Mercurial > hg > mauch-mirex-2010
changeset 8:b5b38998ef3b
added all that other stuff
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/@assocarray/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,3 @@ +/assocarray.m/1.1.1.1/Wed May 29 15:59:52 2002// +/subsref.m/1.1.1.1/Wed Aug 4 19:36:30 2004// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/@assocarray/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/@assocarray
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/@assocarray/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/@assocarray/assocarray.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,10 @@ +function A = assocarray(keys, vals) +% ASSOCARRAY Make an associative array +% function A = assocarray(keys, vals) +% +% keys{i} is the i'th string, vals{i} is the i'th value. +% After construction, A('foo') will return the value associated with foo. + +A.keys = keys; +A.vals = vals; +A = class(A, 'assocarray');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/@assocarray/subsref.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +function val = subsref(A, S) +% SUBSREF Subscript reference for an associative array +% A('foo') will return the value associated with foo. +% If there are multiple identicaly keys, the first match is returned. +% Currently the search is sequential. + +i = 1; +while i <= length(A.keys) + if strcmp(S.subs{1}, A.keys{i}) + val = A.vals{i}; + return; + end + i = i + 1; +end +error(['can''t find ' S.subs{1}])
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@boolean_CPD/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +/boolean_CPD.m/1.1.1.1/Wed May 29 15:59:52 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@boolean_CPD/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@boolean_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@boolean_CPD/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@boolean_CPD/boolean_CPD.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,179 @@ +function CPD = boolean_CPD(bnet, self, ftype, fname, pfail) +% BOOLEAN_CPD Make a tabular CPD representing a (noisy) boolean function +% +% CPD = boolean_cpd(bnet, self, 'inline', f) uses the inline function f +% to specify the CPT. +% e.g., suppose X4 = X2 AND (NOT X3). Then we can write +% bnet.CPD{4} = boolean_CPD(bnet, 4, 'inline', inline('(x(1) & ~x(2)')); +% Note that x(1) refers pvals(1) = X2, and x(2) refers to pvals(2)=X3. +% +% CPD = boolean_cpd(bnet, self, 'named', f) assumes f is a function name. +% f can be built-in to matlab, or a file. +% e.g., If X4 = X2 AND X3, we can write +% bnet.CPD{4} = boolean_CPD(bnet, 4, 'named', 'and'); +% e.g., If X4 = X2 OR X3, we can write +% bnet.CPD{4} = boolean_CPD(bnet, 4, 'named', 'any'); +% +% CPD = boolean_cpd(bnet, self, 'rnd') makes a random non-redundant bool fn. +% +% CPD = boolean_CPD(bnet, self, 'inline'/'named', f, pfail) +% will put probability mass 1-pfail on f(parents), and put pfail on the other value. +% This is useful for simulating noisy boolean functions. +% If pfail is omitted, it is set to 0. +% (Note that adding noise to a random (non-redundant) boolean function just creates a different +% (potentially redundant) random boolean function.) +% +% Note: This cannot be used to simulate a noisy-OR gate. +% Example: suppose C has parents A and B, and the +% link of A->C fails with prob pA and the link B->C fails with pB. +% Then the noisy-OR gate defines the following distribution +% +% A B P(C=0) +% 0 0 1.0 +% 1 0 pA +% 0 1 pB +% 1 1 pA * PB +% +% By contrast, boolean_CPD(bnet, C, 'any', p) would define +% +% A B P(C=0) +% 0 0 1-p +% 1 0 p +% 0 1 p +% 1 1 p + + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = tabular_CPD(bnet, self); + return; +elseif isa(bnet, 'boolean_CPD') + % This might occur if we are copying an object. + CPD = bnet; + return; +end + +if nargin < 5, pfail = 0; end + +ps = parents(bnet.dag, self); +ns = bnet.node_sizes; +psizes = ns(ps); +self_size = ns(self); + +psucc = 1-pfail; + +k = length(ps); +switch ftype + case 'inline', f = eval_bool_fn(fname, k); + case 'named', f = eval_bool_fn(fname, k); + case 'rnd', f = mk_rnd_bool_fn(k); + otherwise, error(['unknown function type ' ftype]); +end + +CPT = zeros(prod(psizes), self_size); +ndx = find(f==0); +CPT(ndx, 1) = psucc; +CPT(ndx, 2) = pfail; +ndx = find(f==1); +CPT(ndx, 2) = psucc; +CPT(ndx, 1) = pfail; +if k > 0 + CPT = reshape(CPT, [psizes self_size]); +end + +clamp = 1; +CPD = tabular_CPD(bnet, self, CPT, [], clamp); + + + +%%%%%%%%%%%% + +function f = eval_bool_fn(fname, n) +% EVAL_BOOL_FN Evaluate a boolean function on all bit vectors of length n +% f = eval_bool_fn(fname, n) +% +% e.g. f = eval_bool_fn(inline('x(1) & x(3)'), 3) +% returns 0 0 0 0 0 1 0 1 + +ns = 2*ones(1, n); +f = zeros(1, 2^n); +bits = ind2subv(ns, 1:2^n); +for i=1:2^n + f(i) = feval(fname, bits(i,:)-1); +end + +%%%%%%%%%%%%%%% + +function f = mk_rnd_bool_fn(n) +% MK_RND_BOOL_FN Make a random bit vector of length n that encodes a non-redundant boolean function +% f = mk_rnd_bool_fn(n) + +red = 1; +while red + f = sample_discrete([0.5 0.5], 2^n, 1)-1; + red = redundant_bool_fn(f); +end + +%%%%%%%% + + +function red = redundant_bool_fn(f) +% REDUNDANT_BOOL_FN Does a boolean function depend on all its input values? +% r = redundant_bool_fn(f) +% +% f is a vector of length 2^n, representing the output for each bit vector. +% An input is redundant if there is no assignment to the other bits +% which changes the output e.g., input 1 is redundant if u(2:n) s.t., +% f([0 u(2:n)]) <> f([1 u(2:n)]). +% A function is redundant it it has any redundant inputs. + +n = log2(length(f)); +ns = 2*ones(1,n); +red = 0; +for i=1:n + ens = ns; + ens(i) = 1; + U = ind2subv(ens, 1:2^(n-1)); + U(:,i) = 1; + f1 = f(subv2ind(ns, U)); + U(:,i) = 2; + f2 = f(subv2ind(ns, U)); + if isequal(f1, f2) + red = 1; + return; + end +end + + +%%%%%%%%%% + +function [b, iter] = rnd_truth_table(N) +% RND_TRUTH_TABLE Construct the output of a random truth table s.t. each input is non-redundant +% b = rnd_truth_table(N) +% +% N is the number of inputs. +% b is a random bit string of length N, representing the output of the truth table. +% Non-redundant means that, for each input position k, +% there are at least two bit patterns, u and v, that differ only in the k'th position, +% s.t., f(u) ~= f(v), where f is the function represented by b. +% We use rejection sampling to ensure non-redundancy. +% +% Example: b = [0 0 0 1 0 0 0 1] is indep of 3rd input (AND of inputs 1 and 2) + +bits = ind2subv(2*ones(1,N), 1:2^N)-1; +redundant = 1; +iter = 0; +while redundant & (iter < 4) + iter = iter + 1; + b = sample_discrete([0.5 0.5], 1, 2^N)-1; + redundant = 0; + for i=1:N + on = find(bits(:,i)==1); + off = find(bits(:,i)==0); + if isequal(b(on), b(off)) + redundant = 1; + break; + end + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@deterministic_CPD/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +/deterministic_CPD.m/1.1.1.1/Mon Oct 7 13:26:36 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@deterministic_CPD/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@deterministic_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@deterministic_CPD/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@deterministic_CPD/deterministic_CPD.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,59 @@ +function CPD = deterministic_CPD(bnet, self, fname, pfail) +% DETERMINISTIC_CPD Make a tabular CPD representing a (noisy) deterministic function +% +% CPD = deterministic_CPD(bnet, self, fname) +% This calls feval(fname, pvals) for each possible vector of parent values. +% e.g., suppose there are 2 ternary parents, then pvals = +% [1 1], [2 1], [3 1], [1 2], [2 2], [3 2], [1 3], [2 3], [3 3] +% If v = feval(fname, pvals(i)), then +% CPD(x | parents=pvals(i)) = 1 if x==v, and = 0 if x<>v +% e.g., suppose X4 = X2 AND (NOT X3). Then +% bnet.CPD{4} = deterministic_CPD(bnet, 4, inline('((x(1)-1) & ~(x(2)-1)) + 1')); +% Note that x(1) refers pvals(1) = X2, and x(2) refers to pvals(2)=X3 +% See also boolean_CPD. +% +% CPD = deterministic_CPD(bnet, self, fname, pfail) +% will put probability mass 1-pfail on f(parents), and distribute pfail over the other values. +% This is useful for simulating noisy deterministic functions. +% If pfail is omitted, it is set to 0. +% + + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = tabular_CPD(bnet, self); + return; +elseif isa(bnet, 'deterministic_CPD') + % This might occur if we are copying an object. + CPD = bnet; + return; +end + +if nargin < 4, pfail = 0; end + +ps = parents(bnet.dag, self); +ns = bnet.node_sizes; +psizes = ns(ps); +self_size = ns(self); + +psucc = 1-pfail; + +CPT = zeros(prod(psizes), self_size); +pvals = zeros(1, length(ps)); +for i=1:prod(psizes) + pvals = ind2subv(psizes, i); + x = feval(fname, pvals); + %fprintf('%d ', [pvals x]); fprintf('\n'); + if psucc == 1 + CPT(i, x) = 1; + else + CPT(i, x) = psucc; + rest = mysetdiff(1:self_size, x); + CPT(i, rest) = pfail/length(rest); + end +end +CPT = reshape(CPT, [psizes self_size]); + +CPD = tabular_CPD(bnet, self, 'CPT',CPT, 'clamped',1); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@discrete_CPD/CPD_to_lambda_msg.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +function lam_msg = CPD_to_lambda_msg(CPD, msg_type, n, ps, msg, p, evidence) +% CPD_TO_LAMBDA_MSG Compute lambda message (discrete) +% lam_msg = compute_lambda_msg(CPD, msg_type, n, ps, msg, p, evidence) +% Pearl p183 eq 4.52 + +switch msg_type + case 'd', + T = prod_CPT_and_pi_msgs(CPD, n, ps, msg, p); + mysize = length(msg{n}.lambda); + lambda = dpot(n, mysize, msg{n}.lambda); + T = multiply_by_pot(T, lambda); + lam_msg = pot_to_marginal(marginalize_pot(T, p)); + lam_msg = lam_msg.T; + case 'g', + error('discrete_CPD can''t create Gaussian msgs') +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@discrete_CPD/CPD_to_pi.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function pi = CPD_to_pi(CPD, msg_type, n, ps, msg, evidence) +% COMPUTE_PI Compute pi vector (discrete) +% pi = compute_pi(CPD, msg_type, n, ps, msg, evidence) +% Pearl p183 eq 4.51 + +switch msg_type + case 'd', + T = prod_CPT_and_pi_msgs(CPD, n, ps, msg); + pi = pot_to_marginal(marginalize_pot(T, n)); + pi = pi.T(:); + case 'g', + error('can only convert discrete CPD to Gaussian pi if observed') +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@discrete_CPD/CPD_to_scgpot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,25 @@ +function pot = CPD_to_scgpot(CPD, domain, ns, cnodes, evidence) +% CPD_TO_SCGPOT Convert a CPD to a CG potential, incorporating any evidence (discrete) +% pot = CPD_to_scgpot(CPD, domain, ns, cnodes, evidence) +% +% domain is the domain of CPD. +% node_sizes(i) is the size of node i. +% cnodes +% evidence{i} is the evidence on the i'th node. + +%odom = domain(~isemptycell(evidence(domain))); + +%vals = cat(1, evidence{odom}); +%map = find_equiv_posns(odom, domain); +%index = mk_multi_index(length(domain), map, vals); +CPT = CPD_to_CPT(CPD); +%CPT = CPT(index{:}); +CPT = CPT(:); +%ns(odom) = 1; +potarray = cell(1, length(CPT)); +for i=1:length(CPT) + %p = CPT(i); + potarray{i} = scgcpot(0, 0, CPT(i)); + %scpot{i} = scpot(0, 0); +end +pot = scgpot(domain, [], [], ns, potarray);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@discrete_CPD/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +/CPD_to_lambda_msg.m/1.1.1.1/Wed May 29 15:59:52 2002// +/CPD_to_pi.m/1.1.1.1/Wed May 29 15:59:52 2002// +/CPD_to_scgpot.m/1.1.1.1/Wed May 29 15:59:52 2002// +/README/1.1.1.1/Wed May 29 15:59:52 2002// +/convert_CPD_to_table_hidden_ps.m/1.1.1.1/Wed May 29 15:59:52 2002// +/convert_obs_CPD_to_table.m/1.1.1.1/Wed May 29 15:59:52 2002// +/convert_to_pot.m/1.1.1.1/Fri Feb 20 22:00:38 2004// +/convert_to_sparse_table.c/1.1.1.1/Wed May 29 15:59:52 2002// +/convert_to_table.m/1.1.1.1/Wed May 29 15:59:52 2002// +/discrete_CPD.m/1.1.1.1/Wed May 29 15:59:52 2002// +/dom_sizes.m/1.1.1.1/Wed May 29 15:59:52 2002// +/log_prob_node.m/1.1.1.1/Wed May 29 15:59:52 2002// +/prob_node.m/1.1.1.1/Wed May 29 15:59:52 2002// +/sample_node.m/1.1.1.1/Wed May 29 15:59:52 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@discrete_CPD/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +A D/Old//// +A D/private////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@discrete_CPD/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@discrete_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@discrete_CPD/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@discrete_CPD/Old/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +/convert_to_pot.m/1.1.1.1/Wed May 29 15:59:52 2002// +/convert_to_table.m/1.1.1.1/Wed May 29 15:59:52 2002// +/prob_CPD.m/1.1.1.1/Wed May 29 15:59:52 2002// +/prob_node.m/1.1.1.1/Wed May 29 15:59:52 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@discrete_CPD/Old/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@discrete_CPD/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@discrete_CPD/Old/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@discrete_CPD/Old/convert_to_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,44 @@ +function pot = convert_to_pot(CPD, pot_type, domain, evidence) +% CONVERT_TO_POT Convert a tabular CPD to one or more potentials +% pots = convert_to_pot(CPD, pot_type, domain, evidence) +% +% pots{i} = CPD evaluated using evidence(domain(:,i)) +% If 'domains' is a single row vector, pots will be an object, not a cell array. + +ncases = size(domain,2); +assert(ncases==1); % not yet vectorized + +sz = dom_sizes(CPD); +ns = zeros(1, max(domain)); +ns(domain) = sz; + +local_ev = evidence(domain); +obs_bitv = ~isemptycell(local_ev); +odom = domain(obs_bitv); +T = convert_to_table(CPD, domain, local_ev, obs_bitv); + +switch pot_type + case 'u', + pot = upot(domain, sz, T, 0*myones(sz)); + case 'd', + ns(odom) = 1; + pot = dpot(domain, ns(domain), T); + case {'c','g'}, + % Since we want the output to be a Gaussian, the whole family must be observed. + % In other words, the potential is really just a constant. + p = T; + %p = prob_node(CPD, evidence(domain(end)), evidence(domain(1:end-1))); + ns(domain) = 0; + pot = cpot(domain, ns(domain), log(p)); + case 'cg', + T = T(:); + ns(odom) = 1; + can = cell(1, length(T)); + for i=1:length(T) + can{i} = cpot([], [], log(T(i))); + end + pot = cgpot(domain, [], ns, can); + otherwise, + error(['unrecognized pot type ' pot_type]) +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@discrete_CPD/Old/convert_to_table.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,23 @@ +function T = convert_to_table(CPD, domain, local_ev, obs_bitv) +% CONVERT_TO_TABLE Convert a discrete CPD to a table +% function T = convert_to_table(CPD, domain, local_ev, obs_bitv) +% +% We convert the CPD to a CPT, and then lookup the evidence on the discrete parents. +% The resulting table can easily be converted to a potential. + + +CPT = CPD_to_CPT(CPD); +obs_child_only = ~any(obs_bitv(1:end-1)) & obs_bitv(end); + +if obs_child_only + sz = size(CPT); + CPT = reshape(CPT, prod(sz(1:end-1)), sz(end)); + o = local_ev{end}; + T = CPT(:, o); +else + odom = domain(obs_bitv); + vals = cat(1, local_ev{find(obs_bitv)}); % undo cell array + map = find_equiv_posns(odom, domain); + index = mk_multi_index(length(domain), map, vals); + T = CPT(index{:}); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@discrete_CPD/Old/prob_CPD.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,25 @@ +function p = prob_CPD(CPD, domain, ns, cnodes, evidence) +% PROB_CPD Compute prob of a node given evidence on the parents (discrete) +% p = prob_CPD(CPD, domain, ns, cnodes, evidence) +% +% domain is the domain of CPD. +% node_sizes(i) is the size of node i. +% cnodes = all the cts nodes +% evidence{i} is the evidence on the i'th node. + +ps = domain(1:end-1); +self = domain(end); +CPT = CPD_to_CPT(CPD); + +if isempty(ps) + T = CPT; +else + assert(~any(isemptycell(evidence(ps)))); + pvals = cat(1, evidence{ps}); + i = subv2ind(ns(ps), pvals(:)'); + T = reshape(CPT, [prod(ns(ps)) ns(self)]); + T = T(i,:); +end +p = T(evidence{self}); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@discrete_CPD/Old/prob_node.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,51 @@ +function [P, p] = prob_node(CPD, self_ev, pev) +% PROB_NODE Compute prod_m P(x(i,m)| x(pi_i,m), theta_i) for node i (discrete) +% [P, p] = prob_node(CPD, self_ev, pev) +% +% self_ev(m) is the evidence on this node in case m. +% pev(i,m) is the evidence on the i'th parent in case m (if there are any parents). +% (These may also be cell arrays.) +% +% p(m) = P(x(i,m)| x(pi_i,m), theta_i) +% P = prod p(m) + +if iscell(self_ev), usecell = 1; else usecell = 0; end + +ncases = length(self_ev); +sz = dom_sizes(CPD); + +nparents = length(sz)-1; +if nparents == 0 + assert(isempty(pev)); +else + assert(isequal(size(pev), [nparents ncases])); +end + +n = length(sz); +dom = 1:n; +p = zeros(1, ncases); +if nparents == 0 + for m=1:ncases + if usecell + evidence = {self_ev{m}}; + else + evidence = num2cell(self_ev(m)); + end + T = convert_to_table(CPD, dom, evidence); + p(m) = T; + end +else + for m=1:ncases + if usecell + evidence = cell(1,n); + evidence(1:n-1) = pev(:,m); + evidence(n) = self_ev(m); + else + evidence = num2cell([pev(:,m)', self_ev(m)]); + end + T = convert_to_table(CPD, dom, evidence); + p(m) = T; + end +end +P = prod(p); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@discrete_CPD/README Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +Any CPD on a discrete child with discrete parents +can be represented as a table (although this might be quite big). +discrete_CPD uses this tabular representation to implement various +functions. Subtypes are free to implement more efficient versions. +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@discrete_CPD/convert_CPD_to_table_hidden_ps.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,20 @@ +function T = convert_CPD_to_table_hidden_ps(CPD, child_obs) +% CONVERT_CPD_TO_TABLE_HIDDEN_PS Convert a discrete CPD to a table +% T = convert_CPD_to_table_hidden_ps(CPD, child_obs) +% +% This is like convert_to_table, except that we are guaranteed that +% none of the parents have evidence on them. +% child_obs may be an integer (1,2,...) or []. + +CPT = CPD_to_CPT(CPD); +if isempty(child_obs) + T = CPT(:); +else + sz = dom_sizes(CPD); + if length(sz)==1 % no parents + T = CPT(child_obs); + else + CPT = reshape(CPT, prod(sz(1:end-1)), sz(end)); + T = CPT(:, child_obs); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@discrete_CPD/convert_obs_CPD_to_table.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function T = convert_to_table(CPD, domain, evidence) +% CONVERT_TO_TABLE Convert a discrete CPD to a table +% T = convert_to_table(CPD, domain, evidence) +% +% We convert the CPD to a CPT, and then lookup the evidence on the discrete parents. +% The resulting table can easily be converted to a potential. + +CPT = CPD_to_CPT(CPD); +odom = domain(~isemptycell(evidence(domain))); +vals = cat(1, evidence{odom}); +map = find_equiv_posns(odom, domain); +index = mk_multi_index(length(domain), map, vals); +T = CPT(index{:});
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@discrete_CPD/convert_to_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,62 @@ +function pot = convert_to_pot(CPD, pot_type, domain, evidence) +% CONVERT_TO_POT Convert a discrete CPD to a potential +% pot = convert_to_pot(CPD, pot_type, domain, evidence) +% +% pots = CPD evaluated using evidence(domain) + +ncases = size(domain,2); +assert(ncases==1); % not yet vectorized + +sz = dom_sizes(CPD); +ns = zeros(1, max(domain)); +ns(domain) = sz; + +CPT1 = CPD_to_CPT(CPD); +spar = issparse(CPT1); +odom = domain(~isemptycell(evidence(domain))); +if spar + T = convert_to_sparse_table(CPD, domain, evidence); +else + T = convert_to_table(CPD, domain, evidence); +end + +switch pot_type + case 'u', + pot = upot(domain, sz, T, 0*myones(sz)); + case 'd', + ns(odom) = 1; + pot = dpot(domain, ns(domain), T); + case {'c','g'}, + % Since we want the output to be a Gaussian, the whole family must be observed. + % In other words, the potential is really just a constant. + p = T; + %p = prob_node(CPD, evidence(domain(end)), evidence(domain(1:end-1))); + ns(domain) = 0; + pot = cpot(domain, ns(domain), log(p)); + + case 'cg', + T = T(:); + ns(odom) = 1; + can = cell(1, length(T)); + for i=1:length(T) + if T(i) == 0 + can{i} = cpot([], [], -Inf); % bug fix by Bob Welch 20/2/04 + else + can{i} = cpot([], [], log(T(i))); + end; + end + pot = cgpot(domain, [], ns, can); + + case 'scg' + T = T(:); + ns(odom) = 1; + pot_array = cell(1, length(T)); + for i=1:length(T) + pot_array{i} = scgcpot([], [], T(i)); + end + pot = scgpot(domain, [], [], ns, pot_array); + + otherwise, + error(['unrecognized pot type ' pot_type]) +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@discrete_CPD/convert_to_sparse_table.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,154 @@ +/* convert_to_sparse_table.c convert a sparse discrete CPD with evidence into sparse table */ +/* convert_to_pot.m located in ../CPDs/discrete_CPD call it */ +/* 3 input */ +/* CPD prhs[0] with 1D sparse CPT */ +/* domain prhs[1] */ +/* evidence prhs[2] */ +/* 1 output */ +/* T plhs[0] sparse table */ + +#include <math.h> +#include "mex.h" + +void ind_subv(int index, const int *cumprod, const int n, int *bsubv){ + int i; + + for (i = n-1; i >= 0; i--) { + bsubv[i] = ((int)floor(index / cumprod[i])); + index = index % cumprod[i]; + } +} + +int subv_ind(const int n, const int *cumprod, const int *subv){ + int i, index=0; + + for(i=0; i<n; i++){ + index += subv[i] * cumprod[i]; + } + return index; +} + +void reset_nzmax(mxArray *spArray, const int old_nzmax, const int new_nzmax){ + double *ptr; + void *newptr; + int *ir, *jc; + int nbytes; + + if(new_nzmax == old_nzmax) return; + nbytes = new_nzmax * sizeof(*ptr); + ptr = mxGetPr(spArray); + newptr = mxRealloc(ptr, nbytes); + mxSetPr(spArray, newptr); + nbytes = new_nzmax * sizeof(*ir); + ir = mxGetIr(spArray); + newptr = mxRealloc(ir, nbytes); + mxSetIr(spArray, newptr); + jc = mxGetJc(spArray); + jc[0] = 0; + jc[1] = new_nzmax; + mxSetNzmax(spArray, new_nzmax); +} + + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, NS, NZB, count, bdim, match, domain, bindex, sindex, nzCounts=0; + int *observed, *bsubv, *ssubv, *bir, *sir, *bjc, *sjc, *mask, *ssize, *bcumprod, *scumprod; + double *pDomain, *pSize, *bpr, *spr; + mxArray *pTemp; + + pTemp = mxGetField(prhs[0], 0, "CPT"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + pTemp = mxGetField(prhs[0], 0, "sizes"); + pSize = mxGetPr(pTemp); + + pDomain = mxGetPr(prhs[1]); + bdim = mxGetNumberOfElements(prhs[1]); + + mask = malloc(bdim * sizeof(int)); + ssize = malloc(bdim * sizeof(int)); + observed = malloc(bdim * sizeof(int)); + + for(i=0; i<bdim; i++){ + ssize[i] = (int)pSize[i]; + } + + count = 0; + for(i=0; i<bdim; i++){ + domain = (int)pDomain[i] - 1; + pTemp = mxGetCell(prhs[2], domain); + if(pTemp){ + mask[count] = i; + ssize[i] = 1; + observed[count] = (int)mxGetScalar(pTemp) - 1; + count++; + } + } + + if(count == 0){ + pTemp = mxGetField(prhs[0], 0, "CPT"); + plhs[0] = mxDuplicateArray(pTemp); + free(mask); + free(ssize); + free(observed); + return; + } + + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(count * sizeof(int)); + bcumprod = malloc(bdim * sizeof(int)); + scumprod = malloc(bdim * sizeof(int)); + + NS = 1; + for(i=0; i<bdim; i++){ + NS *= ssize[i]; + } + + plhs[0] = mxCreateSparse(NS, 1, NS, mxREAL); + spr = mxGetPr(plhs[0]); + sir = mxGetIr(plhs[0]); + sjc = mxGetJc(plhs[0]); + sjc[0] = 0; + sjc[1] = NS; + + bcumprod[0] = 1; + scumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bcumprod[i+1] = bcumprod[i] * (int)pSize[i]; + scumprod[i+1] = scumprod[i] * ssize[i]; + } + + nzCounts = 0; + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bcumprod, bdim, bsubv); + for(j=0; j<count; j++){ + ssubv[j] = bsubv[mask[j]]; + } + match = 1; + for(j=0; j<count; j++){ + if((ssubv[j]) != observed[j]){ + match = 0; + break; + } + } + if(match){ + spr[nzCounts] = bpr[i]; + sindex = subv_ind(bdim, scumprod, bsubv); + sir[nzCounts] = sindex; + nzCounts++; + } + } + + reset_nzmax(plhs[0], NS, nzCounts); + free(mask); + free(ssize); + free(observed); + free(bsubv); + free(ssubv); + free(bcumprod); + free(scumprod); +} +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@discrete_CPD/convert_to_table.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +function T = convert_to_table(CPD, domain, evidence) +% CONVERT_TO_TABLE Convert a discrete CPD to a table +% T = convert_to_table(CPD, domain, evidence) +% +% We convert the CPD to a CPT, and then lookup the evidence on the discrete parents. +% The resulting table can easily be converted to a potential. + +domain = domain(:); +CPT = CPD_to_CPT(CPD); +odom = domain(~isemptycell(evidence(domain))); +vals = cat(1, evidence{odom}); +map = find_equiv_posns(odom, domain); +index = mk_multi_index(length(domain), map, vals); +T = CPT(index{:}); +T = T(:);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@discrete_CPD/discrete_CPD.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +function CPD = discrete_CPD(clamped, dom_sizes) +% DISCRETE_CPD Virtual constructor for generic discrete CPD +% CPD = discrete_CPD(clamped, dom_sizes) + +CPD.dom_sizes = dom_sizes; +CPD = class(CPD, 'discrete_CPD', generic_CPD(clamped));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@discrete_CPD/dom_sizes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function sz = dom_sizes(CPD) +% DOM_SIZES Return the size of each node in the domain +% sz = dom_sizes(CPD) + +sz = CPD.dom_sizes;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@discrete_CPD/log_prob_node.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +function L = log_prob_node(CPD, self_ev, pev) +% LOG_PROB_NODE Compute sum_m log P(x(i,m)| x(pi_i,m), theta_i) for node i (discrete) +% L = log_prob_node(CPD, self_ev, pev) +% +% self_ev(m) is the evidence on this node in case m. +% pev(i,m) is the evidence on the i'th parent in case m (if there are any parents). +% (These may also be cell arrays.) + +[P, p] = prob_node(CPD, self_ev, pev); % P may underflow, so we use p +tiny = exp(-700); +p = p + (p==0)*tiny; % replace 0s by tiny +L = sum(log(p));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@discrete_CPD/private/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +/prod_CPT_and_pi_msgs.m/1.1.1.1/Wed May 29 15:59:52 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@discrete_CPD/private/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@discrete_CPD/private
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@discrete_CPD/private/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@discrete_CPD/private/prod_CPT_and_pi_msgs.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,18 @@ +function T = prod_CPT_and_pi_msgs(CPD, n, ps, msgs, except) +% PROD_CPT_AND_PI_MSGS Multiply the CPD and all the pi messages from parents, perhaps excepting one +% T = prod_CPY_and_pi_msgs(CPD, n, ps, msgs, except) + +if nargin < 5, except = -1; end + +dom = [ps n]; +%ns = sparse(1, max(dom)); +ns = zeros(1, max(dom)); +CPT = CPD_to_CPT(CPD); +ns(dom) = mysize(CPT); +T = dpot(dom, ns(dom), CPT); +for i=1:length(ps) + p = ps(i); + if p ~= except + T = multiply_by_pot(T, dpot(p, ns(p), msgs{n}.pi_from_parent{i})); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@discrete_CPD/prob_node.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,81 @@ +function [P, p] = prob_node(CPD, self_ev, pev) +% PROB_NODE Compute prod_m P(x(i,m)| x(pi_i,m), theta_i) for node i (discrete) +% [P, p] = prob_node(CPD, self_ev, pev) +% +% self_ev(m) is the evidence on this node in case m. +% pev(i,m) is the evidence on the i'th parent in case m (if there are any parents). +% (These may also be cell arrays.) +% +% p(m) = P(x(i,m)| x(pi_i,m), theta_i) +% P = prod p(m) + +if iscell(self_ev), usecell = 1; else usecell = 0; end + +ncases = length(self_ev); +sz = dom_sizes(CPD); + +nparents = length(sz)-1; +if nparents == 0 + assert(isempty(pev)); +else + assert(isequal(size(pev), [nparents ncases])); +end + +n = length(sz); +dom = 1:n; +p = zeros(1, ncases); +if isa(CPD, 'tabular_CPD') + % speed up by looking up CPT using index Zhang Yimin 2001-12-31 + if usecell + if nparents == 0 + data = [cell2num(self_ev)]; + else + data = [cell2num(pev); cell2num(self_ev)]; + end + else + if nparents == 0 + data = [self_ev]; + else + data = [pev; self_ev]; + end + end + + indices = subv2ind(sz, data'); % each row of data' is a case + + CPT=CPD_to_CPT(CPD); + p = CPT(indices); + + %get the prob list + %cpt_size = prod(sz); + %prob_list=reshape(CPT, cpt_size, 1); + %for m=1:ncases %here we assume we get evidence for node and all its parents + % idx=indices(m); + % p(m)=prob_list(idx); + %end + +else % eg. softmax + + for m=1:ncases + if usecell + if nparents == 0 + evidence = {self_ev{m}}; + else + evidence = cell(1,n); + evidence(1:n-1) = pev(:,m); + evidence(n) = self_ev(m); + end + else + if nparents == 0 + evidence = num2cell(self_ev(m)); + else + evidence = num2cell([pev(:,m)', self_ev(m)]); + end + end + T = convert_to_table(CPD, dom, evidence); + p(m) = T; + end +end + +P = prod(p); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@discrete_CPD/sample_node.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,34 @@ +function y = sample_node(CPD, pvals) +% SAMPLE_NODE Draw a random sample from P(Xi | x(pi_i), theta_i) (discrete) +% y = sample_node(CPD, parent_evidence) +% +% parent_evidence{i} is the value of the i'th parent + +if 0 +n = length(pvals)+1; +dom = 1:n; +evidence = cell(1,n); +evidence(1:n-1) = pvals; +T = convert_to_table(CPD, dom, evidence); +y = sample_discrete(T); +end + + +CPT = CPD_to_CPT(CPD); +sz = mysize(CPT); +nparents = length(sz)-1; +switch nparents + case 0, T = CPT; + case 1, T = CPT(pvals{1}, :); + case 2, T = CPT(pvals{1}, pvals{2}, :); + case 3, T = CPT(pvals{1}, pvals{2}, pvals{3}, :); + case 4, T = CPT(pvals{1}, pvals{2}, pvals{3}, pvals{4}, :); + otherwise, + pvals = cat(1, pvals{:}); + psz = sz(1:end-1); + ssz = sz(end); + i = subv2ind(psz, pvals(:)'); + T = reshape(CPT, [prod(psz) ssz]); + T = T(i,:); +end +y = sample_discrete(T);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/CPD_to_lambda_msg.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,59 @@ +function lam_msg = CPD_to_lambda_msg(CPD, msg_type, n, ps, msg, p, evidence) +% CPD_TO_LAMBDA_MSG Compute lambda message (gaussian) +% lam_msg = compute_lambda_msg(CPD, msg_type, n, ps, msg, p, evidence) +% Pearl p183 eq 4.52 + +switch msg_type + case 'd', + error('gaussian_CPD can''t create discrete msgs') + case 'g', + cps = ps(CPD.cps); + cpsizes = CPD.sizes(CPD.cps); + self_size = CPD.sizes(end); + i = find_equiv_posns(p, cps); % p is n's i'th cts parent + psz = cpsizes(i); + if all(msg{n}.lambda.precision == 0) % no info to send on + lam_msg.precision = zeros(psz, psz); + lam_msg.info_state = zeros(psz, 1); + return; + end + [m, Q, W] = gaussian_CPD_params_given_dps(CPD, [ps n], evidence); + Bmu = m; + BSigma = Q; + for k=1:length(cps) % only get pi msgs from cts parents + pk = cps(k); + if pk ~= p + %bk = block(k, cpsizes); + bk = CPD.cps_block_ndx{k}; + Bk = W(:, bk); + m = msg{n}.pi_from_parent{k}; + BSigma = BSigma + Bk * m.Sigma * Bk'; + Bmu = Bmu + Bk * m.mu; + end + end + % BSigma = Q + sum_{k \neq i} B_k Sigma_k B_k' + %bi = block(i, cpsizes); + bi = CPD.cps_block_ndx{i}; + Bi = W(:,bi); + P = msg{n}.lambda.precision; + if (rcond(P) > 1e-3) | isinf(P) + if isinf(P) % Y is observed + Sigma_lambda = zeros(self_size, self_size); % infinite precision => 0 variance + mu_lambda = msg{n}.lambda.mu; % observed_value; + else + Sigma_lambda = inv(P); + mu_lambda = Sigma_lambda * msg{n}.lambda.info_state; + end + C = inv(Sigma_lambda + BSigma); + lam_msg.precision = Bi' * C * Bi; + lam_msg.info_state = Bi' * C * (mu_lambda - Bmu); + else + % method that uses matrix inversion lemma to avoid inverting P + A = inv(P + inv(BSigma)); + C = P - P*A*P; + lam_msg.precision = Bi' * C * Bi; + D = eye(self_size) - P*A; + z = msg{n}.lambda.info_state; + lam_msg.info_state = Bi' * (D*z - D*P*Bmu); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/CPD_to_pi.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,22 @@ +function pi = CPD_to_pi(CPD, msg_type, n, ps, msg, evidence) +% CPD_TO_PI Compute the pi vector (gaussian) +% function pi = CPD_to_pi(CPD, msg_type, n, ps, msg, evidence) + +switch msg_type + case 'd', + error('gaussian_CPD can''t create discrete msgs') + case 'g', + [m, Q, W] = gaussian_CPD_params_given_dps(CPD, [ps n], evidence); + cps = ps(CPD.cps); + cpsizes = CPD.sizes(CPD.cps); + pi.mu = m; + pi.Sigma = Q; + for k=1:length(cps) % only get pi msgs from cts parents + %bk = block(k, cpsizes); + bk = CPD.cps_block_ndx{k}; + Bk = W(:, bk); + m = msg{n}.pi_from_parent{k}; + pi.Sigma = pi.Sigma + Bk * m.Sigma * Bk'; + pi.mu = pi.mu + Bk * m.mu; % m.mu = u(k) + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/CPD_to_scgpot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,58 @@ +function pot = CPD_to_scgpot(CPD, domain, ns, cnodes, evidence) +% CPD_TO_CGPOT Convert a Gaussian CPD to a CG potential, incorporating any evidence +% pot = CPD_to_cgpot(CPD, domain, ns, cnodes, evidence) + +self = CPD.self; +dnodes = mysetdiff(1:length(ns), cnodes); +odom = domain(~isemptycell(evidence(domain))); +cdom = myintersect(cnodes, domain); +cheaddom = myintersect(self, domain); +ctaildom = mysetdiff(cdom,cheaddom); +ddom = myintersect(dnodes, domain); +cobs = myintersect(cdom, odom); +dobs = myintersect(ddom, odom); +ens = ns; % effective node size +ens(cobs) = 0; +ens(dobs) = 1; + +% Extract the params compatible with the observations (if any) on the discrete parents (if any) +% parents are all but the last domain element +ps = domain(1:end-1); +dps = myintersect(ps, ddom); +dops = myintersect(dps, odom); + +map = find_equiv_posns(dops, dps); +dpvals = cat(1, evidence{dops}); +index = mk_multi_index(length(dps), map, dpvals); + +dpsize = prod(ens(dps)); +cpsize = size(CPD.weights(:,:,1), 2); % cts parents size +ss = size(CPD.mean, 1); % self size +% the reshape acts like a squeeze +m = reshape(CPD.mean(:, index{:}), [ss dpsize]); +C = reshape(CPD.cov(:, :, index{:}), [ss ss dpsize]); +W = reshape(CPD.weights(:, :, index{:}), [ss cpsize dpsize]); + + +% Convert each conditional Gaussian to a canonical potential +pot = cell(1, dpsize); +for i=1:dpsize + %pot{i} = linear_gaussian_to_scgcpot(m(:,i), C(:,:,i), W(:,:,i), cdom, ns, cnodes, evidence); + pot{i} = scgcpot(ss, cpsize, 1, m(:,i), W(:,:,i), C(:,:,i)); +end + +pot = scgpot(ddom, cheaddom, ctaildom, ens, pot); + + +function pot = linear_gaussian_to_scgcpot(mu, Sigma, W, domain, ns, cnodes, evidence) +% LINEAR_GAUSSIAN_TO_CPOT Convert a linear Gaussian CPD to a stable conditional potential element. +% pot = linear_gaussian_to_cpot(mu, Sigma, W, domain, ns, cnodes, evidence) + +p = 1; +A = mu; +B = W; +C = Sigma; +ns(odom) = 0; +%pot = scgcpot(, ns(domain), p, A, B, C); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,20 @@ +/CPD_to_lambda_msg.m/1.1.1.1/Wed May 29 15:59:52 2002// +/CPD_to_pi.m/1.1.1.1/Wed May 29 15:59:52 2002// +/CPD_to_scgpot.m/1.1.1.1/Wed May 29 15:59:52 2002// +/adjustable_CPD.m/1.1.1.1/Wed May 29 15:59:52 2002// +/convert_CPD_to_table_hidden_ps.m/1.1.1.1/Wed May 29 15:59:52 2002// +/convert_to_pot.m/1.1.1.1/Sun Mar 9 23:03:16 2003// +/convert_to_table.m/1.1.1.1/Sun May 11 23:31:54 2003// +/display.m/1.1.1.1/Wed May 29 15:59:52 2002// +/gaussian_CPD.m/1.1.1.1/Wed Jun 15 21:13:06 2005// +/gaussian_CPD_params_given_dps.m/1.1.1.1/Sun May 11 23:13:40 2003// +/get_field.m/1.1.1.1/Wed May 29 15:59:52 2002// +/learn_params.m/1.1.1.1/Thu Jun 10 01:28:10 2004// +/log_prob_node.m/1.1.1.1/Tue Sep 10 17:44:00 2002// +/maximize_params.m/1.1.1.1/Tue May 20 14:10:06 2003// +/maximize_params_debug.m/1.1.1.1/Fri Jan 31 00:13:10 2003// +/reset_ess.m/1.1.1.1/Wed May 29 15:59:52 2002// +/sample_node.m/1.1.1.1/Wed May 29 15:59:52 2002// +/set_fields.m/1.1.1.1/Wed May 29 15:59:52 2002// +/update_ess.m/1.1.1.1/Tue Jul 22 22:55:46 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +A D/Old//// +A D/private////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@gaussian_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/Old/CPD_to_lambda_msg.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,64 @@ +function lam_msg = CPD_to_lambda_msg(CPD, msg_type, n, ps, msg, p) +% CPD_TO_LAMBDA_MSG Compute lambda message (gaussian) +% lam_msg = compute_lambda_msg(CPD, msg_type, n, ps, msg, p) +% Pearl p183 eq 4.52 + +switch msg_type + case 'd', + error('gaussian_CPD can''t create discrete msgs') + case 'g', + self_size = CPD.sizes(end); + if all(msg{n}.lambda.precision == 0) % no info to send on + lam_msg.precision = zeros(self_size); + lam_msg.info_state = zeros(self_size, 1); + return; + end + cpsizes = CPD.sizes(CPD.cps); + dpval = 1; + Q = CPD.cov(:,:,dpval); + Sigmai = Q; + wmu = zeros(self_size, 1); + for k=1:length(ps) + pk = ps(k); + if pk ~= p + bk = block(k, cpsizes); + Bk = CPD.weights(:, bk, dpval); + m = msg{n}.pi_from_parent{k}; + Sigmai = Sigmai + Bk * m.Sigma * Bk'; + wmu = wmu + Bk * m.mu; % m.mu = u(k) + end + end + % Sigmai = Q + sum_{k \neq i} B_k Sigma_k B_k' + i = find_equiv_posns(p, ps); + bi = block(i, cpsizes); + Bi = CPD.weights(:,bi, dpval); + + if 0 + P = msg{n}.lambda.precision; + if isinf(P) % inv(P)=Sigma_lambda=0 + precision_temp = inv(Sigmai); + lam_msg.precision = Bi' * precision_temp * Bi; + lam_msg.info_state = precision_temp * (msg{n}.lambda.mu - wmu); + else + A = inv(P + inv(Sigmai)); + precision_temp = P + P*A*P; + lam_msg.precision = Bi' * precision_temp * Bi; + self_size = length(P); + C = eye(self_size) + P*A; + z = msg{n}.lambda.info_state; + lam_msg.info_state = C*z - C*P*wmu; + end + end + + if isinf(msg{n}.lambda.precision) + Sigma_lambda = zeros(self_size, self_size); % infinite precision => 0 variance + mu_lambda = msg{n}.lambda.mu; % observed_value; + else + Sigma_lambda = inv(msg{n}.lambda.precision); + mu_lambda = Sigma_lambda * msg{n}.lambda.info_state; + end + precision_temp = inv(Sigma_lambda + Sigmai); + lam_msg.precision = Bi' * precision_temp * Bi; + lam_msg.info_state = Bi' * precision_temp * (mu_lambda - wmu); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/Old/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +/CPD_to_lambda_msg.m/1.1.1.1/Wed May 29 15:59:52 2002// +/gaussian_CPD.m/1.1.1.1/Wed May 29 15:59:52 2002// +/log_prob_node.m/1.1.1.1/Wed May 29 15:59:52 2002// +/maximize_params.m/1.1.1.1/Thu Jan 30 22:38:16 2003// +/update_ess.m/1.1.1.1/Wed May 29 15:59:52 2002// +/update_tied_ess.m/1.1.1.1/Wed May 29 15:59:52 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/Old/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@gaussian_CPD/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/Old/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/Old/gaussian_CPD.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,184 @@ +function CPD = gaussian_CPD(varargin) +% GAUSSIAN_CPD Make a conditional linear Gaussian distrib. +% +% To define this CPD precisely, call the continuous (cts) parents (if any) X, +% the discrete parents (if any) Q, and this node Y. Then the distribution on Y is: +% - no parents: Y ~ N(mu, Sigma) +% - cts parents : Y|X=x ~ N(mu + W x, Sigma) +% - discrete parents: Y|Q=i ~ N(mu(i), Sigma(i)) +% - cts and discrete parents: Y|X=x,Q=i ~ N(mu(i) + W(i) x, Sigma(i)) +% +% CPD = gaussian_CPD(bnet, node, ...) will create a CPD with random parameters, +% where node is the number of a node in this equivalence class. +% +% The list below gives optional arguments [default value in brackets]. +% (Let ns(i) be the size of node i, X = ns(X), Y = ns(Y) and Q = prod(ns(Q)).) +% +% mean - mu(:,i) is the mean given Q=i [ randn(Y,Q) ] +% cov - Sigma(:,:,i) is the covariance given Q=i [ repmat(eye(Y,Y), [1 1 Q]) ] +% weights - W(:,:,i) is the regression matrix given Q=i [ randn(Y,X,Q) ] +% cov_type - if 'diag', Sigma(:,:,i) is diagonal [ 'full' ] +% tied_cov - if 1, we constrain Sigma(:,:,i) to be the same for all i [0] +% clamp_mean - if 1, we do not adjust mu(:,i) during learning [0] +% clamp_cov - if 1, we do not adjust Sigma(:,:,i) during learning [0] +% clamp_weights - if 1, we do not adjust W(:,:,i) during learning [0] +% cov_prior_weight - weight given to I prior for estimating Sigma [0.01] +% +% e.g., CPD = gaussian_CPD(bnet, i, 'mean', [0; 0], 'clamp_mean', 'yes') +% +% For backwards compatibility with BNT2, you can also specify the parameters in the following order +% CPD = gaussian_CPD(bnet, self, mu, Sigma, W, cov_type, tied_cov, clamp_mean, clamp_cov, clamp_weight) +% +% Sometimes it is useful to create an "isolated" CPD, without needing to pass in a bnet. +% In this case, you must specify the discrete and cts parents (dps, cps) and the family sizes, followed +% by the optional arguments above: +% CPD = gaussian_CPD('self', i, 'dps', dps, 'cps', cps, 'sz', fam_size, ...) + + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + clamp = 0; + CPD = class(CPD, 'gaussian_CPD', generic_CPD(clamp)); + return; +elseif isa(varargin{1}, 'gaussian_CPD') + % This might occur if we are copying an object. + CPD = varargin{1}; + return; +end +CPD = init_fields; + +CPD = class(CPD, 'gaussian_CPD', generic_CPD(0)); + + +% parse mandatory arguments +if ~isstr(varargin{1}) % pass in bnet + bnet = varargin{1}; + self = varargin{2}; + args = varargin(3:end); + ns = bnet.node_sizes; + ps = parents(bnet.dag, self); + dps = myintersect(ps, bnet.dnodes); + cps = myintersect(ps, bnet.cnodes); + fam_sz = ns([ps self]); +else + disp('parsing new style') + for i=1:2:length(varargin) + switch varargin{i}, + case 'self', self = varargin{i+1}; + case 'dps', dps = varargin{i+1}; + case 'cps', cps = varargin{i+1}; + case 'sz', fam_sz = varargin{i+1}; + end + end + ps = myunion(dps, cps); + args = varargin; +end + +CPD.self = self; +CPD.sizes = fam_sz; + +% Figure out which (if any) of the parents are discrete, and which cts, and how big they are +% dps = discrete parents, cps = cts parents +CPD.cps = find_equiv_posns(cps, ps); % cts parent index +CPD.dps = find_equiv_posns(dps, ps); +ss = fam_sz(end); +psz = fam_sz(1:end-1); +dpsz = prod(psz(CPD.dps)); +cpsz = sum(psz(CPD.cps)); + +% set default params +CPD.mean = randn(ss, dpsz); +CPD.cov = 100*repmat(eye(ss), [1 1 dpsz]); +CPD.weights = randn(ss, cpsz, dpsz); +CPD.cov_type = 'full'; +CPD.tied_cov = 0; +CPD.clamped_mean = 0; +CPD.clamped_cov = 0; +CPD.clamped_weights = 0; +CPD.cov_prior_weight = 0.01; + +nargs = length(args); +if nargs > 0 + if ~isstr(args{1}) + % gaussian_CPD(bnet, self, mu, Sigma, W, cov_type, tied_cov, clamp_mean, clamp_cov, clamp_weights) + if nargs >= 1 & ~isempty(args{1}), CPD.mean = args{1}; end + if nargs >= 2 & ~isempty(args{2}), CPD.cov = args{2}; end + if nargs >= 3 & ~isempty(args{3}), CPD.weights = args{3}; end + if nargs >= 4 & ~isempty(args{4}), CPD.cov_type = args{4}; end + if nargs >= 5 & ~isempty(args{5}) & strcmp(args{5}, 'tied'), CPD.tied_cov = 1; end + if nargs >= 6 & ~isempty(args{6}), CPD.clamped_mean = 1; end + if nargs >= 7 & ~isempty(args{7}), CPD.clamped_cov = 1; end + if nargs >= 8 & ~isempty(args{8}), CPD.clamped_weights = 1; end + else + CPD = set_fields(CPD, args{:}); + end +end + +% Make sure the matrices have 1 dimension per discrete parent. +% Bug fix due to Xuejing Sun 3/6/01 +CPD.mean = myreshape(CPD.mean, [ss ns(dps)]); +CPD.cov = myreshape(CPD.cov, [ss ss ns(dps)]); +CPD.weights = myreshape(CPD.weights, [ss cpsz ns(dps)]); + +CPD.init_cov = CPD.cov; % we reset to this if things go wrong during learning + +% expected sufficient statistics +CPD.Wsum = zeros(dpsz,1); +CPD.WYsum = zeros(ss, dpsz); +CPD.WXsum = zeros(cpsz, dpsz); +CPD.WYYsum = zeros(ss, ss, dpsz); +CPD.WXXsum = zeros(cpsz, cpsz, dpsz); +CPD.WXYsum = zeros(cpsz, ss, dpsz); + +% For BIC +CPD.nsamples = 0; +switch CPD.cov_type + case 'full', + ncov_params = ss*(ss-1)/2; % since symmetric (and positive definite) + case 'diag', + ncov_params = ss; + otherwise + error(['unrecognized cov_type ' cov_type]); +end +% params = weights + mean + cov +if CPD.tied_cov + CPD.nparams = ss*cpsz*dpsz + ss*dpsz + ncov_params; +else + CPD.nparams = ss*cpsz*dpsz + ss*dpsz + dpsz*ncov_params; +end + + + +clamped = CPD.clamped_mean & CPD.clamped_cov & CPD.clamped_weights; +CPD = set_clamped(CPD, clamped); + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +CPD.self = []; +CPD.sizes = []; +CPD.cps = []; +CPD.dps = []; +CPD.mean = []; +CPD.cov = []; +CPD.weights = []; +CPD.clamped_mean = []; +CPD.clamped_cov = []; +CPD.clamped_weights = []; +CPD.init_cov = []; +CPD.cov_type = []; +CPD.tied_cov = []; +CPD.Wsum = []; +CPD.WYsum = []; +CPD.WXsum = []; +CPD.WYYsum = []; +CPD.WXXsum = []; +CPD.WXYsum = []; +CPD.nsamples = []; +CPD.nparams = []; +CPD.cov_prior_weight = [];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/Old/log_prob_node.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,59 @@ +function L = log_prob_node(CPD, self_ev, pev) +% LOG_PROB_NODE Compute prod_m log P(x(i,m)| x(pi_i,m), theta_i) for node i (gaussian) +% L = log_prob_node(CPD, self_ev, pev) +% +% self_ev(m) is the evidence on this node in case m. +% pev(i,m) is the evidence on the i'th parent in case m (if there are any parents). +% (These may also be cell arrays.) + +if iscell(self_ev), usecell = 1; else usecell = 0; end + +use_log = 1; +ncases = length(self_ev); +nparents = length(CPD.sizes)-1; +assert(ncases == size(pev, 2)); + +if ncases == 0 + L = 0; + return; +end + +if length(CPD.dps)==0 % no discrete parents, so we can vectorize + i = 1; + if usecell + Y = cell2num(self_ev); + else + Y = self_ev; + end + if length(CPD.cps) == 0 + L = gaussian_prob(Y, CPD.mean(:,i), CPD.cov(:,:,i), use_log); + else + if usecell + X = cell2num(pev); + else + X = pev; + end + L = gaussian_prob(Y, CPD.mean(:,i) + CPD.weights(:,:,i)*X, CPD.cov(:,:,i), use_log); + end +else % each case uses a (potentially) different set of parameters + L = 0; + for m=1:ncases + if usecell + dpvals = cat(1, pev{CPD.dps, m}); + else + dpvals = pev(CPD.dps, m); + end + i = subv2ind(CPD.sizes(CPD.dps), dpvals(:)'); + y = self_ev{m}; + if length(CPD.cps) == 0 + L = L + gaussian_prob(y, CPD.mean(:,i), CPD.cov(:,:,i), use_log); + else + if usecell + x = cat(1, pev{CPD.cps, m}); + else + x = pev(CPD.cps, m); + end + L = L + gaussian_prob(y, CPD.mean(:,i) + CPD.weights(:,:,i)*x, CPD.cov(:,:,i), use_log); + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/Old/maximize_params.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,147 @@ +function CPD = maximize_params(CPD, temp) +% MAXIMIZE_PARAMS Set the params of a CPD to their ML values (Gaussian) +% CPD = maximize_params(CPD, temperature) +% +% Temperature is currently only used for entropic prior on Sigma + +% For details, see "Fitting a Conditional Gaussian Distribution", Kevin Murphy, tech. report, +% 1998, available at www.cs.berkeley.edu/~murphyk/papers.html +% Refering to table 2, we use equations 1/2 to estimate the covariance matrix in the untied/tied case, +% and equation 9 to estimate the weight matrix and mean. +% We do not implement spherical Gaussians - the code is already pretty complicated! + +if ~adjustable_CPD(CPD), return; end + +%assert(approxeq(CPD.nsamples, sum(CPD.Wsum))); +assert(~any(isnan(CPD.WXXsum))) +assert(~any(isnan(CPD.WXYsum))) +assert(~any(isnan(CPD.WYYsum))) + +[self_size cpsize dpsize] = size(CPD.weights); + +% Append 1s to the parents, and derive the corresponding cross products. +% This is used when estimate the means and weights simultaneosuly, +% and when estimatting Sigma. +% Let x2 = [x 1]' +XY = zeros(cpsize+1, self_size, dpsize); % XY(:,:,i) = sum_l w(l,i) x2(l) y(l)' +XX = zeros(cpsize+1, cpsize+1, dpsize); % XX(:,:,i) = sum_l w(l,i) x2(l) x2(l)' +YY = zeros(self_size, self_size, dpsize); % YY(:,:,i) = sum_l w(l,i) y(l) y(l)' +for i=1:dpsize + XY(:,:,i) = [CPD.WXYsum(:,:,i) % X*Y + CPD.WYsum(:,i)']; % 1*Y + % [x * [x' 1] = [xx' x + % 1] x' 1] + XX(:,:,i) = [CPD.WXXsum(:,:,i) CPD.WXsum(:,i); + CPD.WXsum(:,i)' CPD.Wsum(i)]; + YY(:,:,i) = CPD.WYYsum(:,:,i); +end + +w = CPD.Wsum(:); +% Set any zeros to one before dividing +% This is valid because w(i)=0 => WYsum(:,i)=0, etc +w = w + (w==0); + +if CPD.clamped_mean + % Estimating B2 and then setting the last column (the mean) to the clamped mean is *not* equivalent + % to estimating B and then adding the clamped_mean to the last column. + if ~CPD.clamped_weights + B = zeros(self_size, cpsize, dpsize); + for i=1:dpsize + if det(CPD.WXXsum(:,:,i))==0 + B(:,:,i) = 0; + else + % Eqn 9 in table 2 of TR + %B(:,:,i) = CPD.WXYsum(:,:,i)' * inv(CPD.WXXsum(:,:,i)); + B(:,:,i) = (CPD.WXXsum(:,:,i) \ CPD.WXYsum(:,:,i))'; + end + end + %CPD.weights = reshape(B, [self_size cpsize dpsize]); + CPD.weights = B; + end +elseif CPD.clamped_weights % KPM 1/25/02 + if ~CPD.clamped_mean % ML estimate is just sample mean of the residuals + for i=1:dpsize + CPD.mean(:,i) = (CPD.WYsum(:,i) - CPD.weights(:,:,i) * CPD.WXsum(:,i)) / w(i); + end + end +else % nothing is clamped, so estimate mean and weights simultaneously + B2 = zeros(self_size, cpsize+1, dpsize); + for i=1:dpsize + if det(XX(:,:,i))==0 % fix by U. Sondhauss 6/27/99 + B2(:,:,i)=0; + else + % Eqn 9 in table 2 of TR + %B2(:,:,i) = XY(:,:,i)' * inv(XX(:,:,i)); + B2(:,:,i) = (XX(:,:,i) \ XY(:,:,i))'; + end + CPD.mean(:,i) = B2(:,cpsize+1,i); + CPD.weights(:,:,i) = B2(:,1:cpsize,i); + end +end + +% Let B2 = [W mu] +if cpsize>0 + B2(:,1:cpsize,:) = reshape(CPD.weights, [self_size cpsize dpsize]); +end +B2(:,cpsize+1,:) = reshape(CPD.mean, [self_size dpsize]); + +% To avoid singular covariance matrices, +% we use the regularization method suggested in "A Quasi-Bayesian approach to estimating +% parameters for mixtures of normal distributions", Hamilton 91. +% If the ML estimate is Sigma = M/N, the MAP estimate is (M+gamma*I) / (N+gamma), +% where gamma >=0 is a smoothing parameter (equivalent sample size of I prior) + +gamma = CPD.cov_prior_weight; + +if ~CPD.clamped_cov + if CPD.cov_prior_entropic % eqn 12 of Brand AI/Stat 99 + Z = 1-temp; + % When temp > 1, Z is negative, so we are dividing by a smaller + % number, ie. increasing the variance. + else + Z = 0; + end + if CPD.tied_cov + S = zeros(self_size, self_size); + % Eqn 2 from table 2 in TR + for i=1:dpsize + S = S + (YY(:,:,i) - B2(:,:,i)*XY(:,:,i)); + end + %denom = max(1, CPD.nsamples + gamma + Z); + denom = CPD.nsamples + gamma + Z; + S = (S + gamma*eye(self_size)) / denom; + if strcmp(CPD.cov_type, 'diag') + S = diag(diag(S)); + end + CPD.cov = repmat(S, [1 1 dpsize]); + else + for i=1:dpsize + % Eqn 1 from table 2 in TR + S = YY(:,:,i) - B2(:,:,i)*XY(:,:,i); + %denom = max(1, w(i) + gamma + Z); % gives wrong answers on mhmm1 + denom = w(i) + gamma + Z; + S = (S + gamma*eye(self_size)) / denom; + CPD.cov(:,:,i) = S; + end + if strcmp(CPD.cov_type, 'diag') + for i=1:dpsize + CPD.cov(:,:,i) = diag(diag(CPD.cov(:,:,i))); + end + end + end +end + + +check_covars = 0; +min_covar = 1e-5; +if check_covars % prevent collapsing to a point + for i=1:dpsize + if min(svd(CPD.cov(:,:,i))) < min_covar + disp(['resetting singular covariance for node ' num2str(CPD.self)]); + CPD.cov(:,:,i) = CPD.init_cov(:,:,i); + end + end +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/Old/update_ess.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,85 @@ +function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) +% UPDATE_ESS Update the Expected Sufficient Statistics of a Gaussian node +% function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) + +%if nargin < 6 +% hidden_bitv = zeros(1, max(fmarginal.domain)); +% hidden_bitv(find(isempty(evidence)))=1; +%end + +dom = fmarginal.domain; +self = dom(end); +ps = dom(1:end-1); +hidden_self = hidden_bitv(self); +cps = myintersect(ps, cnodes); +dps = mysetdiff(ps, cps); +hidden_cps = all(hidden_bitv(cps)); +hidden_dps = all(hidden_bitv(dps)); + +CPD.nsamples = CPD.nsamples + 1; +[ss cpsz dpsz] = size(CPD.weights); % ss = self size + +% Let X be the cts parent (if any), Y be the cts child (self). + +if ~hidden_self & (isempty(cps) | ~hidden_cps) & hidden_dps % all cts nodes are observed, all discrete nodes are hidden + % Since X and Y are observed, SYY = 0, SXX = 0, SXY = 0 + % Since discrete parents are hidden, we do not need to add evidence to w. + w = fmarginal.T(:); + CPD.Wsum = CPD.Wsum + w; + y = evidence{self}; + Cyy = y*y'; + if ~CPD.useC + W = repmat(w(:)',ss,1); % W(y,i) = w(i) + W2 = repmat(reshape(W, [ss 1 dpsz]), [1 ss 1]); % W2(x,y,i) = w(i) + CPD.WYsum = CPD.WYsum + W .* repmat(y(:), 1, dpsz); + CPD.WYYsum = CPD.WYYsum + W2 .* repmat(reshape(Cyy, [ss ss 1]), [1 1 dpsz]); + else + W = w(:)'; + W2 = reshape(W, [1 1 dpsz]); + CPD.WYsum = CPD.WYsum + rep_mult(W, y(:), size(CPD.WYsum)); + CPD.WYYsum = CPD.WYYsum + rep_mult(W2, Cyy, size(CPD.WYYsum)); + end + if cpsz > 0 % X exists + x = cat(1, evidence{cps}); x = x(:); + Cxx = x*x'; + Cxy = x*y'; + if ~CPD.useC + CPD.WXsum = CPD.WXsum + W .* repmat(x(:), 1, dpsz); + CPD.WXXsum = CPD.WXXsum + W2 .* repmat(reshape(Cxx, [cpsz cpsz 1]), [1 1 dpsz]); + CPD.WXYsum = CPD.WXYsum + W2 .* repmat(reshape(Cxy, [cpsz ss 1]), [1 1 dpsz]); + else + CPD.WXsum = CPD.WXsum + rep_mult(W, x(:), size(CPD.WXsum)); + CPD.WXXsum = CPD.WXXsum + rep_mult(W2, Cxx, size(CPD.WXXsum)); + CPD.WXYsum = CPD.WXYsum + rep_mult(W2, Cxy, size(CPD.WXYsum)); + end + end + return; +end + +% general (non-vectorized) case +fullm = add_evidence_to_gmarginal(fmarginal, evidence, ns, cnodes); % slow! + +if dpsz == 1 % no discrete parents + w = 1; +else + w = fullm.T(:); +end + +CPD.Wsum = CPD.Wsum + w; +xi = 1:cpsz; +yi = (cpsz+1):(cpsz+ss); +for i=1:dpsz + muY = fullm.mu(yi, i); + SYY = fullm.Sigma(yi, yi, i); + CPD.WYsum(:,i) = CPD.WYsum(:,i) + w(i)*muY; + CPD.WYYsum(:,:,i) = CPD.WYYsum(:,:,i) + w(i)*(SYY + muY*muY'); % E[X Y] = Cov[X,Y] + E[X] E[Y] + if cpsz > 0 + muX = fullm.mu(xi, i); + SXX = fullm.Sigma(xi, xi, i); + SXY = fullm.Sigma(xi, yi, i); + CPD.WXsum(:,i) = CPD.WXsum(:,i) + w(i)*muX; + CPD.WXXsum(:,:,i) = CPD.WXXsum(:,:,i) + w(i)*(SXX + muX*muX'); + CPD.WXYsum(:,:,i) = CPD.WXYsum(:,:,i) + w(i)*(SXY + muX*muY'); + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/Old/update_tied_ess.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,118 @@ +function CPD = update_tied_ess(CPD, domain, engine, evidence, ns, cnodes) + +if ~adjustable_CPD(CPD), return; end +nCPDs = size(domain, 2); +fmarginal = cell(1, nCPDs); +for l=1:nCPDs + fmarginal{l} = marginal_family(engine, nodes(l)); +end + +[ss cpsz dpsz] = size(CPD.weights); +if const_evidence_pattern(engine) + dom = domain(:,1); + dnodes = mysetdiff(1:length(ns), cnodes); + ddom = myintersect(dom, dnodes); + cdom = myintersect(dom, cnodes); + odom = dom(~isemptycell(evidence(dom))); + hdom = dom(isemptycell(evidence(dom))); + % If all hidden nodes are discrete and all cts nodes are observed + % (e.g., HMM with Gaussian output) + % we can add the observed evidence in parallel + if mysubset(ddom, hdom) & mysubset(cdom, odom) + [mu, Sigma, T] = add_cts_ev_to_marginals(fmarginal, evidence, ns, cnodes); + else + mu = zeros(ss, dpsz, nCPDs); + Sigma = zeros(ss, ss, dpsz, nCPDs); + T = zeros(dpsz, nCPDs); + for l=1:nCPDs + [mu(:,:,l), Sigma(:,:,:,l), T(:,l)] = add_ev_to_marginals(fmarginal{l}, evidence, ns, cnodes); + end + end +end +CPD.nsamples = CPD.nsamples + nCPDs; + + +if dpsz == 1 % no discrete parents + w = 1; +else + w = fullm.T(:); +end +CPD.Wsum = CPD.Wsum + w; +% Let X be the cts parent (if any), Y be the cts child (self). +xi = 1:cpsz; +yi = (cpsz+1):(cpsz+ss); +for i=1:dpsz + muY = fullm.mu(yi, i); + SYY = fullm.Sigma(yi, yi, i); + CPD.WYsum(:,i) = CPD.WYsum(:,i) + w(i)*muY; + CPD.WYYsum(:,:,i) = CPD.WYYsum(:,:,i) + w(i)*(SYY + muY*muY'); % E[X Y] = Cov[X,Y] + E[X] E[Y] + if cpsz > 0 + muX = fullm.mu(xi, i); + SXX = fullm.Sigma(xi, xi, i); + SXY = fullm.Sigma(xi, yi, i); + CPD.WXsum(:,i) = CPD.WXsum(:,i) + w(i)*muX; + CPD.WXYsum(:,:,i) = CPD.WXYsum(:,:,i) + w(i)*(SXY + muX*muY'); + CPD.WXXsum(:,:,i) = CPD.WXXsum(:,:,i) + w(i)*(SXX + muX*muX'); + end +end + + +%%%%%%%%%%%%% + +function fullm = add_evidence_to_marginal(fmarginal, evidence, ns, cnodes) + + +dom = fmarginal.domain; + +% Find out which values of the discrete parents (if any) are compatible with +% the discrete evidence (if any). +dnodes = mysetdiff(1:length(ns), cnodes); +ddom = myintersect(dom, dnodes); +cdom = myintersect(dom, cnodes); +odom = dom(~isemptycell(evidence(dom))); +hdom = dom(isemptycell(evidence(dom))); + +dobs = myintersect(ddom, odom); +dvals = cat(1, evidence{dobs}); +ens = ns; % effective node sizes +ens(dobs) = 1; +S = prod(ens(ddom)); +subs = ind2subv(ens(ddom), 1:S); +mask = find_equiv_posns(dobs, ddom); +subs(mask) = dvals; +supportedQs = subv2ind(ns(ddom), subs); + +if isempty(ddom) + Qarity = 1; +else + Qarity = prod(ns(ddom)); +end +fullm.T = zeros(Qarity, 1); +fullm.T(supportedQs) = fmarginal.T(:); + +% Now put the hidden cts parts into their right blocks, +% leaving the observed cts parts as 0. +cobs = myintersect(cdom, odom); +chid = myintersect(cdom, hdom); +cvals = cat(1, evidence{cobs}); +n = sum(ns(cdom)); +fullm.mu = zeros(n,Qarity); +fullm.Sigma = zeros(n,n,Qarity); + +if ~isempty(chid) + chid_blocks = block(find_equiv_posns(chid, cdom), ns(cdom)); +end +if ~isempty(cobs) + cobs_blocks = block(find_equiv_posns(cobs, cdom), ns(cdom)); +end + +for i=1:length(supportedQs) + Q = supportedQs(i); + if ~isempty(chid) + fullm.mu(chid_blocks, Q) = fmarginal.mu(:, i); + fullm.Sigma(chid_blocks, chid_blocks, Q) = fmarginal.Sigma(:,:,i); + end + if ~isempty(cobs) + fullm.mu(cobs_blocks, Q) = cvals(:); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/adjustable_CPD.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function p = adjustable_CPD(CPD) +% ADJUSTABLE_CPD Does this CPD have any adjustable params? (gaussian) +% p = adjustable_CPD(CPD) + +p = ~CPD.clamped_mean | ~CPD.clamped_cov | ~CPD.clamped_weights;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/convert_CPD_to_table_hidden_ps.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,20 @@ +function T = convert_CPD_to_table_hidden_ps(CPD, self_val) +% CONVERT_CPD_TO_TABLE_HIDDEN_PS Convert a Gaussian CPD to a table +% function T = convert_CPD_to_table_hidden_ps(CPD, self_val) +% +% self_val must be a non-empty vector. +% All the parents are hidden. +% +% This is used by misc/convert_dbn_CPDs_to_tables + +m = CPD.mean; +C = CPD.cov; +W = CPD.weights; + +[ssz dpsize] = size(m); + +T = zeros(dpsize, 1); +for i=1:dpsize + T(i) = gaussian_prob(self_val, m(:,i), C(:,:,i)); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/convert_to_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,71 @@ +function pot = convert_to_pot(CPD, pot_type, domain, evidence) +% CONVERT_TO_POT Convert a Gaussian CPD to one or more potentials +% pot = convert_to_pot(CPD, pot_type, domain, evidence) + +sz = CPD.sizes; +ns = zeros(1, max(domain)); +ns(domain) = sz; + +odom = domain(~isemptycell(evidence(domain))); +ps = domain(1:end-1); +cps = ps(CPD.cps); +dps = ps(CPD.dps); +self = domain(end); +cdom = [cps(:)' self]; +ddom = dps; +cnodes = cdom; + +switch pot_type + case 'u', + error('gaussian utility potentials not yet supported'); + + case 'd', + T = convert_to_table(CPD, domain, evidence); + ns(odom) = 1; + pot = dpot(domain, ns(domain), T); + + case {'c','g'}, + [m, C, W] = gaussian_CPD_params_given_dps(CPD, domain, evidence); + pot = linear_gaussian_to_cpot(m, C, W, domain, ns, cnodes, evidence); + + case 'cg', + [m, C, W] = gaussian_CPD_params_given_dps(CPD, domain, evidence); + % Convert each conditional Gaussian to a canonical potential + cobs = myintersect(cdom, odom); + dobs = myintersect(ddom, odom); + ens = ns; % effective node size + ens(cobs) = 0; + ens(dobs) = 1; + dpsize = prod(ens(dps)); + can = cell(1, dpsize); + for i=1:dpsize + if isempty(W) + can{i} = linear_gaussian_to_cpot(m(:,i), C(:,:,i), [], cdom, ns, cnodes, evidence); + else + can{i} = linear_gaussian_to_cpot(m(:,i), C(:,:,i), W(:,:,i), cdom, ns, cnodes, evidence); + end + end + pot = cgpot(ddom, cdom, ens, can); + + case 'scg', + [m, C, W] = gaussian_CPD_params_given_dps(CPD, domain, evidence); + cobs = myintersect(cdom, odom); + dobs = myintersect(ddom, odom); + ens = ns; % effective node size + ens(cobs) = 0; + ens(dobs) = 1; + dpsize = prod(ens(dps)); + cpsize = size(W, 2); % cts parents size + ss = size(m, 1); % self size + cheaddom = self; + ctaildom = cps(:)'; + pot_array = cell(1, dpsize); + for i=1:dpsize + pot_array{i} = scgcpot(ss, cpsize, 1, m(:,i), W(:,:,i), C(:,:,i)); + end + pot = scgpot(ddom, cheaddom, ctaildom, ens, pot_array); + + otherwise, + error(['unrecognized pot_type' pot_type]) +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/convert_to_table.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,38 @@ +function T = convert_to_table(CPD, domain, evidence) +% CONVERT_TO_TABLE Convert a Gaussian CPD to a table +% T = convert_to_table(CPD, domain, evidence) + + +sz = CPD.sizes; +ns = zeros(1, max(domain)); +ns(domain) = sz; + +odom = domain(~isemptycell(evidence(domain))); +ps = domain(1:end-1); +cps = ps(CPD.cps); +dps = ps(CPD.dps); +self = domain(end); +cdom = [cps(:)' self]; +ddom = dps; +cnodes = cdom; + +[m, C, W] = gaussian_CPD_params_given_dps(CPD, domain, evidence); + + +ns(odom) = 1; +dpsize = prod(ns(dps)); +self = domain(end); +assert(myismember(self, odom)); +self_val = evidence{self}; +T = zeros(dpsize, 1); +if length(cps) > 0 + assert(~any(isemptycell(evidence(cps)))); + cps_vals = cat(1, evidence{cps}); + for i=1:dpsize + T(i) = gaussian_prob(self_val, m(:,i) + W(:,:,i)*cps_vals, C(:,:,i)); + end +else + for i=1:dpsize + T(i) = gaussian_prob(self_val, m(:,i), C(:,:,i)); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/display.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,4 @@ +function display(CPD) + +disp('gaussian_CPD object'); +disp(struct(CPD));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/gaussian_CPD.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,161 @@ +function CPD = gaussian_CPD(bnet, self, varargin) +% GAUSSIAN_CPD Make a conditional linear Gaussian distrib. +% +% CPD = gaussian_CPD(bnet, node, ...) will create a CPD with random parameters, +% where node is the number of a node in this equivalence class. + +% To define this CPD precisely, call the continuous (cts) parents (if any) X, +% the discrete parents (if any) Q, and this node Y. Then the distribution on Y is: +% - no parents: Y ~ N(mu, Sigma) +% - cts parents : Y|X=x ~ N(mu + W x, Sigma) +% - discrete parents: Y|Q=i ~ N(mu(i), Sigma(i)) +% - cts and discrete parents: Y|X=x,Q=i ~ N(mu(i) + W(i) x, Sigma(i)) +% +% The list below gives optional arguments [default value in brackets]. +% (Let ns(i) be the size of node i, X = ns(X), Y = ns(Y) and Q = prod(ns(Q)).) +% Parameters will be reshaped to the right size if necessary. +% +% mean - mu(:,i) is the mean given Q=i [ randn(Y,Q) ] +% cov - Sigma(:,:,i) is the covariance given Q=i [ repmat(100*eye(Y,Y), [1 1 Q]) ] +% weights - W(:,:,i) is the regression matrix given Q=i [ randn(Y,X,Q) ] +% cov_type - if 'diag', Sigma(:,:,i) is diagonal [ 'full' ] +% tied_cov - if 1, we constrain Sigma(:,:,i) to be the same for all i [0] +% clamp_mean - if 1, we do not adjust mu(:,i) during learning [0] +% clamp_cov - if 1, we do not adjust Sigma(:,:,i) during learning [0] +% clamp_weights - if 1, we do not adjust W(:,:,i) during learning [0] +% cov_prior_weight - weight given to I prior for estimating Sigma [0.01] +% cov_prior_entropic - if 1, we also use an entropic prior for Sigma [0] +% +% e.g., CPD = gaussian_CPD(bnet, i, 'mean', [0; 0], 'clamp_mean', 1) + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + clamp = 0; + CPD = class(CPD, 'gaussian_CPD', generic_CPD(clamp)); + return; +elseif isa(bnet, 'gaussian_CPD') + % This might occur if we are copying an object. + CPD = bnet; + return; +end +CPD = init_fields; + +CPD = class(CPD, 'gaussian_CPD', generic_CPD(0)); + +args = varargin; +ns = bnet.node_sizes; +ps = parents(bnet.dag, self); +dps = myintersect(ps, bnet.dnodes); +cps = myintersect(ps, bnet.cnodes); +fam_sz = ns([ps self]); + +CPD.self = self; +CPD.sizes = fam_sz; + +% Figure out which (if any) of the parents are discrete, and which cts, and how big they are +% dps = discrete parents, cps = cts parents +CPD.cps = find_equiv_posns(cps, ps); % cts parent index +CPD.dps = find_equiv_posns(dps, ps); +ss = fam_sz(end); +psz = fam_sz(1:end-1); +dpsz = prod(psz(CPD.dps)); +cpsz = sum(psz(CPD.cps)); + +% set default params +CPD.mean = randn(ss, dpsz); +CPD.cov = 100*repmat(eye(ss), [1 1 dpsz]); +CPD.weights = randn(ss, cpsz, dpsz); +CPD.cov_type = 'full'; +CPD.tied_cov = 0; +CPD.clamped_mean = 0; +CPD.clamped_cov = 0; +CPD.clamped_weights = 0; +CPD.cov_prior_weight = 0.01; +CPD.cov_prior_entropic = 0; +nargs = length(args); +if nargs > 0 + CPD = set_fields(CPD, args{:}); +end + +% Make sure the matrices have 1 dimension per discrete parent. +% Bug fix due to Xuejing Sun 3/6/01 +CPD.mean = myreshape(CPD.mean, [ss ns(dps)]); +CPD.cov = myreshape(CPD.cov, [ss ss ns(dps)]); +CPD.weights = myreshape(CPD.weights, [ss cpsz ns(dps)]); + +% Precompute indices into block structured matrices +% to speed up CPD_to_lambda_msg and CPD_to_pi +cpsizes = CPD.sizes(CPD.cps); +CPD.cps_block_ndx = cell(1, length(cps)); +for i=1:length(cps) + CPD.cps_block_ndx{i} = block(i, cpsizes); +end + +%%%%%%%%%%% +% Learning stuff + +% expected sufficient statistics +CPD.Wsum = zeros(dpsz,1); +CPD.WYsum = zeros(ss, dpsz); +CPD.WXsum = zeros(cpsz, dpsz); +CPD.WYYsum = zeros(ss, ss, dpsz); +CPD.WXXsum = zeros(cpsz, cpsz, dpsz); +CPD.WXYsum = zeros(cpsz, ss, dpsz); + +% For BIC +CPD.nsamples = 0; +switch CPD.cov_type + case 'full', + % since symmetric + %ncov_params = ss*(ss-1)/2; + ncov_params = ss*(ss+1)/2; + case 'diag', + ncov_params = ss; + otherwise + error(['unrecognized cov_type ' cov_type]); +end +% params = weights + mean + cov +if CPD.tied_cov + CPD.nparams = ss*cpsz*dpsz + ss*dpsz + ncov_params; +else + CPD.nparams = ss*cpsz*dpsz + ss*dpsz + dpsz*ncov_params; +end + +% for speeding up maximize_params +CPD.useC = exist('rep_mult'); + +clamped = CPD.clamped_mean & CPD.clamped_cov & CPD.clamped_weights; +CPD = set_clamped(CPD, clamped); + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +CPD.self = []; +CPD.sizes = []; +CPD.cps = []; +CPD.dps = []; +CPD.mean = []; +CPD.cov = []; +CPD.weights = []; +CPD.clamped_mean = []; +CPD.clamped_cov = []; +CPD.clamped_weights = []; +CPD.cov_type = []; +CPD.tied_cov = []; +CPD.Wsum = []; +CPD.WYsum = []; +CPD.WXsum = []; +CPD.WYYsum = []; +CPD.WXXsum = []; +CPD.WXYsum = []; +CPD.nsamples = []; +CPD.nparams = []; +CPD.cov_prior_weight = []; +CPD.cov_prior_entropic = []; +CPD.useC = []; +CPD.cps_block_ndx = [];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/gaussian_CPD_params_given_dps.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,28 @@ +function [m, C, W] = gaussian_CPD_params_given_dps(CPD, domain, evidence) +% GAUSSIAN_CPD_PARAMS_GIVEN_EV_ON_DPS Extract parameters given evidence on all discrete parents +% function [m, C, W] = gaussian_CPD_params_given_ev_on_dps(CPD, domain, evidence) + +ps = domain(1:end-1); +dps = ps(CPD.dps); +if isempty(dps) + m = CPD.mean; + C = CPD.cov; + W = CPD.weights; +else + odom = domain(~isemptycell(evidence(domain))); + dops = myintersect(dps, odom); + dpvals = cat(1, evidence{dops}); + if length(dops) == length(dps) + dpsizes = CPD.sizes(CPD.dps); + dpval = subv2ind(dpsizes, dpvals(:)'); + m = CPD.mean(:, dpval); + C = CPD.cov(:, :, dpval); + W = CPD.weights(:, :, dpval); + else + map = find_equiv_posns(dops, dps); + index = mk_multi_index(length(dps), map, dpvals); + m = CPD.mean(:, index{:}); + C = CPD.cov(:, :, index{:}); + W = CPD.weights(:, :, index{:}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/get_field.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,19 @@ +function val = get_params(CPD, name) +% GET_PARAMS Get the parameters (fields) for a gaussian_CPD object +% val = get_params(CPD, name) +% +% The following fields can be accessed +% +% mean - mu(:,i) is the mean given Q=i +% cov - Sigma(:,:,i) is the covariance given Q=i +% weights - W(:,:,i) is the regression matrix given Q=i +% +% e.g., mean = get_params(CPD, 'mean') + +switch name + case 'mean', val = CPD.mean; + case 'cov', val = CPD.cov; + case 'weights', val = CPD.weights; + otherwise, + error(['invalid argument name ' name]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/learn_params.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,31 @@ +function CPD = learn_params(CPD, fam, data, ns, cnodes) +%function CPD = learn_params(CPD, fam, data, ns, cnodes) +% LEARN_PARAMS Compute the maximum likelihood estimate of the params of a gaussian CPD given complete data +% CPD = learn_params(CPD, fam, data, ns, cnodes) +% +% data(i,m) is the value of node i in case m (can be cell array). +% We assume this node has a maximize_params method. + +ncases = size(data, 2); +CPD = reset_ess(CPD); +% make a fully observed joint distribution over the family +fmarginal.domain = fam; +fmarginal.T = 1; +fmarginal.mu = []; +fmarginal.Sigma = []; +if ~iscell(data) + cases = num2cell(data); +else + cases = data; +end +hidden_bitv = zeros(1, max(fam)); +for m=1:ncases + % specify (as a bit vector) which elements in the family domain are hidden + hidden_bitv = zeros(1, max(fmarginal.domain)); + ev = cases(:,m); + hidden_bitv(find(isempty(ev)))=1; + CPD = update_ess(CPD, fmarginal, ev, ns, cnodes, hidden_bitv); +end +CPD = maximize_params(CPD); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/log_prob_node.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,49 @@ +function L = log_prob_node(CPD, self_ev, pev) +% LOG_PROB_NODE Compute prod_m log P(x(i,m)| x(pi_i,m), theta_i) for node i (gaussian) +% L = log_prob_node(CPD, self_ev, pev) +% +% self_ev(m) is the evidence on this node in case m. +% pev(i,m) is the evidence on the i'th parent in case m (if there are any parents). +% (These may also be cell arrays.) + +if iscell(self_ev), usecell = 1; else usecell = 0; end + +use_log = 1; +ncases = length(self_ev); +nparents = length(CPD.sizes)-1; +assert(ncases == size(pev, 2)); + +if ncases == 0 + L = 0; + return; +end + +L = 0; +for m=1:ncases + if isempty(CPD.dps) + i = 1; + else + if usecell + dpvals = cat(1, pev{CPD.dps, m}); + else + dpvals = pev(CPD.dps, m); + end + i = subv2ind(CPD.sizes(CPD.dps), dpvals(:)'); + end + if usecell + y = self_ev{m}; + else + y = self_ev(m); + end + if length(CPD.cps) == 0 + L = L + gaussian_prob(y, CPD.mean(:,i), CPD.cov(:,:,i), use_log); + else + if usecell + x = cat(1, pev{CPD.cps, m}); + else + x = pev(CPD.cps, m); + end + L = L + gaussian_prob(y, CPD.mean(:,i) + CPD.weights(:,:,i)*x, CPD.cov(:,:,i), use_log); + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/maximize_params.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,68 @@ +function CPD = maximize_params(CPD, temp) +% MAXIMIZE_PARAMS Set the params of a CPD to their ML values (Gaussian) +% CPD = maximize_params(CPD, temperature) +% +% Temperature is currently ignored. + +if ~adjustable_CPD(CPD), return; end + + +if CPD.clamped_mean + cl_mean = CPD.mean; +else + cl_mean = []; +end + +if CPD.clamped_cov + cl_cov = CPD.cov; +else + cl_cov = []; +end + +if CPD.clamped_weights + cl_weights = CPD.weights; +else + cl_weights = []; +end + +[ssz psz Q] = size(CPD.weights); + +[ss cpsz dpsz] = size(CPD.weights); % ss = self size = ssz +if cpsz > CPD.nsamples + fprintf('gaussian_CPD/maximize_params: warning: input dimension (%d) > nsamples (%d)\n', ... + cpsz, CPD.nsamples); +end + +prior = repmat(CPD.cov_prior_weight*eye(ssz,ssz), [1 1 Q]); + + +[CPD.mean, CPD.cov, CPD.weights] = ... + clg_Mstep(CPD.Wsum, CPD.WYsum, CPD.WYYsum, [], CPD.WXsum, CPD.WXXsum, CPD.WXYsum, ... + 'cov_type', CPD.cov_type, 'clamped_mean', cl_mean, ... + 'clamped_cov', cl_cov, 'clamped_weights', cl_weights, ... + 'tied_cov', CPD.tied_cov, ... + 'cov_prior', prior); + +if 0 +CPD.mean = reshape(CPD.mean, [ss dpsz]); +CPD.cov = reshape(CPD.cov, [ss ss dpsz]); +CPD.weights = reshape(CPD.weights, [ss cpsz dpsz]); +end + +% Bug fix 11 May 2003 KPM +% clg_Mstep collapses all discrete parents into one mega-node +% but convert_to_CPT needs access to each parent separately +sz = CPD.sizes; +ss = sz(end); + +% Bug fix KPM 20 May 2003: +cpsz = sum(sz(CPD.cps)); +%if isempty(CPD.cps) +% cpsz = 0; +%else +% cpsz = sz(CPD.cps); +%end +dpsz = sz(CPD.dps); +CPD.mean = myreshape(CPD.mean, [ss dpsz]); +CPD.cov = myreshape(CPD.cov, [ss ss dpsz]); +CPD.weights = myreshape(CPD.weights, [ss cpsz dpsz]);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/maximize_params_debug.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,189 @@ +function CPD = maximize_params(CPD, temp) +% MAXIMIZE_PARAMS Set the params of a CPD to their ML values (Gaussian) +% CPD = maximize_params(CPD, temperature) +% +% Temperature is currently ignored. + +if ~adjustable_CPD(CPD), return; end + +CPD1 = struct(new_maximize_params(CPD)); +CPD2 = struct(old_maximize_params(CPD)); +assert(approxeq(CPD1.mean, CPD2.mean)) +assert(approxeq(CPD1.cov, CPD2.cov)) +assert(approxeq(CPD1.weights, CPD2.weights)) + +CPD = new_maximize_params(CPD); + +%%%%%%% +function CPD = new_maximize_params(CPD) + +if CPD.clamped_mean + cl_mean = CPD.mean; +else + cl_mean = []; +end + +if CPD.clamped_cov + cl_cov = CPD.cov; +else + cl_cov = []; +end + +if CPD.clamped_weights + cl_weights = CPD.weights; +else + cl_weights = []; +end + +[ssz psz Q] = size(CPD.weights); + +prior = repmat(CPD.cov_prior_weight*eye(ssz,ssz), [1 1 Q]); +[CPD.mean, CPD.cov, CPD.weights] = ... + Mstep_clg('w', CPD.Wsum, 'YY', CPD.WYYsum, 'Y', CPD.WYsum, 'YTY', [], ... + 'XX', CPD.WXXsum, 'XY', CPD.WXYsum, 'X', CPD.WXsum, ... + 'cov_type', CPD.cov_type, 'clamped_mean', cl_mean, ... + 'clamped_cov', cl_cov, 'clamped_weights', cl_weights, ... + 'tied_cov', CPD.tied_cov, ... + 'cov_prior', prior); + + +%%%%%%%%%%% + +function CPD = old_maximize_params(CPD) + + +if ~adjustable_CPD(CPD), return; end + +%assert(approxeq(CPD.nsamples, sum(CPD.Wsum))); +assert(~any(isnan(CPD.WXXsum))) +assert(~any(isnan(CPD.WXYsum))) +assert(~any(isnan(CPD.WYYsum))) + +[self_size cpsize dpsize] = size(CPD.weights); + +% Append 1s to the parents, and derive the corresponding cross products. +% This is used when estimate the means and weights simultaneosuly, +% and when estimatting Sigma. +% Let x2 = [x 1]' +XY = zeros(cpsize+1, self_size, dpsize); % XY(:,:,i) = sum_l w(l,i) x2(l) y(l)' +XX = zeros(cpsize+1, cpsize+1, dpsize); % XX(:,:,i) = sum_l w(l,i) x2(l) x2(l)' +YY = zeros(self_size, self_size, dpsize); % YY(:,:,i) = sum_l w(l,i) y(l) y(l)' +for i=1:dpsize + XY(:,:,i) = [CPD.WXYsum(:,:,i) % X*Y + CPD.WYsum(:,i)']; % 1*Y + % [x * [x' 1] = [xx' x + % 1] x' 1] + XX(:,:,i) = [CPD.WXXsum(:,:,i) CPD.WXsum(:,i); + CPD.WXsum(:,i)' CPD.Wsum(i)]; + YY(:,:,i) = CPD.WYYsum(:,:,i); +end + +w = CPD.Wsum(:); +% Set any zeros to one before dividing +% This is valid because w(i)=0 => WYsum(:,i)=0, etc +w = w + (w==0); + +if CPD.clamped_mean + % Estimating B2 and then setting the last column (the mean) to the clamped mean is *not* equivalent + % to estimating B and then adding the clamped_mean to the last column. + if ~CPD.clamped_weights + B = zeros(self_size, cpsize, dpsize); + for i=1:dpsize + if det(CPD.WXXsum(:,:,i))==0 + B(:,:,i) = 0; + else + % Eqn 9 in table 2 of TR + %B(:,:,i) = CPD.WXYsum(:,:,i)' * inv(CPD.WXXsum(:,:,i)); + B(:,:,i) = (CPD.WXXsum(:,:,i) \ CPD.WXYsum(:,:,i))'; + end + end + %CPD.weights = reshape(B, [self_size cpsize dpsize]); + CPD.weights = B; + end +elseif CPD.clamped_weights % KPM 1/25/02 + if ~CPD.clamped_mean % ML estimate is just sample mean of the residuals + for i=1:dpsize + CPD.mean(:,i) = (CPD.WYsum(:,i) - CPD.weights(:,:,i) * CPD.WXsum(:,i)) / w(i); + end + end +else % nothing is clamped, so estimate mean and weights simultaneously + B2 = zeros(self_size, cpsize+1, dpsize); + for i=1:dpsize + if det(XX(:,:,i))==0 % fix by U. Sondhauss 6/27/99 + B2(:,:,i)=0; + else + % Eqn 9 in table 2 of TR + %B2(:,:,i) = XY(:,:,i)' * inv(XX(:,:,i)); + B2(:,:,i) = (XX(:,:,i) \ XY(:,:,i))'; + end + CPD.mean(:,i) = B2(:,cpsize+1,i); + CPD.weights(:,:,i) = B2(:,1:cpsize,i); + end +end + +% Let B2 = [W mu] +if cpsize>0 + B2(:,1:cpsize,:) = reshape(CPD.weights, [self_size cpsize dpsize]); +end +B2(:,cpsize+1,:) = reshape(CPD.mean, [self_size dpsize]); + +% To avoid singular covariance matrices, +% we use the regularization method suggested in "A Quasi-Bayesian approach to estimating +% parameters for mixtures of normal distributions", Hamilton 91. +% If the ML estimate is Sigma = M/N, the MAP estimate is (M+gamma*I) / (N+gamma), +% where gamma >=0 is a smoothing parameter (equivalent sample size of I prior) + +gamma = CPD.cov_prior_weight; + +if ~CPD.clamped_cov + if CPD.cov_prior_entropic % eqn 12 of Brand AI/Stat 99 + Z = 1-temp; + % When temp > 1, Z is negative, so we are dividing by a smaller + % number, ie. increasing the variance. + else + Z = 0; + end + if CPD.tied_cov + S = zeros(self_size, self_size); + % Eqn 2 from table 2 in TR + for i=1:dpsize + S = S + (YY(:,:,i) - B2(:,:,i)*XY(:,:,i)); + end + %denom = CPD.nsamples + gamma + Z; + denom = CPD.nsamples + Z; + S = (S + gamma*eye(self_size)) / denom; + if strcmp(CPD.cov_type, 'diag') + S = diag(diag(S)); + end + CPD.cov = repmat(S, [1 1 dpsize]); + else + for i=1:dpsize + % Eqn 1 from table 2 in TR + S = YY(:,:,i) - B2(:,:,i)*XY(:,:,i); + %denom = w(i) + gamma + Z; + denom = w(i) + Z; + S = (S + gamma*eye(self_size)) / denom; + CPD.cov(:,:,i) = S; + end + if strcmp(CPD.cov_type, 'diag') + for i=1:dpsize + CPD.cov(:,:,i) = diag(diag(CPD.cov(:,:,i))); + end + end + end +end + + +check_covars = 0; +min_covar = 1e-5; +if check_covars % prevent collapsing to a point + for i=1:dpsize + if min(svd(CPD.cov(:,:,i))) < min_covar + disp(['resetting singular covariance for node ' num2str(CPD.self)]); + CPD.cov(:,:,i) = CPD.init_cov(:,:,i); + end + end +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/private/CPD_to_linear_gaussian.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,19 @@ +function [mu, Sigma, W] = CPD_to_linear_gaussian(CPD, domain, ns, cnodes, evidence) + +ps = domain(1:end-1); +dnodes = mysetdiff(1:length(ns), cnodes); +dps = myintersect(ps, dnodes); % discrete parents + +if isempty(dps) + Q = 1; +else + assert(~any(isemptycell(evidence(dps)))); + dpvals = cat(1, evidence{dps}); + Q = subv2ind(ns(dps), dpvals(:)'); +end + +mu = CPD.mean(:,Q); +Sigma = CPD.cov(:,:,Q); +W = CPD.weights(:,:,Q); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/private/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +/CPD_to_linear_gaussian.m/1.1.1.1/Wed May 29 15:59:52 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/private/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@gaussian_CPD/private
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/private/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/reset_ess.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function CPD = reset_ess(CPD) +% RESET_ESS Reset the Expected Sufficient Statistics for a Gaussian CPD. +% CPD = reset_ess(CPD) + +CPD.nsamples = 0; +CPD.Wsum = zeros(size(CPD.Wsum)); +CPD.WYsum = zeros(size(CPD.WYsum)); +CPD.WYYsum = zeros(size(CPD.WYYsum)); +CPD.WXsum = zeros(size(CPD.WXsum)); +CPD.WXXsum = zeros(size(CPD.WXXsum)); +CPD.WXYsum = zeros(size(CPD.WXYsum));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/sample_node.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,22 @@ +function y = sample_node(CPD, pev) +% SAMPLE_NODE Draw a random sample from P(Xi | x(pi_i), theta_i) (gaussian) +% y = sample_node(CPD, parent_evidence) +% +% pev{i} is the value of the i'th parent (if there are any parents) +% y is the sampled value (a scalar or vector) + +if length(CPD.dps)==0 + i = 1; +else + dpvals = cat(1, pev{CPD.dps}); + i = subv2ind(CPD.sizes(CPD.dps), dpvals(:)'); +end + +if length(CPD.cps) == 0 + y = gsamp(CPD.mean(:,i), CPD.cov(:,:,i), 1); +else + pev = pev(:); + x = cat(1, pev{CPD.cps}); + y = gsamp(CPD.mean(:,i) + CPD.weights(:,:,i)*x(:), CPD.cov(:,:,i), 1); +end +y = y(:);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/set_fields.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,43 @@ +function CPD = set_fields(CPD, varargin) +% SET_PARAMS Set the parameters (fields) for a gaussian_CPD object +% CPD = set_params(CPD, name/value pairs) +% +% The following optional arguments can be specified in the form of name/value pairs: +% +% mean - mu(:,i) is the mean given Q=i +% cov - Sigma(:,:,i) is the covariance given Q=i +% weights - W(:,:,i) is the regression matrix given Q=i +% cov_type - if 'diag', Sigma(:,:,i) is diagonal +% tied_cov - if 1, we constrain Sigma(:,:,i) to be the same for all i +% clamp_mean - if 1, we do not adjust mu(:,i) during learning +% clamp_cov - if 1, we do not adjust Sigma(:,:,i) during learning +% clamp_weights - if 1, we do not adjust W(:,:,i) during learning +% clamp - if 1, we do not adjust any params +% cov_prior_weight - weight given to I prior for estimating Sigma +% cov_prior_entropic - if 1, we also use an entropic prior for Sigma [0] +% +% e.g., CPD = set_params(CPD, 'mean', [0;0]) + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'mean', CPD.mean = args{i+1}; + case 'cov', CPD.cov = args{i+1}; + case 'weights', CPD.weights = args{i+1}; + case 'cov_type', CPD.cov_type = args{i+1}; + %case 'tied_cov', CPD.tied_cov = strcmp(args{i+1}, 'yes'); + case 'tied_cov', CPD.tied_cov = args{i+1}; + case 'clamp_mean', CPD.clamped_mean = args{i+1}; + case 'clamp_cov', CPD.clamped_cov = args{i+1}; + case 'clamp_weights', CPD.clamped_weights = args{i+1}; + case 'clamp', clamp = args{i+1}; + CPD.clamped_mean = clamp; + CPD.clamped_cov = clamp; + CPD.clamped_weights = clamp; + case 'cov_prior_weight', CPD.cov_prior_weight = args{i+1}; + case 'cov_prior_entropic', CPD.cov_prior_entropic = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gaussian_CPD/update_ess.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,88 @@ +function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) +% UPDATE_ESS Update the Expected Sufficient Statistics of a Gaussian node +% function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) + +%if nargin < 6 +% hidden_bitv = zeros(1, max(fmarginal.domain)); +% hidden_bitv(find(isempty(evidence)))=1; +%end + +dom = fmarginal.domain; +self = dom(end); +ps = dom(1:end-1); +cps = myintersect(ps, cnodes); +dps = mysetdiff(ps, cps); + +CPD.nsamples = CPD.nsamples + 1; +[ss cpsz dpsz] = size(CPD.weights); % ss = self size +[ss dpsz] = size(CPD.mean); + +% Let X be the cts parent (if any), Y be the cts child (self). + +if ~hidden_bitv(self) & ~any(hidden_bitv(cps)) & all(hidden_bitv(dps)) + % Speedup for the common case that all cts nodes are observed, all discrete nodes are hidden + % Since X and Y are observed, SYY = 0, SXX = 0, SXY = 0 + % Since discrete parents are hidden, we do not need to add evidence to w. + w = fmarginal.T(:); + CPD.Wsum = CPD.Wsum + w; + y = evidence{self}; + Cyy = y*y'; + if ~CPD.useC + WY = repmat(w(:)',ss,1); % WY(y,i) = w(i) + WYY = repmat(reshape(WY, [ss 1 dpsz]), [1 ss 1]); % WYY(y,y',i) = w(i) + %CPD.WYsum = CPD.WYsum + WY .* repmat(y(:), 1, dpsz); + CPD.WYsum = CPD.WYsum + y(:) * w(:)'; + CPD.WYYsum = CPD.WYYsum + WYY .* repmat(reshape(Cyy, [ss ss 1]), [1 1 dpsz]); + else + W = w(:)'; + W2 = reshape(W, [1 1 dpsz]); + CPD.WYsum = CPD.WYsum + rep_mult(W, y(:), size(CPD.WYsum)); + CPD.WYYsum = CPD.WYYsum + rep_mult(W2, Cyy, size(CPD.WYYsum)); + end + if cpsz > 0 % X exists + x = cat(1, evidence{cps}); x = x(:); + Cxx = x*x'; + Cxy = x*y'; + WX = repmat(w(:)',cpsz,1); % WX(x,i) = w(i) + WXX = repmat(reshape(WX, [cpsz 1 dpsz]), [1 cpsz 1]); % WXX(x,x',i) = w(i) + WXY = repmat(reshape(WX, [cpsz 1 dpsz]), [1 ss 1]); % WXY(x,y,i) = w(i) + if ~CPD.useC + CPD.WXsum = CPD.WXsum + WX .* repmat(x(:), 1, dpsz); + CPD.WXXsum = CPD.WXXsum + WXX .* repmat(reshape(Cxx, [cpsz cpsz 1]), [1 1 dpsz]); + CPD.WXYsum = CPD.WXYsum + WXY .* repmat(reshape(Cxy, [cpsz ss 1]), [1 1 dpsz]); + else + CPD.WXsum = CPD.WXsum + rep_mult(W, x(:), size(CPD.WXsum)); + CPD.WXXsum = CPD.WXXsum + rep_mult(W2, Cxx, size(CPD.WXXsum)); + CPD.WXYsum = CPD.WXYsum + rep_mult(W2, Cxy, size(CPD.WXYsum)); + end + end + return; +end + +% general (non-vectorized) case +fullm = add_evidence_to_gmarginal(fmarginal, evidence, ns, cnodes); % slow! + +if dpsz == 1 % no discrete parents + w = 1; +else + w = fullm.T(:); +end + +CPD.Wsum = CPD.Wsum + w; +xi = 1:cpsz; +yi = (cpsz+1):(cpsz+ss); +for i=1:dpsz + muY = fullm.mu(yi, i); + SYY = fullm.Sigma(yi, yi, i); + CPD.WYsum(:,i) = CPD.WYsum(:,i) + w(i)*muY; + CPD.WYYsum(:,:,i) = CPD.WYYsum(:,:,i) + w(i)*(SYY + muY*muY'); % E[X Y] = Cov[X,Y] + E[X] E[Y] + if cpsz > 0 + muX = fullm.mu(xi, i); + SXX = fullm.Sigma(xi, xi, i); + SXY = fullm.Sigma(xi, yi, i); + CPD.WXsum(:,i) = CPD.WXsum(:,i) + w(i)*muX; + CPD.WXXsum(:,:,i) = CPD.WXXsum(:,:,i) + w(i)*(SXX + muX*muX'); + CPD.WXYsum(:,:,i) = CPD.WXYsum(:,:,i) + w(i)*(SXY + muX*muY'); + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@generic_CPD/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +/README/1.1.1.1/Wed May 29 15:59:52 2002// +/adjustable_CPD.m/1.1.1.1/Wed May 29 15:59:52 2002// +/display.m/1.1.1.1/Wed May 29 15:59:52 2002// +/generic_CPD.m/1.1.1.1/Wed May 29 15:59:52 2002// +/learn_params.m/1.1.1.1/Thu Jun 10 01:53:20 2004// +/log_prior.m/1.1.1.1/Wed May 29 15:59:52 2002// +/set_clamped.m/1.1.1.1/Wed May 29 15:59:52 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@generic_CPD/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@generic_CPD/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@generic_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@generic_CPD/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@generic_CPD/Old/BIC_score_CPD.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,26 @@ +function score = BIC_score_CPD(CPD, fam, data, ns, cnodes) +% BIC_score_CPD Compute the BIC score of a generic CPD +% score = BIC_score_CPD(CPD, fam, data, ns, cnodes) +% +% We assume this node has a maximize_params method + +ncases = size(data, 2); +CPD = reset_ess(CPD); +% make a fully observed joint distribution over the family +fmarginal.domain = fam; +fmarginal.T = 1; +fmarginal.mu = []; +fmarginal.Sigma = []; +if ~iscell(data) + cases = num2cell(data); +else + cases = data; +end +for m=1:ncases + CPD = update_ess(CPD, fmarginal, cases(:,m), ns, cnodes); +end +CPD = maximize_params(CPD); +self = fam(end); +ps = fam(1:end-1); +L = log_prob_node(CPD, cases(self,:), cases(ps,:)); +score = L - 0.5*CPD.nparams*log(ncases);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@generic_CPD/Old/CPD_to_dpots.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +function pots = CPD_to_dpots(CPD, domain, ns, cnodes, evidence) +% CPD_TO_DPOTS Convert the CPD to several discrete potentials, for different instantiations (generic) +% pots = CPD_to_dpots(CPD, domain, ns, cnodes, evidence) +% +% domain(:,i) is the domain of the i'th instantiation of CPD. +% node_sizes(i) is the size of node i. +% cnodes = all the cts nodes +% evidence{i} is the evidence on the i'th node. +% +% This just calls CPD_to_dpot for each domain. + +nCPDs = size(domain,2); +pots = cell(1,nCPDs); +for i=1:nCPDs + pots{i} = CPD_to_dpot(CPD, domain(:,i), ns, cnodes, evidence); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@generic_CPD/Old/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,3 @@ +/BIC_score_CPD.m/1.1.1.1/Wed May 29 15:59:52 2002// +/CPD_to_dpots.m/1.1.1.1/Wed May 29 15:59:52 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@generic_CPD/Old/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@generic_CPD/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@generic_CPD/Old/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@generic_CPD/README Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +A generic CPD implements general purpose functions like 'display', +that subtypes can inherit.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@generic_CPD/adjustable_CPD.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function p = adjustable_CPD(CPD) +% ADJUSTABLE_CPD Does this CPD have any adjustable params? (generic) +% p = adjustable_CPD(CPD) + +p = ~CPD.clamped;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@generic_CPD/display.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,3 @@ +function display(CPD) + +disp(struct(CPD));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@generic_CPD/generic_CPD.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +function CPD = generic_CPD(clamped) +% GENERIC_CPD Virtual constructor for generic CPD +% CPD = discrete_CPD(clamped) + +if nargin < 1, clamped = 0; end + +CPD.clamped = clamped; +CPD = class(CPD, 'generic_CPD');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@generic_CPD/learn_params.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,32 @@ +function CPD = learn_params(CPD, fam, data, ns, cnodes) +% LEARN_PARAMS Compute the maximum likelihood estimate of the params of a generic CPD given complete data +% CPD = learn_params(CPD, fam, data, ns, cnodes) +% +% data(i,m) is the value of node i in case m (can be cell array). +% We assume this node has a maximize_params method. + +%error('no longer supported') % KPM 1 Feb 03 + +if 1 +ncases = size(data, 2); +CPD = reset_ess(CPD); +% make a fully observed joint distribution over the family +fmarginal.domain = fam; +fmarginal.T = 1; +fmarginal.mu = []; +fmarginal.Sigma = []; +if ~iscell(data) + cases = num2cell(data); +else + cases = data; +end +hidden_bitv = zeros(1, max(fam)); +for m=1:ncases + % specify (as a bit vector) which elements in the family domain are hidden + hidden_bitv = zeros(1, max(fmarginal.domain)); + ev = cases(:,m); + hidden_bitv(find(isempty(evidence)))=1; + CPD = update_ess(CPD, fmarginal, ev, ns, cnodes, hidden_bitv); +end +CPD = maximize_params(CPD); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@generic_CPD/log_prior.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function L = log_prior(CPD) +% LOG_PRIOR Return log P(theta) for a generic CPD - we return 0 +% L = log_prior(CPD) + +L = 0;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@generic_CPD/set_clamped.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,3 @@ +function CPD = set_clamped(CPD, bit) + +CPD.clamped = bit;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gmux_CPD/CPD_to_lambda_msg.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,62 @@ +function lam_msg = CPD_to_lambda_msg(CPD, msg_type, n, ps, msg, p, evidence) +% CPD_TO_LAMBDA_MSG Compute lambda message (gmux) +% lam_msg = compute_lambda_msg(CPD, msg_type, n, ps, msg, p, evidence) +% Pearl p183 eq 4.52 + +% Let Y be this node, X1..Xn be the cts parents and M the discrete switch node. +% e.g., for n=3, M=1 +% +% X1 X2 X3 M +% \ +% \ +% Y +% +% So the only case in which we send an informative message is if p=1=M. +% To the other cts parents, we send the "know nothing" message. + +switch msg_type + case 'd', + error('gaussian_CPD can''t create discrete msgs') + case 'g', + cps = ps(CPD.cps); + cpsizes = CPD.sizes(CPD.cps); + self_size = CPD.sizes(end); + i = find_equiv_posns(p, cps); % p is n's i'th cts parent + psz = cpsizes(i); + dps = ps(CPD.dps); + M = evidence{dps}; + if isempty(M) + error('gmux node must have observed discrete parent') + end + P = msg{n}.lambda.precision; + if all(P == 0) | (cps(M) ~= p) % if we know nothing, or are sending to a disconnected parent + lam_msg.precision = zeros(psz, psz); + lam_msg.info_state = zeros(psz, 1); + return; + end + % We are sending a message to the only effectively connected parent. + % There are no other incoming pi messages. + Bmu = CPD.mean(:,M); + BSigma = CPD.cov(:,:,M); + Bi = CPD.weights(:,:,M); + if (det(P) > 0) | isinf(P) + if isinf(P) % Y is observed + Sigma_lambda = zeros(self_size, self_size); % infinite precision => 0 variance + mu_lambda = msg{n}.lambda.mu; % observed_value; + else + Sigma_lambda = inv(P); + mu_lambda = Sigma_lambda * msg{n}.lambda.info_state; + end + C = inv(Sigma_lambda + BSigma); + lam_msg.precision = Bi' * C * Bi; + lam_msg.info_state = Bi' * C * (mu_lambda - Bmu); + else + % method that uses matrix inversion lemma + A = inv(P + inv(BSigma)); + C = P - P*A*P; + lam_msg.precision = Bi' * C * Bi; + D = eye(self_size) - P*A; + z = msg{n}.lambda.info_state; + lam_msg.info_state = Bi' * (D*z - D*P*Bmu); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gmux_CPD/CPD_to_pi.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,18 @@ +function pi = CPD_to_pi(CPD, msg_type, n, ps, msg, evidence) +% CPD_TO_PI Compute the pi vector (gaussian) +% function pi = CPD_to_pi(CPD, msg_type, n, ps, msg, evidence) + +switch msg_type + case 'd', + error('gaussian_CPD can''t create discrete msgs') + case 'g', + dps = ps(CPD.dps); + k = evidence{dps}; + if isempty(k) + error('gmux node must have observed discrete parent') + end + m = msg{n}.pi_from_parent{k}; + B = CPD.weights(:,:,k); + pi.mu = CPD.mean(:,k) + B * m.mu; + pi.Sigma = CPD.cov(:,:,k) + B * m.Sigma * B'; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gmux_CPD/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +/CPD_to_lambda_msg.m/1.1.1.1/Wed May 29 15:59:52 2002// +/CPD_to_pi.m/1.1.1.1/Wed May 29 15:59:54 2002// +/convert_to_pot.m/1.1.1.1/Wed May 29 15:59:52 2002// +/display.m/1.1.1.1/Wed May 29 15:59:54 2002// +/gmux_CPD.m/1.1.1.1/Wed May 29 15:59:54 2002// +/sample_node.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gmux_CPD/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gmux_CPD/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@gmux_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gmux_CPD/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gmux_CPD/Old/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +/gmux_CPD.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gmux_CPD/Old/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@gmux_CPD/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gmux_CPD/Old/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gmux_CPD/Old/gmux_CPD.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,92 @@ +function CPD = gmux_CPD(bnet, self, varargin) +% GMUX_CPD Make a Gaussian multiplexer node +% +% CPD = gmux_CPD(bnet, node, ...) is used similarly to gaussian_CPD, +% except we assume there is exactly one discrete parent (call it M) +% which is used to select which cts parent to pass through to the output. +% i.e., we define P(Y=y|M=m, X1, ..., XK) = N(y | W*x(m) + mu, Sigma) +% where Y represents this node, and the Xi's are the cts parents. +% All the Xi must have the same size, and the num values for M must be K. +% +% Currently the params for this kind of CPD cannot be learned. +% +% Optional arguments [ default in brackets ] +% +% mean - mu [zeros(Y,1)] +% cov - Sigma [eye(Y,Y)] +% weights - W [ randn(Y,X) ] + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + clamp = 0; + CPD = class(CPD, 'gmux_CPD', generic_CPD(clamp)); + return; +elseif isa(bnet, 'gmux_CPD') + % This might occur if we are copying an object. + CPD = bnet; + return; +end +CPD = init_fields; + +CPD = class(CPD, 'gmux_CPD', generic_CPD(1)); + +ns = bnet.node_sizes; +ps = parents(bnet.dag, self); +dps = myintersect(ps, bnet.dnodes); +cps = myintersect(ps, bnet.cnodes); +fam_sz = ns([ps self]); + +CPD.self = self; +CPD.sizes = fam_sz; + +% Figure out which (if any) of the parents are discrete, and which cts, and how big they are +% dps = discrete parents, cps = cts parents +CPD.cps = find_equiv_posns(cps, ps); % cts parent index +CPD.dps = find_equiv_posns(dps, ps); +if length(CPD.dps) ~= 1 + error('gmux must have exactly 1 discrete parent') +end +ss = fam_sz(end); +cpsz = fam_sz(CPD.cps(1)); % in gaussian_CPD, cpsz = sum(fam_sz(CPD.cps)) +if ~all(fam_sz(CPD.cps) == cpsz) + error('all cts parents must have same size') +end +dpsz = fam_sz(CPD.dps); +if dpsz ~= length(cps) + error(['the arity of the mux node is ' num2str(dpsz) ... + ' but there are ' num2str(length(cps)) ' cts parents']); +end + +% set default params +CPD.mean = zeros(ss, 1); +CPD.cov = eye(ss); +CPD.weights = randn(ss, cpsz); + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'mean', CPD.mean = args{i+1}; + case 'cov', CPD.cov = args{i+1}; + case 'weights', CPD.weights = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end +end + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +CPD.self = []; +CPD.sizes = []; +CPD.cps = []; +CPD.dps = []; +CPD.mean = []; +CPD.cov = []; +CPD.weights = []; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gmux_CPD/convert_to_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,37 @@ +function pot = convert_to_pot(CPD, pot_type, domain, evidence) +% CONVERT_TO_POT Convert a gmux CPD to a Gaussian potential +% pot = convert_to_pot(CPD, pot_type, domain, evidence) + +switch pot_type + case {'d', 'u', 'cg', 'scg'}, + error(['can''t convert gmux to potential of type ' pot_type]) + + case {'c','g'}, + % We create a large weight matrix with zeros in all blocks corresponding + % to the non-chosen parents, since they are effectively disconnected. + % The chosen parent is determined by the value, m, of the discrete parent. + % Thus the potential is as large as the whole family. + ps = domain(1:end-1); + dps = ps(CPD.dps); % CPD.dps is an index, not a node number (because of param tying) + cps = ps(CPD.cps); + m = evidence{dps}; + if isempty(m) + error('gmux node must have observed discrete parent') + end + bs = CPD.sizes(CPD.cps); + b = block(m, bs); + sum_cpsz = sum(CPD.sizes(CPD.cps)); + selfsz = CPD.sizes(end); + W = zeros(selfsz, sum_cpsz); + W(:,b) = CPD.weights(:,:,m); + + ns = zeros(1, max(domain)); + ns(domain) = CPD.sizes; + self = domain(end); + cdom = [cps(:)' self]; + pot = linear_gaussian_to_cpot(CPD.mean(:,m), CPD.cov(:,:,m), W, domain, ns, cdom, evidence); + + otherwise, + error(['unrecognized pot_type' pot_type]) +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gmux_CPD/display.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,4 @@ +function display(CPD) + +disp('gmux_CPD object'); +disp(struct(CPD));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gmux_CPD/gmux_CPD.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,95 @@ +function CPD = gmux_CPD(bnet, self, varargin) +% GMUX_CPD Make a Gaussian multiplexer node +% +% CPD = gmux_CPD(bnet, node, ...) is used similarly to gaussian_CPD, +% except we assume there is exactly one discrete parent (call it M) +% which is used to select which cts parent to pass through to the output. +% i.e., we define P(Y=y|M=m, X1, ..., XK) = N(y | W(m)*x(m) + mu(m), Sigma(m)) +% where Y represents this node, and the Xi's are the cts parents. +% All the Xi must have the same size, and the num values for M must be K. +% +% Currently the params for this kind of CPD cannot be learned. +% +% Optional arguments [ default in brackets ] +% +% mean - mu(:,i) is the mean given M=i [ zeros(Y,K) ] +% cov - Sigma(:,:,i) is the covariance given M=i [ repmat(1*eye(Y,Y), [1 1 K]) ] +% weights - W(:,:,i) is the regression matrix given M=i [ randn(Y,X,K) ] + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + clamp = 0; + CPD = class(CPD, 'gmux_CPD', generic_CPD(clamp)); + return; +elseif isa(bnet, 'gmux_CPD') + % This might occur if we are copying an object. + CPD = bnet; + return; +end +CPD = init_fields; + +CPD = class(CPD, 'gmux_CPD', generic_CPD(1)); + +ns = bnet.node_sizes; +ps = parents(bnet.dag, self); +dps = myintersect(ps, bnet.dnodes); +cps = myintersect(ps, bnet.cnodes); +fam_sz = ns([ps self]); + +CPD.self = self; +CPD.sizes = fam_sz; + +% Figure out which (if any) of the parents are discrete, and which cts, and how big they are +% dps = discrete parents, cps = cts parents +CPD.cps = find_equiv_posns(cps, ps); % cts parent index +CPD.dps = find_equiv_posns(dps, ps); +if length(CPD.dps) ~= 1 + error('gmux must have exactly 1 discrete parent') +end +ss = fam_sz(end); +cpsz = fam_sz(CPD.cps(1)); % in gaussian_CPD, cpsz = sum(fam_sz(CPD.cps)) +if ~all(fam_sz(CPD.cps) == cpsz) + error('all cts parents must have same size') +end +dpsz = fam_sz(CPD.dps); +if dpsz ~= length(cps) + error(['the arity of the mux node is ' num2str(dpsz) ... + ' but there are ' num2str(length(cps)) ' cts parents']); +end + +% set default params +%CPD.mean = zeros(ss, 1); +%CPD.cov = eye(ss); +%CPD.weights = randn(ss, cpsz); +CPD.mean = zeros(ss, dpsz); +CPD.cov = 1*repmat(eye(ss), [1 1 dpsz]); +CPD.weights = randn(ss, cpsz, dpsz); + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'mean', CPD.mean = args{i+1}; + case 'cov', CPD.cov = args{i+1}; + case 'weights', CPD.weights = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end +end + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +CPD.self = []; +CPD.sizes = []; +CPD.cps = []; +CPD.dps = []; +CPD.mean = []; +CPD.cov = []; +CPD.weights = []; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@gmux_CPD/sample_node.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,10 @@ +function y = sample_node(CPD, pev) +% SAMPLE_NODE Draw a random sample from P(Xi | x(pi_i), theta_i) (gmux) +% y = sample_node(CPD, parent_evidence) +% +% parent_ev{i} is the value of the i'th parent + +dpval = pev{CPD.dps}; +x = pev{CPD.cps(dpval)}; +y = gsamp(CPD.mean(:,dpval) + CPD.weights(:,:,dpval)*x(:), CPD.cov(:,:,dpval), 1); +y = y(:);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmm2Q_CPD/CPD_to_CPT.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,35 @@ +function CPT = CPD_to_CPT(CPD) +% Compute the big CPT for an HHMM Q node (including F parents) +% by combining internal transprob and startprob +% function CPT = CPD_to_CPT(CPD) + +Qsz = CPD.Qsz; + +if ~isempty(CPD.Fbelow_ndx) + if ~isempty(CPD.Fself_ndx) % general case + error('not implemented') + else % no F from self, hence no startprob (top level) + nps = length(CPD.dom_sz)-1; % num parents + CPT = 0*myones(CPD.dom_sz); + % when Fself=1, the CPT(i,j) = delta(i,j) for all k + for k=1:prod(CPD.Qpsizes) + Qps_vals = ind2subv(CPD.Qpsizes, k); + ndx = mk_multi_index(nps+1, [CPD.Fbelow_ndx CPD.Qps_ndx], [1 Qps_vals]); + CPT(ndx{:}) = eye(Qsz); % CPT(:,2,k,:) or CPT(:,k,2,:) etc + end + ndx = mk_multi_index(nps+1, CPD.Fbelow_ndx, 2); + CPT(ndx{:}) = CPD.transprob; % we assume transprob is in topo order + end +else % no F signal from below + if ~isempty(CPD.Fself_ndx) % bottom level + nps = length(CPD.dom_sz)-1; % num parents + CPT = 0*myones(CPD.dom_sz); + ndx = mk_multi_index(nps+1, CPD.Fself_ndx, 1); + CPT(ndx{:}) = CPD.transprob; + ndx = mk_multi_index(nps+1, CPD.Fself_ndx, 2); + CPT(ndx{:}) = CPD.startprob; + else % no F from self + error('An hhmmQ node without any F parents is just a tabular_CPD') + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmm2Q_CPD/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +/CPD_to_CPT.m/1.1.1.1/Tue Sep 24 12:46:46 2002// +/hhmm2Q_CPD.m/1.1.1.1/Tue Sep 24 22:34:40 2002// +/maximize_params.m/1.1.1.1/Tue Sep 24 22:44:36 2002// +/reset_ess.m/1.1.1.1/Tue Sep 24 22:36:16 2002// +/update_ess.m/1.1.1.1/Tue Sep 24 22:43:30 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmm2Q_CPD/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@hhmm2Q_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmm2Q_CPD/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmm2Q_CPD/hhmm2Q_CPD.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,65 @@ +function CPD = hhmm2Q_CPD(bnet, self, varargin) +% HHMMQ_CPD Make the CPD for a Q node in a 2 level hierarchical HMM +% CPD = hhmmQ_CPD(bnet, self, ...) +% +% Fself(t-1) Qps +% \ | +% \ v +% Qold(t-1) -> Q(t) +% / +% / +% Fbelow(t-1) +% +% +% optional args [defaults] +% +% Fself - node number <= ss +% Fbelow - node number <= ss +% Qps - node numbers (all <= 2*ss) - uses 2TBN indexing +% transprob - CPT for when Fbelow=2 and Fself=1 +% startprob - CPT for when Fbelow=2 and Fself=2 +% If Fbelow=1, we cannot change state. + +ss = bnet.nnodes_per_slice; +ns = bnet.node_sizes(:); + +% set default arguments +Fself = []; +Fbelow = []; +Qps = []; +startprob = []; +transprob = []; + +for i=1:2:length(varargin) + switch varargin{i}, + case 'Fself', Fself = varargin{i+1}; + case 'Fbelow', Fbelow = varargin{i+1}; + case 'Qps', Qps = varargin{i+1}; + case 'transprob', transprob = varargin{i+1}; + case 'startprob', startprob = varargin{i+1}; + end +end + +ps = parents(bnet.dag, self); +old_self = self-ss; +ndsz = ns(:)'; +CPD.dom_sz = [ndsz(ps) ns(self)]; +CPD.Fself_ndx = find_equiv_posns(Fself, ps); +CPD.Fbelow_ndx = find_equiv_posns(Fbelow, ps); +Qps = mysetdiff(ps, [Fself Fbelow old_self]); +CPD.Qps_ndx = find_equiv_posns(Qps, ps); +CPD.old_self_ndx = find_equiv_posns(old_self, ps); + +Qps = ps(CPD.Qps_ndx); +CPD.Qsz = ns(self); +CPD.Qpsizes = ns(Qps); + +CPD.transprob = transprob; +CPD.startprob = startprob; +CPD.start_counts = []; +CPD.trans_counts = []; + +CPD = class(CPD, 'hhmm2Q_CPD', discrete_CPD(0, CPD.dom_sz)); + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmm2Q_CPD/maximize_params.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,10 @@ +function CPD = maximize_params(CPD, temp) +% MAXIMIZE_PARAMS Set the params of a hhmmQ node to their ML/MAP values. +% CPD = maximize_params(CPD, temperature) + +if sum(CPD.start_counts(:)) > 0 + CPD.startprob = mk_stochastic(CPD.start_counts); +end +if sum(CPD.trans_counts(:)) > 0 + CPD.transprob = mk_stochastic(CPD.trans_counts); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmm2Q_CPD/reset_ess.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +function CPD = reset_ess(CPD) +% RESET_ESS Reset the Expected Sufficient Statistics of a hhmm2 Q node. +% CPD = reset_ess(CPD) + +domsz = CPD.dom_sz; +domsz(CPD.Fself_ndx) = 1; +domsz(CPD.Fbelow_ndx) = 1; +Qdom_sz = domsz; +Qdom_sz(Qdom_sz==1)=[]; % get rid of dimensions of size 1 + +CPD.start_counts = zeros(Qdom_sz); +CPD.trans_counts = zeros(Qdom_sz);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmm2Q_CPD/update_ess.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,26 @@ +function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) + +marg = add_ev_to_dmarginal(fmarginal, evidence, ns); + +nps = length(CPD.dom_sz)-1; % num parents + +if ~isempty(CPD.Fbelow_ndx) + if ~isempty(CPD.Fself_ndx) % general case + ndx = mk_multi_index(nps+1, [CPD.Fbelow_ndx CPD.Fself_ndx], [2 1]); + CPD.trans_counts = CPD.trans_counts + squeeze(marg.T(ndx{:})); + ndx = mk_multi_index(nps+1, [CPD.Fbelow_ndx CPD.Fself_ndx], [2 2]); + CPD.start_counts = CPD.start_counts + squeeze(marg.T(ndx{:})); + else % no F from self, hence no startprob (top level) + ndx = mk_multi_index(nps+1, CPD.Fbelow_ndx, 2); + CPD.trans_counts = CPD.trans_counts + squeeze(marg.T(ndx{:})); + end +else % no F signal from below + if ~isempty(CPD.Fself_ndx) % self F (bottom level) + ndx = mk_multi_index(nps+1, CPD.Fself_ndx, 1); + CPD.trans_counts = CPD.trans_counts + squeeze(marg.T(ndx{:})); + ndx = mk_multi_index(nps+1, CPD.Fself_ndx, 2); + CPD.start_counts = CPD.start_counts + squeeze(marg.T(ndx{:})); + else % no F from self or below + error('no F signal') + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmF_CPD/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +/hhmmF_CPD.m/1.1.1.1/Mon Jun 24 23:38:24 2002// +/log_prior.m/1.1.1.1/Wed May 29 15:59:54 2002// +/maximize_params.m/1.1.1.1/Wed May 29 15:59:54 2002// +/reset_ess.m/1.1.1.1/Wed May 29 15:59:54 2002// +/update_CPT.m/1.1.1.1/Mon Jun 24 22:45:04 2002// +/update_ess.m/1.1.1.1/Mon Jun 24 23:54:30 2002// +D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmF_CPD/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@hhmmF_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmF_CPD/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmF_CPD/Old/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +/hhmmF_CPD.m/1.1.1.1/Mon Jun 24 22:35:06 2002// +/log_prior.m/1.1.1.1/Mon Jun 24 22:35:06 2002// +/maximize_params.m/1.1.1.1/Mon Jun 24 22:35:06 2002// +/reset_ess.m/1.1.1.1/Mon Jun 24 22:35:06 2002// +/update_CPT.m/1.1.1.1/Mon Jun 24 22:35:06 2002// +/update_ess.m/1.1.1.1/Mon Jun 24 22:35:06 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmF_CPD/Old/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@hhmmF_CPD/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmF_CPD/Old/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmF_CPD/Old/hhmmF_CPD.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,76 @@ +function CPD = hhmmF_CPD(bnet, self, Qnodes, d, D, varargin) +% HHMMF_CPD Make the CPD for an F node at depth D of a D-level hierarchical HMM +% CPD = hhmmF_CPD(bnet, self, Qnodes, d, D, ...) +% +% Q(d-1) +% \ +% \ +% F(d) +% / | +% / | +% Q(d) F(d+1) +% +% We assume nodes are ordered (numbered) as follows: +% Q(1), ... Q(d), F(d+1), F(d) +% +% F(d)=2 means level d has finished. The prob this happens depends on Q(d) +% and optionally on Q(d-1), Q(d=1), ..., Q(1). +% Also, level d can only finish if the level below has finished +% (hence the F(d+1) -> F(d) arc). +% +% If d=D, there is no F(d+1), so F(d) is just a regular tabular_CPD. +% If all models always finish in the same state (e.g., their last), +% we don't need to condition on the state of parent models (Q(d-1), ...) +% +% optional args [defaults] +% +% termprob - termprob(k,i,2) = prob finishing given Q(d)=i and Q(1:d-1)=k [ finish in last state ] +% +% hhmmF_CPD is a subclass of tabular_CPD so we inherit inference methods like CPD_to_pot, etc. +% +% We create an isolated tabular_CPD with no F parent to learn termprob +% so we can avail of e.g., entropic or Dirichlet priors. +% +% For details, see "Linear-time inference in hierarchical HMMs", Murphy and Paskin, NIPS'01. + + +ps = parents(bnet.dag, self); +Qps = myintersect(ps, Qnodes); +F = mysetdiff(ps, Qps); +CPD.Q = Qps(end); % Q(d) +assert(CPD.Q == Qnodes(d)); +CPD.Qps = Qps(1:end-1); % all Q parents except Q(d), i.e., calling context + +ns = bnet.node_sizes(:); +CPD.Qsizes = ns(Qnodes); +CPD.d = d; +CPD.D = D; + +Qsz = ns(CPD.Q); +Qpsz = prod(ns(CPD.Qps)); + +% set default arguments +p = 0.9; +%termprob(k,i,t) Might terminate if i=Qsz; will not terminate if i<Qsz +termprob = zeros(Qpsz, Qsz, 2); +termprob(:, Qsz, 2) = p; +termprob(:, Qsz, 1) = 1-p; +termprob(:, 1:(Qsz-1), 1) = 1; + +for i=1:2:length(varargin) + switch varargin{i}, + case 'termprob', termprob = varargin{i+1}; + otherwise, error(['unrecognized argument ' varargin{i}]) + end +end + +ps = [CPD.Qps CPD.Q]; +% ns(self) = 2 since this is an F node +CPD.sub_CPD_term = mk_isolated_tabular_CPD(ps, ns([ps self]), {'CPT', termprob}); +S = struct(CPD.sub_CPD_term); +CPD.termprob = S.CPT; + +CPD = class(CPD, 'hhmmF_CPD', tabular_CPD(bnet, self)); + +CPD = update_CPT(CPD); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmF_CPD/Old/log_prior.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function L = log_prior(CPD) +% LOG_PRIOR Return log P(theta) for a hhmm F CPD +% L = log_prior(CPD) + +L = log_prior(CPD.sub_CPD_term);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmF_CPD/Old/maximize_params.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,9 @@ +function CPD = maximize_params(CPD, temp) +% MAXIMIZE_PARAMS Set the params of a hhmmF node to their ML/MAP values. +% CPD = maximize_params(CPD, temperature) + +CPD.sub_CPD_term = maximize_params(CPD.sub_CPD_term, temp); +S = struct(CPD.sub_CPD_term); +CPD.termprob = S.CPT; + +CPD = update_CPT(CPD);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmF_CPD/Old/reset_ess.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function CPD = reset_ess(CPD) +% RESET_ESS Reset the Expected Sufficient Statistics of a hhmm F node. +% CPD = reset_ess(CPD) + +CPD.sub_CPD_term = reset_ess(CPD.sub_CPD_term);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmF_CPD/Old/update_CPT.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function CPD = update_CPT(CPD) +% Compute the big CPT for an HHMM F node given internal termprob +% function CPD = update_CPT(CPD) + +Qsz = CPD.Qsizes(CPD.Q); +Qpsz = prod(CPD.Qsizes(CPD.Qps)); + +% P(Q(1:d-1), Q(d), F(d+1), F(d)) +CPT = zeros(Qpsz, Qsz, 2, 2); +CPT(:,:,1,1) = 1; % if F(d+1)=1, then F(d)=1 +CPT(:,:,2,:) = CPD.termprob; + +CPD = set_fields(CPD, 'CPT', CPT);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmF_CPD/Old/update_ess.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,61 @@ +function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) +% UPDATE_ESS Update the Expected Sufficient Statistics of a hhmmF node. +% function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) + +% Figure out the node numbers associated with each parent +% so we extract evidence from the right place +dom = fmarginal.domain; % Q(1) .. Q(d) F(d+1) F(d) +Qps = fmarginal.domain(1:end-2); +Q = Qps(end); +Qps = Qps(1:end-1); + +Qsz = CPD.Qsizes(CPD.Q); +Qpsz = prod(CPD.Qsizes(CPD.Qps)); % may be 1 + +% We assume the F node are always hidden, but allow some of the Q nodes +% to be observed. We do case analysis for speed. +%We only extract prob from fmarginal.T when F(d+1)=2 i.e., model below has finished. +% wrong -> % We sum over the possibilities that F(d+1) = 1 or 2 + +obs_self = ~hidden_bitv(Q); +if obs_self + self_val = evidence{Q}; +end + +if isempty(Qps) % independent of parent context + counts = zeros(Qsz, 2); + %fmarginal.T(Q(d), F(d+1), F(d)) + if obs_self + marg = myreshape(fmarginal.T, [1 2 2]); + counts(self_val,:) = marg(1,2,:); + %counts(self_val,:) = marg(1,1,:) + marg(1,2,:); + else + marg = myreshape(fmarginal.T, [Qsz 2 2]); + counts = squeeze(marg(:,2,:)); + %counts = squeeze(marg(:,2,:)) + squeeze(marg(:,1,:)); + end +else + counts = zeros(Qpsz, Qsz, 2); + %fmarginal.T(Q(1:d-1), Q(d), F(d+1), F(d)) + obs_Qps = ~any(hidden_bitv(Qps)); % we assume that all or none of the Q parents are observed + if obs_Qps + Qps_val = subv2ind(Qpsz, cat(1, evidence{Qps})); + end + if obs_self & obs_Qps + marg = myreshape(fmarginal.T, [1 1 2 2]); + counts(Qps_val, self_val, :) = squeeze(marg(1,1,2,:)); + %counts(Qps_val, self_val, :) = squeeze(marg(1,1,2,:)) + squeeze(marg(1,1,1,:)); + elseif ~obs_self & obs_Qps + marg = myreshape(fmarginal.T, [1 Qsz 2 2]); + counts(Qps_val, :, :) = squeeze(marg(1,:,2,:)); + %counts(Qps_val, :, :) = squeeze(marg(1,:,2,:)) + squeeze(marg(1,:,1,:)); + elseif obs_self & ~obs_Qps + error('not yet implemented') + else + marg = myreshape(fmarginal.T, [Qpsz Qsz 2 2]); + counts(:, :, :) = squeeze(marg(:,:,2,:)); + %counts(:, :, :) = squeeze(marg(:,:,2,:)) + squeeze(marg(:,:,1,:)); + end +end + +CPD.sub_CPD_term = update_ess_simple(CPD.sub_CPD_term, counts);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmF_CPD/hhmmF_CPD.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,73 @@ +function CPD = hhmmF_CPD(bnet, self, Qself, Fbelow, varargin) +% HHMMF_CPD Make the CPD for an F node in a hierarchical HMM +% CPD = hhmmF_CPD(bnet, self, Qself, Fbelow, ...) +% +% Qps +% \ +% \ +% Fself +% / | +% / | +% Qself Fbelow +% +% We assume nodes are ordered (numbered) as follows: Qps, Q, Fbelow, F +% All nodes numbers should be from slice 1. +% +% If Fbelow if missing, this becomes a regular tabular_CPD. +% Qps may be omitted. +% +% optional args [defaults] +% +% Qps - node numbers. +% termprob - termprob(k,i,2) = prob finishing given Q(d)=i and Q(1:d-1)=k [ finish in last state wp 0.9] +% +% hhmmF_CPD is a subclass of tabular_CPD so we inherit inference methods like CPD_to_pot, etc. +% +% We create an isolated tabular_CPD with no F parent to learn termprob +% so we can avail of e.g., entropic or Dirichlet priors. +% +% For details, see "Linear-time inference in hierarchical HMMs", Murphy and Paskin, NIPS'01. + + + +Qps = []; +% get parents +for i=1:2:length(varargin) + switch varargin{i}, + case 'Qps', Qps = varargin{i+1}; + end +end + +ns = bnet.node_sizes(:); +Qsz = ns(Qself); +Qpsz = prod(ns(Qps)); +CPD.Qsz = Qsz; +CPD.Qpsz = Qpsz; + +ps = parents(bnet.dag, self); +CPD.Fbelow_ndx = find_equiv_posns(Fbelow, ps); +CPD.Qps_ndx = find_equiv_posns(Qps, ps); +CPD.Qself_ndx = find_equiv_posns(Qself, ps); + +% set default arguments +p = 0.9; +%termprob(k,i,t) Might terminate if i=Qsz; will not terminate if i<Qsz +termprob = zeros(Qpsz, Qsz, 2); +termprob(:, Qsz, 2) = p; +termprob(:, Qsz, 1) = 1-p; +termprob(:, 1:(Qsz-1), 1) = 1; + +for i=1:2:length(varargin) + switch varargin{i}, + case 'termprob', termprob = varargin{i+1}; + end +end + +CPD.sub_CPD_term = mk_isolated_tabular_CPD([Qpsz Qsz 2], {'CPT', termprob}); +S = struct(CPD.sub_CPD_term); +CPD.termprob = S.CPT; + +CPD = class(CPD, 'hhmmF_CPD', tabular_CPD(bnet, self)); + +CPD = update_CPT(CPD); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmF_CPD/log_prior.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function L = log_prior(CPD) +% LOG_PRIOR Return log P(theta) for a hhmm F CPD +% L = log_prior(CPD) + +L = log_prior(CPD.sub_CPD_term);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmF_CPD/maximize_params.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,9 @@ +function CPD = maximize_params(CPD, temp) +% MAXIMIZE_PARAMS Set the params of a hhmmF node to their ML/MAP values. +% CPD = maximize_params(CPD, temperature) + +CPD.sub_CPD_term = maximize_params(CPD.sub_CPD_term, temp); +S = struct(CPD.sub_CPD_term); +CPD.termprob = S.CPT; + +CPD = update_CPT(CPD);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmF_CPD/reset_ess.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function CPD = reset_ess(CPD) +% RESET_ESS Reset the Expected Sufficient Statistics of a hhmm F node. +% CPD = reset_ess(CPD) + +CPD.sub_CPD_term = reset_ess(CPD.sub_CPD_term);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmF_CPD/update_CPT.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function CPD = update_CPT(CPD) +% Compute the big CPT for an HHMM F node given internal termprob +% function CPD = update_CPT(CPD) + +Qsz = CPD.Qsz; +Qpsz = CPD.Qpsz; + +% CPT(Qpsz, Q, Fbelow, Fself) +CPT = zeros(Qpsz, Qsz, 2, 2); +CPT(:,:,1,1) = 1; % if Fbelow=1 (off), then Fself=1 (off) +CPT(:,:,2,:) = CPD.termprob; + +CPD = set_fields(CPD, 'CPT', CPT);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmF_CPD/update_ess.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,40 @@ +function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) +% UPDATE_ESS Update the Expected Sufficient Statistics of a hhmmF node. +% function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) +% +% We assume the F nodes are always hidden + +% Figure out the node numbers associated with each parent +dom = fmarginal.domain; +%Fself = dom(end); +%Fbelow = dom(CPD.Fbelow_ndx); +Qself = dom(CPD.Qself_ndx); +Qps = dom(CPD.Qps_ndx); + +Qsz = CPD.Qsz; +Qpsz = CPD.Qpsz; + +if all(hidden_bitv(Qps)) % we assume all are hidden or all are observed + k_ndx = 1:Qpsz; + eff_Qpsz = Qpsz; +else + k_ndx = subv2ind(Qpsz, cat(1, evidence{Qps})); + eff_Qpsz = 1; +end + +if hidden_bitv(Qself) + j_ndx = 1:Qsz; + eff_Qsz = Qsz; +else + j_ndx = evidence{Qself}; + eff_Qsz = 1; +end + +% Fmarginal(Qps, Q, Fbelow, F) +fmarg = myreshape(fmarginal.T, [eff_Qpsz eff_Qsz 2 2]); + +counts = zeros(Qpsz, Qsz, 2); +%counts(k_ndx, j_ndx, :) = sum(fmarginal.T(:, :, :, :), 3); % sum over Fbelow +counts(k_ndx, j_ndx, :) = fmarg(:, :, 2, :); % Fbelow = 2 + +CPD.sub_CPD_term = update_ess_simple(CPD.sub_CPD_term, counts);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmQ_CPD/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +/hhmmQ_CPD.m/1.1.1.1/Tue Sep 24 04:19:26 2002// +/log_prior.m/1.1.1.1/Wed May 29 15:59:54 2002// +/maximize_params.m/1.1.1.1/Tue Sep 24 13:10:18 2002// +/reset_ess.m/1.1.1.1/Wed May 29 15:59:54 2002// +/update_CPT.m/1.1.1.1/Tue Sep 24 02:58:18 2002// +/update_ess.m/1.1.1.1/Thu Jul 24 13:41:34 2003// +D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmQ_CPD/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@hhmmQ_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmQ_CPD/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmQ_CPD/Old/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,10 @@ +/hhmmQ_CPD.m/1.1.1.1/Mon Jun 24 18:19:00 2002// +/log_prior.m/1.1.1.1/Mon Jun 24 18:19:00 2002// +/maximize_params.m/1.1.1.1/Mon Jun 24 18:19:00 2002// +/reset_ess.m/1.1.1.1/Mon Jun 24 18:19:00 2002// +/update_CPT.m/1.1.1.1/Tue Sep 24 02:30:32 2002// +/update_ess.m/1.1.1.1/Mon Jun 24 18:19:00 2002// +/update_ess2.m/1.1.1.1/Mon Jun 24 21:20:52 2002// +/update_ess3.m/1.1.1.1/Mon Jun 24 22:08:08 2002// +/update_ess4.m/1.1.1.1/Mon Jun 24 22:23:32 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmQ_CPD/Old/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@hhmmQ_CPD/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmQ_CPD/Old/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmQ_CPD/Old/hhmmQ_CPD.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,126 @@ +function CPD = hhmmQ_CPD(bnet, self, Qnodes, d, D, varargin) +% HHMMQ_CPD Make the CPD for a Q node at depth D of a D-level hierarchical HMM +% CPD = hhmmQ_CPD(bnet, self, Qnodes, d, D, ...) +% +% Fd(t-1) \ Q1:d-1(t) +% \ | +% \ v +% Qd(t-1) -> Qd(t) +% / +% / +% Fd+1(t-1) +% +% We assume parents are ordered (numbered) as follows: +% Qd(t-1), Fd+1(t-1), Fd(t-1), Q1(t), ..., Qd(t) +% +% The parents of Qd(t) can either be just Qd-1(t) or the whole stack Q1:d-1(t) (allQ) +% In either case, we will call them Qps. +% If d=1, Qps does not exist. Also, the F1(t-1) -> Q1(t) arc is optional. +% If the arc is missing, startprob does not need to be specified, +% since the toplevel is assumed to never reset (F1 does not exist). +% If d=D, Fd+1(t-1) does not exist (there is no signal from below). +% +% optional args [defaults] +% +% transprob - transprob(i,k,j) = prob transition from i to j given Qps = k ['leftright'] +% selfprob - prob of a transition from i to i given Qps=k [0.1] +% startprob - startprob(k,j) = prob start in j given Qps = k ['leftstart'] +% startargs - other args to be passed to the sub tabular_CPD for learning startprob +% transargs - other args will be passed to the sub tabular_CPD for learning transprob +% allQ - 1 means use all Q nodes above d as parents, 0 means just level d-1 [0] +% F1toQ1 - 1 means add F1(t-1) -> Q1(t) arc, 0 means level 1 never resets [0] +% +% For d=1, startprob(1,j) is only needed if F1toQ1=1 +% Also, transprob(i,j) can be used instead of transprob(i,1,j). +% +% hhmmQ_CPD is a subclass of tabular_CPD so we inherit inference methods like CPD_to_pot, etc. +% +% We create isolated tabular_CPDs with no F parents to learn transprob/startprob +% so we can avail of e.g., entropic or Dirichlet priors. +% In the future, we will be able to represent the transprob using a tree_CPD. +% +% For details, see "Linear-time inference in hierarchical HMMs", Murphy and Paskin, NIPS'01. + + +ss = bnet.nnodes_per_slice; +%assert(self == Qnodes(d)+ss); +ns = bnet.node_sizes(:); +CPD.Qsizes = ns(Qnodes); +CPD.d = d; +CPD.D = D; +allQ = 0; + +% find out which parents to use, to get right size +for i=1:2:length(varargin) + switch varargin{i}, + case 'allQ', allQ = varargin{i+1}; + end +end + +if d==1 + CPD.Qps = []; +else + if allQ + CPD.Qps = Qnodes(1:d-1); + else + CPD.Qps = Qnodes(d-1); + end +end + +Qsz = ns(self); +Qpsz = prod(ns(CPD.Qps)); + +% set default arguments +startprob = 'leftstart'; +transprob = 'leftright'; +startargs = {}; +transargs = {}; +CPD.F1toQ1 = 0; +selfprob = 0.1; + +for i=1:2:length(varargin) + switch varargin{i}, + case 'transprob', transprob = varargin{i+1}; + case 'selfprob', selfprob = varargin{i+1}; + case 'startprob', startprob = varargin{i+1}; + case 'startargs', startargs = varargin{i+1}; + case 'transargs', transargs = varargin{i+1}; + case 'F1toQ1', CPD.F1toQ1 = varargin{i+1}; + end +end + +Qps = CPD.Qps + ss; +old_self = self-ss; + +if strcmp(transprob, 'leftright') + LR = mk_leftright_transmat(Qsz, selfprob); + transprob = repmat(reshape(LR, [1 Qsz Qsz]), [Qpsz 1 1]); % transprob(k,i,j) + transprob = permute(transprob, [2 1 3]); % now transprob(i,k,j) +end +transargs{end+1} = 'CPT'; +transargs{end+1} = transprob; +CPD.sub_CPD_trans = mk_isolated_tabular_CPD([old_self Qps], ns([old_self Qps self]), transargs); +S = struct(CPD.sub_CPD_trans); +CPD.transprob = myreshape(S.CPT, [Qsz Qpsz Qsz]); + + +if strcmp(startprob, 'leftstart') + startprob = zeros(Qpsz, Qsz); + startprob(:,1) = 1; +end + +if (d==1) & ~CPD.F1toQ1 + CPD.sub_CPD_start = []; + CPD.startprob = []; +else + startargs{end+1} = 'CPT'; + startargs{end+1} = startprob; + CPD.sub_CPD_start = mk_isolated_tabular_CPD(Qps, ns([Qps self]), startargs); + S = struct(CPD.sub_CPD_start); + CPD.startprob = myreshape(S.CPT, [Qpsz Qsz]); +end + +CPD = class(CPD, 'hhmmQ_CPD', tabular_CPD(bnet, self)); + +CPD = update_CPT(CPD); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmQ_CPD/Old/log_prior.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +function L = log_prior(CPD) +% LOG_PRIOR Return log P(theta) for a hhmm CPD +% L = log_prior(CPD) + +L = log_prior(CPD.sub_CPD_trans); +if ~isempty(CPD.sub_CPD_start) + L = L + log_prior(CPD.sub_CPD_start); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmQ_CPD/Old/maximize_params.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,40 @@ +function CPD = maximize_params(CPD, temp) +% MAXIMIZE_PARAMS Set the params of a hhmmQ node to their ML/MAP values. +% CPD = maximize_params(CPD, temperature) + +Qsz = CPD.Qsizes(CPD.d); +Qpsz = prod(CPD.Qsizes(CPD.Qps)); + +if ~isempty(CPD.sub_CPD_start) + CPD.sub_CPD_start = maximize_params(CPD.sub_CPD_start, temp); + S = struct(CPD.sub_CPD_start); + CPD.startprob = myreshape(S.CPT, [Qpsz Qsz]); + %CPD.startprob = S.CPT; +end + +if 1 + % If we are in a state that can only go the end state, + % we will never see a transition to another (non-end) state, + % so counts(i,k,j)=0 (and termprob(k,i)=1). + % We set counts(i,k,i)=1 in this case. + % This will cause remove_hhmm_end_state to return a + % stochastic matrix, but otherwise has no effect on EM. + counts = get_field(CPD.sub_CPD_trans, 'counts'); + counts = reshape(counts, [Qsz Qpsz Qsz]); + for k=1:Qpsz + for i=1:Qsz + if sum(counts(i,k,:))==0 % never witnessed a transition out of i + counts(i,k,i)=1; % add self loop + %fprintf('CPDQ d=%d i=%d k=%d\n', CPD.d, i, k); + end + end + end + CPD.sub_CPD_trans = set_fields(CPD.sub_CPD_trans, 'counts', counts(:)); +end + +CPD.sub_CPD_trans = maximize_params(CPD.sub_CPD_trans, temp); +S = struct(CPD.sub_CPD_trans); +%CPD.transprob = S.CPT; +CPD.transprob = myreshape(S.CPT, [Qsz Qpsz Qsz]); + +CPD = update_CPT(CPD);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmQ_CPD/Old/reset_ess.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +function CPD = reset_ess(CPD) +% RESET_ESS Reset the Expected Sufficient Statistics of a hhmm Q node. +% CPD = reset_ess(CPD) + +if ~isempty(CPD.sub_CPD_start) + CPD.sub_CPD_start = reset_ess(CPD.sub_CPD_start); +end +CPD.sub_CPD_trans = reset_ess(CPD.sub_CPD_trans);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmQ_CPD/Old/update_CPT.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,74 @@ +function CPD = update_CPT(CPD) +% Compute the big CPT for an HHMM Q node (including F parents) given internal transprob and startprob +% function CPD = update_CPT(CPD) + +Qsz = CPD.Qsz; +Qpsz = CPD.Qpsz; + +if ~isempty(CPD.Fbelow_ndx) + if ~isempty(CPD.Fself_ndx) % general case + % Fb(t-1) Fself(t-1) P(Q(t)=j| Q(t-1)=i, Qps(t)=k) + % ------------------------------------------------------ + % 1 1 delta(i,j) + % 2 1 transprob(i,k,j) + % 1 2 impossible + % 2 2 startprob(k,j) + CPT = zeros(Qsz, 2, 2, Qpsz, Qsz); + I = repmat(eye(Qsz), [1 1 Qpsz]); % i,j,k + I = permute(I, [1 3 2]); % i,k,j + CPT(:, 1, 1, :, :) = I; + CPT(:, 2, 1, :, :) = CPD.transprob; + CPT(:, 1, 2, :, :) = I; + CPT(:, 2, 2, :, :) = repmat(reshape(CPD.startprob, [1 Qpsz Qsz]), [Qsz 1 1]); % replicate over i + else % no F from self, hence no startprob + % Fb(t-1) P(Q(t)=j| Q(t-1)=i, Qps(t)=k) + % ------------------------------------------------------ + % 1 delta(i,j) + % 2 transprob(i,k,j) + + nps = length(CPD.dom_sz)-1; % num parents + CPT = 0*myones(CPD.dom_sz); + %CPT = zeros(Qsz, 2, Qpsz, Qsz); % assumes CPT(Q(t-1), F(t-1), Qps, Q(t)) + % but a member of Qps may preceed Q(t-1) or F(t-1) in the ordering + + I = repmat(eye(Qsz), [1 1 Qpsz]); % i,j,k + I = permute(I, [1 3 2]); % i,k,j + + % the following fails if there is a member of Qps with a lower + % number than F + %CPT(:, 1, :, :) = I; + %CPT(:, 2, :, :) = CPD.transprob; + + ndx = mk_multi_index(nps+1, CPD.Fbelow_ndx, 1); + CPT(ndx{:}) = I; + ndx = mk_multi_index(nps+1, CPD.Fbelow_ndx, 2); + CPT(ndx{:}) = CPD.transprob; + keyboard + end +else % no F signal from below + if ~isempty(CPD.Fself_ndx) + % Q(t-1), Fself(t-1), Qps, Q(t) + + % if condition start on previous concrete state (as in map learning), + % CPT(:, 1, :, :, :) = CPD.transprob(Q(t-1), Qps, Q(t)) + % CPT(:, 2, :, :, :) = CPD.startprob(Q(t-1), Qps, Q(t)) + + % Fself(t-1) P(Q(t-1)=i, Qps(t)=k -> Q(t)=j) + % ------------------------------------------------------ + % 1 transprob(i,k,j) + % 2 startprob(k,j) + CPT = zeros(Qsz, 2, Qpsz, Qsz); + I = repmat(eye(Qsz), [1 1 Qpsz]); % i,j,k + I = permute(I, [1 3 2]); % i,k,j + CPT(:, 1, :, :) = CPD.transprob; + if CPD.fullstartprob + CPT(:, 2, :, :) = CPD.startprob; + else + CPT(:, 2, :, :) = repmat(reshape(CPD.startprob, [1 Qpsz Qsz]), [Qsz 1 1]); % replicate over i + end + else % no F from self + error('An hhmmQ node without any F parents is just a tabular_CPD') + end +end + +CPD = set_fields(CPD, 'CPT', CPT);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmQ_CPD/Old/update_ess.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,141 @@ +function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) +% UPDATE_ESS Update the Expected Sufficient Statistics of a hhmm Q node. +% function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, idden_bitv) + +% Figure out the node numbers associated with each parent +% e.g., D=4, d=3, Qps = all Qs above, so dom = [Q3(t-1) F4(t-1) F3(t-1) Q1(t) Q2(t) Q3(t)]. +% so self = Q3(t), old_self = Q3(t-1), CPD.Qps = [1 2], Qps = [Q1(t) Q2(t)] +dom = fmarginal.domain; +self = dom(end); +old_self = dom(1); +Qps = dom(length(dom)-length(CPD.Qps):end-1); + +Qsz = CPD.Qsizes(CPD.d); +Qpsz = prod(CPD.Qsizes(CPD.Qps)); + +% If some of the Q nodes are observed (which happens during supervised training) +% the counts will only be non-zero in positions +% consistent with the evidence. We put the computed marginal responsibilities +% into the appropriate slots of the big counts array. +% (Recall that observed discrete nodes only have a single effective value.) +% (A more general, but much slower, way is to call add_evidence_to_dmarginal.) +% We assume the F nodes are never observed. + +obs_self = ~hidden_bitv(self); +obs_Qps = (~isempty(Qps)) & (~any(hidden_bitv(Qps))); % we assume that all or none of the Q parents are observed + +if obs_self + self_val = evidence{self}; + oldself_val = evidence{old_self}; +end + +if obs_Qps + Qps_val = subv2ind(Qpsz, cat(1, evidence{Qps})); + if Qps_val == 0 + keyboard + end +end + +if CPD.d==1 % no Qps from above + if ~CPD.F1toQ1 % no F from self + % marg(Q1(t-1), F2(t-1), Q1(t)) + % F2(t-1) P(Q1(t)=j | Q1(t-1)=i) + % 1 delta(i,j) + % 2 transprob(i,j) + if obs_self + hor_counts = zeros(Qsz, Qsz); + hor_counts(oldself_val, self_val) = fmarginal.T(2); + else + marg = reshape(fmarginal.T, [Qsz 2 Qsz]); + hor_counts = squeeze(marg(:,2,:)); + end + else + % marg(Q1(t-1), F2(t-1), F1(t-1), Q1(t)) + % F2(t-1) F1(t-1) P(Qd(t)=j| Qd(t-1)=i) + % ------------------------------------------------------ + % 1 1 delta(i,j) + % 2 1 transprob(i,j) + % 1 2 impossible + % 2 2 startprob(j) + if obs_self + marg = myreshape(fmarginal.T, [1 2 2 1]); + hor_counts = zeros(Qsz, Qsz); + hor_counts(oldself_val, self_val) = marg(1,2,1,1); + ver_counts = zeros(Qsz, 1); + %ver_counts(self_val) = marg(1,2,2,1); + ver_counts(self_val) = marg(1,2,2,1) + marg(1,1,2,1); + else + marg = reshape(fmarginal.T, [Qsz 2 2 Qsz]); + hor_counts = squeeze(marg(:,2,1,:)); + %ver_counts = squeeze(sum(marg(:,2,2,:),1)); % sum over i + ver_counts = squeeze(sum(marg(:,2,2,:),1)) + squeeze(sum(marg(:,1,2,:),1)); % sum i,b + end + end % F1toQ1 +else % d ~= 1 + if CPD.d < CPD.D % general case + % marg(Qd(t-1), Fd+1(t-1), Fd(t-1), Qps(t), Qd(t)) + % Fd+1(t-1) Fd(t-1) P(Qd(t)=j| Qd(t-1)=i, Qps(t)=k) + % ------------------------------------------------------ + % 1 1 delta(i,j) + % 2 1 transprob(i,k,j) + % 1 2 impossible + % 2 2 startprob(k,j) + if obs_Qps & obs_self + marg = myreshape(fmarginal.T, [1 2 2 1 1]); + k = 1; + hor_counts = zeros(Qsz, Qpsz, Qsz); + hor_counts(oldself_val, Qps_val, self_val) = marg(1, 2,1, k,1); + ver_counts = zeros(Qpsz, Qsz); + %ver_counts(Qps_val, self_val) = marg(1, 2,2, k,1); + ver_counts(Qps_val, self_val) = marg(1, 2,2, k,1) + marg(1, 1,2, k,1); + elseif obs_Qps & ~obs_self + marg = myreshape(fmarginal.T, [Qsz 2 2 1 Qsz]); + k = 1; + hor_counts = zeros(Qsz, Qpsz, Qsz); + hor_counts(:, Qps_val, :) = marg(:, 2,1, k,:); + ver_counts = zeros(Qpsz, Qsz); + %ver_counts(Qps_val, :) = sum(marg(:, 2,2, k,:), 1); + ver_counts(Qps_val, :) = sum(marg(:, 2,2, k,:), 1) + sum(marg(:, 1,2, k,:), 1); + elseif ~obs_Qps & obs_self + error('not yet implemented') + else % everything is hidden + marg = reshape(fmarginal.T, [Qsz 2 2 Qpsz Qsz]); + hor_counts = squeeze(marg(:,2,1,:,:)); % i,k,j + %ver_counts = squeeze(sum(marg(:,2,2,:,:),1)); % sum over i + ver_counts = squeeze(sum(marg(:,2,2,:,:),1)) + squeeze(sum(marg(:,1,2,:,:),1)); % sum over i,b + end + else % d == D, so no F from below + % marg(QD(t-1), FD(t-1), Qps(t), QD(t)) + % FD(t-1) P(QD(t)=j | QD(t-1)=i, Qps(t)=k) + % 1 transprob(i,k,j) + % 2 startprob(k,j) + if obs_Qps & obs_self + marg = myreshape(fmarginal.T, [1 2 1 1]); + k = 1; + hor_counts = zeros(Qsz, Qpsz, Qsz); + hor_counts(oldself_val, Qps_val, self_val) = marg(1, 1, k,1); + ver_counts = zeros(Qpsz, Qsz); + ver_counts(Qps_val, self_val) = marg(1, 2, k,1); + elseif obs_Qps & ~obs_self + marg = myreshape(fmarginal.T, [Qsz 2 1 Qsz]); + k = 1; + hor_counts = zeros(Qsz, Qpsz, Qsz); + hor_counts(:, Qps_val, :) = marg(:, 1, k,:); + ver_counts = zeros(Qpsz, Qsz); + ver_counts(Qps_val, :) = sum(marg(:, 2, k, :), 1); + elseif ~obs_Qps & obs_self + error('not yet implemented') + else % everything is hidden + marg = reshape(fmarginal.T, [Qsz 2 Qpsz Qsz]); + hor_counts = squeeze(marg(:,1,:,:)); + ver_counts = squeeze(sum(marg(:,2,:,:),1)); % sum over i + end + end +end + +CPD.sub_CPD_trans = update_ess_simple(CPD.sub_CPD_trans, hor_counts); + +if ~isempty(CPD.sub_CPD_start) + CPD.sub_CPD_start = update_ess_simple(CPD.sub_CPD_start, ver_counts); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmQ_CPD/Old/update_ess2.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,178 @@ +function CPD = update_ess2(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) +% UPDATE_ESS Update the Expected Sufficient Statistics of a hhmm Q node. +% function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, idden_bitv) + +% Figure out the node numbers associated with each parent +dom = fmarginal.domain; +self = dom(end); % by assumption +old_self = dom(CPD.old_self_ndx); +Fself = dom(CPD.Fself_ndx); +Fbelow = dom(CPD.Fbelow_ndx); +Qps = dom(CPD.Qps_ndx); + +Qsz = CPD.Qsz; +Qpsz = CPD.Qpsz; + + +fmarg = add_ev_to_dmarginal(fmarginal, evidence, ns); + + + +% hor_counts(old_self, Qps, self), +% fmarginal(old_self, Fbelow, Fself, Qps, self) +% hor_counts(i,k,j) = fmarginal(i,2,1,k,j) % below has finished, self has not +% ver_counts(i,k,j) = fmarginal(i,2,2,k,j) % below has finished, and so has self (reset) +% Since any of i,j,k may be observed, we write +% hor_counts(counts_ndx{:}) = fmarginal(fmarg_ndx{:}) +% where e.g., counts_ndx = {1, ':', 2} if Qps is hidden but we observe old_self=1, self=2. +% To create this counts_ndx, we write counts_ndx = mk_multi_ndx(3, obs_dim, obs_val) +% where counts_obs_dim = [1 3], counts_obs_val = [1 2] specifies the values of dimensions 1 and 3. + +counts_obs_dim = []; +fmarg_obs_dim = []; +obs_val = []; +if hidden_bitv(self) + effQsz = Qsz; +else + effQsz = 1; + counts_obs_dim = [counts_obs_dim 3]; + fmarg_obs_dim = [fmarg_obs_dim 5]; + obs_val = [obs_val evidence{self}]; +end + +% e.g., D=4, d=3, Qps = all Qs above, so dom = [Q3(t-1) F4(t-1) F3(t-1) Q1(t) Q2(t) Q3(t)]. +% so self = Q3(t), old_self = Q3(t-1), CPD.Qps = [1 2], Qps = [Q1(t) Q2(t)] +dom = fmarginal.domain; +self = dom(end); +old_self = dom(1); +Qps = dom(length(dom)-length(CPD.Qps):end-1); + +Qsz = CPD.Qsizes(CPD.d); +Qpsz = prod(CPD.Qsizes(CPD.Qps)); + +% If some of the Q nodes are observed (which happens during supervised training) +% the counts will only be non-zero in positions +% consistent with the evidence. We put the computed marginal responsibilities +% into the appropriate slots of the big counts array. +% (Recall that observed discrete nodes only have a single effective value.) +% (A more general, but much slower, way is to call add_evidence_to_dmarginal.) +% We assume the F nodes are never observed. + +obs_self = ~hidden_bitv(self); +obs_Qps = (~isempty(Qps)) & (~any(hidden_bitv(Qps))); % we assume that all or none of the Q parents are observed + +if obs_self + self_val = evidence{self}; + oldself_val = evidence{old_self}; +end + +if obs_Qps + Qps_val = subv2ind(Qpsz, cat(1, evidence{Qps})); + if Qps_val == 0 + keyboard + end +end + +if CPD.d==1 % no Qps from above + if ~CPD.F1toQ1 % no F from self + % marg(Q1(t-1), F2(t-1), Q1(t)) + % F2(t-1) P(Q1(t)=j | Q1(t-1)=i) + % 1 delta(i,j) + % 2 transprob(i,j) + if obs_self + hor_counts = zeros(Qsz, Qsz); + hor_counts(oldself_val, self_val) = fmarginal.T(2); + else + marg = reshape(fmarginal.T, [Qsz 2 Qsz]); + hor_counts = squeeze(marg(:,2,:)); + end + else + % marg(Q1(t-1), F2(t-1), F1(t-1), Q1(t)) + % F2(t-1) F1(t-1) P(Qd(t)=j| Qd(t-1)=i) + % ------------------------------------------------------ + % 1 1 delta(i,j) + % 2 1 transprob(i,j) + % 1 2 impossible + % 2 2 startprob(j) + if obs_self + marg = myreshape(fmarginal.T, [1 2 2 1]); + hor_counts = zeros(Qsz, Qsz); + hor_counts(oldself_val, self_val) = marg(1,2,1,1); + ver_counts = zeros(Qsz, 1); + %ver_counts(self_val) = marg(1,2,2,1); + ver_counts(self_val) = marg(1,2,2,1) + marg(1,1,2,1); + else + marg = reshape(fmarginal.T, [Qsz 2 2 Qsz]); + hor_counts = squeeze(marg(:,2,1,:)); + %ver_counts = squeeze(sum(marg(:,2,2,:),1)); % sum over i + ver_counts = squeeze(sum(marg(:,2,2,:),1)) + squeeze(sum(marg(:,1,2,:),1)); % sum i,b + end + end % F1toQ1 +else % d ~= 1 + if CPD.d < CPD.D % general case + % marg(Qd(t-1), Fd+1(t-1), Fd(t-1), Qps(t), Qd(t)) + % Fd+1(t-1) Fd(t-1) P(Qd(t)=j| Qd(t-1)=i, Qps(t)=k) + % ------------------------------------------------------ + % 1 1 delta(i,j) + % 2 1 transprob(i,k,j) + % 1 2 impossible + % 2 2 startprob(k,j) + if obs_Qps & obs_self + marg = myreshape(fmarginal.T, [1 2 2 1 1]); + k = 1; + hor_counts = zeros(Qsz, Qpsz, Qsz); + hor_counts(oldself_val, Qps_val, self_val) = marg(1, 2,1, k,1); + ver_counts = zeros(Qpsz, Qsz); + %ver_counts(Qps_val, self_val) = marg(1, 2,2, k,1); + ver_counts(Qps_val, self_val) = marg(1, 2,2, k,1) + marg(1, 1,2, k,1); + elseif obs_Qps & ~obs_self + marg = myreshape(fmarginal.T, [Qsz 2 2 1 Qsz]); + k = 1; + hor_counts = zeros(Qsz, Qpsz, Qsz); + hor_counts(:, Qps_val, :) = marg(:, 2,1, k,:); + ver_counts = zeros(Qpsz, Qsz); + %ver_counts(Qps_val, :) = sum(marg(:, 2,2, k,:), 1); + ver_counts(Qps_val, :) = sum(marg(:, 2,2, k,:), 1) + sum(marg(:, 1,2, k,:), 1); + elseif ~obs_Qps & obs_self + error('not yet implemented') + else % everything is hidden + marg = reshape(fmarginal.T, [Qsz 2 2 Qpsz Qsz]); + hor_counts = squeeze(marg(:,2,1,:,:)); % i,k,j + %ver_counts = squeeze(sum(marg(:,2,2,:,:),1)); % sum over i + ver_counts = squeeze(sum(marg(:,2,2,:,:),1)) + squeeze(sum(marg(:,1,2,:,:),1)); % sum over i,b + end + else % d == D, so no F from below + % marg(QD(t-1), FD(t-1), Qps(t), QD(t)) + % FD(t-1) P(QD(t)=j | QD(t-1)=i, Qps(t)=k) + % 1 transprob(i,k,j) + % 2 startprob(k,j) + if obs_Qps & obs_self + marg = myreshape(fmarginal.T, [1 2 1 1]); + k = 1; + hor_counts = zeros(Qsz, Qpsz, Qsz); + hor_counts(oldself_val, Qps_val, self_val) = marg(1, 1, k,1); + ver_counts = zeros(Qpsz, Qsz); + ver_counts(Qps_val, self_val) = marg(1, 2, k,1); + elseif obs_Qps & ~obs_self + marg = myreshape(fmarginal.T, [Qsz 2 1 Qsz]); + k = 1; + hor_counts = zeros(Qsz, Qpsz, Qsz); + hor_counts(:, Qps_val, :) = marg(:, 1, k,:); + ver_counts = zeros(Qpsz, Qsz); + ver_counts(Qps_val, :) = sum(marg(:, 2, k, :), 1); + elseif ~obs_Qps & obs_self + error('not yet implemented') + else % everything is hidden + marg = reshape(fmarginal.T, [Qsz 2 Qpsz Qsz]); + hor_counts = squeeze(marg(:,1,:,:)); + ver_counts = squeeze(sum(marg(:,2,:,:),1)); % sum over i + end + end +end + +CPD.sub_CPD_trans = update_ess_simple(CPD.sub_CPD_trans, hor_counts); + +if ~isempty(CPD.sub_CPD_start) + CPD.sub_CPD_start = update_ess_simple(CPD.sub_CPD_start, ver_counts); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmQ_CPD/Old/update_ess3.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,80 @@ +function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) +% UPDATE_ESS Update the Expected Sufficient Statistics of a hhmm Q node. +% function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, idden_bitv) +% +% we assume if one of the Qps is observed, all of them are +% We assume the F nodes are already hidden + +% Figure out the node numbers associated with each parent +dom = fmarginal.domain; +self = dom(CPD.self_ndx); +old_self = dom(CPD.old_self_ndx); +%Fself = dom(CPD.Fself_ndx); +%Fbelow = dom(CPD.Fbelow_ndx); +Qps = dom(CPD.Qps_ndx); + +Qsz = CPD.Qsz; +Qpsz = CPD.Qpsz; + + +% hor_counts(old_self, Qps, self), +% fmarginal(old_self, Fbelow, Fself, Qps, self) +% hor_counts(i,k,j) = fmarginal(i,2,1,k,j) % below has finished, self has not +% ver_counts(i,k,j) = fmarginal(i,2,2,k,j) % below has finished, and so has self (reset) +% Since any of i,j,k may be observed, we write +% hor_counts(ndx{:}) = fmarginal(...) +% where e.g., ndx = {1, ':', 2} if Qps is hidden but we observe old_self=1, self=2. + +% ndx{i,k,j} +if hidden_bitv(old_self) + ndx{1} = ':'; +else + ndx{1} = evidence{old_self}; +end +if hidden_bitv(Qps) + ndx{2} = ':'; +else + ndx{2} = subv2ind(Qpsz, cat(1, evidence{Qps})); +end +if hidden_bitv(self) + ndx{3} = ':'; +else + ndx{3} = evidence{self}; +end + +fmarg = add_ev_to_dmarginal(fmarginal, evidence, ns); +% marg(Qold(t-1), Fbelow(t-1), Fself(t-1), Qps(t), Qself(t)) +hor_counts = zeros(Qsz, Qpsz, Qsz); +ver_counts = zeros(Qpsz, Qsz); + +if ~isempty(CPD.Fbelow_ndx) + if ~isempty(CPD.Fself_ndx) % general case + fmarg.T = myreshape(fmarg.T, [Qsz 2 2 Qpsz Qsz]); + marg_ndx = {ndx{1}, 2, 1, ndx{2}, ndx{3}}; + hor_counts(ndx{:}) = fmarg.T(marg_ndx{:}); + ver_counts(ndx{2:3}) = ... % sum over Fbelow and Qold=i + sum(fmarg.T({ndx{1}, 1, 2, ndx{2}, ndx{3}}),1) + .. + sum(fmarg.T({ndx{1}, 2, 2, ndx{2}, ndx{3}}),1); + else % no F from self, hence no startprob + fmarg.T = myreshape(fmarg.T, [Qsz 2 Qpsz Qsz]); + hor_counts(ndx{:}) = fmarg.T({ndx{1}, 2, ndx{2}, ndx{3}}); + end +else % no F signal from below + if ~isempty(CPD.Fself_ndx) % self F + fmarg.T = myreshape(fmarg.T, [Qsz 2 Qpsz Qsz]); + hor_counts(ndx{:}) = fmarg.T({ndx{1}, 1, ndx{2}, ndx{3}}); + ver_counts(ndx{2:3}) = ... % sum over Qold=i + sum(fmarg.T({ndx{1}, 2, ndx{2}, ndx{3}}),1); + else % no F from self + error('An hhmmQ node without any F parents is just a tabular_CPD') + end +end + + +CPD.sub_CPD_trans = update_ess_simple(CPD.sub_CPD_trans, hor_counts); + +if ~isempty(CPD.sub_CPD_start) + CPD.sub_CPD_start = update_ess_simple(CPD.sub_CPD_start, ver_counts); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmQ_CPD/Old/update_ess4.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,95 @@ +function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) +% UPDATE_ESS Update the Expected Sufficient Statistics of a hhmm Q node. +% function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, idden_bitv) +% +% we assume if one of the Qps is observed, all of them are +% We assume the F nodes are already hidden + +% Figure out the node numbers associated with each parent +dom = fmarginal.domain; +self = dom(CPD.self_ndx); +old_self = dom(CPD.old_self_ndx); +%Fself = dom(CPD.Fself_ndx); +%Fbelow = dom(CPD.Fbelow_ndx); +Qps = dom(CPD.Qps_ndx); + +Qsz = CPD.Qsz; +Qpsz = CPD.Qpsz; + + +% hor_counts(old_self, Qps, self), +% fmarginal(old_self, Fbelow, Fself, Qps, self) +% hor_counts(i,k,j) = fmarginal(i,2,1,k,j) % below has finished, self has not +% ver_counts(i,k,j) = fmarginal(i,2,2,k,j) % below has finished, and so has self (reset) +% Since any of i,j,k may be observed, we write +% hor_counts(i_counts_ndx, kndx, jndx) = fmarginal(i_fmarg_ndx...) +% where i_fmarg_ndx = 1 and i_counts_ndx = i if old_self is observed to have value i, +% i_fmarg_ndx = 1:Qsz and i_counts_ndx = 1:Qsz if old_self is hidden, etc. + + +if hidden_bitv(old_self) + i_counts_ndx = 1:Qsz; + i_fmarg_ndx = 1:Qsz; + eff_oldQsz = Qsz; +else + i_counts_ndx = evidence{old_self}; + i_fmarg_ndx = 1; + eff_oldQsz = 1; +end + +if all(hidden_bitv(Qps)) % we assume all are hidden or all are observed + k_counts_ndx = 1:Qpsz; + k_fmarg_ndx = 1:Qpsz; + eff_Qpsz = Qpsz; +else + k_counts_ndx = subv2ind(Qpsz, cat(1, evidence{Qps})); + k_fmarg_ndx = 1; + eff_Qpsz = 1; +end + +if hidden_bitv(self) + j_counts_ndx = 1:Qsz; + j_fmarg_ndx = 1:Qsz; + eff_Qsz = Qsz; +else + j_counts_ndx = evidence{self}; + j_fmarg_ndx = 1; + eff_Qsz = 1; +end + +hor_counts = zeros(Qsz, Qpsz, Qsz); +ver_counts = zeros(Qpsz, Qsz); + +if ~isempty(CPD.Fbelow_ndx) + if ~isempty(CPD.Fself_ndx) % general case + fmarg.T = myreshape(fmarg.T, [eff_oldQsz 2 2 eff_Qpsz eff_Qsz]); + hor_counts(i_counts_ndx, k_counts_ndx, j_counts_ndx) = ... + fmarg.T(:, i_fmarg_ndx, 2, 1, k_fmarg_ndx, j_fmarg_ndx); + ver_counts(k_counts_ndx, j_counts_ndx) = ... % sum over Fbelow and Qold + sum(fmarg.T(:, 1, 2, k_fmarg_ndx, j_fmarg_ndx), 1) + ... + sum(fmarg.T(:, 2, 2, k_fmarg_ndx, j_fmarg_ndx), 1); + else % no F from self, hence no startprob + fmarg.T = myreshape(fmarg.T, [eff_oldQsz 2 eff_Qpsz eff_Qsz]); + hor_counts(i_counts_ndx, k_counts_ndx, j_counts_ndx) = ... + fmarg.T(i_fmarg_ndx, 2, k_fmarg_ndx, j_fmarg_ndx); + end +else % no F signal from below + if ~isempty(CPD.Fself_ndx) % self F + fmarg.T = myreshape(fmarg.T, [eff_oldQsz 2 eff_Qpsz eff_Qsz]); + hor_counts(i_counts_ndx, k_counts_ndx, j_counts_ndx) = ... + fmarg.T(i_fmarg_ndx, 1, k_fmarg_ndx, j_fmarg_ndx); + ver_counts(k_counts_ndx, j_counts_ndx) = ... % sum over Qold + sum(fmarg.T(:, 2, k_fmarg_ndx, j_fmarg_ndx), 1); + else % no F from self + error('An hhmmQ node without any F parents is just a tabular_CPD') + end +end + + +CPD.sub_CPD_trans = update_ess_simple(CPD.sub_CPD_trans, hor_counts); + +if ~isempty(CPD.sub_CPD_start) + CPD.sub_CPD_start = update_ess_simple(CPD.sub_CPD_start, ver_counts); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmQ_CPD/hhmmQ_CPD.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,132 @@ +function CPD = hhmmQ_CPD(bnet, self, varargin) +% HHMMQ_CPD Make the CPD for a Q node in a hierarchical HMM +% CPD = hhmmQ_CPD(bnet, self, ...) +% +% Fself(t-1) Qps(t) +% \ | +% \ v +% Qold(t-1) -> Q(t) +% / +% / +% Fbelow(t-1) +% +% Let ss = slice size = num. nodes per slice. +% This node is Q(t), and has mandatory parents Qold(t-1) (assumed to be numbered Q(t)-ss) +% and optional parents Fbelow, Fself, Qps. +% We require parents to be ordered (numbered) as follows: +% Qold, Fbelow, Fself, Qps, Q. +% +% If Fself=2, we use the transition matrix, else we use the prior matrix. +% If Fself node is omitted (eg. top level), we always use the transition matrix. +% If Fbelow=2, we may change state, otherwise we must stay in the same state. +% If Fbelow node is omitted (eg., bottom level), we may change state at every step. +% If Qps (Q parents) are specified, all parameters are conditioned on their joint value. +% We may choose any subset of nodes to condition on, as long as they as numbered lower than self. +% +% optional args [defaults] +% +% Fself - node number <= ss +% Fbelow - node number <= ss +% Qps - node numbers (all <= 2*ss) - uses 2TBN indexing +% transprob - transprob(i,k,j) = prob transition from i to j given Qps = k ['leftright'] +% selfprob - prob of a transition from i to i given Qps=k [0.1] +% startprob - startprob(k,j) = prob start in j given Qps = k ['leftstart'] +% startargs - other args to be passed to the sub tabular_CPD for learning startprob +% transargs - other args will be passed to the sub tabular_CPD for learning transprob +% fullstartprob - 1 means startprob depends on Q(t-1) [0] +% hhmmQ_CPD is a subclass of tabular_CPD so we inherit inference methods like CPD_to_pot, etc. +% +% We create isolated tabular_CPDs with no F parents to learn transprob/startprob +% so we can avail of e.g., entropic or Dirichlet priors. +% In the future, we will be able to represent the transprob using a tree_CPD. +% +% For details, see "Linear-time inference in hierarchical HMMs", Murphy and Paskin, NIPS'01. + + +ss = bnet.nnodes_per_slice; +ns = bnet.node_sizes(:); + +% set default arguments +Fself = []; +Fbelow = []; +Qps = []; +startprob = 'leftstart'; +transprob = 'leftright'; +startargs = {}; +transargs = {}; +selfprob = 0.1; +fullstartprob = 0; + +for i=1:2:length(varargin) + switch varargin{i}, + case 'Fself', Fself = varargin{i+1}; + case 'Fbelow', Fbelow = varargin{i+1}; + case 'Qps', Qps = varargin{i+1}; + case 'transprob', transprob = varargin{i+1}; + case 'selfprob', selfprob = varargin{i+1}; + case 'startprob', startprob = varargin{i+1}; + case 'startargs', startargs = varargin{i+1}; + case 'transargs', transargs = varargin{i+1}; + case 'fullstartprob', fullstartprob = varargin{i+1}; + end +end + +CPD.fullstartprob = fullstartprob; + +ps = parents(bnet.dag, self); +ndsz = ns(:)'; +CPD.dom_sz = [ndsz(ps) ns(self)]; +CPD.Fself_ndx = find_equiv_posns(Fself, ps); +CPD.Fbelow_ndx = find_equiv_posns(Fbelow, ps); +%CPD.Qps_ndx = find_equiv_posns(Qps+ss, ps); +CPD.Qps_ndx = find_equiv_posns(Qps, ps); +old_self = self-ss; +CPD.old_self_ndx = find_equiv_posns(old_self, ps); + +Qps = ps(CPD.Qps_ndx); +CPD.Qsz = ns(self); +CPD.Qpsz = prod(ns(Qps)); +CPD.Qpsizes = ns(Qps); +Qsz = CPD.Qsz; +Qpsz = CPD.Qpsz; + +if strcmp(transprob, 'leftright') + LR = mk_leftright_transmat(Qsz, selfprob); + transprob = repmat(reshape(LR, [1 Qsz Qsz]), [Qpsz 1 1]); % transprob(k,i,j) + transprob = permute(transprob, [2 1 3]); % now transprob(i,k,j) +end +transargs{end+1} = 'CPT'; +transargs{end+1} = transprob; +CPD.sub_CPD_trans = mk_isolated_tabular_CPD(ns([old_self Qps self]), transargs); +S = struct(CPD.sub_CPD_trans); +%CPD.transprob = myreshape(S.CPT, [Qsz Qpsz Qsz]); +CPD.transprob = S.CPT; + + +if strcmp(startprob, 'leftstart') + startprob = zeros(Qpsz, Qsz); + startprob(:,1) = 1; +end +if isempty(CPD.Fself_ndx) + CPD.sub_CPD_start = []; + CPD.startprob = []; +else + startargs{end+1} = 'CPT'; + startargs{end+1} = startprob; + if CPD.fullstartprob + CPD.sub_CPD_start = mk_isolated_tabular_CPD(ns([self Qps self]), startargs); + S = struct(CPD.sub_CPD_start); + %CPD.startprob = myreshape(S.CPT, [Qsz Qpsz Qsz]); + CPD.startprob = S.CPT; + else + CPD.sub_CPD_start = mk_isolated_tabular_CPD(ns([Qps self]), startargs); + S = struct(CPD.sub_CPD_start); + %CPD.startprob = myreshape(S.CPT, [CPD.Qpsizes Qsz]); + CPD.startprob = S.CPT; + end +end + +CPD = class(CPD, 'hhmmQ_CPD', tabular_CPD(bnet, self)); + +CPD = update_CPT(CPD); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmQ_CPD/log_prior.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +function L = log_prior(CPD) +% LOG_PRIOR Return log P(theta) for a hhmm CPD +% L = log_prior(CPD) + +L = log_prior(CPD.sub_CPD_trans); +if ~isempty(CPD.sub_CPD_start) + L = L + log_prior(CPD.sub_CPD_start); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmQ_CPD/maximize_params.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,40 @@ +function CPD = maximize_params(CPD, temp) +% MAXIMIZE_PARAMS Set the params of a hhmmQ node to their ML/MAP values. +% CPD = maximize_params(CPD, temperature) + +Qsz = CPD.Qsz; +Qpsz = CPD.Qpsz; + +if ~isempty(CPD.sub_CPD_start) + CPD.sub_CPD_start = maximize_params(CPD.sub_CPD_start, temp); + S = struct(CPD.sub_CPD_start); + CPD.startprob = myreshape(S.CPT, [Qpsz Qsz]); + %CPD.startprob = S.CPT; +end + +if 1 + % If we are in a state that can only go the end state, + % we will never see a transition to another (non-end) state, + % so counts(i,k,j)=0 (and termprob(k,i)=1). + % We set counts(i,k,i)=1 in this case. + % This will cause remove_hhmm_end_state to return a + % stochastic matrix, but otherwise has no effect on EM. + counts = get_field(CPD.sub_CPD_trans, 'counts'); + counts = reshape(counts, [Qsz Qpsz Qsz]); + for k=1:Qpsz + for i=1:Qsz + if sum(counts(i,k,:))==0 % never witnessed a transition out of i + counts(i,k,i)=1; % add self loop + %fprintf('CPDQ d=%d i=%d k=%d\n', CPD.d, i, k); + end + end + end + CPD.sub_CPD_trans = set_fields(CPD.sub_CPD_trans, 'counts', counts(:)); +end + +CPD.sub_CPD_trans = maximize_params(CPD.sub_CPD_trans, temp); +S = struct(CPD.sub_CPD_trans); +%CPD.transprob = S.CPT; +CPD.transprob = myreshape(S.CPT, [Qsz Qpsz Qsz]); + +CPD = update_CPT(CPD);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmQ_CPD/reset_ess.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +function CPD = reset_ess(CPD) +% RESET_ESS Reset the Expected Sufficient Statistics of a hhmm Q node. +% CPD = reset_ess(CPD) + +if ~isempty(CPD.sub_CPD_start) + CPD.sub_CPD_start = reset_ess(CPD.sub_CPD_start); +end +CPD.sub_CPD_trans = reset_ess(CPD.sub_CPD_trans);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmQ_CPD/update_CPT.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,70 @@ +function CPD = update_CPT(CPD) +% Compute the big CPT for an HHMM Q node (including F parents) given internal transprob and startprob +% function CPD = update_CPT(CPD) + +Qsz = CPD.Qsz; +Qpsz = CPD.Qpsz; + +if ~isempty(CPD.Fbelow_ndx) + if ~isempty(CPD.Fself_ndx) % general case + % Fb(t-1) Fself(t-1) P(Q(t)=j| Q(t-1)=i, Qps(t)=k) + % ------------------------------------------------------ + % 1 1 delta(i,j) + % 2 1 transprob(i,k,j) + % 1 2 impossible + % 2 2 startprob(k,j) + CPT = zeros(Qsz, 2, 2, Qpsz, Qsz); + I = repmat(eye(Qsz), [1 1 Qpsz]); % i,j,k + I = permute(I, [1 3 2]); % i,k,j + CPT(:, 1, 1, :, :) = I; + CPT(:, 2, 1, :, :) = CPD.transprob; + CPT(:, 1, 2, :, :) = I; + CPT(:, 2, 2, :, :) = repmat(reshape(CPD.startprob, [1 Qpsz Qsz]), ... + [Qsz 1 1]); % replicate over i + else % no F from self, hence no startprob + % Fb(t-1) P(Q(t)=j| Q(t-1)=i, Qps(t)=k) + % ------------------------------------------------------ + % 1 delta(i,j) + % 2 transprob(i,k,j) + + nps = length(CPD.dom_sz)-1; % num parents + CPT = 0*myones(CPD.dom_sz); + %CPT = zeros(Qsz, 2, Qpsz, Qsz); % assumes CPT(Q(t-1), F(t-1), Qps, Q(t)) + % but a member of Qps may preceed Q(t-1) or F(t-1) in the ordering + + for k=1:CPD.Qpsz + Qps_vals = ind2subv(CPD.Qpsizes, k); + ndx = mk_multi_index(nps+1, [CPD.Fbelow_ndx CPD.Qps_ndx], [1 Qps_vals]); + CPT(ndx{:}) = eye(Qsz); % CPT(:,2,k,:) or CPT(:,k,2,:) etc + end + ndx = mk_multi_index(nps+1, CPD.Fbelow_ndx, 2); + CPT(ndx{:}) = CPD.transprob; % we assume transprob is in topo order + end +else % no F signal from below + if ~isempty(CPD.Fself_ndx) + % Q(t-1), Fself(t-1), Qps, Q(t) + + % Fself(t-1) P(Q(t-1)=i, Qps(t)=k -> Q(t)=j) + % ------------------------------------------------------ + % 1 transprob(i,k,j) + % 2 startprob(k,j) + + nps = length(CPD.dom_sz)-1; % num parents + CPT = 0*myones(CPD.dom_sz); + ndx = mk_multi_index(nps+1, CPD.Fself_ndx, 1); + CPT(ndx{:}) = CPD.transprob; + if CPD.fullstartprob + ndx = mk_multi_index(nps+1, CPD.Fself_ndx, 2); + CPT(ndx{:}) = CPD.startprob; + else + for i=1:CPD.Qsz + ndx = mk_multi_index(nps+1, [CPD.Fself_ndx CPD.old_self_ndx], [2 i]); + CPT(ndx{:}) = CPD.startprob; + end + end + else % no F from self + error('An hhmmQ node without any F parents is just a tabular_CPD') + end +end + +CPD = set_fields(CPD, 'CPT', CPT);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@hhmmQ_CPD/update_ess.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,86 @@ +function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) +% UPDATE_ESS Update the Expected Sufficient Statistics of a hhmm Q node. +% function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, idden_bitv) +% +% we assume if one of the Qps is observed, all of them are +% We assume the F nodes are already hidden + +% Figure out the node numbers associated with each parent +dom = fmarginal.domain; +self = dom(end); +old_self = dom(CPD.old_self_ndx); +%Fself = dom(CPD.Fself_ndx); +%Fbelow = dom(CPD.Fbelow_ndx); +Qps = dom(CPD.Qps_ndx); + +Qsz = CPD.Qsz; +Qpsz = CPD.Qpsz; + + +% hor_counts(old_self, Qps, self), +% fmarginal(old_self, Fbelow, Fself, Qps, self) +% hor_counts(i,k,j) = fmarginal(i,2,1,k,j) % below has finished, self has not +% ver_counts(i,k,j) = fmarginal(i,2,2,k,j) % below has finished, and so has self (reset) +% Since any of i,j,k may be observed, we write +% hor_counts(i_counts_ndx, kndx, jndx) = fmarginal(i_fmarg_ndx...) +% where i_fmarg_ndx = 1 and i_counts_ndx = i if old_self is observed to have value i, +% i_fmarg_ndx = 1:Qsz and i_counts_ndx = 1:Qsz if old_self is hidden, etc. + + +if hidden_bitv(old_self) + i_counts_ndx = 1:Qsz; + eff_oldQsz = Qsz; +else + i_counts_ndx = evidence{old_self}; + eff_oldQsz = 1; +end + +if all(hidden_bitv(Qps)) % we assume all are hidden or all are observed + k_counts_ndx = 1:Qpsz; + eff_Qpsz = Qpsz; +else + k_counts_ndx = subv2ind(Qpsz, cat(1, evidence{Qps})); + eff_Qpsz = 1; +end + +if hidden_bitv(self) + j_counts_ndx = 1:Qsz; + eff_Qsz = Qsz; +else + j_counts_ndx = evidence{self}; + eff_Qsz = 1; +end + +hor_counts = zeros(Qsz, Qpsz, Qsz); +ver_counts = zeros(Qpsz, Qsz); + +if ~isempty(CPD.Fbelow_ndx) + if ~isempty(CPD.Fself_ndx) % general case + fmarg = myreshape(fmarginal.T, [eff_oldQsz 2 2 eff_Qpsz eff_Qsz]); + hor_counts(i_counts_ndx, k_counts_ndx, j_counts_ndx) = fmarg(:, 2, 1, :, :); + ver_counts(k_counts_ndx, j_counts_ndx) = ... % sum over Fbelow and Qold + sumv(fmarg(:, :, 2, :, :), [1 2]); % require Fself=2 + else % no F from self, hence no startprob + fmarg = myreshape(fmarginal.T, [eff_oldQsz 2 eff_Qpsz eff_Qsz]); + hor_counts(i_counts_ndx, k_counts_ndx, j_counts_ndx) = ... + fmarg(:, 2, :, :); % require Fbelow = 2 + end +else % no F signal from below + if ~isempty(CPD.Fself_ndx) % self F + fmarg = myreshape(fmarginal.T, [eff_oldQsz 2 eff_Qpsz eff_Qsz]); + hor_counts(i_counts_ndx, k_counts_ndx, j_counts_ndx) = fmarg(:, 1, :, :); + ver_counts(k_counts_ndx, j_counts_ndx) = ... % sum over Qold + squeeze(sum(fmarg(:, 2, :, :), 1)); % Fself=2 + else % no F from self + error('An hhmmQ node without any F parents is just a tabular_CPD') + end +end + + +CPD.sub_CPD_trans = update_ess_simple(CPD.sub_CPD_trans, hor_counts); + +if ~isempty(CPD.sub_CPD_start) + CPD.sub_CPD_start = update_ess_simple(CPD.sub_CPD_start, ver_counts); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@mlp_CPD/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +/convert_to_table.m/1.1.1.1/Wed May 29 15:59:54 2002// +/maximize_params.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mlp_CPD.m/1.1.1.1/Wed May 29 15:59:54 2002// +/reset_ess.m/1.1.1.1/Wed May 29 15:59:54 2002// +/update_ess.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@mlp_CPD/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@mlp_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@mlp_CPD/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@mlp_CPD/convert_to_table.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,80 @@ +function T = convert_to_table(CPD, domain, evidence) +% CONVERT_TO_TABLE Convert a mlp CPD to a table, incorporating any evidence +% T = convert_to_table(CPD, domain, evidence) + +self = domain(end); +ps = domain(1:end-1); % self' parents +%cps = myintersect(ps, cnodes); % self' continous parents +cnodes = domain(CPD.cpndx); +cps = myintersect(ps, cnodes); +odom = domain(~isemptycell(evidence(domain))); % obs nodes in the net +assert(myismember(cps, odom)); % !ALL the CTS parents must be observed! +ns(cps)=1; +dps = mysetdiff(ps, cps); % self' discrete parents +dobs = myintersect(dps, odom); % discrete obs parents + +% Extract the params compatible with the observations (if any) on the discrete parents (if any) + +if ~isempty(dobs), + dvals = cat(1, evidence{dobs}); + ns_eff= CPD.sizes; % effective node sizes + ens=ns_eff; + ens(dobs) = 1; + S=prod(ens(dps)); + subs = ind2subv(ens(dps), 1:S); + mask = find_equiv_posns(dobs, dps); + for i=1:length(mask), + subs(:,mask(i)) = dvals(i); + end + support = subv2ind(ns_eff(dps), subs)'; +else + ns_eff= CPD.sizes; + support=[1:prod(ns_eff(dps))]; +end + +W1=[]; b1=[]; W2=[]; b2=[]; + +W1 = CPD.W1(:,:,support); +b1= CPD.b1(support,:); +W2 = CPD.W2(:,:,support); +b2= CPD.b2(support,:); +ns(odom) = 1; +dpsize = prod(ns(dps)); % overall size of the self' discrete parents + +x = cat(1, evidence{cps}); +ndata=size(x,2); + +if ~isempty(evidence{self}) % + app=struct(CPD); % + ns(self)=app.mlp{1}.nout; % pump up self to the original dimension if observed + clear app; % +end % + +T =zeros(dpsize, ns(self)); % +for i=1:dpsize % + W1app = W1(:,:,i); % + b1app = b1(i,:); % + W2app = W2(:,:,i); % + b2app = b2(i,:); % for each of the dpsize combinations of self'parents values + z = tanh(x(:)'*W1app + ones(ndata, 1)*b1app); % we tabulate the corrisponding glm model + a = z*W2app + ones(ndata, 1)*b2app; % (element of the cell array CPD.glim) + appoggio = normalise(exp(a)); % + T(i,:)=appoggio; % + W1app=[]; W2app=[]; b1app=[]; b2app=[]; % + z=[]; a=[]; appoggio=[]; % +end % + +if ~isempty(evidence{self}) + appoggio=[]; % + appoggio=zeros(1,ns(self)); % + r = evidence{self}; %...if self is observed => in output there's only the probability of the 'true' class + for i=1:dpsize % + appoggio(i)=T(i,r); % + end + T=zeros(dpsize,1); + for i=1:dpsize + T(i,1)=appoggio(i); + end + clear appoggio; + ns(self) = 1; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@mlp_CPD/maximize_params.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,34 @@ +function CPD = maximize_params(CPD, temp) +% MAXIMIZE_PARAMS Find ML params of an MLP using Scaled Conjugated Gradient (SCG) +% CPD = maximize_params(CPD, temperature) +% temperature parameter is ignored + +if ~adjustable_CPD(CPD), return; end +options = foptions; + +% options(1) >= 0 means print an annoying message when the max. num. iter. is reached +if CPD.verbose + options(1) = 1; +else + options(1) = -1; +end +%options(1) = CPD.verbose; + +options(2) = CPD.wthresh; +options(3) = CPD.llthresh; +options(14) = CPD.max_iter; + +dpsz=length(CPD.mlp); + +for i=1:dpsz + mask=[]; + mask=find(CPD.eso_weights(:,:,i)>0); % for adapting the parameters we use only positive weighted example + if ~isempty(mask), + CPD.mlp{i} = netopt_weighted(CPD.mlp{i}, options, CPD.parent_vals(mask',:), CPD.self_vals(mask',:,i), CPD.eso_weights(mask',:,i), 'scg'); + + CPD.W1(:,:,i)=CPD.mlp{i}.w1; % update the parameters matrix + CPD.b1(i,:)=CPD.mlp{i}.b1; % + CPD.W2(:,:,i)=CPD.mlp{i}.w2; % update the parameters matrix + CPD.b2(i,:)=CPD.mlp{i}.b2; % + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@mlp_CPD/mlp_CPD.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,139 @@ +function CPD = mlp_CPD(bnet, self, nhidden, w1, b1, w2, b2, clamped, max_iter, verbose, wthresh, llthresh) +% MLP_CPD Make a CPD from a Multi Layer Perceptron (i.e., feedforward neural network) +% +% We use a different MLP for each discrete parent combination (if there are any discrete parents). +% We currently assume this node (the child) is discrete. +% +% CPD = mlp_CPD(bnet, self, nhidden) +% will create a CPD with random parameters, where self is the number of this node and nhidden the number of the hidden nodes. +% The params are drawn from N(0, s*I), where s = 1/sqrt(n+1), n = length(X). +% +% CPD = mlp_CPD(bnet, self, nhidden, w1, b1, w2, b2) allows you to specify the params, where +% w1 = first-layer weight matrix +% b1 = first-layer bias vector +% w2 = second-layer weight matrix +% b2 = second-layer bias vector +% These are assumed to be the same for each discrete parent combination. +% If any of these are [], random values will be created. +% +% CPD = mlp_CPD(bnet, self, nhidden, w1, b1, w2, b2, clamped) allows you to prevent the params from being +% updated during learning (if clamped = 1). Default: clamped = 0. +% +% CPD = mlp_CPD(bnet, self, nhidden, w1, b1, w2, b2, clamped, max_iter, verbose, wthresh, llthresh) +% alllows you to specify params that control the M step: +% max_iter - the maximum number of steps to take (default: 10) +% verbose - controls whether to print (default: 0 means silent). +% wthresh - a measure of the precision required for the value of +% the weights W at the solution. Default: 1e-2. +% llthresh - a measure of the precision required of the objective +% function (log-likelihood) at the solution. Both this and the previous condition must +% be satisfied for termination. Default: 1e-2. +% +% For learning, we use a weighted version of scaled conjugated gradient in the M step. + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + CPD = class(CPD, 'mlp_CPD', discrete_CPD(0,[])); + return; +elseif isa(bnet, 'mlp_CPD') + % This might occur if we are copying an object. + CPD = bnet; + return; +end +CPD = init_fields; + +assert(myismember(self, bnet.dnodes)); +ns = bnet.node_sizes; + +ps = parents(bnet.dag, self); +dnodes = mysetdiff(1:length(bnet.dag), bnet.cnodes); +dps = myintersect(ps, dnodes); +cps = myintersect(ps, bnet.cnodes); +dpsz = prod(ns(dps)); +cpsz = sum(ns(cps)); +self_size = ns(self); + +% discrete/cts parent index - which ones of my parents are discrete/cts? +CPD.dpndx = find_equiv_posns(dps, ps); +CPD.cpndx = find_equiv_posns(cps, ps); + +CPD.mlp = cell(1,dpsz); +for i=1:dpsz + CPD.mlp{i} = mlp(cpsz, nhidden, self_size, 'softmax'); + if nargin >=4 & ~isempty(w1) + CPD.mlp{i}.w1 = w1; + end + if nargin >=5 & ~isempty(b1) + CPD.mlp{i}.b1 = b1; + end + if nargin >=6 & ~isempty(w2) + CPD.mlp{i}.w2 = w2; + end + if nargin >=7 & ~isempty(b2) + CPD.mlp{i}.b2 = b2; + end + W1app(:,:,i)=CPD.mlp{i}.w1; + W2app(:,:,i)=CPD.mlp{i}.w2; + b1app(i,:)=CPD.mlp{i}.b1; + b2app(i,:)=CPD.mlp{i}.b2; +end +if nargin < 8, clamped = 0; end +if nargin < 9, max_iter = 10; end +if nargin < 10, verbose = 0; end +if nargin < 11, wthresh = 1e-2; end +if nargin < 12, llthresh = 1e-2; end + +CPD.self = self; +CPD.max_iter = max_iter; +CPD.verbose = verbose; +CPD.wthresh = wthresh; +CPD.llthresh = llthresh; + +% sufficient statistics +% Since MLP is not in the exponential family, we must store all the raw data. +% +CPD.W1=W1app; % Extract all the parameters of the node for handling discrete obs parents +CPD.W2=W2app; % +nparaW=[size(W1app) size(W2app)]; % +CPD.b1=b1app; % +CPD.b2=b2app; % +nparab=[size(b1app) size(b2app)]; % + +CPD.sizes=bnet.node_sizes(:); % used in CPD_to_table to pump up the node sizes + +CPD.parent_vals = []; % X(l,:) = value of cts parents in l'th example + +CPD.eso_weights=[]; % weights used by the SCG algorithm + +CPD.self_vals = []; % Y(l,:) = value of self in l'th example + +% For BIC +CPD.nsamples = 0; +CPD.nparams=prod(nparaW)+prod(nparab); +CPD = class(CPD, 'mlp_CPD', discrete_CPD(clamped, ns([ps self]))); + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +CPD.mlp = {}; +CPD.self = []; +CPD.max_iter = []; +CPD.verbose = []; +CPD.wthresh = []; +CPD.llthresh = []; +CPD.approx_hess = []; +CPD.W1 = []; +CPD.W2 = []; +CPD.b1 = []; +CPD.b2 = []; +CPD.sizes = []; +CPD.parent_vals = []; +CPD.eso_weights=[]; +CPD.self_vals = []; +CPD.nsamples = []; +CPD.nparams = [];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@mlp_CPD/reset_ess.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +function CPD = reset_ess(CPD) +% RESET_ESS Reset the Expected Sufficient Statistics for a CPD (mlp) +% CPD = reset_ess(CPD) + +CPD.W1 = []; +CPD.W2 = []; +CPD.b1 = []; +CPD.b2 = []; +CPD.parent_vals = []; +CPD.eso_weights=[]; +CPD.self_vals = []; +CPD.nsamples = 0; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@mlp_CPD/update_ess.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,131 @@ +function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) +% UPDATE_ESS Update the Expected Sufficient Statistics of a CPD (MLP) +% CPD = update_ess(CPD, family_marginal, evidence, node_sizes, cnodes, hidden_bitv) +% +% fmarginal = overall posterior distribution of self and its parents +% fmarginal(i1,i2...,ik,s)=prob(Pa1=i1,...,Pak=ik, self=s| X) +% +% => 1) prob(self|Pa1,...,Pak)=fmarginal/prob(Pa1,...,Pak) with prob(Pa1,...,Pak)=sum{s,fmarginal} +% [self estimation -> CPD.self_vals] +% 2) prob(Pa1,...,Pak) [SCG weights -> CPD.eso_weights] +% +% Hidden_bitv is ignored + +% Written by Pierpaolo Brutti + +if ~adjustable_CPD(CPD), return; end + +dom = fmarginal.domain; +cdom = myintersect(dom, cnodes); +assert(~any(isemptycell(evidence(cdom)))); +ns(cdom)=1; + +self = dom(end); +ps=dom(1:end-1); +dpdom=mysetdiff(ps,cdom); + +dnodes = mysetdiff(1:length(ns), cnodes); + +ddom = myintersect(ps, dnodes); % +if isempty(evidence{self}), % if self is hidden in what follow we must + ddom = myintersect(dom, dnodes); % consider its dimension +end % + +odom = dom(~isemptycell(evidence(dom))); +hdom = dom(isemptycell(evidence(dom))); % hidden parents in domain + +dobs = myintersect(ddom, odom); +dvals = cat(1, evidence{dobs}); +ens = ns; % effective node sizes +ens(dobs) = 1; + +dpsz=prod(ns(dpdom)); +S=prod(ens(ddom)); +subs = ind2subv(ens(ddom), 1:S); +mask = find_equiv_posns(dobs, ddom); +for i=1:length(mask), + subs(:,mask(i)) = dvals(i); +end +supportedQs = subv2ind(ns(ddom), subs); + +Qarity = prod(ns(ddom)); +if isempty(ddom), + Qarity = 1; +end +fullm.T = zeros(Qarity, 1); +fullm.T(supportedQs) = fmarginal.T(:); + +% For dynamic (recurrent) net------------------------------------------------------------- +% ---------------------------------------------------------------------------------------- +high=size(evidence,1); % slice height +ss_ns=ns(1:high); % single slice nodes sizes +pos=self; % +slice_num=0; % +while pos>high, % + slice_num=slice_num+1; % find active slice + pos=pos-high; % pos=self posistion into a single slice +end % + +last_dim=pos-1; % +if isempty(evidence{self}), % + last_dim=pos; % +end % last_dim=last reshaping dimension +reg=dom-slice_num*high; +dex=myintersect(reg(find(reg>=0)), [1:last_dim]); % +rs_dim=ss_ns(dex); % reshaping dimensions + +if slice_num>0, + act_slice=[]; past_ancest=[]; % + act_slice=slice_num*high+[1:high]; % recover the active slice nodes + % past_ancest=mysetdiff(ddom, act_slice); + past_ancest=mysetdiff(ps, act_slice); % recover ancestors contained into past slices + app=ns(past_ancest); + rs_dim=[app(:)' rs_dim(:)']; % +end % +if length(rs_dim)==1, rs_dim=[1 rs_dim]; end % +if size(rs_dim,1)~=1, rs_dim=rs_dim'; end % + +fullm.T=reshape(fullm.T, rs_dim); % reshaping the marginal + +% ---------------------------------------------------------------------------------------- +% ---------------------------------------------------------------------------------------- + +% X = cts parent, R = discrete self + +% 1) observations vector -> CPD.parents_vals ------------------------------------------------- +x = cat(1, evidence{cdom}); + +% 2) weights vector -> CPD.eso_weights ------------------------------------------------------- +if isempty(evidence{self}) % R is hidden + sum_over=length(rs_dim); + app=sum(fullm.T, sum_over); + pesi=reshape(app,[dpsz,1]); + clear app; +else + pesi=reshape(fullm.T,[dpsz,1]); +end + +assert(approxeq(sum(pesi),1)); + +% 3) estimate (if R is hidden) or recover (if R is obs) self'value---------------------------- +if isempty(evidence{self}) % R is hidden + app=mk_stochastic(fullm.T); % P(self|Pa1,...,Pak)=fmarginal/prob(Pa1,...,Pak) + app=reshape(app,[dpsz ns(self)]); % matrix size: prod{j,ns(Paj)} x ns(self) + r=app; + clear app; +else + r = zeros(dpsz,ns(self)); + for i=1:dpsz + if pesi(i)~=0, r(i,evidence{self}) = 1; end + end +end +for i=1:dpsz + if pesi(i) ~=0, assert(approxeq(sum(r(i,:)),1)); end +end + +CPD.nsamples = CPD.nsamples + 1; +CPD.parent_vals(CPD.nsamples,:) = x(:)'; +for i=1:dpsz + CPD.eso_weights(CPD.nsamples,:,i)=pesi(i); + CPD.self_vals(CPD.nsamples,:,i) = r(i,:); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@noisyor_CPD/CPD_to_CPT.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,34 @@ +function CPT = CPD_to_CPT(CPD) +% CPD_TO_CPT Convert the discrete CPD to tabular form (noisyor) +% CPT = CPD_to_CPT(CPD) +% +% CPT(U1,...,Un, X) = Pr(X|U1,...,Un) where the Us are the parents (excluding leak). + +if ~isempty(CPD.CPT) + CPT = CPD.CPT; % remember to flush cache if params change (e.g., during learning) + return; +end + +q = [CPD.leak_inhibit CPD.inhibit(:)']; +% q(i) is the prob. that the i'th parent will be inhibited (flipped from 1 to 0). +% q(1) is the leak inhibition probability, and length(q) = n + 1. + +if length(q)==1 + CPT = [q 1-q]; + return; +end + +n = length(q); +Bn = ind2subv(2*ones(1,n), 1:(2^n))-1; % all n bit vectors, with the left most column toggling fastest (LSB) +CPT = zeros(2^n, 2); +% Pr(X=0 | U_1 .. U_n) = prod_{i: U_i = on} q_i = prod_i q_i ^ U_i = exp(u' * log(q_i)) +% This method is problematic when q contains zeros + +Q = repmat(q(:)', 2^n, 1); +Q(logical(~Bn)) = 1; +CPT(:,1) = prod(Q,2); +CPT(:,2) = 1-CPT(:,1); + +CPT = reshape(CPT(2:2:end), 2*ones(1,n)); % skip cases in which the leak is off + +CPD.CPT = CPT;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@noisyor_CPD/CPD_to_lambda_msg.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,19 @@ +function lam_msg = CPD_to_lambda_msg(CPD, msg_type, n, ps, msg, p, evidence) +% CPD_TO_LAMBDA_MSG Compute lambda message (noisyor) +% lam_msg = CPD_to_lambda_msg(CPD, msg_type, n, ps, msg, p) +% Pearl p190 top eqn + +switch msg_type + case 'd', + l0 = msg{n}.lambda(1); + l1 = msg{n}.lambda(2); + Pi = sum_prod_CPD_and_pi_msgs(CPD, n, ps, msg, p); + i = find(p==ps); % p is n's i'th parent + q = CPD.inhibit(i); + lam_msg = zeros(2,1); + for u=0:1 + lam_msg(u+1) = l1 - (q^u)*(l1 - l0)*Pi; + end + case 'g', + error('noisyor_CPD can''t create Gaussian msgs') +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@noisyor_CPD/CPD_to_pi.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +function pi = CPD_to_pi(CPD, msg_type, n, ps, msg, evidence) +% CPD_TO_PI Compute pi vector (noisyor) +% pi = CPD_to_pi(CPD, msg_type, n, ps, msg) +% Pearl p188 eqn 4.57 + +switch msg_type + case 'd', + pi = sum_prod_CPD_and_pi_msgs(CPD, n, ps, msg); + pi = [pi 1-pi]'; + case 'g', + error('can''t convert noisy-or CPD to Gaussian pi') +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@noisyor_CPD/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +/CPD_to_CPT.m/1.1.1.1/Mon Aug 2 22:23:32 2004// +/CPD_to_lambda_msg.m/1.1.1.1/Wed May 29 15:59:54 2002// +/CPD_to_pi.m/1.1.1.1/Wed May 29 15:59:54 2002// +/noisyor_CPD.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@noisyor_CPD/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +A D/private////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@noisyor_CPD/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@noisyor_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@noisyor_CPD/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@noisyor_CPD/noisyor_CPD.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,79 @@ +function CPD = noisyor_CPD(bnet, self, leak_inhibit, inhibit) +% NOISYOR_CPD Make a noisy-or CPD +% CPD = NOISYOR_CPD(BNET, NODE_NUM, LEAK_INHIBIT, INHIBIT) +% +% A noisy-or node turns on if any of its parents are on, provided they are not inhibited. +% The prob. that the i'th parent gets inhibited (flipped from 1 to 0) is inhibit(i). +% The prob that the leak node (a dummy parent that is always on) gets inhibit is leak_inhibit. +% These params default to random values if omitted. +% +% Example: suppose C has parents A and B, and the +% link of A->C fails with prob pA and the link B->C fails with pB. +% Then the noisy-OR gate defines the following distribution +% +% A B P(C=0) +% 0 0 1.0 +% 1 0 pA +% 0 1 pB +% 1 1 pA * PB +% +% Currently, learning is not supported for noisy-or nodes +% (since the M step is somewhat complicated). +% +% For simple generalizations of the noisy-OR model, see e.g., +% - Srinivas, "A generalization of the noisy-OR model", UAI 93 +% - Meek and Heckerman, "Learning Causal interaction models", UAI 97. + + + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + CPD = class(CPD, 'noisyor_CPD', discrete_CPD(1, [])); + return; +elseif isa(bnet, 'noisyor_CPD') + % This might occur if we are copying an object. + CPD = bnet; + return; +end +CPD = init_fields; + + +ps = parents(bnet.dag, self); +fam = [ps self]; +ns = bnet.node_sizes; +assert(all(ns(fam)==2)); +assert(isempty(myintersect(fam, bnet.cnodes))); + +if nargin < 3, leak_inhibit = rand(1, 1); end +if nargin < 4, inhibit = rand(1, length(ps)); end + +CPD.self = self; +CPD.inhibit = inhibit; +CPD.leak_inhibit = leak_inhibit; + + +% For BIC +CPD.nparams = 0; +CPD.nsamples = 0; + +CPD.CPT = []; % cached copy, to speed up CPD_to_CPT + +clamped = 1; +CPD = class(CPD, 'noisyor_CPD', discrete_CPD(clamped, ns([ps self]))); + + + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +CPD.self = []; +CPD.inhibit = []; +CPD.leak_inhibit = []; +CPD.nparams = []; +CPD.nsamples = []; +CPD.CPT = [];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@noisyor_CPD/private/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +/sum_prod_CPD_and_pi_msgs.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@noisyor_CPD/private/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@noisyor_CPD/private
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@noisyor_CPD/private/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@noisyor_CPD/private/sum_prod_CPD_and_pi_msgs.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,25 @@ +function pi = sum_prod_CPD_and_pi_msgs(CPD, n, ps, msg, except) +% SUM_PROD_CPD_AND_PI_MSGS Compute pi = sum_{u\p} P(n|u) prod_{ui in ps\p} pi_msg(ui->n) +% pi = sum_prod_CPD_and_pi_msgs(CPD, n, ps, msg, p) +% +% pi = prod_i (qi pi_msg(ui->n) + 1 - pi_msg(ui->n)) = prod_i (1 - ci pi_msg(ui->n)) +% is the product of the endorsement withheld (Pearl p188 eqn 4.56) +% We skip p from this product, if specified. + +if nargin < 5, except = -1; end +pi = 1; +for i=1:length(ps) + p = ps(i); + if p ~= except + pi_from_parent = msg{n}.pi_from_parent{i}; + q = CPD.inhibit(i); + c = 1-q; + pi = pi * (1 - c*pi_from_parent(2)); + end +end +% The pi msg that a leak node sends to its child is [0 1] +% since its own pi is [0 1] and its lambda to self is [0 1]. +q = CPD.leak_inhibit; +% 1 - c*pi_from_parent = 1-c*1 = q +pi = pi * q; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@root_CPD/CPD_to_pi.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +function pi = CPD_to_pi(CPD, msg_type, n, ps, msg, evidence) +% CPD_TO_PI Compute the pi vector (root) +% function pi = CPD_to_pi(CPD, msg_type, n, ps, msg, evidence) + +self_ev = evidence{n}; +switch msg_type + case 'd', + error('root_CPD can''t create discrete msgs') + case 'g', + pi.mu = self_ev; + pi.Sigma = zeros(size(self_ev)); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@root_CPD/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +/CPD_to_pi.m/1.1.1.1/Wed May 29 15:59:54 2002// +/convert_to_pot.m/1.1.1.1/Wed May 29 15:59:54 2002// +/log_marg_prob_node.m/1.1.1.1/Wed May 29 15:59:54 2002// +/log_prob_node.m/1.1.1.1/Wed May 29 15:59:54 2002// +/root_CPD.m/1.1.1.1/Wed May 29 15:59:54 2002// +/sample_node.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@root_CPD/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@root_CPD/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@root_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@root_CPD/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@root_CPD/Old/CPD_to_CPT.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function CPT = CPD_to_CPT(CPD) +% CPD_TO_CPT Convert the CPD to tabular form (root) +% CPT = CPD_to_CPT(CPD) + +CPT = 1;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@root_CPD/Old/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +/CPD_to_CPT.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@root_CPD/Old/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@root_CPD/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@root_CPD/Old/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@root_CPD/convert_to_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,28 @@ +function pot = convert_to_pot(CPD, pot_type, domain, evidence) +% CONVERT_TO_POT Convert a root CPD to one or more potentials +% pots = convert_to_pot(CPD, pot_type, domain, evidence) + +assert(length(domain)==1); +assert(~isempty(evidence(domain))); +T = 1; + +sz = CPD.sizes; +ns = zeros(1, max(domain)); +ns(domain) = sz; + +switch pot_type + case 'u', + pot = upot(domain, 1, T, 0); + case 'd', + ns(domain) = 1; + pot = dpot(domain, ns(domain), T); + case {'c','g'}, + ns(domain) = 0; + pot = cpot(domain, ns(domain), 0); + case 'cg', + ddom = []; + cdom = domain; % we assume the root node is cts + %pot = cgpot(ddom, cdom, ns, {cpot([],[],0)}); + pot = cgpot(ddom, cdom, ns); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@root_CPD/log_marg_prob_node.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,9 @@ +function L = log_marg_prob_node(CPD, self_ev, pev) +% LOG_MARG_PROB_NODE Compute prod_m log int_{theta_i} P(x(i,m)| x(pi_i,m), theta_i) for node i (root) +% L = log_marg_prob_node(CPD, self_ev, pev) +% +% self_ev{m} is the evidence on this node in case m +% pev{i,m} is the evidence on the i'th parent in case m (ignored) +% We always return L = 0. + +L = 0;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@root_CPD/log_prob_node.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,9 @@ +function L = log_prob_node(CPD, self_ev, pev) +% LOG_PROB_NODE Compute prod_m log P(x(i,m)| x(pi_i,m), theta_i) for node i (root) +% L = log_prob_node(CPD, self_ev, pev) +% +% self_ev{m} is the evidence on this node in case m +% pev{i,m} is the evidence on the i'th parent in case m (ignored) +% We always return L = 0. + +L = 0;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@root_CPD/root_CPD.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,48 @@ +function CPD = root_CPD(bnet, self, val) +% ROOT_CPD Make a conditional prob. distrib. which has no parameters. +% CPD = ROOT_CPD(BNET, NODE_NUM, VAL) +% +% The node must not have any parents and is assumed to always be observed. +% It is a way of modelling exogenous inputs to a model. +% VAL is the value to which the root is clamped (default: []) + + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + CPD = class(CPD, 'root_CPD', generic_CPD(1)); + return; +elseif isa(bnet, 'root_CPD') + % This might occur if we are copying an object. + CPD = bnet; + return; +end +CPD = init_fields; + + +if nargin < 3, val = []; end + +ns = bnet.node_sizes; +ps = parents(bnet.dag, self); +if ~isempty(ps) + error('root CPDs should have no parents') +end + +CPD.self = self; +CPD.val = val; +CPD.sizes = ns(self); + +clamped = 1; +CPD = class(CPD, 'root_CPD', generic_CPD(clamped)); + + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +CPD.self = []; +CPD.val = []; +CPD.sizes = [];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@root_CPD/sample_node.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,9 @@ +function y = sample_node(CPD, pev) +% SAMPLE_NODE Draw a random sample from P(Y|pa(y), theta) (root) +% Y = SAMPLE_NODE(CPD, PEV) +% +% pev{i} is the evidence on the i'th parent. +% Since a root has no parents, we ignore pev, +% and return the value the root was clamped to when it was created. + +y = CPD.val;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@softmax_CPD/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +/convert_to_pot.m/1.1.1.1/Wed May 29 15:59:54 2002// +/convert_to_table.m/1.1.1.1/Tue Mar 30 17:19:22 2004// +/display.m/1.1.1.1/Wed May 29 15:59:54 2002// +/get_field.m/1.1.1.1/Wed May 29 15:59:54 2002// +/maximize_params.m/1.1.1.1/Wed May 29 15:59:54 2002// +/reset_ess.m/1.1.1.1/Wed May 29 15:59:54 2002// +/sample_node.m/1.1.1.1/Wed May 29 15:59:54 2002// +/set_fields.m/1.1.1.1/Wed May 29 15:59:54 2002// +/softmax_CPD.m/1.1.1.1/Tue Jan 7 16:25:14 2003// +/update_ess.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@softmax_CPD/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +A D/private////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@softmax_CPD/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@softmax_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@softmax_CPD/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@softmax_CPD/convert_to_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,58 @@ +function pot = convert_to_pot(CPD, pot_type, domain, evidence) +% CONVERT_TO_POT Convert a softmax CPD to a potential +% pots = convert_to_pot(CPD, pot_type, domain, evidence) +% +% pots = CPD evaluated using evidence(domain) + +ncases = size(domain,2); +assert(ncases==1); % not yet vectorized + +sz = dom_sizes(CPD); +ns = zeros(1, max(domain)); +ns(domain) = sz; + +odom = domain(~isemptycell(evidence(domain))); +T = convert_to_table(CPD, domain, evidence); + +switch pot_type + case 'u', + pot = upot(domain, sz, T, 0*myones(sz)); + case 'd', + ns(odom) = 1; + pot = dpot(domain, ns(domain), T); + + case {'c','g'}, + % Since we want the output to be a Gaussian, the whole family must be observed. + % In other words, the potential is really just a constant. + p = T; + %p = prob_node(CPD, evidence(domain(end)), evidence(domain(1:end-1))); + ns(domain) = 0; + pot = cpot(domain, ns(domain), log(p)); + + case 'cg', + T = T(:); + ns(odom) = 1; + can = cell(1, length(T)); + for i=1:length(T) + can{i} = cpot([], [], log(T(i))); + end + ps = domain(1:end-1); + dps = ps(CPD.dpndx); + cps = ps(CPD.cpndx); + ddom = [dps CPD.self]; + cdom = cps; + pot = cgpot(ddom, cdom, ns, can); + + case 'scg' + T = T(:); + ns(odom) = 1; + pot_array = cell(1, length(T)); + for i=1:length(T) + pot_array{i} = scgcpot([], [], T(i)); + end + pot = scgpot(domain, [], [], ns, pot_array); + + otherwise, + error(['unrecognized pot type ' pot_type]) +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@softmax_CPD/convert_to_table.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,52 @@ +function T = convert_to_table(CPD, domain, evidence) +% CONVERT_TO_TABLE Convert a softmax CPD to a table, incorporating any evidence +% T = convert_to_table(CPD, domain, evidence) + +self = domain(end); +ps = domain(1:end-1); +cnodes = domain(CPD.cpndx); +cps = myintersect(ps, cnodes); +dps = domain(CPD.dpndx); +dps_as_cps = domain(CPD.dps_as_cps.ndx); +all_dps = union(dps,dps_as_cps); +odom = domain(~isemptycell(evidence(domain))); +if ~isempty(cps), assert(myismember(cps, odom)); end % all cts parents must be observed + +ns = zeros(1, max(domain)); +ns(domain) = CPD.sizes; +ens = ns; % effective node sizes +ens(odom) = 1; + +% dpsize >= glimsz because the glm parameters are tied across the dps_as_cps parents +dpsize = prod(ens(all_dps)); % size of ALL self'discrete parents +dpvals = cat(1, evidence{myintersect(all_dps, odom)}); +cpvals = cat(1, evidence{cps}); +if ~isempty(dps_as_cps), + separator = CPD.dps_as_cps.separator; + dp_as_cpmap = find_equiv_posns(dps_as_cps, all_dps); + dops_map = find_equiv_posns(myintersect(all_dps, odom), all_dps); + puredp_map = find_equiv_posns(dps, all_dps); + subs = ind2subv(ens(all_dps), 1:prod(ens(all_dps))); + if ~isempty(dops_map), subs(:,dops_map) = subs(:,dops_map)+repmat(dpvals(:)',[size(subs,1) 1])-1; end +end + +[w,b] = extract_params(CPD); +T = zeros(dpsize, ns(self)); +for i=1:dpsize, + active_glm = i; + dp_as_cpvals=zeros(1,sum(ns(dps_as_cps))); + if ~isempty(dps_as_cps), + active_glm = max([1,subv2ind(ns(dps), subs(i,puredp_map))]); + % Extract the params compatible with the observations (if any) on the 'pure' discrete parents (if any) + where_one = separator + subs(i,dp_as_cpmap); + % and get in the dp_as_cp parents... + dp_as_cpvals(where_one)=1; + end + T(i,:) = normalise(exp([dp_as_cpvals(:); cpvals(:)]'*w(:,:,active_glm) + b(:,active_glm)')); +end +if myismember(self, odom) + r = evidence{self}; + T = T(:,r); +end + +T = myreshape(T, ens(domain));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@softmax_CPD/display.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,4 @@ +function display(CPD) + +disp('softmax_CPD object'); +disp(struct(CPD));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@softmax_CPD/get_field.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,18 @@ +function val = get_params(CPD, name) +% GET_PARAMS Get the parameters (fields) for a softmax_CPD object +% val = get_params(CPD, name) +% +% The following fields can be accessed +% +% weights - W(X,Y,Q) +% offset - b(Y,Q) +% +% e.g., W = get_params(CPD, 'weights') + +[W, b] = extract_params(CPD); +switch name + case 'weights', val = W; + case 'offset', val = b; + otherwise, + error(['invalid argument name ' name]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@softmax_CPD/maximize_params.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,41 @@ +function CPD = maximize_params(CPD, temp) +% MAXIMIZE_PARAMS Set the params of a CPD to their ML values (dsoftmax) using IRLS +% CPD = maximize_params(CPD, temperature) +% temperature parameter is ignored + +% Written by Pierpaolo Brutti + +if ~adjustable_CPD(CPD), return; end +options = foptions; + +if CPD.verbose + options(1) = 1; +else + options(1) = -1; +end +%options(1) = CPD.verbose; + +options(2) = CPD.wthresh; +options(3) = CPD.llthresh; +options(5) = CPD.approx_hess; +options(14) = CPD.max_iter; + +dpsize = size(CPD.self_vals,3); +for i=1:dpsize, + mask=find(CPD.eso_weights(:,:,i)>0); % for adapting the parameters we use only positive weighted example + if ~isempty(mask), + if ~isempty(CPD.dps_as_cps.ndx), + puredp_map = find_equiv_posns(CPD.dpndx, union(CPD.dpndx, CPD.dps_as_cps.ndx)); % find the glm structure + subs = ind2subv(CPD.sizes(union(CPD.dpndx, CPD.dps_as_cps.ndx)),i); % that corrisponds to the + active_glm = max([1,subv2ind(CPD.sizes(CPD.dpndx), subs(puredp_map))]); % i-th 'fictitious' example + + CPD.glim{active_glm} = netopt_weighted(CPD.glim{active_glm}, options, CPD.parent_vals(mask',:,i),... + CPD.self_vals(mask',:,i), CPD.eso_weights(mask',:,i), 'scg'); + else + alfa = 0.4; if CPD.solo, alfa = 1; end % learning step = 1 <=> self is all alone in the net + CPD.glim{i} = glmtrain_weighted(CPD.glim{i}, options, CPD.parent_vals(mask',:),... + CPD.self_vals(mask',:,i), CPD.eso_weights(mask',:,i), alfa); + end + end + mask=[]; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@softmax_CPD/private/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +/extract_params.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@softmax_CPD/private/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@softmax_CPD/private
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@softmax_CPD/private/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@softmax_CPD/private/extract_params.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,18 @@ +function [W, b] = extract_params(CPD) + +% W(X,Y,Q), b(Y,Q) where Y = ns(self), X = ns(cps), Q = prod(ns(dps)) + +glimsz = prod(CPD.sizes(CPD.dpndx)); +ss = CPD.sizes(end); +cpsz = sum(CPD.sizes(CPD.cpndx)); +dp_as_cpsz = sum(CPD.sizes(CPD.dps_as_cps.ndx)); +W = zeros(dp_as_cpsz + cpsz, ss, glimsz); +b = zeros(ss, glimsz); + +for i=1:glimsz + W(:,:,i) = CPD.glim{i}.w1; + b(:,i) = CPD.glim{i}.b1(:); +end + +W = myreshape(W, [dp_as_cpsz + cpsz ss CPD.sizes(CPD.dpndx)]); +b = myreshape(b, [ss CPD.sizes(CPD.dpndx)]);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@softmax_CPD/reset_ess.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +function CPD = reset_ess(CPD) +% RESET_ESS Reset the Expected Sufficient Statistics for a CPD (dsoftmax) +% CPD = reset_ess(CPD) + +CPD.parent_vals = []; +CPD.eso_weights=[]; +CPD.self_vals = []; +CPD.nsamples = 0;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@softmax_CPD/sample_node.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,14 @@ +function y = sample_node(CPD, pvals) +% SAMPLE_NODE Draw a random sample from P(Xi | x(pi_i), theta_i) (discrete) +% y = sample_node(CPD, parent_evidence) +% +% parent_evidence{i} is the value of the i'th parent + +n = length(pvals)+1; +dom = 1:n; +%evidence = cell(1,n); +%evidence(1:n-1) = pvals(:)'; +evidence = pvals; +evidence{end+1} = []; +T = convert_to_table(CPD, dom, evidence); +y = sample_discrete(T);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@softmax_CPD/set_fields.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,45 @@ +function CPD = set_params(CPD, varargin) +% SET_PARAMS Set the parameters (fields) for a softmax_CPD object +% CPD = set_params(CPD, name/value pairs) +% +% The following optional arguments can be specified in the form of name/value pairs: +% (Let ns(i) be the size of node i, X = ns(X), Y = ns(Y), Q1=ns(dps(1)), Q2=ns(dps(2)), ... +% where dps are the discrete parents; if there are no discrete parents, we set Q1=1.) +% +% weights - (W(:,j,a,b,...) - W(:,j',a,b,...)) is ppn to dec. boundary +% between j,j' given Q1=a,Q2=b,... [ randn(X,Y,Q1,Q2,...) ] +% offset - (offset(j,a,b,...) - offset(j',a,b,...)) is the offset to dec. boundary +% between j,j' given Q1=a,Q2=b,... [ randn(Y,Q1,Q2,...) ] +% clamped - 'yes' means don't adjust params during learning ['no'] +% max_iter - the maximum number of steps to take [10] +% verbose - 'yes' means print the LL at each step of IRLS ['no'] +% wthresh - convergence threshold for weights [1e-2] +% llthresh - convergence threshold for log likelihood [1e-2] +% approx_hess - 'yes' means approximate the Hessian for speed ['no'] +% +% e.g., CPD = set_params(CPD,'offset', zeros(ns(i),1)); + +args = varargin; +nargs = length(args); +glimsz = prod(CPD.sizes(CPD.dpndx)); +for i=1:2:nargs + switch args{i}, + case 'discrete', str='nothing to do'; + case 'clamped', CPD = set_clamped(CPD, strcmp(args{i+1}, 'yes')); + case 'max_iter', CPD.max_iter = args{i+1}; + case 'verbose', CPD.verbose = strcmp(args{i+1}, 'yes'); + case 'max_iter', CPD.max_iter = args{i+1}; + case 'wthresh', CPD.wthresh = args{i+1}; + case 'llthresh', CPD.llthresh = args{i+1}; + case 'approx_hess', CPD.approx_hess = strcmp(args{i+1}, 'yes'); + case 'weights', for q=1:glimsz, CPD.glim{q}.w1 = args{i+1}(:,:,q); end; + case 'offset', + if glimsz == 1 + CPD.glim{1}.b1 = args{i+1}; + else + for q=1:glimsz, CPD.glim{q}.b1 = args{i+1}(:,q); end; + end + otherwise, + error(['invalid argument name ' args{i}]); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@softmax_CPD/softmax_CPD.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,187 @@ +function CPD = softmax_CPD(bnet, self, varargin) +% SOFTMAX_CPD Make a softmax (multinomial logit) CPD +% +% To define this CPD precisely, let W be an (m x n) matrix with W(i,:) = {i-th row of B} +% => we can define the following vectorial function: +% +% softmax: R^n |--> R^m +% softmax(z,i-th)=exp(W(i,:)*z)/sum_k(exp(W(k,:)*z)) +% +% (this constructor augments z with a one at the beginning to introduce an offset term (=bias, intercept)) +% Now call the continuous (cts) and always observed (obs) parents X, +% the discrete parents (if any) Q, and this node Y then we use the discrete parent(s) just to index +% the parameter vectors (c.f., conditional Gaussian nodes); that is: +% prob(Y=i | X=x, Q=j) = softmax(x,i-th|j) +% where '|j' means that we are using the j-th (m x n) parameters matrix W(:,:,j). +% If there are no discrete parents, this is a regular softmax node. +% If Y is binary, this is a logistic (sigmoid) function. +% +% CPD = softmax_CPD(bnet, node_num, ...) will create a softmax CPD with random parameters, +% where node is the number of a node in this equivalence class. +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% (Let ns(i) be the size of node i, X = ns(X), Y = ns(Y), Q1=ns(dps(1)), Q2=ns(dps(2)), ... +% where dps are the discrete parents; if there are no discrete parents, we set Q1=1.) +% +% discrete - the discrete parents that we want to treat like the cts ones [ [] ]. +% This can be used to define sigmoid belief network - see below the reference. +% For example suppose that Y has one cts parents X and two discrete ones: Q, C1 where: +% -> Q is binary (1/2) and used just to index the parameters of 'self' +% -> C1 is ternary (1/2/3) and treated as a cts node <=> its values appear into the linear +% part of the softmax function +% then: +% prob(Y|X=x, Q=q, C1=c1)= softmax(W(:,:,q)' * y) +% where y = [1 | delta(C1,1) delta(C1,2) delta(C1,3) | x(:)']' and delta(Y,a)=indicator(Y=a). +% weights - (w(:,j,a,b,...) - w(:,j',a,b,...)) is ppn to dec. boundary +% between j,j' given Q1=a,Q2=b,... [ randn(X,Y,Q1,Q2,...) ] +% offset - (b(j,a,b,...) - b(j',a,b,...)) is the offset to dec. boundary +% between j,j' given Q1=a,Q2=b,... [ randn(Y,Q1,Q2,...) ] +% +% e.g., CPD = softmax_CPD(bnet, i, 'offset', zeros(ns(i),1)); +% +% The following fields control the behavior of the M step, which uses +% a weighted version of the Iteratively Reweighted Least Squares (WIRLS) if dps_as_cps=[]; or +% a weighted SCG otherwise, as implemented in Netlab, and modified by Pierpaolo Brutti. +% +% clamped - 'yes' means don't adjust params during learning ['no'] +% max_iter - the maximum number of steps to take [10] +% verbose - 'yes' means print the LL at each step of IRLS ['no'] +% wthresh - convergence threshold for weights [1e-2] +% llthresh - convergence threshold for log likelihood [1e-2] +% approx_hess - 'yes' means approximate the Hessian for speed ['no'] +% +% For backwards compatibility with BNT2, you can also specify the parameters in the following order +% softmax_CPD(bnet, self, w, b, clamped, max_iter, verbose, wthresh, llthresh, approx_hess) +% +% REFERENCE +% For details on the sigmoid belief nets, see: +% - Neal (1992). Connectionist learning of belief networks, Artificial Intelligence, 56, 71-113. +% - Saul, Jakkola, Jordan (1996). Mean field theory for sigmoid belief networks, Journal of Artificial Intelligence Reseach (4), pagg. 61-76. +% +% For details on the M step, see: +% - K. Chen, L. Xu, H. Chi (1999). Improved learning algorithms for mixtures of experts in multiclass +% classification. Neural Networks 12, pp. 1229-1252. +% - M.I. Jordan, R.A. Jacobs (1994). Hierarchical Mixtures of Experts and the EM algorithm. +% Neural Computation 6, pp. 181-214. +% - S.R. Waterhouse, A.J. Robinson (1994). Classification Using Hierarchical Mixtures of Experts. In Proc. IEEE +% Workshop on Neural Network for Signal Processing IV, pp. 177-186 + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + CPD = class(CPD, 'softmax_CPD', discrete_CPD(0, [])); + return; +elseif isa(bnet, 'softmax_CPD') + % This might occur if we are copying an object. + CPD = bnet; + return; +end +CPD = init_fields; + +assert(myismember(self, bnet.dnodes)); +ns = bnet.node_sizes; +ps = parents(bnet.dag, self); +dps = myintersect(ps, bnet.dnodes); +cps = myintersect(ps, bnet.cnodes); + +clamped = 0; +CPD = class(CPD, 'softmax_CPD', discrete_CPD(clamped, ns([ps self]))); + +dps_as_cpssz = 0; +dps_as_cps = []; +% determine if any discrete parents are to be treated as cts +if nargin >= 3 & isstr(varargin{1}) % might have passed in 'discrete' + for i=1:2:length(varargin) + if strcmp(varargin{i}, 'discrete') + dps_as_cps = varargin{i+1}; + assert(myismember(dps_as_cps, dps)); + dps = mysetdiff(dps, dps_as_cps); % put out the dps treated as cts + CPD.dps_as_cps.ndx = find_equiv_posns(dps_as_cps, ps); + CPD.dps_as_cps.separator = [0 cumsum(ns(dps_as_cps(1:end-1)))]; % concatenated dps_as_cps dims separators + dps_as_cpssz = sum(ns(dps_as_cps)); + break; + end + end +end +assert(~isempty(union(cps, dps_as_cps))); % It have to be at least a cts or a dps_as_cps parents +self_size = ns(self); +cpsz = sum(ns(cps)); +glimsz = prod(ns(dps)); +CPD.dpndx = find_equiv_posns(dps, ps); % it contains only the indeces of the 'pure' dps +CPD.cpndx = find_equiv_posns(cps, ps); + +CPD.self = self; +CPD.solo = (length(ns)<=2); +CPD.sizes = bnet.node_sizes([ps self]); + +% set default params +CPD.max_iter = 10; +CPD.verbose = 0; +CPD.wthresh = 1e-2; +CPD.llthresh = 1e-2; +CPD.approx_hess = 0; +CPD.glim = cell(1,glimsz); +for i=1:glimsz + CPD.glim{i} = glm(dps_as_cpssz + cpsz, self_size, 'softmax'); +end + +if nargin >= 3 + args = varargin; + nargs = length(args); + if ~isstr(args{1}) + % softmax_CPD(bnet, self, w, b, clamped, max_iter, verbose, wthresh, llthresh, approx_hess) + if nargs >= 1 & ~isempty(args{1}), CPD = set_fields(CPD, 'weights', args{1}); end + if nargs >= 2 & ~isempty(args{2}), CPD = set_fields(CPD, 'offset', args{2}); end + if nargs >= 3 & ~isempty(args{3}), CPD = set_clamped(CPD, args{3}); end + if nargs >= 4 & ~isempty(args{4}), CPD.max_iter = args{4}; end + if nargs >= 5 & ~isempty(args{5}), CPD.verbose = args{5}; end + if nargs >= 6 & ~isempty(args{6}), CPD.wthresh = args{6}; end + if nargs >= 7 & ~isempty(args{7}), CPD.llthresh = args{7}; end + if nargs >= 8 & ~isempty(args{8}), CPD.approx_hess = args{8}; end + else + CPD = set_fields(CPD, args{:}); + end +end + +% sufficient statistics +% Since dsoftmax is not in the exponential family, we must store all the raw data. +CPD.parent_vals = []; % X(l,:) = value of cts parents in l'th example +CPD.self_vals = []; % Y(l,:) = value of self in l'th example + +CPD.eso_weights=[]; % weights used by the WIRLS algorithm + +% For BIC +CPD.nsamples = 0; +if ~adjustable_CPD(CPD), + CPD.nparams=0; +else + [W, b] = extract_params(CPD); + CPD.nparams= prod(size(W)) + prod(size(b)); +end + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +CPD.glim = {}; +CPD.self = []; +CPD.solo = []; +CPD.max_iter = []; +CPD.verbose = []; +CPD.wthresh = []; +CPD.llthresh = []; +CPD.approx_hess = []; +CPD.sizes = []; +CPD.parent_vals = []; +CPD.eso_weights=[]; +CPD.self_vals = []; +CPD.nsamples = []; +CPD.nparams = []; +CPD.dpndx = []; +CPD.cpndx = []; +CPD.dps_as_cps.ndx = []; +CPD.dps_as_cps.separator = [];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@softmax_CPD/update_ess.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,97 @@ +function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) +% UPDATE_ESS Update the Expected Sufficient Statistics of a softmax node +% function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) +% +% fmarginal = overall posterior distribution of self and its parents +% fmarginal(i1,i2...,ik,s)=prob(Pa1=i1,...,Pak=ik, self=s| X) +% +% => 1) prob(self|Pa1,...,Pak)=fmarginal/prob(Pa1,...,Pak) with prob(Pa1,...,Pak)=sum{s,fmarginal} +% [self estimation -> CPD.self_vals] +% 2) prob(Pa1,...,Pak) [WIRLS weights -> CPD.eso_weights] +% +% Hidden_bitv is ignored + +% Written by Pierpaolo Brutti + +if ~adjustable_CPD(CPD), return; end + +domain = fmarginal.domain; +self = domain(end); +ps = domain(1:end-1); +cnodes = domain(CPD.cpndx); +cps = myintersect(domain, cnodes); +dps = mysetdiff(ps, cps); +dn_use = dps; +if isempty(evidence{self}) dn_use = [dn_use self]; end % if self is hidden we must consider its dimension +dps_as_cps = domain(CPD.dps_as_cps.ndx); +odom = domain(~isemptycell(evidence(domain))); + +ns = zeros(1, max(domain)); +ns(domain) = CPD.sizes; % CPD.sizes = bnet.node_sizes([ps self]); +ens = ns; % effective node sizes +ens(odom) = 1; +dpsize = prod(ns(dps)); + +% Extract the params compatible with the observations (if any) on the discrete parents (if any) +dops = myintersect(dps, odom); +dpvals = cat(1, evidence{dops}); + +subs = ind2subv(ens(dn_use), 1:prod(ens(dn_use))); +dpmap = find_equiv_posns(dops, dn_use); +if ~isempty(dpmap), subs(:,dpmap) = subs(:,dpmap)+repmat(dpvals(:)',[size(subs,1) 1])-1; end +supportedQs = subv2ind(ns(dn_use), subs); subs=subs(1:prod(ens(dps)),1:length(dps)); +Qarity = prod(ns(dn_use)); +if isempty(dn_use), Qarity = 1; end + +fullm.T = zeros(Qarity, 1); +fullm.T(supportedQs) = fmarginal.T(:); +rs_dim = CPD.sizes; rs_dim(CPD.cpndx) = 1; % +if ~isempty(evidence{self}), rs_dim(end)=1; end % reshaping the marginal +fullm.T = reshape(fullm.T, rs_dim); % + +% --------------------------------------------------------------------------------UPDATE-- + +CPD.nsamples = CPD.nsamples + 1; + +% 1) observations vector -> CPD.parents_vals --------------------------------------------- +cpvals = cat(1, evidence{cps}); + +if ~isempty(dps_as_cps), % ...get in the dp_as_cp parents... + separator = CPD.dps_as_cps.separator; + dp_as_cpmap = find_equiv_posns(dps_as_cps, dps); + for i=1:dpsize, + dp_as_cpvals=zeros(1,sum(ns(dps_as_cps))); + possible_vals = ind2subv(ns(dps),i); + ll=find(ismember(subs(:,dp_as_cpmap), possible_vals(dp_as_cpmap), 'rows')==1); + if ~isempty(ll), + where_one = separator + possible_vals(dp_as_cpmap); + dp_as_cpvals(where_one)=1; + end + CPD.parent_vals(CPD.nsamples,:,i) = [dp_as_cpvals(:); cpvals(:)]'; + end +else + CPD.parent_vals(CPD.nsamples,:) = cpvals(:)'; +end + +% 2) weights vector -> CPD.eso_weights ---------------------------------------------------- +if isempty(evidence{self}), % self is hidden + pesi=reshape(sum(fullm.T, length(rs_dim)),[dpsize,1]); +else + pesi=reshape(fullm.T,[dpsize,1]); +end +assert(approxeq(sum(pesi),1)); % check + +% 3) estimate (if R is hidden) or recover (if R is obs) self'value------------------------- +if isempty(evidence{self}) % P(self|Pa1,...,Pak)=fmarginal/prob(Pa1,...,Pak) + r=reshape(mk_stochastic(fullm.T), [dpsize ns(self)]); % matrix size: prod{j,ns(Paj)} x ns(self) +else + r = zeros(dpsize,ns(self)); + for i=1:dpsize, if pesi(i)~=0, r(i,evidence{self}) = 1; end; end +end +for i=1:dpsize, if pesi(i)~=0, assert(approxeq(sum(r(i,:)),1)); end; end % check + +% 4) save the previous values -------------------------------------------------------------- +for i=1:dpsize + CPD.eso_weights(CPD.nsamples,:,i)=pesi(i); + CPD.self_vals(CPD.nsamples,:,i) = r(i,:); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/CPD_to_CPT.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function CPT = CPD_to_CPT(CPD) +% CPD_TO_CPT Convert the discrete CPD to tabular form (tabular) +% CPT = CPD_to_CPT(CPD) + +CPT = CPD.CPT;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +/CPD_to_CPT.m/1.1.1.1/Wed May 29 15:59:54 2002// +/bayes_update_params.m/1.1.1.1/Wed May 29 15:59:54 2002// +/display.m/1.1.1.1/Tue Apr 22 21:00:02 2003// +/get_field.m/1.1.1.1/Sun Jan 16 02:27:30 2005// +/learn_params.m/1.1.1.1/Thu Jun 10 01:25:02 2004// +/log_marg_prob_node.m/1.1.1.1/Fri Jun 11 21:16:00 2004// +/log_nextcase_prob_node.m/1.1.1.1/Wed May 29 15:59:54 2002// +/log_prior.m/1.1.1.1/Wed May 29 15:59:54 2002// +/maximize_params.m/1.1.1.1/Sun Mar 9 22:44:40 2003// +/reset_ess.m/1.1.1.1/Wed May 29 15:59:54 2002// +/set_fields.m/1.1.1.1/Sun Jan 16 02:27:30 2005// +/tabular_CPD.m/1.1.1.1/Sun Jan 16 02:27:32 2005// +/update_ess.m/1.1.1.1/Wed May 29 15:59:54 2002// +/update_ess_simple.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@tabular_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/Old/BIC_score_CPD.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,17 @@ +function score = BIC_score_CPD(CPD, fam, data, ns, cnodes) +% BIC_score_CPD Compute the BIC score of a tabular CPD +% score = BIC_score_CPD(CPD, fam, data, ns, cnodes) + +if iscell(data) + local_data = cell2num(data(fam,:)); +else + local_data = data(fam, :); +end +counts = compute_counts(local_data, CPD.sizes); +CPT = mk_stochastic(counts); % MLE +tiny = exp(-700); +CPT = CPT + (CPT==0)*tiny; % replace 0s by tiny +LL = sum(log(CPT(:)) .* counts(:)); +N = size(data, 2); +score = LL - 0.5*CPD.nparams*log(N); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/Old/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +/BIC_score_CPD.m/1.1.1.1/Wed May 29 15:59:54 2002// +/bayesian_score_CPD.m/1.1.1.1/Wed May 29 15:59:54 2002// +/log_marg_prob_node_case.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mult_CPD_and_pi_msgs.m/1.1.1.1/Wed May 29 15:59:54 2002// +/prob_CPT.m/1.1.1.1/Wed May 29 15:59:54 2002// +/prob_node.m/1.1.1.1/Wed May 29 15:59:54 2002// +/sample_node.m/1.1.1.1/Wed May 29 15:59:54 2002// +/sample_node_single_case.m/1.1.1.1/Wed May 29 15:59:54 2002// +/tabular_CPD.m/1.1.1.1/Wed May 29 15:59:54 2002// +/update_params.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/Old/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@tabular_CPD/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/Old/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/Old/bayesian_score_CPD.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function score = bayesian_score_CPD(CPD, local_ev) +% bayesian_score_CPD Compute the Bayesian score of a tabular CPD using uniform Dirichlet prior +% score = bayesian_score_CPD(CPD, local_ev) +% +% The Bayesian score is the log marginal likelihood + +if iscell(local_ev) + data = num2cell(local_ev); +else + data = local_ev; +end + +score = dirichlet_score_family(compute_counts(data, CPD.sizes));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/Old/log_marg_prob_node_case.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,22 @@ +function L = log_marg_prob_node_case(CPD, y, x) +% LOG_MARG_PROB_NODE_CASE Compute prod_m log P(x(i,m)| x(pi_i,m)) for node i (tabular) +% L = log_marg_prob_node_case(CPD, self_ev, parent_ev) +% +% This is a slightly optimised version of log_marg_prob_node. +% We assume we have exactly 1 case, i.e., y is a scalar and x is a vector (not a cell array). + +sz = CPD.sizes; +nparents = length(sz)-1; + +% We assume the CPTs are already set to the mean of the posterior (due to update_params) + +switch nparents + case 0, p = CPD.CPT(y); + case 1, p = CPD.CPT(x(1), y); + case 2, p = CPD.CPT(x(1), x(2), y); + case 3, p = CPD.CPT(x(1), x(2), x(3), y); + otherwise, + ind = subv2ind(sz, [x y]); + p = CPD.CPT(ind); +end +L = log(p);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/Old/mult_CPD_and_pi_msgs.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,17 @@ +function T = mult_CPD_and_pi_msgs(CPD, n, ps, msgs, except) +% MULT_CPD_AND_PI_MSGS Multiply the CPD and all the pi messages from parents, perhaps excepting one +% T = mult_CPD_and_pi_msgs(CPD, n, ps, msgs, except) + +if nargin < 5, except = -1; end + +dom = [ps n]; +%ns = sparse(1, max(dom)); +ns = zeros(1, max(dom)); +ns(dom) = mysize(CPD.CPT); +T = dpot(dom, ns(dom), CPD.CPT); +for i=1:length(ps) + p = ps(i); + if p ~= except + T = multiply_by_pot(T, dpot(p, ns(p), msgs{n}.pi_from_parent{i}.T)); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/Old/prob_CPT.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +function p = prob_CPT(CPD, x) +% PROB_CPT Lookup the prob. of a family value in a tabular CPD +% p = prob_CPT(CPD, x) +% +% This is a version of prob_CPD optimized for tables. + +switch length(x) + case 1, p = CPD.CPT(x); + case 2, p = CPD.CPT(x(1), x(2)); + case 3, p = CPD.CPT(x(1), x(2), x(3)); + case 4, p = CPD.CPT(x(1), x(2), x(3), x(4)); + otherwise, + ind = subv2ind(mysize(CPD.CPT), x); + p = CPD.CPT(ind); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/Old/prob_node.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,40 @@ +function p = prob_node(CPD, self_ev, pev) +% PROB_NODE Compute P(y|pa(y), theta) (tabular) +% p = prob_node(CPD, self_ev, pev) +% +% self_ev{m} is the evidence on this node in case m +% pev{i,m} is the evidence on the i'th parent in case m +% If there is a single case, self_ev can be a scalar instead of a cell array + +ncases = size(pev, 2); + +%assert(~any(isemptycell(pev))); % slow +%assert(~any(isemptycell(self_ev))); % slow + +CPT = CPD_to_CPT(CPD); +sz = mysize(CPT); +nparents = length(sz)-1; +assert(nparents == size(pev, 1)); + +if ncases==1 + x = cat(1, pev{:}); + if iscell(y) + y = self_ev{1}; + else + y = self_ev; + end + switch nparents + case 0, p = CPT(y); + case 1, p = CPT(x(1), y); + case 2, p = CPT(x(1), x(2), y); + case 3, p = CPT(x(1), x(2), x(3), y); + otherwise, + ind = subv2ind(CPD.sizes, [x y]); + p = CPT(ind); + end +else + x = num2cell(pev)'; % each row is a case + y = cat(1, self_ev{:})'; + ind = subv2ind(CPD.sizes, [x y]); + p = CPT(ind); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/Old/sample_node.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,53 @@ +function y = sample_node(CPD, pev, nsamples) +% SAMPLE_NODE Draw a random sample from P(Xi | x(pi_i), theta_i) (tabular) +% Y = SAMPLE_NODE(CPD, PEV, NSAMPLES) +% +% pev(i,m) is the value of the i'th parent in sample m (if there are any parents). +% y(m) is the m'th sampled value (a row vector). +% (If pev is a cell array, so is y.) +% nsamples defaults to 1. + +if nargin < 3, nsamples = 1; end + +%if nargin < 4, usecell = 0; end +if iscell(pev), usecell = 1; else usecell = 0; end + +if nsamples == 1, pev = pev(:); end + +sz = CPD.sizes; +nparents = length(sz)-1; +if nparents==0 + y = sample_discrete(CPD.CPT, 1, nsamples); + if usecell + y = num2cell(y); + end + return; +end + +sz = CPD.sizes; +[nparents nsamples] = size(pev); + +if usecell + pvals = cell2num(pev)'; % each row is a case +else + pvals = pev'; +end + +psz = sz(1:end-1); +ssz = sz(end); +ndx = subv2ind(psz, pvals); +T = reshape(CPD.CPT, [prod(psz) ssz]); +T2 = T(ndx,:); % each row is a distribution selected by the parents +C = cumsum(T2, 2); % sum across columns +R = rand(nsamples, 1); +y = ones(nsamples, 1); +for i=1:ssz-1 + y = y + (R > C(:,i)); +end +y = y(:)'; +if usecell + y = num2cell(y); +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/Old/sample_node_single_case.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,39 @@ +function y = sample_node(CPD, pev) +% SAMPLE_NODE Draw a random sample from P(Xi | x(pi_i), theta_i) (tabular) +% y = sample_node(CPD, pev) +% +% pev{i} is the value of the i'th parent (if any) + +%assert(~any(isemptycell(pev))); + +%CPT = CPD_to_CPT(CPD); +%sz = mysize(CPT); +sz = CPD.sizes; +nparents = length(sz)-1; +if nparents > 0 + pvals = cat(1, pev{:}); +end +switch nparents + case 0, T = CPD.CPT; + case 1, T = CPD.CPT(pvals(1), :); + case 2, T = CPD.CPT(pvals(1), pvals(2), :); + case 3, T = CPD.CPT(pvals(1), pvals(2), pvals(3), :); + case 4, T = CPD.CPT(pvals(1), pvals(2), pvals(3), pvals(4), :); + otherwise, + psz = sz(1:end-1); + ssz = sz(end); + i = subv2ind(psz, pvals(:)'); + T = reshape(CPD.CPT, [prod(psz) ssz]); + T = T(i,:); +end + +if sz(end)==2 + r = rand(1,1); + if r > T(1) + y = 2; + else + y = 1; + end +else + y = sample_discrete(T); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/Old/tabular_CPD.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,186 @@ +function CPD = tabular_CPD(bnet, self, varargin) +% TABULAR_CPD Make a multinomial conditional prob. distrib. (CPT) +% +% CPD = tabular_CPD(bnet, node) creates a random CPT. +% +% The following arguments can be specified [default in brackets] +% +% CPT - specifies the params ['rnd'] +% - T means use table T; it will be reshaped to the size of node's family. +% - 'rnd' creates rnd params (drawn from uniform) +% - 'unif' creates a uniform distribution +% - 'leftright' only transitions from i to i/i+1 are allowed, for each non-self parent context. +% The non-self parents are all parents except oldself. +% selfprob - The prob of transition from i to i if CPT = 'leftright' [0.1] +% old_self - id of the node corresponding to self in the previous slice [self-ss] +% adjustable - 0 means don't adjust the parameters during learning [1] +% prior_type - defines type of prior ['none'] +% - 'none' means do ML estimation +% - 'dirichlet' means add pseudo-counts to every cell +% - 'entropic' means use a prior P(theta) propto exp(-H(theta)) (see Brand) +% dirichlet_weight - equivalent sample size (ess) of the dirichlet prior [1] +% dirichlet_type - defines the type of Dirichlet prior ['BDeu'] +% - 'unif' means put dirichlet_weight in every cell +% - 'BDeu' means we put 'dirichlet_weight/(r q)' in every cell +% where r = self_sz and q = prod(parent_sz) (see Heckerman) +% trim - 1 means trim redundant params (rows in CPT) when using entropic prior [0] +% +% e.g., tabular_CPD(bnet, i, 'CPT', T) +% e.g., tabular_CPD(bnet, i, 'CPT', 'unif', 'dirichlet_weight', 2, 'dirichlet_type', 'unif') +% +% REFERENCES +% M. Brand - "Structure learning in conditional probability models via an entropic prior +% and parameter extinction", Neural Computation 11 (1999): 1155--1182 +% M. Brand - "Pattern discovery via entropy minimization" [covers annealing] +% AI & Statistics 1999. Equation numbers refer to this paper, which is available from +% www.merl.com/reports/docs/TR98-21.pdf +% D. Heckerman, D. Geiger and M. Chickering, +% "Learning Bayesian networks: the combination of knowledge and statistical data", +% Microsoft Research Tech Report, 1994 + + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + CPD = class(CPD, 'tabular_CPD', discrete_CPD(0, [])); + return; +elseif isa(bnet, 'tabular_CPD') + % This might occur if we are copying an object. + CPD = bnet; + return; +end +CPD = init_fields; + +ns = bnet.node_sizes; +ps = parents(bnet.dag, self); +fam_sz = ns([ps self]); +CPD.sizes = fam_sz; +CPD.leftright = 0; + +% set defaults +CPD.CPT = mk_stochastic(myrand(fam_sz)); +CPD.adjustable = 1; +CPD.prior_type = 'none'; +dirichlet_type = 'BDeu'; +dirichlet_weight = 1; +CPD.trim = 0; +selfprob = 0.1; + +% extract optional args +args = varargin; +% check for old syntax CPD(bnet, i, CPT) as opposed to CPD(bnet, i, 'CPT', CPT) +if ~isempty(args) & ~isstr(args{1}) + CPD.CPT = myreshape(args{1}, fam_sz); + args = []; +end + +% if old_self is specified, read in the value before CPT is created +old_self = []; +for i=1:2:length(args) + switch args{i}, + case 'old_self', old_self = args{i+1}; + end +end + +for i=1:2:length(args) + switch args{i}, + case 'CPT', + T = args{i+1}; + if ischar(T) + switch T + case 'unif', CPD.CPT = mk_stochastic(myones(fam_sz)); + case 'rnd', CPD.CPT = mk_stochastic(myrand(fam_sz)); + case 'leftright', + % we just initialise the CPT to leftright - this structure will + % be maintained by EM, assuming we don't use a prior... + CPD.leftright = 1; + if isempty(old_self) % we assume the network is a DBN + ss = bnet.nnodes_per_slice; + old_self = self-ss; + end + other_ps = mysetdiff(ps, old_self); + Qps = prod(ns(other_ps)); + Q = ns(self); + p = selfprob; + LR = mk_leftright_transmat(Q, p); + transprob = repmat(reshape(LR, [1 Q Q]), [Qps 1 1]); % transprob(k,i,j) + transprob = permute(transprob, [2 1 3]); % now transprob(i,k,j) + CPD.CPT = myreshape(transprob, fam_sz); + otherwise, error(['invalid CPT ' T]); + end + else + CPD.CPT = myreshape(T, fam_sz); + end + + case 'prior_type', CPD.prior_type = args{i+1}; + case 'dirichlet_type', dirichlet_type = args{i+1}; + case 'dirichlet_weight', dirichlet_weight = args{i+1}; + case 'adjustable', CPD.adjustable = args{i+1}; + case 'clamped', CPD.adjustable = ~args{i+1}; + case 'trim', CPD.trim = args{i+1}; + case 'old_self', noop = 1; % already read in + otherwise, error(['invalid argument name: ' args{i}]); + end +end + +switch CPD.prior_type + case 'dirichlet', + switch dirichlet_type + case 'unif', CPD.dirichlet = dirichlet_weight * myones(fam_sz); + case 'BDeu', CPD.dirichlet = dirichlet_weight * mk_stochastic(myones(fam_sz)); + otherwise, error(['invalid dirichlet_type ' dirichlet_type]) + end + case {'entropic', 'none'} + CPD.dirichlet = []; + otherwise, error(['invalid prior_type ' prior_type]) +end + + + +% fields to do with learning +if ~CPD.adjustable + CPD.counts = []; + CPD.nparams = 0; + CPD.nsamples = []; +else + CPD.counts = zeros(size(CPD.CPT)); + psz = fam_sz(1:end-1); + ss = fam_sz(end); + if CPD.leftright + % For each of the Qps contexts, we specify Q elements on the diagoanl + CPD.nparams = Qps * Q; + else + % sum-to-1 constraint reduces the effective arity of the node by 1 + CPD.nparams = prod([psz ss-1]); + end + CPD.nsamples = 0; +end + +fam_sz = CPD.sizes; +psz = prod(fam_sz(1:end-1)); +ssz = fam_sz(end); +CPD.trimmed_trans = zeros(psz, ssz); % must declare before reading + +CPD = class(CPD, 'tabular_CPD', discrete_CPD(~CPD.adjustable, fam_sz)); + + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +CPD.CPT = []; +CPD.sizes = []; +CPD.prior_type = []; +CPD.dirichlet = []; +CPD.adjustable = []; +CPD.counts = []; +CPD.nparams = []; +CPD.nsamples = []; +CPD.trim = []; +CPD.trimmed_trans = []; +CPD.leftright = []; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/Old/update_params.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +function CPD = update_params(CPD, ev, counts) +% UPDATE_PARAMS Update the Dirichlet pseudo counts and compute the new MAP param estimates (tabular) +% +% CPD = update_params(CPD, ev) uses the evidence on the family from a single case. +% +% CPD = update_params(CPD, [], counts) does a batch update using the specified suff. stats. + +if nargin < 3 + n = length(ev); + data = cat(1, ev{:}); % convert to a vector of scalars + counts = compute_counts(data(:)', 1:n, mysize(CPD.CPT)); +end + +CPD.prior = CPD.prior + counts; +CPD.CPT = mk_stochastic(CPD.prior);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/bayes_update_params.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,55 @@ +function CPD = bayes_update_params(CPD, self_ev, pev) +% UPDATE_PARAMS_COMPLETE Bayesian parameter updating given completely observed data (tabular) +% CPD = update_params_complete(CPD, self_ev, pev) +% +% self_ev(m) is the evidence on this node in case m. +% pev(i,m) is the evidence on the i'th parent in case m (if there are any parents). +% These can be arrays or cell arrays. +% +% We update the Dirichlet pseudo counts and set the CPT to the mean of the posterior. + +if iscell(self_ev), usecell = 1; else usecell = 0; end + +ncases = length(self_ev); +sz = CPD.sizes; +nparents = length(sz)-1; +assert(nparents == size(pev,1)); + +if ncases == 0 | ~adjustable_CPD(CPD) + return; +elseif ncases == 1 % speedup the sequential learning case by avoiding normalization of the whole array + if usecell + x = cat(1, pev{:})'; + y = self_ev{1}; + else + x = pev(:)'; + y = self_ev; + end + switch nparents + case 0, + CPD.dirichlet(y) = CPD.dirichlet(y)+1; + CPD.CPT = CPD.dirichlet / sum(CPD.dirichlet); + case 1, + CPD.dirichlet(x(1), y) = CPD.dirichlet(x(1), y)+1; + CPD.CPT(x(1), :) = CPD.dirichlet(x(1), :) ./ sum(CPD.dirichlet(x(1), :)); + case 2, + CPD.dirichlet(x(1), x(2), y) = CPD.dirichlet(x(1), x(2), y)+1; + CPD.CPT(x(1), x(2), :) = CPD.dirichlet(x(1), x(2), :) ./ sum(CPD.dirichlet(x(1), x(2), :)); + case 3, + CPD.dirichlet(x(1), x(2), x(3), y) = CPD.dirichlet(x(1), x(2), x(3), y)+1; + CPD.CPT(x(1), x(2), x(3), :) = CPD.dirichlet(x(1), x(2), x(3), :) ./ sum(CPD.dirichlet(x(1), x(2), x(3), :)); + otherwise, + ind = subv2ind(sz, [x y]); + CPD.dirichlet(ind) = CPD.dirichlet(ind) + 1; + CPD.CPT = mk_stochastic(CPD.dirichlet); + end +else + if usecell + data = [cell2num(pev); cell2num(self_ev)]; + else + data = [pev; self_ev]; + end + counts = compute_counts(data, sz); + CPD.dirichlet = CPD.dirichlet + counts; + CPD.CPT = mk_stochastic(CPD.dirichlet); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/display.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function display(CPD) + +disp('tabular_CPD object'); +disp(struct(CPD)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/get_field.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +function val = get_field(CPD, name) +% GET_PARAMS Get the parameters (fields) for a tabular_CPD object +% val = get_params(CPD, name) +% +% The following fields can be accessed +% +% cpt, counts +% +% e.g., CPT = get_params(CPD, 'cpt') + +switch name + case 'cpt', val = CPD.CPT; + case 'counts', val = CPD.counts; + otherwise, + error(['invalid argument name ' name]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/learn_params.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,17 @@ +function CPD = learn_params(CPD, fam, data, ns, cnodes) +%function CPD = learn_params(CPD, local_data) +% LEARN_PARAMS Compute the ML/MAP estimate of the params of a tabular CPD given complete data +% CPD = learn_params(CPD, local_data) +% +% local_data(i,m) is the value of i'th family member in case m (can be cell array). + +local_data = data(fam, :); +if iscell(local_data) + local_data = cell2num(local_data); +end +counts = compute_counts(local_data, CPD.sizes); +switch CPD.prior_type + case 'none', CPD.CPT = mk_stochastic(counts); + case 'dirichlet', CPD.CPT = mk_stochastic(counts + CPD.dirichlet); + otherwise, error(['unrecognized prior ' CPD.prior_type]) +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/log_marg_prob_node.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,69 @@ +function L = log_marg_prob_node(CPD, self_ev, pev, usecell) +% LOG_MARG_PROB_NODE Compute sum_m log P(x(i,m)| x(pi_i,m)) for node i (tabular) +% L = log_marg_prob_node(CPD, self_ev, pev) +% +% This differs from log_prob_node because we integrate out the parameters. +% self_ev(m) is the evidence on this node in case m. +% pev(i,m) is the evidence on the i'th parent in case m (if there are any parents). +% (These may also be cell arrays.) + +ncases = length(self_ev); +sz = CPD.sizes; +nparents = length(sz)-1; +assert(ncases == size(pev, 2)); + +if nargin < 4 + %usecell = 0; + if iscell(self_ev) + usecell = 1; + else + usecell = 0; + end +end + + +if ncases==0 + L = 0; + return; +elseif ncases==1 % speedup the sequential learning case + CPT = CPD.CPT; + % We assume the CPTs are already set to the mean of the posterior (due to bayes_update_params) + if usecell + x = cat(1, pev{:})'; + y = self_ev{1}; + else + %x = pev(:)'; + x = pev; + y = self_ev; + end + switch nparents + case 0, p = CPT(y); + case 1, p = CPT(x(1), y); + case 2, p = CPT(x(1), x(2), y); + case 3, p = CPT(x(1), x(2), x(3), y); + otherwise, + ind = subv2ind(sz, [x y]); + p = CPT(ind); + end + L = log(p); +else + % We ignore the CPTs here and assume the prior has not been changed + + % We arrange the data as in the following example. + % Let there be 2 parents and 3 cases. Let p(i,m) be parent i in case m, + % and y(m) be the child in case m. Then we create the data matrix + % + % p(1,1) p(1,2) p(1,3) + % p(2,1) p(2,2) p(2,3) + % y(1) y(2) y(3) + if usecell + data = [cell2num(pev); cell2num(self_ev)]; + else + data = [pev; self_ev]; + end + %S = struct(CPD); fprintf('log marg prob node %d, ps\n', S.self); disp(S.parents) + counts = compute_counts(data, sz); + L = dirichlet_score_family(counts, CPD.dirichlet); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/log_nextcase_prob_node.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,72 @@ +function L = log_nextcase_prob_node(CPD, self_ev, pev, test_self_ev, test_pev) +% LOG_NEXTCASE_PROB_NODE compute the joint distribution of a node (tabular) of a new case given +% completely observed data. +% +% The input arguments are mainly similar with log_marg_prob_node(CPD, self_ev, pev, usecell), +% but add test_self_ev, test_pev, and without usecell +% test_self_ev(m) is the evidence on this node in a test case. +% test_pev(i) is the evidence on the i'th parent in the test case (if there are any parents). +% +% Written by qian.diao@intel.com + +ncases = length(self_ev); +sz = CPD.sizes; +nparents = length(sz)-1; +assert(ncases == size(pev, 2)); + +if nargin < 6 + %usecell = 0; + if iscell(self_ev) + usecell = 1; + else + usecell = 0; + end +end + + +if ncases==0 + L = 0; + return; +elseif ncases==1 % speedup the sequential learning case; here need correction!!! + CPT = CPD.CPT; + % We assume the CPTs are already set to the mean of the posterior (due to bayes_update_params) + if usecell + x = cat(1, pev{:})'; + y = self_ev{1}; + else + %x = pev(:)'; + x = pev; + y = self_ev; + end + switch nparents + case 0, p = CPT(y); + case 1, p = CPT(x(1), y); + case 2, p = CPT(x(1), x(2), y); + case 3, p = CPT(x(1), x(2), x(3), y); + otherwise, + ind = subv2ind(sz, [x y]); + p = CPT(ind); + end + L = log(p); +else + % We ignore the CPTs here and assume the prior has not been changed + + % We arrange the data as in the following example. + % Let there be 2 parents and 3 cases. Let p(i,m) be parent i in case m, + % and y(m) be the child in case m. Then we create the data matrix + % + % p(1,1) p(1,2) p(1,3) + % p(2,1) p(2,2) p(2,3) + % y(1) y(2) y(3) + if usecell + data = [cell2num(pev); cell2num(self_ev)]; + else + data = [pev; self_ev]; + end + counts = compute_counts(data, sz); + + % compute the (N_ijk'+ N_ijk)/(N_ij' + N_ij) under the condition of 1_m+1,ijk = 1 + L = predict_family(counts, CPD.prior, test_self_ev, test_pev); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/log_prior.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,18 @@ +function L = log_prior(CPD) +% LOG_PRIOR Return log P(theta) for a tabular CPD +% L = log_prior(CPD) + +switch CPD.prior_type + case 'none', + L = 0; + case 'dirichlet', + D = CPD.dirichlet(:); + L = sum(log(D + (D==0))); + case 'entropic', + % log-prior = log exp(-H(theta)) = sum_i theta_i log (theta_i) + fam_sz = CPD.sizes; + psz = prod(fam_sz(1:end-1)); + ssz = fam_sz(end); + C = reshape(CPD.CPT, psz, ssz); + L = sum(sum(C .* log(C + (C==0)))); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/maximize_params.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,52 @@ +function CPD = maximize_params(CPD, temp) +% MAXIMIZE_PARAMS Set the params of a tabular node to their ML/MAP values. +% CPD = maximize_params(CPD, temp) + +if ~adjustable_CPD(CPD), return; end + +%assert(approxeq(sum(CPD.counts(:)), CPD.nsamples)); % false! +switch CPD.prior_type + case 'none', + counts = reshape(CPD.counts, size(CPD.CPT)); + CPD.CPT = mk_stochastic(counts); + case 'dirichlet', + counts = reshape(CPD.counts, size(CPD.CPT)); + CPD.CPT = mk_stochastic(counts + CPD.dirichlet); + + % case 'entropic', +% % For an HMM, +% % CPT(i,j) = pr(X(t)=j | X(t-1)=i) = transprob(i,j) +% % counts(i,j) = E #(X(t-1)=i, X(t)=j) = exp_num_trans(i,j) +% Z = 1-temp; +% fam_sz = CPD.sizes; +% psz = prod(fam_sz(1:end-1)); +% ssz = fam_sz(end); +% counts = reshape(CPD.counts, psz, ssz); +% CPT = zeros(psz, ssz); +% for i=CPD.entropic_pcases(:)' +% [CPT(i,:), logpost] = entropic_map_estimate(counts(i,:), Z); +% end +% non_entropic_pcases = mysetdiff(1:psz, CPD.entropic_pcases); +% for i=non_entropic_pcases(:)' +% CPT(i,:) = mk_stochastic(counts(i,:)); +% end +% %for i=1:psz +% % [CPT(i,:), logpost] = entropic_map(counts(i,:), Z); +% %end +% if CPD.trim & (temp < 2) % at high temps, we would trim everything! +% % grad(j) = d log lik / d theta(i ->j) +% % CPT(i,j) = 0 => counts(i,j) = 0 +% % so we can safely replace 0s by 1s in the denominator +% denom = CPT(i,:) + (CPT(i,:)==0); +% grad = counts(i,:) ./ denom; +% trim = find(CPT(i,:) <= exp(-(1/Z)*grad)); % eqn 32 +% if ~isempty(trim) +% CPT(i,trim) = 0; +% if all(CPD.trimmed_trans(i,trim)==0) % trimming for 1st time +% disp(['trimming CPT(' num2str(i) ',' num2str(trim) ')']) +% end +% CPD.trimmed_trans(i,trim) = 1; +% end +% end +% CPD.CPT = myreshape(CPT, CPD.sizes); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/reset_ess.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +function CPD = reset_ess(CPD) +% RESET_ESS Reset the Expected Sufficient Statistics of a tabular node. +% CPD = reset_ess(CPD) + +%CPD.counts = zeros(size(CPD.CPT)); +CPD.counts = zeros(prod(size(CPD.CPT)), 1); +CPD.nsamples = 0;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/set_fields.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,52 @@ +function CPD = set_fields(CPD, varargin) +% SET_PARAMS Set the parameters (fields) for a tabular_CPD object +% CPD = set_params(CPD, name/value pairs) +% +% The following optional arguments can be specified in the form of name/value pairs: +% +% CPT, prior, clamped, counts +% +% e.g., CPD = set_params(CPD, 'CPT', 'rnd') + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'CPT', + if ischar(args{i+1}) + switch args{i+1} + case 'unif', CPD.CPT = mk_stochastic(myones(CPD.sizes)); + case 'rnd', CPD.CPT = mk_stochastic(myrand(CPD.sizes)); + otherwise, error(['invalid type ' args{i+1}]); + end + elseif isscalar(args{i+1}) + p = args{i+1}; + k = CPD.sizes(end); + % Bug fix by Hervé Boutrouille 10/1/01 + CPD.CPT = myreshape(sample_dirichlet(p*ones(1,k), prod(CPD.sizes(1:end-1)), CPD.sizes)); + %CPD.CPT = myreshape(sample_dirichlet(p*ones(1,k), prod(CPD.sizes(1:end-1))), CPD.sizes); + else + CPD.CPT = myreshape(args{i+1}, CPD.sizes); + end + + case 'prior', + if ischar(args{i+1}) & strcmp(args{i+1}, 'unif') + CPD.prior = myones(CPD.sizes); + elseif isscalar(args{i+1}) + CPD.prior = args{i+1} * normalise(myones(CPD.sizes)); + else + CPD.prior = myreshape(args{i+1}, CPD.sizes); + end + + %case 'clamped', CPD.clamped = strcmp(args{i+1}, 'yes'); + %case 'clamped', CPD = set_clamped(CPD, strcmp(args{i+1}, 'yes')); + case 'clamped', CPD = set_clamped(CPD, args{i+1}); + + case 'counts', CPD.counts = args{i+1}; + + otherwise, + %error(['invalid argument name ' args{i}]); + end +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/tabular_CPD.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,173 @@ +function CPD = tabular_CPD(bnet, self, varargin) +% TABULAR_CPD Make a multinomial conditional prob. distrib. (CPT) +% +% CPD = tabular_CPD(bnet, node) creates a random CPT. +% +% The following arguments can be specified [default in brackets] +% +% CPT - specifies the params ['rnd'] +% - T means use table T; it will be reshaped to the size of node's family. +% - 'rnd' creates rnd params (drawn from uniform) +% - 'unif' creates a uniform distribution +% adjustable - 0 means don't adjust the parameters during learning [1] +% prior_type - defines type of prior ['none'] +% - 'none' means do ML estimation +% - 'dirichlet' means add pseudo-counts to every cell +% - 'entropic' means use a prior P(theta) propto exp(-H(theta)) (see Brand) +% dirichlet_weight - equivalent sample size (ess) of the dirichlet prior [1] +% dirichlet_type - defines the type of Dirichlet prior ['BDeu'] +% - 'unif' means put dirichlet_weight in every cell +% - 'BDeu' means we put 'dirichlet_weight/(r q)' in every cell +% where r = self_sz and q = prod(parent_sz) (see Heckerman) +% trim - 1 means trim redundant params (rows in CPT) when using entropic prior [0] +% entropic_pcases - list of assignments to the parents nodes when we should use +% the entropic prior; all other cases will be estimated using ML [1:psz] +% sparse - 1 means use 1D sparse array to represent CPT [0] +% +% e.g., tabular_CPD(bnet, i, 'CPT', T) +% e.g., tabular_CPD(bnet, i, 'CPT', 'unif', 'dirichlet_weight', 2, 'dirichlet_type', 'unif') +% +% REFERENCES +% M. Brand - "Structure learning in conditional probability models via an entropic prior +% and parameter extinction", Neural Computation 11 (1999): 1155--1182 +% M. Brand - "Pattern discovery via entropy minimization" [covers annealing] +% AI & Statistics 1999. Equation numbers refer to this paper, which is available from +% www.merl.com/reports/docs/TR98-21.pdf +% D. Heckerman, D. Geiger and M. Chickering, +% "Learning Bayesian networks: the combination of knowledge and statistical data", +% Microsoft Research Tech Report, 1994 + + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + CPD = class(CPD, 'tabular_CPD', discrete_CPD(0, [])); + return; +elseif isa(bnet, 'tabular_CPD') + % This might occur if we are copying an object. + CPD = bnet; + return; +end +CPD = init_fields; + +ns = bnet.node_sizes; +ps = parents(bnet.dag, self); +fam_sz = ns([ps self]); +psz = prod(ns(ps)); +CPD.sizes = fam_sz; +CPD.leftright = 0; +CPD.sparse = 0; + +% set defaults +CPD.CPT = mk_stochastic(myrand(fam_sz)); +CPD.adjustable = 1; +CPD.prior_type = 'none'; +dirichlet_type = 'BDeu'; +dirichlet_weight = 1; +CPD.trim = 0; +selfprob = 0.1; +CPD.entropic_pcases = 1:psz; + +% extract optional args +args = varargin; +% check for old syntax CPD(bnet, i, CPT) as opposed to CPD(bnet, i, 'CPT', CPT) +if ~isempty(args) & ~isstr(args{1}) + CPD.CPT = myreshape(args{1}, fam_sz); + args = []; +end + +for i=1:2:length(args) + switch args{i}, + case 'CPT', + T = args{i+1}; + if ischar(T) + switch T + case 'unif', CPD.CPT = mk_stochastic(myones(fam_sz)); + case 'rnd', CPD.CPT = mk_stochastic(myrand(fam_sz)); + otherwise, error(['invalid CPT ' T]); + end + else + CPD.CPT = myreshape(T, fam_sz); + end + case 'prior_type', CPD.prior_type = args{i+1}; + case 'dirichlet_type', dirichlet_type = args{i+1}; + case 'dirichlet_weight', dirichlet_weight = args{i+1}; + case 'adjustable', CPD.adjustable = args{i+1}; + case 'clamped', CPD.adjustable = ~args{i+1}; + case 'trim', CPD.trim = args{i+1}; + case 'entropic_pcases', CPD.entropic_pcases = args{i+1}; + case 'sparse', CPD.sparse = args{i+1}; + otherwise, error(['invalid argument name: ' args{i}]); + end +end + +switch CPD.prior_type + case 'dirichlet', + switch dirichlet_type + case 'unif', CPD.dirichlet = dirichlet_weight * myones(fam_sz); + case 'BDeu', CPD.dirichlet = (dirichlet_weight/psz) * mk_stochastic(myones(fam_sz)); + otherwise, error(['invalid dirichlet_type ' dirichlet_type]) + end + case {'entropic', 'none'} + CPD.dirichlet = []; + otherwise, error(['invalid prior_type ' prior_type]) +end + + + +% fields to do with learning +if ~CPD.adjustable + CPD.counts = []; + CPD.nparams = 0; + CPD.nsamples = []; +else + %CPD.counts = zeros(size(CPD.CPT)); + CPD.counts = zeros(prod(size(CPD.CPT)), 1); + psz = fam_sz(1:end-1); + ss = fam_sz(end); + if CPD.leftright + % For each of the Qps contexts, we specify Q elements on the diagoanl + CPD.nparams = Qps * Q; + else + % sum-to-1 constraint reduces the effective arity of the node by 1 + CPD.nparams = prod([psz ss-1]); + end + CPD.nsamples = 0; +end + +CPD.trimmed_trans = []; +fam_sz = CPD.sizes; + +%psz = prod(fam_sz(1:end-1)); +%ssz = fam_sz(end); +%CPD.trimmed_trans = zeros(psz, ssz); % must declare before reading + +%sparse CPT +if CPD.sparse + CPD.CPT = sparse(CPD.CPT(:)); +end + +CPD = class(CPD, 'tabular_CPD', discrete_CPD(~CPD.adjustable, fam_sz)); + + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +CPD.CPT = []; +CPD.sizes = []; +CPD.prior_type = []; +CPD.dirichlet = []; +CPD.adjustable = []; +CPD.counts = []; +CPD.nparams = []; +CPD.nsamples = []; +CPD.trim = []; +CPD.trimmed_trans = []; +CPD.leftright = []; +CPD.entropic_pcases = []; +CPD.sparse = []; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/update_ess.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) +% UPDATE_ESS Update the Expected Sufficient Statistics of a tabular node. +% function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) + +dom = fmarginal.domain; + +if all(hidden_bitv(dom)) + CPD = update_ess_simple(CPD, fmarginal.T); + %fullm = add_ev_to_dmarginal(fmarginal, evidence, ns); + %assert(approxeq(fullm.T(:), fmarginal.T(:))) +else + fullm = add_ev_to_dmarginal(fmarginal, evidence, ns); + CPD = update_ess_simple(CPD, fullm.T); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_CPD/update_ess_simple.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +function CPD = update_ess_simple(CPD, counts) +% UPDATE_ESS_SIMPLE Update the Expected Sufficient Statistics of a tabular node. +% function CPD = update_ess_simple(CPD, counts) + +CPD.nsamples = CPD.nsamples + 1; +CPD.counts = CPD.counts + counts(:);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_decision_node/CPD_to_CPT.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function CPT = CPD_to_CPT(CPD) +% CPD_TO_CPT Convert the tabular_decision_node to a CPT +% CPT = CPD_to_CPT(CPD) + +CPT = CPD.CPT;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_decision_node/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +/CPD_to_CPT.m/1.1.1.1/Wed May 29 15:59:54 2002// +/display.m/1.1.1.1/Wed May 29 15:59:54 2002// +/get_field.m/1.1.1.1/Wed May 29 15:59:54 2002// +/set_fields.m/1.1.1.1/Wed May 29 15:59:54 2002// +/tabular_decision_node.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_decision_node/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_decision_node/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@tabular_decision_node
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_decision_node/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_decision_node/Old/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +/tabular_decision_node.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_decision_node/Old/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@tabular_decision_node/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_decision_node/Old/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_decision_node/Old/tabular_decision_node.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,39 @@ +function CPD = tabular_decision_node(sz, CPT) +% TABULAR_DECISION_NODE Represent the randomized policy over a discrete decision/action node as a table +% CPD = tabular_decision_node(sz, CPT) +% +% sz(1:end-1) is the sizes of the parents, sz(end) is the size of this node +% By default, CPT is set to the uniform random policy + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + CPD = class(CPD, 'tabular_decision_node'); + return; +elseif isa(sz, 'tabular_decision_node') + % This might occur if we are copying an object. + CPD = sz; + return; +end +CPD = init_fields; + +if nargin < 2 + CPT = mk_stochastic(myones(sz)); +else + CPT = myreshape(CPT, sz); +end + +CPD.CPT = CPT; +CPD.size = sz; + +CPD = class(CPD, 'tabular_decision_node'); + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +CPD.CPT = []; +CPD.size = [];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_decision_node/display.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,4 @@ +function display(CPD) + +disp('tabular decision node object'); +disp(struct(CPD));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_decision_node/get_field.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,19 @@ +function vals = get_field(CPD, name) +% GET_PARAMS Get the parameters (fields) for a tabular_decision_node object +% vals = get_params(CPD, name) +% +% The following fields can be accessed +% +% policy - the table containing the policy +% +% e.g., policy = get_params(CPD, 'policy') + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'policy', vals = CPD.CPT; + otherwise, + error(['invalid argument name ' args{i}]); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_decision_node/set_fields.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,19 @@ +function CPD = set_params(CPD, varargin) +% SET_PARAMS Set the parameters (fields) for a tabular_decision_node object +% CPD = set_params(CPD, name/value pairs) +% +% The following optional arguments can be specified in the form of name/value pairs: +% +% policy - the table containing the policy +% +% e.g., CPD = set_params(CPD, 'policy', T) + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'policy', CPD.CPT = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_decision_node/tabular_decision_node.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,45 @@ +function CPD = tabular_decision_node(bnet, self, CPT) +% TABULAR_DECISION_NODE Represent a stochastic policy over a discrete decision/action node as a table +% CPD = tabular_decision_node(bnet, self, CPT) +% +% node is the number of a node in this equivalence class. +% CPT is an optional argument (see tabular_CPD for details); by default, it is the uniform policy. + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + CPD = class(CPD, 'tabular_decision_node', discrete_CPD(1, [])); + return; +elseif isa(bnet, 'tabular_decision_node') + % This might occur if we are copying an object. + CPD = bnet; + return; +end +CPD = init_fields; + +ns = bnet.node_sizes; +fam = family(bnet.dag, self); +ps = parents(bnet.dag, self); +sz = ns(fam); + +if nargin < 3 + CPT = mk_stochastic(myones(sz)); +else + CPT = myreshape(CPT, sz); +end + +CPD.CPT = CPT; +CPD.sizes = sz; + +clamped = 1; % don't update using EM +CPD = class(CPD, 'tabular_decision_node', discrete_CPD(clamped, ns([ps self]))); + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +CPD.CPT = []; +CPD.sizes = [];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_kernel/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +/convert_to_pot.m/1.1.1.1/Wed May 29 15:59:54 2002// +/convert_to_table.m/1.1.1.1/Wed May 29 15:59:54 2002// +/get_field.m/1.1.1.1/Wed May 29 15:59:54 2002// +/set_fields.m/1.1.1.1/Wed May 29 15:59:54 2002// +/tabular_kernel.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_kernel/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_kernel/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@tabular_kernel
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_kernel/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_kernel/Old/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +/tabular_kernel.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_kernel/Old/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@tabular_kernel/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_kernel/Old/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_kernel/Old/tabular_kernel.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,45 @@ +function K = tabular_kernel(fg, self) +% TABULAR_KERNEL Make a table-based local kernel (discrete potential) +% K = tabular_kernel(fg, self) +% +% fg is a factor graph +% self is the number of a representative domain +% +% Use 'set_params_kernel' to adjust the following fields +% table - a q[1]xq[2]x... array, where q[i] is the number of values for i'th node +% in this domain [default: random values from [0,1], which need not sum to 1] + + +if nargin==0 + % This occurs if we are trying to load an object from a file. + K = init_fields; + K = class(K, 'tabular_kernel'); + return; +elseif isa(fg, 'tabular_kernel') + % This might occur if we are copying an object. + K = fg; + return; +end +K = init_fields; + +ns = fg.node_sizes; +dom = fg.doms{self}; +% we don't store the actual domain since it may vary due to parameter tieing +K.sz = ns(dom); +K.table = myrand(K.sz); + +K = class(K, 'tabular_kernel'); + + +%%%%%%% + + +function K = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +K.table = []; +K.sz = []; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_kernel/convert_to_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,37 @@ +function pot = convert_to_pot(CPD, pot_type, domain, evidence) +% CONVERT_TO_POT Convert a tabular CPD to one or more potentials +% pot = convert_to_pot(CPD, pot_type, domain, evidence) + +% This is the same as discrete_CPD/convert_to_pot, +% except we didn't want to the kernel to inherit methods like sample_node etc. + +sz = CPD.sz; +ns = zeros(1, max(domain)); +ns(domain) = sz; + +odom = domain(~isemptycell(evidence(domain))); +T = convert_to_table(CPD, domain, evidence); + +switch pot_type + case 'u', + pot = upot(domain, sz, T, 0*myones(sz)); + case 'd', + ns(odom) = 1; + pot = dpot(domain, ns(domain), T); + case 'c', + % Since we want the output to be a Gaussian, the whole family must be observed. + % In other words, the potential is really just a constant. + p = T.p; + %p = prob_node(CPD, evidence(domain(end)), evidence(domain(1:end-1))); + ns(domain) = 0; + pot = cpot(domain, ns(domain), log(p)); + case 'cg', + T = T(:); + ns(odom) = 1; + can = cell(1, length(T)); + for i=1:length(T) + can{i} = cpot([], [], log(T(i))); + end + pot = cgpot(domain, [], ns, can); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_kernel/convert_to_table.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function T = convert_to_table(CPD, domain, evidence) +% CONVERT_TO_TABLE Convert a discrete CPD to a table +% T = convert_to_table(CPD, domain, evidence) +% +% We convert the CPD to a CPT, and then lookup the evidence on the discrete parents. +% The resulting table can easily be converted to a potential. + +CPT = CPD.table; +odom = domain(~isemptycell(evidence(domain))); +vals = cat(1, evidence{odom}); +map = find_equiv_posns(odom, domain); +index = mk_multi_index(length(domain), map, vals); +T = CPT(index{:});
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_kernel/get_field.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function val = get_params_kernel(K, name) +% GET_PARAMS_KERNEL Accessor function for a field (tabular_kernel) +% val = get_params_kernel(K, name) +% +% e.g., get_params_kernel(K, 'table') + +switch name + case 'table', val = K.table; + otherwise, + error(['invalid field name ' name]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_kernel/set_fields.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function K = set_params_kernel(K, name, val) +% SET_PARAMS_KERNEL Accessor function for a field (table_kernel) +% K = set_params_kernel(K, name, val) +% +% e.g., K = set_params_kernel(K, 'table', rand(2,3,2)) for a kernel on 3 nodes with 2,3,2 values each + +% We should check if the arguments are valid... + +switch name + case 'table', K.table = val; + otherwise, + error(['invalid field name ' name]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_kernel/tabular_kernel.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,40 @@ +function K = tabular_kernel(sz, table) +% TABULAR_KERNEL Make a table-based local kernel (discrete potential) +% K = tabular_kernel(sz, table) +% +% sz(i) is the number of values the i'th member of this kernel can have +% table is an optional array of size sz[1] x sz[2] x... [default: random] + +if nargin==0 + % This occurs if we are trying to load an object from a file. + K = init_fields; + K = class(K, 'tabular_kernel'); + return; +elseif isa(sz, 'tabular_kernel') + % This might occur if we are copying an object. + K = sz; + return; +end +K = init_fields; + +if nargin < 2, table = myrand(sz); end + +K.sz = sz; +K.table = table; + +K = class(K, 'tabular_kernel'); + + +%%%%%%% + + +function K = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +K.sz = []; +K.table = []; + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_utility_node/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,4 @@ +/convert_to_pot.m/1.1.1.1/Wed May 29 15:59:54 2002// +/display.m/1.1.1.1/Wed May 29 15:59:54 2002// +/tabular_utility_node.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_utility_node/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@tabular_utility_node
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_utility_node/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_utility_node/convert_to_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function pot = convert_to_pot(CPD, pot_type, domain, evidence) +% CONVERT_TO_POT Convert a tabular utility node to one or more potentials +% pot = convert_to_pot(CPD, pot_type, domain, evidence) + +switch pot_type + case 'u', + sz = [CPD.sizes 1]; % the utility node itself has size 1 + pot = upot(domain, sz, 1*myones(sz), myreshape(CPD.T, sz)); + otherwise, + error(['can''t convert a utility node to a ' pot_type ' potential']); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_utility_node/display.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,4 @@ +function display(CPD) + +disp('tabular utility node object'); +disp(struct(CPD));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tabular_utility_node/tabular_utility_node.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,46 @@ +function CPD = tabular_utility_node(bnet, node, T) +% TABULAR_UTILITY_NODE Represent a utility function as a table +% CPD = tabular_utility_node(bnet, node, T) +% +% node is the number of a node in this equivalence class. +% T is an optional argument (same shape as the CPT in tabular_CPD, but missing the last (child) +% dimension). By default, entries in T are chosen u.a.r. from 0:1 (using 'rand'). + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + clamp = 0; + CPD = class(CPD, 'tabular_utility_node'); + return; +elseif isa(bnet, 'tabular_utility_node') + % This might occur if we are copying an object. + CPD = bnet; + return; +end +CPD = init_fields; + + +ns = bnet.node_sizes; +ps = parents(bnet.dag, node); +sz = ns(ps); + +if nargin < 3 + T = myrand(sz); +else + T = myreshape(T, sz); +end + +CPD.T = T; +CPD.sizes = sz; + +CPD = class(CPD, 'tabular_utility_node'); + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +CPD.T = []; +CPD.sizes = [];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tree_CPD/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +/display.m/1.1.1.1/Wed May 29 15:59:54 2002// +/evaluate_tree_performance.m/1.1.1.1/Wed May 29 15:59:54 2002// +/get_field.m/1.1.1.1/Wed May 29 15:59:54 2002// +/learn_params.m/1.1.1.1/Wed May 29 15:59:54 2002// +/readme.txt/1.1.1.1/Wed May 29 15:59:54 2002// +/set_fields.m/1.1.1.1/Wed May 29 15:59:54 2002// +/tree_CPD.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tree_CPD/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@tree_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tree_CPD/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tree_CPD/display.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,4 @@ +function display(CPD) + +disp('dtree_CPD object'); +disp(struct(CPD));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tree_CPD/evaluate_tree_performance.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,82 @@ +function [score,outputs] = evaluate(CPD, fam, data, ns, cnodes) +% Evaluate evaluate the performance of the classification/regression tree on given complete data +% score = evaluate(CPD, fam, data, ns, cnodes) +% +% fam(i) is the node id of the i-th node in the family of nodes, self node is the last one +% data(i,m) is the value of node i in case m (can be cell array). +% ns(i) is the node size for the i-th node in the whold bnet +% cnodes(i) is the node id for the i-th continuous node in the whole bnet +% +% Output +% score is the classification accuracy (for classification) +% or mean square deviation (for regression) +% here for every case we use the mean value at the tree leaf node as its predicted value +% outputs(i) is the predicted output value for case i +% +% Author: yimin.zhang@intel.com +% Last updated: Jan. 19, 2002 + + +if iscell(data) + local_data = cell2num(data(fam,:)); +else + local_data = data(fam, :); +end + +%get local node sizes and node types +node_sizes = ns(fam); +node_types = zeros(1,size(ns,2)); %all nodes are disrete +node_types(cnodes)=1; +node_types=node_types(fam); + +fam_size=size(fam,2); +output_type = node_types(fam_size); + +num_cases=size(local_data,2); +total_error=0; + +outputs=zeros(1,num_cases); +for i=1:num_cases + %class one case using the tree + cur_node=CPD.tree.root; % at the root node of the tree + while (1) + if (CPD.tree.nodes(cur_node).is_leaf==1) + if (output_type==0) %output is discrete + %use the class with max probability as the output + [maxvalue,class_id]=max(CPD.tree.nodes(cur_node).probs); + outputs(i)=class_id; + if (class_id~=local_data(fam_size,i)) + total_error=total_error+1; + end + else %output is continuous + %use the mean as the value + outputs(i)=CPD.tree.nodes(cur_node).mean; + cur_deviation = CPD.tree.nodes(cur_node).mean-local_data(fam_size,i); + total_error=total_error+cur_deviation*cur_deviation; + end + break; + end + cur_attr = CPD.tree.nodes(cur_node).split_id; + attr_val = local_data(cur_attr,i); + if (node_types(cur_attr)==0) %discrete attribute + % goto the attr_val -th child + cur_node = CPD.tree.nodes(cur_node).children(attr_val); + else + if (attr_val <= CPD.tree.nodes(cur_node).split_threshhold) + cur_node = CPD.tree.nodes(cur_node).children(1); + else + cur_node = CPD.tree.nodes(cur_node).children(2); + end + end + if (cur_node > CPD.tree.num_node) + fprintf('Fatal error: Tree structure corrupted.\n'); + return; + end + end + %update the classification error number +end +if (output_type==0) + score=1-total_error/num_cases; +else + score=total_error/num_cases; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tree_CPD/get_field.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +function val = get_params(CPD, name) +% GET_PARAMS Get the parameters (fields) for a tabular_CPD object +% val = get_params(CPD, name) +% +% The following fields can be accessed +% +% cpt - the CPT +% +% e.g., CPT = get_params(CPD, 'cpt') + +switch name + case 'cpt', val = CPD.CPT; + case 'tree', val = CPD.tree; + otherwise, + error(['invalid argument name ' name]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tree_CPD/learn_params.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,642 @@ +function CPD = learn_params(CPD, fam, data, ns, cnodes, varargin) +% LEARN_PARAMS Construct classification/regression tree given complete data +% CPD = learn_params(CPD, fam, data, ns, cnodes) +% +% fam(i) is the node id of the i-th node in the family of nodes, self node is the last one +% data(i,m) is the value of node i in case m (can be cell array). +% ns(i) is the node size for the i-th node in the whold bnet +% cnodes(i) is the node id for the i-th continuous node in the whole bnet +% +% The following optional arguments can be specified in the form of name/value pairs: +% stop_cases: for early stop (pruning). A node is not split if it has less than k cases. default is 0. +% min_gain: for early stop (pruning). +% For discrete output: A node is not split when the gain of best split is less than min_gain. default is 0. +% For continuous (cts) outpt: A node is not split when the gain of best split is less than min_gain*score(root) +% (we denote it cts_min_gain). default is 0.006 +% %%%%%%%%%%%%%%%%%%%Struction definition of dtree_CPD.tree%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% tree.num_node the last position in tree.nodes array for adding new nodes, +% it is not always same to number of nodes in a tree, because some position in the +% tree.nodes array can be set to unused (e.g. in tree pruning) +% tree.nodes is the array of nodes in the tree plus some unused nodes. +% tree.nodes(1) is the root for the tree. +% +% Below is the attributes for each node +% tree.nodes(i).used; % flag this node is used (0 means node not used, it can be removed from tree to save memory) +% tree.nodes(i).is_leaf; % if 1 means this node is a leaf, if 0 not a leaf. +% tree.nodes(i).children; % children(i) is the node number in tree.nodes array for the i-th child node +% tree.nodes(i).split_id; % the attribute id used to split this node +% tree.nodes(i).split_threshhold; % the threshhold for continuous attribute to split this node +% %%%%%attributes specially for classification tree (discrete output) +% tree.nodes(i).probs % probs(i) is the prob for i-th value of class node +% % For three output class, the probs = [0.9 0.1 0.0] means the probability of +% % class 1 is 0.9, for class 2 is 0.1, for class 3 is 0.0. +% %%%%%attributes specially for regression tree (continuous output) +% tree.nodes(i).mean % mean output value for this node +% tree.nodes(i).std % standard deviation for output values in this node +% +% Author: yimin.zhang@intel.com +% Last updated: Jan. 19, 2002 + +% Want list: +% (1) more efficient for cts attributes: get the values of cts attributes at first (the begining of build_tree function), then doing bi_search in finding threshhold +% (2) pruning classification tree using Pessimistic Error Pruning +% (3) bi_search for strings (used for transform data to BNT format) + +global tree %tree must be global so that it can be accessed in recursive slitting function +global cts_min_gain +tree=[]; % clear the tree +tree.num_node=0; +cts_min_gain=0; + +stop_cases=0; +min_gain=0; + +args = varargin; +nargs = length(args); +if (nargs>0) + if isstr(args{1}) + for i=1:2:nargs + switch args{i}, + case 'stop_cases', stop_cases = args{i+1}; + case 'min_gain', min_gain = args{i+1}; + end + end + else + error(['error in input parameters']); + end +end + +if iscell(data) + local_data = cell2num(data(fam,:)); +else + local_data = data(fam, :); +end +%counts = compute_counts(local_data, CPD.sizes); +%CPD.CPT = mk_stochastic(counts + CPD.prior); % bug fix 11/5/01 +node_types = zeros(1,size(ns,2)); %all nodes are disrete +node_types(cnodes)=1; +%make the data be BNT compliant (values for discrete nodes are from 1-n, here n is the node size) +%trans_data=transform_data(local_data,'tmp.dat',[]); %here no cts nodes + +build_dtree (CPD, local_data, ns(fam), node_types(fam),stop_cases,min_gain); +%CPD.tree=copy_tree(tree); +CPD.tree=tree; %copy the tree constructed to CPD + + +function new_tree = copy_tree(tree) +% copy the tree to new_tree +new_tree.num_node=tree.num_node; +new_tree.root = tree.root; +for i=1:tree.num_node + new_tree.nodes(i)=tree.nodes(i); +end + + +function build_dtree (CPD, fam_ev, node_sizes, node_types,stop_cases,min_gain) +global tree +global cts_min_gain + +tree.num_node=0; %the current number of nodes in the tree +tree.root=1; + +T = 1:size(fam_ev,2) ; %all cases +candidate_attrs = 1:(size(node_sizes,2)-1); %all attributes +node_id=1; %the root node +lastnode=size(node_sizes,2); %the last element in all nodes is the dependent variable (category node) +num_cat=node_sizes(lastnode); + +% get minimum gain for cts output (used in stop splitting) +if (node_types(size(fam_ev,1))==1) %cts output + N = size(fam_ev,2); + output_id = size(fam_ev,1); + cases_T = fam_ev(output_id,:); %get all the output value for cases T + std_T = std(cases_T); + avg_y_T = mean(cases_T); + sqr_T = cases_T - avg_y_T; + cts_min_gain = min_gain*(sum(sqr_T.*sqr_T)/N); % min_gain * (R(root) = 1/N * SUM(y-avg_y)^2) +end + +split_dtree (CPD, fam_ev, node_sizes, node_types, stop_cases,min_gain, T, candidate_attrs, num_cat); + + + +% pruning method +% (1) Restrictions on minimum node size: A node is not split if it has smaller than k cases. +% (2) Threshholds on impurity: a threshhold is imposed on the splitting test score. Threshhold can be +% imposed on local goodness measure (the gain_ratio of a node) or global goodness. +% (3) Mininum Error Pruning (MEP), (no need pruning set) +% Prune if static error<=backed-up error +% Static error at node v: e(v) = (Nc + 1)/(N+k) (laplace estimate, prior for each class equal) +% here N is # of all examples, Nc is # of majority class examples, k is number of classes +% Backed-up error at node v: (Ti is the i-th subtree root) +% E(T) = Sum_1_to_n(pi*e(Ti)) +% (4) Pessimistic Error Pruning (PEP), used in Quilan C4.5 (no need pruning set, efficient because of pruning top-down) +% Probability of error (apparent error rate) +% q = (N-Nc+0.5)/N +% where N=#examples, Nc=#examples in majority class +% Error of a node v (if pruned) q(v)= (Nv- Nc,v + 0.5)/Nv +% Error of a subtree q(T)= Sum_of_l_leaves(Nl - Nc,l + 0.5)/Sum_of_l_leaves(Nl) +% Prune if q(v)<=q(T) +% +% Implementation statuts: +% (1)(2) has been implemented as the input parameters of learn_params. +% (4) is implemented in this function +function pruning(fam_ev,node_sizes,node_types) +% PRUNING prune the constructed tree using PEP +% pruning(fam_ev,node_sizes,node_types) +% +% fam_ev(i,j) is the value of attribute i in j-th training cases (for whole tree), the last row is for the class label (self_ev) +% node_sizes(i) is the node size for the i-th node in the family +% node_types(i) is the node type for the i-th node in the family, 0 for disrete node, 1 for continous node +% the global parameter 'tree' is for storing the input tree and the pruned tree + + +function split_T = split_cases(fam_ev,node_sizes,node_types,T,node_i, threshhold) +% SPLIT_CASES split the cases T according to values of node_i in the family +% split_T = split_cases(fam_ev,node_sizes,node_types,T,node_i) +% +% fam_ev(i,j) is the value of attribute i in j-th training cases (for whole tree), the last row is for the class label (self_ev) +% node_sizes(i) is the node size for the i-th node in the family +% node_types(i) is the node type for the i-th node in the family, 0 for disrete node, 1 for continous node +% node_i is the attribute we need to split + +if (node_types(node_i)==0) %discrete attribute + %init the subsets of T + split_T = cell(1,node_sizes(node_i)); %T will be separated into |node_size of i| subsets according to different values of node i + for i=1:node_sizes(node_i) % here we assume that the value of an attribute is 1:node_size + split_T{i}=zeros(1,0); + end + + size_t = size(T,2); + for i=1:size_t + case_id = T(i); + %put this case into one subset of split_T according to its value for node_i + value = fam_ev(node_i,case_id); + pos = size(split_T{value},2)+1; + split_T{value}(pos)=case_id; % here assumes the value of an attribute is 1:node_size + end +else %continuous attribute + %init the subsets of T + split_T = cell(1,2); %T will be separated into 2 subsets (<=threshhold) (>threshhold) + for i=1:2 + split_T{i}=zeros(1,0); + end + + size_t = size(T,2); + for i=1:size_t + case_id = T(i); + %put this case into one subset of split_T according to its value for node_i + value = fam_ev(node_i,case_id); + subset_num=1; + if (value>threshhold) + subset_num=2; + end + pos = size(split_T{subset_num},2)+1; + split_T{subset_num}(pos)=case_id; + end +end + + + +function new_node = split_dtree (CPD, fam_ev, node_sizes, node_types, stop_cases, min_gain, T, candidate_attrs, num_cat) +% SPLIT_TREE Split the tree at node node_id with cases T (actually it is just indexes to family evidences). +% new_node = split_dtree (fam_ev, node_sizes, node_types, T, node_id, num_cat, method) +% +% fam_ev(i,j) is the value of attribute i in j-th training cases (for whole tree), the last row is for the class label (self_ev) +% node_sizes{i} is the node size for the i-th node in the family +% node_types{i} is the node type for the i-th node in the family, 0 for disrete node, 1 for continous node +% stop_cases is the threshold of number of cases to stop slitting +% min_gain is the minimum gain need to split a node +% T(i) is the index of i-th cases in current decision tree node, we need split it further +% candidate_attrs(i) the node id for the i-th attribute that still need to be considered as split attribute +%%%%% node_id is the index of current node considered for a split +% num_cat is the number of output categories for the decision tree +% output: +% new_node is the new node created +global tree +global cts_min_gain + +size_fam = size(fam_ev,1); %number of family size +output_type = node_types(size_fam); %the type of output for the tree (0 is discrete, 1 is continuous) +size_attrs = size(candidate_attrs,2); %number of candidate attributes +size_t = size(T,2); %number of training cases in this tree node + +%(1)computeFrequenceyForEachClass(T) +if (output_type==0) %discrete output + class_freqs = zeros(1,num_cat); + for i=1:size_t + case_id = T(i); + case_class = fam_ev(size_fam,case_id); %get the class label for this case + class_freqs(case_class)=class_freqs(case_class)+1; + end +else %cts output + N = size(fam_ev,2); + cases_T = fam_ev(size(fam_ev,1),T); %get the output value for cases T + std_T = std(cases_T); +end + +%(2) if OneClass (for discrete output) or same output value (for cts output) or Class With #examples < stop_cases +% return a leaf; +% create a decision node N; + +% get majority class in this node +if (output_type == 0) + top1_class = 0; %the class with the largest number of cases + top1_class_cases = 0; %the number of cases in top1_class + [top1_class_cases,top1_class]=max(class_freqs); +end + +if (size_t==0) %impossble + new_node=-1; + fprintf('Fatal error: please contact the author. \n'); + return; +end + +% stop splitting if needed + %for discrete output: one class + %for cts output, all output value in cases are same + %cases too little +if ( (output_type==0 & top1_class_cases == size_t) | (output_type==1 & std_T == 0) | (size_t < stop_cases)) + %create one new leaf node + tree.num_node=tree.num_node+1; + tree.nodes(tree.num_node).used=1; %flag this node is used (0 means node not used, it will be removed from tree at last to save memory) + tree.nodes(tree.num_node).is_leaf=1; + tree.nodes(tree.num_node).children=[]; + tree.nodes(tree.num_node).split_id=0; %the attribute(parent) id to split this tree node + tree.nodes(tree.num_node).split_threshhold=0; + if (output_type==0) + tree.nodes(tree.num_node).probs=class_freqs/size_t; %the prob for each value of class node + + % tree.nodes(tree.num_node).probs=zeros(1,num_cat); %the prob for each value of class node + % tree.nodes(tree.num_node).probs(top1_class)=1; %use the majority class of parent node, like for binary class, + %and majority is class 2, then the CPT is [0 1] + %we may need to use prior to do smoothing, to get [0.001 0.999] + tree.nodes(tree.num_node).error.self_error=1-top1_class_cases/size_t; %the classfication error in this tree node when use default class + tree.nodes(tree.num_node).error.all_error=1-top1_class_cases/size_t; %no total classfication error in this tree node and its subtree + tree.nodes(tree.num_node).error.all_error_num=size_t - top1_class_cases; + fprintf('Create leaf node(onecla) %d. Class %d Cases %d Error %d \n',tree.num_node, top1_class, size_t, size_t - top1_class_cases ); + else + avg_y_T = mean(cases_T); + tree.nodes(tree.num_node).mean = avg_y_T; + tree.nodes(tree.num_node).std = std_T; + fprintf('Create leaf node(samevalue) %d. Mean %8.4f Std %8.4f Cases %d \n',tree.num_node, avg_y_T, std_T, size_t); + end + new_node = tree.num_node; + return; +end + +%create one new node +tree.num_node=tree.num_node+1; +tree.nodes(tree.num_node).used=1; %flag this node is used (0 means node not used, it will be removed from tree at last to save memory) +tree.nodes(tree.num_node).is_leaf=1; +tree.nodes(tree.num_node).children=[]; +tree.nodes(tree.num_node).split_id=0; +tree.nodes(tree.num_node).split_threshhold=0; +if (output_type==0) + tree.nodes(tree.num_node).error.self_error=1-top1_class_cases/size_t; + tree.nodes(tree.num_node).error.all_error=0; + tree.nodes(tree.num_node).error.all_error_num=0; +else + avg_y_T = mean(cases_T); + tree.nodes(tree.num_node).mean = avg_y_T; + tree.nodes(tree.num_node).std = std_T; +end +new_node = tree.num_node; + +%Stop splitting if no attributes left in this node +if (size_attrs==0) + if (output_type==0) + tree.nodes(tree.num_node).probs=class_freqs/size_t; %the prob for each value of class node + tree.nodes(tree.num_node).error.all_error=1-top1_class_cases/size_t; + tree.nodes(tree.num_node).error.all_error_num=size_t - top1_class_cases; + fprintf('Create leaf node(noattr) %d. Class %d Cases %d Error %d \n',tree.num_node, top1_class, size_t, size_t - top1_class_cases ); + else + fprintf('Create leaf node(noattr) %d. Mean %8.4f Std %8.4f Cases %d \n',tree.num_node, avg_y_T, std_T, size_t); + end + return; +end + + +%(3) for each attribute A +% ComputeGain(A); +max_gain=0; %the max gain score (for discrete information gain or gain ration, for cts node the R(T)) +best_attr=0; %the attribute with the max_gain +best_split = []; %the split of T according to the value of best_attr +cur_best_threshhold = 0; %the threshhold for split continuous attribute +best_threshhold=0; + +% compute Info(T) (for discrete output) +if (output_type == 0) + class_split_T = split_cases(fam_ev,node_sizes,node_types,T,size(fam_ev,1),0); %split cases according to class + info_T = compute_info (fam_ev, T, class_split_T); +else % compute R(T) (for cts output) +% N = size(fam_ev,2); +% cases_T = fam_ev(size(fam_ev,1),T); %get the output value for cases T +% std_T = std(cases_T); +% avg_y_T = mean(cases_T); + sqr_T = cases_T - avg_y_T; + R_T = sum(sqr_T.*sqr_T)/N; % get R(T) = 1/N * SUM(y-avg_y)^2 + info_T = R_T; +end + +for i=1:(size_fam-1) + if (myismember(i,candidate_attrs)) %if this attribute still in the candidate attribute set + if (node_types(i)==0) %discrete attibute + split_T = split_cases(fam_ev,node_sizes,node_types,T,i,0); %split cases according to value of attribute i + % For cts output, we compute the least square gain. + % For discrete output, we compute gain ratio + cur_gain = compute_gain(fam_ev,node_sizes,node_types,T,info_T,i,split_T,0,output_type); %gain ratio + else %cts attribute + %get the values of this attribute + ev = fam_ev(:,T); + values = ev(i,:); + sort_v = sort(values); + %remove the duplicate values in sort_v + v_set = unique(sort_v); + best_gain = 0; + best_threshhold = 0; + best_split1 = []; + + %find the best split for this cts attribute + % see "Quilan 96: Improved Use of Continuous Attributes in C4.5" + for j=1:(size(v_set,2)-1) + mid_v = (v_set(j)+v_set(j+1))/2; + split_T = split_cases(fam_ev,node_sizes,node_types,T,i,mid_v); %split cases according to value of attribute i (<=mid_v) + % For cts output, we compute the least square gain. + % For discrete output, we use Quilan 96: use information gain instead of gain ratio to select threshhold + cur_gain = compute_gain(fam_ev,node_sizes,node_types,T,info_T,i,split_T,1,output_type); + %if (i==6) + % fprintf('gain %8.5f threshhold %6.3f spliting %d\n', cur_gain, mid_v, size(split_T{1},2)); + %end + + if (best_gain < cur_gain) + best_gain = cur_gain; + best_threshhold = mid_v; + %best_split1 = split_T; %here we need to copy array, not good!!! (maybe we can compute after we get best_attr + end + end + %recalculate the gain_ratio of the best_threshhold + split_T = split_cases(fam_ev,node_sizes,node_types,T,i,best_threshhold); + best_gain = compute_gain(fam_ev,node_sizes,node_types,T,info_T,i,split_T,0,output_type); %gain_ratio + if (output_type==0) %for discrete output + cur_gain = best_gain-log2(size(v_set,2)-1)/size_t; % Quilan 96: use the gain_ratio-log2(N-1)/|D| as the gain of this attr + else %for cts output + cur_gain = best_gain; + end + end + + if (max_gain < cur_gain) + max_gain = cur_gain; + best_attr = i; + cur_best_threshhold=best_threshhold; %save the threshhold + %best_split = split_T; %here we need to copy array, not good!!! So we will recalculate in below line 313 + end + end +end + +% stop splitting if gain is too small +if (max_gain==0 | (output_type==0 & max_gain < min_gain) | (output_type==1 & max_gain < cts_min_gain)) + if (output_type==0) + tree.nodes(tree.num_node).probs=class_freqs/size_t; %the prob for each value of class node + tree.nodes(tree.num_node).error.all_error=1-top1_class_cases/size_t; + tree.nodes(tree.num_node).error.all_error_num=size_t - top1_class_cases; + fprintf('Create leaf node(nogain) %d. Class %d Cases %d Error %d \n',tree.num_node, top1_class, size_t, size_t - top1_class_cases ); + else + fprintf('Create leaf node(nogain) %d. Mean %8.4f Std %8.4f Cases %d \n',tree.num_node, avg_y_T, std_T, size_t); + end + return; +end + +%get the split of cases according to the best split attribute +if (node_types(best_attr)==0) %discrete attibute + best_split = split_cases(fam_ev,node_sizes,node_types,T,best_attr,0); +else + best_split = split_cases(fam_ev,node_sizes,node_types,T,best_attr,cur_best_threshhold); +end + +%(4) best_attr = AttributeWithBestGain; +%(5) if best_attr is continuous ???? why need this? maybe the value in the decision tree must appeared in data +% find threshhold in all cases that <= max_V +% change the split of T +tree.nodes(tree.num_node).split_id=best_attr; +tree.nodes(tree.num_node).split_threshhold=cur_best_threshhold; %for cts attribute only + +%note: below threshhold rejust is linera search, so it is slow. A better method is described in paper "Efficient C4.5" +%if (output_type==0) +if (node_types(best_attr)==1) %is a continuous attribute + %find the value that approximate best_threshhold from below (the largest that <= best_threshhold) + best_value=0; + for i=1:size(fam_ev,2) %note: need to search in all cases for all tree, not just in cases for this node + val = fam_ev(best_attr,i); + if (val <= cur_best_threshhold & val > best_value) %val is more clear to best_threshhold + best_value=val; + end + end + tree.nodes(tree.num_node).split_threshhold=best_value; %for cts attribute only +end +%end + +if (output_type == 0) + fprintf('Create node %d split at %d gain %8.4f Th %d. Class %d Cases %d Error %d \n',tree.num_node, best_attr, max_gain, tree.nodes(tree.num_node).split_threshhold, top1_class, size_t, size_t - top1_class_cases ); +else + fprintf('Create node %d split at %d gain %8.4f Th %d. Mean %8.4f Cases %d\n',tree.num_node, best_attr, max_gain, tree.nodes(tree.num_node).split_threshhold, avg_y_T, size_t ); +end + +%(6) Foreach T' in the split_T +% if T' is Empty +% Child of node_id is a leaf +% else +% Child of node_id = split_tree (T') +tree.nodes(new_node).is_leaf=0; %because this node will be split, it is not leaf now +for i=1:size(best_split,2) + if (size(best_split{i},2)==0) %T(i) is empty + %create one new leaf node + tree.num_node=tree.num_node+1; + tree.nodes(tree.num_node).used=1; %flag this node is used (0 means node not used, it will be removed from tree at last to save memory) + tree.nodes(tree.num_node).is_leaf=1; + tree.nodes(tree.num_node).children=[]; + tree.nodes(tree.num_node).split_id=0; + tree.nodes(tree.num_node).split_threshhold=0; + if (output_type == 0) + tree.nodes(tree.num_node).probs=zeros(1,num_cat); %the prob for each value of class node + tree.nodes(tree.num_node).probs(top1_class)=1; %use the majority class of parent node, like for binary class, + %and majority is class 2, then the CPT is [0 1] + %we may need to use prior to do smoothing, to get [0.001 0.999] + tree.nodes(tree.num_node).error.self_error=0; + tree.nodes(tree.num_node).error.all_error=0; + tree.nodes(tree.num_node).error.all_error_num=0; + else + tree.nodes(tree.num_node).mean = avg_y_T; %just use parent node's mean value + tree.nodes(tree.num_node).std = std_T; + end + %add the new leaf node to parents + num_children=size(tree.nodes(new_node).children,2); + tree.nodes(new_node).children(num_children+1)=tree.num_node; + if (output_type==0) + fprintf('Create leaf node(nullset) %d. %d-th child of Father %d Class %d\n',tree.num_node, i, new_node, top1_class ); + else + fprintf('Create leaf node(nullset) %d. %d-th child of Father %d \n',tree.num_node, i, new_node ); + end + + else + if (node_types(best_attr)==0) % if attr is discrete, it should be removed from the candidate set + new_candidate_attrs = mysetdiff(candidate_attrs,[best_attr]); + else + new_candidate_attrs = candidate_attrs; + end + new_sub_node = split_dtree (CPD, fam_ev, node_sizes, node_types, stop_cases, min_gain, best_split{i}, new_candidate_attrs, num_cat); + %tree.nodes(parent_id).error.all_error += tree.nodes(new_sub_node).error.all_error; + fprintf('Add subtree node %d to %d. #nodes %d\n',new_sub_node,new_node, tree.num_node ); + +% tree.nodes(new_node).error.all_error_num = tree.nodes(new_node).error.all_error_num + tree.nodes(new_sub_node).error.all_error_num; + %add the new leaf node to parents + num_children=size(tree.nodes(new_node).children,2); + tree.nodes(new_node).children(num_children+1)=new_sub_node; + end +end + +%(7) Compute errors of N; for doing pruning +% get the total error for the subtree +if (output_type==0) + tree.nodes(new_node).error.all_error=tree.nodes(new_node).error.all_error_num/size_t; +end +%doing pruning, but doing here is not so efficient, because it is bottom up. +%if tree.nodes() +%after doing pruning, need to update the all_error to self_error + +%(8) Return N + + + + +%(1) For discrete output, we use GainRatio defined as below +% Gain(X,T) +% GainRatio(X,T) = ---------- +% SplitInfo(X,T) +% where +% Gain(X,T) = Info(T) - Info(X,T) +% |Ti| +% Info(X,T) = Sum for i from 1 to n of ( ---- * Info(Ti)) +% |T| + +% SplitInfo(D,T) is the information due to the split of T on the basis +% of the value of the categorical attribute D. Thus SplitInfo(D,T) is +% I(|T1|/|T|, |T2|/|T|, .., |Tm|/|T|) +% where {T1, T2, .. Tm} is the partition of T induced by the value of D. + +% Definition of Info(Ti) +% If a set T of records is partitioned into disjoint exhaustive classes C1, C2, .., Ck on the basis of the +% value of the categorical attribute, then the information needed to identify the class of an element of T +% is Info(T) = I(P), where P is the probability distribution of the partition (C1, C2, .., Ck): +% P = (|C1|/|T|, |C2|/|T|, ..., |Ck|/|T|) +% Here I(P) is defined as +% I(P) = -(p1*log(p1) + p2*log(p2) + .. + pn*log(pn)) +% +%(2) For continuous output (regression tree), we use least squares score (adapted from Leo Breiman's book "Classification and regression trees", page 231 +% The original support only binary split, we further extend it to permit multiple-child split +% +% Delta_R = R(T) - Sum for all childe nodes Ti (R(Ti)) +% Where R(Ti)= 1/N * Sum for all cases i in node Ti ((yi - avg_y(Ti))^2) +% here N is the number of all training cases for construct the regression tree +% avg_y(Ti) is the average value for output variable for the cases in node Ti + +function gain_score = compute_gain (fam_ev, node_sizes, node_types, T, info_T, attr_id, split_T, score_type, output_type) +% COMPUTE_GAIN Compute the score for the split of cases T using attribute attr_id +% gain_score = compute_gain (fam_ev, T, attr_id, node_size, method) +% +% fam_ev(i,j) is the value of attribute i in j-th training cases, the last row is for the class label (self_ev) +% T(i) is the index of i-th cases in current decision tree node, we need split it further +% attr_id is the index of current node considered for a split +% split_T{i} is the i_th subset in partition of cases T according to the value of attribute attr_id +% score_type if 0, is gain ratio, 1 is information gain (only apply to discrete output) +% node_size(i) the node size of i-th node in the family +% output_type: 0 means discrete output, 1 means continuous output. +gain_score=0; +% ***********for DISCRETE output******************************************************* +if (output_type == 0) + % compute Info(T) + total_cnt = size(T,2); + if (total_cnt==0) + return; + end; + %class_split_T = split_cases(fam_ev,node_sizes,node_types,T,size(fam_ev,1),0); %split cases according to class + %info_T = compute_info (fam_ev, T, class_split_T); + + % compute Info(X,T) + num_class = size(split_T,2); + subset_sizes = zeros(1,num_class); + info_ti = zeros(1,num_class); + for i=1:num_class + subset_sizes(i)=size(split_T{i},2); + if (subset_sizes(i)~=0) + class_split_Ti = split_cases(fam_ev,node_sizes,node_types,split_T{i},size(fam_ev,1),0); %split cases according to class + info_ti(i) = compute_info(fam_ev, split_T{i}, class_split_Ti); + end + end + ti_ratios = subset_sizes/total_cnt; %get the |Ti|/|T| + info_X_T = sum(ti_ratios.*info_ti); + + %get Gain(X,T) + gain_X_T = info_T - info_X_T; + + if (score_type == 1) %information gain + gain_score=gain_X_T; + return; + end + %compute the SplitInfo(X,T) //is this also for cts attr, only split into two subsets + splitinfo_T = compute_info (fam_ev, T, split_T); + if (splitinfo_T~=0) + gain_score = gain_X_T/splitinfo_T; + end + +% ************for continuous output************************************************** +else + N = size(fam_ev,2); + + % compute R(Ti) + num_class = size(split_T,2); + R_Ti = zeros(1,num_class); + for i=1:num_class + if (size(split_T{i},2)~=0) + cases_T = fam_ev(size(fam_ev,1),split_T{i}); + avg_y_T = mean(cases_T); + sqr_T = cases_T - avg_y_T; + R_Ti(i) = sum(sqr_T.*sqr_T)/N; % get R(Ti) = 1/N * SUM(y-avg_y)^2 + end + end + %delta_R = R(T) - SUM(R(Ti)) + gain_score = info_T - sum(R_Ti); + +end + + +% Definition of Info(Ti) +% If a set T of records is partitioned into disjoint exhaustive classes C1, C2, .., Ck on the basis of the +% value of the categorical attribute, then the information needed to identify the class of an element of T +% is Info(T) = I(P), where P is the probability distribution of the partition (C1, C2, .., Ck): +% P = (|C1|/|T|, |C2|/|T|, ..., |Ck|/|T|) +% Here I(P) is defined as +% I(P) = -(p1*log(p1) + p2*log(p2) + .. + pn*log(pn)) +function info = compute_info (fam_ev, T, split_T) +% COMPUTE_INFO compute the information for the split of T into split_T +% info = compute_info (fam_ev, T, split_T) + +total_cnt = size(T,2); +num_class = size(split_T,2); +subset_sizes = zeros(1,num_class); +probs = zeros(1,num_class); +log_probs = zeros(1,num_class); +for i=1:num_class + subset_sizes(i)=size(split_T{i},2); +end + +probs = subset_sizes/total_cnt; +%log_probs = log2(probs); % if probs(i)=0, the log2(probs(i)) will be Inf +for i=1:size(probs,2) + if (probs(i)~=0) + log_probs(i)=log2(probs(i)); + end +end + +info = sum(-(probs.*log_probs)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tree_CPD/readme.txt Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +Decision/regression tree CPD +Author: Yimin Zhang yimin.zhang@intel.com +21 Jan 2002 + + +See also Paul Bradley's Multisurface Method-Tree matlab code + http://www.cs.wisc.edu/~paulb/msmt/ +http://www.cs.wisc.edu/~olvi/uwmp/msmt.html
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tree_CPD/set_fields.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,52 @@ +function CPD = set_fields(CPD, varargin) +% SET_PARAMS Set the parameters (fields) for a tabular_CPD object +% CPD = set_params(CPD, name/value pairs) +% +% The following optional arguments can be specified in the form of name/value pairs: +% +% CPT - the CPT +% prior - the prior +% clamped - 1 means don't adjust during EM +% +% e.g., CPD = set_params(CPD, 'CPT', 'rnd') + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'CPT', + if ischar(args{i+1}) + switch args{i+1} + case 'unif', CPD.CPT = mk_stochastic(myones(CPD.sizes)); + case 'rnd', CPD.CPT = mk_stochastic(myrand(CPD.sizes)); + otherwise, error(['invalid type ' args{i+1}]); + end + elseif isscalar(args{i+1}) + p = args{i+1}; + k = CPD.sizes(end); + % Bug fix by Hervé BOUTROUILLE 10/1/01 + CPD.CPT = myreshape(sample_dirichlet(p*ones(1,k), prod(CPD.sizes(1:end-1)), CPD.sizes)); + %CPD.CPT = myreshape(sample_dirichlet(p*ones(1,k), prod(CPD.sizes(1:end-1))), CPD.sizes); + else + CPD.CPT = myreshape(args{i+1}, CPD.sizes); + end + + case 'prior', + if ischar(args{i+1}) & strcmp(args{i+1}, 'unif') + CPD.prior = myones(CPD.sizes); + elseif isscalar(args{i+1}) + CPD.prior = args{i+1} * normalise(myones(CPD.sizes)); + else + CPD.prior = myreshape(args{i+1}, CPD.sizes); + end + + %case 'clamped', CPD.clamped = strcmp(args{i+1}, 'yes'); + %case 'clamped', CPD = set_clamped(CPD, strcmp(args{i+1}, 'yes')); + case 'clamped', CPD = set_clamped(CPD, args{i+1}); + + otherwise, + %error(['invalid argument name ' args{i}]); + end +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/@tree_CPD/tree_CPD.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,37 @@ +function CPD = tree_CPD(varargin) +%DTREE_CPD Make a conditional prob. distrib. which is a decision/regression tree. +% +% CPD =dtree_CPD() will create an empty tree. + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + clamp = 0; + CPD = class(CPD, 'tree_CPD', discrete_CPD(clamp, [])); + return; +elseif isa(varargin{1}, 'tree_CPD') + % This might occur if we are copying an object. + CPD = varargin{1}; + return; +end + +CPD = init_fields; + + +clamped = 0; +fam_sz = []; +CPD = class(CPD, 'tree_CPD', discrete_CPD(clamped, fam_sz)); + + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +%init the decision tree set the root to null +CPD.tree.num_node = 0; +CPD.tree.root=1; +CPD.tree.nodes=[]; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +/mk_isolated_tabular_CPD.m/1.1.1.1/Mon Jun 24 18:58:32 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,19 @@ +A D/@boolean_CPD//// +A D/@deterministic_CPD//// +A D/@discrete_CPD//// +A D/@gaussian_CPD//// +A D/@generic_CPD//// +A D/@gmux_CPD//// +A D/@hhmm2Q_CPD//// +A D/@hhmmF_CPD//// +A D/@hhmmQ_CPD//// +A D/@mlp_CPD//// +A D/@noisyor_CPD//// +A D/@root_CPD//// +A D/@softmax_CPD//// +A D/@tabular_CPD//// +A D/@tabular_decision_node//// +A D/@tabular_kernel//// +A D/@tabular_utility_node//// +A D/@tree_CPD//// +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CPDs/mk_isolated_tabular_CPD.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,14 @@ +function CPD = mk_isolated_tabular_CPD(fam_sz, args) +% function CPD = mk_isolated_tabular_CPD(fam_sz, args) +% function CPD = mk_isolated_tabular_CPD(fam_sz, args) +% Make a single CPD by creating a mini-bnet containing just this one family. +% This is necessary because the CPD constructor requires a bnet. + +n = length(fam_sz); +dag = zeros(n,n); +ps = 1:(n-1); +if ~isempty(ps) + dag(ps,n) = 1; +end +bnet = mk_bnet(dag, fam_sz); +CPD = tabular_CPD(bnet, n, args{:});
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,9 @@ +/README.txt/1.1.1.1/Thu Jun 10 01:56:50 2004// +/add_BNT_to_path.m/1.2/Sat Sep 17 16:58:43 2005// +/copyright.txt/1.1.1.1/Sat Jan 18 23:00:58 2003// +/installC_BNT.m/1.1.1.1/Thu Mar 31 19:20:56 2005// +/installC_graph.m/1.1.1.1/Tue May 18 14:50:12 2004// +/license.gpl.txt/1.1.1.1/Wed May 29 15:59:56 2002// +/test_BNT.m/1.3/Sat Sep 17 16:56:27 2005// +/uninstallC_BNT.m/1.1.1.1/Sun May 4 22:31:38 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +A D/@assocarray//// +A D/CPDs//// +A D/examples//// +A D/general//// +A D/inference//// +A D/learning//// +A D/potentials////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/README.txt Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,4 @@ +The Bayes Net Toolbox for Matlab was written by Kevin Patrick Murphy et al. +This version was last updated on 17 Oct 2007.. +To download the latest version, and to get documentation, please go to + http://www.cs.ubc.ca/~murphyk/Software/BNT/bnt.html
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/copyright.txt Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +Written by Kevin Murphy (murphyk@ai.mit.edu), 1997-2003 +Distributed under the GNU Library GPL - see license.gpl for details.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,50 @@ +/add_ev_to_dmarginal.m/1.1.1.1/Thu Jun 27 20:34:32 2002// +/add_evidence_to_gmarginal.m/1.1.1.1/Wed May 29 15:59:56 2002// +/bnet_to_fgraph.m/1.1.1.1/Wed May 29 15:59:54 2002// +/compute_fwd_interface.m/1.1.1.1/Wed May 29 15:59:54 2002// +/compute_interface_nodes.m/1.1.1.1/Wed May 29 15:59:54 2002// +/compute_joint_pot.m/1.1.1.1/Mon Jun 7 15:50:34 2004// +/compute_minimal_interface.m/1.1.1.1/Wed May 29 15:59:54 2002// +/convert_dbn_CPDs_to_pots.m/1.1.1.1/Fri Nov 22 22:35:00 2002// +/convert_dbn_CPDs_to_tables.m/1.1.1.1/Thu Jan 23 18:44:50 2003// +/convert_dbn_CPDs_to_tables1.m/1.1.1.1/Thu Jan 23 18:49:48 2003// +/convert_dbn_CPDs_to_tables_slow.m/1.1.1.1/Wed May 29 15:59:58 2002// +/dbn_to_bnet.m/1.1.1.1/Wed May 29 15:59:54 2002// +/dbn_to_hmm.m/1.1.1.1/Sun Feb 2 00:23:38 2003// +/determine_elim_constraints.m/1.1.1.1/Wed May 29 15:59:54 2002// +/dispcpt.m/1.1.1.1/Wed May 29 15:59:58 2002// +/do_intervention.m/1.1.1.1/Wed May 29 15:59:54 2002// +/dsep.m/1.1.1.1/Wed May 29 15:59:54 2002// +/dsep_test.m/1.1.1.1/Sat Jan 18 23:10:16 2003// +/enumerate_scenarios.m/1.1.1.1/Wed May 29 15:59:54 2002// +/fgraph_to_bnet.m/1.1.1.1/Wed May 29 15:59:54 2002// +/hodbn_to_bnet.m/1.1.1.1/Wed Jul 24 14:48:06 2002// +/is_mnet.m/1.1.1.1/Sun Jun 16 20:01:22 2002// +/linear_gaussian_to_cpot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/log_lik_complete.m/1.1.1.1/Wed May 29 15:59:54 2002// +/log_marg_lik_complete.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_bnet.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_dbn.m/1.1.1.1/Sat Feb 1 19:42:14 2003// +/mk_fgraph.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_fgraph_given_ev.m/1.1.1.1/Mon Jun 24 18:56:26 2002// +/mk_higher_order_dbn.m/1.1.1.1/Tue Jul 23 13:17:04 2002// +/mk_limid.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_mnet.m/1.1.1.1/Sun Jun 16 19:52:12 2002// +/mk_mrf2.m/1.1.1.1/Tue Dec 31 22:06:48 2002// +/mk_mutilated_samples.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_named_CPT.m/1.1.1.1/Tue Mar 30 17:18:54 2004// +/mk_slice_and_half_dbn.m/1.1.1.1/Wed May 29 15:59:54 2002// +/noisyORtoTable.m/1.1.1.1/Mon Aug 2 22:05:58 2004// +/partition_dbn_nodes.m/1.1.1.1/Wed May 29 15:59:54 2002// +/partition_matrix_vec_3.m/1.1.1.1/Wed May 29 15:59:58 2002// +/sample_bnet.m/1.1.1.1/Thu Jun 10 01:17:46 2004// +/sample_bnet_nocell.m/1.1.1.1/Wed May 29 15:59:54 2002// +/sample_dbn.m/1.1.1.1/Wed May 29 15:59:54 2002// +/score_bnet_complete.m/1.1.1.1/Wed May 29 15:59:54 2002// +/shrink_obs_dims_in_gaussian.m/1.1.1.1/Wed May 29 15:59:58 2002// +/shrink_obs_dims_in_table.m/1.1.1.1/Wed May 29 15:59:58 2002// +/solve_limid.m/1.1.1.1/Mon Jun 7 15:48:02 2004// +/unroll_dbn_topology.m/1.1.1.1/Wed May 29 15:59:54 2002// +/unroll_higher_order_topology.m/1.1.1.1/Fri May 31 10:25:58 2002// +/unroll_set.m/1.1.1.1/Mon Dec 16 17:57:14 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/general
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/Old/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,9 @@ +/bnet_to_gdl_graph.m/1.1.1.1/Wed May 29 15:59:54 2002// +/calc_mpe.m/1.1.1.1/Mon Jun 17 21:58:38 2002// +/calc_mpe_bucket.m/1.1.1.1/Wed May 29 15:59:54 2002// +/calc_mpe_dbn.m/1.1.1.1/Wed May 29 15:59:54 2002// +/calc_mpe_given_inf_engine.m/1.1.1.1/Wed May 29 15:59:54 2002// +/calc_mpe_global.m/1.1.1.1/Wed May 29 15:59:54 2002// +/compute_interface_nodes.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_gdl_graph.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/Old/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/general/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/Old/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/Old/bnet_to_gdl_graph.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,18 @@ +function gdl = bnet_to_gdl_graph(bnet) +% BNET_TO_GDL_GRAPH Convert a Bayesian network to a GDL graph +% gdl = bnet_to_gdl_graph(bnet) +% +% Each node in the BN gets converted to a single node in the GDL graph, +% representing its family; its kernel function is the corresponding CPD. + +N = length(bnet.dag); +doms = cell(1,N); +for i=1:N + doms{i} = family(bnet.dag, i); +end + +U = mk_undirected(bnet.dag); +gdl = mk_gdl_graph(U, doms, bnet.node_sizes, bnet.CPD, 'equiv_class', bnet.equiv_class, ... + 'discrete', bnet.dnodes, 'chance', bnet.chance_nodes, ... + 'decision', bnet.decision_nodes, 'utility', bnet.utility_nodes); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/Old/calc_mpe.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,58 @@ +function [mpe, ll] = calc_mpe(engine, evidence, break_ties) +% CALC_MPE Computes the most probable explanation of the evidence +% [mpe, ll] = calc_mpe_given_inf_engine(engine, evidence, break_ties) +% +% INPUT +% engine must support max-propagation +% evidence{i} is the observed value of node i, or [] if hidden +% break_ties is optional. If 1, we will force ties to be broken consistently +% by calling enter_evidence N times. +% +% OUTPUT +% mpe{i} is the most likely value of node i (cell array!) +% ll is the log-likelihood of the globally best assignment +% +% This currently only works when all hidden nodes are discrete + +if nargin < 3, break_ties = 0; end + + +[engine, ll] = enter_evidence(engine, evidence, 'maximize', 1); + +observed = ~isemptycell(evidence); + +if 0 % fgraphs don't support bnet_from_engine +onodes = find(observed); +bnet = bnet_from_engine(engine); +pot_type = determine_pot_type(bnet, onodes); +assert(pot_type == 'd'); +end + +scalar = 1; +evidence = evidence(:); % hack to handle unrolled DBNs +N = length(evidence); +mpe = cell(1,N); +for i=1:N + m = marginal_nodes(engine, i); + % observed nodes are all set to 1 inside the inference engine, so we must undo this + if observed(i) + mpe{i} = evidence{i}; + else + mpe{i} = argmax(m.T); + % Bug fix by Ron Zohar, 8/15/01 + % If there are ties, we must break them as follows (see Jensen96, p106) + if break_ties + evidence{i} = mpe{i}; + [engine, ll] = enter_evidence(engine, evidence, 'maximize', 1); + end + end + if length(mpe{i}) > 1, scalar = 0; end +end + +if nargout >= 2 + bnet = bnet_from_engine(engine); + ll = log_lik_complete(bnet, mpe(:)); +end +if 0 % scalar + mpe = cell2num(mpe); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/Old/calc_mpe_bucket.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,160 @@ +function [mpe, ll] = calc_mpe_bucket(bnet, new_evidence, max_over) +% +% PURPOSE: +% CALC_MPE Computes the most probable explanation to the network nodes +% given the evidence. +% +% [mpe, ll] = calc_mpe(engine, new_evidence, max_over) +% +% INPUT: +% bnet - the bayesian network +% new_evidence - optional, if specified - evidence to be incorporated [cell(1,n)] +% max_over - optional, if specified determines the variable elimination order [1:n] +% +% OUTPUT: +% mpe - the MPE assignmet for the net variables (or [] if no satisfying assignment) +% ll - log assignment probability. +% +% Notes: +% 1. Adapted from '@var_elim_inf_engine\marginal_nodes' for MPE by Ron Zohar, 8/7/01 +% 2. Only discrete potentials are supported at this time. +% 3. Complexity: O(nw*) where n is the number of nodes and w* is the induced tree width. +% 4. Implementation based on: +% - R. Dechter, "Bucket Elimination: A Unifying Framework for Probabilistic Inference", +% UA1 96, pp. 211-219. + + +ns = bnet.node_sizes; +n = length(bnet.dag); +evidence = cell(1,n); +if (nargin<2) + new_evidence = evidence; +end + +onodes = find(~isemptycell(new_evidence)); % observed nodes +hnodes = find(isemptycell(new_evidence)); % hidden nodes +pot_type = determine_pot_type(bnet, onodes); + +if pot_type ~= 'd' + error('only disrete potentials supported at this time') +end + +for i=1:n + fam = family(bnet.dag, i); + CPT{i} = convert_to_pot(bnet.CPD{bnet.equiv_class(i)}, pot_type, fam(:), evidence); +end + +% handle observed nodes: set impossible cases' probability to zero +% rather than prun matrix (this makes backtracking easier) + +for ii=onodes + lIdx = 1:ns(ii); + lIdx = setdiff(lIdx, new_evidence{ii}); + + sCPT=struct(CPT{ii}); % violate object privacy + + sargs = ''; + for jj=1:(length(sCPT.domain)-1) + sargs = [sargs, ':,']; + end + for jj=lIdx + eval(['sCPT.T(', sargs, num2str(jj), ')=0;']); + end + CPT{ii}=dpot(sCPT.domain, sCPT.sizes, sCPT.T); +end + +B = cell(1,n); +for b=1:n + B{b} = mk_initial_pot(pot_type, [], [], [], []); +end + +if (nargin<3) + max_over = (1:n); +end +order = max_over; % no attempt to optimize this + + +% Initialize the buckets with the CPDs assigned to them +for i=1:n + b = bucket_num(domain_pot(CPT{i}), order); + B{b} = multiply_pots(B{b}, CPT{i}); +end + +% Do backward phase +max_over = max_over(length(max_over):-1:1); % reverse +for i=max_over(1:end-1) + % max-ing over variable i which occurs in bucket j + j = bucket_num(i, order); + rest = mysetdiff(domain_pot(B{j}), i); + %temp = marginalize_pot_max(B{j}, rest); + temp = marginalize_pot(B{j}, rest, 1); + b = bucket_num(domain_pot(temp), order); + % fprintf('maxing over bucket %d (var %d), putting result into bucket %d\n', j, i, b); + sB=struct(B{b}); % violate object privacy + if ~isempty(sB.domain) + B{b} = multiply_pots(B{b}, temp); + else + B{b} = temp; + end +end +result = B{1}; +marginal = pot_to_marginal(result); +[prob, mpe] = max(marginal.T); + +% handle impossible cases +if ~(prob>0) + mpe = []; + ll = -inf; + %warning('evidence has zero probability') + return +end + +ll = log(prob); + +% Do forward phase +for ii=2:n + marginal = pot_to_marginal(B{ii}); + mpeidx = []; + for jj=order(1:length(mpe)) + assert(ismember(jj, marginal.domain)) %%% bug + temp = find_equiv_posns(jj, marginal.domain); + mpeidx = [mpeidx, temp] ; + if isempty(temp) + mpeidx = [mpeidx, Inf] ; + end + end + [mpeidxsorted sortedtompe] = sort(mpeidx) ; + + % maximize the matrix obtained from assigning values from previous buckets. + % this is done by building a string and using eval. + + kk=1; + sargs = '('; + for jj=1:length(marginal.domain) + if (jj~=1) + sargs = [sargs, ',']; + end + if (mpeidxsorted(kk)==jj) + sargs = [sargs, num2str(mpe(sortedtompe(kk)))]; + if (kk<length(mpe)) + kk = kk+1 ; + end + else + sargs = [sargs, ':']; + end + end + sargs = [sargs, ')'] ; + eval(['[val, loc] = max(marginal.T', sargs, ');']) + mpe = [mpe loc]; +end +[I,J] = sort(order); +mpe = mpe(J); + + + +%%%%%%%%% + +function b = bucket_num(domain, order) + +b = max(find_equiv_posns(domain, order)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/Old/calc_mpe_dbn.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,41 @@ +function [mpe, ll] = calc_mpe_dbn(engine, evidence, break_ties) +% CALC_MPE Computes the most probable explanation of the evidence +% [mpe, ll] = calc_mpe_dbn(engine, evidence, break_ties) +% +% INPUT +% engine must support max-propagation +% evidence{i,t} is the observed value of node i in slice t, or [] if hidden +% +% OUTPUT +% mpe{i,t} is the most likely value of node i (cell array!) +% ll is the log-likelihood of the globally best assignment +% +% This currently only works when all hidden nodes are discrete + +if nargin < 3, break_ties = 0; end + +if break_ties + disp('warning: break ties is ignored') +end + +[engine, ll] = enter_evidence(engine, evidence, 'maximize', 1); + +observed = ~isemptycell(evidence); +[ss T] = size(evidence); +scalar = 1; +N = length(evidence); +mpe = cell(ss,T); +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes; +for t=1:T + for i=1:ss + m = marginal_nodes(engine, i, t); + % observed nodes are all set to 1 inside the inference engine, so we must undo this + if observed(i,t) + mpe{i,t} = evidence{i,t}; + else + assert(length(m.T) == ns(i)); + mpe{i,t} = argmax(m.T); + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/Old/calc_mpe_given_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,32 @@ +function [mpe, prob] = calc_mpe_given_inf_engine(engine, evidence) +% CALC_MPE_GIVEN_ENGINE Computes the most probable explanation of the evidence +% [mpe, prob] = calc_mpe_given_inf_engine(engine, evidence) +% +% INPUT +% engine must support max-propagation +% evidence{i} is the obsevred value of node i, or [] if hidden +% +% OUTPUT +% mpe(i) is the most likely value of node i +% prob is the likelihood of the globally best assignment +% +% This currently only works when all nodes are discrete + +[engine, ll] = enter_evidence(engine, evidence); + +observed = ~isemptycell(evidence); +N = length(evidence); +mpe = zeros(1,N); +for i=1:N + m = marginal_nodes(engine, i); + % discrete observed nodes are all set to 1 inside the inference engine, so we must undo this + if observed(i) + mpe(i) = evidence{i}; + else + mpe(i) = argmax(m.T); + end +end + +bnet = bnet_from_engine(engine); +ll = log_lik_complete(bnet, num2cell(mpe(:))); +prob = exp(ll);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/Old/calc_mpe_global.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,28 @@ +function [mpe, ll] = calc_mpe_global(bnet, evidence) +% CALC_MPE_GLOBAL Compute the most probable explanation(s) from the global joint +% [mpe, ll] = calc_mpe_global(bnet, evidence) +% +% mpe(k,i) is the most probable value of node i in the k'th global mode +% ll is the log likelihood +% +% We assume all nodes are discrete + +engine = global_joint_inf_engine(bnet); +engine = enter_evidence(engine, evidence); +S1 = struct(engine); % violate object privacy +S2 = struct(S1.jpot); % joint potential +prob = max(S2.T(:)); +modes = find(S2.T(:) == prob); + +ens = bnet.node_sizes; +onodes = find(~isemptycell(evidence)); +ens(onodes) = 1; +mpe = ind2subv(ens, modes); +for k=1:length(modes) + for i=onodes(:)' + mpe(k,i) = evidence{i}; + end +end +ll = log(prob); + +mpe = num2cell(mpe);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/Old/compute_interface_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,31 @@ +function [int, persist, transient] = compute_interface_nodes(intra, inter) +% COMPUTE_INTERFACE_NODES Find the nodes in a DBN that represent a sufficient statistic +% [int, persist, transient] = compute_interface_nodes(intra, inter) +% +% The interface nodes are all those that has an incoming temporal arc, +% or which have a child which has an incoming temporal arc, +% where a temporal arc means one coming from the previous slice. +% (The parents of nodes with incoming temporal arcs are needed +% because moralization will bring them into the clique.) +% +% The persisent nodes are all those that have one or more incoming temporal arc. +% The transient nodes are all the non-persistent. +% +% See U. Kjaerulff, "dHugin: A computational system for dynamic +% time-sliced Bayesian networks", Intl. J. Forecasting (11) 89-111, 1995 + +n = length(intra); +int = []; +persist = []; +for u=1:n + if any(inter(:,u)) + int = [int u]; + persist = [persist u]; + end + if any(inter(:, children(intra, u))) + int = [int u]; + end +end +int = unique(int); +persist = unique(persist); +transient = mysetdiff(1:n, persist);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/Old/mk_gdl_graph.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,86 @@ +function gdl = mk_gdl_graph(G, domains, node_sizes, kernels, varargin) +% MK_GDL_GRAPH Make a GDL (generalized distributed law) graph +% gdl = mk_gdl_graph(G, domains, node_sizes, kernels, ...) +% +% A GDL graph is like a moralized, but untriangulated, Bayes net: +% each "node" represents a domain with a corresponding kernel function. +% For details, see "The Generalized Distributive Law", Aji and McEliece, +% IEEE Trans. Info. Theory, 46(2): 325--343, 2000 +% +% G(i,j) = 1 if there is an (undirected) edge between domains i,j +% +% domains{i} is the domain of node i +% +% node_sizes(i) is the number of values node i can take on, +% or the length of node i if i is a continuous-valued vector. +% node_sizes(i) = 1 if i is a utility node. +% +% kernels is the list of kernel functions +% +% The list below gives optional arguments [default value in brackets]. +% +% equiv_class - equiv_class(i)=j means factor node i gets its params from factors{j} [1:F] +% discrete - the list of nodes which are discrete random variables [1:N] +% chance - the list of nodes which are random variables [1:N] +% decision - the list of nodes which are decision nodes [ [] ] +% utility - the list of nodes which are utility nodes [ [] ] + + +ns = node_sizes; +N = length(domains); +vars = []; +for i=1:N + vars = myunion(vars, domains{i}); +end +Nvars = length(vars); + +gdl.equiv_class = 1:length(kernels); +gdl.chance_nodes = 1:Nvars; +gdl.utility_nodes = []; +gdl.decision_nodes = []; +gdl.dnodes = 1:Nvars; + +if nargin >= 5 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'equiv_class', bnet.equiv_class = args{i+1}; + case 'chance', bnet.chance_nodes = args{i+1}; + case 'utility', bnet.utility_nodes = args{i+1}; + case 'decision', bnet.decision_nodes = args{i+1}; + case 'discrete', bnet.dnodes = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + + +gdl.G = G; +gdl.vars = vars; +gdl.doms = domains; +gdl.node_sizes = node_sizes; +gdl.cnodes = mysetdiff(vars, gdl.dnodes); +gdl.kernels = kernels; +gdl.type = 'gdl'; + +% Compute a bit vector representation of the set of domains +% dom_bitv(i,j) = 1 iff variable j occurs in domain i +gdl.dom_bitv = zeros(N, length(vars)); +for i=1:N + gdl.dom_bitv(i, domains{i}) = 1; +end + +% compute the interesection of the domains on either side of each edge (separating set) +gdl.sepset = cell(N, N); +gdl.nbrs = cell(1,N); +for i=1:N + nbrs = neighbors(G, i); + gdl.nbrs{i} = nbrs; + for j = nbrs(:)' + gdl.sepset{i,j} = myintersect(domains{i}, domains{j}); + end +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/add_ev_to_dmarginal.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +function fmarginal = add_ev_to_dmarginal(fmarginal, evidence, ns) +% ADD_EV_TO_DMARGINAL 'pump up' observed nodes back to their original size. +% fmarginal = add_ev_to_dmarginal(fmarginal, evidence, ns) +% +% We introduce 0s into the array in positions which are incompatible with the evidence. + +dom = fmarginal.domain; +odom = dom(~isemptycell(evidence(dom))); +vals = cat(1, evidence{odom}); +index = mk_multi_index(length(dom), find_equiv_posns(odom, dom), vals); +T = 0*myones(ns(dom)); +ens = ns(:)'; +ens(odom) = 1; +T(index{:}) = myreshape(fmarginal.T, ens(dom)); +fmarginal.T = T;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/add_evidence_to_gmarginal.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,78 @@ +function fullm = add_evidence_to_gmarginal(fmarginal, evidence, ns, cnodes) +% ADD_EVIDENCE_TO_GMARGINAL 'pump up' observed nodes back to their original size. +% function fullm = add_evidence_to_gmarginal(fmarginal, evidence, ns, cnodes) +% +% We introduce 0s into the array in positions which are incompatible with the evidence. +% for both discrete and continuous nodes. +% +% See also add_ev_to_dmarginal + +dom = fmarginal.domain; +fullm.domain = fmarginal.domain; + +% Find out which values of the discrete parents (if any) are compatible with +% the discrete evidence (if any). +dnodes = mysetdiff(1:length(ns), cnodes); +ddom = myintersect(dom, dnodes); +cdom = myintersect(dom, cnodes); +odom = dom(~isemptycell(evidence(dom))); +hdom = dom(isemptycell(evidence(dom))); + +% Find the entries in the big table that are compatible with the discrete evidence. +% (We will put the probabilities from the small inferred table into these positions.) +% We could use add_ev_to_dmarginal to do this. +dobs = myintersect(ddom, odom); +dvals = cat(1, evidence{dobs}); +ens = ns; % effective node sizes +ens(dobs) = 1; +S = prod(ens(ddom)); +subs = ind2subv(ens(ddom), 1:S); +mask = find_equiv_posns(dobs, ddom); +%subs(mask) = dvals; % bug fix by P. Brutti +for i=1:length(mask), + subs(:,mask(i)) = dvals(i); +end +supportedQs = subv2ind(ns(ddom), subs); + +if isempty(ddom) + Qarity = 1; +else + Qarity = prod(ns(ddom)); +end +fullm.T = zeros(Qarity, 1); +fullm.T(supportedQs) = fmarginal.T(:); +fullm.T = myreshape(fullm.T, ns(ddom)); + + +if isempty(cdom) + fullm.mu = []; + fullm.sigma = []; + return; +end + +% Now put the hidden cts parts into their right blocks, +% leaving the observed cts parts as 0. +cobs = myintersect(cdom, odom); +chid = myintersect(cdom, hdom); +cvals = cat(1, evidence{cobs}); +n = sum(ns(cdom)); +fullm.mu = zeros(n,Qarity); +fullm.Sigma = zeros(n,n,Qarity); + +if ~isempty(chid) + chid_blocks = block(find_equiv_posns(chid, cdom), ns(cdom)); +end +if ~isempty(cobs) + cobs_blocks = block(find_equiv_posns(cobs, cdom), ns(cdom)); +end + +for i=1:length(supportedQs) + Q = supportedQs(i); + if ~isempty(chid) + fullm.mu(chid_blocks, Q) = fmarginal.mu(:, i); + fullm.Sigma(chid_blocks, chid_blocks, Q) = fmarginal.Sigma(:,:,i); + end + if ~isempty(cobs) + fullm.mu(cobs_blocks, Q) = cvals(:); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/bnet_to_fgraph.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +function fg = bnet_to_fgraph(bnet) +% BNET_TO_FGRAPH Convert a Bayes net to a factor graph +% fg = bnet_to_fgraph(bnet) +% +% We create one factor per family, whose kernel is the CPD + +nnodes = length(bnet.dag); +G = zeros(nnodes, nnodes); +for i=1:nnodes + G(family(bnet.dag, i), i) = 1; +end + +fg = mk_fgraph(G, bnet.node_sizes, bnet.CPD, 'equiv_class', bnet.equiv_class, 'discrete', bnet.dnodes); + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/compute_fwd_interface.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function int = compute_fwd_interface(intra, inter) +% COMPUTE_FWD_INTERFACE Compute nodes with children in the next slice +% function int = compute_fwd_interface(intra, inter) + +int = []; +ss = length(intra); +for u=1:ss + if any(inter(u,:)) + int = [int u]; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/compute_interface_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,42 @@ +function [interface, persist, transient] = compute_interface_nodes(intra, inter) +% COMPUTE_INTERFACE_NODES Find the nodes in a DBN that represent a sufficient statistic +% [interface, persist, transient] = compute_interface_nodes(intra, inter) +% +% The interface nodes are all those that has an incoming temporal arc, +% or which are parents of such nodes. +% If the parents are in the previous slice, this just means they have an +% outgoing temporal arc. +% (The parents of nodes with incoming temporal arcs are needed +% because moralization will bring them into the clique.) +% +% The persisent nodes are all those that have one or more incoming temporal arc. +% The transient nodes are all the non-persistent. +% +% See U. Kjaerulff, "dHugin: A computational system for dynamic +% time-sliced Bayesian networks", Intl. J. Forecasting (11) 89-111, 1995 + +n = length(intra); +interface = []; +persist = []; +% any nodes with incoming arcs +for u=1:n + if any(inter(:,u)) + interface = [interface u]; + persist = [persist u]; + end +end +% Any nodes which are parents of nodes with incoming arcs +for u=1:n + cs = children(intra, u); + if any(inter(:, cs)) + interface = [interface u]; + end + %cs = children(inter, u); + % if ~isempty(myintersect(cs, persist)) + % interface = [interface u]; + %end +end +interface = unique(interface); +persist = unique(persist); +transient = mysetdiff(1:n, persist); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/compute_joint_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,17 @@ +function [jpot, loglik] = compute_joint_pot(bnet, nodes, evidence, domain) +% COMPUTE_JOINT_POT Compute the global joint potential of a Bayes net +% function jpot = compute_joint_pot(bnet, nodes, evidence, domain) + +if nargin < 4, domain = nodes; end + +onodes = find(~isemptycell(evidence)); +pot_type = determine_pot_type(bnet, onodes, domain); + +jpot = mk_initial_pot(pot_type, domain, bnet.node_sizes, bnet.cnodes, onodes); +for i=nodes(:)' + e = bnet.equiv_class(i); + fam = family(bnet.dag, i); + pot = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), evidence); + jpot = multiply_by_pot(jpot, pot); +end +%[jpot, loglik] = normalize_pot(jpot); % causes errors in asia_dt1 etc
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/compute_minimal_interface.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,25 @@ +function clqs = compute_minimal_interface(intra, inter) + +int = compute_fwd_interface(intra, inter); +ss = length(intra); +Z = zeros(ss); +dag = [intra inter; + Z intra]; +G = moralize(dag); +intra2 = G(1:ss,1:ss); +inter2 = G(1:ss,(1:ss)+ss); +G = unroll_dbn_topology(intra2, inter2, ss); +T = ss; +last_slice = (1:ss) + (T-1)*ss; +G = (G + G')/2; % mk symmetric +G2 = (expm(full(G)) > 0); % closure of graph +G3 = G2(last_slice, last_slice); +[c,v] = scc(G3); % connected components +ncomp = size(v,1); +clqs = cell(1,ncomp); +for i=1:ncomp + ndx = find(v(i,:)>0); + clqs{i} = v(i,ndx); + clqs{i} = myintersect(clqs{i}, int); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/convert_dbn_CPDs_to_pots.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,30 @@ +function CPDpot = convert_dbn_CPDs_to_pots(bnet, evidence, pot_type, softCPDpot) +% CONVERT_DBN_CPDS_TO_POTS Convert CPDs of (possibly instantiated) DBN nodes to potentials +% CPDpot = convert_dbn_CPDs_to_pots(bnet, evidence, pot_type, softCPDpot) +% +% CPDpot{n,t} is a potential containing P(n,t|pa(n,t), ev) +% softCPDpot{n,t} is a potential containing P(n,t|pa(n,t), ev) insted of using n's CPD + +[ss T] = size(evidence); + +if nargin < 4, softCPDpot = cell(ss,T); end +CPDpot = softCPDpot; + +% Convert CPDs of instantiated nodes to potential form +t = 1; +for n=1:ss + fam = family(bnet.dag, n); + e = bnet.equiv_class(n, 1); + if isempty(softCPDpot{n,t}) + CPDpot{n,t} = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), evidence(:,1)); + end +end +for n=1:ss + fam = family(bnet.dag, n, 2); + e = bnet.equiv_class(n, 2); + for t=2:T + if isempty(softCPDpot{n,t}) + CPDpot{n,t} = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), evidence(:,t-1:t)); + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/convert_dbn_CPDs_to_tables.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,201 @@ +function CPDpot = convert_dbn_CPDs_to_tables(bnet, evidence) +% CONVERT_DBN_CPDS_TO_TABLES Convert CPDs of (possibly instantiated) DBN nodes to tables +% CPDpot = convert_dbn_CPDs_to_tables(bnet, evidence) +% +% CPDpot{n,t} is a table containing P(n,t|pa(n,t), ev) +% All hidden nodes are assumed to be discrete. +% We assume the observed nodes are the same in every slice. +% +% Evaluating the conditional likelihood of long evidence sequences can be very slow, +% so we take pains to vectorize where possible. + +[ss T] = size(evidence); +%obs_bitv = ~isemptycell(evidence(:)); +obs_bitv = zeros(1, 2*ss); +obs_bitv(bnet.observed) = 1; +obs_bitv(bnet.observed+ss) = 1; + +ns = bnet.node_sizes(:); +CPDpot = cell(ss,T); + +for n=1:ss + % slice 1 + t = 1; + ps = parents(bnet.dag, n); + e = bnet.equiv_class(n, 1); + if ~any(obs_bitv(ps)) + CPDpot{n,t} = convert_CPD_to_table_hidden_ps(bnet.CPD{e}, evidence{n,t}); + else + CPDpot{n,t} = convert_to_table(bnet.CPD{e}, [ps n], evidence(:,1)); + end + +% special cases: c=child, p=parents, d=discrete, h=hidden, 1sl=1slice +% if c=h=1 then c=d=1, since hidden nodes must be discrete +% c=h c=d p=h p=d 1sl method +% --------------------------- +% 1 1 1 1 - replicate CPT +% - 1 - 1 - evaluate CPT on evidence * +% 0 1 1 1 1 dhmm +% 0 0 1 1 1 ghmm +% other loop +% +% * = any subset of the domain may be observed + +% Example where all of the special cases occur - a hierarchical HMM +% where the top layer (G) and leaves (Y) are observed and +% all nodes are discrete except Y. +% (O turns on if Y is an outlier) + +% G ---------> G +% | | +% v v +% S --------> S +% | | +% v v +% Y Y +% ^ ^ +% | | +% O O + +% Evaluating P(yt|St,Ot) is the ghmm case +% Evaluating P(St|S(t-1),gt) is the eval CPT case +% Evaluating P(gt|g(t-1) is the eval CPT case (hdom = []) +% Evaluating P(Ot) is the replicated CPT case + +% Cts parents (e.g., inputs) would require an additional special case for speed + + + % slices 2..T + [ss T] = size(evidence); + self = n+ss; + ps = parents(bnet.dag, self); + e = bnet.equiv_class(n, 2); + + if 1 + debug = 0; + hidden_child = ~obs_bitv(n); + discrete_child = myismember(n, bnet.dnodes); + hidden_ps = all(~obs_bitv(ps)); + discrete_ps = mysubset(ps, bnet.dnodes); + parents_in_same_slice = all(ps > ss); + + if hidden_child & discrete_child & hidden_ps & discrete_ps + CPDpot = helper_repl(bnet, evidence, n, CPDpot, obs_bitv, debug); + elseif discrete_child & discrete_ps + CPDpot = helper_eval(bnet, evidence, n, CPDpot, obs_bitv, debug); + elseif discrete_child & hidden_ps & discrete_ps & parents_in_same_slice + CPDpot = helper_dhmm(bnet, evidence, n, CPDpot, obs_bitv, debug); + elseif ~discrete_child & hidden_ps & discrete_ps & parents_in_same_slice + CPDpot = helper_ghmm(bnet, evidence, n, CPDpot, obs_bitv, debug); + else + if debug, fprintf('node %d, slow\n', n); end + for t=2:T + CPDpot{n,t} = convert_to_table(bnet.CPD{e}, [ps self], evidence(:,t-1:t)); + end + end + end + + if 0 + for t=2:T + CPDpot2{n,t} = convert_to_table(bnet.CPD{e}, [ps self], evidence(:,t-1:t)); + if ~approxeq(CPDpot{n,t}, CPDpot2{n,t}) + fprintf('CPDpot n=%d, t=%d\n',n,t); + keyboard + end + end + end + + +end + + + + +%%%%%%% +function CPDpot = helper_repl(bnet, evidence, n, CPDpot, obs_bitv, debug) + +[ss T] = size(evidence); +if debug, fprintf('node %d, repl\n', n); end +e = bnet.equiv_class(n, 2); +CPT = convert_CPD_to_table_hidden_ps(bnet.CPD{e}, []); +CPDpot(n,2:T) = num2cell(repmat(CPT, [1 1 T-1]), [1 2]); + + + +%%%%%%% +function CPDpot = helper_eval(bnet, evidence, n, CPDpot, obs_bitv, debug) + +[ss T] = size(evidence); +self = n+ss; +ps = parents(bnet.dag, self); +e = bnet.equiv_class(n, 2); +ns = bnet.node_sizes(:); +% Example: given CPT(p1, p2, p3, p4, c), where p1,p3 are observed +% we create CPT([p2 p4 c], [p1 p3]). +% We then convert all observed p1,p3 into indices ndx +% and return CPT(:, ndx) +CPT = CPD_to_CPT(bnet.CPD{e}); +domain = [ps self]; +% if dom is [3 7 8] and 3,8 are observed, odom_rel = [1 3], hdom_rel = 2, +% odom = [3 8], hdom = 7 +odom_rel = find(obs_bitv(domain)); +hdom_rel = find(~obs_bitv(domain)); +odom = domain(odom_rel); +hdom = domain(hdom_rel); +if isempty(hdom) + CPT = CPT(:); +else + CPT = permute(CPT, [hdom_rel odom_rel]); + CPT = reshape(CPT, prod(ns(hdom)), prod(ns(odom))); +end +parents_in_same_slice = all(ps > ss); +if parents_in_same_slice + if debug, fprintf('node %d eval 1 slice\n', n); end + data = cell2num(evidence(odom-ss,2:T)); %data(i,t) = val of i'th obs parent at t+1 +else + if debug, fprintf('node %d eval 2 slice\n', n); end + % there's probably a way of vectorizing this... + data = zeros(length(odom), T-1); + for t=2:T + ev = evidence(:,t-1:t); + ev = ev(:); + ev2 = ev(odom); + data(:,t-1) = cat(1, ev2{:}); + %data(:,t-1) = cell2num(ev2); + end +end +ndx = subv2ind(ns(odom), data'); % ndx(t) encodes data(:,t) +if isempty(hdom) + CPDpot(n,2:T) = num2cell(CPT(ndx)); % a cell array of floats +else + CPDpot(n,2:T) = num2cell(CPT(:, ndx), 1); % a cell array of column vectors +end + +%%%%%%% +function CPDpot = helper_dhmm(bnet, evidence, n, CPDpot, obs_bitv, debug) + +if debug, fprintf('node %d, dhmm\n', n); end +[ss T] = size(evidence); +self = n+ss; +ps = parents(bnet.dag, self); +e = bnet.equiv_class(n, 2); +ns = bnet.node_sizes(:); +CPT = CPD_to_CPT(bnet.CPD{e}); +CPT = reshape(CPT, [prod(ns(ps)) ns(self)]); % what if no parents? +%obslik = mk_dhmm_obs_lik(cell2num(evidence(n,2:T)), CPT); +obslik = eval_pdf_cond_multinomial(cell2num(evidence(n,2:T)), CPT); +CPDpot(n,2:T) = num2cell(obslik, 1); + + +%%%%%%% +function CPDpot = helper_ghmm(bnet, evidence, n, CPDpot, obs_bitv, debug) + +if debug, fprintf('node %d, ghmm\n', n); end +[ss T] = size(evidence); +e = bnet.equiv_class(n, 2); +S = struct(bnet.CPD{e}); +ev2 = cell2num(evidence(n,2:T)); +%obslik = mk_ghmm_obs_lik(ev2, S.mean, S.cov); +obslik = eval_pdf_cond_gauss(ev2, S.mean, S.cov); +CPDpot(n,2:T) = num2cell(obslik, 1); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/convert_dbn_CPDs_to_tables1.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,162 @@ +function CPDpot = convert_dbn_CPDs_to_tables1(bnet, evidence) +% CONVERT_DBN_CPDS_TO_TABLES Convert CPDs of (possibly instantiated) DBN nodes to tables +% CPDpot = convert_dbn_CPDs_to_tables(bnet, evidence) +% +% CPDpot{n,t} is a table containing P(n,t|pa(n,t), ev) +% All hidden nodes are assumed to be discrete +% We assume the observed nodes are the same in every slice +% +% Evaluating the conditional likelihood of the evidence can be very slow, +% so we take pains to vectorize where possible, i.e., we try to avoid +% calling convert_to_table + +[ss T] = size(evidence); +%obs_bitv = ~isemptycell(evidence(:)); +obs_bitv = zeros(1, 2*ss); +obs_bitv(bnet.observed) = 1; +obs_bitv(bnet.observed+ss) = 1; + +ns = bnet.node_sizes(:); +CPDpot = cell(ss,T); + +for n=1:ss + % slice 1 + t = 1; + ps = parents(bnet.dag, n); + e = bnet.equiv_class(n, 1); + if ~any(obs_bitv(ps)) + CPDpot{n,t} = convert_CPD_to_table_hidden_ps(bnet.CPD{e}, evidence{n,t}); + else + CPDpot{n,t} = convert_to_table(bnet.CPD{e}, [ps n], evidence(:,1)); + end + + % slices 2..T + debug = 1; + if ~obs_bitv(n) + CPDpot = helper_hidden_child(bnet, evidence, n, CPDpot, obs_bitv, debug); + else + CPDpot = helper_obs_child(bnet, evidence, n, CPDpot, obs_bitv, debug); + end +end + +if 0 +CPDpot2 = convert_dbn_CPDs_to_tables_slow(bnet, evidence); +for t=1:T + for n=1:ss + if ~approxeq(CPDpot{n,t}, CPDpot2{n,t}) + fprintf('CPDpot n=%d, t=%d\n',n,t); + keyboard + end + end +end +end + + +% special cases: c=child, p=parents, d=discrete, h=hidden, 1=1slice +% if c=h=1 then c=d=1, since hidden nodes must be discrete +% c=h c=d p=h p=d p=1 method +% --------------------------- +% 1 1 1 1 - replicate CPT +% 0 1 1 1 1 dhmm +% 0 0 1 1 1 ghmm +% - 1 - 1 - evaluate CPT on evidence +% other loop + +%%%%%%% +function CPDpot = helper_hidden_child(bnet, evidence, n, CPDpot, obs_bitv, debug) + +[ss T] = size(evidence); +self = n+ss; +ps = parents(bnet.dag, self); +e = bnet.equiv_class(n, 2); +ns = bnet.node_sizes(:); +if ~any(obs_bitv(ps)) % all parents are hidden (hence discrete) + if debug, fprintf('node %d is hidden, all ps are hidden\n', n); end + if myismember(n, bnet.dnodes) + %CPT = CPD_to_CPT(bnet.CPD{e}); + %CPT = reshape(CPT, [prod(ns(ps)) ns(self)]); + CPT = convert_CPD_to_table_hidden_ps(bnet.CPD{e}, []); + CPDpot(n,2:T) = num2cell(repmat(CPT, [1 1 T-1]), [1 2]); + else + error(['hidden cts node disallowed']) + end +else % some parents are observed - slow + if mysubset(ps, bnet.dnodes) % all parents are discrete + % given CPT(p1, p2, p3, p4, c), where p1,p3 are observed + % we create CPT([p2 p4 c], [p1 p3]). + % We then convert all observed p1,p3 into indices ndx + % and return CPT(:, ndx) + CPT = CPD_to_CPT(bnet.CPD{e}); + domain = [ps self]; + % if dom is [3 7 8] and 3,8 are observed, odom_rel = [1 3], hdom_rel = 2, + % odom = [3 8], hdom = 7 + odom_rel = find(obs_bitv(domain)); + hdom_rel = find(~obs_bitv(domain)); + odom = domain(odom_rel); + hdom = domain(hdom_rel); + CPT = permute(CPT, [hdom_rel odom_rel]); + CPT = reshape(CPT, prod(ns(hdom)), prod(ns(odom))); + parents_in_same_slice = all(ps > ss); + if parents_in_same_slice + if debug, fprintf('node %d is hidden, some ps are obs, all ps discrete, 1 slice\n', n); end + data = cell2num(evidence(odom-ss,2:T)); %data(i,t) = val of i'th obs parent at t+1 + else + if debug, fprintf('node %d is hidden, some ps are obs, all ps discrete, 2 slice\n', n); end + data = zeros(length(odom), T-1); + for t=2:T + ev = evidence(:,t-1:t); + data(:,t-1) = cell2num(ev(odom)); + end + end + ndx = subv2ind(ns(odom), data'); % ndx(t) encodes data(:,t) + CPDpot(n,2:T) = num2cell(CPT(:, ndx), [1 2]); + else % some parents are cts - v slow + if debug, fprintf('node %d is hidden, some ps are obs, some ps cts\n', n); end + for t=2:T + CPDpot{n,t} = convert_to_table(bnet.CPD{e}, [ps self], evidence(:,t-1:t)); + end + end +end + +%%%%%%% +function CPDpot = helper_obs_child(bnet, evidence, n, CPDpot, obs_bitv, debug) + +[ss T] = size(evidence); +self = n+ss; +ps = parents(bnet.dag, self); +e = bnet.equiv_class(n, 2); +ns = bnet.node_sizes(:); +if ~any(obs_bitv(ps)) % all parents are hidden + parents_in_same_slice = all(ps > ss); + if parents_in_same_slice + if debug, fprintf('node %d is obs, all ps are hidden, 1 slice\n', n); end + ps1 = ps - ss; + if myismember(n, bnet.dnodes) + CPT = CPD_to_CPT(bnet.CPD{e}); + CPT = reshape(CPT, [prod(ns(ps)) ns(self)]); % what if no parents? + obslik = eval_pdf_cond_multinomial(cell2num(evidence(n,2:T)), CPT); + CPDpot(n,2:T) = num2cell(obslik, 1); + else + S = struct(bnet.CPD{e}); + obslik = eval_pdf_cond_gauss(cell2num(evidence(n,2:T)), S.mean, S.cov); + CPDpot(n,2:T) = num2cell(obslik, 1); + end + else % parents span 2 slices - slow + if debug, fprintf('node %d is obs, all ps are hidden , 2 slice\n', n); end + for t=2:T + CPDpot{n,t} = convert_to_table(bnet.CPD{e}, [ps self], evidence(:,t-1:t)); + end + end +else + if isempty(ps) % observed root + if debug, fprintf('node %d is obs, no ps\n', n); end + CPT = CPD_to_CPT(bnet.CPD{e}); + data = cell2num(evidence(n,2:T)); + CPDpot(n,2:T) = CPT(data); + else % some parents are observed - slow + if debug, fprintf('node %d is obs, some ps are obs\n', n); end + for t=2:T + CPDpot{n,t} = convert_to_table(bnet.CPD{e}, [ps self], evidence(:,t-1:t)); + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/convert_dbn_CPDs_to_tables_slow.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,41 @@ +function CPDpot = convert_dbn_CPDs_to_tables_slow(bnet, evidence) +% CONVERT_DBN_CPDS_TO_TABLES_SLOW Convert CPDs of (possibly instantiated) DBN nodes to tables +% CPDpot = convert_dbn_CPDs_to_tables_slow(bnet, evidence) +% +% CPDpot{n,t} is a table containing P(n,t|pa(n,t), ev) +% All hidden nodes are assumed to be discrete +% +% Non-vectorized method; this is less efficient for long sequences of observed Gaussian +% nodes, because of the (unnecessary) repeated matrix inversion. + +obs_bitv = ~isemptycell(evidence(:)); +[ss T] = size(evidence); +ns = bnet.node_sizes(:); + +CPDpot = cell(ss,T); + +t = 1; +for n=1:ss + %ps = engine.bnet_parents{n}; + ps = parents(bnet.dag, n); + e = bnet.equiv_class(n, 1); + if ~any(obs_bitv(ps)) + CPDpot{n,t} = convert_CPD_to_table_hidden_ps(bnet.CPD{e}, evidence{n,t}); + else + CPDpot{n,t} = convert_to_table(bnet.CPD{e}, [ps n], evidence(:,1)); + end +end +for t=2:T + for n=1:ss + self = n+ss; + ps = parents(bnet.dag, self); + e = bnet.equiv_class(n, 2); + if ~any(obs_bitv(ps)) + CPDpot{n,t} = convert_CPD_to_table_hidden_ps(bnet.CPD{e}, evidence{n,t}); + else + CPDpot{n,t} = convert_to_table(bnet.CPD{e}, [ps self], evidence(:,t-1:t)); + end + end +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/dbn_to_bnet.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function bnet = dbn_to_bnet(dbn, T) +% DBN_TO_BNET Convert a DBN to a static network by unroll for T slices +% bnet = dbn_to_bnet(dbn, T) + +ss = length(dbn.intra); +eclass = [dbn.equiv_class(:,1) repmat(dbn.equiv_class(:,2), 1, T-1)]; +dnodes = unroll_set(dbn.dnodes_slice, ss, T); +ns = repmat(dbn.node_sizes_slice(:), 1, T); +dag = unroll_dbn_topology(dbn.intra, dbn.inter, T, dbn.intra1); +onodes = unroll_set(dbn.observed(:), ss, T); +bnet = mk_bnet(dag, ns(:), 'discrete', dnodes(:), 'equiv_class', eclass(:), 'observed', onodes(:)); +bnet.CPD = dbn.CPD; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/dbn_to_hmm.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,81 @@ +function [startprob, transprob, obsprob] = dbn_to_hmm(bnet) +% DBN_TO_HMM % Convert DBN params to HMM params +% [startprob, transprob, obsprob] = dbn_to_hmm(bnet, onodes) +% startprob(i) +% transprob(i,j) +% obsprob{k}.big_CPT(i,o) if k'th observed node is discrete +% obsprob{k}.big_mu(:,i), .big_Sigma(:,:,i) if k'th observed node is Gaussian +% Big means the domain contains all the hidden discrete nodes, not just the parents. + +% Called by constructor and by update_engine + +ss = length(bnet.intra); +onodes = bnet.observed; +hnodes = mysetdiff(1:ss, onodes); +evidence = cell(ss, 2); +ns = bnet.node_sizes(:); +Qh = prod(ns(hnodes)); +tmp = dpot_to_table(compute_joint_pot(bnet, hnodes, evidence)); +startprob = reshape(tmp, Qh, 1); + +tmp = dpot_to_table(compute_joint_pot(bnet, hnodes+ss, evidence, [hnodes hnodes+ss])); +transprob = mk_stochastic(reshape(tmp, Qh, Qh)); + +% P(o|ps) is used by mk_hmm_obs_lik_vec for a single time slice +% P(o|h) (the big version), where h = all hidden nodes, is used by enter_evidence + +obsprob = cell(1, length(onodes)); +for i=1:length(onodes) + o = onodes(i); + if bnet.auto_regressive(o) + % We assume the parents of this node are all the hidden nodes in the slice, + % so the params already are "big". Also, we assume we regress only on our old selves. + % slice 1 + e = bnet.equiv_class(o); + CPD = struct(bnet.CPD{e}); + O = ns(o); + ps = bnet.parents{o}; + Qps = prod(ns(ps)); + obsprob{i}.big_mu0 = reshape(CPD.mean, [O Qps]); + obsprob{i}.big_Sigma0 = reshape(CPD.cov, [O O Qps]); + + % slice t>1 + e = bnet.equiv_class(o+ss); + CPD = struct(bnet.CPD{e}); + O = ns(o); + dps = mysetdiff(bnet.parents{o+ss}, o); + Qdps = prod(ns(dps)); + obsprob{i}.big_mu = reshape(CPD.mean, [O Qdps]); + obsprob{i}.big_Sigma = reshape(CPD.cov, [O O Qdps]); + obsprob{i}.big_W = reshape(CPD.weights, [O O Qdps]); + else + e = bnet.equiv_class(o+ss); + CPD = struct(bnet.CPD{e}); + O = ns(o); + ps = bnet.parents{o}; + Qps = prod(ns(ps)); + % We make a big potential, replicating the params if necessary + % e.g., for a 2 chain coupled HMM, mu(:,Q1) becomes mu(:,Q1,Q2) + bigpot = pot_to_marginal(compute_joint_pot(bnet, onodes(i), evidence, [hnodes onodes(i)])); + + if myismember(o, bnet.dnodes) + obsprob{i}.CPT = reshape(CPD.CPT, [Qps O]); + obsprob{i}.big_CPT = reshape(bigpot.T, Qh, O); + else + obsprob{i}.big_mu = bigpot.mu; + obsprob{i}.big_Sigma = bigpot.Sigma; + + if 1 + obsprob{i}.mu = reshape(CPD.mean, [O Qps]); + C = reshape(CPD.cov, [O O Qps]); + obsprob{i}.Sigma = C; + d = size(obsprob{i}.mu, 1); + for j=1:Qps + obsprob{i}.inv_Sigma(:,:,j) = inv(C(:,:,j)); + obsprob{i}.denom(j) = (2*pi)^(d/2)*sqrt(abs(det(C(:,:,j)))); + end + end + + end % if discrete + end % if ar +end % for
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/determine_elim_constraints.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,43 @@ +function partial_order = determine_elim_constraints(bnet, onodes) +% DETERMINE_ELIM_CONSTRAINTS Determine what the constraints are (if any) on the elimination ordering. +% partial_order = determine_elim_constraints(bnet, onodes) +% +% A graph with different kinds of nodes (e.g., discrete and cts, or decision and rnd) is called marked. +% A strong root is guaranteed to exist if the marked graph is triangulated and does not have any paths of +% the form discrete -> cts -> discrete. In general we need to add extra edges to +% the moral graph to ensure this (see example in Lauritzen (1992) fig 3b). +% However, a simpler sufficient condition is to eliminate all the cts nodes before the discrete ones, +% because then, as we move from the leaves to the root, the cts nodes get marginalized away +% and we are left with purely discrete cliques. +% +% partial_order(i,j)=1 if we must marginalize j *before* i +% (so i will be nearer the strong root). +% If the hidden nodes are either all discrete or all cts, we set partial_order = []. +% +% For details, see +% - Jensen, Jensen and Dittmer, "From influence diagrams to junction trees", UAI 94. +% - Lauritzen, "Propgation of probabilities, means, and variances in mixed graphical +% association models", JASA 87(420):1098--1108, 1992. +% - K. Olesen, "Causal probabilistic networks with both discrete and continuous variables", +% IEEE Pami 15(3), 1993 + + +n = length(bnet.dag); +pot_type = determine_pot_type(bnet, onodes); +if (pot_type == 'd') | (pot_type == 'g') + partial_order = []; + return; +end + + +partial_order = sparse(n,n); +partial_order(bnet.dnodes, bnet.cnodes) = 1; + +% Integrate out cts nodes before their discrete parents - see Olesen (1993) p9 +% This method gives the wrong results on cg1.m! +if 0 +for i=bnet.cnodes(:)' + dps = myintersect(parents(bnet.dag, i), bnet.dnodes); + partial_order(dps, i)=1; +end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/dispcpt.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +function display_CPT(CPT) + +n = ndims(CPT); +parents_size = size(CPT); +parents_size = parents_size(1:end-1); +child_size = size(CPT,n); +c = 1; +for i=1:prod(parents_size) + parent_inst = ind2subv(parents_size, i); + fprintf(1, '%d ', parent_inst); + fprintf(1, ': '); + index = num2cell([parent_inst 1]); + index{n} = ':'; + fprintf(1, '%6.4f ', CPT(index{:})); + fprintf(1, '\n'); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/do_intervention.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function bnet = mutilate_bnet(bnet, nodes, vals) +% MUTILATE_BNET Clamp nodes to specific values (perform a surgical intervention) +% bnet = mutilate_bnet(bnet, nodes, vals) +% +% We make all the clamped nodes roots. + +ns = bnet.node_sizes; +for i=1:length(nodes) + X = nodes(i); + x = vals(i); + bnet.dag(:,X) = 0; + bnet.CPD{X} = root_CPD(bnet, X, x); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/dsep.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +function sep = dsep(X, Y, S, G) +% DSEP Is X indep Y given S wrt DAG G? +% sep = dsep(X, Y, S, G) +% +% Instead of using the Bayes-Ball criterion, we see if S separates X and Y +% in the moralized ancestral graph. + +conn = reachability_graph(G); +M = myunion(myunion(X, Y), S); +[A,junk] = find(conn(:, M)); +A = unique(A); +A = myunion(A, M); +GM = moralize(G(A,A)); +%sep = graph_separated(GM, X, Y, S); +sep = graph_separated(GM, find_equiv_posns(X,A), find_equiv_posns(Y,A), find_equiv_posns(S,A));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/dsep_test.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ + +% Cowell et al p72 +G = zeros(10); +G(1,2)=1; +G(2,3)=1; +G(3,7)=1; +G(4,[5 8])=1; +G(5,6)=1; +G(6,7)=1; +G(7,[9 10])=1; +G(8,9)=1; + +dsep(1, 4, [5 7], G) +dsep(1, 4, [7], G) +dsep(1, 4, [10 5], G)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/enumerate_scenarios.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,21 @@ +function [scenarios, log_probs] = enumerate_scenarios(bnet, evidence) +% ENUMERATE_SCENARIOS Enumerate all assignments, and return the prob. of the non-zeros ones +% function [scenarios, log_probs] = enumerate_scenarios(bnet, evidence) + +assert(isempty(bnet.cnodes)); +n = length(bnet.dag); +observed = ~isemptycell(evidence); +vals = cat(1,evidence{observed}); +vals = vals(:)'; +ns = bnet.node_sizes; + +log_probs = []; +scenarios = []; +for i=1:prod(ns) + inst = ind2subv(ns, i); % i'th instantiation + if isempty(vals) | inst(observed) == vals % agrees with evidence + ll = log_lik_complete(bnet, num2cell(inst(:))); + log_probs = [log_probs ll]; + scenarios = [scenarios(:)' inst]; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/fgraph_to_bnet.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,30 @@ +function bnet = fgraph_to_bnet(fg) +% FGRAPH_TO_BNET Convert a factor graph to a Bayes net +% bnet = fgraph_to_bnet(fg) +% +% We assume all factors are tabular_CPD. +% We create 1 dummy observed node for every factor. + +N = fg.nvars + fg.nfactors; +vnodes = 1:fg.nvars; +fnodes = fg.nvars+1:N; +dag = zeros(N); +for x=1:fg.nvars + dag(x, fnodes(fg.dep{x})) = 1; +end +ns = [fg.node_sizes ones(1, fg.nfactors)]; +discrete = [fg.dnodes fnodes]; +bnet = mk_bnet(dag, ns, 'discrete', discrete); +for x=1:fg.nvars + bnet.CPD{x} = tabular_CPD(bnet, x, 'CPT', 'unif'); +end +ev = cell(1, fg.nvars); % no evidence +for i=1:fg.nfactors + f = fnodes(i); + e = fg.equiv_class(i); + pot = convert_to_pot(fg.factors{e}, 'd', fg.dom{i}, ev); + m = pot_to_marginal(pot); + bnet.CPD{f} = tabular_CPD(bnet, f, 'CPT', m.T); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/hodbn_to_bnet.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,21 @@ +function bnet = hodbn_to_bnet(dbn, T) +% DBN_TO_BNET Convert a DBN to a static network by unroll for T slices +% bnet = dbn_to_bnet(dbn, T) +ss = length(dbn.intra); +[row,order] = size(dbn.equiv_class); +eclass = []; +for i = 1:min(order,T) + eclass = [eclass ; dbn.equiv_class(:,i)]; +end +if T > order + eclass = [eclass ; repmat(dbn.equiv_class(:,order),T-order,1)]; +end + +dnodes = unroll_set(dbn.dnodes_slice, ss, T); +ns = repmat(dbn.node_sizes_slice(:), 1, T); +dag = unroll_higher_order_topology(dbn.intra, dbn.inter, T, dbn.intra1); +onodes = unroll_set(dbn.observed(:), ss, T); +bnet = mk_bnet(dag, ns(:), 'discrete', dnodes(:), 'equiv_class', eclass(:), 'observed', onodes(:)); +bnet.CPD = dbn.CPD; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/is_mnet.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,3 @@ +function m = is_mnet(model) + +m = isfield(model, 'markov_net');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/linear_gaussian_to_cpot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,43 @@ +function pot = linear_gaussian_to_cpot(mu, Sigma, W, domain, ns, cnodes, evidence) +% LINEAR_GAUSSIAN_TO_CPOT Convert a linear Gaussian CPD to a canonical potential. +% pot = linear_gaussian_to_cpot(mu, Sigma, W, domain, ns, cnodes, evidence) +% +% We include any cts evidence, but ignore any discrete evidence. +% (Use gaussian_CPD_params_given_dps to use discrete evidence to select mu, Sigma, W.) + +odom = domain(~isemptycell(evidence(domain))); +hdom = domain(isemptycell(evidence(domain))); +cobs = myintersect(cnodes, odom); +chid = myintersect(cnodes, hdom); +cvals = cat(1, evidence{cobs}); + +%[g,h,K] = gaussian_to_canonical(mu, Sigma, W); +Sinv = inv(Sigma); +g = -0.5*mu'*Sinv*mu + log(normal_coef(Sigma)); +if isempty(W) | (size(W,2)==0) % no cts parents + h = Sinv*mu; + K = Sinv; +else + h = [-W'*Sinv*mu; Sinv*mu]; + K = [W'*Sinv*W -W'*Sinv'; + -Sinv*W Sinv]; +end + +if ~isempty(cvals) + %[g, h, K] = enter_evidence_canonical(g, h, K, chid, cobs, cvals(:), ns); + [hx, hy, KXX, KXY, KYX, KYY] = partition_matrix_vec(h, K, chid, cobs, ns); + y = cvals(:); + g = g + hy'*y - 0.5*y'*KYY*y; + if length(hx)==0 % isempty(X) % i.e., we have instantiated everything away + h = []; + K = []; + else + h = hx - KXY*y; + K = KXX; + end +end + +ns(odom) = 0; +pot = cpot(domain, ns(domain), g, h, K); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/log_lik_complete.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,30 @@ +function L = log_lik_complete(bnet, cases, clamped) +% LOG_LIK_COMPLETE Compute sum_m sum_i log P(x(i,m)| x(pi_i,m), theta_i) for a completely observed data set +% L = log_lik_complete(bnet, cases, clamped) +% +% If there is a missing data, you must use an inference engine. +% cases(i,m) is the value assigned to node i in case m. +% (If there are vector-valued nodes, cases should be a cell array.) +% clamped(i,m) = 1 if node i was set by intervention in case m (default: clamped = zeros) +% Clamped nodes contribute a factor of 1.0 to the likelihood. + +if iscell(cases), usecell = 1; else usecell = 0; end + +n = length(bnet.dag); +ncases = size(cases, 2); +if n ~= size(cases, 1) + error('data should be of size nnodes * ncases'); +end + +if nargin < 3, clamped = zeros(n,ncases); end + +L = 0; +for i=1:n + ps = parents(bnet.dag, i); + e = bnet.equiv_class(i); + u = find(clamped(i,:)==0); + ll = log_prob_node(bnet.CPD{e}, cases(i,u), cases(ps,u)); + if approxeq(exp(ll), 0), fprintf('node %d has very low likelihood\n'); end + L = L + ll; +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/log_marg_lik_complete.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,40 @@ +function L = log_marg_lik_complete(bnet, cases, clamped) +% LOG_MARG_LIK_COMPLETE Compute sum_m sum_i log P(x(i,m)| x(pi_i,m)) for a completely observed data set +% L = log_marg_lik_complete(bnet, cases, clamped) +% +% This differs from log_lik_complete because we integrate out the parameters. +% If there is a missing data, you must use an inference engine. +% cases(i,m) is the value assigned to node i in case m. +% (If there are vector-valued nodes, cases should be a cell array.) +% clamped(i,m) = 1 if node i was set by intervention in case m (default: clamped = zeros) +% Clamped nodes contribute a factor of 1.0 to the likelihood. +% +% If there is a single case, clamped is a list of the clamped nodes, not a bit vector. + +if iscell(cases), usecell = 1; else usecell = 0; end + +n = length(bnet.dag); +ncases = size(cases, 2); +if n ~= size(cases, 1) + error('data should be of size nnodes * ncases'); +end + +if ncases == 1 + if nargin < 3, clamped = []; end + clamp_set = clamped; + clamped = zeros(n,1); + clamped(clamp_set) = 1; +else + if nargin < 3, clamped = zeros(n,ncases); end +end + +L = 0; +for i=1:n + ps = parents(bnet.dag, i); + e = bnet.equiv_class(i); + u = find(clamped(i,:)==0); + L = L + log_marg_prob_node(bnet.CPD{e}, cases(i,u), cases(ps,u)); +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/mk_bnet.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,93 @@ +function bnet = mk_bnet(dag, node_sizes, varargin) +% MK_BNET Make a Bayesian network. +% +% BNET = MK_BNET(DAG, NODE_SIZES, ...) makes a graphical model with an arc from i to j iff DAG(i,j) = 1. +% Thus DAG is the adjacency matrix for a directed acyclic graph. +% The nodes are assumed to be in topological order. Use TOPOLOGICAL_SORT if necessary. +% +% node_sizes(i) is the number of values node i can take on, +% or the length of node i if i is a continuous-valued vector. +% node_sizes(i) = 1 if i is a utility node. +% +% Below are the names of optional arguments [and their default value in brackets]. +% Pass as 'PropertyName1', PropertyValue1, 'PropertyName2', PropertyValue2, ... +% +% discrete - the list of nodes which are discrete random variables [1:N] +% equiv_class - equiv_class(i)=j means node i gets its params from CPD{j} [1:N] +% observed - the list of nodes which will definitely be observed in every case [ [] ] +% 'names' - a cell array of strings to be associated with nodes 1:n [{}] +% This creates an associative array, so you write e.g. +% 'evidence(bnet.names{'bar'}) = 42' instead of 'evidence(2} = 42' +% assuming names = { 'foo', 'bar', ...}. +% +% e.g., bnet = mk_bnet(dag, ns, 'discrete', [1 3]) +% +% For backwards compatibility with BNT2, you can also specify the parameters in the following order +% bnet = mk_bnet(dag, node_sizes, discrete_nodes, equiv_class) + +n = length(dag); + +% default values for parameters +bnet.equiv_class = 1:n; +bnet.dnodes = 1:n; % discrete +bnet.observed = []; +bnet.names = {}; + +if nargin >= 3 + args = varargin; + nargs = length(args); + if ~isstr(args{1}) + if nargs >= 1, bnet.dnodes = args{1}; end + if nargs >= 2, bnet.equiv_class = args{2}; end + else + for i=1:2:nargs + switch args{i}, + case 'equiv_class', bnet.equiv_class = args{i+1}; + case 'discrete', bnet.dnodes = args{i+1}; + case 'observed', bnet.observed = args{i+1}; + case 'names', bnet.names = assocarray(args{i+1}, num2cell(1:n)); + otherwise, + error(['invalid argument name ' args{i}]); + end + end + end +end + +bnet.observed = sort(bnet.observed); % for comparing sets +bnet.hidden = mysetdiff(1:n, bnet.observed(:)'); +bnet.hidden_bitv = zeros(1,n); +bnet.hidden_bitv(bnet.hidden) = 1; +bnet.dag = dag; +bnet.node_sizes = node_sizes(:)'; + +bnet.cnodes = mysetdiff(1:n, bnet.dnodes); +% too many functions refer to cnodes to rename it to cts_nodes - +% We hope it won't be confused with chance nodes! + +bnet.parents = cell(1,n); +for i=1:n + bnet.parents{i} = parents(dag, i); +end + +E = max(bnet.equiv_class); +mem = cell(1,E); +for i=1:n + e = bnet.equiv_class(i); + mem{e} = [mem{e} i]; +end +bnet.members_of_equiv_class = mem; + +bnet.CPD = cell(1, E); + +bnet.rep_of_eclass = zeros(1,E); +for e=1:E + mems = bnet.members_of_equiv_class{e}; + bnet.rep_of_eclass(e) = mems(1); +end + +directed = 1; +if ~acyclic(dag,directed) + error('graph must be acyclic') +end + +bnet.order = topological_sort(bnet.dag);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/mk_dbn.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,133 @@ +function bnet = mk_dbn(intra, inter, node_sizes, varargin) +% MK_DBN Make a Dynamic Bayesian Network. +% +% BNET = MK_DBN(INTRA, INTER, NODE_SIZES, ...) makes a DBN with arcs +% from i in slice t to j in slice t iff intra(i,j) = 1, and +% from i in slice t to j in slice t+1 iff inter(i,j) = 1, +% for i,j in {1, 2, ..., n}, where n = num. nodes per slice, and t >= 1. +% node_sizes(i) is the number of values node i can take on. +% The nodes are assumed to be in topological order. Use TOPOLOGICAL_SORT if necessary. +% See also mk_bnet. +% +% Optional arguments [default in brackets] +% 'discrete' - list of discrete nodes [1:n] +% 'observed' - the list of nodes which will definitely be observed in every slice of every case [ [] ] +% 'eclass1' - equiv class for slice 1 [1:n] +% 'eclass2' - equiv class for slice 2 [tie nodes with equivalent parents to slice 1] +% equiv_class1(i) = j means node i in slice 1 gets its parameters from bnet.CPD{j}, +% i.e., nodes i and j have tied parameters. +% 'intra1' - topology of first slice, if different from others +% 'names' - a cell array of strings to be associated with nodes 1:n [{}] +% This creates an associative array, so you write e.g. +% 'evidence(bnet.names{'bar'}) = 42' instead of 'evidence(2} = 42' +% assuming names = { 'foo', 'bar', ...}. +% +% For backwards compatibility with BNT2, arguments can also be specified as follows +% bnet = mk_dbn(intra, inter, node_sizes, dnodes, eclass1, eclass2, intra1) +% +% After calling this function, you must specify the parameters (conditional probability +% distributions) using bnet.CPD{i} = gaussian_CPD(...) or tabular_CPD(...) etc. + + +n = length(intra); +ss = n; +bnet.nnodes_per_slice = ss; +bnet.intra = intra; +bnet.inter = inter; +bnet.intra1 = intra; +dag = zeros(2*n); +dag(1:n,1:n) = bnet.intra1; +dag(1:n,(1:n)+n) = bnet.inter; +dag((1:n)+n,(1:n)+n) = bnet.intra; +bnet.dag = dag; +bnet.names = {}; + +directed = 1; +if ~acyclic(dag,directed) + error('graph must be acyclic') +end + + +bnet.eclass1 = 1:n; +%bnet.eclass2 = (1:n)+n; +bnet.eclass2 = bnet.eclass1; +for i=1:ss + if isequal(parents(dag, i+ss), parents(dag, i)+ss) + %fprintf('%d has isomorphic parents, eclass %d\n', i, bnet.eclass2(i)) + else + bnet.eclass2(i) = max(bnet.eclass2) + 1; + %fprintf('%d has non isomorphic parents, eclass %d\n', i, bnet.eclass2(i)) + end +end + +dnodes = 1:n; +bnet.observed = []; + +if nargin >= 4 + args = varargin; + nargs = length(args); + if ~isstr(args{1}) + if nargs >= 1, dnodes = args{1}; end + if nargs >= 2, bnet.eclass1 = args{2}; end + if nargs >= 3, bnet.eclass2 = args{3}; end + if nargs >= 4, bnet.intra1 = args{4}; end + else + for i=1:2:nargs + switch args{i}, + case 'discrete', dnodes = args{i+1}; + case 'observed', bnet.observed = args{i+1}; + case 'eclass1', bnet.eclass1 = args{i+1}; + case 'eclass2', bnet.eclass2 = args{i+1}; + case 'intra1', bnet.intra1 = args{i+1}; + %case 'ar_hmm', bnet.ar_hmm = args{i+1}; % should check topology + case 'names', bnet.names = assocarray(args{i+1}, num2cell(1:n)); + otherwise, + error(['invalid argument name ' args{i}]); + end + end + end +end + + +bnet.observed = sort(bnet.observed); % for comparing sets +ns = node_sizes; +bnet.node_sizes_slice = ns(:)'; +bnet.node_sizes = [ns(:) ns(:)]; + +cnodes = mysetdiff(1:n, dnodes); +bnet.dnodes_slice = dnodes; +bnet.cnodes_slice = cnodes; +bnet.dnodes = [dnodes dnodes+n]; +bnet.cnodes = [cnodes cnodes+n]; + +bnet.equiv_class = [bnet.eclass1(:) bnet.eclass2(:)]; +bnet.CPD = cell(1,max(bnet.equiv_class(:))); +eclass = bnet.equiv_class(:); +E = max(eclass); +bnet.rep_of_eclass = zeros(1,E); +for e=1:E + mems = find(eclass==e); + bnet.rep_of_eclass(e) = mems(1); +end + +ss = n; +onodes = bnet.observed; +hnodes = mysetdiff(1:ss, onodes); +bnet.hidden_bitv = zeros(1,2*ss); +bnet.hidden_bitv(hnodes) = 1; +bnet.hidden_bitv(hnodes+ss) = 1; + +bnet.parents = cell(1, 2*ss); +for i=1:ss + bnet.parents{i} = parents(bnet.dag, i); + bnet.parents{i+ss} = parents(bnet.dag, i+ss); +end + +bnet.auto_regressive = zeros(1,ss); +% ar(i)=1 means (observed) node i depends on i in the previous slice +for o=bnet.observed(:)' + if any(bnet.parents{o+ss} <= ss) + bnet.auto_regressive(o) = 1; + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/mk_fgraph.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,60 @@ +function fg = mk_fgraph(G, node_sizes, factors, varargin) +% MK_FGRAPH Make a factor graph +% fg = mk_fgraph(G, node_sizes, factors, ...) +% +% A factor graph is a bipartite graph, with one side containing variables, +% and the other containing functions of (subsets of) these variables. +% For details, see "Factor Graphs and the Sum-Product Algorithm", +% F. Kschischang and B. Frey and H-A. Loeliger, +% IEEE Trans. Info. Theory, 2001 +% +% G(i,j) = 1 if there is an arc from variable i to factor j +% +% node_sizes(i) is the number of values node i can take on, +% or the length of node i if i is a continuous-valued vector. +% +% 'factors' is the list of factors (kernel functions) +% +% The list below gives optional arguments [default value in brackets]. +% +% equiv_class - equiv_class(i)=j means factor node i gets its params from factors{j} [1:F] +% discrete - the list of nodes which are discrete random variables [1:N] +% +% e.g., fg = mk_fgraph(G, [2 2], {bnet.CPD{1},bnet.CPD{2}}, 'discrete', [1 2]) + +fg.G = G; +fg.node_sizes = node_sizes; +fg.factors = factors; +[fg.nvars fg.nfactors] = size(G); + +% default values for parameters +fg.equiv_class = 1:fg.nfactors; +fg.dnodes = 1:fg.nvars; + +if nargin >= 4 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'equiv_class', fg.equiv_class = args{i+1}; + case 'discrete', fg.dnodes = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +% so that determine_pot_type will work... +fg.utility_nodes = []; +%fg.decision_nodes = []; +%fg.chance_nodes = fg.nvars; + +fg.dom = cell(1, fg.nfactors); +for f=1:fg.nfactors + fg.dom{f} = find(G(:,f)); +end +fg.dep = cell(1, fg.nvars); +for x=1:fg.nvars + fg.dep{x} = find(G(x,:)); +end +fg.cnodes = mysetdiff(1:fg.nvars, fg.dnodes);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/mk_fgraph_given_ev.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,48 @@ +function fg = mk_fgraph_given_ev(G, node_sizes, factors, ev_CPD, evidence, varargin) +% MK_FGRAPH_GIVEN_EV Make a factor graph where each node has its own private evidence term +% fg = mk_fgraph(G, node_sizes, factors, ev_CPD, evidence, ...) +% +% G, node_sizes and factors are as in mk_fgraph, but they refer to the hidden nodes. +% ev_CPD{i} is a CPD for the i'th hidden node; this will be converted into a factor +% for node i using evidence{i}. +% We currently assume all hidden nodes are discrete, for simplicity. +% +% The list below gives optional arguments [default value in brackets]. +% +% equiv_class - equiv_class(i)=j means factor node i gets its params from factors{j} [1:F] +% ev_equiv_class - ev_equiv_class(i)=j means evidence node i gets its params from ev_CPD{j} [1:N] + + +N = length(node_sizes); +nfactors = length(factors); + +% default values for parameters +eclass = 1:nfactors; +ev_eclass = 1:N; + +if nargin >= 6 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'equiv_class', eclass = args{i+1}; + case 'ev_equiv_class', ev_eclass = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +pot_type = 'd'; +for x=1:N + ev = cell(1,2); % cell 1 is the hidden parent, cell 2 is the observed child + ev(2) = evidence(x); + dom = 1:2; + F = convert_to_pot(ev_CPD{ev_eclass(x)}, pot_type, dom(:), ev); + M = pot_to_marginal(F); + %factors{end+1} = tabular_CPD('self', 1, 'ps', [], 'sz', node_sizes(x), 'CPT', M.T); + factors{end+1} = mk_isolated_tabular_CPD(node_sizes(x), {'CPT', M.T}); +end + +E = max(eclass); +fg = mk_fgraph([G eye(N)], node_sizes, factors, 'equiv_class', [eclass E+1:E+N]);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/mk_higher_order_dbn.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,181 @@ +function bnet = mk_higher_order_dbn(intra, inter, node_sizes, varargin) +% MK_DBN Make a Dynamic Bayesian Network. +% +% BNET = MK_DBN(INTRA, INTER, NODE_SIZES, ...) makes a DBN with arcs +% from i in slice t to j in slice t iff intra(i,j) = 1, and +% from i in slice t to j in slice t+1 iff inter(i,j) = 1, +% for i,j in {1, 2, ..., n}, where n = num. nodes per slice, and t >= 1. +% node_sizes(i) is the number of values node i can take on. +% The nodes are assumed to be in topological order. Use TOPOLOGICAL_SORT if necessary. +% See also mk_bnet. +% +% Optional arguments [default in brackets] +% 'discrete' - list of discrete nodes [1:n] +% 'observed' - the list of nodes which will definitely be observed in every slice of every case [ [] ] +% 'eclass1' - equiv class for slice 1 [1:n] +% 'eclass2' - equiv class for slice 2 [tie nodes with equivalent parents to slice 1] +% equiv_class1(i) = j means node i in slice 1 gets its parameters from bnet.CPD{j}, +% i.e., nodes i and j have tied parameters. +% 'intra1' - topology of first slice, if different from others +% 'names' - a cell array of strings to be associated with nodes 1:n [{}] +% This creates an associative array, so you write e.g. +% 'evidence(bnet.names{'bar'}) = 42' instead of 'evidence(2} = 42' +% assuming names = { 'foo', 'bar', ...}. +% +% For backwards compatibility with BNT2, arguments can also be specified as follows +% bnet = mk_dbn(intra, inter, node_sizes, dnodes, eclass1, eclass2, intra1) +% +% After calling this function, you must specify the parameters (conditional probability +% distributions) using bnet.CPD{i} = gaussian_CPD(...) or tabular_CPD(...) etc. + + +n = length(intra); +ss = n; +bnet.nnodes_per_slice = ss; +bnet.intra = intra; +bnet.inter = inter; +bnet.intra1 = intra; + +% As this method is used to generate a higher order Markov Model +% also connect from time slice t - i -> t with i > 1 has to be +% taken into account. + +%inter should be a three dimensional array where inter(:,:,i) +%describes the connections from time-slice t - i to t. +[rows,columns,order] = size(inter); +assert(rows == n); +assert(columns == n); +dag = zeros((order + 1)*n); + +i = 0; +while i <= order + j = i; + while j <= order + if j == i + dag(1 + i*n:(i+1)*n,1+i*n:(i+1)*n) = intra; + else + dag(1+i*n:(i+1)*n,1+j*n:(j+1)*n) = inter(:,:,j - i); + end + j = j + 1; + end; + i = i + 1; +end; + +bnet.dag = dag; +bnet.names = {}; + +directed = 1; +if ~acyclic(dag,directed) + error('graph must be acyclic') +end + +% Calculation of the equivalence classes +bnet.eclass1 = 1:n; +bnet.eclass = zeros(order + 1,ss); +bnet.eclass(1,:) = 1:n; +for i = 1:order + bnet.eclass(i+1,:) = bnet.eclass(i,:); + for j = 1:ss + if(isequal(parents(dag,(i-1)*n+j)+ss,parents(dag,(i*n + j)))) + %fprintf('%d has isomorphic parents, eclass %d \n',j,bnet.eclass(i,j)) + else + bnet.eclass(i + 1,j) = max(bnet.eclass(i+1,:))+1; + %fprintf('%d has non isomorphic parents, eclass %d \n',j,bnet.eclass(i,j)) + end; + end; +end; +bnet.eclass1 = 1:n; + +% To be compatible with whe rest of the code +bnet.eclass2 = bnet.eclass(2,:); + +dnodes = 1:n; +bnet.observed = []; + +if nargin >= 4 + args = varargin; + nargs = length(args); + if ~isstr(args{1}) + if nargs >= 1 dnodes = args{1}; end + if nargs >= 2 bnet.eclass1 = args{2}; bnet.eclass(1,:) = args{2}; end + if nargs >= 3 bnet.eclass2 = args{3}; bnet.eclass(2,:) = args{2}; end + if nargs >= 4 bnet.intra1 = args{4}; end + else + for i=1:2:nargs + switch args{i}, + case 'discrete', dnodes = args{i+1}; + case 'observed', bnet.observed = args{i+1}; + case 'eclass1', bnet.eclass1 = args{i+1}; bnet.eclass(1,:) = args{i+1}; + case 'eclass2', bnet.eclass2 = args{i+1}; bnet.eclass(2,:) = args{i+1}; + case 'eclass', bnet.eclass = args{i+1}; + case 'intra1', bnet.intra1 = args{i+1}; + %case 'ar_hmm', bnet.ar_hmm = args{i+1}; % should check topology + case 'names', bnet.names = assocarray(args{i+1}, num2cell(1:n)); + otherwise, + error(['invalid argument name ' args{i}]); + end + end + end +end + +bnet.observed = sort(bnet.observed); % for comparing sets +ns = node_sizes; +bnet.node_sizes_slice = ns(:)'; +bnet.node_sizes = repmat(ns(:),1,order + 1); + +cnodes = mysetdiff(1:n, dnodes); +bnet.dnodes_slice = dnodes; +bnet.cnodes_slice = cnodes; +bnet.dnodes = dnodes; +bnet.cnodes = cnodes; +% To adapt the function to higher order Markov models include dnodes for more +% time slices +for i = 1:order + bnet.dnodes = [bnet.dnodes dnodes+i*n]; + bnet.cnodes = [bnet.cnodes cnodes+i*n]; +end + +% Generieren einer Matrix, deren i-te Spalte die Aequivalenzklassen +% der i-ten Zeitscheibe enthaelt. +bnet.equiv_class = [bnet.eclass(1,:)]'; +for i = 2:(order + 1) + bnet.equiv_class = [bnet.equiv_class bnet.eclass(i,:)']; +end + +bnet.CPD = cell(1,max(bnet.equiv_class(:))); + +ss = n; +onodes = bnet.observed; +hnodes = mysetdiff(1:ss, onodes); +bnet.hidden_bitv = zeros(1,(order + 1)*ss); +for i = 0:order + bnet.hidden_bitv(hnodes +i*ss) = 1; +end; + +bnet.parents = cell(1, (order + 1)*ss); +for i=1:(order + 1)*ss + bnet.parents{i} = parents(bnet.dag, i); +end + +bnet.auto_regressive = zeros(1,ss); +% ar(i)=1 means (observed) node i depends on i in the previous slice +for o=bnet.observed(:)' + if any(bnet.parents{o+ss} <= ss) + bnet.auto_regressive(o) = 1; + end +end + + + + + + + + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/mk_limid.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,93 @@ +function bnet = mk_limid(dag, node_sizes, varargin) +% MK_LIMID Make a limited information influence diagram +% +% BNET = MK_LIMID(DAG, NODE_SIZES, ...) +% DAG is the adjacency matrix for a directed acyclic graph. +% The nodes are assumed to be in topological order. Use TOPOLOGICAL_SORT if necessary. +% For decision nodes, the parents must explicitely include all nodes +% on which it can depends, in contrast to the implicit no-forgetting assumption of influence diagrams. +% (For details, see "Representing and solving decision problems with limited information", +% Lauritzen and Nilsson, Management Science, 2001.) +% +% node_sizes(i) is the number of values node i can take on, +% or the length of node i if i is a continuous-valued vector. +% node_sizes(i) = 1 if i is a utility node. +% +% The list below gives optional arguments [default value in brackets]. +% +% chance - the list of nodes which are random variables [1:N] +% decision - the list of nodes which are decision nodes [ [] ] +% utility - the list of nodes which are utility nodes [ [] ] +% equiv_class - equiv_class(i)=j means node i gets its params from CPD{j} [1:N] +% +% e.g., limid = mk_limid(dag, ns, 'chance', [1 3], 'utility', [2]) + +n = length(dag); + +% default values for parameters +bnet.chance_nodes = 1:n; +bnet.equiv_class = 1:n; +bnet.utility_nodes = []; +bnet.decision_nodes = []; +bnet.dnodes = 1:n; % discrete + +if nargin >= 3 + args = varargin; + nargs = length(args); + if ~isstr(args{1}) + if nargs >= 1, bnet.dnodes = args{1}; end + if nargs >= 2, bnet.equiv_class = args{2}; end + else + for i=1:2:nargs + switch args{i}, + case 'equiv_class', bnet.equiv_class = args{i+1}; + case 'chance', bnet.chance_nodes = args{i+1}; + case 'utility', bnet.utility_nodes = args{i+1}; + case 'decision', bnet.decision_nodes = args{i+1}; + case 'discrete', bnet.dnodes = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end + end +end + +bnet.limid = 1; + +bnet.dag = dag; +bnet.node_sizes = node_sizes(:)'; + +bnet.cnodes = mysetdiff(1:n, bnet.dnodes); +% too many functions refer to cnodes to rename it to cts_nodes - +% We hope it won't be confused with chance nodes! + +bnet.parents = cell(1,n); +for i=1:n + bnet.parents{i} = parents(dag, i); +end + +E = max(bnet.equiv_class); +mem = cell(1,E); +for i=1:n + e = bnet.equiv_class(i); + mem{e} = [mem{e} i]; +end +bnet.members_of_equiv_class = mem; + +bnet.CPD = cell(1, E); + +% for e=1:E +% i = bnet.members_of_equiv_class{e}(1); % pick arbitrary member +% switch type{e} +% case 'tabular', bnet.CPD{e} = tabular_CPD(bnet, i); +% case 'gaussian', bnet.CPD{e} = gaussian_CPD(bnet, i); +% otherwise, error(['unrecognized CPD type ' type{e}]); +% end +% end + +directed = 1; +if ~acyclic(dag,directed) + error('graph must be acyclic') +end + +bnet.order = topological_sort(bnet.dag);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/mk_mnet.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,14 @@ +function mnet = mk_mnet(graph, node_sizes, cliques, potentials) +% MK_MNET Make a Markov network (Markov Random Field) +% +% mnet = mk_mnet(adj_mat, node_sizes, cliques, potentials) +% +% cliques{i} is a list of the nodes in clq i +% potentials{i} is a dpot object corresponding to the potential for clique i +% + +mnet.markov_net = 1; +mnet.graph = graph; +mnet.node_sizes = node_sizes; +mnet.user_cliques = cliques; +mnet.user_potentials = potentials;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/mk_mrf2.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +function mrf2 = mk_mrf2(adj_mat, pot) +% MK_MRF2 Make a Markov random field with pairwise potentials +% function mrf2 = mk_mrf2(adj_mat, pot) +% +% pot{i,j}(k1,k2) + +mrf2.adj_mat = adj_mat; +mrf2.pot = pot;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/mk_mutilated_samples.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,38 @@ +function [data, clamped] = mk_mutilated_samples(bnet, ncases, max_clamp, usecell) +% GEN_MUTILATED_SAMPLES Do random interventions and then draw random samples +% [data, clamped] = gen_mutilated_samples(bnet, ncases, max_clamp, usecell) +% +% At each step, we pick a random subset of size 0 .. max_clamp, and +% clamp these nodes to random values. +% +% data(i,m) is the value of node i in case m. +% clamped(i,m) = 1 if node i in case m was set by intervention. + +if nargin < 4, usecell = 1; end + +ns = bnet.node_sizes; +n = length(bnet.dag); +if usecell + data = cell(n, ncases); +else + data = zeros(n, ncases); +end +clamped = zeros(n, ncases); + +csubsets = subsets(1:n, max_clamp, 0); % includes the empty set +distrib_cset = normalise(ones(1, length(csubsets))); + +for m=1:ncases + cset = csubsets{sample_discrete(distrib_cset)}; + nvals = prod(ns(cset)); + distrib_cvals = normalise(ones(1, nvals)); + cvals = ind2subv(ns(cset), sample_discrete(distrib_cvals)); + mutilated_bnet = do_intervention(bnet, cset, cvals); + ev = sample_bnet(mutilated_bnet); + if usecell + data(:,m) = ev; + else + data(:,m) = cell2num(ev); + end + clamped(cset,m) = 1; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/mk_named_CPT.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,52 @@ +function CPT2 = mk_named_CPT(family_names, names, dag, CPT1) +% MK_NAMED_CPT Permute the dimensions of a CPT so they agree with the internal numbering convention +% CPT2 = mk_named_CPT(family_names, names, dag, CPT1) +% +% This is best explained by example. +% Consider the following directed acyclic graph +% +% C +% / \ +% R S +% \ / +% W +% +% where all arcs point down. +% When we create the CPT for node W, we consider S as its first parent, and R as its +% second, and hence write +% +% S R W +% CPT1(1,1,:) = [1.0 0.0]; +% CPT1(2,1,:) = [0.2 0.8]; % P(W=1 | R=1, S=2) = 0.2 +% CPT1(1,2,:) = [0.1 0.9]; +% CPT1(2,2,:) = [0.01 0.99]; +% +% However, when we create the dag using mk_adj_mat, the nodes get topologically sorted, +% and by chance, node R preceeds node S in this ordering. +% Hence we should have written +% +% R S W +% CPT2(1,1,:) = [1.0 0.0]; +% CPT2(2,1,:) = [0.1 0.9]; +% CPT2(1,2,:) = [0.2 0.8]; % P(W=1 | R=1, S=2) = 0.2 +% CPT2(2,2,:) = [0.01 0.99]; +% +% Since we do not know the order of the nodes in advance, we can write +% CPT2 = mk_named_CPT({'S', 'R', 'W'}, names, dag, CPT1) +% where 'S', 'R', 'W' are the order of the dimensions we assumed (the child node must be last in this list), +% and names{i} is the name of the i'th node. + +n = length(family_names); +family_nums = zeros(1,n); +for i=1:n + family_nums(i) = stringmatch(family_names{i}, names); % was strmatch +end + +fam = family(dag, family_nums(end)); +perm = zeros(1,n); +for i=1:n + % perm(i) = find(family_nums(i) == fam); + perm(i) = find(fam(i) == family_nums); +end + +CPT2 = permute(CPT1, perm);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/mk_slice_and_half_dbn.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,28 @@ +function bnet15 = mk_slice_and_half_dbn(bnet, int) +% function bnet = mk_slice_and_half_dbn(bnet, int) +% function bnet = mk_slice_and_half_dbn(bnet, int) +% +% Create a "1.5 slice" jtree, containing the interface nodes of slice 1 +% and all the nodes of slice 2 +% To keep the node numbering the same, we simply disconnect the non-interface nodes +% from slice 1, and set their size to 1. +% We do this to speed things up, and so that the likelihood is computed correctly. +% We do not need to do +% this if we just want to compute marginals (i.e., we can include nodes whose potentials will +% be left as all 1s). + +intra15 = bnet.intra; +ss = length(bnet.intra); +nonint = mysetdiff(1:ss, int); +for i=nonint(:)' + intra15(:,i) = 0; + intra15(i,:) = 0; + %assert(~any(bnet.inter(i,:))) +end +dag15 = [intra15 bnet.inter; + zeros(ss) bnet.intra]; +ns = bnet.node_sizes(:); +ns(nonint) = 1; % disconnected nodes get size 1 +obs_nodes = [bnet.observed(:) bnet.observed(:)+ss]; +bnet15 = mk_bnet(dag15, ns, 'discrete', bnet.dnodes, 'equiv_class', bnet.equiv_class(:), ... + 'observed', obs_nodes(:));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/noisyORtoTable.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,32 @@ +function CPT = noisyORtoTable(inhibit, leak_inhibit) +% NOISYORTOTABLE Convert noisyOR distribution to CPT +% function CPT = noisyORtoTable(inhibit, leak_inhibit) +% +% inhibit(i) = prob i'th parent will be inhibited (flipped from 1 to 0) +% leak_inhibit - optional suppression of leak +% CPT(U1,...,Un, X) = Pr(X|U1,...,Un) where the Us are the parents (excluding leak). +% State 1 = off, 2 = on + +if nargin < 2, leak_inhibit = 1; end + +q = [leak_inhibit inhibit(:)']; + +if length(q)==1 + CPT = [q 1-q]; + return; +end + +n = length(q); +Bn = ind2subv(2*ones(1,n), 1:(2^n))-1; % all n bit vectors, with the left most column toggling fastest (LSB) +CPT = zeros(2^n, 2); +% Pr(X=0 | U_1 .. U_n) = prod_{i: U_i = on} q_i = prod_i q_i ^ U_i = exp(u' * log(q_i)) +% This method is problematic when q contains zeros + +Q = repmat(q(:)', 2^n, 1); +Q(logical(~Bn)) = 1; +CPT(:,1) = prod(Q,2); +CPT(:,2) = 1-CPT(:,1); + +CPT = reshape(CPT(2:2:end), 2*ones(1,n)); % skip cases in which the leak is off + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/partition_dbn_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,17 @@ +function [pnodes, tnodes] = partition_dbn_nodes(intra, inter) +% PARTITION_DBN_NODES Divide the nodes into a DBN into persistent and transient. +% [pnodes, tnodes] = partition_dbn_nodes(intra, inter) +% Persistent nodes have children in the next time slice, transient nodes do not. + +ss = length(intra); +pnodes = []; +tnodes = []; +for i=1:ss + cs = children(inter, i); + if isempty(cs) + tnodes = [tnodes i]; + else + pnodes = [pnodes i]; + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/partition_matrix_vec_3.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,22 @@ +function [A1, A2, B1, B2, C11, C12, C21, C22] = partition_matrix_vec_3(A, B, C, n1, n2, bs) + +dom = myunion(n1, n2); +n1i = block(find_equiv_posns(n1, dom), bs(dom)); +n2i = block(find_equiv_posns(n2, dom), bs(dom)); + + + A1 = A(n1i); + A2 = A(n2i); + if isempty(B) + B1 = zeros(size(n1i, 2),size(B, 2)); + B2 = zeros(size(n2i, 2),size(B, 2)); + else + B1 = B(n1i, :); + B2 = B(n2i, :); + end + + + C11 = C(n1i, n1i); + C12 = C(n1i, n2i); + C21 = C(n2i, n1i); + C22 = C(n2i, n2i);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/sample_bnet.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,34 @@ +function sample = sample_bnet(bnet, varargin) +% SAMPLE_BNET Generate a random sample from a Bayes net. +% SAMPLE = SAMPLE_BNET(BNET, ...) +% +% sample{i} contains the value of the i'th node. +% i.e., the result is an Nx1 cell array. +% Nodes are sampled in the order given by bnet.order. +% +% Optional arguments: +% +% evidence - initial evidence; if evidence{i} is non-empty, node i won't be sampled. + +% set defauly params +n = length(bnet.dag); +sample = cell(n,1); + +% get optional params +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'evidence', sample = args{i+1}(:); + otherwise, error(['unrecognized argument ' args{i}]) + end +end + +for j=bnet.order(:)' + if isempty(sample{j}) + %ps = parents(bnet.dag, j); + ps = bnet.parents{j}; + e = bnet.equiv_class(j); + sample{j} = sample_node(bnet.CPD{e}, sample(ps)); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/sample_dbn.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,73 @@ +function seq = sample_dbn(bnet, varargin) +% SAMPLE_DBN Generate a random sequence from a DBN. +% seq = sample_dbn(bnet, ...) +% +% seq{i,t} contains the values of the i'th node in the t'th slice. +% +% Optional arguments: +% +% length - length of sequence to be generated (can also just use sample_dbn(bnet,T)) +% stop_test - name of a function which is used to decide when to stop; +% This will be called as feval(stop_test, seq(:,t)) +% i.e., stop_test is passed a cell array containing all the nodes in the current slice. +% evidence - initial evidence; if evidence{i,t} is non-empty, this node won't be sampled. + +args = varargin; +nargs = length(args); + +if (nargs == 1) & ~isstr(args{1}) + % Old syntax: sample_dbn(bnet, T) + T = args{1}; +else + % get length + T = 1; + for i=1:2:nargs + switch args{i}, + case 'length', T = args{i+1}; + case 'evidence', T = size(args{i+1}, 2); + end + end +end + +ss = length(bnet.intra); +% set default arguments +seq = cell(ss, T); +stop_test = []; +for i=1:2:nargs + switch args{i}, + case 'evidence', seq = args{i+1}; % initialise observed nodes + case 'stop_test', stop_test = args{i+1}; + end +end + +t = 1; +for i=1:ss + if ~isempty(stop_test) | isempty(seq{i,t}) + ps = parents(bnet.dag, i); + e = bnet.equiv_class(i,1); + pvals = seq(ps); + seq{i,t} = sample_node(bnet.CPD{e}, pvals); + %fprintf('sample i=%d,t=%d,val=%d,ps\n', i, t, seq(i,t)); pvals(:)' + end +end +t = 2; +done = 0; +while ~done + for i=1:ss + if ~isempty(stop_test) | isempty(seq{i,t}) + ps = parents(bnet.dag, i+ss) + (t-2)*ss; + e = bnet.equiv_class(i,2); + pvals = seq(ps); + seq{i,t} = sample_node(bnet.CPD{e}, pvals); + %fprintf('sample i=%d,t=%d,val=%d,ps\n', i, t, seq(i,t)); pvals(:)' + end + end + if ~isempty(stop_test) + done = feval(stop_test, seq(:,t)); + else + if t==T + done = 1; + end + end + t = t + 1; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/score_bnet_complete.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,28 @@ +function L = log_lik_complete(bnet, cases, clamped) +% LOG_LIK_COMPLETE Compute sum_m sum_i log P(x(i,m)| x(pi_i,m), theta_i) for a completely observed data set +% L = log_lik_complete(bnet, cases, clamped) +% +% If there is a missing data, you must use an inference engine. +% cases(i,m) is the value assigned to node i in case m. +% (If there are vector-valued nodes, cases should be a cell array.) +% clamped(i,m) = 1 if node i was set by intervention in case m (default: clamped = zeros) +% Clamped nodes contribute a factor of 1.0 to the likelihood. + +if iscell(cases), usecell = 1; else usecell = 0; end + +n = length(bnet.dag); +ncases = size(cases, 2); +if n ~= size(cases, 1) + error('data should be of size nnodes * ncases'); +end + +if nargin < 3, clamped = zeros(n,ncases); end + +L = 0; +for i=1:n + ps = parents(bnet.dag, i); + e = bnet.equiv_class(i); + u = find(clamped(i,:)==0); + L = L + log_prob_node(bnet.CPD{e}, cases(i,u), cases(ps,u)); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/shrink_obs_dims_in_gaussian.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +function marg2 = shrink_obs_dims_in_gaussian(marg1, dom, evidence, ns) +% SHRINK_OBS_DIMS_IN_GAUSSIAN Remove observed dimensions from mu/Sigma +% function marg2 = shrink_obs_dims_in_gaussian(marg1, dom, evidence, ns) + +% This is used by loopy + +hdom = dom(isemptycell(evidence(dom))); +ndx = find_equiv_posns(hdom, dom); +b = block(ndx, ns(dom)); +marg2.mu = marg1.mu(b); +marg2.Sigma = marg1.Sigma(b,b); +marg2.domain = marg1.domain;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/shrink_obs_dims_in_table.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +function T2 = shrink_obs_dims_in_table(T1, dom, evidence) +% SHRINK_OBS_DIMS_IN_TABLE Set observed dimensions to size 1 +% T2 = shrink_obs_dims_in_table(T1, dom, evidence) +% +% If 'T1' contains observed nodes, it will have 0s in the positions that are +% inconsistent with the evidence. We now remove these 0s and set the corresponding dimensions to +% size 1, to be consistent with the way most inference engines handle evidence, which is to +% shrink observed nodes before doing inference. + +% This is used by pearl and enumerative inf. engines. + +odom = dom(~isemptycell(evidence(dom))); +vals = cat(1,evidence{odom}); +ndx = mk_multi_index(length(dom), find_equiv_posns(odom, dom), vals(:)); +T2 = T1(ndx{:});
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/solve_limid.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,68 @@ +function [strategy, MEU, niter] = solve_limid(engine, varargin) +% SOLVE_LIMID Find the (locally) optimal strategy for a LIMID +% [strategy, MEU, niter] = solve_limid(inf_engine, ...) +% +% strategy{d} = stochastic policy for node d (a decision node) +% MEU = maximum expected utility +% niter = num iterations used +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default in brackets] +% +% max_iter - max. num. iterations [ 1 ] +% tol - tolerance required of consecutive MEU values, used to assess convergence [1e-3] +% order - order in which decision nodes are optimized [ reverse numerical order ] +% +% e.g., solve_limid(engine, 'tol', 1e-2, 'max_iter', 10) + +bnet = bnet_from_engine(engine); + +% default values +max_iter = 1; +tol = 1e-3; +D = bnet.decision_nodes; +order = D(end:-1:1); + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'max_iter', max_iter = args{i+1}; + case 'tol', tol = args{i+1}; + case 'order', order = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end +end + +CPDs = bnet.CPD; +ns = bnet.node_sizes; +N = length(ns); +evidence = cell(1,N); +strategy = cell(1, N); + +iter = 1; +converged = 0; +oldMEU = 0; +while ~converged & (iter <= max_iter) + for d=order(:)' + engine = enter_evidence(engine, evidence, 'exclude', d); + [m, pot] = marginal_family(engine, d); + %pot = marginal_family_pot(engine, d); + [policy, score] = upot_to_opt_policy(pot); + e = bnet.equiv_class(d); + CPDs{e} = set_fields(CPDs{e}, 'policy', policy); + engine = update_engine(engine, CPDs); + strategy{d} = policy; + end + engine = enter_evidence(engine, evidence); + [m, pot] = marginal_nodes(engine, []); + %pot = marginal_family_pot(engine, []); + [dummy, MEU] = upot_to_opt_policy(pot); + if approxeq(MEU, oldMEU, tol) + converged = 1; + end + oldMEU = MEU; + iter = iter + 1; +end +niter = iter - 1;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/unroll_dbn_topology.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,28 @@ +function M = unroll_dbn_topology(intra, inter, T, intra1) +% UNROLL_DBN_TOPOLOGY Make the block diagonal adjacency matrix for a DBN consisting of T slices +% M = unroll_dbn_topology(intra, inter, T, intra1) +% +% intra is the connectivity within a slice, inter between two slices. +% M will have intra along the diagonal, and inter one above the diagonal. +% intra1 is an optional argumnet, in case the intra is different for the first slice. + +if nargin < 4, intra1 = intra; end + +ss = length(intra); % slice size +M = sparse(ss*T, ss*T); + +b = 1:ss; +M(b,b) = intra1; +M(b,b+ss) = inter; + +for t=2:T-1 + b = (1:ss) + (t-1)*ss; + M(b,b) = intra; + M(b,b+ss) = inter; +end + +t = T; +b = (1:ss) + (t-1)*ss; +M(b,b) = intra; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/unroll_higher_order_topology.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,30 @@ +function M = unroll_higher_order_topology(intra, inter, T, intra1) +% UNROLL_DBN_TOPOLOGY Make the block diagonal adjacency matrix for a DBN consisting of T slices +% M = unroll_dbn_topology(intra, inter, T, intra1) +% +% intra is the connectivity within a slice, inter between two slices. +% M will have intra along the diagonal, and inter one above the diagonal. +% intra1 is an optional argumnet, in case the intra is different for the first slice. + +if nargin < 4 + intra1 = intra; +end; + + +ss = length(intra); % slice size +M = sparse(ss*T, ss*T); +[rows,columns,order] = size(inter); +for t1 = 1:T + b = 1 + (t1 - 1)*ss : t1*ss; + if t1 == 1 + M(b,b) = intra1; + else + M(b,b) = intra; + end + for t2 = 1:order + if t1 + t2 <= T + M(b,b+t2*ss) = inter(:,:,t2); + end + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/general/unroll_set.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +function U = unroll_set(S, ss, T) +% UNROLL_SET Make T shifted copies of the set of nodes S in a slice of size ss. +% U = unroll_set(S, ss, T) + +offset = repmat(0:ss:(T-1)*ss, [length(S) 1]); +U = repmat(S(:), [1 T]) + offset; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/CVS/.svn/all-wcprops Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,29 @@ +K 25 +svn:wc:ra_dav:version-url +V 62 +/svnroot/!svn/ver/1127/matlabtoolboxes/FullBNT-1.0.4/graph/CVS +END +Repository +K 25 +svn:wc:ra_dav:version-url +V 73 +/svnroot/!svn/ver/1127/matlabtoolboxes/FullBNT-1.0.4/graph/CVS/Repository +END +Root +K 25 +svn:wc:ra_dav:version-url +V 67 +/svnroot/!svn/ver/1127/matlabtoolboxes/FullBNT-1.0.4/graph/CVS/Root +END +Entries +K 25 +svn:wc:ra_dav:version-url +V 70 +/svnroot/!svn/ver/1127/matlabtoolboxes/FullBNT-1.0.4/graph/CVS/Entries +END +Entries.Log +K 25 +svn:wc:ra_dav:version-url +V 74 +/svnroot/!svn/ver/1127/matlabtoolboxes/FullBNT-1.0.4/graph/CVS/Entries.Log +END
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/CVS/.svn/entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,164 @@ +10 + +dir +7329 +https://parrot.dcs.qmul.ac.uk/svnroot/matlabtoolboxes/FullBNT-1.0.4/graph/CVS +https://parrot.dcs.qmul.ac.uk/svnroot + + + +2008-11-18T12:42:45.431389Z +1127 +matthiasm + + + + + + + + + + + + + + +214cd2a9-030a-0410-b9a1-da4e402363b6 + +Repository +file + + + + +2010-04-08T01:08:23.000000Z +5547e28254023e278f909952604e906e +2008-11-18T12:42:45.431389Z +1127 +matthiasm +has-props + + + + + + + + + + + + + + + + + + + + +14 + +Root +file + + + + +2010-04-08T01:08:23.000000Z +a49432e9bea5bbd5666443ab187b4558 +2008-11-18T12:42:45.431389Z +1127 +matthiasm +has-props + + + + + + + + + + + + + + + + + + + + +51 + +Entries +file + + + + +2010-04-08T01:08:23.000000Z +409b7f319e28ba20e594a8c1c77bf619 +2008-11-18T12:42:45.431389Z +1127 +matthiasm +has-props + + + + + + + + + + + + + + + + + + + + +2547 + +Entries.Log +file + + + + +2010-04-08T01:08:23.000000Z +ef0b99de88eda8653a20a0f7a9f177c0 +2008-11-18T12:42:45.431389Z +1127 +matthiasm +has-props + + + + + + + + + + + + + + + + + + + + +12 +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/CVS/.svn/prop-base/Entries.Log.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +K 14 +svn:executable +V 0 + +END
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/CVS/.svn/prop-base/Entries.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +K 14 +svn:executable +V 0 + +END
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/CVS/.svn/prop-base/Repository.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +K 14 +svn:executable +V 0 + +END
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/CVS/.svn/prop-base/Root.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +K 14 +svn:executable +V 0 + +END
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/CVS/.svn/text-base/Entries.Log.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/CVS/.svn/text-base/Entries.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,49 @@ +/README.txt/1.1.1.1/Mon Aug 29 17:44:02 2005// +/acyclic.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/assignEdgeNums.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/best_first_elim_order.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/check_jtree_property.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/check_triangulated.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/children.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/cliques_to_jtree.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/cliques_to_strong_jtree.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/connected_graph.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/dag_to_essential_graph.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/dfs.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/dfs_test.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/dijkstra.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/family.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/graph_separated.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/graph_to_jtree.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/min_subtree_con_nodes.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/minimum_spanning_tree.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/minspan.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_2D_lattice.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_2D_lattice_slow.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_adj_mat.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_adjmat_chain.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_all_dags.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_nbrs_of_dag.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_nbrs_of_digraph.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_nbrs_of_digraph_broken.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_nbrs_of_digraph_not_vectorized.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_rnd_dag.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_rnd_dag_given_edge_prob.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_rooted_tree.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_undirected.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/moralize.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/neighbors.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/parents.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/pred2path.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/reachability_graph.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/scc.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/strong_elim_order.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/test.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/test_strong_root.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/topological_sort.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/trees.txt/1.1.1.1/Mon Aug 29 17:44:08 2005// +/triangulate.c/1.1.1.1/Mon Aug 29 17:44:08 2005// +/triangulate.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/triangulate_2Dlattice_demo.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/triangulate_test.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/CVS/.svn/text-base/Repository.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/graph
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/CVS/.svn/text-base/Root.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,49 @@ +/README.txt/1.1.1.1/Mon Aug 29 17:44:02 2005// +/acyclic.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/assignEdgeNums.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/best_first_elim_order.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/check_jtree_property.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/check_triangulated.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/children.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/cliques_to_jtree.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/cliques_to_strong_jtree.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/connected_graph.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/dag_to_essential_graph.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/dfs.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/dfs_test.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/dijkstra.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/family.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/graph_separated.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/graph_to_jtree.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/min_subtree_con_nodes.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/minimum_spanning_tree.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/minspan.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_2D_lattice.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_2D_lattice_slow.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_adj_mat.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_adjmat_chain.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_all_dags.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_nbrs_of_dag.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_nbrs_of_digraph.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_nbrs_of_digraph_broken.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_nbrs_of_digraph_not_vectorized.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_rnd_dag.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_rnd_dag_given_edge_prob.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_rooted_tree.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_undirected.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/moralize.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/neighbors.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/parents.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/pred2path.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/reachability_graph.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/scc.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/strong_elim_order.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/test.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/test_strong_root.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/topological_sort.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/trees.txt/1.1.1.1/Mon Aug 29 17:44:08 2005// +/triangulate.c/1.1.1.1/Mon Aug 29 17:44:08 2005// +/triangulate.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/triangulate_2Dlattice_demo.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/triangulate_test.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/graph
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/Old/.svn/all-wcprops Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,35 @@ +K 25 +svn:wc:ra_dav:version-url +V 62 +/svnroot/!svn/ver/1127/matlabtoolboxes/FullBNT-1.0.4/graph/Old +END +best_first_elim_order.m +K 25 +svn:wc:ra_dav:version-url +V 86 +/svnroot/!svn/ver/1127/matlabtoolboxes/FullBNT-1.0.4/graph/Old/best_first_elim_order.m +END +mk_2D_lattice_slow.m +K 25 +svn:wc:ra_dav:version-url +V 83 +/svnroot/!svn/ver/1127/matlabtoolboxes/FullBNT-1.0.4/graph/Old/mk_2D_lattice_slow.m +END +dag_to_jtree.m +K 25 +svn:wc:ra_dav:version-url +V 77 +/svnroot/!svn/ver/1127/matlabtoolboxes/FullBNT-1.0.4/graph/Old/dag_to_jtree.m +END +dsep_test.m +K 25 +svn:wc:ra_dav:version-url +V 74 +/svnroot/!svn/ver/1127/matlabtoolboxes/FullBNT-1.0.4/graph/Old/dsep_test.m +END +dfs.m +K 25 +svn:wc:ra_dav:version-url +V 68 +/svnroot/!svn/ver/1127/matlabtoolboxes/FullBNT-1.0.4/graph/Old/dfs.m +END
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/Old/.svn/entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,201 @@ +10 + +dir +7329 +https://parrot.dcs.qmul.ac.uk/svnroot/matlabtoolboxes/FullBNT-1.0.4/graph/Old +https://parrot.dcs.qmul.ac.uk/svnroot + + + +2008-11-18T12:42:45.431389Z +1127 +matthiasm + + + + + + + + + + + + + + +214cd2a9-030a-0410-b9a1-da4e402363b6 + +best_first_elim_order.m +file + + + + +2010-04-08T01:08:23.000000Z +181b4fccdaf1791e1c7f710c26aeaa09 +2008-11-18T12:42:45.431389Z +1127 +matthiasm +has-props + + + + + + + + + + + + + + + + + + + + +2607 + +mk_2D_lattice_slow.m +file + + + + +2010-04-08T01:08:23.000000Z +259c93a5a0170329b60f3d5b3d1be31d +2008-11-18T12:42:45.431389Z +1127 +matthiasm +has-props + + + + + + + + + + + + + + + + + + + + +2974 + +dag_to_jtree.m +file + + + + +2010-04-08T01:08:23.000000Z +f1284e4c382c299b49e64ee7449d4d12 +2008-11-18T12:42:45.431389Z +1127 +matthiasm +has-props + + + + + + + + + + + + + + + + + + + + +1819 + +dsep_test.m +file + + + + +2010-04-08T01:08:23.000000Z +e9c530ff844accff52a321ebe6fa88be +2008-11-18T12:42:45.431389Z +1127 +matthiasm +has-props + + + + + + + + + + + + + + + + + + + + +202 + +dfs.m +file + + + + +2010-04-08T01:08:23.000000Z +da9bdaa08aac3ad2eda9556949b6e46b +2008-11-18T12:42:45.431389Z +1127 +matthiasm +has-props + + + + + + + + + + + + + + + + + + + + +2826 + +CVS +dir +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/Old/.svn/prop-base/best_first_elim_order.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +K 14 +svn:executable +V 0 + +END
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/Old/.svn/prop-base/dag_to_jtree.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +K 14 +svn:executable +V 0 + +END
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/Old/.svn/prop-base/dfs.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +K 14 +svn:executable +V 0 + +END
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/Old/.svn/prop-base/dsep_test.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +K 14 +svn:executable +V 0 + +END
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/Old/.svn/prop-base/mk_2D_lattice_slow.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +K 14 +svn:executable +V 0 + +END
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/Old/.svn/text-base/best_first_elim_order.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,64 @@ +function order = best_first_elim_order(G, node_sizes, stage) +% BEST_FIRST_ELIM_ORDER Greedily search for an optimal elimination order. +% order = best_first_elim_order(moral_graph, node_sizes) +% +% Find an order in which to eliminate nodes from the graph in such a way as to try and minimize the +% weight of the resulting triangulated graph. The weight of a graph is the sum of the weights of each +% of its cliques; the weight of a clique is the product of the weights of each of its members; the +% weight of a node is the number of values it can take on. +% +% Since this is an NP-hard problem, we use the following greedy heuristic: +% at each step, eliminate that node which will result in the addition of the least +% number of fill-in edges, breaking ties by choosing the node that induces the lighest clique. +% For details, see +% - Kjaerulff, "Triangulation of graphs -- algorithms giving small total state space", +% Univ. Aalborg tech report, 1990 (www.cs.auc.dk/~uk) +% - C. Huang and A. Darwiche, "Inference in Belief Networks: A procedural guide", +% Intl. J. Approx. Reasoning, 11, 1994 +% + +% Warning: This code is pretty old and could probably be made faster. + +n = length(G); +if nargin < 3, stage = { 1:n }; end % no constraints + +% For long DBNs, it may be useful to eliminate all the nodes in slice t before slice t+1. +% This will ensure that the jtree has a repeating structure (at least away from both edges). +% This is why we have stages. +% See the discussion of splicing jtrees on p68 of +% Geoff Zweig's PhD thesis, Dept. Comp. Sci., UC Berkeley, 1998. +% This constraint can increase the clique size significantly. + +MG = G; % copy the original graph +uneliminated = ones(1,n); +order = zeros(1,n); +t = 1; % Counts which time slice we are on +for i=1:n + U = find(uneliminated); + valid = myintersect(U, stage{t}); + % Choose the best node from the set of valid candidates + score1 = zeros(1,length(valid)); + score2 = zeros(1,length(valid)); + for j=1:length(valid) + k = valid(j); + ns = myintersect(neighbors(G, k), U); + l = length(ns); + M = MG(ns,ns); + score1(j) = l^2 - sum(M(:)); % num. added edges + score2(j) = prod(node_sizes([k ns])); % weight of clique + end + j1s = find(score1==min(score1)); + j = j1s(argmin(score2(j1s))); + k = valid(j); + uneliminated(k) = 0; + order(i) = k; + ns = myintersect(neighbors(G, k), U); + if ~isempty(ns) + G(ns,ns) = 1; + G = setdiag(G,0); + end + if ~any(logical(uneliminated(stage{t}))) % are we allowed to the next slice? + t = t + 1; + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/Old/.svn/text-base/dag_to_jtree.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,55 @@ +function [jtree, root, cliques, B, w, elim_order, moral_edges, fill_in_edges, strong] = ... + dag_to_jtree(dag, node_sizes, partial_order, stages, clusters) +% DAG_TO_JTREE Moralize and triangulate a DAG, and make a junction tree from its cliques. +% [jtree, root, cliques, B, w, elim_order, moral_edges, fill_in_edges, strong] = ... +% dag_to_jtree(dag, node_sizes, partial_order, stages, clusters) +% +% Input: +% dag(i,j) +% jtree(i,j) = 1 iff there is an arc between clique i and clique j +% root = the root clique +% cliques{i} = the nodes in clique i +% B(i,j) = 1 iff node j occurs in clique i +% w(i) = weight of clique i + +N = length(bnet.dag); +if nargin < 2, obs_nodes = []; end +if nargin < 3, stages = { 1:N }; end +if nargin < 4, clusters = {}; end + +[MG, moral_edges] = moralize(bnet.dag); + +% Add extra arcs between nodes in each cluster to ensure they occur in the same clique +for i=1:length(clusters) + c = clusters{i}; + MG(c,c) = 1; +end +MG = setdiag(MG, 0); + +% Find an optimal elimination ordering (NP-hard problem!) +ns = bnet.node_sizes(:); +ns(obs_nodes) = 1; % observed nodes have only 1 possible value +partial_order = determine_elim_constraints(bnet, obs_nodes); + +if isempty(partial_order) + strong = 0; + elim_order = best_first_elim_order(MG, ns, stages); +else + strong = 1; + elim_order = strong_elim_order(MG, ns, partial_order); +end + +[MTG, cliques, fill_in_edges] = triangulate(MG, elim_order); + +% Connect the cliques up into a jtree, +[jtree, root, B, w] = cliques_to_jtree(cliques, ns); + +if 0 + disp('testing dag to jtree'); + % Find the cliques containing each node, and check they form a connected subtree + clqs_con_node = cell(1,N); + for i=1:N + clqs_con_node{i} = find(B(:,i))'; + end + check_jtree_property(clqs_con_node, jtree); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/Old/.svn/text-base/dfs.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,84 @@ +function [d, pre, post, height, cycle, pred] = dfs(adj_mat, start, directed) +% DFS Perform a depth-first search of the graph starting from 'start'. +% [d, pre, post, height, cycle, pred] = dfs(adj_mat, start, directed) +% +% d(i) is the time at which node i is first discovered. +% pre is a listing of the nodes in the order in which they are first encountered (opened). +% post is a listing of the nodes in the order in which they are last encountered (closed). +% A node is last encountered once we have explored all of its neighbors. +% If the graph is directed, i's neighbors are its children. +% If the graph is a tree, preorder is parents before children, and +% postorder is children before parents. +% For a DAG, topological order = reverse(postorder). +% height(i) is the height (distance) of node i from the start. +% 'cycle' is true iff a (directed) cycle is found. +% pred(i) is the parent of i in the dfs tree rooted at start. +% See Cormen, Leiserson and Rivest, "An intro. to algorithms" 1994, p478. + +% We can detect undirected cycles by checking if we are about to visit a node n which we have +% already visited. To detect *directed* cycles, we need to know if n has been closed or is still open. +% For example (where arcs are directed down) +% 1 2 +% \ / +% 3 +% Assume we visit 1, 3 and then 2 in order. The fact that a child of 2 (namely, 3) has +% already been visited is okay, because 3 has been closed. +% The algorithms in Aho, Hopcroft and Ullman, and Sedgewick, do not detect directed cycles. + +n = length(adj_mat); + +global white gray black +white = 0; gray = 1; black = 2; + +color = white*ones(1,n); +d = zeros(1,n); +height = zeros(1,n); +pred = zeros(1,n); +pre = []; +post = []; +cycle = 0; +global count +count = 0; +h = 0; +[d, pre, post, height, cycle, color, pred] = ... + dfs2(adj_mat, start, directed, h, d, pre, post, height, cycle, color, pred); + + + +%%%%%%%%%% + +function [d, pre, post, height, cycle, color, pred] = ... + dfs2(adj_mat, i, directed, h, d, pre, post, height, cycle, color, pred) + +global count +global white gray black + +color(i) = gray; +count = count + 1; +d(i) = count; +pre = [pre i]; +height(i) = h; +if directed + ns = children(adj_mat, i); +else + ns = neighbors(adj_mat, i); +end +for j=1:length(ns) + n=ns(j); + if ~directed & n==pred(i) % don't go back up the edge you just came down + % continue + else + if color(n) == gray % going back to a non-closed vertex via a new edge + %fprintf(1, 'cycle from %d to %d\n', i, n); + cycle = 1; + end + if color(n) == white % not visited n before + pred(n)=i; + [d, pre, post, height, cycle, color, pred] = ... + dfs2(adj_mat, n, directed, h+1, d, pre, post, height, cycle, color, pred); + end + end +end +color(i) = black; +post = [post i]; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/Old/.svn/text-base/dsep_test.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ + +% Cowell et al p72 +G = zeros(10); +G(1,2)=1; +G(2,3)=1; +G(3,7)=1; +G(4,[5 8])=1; +G(5,6)=1; +G(6,7)=1; +G(7,[9 10])=1; +G(8,9)=1; + +dsep(1, 4, [5 7], G) +dsep(1, 4, [7], G) +dsep(1, 4, [10 5], G)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/Old/.svn/text-base/mk_2D_lattice_slow.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,121 @@ +function G = mk_2D_lattice(nrows, ncols, wrap_around) +% MK_2D_LATTICE Return adjacency matrix for 4-nearest neighbor connected 2D lattice +% G = mk_2D_lattice(nrows, ncols, wrap_around) +% G(k1, k2) = 1 iff k1=(i1,j1) is connected to k2=(i2,j2) +% +% If wrap_around = 1, we use toroidal boundary conditions (default = 0) +% +% Nodes are assumed numbered as in the following 3x3 lattice +% 1 4 7 +% 2 5 8 +% 3 6 9 +% +% e.g., G = mk_2D_lattice(3, 3, 0) returns +% 0 1 0 1 0 0 0 0 0 +% 1 0 1 0 1 0 0 0 0 +% 0 1 0 0 0 1 0 0 0 +% 1 0 0 0 1 0 1 0 0 +% 0 1 0 1 0 1 0 1 0 +% 0 0 1 0 1 0 0 0 1 +% 0 0 0 1 0 0 0 1 0 +% 0 0 0 0 1 0 1 0 1 +% 0 0 0 0 0 1 0 1 0 +% so find(G(5,:)) = [2 4 6 8] +% but find(G(1,:)) = [2 4] +% +% Using wrap around, G = mk_2D_lattice(3, 3, 1), we get +% 0 1 1 1 0 0 1 0 0 +% 1 0 1 0 1 0 0 1 0 +% 1 1 0 0 0 1 0 0 1 +% 1 0 0 0 1 1 1 0 0 +% 0 1 0 1 0 1 0 1 0 +% 0 0 1 1 1 0 0 0 1 +% 1 0 0 1 0 0 0 1 1 +% 0 1 0 0 1 0 1 0 1 +% 0 0 1 0 0 1 1 1 0 +% so find(G(5,:)) = [2 4 6 8] +% and find(G(1,:)) = [2 3 4 7] + +if nargin < 3, wrap_around = 0; end + +% M contains the number of each cell e.g. +% 1 4 7 +% 2 5 8 +% 3 6 9 +% North neighbors (assuming wrap around) are +% 3 6 9 +% 1 4 7 +% 2 5 8 +% Without wrap around, they are +% 1 4 7 +% 1 4 7 +% 2 5 8 +% The first row is arbitrary, since pixels at the top have no north neighbor. + +npixels = nrows*ncols; + +N = 1; E = 2; S = 3; W = 4; +if wrap_around + rows{N} = [nrows 1:nrows-1]; cols{N} = 1:ncols; + rows{E} = 1:nrows; cols{E} = [2:ncols 1]; + rows{S} = [2:nrows 1]; cols{S} = 1:ncols; + rows{W} = 1:nrows; cols{W} = [ncols 1:ncols-1]; +else + rows{N} = [1 1:nrows-1]; cols{N} = 1:ncols; + rows{E} = 1:nrows; cols{E} = [2:ncols 2]; + rows{S} = [2:nrows 2]; cols{S} = 1:ncols; + rows{W} = 1:nrows; cols{W} = [1 1:ncols-1]; +end + +M = reshape(1:npixels, [nrows ncols]); +nbrs = cell(1, 4); +for i=1:4 + nbrs{i} = M(rows{i}, cols{i}); +end + + +G = zeros(npixels, npixels); +if wrap_around + for i=1:4 + if 0 + % naive + for p=1:npixels + G(p, nbrs{i}(p)) = 1; + end + else + % vectorized + ndx2 = sub2ind([npixels npixels], 1:npixels, nbrs{i}(:)'); + G(ndx2) = 1; + end + end +else + i = N; + mask = ones(nrows, ncols); + mask(1,:) = 0; % pixels in row 1 have no nbr to the north + ndx = find(mask); + ndx2 = sub2ind([npixels npixels], ndx, nbrs{i}(ndx)); + G(ndx2) = 1; + + i = E; + mask = ones(nrows, ncols); + mask(:,ncols) = 0; + ndx = find(mask); + ndx2 = sub2ind([npixels npixels], ndx, nbrs{i}(ndx)); + G(ndx2) = 1; + + i = S; + mask = ones(nrows, ncols); + mask(nrows,:)=0; + ndx = find(mask); + ndx2 = sub2ind([npixels npixels], ndx, nbrs{i}(ndx)); + G(ndx2) = 1; + + i = W; + mask = ones(nrows, ncols); + mask(:,1)=0; + ndx = find(mask); + ndx2 = sub2ind([npixels npixels], ndx, nbrs{i}(ndx)); + G(ndx2) = 1; +end + +G = setdiag(G, 0);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/Old/CVS/.svn/all-wcprops Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,23 @@ +K 25 +svn:wc:ra_dav:version-url +V 66 +/svnroot/!svn/ver/1127/matlabtoolboxes/FullBNT-1.0.4/graph/Old/CVS +END +Repository +K 25 +svn:wc:ra_dav:version-url +V 77 +/svnroot/!svn/ver/1127/matlabtoolboxes/FullBNT-1.0.4/graph/Old/CVS/Repository +END +Root +K 25 +svn:wc:ra_dav:version-url +V 71 +/svnroot/!svn/ver/1127/matlabtoolboxes/FullBNT-1.0.4/graph/Old/CVS/Root +END +Entries +K 25 +svn:wc:ra_dav:version-url +V 74 +/svnroot/!svn/ver/1127/matlabtoolboxes/FullBNT-1.0.4/graph/Old/CVS/Entries +END
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/Old/CVS/.svn/entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,130 @@ +10 + +dir +7329 +https://parrot.dcs.qmul.ac.uk/svnroot/matlabtoolboxes/FullBNT-1.0.4/graph/Old/CVS +https://parrot.dcs.qmul.ac.uk/svnroot + + + +2008-11-18T12:42:45.431389Z +1127 +matthiasm + + + + + + + + + + + + + + +214cd2a9-030a-0410-b9a1-da4e402363b6 + +Repository +file + + + + +2010-04-08T01:08:23.000000Z +aab97ca5bf8f143ca69f54882751ca4c +2008-11-18T12:42:45.431389Z +1127 +matthiasm +has-props + + + + + + + + + + + + + + + + + + + + +18 + +Root +file + + + + +2010-04-08T01:08:23.000000Z +a49432e9bea5bbd5666443ab187b4558 +2008-11-18T12:42:45.431389Z +1127 +matthiasm +has-props + + + + + + + + + + + + + + + + + + + + +51 + +Entries +file + + + + +2010-04-08T01:08:23.000000Z +d4180170bdf4e29332494da6ad177c67 +2008-11-18T12:42:45.431389Z +1127 +matthiasm +has-props + + + + + + + + + + + + + + + + + + + + +260 +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/Old/CVS/.svn/prop-base/Entries.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +K 14 +svn:executable +V 0 + +END
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/Old/CVS/.svn/prop-base/Repository.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +K 14 +svn:executable +V 0 + +END
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/Old/CVS/.svn/prop-base/Root.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +K 14 +svn:executable +V 0 + +END
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/Old/CVS/.svn/text-base/Entries.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +/best_first_elim_order.m/1.1.1.1/Mon Aug 29 17:44:02 2005// +/dag_to_jtree.m/1.1.1.1/Mon Aug 29 17:44:02 2005// +/dfs.m/1.1.1.1/Mon Aug 29 17:44:02 2005// +/dsep_test.m/1.1.1.1/Mon Aug 29 17:44:02 2005// +/mk_2D_lattice_slow.m/1.1.1.1/Mon Aug 29 17:44:02 2005// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/Old/CVS/.svn/text-base/Repository.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/graph/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/Old/CVS/.svn/text-base/Root.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/Old/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +/best_first_elim_order.m/1.1.1.1/Mon Aug 29 17:44:02 2005// +/dag_to_jtree.m/1.1.1.1/Mon Aug 29 17:44:02 2005// +/dfs.m/1.1.1.1/Mon Aug 29 17:44:02 2005// +/dsep_test.m/1.1.1.1/Mon Aug 29 17:44:02 2005// +/mk_2D_lattice_slow.m/1.1.1.1/Mon Aug 29 17:44:02 2005// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/Old/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/graph/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/Old/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/Old/best_first_elim_order.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,64 @@ +function order = best_first_elim_order(G, node_sizes, stage) +% BEST_FIRST_ELIM_ORDER Greedily search for an optimal elimination order. +% order = best_first_elim_order(moral_graph, node_sizes) +% +% Find an order in which to eliminate nodes from the graph in such a way as to try and minimize the +% weight of the resulting triangulated graph. The weight of a graph is the sum of the weights of each +% of its cliques; the weight of a clique is the product of the weights of each of its members; the +% weight of a node is the number of values it can take on. +% +% Since this is an NP-hard problem, we use the following greedy heuristic: +% at each step, eliminate that node which will result in the addition of the least +% number of fill-in edges, breaking ties by choosing the node that induces the lighest clique. +% For details, see +% - Kjaerulff, "Triangulation of graphs -- algorithms giving small total state space", +% Univ. Aalborg tech report, 1990 (www.cs.auc.dk/~uk) +% - C. Huang and A. Darwiche, "Inference in Belief Networks: A procedural guide", +% Intl. J. Approx. Reasoning, 11, 1994 +% + +% Warning: This code is pretty old and could probably be made faster. + +n = length(G); +if nargin < 3, stage = { 1:n }; end % no constraints + +% For long DBNs, it may be useful to eliminate all the nodes in slice t before slice t+1. +% This will ensure that the jtree has a repeating structure (at least away from both edges). +% This is why we have stages. +% See the discussion of splicing jtrees on p68 of +% Geoff Zweig's PhD thesis, Dept. Comp. Sci., UC Berkeley, 1998. +% This constraint can increase the clique size significantly. + +MG = G; % copy the original graph +uneliminated = ones(1,n); +order = zeros(1,n); +t = 1; % Counts which time slice we are on +for i=1:n + U = find(uneliminated); + valid = myintersect(U, stage{t}); + % Choose the best node from the set of valid candidates + score1 = zeros(1,length(valid)); + score2 = zeros(1,length(valid)); + for j=1:length(valid) + k = valid(j); + ns = myintersect(neighbors(G, k), U); + l = length(ns); + M = MG(ns,ns); + score1(j) = l^2 - sum(M(:)); % num. added edges + score2(j) = prod(node_sizes([k ns])); % weight of clique + end + j1s = find(score1==min(score1)); + j = j1s(argmin(score2(j1s))); + k = valid(j); + uneliminated(k) = 0; + order(i) = k; + ns = myintersect(neighbors(G, k), U); + if ~isempty(ns) + G(ns,ns) = 1; + G = setdiag(G,0); + end + if ~any(logical(uneliminated(stage{t}))) % are we allowed to the next slice? + t = t + 1; + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/Old/dag_to_jtree.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,55 @@ +function [jtree, root, cliques, B, w, elim_order, moral_edges, fill_in_edges, strong] = ... + dag_to_jtree(dag, node_sizes, partial_order, stages, clusters) +% DAG_TO_JTREE Moralize and triangulate a DAG, and make a junction tree from its cliques. +% [jtree, root, cliques, B, w, elim_order, moral_edges, fill_in_edges, strong] = ... +% dag_to_jtree(dag, node_sizes, partial_order, stages, clusters) +% +% Input: +% dag(i,j) +% jtree(i,j) = 1 iff there is an arc between clique i and clique j +% root = the root clique +% cliques{i} = the nodes in clique i +% B(i,j) = 1 iff node j occurs in clique i +% w(i) = weight of clique i + +N = length(bnet.dag); +if nargin < 2, obs_nodes = []; end +if nargin < 3, stages = { 1:N }; end +if nargin < 4, clusters = {}; end + +[MG, moral_edges] = moralize(bnet.dag); + +% Add extra arcs between nodes in each cluster to ensure they occur in the same clique +for i=1:length(clusters) + c = clusters{i}; + MG(c,c) = 1; +end +MG = setdiag(MG, 0); + +% Find an optimal elimination ordering (NP-hard problem!) +ns = bnet.node_sizes(:); +ns(obs_nodes) = 1; % observed nodes have only 1 possible value +partial_order = determine_elim_constraints(bnet, obs_nodes); + +if isempty(partial_order) + strong = 0; + elim_order = best_first_elim_order(MG, ns, stages); +else + strong = 1; + elim_order = strong_elim_order(MG, ns, partial_order); +end + +[MTG, cliques, fill_in_edges] = triangulate(MG, elim_order); + +% Connect the cliques up into a jtree, +[jtree, root, B, w] = cliques_to_jtree(cliques, ns); + +if 0 + disp('testing dag to jtree'); + % Find the cliques containing each node, and check they form a connected subtree + clqs_con_node = cell(1,N); + for i=1:N + clqs_con_node{i} = find(B(:,i))'; + end + check_jtree_property(clqs_con_node, jtree); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/Old/dfs.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,84 @@ +function [d, pre, post, height, cycle, pred] = dfs(adj_mat, start, directed) +% DFS Perform a depth-first search of the graph starting from 'start'. +% [d, pre, post, height, cycle, pred] = dfs(adj_mat, start, directed) +% +% d(i) is the time at which node i is first discovered. +% pre is a listing of the nodes in the order in which they are first encountered (opened). +% post is a listing of the nodes in the order in which they are last encountered (closed). +% A node is last encountered once we have explored all of its neighbors. +% If the graph is directed, i's neighbors are its children. +% If the graph is a tree, preorder is parents before children, and +% postorder is children before parents. +% For a DAG, topological order = reverse(postorder). +% height(i) is the height (distance) of node i from the start. +% 'cycle' is true iff a (directed) cycle is found. +% pred(i) is the parent of i in the dfs tree rooted at start. +% See Cormen, Leiserson and Rivest, "An intro. to algorithms" 1994, p478. + +% We can detect undirected cycles by checking if we are about to visit a node n which we have +% already visited. To detect *directed* cycles, we need to know if n has been closed or is still open. +% For example (where arcs are directed down) +% 1 2 +% \ / +% 3 +% Assume we visit 1, 3 and then 2 in order. The fact that a child of 2 (namely, 3) has +% already been visited is okay, because 3 has been closed. +% The algorithms in Aho, Hopcroft and Ullman, and Sedgewick, do not detect directed cycles. + +n = length(adj_mat); + +global white gray black +white = 0; gray = 1; black = 2; + +color = white*ones(1,n); +d = zeros(1,n); +height = zeros(1,n); +pred = zeros(1,n); +pre = []; +post = []; +cycle = 0; +global count +count = 0; +h = 0; +[d, pre, post, height, cycle, color, pred] = ... + dfs2(adj_mat, start, directed, h, d, pre, post, height, cycle, color, pred); + + + +%%%%%%%%%% + +function [d, pre, post, height, cycle, color, pred] = ... + dfs2(adj_mat, i, directed, h, d, pre, post, height, cycle, color, pred) + +global count +global white gray black + +color(i) = gray; +count = count + 1; +d(i) = count; +pre = [pre i]; +height(i) = h; +if directed + ns = children(adj_mat, i); +else + ns = neighbors(adj_mat, i); +end +for j=1:length(ns) + n=ns(j); + if ~directed & n==pred(i) % don't go back up the edge you just came down + % continue + else + if color(n) == gray % going back to a non-closed vertex via a new edge + %fprintf(1, 'cycle from %d to %d\n', i, n); + cycle = 1; + end + if color(n) == white % not visited n before + pred(n)=i; + [d, pre, post, height, cycle, color, pred] = ... + dfs2(adj_mat, n, directed, h+1, d, pre, post, height, cycle, color, pred); + end + end +end +color(i) = black; +post = [post i]; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/Old/dsep_test.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ + +% Cowell et al p72 +G = zeros(10); +G(1,2)=1; +G(2,3)=1; +G(3,7)=1; +G(4,[5 8])=1; +G(5,6)=1; +G(6,7)=1; +G(7,[9 10])=1; +G(8,9)=1; + +dsep(1, 4, [5 7], G) +dsep(1, 4, [7], G) +dsep(1, 4, [10 5], G)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/Old/mk_2D_lattice_slow.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,121 @@ +function G = mk_2D_lattice(nrows, ncols, wrap_around) +% MK_2D_LATTICE Return adjacency matrix for 4-nearest neighbor connected 2D lattice +% G = mk_2D_lattice(nrows, ncols, wrap_around) +% G(k1, k2) = 1 iff k1=(i1,j1) is connected to k2=(i2,j2) +% +% If wrap_around = 1, we use toroidal boundary conditions (default = 0) +% +% Nodes are assumed numbered as in the following 3x3 lattice +% 1 4 7 +% 2 5 8 +% 3 6 9 +% +% e.g., G = mk_2D_lattice(3, 3, 0) returns +% 0 1 0 1 0 0 0 0 0 +% 1 0 1 0 1 0 0 0 0 +% 0 1 0 0 0 1 0 0 0 +% 1 0 0 0 1 0 1 0 0 +% 0 1 0 1 0 1 0 1 0 +% 0 0 1 0 1 0 0 0 1 +% 0 0 0 1 0 0 0 1 0 +% 0 0 0 0 1 0 1 0 1 +% 0 0 0 0 0 1 0 1 0 +% so find(G(5,:)) = [2 4 6 8] +% but find(G(1,:)) = [2 4] +% +% Using wrap around, G = mk_2D_lattice(3, 3, 1), we get +% 0 1 1 1 0 0 1 0 0 +% 1 0 1 0 1 0 0 1 0 +% 1 1 0 0 0 1 0 0 1 +% 1 0 0 0 1 1 1 0 0 +% 0 1 0 1 0 1 0 1 0 +% 0 0 1 1 1 0 0 0 1 +% 1 0 0 1 0 0 0 1 1 +% 0 1 0 0 1 0 1 0 1 +% 0 0 1 0 0 1 1 1 0 +% so find(G(5,:)) = [2 4 6 8] +% and find(G(1,:)) = [2 3 4 7] + +if nargin < 3, wrap_around = 0; end + +% M contains the number of each cell e.g. +% 1 4 7 +% 2 5 8 +% 3 6 9 +% North neighbors (assuming wrap around) are +% 3 6 9 +% 1 4 7 +% 2 5 8 +% Without wrap around, they are +% 1 4 7 +% 1 4 7 +% 2 5 8 +% The first row is arbitrary, since pixels at the top have no north neighbor. + +npixels = nrows*ncols; + +N = 1; E = 2; S = 3; W = 4; +if wrap_around + rows{N} = [nrows 1:nrows-1]; cols{N} = 1:ncols; + rows{E} = 1:nrows; cols{E} = [2:ncols 1]; + rows{S} = [2:nrows 1]; cols{S} = 1:ncols; + rows{W} = 1:nrows; cols{W} = [ncols 1:ncols-1]; +else + rows{N} = [1 1:nrows-1]; cols{N} = 1:ncols; + rows{E} = 1:nrows; cols{E} = [2:ncols 2]; + rows{S} = [2:nrows 2]; cols{S} = 1:ncols; + rows{W} = 1:nrows; cols{W} = [1 1:ncols-1]; +end + +M = reshape(1:npixels, [nrows ncols]); +nbrs = cell(1, 4); +for i=1:4 + nbrs{i} = M(rows{i}, cols{i}); +end + + +G = zeros(npixels, npixels); +if wrap_around + for i=1:4 + if 0 + % naive + for p=1:npixels + G(p, nbrs{i}(p)) = 1; + end + else + % vectorized + ndx2 = sub2ind([npixels npixels], 1:npixels, nbrs{i}(:)'); + G(ndx2) = 1; + end + end +else + i = N; + mask = ones(nrows, ncols); + mask(1,:) = 0; % pixels in row 1 have no nbr to the north + ndx = find(mask); + ndx2 = sub2ind([npixels npixels], ndx, nbrs{i}(ndx)); + G(ndx2) = 1; + + i = E; + mask = ones(nrows, ncols); + mask(:,ncols) = 0; + ndx = find(mask); + ndx2 = sub2ind([npixels npixels], ndx, nbrs{i}(ndx)); + G(ndx2) = 1; + + i = S; + mask = ones(nrows, ncols); + mask(nrows,:)=0; + ndx = find(mask); + ndx2 = sub2ind([npixels npixels], ndx, nbrs{i}(ndx)); + G(ndx2) = 1; + + i = W; + mask = ones(nrows, ncols); + mask(:,1)=0; + ndx = find(mask); + ndx2 = sub2ind([npixels npixels], ndx, nbrs{i}(ndx)); + G(ndx2) = 1; +end + +G = setdiag(G, 0);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/README.txt Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,3 @@ +Graph theory toolbox. +All functions written by Kevin Murphy (1998) unless otherwise noted. +The latest version is available from http://www.ai.mit.edu/~murphyk/Software/index.html
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/acyclic.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,23 @@ +function b = acyclic(adj_mat, directed) +% ACYCLIC Returns true iff the graph has no (directed) cycles. +% b = acyclic(adj_mat, directed) + +adj_mat = double(adj_mat); +if nargin < 2, directed = 1; end + +% e.g., G = +% 1 -> 3 +% | +% v +% 2 <- 4 +% In this case, 1->2 in the transitive closure, but 1 cannot get to itself. +% If G was undirected, 1 could get to itself, but this graph is not cyclic. +% So we cannot use the closure test in the undirected case. + +if directed + R = reachability_graph(adj_mat); + b = ~any(diag(R)==1); +else + [d, pre, post, cycle] = dfs(adj_mat,[],directed); + b = ~cycle; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/assignEdgeNums.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,32 @@ +function [edge_id, nedges] = assignEdgeNums(adj_mat) +% give each edge a unique number +% we number (i,j) for j>i first, in row, column order. +% Then we number the reverse links + +nnodes = length(adj_mat); +edge_id = zeros(nnodes); +e = 1; +for i=1:nnodes + for j=i+1:nnodes + if adj_mat(i,j) + edge_id(i,j) = e; + e = e+1; + end + end +end + +nedges = e-1; +tmp = edge_id; +ndx = find(tmp); +tmp(ndx) = tmp(ndx)+nedges; +edge_id = edge_id + triu(tmp)'; + + +if 0 +ndx = find(adj_mat); +nedges = length(ndx); +nnodes = length(adj_mat); +edge_id = zeros(1, nnodes*nnodes); +edge_id(ndx) = 1:nedges; +edge_id = reshape(edge_id, nnodes, nnodes); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/best_first_elim_order.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,68 @@ +function order = best_first_elim_order(G, node_sizes, stage) +% BEST_FIRST_ELIM_ORDER Greedily search for an optimal elimination order. +% order = best_first_elim_order(moral_graph, node_sizes) +% +% Find an order in which to eliminate nodes from the graph in such a way as to try and minimize the +% weight of the resulting triangulated graph. The weight of a graph is the sum of the weights of each +% of its cliques; the weight of a clique is the product of the weights of each of its members; the +% weight of a node is the number of values it can take on. +% +% Since this is an NP-hard problem, we use the following greedy heuristic: +% at each step, eliminate that node which will result in the addition of the least +% number of fill-in edges, breaking ties by choosing the node that induces the lighest clique. +% For details, see +% - Kjaerulff, "Triangulation of graphs -- algorithms giving small total state space", +% Univ. Aalborg tech report, 1990 (www.cs.auc.dk/~uk) +% - C. Huang and A. Darwiche, "Inference in Belief Networks: A procedural guide", +% Intl. J. Approx. Reasoning, 11, 1994 +% + +% Warning: This code is pretty old and could probably be made faster. + +n = length(G); +if nargin < 3, stage = { 1:n }; end % no constraints + +% For long DBNs, it may be useful to eliminate all the nodes in slice t before slice t+1. +% This will ensure that the jtree has a repeating structure (at least away from both edges). +% This is why we have stages. +% See the discussion of splicing jtrees on p68 of +% Geoff Zweig's PhD thesis, Dept. Comp. Sci., UC Berkeley, 1998. +% This constraint can increase the clique size significantly. + +MG = G; % copy the original graph +uneliminated = ones(1,n); +order = zeros(1,n); +t = 1; % Counts which time slice we are on +for i=1:n + U = find(uneliminated); + valid = myintersect(U, stage{t}); + % Choose the best node from the set of valid candidates + min_fill = zeros(1,length(valid)); + min_weight = zeros(1,length(valid)); + for j=1:length(valid) + k = valid(j); + nbrs = myintersect(neighbors(G, k), U); + l = length(nbrs); + M = MG(nbrs,nbrs); + min_fill(j) = l^2 - sum(M(:)); % num. added edges + min_weight(j) = prod(node_sizes([k nbrs])); % weight of clique + end + lightest_nbrs = find(min_weight==min(min_weight)); + % break ties using min-fill heuristic + best_nbr_ndx = argmin(min_fill(lightest_nbrs)); + j = lightest_nbrs(best_nbr_ndx); % we will eliminate the j'th element of valid + %j1s = find(score1==min(score1)); + %j = j1s(argmin(score2(j1s))); + k = valid(j); + uneliminated(k) = 0; + order(i) = k; + ns = myintersect(neighbors(G, k), U); + if ~isempty(ns) + G(ns,ns) = 1; + G = setdiag(G,0); + end + if ~any(logical(uneliminated(stage{t}))) % are we allowed to the next slice? + t = t + 1; + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/check_jtree_property.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,18 @@ +function check_jtree_property(cliques_containing_node, jtree) +% CHECK_JTREE_PROPERTY Raise an error if the graph does not satisfy the join tree property. +% check_jtree_property(cliques_containing_node, jtree_adj_mat) +% +% The join tree property says: +% For each node n in the dag, compute the node-induced subgraph G by looking at all the cliques +% that contain n, and make sure G forms a connected graph. +% This ensures that local propagation leads to global consistency. + +num_bn_nodes = length(cliques_containing_node); +directed = 0; +for i=1:num_bn_nodes + cs = cliques_containing_node{i}; + G = jtree(cs,cs); + if ~connected_graph(G, directed) + error(['node ' num2str(i) ' violates jtree property']); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/check_triangulated.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,34 @@ +function [triangulated, order] = check_triangulated(G) +% CHECK_TRIANGULATED Return 1 if G is a triangulated (chordal) graph, 0 otherwise. +% [triangulated, order] = check_triangulated(G) +% +% A numbering alpha is perfect if Nbrs(alpha(i)) intersect {alpha(1)...alpha(i-1)} is complete. +% A graph is triangulated iff it has a perfect numbering. +% The Maximum Cardinality Search algorithm will create such a perfect numbering if possible. +% See Golumbic, "Algorithmic Graph Theory and Perfect Graphs", Cambridge Univ. Press, 1985, p85. +% or Castillo, Gutierrez and Hadi, "Expert systems and probabilistic network models", Springer 1997, p134. + + +G = setdiag(G, 1); +n = length(G); +order = zeros(1,n); +triangulated = 1; +numbered = [1]; +order(1) = 1; +for i=2:n + U = mysetdiff(1:n, numbered); % unnumbered nodes + score = zeros(1, length(U)); + for ui=1:length(U) + u = U(ui); + score(ui) = length(myintersect(neighbors(G, u), numbered)); + end + u = U(argmax(score)); + numbered = [numbered u]; + order(i) = u; + nns = myintersect(neighbors(G,u), order(1:i-1)); % numbered neighbors + if ~isequal(G(nns,nns), ones(length(nns))) % ~complete(G(nns,nns)) + triangulated = 0; + break; + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/children.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,17 @@ +function cs = children(adj_mat, i, t) +% CHILDREN Return the indices of a node's children in sorted order +% c = children(adj_mat, i, t) +% +% t is an optional argument: if present, dag is assumed to be a 2-slice DBN + +if nargin < 3 + cs = find(adj_mat(i,:)); +else + if t==1 + cs = find(adj_mat(i,:)); + else + ss = length(adj_mat)/2; + j = i+ss; + cs = find(adj_mat(j,:)) + (t-2)*ss; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/cliques_to_jtree.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,57 @@ +function [jtree, root, B, w] = cliques_to_jtree(cliques, ns) +% MK_JTREE Make an optimal junction tree. +% [jtree, root, B, w] = mk_jtree(cliques, ns) +% +% A junction tree is a tree that satisfies the jtree property, which says: +% for each pair of cliques U,V with intersection S, all cliques on the path between U and V +% contain S. (This ensures that local propagation leads to global consistency.) +% +% We can create a junction tree by computing the maximal spanning tree of the junction graph. +% (The junction graph connects all cliques, and the weight of an edge (i,j) is +% |C(i) intersect C(j)|, where C(i) is the i'th clique.) +% +% The best jtree is the maximal spanning tree which minimizes the sum of the costs on each edge, +% where cost(i,j) = w(C(i)) + w(C(j)), and w(C) is the weight of clique C, +% which is the total number of values C can take on. +% +% For details, see +% - Jensen and Jensen, "Optimal Junction Trees", UAI 94. +% +% Input: +% cliques{i} = nodes in clique i +% ns(i) = number of values node i can take on +% Output: +% jtree(i,j) = 1 iff cliques i and j aer connected +% root = the clique that should be used as root +% B(i,j) = 1 iff node j occurs in clique i +% w(i) = weight of clique i + + + +num_cliques = length(cliques); +w = zeros(num_cliques, 1); +B = sparse(num_cliques, 1); +for i=1:num_cliques + B(i, cliques{i}) = 1; + w(i) = prod(ns(cliques{i})); +end + + +% C1(i,j) = length(intersect(cliques{i}, cliques{j})); +% The length of the intersection of two sets is the dot product of their bit vector representation. +C1 = B*B'; +C1 = setdiag(C1, 0); + +% C2(i,j) = w(i) + w(j) +num_cliques = length(w); +W = repmat(w, 1, num_cliques); +C2 = W + W'; +C2 = setdiag(C2, 0); + +jtree = sparse(minimum_spanning_tree(-C1, C2)); % Using -C1 gives *maximum* spanning tree + +% The root is arbitrary, but since the first pass is towards the root, +% we would like this to correspond to going forward in time in a DBN. +root = num_cliques; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/cliques_to_strong_jtree.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,114 @@ +function [jtree, root, cliques, B, w] = mk_strong_jtree(cliques, ns, elim_order, MTG) +% MK_SRONG_JTREE Make a strong junction tree. +% [jtree, root, cliques, B, w] = mk_strong_jtree(cliques, ns, elim_order, MTG) +% +% Here is a definition of a strong jtree from Jensen et al. 1994: +% "A junction tree is said to be strong if it has at least one distinguished clique R, +% called a strong root, s.t. for each pair (C1,C2) of adjacent cliques in the tree, +% with C1 closer to R than C2, there exists and ordering of [the nodes below] C2 +% that respects [the partial order] and with the vertices of the separator C1 intersect C2 +% preceeding the vertices [below C2] of C2 \ C1." +% +% For details, see +% - Jensen, Jensen and Dittmer, "From influence diagrams to junction trees", UAI 94. +% +% MTG is the moralized, triangulated graph. +% elim_order is the elimination ordering used to compute MTG. + + +% Warning: this is a very naive implementation of the algorithm in Jensen et al. + +n = length(elim_order); +alpha(elim_order) = n:-1:1; +% alpha(u) = i if we eliminate u at step n-i+1 +% i.e., vertices with higher alpha numbers are eliminated before vertices with lower numbers. +% e.g., from the Jensen et al paper +% node a=1 eliminated at step 6, so alpha(a)=16-6+1=11. +% alpha = [11 1 2 10 9 3 4 7 5 8 13 12 6 16 15 14] + + +% We sort the cliques in order of increasing index. The index of a clique C is defined as follows. +% Let lower = {u | alpha(u) < alpha(v)}, and +% let v in C be the highest-numbered vertex s.t. the vertices in W = lower intersect C +% have a common neighbor u in U, where U = lower \ C. +% If such a v exists, define index(C) = alpha(v), otherwise, index(C) = 1. +% Intuitively, index(C) is the step in the elimination process at which C disappears. + +num_cliques = length(cliques); +index = zeros(1, num_cliques); +for c = 1:num_cliques + C = cliques{c}; + highest_num = -inf; + for vi = 1:length(C) + v = C(vi); + lower = find(alpha < alpha(v)); + W = myintersect(lower, C); + U = mysetdiff(lower, C); + found = 0; + for ui=1:length(U) + u = U(ui); + if mysubset(W, neighbors(MTG, u)) + found = 1; + break; + end + end + if found + if alpha(v) > highest_num + highest_num = alpha(v); + end + end + end + if highest_num == -inf + index(c) = 1; + else + index(c) = highest_num; + end +end + + +% Permute the cliques so that they are ordered according to index +[dummy, clique_order] = sort(index); +cliques = cliques(clique_order); + +w = zeros(num_cliques, 1); +B = sparse(num_cliques, 1); +for i=1:num_cliques + B(i, cliques{i}) = 1; + w(i) = prod(ns(cliques{i})); +end + +% Pearl p113 suggests ordering the cliques by rank of the highest vertex in each clique. +% However, this will only work if we use maximum cardinality search. + + +% Join up the cliques so that they satisfy the Running Intersection Property. +% This states that, for all k > 1, S(k) subseteq C(j) for some j < k, where +% S(k) = C(k) intersect (union_{i=1}^{k-1} C(i)) +jtree = sparse(num_cliques, num_cliques); +for k=2:num_cliques + S = []; + for i=1:k-1 + S = myunion(S, cliques{i}); + end + S = myintersect(S, cliques{k}); + found = 0; + for j=1:k-1 + if mysubset(S, cliques{j}) + found = 1; + break; + end + end + if ~found + disp(['RIP is violated for clique ' num2str(k)]); + end + jtree(k,j)=1; + jtree(j,k)=1; +end + +% Pearl p113 suggests connecting Ci to a predecessor Cj (j < i) sharing +% the highest number of vertices with Ci (i.e., the heaviest i-j edge +% in the jgraph). However, this will only work if we use maximum cardinality search. + +root = 1; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/connected_graph.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,9 @@ +function b = connected(adj_mat, directed) +% +% b = connected(adj_mat). +% Returns true iff the graph is connected. + +n = length(adj_mat); +start = 1; +[d, pre] = dfs(adj_mat, start, directed); +b = (length(pre) == n);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/dag_to_essential_graph.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,112 @@ + +function [eg] = dag_to_essential_graph(dag) +cpdag = dag_to_cpdag(dag); +eg = dag + dag .* (cpdag + cpdag'); + +return; + + + + +% Coverts a DAG into Essential Graph where edges are coded by 2 and 3, 2 is +% directed edge and 3 is bidirected edge and is at one (the same as the original DAG) of the two +% symetrical places. + +% Is implemented by the algorithm of Max Chickering in D.M.Chickering (1995). +% A transformational characterization of equivalent Bayesian network structures. +% In Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, QU, +% pages 87-98. Morgan Kaufmann +% http://research.microsoft.com/~dmax/publications/uai95.pdf + +% Implemented by Tomas Kocka, AAU. + +function [eg] = dag_to_essential_graph(dagx) + +%print_dag(dagx); % Just checking input + +order = topological_sort(dagx); % get the topological order of nodes and their number + +% fprintf('the topological order is: %d',order); +% fprintf('\n'); + +[nx,ny] = size(dagx); % gets the number of nodes, note that nx == ny +[I,J] = find(dagx); % finds all nonzero elements in the adjacency matrix, i.e. arcs in the DAG - however we will overwrite it in a special order +% we will sort the arcs from lowest possible y and highest possible x, arcs are x->y +e = 1; +for y = 1:ny + for x = nx:-1:1 + %fprintf('x %d ',order(x)); fprintf('y %d ',order(y)); + if dagx(order(x),order(y)) == 1 + I(e) = order(x); + J(e) = order(y); + e = e + 1; + %fprintf('x order %d',x); + %fprintf('y order %d',y); + %fprintf('\n'); + end + end +end + + +% fprintf('the arcs are: %d',I); +% fprintf('\n'); +% fprintf('the arcs are: %d',J); +% fprintf('\n'); + + +% Now we have to decide which arcs are part of the essential graph and +% which are undirected edges in the essential graph. +% Undecided arc in the DAG are 1, directed in EG are 2 and undirected in EG +% are 3. + + +for e = 1:length(I) + if dagx(I(e),J(e)) == 1 + cont = true; + for w = 1:nx + if dagx(w,I(e)) == 2 + if dagx(w,J(e)) ~= 0 + dagx(w,J(e)) = 2; + else + for ww = 1:nx + if dagx(ww,J(e)) ~= 0 + dagx(ww,J(e)) = 2; + end + end % and now skip the rest and start with another arc from the list + w = nx; + cont = false; + end + end + end + if cont + exists = false; + for z = 1:nx + %fprintf('test %d',dagx(z,J(e))); + if dagx(z,J(e)) ~= 0 & z ~= I(e) & dagx(z,I(e)) == 0 + exists = true; + for ww = 1:nx + if dagx(ww,J(e)) == 1 + dagx(ww,J(e)) = 2; + end + end + end + end + if ~ exists + for ww = 1:nx + if dagx(ww,J(e)) == 1 + dagx(ww,J(e)) = 3; + end + end + end + end + end +end + +%print_dag(dagx); % Just checking output + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/dfs.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,91 @@ +function [d, pre, post, cycle, f, pred] = dfs(adj_mat, start, directed) +% DFS Perform a depth-first search of the graph starting from 'start'. +% [d, pre, post, cycle, f, pred] = dfs(adj_mat, start, directed) +% +% Input: +% adj_mat(i,j)=1 iff i is connected to j. +% start is the root vertex of the dfs tree; if [], all nodes are searched +% directed = 1 if the graph is directed +% +% Output: +% d(i) is the time at which node i is first discovered. +% pre is a list of the nodes in the order in which they are first encountered (opened). +% post is a list of the nodes in the order in which they are last encountered (closed). +% 'cycle' is true iff a (directed) cycle is found. +% f(i) is the time at which node i is finished. +% pred(i) is the predecessor of i in the dfs tree. +% +% If the graph is a tree, preorder is parents before children, +% and postorder is children before parents. +% For a DAG, topological order = reverse(postorder). +% +% See Cormen, Leiserson and Rivest, "An intro. to algorithms" 1994, p478. + +n = length(adj_mat); + +global white gray black color +white = 0; gray = 1; black = 2; +color = white*ones(1,n); + +global time_stamp +time_stamp = 0; + +global d f +d = zeros(1,n); +f = zeros(1,n); + +global pred +pred = zeros(1,n); + +global cycle +cycle = 0; + +global pre post +pre = []; +post = []; + +if ~isempty(start) + dfs_visit(start, adj_mat, directed); +end +for u=1:n + if color(u)==white + dfs_visit(u, adj_mat, directed); + end +end + + +%%%%%%%%%% + +function dfs_visit(u, adj_mat, directed) + +global white gray black color time_stamp d f pred cycle pre post + +pre = [pre u]; +color(u) = gray; +time_stamp = time_stamp + 1; +d(u) = time_stamp; +if directed + ns = children(adj_mat, u); +else + ns = neighbors(adj_mat, u); + ns = setdiff(ns, pred(u)); % don't go back to visit the guy who called you! +end +for v=ns(:)' + %fprintf('u=%d, v=%d, color(v)=%d\n', u, v, color(v)) + switch color(v) + case white, % not visited v before (tree edge) + pred(v)=u; + dfs_visit(v, adj_mat, directed); + case gray, % back edge - v has been visited, but is still open + cycle = 1; + %fprintf('cycle: back edge from v=%d to u=%d\n', v, u); + case black, % v has been visited, but is closed + % no-op + end +end +color(u) = black; +post = [post u]; +time_stamp = time_stamp + 1; +f(u) = time_stamp; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/dfs_test.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,30 @@ +% Do the example in fig 23.4 p479 of Cormen, Leiserson and Rivest (1994) + +u = 1; v = 2; w = 3; x = 4; y = 5; z = 6; +n = 6; +dag=zeros(n,n); +dag(u,[v x])=1; +dag(v,y)=1; +dag(w,[y z])=1; +dag(x,v)=1; +dag(y,x)=1; +dag(z,z)=1; + +[d, pre, post, cycle, f, pred] = dfs(dag, [], 1); +assert(isequal(d, [1 2 9 4 3 10])) +assert(isequal(f, [8 7 12 5 6 11]) +assert(cycle) + +% Now give it an undirected cyclic graph +G = mk_2D_lattice(2,2,0); +% 1 - 3 +% | | +% 2 - 4 +[d, pre, post, cycle, f, pred] = dfs(G, [], 0); +% d = [1 2 4 3] +assert(cycle) + +% Now break the cycle +G(1,2)=0; G(2,1)=0; +[d, pre, post, cycle, f, pred] = dfs(G, [], 0); +assert(~cycle)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/dijkstra.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,112 @@ +function [D,P] = dijk(A,s,t) +%DIJK Shortest paths from nodes 's' to nodes 't' using Dijkstra algorithm. +% [D,p] = dijk(A,s,t) +% A = n x n node-node weighted adjacency matrix of arc lengths +% (Note: A(i,j) = 0 => Arc (i,j) does not exist; +% A(i,j) = NaN => Arc (i,j) exists with 0 weight) +% s = FROM node indices +% = [] (default), paths from all nodes +% t = TO node indices +% = [] (default), paths to all nodes +% D = |s| x |t| matrix of shortest path distances from 's' to 't' +% = [D(i,j)], where D(i,j) = distance from node 'i' to node 'j' +% P = |s| x n matrix of predecessor indices, where P(i,j) is the +% index of the predecessor to node 'j' on the path from 's(i)' to 'j' +% (use PRED2PATH to convert P to paths) +% = path from 's' to 't', if |s| = |t| = 1 +% +% (If A is a triangular matrix, then computationally intensive node +% selection step not needed since graph is acyclic (triangularity is a +% sufficient, but not a necessary, condition for a graph to be acyclic) +% and A can have non-negative elements) +% +% (If |s| >> |t|, then DIJK is faster if DIJK(A',t,s) used, where D is now +% transposed and P now represents successor indices) +% +% (Based on Fig. 4.6 in Ahuja, Magnanti, and Orlin, Network Flows, +% Prentice-Hall, 1993, p. 109.) + +% Copyright (c) 1998-2001 by Michael G. Kay +% Matlog Version 5 22-Aug-2001 + +% Input Error Checking ****************************************************** +error(nargchk(1,3,nargin)); + +[n,cA] = size(A); + +if nargin < 2 | isempty(s), s = (1:n)'; else s = s(:); end +if nargin < 3 | isempty(t), t = (1:n)'; else t = t(:); end + +if ~any(any(tril(A) ~= 0)) % A is upper triangular + isAcyclic = 1; +elseif ~any(any(triu(A) ~= 0)) % A is lower triangular + isAcyclic = 2; +else % Graph may not be acyclic + isAcyclic = 0; +end + +if n ~= cA + error('A must be a square matrix'); +elseif ~isAcyclic & any(any(A < 0)) + error('A must be non-negative'); +elseif any(s < 1 | s > n) + error(['''s'' must be an integer between 1 and ',num2str(n)]); +elseif any(t < 1 | t > n) + error(['''t'' must be an integer between 1 and ',num2str(n)]); +end +% End (Input Error Checking) ************************************************ + +A = A'; % Use transpose to speed-up FIND for sparse A + +D = zeros(length(s),length(t)); +if nargout > 1, P = zeros(length(s),n); end + +for i = 1:length(s) + j = s(i); + + Di = Inf*ones(n,1); Di(j) = 0; + + isLab = logical(zeros(length(t),1)); + if isAcyclic == 1 + nLab = j - 1; + elseif isAcyclic == 2 + nLab = n - j; + else + nLab = 0; + UnLab = 1:n; + isUnLab = logical(ones(n,1)); + end + + while nLab < n & ~all(isLab) + if isAcyclic + Dj = Di(j); + else % Node selection + [Dj,jj] = min(Di(isUnLab)); + j = UnLab(jj); + UnLab(jj) = []; + isUnLab(j) = 0; + end + + nLab = nLab + 1; + if length(t) < n, isLab = isLab | (j == t); end + + [jA,kA,Aj] = find(A(:,j)); + Aj(isnan(Aj)) = 0; + + if isempty(Aj), Dk = Inf; else Dk = Dj + Aj; end + + if nargout > 1, P(i,jA(Dk < Di(jA))) = j; end + Di(jA) = min(Di(jA),Dk); + + if isAcyclic == 1 % Increment node index for upper triangular A + j = j + 1; + elseif isAcyclic == 2 % Decrement node index for lower triangular A + j = j - 1; + end + end + D(i,:) = Di(t)'; +end + +if nargout > 1 & length(s) == 1 & length(t) == 1 + P = pred2path(P,s,t); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,17 @@ +function f = family(A,i,t) +% FAMILY Return the indices of parents and self in sorted order +% f = family(dag,i,t) +% +% t is an optional argument: if present, dag is assumed to be a 2-slice DBN + +if nargin < 3 + f = [parents(A,i) i]; +else + if t == 1 + f = [parents(A,i) i]; + else + ss = length(A)/2; + j = i+ss; + f = [parents(A,j) j] + (t-2)*ss; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/graph_separated.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,9 @@ +function sep = graph_separated(G, X, Y, S) + +G2 = G; +G2(S,:) = 0; +G2(:,S) = 0; +conn = reachability_graph(G2); +conn2 = conn(X,Y); +sep = all(conn2(:)==0); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/graph_to_jtree.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,59 @@ +function [jtree, root, cliques, B, w, elim_order] = graph_to_jtree(MG, ns, partial_order, stages, clusters) +% GRAPH_TO_JTREE Triangulate a graph and make a junction tree from its cliques. +% [jtree, root, cliques, B, w, elim_order] = ... +% graph_to_jtree(graph, node_sizes, partial_order, stages, clusters) +% +% INPUT: +% graph(i,j) = 1 iff there is an edge between i,j +% node_weights(i) = num discrete values node i can take on [1 if observed] +% partial_order = {} if no constraints on elimination ordering +% stages{i} = nodes that must be eliminated at i'th stage (if porder is empty) +% clusters{i} = list of nodes that must get connected together in the moral graph +% +% OUTPUT: +% jtree(i,j) = 1 iff there is an arc between clique i and clique j +% root = the root clique +% cliques{i} = the nodes in clique i +% B(i,j) = 1 iff node j occurs in clique i +% w(i) = weight of clique i + +N = length(MG); + +if nargin >= 5 + % Add extra arcs between nodes in each cluster to ensure they occur in the same clique + for i=1:length(clusters) + c = clusters{i}; + MG(c,c) = 1; + end +end +MG = setdiag(MG, 0); + +% Find an optimal elimination ordering (NP-hard problem!) +if nargin < 4 + stages = {1:N}; +end +if nargin < 3 + partial_order = {}; +end +if isempty(partial_order) + strong = 0; + elim_order = best_first_elim_order(MG, ns, stages); +else + strong = 1; + elim_order = strong_elim_order(MG, ns, partial_order); +end + +[MTG, cliques, fill_in_edges] = triangulate(MG, elim_order); + +% Connect the cliques up into a jtree, +[jtree, root, B, w] = cliques_to_jtree(cliques, ns); + +if 0 + disp('testing dag to jtree'); + % Find the cliques containing each node, and check they form a connected subtree + clqs_con_node = cell(1,N); + for i=1:N + clqs_con_node{i} = find(B(:,i))'; + end + check_jtree_property(clqs_con_node, jtree); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/min_subtree_con_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,60 @@ +function [subtree, nroot_node] = min_subtree_con_nodes(jtree, root, nodes) +%min_subtree_con_nodes get the minimum subtree of tree which contains the nodes + +if isempty(jtree) | isempty(nodes) + subtree = []; + nroot_node = []; + return; +end + +rnodes = min_subtree_nodes(jtree, nodes); +nea_node = nearest_node(jtree, root, nodes); +node_num = length(jtree); +subtree = zeros(node_num); +subtree(rnodes, rnodes) = jtree(rnodes, rnodes); +nroot_node = nea_node; + + +function rnodes = min_subtree_nodes(tree, nodes) +rnodes = []; +if isempty(tree) | isempty(nodes) + return +end + +rnodes = nodes(1); +newnodes = neighbors(tree, nodes(1)); +while ~mysubset(nodes, rnodes) + swapnodes = newnodes; + newnodes = []; + added = 0; + for i=1:length(swapnodes) + inode = swapnodes(i); + tnodes = myunion(inode, rnodes); + if mysubset(nodes, tnodes) + added = 1; + break; + end + nns = neighbors(tree, inode); + add_nodes = mysetdiff(nns, tnodes); + newnodes = myunion(newnodes, add_nodes); + end + if added + rnodes = tnodes; + else + rnodes = myunion(rnodes, newnodes); + end +end + +function nea_node = nearest_node(tree, inode, nodes) +if myismember(inode, nodes) + nea_node = inode; + return; +end +cs = children(tree, inode); +for i = 1:length(cs) + n = cs(i); + nea_node = nearest_node(tree, n, nodes); +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/minimum_spanning_tree.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,51 @@ +function A = minimum_spanning_tree(C1, C2) +% +% Find the minimum spanning tree using Prim's algorithm. +% C1(i,j) is the primary cost of connecting i to j. +% C2(i,j) is the (optional) secondary cost of connecting i to j, used to break ties. +% We assume that absent edges have 0 cost. +% To find the maximum spanning tree, used -1*C. +% See Aho, Hopcroft & Ullman 1983, "Data structures and algorithms", p 237. + +% Prim's is O(V^2). Kruskal's algorithm is O(E log E) and hence is more efficient +% for sparse graphs, but is implemented in terms of a priority queue. + +% We partition the nodes into those in U and those not in U. +% closest(i) is the vertex in U that is closest to i in V-U. +% lowcost(i) is the cost of the edge (i, closest(i)), or infinity is i has been used. +% In Aho, they say C(i,j) should be "some appropriate large value" if the edge is missing. +% We set it to infinity. +% However, since lowcost is initialized from C, we must distinguish absent edges from used nodes. + +n = length(C1); +if nargin==1, C2 = zeros(n); end +A = zeros(n); + +closest = ones(1,n); +used = zeros(1,n); % contains the members of U +used(1) = 1; % start with node 1 +C1(find(C1==0))=inf; +C2(find(C2==0))=inf; +lowcost1 = C1(1,:); +lowcost2 = C2(1,:); + +for i=2:n + ks = find(lowcost1==min(lowcost1)); + k = ks(argmin(lowcost2(ks))); + A(k, closest(k)) = 1; + A(closest(k), k) = 1; + lowcost1(k) = inf; + lowcost2(k) = inf; + used(k) = 1; + NU = find(used==0); + for ji=1:length(NU) + for j=NU(ji) + if C1(k,j) < lowcost1(j) + lowcost1(j) = C1(k,j); + lowcost2(j) = C2(k,j); + closest(j) = k; + end + end + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/minspan.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,56 @@ +function [t,nk] = minspan(IJC) +%MINSPAN Minimum weight spanning tree using Kruskal algorithm. +%[t,nk] = minspan(IJC) +% IJC = n x 3 matrix arc list [i j c] of arc heads, tails, and costs +% t = n-element logical vector, where +% t(i) = 1, if IJC(i,:) arc in spanning tree +% t(i) = k, if IJC(i,:) arc in component k of forest +% nk = number of components + +% Copyright (c) 1998-2001 by Michael G. Kay +% Matlog Version 5 22-Aug-2001 + +% Input Error Checking ****************************************************** +[n,cIJC] = size(IJC); +if cIJC ~= 3 + error('''IJC'' must be a 3-column matrix.') +elseif n < 1 + error('''IJC'' must have at least one row.') +elseif any(IJC(:,1) < 1) | any(any(~isint(IJC(:,[1 2])))) + error('Invalid arc index in IJC.') +end +% End (Input Error Checking) ************************************************ + +i = IJC(:,1); j = abs(IJC(:,2)); +m = max(max([i j])); + +sidxIJ = argsort(IJC(:,3)); +i = i(sidxIJ); j = j(sidxIJ); + +t = logical(zeros(n,1)); +k = 1; % Current arc +nt = 0; % Number of arcs in spanning tree +v = (1:m)'; % Arc labels + +while nt < m - 1 & k <= n + if (v(i(k)) ~= v(j(k))) + v(v==v(j(k))) = v(i(k)); + t(k) = 1; + nt = nt + 1; + end + k = k + 1; +end + +idxIJ = invperm(sidxIJ); +t = t(idxIJ); i = i(idxIJ); j = j(idxIJ); + +c = unique(v(unique([i; j]))); % Unique labels of arc vertices +nk = length(c); +if ~any(t), nk = 0; end % Self-loop not a component + +if nk > 1 + for k = 1:nk + t(t~=0 & v(i)==c(k)) = k; % Relabel to consecutive component numbers + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/mk_2D_lattice.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,59 @@ +function G = mk_2D_lattice(nrows, ncols, con) +% MK_2D_LATTICE Return adjacency matrix for nearest neighbor connected 2D lattice +% G = mk_2D_lattice(nrows, ncols, con) +% G(k1, k2) = 1 iff k1=(i1,j1) is a neighbor of k2=(i2,j2) +% (Two pixels are neighbors if their Euclidean distance is less than r.) +% Default connectivity = 4. +% +% WE ASSUME NO WRAP AROUND. +% +% This is the neighborhood as a function of con: +% +% con=4,r=1 con=8,r=sqrt(2) con=12,r=2 con=24,r=sqrt(8) +% nn 2nd order 4th order +% x x x x x x +% x x x x x x x x x x x x +% x o x x o x x x o x x x x o x x +% x x x x x x x x x x x x +% x x x x x x +% +% Examples: +% Consider a 3x4 grid +% 1 4 7 10 +% 2 5 8 11 +% 3 6 9 12 +% +% 4-connected: +% G=mk_2D_lattice(3,4,4); +% find(G(1,:)) = [2 4] +% find(G(5,:)) = [2 4 6 8] +% +% 8-connected: +% G=mk_2D_lattice(3,4,8); +% find(G(1,:)) = [2 4 5] +% find(G(5,:)) = [1 2 3 4 6 7 8 9] + +% meshgrid trick due to Temu Gautama (temu@neuro.kuleuven.ac.be) + +if nargin < 3, con = 4; end + +switch con, + case 4, r = 1; + case 8, r = sqrt(2); + case 12, r = 2; + case 24, r = sqrt(8); + otherwise, error(['unrecognized connectivity ' num2str(con)]) +end + + +npixels = nrows*ncols; + +[x y]=meshgrid(1:ncols, 1:nrows); +M = [x(:) y(:)]; +M1 = repmat(reshape(M',[1 2 npixels]),[npixels 1 1]); +M2 = repmat(M,[1 1 npixels]); +%D = squeeze(sum(abs(M1-M2),2)); % Manhattan distance +M3 = M1-M2; +D = sqrt(squeeze(M3(:,1,:)) .^2 + squeeze(M3(:,2,:)) .^2); % Euclidean distance +G = reshape(D <= r,npixels,npixels); +G = setdiag(G, 0);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/mk_2D_lattice_slow.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,135 @@ +function G = mk_2D_lattice_slow(nrows, ncols, wrap_around) +% MK_2D_LATTICE Return adjacency matrix for 4-nearest neighbor connected 2D lattice +% G = mk_2D_lattice(nrows, ncols, wrap_around) +% G(k1, k2) = 1 iff k1=(i1,j1) is connected to k2=(i2,j2) +% +% If wrap_around = 1, we use toroidal boundary conditions (default = 0) +% +% Nodes are assumed numbered as in the following 3x3 lattice +% 1 4 7 +% 2 5 8 +% 3 6 9 +% +% e.g., G = mk_2D_lattice(3, 3, 0) returns +% 0 1 0 1 0 0 0 0 0 +% 1 0 1 0 1 0 0 0 0 +% 0 1 0 0 0 1 0 0 0 +% 1 0 0 0 1 0 1 0 0 +% 0 1 0 1 0 1 0 1 0 +% 0 0 1 0 1 0 0 0 1 +% 0 0 0 1 0 0 0 1 0 +% 0 0 0 0 1 0 1 0 1 +% 0 0 0 0 0 1 0 1 0 +% so find(G(5,:)) = [2 4 6 8] +% but find(G(1,:)) = [2 4] +% +% Using wrap around, G = mk_2D_lattice(3, 3, 1), we get +% 0 1 1 1 0 0 1 0 0 +% 1 0 1 0 1 0 0 1 0 +% 1 1 0 0 0 1 0 0 1 +% 1 0 0 0 1 1 1 0 0 +% 0 1 0 1 0 1 0 1 0 +% 0 0 1 1 1 0 0 0 1 +% 1 0 0 1 0 0 0 1 1 +% 0 1 0 0 1 0 1 0 1 +% 0 0 1 0 0 1 1 1 0 +% so find(G(5,:)) = [2 4 6 8] +% and find(G(1,:)) = [2 3 4 7] + +if nargin < 3, wrap_around = 0; end + +% M contains the number of each cell e.g. +% 1 4 7 +% 2 5 8 +% 3 6 9 +% North neighbors (assuming wrap around) are +% 3 6 9 +% 1 4 7 +% 2 5 8 +% Without wrap around, they are +% 1 4 7 +% 1 4 7 +% 2 5 8 +% The first row is arbitrary, since pixels at the top have no north neighbor. + +if nrows==1 + G = zeros(1, ncols); + for i=1:ncols-1 + G(i,i+1) = 1; + G(i+1,i) = 1; + end + if wrap_around + G(1,ncols) = 1; + G(ncols,1) = 1; + end + return; +end + + +npixels = nrows*ncols; + +N = 1; E = 2; S = 3; W = 4; +if wrap_around + rows{N} = [nrows 1:nrows-1]; cols{N} = 1:ncols; + rows{E} = 1:nrows; cols{E} = [2:ncols 1]; + rows{S} = [2:nrows 1]; cols{S} = 1:ncols; + rows{W} = 1:nrows; cols{W} = [ncols 1:ncols-1]; +else + rows{N} = [1 1:nrows-1]; cols{N} = 1:ncols; + rows{E} = 1:nrows; cols{E} = [1 1:ncols-1]; + rows{S} = [2:nrows nrows]; cols{S} = 1:ncols; + rows{W} = 1:nrows; cols{W} = [2:ncols ncols]; +end + +M = reshape(1:npixels, [nrows ncols]); +nbrs = cell(1, 4); +for i=1:4 + nbrs{i} = M(rows{i}, cols{i}); +end + + +G = zeros(npixels, npixels); +if wrap_around + for i=1:4 + if 0 + % naive + for p=1:npixels + G(p, nbrs{i}(p)) = 1; + end + else + % vectorized + ndx2 = sub2ind([npixels npixels], 1:npixels, nbrs{i}(:)'); + G(ndx2) = 1; + end + end +else + i = N; + mask = ones(nrows, ncols); + mask(1,:) = 0; % pixels in row 1 have no nbr to the north + ndx = find(mask); + ndx2 = sub2ind([npixels npixels], ndx, nbrs{i}(ndx)); + G(ndx2) = 1; + + i = E; + mask = ones(nrows, ncols); + mask(:,ncols) = 0; + ndx = find(mask); + ndx2 = sub2ind([npixels npixels], ndx, nbrs{i}(ndx)); + G(ndx2) = 1; + + i = S; + mask = ones(nrows, ncols); + mask(nrows,:)=0; + ndx = find(mask); + ndx2 = sub2ind([npixels npixels], ndx, nbrs{i}(ndx)); + G(ndx2) = 1; + + i = W; + mask = ones(nrows, ncols); + mask(:,1)=0; + ndx = find(mask); + ndx2 = sub2ind([npixels npixels], ndx, nbrs{i}(ndx)); + G(ndx2) = 1; +end + +G = setdiag(G, 0);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/mk_adj_mat.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,36 @@ +function [A, names] = mk_adj_mat(connections, names, topological) +% MK_ADJ_MAT Make a directed adjacency matrix from a list of connections between named nodes. +% +% A = mk_adj_mat(connections, name) +% This is best explaine by an example: +% names = {'WetGrass', 'Sprinkler', 'Cloudy', 'Rain'}; +% connections = {'Cloudy', 'Sprinkler'; 'Cloudy', 'Rain'; 'Sprinkler', 'WetGrass'; 'Rain', 'WetGrass'}; +% adds the arcs C -> S, C -> R, S -> W, R -> W. Node 1 is W, 2 is S, 3 is C, 4 is R. +% +% [A, names] = mk_adj_mat(connections, name, 1) +% The last argument of 1 indicates that we should topologically sort the nodes (parents before children). +% In the example, the numbering becomes: node 1 is C, 2 is R, 3 is S, 4 is W +% and the return value of names gets permuted to {'Cloudy', 'Rain', 'Sprinkler', 'WetGrass'}. +% Note that topological sorting the graph is only possible if it has no directed cycles. + +if nargin < 3, topological = 0; end + +n=length(names); +A=zeros(n); +[nr nc] = size(connections); +for r=1:nr + from = strmatch(connections{r,1}, names, 'exact'); + assert(~isempty(from)); + to = strmatch(connections{r,2}, names, 'exact'); + assert(~isempty(to)); + %fprintf(1, 'from %s %d to %s %d\n', connections{r,1}, from, connections{r,2}, to); + A(from,to) = 1; +end + +if topological + order = topological_sort(A); + A = A(order, order); + names = names(order); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/mk_adjmat_chain.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +function G = mk_adjmat_chain(T) +% MK_DAG_CHAIN Make adjacency matrix for bi-directional Markov chain of T nodes +% function G = mk_dag_chain(T) +% +% G(t,t+1) = 1 for all t<T + +G = diag(ones(1,T-1),1) + diag(ones(1,T-1),-1); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/mk_all_dags.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,52 @@ +function Gs = mk_all_dags(N, order) +% MK_ALL_DAGS generate all DAGs on N variables +% G = mk_all_dags(N) +% +% G = mk_all_dags(N, order) only generates DAGs in which node i has parents from +% nodes in order(1:i-1). Default: order=[] (no constraints). +% +% G{i} is the i'th dag +% +% Note: the number of DAGs is super-exponential in N, so don't call this with N > 4. + +if nargin < 2, order = []; end + +use_file = 0; + +global BNT_HOME +fname = sprintf('%s/DAGS%d.mat', BNT_HOME, N); +if use_file & exist(fname, 'file') + S = load(fname, '-mat'); + fprintf('loading %s\n', fname); + Gs = S.Gs; + return; +end + +m = 2^(N*N); +ind = ind2subv(2*ones(1,N^2), 1:m); +Gs = {}; +j = 1; +directed = 1; +for i=1:m + dag = reshape(ind(i,:)-1, N, N); + if acyclic(dag, directed) + out_of_order = 0; + if ~isempty(order) + for k=1:N-1 + if any(dag(order(k+1:end), k)) + out_of_order = 1; + break; + end + end + end + if ~out_of_order + Gs{j} = dag; + j = j + 1; + end + end +end + +if use_file + disp(['mk_all_dags: saving to ' fname '!']); + save(fname, 'Gs'); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/mk_nbrs_of_dag.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,64 @@ +function [Gs, op, nodes] = mk_nbrs_of_dag(G0) +% MK_NBRS_OF_DAG Make all DAGs that differ from G0 by a single edge deletion, addition or reversal +% [Gs, op, nodes] = mk_nbrs_of_dag(G0) +% +% Gs{i} is the i'th neighbor. +% op{i} = 'add', 'del', or 'rev' is the operation used to create the i'th neighbor. +% nodes(i,1:2) are the head and tail of the operated-on arc. + +Gs = {}; +op = {}; +nodes = []; + +[I,J] = find(G0); +nnbrs = 1; +% all single edge deletions +for e=1:length(I) + i = I(e); j = J(e); + G = G0; + G(i,j) = 0; + Gs{nnbrs} = G; + op{nnbrs} = 'del'; + nodes(nnbrs, :) = [i j]; + nnbrs = nnbrs + 1; +end + +% all single edge reversals +for e=1:length(I) + i = I(e); j = J(e); + G = G0; + G(i,j) = 0; + G(j,i) = 1; + if acyclic(G) + Gs{nnbrs} = G; + op{nnbrs} = 'rev'; + nodes(nnbrs, :) = [i j]; + nnbrs = nnbrs + 1; + end +end + +[I,J] = find(~G0); +% all single edge additions +for e=1:length(I) + i = I(e); j = J(e); + if i ~= j % don't add self arcs + G = G0; + G(i,j) = 1; + if G(j,i)==0 % don't add i->j if j->i exists already + if acyclic(G) + Gs{nnbrs} = G; + op{nnbrs} = 'add'; + nodes(nnbrs, :) = [i j]; + nnbrs = nnbrs + 1; + end + end + end +end + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/mk_nbrs_of_digraph.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,132 @@ +function [Gs, op, nodes, A] = my_mk_nbs_of_digraph(G0,A) +% MY_MK_NBRS_OF_DIGRAPH Make all digraphs that differ from G0 by a single edge deletion, addition or reversal, subject to acyclicity +% [Gs, op, nodes, A] = my_mk_nbrs_of_digraph(G0,<A>) +% +% G0 is an adj matrix s.t. G0(i,j)=1 iff i->j in graph +% A is the ancestor matrix for G0 (opt, creates if necessary) +% +% Gs(:,:,i) is the i'th neighbor +% op{i} = 'add', 'del', or 'rev' is the operation used to create the i'th neighbor. +% nodes(i,1:2) are the head and tail of the operated-on arc. +% Modified from mk_nbrs_of_digraph by Sonia Leach +% +% Modified by Sonia Leach Feb 02 + +if nargin ==1, A = reachability_graph(G0');, end + +n = length(G0); +[I,J] = find(G0); % I(k), J(k) is the k'th edge +E = length(I); % num edges present in G0 + + +% SINGLE EDGE DELETIONS +% all deletions are valid wrt acyclity + +Grep = repmat(G0(:), 1, E); % each column is a copy of G0 +% edge_ndx(k) is the scalar location of the k'th edge +edge_ndx = find(G0); + +% edge_ndx = subv2ind([n n], [I J]); % equivalent +% We set (ndx(k), k) to 0 for k=1:E in Grep +ndx = subv2ind(size(Grep), [edge_ndx(:) (1:E)']); +G1 = Grep; +G1(ndx) = 0; +Gdel = reshape(G1, [n n E]); + + +% SINGLE EDGE REVERSALS + +% SML: previously Kevin had that legal structure was if +% A(P,i)=1 for any P = { p | p in parents(j), p~=i} +% specifically he said +% "if any(A(ps,i)) then there is a path i -> parent of j -> j +% so reversing i->j would create a cycle" +% Thus put in another way: +% for each i,j if sum(G0(:,j)' * A(:,i)) > 0, reversing i->j +% is not legal. +% +% Ex. Suppose we want to check if 2->4 can be reversed in the +% following graph: +% G0 = A = +% 0 0 1 0 0 0 0 0 +% 0 0 1 1 0 0 0 0 +% 0 0 0 1 1 1 0 0 +% 0 0 0 0 1 1 1 0 +% +% Then parents(4) = G0(:,4) = [0 1 1 0]' +% and A(:,2) = [0 0 1 1]. Thus G0(:,4)'*A(:,2) = 1 b/c 3 is +% an ancestor of 4 and a child of 2. Note that this works b/c +% matrix multiplication has the effect of ANDing the two vectors +% and summing up the result (equiv. to the any(A(ps,i)) in kevin's code) +% +% So, we vectorize and check for all i,j pairs by looking for +% 1's in L = (G0'*A)' which has L(i,j)=1 if rev(i,j) not legal +% Note that this will give 1's where there are none in the G0 +% so we do a L=max(0, G0-L) to cancel out only the existing edges that +% aren't legal (subtracting where both are 1 and setting where +% G0=0 and A=1 back to 0). + +L = max(0, G0-(G0'*A)'); +[IL, JL] = find(L); % I(k), J(k) is the k'th legal edge to rev. +EL = length(IL); + + +% SML: First we have to DELETE THE EDGES WE ARE REVERSING +% We can't use G1 w/ reversed edges already deleted (as +% Kevin did) b/c the space of possible deletions are different +% now (some reverses aren't legal) + +Grep = repmat(G0(:), 1, EL); % each column is a copy of G0 +% edge_ndx(k) is the scalar location of the k'th edge +edge_ndx = subv2ind([n n], [IL JL]); +% We set (ndx(k), k) to 0 for k=1:E in Grep +ndx = subv2ind(size(Grep), [edge_ndx(:) (1:EL)']); +G1 = Grep; +G1(ndx) = 0; + +% SML: Now we add in our REVERSED EDGES +% rev_edge_ndx(k) is the scalar location of the k'th legal reversed edge +rev_edge_ndx = subv2ind([n n], [JL IL]); + +% We set (rev_edge_ndx(k), k) to 1 for k=1:EL in G1 +% We have already deleted i->j in the previous step +ndx = subv2ind(size(Grep), [rev_edge_ndx(:) (1:EL)']); +G1(ndx) = 1; +Grev = reshape(G1, [n n EL]); + +% SINGLE EDGE ADDITIONS + +% SML: previously Kevin had that any addition was legal if A(i,j)=0 +% however, you can not add i->j if j is a descendent of i. +% Thus, we create all possible additions in Gbar and then +% subtract the descendants of each edge as possible parents +% This means the potential parents of i (i.e. Gbar(:,i)) +% can not also be descendants if i i.e. (A(:,i)) which is accomplished +% by subtracting (Gbar-A == 1 iff Gbar=1 & A=0) + +Gbar = ~G0; % Gbar(i,j)=1 iff there is no i->j edge in G0 +Gbar = setdiag(Gbar, 0); % turn off self loops + +GbarL = Gbar-A; +[IbarL, JbarL] = find(GbarL); % I(k), J(k) is the k'th legal edge to add +EbarL = length(IbarL); + +bar_edge_ndx = find(GbarL); + +Grep = repmat(G0(:), 1, EbarL); % each column is a copy of G0 +ndx = subv2ind(size(Grep), [bar_edge_ndx(:) (1:EbarL)']); +Grep(ndx) = 1; +Gadd = reshape(Grep, [n n EbarL]); + + +Gs = cat(3, Gdel, Grev, Gadd); + +nodes = [I J; + IL JL; + IbarL JbarL]; + +op = cell(1, E+EL+EbarL); +op(1:E) = {'del'}; +op(E+(1:EL)) = {'rev'}; +op((E+EL+1):end) = {'add'}; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/mk_nbrs_of_digraph_broken.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,111 @@ +function [Gs, op, nodes] = mk_nbrs_of_digraph(G0) +% MK_NBRS_OF_DIGRAPH Make all digraphs that differ from G0 by a single edge deletion, addition or reversal +% [Gs, op, nodes] = mk_nbrs_of_digraph(G0) +% +% Gs(:,:,i) is the i'th neighbor +% op{i} = 'add', 'del', or 'rev' is the operation used to create the i'th neighbor. +% nodes(i,1:2) are the head and tail of the operated-on arc. + +debug = 0; % the vectorized version is about 3 to 10 times faster + +n = length(G0); +[I,J] = find(G0); % I(k), J(k) is the k'th edge +E = length(I); % num edges present in G0 + +% SINGLE EDGE DELETIONS + +Grep = repmat(G0(:), 1, E); % each column is a copy of G0 +% edge_ndx(k) is the scalar location of the k'th edge +edge_ndx = find(G0); +% edge_ndx = subv2ind([n n], [I J]); % equivalent +% We set (ndx(k), k) to 0 for k=1:E in Grep +ndx = subv2ind(size(Grep), [edge_ndx(:) (1:E)']); +G1 = Grep; +G1(ndx) = 0; +Gdel = reshape(G1, [n n E]); + + +% if debug +% % Non-vectorized version +% ctr = 1; +% for e=1:E +% i = I(e); j = J(e); +% Gdel2(:,:,ctr) = G0; +% Gdel2(i,j,ctr) = 0; +% ctr = ctr + 1; +% end +% assert(isequal(Gdel, Gdel2)); +% end + + +% SINGLE EDGE REVERSALS + +% rev_edge_ndx(k) is the scalar location of the k'th reversed edge +%rev_edge_ndx = find(G0'); % different order to edge_ndx, which is bad +rev_edge_ndx = subv2ind([n n], [J I]); +% We set (rev_edge_ndx(k), k) to 1 for k=1:E in G1 +% We have already deleted i->j in the previous step +ndx = subv2ind(size(Grep), [rev_edge_ndx(:) (1:E)']); +G1(ndx) = 1; +Grev = reshape(G1, [n n E]); + +% if debug +% % Non-vectorized version +% ctr = 1; +% for e=1:E +% i = I(e); j = J(e); +% Grev2(:,:,ctr) = G0; +% Grev2(i,j,ctr) = 0; +% Grev2(j,i,ctr) = 1; +% ctr = ctr + 1; +% end +% assert(isequal(Grev, Grev2)); +% end + + +% SINGLE EDGE ADDITIONS + +Gbar = ~G0; % Gbar(i,j)=1 iff there is no i->j edge in G0 +Gbar = setdiag(Gbar, 0); % turn off self loops +[Ibar,Jbar] = find(Gbar); + +bar_edge_ndx = find(Gbar); +Ebar = length(Ibar); % num edges present in Gbar +Grep = repmat(G0(:), 1, Ebar); % each column is a copy of G0 +ndx = subv2ind(size(Grep), [bar_edge_ndx(:) (1:Ebar)']); +Grep(ndx) = 1; +Gadd = reshape(Grep, [n n Ebar]); + +% if debug +% % Non-vectorized version +% ctr = 1; +% for e=1:length(Ibar) +% i = Ibar(e); j = Jbar(e); +% Gadd2(:,:,ctr) = G0; +% Gadd2(i,j,ctr) = 1; +% ctr = ctr + 1; +% end +% assert(isequal(Gadd, Gadd2)); +% end + + +Gs = cat(3, Gdel, Grev, Gadd); + +nodes = [I J; + I J; + Ibar Jbar]; + +op = cell(1, E+E+Ebar); +op(1:E) = {'del'}; +op(E+1:2*E) = {'rev'}; +op(2*E+1:end) = {'add'}; + + +% numeric output: +% op(i) = 1, 2, or 3, if the i'th neighbor was created by adding, deleting or reversing an arc. + +ADD = 1; +DEL = 2; +REV = 3; + +%op = [repmat(DEL, 1, E) repmat(REV, 1, E) repmat(ADD, 1, Ebar)];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/mk_nbrs_of_digraph_not_vectorized.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,53 @@ +function [Gs, op, nodes] = mk_nbrs_of_digraph2(G0) +% MK_NBRS_OF_DIGRAPH Make all digraphs that differ from G0 by a single edge deletion, addition or reversal +% [Gs, op, nodes] = mk_nbrs_of_digraph(G0) +% op{i} = 'add', 'del', or 'rev' is the operation used to create the i'th neighbor. +% nodes(i,1:2) are the head and tail of the operated-on arc. + +[I,J] = find(G0); +G0bar = setdiag(~G0, 0); % exclude self loops in graph complement +[Ibar,Jbar] = find(G0bar); +nnbrs = 2*length(I) + length(Ibar); +Gs = cell(1, nnbrs); +op = cell(1, nnbrs); +nodes = zeros(nnbrs, 2); + +nbr = 1; +% all single edge deletions +for e=1:length(I) + i = I(e); j = J(e); + G = G0; + G(i,j) = 0; + Gs{nbr} = G; + op{nbr} = 'del'; + nodes(nbr, :) = [i j]; + nbr = nbr + 1; +end + +% all single edge reversals +for e=1:length(I) + i = I(e); j = J(e); + G = G0; + G(i,j) = 0; + G(j,i) = 1; + Gs{nbr} = G; + op{nbr} = 'rev'; + nodes(nbr, :) = [i j]; + nbr = nbr + 1; +end + +[I,J] = find(~G0); +% all single edge additions +for e=1:length(I) + i = I(e); j = J(e); + G = G0; + if i ~= j % don't add self loops + G(i,j) = 1; + Gs{nbr} = G; + op{nbr} = 'add'; + nodes(nbr, :) = [i j]; + nbr = nbr + 1; + end +end + +assert(nnbrs == nbr-1);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/mk_rnd_dag.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,27 @@ +function [dag, order] = mk_rnd_dag(N, max_fan_in) +% MY_MK_RND_DAG Create a random directed acyclic graph +% +% [dag, order] = my_mk_rnd_dag(N, max_fan_in) +% max_fan_in defaults to N. +% order is the random topological order that was chosen + +% Modified by Sonia Leach 2/25/02 + +if nargin < 2, max_fan_in = N; end + +order = randperm(N); +dag = zeros(N,N); +for i=2:N + j = order(i); + %k = sample_discrete(normalise(ones(1, min(i-1, max_fan_in)))); + k = sample_discrete(normalise(ones(1, min(i-1, max_fan_in)+1))) - 1; % min = 0 (bug fix due to + % Pedrito, 7/28/04) + SS = order(1:i-1); % get Set of possible parentS + p = randperm(length(SS)); % permute order of set + dag(SS(p(1:k)),j) = 1; % take first k in permuted order + + % Kevin had: + %SS = subsets(order(1:i-1), k, k); + %p = sample_discrete(normalise(ones(1, length(SS)))); + %dag(SS{p}, j) = 1; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/mk_rnd_dag_given_edge_prob.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +function dag = sample_dag(P) +% SAMPLE_DAG Create a random directed acyclic graph with edge probabilities P(i,j) +% dag = sample_dag(P) +% +% This uses rejection sampling to reject graphs with directed cycles. + +done = 0; +directed = 1; +iter = 1; +while ~done + dag = binornd(1, P); % each edge is an indep Bernoulli (0/1) random variable + dag = setdiag(dag, 0); + done = acyclic(dag, directed); + iter = iter + 1 +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/mk_rooted_tree.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,19 @@ +function [T, pre, post, cycle] = mk_rooted_tree(G, root) +% MK_ROOTED_TREE Make a directed tree pointing away from root +% [T, pre, post, cycle] = mk_rooted_tree(G, root) + +n = length(G); +T = sparse(n,n); % not the same as T = sparse(n) ! +directed = 0; +[d, pre, post, cycle, f, pred] = dfs(G, root, directed); +[junk, pre2] = sort(d); +assert(isequal(pre, pre2)) +[junk, post2] = sort(f); +assert(isequal(post, post2)); +%[d, pre, post, cycle, f, pred] = dfs(G, [], directed); +for i=1:length(pred) + if pred(i)>0 + T(pred(i),i)=1; + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/mk_undirected.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function U = mk_undirected(G) + +[nr nc] = size(G); +U = G; +for i=1:nr + for j=1:nc + if U(i,j)==1 + U(j,i) = 1; + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/moralize.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +function [M, moral_edges] = moralize(G) +% MORALIZE Ensure that for every child, all its parents are married, and drop directionality of edges. +% [M, moral_edges] = moralize(G) + +M = G; +n = length(M); +for i=1:n + fam = family(G,i); + M(fam,fam)=1; +end +M = setdiag(M,0); +moral_edges = sparse(triu(max(0,M-G),1));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/neighbors.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +function ns = neighbors(adj_mat, i) +% NEIGHBORS Find the parents and children of a node in a graph. +% ns = neighbors(adj_mat, i) + +%ns = myunion(children(adj_mat, i), parents(adj_mat, i)); +ns = [find(adj_mat(i,:)) find(adj_mat(:,i))']; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/parents.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function ps = parents(adj_mat, i) +% PARENTS Return the list of parents of node i +% ps = parents(adj_mat, i) + +ps = find(adj_mat(:,i))';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/pred2path.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,78 @@ +function rte = pred2path(P,s,t) +%PRED2PATH Convert predecessor indices to shortest paths from node 's' to 't'. +% rte = pred2path(P,s,t) +% P = |s| x n matrix of predecessor indices (from DIJK) +% s = FROM node indices +% = [] (default), paths from all nodes +% t = TO node indices +% = [] (default), paths to all nodes +% rte = |s| x |t| cell array of paths (or routes) from 's' to 't', where +% rte{i,j} = path from s(i) to t(j) +% = [], if no path exists from s(i) to t(j) +% +% (Used with output of DIJK) + +% Copyright (c) 1998-2001 by Michael G. Kay +% Matlog Version 5 22-Aug-2001 + +% Input Error Checking ****************************************************** +error(nargchk(1,3,nargin)); + +[rP,n] = size(P); + +if nargin < 2 | isempty(s), s = (1:n)'; else s = s(:); end +if nargin < 3 | isempty(t), t = (1:n)'; else t = t(:); end + +if any(P < 0 | P > n) + error(['Elements of P must be integers between 1 and ',num2str(n)]); +elseif any(s < 1 | s > n) + error(['''s'' must be an integer between 1 and ',num2str(n)]); +elseif any(t < 1 | t > n) + error(['''t'' must be an integer between 1 and ',num2str(n)]); +end +% End (Input Error Checking) ************************************************ + +rte = cell(length(s),length(t)); + +for i = 1:length(s) + if rP == 1 + si = 1; + else + si = s(i); + if si < 1 | si > rP + error('Invalid P matrix.') + end + end + for j = 1:length(t) + tj = t(j); + if tj == s(i) + r = tj; + elseif P(si,tj) == 0 + r = []; + else + r = tj; + while tj ~= s(i) + if tj < 1 | tj > n + error('Invalid element of P matrix found.') + end + r = [P(si,tj) r]; + tj = P(si,tj); + end + end + rte{i,j} = r; + end +end + +if length(s) == 1 & length(t) == 1 + rte = rte{:}; +end + +%rte = t; +while 0%t ~= s + if t < 1 | t > n | round(t) ~= t + error('Invalid ''pred'' element found prior to reaching ''s'''); + end + rte = [P(t) rte]; + t = P(t); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/reachability_graph.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,19 @@ +function C = reachability_graph(G) +% REACHABILITY_GRAPH C(i,j) = 1 iff there is a path from i to j in DAG G +% C = reachability_graph(G) + +if 1 + % expm(G) = I + G + G^2 / 2! + G^3 / 3! + ... + M = expm(double(full(G))) - eye(length(G)); + C = (M>0); +else + % This computes C = G + G^2 + ... + G^{n-1} + n = length(G); + A = G; + C = zeros(n); + for i=1:n-1 + C = C + A; + A = A * G; + end + C = (C > 0); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/scc.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,64 @@ +function [c,v] = scc(a,tol) + +% Finds the strongly connected sets of vertices +% in the DI-rected G-raph of A +% c = 0-1 matrix displaying accessibility +% v = displays the equivalent classes +% +% v(i,j) is the j'th member of the i'th equiv class (0 padded) +% +% http://www.math.wsu.edu/math/faculty/tsat/matlab.html + +[m,n] = size(a); +if m~=n 'Not a Square Matrix', break, end +b=abs(a); o=ones(size(a)); x=zeros(1,n); +msg='The Matrix is Irreducible !'; v='Connected Directed Graph !'; +if (nargin==1) tol=n*eps*norm(a,'inf'); end + +% Create a companion matrix +c = b>tol*o; +if (c==o) + % msg, break + v = 1:length(a); + return +end + + +% Compute accessibility in at most n-step paths +for k=1:n + for j=1:n + for i=1:n + % If index i accesses j, where can you go ? + if c(i,j) > 0 c(i,:) = c(i,:)+c(j,:); end + end + end +end +% Create a 0-1 matrix with the above information +c>zeros(size(a)); c=ans; if (c==o) msg, break, end + +% Identify equivalence classes +d=c.*c'+eye(size(a)); d>zeros(size(a)); d=ans; +v=zeros(size(a)); +for i=1:n find(d(i,:)); ans(n)=0; v(i,:)=ans; end + +% Eliminate displaying of identical rows +i=1; +while(i<n) + for k=i+1:n + if v(k,1) == v(i,1) + v(k,:)=x; + end + end + i=i+1; +end +j=1; +for i=1:n + if v(i,1)>0 + h(j,:)=v(i,:); + j=j+1; + end +end +v=h; + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/strong_elim_order.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,75 @@ +function order = strong_elim_order(G, node_sizes, partial_order) +% STRONG_ELIM_ORDER Find an elimination order to produce a strongly triangulated graph. +% order = strong_elim_order(moral_graph, node_sizes, partial_order) +% +% partial_order(i,j)=1 if we must marginalize i *after* j +% (so i will be nearer the strong root). +% e.g., if j is a decision node and i is its information set: +% we cannot maximize j if we have marginalized out some of i +% e.g., if j is a continuous child and i is its discrete parent: +% we want to integrate out the cts nodes before the discrete ones, +% so that the marginal is strong. +% +% For details, see +% - Jensen, Jensen and Dittmer, "From influence diagrams to junction trees", UAI 94. +% - Lauritzen, "Propgation of probabilities, means, and variances in mixed graphical +% association models", JASA 87(420):1098--1108, 1992. +% +% On p369 of the Jensen paper, they state "the reverse of the elimination order must be some +% extension of [the partial order] to a total order". +% We make no attempt to find the best such total ordering, in the sense of minimizing the weight +% of the resulting cliques. + +% Example from the Jensen paper: +% Let us number the nodes in Fig 1 from top to bottom, left to right, +% so a=1,b=2,D1=3,c=4,...,l=14,j=15,k=16. +% The elimination ordering they propose on p370 is [14 15 16 11 12 1 4 5 10 8 13 9 7 6 3 2]; + +if 0 + total_order = topological_sort(partial_order); + order = total_order(end:-1:1); % no attempt to find an optimal constrained ordering! + return; +end + +% The following implementation is due to Ilya Shpitser and seems to give wrong +% results on cg1 + +n = length(G); +MG = G; % copy the original graph +uneliminated = ones(1,n); +order = zeros(1,n); + +for i=1:n + roots = []; + k = 1; + for j=1:n + if sum(partial_order(j,:)) == 0 + roots(k) = j; + k = k + 1; + end + end + U = find(uneliminated); + valid = myintersect(U, roots); + % Choose the best node from the set of valid candidates + score1 = zeros(1,length(valid)); + score2 = zeros(1,length(valid)); + for j=1:length(valid) + k = valid(j); + ns = myintersect(neighbors(G, k), U); + l = length(ns); + M = MG(ns,ns); + score1(j) = l^2 - sum(M(:)); % num. added edges + score2(j) = prod(node_sizes([k ns])); % weight of clique + end + j1s = find(score1==min(score1)); + j = j1s(argmin(score2(j1s))); + k = valid(j); + uneliminated(k) = 0; + order(i) = k; + ns = myintersect(neighbors(G, k), U); + if ~isempty(ns) + G(ns,ns) = 1; + G = setdiag(G,0); + end + partial_order(:,k) = 0; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/test.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +% A - B +% | +% D - C - E + +A=1;B=2;C=3;D=4;E=5; +dag = zeros(5,5); +dag(A,B)=1; +%dag(A,D)=1; +dag(B,C)=1; +dag(C,D)=1; +dag(E,C)=1; +[d, pre, post, cycle, f, pred] = dfs(dag, A, 0) + +[T, pre, post, cycle] = mk_rooted_tree(dag, A) + +%[T, pre, post, cycle] = mkRootedTree(dag, A)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/test_strong_root.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,34 @@ +function strong = test_strong_root(jtree,cliques,dnodes,root) +% This function tests, whether root is a strong root of jtree. +% The following parameters are used +% Input: +% jtree An matrix with two colums. jtree(i,j) == jtree(j,i) is 1 if node +% i is connected with node j +% cliques Cells which contain the nodes in each clique +% dnodes An array with the discrete nodes of the juntion tree. +% root It is tested whether root is the strong root of the junction tree +% Output: +% strong The output is 1 if root is the strong root of the junction tree jtree. +% Please note, that the running intersection property is not tested. +if isempty(dnodes) + strong = 1; + return; +end + +children = find(jtree(root,:)==1); +i = 1; +strong = 1; +while (i <= length(children)) & (strong==1) + child = children(i); + jtree(child,root) = 0; + jtree(root,child) = 0; + sep = myintersect(cliques{child},cliques{root}); + diff = mysetdiff(cliques{child},cliques{root}); + if (mysubset(sep,dnodes) | isempty(myintersect(diff,dnodes))) + strong = test_strong_root(jtree,cliques,dnodes,child); + else + strong = 0; + end; + i = i+1; +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/topological_sort.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,30 @@ +function order = topological_sort(A) +% TOPOLOGICAL_SORT Return the nodes in topological order (parents before children). +% order = topological_sort(adj_mat) + +n = length(A); +indeg = zeros(1,n); +zero_indeg = []; % a stack of nodes with no parents +for i=1:n + indeg(i) = length(parents(A,i)); + if indeg(i)==0 + zero_indeg = [i zero_indeg]; + end +end + +t=1; +order = zeros(1,n); +while ~isempty(zero_indeg) + v = zero_indeg(1); % pop v + zero_indeg = zero_indeg(2:end); + order(t) = v; + t = t + 1; + cs = children(A, v); + for j=1:length(cs) + c = cs(j); + indeg(c) = indeg(c) - 1; + if indeg(c) == 0 + zero_indeg = [c zero_indeg]; % push c + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/trees.txt Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,168 @@ + +% make undirected adjacency matrix of graph/tree +% e.g., +% 1 +% / \ +% 2 3 +T = zeros(3,3); +T(1,2) = 1; T(2,1)=1; +T(1,3)=1; T(3,1) = 1; + +root = 1; +[T, preorder, postorder] = mk_rooted_tree(T, root); + +% bottom up message passing leaves to root +for n=postorder(:)' + for p = parents(T, n) + % p is parent of n + end +end + +% top down, root to leaves +for n=preorder(:)' + for c= children(T,n) + % c is child of n + end +end + + +%%%%%%%%%%%%% + +function ps = parents(adj_mat, i) +% PARENTS Return the list of parents of node i +% ps = parents(adj_mat, i) + +ps = find(adj_mat(:,i))'; + + +%%%%%%%%%%%% + +function cs = children(adj_mat, i, t) +% CHILDREN Return the indices of a node's children in sorted order +% c = children(adj_mat, i, t) +% +% t is an optional argument: if present, dag is assumed to be a 2-slice DBN + +if nargin < 3 + cs = find(adj_mat(i,:)); +else + if t==1 + cs = find(adj_mat(i,:)); + else + ss = length(adj_mat)/2; + j = i+ss; + cs = find(adj_mat(j,:)) + (t-2)*ss; + end +end + +%%%%%%%%%%% + +function [T, pre, post, cycle] = mk_rooted_tree(G, root) +% MK_ROOTED_TREE Make a directed, rooted tree out of an undirected tree. +% [T, pre, post, cycle] = mk_rooted_tree(G, root) + +n = length(G); +T = sparse(n,n); % not the same as T = sparse(n) ! +directed = 0; +[d, pre, post, cycle, f, pred] = dfs(G, root, directed); +for i=1:length(pred) + if pred(i)>0 + T(pred(i),i)=1; + end +end + + +%%%%%%%%%%% + +function [d, pre, post, cycle, f, pred] = dfs(adj_mat, start, directed) +% DFS Perform a depth-first search of the graph starting from 'start'. +% [d, pre, post, cycle, f, pred] = dfs(adj_mat, start, directed) +% +% Input: +% adj_mat(i,j)=1 iff i is connected to j. +% start is the root vertex of the dfs tree; if [], all nodes are searched +% directed = 1 if the graph is directed +% +% Output: +% d(i) is the time at which node i is first discovered. +% pre is a list of the nodes in the order in which they are first encountered (opened). +% post is a list of the nodes in the order in which they are last encountered (closed). +% 'cycle' is true iff a (directed) cycle is found. +% f(i) is the time at which node i is finished. +% pred(i) is the predecessor of i in the dfs tree. +% +% If the graph is a tree, preorder is parents before children, +% and postorder is children before parents. +% For a DAG, topological order = reverse(postorder). +% +% See Cormen, Leiserson and Rivest, "An intro. to algorithms" 1994, p478. + +n = length(adj_mat); + +global white gray black color +white = 0; gray = 1; black = 2; +color = white*ones(1,n); + +global time_stamp +time_stamp = 0; + +global d f +d = zeros(1,n); +f = zeros(1,n); + +global pred +pred = zeros(1,n); + +global cycle +cycle = 0; + +global pre post +pre = []; +post = []; + +if ~isempty(start) + dfs_visit(start, adj_mat, directed); +else + for u=1:n + if color(u)==white + dfs_visit(u, adj_mat, directed); + end + end +end + + +%%%%%%%%%% + +function dfs_visit(u, adj_mat, directed) + +global white gray black color time_stamp d f pred cycle pre post + +pre = [pre u]; +color(u) = gray; +time_stamp = time_stamp + 1; +d(u) = time_stamp; +if directed + ns = children(adj_mat, u); +else + ns = neighbors(adj_mat, u); + ns = mysetdiff(ns, pred(u)); % don't go back to visit the guy who called you! +end +for v=ns(:)' + %fprintf('u=%d, v=%d, color(v)=%d\n', u, v, color(v)) + switch color(v) + case white, % not visited v before (tree edge) + pred(v)=u; + dfs_visit(v, adj_mat, directed); + case gray, % back edge - v has been visited, but is still open + cycle = 1; + %fprintf('cycle: back edge from v=%d to u=%d\n', v, u); + case black, % v has been visited, but is closed + % no-op + end +end +color(u) = black; +post = [post u]; +time_stamp = time_stamp + 1; +f(u) = time_stamp; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/triangulate.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,159 @@ +/* triangulate.c written by Ilya Shpitser */ + +#include <stdlib.h> + +#ifdef UNIX +#include "matlab.h" +#endif + +#include "matrix.h" +#include "mex.h" + +#include "elim.h" +#include "map.h" +#include "misc.h" + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + + int dims [2]; + int i, j, k, m, n; + long index; + double * G_pr; + double * stage_pr; + double * answer_G_pr, * fill_ins_pr; + double * matlab_clique_pr; + mxArray * matlab_clique; + Elimination e; + float ** adj_mat; + int ** order = (int **) NULL; + Iterator iter, iter2; + word w, w2; + int ** fill_ins; + Map cliques; + Map clique; + mxArray * fill_ins_mat; + int * nodes; + mxArray * full; + +// (original) full = mlfFull((mxArray *) prhs[0]); + full = (mxArray *) mlfFull((mxArray *) prhs[0]); // added typecasting + /* Obtain graph matrix information. */ + m = mxGetM(full); + n = mxGetN(full); + G_pr = mxGetPr(full); + + if(n < 1 || m < 1){ + return; + } + + /* Allocate and populate the log weight adjacency matrix corresponding + to the input graph. */ + adj_mat = (float **) malloc(sizeof(float *) * m); + adj_mat[0] = (float *) malloc(sizeof(float) * m * n); + for(i = 1; i < m; i++){ + adj_mat[i] = adj_mat[i - 1] + n; + } + /* We no longer have log weight info, but we have a (total) ordering on + the nodes already, so we do not need this information. */ + for(i = 0; i < m; i++){ + for(j = 0; j < n; j++){ + index = j * m + i; + if(G_pr[index] > 0){ + adj_mat[i][j] = 1; + } else { + adj_mat[i][j] = 0; + } + } + } + + /* Convert the total elimination ordering into a partial order argument + for the elimination routine. The elimination routine's purpose in this + mode of operation is to return cliques and fill-in edges. */ + if(nrhs > 1){ + order = (int **) malloc(sizeof(int *) * m); + order[0] = (int *) malloc(sizeof(int) * m * n); + for(i = 1; i < m; i++){ + order[i] = order[i - 1] + n; + } + for(i = 0; i < m; i++){ + for(j = 0; j < n; j++){ + order[i][j] = 0; + } + } + stage_pr = mxGetPr(prhs[1]); + for(i = 0; i < mxGetN(prhs[1]) - 1; i++){ + order[(int) stage_pr[i] - 1][(int) stage_pr[i + 1] - 1] = 1; + } + } + + /* Find the elimination ordering. */ + e = find_elim(n, adj_mat, order, -1); + + /* Allocate memory for the answer, and set the answer. */ + plhs[0] = mxCreateDoubleMatrix(m, n, mxREAL); + answer_G_pr = mxGetPr(plhs[0]); + cliques = get_cliques(e); +/* + dims[0] = 1; + dims[1] = get_size_Map(cliques); + plhs[1] = mxCreateCellArray(2, (const int *) dims);*/ + plhs[1] = mxCreateCellMatrix(get_size_Map(cliques), 1); + fill_ins = get_fill_ins(e); + fill_ins_mat = mxCreateDoubleMatrix(m, n, mxREAL); + fill_ins_pr = mxGetPr(fill_ins_mat); + + for(i = 0; i < n; i++){ + for(j = 0; j < m; j++){ + index = j * m + i; + answer_G_pr[index] = G_pr[index]; + if(fill_ins[i][j] > 0){ + answer_G_pr[index] = 1; + fill_ins_pr[index] = 1; + } + } + } + mxDestroyArray(full); +// (original) plhs[2] = mlfSparse(fill_ins_mat, NULL, NULL, NULL, NULL, NULL); + plhs[2] = (mxArray *) mlfSparse(fill_ins_mat, NULL, NULL, NULL, NULL, NULL); // added typecasting + mxDestroyArray(fill_ins_mat); + nodes = (int *) malloc(sizeof(int) * n); + k = 0; + iter = get_Iterator(cliques); + while(!is_empty(iter)){ + w = next_key(iter); + clique = (Map) w.v; + matlab_clique = mxCreateDoubleMatrix(1, get_size_Map(clique), mxREAL); + matlab_clique_pr = mxGetPr(matlab_clique); + for(i = 0; i < n; i++){ + nodes[i] = 0; + } + iter2 = get_Iterator(clique); + while(!is_empty(iter2)){ + w2 = next_key(iter2); + nodes[w2.i] = w2.i + 1; + } + j = 0; + for(i = 0; i < n; i++){ + if(nodes[i] > 0){ + matlab_clique_pr[j++] = nodes[i]; + } + } + mxSetCell(plhs[1], k++, matlab_clique); + } + free(nodes); + + /* Finally, free the allocated memory. */ + destroy_Elimination(e); + if(adj_mat){ + if(adj_mat[0]){ + free(adj_mat[0]); + } + free(adj_mat); + } + if(order){ + if(order[0]){ + free(order[0]); + } + free(order); + } +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/triangulate.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,42 @@ +function [G, cliques, fill_ins] = triangulate(G, order) +% TRIANGULATE Ensure G is triangulated (chordal), i.e., every cycle of length > 3 has a chord. +% [G, cliques, fill_ins, cliques_containing_node] = triangulate(G, order) +% +% cliques{i} is the i'th maximal complete subgraph of the triangulated graph. +% fill_ins(i,j) = 1 iff we add a fill-in arc between i and j. +% +% To find the maximal cliques, we save each induced cluster (created by adding connecting +% neighbors) that is not a subset of any previously saved cluster. (A cluster is a complete, +% but not necessarily maximal, set of nodes.) + +MG = G; +n = length(G); +eliminated = zeros(1,n); +cliques = {}; +for i=1:n + u = order(i); + U = find(~eliminated); % uneliminated + nodes = myintersect(neighbors(G,u), U); % look up neighbors in the partially filled-in graph + nodes = myunion(nodes, u); % the clique will always contain at least u + G(nodes,nodes) = 1; % make them all connected to each other + G = setdiag(G,0); + eliminated(u) = 1; + + exclude = 0; + for c=1:length(cliques) + if mysubset(nodes,cliques{c}) % not maximal + exclude = 1; + break; + end + end + if ~exclude + cnum = length(cliques)+1; + cliques{cnum} = nodes; + end +end + +fill_ins = sparse(triu(max(0, G - MG), 1)); + +%assert(check_triangulated(G)); % takes 72% of the time! + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/triangulate_2Dlattice_demo.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,131 @@ +% Consider a 3x3 lattice with 4-nearest neighbor connectivity + +% 1 - 2 - 3 +% | | | +% 4 - 5 - 6 +% | | | +% 7 - 8 - 9 + +N = 3; +G = mk_2D_lattice(N,N,4); +G0 = G; + +% Now add in the diagonal edges + +if 0 +% 1 - 2 - 3 +% | x | x | +% 4 - 5 - 6 +% | x | x | +% 7 - 8 - 9 + +G(1,5)=1; G(5,1)=1; +G(2,6)=1; G(6,2)=1; +G(4,2)=1; G(2,4)=1; +G(5,3)=1; G(3,5)=1; + +G(4,8)=1; G(8,4)=1; +G(5,9)=1; G(9,5)=1; +G(7,5)=1; G(5,7)=1; +G(8,6)=1; G(6,8)=1; +end + +% 1 - 2 - 3 +% | / | \ | +% 4 - 5 - 6 +% | \ | / | +% 7 - 8 - 9 + +G(2,6)=1; G(6,2)=1; +G(4,2)=1; G(2,4)=1; +G(4,8)=1; G(8,4)=1; +G(8,6)=1; G(6,8)=1; + +% Is this a chordal (triangulated) graph? No! + +assert(~check_triangulated(G)) + +% The reason is that there is a chordless cycle around the outside nodes. +% To see this, imagine "picking up" node 5, leaving the rest on the plane +% (like a hoop skirt, or a tent), as shown below + +% 1 - 2 - 3 +% | / \ | +% 4 6 +% | \ / | +% 7 - 8 - 9 + + +% However, if we add in the 4-6 arc, it will be chordal. + +G2 = G; +G2(4,6)=1; G2(6,4)=1; +assert(check_triangulated(G2)) + +% Or we can add in the 2-8 arc +G2 = G; +G2(2,8)=1; G2(8,2)=1; +assert(check_triangulated(G2)) + + +if 0 +% 4x4 lattice with cross arcs +N=4;G0 = mk_2D_lattice(N,N,4); +vs = [1 6; 2 5; 2 7; 3 6; 3 8; 4 7; ... + 5 10; 6 9; 6 11; 7 10; 7 12; 8 11;... + 9 14; 10 13; 10 15; 11 14; 11 16; 12 15]; +for i=1:size(vs,1) + u = vs(i,1); v= vs(i,2); + G0(u,v) = 1; G0(v,u) = 1; +end +end + +% Here is how we can discover which edges to fill in automatically +% (although possibly sub-optimally) +weights = 2*ones(1,N*N); % all nodes are binar + +% fill-ins = 2-4, 2-6, 4-8, 6-8 and 4-6 +% cliques = 124, etc and 2456 4568 +greedy_order = best_first_elim_order(G0, weights); +[GT, cliques, fill_ins] = triangulate(G0, greedy_order) +assert(check_triangulated(GT)) + + + +greedy_order = best_first_elim_order(G, weights); +[GT, cliques, fill_ins] = triangulate(G, greedy_order) +assert(check_triangulated(GT)) + +% fill-ins = [4 6] + +% Cliques are the overlapping squares [1,2,4,5], [2 3 5 6], [4 5 7 8], [5 6 8 9] +% and the following caused by the fill-in: [2 4 5 6], [4 5 6 8] + +% Connect the maximal cliques of the triangulate graph into a junction tree +[jtree, root, B, clq_weights] = cliques_to_jtree(cliques, weights); + +% In this case, all cliques have weight 2^4 = 16 + + +% Now consider size of max clique as a function of grid size +% Note: this is not necessarily the optimal triangulation + +% N 5 10 15 16 17 18 +% m 6 15 23 25 28 28 +Ns = [5 10 15 16 17 18]; +for i=1:length(Ns) + N = Ns(i) + G = mk_2D_lattice(N,N,4); + weights = 2*ones(1,N*N); % all nodes are binary + greedy_order = best_first_elim_order(G, weights); % slow! + [GT, cliques, fill_ins] = triangulate(G, greedy_order); + %assert(check_triangulated(GT)) + [jtree, root, B, clq_weights] = cliques_to_jtree(cliques, weights); + m(i) = log2(max(clq_weights)); +end + +% plot distribution of clique sizes for fixed N +for c=1:length(cliques) + l(c) = length(cliques{c}); +end +hist(l)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/graph/triangulate_test.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,36 @@ +% Test the code using the dag in Fig 1 of Jensen, Jensen, Dittmer, +% "From influence diagrams to junction trees", UAI 94 + +% By reverse enginering Fig 2, we infer that the following arcs should +% be absent from the original dag: b->d1, e->d2, f->d2, g->d4 +a=1; b=2; d1=3; c=4; d=5; e=6; f=7; g=8; d2=9; d4=10; i=11; h=12; d3=13; l=14; j=15; k=16; +dag=zeros(16); +dag(a,c)=1; +%dag(b,[c d d1])=1; +dag(b,[c d])=1; +dag(d1,d)=1; +dag(c,e)=1; +dag(d,[e f])=1; +%dag(e,[g d2])=1; +dag(e,[g])=1; +%dag(f,[d2 h])=1; +dag(f,[h])=1; +%dag(g,[d4 i])=1; +dag(g,[i])=1; +dag(d2,i)=1; +dag(d4,l)=1; +dag(i,l)=1; +dag(h,[j k])=1; +dag(d3,k)=1; + + +[MG, moral_edges] = moralize(dag); +MG(j,k)=1; MG(k,j)=1; % simulate having a common utility child +% MG now equals fig 2 +order = [l j k i h a c d d4 g d3 d2 f e d1 b]; +[MTG, cliques, fill_ins] = triangulate(MG, order); +% MTG equals fig 3 +ns = 2*ones(1,16); +[jtree, root, cliques2] = mk_strong_jtree(cliques, ns, order, MTG); +jtree2 = mk_rooted_tree(jtree, root); +% jtree2 equals fig 4, with their arrows reversed
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/@inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +/bnet_from_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/get_field.m/1.1.1.1/Wed May 29 15:59:56 2002// +/inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/set_fields.m/1.1.1.1/Wed May 29 15:59:56 2002// +/update_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/@inf_engine/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/@inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/@inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/@inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/@inf_engine/Old/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,3 @@ +/marginal_family_pot.m/1.1.1.1/Wed May 29 15:59:56 2002// +/observed_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/@inf_engine/Old/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/@inf_engine/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/@inf_engine/Old/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/@inf_engine/Old/marginal_family_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,14 @@ +function pot = marginal_family_pot(engine, i) +% MARGINAL_FAMILY_POT Compute the marginal on i's family and return as a potentila (inf_engine) +% function pot = marginal_family_pot(engine,i) + +% This function is only called by solve_limid. +% It requires that engine's marginal_family function return a potential. +% This is true for jtree_inf_engine, but not for, say, jtree_ndx_inf_engine. +% All limids must be solved using potentials, +% but this is not true for bnets. + +%[m, pot] = marginal_family(engine, i); + +bnet = bnet_from_engine(engine); +[m, pot] = marginal_nodes(engine, family(bnet.dag, i));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/@inf_engine/Old/observed_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function onodes = observed_nodes(engine) +% OBSERVED_NODES Return nodes that are guaranteed to be observed, indep of evidence (generic inf_engine) +% onodes = observed_nodes(engine) + +onodes = [];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/@inf_engine/bnet_from_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +function bnet = bnet_from_engine(engine) +% BNET_FROM_ENGINE Return the bnet structure stored inside the engine (inf_engine) +% bnet = bnet_from_engine(engine) + +bnet = engine.bnet; + +% We cannot write 'engine.bnet' without writing a 'subsref' function, +% since engine is an object with private parts. +% The bnet field should be the only thing external users of the engine should need access to. +% We do not pass bnet as a separate argument, since it could get out of synch with the one +% encoded inside the engine. +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/@inf_engine/get_field.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +function val = get_field(engine, name) +% GET_FIELD Get the value of a named field from a generic engine +% val = get_field(engine, name) +% +% The following fields can be accessed +% +% bnet +% +% e.g., bnet = get_field(engine, 'bnet') + +switch name + case 'bnet', val = engine.bnet; + otherwise, + error(['invalid argument name ' name]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/@inf_engine/inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +function engine = inf_engine(bnet) + +engine.bnet = bnet; +engine = class(engine, 'inf_engine'); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/@inf_engine/marginal_family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,24 @@ +function m = marginal_family(engine, i, t) +% MARGINAL_FAMILY Compute the marginal on i's family (inf_engine) +% m = marginal_family(engine, i, t) +% +% t defaults to 1. + +if nargin < 3, t = 1; end + +bnet = bnet_from_engine(engine); +if t==1 + m = marginal_nodes(engine, family(bnet.dag, i)); +else + ss = length(bnet.intra); + fam = family(bnet.dag, i+ss); + if any(fam<=ss) + % i has a parent in the preceeding slice + % Hence the lowest numbered slice containing the family is t-1 + m = marginal_nodes(engine, fam, t-1); + else + % The family all fits inside slice t + % Hence shift the indexes back to slice 1 + m = marginal_nodes(engine, fam-ss, t); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/@inf_engine/set_fields.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function engine = set_fields(engine, varargin) +% SET_FIELDS Set the fields for a generic engine +% engine = set_fields(engine, name/value pairs) +% +% e.g., engine = set_fields(engine, 'maximize', 1) + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'maximize', engine.maximize = args{i+1}; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/@inf_engine/update_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +function engine = update_engine(engine, newCPDs) +% UPDATE_ENGINE Update the engine to take into account the new parameters (inf_engine). +% engine = update_engine(engine, newCPDs) +% +% This generic method is suitable for engines that do not process the parameters until 'enter_evidence'. + +engine.bnet.CPD = newCPDs;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +/dummy/1.1.1.1/Sat Jan 18 22:22:22 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,4 @@ +A D/@inf_engine//// +A D/dynamic//// +A D/online//// +A D/static////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_ff_hmm_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,10 @@ +/bk_ff_hmm_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/dbn_init_bel.m/1.1.1.1/Wed May 29 15:59:56 2002// +/dbn_marginal_from_bel.m/1.1.1.1/Wed May 29 15:59:56 2002// +/dbn_predict_bel.m/1.1.1.1/Wed May 29 15:59:56 2002// +/dbn_update_bel.m/1.1.1.1/Wed May 29 15:59:56 2002// +/dbn_update_bel1.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_ff_hmm_inf_engine/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +A D/private////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_ff_hmm_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@bk_ff_hmm_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_ff_hmm_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_ff_hmm_inf_engine/bk_ff_hmm_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,21 @@ +function engine = bk_ff_hmm_inf_engine(bnet) +% BK_FF_HMM_INF_ENGINE Naive (HMM-based) implementation of fully factored form of Boyen-Koller +% engine = bk_ff_hmm_inf_engine(bnet) +% +% This is implemented on top of the forwards-backwards algo for HMMs, +% so it is *less* efficient than exact inference! However, it is good for educational purposes, +% because it illustrates the BK algorithm very clearly. + +[persistent_nodes, transient_nodes] = partition_dbn_nodes(bnet.intra, bnet.inter); +assert(isequal(sort(bnet.observed), transient_nodes)); +[engine.prior, engine.transmat] = dbn_to_hmm(bnet); + +ss = length(bnet.intra); + +engine.bel = []; +engine.bel_marginals = []; +engine.marginals = []; + + +engine = class(engine, 'bk_ff_hmm_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_ff_hmm_inf_engine/dbn_init_bel.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function engine = dbn_init_bel(engine) +% DBN_INIT_BEL Compute the initial belief state (bk_ff_hmm) +% engine = dbn_init_bel(engine) + +engine.bel = engine.prior(:);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_ff_hmm_inf_engine/dbn_marginal_from_bel.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function marginal = dbn_marginal_from_bel(engine, i) +% DBN_MARGINAL_FROM_BEL Compute the marginal on a node given the current belief state (bk_ff_hmm) +% marginal = dbn_marginal_from_bel(engine, i) + +marginal = pot_to_marginal(engine.bel_marginals{i});
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_ff_hmm_inf_engine/dbn_predict_bel.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,19 @@ +function engine = dbn_predict_bel(engine, lag) +% DBN_PREDICT_BEL Predict the belief state 'lag' steps into the future (bk_ff_hmm) +% engine = dbn_predict_bel(engine, lag) +% 'lag' defaults to 1 + +if nargin < 2, lag = 1; end + +for d=1:lag + %newbel = engine.transmat' * engine.bel; + newbel = normalise(engine.transmat' * engine.bel); + + hnodes = engine.hnodes; + bnet = bnet_from_engine(engine); + ns = bnet.node_sizes; + [marginals, marginalsT] = project_joint_onto_marginals(newbel, hnodes, ns); + newbel = combine_marginals_into_joint(marginalsT, hnodes, ns); + engine.bel_marginals = marginalsT; + engine.bel = newbel; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_ff_hmm_inf_engine/dbn_update_bel.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,19 @@ +function [engine, loglik] = dbn_update_bel(engine, evidence) +% DBN_UPDATE_BEL Update the belief state (bk_ff_hmm) +% [engine, loglik] = dbn_update_bel(engine, evidence) +% +% evidence{i,1} contains the evidence on node i in slice t-1 +% evidence{i,2} contains the evidence on node i in slice t + +oldbel = engine.bel; +bnet = bnet_from_engine(engine); +obslik = mk_hmm_obs_lik_vec(bnet, evidence); +[newbel, lik] = normalise((engine.transmat' * oldbel) .* obslik); +loglik = log(lik); + +hnodes = engine.hnodes; +ns = bnet.node_sizes; +[marginals, marginalsT] = project_joint_onto_marginals(newbel, hnodes, ns); +newbel = combine_marginals_into_joint(marginalsT, hnodes, ns); +engine.bel_marginals = marginalsT; +engine.bel = newbel;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_ff_hmm_inf_engine/dbn_update_bel1.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,18 @@ +function [engine, loglik] = dbn_update_bel1(engine, evidence) +% DBN_UPDATE_BEL Update the initial belief state (bk_ff_hmm) +% [engine, loglik] = dbn_update_bel(engine, evidence) +% +% evidence{i} contains the evidence on node i in slice 1 + +oldbel = engine.bel; +bnet = bnet_from_engine(engine); +obslik = mk_hmm_obs_lik_vec1(bnet, evidence); +[newbel, lik] = normalise(oldbel .* obslik); +loglik = log(lik); + +hnodes = engine.hnodes; +ns = bnet.node_sizes; +[marginals, marginalsT] = project_joint_onto_marginals(newbel, hnodes, ns); +newbel = combine_marginals_into_joint(marginalsT, hnodes, ns); +engine.bel_marginals = marginalsT; +engine.bel = newbel;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_ff_hmm_inf_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,60 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (bk_ff_hmm) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product (not yet supported), else sum-product [0] +% filter - if 1, do filtering, else smoothing [0] +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) + +maximize = 0; +filter = 0; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + for i=1:2:nargs + switch args{i}, + case 'maximize', maximize = args{i+1}; + case 'filter', filter = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +assert(~maximize); + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +onodes = bnet.observed; +hnodes = mysetdiff(1:ss, onodes); +T = size(evidence, 2); +assert(~any(isemptycell(evidence(onodes,:)))); + +obslik = mk_hmm_obs_lik_mat(bnet, onodes, evidence); + +ns = bnet.node_sizes_slice; +ns(onodes) = 1; + +[gamma, loglik, marginals, marginalsT] = bk_ff_fb(engine.prior, engine.transmat, obslik, filter, hnodes, ns); + +for t=1:T + for i=hnodes(:)' + engine.marginals{i,t} = pot_to_marginal(marginalsT{i,t}); + end + for i=onodes(:)' + m.domain = i + (t-1)*ss; + m.T = 1; + engine.marginals{i,t} = m; + end +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_ff_hmm_inf_engine/marginal_family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function m = marginal_family(engine, i, t) +% MARGINAL_FAMILY Compute the marginal on the specified family (bk_ff_hmm) +% marginal = marginal_family(engine, i, t) + +error('bk_ff_hmm doesn''t support marginal_family');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_ff_hmm_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function marginal = marginal_nodes(engine, nodes, t) +% MARGINAL_NODES Compute the marginal on the specified query nodes (bk_ff_hmm) +% marginal = marginal_nodes(engine, i, t) + +assert(length(nodes)==1); +i = nodes(end); +%assert(myismember(i, engine.hnodes)); +marginal = engine.marginals{i,t}; +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +marginal.domain = i + (t-1)*ss;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_ff_hmm_inf_engine/private/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +/bk_ff_fb.m/1.1.1.1/Wed May 29 15:59:56 2002// +/combine_marginals_into_joint.m/1.1.1.1/Wed May 29 15:59:56 2002// +/dbn_to_hmm.m/1.1.1.1/Wed May 29 15:59:56 2002// +/mk_hmm_obs_lik_mat.m/1.1.1.1/Wed May 29 15:59:56 2002// +/mk_hmm_obs_lik_vec.m/1.1.1.1/Wed May 29 15:59:56 2002// +/mk_hmm_obs_lik_vec1.m/1.1.1.1/Wed May 29 15:59:56 2002// +/project_joint_onto_marginals.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_ff_hmm_inf_engine/private/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@bk_ff_hmm_inf_engine/private
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_ff_hmm_inf_engine/private/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_ff_hmm_inf_engine/private/bk_ff_fb.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,59 @@ +function [gamma, loglik, marginals, marginalsT] = bk_ff_fb(prior, transmat, obslik, filter_only, hnodes, ns) +% BK_FF_FB Fully factored Boyen-Koller version of forwards-backwards +% [gamma, loglik, marginals, marginalsT] = bk_ff_hmm(prior, transmat, obslik, filter_only, hnodes, ns) + +ss = length(ns); +S = length(prior); +T = size(obslik, 2); +marginals = cell(ss,T); +marginalsT = cell(ss,T); +scale = zeros(1,T); +alpha = zeros(S, T); + +transmat2 = transmat'; +for t=1:T + if t==1 + [alpha(:,t), scale(t)] = normalise(prior(:) .* obslik(:,t)); + else + [alpha(:,t), scale(t)] = normalise((transmat2 * alpha(:,t-1)) .* obslik(:,t)); + end + [marginals(:,t), marginalsT(:,t)] = project_joint_onto_marginals(alpha(:,t), hnodes, ns); + alpha(:,t) = combine_marginals_into_joint(marginalsT(:,t), hnodes, ns); + %fprintf('alpha t=%d\n', t); + %celldisp(marginals(1:8,t)) +end +loglik = sum(log(scale)); + +if filter_only + gamma = alpha; + return; +end + +beta = zeros(S,T); +gamma = zeros(S,T); +t = T; +beta(:,t) = ones(S,1); +gamma(:,t) = normalise(alpha(:,t) .* beta(:,t)); +[marginals(:,t), marginalsT(:,t)] = project_joint_onto_marginals(gamma(:,t), hnodes, ns); + +for t=T-1:-1:1 + b = beta(:,t+1) .* obslik(:,t+1); + beta(:,t) = normalise((transmat * b)); + [junk, tempT] = project_joint_onto_marginals(beta(:,t), hnodes, ns); + beta(:,t) = combine_marginals_into_joint(tempT, hnodes, ns); + %gamma(:,t) = normalise(alpha(:,t) .* beta(:,t)); + %[marginals(:,t), marginalsT(:,t)] = project_joint_onto_marginals(gamma(:,t), hnodes, ns); +end + +gamma2 = zeros(S,T); +for t=T-1:-1:1 + b = beta(:,t+1) .* obslik(:,t+1); + xi(:,:,t) = normalise((transmat .* (alpha(:,t) * b'))); + if t==T-1 + gamma2(:,T) = sum(xi(:,:,T-1), 1)'; + end + gamma2(:,t) = sum(xi(:,:,t), 2); + [marginals(:,t), marginalsT(:,t)] = project_joint_onto_marginals(gamma2(:,t), hnodes, ns); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_ff_hmm_inf_engine/private/combine_marginals_into_joint.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +function joint = combine_marginals_into_joint(marginalsT, hnodes, ns) + +jointT = dpot(hnodes, ns(hnodes)); +for i=hnodes(:)' + jointT = multiply_by_pot(jointT, marginalsT{i}); +end +m = pot_to_marginal(jointT); +joint = m.T(:);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_ff_hmm_inf_engine/private/dbn_to_hmm.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,41 @@ +function [prior, transmat] = dbn_to_hmm(bnet) +% DBN_TO_HMM Compute the discrete HMM matrices from a simple DBN +% [prior, transmat] = dbn_to_hmm(bnet) + +onodes = bnet.observed; +ss = length(bnet.intra); +evidence = cell(1,2*ss); +hnodes = mysetdiff(1:ss, onodes); +prior = multiply_CPTs(bnet, [], hnodes, evidence); +transmat = multiply_CPTs(bnet, hnodes, hnodes+ss, evidence); +%obsmat1 = multiply_CPTs(bnet, hnodes, onodes, evidence); +%obsmat = multiply_CPTs(bnet, hnodes+ss, onodes+ss, evidence); +%obsmat1 = obsmat if the observation matrices are tied across slices + + + +%%%%%%%%%%%% + +function mat = multiply_CPTs(bnet, pdom, cdom, evidence) + +% MULTIPLY_CPTS Make a matrix Pr(Y|X), where X represents all the parents, and Y all the children +% We assume the children have no intra-connections. +% +% e.g., Consider the DBN with interconnectivity i->i', j->j',k', k->i',k' +% Then transition matrix = Pr(i,j,k -> i',j',k') = Pr(i,k->i') Pr(j->j') Pr(j,k->k') + +dom = [pdom cdom]; +ns = bnet.node_sizes; +bigpot = dpot(dom, ns(dom)); +for j=cdom(:)' + e = bnet.equiv_class(j); + fam = family(bnet.dag, j); + pot = convert_to_pot(bnet.CPD{e}, 'd', fam(:), evidence); + bigpot = multiply_by_pot(bigpot, pot); +end +psize = prod(ns(pdom)); +csize = prod(ns(cdom)); +T = pot_to_marginal(bigpot); +mat = reshape(T.T, [psize csize]); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_ff_hmm_inf_engine/private/mk_hmm_obs_lik_mat.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,34 @@ +function obslik = mk_hmm_obs_lik_mat(bnet, onodes, evidence) +% MK_HMM_OBS_LIK_MAT Make the observation likelihood matrix for all slices +% obslik = mk_hmm_obs_lik_mat(bnet, onodes, evidence) +% +% obslik(i,t) = Pr(Y(t) | X(t)=i) + +[ss T] = size(evidence); + +hnodes = mysetdiff(1:ss, onodes); +ns = bnet.node_sizes_slice; +ns(onodes) = 1; +Q = prod(ns(hnodes)); +obslik = zeros(Q,T); + +dom = 1:ss; +for t=1:T + bigpot = dpot(dom, ns(dom)); + for i=onodes(:)' + if t==1 + e = bnet.equiv_class(i,1); + fam = family(bnet.dag, i); + else + e = bnet.equiv_class(i,2); + fam = family(bnet.dag, i, 2) + ss*(t-2); + end + pot = convert_to_pot(bnet.CPD{e}, 'd', fam(:), evidence); + pot = set_domain_pot(pot, family(bnet.dag, i)); + bigpot = multiply_by_pot(bigpot, pot); + end + m = pot_to_marginal(bigpot); + obslik(:,t) = m.T(:); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_ff_hmm_inf_engine/private/mk_hmm_obs_lik_vec.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,27 @@ +function obslik = mk_hmm_obs_lik_vec(bnet, evidence) +% MK_HMM_OBS_LIK_VEC Make the observation likelihood vector for one slice +% obslik = mk_obs_lik(bnet, evidence) +% +% obslik(i) = Pr(y(t) | X(t)=i) +% evidence{i,1} contains the evidence on node i in slice t-1 +% evidence{i,2} contains the evidence on node i in slice t + +ns = bnet.node_sizes; +ss = length(bnet.intra); +onodes = find(~isemptycell(evidence(:))); +hnodes = find(isemptycell(evidence(:))); +ens = ns; +ens(onodes) = 1; +Q = prod(ens(hnodes)); +obslik = zeros(1,Q); +dom = (1:ss)+ss; +bigpot = dpot(dom, ens(dom)); +onodes1 = find(~isemptycell(evidence(:,1))); +for i=onodes1(:)' + e = bnet.equiv_class(i,2); + fam = family(bnet.dag, i, 2); + pot = convert_to_pot(bnet.CPD{e}, 'd', fam, evidence); + bigpot = multiply_by_pot(bigpot, pot); +end +m = pot_to_marginal(bigpot); +obslik = m.T(:);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_ff_hmm_inf_engine/private/mk_hmm_obs_lik_vec1.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,25 @@ +function obslik = mk_hmm_obs_lik_vec1(bnet, evidence) +% MK_HMM_OBS_LIK_VEC1 Make the observation likelihood vector for the first slice +% obslik = mk_hmm_obs_lik_vec1(engine, evidence) +% +% obslik(i) = Pr(y(1) | X(1)=i) +% evidence{i} contains the evidence on node i in slice 1 + +ns = bnet.node_sizes; +ss = length(ns); +onodes = find(~isemptycell(evidence(:))); +hnodes = find(isemptycell(evidence(:))); +ens = ns; +ens(onodes) = 1; +Q = prod(ens(hnodes)); +obslik = zeros(1,Q); +dom = (1:ss); +bigpot = dpot(dom, ens(dom)); +for i=onodes(:)' + e = bnet.equiv_class(i,1); + fam = family(bnet.dag, i); + pot = convert_to_pot(bnet.CPD{e}, 'd', fam(:), evidence); + bigpot = multiply_by_pot(bigpot, pot); +end +m = pot_to_marginal(bigpot); +obslik = m.T(:);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_ff_hmm_inf_engine/private/project_joint_onto_marginals.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function [marginals, marginalsT] = project_joint_onto_marginals(joint, hnodes, ns) + +ss = length(ns); +jointT = dpot(hnodes, ns(hnodes), joint); +marginalsT = cell(1, ss); +marginals = cell(1,ss); +for i=hnodes(:)' + marginalsT{i} = marginalize_pot(jointT, i); + m = pot_to_marginal(marginalsT{i}); + marginals{i} = m.T(:); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +/bk_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/dbn_init_bel.m/1.1.1.1/Wed May 29 15:59:56 2002// +/dbn_marginal_from_bel.m/1.1.1.1/Wed May 29 15:59:56 2002// +/dbn_update_bel.m/1.1.1.1/Wed May 29 15:59:56 2002// +/dbn_update_bel1.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_soft_evidence.m/1.1.1.1/Sat Jan 11 18:13:50 2003// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/update_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@bk_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_inf_engine/bk_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,107 @@ +function engine = bk_inf_engine(bnet, varargin) +% BK_INF_ENGINE Boyen-Koller approximate inference algorithm for DBNs. +% +% In the BK algorithm, the belief state is represented as a product of marginals, +% even though the factors may not be independent. +% +% engine = bk_inf_engine(bnet, ...) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% clusters - if a cell array, clusters{i} specifies the terms in the i'th factor. +% - 'exact' means create one cluster that contains all the nodes in a slice [exact] +% - 'ff' means create one cluster per node (ff = fully factorised). +% +% +% For details, see +% - "Tractable Inference for Complex Stochastic Processes", X. Boyen and D. Koller, UAI 98. +% - "Approximate learning of dynamic models", X. Boyen and D. Koller, NIPS 98. +% (The UAI98 paper discusses filtering and theory, and the NIPS98 paper discusses smoothing.) + +ss = length(bnet.intra); +% set default params +clusters = 'exact'; + + +if nargin >= 2 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'clusters', clusters = args{i+1}; + otherwise, error(['unrecognized argument ' args{i}]) + end + end +end + +if strcmp(clusters, 'exact') + %clusters = { compute_interface_nodes(bnet.intra, bnet.inter) }; + clusters = { 1:ss }; +elseif strcmp(clusters, 'ff') + clusters = num2cell(1:ss); +end + + +% We need to insert the prior on the clusters in slice 1, +% and extract the posterior on the clusters in slice 2. +C = length(clusters); +clusters2 = cell(1,2*C); +clusters2(1:C) = clusters; +for c=1:C + clusters2{c+C} = clusters{c} + ss; +end + +onodes = bnet.observed; +obs_nodes = [onodes(:) onodes(:)+ss]; +engine.sub_engine = jtree_inf_engine(bnet, 'clusters', clusters2); + +engine.clq_ass_to_cluster = zeros(C, 2); +for c=1:C + engine.clq_ass_to_cluster(c,1) = clq_containing_nodes(engine.sub_engine, clusters{c}); + engine.clq_ass_to_cluster(c,2) = clq_containing_nodes(engine.sub_engine, clusters{c}+ss); +end +engine.clusters = clusters; + +engine.clq_ass_to_node = zeros(ss, 2); +for i=1:ss + engine.clq_ass_to_node(i, 1) = clq_containing_nodes(engine.sub_engine, i); + engine.clq_ass_to_node(i, 2) = clq_containing_nodes(engine.sub_engine, i+ss); +end + + + +% Also create an engine just for slice 1 +bnet1 = mk_bnet(bnet.intra1, bnet.node_sizes_slice, 'discrete', myintersect(bnet.dnodes, 1:ss), ... + 'equiv_class', bnet.equiv_class(:,1), 'observed', onodes); +for i=1:max(bnet1.equiv_class) + bnet1.CPD{i} = bnet.CPD{i}; +end + +engine.sub_engine1 = jtree_inf_engine(bnet1, 'clusters', clusters); + +engine.clq_ass_to_cluster1 = zeros(1,C); +for c=1:C + engine.clq_ass_to_cluster1(c) = clq_containing_nodes(engine.sub_engine1, clusters{c}); +end + +engine.clq_ass_to_node1 = zeros(1, ss); +for i=1:ss + engine.clq_ass_to_node1(i) = clq_containing_nodes(engine.sub_engine1, i); +end + +engine.clpot = []; % this is where we store the results between enter_evidence and marginal_nodes +engine.filter = []; +engine.maximize = []; +engine.T = []; + +engine.bel = []; +engine.bel_clpot = []; +engine.slice1 = []; +%engine.pot_type = 'cg'; +% hack for online inference so we can cope with hidden Gaussians and discrete +% it will not affect the pot type used in enter_evidence +engine.pot_type = determine_pot_type(bnet, onodes); + +engine = class(engine, 'bk_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_inf_engine/dbn_init_bel.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +function engine = dbn_init_bel(engine) +% DBN_INIT_BEL Compute the initial belief state (bk) +% engine = dbn_init_bel(engine)) + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +evidence = cell(1,ss); +engine = dbn_update_bel1(engine, evidence);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_inf_engine/dbn_marginal_from_bel.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,18 @@ +function marginal = dbn_marginal_from_bel(engine, i) +% DBN_MARGINAL_FROM_BEL Compute the marginal on a node given the current belief state (bk) +% marginal = dbn_marginal_from_bel(engine, i) + +if engine.slice1 + j = i; + c = clq_containing_nodes(engine.sub_engine1, j); +else + bnet = bnet_from_engine(engine); + ss = length(bnet.intra); + j = i+ss; + c = clq_containing_nodes(engine.sub_engine, j); +end +assert(c >= 1); +bigpot = engine.bel_clpot{c}; + +pot = marginalize_pot(bigpot, j); +marginal = pot_to_marginal(pot);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_inf_engine/dbn_update_bel.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,37 @@ +function [engine, loglik] = dbn_update_bel(engine, evidence) +% DBN_UPDATE_BEL Update the belief state (bk) +% [engine, loglik] = dbn_update_bel(engine, evidence) +% +% evidence{i,1} contains the evidence on node i in slice t-1 +% evidence{i,2} contains the evidence on node i in slice t + +oldbel = engine.bel; + +ss = size(evidence, 1); +bnet = bnet_from_engine(engine); +CPDpot = cell(1, ss); +for n=1:ss + fam = family(bnet.dag, n, 2); + e = bnet.equiv_class(n, 2); + CPDpot{n} = convert_to_pot(bnet.CPD{e}, engine.pot_type, fam(:), evidence); +end + +observed = ~isemptycell(evidence); +onodes2 = find(observed(:)); +clqs = [engine.clq_ass_to_cluster(:,1); engine.clq_ass_to_node(:,2)]; +pots = [oldbel(:); CPDpot(:)]; + +[clpot, loglik] = enter_soft_evidence(engine.sub_engine, clqs, pots, onodes2(:), engine.pot_type); + +C = length(engine.clusters); +newbel = cell(1,C); +for c=1:C + k = engine.clq_ass_to_cluster(c,2); + cl = engine.clusters{c}; + newbel{c} = marginalize_pot(clpot{k}, cl+ss); % extract slice 2 posterior + newbel{c} = set_domain_pot(newbel{c}, cl); % shift back to slice 1 for re-use as prior +end + +engine.bel = newbel; +engine.bel_clpot = clpot; +engine.slice1 = 0;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_inf_engine/dbn_update_bel1.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,30 @@ +function [engine, loglik] = dbn_update_bel1(engine, evidence) +% DBN_UPDATE_BEL1 Update the initial belief state (bk) +% engine = dbn_update_bel1(engine, evidence) +% +% evidence{i} has the evidence on node i for slice 1 + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +CPDpot = cell(1,ss); +t = 1; +for n=1:ss + fam = family(bnet.dag, n); + e = bnet.equiv_class(n, 1); + CPDpot{n} = convert_to_pot(bnet.CPD{e}, engine.pot_type, fam(:), evidence); +end + +onodes = find(~isemptycell(evidence)); + +[clpot, loglik] = enter_soft_evidence(engine.sub_engine1, engine.clq_ass_to_node1, CPDpot, onodes, engine.pot_type); + +C = length(engine.clusters); +newbel = cell(1,C); +for c=1:C + k = engine.clq_ass_to_cluster1(c); + newbel{c} = marginalize_pot(clpot{k}, engine.clusters{c}); +end + +engine.bel = newbel; +engine.bel_clpot = clpot; +engine.slice1 = 1;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_inf_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,48 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (bk) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product instead of sum-product [0] +% filter - if 1, do filtering, else smoothing [0] +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) + +maximize = 0; +filter = 0; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + for i=1:2:nargs + switch args{i}, + case 'maximize', maximize = args{i+1}; + case 'filter', filter = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +[ss T] = size(evidence); +engine.filter = filter; +engine.maximize = maximize; +engine.T = T; + +if maximize + error('BK does not yet support max propagation') + % because it calls enter_soft_evidence, not enter_evidence +end + +observed_bitv = ~isemptycell(evidence); +onodes = find(observed_bitv); +bnet = bnet_from_engine(engine); +pot_type = determine_pot_type(bnet, onodes); +CPDpot = convert_dbn_CPDs_to_pots(bnet, evidence, pot_type); +[engine.clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed_bitv, pot_type, filter); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_inf_engine/enter_soft_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,88 @@ +function [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) +% ENTER_SOFT_EVIDENCE Add the specified soft evidence to the network (bk) +% [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) + +[ss T] = size(CPDpot); +C = length(engine.clusters); +Q = length(cliques_from_engine(engine.sub_engine)); +Q1 = length(cliques_from_engine(engine.sub_engine1)); +clpot = cell(Q,T); +alpha = cell(C,T); + +% Forwards +% The method is a generalization of the following HMM equation: +% alpha(j,t) = normalise( (sum_i alpha(i,t-1) * transmat(i,j)) * obsmat(j,t) ) +% where alpha(j,t) = Pr(Q(t)=j | y(1:t)) +t = 1; +[clpot(1:Q1,t), logscale(t)] = enter_soft_evidence(engine.sub_engine1, engine.clq_ass_to_node1(:), ... + CPDpot(:,1), find(observed(:,1)), pot_type); +for c=1:C + k = engine.clq_ass_to_cluster1(c); + alpha{c,t} = marginalize_pot(clpot{k,t}, engine.clusters{c}); +end +% For filtering, clpot{1} contains evidence on slice 1 only + +%fprintf('alphas t=%d\n', t); +%for c=1:8 +% temp = pot_to_marginal(alpha{c,t}); +% temp.T +%end + +% clpot{t} contains evidence from slices t-1, t for t > 1 +clqs = [engine.clq_ass_to_cluster(:,1); engine.clq_ass_to_node(:,2)]; +for t=2:T + pots = [alpha(:,t-1); CPDpot(:,t)]; + [clpot(:,t), logscale(t)] = enter_soft_evidence(engine.sub_engine, clqs, pots, find(observed(:,t-1:t)), pot_type); + for c=1:C + k = engine.clq_ass_to_cluster(c,2); + cl = engine.clusters{c}; + alpha{c,t} = marginalize_pot(clpot{k,t}, cl+ss); % extract slice 2 posterior + alpha{c,t} = set_domain_pot(alpha{c,t}, cl); % shift back to slice 1 for re-use as prior + end + +end + +loglik = sum(logscale); + +if filter + return; +end + +% Backwards +% The method is a generalization of the following HMM equation: +% beta(i,t) = (sum_j transmat(i,j) * obsmat(j,t+1) * beta(j,t+1)) +% where beta(i,t) = Pr(y(t+1:T) | Q(t)=i) +t = T; +bnet = bnet_from_engine(engine); +beta = cell(C,T); +for c=1:C + beta{c,t} = mk_initial_pot(pot_type, engine.clusters{c} + ss, bnet.node_sizes(:), bnet.cnodes(:), ... + find(observed(:,t-1:t))); +end +for t=T-1:-1:1 + clqs = [engine.clq_ass_to_cluster(:,2); engine.clq_ass_to_node(:,2)]; + pots = [beta(:,t+1); CPDpot(:,t+1)]; + temp = enter_soft_evidence(engine.sub_engine, clqs, pots, find(observed(:,t:t+1)), pot_type); + for c=1:C + k = engine.clq_ass_to_cluster(c,1); + cl = engine.clusters{c}; + beta{c,t} = marginalize_pot(temp{k}, cl); % extract slice 1 + beta{c,t} = set_domain_pot(beta{c,t}, cl + ss); % shift fwd to slice 2 + end +end + +% Combine +% The method is a generalization of the following HMM equation: +% xi(i,j,t) = normalise( alpha(i,t) * transmat(i,j) * obsmat(j,t+1) * beta(j,t+1) ) +% where xi(i,j,t) = Pr(Q(t)=i, Q(t+1)=j | y(1:T)) +for t=1:T-1 + clqs = [engine.clq_ass_to_cluster(:); engine.clq_ass_to_node(:,2)]; + pots = [alpha(:,t); beta(:,t+1); CPDpot(:,t+1)]; + clpot(:,t+1) = enter_soft_evidence(engine.sub_engine, clqs, pots, find(observed(:,t:t+1)), pot_type); +end +% for smoothing, clpot{1} is undefined +for k=1:Q1 + clpot{k,1} = []; +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_inf_engine/marginal_family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,25 @@ +function m = marginal_family(engine, i, t) +% MARGINAL_FAMILY Compute the marginal on the specified family (bk) +% marginal = marginal_family(engine, i, t) + +% This is just like inf_engine/marginal_family, except when we call +% marginal_nodes, we provide a 4th argument, to tell it's a family. + +if nargin < 3, t = 1; end + +bnet = bnet_from_engine(engine); +if t==1 + m = marginal_nodes(engine, family(bnet.dag, i), t, 1); +else + ss = length(bnet.intra); + fam = family(bnet.dag, i+ss); + if any(fam<=ss) + % i has a parent in the preceeding slice + % Hence the lowest numbered slice containing the family is t-1 + m = marginal_nodes(engine, fam, t-1, 1); + else + % The family all fits inside slice t + % Hence shift the indexes back to slice 1 + m = marginal_nodes(engine, fam-ss, t, 1); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,67 @@ +function marginal = marginal_nodes(engine, nodes, t, fam) +% MARGINAL_NODES Compute the marginal on the specified query nodes (bk) +% +% marginal = marginal_nodes(engine, i, t) +% returns Pr(X(i,t) | Y(1:T)), where X(i,t) is the i'th node in the t'th slice. +% If enter_evidence used filtering instead of smoothing, this will return Pr(X(i,t) | Y(1:t)). +% +% marginal = marginal_nodes(engine, query, t) +% returns Pr(X(query(1),t), ... X(query(end),t) | Y(1:T)), +% where X(q,t) is the q'th node in the t'th slice. If q > ss (slice size), this is equal +% to X(q mod ss, t+1). That is, 't' specifies the time slice of the earliest node. +% 'query' cannot span more than 2 time slices. +% Example: +% Consider a DBN with 2 nodes per slice. +% Then t=2, nodes=[1 3] refers to node 1 in slice 2 and node 1 in slice 3. + +if nargin < 3, t = 1; end +if nargin < 4, fam = 0; else fam = 1; end + + +% clpot{t} contains slice t-1 and t +% Example +% clpot #: 1 2 3 +% slices: 1 1,2 2,3 +% For filtering, we must take care not to take future evidence into account. +% For smoothing, clpot{1} does not exist. + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); + +nodes2 = nodes; +if ~engine.filter + if t < engine.T + slice = t+1; + else % earliest t is T, so all nodes fit in one slice + slice = engine.T; + nodes2 = nodes + ss; + end +else + if t == 1 + slice = 1; + else + if all(nodes<=ss) + slice = t; + nodes2 = nodes + ss; + elseif t == engine.T + slice = t; + else + slice = t + 1; + end + end +end + +if engine.filter & t==1 + c = clq_containing_nodes(engine.sub_engine1, nodes2, fam); +else + c = clq_containing_nodes(engine.sub_engine, nodes2, fam); +end +assert(c >= 1); +bigpot = engine.clpot{c, slice}; + +pot = marginalize_pot(bigpot, nodes2); +marginal = pot_to_marginal(pot); + +% we convert the domain to the unrolled numbering system +% so that update_ess extracts the right evidence. +marginal.domain = nodes+(t-1)*ss;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@bk_inf_engine/update_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function engine = update_engine(engine, newCPDs) +% UPDATE_ENGINE Update the engine to take into account the new parameters (bk) +% engine = update_engine(engine, newCPDs) + +engine.inf_engine = update_engine(engine.inf_engine, newCPDs); +engine.sub_engine = update_engine(engine.sub_engine, newCPDs); + +bnet = bnet_from_engine(engine); +eclass1 = bnet.equiv_class(:,1); +engine.sub_engine1 = update_engine(engine.sub_engine1, newCPDs(1:max(eclass1))); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@cbk_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +/cbk_inf_engine.m/1.1.1.1/Mon Nov 22 22:15:34 2004// +/dbn_init_bel.m/1.1.1.1/Tue Jul 29 02:44:58 2003// +/dbn_marginal_from_bel.m/1.1.1.1/Tue Jul 29 02:44:58 2003// +/dbn_update_bel.m/1.1.1.1/Tue Jul 29 02:44:58 2003// +/dbn_update_bel1.m/1.1.1.1/Tue Jul 29 02:44:58 2003// +/enter_evidence.m/1.1.1.1/Mon Jan 12 20:53:54 2004// +/enter_soft_evidence.m/1.1.1.1/Wed Feb 4 07:42:38 2004// +/junk/1.1.1.1/Wed Nov 24 20:12:38 2004// +/marginal_family.m/1.1.1.1/Tue Jul 29 02:44:58 2003// +/marginal_nodes.m/1.1.1.1/Tue Dec 16 06:17:18 2003// +/update_engine.m/1.1.1.1/Tue Jul 29 02:44:58 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@cbk_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@cbk_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@cbk_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@cbk_inf_engine/cbk_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,175 @@ +function engine = cbk_inf_engine(bnet, varargin) +% Just the same as bk_inf_engine, but you can specify overlapping clusters. + +ss = length(bnet.intra); +% set default params +clusters = 'exact'; + +if nargin >= 2 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'clusters', clusters = args{i+1}; + otherwise, error(['unrecognized argument ' args{i}]) + end + end +end + +if strcmp(clusters, 'exact') + %clusters = { compute_interface_nodes(bnet.intra, bnet.inter) }; + clusters = { 1:ss }; +elseif strcmp(clusters, 'ff') + clusters = num2cell(1:ss); +end + + +% We need to insert the prior on the clusters in slice 1, +% and extract the posterior on the clusters in slice 2. +% We don't need to care about the separators, b/c they're subsets of the clusters. +C = length(clusters); +clusters2 = cell(1,2*C); +clusters2(1:C) = clusters; +for c=1:C + clusters2{c+C} = clusters{c} + ss; +end + +onodes = bnet.observed; +obs_nodes = [onodes(:) onodes(:)+ss]; +engine.sub_engine = jtree_inf_engine(bnet, 'clusters', clusters2); + +%FH >>> +%Compute separators. +ns = bnet.node_sizes(:,1); +ns(onodes) = 1; +[clusters, separators] = build_jt(clusters, 1:length(ns), ns); +S = length(separators); +engine.separators = separators; + +%Compute size of clusters. +cl_sizes = zeros(1,C); +for c=1:C + cl_sizes(c) = prod(ns(clusters{c})); +end + +%Assign separators to the smallest cluster subsuming them. +engine.cluster_ass_to_separator = zeros(S, 1); +for s=1:S + subsuming_clusters = []; + %find smallest cluster containing s + for c=1:C + if mysubset(separators{s}, clusters{c}) + subsuming_clusters(end+1) = c; + end + end + c = argmin(cl_sizes(subsuming_clusters)); + engine.cluster_ass_to_separator(s) = subsuming_clusters(c); +end + +%<<< FH + +engine.clq_ass_to_cluster = zeros(C, 2); +for c=1:C + engine.clq_ass_to_cluster(c,1) = clq_containing_nodes(engine.sub_engine, clusters{c}); + engine.clq_ass_to_cluster(c,2) = clq_containing_nodes(engine.sub_engine, clusters{c}+ss); +end +engine.clusters = clusters; + +engine.clq_ass_to_node = zeros(ss, 2); +for i=1:ss + engine.clq_ass_to_node(i, 1) = clq_containing_nodes(engine.sub_engine, i); + engine.clq_ass_to_node(i, 2) = clq_containing_nodes(engine.sub_engine, i+ss); +end + + + +% Also create an engine just for slice 1 +bnet1 = mk_bnet(bnet.intra1, bnet.node_sizes_slice, 'discrete', myintersect(bnet.dnodes, 1:ss), ... + 'equiv_class', bnet.equiv_class(:,1), 'observed', onodes); +for i=1:max(bnet1.equiv_class) + bnet1.CPD{i} = bnet.CPD{i}; +end + +engine.sub_engine1 = jtree_inf_engine(bnet1, 'clusters', clusters); + +engine.clq_ass_to_cluster1 = zeros(1,C); +for c=1:C + engine.clq_ass_to_cluster1(c) = clq_containing_nodes(engine.sub_engine1, clusters{c}); +end + +engine.clq_ass_to_node1 = zeros(1, ss); +for i=1:ss + engine.clq_ass_to_node1(i) = clq_containing_nodes(engine.sub_engine1, i); +end + +engine.clpot = []; % this is where we store the results between enter_evidence and marginal_nodes +engine.filter = []; +engine.maximize = []; +engine.T = []; + +engine.bel = []; +engine.bel_clpot = []; +engine.slice1 = []; +%engine.pot_type = 'cg'; +% hack for online inference so we can cope with hidden Gaussians and discrete +% it will not affect the pot type used in enter_evidence +engine.pot_type = determine_pot_type(bnet, onodes); + +engine = class(engine, 'cbk_inf_engine', inf_engine(bnet)); + + + + +function [cliques, seps, jt_size] = build_jt(cliques, vars, ns) +% BUILD_JT connects the cliques into a jtree, computes the respective +% separators and the size of the resulting jtree. +% +% [cliques, seps, jt_size] = build_jt(cliques, vars, ns) +% ns(i) has to hold the size of vars(i) +% vars has to be a superset of the union of cliques. + +%======== Compute the jtree with tool from BNT. This wants the vars to be 1:N. +%==== Map from nodes to their indices. +%disp('Computing jtree for cliques with vars and ns:'); +%cliques +%vars +%ns' + +inv_nodes = sparse(1,max(vars)); +N = length(vars); +for i=1:N + inv_nodes(vars(i)) = i; +end + +tmp_cliques = cell(1,length(cliques)); +%==== Temporarily map clique vars to their indices. +for i=1:length(cliques) + tmp_cliques{i} = inv_nodes(cliques{i}); +end + +%=== Compute the jtree, using BNT. +[jtree, root, B, w] = cliques_to_jtree(tmp_cliques, ns); + + +%======== Now, compute the separators between connected cliques and their weights. +seps = {}; +s_w = []; +[is,js] = find(jtree > 0); +for k=1:length(is) + i = is(k); j = js(k); + sep = vars(find(B(i,:) & B(j,:))); % intersect(cliques{i}, cliques{j}); + if i>j | length(sep) == 0, continue; end; + seps{end+1} = sep; + s_w(end+1) = prod(ns(inv_nodes(seps{end}))); +end + +cl_w = sum(w); +sep_w = sum(s_w); +assert(cl_w > sep_w, 'Weight of cliques must be bigger than weight of separators'); + +jt_size = cl_w + sep_w; +% jt.cliques = cliques; +% jt.seps = seps; +% jt.size = jt_size; +% jt.ns = ns'; +% jt; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@cbk_inf_engine/dbn_init_bel.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +function engine = dbn_init_bel(engine) +% DBN_INIT_BEL Compute the initial belief state (bk) +% engine = dbn_init_bel(engine)) + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +evidence = cell(1,ss); +engine = dbn_update_bel1(engine, evidence);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@cbk_inf_engine/dbn_marginal_from_bel.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,18 @@ +function marginal = dbn_marginal_from_bel(engine, i) +% DBN_MARGINAL_FROM_BEL Compute the marginal on a node given the current belief state (bk) +% marginal = dbn_marginal_from_bel(engine, i) + +if engine.slice1 + j = i; + c = clq_containing_nodes(engine.sub_engine1, j); +else + bnet = bnet_from_engine(engine); + ss = length(bnet.intra); + j = i+ss; + c = clq_containing_nodes(engine.sub_engine, j); +end +assert(c >= 1); +bigpot = engine.bel_clpot{c}; + +pot = marginalize_pot(bigpot, j); +marginal = pot_to_marginal(pot);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@cbk_inf_engine/dbn_update_bel.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,37 @@ +function [engine, loglik] = dbn_update_bel(engine, evidence) +% DBN_UPDATE_BEL Update the belief state (bk) +% [engine, loglik] = dbn_update_bel(engine, evidence) +% +% evidence{i,1} contains the evidence on node i in slice t-1 +% evidence{i,2} contains the evidence on node i in slice t + +oldbel = engine.bel; + +ss = size(evidence, 1); +bnet = bnet_from_engine(engine); +CPDpot = cell(1, ss); +for n=1:ss + fam = family(bnet.dag, n, 2); + e = bnet.equiv_class(n, 2); + CPDpot{n} = convert_to_pot(bnet.CPD{e}, engine.pot_type, fam(:), evidence); +end + +observed = ~isemptycell(evidence); +onodes2 = find(observed(:)); +clqs = [engine.clq_ass_to_cluster(:,1); engine.clq_ass_to_node(:,2)]; +pots = [oldbel(:); CPDpot(:)]; + +[clpot, loglik] = enter_soft_evidence(engine.sub_engine, clqs, pots, onodes2(:), engine.pot_type); + +C = length(engine.clusters); +newbel = cell(1,C); +for c=1:C + k = engine.clq_ass_to_cluster(c,2); + cl = engine.clusters{c}; + newbel{c} = marginalize_pot(clpot{k}, cl+ss); % extract slice 2 posterior + newbel{c} = set_domain_pot(newbel{c}, cl); % shift back to slice 1 for re-use as prior +end + +engine.bel = newbel; +engine.bel_clpot = clpot; +engine.slice1 = 0;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@cbk_inf_engine/dbn_update_bel1.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,30 @@ +function [engine, loglik] = dbn_update_bel1(engine, evidence) +% DBN_UPDATE_BEL1 Update the initial belief state (bk) +% engine = dbn_update_bel1(engine, evidence) +% +% evidence{i} has the evidence on node i for slice 1 + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +CPDpot = cell(1,ss); +t = 1; +for n=1:ss + fam = family(bnet.dag, n); + e = bnet.equiv_class(n, 1); + CPDpot{n} = convert_to_pot(bnet.CPD{e}, engine.pot_type, fam(:), evidence); +end + +onodes = find(~isemptycell(evidence)); + +[clpot, loglik] = enter_soft_evidence(engine.sub_engine1, engine.clq_ass_to_node1, CPDpot, onodes, engine.pot_type); + +C = length(engine.clusters); +newbel = cell(1,C); +for c=1:C + k = engine.clq_ass_to_cluster1(c); + newbel{c} = marginalize_pot(clpot{k}, engine.clusters{c}); +end + +engine.bel = newbel; +engine.bel_clpot = clpot; +engine.slice1 = 1;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@cbk_inf_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,48 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% this is unchanged from bk_inf_engine. +% ENTER_EVIDENCE Add the specified evidence to the network (bk) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product instead of sum-product [0] +% filter - if 1, do filtering, else smoothing [0] +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) + +maximize = 0; +filter = 0; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + for i=1:2:nargs + switch args{i}, + case 'maximize', maximize = args{i+1}; + case 'filter', filter = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +[ss T] = size(evidence); +engine.filter = filter; +engine.maximize = maximize; +engine.T = T; + +if maximize + error('BK does not yet support max propagation') + % because it calls enter_soft_evidence, not enter_evidence +end + +observed_bitv = ~isemptycell(evidence); +onodes = find(observed_bitv); +bnet = bnet_from_engine(engine); +pot_type = determine_pot_type(bnet, onodes); +CPDpot = convert_dbn_CPDs_to_pots(bnet, evidence, pot_type); +[engine.clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed_bitv, pot_type, filter); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@cbk_inf_engine/enter_soft_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,115 @@ +function [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) +% ENTER_SOFT_EVIDENCE Add the specified soft evidence to the network (bk) +% [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) + +[ss T] = size(CPDpot); +C = length(engine.clusters); +S = length(engine.separators); +Q = length(cliques_from_engine(engine.sub_engine)); +Q1 = length(cliques_from_engine(engine.sub_engine1)); +clpot = cell(Q,T); +alpha = cell(C,T); + +% Forwards +% The method is a generalization of the following HMM equation: +% alpha(j,t) = normalise( (sum_i alpha(i,t-1) * transmat(i,j)) * obsmat(j,t) ) +% where alpha(j,t) = Pr(Q(t)=j | y(1:t)) +t = 1; +[clpot(1:Q1,t), logscale(t)] = enter_soft_evidence(engine.sub_engine1, engine.clq_ass_to_node1(:), ... + CPDpot(:,1), find(observed(:,1)), pot_type); +for c=1:C + k = engine.clq_ass_to_cluster1(c); + alpha{c,t} = marginalize_pot(clpot{k,t}, engine.clusters{c}); +end + +%=== FH: For each separator s, divide some cluster potential by s's potential +alpha_orig = alpha(:,t); +for s=1:S + c = engine.cluster_ass_to_separator(s); + alpha{c,t} = divide_by_pot(alpha{c,t}, marginalize_pot(alpha_orig{c}, engine.separators{s})); +end + +% For filtering, clpot{1} contains evidence on slice 1 only + +%fprintf('alphas t=%d\n', t); +%for c=1:8 +% temp = pot_to_marginal(alpha{c,t}); +% temp.T +%end + +% clpot{t} contains evidence from slices t-1, t for t > 1 +clqs = [engine.clq_ass_to_cluster(:,1); engine.clq_ass_to_node(:,2)]; +for t=2:T + pots = [alpha(:,t-1); CPDpot(:,t)]; + [clpot(:,t), logscale(t)] = enter_soft_evidence(engine.sub_engine, clqs, pots, find(observed(:,t-1:t)), pot_type); + for c=1:C + k = engine.clq_ass_to_cluster(c,2); + cl = engine.clusters{c}; + alpha{c,t} = marginalize_pot(clpot{k,t}, cl+ss); % extract slice 2 posterior + alpha{c,t} = set_domain_pot(alpha{c,t}, cl); % shift back to slice 1 for re-use as prior + end + %=== FH: For each separator s, divide some cluster potential by s's potential + alpha_orig = alpha(:,t); + for s=1:S + c = engine.cluster_ass_to_separator(s); + alpha{c,t} = divide_by_pot(alpha{c,t}, marginalize_pot(alpha_orig{c}, engine.separators{s})); + end +end + +loglik = sum(logscale); + +if filter + return; +end + +% Backwards +% The method is a generalization of the following HMM equation: +% beta(i,t) = (sum_j transmat(i,j) * obsmat(j,t+1) * beta(j,t+1)) +% where beta(i,t) = Pr(y(t+1:T) | Q(t)=i) +t = T; +bnet = bnet_from_engine(engine); +beta = cell(C,T); +for c=1:C + beta{c,t} = mk_initial_pot(pot_type, engine.clusters{c} + ss, bnet.node_sizes(:), bnet.cnodes(:), ... + find(observed(:,t-1:t))); +end +%=== FH: For each separator s, divide some cluster potential by s's potential +beta_orig = beta(:,t); +for s=1:S + c = engine.cluster_ass_to_separator(s); + beta{c,t} = divide_by_pot(beta{c,t}, marginalize_pot(beta_orig{c}, engine.separators{s}+ss)); +end + +for t=T-1:-1:1 + clqs = [engine.clq_ass_to_cluster(:,2); engine.clq_ass_to_node(:,2)]; + pots = [beta(:,t+1); CPDpot(:,t+1)]; + temp = enter_soft_evidence(engine.sub_engine, clqs, pots, find(observed(:,t:t+1)), pot_type); + for c=1:C + k = engine.clq_ass_to_cluster(c,1); + cl = engine.clusters{c}; + beta{c,t} = marginalize_pot(temp{k}, cl); % extract slice 1 + beta{c,t} = set_domain_pot(beta{c,t}, cl + ss); % shift fwd to slice 2 + end + %=== FH: For each separator s, divide some cluster potential by s's potential + beta_orig = beta(:,t); + for s=1:S + c = engine.cluster_ass_to_separator(s); + beta{c,t} = divide_by_pot(beta{c,t}, marginalize_pot(beta_orig{c}, engine.separators{s}+ss)); + end +end + +% Combine +% The method is a generalization of the following HMM equation: +% xi(i,j,t) = normalise( alpha(i,t) * transmat(i,j) * obsmat(j,t+1) * beta(j,t+1) ) +% where xi(i,j,t) = Pr(Q(t)=i, Q(t+1)=j | y(1:T)) +for t=1:T-1 + clqs = [engine.clq_ass_to_cluster(:); engine.clq_ass_to_node(:,2)]; + pots = [alpha(:,t); beta(:,t+1); CPDpot(:,t+1)]; + clpot(:,t+1) = enter_soft_evidence(engine.sub_engine, clqs, pots, find(observed(:,t:t+1)), pot_type); +end +% for smoothing, clpot{1} is undefined +for k=1:Q1 + clpot{k,1} = []; +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@cbk_inf_engine/junk Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,176 @@ +function engine = cbk_inf_engine(bnet, varargin) +% Just the same as bk_inf_engine, but you can specify overlapping clusters. + +ss = length(bnet.intra); +% set default params +clusters = 'exact'; + +if nargin >= 2 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'clusters', clusters = args{i+1}; + otherwise, error(['unrecognized argument ' args{i}]) + end + end +end + +if strcmp(clusters, 'exact') + %clusters = { compute_interface_nodes(bnet.intra, bnet.inter) }; + clusters = { 1:ss }; +elseif strcmp(clusters, 'ff') + clusters = num2cell(1:ss); +end + + +% We need to insert the prior on the clusters in slice 1, +% and extract the posterior on the clusters in slice 2. +% We don't need to care about the separators, b/c they're subsets of the clusters. +C = length(clusters); +clusters2 = cell(1,2*C); +clusters2(1:C) = clusters; +for c=1:C + clusters2{c+C} = clusters{c} + ss; +end + +onodes = bnet.observed; +obs_nodes = [onodes(:) onodes(:)+ss]; +engine.sub_engine = jtree_inf_engine(bnet, 'clusters', clusters2); + +%FH >>> +%Compute separators. +ns = bnet.node_sizes(:,1); +ns(onodes) = 1; +[clusters, separators] = build_jt(clusters, 1:length(ns), ns); +S = length(separators); +engine.separators = separators; + +%Compute size of clusters. +cl_sizes = zeros(1,C); +for c=1:C + cl_sizes(c) = prod(ns(clusters{c})); +end + +%Assign separators to the smallest cluster subsuming them. +engine.cluster_ass_to_separator = zeros(S, 1); +for s=1:S + subsuming_clusters = []; + %find smaunk + + for c=1:C + if mysubset(separators{s}, clusters{c}) + subsuming_clusters(end+1) = c; + end + end + c = argmin(cl_sizes(subsuming_clusters)); + engine.cluster_ass_to_separator(s) = subsuming_clusters(c); +end + +%<<< FH + +engine.clq_ass_to_cluster = zeros(C, 2); +for c=1:C + engine.clq_ass_to_cluster(c,1) = clq_containing_nodes(engine.sub_engine, clusters{c}); + engine.clq_ass_to_cluster(c,2) = clq_containing_nodes(engine.sub_engine, clusters{c}+ss); +end +engine.clusters = clusters; + +engine.clq_ass_to_node = zeros(ss, 2); +for i=1:ss + engine.clq_ass_to_node(i, 1) = clq_containing_nodes(engine.sub_engine, i); + engine.clq_ass_to_node(i, 2) = clq_containing_nodes(engine.sub_engine, i+ss); +end + + + +% Also create an engine just for slice 1 +bnet1 = mk_bnet(bnet.intra1, bnet.node_sizes_slice, 'discrete', myintersect(bnet.dnodes, 1:ss), ... + 'equiv_class', bnet.equiv_class(:,1), 'observed', onodes); +for i=1:max(bnet1.equiv_class) + bnet1.CPD{i} = bnet.CPD{i}; +end + +engine.sub_engine1 = jtree_inf_engine(bnet1, 'clusters', clusters); + +engine.clq_ass_to_cluster1 = zeros(1,C); +for c=1:C + engine.clq_ass_to_cluster1(c) = clq_containing_nodes(engine.sub_engine1, clusters{c}); +end + +engine.clq_ass_to_node1 = zeros(1, ss); +for i=1:ss + engine.clq_ass_to_node1(i) = clq_containing_nodes(engine.sub_engine1, i); +end + +engine.clpot = []; % this is where we store the results between enter_evidence and marginal_nodes +engine.filter = []; +engine.maximize = []; +engine.T = []; + +engine.bel = []; +engine.bel_clpot = []; +engine.slice1 = []; +%engine.pot_type = 'cg'; +% hack for online inference so we can cope with hidden Gaussians and discrete +% it will not affect the pot type used in enter_evidence +engine.pot_type = determine_pot_type(bnet, onodes); + +engine = class(engine, 'cbk_inf_engine', inf_engine(bnet)); + + + + +function [cliques, seps, jt_size] = build_jt(cliques, vars, ns) +% BUILD_JT connects the cliques into a jtree, computes the respective +% separators and the size of the resulting jtree. +% +% [cliques, seps, jt_size] = build_jt(cliques, vars, ns) +% ns(i) has to hold the size of vars(i) +% vars has to be a superset of the union of cliques. + +%======== Compute the jtree with tool from BNT. This wants the vars to be 1:N. +%==== Map from nodes to their indices. +%disp('Computing jtree for cliques with vars and ns:'); +%cliques +%vars +%ns' + +inv_nodes = sparse(1,max(vars)); +N = length(vars); +for i=1:N + inv_nodes(vars(i)) = i; +end + +tmp_cliques = cell(1,length(cliques)); +%==== Temporarily map clique vars to their indices. +for i=1:length(cliques) + tmp_cliques{i} = inv_nodes(cliques{i}); +end + +%=== Compute the jtree, using BNT. +[jtree, root, B, w] = cliques_to_jtree(tmp_cliques, ns); + + +%======== Now, compute the separators between connected cliques and their weights. +seps = {}; +s_w = []; +[is,js] = find(jtree > 0); +for k=1:length(is) + i = is(k); j = js(k); + sep = vars(find(B(i,:) & B(j,:))); % intersect(cliques{i}, cliques{j}); + if i>j | length(sep) == 0, continue; end; + seps{end+1} = sep; + s_w(end+1) = prod(ns(inv_nodes(seps{end}))); +end + +cl_w = sum(w); +sep_w = sum(s_w); +assert(cl_w > sep_w, 'Weight of cliques must be bigger than weight of separators'); + +jt_size = cl_w + sep_w; +% jt.cliques = cliques; +% jt.seps = seps; +% jt.size = jt_size; +% jt.ns = ns'; +% jt;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@cbk_inf_engine/marginal_family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,25 @@ +function m = marginal_family(engine, i, t) +% MARGINAL_FAMILY Compute the marginal on the specified family (bk) +% marginal = marginal_family(engine, i, t) + +% This is just like inf_engine/marginal_family, except when we call +% marginal_nodes, we provide a 4th argument, to tell it's a family. + +if nargin < 3, t = 1; end + +bnet = bnet_from_engine(engine); +if t==1 + m = marginal_nodes(engine, family(bnet.dag, i), t, 1); +else + ss = length(bnet.intra); + fam = family(bnet.dag, i+ss); + if any(fam<=ss) + % i has a parent in the preceeding slice + % Hence the lowest numbered slice containing the family is t-1 + m = marginal_nodes(engine, fam, t-1, 1); + else + % The family all fits inside slice t + % Hence shift the indexes back to slice 1 + m = marginal_nodes(engine, fam-ss, t, 1); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@cbk_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,67 @@ +function marginal = marginal_nodes(engine, nodes, t, fam) +% MARGINAL_NODES Compute the marginal on the specified query nodes (bk) +% +% marginal = marginal_nodes(engine, i, t) +% returns Pr(X(i,t) | Y(1:T)), where X(i,t) is the i'th node in the t'th slice. +% If enter_evidence used filtering instead of smoothing, this will return Pr(X(i,t) | Y(1:t)). +% +% marginal = marginal_nodes(engine, query, t) +% returns Pr(X(query(1),t), ... X(query(end),t) | Y(1:T)), +% where X(q,t) is the q'th node in the t'th slice. If q > ss (slice size), this is equal +% to X(q mod ss, t+1). That is, 't' specifies the time slice of the earliest node. +% 'query' cannot span more than 2 time slices. +% Example: +% Consider a DBN with 2 nodes per slice. +% Then t=2, nodes=[1 3] refers to node 1 in slice 2 and node 1 in slice 3. + +if nargin < 3, t = 1; end +if nargin < 4, fam = 0; else fam = 1; end + + +% clpot{t} contains slice t-1 and t +% Example +% clpot #: 1 2 3 +% slices: 1 1,2 2,3 +% For filtering, we must take care not to take future evidence into account. +% For smoothing, clpot{1} does not exist. + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); + +nodes2 = nodes; +if ~engine.filter + if t < engine.T + slice = t+1; + else % earliest t is T, so all nodes fit in one slice + slice = engine.T; + nodes2 = nodes + ss; + end +else + if t == 1 + slice = 1; + else + if all(nodes<=ss) + slice = t; + nodes2 = nodes + ss; + elseif t == engine.T + slice = t; + else + slice = t + 1; + end + end +end + +if engine.filter & t==1 + c = clq_containing_nodes(engine.sub_engine1, nodes2, fam); +else + c = clq_containing_nodes(engine.sub_engine, nodes2, fam); +end +assert(c >= 1); +bigpot = engine.clpot{c, slice}; + +pot = marginalize_pot(bigpot, nodes2); +marginal = pot_to_marginal(pot); + +% we convert the domain to the unrolled numbering system +% so that update_ess extracts the right evidence. +marginal.domain = nodes+(t-1)*ss;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@cbk_inf_engine/update_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function engine = update_engine(engine, newCPDs) +% UPDATE_ENGINE Update the engine to take into account the new parameters (bk) +% engine = update_engine(engine, newCPDs) + +engine.inf_engine = update_engine(engine.inf_engine, newCPDs); +engine.sub_engine = update_engine(engine.sub_engine, newCPDs); + +bnet = bnet_from_engine(engine); +eclass1 = bnet.equiv_class(:,1); +engine.sub_engine1 = update_engine(engine.sub_engine1, newCPDs(1:max(eclass1))); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@ff_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_soft_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/ff_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/filter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/smooth_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@ff_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@ff_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@ff_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@ff_inf_engine/Old/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,4 @@ +/enter_soft_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_soft_evidence1.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@ff_inf_engine/Old/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@ff_inf_engine/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@ff_inf_engine/Old/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@ff_inf_engine/Old/enter_soft_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,59 @@ +function [marginals, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) +% ENTER_SOFT_EVIDENCE Add the specified soft evidence to the network (bk_ff) +% [marginals, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) + +assert(pot_type == 'd'); +[ss T] = size(CPDpot); +fwd = cell(ss,T); +hnodes = engine.hnodes(:)'; +onodes = engine.onodes(:)'; +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes; +onodes2 = [onodes onodes+ss]; +ns(onodes2) = 1; + +logscale = zeros(1,T); +local_logscale = zeros(1,length(hnodes)); + +t = 1; +for i=hnodes + fwd{i,t} = CPDpot{i,t}; +end +for i=onodes + p = parents(bnet.dag, i); + assert(length(p)==1); + ev = marginalize_pot(CPDpot{i,t}, p); + fwd{p,t} = multiply_by_pot(fwd{p,t}, ev); +end +for i=hnodes + [fwd{i,t}, local_logscale(i)] = normalize_pot(fwd{i,t}); +end +logscale(t) = sum(local_logscale); + +for t=2:T + for i=hnodes + ps = parents(bnet.dag, i+ss); + assert(all(ps<=ss)); % in previous slice + prior = CPDpot{i,t}; + for p=ps(:)' + prior = multiply_by_pot(prior, fwd{p,t-1}); + end + fwd{i,t} = marginalize_pot(prior, i+ss); + fwd{i,t} = set_domain_pot(fwd{i,t}, i); + end + for i=onodes + p = parents(bnet.dag, i); + assert(length(p)==1); + temp = pot_to_marginal(CPDpot{i,t}); + ev = dpot(p, ns(p), temp.T); + fwd{p,t} = multiply_by_pot(fwd{p,t}, ev); + end + + for i=hnodes + [fwd{i,t}, local_logscale(i)] = normalize_pot(fwd{i,t}); + end + logscale(t) = sum(local_logscale); +end + +marginals = fwd; +loglik = sum(logscale);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@ff_inf_engine/Old/enter_soft_evidence1.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,94 @@ +function [marginals, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) +% ENTER_SOFT_EVIDENCE Add the specified soft evidence to the network (ff) +% [marginals, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) + +assert(pot_type == 'd'); +[ss T] = size(CPDpot); +fwd = cell(ss,T); +hnodes = engine.hnodes(:)'; +onodes = engine.onodes(:)'; +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes; +onodes2 = [onodes onodes+ss]; +ns(onodes2) = 1; + +logscale = zeros(1,T); +H = length(hnodes); +local_logscale = zeros(1,ss); + +obschild = zeros(1,ss); +for i=hnodes + ocs = myintersect(children(bnet.dag, i), onodes); + assert(length(ocs)==1); + obschild(i) = ocs(1); +end + +t = 1; +for i=hnodes + fwd{i,t} = CPDpot{i,t}; + c = obschild(i); + temp = pot_to_marginal(CPDpot{c,t}); + ev = dpot(i, ns(i), temp.T); + fwd{i,t} = multiply_by_pot(fwd{i,t}, ev); + [fwd{i,t}, local_logscale(i)] = normalize_pot(fwd{i,t}); +end +logscale(t) = sum(local_logscale); + +for t=2:T + for i=hnodes + ps = parents(bnet.dag, i+ss); + assert(all(ps<=ss)); % in previous slice + prior = CPDpot{i,t}; + for p=ps(:)' + prior = multiply_by_pot(prior, fwd{p,t-1}); + end + fwd{i,t} = marginalize_pot(prior, i+ss); + fwd{i,t} = set_domain_pot(fwd{i,t}, i); + c = obschild(i); + temp = pot_to_marginal(CPDpot{c,t}); + ev = dpot(i, ns(i), temp.T); + fwd{i,t} = multiply_by_pot(fwd{i,t}, ev); + [fwd{i,t}, local_logscale(i)] = normalize_pot(fwd{i,t}); + end + logscale(t) = sum(local_logscale); +end + +loglik = sum(logscale); + + +if filter + marginals = fwd; + return; +end + +back = cell(ss,T); +t = T; +for i=hnodes + back{i,t} = dpot(i, ns(i)); + back{i,t} = set_domain_pot(back{i,t}, i+ss); +end +for t=T-1:-1:1 + for i=hnodes + pot = CPDpot{i,t+1}; + pot = multiply_by_pot(pot, back{i,t+1}); + c = obschild(i); + temp = pot_to_marginal(CPDpot{c,t+1}); + ev = dpot(i, ns(i), temp.T); + pot = multiply_by_pot(pot, ev); + back{i,t} = marginalize_pot(pot, i); + back{i,t} = normalize_pot(back{i,t}); + back{i,t} = set_domain_pot(back{i,t}, i+ss); + end +end + + + +% COMBINE +for t=1:T + for i=hnodes + back{i,t} = set_domain_pot(back{i,t}, i); + fwd{i,t} = multiply_by_pot(fwd{i,t}, back{i,t}); + marginals{i,t} = normalize_pot(fwd{i,t}); + %fwdback{i,t} = normalize_pot(multiply_pots(fwd{i,t}, back{i,t})); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@ff_inf_engine/Old/marginal_family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,38 @@ +function marginal = marginal_family(engine, i, t) +% MARGINAL_FAMILY Compute the marginal on the specified family (ff) +% marginal = marginal_family(engine, i, t) + +if nargin < 3, t = 1; end + +% The method is similar to the following HMM equation: +% xi(i,j,t) = normalise( alpha(i,t) * transmat(i,j) * obsmat(j,t+1) * beta(j,t+1) ) +% where xi(i,j,t) = Pr(Q(t)=i, Q(t+1)=j | y(1:T)) + +bnet = bnet_from_engine(engine); + +if myismember(i, engine.onodes) + ps = parents(bnet.dag, i); + p = ps(1); + marginal = pot_to_marginal(engine.marginals{p,t}); + marginal.domain = [p i]; + return; +end + +if t==1 + marginal = pot_to_marginal(engine.marginals{i,t}); + return; +end + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +pot = engine.CPDpot{i,t}; +c = engine.obschild(i); +pot = multiply_by_pot(pot, engine.CPDpot{c,t}); +pot = multiply_by_pot(pot, engine.back{i,t}); +ps = parents(bnet.dag, i+ss); +for p=ps(:)' + pot = multiply_by_pot(pot, engine.fwd{p,t-1}); +end +marginal = pot_to_marginal(normalize_pot(pot)); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@ff_inf_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,62 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (ff) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or +% column vector) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product (not yet supported), else sum-product [0] +% filter - if 1, do filtering, else smoothing [0] +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) + +maximize = 0; +filter = 0; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + for i=1:2:nargs + switch args{i}, + case 'maximize', maximize = args{i+1}; + case 'filter', filter = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +assert(~maximize); + + +[ss T] = size(evidence); +observed = ~isemptycell(evidence); +bnet = bnet_from_engine(engine); +%pot_type = determine_pot_type(find(observed(:,1)), bnet.cnodes_slice, bnet.intra); +pot_type = determine_pot_type(bnet, observed); +% we assume we can use the same pot_type in all slices + +CPDpot = convert_dbn_CPDs_to_pots(bnet, evidence, pot_type); + +% Now convert CPDs on observed nodes to be potentials just on their parents +assert(pot_type == 'd'); +onodes = bnet.observed(:); +ns = bnet.node_sizes_slice; +ns(onodes) = 1; +for t=1:T + for i=onodes + p = parents(bnet.dag, i); + %CPDpot{i,t} = set_domain_pot(CPDpot{i,t}, p); % leaves size too long + temp = pot_to_marginal(CPDpot{i,t}); + CPDpot{i,t} = dpot(p, ns(p), temp.T); % assumes pot_type = d + end +end + +[engine.marginals, engine.fwd, engine.back, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter); + +engine.CPDpot = CPDpot; +engine.filter = filter;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@ff_inf_engine/enter_soft_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function [marginals, fwd, back, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) +% ENTER_SOFT_EVIDENCE Add the specified soft evidence to the network (ff) +% [marginals, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) + +if filter + [fwd, loglik] = filter_evidence(engine, CPDpot, observed, pot_type); + marginals = fwd; + back = []; +else + [marginals, fwd, back, loglik] = smooth_evidence(engine, CPDpot, observed, pot_type); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@ff_inf_engine/ff_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,44 @@ +function engine = ff_inf_engine(bnet) +% FF_INF_ENGINE Factored frontier inference engine for DBNs +% engine = ff_inf_engine(bnet) +% +% The model must be topologically isomorphic to an HMM. +% In addition, each hidden node is assumed to have at most one observed child, +% and each observed child is assumed to have exactly one hidden parent. +% +% For details of this algorithm, see +% "The Factored Frontier Algorithm for Approximate Inference in DBNs", +% Kevin Murphy and Yair Weiss, UAI 2001. +% +% THIS IS HIGHLY EXPERIMENTAL CODE! + +ss = length(bnet.intra); +onodes = bnet.observed; +hnodes = mysetdiff(1:ss, onodes); + +[persistent_nodes, transient_nodes] = partition_dbn_nodes(bnet.intra, bnet.inter); +assert(isequal(onodes, transient_nodes)); +assert(isequal(hnodes, persistent_nodes)); + +engine.onodes = onodes; +engine.hnodes = hnodes; +engine.marginals = []; +engine.fwd = []; +engine.back = []; +engine.CPDpot = []; +engine.filter = []; + +obschild = zeros(1,ss); +for i=engine.hnodes(:)' + %ocs = myintersect(children(bnet.dag, i), onodes); + ocs = children(bnet.intra, i); + assert(length(ocs) <= 1); + if length(ocs)==1 + obschild(i) = ocs(1); + end +end +engine.obschild = obschild; + + +engine = class(engine, 'ff_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@ff_inf_engine/filter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,48 @@ +function [fwd, loglik] = filter_evidence(engine, CPDpot, observed, pot_type) +% [fwd, loglik] = filter_evidence(engine, CPDpot, observed, pot_type) (ff) + +[ss T] = size(CPDpot); +fwd = cell(ss,T); +hnodes = engine.hnodes(:)'; +onodes = engine.onodes(:)'; +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes; +onodes2 = [onodes onodes+ss]; +ns(onodes2) = 1; + +logscale = zeros(1,T); +H = length(hnodes); +local_logscale = zeros(1,ss); + +t = 1; +for i=hnodes + fwd{i,t} = CPDpot{i,t}; + c = engine.obschild(i); + if c > 0 + fwd{i,t} = multiply_by_pot(fwd{i,t}, CPDpot{c, t}); + end + [fwd{i,t}, local_logscale(i)] = normalize_pot(fwd{i,t}); +end +logscale(t) = sum(local_logscale); + +for t=2:T + for i=hnodes + ps = parents(bnet.dag, i+ss); + assert(all(ps<=ss)); % in previous slice + prior = CPDpot{i,t}; + for p=ps(:)' + prior = multiply_by_pot(prior, fwd{p,t-1}); + end + fwd{i,t} = marginalize_pot(prior, i+ss); + fwd{i,t} = set_domain_pot(fwd{i,t}, i); + c = engine.obschild(i); + if c > 0 + fwd{i,t} = multiply_by_pot(fwd{i,t}, CPDpot{c,t}); + end + [fwd{i,t}, local_logscale(i)] = normalize_pot(fwd{i,t}); + end + logscale(t) = sum(local_logscale); +end + +loglik = sum(logscale); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@ff_inf_engine/marginal_family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,44 @@ +function marginal = marginal_family(engine, i, t) +% MARGINAL_FAMILY Compute the marginal on the specified family (ff) +% marginal = marginal_family(engine, i, t) + + +if engine.filter + error('can''t currently use marginal_family when filtering with ff'); +end + +if nargin < 3, t = 1; end + +% The method is similar to the following HMM equation: +% xi(i,j,t) = normalise( alpha(i,t) * transmat(i,j) * obsmat(j,t+1) * beta(j,t+1) ) +% where xi(i,j,t) = Pr(Q(t)=i, Q(t+1)=j | y(1:T)) + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); + +if myismember(i, engine.onodes) + ps = parents(bnet.dag, i); + p = ps(1); + marginal = pot_to_marginal(engine.marginals{ps(1),t}); + fam = ([ps i]) + (t-1)*ss; +elseif t==1 + marginal = pot_to_marginal(engine.marginals{i,t}); + fam = i + (t-1)*ss; +else + pot = engine.CPDpot{i,t}; + c = engine.obschild(i); + if c>0 + pot = multiply_by_pot(pot, engine.CPDpot{c,t}); + end + pot = multiply_by_pot(pot, engine.back{i,t}); + ps = parents(bnet.dag, i+ss); + for p=ps(:)' + pot = multiply_by_pot(pot, engine.fwd{p,t-1}); + end + marginal = pot_to_marginal(normalize_pot(pot)); + fam = ([ps i+ss]) + (t-2)*ss; +end + +% we convert the domain to the unrolled numbering system +% so that update_ess extracts the right evidence. +marginal.domain = fam;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@ff_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,20 @@ +function marginal = marginal_nodes(engine, nodes, t) +% MARGINAL_NODES Compute the marginal on the specified query nodes (ff) +% marginal = marginal_nodes(engine, i, t) +% returns Pr(X(i,t) | Y(1:T)), where X(i,t) is the i'th node in the t'th slice. +% If enter_evidence used filtering instead of smoothing, this will return Pr(X(i,t) | Y(1:t)). + +if nargin < 3, t = 1; end +assert(length(nodes)==1); +i = nodes(end); +if myismember(i, engine.hnodes) + marginal = pot_to_marginal(engine.marginals{i,t}); +else + marginal = pot_to_marginal(dpot(i, 1, 1)); % observed +end + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +% we convert the domain to the unrolled numbering system +% so that update_ess extracts the right evidence. +marginal.domain = nodes+(t-1)*ss;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@ff_inf_engine/smooth_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,89 @@ +function [marginals, fwd, back, loglik] = smooth_evidence(engine, CPDpot, observed, pot_type) +% [marginals, fwd, back, loglik] = smooth_evidence(engine, CPDpot, observed, pot_type) (ff) + +error('ff smoothing is broken'); + +[ss T] = size(CPDpot); +fwd = cell(ss,T); +hnodes = engine.hnodes(:)'; +onodes = engine.onodes(:)'; +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes; +onodes2 = [onodes onodes+ss]; +ns(onodes2) = 1; + +logscale = zeros(1,T); +H = length(hnodes); +local_logscale = zeros(1,ss); + +t = 1; +for i=hnodes + fwd{i,t} = CPDpot{i,t}; + c = engine.obschild(i); + if 0 % c > 0 + fwd{i,t} = multiply_by_pot(fwd{i,t}, CPDpot{c, t}); + end + [fwd{i,t}, local_logscale(i)] = normalize_pot(fwd{i,t}); +end +logscale(t) = sum(local_logscale); + +for t=2:T + for i=hnodes + ps = parents(bnet.dag, i+ss); + assert(all(ps<=ss)); % in previous slice + prior = CPDpot{i,t}; + for p=ps(:)' + prior = multiply_by_pot(prior, fwd{p,t-1}); + end + fwd{i,t} = marginalize_pot(prior, i+ss); + fwd{i,t} = set_domain_pot(fwd{i,t}, i); + c = engine.obschild(i); + if 0 % c > 0 + fwd{i,t} = multiply_by_pot(fwd{i,t}, CPDpot{c,t}); + end + [fwd{i,t}, local_logscale(i)] = normalize_pot(fwd{i,t}); + end + logscale(t) = sum(local_logscale); +end + +loglik = sum(logscale); + +back = cell(ss,T); +t = T; +for i=hnodes + pot = dpot(i, ns(i)); + cs = children(bnet.intra, i); + for c=cs(:)' + pot = multiply_pots(pot, CPDpot{c,t}); + end + back{i,t} = marginalize_pot(pot, i); + back{i,t} = normalize_pot(back{i,t}); + back{i,t} = set_domain_pot(back{i,t}, i+ss); +end +for t=T-1:-1:1 + for i=hnodes + pot = dpot(i, ns(i)); + cs = children(bnet.inter, i); + for c=cs(:)' + pot = multiply_pots(pot, back{c,t+1}); + pot = multiply_pots(pot, CPDpot{c,t+1}); + end + cs = children(bnet.intra, i); + for c=cs(:)' + pot = multiply_pots(pot, CPDpot{c,t}); + end + back{i,t} = marginalize_pot(pot, i); + back{i,t} = normalize_pot(back{i,t}); + back{i,t} = set_domain_pot(back{i,t}, i+ss); + end +end + + +% COMBINE +for t=1:T + for i=hnodes + back{i,t} = set_domain_pot(back{i,t}, i); + fwd{i,t} = multiply_by_pot(fwd{i,t}, back{i,t}); + marginals{i,t} = normalize_pot(fwd{i,t}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@frontier_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_soft_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/frontier_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/set_fwdback.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@frontier_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@frontier_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@frontier_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@frontier_inf_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,44 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (frontier) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product (not yet supported), else sum-product [0] +% filter - if 1, do filtering, else smoothing [0] +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) + +maximize = 0; +filter = 0; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + for i=1:2:nargs + switch args{i}, + case 'maximize', maximize = args{i+1}; + case 'filter', filter = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +assert(~maximize); + +[ss T] = size(evidence); +bnet = bnet_from_engine(engine); +onodes = find(~isemptycell(evidence)); +cnodes = unroll_set(bnet.cnodes(:), ss, T); +pot_type = determine_pot_type(bnet, onodes); + +CPDpot = convert_dbn_CPDs_to_pots(bnet, evidence, pot_type); + +[engine.fwdback, loglik, engine.fwd_frontier, engine.back_frontier] = ... + enter_soft_evidence(engine, CPDpot, onodes, pot_type, filter); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@frontier_inf_engine/enter_soft_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,142 @@ +function [fwdback, loglik, fwd_frontier, back_frontier] = enter_soft_evidence(engine, CPD, onodes, pot_type, filter) +% ENTER_SOFT_EVIDENCE Add soft evidence to network (frontier) +% [fwdback, loglik] = enter_soft_evidence(engine, CPDpot, onodes, filter) + +if nargin < 3, filter = 0; end + +[ss T] = size(CPD); +bnet = bnet_from_engine(engine); +ns = repmat(bnet.node_sizes_slice(:), 1, T); +cnodes = unroll_set(bnet.cnodes(:), ss, T); + +% FORWARDS +fwd = cell(ss,T); +ll = zeros(1,T); +S = 2*ss; % num. intermediate frontiers to get from t to t+1 +frontier = cell(S,T); + +% Start with empty frontier, and add each node in slice 1 +init = mk_initial_pot(pot_type, [], ns, cnodes, onodes); +t = 1; +s = 1; +j = 1; +frontier{s,t} = update(init, j, 1, CPD{j}, engine.fdom1{s}, pot_type, ns, cnodes, onodes); +fwd{j} = frontier{s,t}; +for s=2:ss + j = s; % add node j at step s + frontier{s,t} = update(frontier{s-1,t}, j, 1, CPD{j}, engine.fdom1{s}, pot_type, ns, cnodes, onodes); + fwd{j} = frontier{s,t}; +end +frontier{S,t} = frontier{ss,t}; +[frontier{S,t}, ll(1)] = normalize_pot(frontier{S,t}); + +% Now move frontier from slice to slice +OPS = engine.ops; +add = OPS>0; +nodes = [zeros(S,1) unroll_set(abs(OPS(:)), ss, T-1)]; +for t=2:T + offset = (t-2)*ss; + for s=1:S + if s==1 + prev_ndx = (t-2)*S + S; % S,t-1 + else + prev_ndx = (t-1)*S + s-1; % s-1,t + end + j = nodes(s,t); + frontier{s,t} = update(frontier{prev_ndx}, j, add(s), CPD{j}, engine.fdom{s}+offset, pot_type, ns, cnodes, onodes); + if add(s) + fwd{j} = frontier{s,t}; + end + end + [frontier{S,t}, ll(t)] = normalize_pot(frontier{S,t}); +end +loglik = sum(ll); + + +fwd_frontier = frontier; + +if filter + fwdback = fwd; + return; +end + + +% BACKWARDS +back = cell(ss,T); +add = ~add; % forwards add = backwards remove +frontier = cell(S,T+1); +t = T; +dom = (1:ss) + (t-1)*ss; +frontier{1,T+1} = mk_initial_pot(pot_type, dom, ns, cnodes, onodes); % all 1s for last slice +for t=T:-1:2 + offset = (t-2)*ss; + for s=S:-1:1 % reverse order + if s==S + prev_ndx = t*S + 1; % 1,t+1 + else + prev_ndx = (t-1)*S + (s+1); % s+1,t + end + j = nodes(s,t); + if ~add(s) + back{j} = frontier{prev_ndx}; % save frontier before removing + end + frontier{s,t} = rev_update(frontier{prev_ndx}, t, s, j, add(s), CPD{j}, engine.fdom{s}+offset, pot_type, ns, cnodes, onodes); + end + frontier{1,t} = normalize_pot(frontier{1,t}); +end +% Remove each node in first slice until left with empty set +t = 1; +frontier{ss+1,t} = frontier{1,2}; +add = 0; +for s=ss:-1:1 + j = s; % remove node j at step s + back{j} = frontier{s+1,t}; + frontier{s,t} = rev_update(frontier{s+1,t}, t, s, j, add, CPD{j}, 1:s, pot_type, ns, cnodes, onodes); +end + +% COMBINE +for t=1:T + for i=1:ss + %fwd{i,t} = multiply_by_pot(fwd{i,t}, back{i,t}); + %fwdback{i,t} = normalize_pot(fwd{i,t}); + fwdback{i,t} = normalize_pot(multiply_pots(fwd{i,t}, back{i,t})); + end +end + +back_frontier = frontier; + +%%%%%%%%%% +function new_frontier = update(old_frontier, j, add, CPD, newdom, pot_type, ns, cnodes, onodes) + +if add + new_frontier = mk_initial_pot(pot_type, newdom, ns, cnodes, onodes); + new_frontier = multiply_by_pot(new_frontier, old_frontier); + new_frontier = multiply_by_pot(new_frontier, CPD); +else + new_frontier = marginalize_pot(old_frontier, mysetdiff(domain_pot(old_frontier), j)); +end + + +%%%%%% +function new_frontier = rev_update(old_frontier, t, s, j, add, CPD, junk, pot_type, ns, cnodes, onodes) + +olddom = domain_pot(old_frontier); +assert(isequal(junk, olddom)); + +if add + % add: extend domain to include j by multiplying by 1 + newdom = myunion(olddom, j); + new_frontier = mk_initial_pot(pot_type, newdom, ns, cnodes, onodes); + new_frontier = multiply_by_pot(new_frontier, old_frontier); + %fprintf('t=%d, s=%d, add %d to %s to make %s\n', t, s, j, num2str(olddom), num2str(newdom)); +else + % remove: multiply in CPT and then marginalize out j + % parents of j are guaranteed to be in old_frontier, else couldn't have added j on fwds pass + old_frontier = multiply_by_pot(old_frontier, CPD); + newdom = mysetdiff(olddom, j); + new_frontier = marginalize_pot(old_frontier, newdom); + %newdom2 = domain_pot(new_frontier); + %fprintf('t=%d, s=%d, rem %d from %s to make %s\n', t, s, j, num2str(olddom), num2str(newdom2)); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@frontier_inf_engine/frontier_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,121 @@ +function engine = frontier_inf_engine(bnet) +% FRONTIER_INF_ENGINE Inference engine for DBNs which which uses the frontier algorithm. +% engine = frontier_inf_engine(bnet) +% +% The frontier algorithm extends the forwards-backwards algorithm to DBNs in the obvious way, +% maintaining a joint distribution (frontier) over all the nodes in a time slice. +% When all the hidden nodes in the DBN are persistent (have children in the next time slice), +% its theoretical running time is often similar to that of the junction tree algorithm, +% although in practice, this algorithm seems to very slow (at least in matlab). +% However, it is extremely simple to describe and implement. +% +% Suppose there are n binary nodes per slice, so the frontier takes O(2^n) space. +% Each time step takes between O(n 2^{n+1}) and O(n 2^{2n}) operations, depending on the graph structure. +% The lower bound is achieved by a set of n independent chains, as in a factorial HMM. +% The upper bound is achieved by a set of n fully interconnected chains, as in an HMM. +% +% The factor of n arises because we need to multiply in each CPD from slice t+1. +% The second factor depends on the size of the frontier to which we add the new node. +% In an FHMM, once we have added X(i,t+1), we can marginalize out X(i,t) from the frontier, since +% no other nodes depend on it; hence the frontier never contains more than n+1 nodes. +% In a fully coupled HMM, we must leave X(i,t) in the frontier until all X(j,t+1) have been +% added; hence the frontier will contain 2*n nodes at its peak. +% +% For details, see +% "The Factored Frontier Algorithm for Approximate Inference in DBNs", +% Kevin Murphy and Yair Weiss, UAI 01. + +ns = bnet.node_sizes_slice; +onodes = bnet.observed; +ns(onodes) = 1; +ss = length(bnet.intra); + +[engine.ops, engine.fdom] = best_first_frontier_seq(ns, bnet.dag); +engine.ops1 = 1:ss; + +engine.fwdback = []; +engine.fwd_frontier = []; +engine.back_frontier = []; + +engine.fdom1 = cell(1,ss); +for s=1:ss + engine.fdom1{s} = 1:s; +end + +engine = class(engine, 'frontier_inf_engine', inf_engine(bnet)); + + +%%%%%%%%% + +function [ops, frontier_set] = best_first_frontier_seq(ns, dag) +% BEST_FIRST_FRONTIER_SEQ Do a greedy search for the sequence of additions/removals to the frontier. +% [ops, frontier_set] = best_first_frontier_seq(ns, dag) +% +% We maintain 3 sets: the frontier (F), the right set (R), and the left set (L). +% The invariant is that the nodes in R are d-separated from L given F. +% We start with slice 1 in F and slice 2 in R. +% The goal is to move slice 1 from F to L, and slice 2 from R to F, so as to minimize the size +% of the frontier at each step, where the size(F) = product of the node-sizes of nodes in F. +% A node may be removed (from F to L) if it has no children in R. +% A node may be added (from R to F) if its parents are in F. +% +% ns(i) = num. discrete values node i can take on (i=1..ss, where ss = slice size) +% dag is the (2*ss) x (2*ss) adjacency matrix for the 2-slice DBN. + +% Example: +% +% 4 9 +% ^ ^ +% | | +% 2 -> 7 +% ^ ^ +% | | +% 1 -> 6 +% | | +% v v +% 3 -> 8 +% | | +% v V +% 5 10 +% +% ops = -4, -5, 6, -1, 7, -2, 8, -3, 9, 10 + +ss = length(ns); +ns = [ns(:)' ns(:)']; +ops = zeros(1,ss); +L = []; F = 1:ss; R = (1:ss)+ss; +frontier_set = cell(1,2*ss); +for s=1:2*ss + remcost = inf*ones(1,2*ss); + %disp(['L: ' num2str(L) ', F: ' num2str(F) ', R: ' num2str(R)]); + maybe_removable = myintersect(F, 1:ss); + for n=maybe_removable(:)' + cs = children(dag, n); + if isempty(myintersect(cs, R)) + remcost(n) = prod(ns(mysetdiff(F, n))); + end + end + %remcost + if any(remcost < inf) + n = argmin(remcost); + ops(s) = -n; + L = myunion(L, n); + F = mysetdiff(F, n); + else + addcost = inf*ones(1,2*ss); + for n=R(:)' + ps = parents(dag, n); + if mysubset(ps, F) + addcost(n) = prod(ns(myunion(F, [ps n]))); + end + end + %addcost + assert(any(addcost < inf)); + n = argmin(addcost); + ops(s) = n; + R = mysetdiff(R, n); + F = myunion(F, n); + end + %fprintf('op at step %d = %d\n\n', s, ops(s)); + frontier_set{s} = F; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@frontier_inf_engine/marginal_family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +function marginal = marginal_family(engine, i, t) +% MARGINAL_FAMILY Compute the marginal on node i in slice t and its parents (frontier) +% marginal = marginal_family(engine, i, t) + +bnet = bnet_from_engine(engine); +fam = family(bnet.dag, i, t); +marginal = pot_to_marginal(normalize_pot(marginalize_pot(engine.fwdback{i,t}, fam)));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@frontier_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,21 @@ +function marginal = marginal_nodes(engine, nodes, t) +% MARGINAL_NODES Compute the marginal on the specified query nodes (frontier) +% marginal = marginal_nodes(engine, nodes, t) +% +% 't' specifies the time slice of the earliest node in 'nodes'. +% 'nodes' cannot span more than 2 time slices. +% +% Example: +% Consider a DBN with 2 nodes per slice. +% Then t=2, nodes=[1 3] refers to node 1 in slice 2 and node 1 in slice 3, +% i.e., nodes 3 and 5 in the unrolled network, + +if nargin < 3, t = 1; end +assert(length(nodes)==1); +i = nodes(1); +bigpot = engine.fwdback{i,t}; +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +nodes = nodes + (t-1)*ss; +%if t > 1, nodes = nodes + ss; end +marginal = pot_to_marginal(marginalize_pot(bigpot, nodes));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@frontier_inf_engine/set_fwdback.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +function engine = set_fwdback(engine, fb) +% SET_FWDBACK Set the field 'fwdback', which contains the frontiers after propagation +% engine = set_fwdback(engine, fb) +% +% This is used by frontier_fast_inf_engine/enter_evidence +% as a workaround for Matlab's annoying privacy control + +engine.fwdback = fb;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@hmm_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,9 @@ +/enter_evidence.m/1.2/Sat Sep 17 17:00:30 2005// +/find_mpe.m/1.1.1.1/Thu Jun 20 00:18:24 2002// +/fwdback_twoslice.m/1.1/Sat Nov 26 01:24:09 2005// +/hmm_inf_engine.m/1.1.1.1/Thu Nov 14 20:05:36 2002// +/marginal_family.m/1.1.1.1/Thu Nov 14 20:05:36 2002// +/marginal_nodes.m/1.1.1.1/Thu Nov 14 20:03:28 2002// +/update_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +D/Old//// +D/private////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@hmm_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@hmm_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@hmm_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@hmm_inf_engine/Old/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,4 @@ +/dhmm_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@hmm_inf_engine/Old/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@hmm_inf_engine/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@hmm_inf_engine/Old/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@hmm_inf_engine/Old/dhmm_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,34 @@ +function engine = dhmm_inf_engine(bnet, onodes) +% DHMM_INF_ENGINE Inference engine for discrete DBNs which uses the forwards-backwards algorithm. +% engine = dhmm_inf_engine(bnet, onodes) +% +% 'onodes' specifies which nodes are observed; these must be leaves, and can be discrete or continuous. +% The remaining nodes are all hidden, and must be discrete. +% The DBN is converted to an HMM, with a single meganode, but which may have factored obs. + +ss = length(bnet.intra); +hnodes = mysetdiff(1:ss, onodes); +evidence = cell(ss, 2); +ns = bnet.node_sizes; +Q = prod(ns(hnodes)); +tmp = dpot_to_table(compute_joint_pot(bnet, hnodes, evidence)); +engine.startprob = reshape(tmp, Q, 1); +tmp = dpot_to_table(compute_joint_pot(bnet, [hnodes hnodes+ss], evidence)); +engine.transprob = mk_stochastic(reshape(tmp, Q, Q)); +engine.obsprob = cell(1, length(onodes)); +for i=1:length(onodes) + tmp = dpot_to_table(compute_joint_pot(bnet, [hnodes onodes(i)], evidence)); + O = ns(onodes(i)); + engine.obsprob{i} = mk_stochastic(reshape(tmp, Q, O)); +end + +% This is where we will store the results between enter_evidence and marginal_nodes +engine.gamma = []; +engine.xi = []; + +engine.onodes = onodes; +engine.hnodes = hnodes; +engine.maximize = []; + +engine = class(engine, 'dhmm_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@hmm_inf_engine/Old/marginal_family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,31 @@ +function marginal = marginal_family(engine, i, t, add_ev) +% MARGINAL_FAMILY Compute the marginal on the specified family (hmm) +% marginal = marginal_nodes(engine, i, t, add_ev) +% + +if nargin < 3, t = 1; end +if nargin < 4, add_ev = 0; end + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +if t==1 + fam = family(bnet.dag, i); + bigpot = engine.one_slice_marginal{t}; + nodes = fam; +else + fam = family(bnet.dag, i+ss); + if any(fam <= ss) % family spans 2 slices + bigpot = engine.two_slice_marginal{t-1}; % t-1 and t + nodes = fam + (t-2)*ss; + else + bigpot = engine.one_slice_marginal{t}; + nodes = fam-ss + (t-1)*ss; + end +end + +marginal = pot_to_marginal(marginalize_pot(bigpot, nodes, engine.maximize)); + +if add_ev + marginal = add_ev_to_dmarginal(marginal, engine.evidence, engine.node_sizes); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@hmm_inf_engine/Old/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,30 @@ +function marginal = marginal_nodes(engine, nodes, t, add_ev) +% MARGINAL_NODES Compute the marginal on the specified query nodes (hmm) +% marginal = marginal_nodes(engine, nodes, t, add_ev) +% +% 't' specifies the time slice of the earliest node in 'nodes'. +% 'nodes' cannot span more than 2 time slices. +% +% Example: +% Consider a DBN with 2 nodes per slice. +% Then t=2, nodes=[1 3] refers to node 1 in slice 2 and node 1 in slice 3, +% i.e., nodes 3 and 5 in the unrolled network, + +if nargin < 3, t = 1; end +if nargin < 4, add_ev = 0; end + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +if all(nodes <= ss) + bigpot = engine.one_slice_marginal{t}; +else + bigpot = engine.two_slice_marginal{t}; +end + +nodes = nodes + (t-1)*ss; +marginal = pot_to_marginal(marginalize_pot(bigpot, nodes, engine.maximize)); + +if add_ev + marginal = add_ev_to_dmarginal(marginal, engine.evidence, engine.node_sizes); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@hmm_inf_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,64 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (hmm) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product (not yet supported), else sum-product [0] +% filter - if 1, does filtering, else smoothing [0] +% oneslice - 1 means only compute marginals on nodes within a single slice [0] +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) + +maximize = 0; +filter = 0; +oneslice = 0; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + for i=1:2:nargs + switch args{i}, + case 'maximize', maximize = args{i+1}; + case 'filter', filter = args{i+1}; + case 'oneslice', oneslice = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +[ss T] = size(evidence); +engine.maximize = maximize; +engine.evidence = evidence; +bnet = bnet_from_engine(engine); +engine.node_sizes = repmat(bnet.node_sizes_slice(:), [1 T]); + +obs_bitv = ~isemptycell(evidence(:)); +bitv = reshape(obs_bitv, ss, T); +for t=1:T + onodes = find(bitv(:,t)); + if ~isequal(onodes, bnet.observed(:)) + error(['dbn was created assuming observed nodes per slice were '... + num2str(bnet.observed(:)') ' but the evidence in slice ' num2str(t) ... + ' has observed nodes ' num2str(onodes(:)')]); + end +end + +obslik = mk_hmm_obs_lik_matrix(engine, evidence); + +%[alpha, beta, gamma, loglik, xi] = fwdback(engine.startprob, engine.transprob, obslik, ... +[alpha, beta, gamma, loglik, xi] = fwdback_twoslice(engine, engine.startprob,... + engine.transprob, obslik, ... + 'maximize', maximize, 'fwd_only', filter, ... + 'compute_xi', ~oneslice); + +engine.one_slice_marginal = gamma; % gamma(:,t) for t=1:T +if ~oneslice + Q = size(gamma,1); + engine.two_slice_marginal = reshape(xi, [Q*Q T-1]); % xi(:,t) for t=1:T-1 +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@hmm_inf_engine/find_mpe.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +function mpe = find_mpe(engine, evidence) +% FIND_MPE Find the most probable explanation (Viterbi) +% mpe = enter_evidence(engine, evidence, ...) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% + +obslik = mk_hmm_obs_lik_matrix(engine, evidence); +path = viterbi_path(engine.startprob, engine.transprob, obslik); +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes_slice; +ns(bnet.observed) = 1; +ass = ind2subv(ns, path); +mpe = num2cell(ass'); +mpe(bnet.observed,:) = evidence(bnet.observed,:); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@hmm_inf_engine/fwdback_twoslice.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,198 @@ +function [alpha, beta, gamma, loglik, xi, gamma2] = fwdback_twoslice(engine, init_state_distrib, transmat, obslik, varargin) +% FWDBACK Compute the posterior probs. in an HMM using the forwards backwards algo. +% +% [alpha, beta, gamma, loglik, xi, gamma2] = fwdback(init_state_distrib, transmat, obslik, ...) +% +% Notation: +% Y(t) = observation, Q(t) = hidden state, M(t) = mixture variable (for MOG outputs) +% A(t) = discrete input (action) (for POMDP models) +% +% INPUT: +% init_state_distrib(i) = Pr(Q(1) = i) +% transmat(i,j) = Pr(Q(t) = j | Q(t-1)=i) +% or transmat{a}(i,j) = Pr(Q(t) = j | Q(t-1)=i, A(t-1)=a) if there are discrete inputs +% obslik(i,t) = Pr(Y(t)| Q(t)=i) +% (Compute obslik using eval_pdf_xxx on your data sequence first.) +% +% Optional parameters may be passed as 'param_name', param_value pairs. +% Parameter names are shown below; default values in [] - if none, argument is mandatory. +% +% For HMMs with MOG outputs: if you want to compute gamma2, you must specify +% 'obslik2' - obslik(i,j,t) = Pr(Y(t)| Q(t)=i,M(t)=j) [] +% 'mixmat' - mixmat(i,j) = Pr(M(t) = j | Q(t)=i) [] +% +% For HMMs with discrete inputs: +% 'act' - act(t) = action performed at step t +% +% Optional arguments: +% 'fwd_only' - if 1, only do a forwards pass and set beta=[], gamma2=[] [0] +% 'scaled' - if 1, normalize alphas and betas to prevent underflow [1] +% 'maximize' - if 1, use max-product instead of sum-product [0] +% +% OUTPUTS: +% alpha(i,t) = p(Q(t)=i | y(1:t)) (or p(Q(t)=i, y(1:t)) if scaled=0) +% beta(i,t) = p(y(t+1:T) | Q(t)=i)*p(y(t+1:T)|y(1:t)) (or p(y(t+1:T) | Q(t)=i) if scaled=0) +% gamma(i,t) = p(Q(t)=i | y(1:T)) +% loglik = log p(y(1:T)) +% xi(i,j,t-1) = p(Q(t-1)=i, Q(t)=j | y(1:T)) +% gamma2(j,k,t) = p(Q(t)=j, M(t)=k | y(1:T)) (only for MOG outputs) +% +% If fwd_only = 1, these become +% alpha(i,t) = p(Q(t)=i | y(1:t)) +% beta = [] +% gamma(i,t) = p(Q(t)=i | y(1:t)) +% xi(i,j,t-1) = p(Q(t-1)=i, Q(t)=j | y(1:t)) +% gamma2 = [] +% +% Note: we only compute xi if it is requested as a return argument, since it can be very large. +% Similarly, we only compute gamma2 on request (and if using MOG outputs). +% +% Examples: +% +% [alpha, beta, gamma, loglik] = fwdback(pi, A, multinomial_prob(sequence, B)); +% +% [B, B2] = mixgauss_prob(data, mu, Sigma, mixmat); +% [alpha, beta, gamma, loglik, xi, gamma2] = fwdback(pi, A, B, 'obslik2', B2, 'mixmat', mixmat); + + +if nargout >= 5, compute_xi = 1; else compute_xi = 0; end +if nargout >= 6, compute_gamma2 = 1; else compute_gamma2 = 0; end + +[obslik2, mixmat, fwd_only, scaled, act, maximize, compute_xi, compute_gamma2] = process_options(varargin, 'obslik2', [], 'mixmat', [], 'fwd_only', 0, 'scaled', 1, 'act', [], 'maximize', 0, 'compute_xi', compute_xi, 'compute_gamma2', compute_gamma2); + + +[Q T] = size(obslik); + +if isempty(obslik2) + compute_gamma2 = 0; +end + +if isempty(act) + act = ones(1,T); + transmat = { transmat } ; +end + +scale = ones(1,T); + +% scale(t) = Pr(O(t) | O(1:t-1)) = 1/c(t) as defined by Rabiner (1989). +% Hence prod_t scale(t) = Pr(O(1)) Pr(O(2)|O(1)) Pr(O(3) | O(1:2)) = Pr(O(1), ... ,O(T)) +% or log P = sum_t log scale(t). +% Rabiner suggests multiplying beta(t) by scale(t), but we can instead +% normalise beta(t) - the constants will cancel when we compute gamma. + +loglik = 0; + +alpha = zeros(Q,T); +gamma = zeros(Q,T); +if compute_xi + xi = zeros(Q,Q,T-1); +else + xi = []; +end + + +%%%%%%%%% Forwards %%%%%%%%%% + +t = 1; +alpha(:,1) = init_state_distrib(:) .* obslik(:,t); +if scaled + %[alpha(:,t), scale(t)] = normaliseC(alpha(:,t)); + [alpha(:,t), scale(t)] = normalise(alpha(:,t)); +end +if scaled, assert(approxeq(sum(alpha(:,t)),1)), end +for t=2:T + %trans = transmat(:,:,act(t-1))'; + trans = transmat{act(t-1)}; + if maximize + m = max_mult(trans', alpha(:,t-1)); + %A = repmat(alpha(:,t-1), [1 Q]); + %m = max(trans .* A, [], 1); + else + m = trans' * alpha(:,t-1); + end + alpha(:,t) = m(:) .* obslik(:,t); + if scaled + %[alpha(:,t), scale(t)] = normaliseC(alpha(:,t)); + [alpha(:,t), scale(t)] = normalise(alpha(:,t)); + end + if compute_xi & fwd_only % useful for online EM + %xi(:,:,t-1) = normaliseC((alpha(:,t-1) * obslik(:,t)') .* trans); + xi(:,:,t-1) = normalise((alpha(:,t-1) * obslik(:,t)') .* trans); + end + if scaled, assert(approxeq(sum(alpha(:,t)),1)), end +end +if scaled + if any(scale==0) + loglik = -inf; + else + loglik = sum(log(scale)); + end +else + loglik = log(sum(alpha(:,T))); +end + +if fwd_only + gamma = alpha; + beta = []; + gamma2 = []; + return; +end + + +%%%%%%%%% Backwards %%%%%%%%%% + +beta = zeros(Q,T); +if compute_gamma2 + M = size(mixmat, 2); + gamma2 = zeros(Q,M,T); +else + gamma2 = []; +end + +beta(:,T) = ones(Q,1); +%gamma(:,T) = normaliseC(alpha(:,T) .* beta(:,T)); +gamma(:,T) = normalise(alpha(:,T) .* beta(:,T)); +t=T; +if compute_gamma2 + denom = obslik(:,t) + (obslik(:,t)==0); % replace 0s with 1s before dividing + gamma2(:,:,t) = obslik2(:,:,t) .* mixmat .* repmat(gamma(:,t), [1 M]) ./ repmat(denom, [1 M]); + %gamma2(:,:,t) = normaliseC(obslik2(:,:,t) .* mixmat .* repmat(gamma(:,t), [1 M])); % wrong! +end +for t=T-1:-1:1 + b = beta(:,t+1) .* obslik(:,t+1); + %trans = transmat(:,:,act(t)); + trans = transmat{act(t)}; + if maximize + B = repmat(b(:)', Q, 1); + beta(:,t) = max(trans .* B, [], 2); + else + beta(:,t) = trans * b; + end + if scaled + %beta(:,t) = normaliseC(beta(:,t)); + beta(:,t) = normalise(beta(:,t)); + end + %gamma(:,t) = normaliseC(alpha(:,t) .* beta(:,t)); + gamma(:,t) = normalise(alpha(:,t) .* beta(:,t)); + if compute_xi + %xi(:,:,t) = normaliseC((trans .* (alpha(:,t) * b'))); + xi(:,:,t) = normalise((trans .* (alpha(:,t) * b'))); + %xi(:,:,t) = (trans .* (alpha(:,t) * b')); + end + if compute_gamma2 + denom = obslik(:,t) + (obslik(:,t)==0); % replace 0s with 1s before dividing + gamma2(:,:,t) = obslik2(:,:,t) .* mixmat .* repmat(gamma(:,t), [1 M]) ./ repmat(denom, [1 M]); + %gamma2(:,:,t) = normaliseC(obslik2(:,:,t) .* mixmat .* repmat(gamma(:,t), [1 M])); + end +end + + +% We now explain the equation for gamma2 +% Let zt=y(1:t-1,t+1:T) be all observations except y(t) +% gamma2(Q,M,t) = P(Qt,Mt|yt,zt) = P(yt|Qt,Mt,zt) P(Qt,Mt|zt) / P(yt|zt) +% = P(yt|Qt,Mt) P(Mt|Qt) P(Qt|zt) / P(yt|zt) +% Now gamma(Q,t) = P(Qt|yt,zt) = P(yt|Qt) P(Qt|zt) / P(yt|zt) +% hence +% P(Qt,Mt|yt,zt) = P(yt|Qt,Mt) P(Mt|Qt) [P(Qt|yt,zt) P(yt|zt) / P(yt|Qt)] / P(yt|zt) +% = P(yt|Qt,Mt) P(Mt|Qt) P(Qt|yt,zt) / P(yt|Qt) +% \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@hmm_inf_engine/hmm_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,71 @@ +function engine = hmm_inf_engine(bnet, varargin) +% HMM_INF_ENGINE Inference engine for DBNs which uses the forwards-backwards algorithm. +% engine = hmm_inf_engine(bnet, ...) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - 1 means max-product, 0 means sum-product [0] +% +% The DBN is converted to an HMM with a single meganode, but the observed nodes remain factored. +% This can be faster than jtree if the num. hidden nodes is low, because of lower constant factors. +% +% All hidden nodes must be discrete. +% All observed nodes are assumed to be leaves, i.e., they cannot be parents of anything. +% The parents of each observed leaf are assumed to be a subset of the hidden nodes within the same slice. +% The only exception is if bnet is an AR-HMM, where the parents are assumed to be self in the +% previous slice (continuous), plus all the discrete nodes in the current slice. + +ss = bnet.nnodes_per_slice; + +engine.maximize = 0; +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + for i=1:2:nargs + switch args{i}, + case 'maximize', engine.maximize = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +% Stuff to do with speeding up marginal_family +[int, engine.persist, engine.transient] = compute_interface_nodes(bnet.intra, bnet.inter); +engine.persist_bitv = zeros(1, ss); +engine.persist_bitv(engine.persist) = 1; + + +ns = bnet.node_sizes(:); +ns(bnet.observed) = 1; +ns(bnet.observed+ss) = 1; +engine.eff_node_sizes = ns; + +for o=bnet.observed(:)' + %if bnet.equiv_class(o,1) ~= bnet.equiv_class(o,2) + % error(['observed node ' num2str(o) ' is not tied']) + %end + cs = children(bnet.dag, o); + if ~isempty(cs) + error(['observed node ' num2str(o) ' is not allowed children']) + end +end + +[engine.startprob, engine.transprob, engine.obsprob] = dbn_to_hmm(bnet); + +% This is where we will store the results between enter_evidence and marginal_nodes +engine.one_slice_marginal = []; +engine.two_slice_marginal = []; + +ss = length(bnet.intra); +engine.evidence = []; +engine.node_sizes = []; + +% avoid the need to do bnet_from_engine, which is slow +engine.slice_size = ss; +engine.parents = bnet.parents; + +engine = class(engine, 'hmm_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@hmm_inf_engine/marginal_family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,35 @@ +function marginal = marginal_family(engine, i, t, add_ev) +% MARGINAL_FAMILY Compute the marginal on the specified family (hmm) +% marginal = marginal_family(engine, i, t, add_ev) + +if nargin < 3, t = 1; end +if nargin < 4, add_ev = 0; end + +ns = engine.eff_node_sizes(:); +ss = engine.slice_size; + +if t==1 | ~engine.persist_bitv(i) + bigT = engine.one_slice_marginal(:,t); + ps = engine.parents{i}; + dom = [ps i] + (t-1)*ss; + bigdom = 1:ss; + bigsz = ns(bigdom); + bigdom = bigdom + (t-1)*ss; +else % some parents are in previous slice + bigT = engine.two_slice_marginal(:,t-1); % t-1 and t + ps = engine.parents{i+ss}; + dom = [ps i+ss] + (t-2)*ss; + bigdom = 1:(2*ss); % domain of xi(:,:,t) + bigsz = ns(bigdom); + bigdom = bigdom + (t-2)*ss; +end +marginal.domain = dom; + +marginal.T = marg_table(bigT, bigdom, bigsz, dom, engine.maximize); +marginal.mu = []; +marginal.Sigma = []; + +if add_ev + marginal = add_ev_to_dmarginal(marginal, engine.evidence, engine.node_sizes); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@hmm_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,29 @@ +function marginal = marginal_nodes(engine, nodes, t, add_ev) +% MARGINAL_NODES Compute the marginal on the specified query nodes (hmm) +% marginal = marginal_nodes(engine, nodes, t, add_ev) +% +% 'nodes' must be a single node. +% t is the time slice. + +if nargin < 3, t = 1; end +if nargin < 4, add_ev = 0; end + +assert(length(nodes)==1) +ss = engine.slice_size; + +i = nodes(1); +bigT = engine.one_slice_marginal(:,t); +dom = i + (t-1)*ss; + +ns = engine.eff_node_sizes(:); +bigdom = 1:ss; +marginal.T = marg_table(bigT, bigdom + (t-1)*ss, ns(bigdom), dom, engine.maximize); + +marginal.domain = dom; +marginal.mu = []; +marginal.Sigma = []; + +if add_ev + marginal = add_ev_to_dmarginal(marginal, engine.evidence, engine.node_sizes); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@hmm_inf_engine/private/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,3 @@ +/mk_hmm_obs_lik_matrix.m/1.1.1.1/Sun May 4 21:42:26 2003// +/mk_hmm_obs_lik_vec.m/1.1.1.1/Thu Jan 23 18:50:10 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@hmm_inf_engine/private/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@hmm_inf_engine/private
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@hmm_inf_engine/private/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@hmm_inf_engine/private/mk_hmm_obs_lik_matrix.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,30 @@ +function obslik = mk_hmm_obs_lik_matrix(engine, evidence) + +T = size(evidence,2); +Q = length(engine.startprob); +obslik = ones(Q, T); +bnet = bnet_from_engine(engine); +% P(o1,o2| Q1,Q2) = P(o1|Q1,Q2) * P(o2|Q1,Q2) +onodes = bnet.observed; +for i=1:length(onodes) + data = cell2num(evidence(onodes(i),:)); + if bnet.auto_regressive(onodes(i)) + params = engine.obsprob{i}; + mu = params.big_mu; + Sigma = params.big_Sigma, + W = params.big_W; + mu0 = params.big_mu0; + Sigma0 = params.big_Sigma0; + %obslik_i = mk_arhmm_obs_lik(data, mu, Sigma, W, mu0, Sigma0 + obslik_i = clg_prob(data(:,1:T-1), data(:,2:T), mu, Sigma, W); + obslik_i = [mixgauss_prob(data(:,1), mu0, Sigma0) obslik_i]; + elseif myismember(onodes(i), bnet.dnodes) + %obslik_i = eval_pdf_cond_multinomial(data, engine.obsprob{i}.big_CPT); + obslik_i = multinomial_prob(data, engine.obsprob{i}.big_CPT); + else + %obslik_i = eval_pdf_cond_gauss(data, engine.obsprob{i}.big_mu, engine.obsprob{i}.big_Sigma); + obslik_i = mixgauss_prob(data, engine.obsprob{i}.big_mu, engine.obsprob{i}.big_Sigma); + end + obslik = obslik .* obslik_i; +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@hmm_inf_engine/private/mk_hmm_obs_lik_vec.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,52 @@ +function obslik = mk_hmm_obs_lik_vec(engine, evidence) + +% P(o1,o2| h) = P(o1|h) * P(o2|h) where h = Q1,Q2,... + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +onodes = bnet.observed; +hnodes = mysetdiff(1:ss, onodes); +ns = bnet.node_sizes(:); +ns(onodes) = 1; + +Q = length(engine.startprob); +obslik = ones(Q, 1); + +for i=1:length(onodes) + o = onodes(i); + %data = cell2num(evidence(o,1)); + data = evidence{o,1}; + if myismember(o, bnet.dnodes) + obslik_i = eval_pdf_cond_multinomial(data, engine.obsprob{i}.CPT); + else + if bnet.auto_regressive(o) + error('can''t handle AR nodes') + end + %% calling mk_ghmm_obs_lik, which calls gaussian_prob, is slow, so we inline it + %% and use the pre-computed inverse matrix + %obslik_i = mk_ghmm_obs_lik(data, engine.obsprob{i}.mu, engine.obsprob{i}.Sigma); + x = data(:); + m = engine.obsprob{i}.mu; + Qi = size(m, 2); + obslik_i = size(Qi, 1); + invC = engine.obsprob{i}.inv_Sigma; + denom = engine.obsprob{i}.denom; + for j=1:Qi + numer = exp(-0.5 * (x-m(:,j))' * invC(:,:,j) * (x-m(:,j))); + obslik_i(j) = numer / denom(j); + end + end + % convert P(o|ps) into P(o|h) by multiplying onto a (h,o) potential of all 1s + ps = bnet.parents{o}; + dom = [ps o]; + obspot_i = dpot(dom, ns(dom), obslik_i); + dom = [hnodes o]; + obspot = dpot(dom, ns(dom)); + obspot = multiply_by_pot(obspot, obspot_i); + % compute p(oi|h) * p(oj|h) + S = struct(obspot); + obslik = obslik .* S.T(:); +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@hmm_inf_engine/update_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +function engine = update_engine(engine, newCPDs) +% UPDATE_ENGINE Update the engine to take into account the new parameters (hmm) +% engine = update_engine(engine, newCPDs) + +%engine.inf_engine.bnet.CPD = newCPDs; +engine.inf_engine = update_engine(engine.inf_engine, newCPDs); +[engine.startprob, engine.transprob, engine.obsprob] = dbn_to_hmm(bnet_from_engine(engine)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine/Broken/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +/enter_soft_evidence1.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_soft_evidence2.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_soft_evidence3.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_soft_evidence4.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine/Broken/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine/Broken
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine/Broken/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine/Broken/enter_soft_evidence1.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,119 @@ +function [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type) +% ENTER_SOFT_EVIDENCE Add the specified soft evidence to the network (jtree_dbn) +% [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) + +[ss T] = size(CPDpot); +Q = length(engine.jtree_struct.cliques); +clpot = cell(Q,T); % clpot{t} contains evidence from slices (t-1, t) +seppot = cell(Q,Q,T); +ll = zeros(1,Q); +logscale = zeros(1,T); +bnet = bnet_from_engine(engine); + +% Forwards pass. +% Compute distribution on clq C, +% where C is the out interface to (t-1,t). +% Then pass this to clq D, where D is the in inferface to (t+1,t). +% Then propagate from D to later slices. + +C = engine.out_clq; +assert(C==engine.jtree_struct.root_clq); +D = engine.in_clq; +slice1 = 1:ss; +slice2 = slice1 + ss; +for t=2:T + if t==2 + clqs = engine.jtree_struct.clq_ass_to_node([slice1 slice2]); + pots = CPDpot(:,t-1:t); + else + %clqs = [D; engine.clq_ass_to_node(:,2)]; + clqs = [D engine.jtree_struct.clq_ass_to_node(slice2)]; + phiC = set_domain_pot(phiC, engine.interface); % shift back to slice 1 + pots = [ {phiC}; CPDpot(:,t)]; % CPDpot domains are always slice 2 + end + [clpot(:,t), seppot(:,:,t)] = init_pot(engine.jtree_struct.cliques, clqs, pots, pot_type, ... + find(observed(:,t-1:t)), bnet.node_sizes(:), bnet.cnodes); + [clpot(:,t), seppot(:,:,t)] = collect_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.postorder, ... + engine.jtree_struct.postorder_parents,... + engine.jtree_struct.separator); + + for c=1:Q + [clpot{c,t}, ll(c)] = normalize_pot(clpot{c,t}); + end + logscale(t) = ll(C); + + phiC = marginalize_pot(clpot{C,t}, engine.interface+ss, engine.maximize); +end + + + +% Backwards pass. +% Pass evidence from clq C to clq D, +% where C is the in interface to (t,t+1) and D is the out inferface to (t-1,t) +% Then propagate evidence from D to earlier slices. +C = engine.in_clq; +D = engine.out_clq; +for t=T:-1:2 + [clpot(:,t), seppot(:,:,t)] = distribute_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.preorder, ... + engine.jtree_struct.preorder_children, ... + engine.jtree_struct.separator); + for c=1:Q + [clpot{c,t}, ll(c)] = normalize_pot(clpot{c,t}); + end + logscale(t) = ll(C); + + if t >= 3 + phiC = marginalize_pot(clpot{C,t}, engine.interface, engine.maximize); + phiC = set_domain_pot(phiC, engine.interface+ss); % shift forward to slice 2 + phiD = marginalize_pot(clpot{D,t-1}, engine.interface+ss, engine.maximize); + ratio = divide_by_pot(phiC, phiD); + clpot{D,t-1} = multiply_by_pot(clpot{D,t-1}, ratio); + end +end + +loglik = sum(logscale); + + +%%%%%%% +function [clpot, seppot] = init_pot(cliques, clqs, pots, pot_type, onodes, ns, cnodes); + +% Set the clique potentials to all 1s +C = length(cliques); +clpot = cell(1,C); +for i=1:C + clpot{i} = mk_initial_pot(pot_type, cliques{i}, ns, cnodes, onodes); +end + +% Multiply on specified potentials +for i=1:length(clqs) + c = clqs(i); + clpot{c} = multiply_by_pot(clpot{c}, pots{i}); +end + +seppot = cell(C,C); % implicitely initialized to 1 + + +%%%% +function [clpot, seppot] = collect_evidence(clpot, seppot, maximize, postorder, postorder_parents,... + separator) +for n=postorder %postorder(1:end-1) + for p=postorder_parents{n} + %clpot{p} = divide_by_pot(clpot{n}, seppot{p,n}); % dividing by 1 is redundant + seppot{p,n} = marginalize_pot(clpot{n}, separator{p,n}, maximize); + clpot{p} = multiply_by_pot(clpot{p}, seppot{p,n}); + end +end + + +%%%% +function [clpot, seppot] = distribute_evidence(clpot, seppot, maximize, preorder, preorder_children,... + separator) +for n=preorder + for c=preorder_children{n} + clpot{c} = divide_by_pot(clpot{c}, seppot{n,c}); + seppot{n,c} = marginalize_pot(clpot{n}, separator{n,c}, maximize); + clpot{c} = multiply_by_pot(clpot{c}, seppot{n,c}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine/Broken/enter_soft_evidence2.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,143 @@ +function [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type) +% ENTER_SOFT_EVIDENCE Add the specified soft evidence to the network (jtree_dbn) +% [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) + +[ss T] = size(CPDpot); +Q = length(engine.jtree_struct.cliques); +clpot = cell(Q,T); % clpot{t} contains evidence from slices (t-1, t) +seppot = cell(Q,Q,T); +ll = zeros(1,Q); +logscale = ones(1,T); % log(logscale(1)) = 0 +bnet = bnet_from_engine(engine); + +slice1 = 1:ss; +slice2 = slice1+ss; + +% calibrate each 2-slice jtree in isolation +for t=2:T + if t==2 + clqs = engine.jtree_struct.clq_ass_to_node([slice1 slice2]); + pots = CPDpot(:,t-1:t); + else + clqs = engine.jtree_struct.clq_ass_to_node(slice2); + pots = CPDpot(:,t); % CPDpot domains are always slice 2 + end + [clpot(:,t), sepot(:,:,t)] = init_pot(engine.jtree_struct.cliques, clqs, pots, pot_type, ... + find(observed(:,t-1:t)), bnet.node_sizes(:), bnet.cnodes); + [clpot(:,t), seppot(:,:,t)] = collect_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.postorder, ... + engine.jtree_struct.postorder_parents,... + engine.jtree_struct.separator); + [clpot(:,t), seppot(:,:,t)] = distribute_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.preorder, ... + engine.jtree_struct.preorder_children, ... + engine.jtree_struct.separator); +end + +% Forwards pass. +% Compute distribution on clq C, +% where C is the out interface to (t-1,t). +% Then pass this to clq D, where D is the in inferface to (t+1,t). +% Then propagate from D to later slices. + +C = engine.out_clq; +D = engine.in_clq; +for t=2:T-1 + phiC = marginalize_pot(clpot{C,t}, engine.interface+ss, engine.maximize); + phiC = set_domain_pot(phiC, engine.interface); % shift back to slice 1 + phiD = marginalize_pot(clpot{D,t+1}, engine.interface, engine.maximize); + ratio = divide_by_pot(phiC, phiD); + clpot{D,t+1} = multiply_by_pot(clpot{D,t+1}, ratio); + + [clpot(:,t), seppot(:,:,t)] = distribute_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.preorder, ... + engine.jtree_struct.preorder_children, ... + engine.jtree_struct.separator); + for c=1:Q + [clpot{c,t}, ll(c)] = normalize_pot(clpot{c,t}); + end + logscale(t) = ll(1); +end + +% Backwards pass. +% Pass evidence from clq C to clq D, +% where C is the in interface to (t,t+1) and D is the out inferface to (t-1,t) +% Then propagate evidence from D to earlier slices. +C = engine.in_clq; +D = engine.out_clq; +for t=T:-1:2 + [clpot(:,t), seppot(:,:,t)] = collect_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.postorder, ... + engine.jtree_struct.postorder_parents,... + engine.jtree_struct.separator); + for c=1:Q + [clpot{c,t}, ll(c)] = normalize_pot(clpot{c,t}); + end + logscale(t) = ll(1); + + if t >= 3 + phiC = marginalize_pot(clpot{C,t}, engine.interface, engine.maximize); + phiC = set_domain_pot(phiC, engine.interface+ss); % shift forward to slice 2 + phiD = marginalize_pot(clpot{D,t-1}, engine.interface+ss, engine.maximize); + ratio = divide_by_pot(phiC, phiD); + clpot{D,t-1} = multiply_by_pot(clpot{D,t-1}, ratio); + end +end + +loglik = sum(logscale); + +%%%%%%%%%% + +function [clpot, seppot] = calibrate(engine, clpot, seppot) + + [clpot(:,t), seppot(:,:,t)] = collect_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.postorder, ... + engine.jtree_struct.postorder_parents,... + engine.jtree_struct.separator); + [clpot(:,t), seppot(:,:,t)] = distribute_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.preorder, ... + engine.jtree_struct.preorder_children, ... + engine.jtree_struct.separator); + + +%%%%%%% +function [clpot, seppot] = init_pot(cliques, clqs, pots, pot_type, onodes, ns, cnodes); + +% Set the clique potentials to all 1s +C = length(cliques); +clpot = cell(1,C); +for i=1:C + clpot{i} = mk_initial_pot(pot_type, cliques{i}, ns, cnodes, onodes); +end + +% Multiply on specified potentials +for i=1:length(clqs) + c = clqs(i); + clpot{c} = multiply_by_pot(clpot{c}, pots{i}); +end + +seppot = cell(C,C); % implicitely initialized to 1 + + +%%%% +function [clpot, seppot] = collect_evidence(clpot, seppot, maximize, postorder, postorder_parents,... + separator) +for n=postorder %postorder(1:end-1) + for p=postorder_parents{n} + %clpot{p} = divide_by_pot(clpot{n}, seppot{p,n}); % dividing by 1 is redundant + seppot{p,n} = marginalize_pot(clpot{n}, separator{p,n}, maximize); + clpot{p} = multiply_by_pot(clpot{p}, seppot{p,n}); + end +end + + +%%%% +function [clpot, seppot] = distribute_evidence(clpot, seppot, maximize, preorder, preorder_children,... + separator) +for n=preorder + for c=preorder_children{n} + clpot{c} = divide_by_pot(clpot{c}, seppot{n,c}); + seppot{n,c} = marginalize_pot(clpot{n}, separator{n,c}, maximize); + clpot{c} = multiply_by_pot(clpot{c}, seppot{n,c}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine/Broken/enter_soft_evidence3.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,125 @@ +function [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type) +% ENTER_SOFT_EVIDENCE Add the specified soft evidence to the network (jtree_dbn) +% [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) + +[ss T] = size(CPDpot); +Q = length(engine.jtree_struct.cliques); +clpot = cell(Q,T); % clpot{t} contains evidence from slices (t-1, t) +seppot = cell(Q,Q,T); +ll = zeros(1,Q); +logscale = zeros(1,T); +bnet = bnet_from_engine(engine); + +% Forwards pass. +% Compute distribution on clq C, +% where C is the out interface to (t-1,t). +% Then pass this to clq D, where D is the in inferface to (t+1,t). +% Then propagate from D to later slices. + +C = engine.out_clq; +assert(C==engine.jtree_struct.root_clq); +D = engine.in_clq; +slice1 = 1:ss; +slice2 = slice1 + ss; +Ntransient = length(engine.transient); +trans = cell(Ntransient,1); +for t=2:T + if t==2 + clqs = engine.jtree_struct.clq_ass_to_node([slice1 slice2]); + pots = CPDpot(:,t-1:t); + else + %clqs = [D; engine.clq_ass_to_node(:,2)]; + clqs = [D engine.jtree_struct.clq_ass_to_node([engine.transient slice2])]; + phiC = set_domain_pot(phiC, engine.interface); % shift back to slice 1 + for i=1:Ntransient + trans{i} = CPDpot{engine.transient(i), t-1}; + trans{i} = set_domain_pot(trans{i}, domain_pot(trans{i})-ss); % shift back to slice 1 + end + pots = [ {phiC}; trans; CPDpot(:,t)]; + end + [clpot(:,t), seppot(:,:,t)] = init_pot(engine.jtree_struct.cliques, clqs, pots, pot_type, ... + find(observed(:,t-1:t)), bnet.node_sizes(:), bnet.cnodes); + [clpot(:,t), seppot(:,:,t)] = collect_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.postorder, ... + engine.jtree_struct.postorder_parents,... + engine.jtree_struct.separator); + + for c=1:Q + [clpot{c,t}, ll(c)] = normalize_pot(clpot{c,t}); + end + logscale(t) = ll(C); + + phiC = marginalize_pot(clpot{C,t}, engine.interface+ss, engine.maximize); +end + + + +% Backwards pass. +% Pass evidence from clq C to clq D, +% where C is the in interface to (t,t+1) and D is the out inferface to (t-1,t) +% Then propagate evidence from D to earlier slices. +C = engine.in_clq; +D = engine.out_clq; +for t=T:-1:2 + [clpot(:,t), seppot(:,:,t)] = distribute_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.preorder, ... + engine.jtree_struct.preorder_children, ... + engine.jtree_struct.separator); + for c=1:Q + [clpot{c,t}, ll(c)] = normalize_pot(clpot{c,t}); + end + logscale(t) = ll(C); + + if t >= 3 + phiC = marginalize_pot(clpot{C,t}, engine.interface, engine.maximize); + phiC = set_domain_pot(phiC, engine.interface+ss); % shift forward to slice 2 + phiD = marginalize_pot(clpot{D,t-1}, engine.interface+ss, engine.maximize); + ratio = divide_by_pot(phiC, phiD); + clpot{D,t-1} = multiply_by_pot(clpot{D,t-1}, ratio); + end +end + +loglik = sum(logscale); + + +%%%%%%% +function [clpot, seppot] = init_pot(cliques, clqs, pots, pot_type, onodes, ns, cnodes); + +% Set the clique potentials to all 1s +C = length(cliques); +clpot = cell(1,C); +for i=1:C + clpot{i} = mk_initial_pot(pot_type, cliques{i}, ns, cnodes, onodes); +end + +% Multiply on specified potentials +for i=1:length(clqs) + c = clqs(i); + clpot{c} = multiply_by_pot(clpot{c}, pots{i}); +end + +seppot = cell(C,C); % implicitely initialized to 1 + + +%%%% +function [clpot, seppot] = collect_evidence(clpot, seppot, maximize, postorder, postorder_parents,... + separator) +for n=postorder %postorder(1:end-1) + for p=postorder_parents{n} + %clpot{p} = divide_by_pot(clpot{n}, seppot{p,n}); % dividing by 1 is redundant + seppot{p,n} = marginalize_pot(clpot{n}, separator{p,n}, maximize); + clpot{p} = multiply_by_pot(clpot{p}, seppot{p,n}); + end +end + + +%%%% +function [clpot, seppot] = distribute_evidence(clpot, seppot, maximize, preorder, preorder_children,... + separator) +for n=preorder + for c=preorder_children{n} + clpot{c} = divide_by_pot(clpot{c}, seppot{n,c}); + seppot{n,c} = marginalize_pot(clpot{n}, separator{n,c}, maximize); + clpot{c} = multiply_by_pot(clpot{c}, seppot{n,c}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine/Broken/enter_soft_evidence4.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,149 @@ +function [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type) +% ENTER_SOFT_EVIDENCE Add the specified soft evidence to the network (jtree_dbn) +% [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) + +[ss T] = size(CPDpot); +Q = length(engine.jtree_struct.cliques); +clpot = cell(Q,T); % clpot{t} contains evidence from slices (t-1, t) +seppot = cell(Q,Q,T); +ll = zeros(1,Q); +logscale = ones(1,T); % log(logscale(1)) = 0 +bnet = bnet_from_engine(engine); + +slice1 = 1:ss; +slice2 = slice1+ss; +Ntransient = length(engine.transient); +trans = cell(Ntransient,1); + +% calibrate each 2-slice jtree in isolation +for t=2:T + if t==2 + clqs = engine.jtree_struct.clq_ass_to_node([slice1 slice2]); + pots = CPDpot(:,t-1:t); + else + clqs = engine.jtree_struct.clq_ass_to_node([engine.transient slice2]); + for i=1:Ntransient + trans{i} = CPDpot{engine.transient(i), t-1}; + trans{i} = set_domain_pot(trans{i}, domain_pot(trans{i})-ss); % shift back to slice 1 + end + pots = [ trans; CPDpot(:,t)]; + end + [clpot(:,t), sepot(:,:,t)] = init_pot(engine.jtree_struct.cliques, clqs, pots, pot_type, ... + find(observed(:,t-1:t)), bnet.node_sizes(:), bnet.cnodes); + [clpot(:,t), seppot(:,:,t)] = collect_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.postorder, ... + engine.jtree_struct.postorder_parents,... + engine.jtree_struct.separator); + [clpot(:,t), seppot(:,:,t)] = distribute_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.preorder, ... + engine.jtree_struct.preorder_children, ... + engine.jtree_struct.separator); +end + +% Forwards pass. +% Compute distribution on clq C, +% where C is the out interface to (t-1,t). +% Then pass this to clq D, where D is the in inferface to (t+1,t). +% Then propagate from D to later slices. + +C = engine.out_clq; +D = engine.in_clq; +for t=2:T-1 + phiC = marginalize_pot(clpot{C,t}, engine.interface+ss, engine.maximize); + phiC = set_domain_pot(phiC, engine.interface); % shift back to slice 1 + phiD = marginalize_pot(clpot{D,t+1}, engine.interface, engine.maximize); + ratio = divide_by_pot(phiC, phiD); + clpot{D,t+1} = multiply_by_pot(clpot{D,t+1}, ratio); + + [clpot(:,t), seppot(:,:,t)] = distribute_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.preorder, ... + engine.jtree_struct.preorder_children, ... + engine.jtree_struct.separator); + for c=1:Q + [clpot{c,t}, ll(c)] = normalize_pot(clpot{c,t}); + end + logscale(t) = ll(1); +end + +% Backwards pass. +% Pass evidence from clq C to clq D, +% where C is the in interface to (t,t+1) and D is the out inferface to (t-1,t) +% Then propagate evidence from D to earlier slices. +C = engine.in_clq; +D = engine.out_clq; +for t=T:-1:2 + [clpot(:,t), seppot(:,:,t)] = collect_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.postorder, ... + engine.jtree_struct.postorder_parents,... + engine.jtree_struct.separator); + for c=1:Q + [clpot{c,t}, ll(c)] = normalize_pot(clpot{c,t}); + end + logscale(t) = ll(1); + + if t >= 3 + phiC = marginalize_pot(clpot{C,t}, engine.interface, engine.maximize); + phiC = set_domain_pot(phiC, engine.interface+ss); % shift forward to slice 2 + phiD = marginalize_pot(clpot{D,t-1}, engine.interface+ss, engine.maximize); + ratio = divide_by_pot(phiC, phiD); + clpot{D,t-1} = multiply_by_pot(clpot{D,t-1}, ratio); + end +end + +loglik = sum(logscale); + +%%%%%%%%%% + +function [clpot, seppot] = calibrate(engine, clpot, seppot) + + [clpot(:,t), seppot(:,:,t)] = collect_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.postorder, ... + engine.jtree_struct.postorder_parents,... + engine.jtree_struct.separator); + [clpot(:,t), seppot(:,:,t)] = distribute_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.preorder, ... + engine.jtree_struct.preorder_children, ... + engine.jtree_struct.separator); + + +%%%%%%% +function [clpot, seppot] = init_pot(cliques, clqs, pots, pot_type, onodes, ns, cnodes); + +% Set the clique potentials to all 1s +C = length(cliques); +clpot = cell(1,C); +for i=1:C + clpot{i} = mk_initial_pot(pot_type, cliques{i}, ns, cnodes, onodes); +end + +% Multiply on specified potentials +for i=1:length(clqs) + c = clqs(i); + clpot{c} = multiply_by_pot(clpot{c}, pots{i}); +end + +seppot = cell(C,C); % implicitely initialized to 1 + + +%%%% +function [clpot, seppot] = collect_evidence(clpot, seppot, maximize, postorder, postorder_parents,... + separator) +for n=postorder %postorder(1:end-1) + for p=postorder_parents{n} + %clpot{p} = divide_by_pot(clpot{n}, seppot{p,n}); % dividing by 1 is redundant + seppot{p,n} = marginalize_pot(clpot{n}, separator{p,n}, maximize); + clpot{p} = multiply_by_pot(clpot{p}, seppot{p,n}); + end +end + + +%%%% +function [clpot, seppot] = distribute_evidence(clpot, seppot, maximize, preorder, preorder_children,... + separator) +for n=preorder + for c=preorder_children{n} + clpot{c} = divide_by_pot(clpot{c}, seppot{n,c}); + seppot{n,c} = marginalize_pot(clpot{n}, separator{n,c}, maximize); + clpot{c} = multiply_by_pot(clpot{c}, seppot{n,c}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine/Broken/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,51 @@ +function marginal = marginal_nodes(engine, nodes, t, fam) +% MARGINAL_NODES Compute the marginal on the specified query nodes (bk) +% +% marginal = marginal_nodes(engine, i, t) +% returns Pr(X(i,t) | Y(1:T)), where X(i,t) is the i'th node in the t'th slice. +% +% marginal = marginal_nodes(engine, query, t) +% returns Pr(X(query(1),t), ... X(query(end),t) | Y(1:T)), +% where 't' specifies the time slice of the earliest node in the query. +% 'query' cannot span more than 2 time slices. +% +% Example: +% Consider a DBN with 2 nodes per slice. +% Then t=2, nodes=[1 3] refers to node 1 in slice 2 and node 1 in slice 3. + +if nargin < 3, t = 1; end +if nargin < 4, fam = 0; else fam = 1; end + + +% clpot{t} contains slice t-1 and t +% Example +% clpot #: 1 2 3 +% slices: 1 1,2 2,3 +% For filtering, we must take care not to take future evidence into account. +% For smoothing, clpot{1} does not exist. + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); + +if t < engine.T + slice = t+1; + nodes2 = nodes; +else % earliest t is T, so all nodes fit in one slice + slice = engine.T; + nodes2 = nodes + ss; +end + +c = clq_containing_nodes(engine.jtree_engine, nodes2, fam); +assert(c >= 1); + +%disp(['computing marginal on ' num2str(nodes) ' t = ' num2str(t)]); +%disp(['using ' num2str(nodes2) ' slice = ' num2str(slice) 'clq = ' num2str(c)]); + +bigpot = engine.clpot{c, slice}; + +pot = marginalize_pot(bigpot, nodes2, engine.maximize); +marginal = pot_to_marginal(pot); + +% we convert the domain to the unrolled numbering system +% so that update_ess extracts the right evidence. +marginal.domain = nodes+(t-1)*ss;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +/enter_evidence.m/1.1.1.1/Sat Jan 11 18:41:30 2003// +/enter_soft_evidence.m/1.1.1.1/Thu Feb 19 01:12:08 2004// +/jtree_dbn_inf_engine.m/1.1.1.1/Thu Nov 14 16:32:00 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Fri Nov 22 23:51:58 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +A D/Broken//// +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine/Old/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +/enter_soft_evidence_nonint.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_soft_evidence_trans.m/1.1.1.1/Wed May 29 15:59:56 2002// +/jtree_dbn_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/jtree_dbn_inf_engine1.m/1.1.1.1/Wed May 29 15:59:56 2002// +/jtree_dbn_inf_engine2.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine/Old/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine/Old/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine/Old/enter_soft_evidence_nonint.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,135 @@ +function [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type) +% ENTER_SOFT_EVIDENCE Add the specified soft evidence to the network (jtree_dbn) +% [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) + +[ss T] = size(CPDpot); +Q = length(engine.jtree_struct.cliques); +clpot = cell(Q,T); % clpot{t} contains evidence from slices (t-1, t) +seppot = cell(Q,Q,T); +ll = zeros(1,Q); +logscale = zeros(1,T); +bnet = bnet_from_engine(engine); + +% Forwards pass. +% Compute distribution on clq C, +% where C is the out interface to (t-1,t). +% Then pass this to clq D, where D is the in inferface to (t+1,t). +% Then propagate from D to later slices. + +C = engine.out_clq; +assert(C==engine.jtree_struct.root_clq); +D = engine.in_clq; +slice1 = 1:ss; +slice2 = slice1 + ss; +Nnonint = length(engine.nonint); +nonint = cell(Nnonint, 1); +for t=1:T + if t==1 + pots = [CPDpot(:,1); CPDpot(engine.interface, 2)]; + clqs = engine.jtree_struct.clq_ass_to_node([slice1 engine.interface+ss]); + obs = find(observed(:,1:2)); + elseif t==T + clqs = [D engine.jtree_struct.clq_ass_to_node(engine.nonint)]; + phiC = set_domain_pot(phiC, engine.interface); % shift back to slice 1 + for i=1:Nnonint + nonint{i} = CPDpot{engine.nonint(i), t}; + nonint{i} = set_domain_pot(nonint{i}, domain_pot(nonint{i})-ss); % shift back to slice 1 + end + pots = [ {phiC}; nonint]; + obs = find(observed(:,T)); + else + clqs = [D engine.jtree_struct.clq_ass_to_node([engine.nonint engine.interface+ss])]; + phiC = set_domain_pot(phiC, engine.interface); % shift back to slice 1 + for i=1:Nnonint + nonint{i} = CPDpot{engine.nonint(i), t}; + nonint{i} = set_domain_pot(nonint{i}, domain_pot(nonint{i})-ss); % shift back to slice 1 + end + pots = [ {phiC}; nonint; CPDpot(engine.interface, t+1)]; + obs = find(observed(:,t:t+1)); + end + [clpot(:,t), seppot(:,:,t)] = init_pot(engine.jtree_struct.cliques, clqs, pots, pot_type, ... + obs, bnet.node_sizes(:), bnet.cnodes); + [clpot(:,t), seppot(:,:,t)] = collect_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.postorder, ... + engine.jtree_struct.postorder_parents,... + engine.jtree_struct.separator); + + for c=1:Q + [clpot{c,t}, ll(c)] = normalize_pot(clpot{c,t}); + end + logscale(t) = ll(C); + + phiC = marginalize_pot(clpot{C,t}, engine.interface+ss, engine.maximize); +end + + + +% Backwards pass. +% Pass evidence from clq C to clq D, +% where C is the in interface to (t,t+1) and D is the out inferface to (t-1,t) +% Then propagate evidence from D to earlier slices. +C = engine.in_clq; +D = engine.out_clq; +for t=T:-1:1 + [clpot(:,t), seppot(:,:,t)] = distribute_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.preorder, ... + engine.jtree_struct.preorder_children, ... + engine.jtree_struct.separator); + for c=1:Q + [clpot{c,t}, ll(c)] = normalize_pot(clpot{c,t}); + end + %logscale(t) = ll(C); + + if t >= 2 + phiC = marginalize_pot(clpot{C,t}, engine.interface, engine.maximize); + phiC = set_domain_pot(phiC, engine.interface+ss); % shift forward to slice 2 + phiD = marginalize_pot(clpot{D,t-1}, engine.interface+ss, engine.maximize); + ratio = divide_by_pot(phiC, phiD); + clpot{D,t-1} = multiply_by_pot(clpot{D,t-1}, ratio); + end +end + +loglik = sum(logscale); + + +%%%%%%% +function [clpot, seppot] = init_pot(cliques, clqs, pots, pot_type, onodes, ns, cnodes); + +% Set the clique potentials to all 1s +C = length(cliques); +clpot = cell(1,C); +for i=1:C + clpot{i} = mk_initial_pot(pot_type, cliques{i}, ns, cnodes, onodes); +end + +% Multiply on specified potentials +for i=1:length(clqs) + c = clqs(i); + clpot{c} = multiply_by_pot(clpot{c}, pots{i}); +end + +seppot = cell(C,C); % implicitely initialized to 1 + + +%%%% +function [clpot, seppot] = collect_evidence(clpot, seppot, maximize, postorder, postorder_parents,... + separator) +for n=postorder %postorder(1:end-1) + for p=postorder_parents{n} + %clpot{p} = divide_by_pot(clpot{n}, seppot{p,n}); % dividing by 1 is redundant + seppot{p,n} = marginalize_pot(clpot{n}, separator{p,n}, maximize); + clpot{p} = multiply_by_pot(clpot{p}, seppot{p,n}); + end +end + + +%%%% +function [clpot, seppot] = distribute_evidence(clpot, seppot, maximize, preorder, preorder_children,... + separator) +for n=preorder + for c=preorder_children{n} + clpot{c} = divide_by_pot(clpot{c}, seppot{n,c}); + seppot{n,c} = marginalize_pot(clpot{n}, separator{n,c}, maximize); + clpot{c} = multiply_by_pot(clpot{c}, seppot{n,c}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine/Old/enter_soft_evidence_trans.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,135 @@ +function [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type) +% ENTER_SOFT_EVIDENCE Add the specified soft evidence to the network (jtree_dbn) +% [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) + +[ss T] = size(CPDpot); +Q = length(engine.jtree_struct.cliques); +clpot = cell(Q,T); % clpot{t} contains evidence from slices (t-1, t) +seppot = cell(Q,Q,T); +ll = zeros(1,Q); +logscale = zeros(1,T); +bnet = bnet_from_engine(engine); + +% Forwards pass. +% Compute distribution on clq C, +% where C is the out interface to (t-1,t). +% Then pass this to clq D, where D is the in inferface to (t+1,t). +% Then propagate from D to later slices. + +C = engine.out_clq; +assert(C==engine.jtree_struct.root_clq); +D = engine.in_clq; +slice1 = 1:ss; +slice2 = slice1 + ss; +Ntransient = length(engine.transient); +trans = cell(Ntransient,1); +for t=1:T + if t==1 + pots = [CPDpot(:,1); CPDpot(engine.persist, 2)]; + clqs = engine.jtree_struct.clq_ass_to_node([slice1 engine.persist+ss]); + obs = find(observed(:,1:2)); + elseif t==T + clqs = [D engine.jtree_struct.clq_ass_to_node(engine.transient)]; + phiC = set_domain_pot(phiC, engine.interface); % shift back to slice 1 + for i=1:Ntransient + trans{i} = CPDpot{engine.transient(i), t}; + trans{i} = set_domain_pot(trans{i}, domain_pot(trans{i})-ss); % shift back to slice 1 + end + pots = [ {phiC}; trans]; + obs = find(observed(:,T)); + else + clqs = [D engine.jtree_struct.clq_ass_to_node([engine.transient engine.persist+ss])]; + phiC = set_domain_pot(phiC, engine.interface); % shift back to slice 1 + for i=1:Ntransient + trans{i} = CPDpot{engine.transient(i), t}; + trans{i} = set_domain_pot(trans{i}, domain_pot(trans{i})-ss); % shift back to slice 1 + end + pots = [ {phiC}; trans; CPDpot(engine.persist, t+1)]; + obs = find(observed(:,t:t+1)); + end + [clpot(:,t), seppot(:,:,t)] = init_pot(engine.jtree_struct.cliques, clqs, pots, pot_type, ... + obs, bnet.node_sizes(:), bnet.cnodes); + [clpot(:,t), seppot(:,:,t)] = collect_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.postorder, ... + engine.jtree_struct.postorder_parents,... + engine.jtree_struct.separator); + + for c=1:Q + [clpot{c,t}, ll(c)] = normalize_pot(clpot{c,t}); + end + logscale(t) = ll(C); + + phiC = marginalize_pot(clpot{C,t}, engine.interface+ss, engine.maximize); +end + + + +% Backwards pass. +% Pass evidence from clq C to clq D, +% where C is the in interface to (t,t+1) and D is the out inferface to (t-1,t) +% Then propagate evidence from D to earlier slices. +C = engine.in_clq; +D = engine.out_clq; +for t=T:-1:1 + [clpot(:,t), seppot(:,:,t)] = distribute_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.preorder, ... + engine.jtree_struct.preorder_children, ... + engine.jtree_struct.separator); + for c=1:Q + [clpot{c,t}, ll(c)] = normalize_pot(clpot{c,t}); + end + %logscale(t) = ll(C); + + if t >= 2 + phiC = marginalize_pot(clpot{C,t}, engine.interface, engine.maximize); + phiC = set_domain_pot(phiC, engine.interface+ss); % shift forward to slice 2 + phiD = marginalize_pot(clpot{D,t-1}, engine.interface+ss, engine.maximize); + ratio = divide_by_pot(phiC, phiD); + clpot{D,t-1} = multiply_by_pot(clpot{D,t-1}, ratio); + end +end + +loglik = sum(logscale); + + +%%%%%%% +function [clpot, seppot] = init_pot(cliques, clqs, pots, pot_type, onodes, ns, cnodes); + +% Set the clique potentials to all 1s +C = length(cliques); +clpot = cell(1,C); +for i=1:C + clpot{i} = mk_initial_pot(pot_type, cliques{i}, ns, cnodes, onodes); +end + +% Multiply on specified potentials +for i=1:length(clqs) + c = clqs(i); + clpot{c} = multiply_by_pot(clpot{c}, pots{i}); +end + +seppot = cell(C,C); % implicitely initialized to 1 + + +%%%% +function [clpot, seppot] = collect_evidence(clpot, seppot, maximize, postorder, postorder_parents,... + separator) +for n=postorder %postorder(1:end-1) + for p=postorder_parents{n} + %clpot{p} = divide_by_pot(clpot{n}, seppot{p,n}); % dividing by 1 is redundant + seppot{p,n} = marginalize_pot(clpot{n}, separator{p,n}, maximize); + clpot{p} = multiply_by_pot(clpot{p}, seppot{p,n}); + end +end + + +%%%% +function [clpot, seppot] = distribute_evidence(clpot, seppot, maximize, preorder, preorder_children,... + separator) +for n=preorder + for c=preorder_children{n} + clpot{c} = divide_by_pot(clpot{c}, seppot{n,c}); + seppot{n,c} = marginalize_pot(clpot{n}, separator{n,c}, maximize); + clpot{c} = multiply_by_pot(clpot{c}, seppot{n,c}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine/Old/jtree_dbn_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,67 @@ +function engine = jtree_dbn_inf_engine(bnet, varargin) +% JTREE_DBN_INF_ENGINE Junction tree inference algorithm for DBNs. + +ss = length(bnet.intra); + +onodes = []; + +if nargin >= 2 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'observed', onodes = args{i+1}; + end + end +end + +[int, engine.persist, engine.transient] = compute_interface_nodes(bnet.intra, bnet.inter); +%engine.interface = engine.persist; % WRONG! +engine.interface = int; +engine.nonint = mysetdiff(1:ss, int); + +if 0 + % Create a 2 slice jtree + % We force there to be cliques containing the in and out interfaces for slices t and t+1. + obs_nodes = [onodes(:) onodes(:)+ss]; + engine.jtree_engine = jtree_inf_engine(bnet, 'observed', obs_nodes(:), ... + 'clusters', {int, int+ss}, 'root', int+ss); +else + % Create a "1.5 slice" jtree, containing slice 1 and the interface nodes of slice 2 + nodes15 = [1:ss int+ss]; + N = length(nodes15); + dag15 = bnet.dag(nodes15, nodes15); + ns15 = bnet.node_sizes(nodes15); + eclass15 = bnet.equiv_class(nodes15); + discrete_bitv = zeros(1,2*ss); + discrete_bitv(bnet.dnodes) = 1; + discrete15 = find(discrete_bitv(nodes15)); + bnet15 = mk_bnet(dag15, ns15, 'equiv_class', eclass15, 'discrete', discrete15); + bnet15.CPD = bnet.CPD; % CPDs for non-interface nodes in slice 2 will not be used + obs_bitv = zeros(1, 2*ss); + obs_bitv([onodes onodes+ss]) = 1; + obs_nodes15 = find(obs_bitv(nodes15)); + int_bitv = zeros(1,ss); + int_bitv(int) = 1; + engine.jtree_engine = jtree_inf_engine(bnet15, 'observed', obs_nodes15(:), ... + 'clusters', {int, int+ss}, 'root', int+ss); +end + +engine.in_clq = clq_containing_nodes(engine.jtree_engine, int); +engine.out_clq = clq_containing_nodes(engine.jtree_engine, int+ss); + +engine.clq_ass_to_node = zeros(ss, 2); +for i=1:ss + engine.clq_ass_to_node(i, 1) = clq_containing_nodes(engine.jtree_engine, i); + engine.clq_ass_to_node(i, 2) = clq_containing_nodes(engine.jtree_engine, i+ss); +end + +engine.jtree_struct = struct(engine.jtree_engine); % violate object privacy + +% stuff needed by marginal_nodes +engine.clpot = []; +engine.maximize = []; +engine.T = []; + +engine = class(engine, 'jtree_dbn_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine/Old/jtree_dbn_inf_engine1.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,62 @@ +function engine = jtree_dbn_inf_engine(bnet, varargin) +% JTREE_DBN_INF_ENGINE Junction tree inference algorithm for DBNs. + +ss = length(bnet.intra); + +onodes = []; + +if nargin >= 2 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'observed', onodes = args{i+1}; + end + end +end + +[int, engine.persist, engine.transient] = compute_interface_nodes(bnet.intra, bnet.inter); +%engine.interface = engine.persist; % WRONG! +engine.interface = int; +engine.nonint = mysetdiff(1:ss, int); + +if 1 + % Create a 2 slice jtree + % We force there to be cliques containing the in and out interfaces for slices t and t+1. + obs_nodes = [onodes(:) onodes(:)+ss]; + engine.jtree_engine = jtree_inf_engine(bnet, 'observed', obs_nodes(:), ... + 'clusters', {int, int+ss}, 'root', int+ss); +else + % Create a "1.5 slice" jtree, containing slice 1 and the interface nodes of slice 2 + % To keep the node numbering the same, we simply disconnect the non-interface nodes + % from slice 2. + intra15 = bnet.intra; + for i=engine.nonint(:)' + intra15(i,:) = 0; + intra15(:,i) = 0; + end + bnet15 = mk_dbn(intra15, bnet.inter, bnet.node_sizes_slice, bnet.dnodes_slice, ... + bnet.equiv_class(:,1), bnet.equiv_class(:,2), bnet.intra); + obs_nodes = [onodes(:) onodes(:)+ss]; + engine.jtree_engine = jtree_inf_engine(bnet15, 'observed', obs_nodes(:), ... + 'clusters', {int, int+ss}, 'root', int+ss); +end + +engine.in_clq = clq_containing_nodes(engine.jtree_engine, int); +engine.out_clq = clq_containing_nodes(engine.jtree_engine, int+ss); + +engine.clq_ass_to_node = zeros(ss, 2); +for i=1:ss + engine.clq_ass_to_node(i, 1) = clq_containing_nodes(engine.jtree_engine, i); + engine.clq_ass_to_node(i, 2) = clq_containing_nodes(engine.jtree_engine, i+ss); +end + +engine.jtree_struct = struct(engine.jtree_engine); % violate object privacy + +% stuff needed by marginal_nodes +engine.clpot = []; +engine.maximize = []; +engine.T = []; + +engine = class(engine, 'jtree_dbn_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine/Old/jtree_dbn_inf_engine2.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,57 @@ +function engine = jtree_dbn_inf_engine(bnet, varargin) +% JTREE_DBN_INF_ENGINE Junction tree inference algorithm for DBNs. + +ss = length(bnet.intra); + +onodes = []; + +if nargin >= 2 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'observed', onodes = args{i+1}; + end + end +end + +[int, engine.persist, engine.transient] = compute_interface_nodes(bnet.intra, bnet.inter); +%engine.interface = engine.persist; % WRONG! +engine.interface = int; +engine.nonint = mysetdiff(1:ss, int); + + +% Create a 2 slice jtree +% We force there to be cliques containing the in and out interfaces for slices t and t+1. +obs_nodes = [onodes(:) onodes(:)+ss]; +engine.jtree_engine = jtree_inf_engine(bnet, 'observed', obs_nodes(:), ... + 'clusters', {int, int+ss}, 'root', int+ss); + +engine.in_clq = clq_containing_nodes(engine.jtree_engine, int); +engine.out_clq = clq_containing_nodes(engine.jtree_engine, int+ss); +engine.jtree_struct = struct(engine.jtree_engine); % violate object privacy + + + +% Also create an engine just for slice 1 +bnet1 = mk_bnet(bnet.intra1, bnet.node_sizes_slice, bnet.dnodes, bnet.equiv_class(:,1)); +for i=1:max(bnet1.equiv_class) + bnet1.CPD{i} = bnet.CPD{i}; +end + +engine.jtree_engine1 = jtree_inf_engine(bnet1, 'observed', onodes, 'clusters', {int}, ... + 'root', int); + +engine.in_clq1 = clq_containing_nodes(engine.jtree_engine1, int); +engine.jtree_struct1 = struct(engine.jtree_engine1); % violate object privacy + + + + +% stuff needed by marginal_nodes +engine.clpot = []; +engine.T = []; +engine.maximize = []; + +engine = class(engine, 'jtree_dbn_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,70 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (jtree_dbn) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product instead of sum-product [engine.maximize] +% softCPDpot{n,t} - use soft potential for node n instead of its CPD; set to [] to use CPD +% soft_evidence_nodes(i,1:2) = [n t] means the i'th piece of soft evidence is on node n in slice t +% soft_evidence{i} - prob distribution over values for soft_evidence_nodes(i,:) +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) + + +% for add_ev in marginal_nodes +T = size(evidence, 2); +engine.evidence = evidence; +bnet = bnet_from_engine(engine); +ss = length(bnet.node_sizes_slice); +ns = bnet.node_sizes_slice(:); +engine.node_sizes = repmat(ns, [1 T]); +softCPDpot = cell(ss,T); +soft_evidence = {}; +soft_evidence_nodes = []; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + for i=1:2:nargs + switch args{i}, + case 'maximize', engine.maximize = args{i+1}; + case 'softCPDpot', softCPDpot = args{i+1}; + case 'soft_evidence', soft_evidence = args{i+1}; + case 'soft_evidence_nodes', soft_evidence_nodes = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +engine.jtree_engine = set_fields(engine.jtree_engine, 'maximize', engine.maximize); +engine.jtree_engine1 = set_fields(engine.jtree_engine1, 'maximize', engine.maximize); + +[ss T] = size(evidence); +engine.T = T; +observed_bitv = ~isemptycell(evidence); +onodes = find(observed_bitv); +pot_type = determine_pot_type(bnet, onodes); +CPDpot = convert_dbn_CPDs_to_pots(bnet, evidence, pot_type, softCPDpot); + +if ~isempty(soft_evidence_nodes) + nsoft = size(soft_evidence_nodes,1); + for i=1:nsoft + n = soft_evidence_nodes(i,1); + t = soft_evidence_nodes(i,2); + if t==1 + dom = n; + else + dom = n+ss; + end + pot = dpot(dom, ns(n), soft_evidence{i}); + CPDpot{n,t} = multiply_by_pot(CPDpot{n,t}, pot); + end +end + +[engine.clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed_bitv, pot_type);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine/enter_soft_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,126 @@ +function [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type) +% ENTER_SOFT_EVIDENCE Add the specified soft evidence to the network (jtree_dbn) +% [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type) + +scale = 1; +verbose = 0; + +[ss T] = size(CPDpot); +Q = length(engine.jtree_struct.cliques); +clpot = cell(Q,T); % clpot{t} contains evidence from slices (t-1, t) +seppot = cell(Q,Q,T); +ll = zeros(1,Q); +logscale = zeros(1,T); +bnet = bnet_from_engine(engine); +root = engine.jtree_struct.root_clq; + +% Forwards pass. +% Compute distribution on clq C, +% where C is the out interface to (t-1,t). +% Then pass this to clq D, where D is the in inferface to (t+1,t). + +% Then propagate from D to later slices. + +slice1 = 1:ss; +slice2 = slice1 + ss; +transient = engine.transient; +persist = engine.persist; +Ntransient = length(transient); +trans = cell(Ntransient,1); +if verbose, fprintf('forward pass\n'); end +for t=1:T + if verbose, fprintf('%d ', t); end + if t==1 + pots = [CPDpot(:,1); CPDpot(persist, 2)]; + clqs = engine.jtree_struct.clq_ass_to_node([slice1 persist+ss]); + obs = find(observed(:,1:2)); + elseif t==T + clqs = [engine.in_clq1 engine.jtree_struct1.clq_ass_to_node(transient)]; + phi = set_domain_pot(phi, engine.interface); % shift back to slice 1 + for i=1:Ntransient + trans{i} = CPDpot{transient(i), t}; + trans{i} = set_domain_pot(trans{i}, domain_pot(trans{i})-ss); % shift back to slice 1 + end + pots = [ {phi}; trans]; + obs = find(observed(:,T)); + else + clqs = [engine.in_clq engine.jtree_struct.clq_ass_to_node([transient persist+ss])]; + phi = set_domain_pot(phi, engine.interface); % shift back to slice 1 + for i=1:Ntransient + trans{i} = CPDpot{transient(i), t}; + trans{i} = set_domain_pot(trans{i}, domain_pot(trans{i})-ss); % shift back to slice 1 + end + pots = [ {phi}; trans; CPDpot(persist, t+1)]; + obs = find(observed(:,t:t+1)); + end + + if t < T + [clpot(1:Q,t), seppot(1:Q,1:Q,t)] = init_pot(engine.jtree_engine, clqs, pots, pot_type, obs); + [clpot(1:Q,t), seppot(1:Q,1:Q,t)] = collect_evidence(engine.jtree_engine, clpot(1:Q,t), seppot(1:Q,1:Q,t)); + else + Q = length(engine.jtree_struct1.cliques); + root = engine.jtree_struct1.root_clq; + [clpot(1:Q,t), seppot(1:Q,1:Q,t)] = init_pot(engine.jtree_engine1, clqs, pots, pot_type, obs); + [clpot(1:Q,t), seppot(1:Q,1:Q,t)] = collect_evidence(engine.jtree_engine1, clpot(1:Q,t), seppot(1:Q,1:Q,t)); + end + + + if scale + for c=1:Q + [clpot{c,t}, ll(c)] = normalize_pot(clpot{c,t}); + end + logscale(t) = ll(root); + end + + if t < T + % bug fix by Bob Welch 30 Jan 04 + phi = marginalize_pot(clpot{engine.out_clq,t}, engine.interface+ss,engine.maximize); + %phi = marginalize_pot(clpot{root,t}, engine.interface+ss, engine.maximize); + end +end + +if scale +loglik = sum(logscale); +else +loglik = []; +end + + +% Backwards pass. +% Pass evidence from clq C to clq D, +% where C is the in interface to (t,t+1) and D is the out inferface to (t-1,t) +% Then propagate evidence from D to earlier slices. +% (C and D are reversed names from the tech report!) +D = engine.out_clq; +if verbose, fprintf('\nbackwards pass\n'); end +for t=T:-1:1 + if verbose, fprintf('%d ', t); end + + if t == T + Q = length(engine.jtree_struct1.cliques); + C = engine.in_clq1; + [clpot(1:Q,t), seppot(1:Q,1:Q,t)] = distribute_evidence(engine.jtree_engine1, clpot(1:Q,t), seppot(1:Q,1:Q,t)); + else + Q = length(engine.jtree_struct.cliques); + C = engine.in_clq; + [clpot(1:Q,t), seppot(1:Q,1:Q,t)] = distribute_evidence(engine.jtree_engine, clpot(1:Q,t), seppot(1:Q,1:Q,t)); + end + + if scale + for c=1:Q + [clpot{c,t}, ll(c)] = normalize_pot(clpot{c,t}); + end + end + + if t >= 2 + phiC = marginalize_pot(clpot{C,t}, engine.interface, engine.maximize); + phiC = set_domain_pot(phiC, engine.interface+ss); % shift forward to slice 2 + phiD = marginalize_pot(clpot{D,t-1}, engine.interface+ss, engine.maximize); + ratio = divide_by_pot(phiC, phiD); + clpot{D,t-1} = multiply_by_pot(clpot{D,t-1}, ratio); + end +end +if verbose, fprintf('\n'); end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine/jtree_dbn_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,109 @@ +function engine = jtree_dbn_inf_engine(bnet, varargin) +% JTREE_DBN_INF_ENGINE Junction tree inference algorithm for DBNs. +% engine = jtree_inf_engine(bnet, ...) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% clusters - specifies variables that must be grouped in the 1.5 slice DBN +% maximize - 1 means max-product, 0 means sum-product [0] +% +% e.g., engine = jtree_dbn_inf_engine(dbn, 'clusters', {[1 2]}); +% +% This uses all of slice t-1 plus the backwards interface of slice t. +% By contrast, jtree_2TBN_inf_engine in the online directory uses +% the forwards interface of slice t-1 plus all of slice t. +% See my thesis for details. + +ss = length(bnet.intra); + +engine.maximize = 0; +clusters = {}; + +args = varargin; +for i=1:2:length(args) + switch args{i}, + case 'clusters', clusters = args{i+1}; + case 'maximize', engine.maximize = args{i+1}; + otherwise, error(['unrecognized argument ' args{i}]) + end +end + + +engine.evidence = []; +engine.node_sizes = []; + +[int, engine.persist, engine.transient] = compute_interface_nodes(bnet.intra, bnet.inter); +engine.interface = int; +engine.nonint = mysetdiff(1:ss, int); + +onodes = bnet.observed; + +if 0 + % Create a 2 slice jtree + % We force there to be cliques containing the in and out interfaces for slices t and t+1. + obs_nodes = [onodes(:) onodes(:)+ss]; + engine.jtree_engine = jtree_inf_engine(bnet, 'observed', obs_nodes(:), ... + 'clusters', {int, int+ss}, 'root', int+ss); +else + % Create a "1.5 slice" jtree, containing slice 1 and the interface nodes of slice 2 + % To keep the node numbering the same, we simply disconnect the non-interface nodes + % from slice 2, and set their size to 1. + % We do this to speed things up, and so that the likelihood is computed correctly - we do not need to do + % this if we just want to compute marginals. + intra15 = bnet.intra; + for i=engine.nonint(:)' + intra15(i,:) = 0; + intra15(:,i) = 0; + end + dag15 = [bnet.intra bnet.inter; + zeros(ss) intra15]; + ns = bnet.node_sizes(:); + ns(engine.nonint+ss) = 1; % disconnected nodes get size 1 + obs_nodes = [onodes(:) onodes(:)+ss]; + bnet15 = mk_bnet(dag15, ns, 'discrete', bnet.dnodes, 'equiv_class', bnet.equiv_class(:), ... + 'observed', obs_nodes(:)); + + %bnet15 = mk_dbn(intra15, bnet.inter, bnet.node_sizes_slice, bnet.dnodes_slice, ... + % bnet.equiv_class(:,1), bnet.equiv_class(:,2), bnet.intra); + % with the dbn, we can't independently control the sizes of slice 2 nodes + + if 1 + % use unconstrained elimination, + % but force there to be a clique containing both interfaces + clusters(end+1:end+2) = {int, int+ss}; + engine.jtree_engine = jtree_inf_engine(bnet15, 'clusters', clusters, 'root', int+ss); + else + % Use constrained elimination - this induces a clique that contain the 2nd interface, + % but not the first. + % Hence we throw in the first interface as an extra. + stages = {1:ss, [1:ss]+ss}; + clusters(end+1:end+2) = {int, int+ss}; + engine.jtree_engine = jtree_inf_engine(bnet15, 'clusters', clusters, ... + 'stages', stages, 'root', int+ss); + end +end + +engine.in_clq = clq_containing_nodes(engine.jtree_engine, int); +engine.out_clq = clq_containing_nodes(engine.jtree_engine, int+ss); +engine.jtree_struct = struct(engine.jtree_engine); % violate object privacy + + +% Also create an engine just for slice 1 +bnet1 = mk_bnet(bnet.intra1, bnet.node_sizes_slice, 'discrete', myintersect(bnet.dnodes,1:ss), ... + 'equiv_class', bnet.equiv_class(:,1), 'observed', onodes); +for i=1:max(bnet1.equiv_class) + bnet1.CPD{i} = bnet.CPD{i}; +end + +engine.jtree_engine1 = jtree_inf_engine(bnet1, 'clusters', {int}, 'root', int); + +engine.in_clq1 = clq_containing_nodes(engine.jtree_engine1, int); +engine.jtree_struct1 = struct(engine.jtree_engine1); % violate object privacy + +% stuff needed by marginal_nodes +engine.clpot = []; +engine.T = []; + +engine = class(engine, 'jtree_dbn_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine/marginal_family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,26 @@ +function m = marginal_family(engine, i, t, add_ev) +% MARGINAL_FAMILY Compute the marginal on the specified family (jtree_dbn) +% marginal = marginal_family(engine, i, t) + +% This is just like inf_engine/marginal_family, except when we call +% marginal_nodes, we provide a 4th argument, to tell it's a family. + +if nargin < 3, t = 1; end +if nargin < 4, add_ev = 0; end + +bnet = bnet_from_engine(engine); +if t==1 + m = marginal_nodes(engine, family(bnet.dag, i), t, add_ev, 1); +else + ss = length(bnet.intra); + fam = family(bnet.dag, i+ss); + if any(fam<=ss) + % i has a parent in the preceeding slice + % Hence the lowest numbered slice containing the family is t-1 + m = marginal_nodes(engine, fam, t-1, add_ev, 1); + else + % The family all fits inside slice t + % Hence shift the indexes back to slice 1 + m = marginal_nodes(engine, fam-ss, t, add_ev, 1); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,66 @@ +function marginal = marginal_nodes(engine, nodes, t, add_ev, fam) +% MARGINAL_NODES Compute the marginal on the specified query nodes (bk) +% +% marginal = marginal_nodes(engine, i, t) +% returns Pr(X(i,t) | Y(1:T)), where X(i,t) is the i'th node in the t'th slice. +% +% marginal = marginal_nodes(engine, query, t) +% returns Pr(X(query(1),t), ... X(query(end),t) | Y(1:T)), +% where 't' specifies the time slice of the earliest node in the query. +% 'query' cannot span more than 2 time slices. +% +% Example: +% Consider a DBN with 2 nodes per slice. +% Then t=2, nodes=[1 3] refers to node 1 in slice 2 and node 1 in slice 3. +% +% marginal = marginal_nodes(engine, nodes, t, add_ev, fam) +% add_ev is an optional argument; if 1, we will "inflate" the marginal of observed nodes +% to their original size, adding 0s to the positions which contradict the evidence + +if nargin < 3, t = 1; end +if nargin < 4, add_ev = 0; end +if nargin < 5, fam = 0; end + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); + +if t==1 | t==engine.T + slice = t; + nodes2 = nodes; +elseif mysubset(nodes, engine.persist) + slice = t-1; + nodes2 = nodes+ss; +else + slice = t; + nodes2 = nodes; +end + +%disp(['computing marginal on ' num2str(nodes) ' t = ' num2str(t) ' fam = ' num2str(fam)]); + +if t==engine.T + c = clq_containing_nodes(engine.jtree_engine1, nodes2, fam); +else + c = clq_containing_nodes(engine.jtree_engine, nodes2, fam); +end +if c == -1 + error(['no clique contains ' nodes2]) +end + + +%disp(['using ' num2str(nodes2) ' slice = ' num2str(slice) ' clq = ' num2str(c)]); + +bigpot = engine.clpot{c, slice}; + +pot = marginalize_pot(bigpot, nodes2, engine.maximize); +%pot = normalize_pot(pot); +marginal = pot_to_marginal(pot); + + +% we convert the domain to the unrolled numbering system +% so that add_ev_to_dmarginal (maybe called in update_ess) extracts the right evidence. +marginal.domain = nodes+(t-1)*ss; + +if add_ev + marginal = add_ev_to_dmarginal(marginal, engine.evidence, engine.node_sizes); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_unrolled_dbn_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/jtree_unrolled_dbn_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/update_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_unrolled_dbn_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@jtree_unrolled_dbn_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_unrolled_dbn_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_unrolled_dbn_inf_engine/Old/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,3 @@ +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_unrolled_dbn_inf_engine/Old/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@jtree_unrolled_dbn_inf_engine/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_unrolled_dbn_inf_engine/Old/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_unrolled_dbn_inf_engine/Old/marginal_family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,10 @@ +function marginal = marginal_family(engine, i, t) +% MARGINAL_FAMILY Compute the marginal on the specified family (jtree_unrolled_dbn) +% marginal = marginal_family(engine, i, t) + +if nargin < 3, t = 1; end + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +marginal = marginal_family(engine.sub_engine, i + (t-1)*ss); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_unrolled_dbn_inf_engine/Old/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,18 @@ +function marginal = marginal_nodes(engine, nodes, t) +% MARGINAL_NODES Compute the marginal on the specified query nodes (jtree_unrolled_dbn) +% marginal = marginal_nodes(engine, nodes, t) +% +% 't' specifies the time slice of the earliest node in 'nodes'. +% 'nodes' must occur in some clique. +% +% Example: +% Consider a DBN with 2 nodes per slice. +% Then t=2, nodes=[1 3] refers to node 1 in slice 2 and node 1 in slice 3, +% i.e., nodes 3 and 5 in the unrolled network, + +if nargin < 3, t = 1; end + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +query = nodes + (t-1)*ss; +marginal = marginal_nodes(engine.sub_engine, query);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_unrolled_dbn_inf_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,43 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (jtree_unrolled_dbn) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product instead of sum-product [0] +% filter - if 1, does filtering (not supported), else smoothing [0] +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) + +maximize = 0; +filter = 0; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + for i=1:2:nargs + switch args{i}, + case 'maximize', maximize = args{i+1}; + case 'filter', filter = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +if filter + error('jtree_unrolled_dbn does not support filtering') +end + +if size(evidence,2) ~= engine.nslices + error(['engine was created assuming there are ' num2str(engine.nslices) ... + ' slices, but evidence has ' num2str(size(evidence,2))]) +end + +[engine.unrolled_engine, loglik] = enter_evidence(engine.unrolled_engine, evidence, 'maximize', maximize); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_unrolled_dbn_inf_engine/jtree_unrolled_dbn_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,57 @@ +function engine = jtree_unrolled_dbn_inf_engine(bnet, T, varargin) +% JTREE_UNROLLED_DBN_INF_ENGINE Unroll the DBN for T time-slices and apply jtree to the resulting static net +% engine = jtree_unrolled_dbn_inf_engine(bnet, T, ...) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% useC - 1 means use jtree_C_inf_engine instead of jtree_inf_engine [0] +% constrained - 1 means we constrain ourselves to eliminate slice t before t+1 [1] +% +% e.g., engine = jtree_unrolled_inf_engine(bnet, 'useC', 1); + +% set default params +N = length(bnet.intra); +useC = 0; +constrained = 1; + +if nargin >= 3 + args = varargin; + nargs = length(args); + if isstr(args{1}) + for i=1:2:nargs + switch args{i}, + case 'useC', useC = args{i+1}; + case 'constrained', constrained = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end + else + error(['invalid argument name ' args{1}]); + end +end + +bnet2 = dbn_to_bnet(bnet, T); +ss = length(bnet.intra); +engine.ss = ss; + +% If constrained_order = 1 we constrain ourselves to eliminate slice t before t+1. +% This prevents cliques containing nodes from far-apart time-slices. +if constrained + stages = num2cell(unroll_set(1:ss, ss, T), 1); +else + stages = { 1:length(bnet2.dag) }; +end +if useC + jengine = jtree_C_inf_engine(bnet2, 'stages', stages); +else + jengine = jtree_inf_engine(bnet2, 'stages', stages); +end + +engine.unrolled_engine = jengine; +% we don't inherit from jtree_inf_engine, because that would only store bnet2, +% and we would lose access to the DBN-specific fields like intra/inter + +engine.nslices = T; +engine = class(engine, 'jtree_unrolled_dbn_inf_engine', inf_engine(bnet));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_unrolled_dbn_inf_engine/marginal_family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function marginal = marginal_family(engine, i, t, add_ev) +% MARGINAL_FAMILY Compute the marginal on the specified family (jtree_unrolled_dbn) +% marginal = marginal_family(engine, i, t) + +if nargin < 3, t = 1; end +if nargin < 4, add_ev = 0; end +assert(~add_ev); + +%marginal = marginal_family(engine.unrolled_engine, i + (t-1)*engine.ss, add_ev); +marginal = marginal_family(engine.unrolled_engine, i + (t-1)*engine.ss); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_unrolled_dbn_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +function marginal = marginal_nodes(engine, nodes, t, add_ev) +% MARGINAL_NODES Compute the marginal on the specified query nodes (loopy_unrolled_dbn) +% marginal = marginal_nodes(engine, nodes, t) +% +% 't' specifies the time slice of the earliest node in 'nodes'. +% 'nodes' must occur in some clique. +% +% Example: +% Consider a DBN with 2 nodes per slice. +% Then t=2, nodes=[1 3] refers to node 1 in slice 2 and node 1 in slice 3, +% i.e., nodes 3 and 5 in the unrolled network, + +if nargin < 3, t = 1; end +if nargin < 4, add_ev = 0; end + +marginal = marginal_nodes(engine.unrolled_engine, nodes + (t-1)*engine.ss, add_ev);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@jtree_unrolled_dbn_inf_engine/update_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +function engine = update_engine(engine, newCPDs) +% UPDATE_ENGINE Update the engine to take into account the new parameters (jtree_unrolled_dbn) +% engine = update_engine(engine, newCPDs) + +engine.inf_engine = update_engine(engine.inf_engine, newCPDs); +engine.unrolled_engine = update_engine(engine.unrolled_engine, newCPDs); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@kalman_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/kalman_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/update_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +D/private////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@kalman_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@kalman_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@kalman_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@kalman_inf_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,83 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (kalman) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product (same as sum-product for Gaussians!), else sum-product [0] +% filter - if 1, do filtering, else smoothing [0] +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) + +maximize = 0; +filter = 0; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + for i=1:2:nargs + switch args{i}, + case 'maximize', maximize = args{i+1}; + case 'filter', filter = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +assert(~maximize); + +bnet = bnet_from_engine(engine); +n = length(bnet.intra); +onodes = bnet.observed; +hnodes = mysetdiff(1:n, onodes); +T = size(evidence, 2); +ns = bnet.node_sizes; +O = sum(ns(onodes)); +data = reshape(cat(1, evidence{onodes,:}), [O T]); + +A = engine.trans_mat; +C = engine.obs_mat; +Q = engine.trans_cov; +R = engine.obs_cov; +init_x = engine.init_state; +init_V = engine.init_cov; + +if filter + [x, V, VV, loglik] = kalman_filter(data, A, C, Q, R, init_x, init_V); +else + [x, V, VV, loglik] = kalman_smoother(data, A, C, Q, R, init_x, init_V); +end + + +% Wrap the posterior inside a potential, so it can be marginalized easily +engine.one_slice_marginal = cell(1,T); +engine.two_slice_marginal = cell(1,T); +ns(onodes) = 0; +ns(onodes+n) = 0; +ss = length(bnet.intra); +for t=1:T + dom = (1:n); + engine.one_slice_marginal{t} = mpot(dom+(t-1)*ss, ns(dom), 1, x(:,t), V(:,:,t)); +end +% for t=1:T-1 +% dom = (1:(2*n)); +% mu = [x(:,t); x(:,t)]; +% Sigma = [V(:,:,t) VV(:,:,t+1)'; +% VV(:,:,t+1) V(:,:,t+1)]; +% engine.two_slice_marginal{t} = mpot(dom+(t-1)*ss, ns(dom), 1, mu, Sigma); +% end +for t=2:T + %dom = (1:(2*n)); + current_slice = hnodes; + next_slice = hnodes + ss; + dom = [current_slice next_slice]; + mu = [x(:,t-1); x(:,t)]; + Sigma = [V(:,:,t-1) VV(:,:,t)'; + VV(:,:,t) V(:,:,t)]; + engine.two_slice_marginal{t-1} = mpot(dom+(t-2)*ss, ns(dom), 1, mu, Sigma); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@kalman_inf_engine/kalman_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,23 @@ +function engine = kalman_inf_engine(bnet) +% KALMAN_INF_ENGINE Inference engine for Linear-Gaussian state-space models. +% engine = kalman_inf_engine(bnet) +% +% 'onodes' specifies which nodes are observed; these must be leaves. +% The remaining nodes are all hidden. All nodes must have linear-Gaussian CPDs. +% The hidden nodes must be persistent, i.e., they must have children in +% the next time slice. In addition, they may not have any children within the current slice, +% except to the observed leaves. In other words, the topology must be isomorphic to a standard LDS. +% +% There are many derivations of the filtering and smoothing equations for Linear Dynamical +% Systems in the literature. I particularly like the following +% - "From HMMs to LDSs", T. Minka, MIT Tech Report, (no date), available from +% ftp://vismod.www.media.mit.edu/pub/tpminka/papers/minka-lds-tut.ps.gz + +[engine.trans_mat, engine.trans_cov, engine.obs_mat, engine.obs_cov, engine.init_state, engine.init_cov] = ... + dbn_to_lds(bnet); + +% This is where we will store the results between enter_evidence and marginal_nodes +engine.one_slice_marginal = []; +engine.two_slice_marginal = []; + +engine = class(engine, 'kalman_inf_engine', inf_engine(bnet));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@kalman_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,25 @@ +function marginal = marginal_nodes(engine, nodes, t) +% MARGINAL_NODES Compute the marginal on the specified query nodes (kalman) +% marginal = marginal_nodes(engine, nodes, t) +% +% 't' specifies the time slice of the earliest node in 'nodes'. +% 'nodes' cannot span more than 2 time slices. +% +% Example: +% Consider a DBN with 2 nodes per slice. +% Then t=2, nodes=[1 3] refers to node 1 in slice 2 and node 1 in slice 3, +% i.e., nodes 3 and 5 in the unrolled network, + +if nargin < 3, t = 1; end + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +if all(nodes <= ss) + bigpot = engine.one_slice_marginal{t}; +else + bigpot = engine.two_slice_marginal{t}; +end + +nodes = nodes + (t-1)*ss; +pot = marginalize_pot(bigpot, nodes); +marginal = pot_to_marginal(pot);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@kalman_inf_engine/private/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,3 @@ +/dbn_to_lds.m/1.1.1.1/Wed May 29 15:59:56 2002// +/extract_params_from_gbn.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@kalman_inf_engine/private/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@kalman_inf_engine/private
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@kalman_inf_engine/private/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@kalman_inf_engine/private/dbn_to_lds.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,26 @@ +function [trans_mat, trans_cov, obs_mat, obs_cov, init_state, init_cov] = dbn_to_lds(bnet) +% DBN_TO_LDS Compute the Linear Dynamical System parameters from the Gaussian DBN. +% [trans_mat, trans_cov, obs_mat, obs_cov, init_state, init_cov] = dbn_to_lds(bnet) + +onodes = bnet.observed; +ss = length(bnet.intra); +num_nodes = ss*2; +assert(isequal(bnet.cnodes_slice, 1:ss)); +[W,D,mu] = extract_params_from_gbn(bnet); + +hnodes = mysetdiff(1:ss, onodes); +bs = bnet.node_sizes(:); % block sizes + +obs_mat = W(block(hnodes,bs), block(onodes,bs))'; +u = block(onodes,bs); +obs_cov = D(u,u); + +trans_mat = W(block(hnodes,bs), block(hnodes + ss, bs))'; +u = block(hnodes + ss, bs); +trans_cov = D(u,u); + +u = block(hnodes,bs); +init_cov = D(u,u); +init_state = mu(u); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@kalman_inf_engine/private/extract_params_from_gbn.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,38 @@ +function [B,D,mu] = extract_params_from_gbn(bnet) +% Extract all the local parameters of each Gaussian node, and collect them into global matrices. +% [B,D,mu] = extract_params_from_gbn(bnet) +% +% B(i,j) is a block matrix that contains the transposed weight matrix from node i to node j. +% D(i,i) is a block matrix that contains the noise covariance matrix for node i. +% mu(i) is a block vector that contains the shifted noise mean for node i. + +% In Shachter's model, the mean of each node in the global gaussian is +% the same as the node's local unconditional mean. +% In Alag's model (which we use), the global mean gets shifted. + + +num_nodes = length(bnet.dag); +bs = bnet.node_sizes(:); % bs = block sizes +N = sum(bs); % num scalar nodes + +B = zeros(N,N); +D = zeros(N,N); +mu = zeros(N,1); + +for i=1:num_nodes % in topological order + ps = parents(bnet.dag, i); + e = bnet.equiv_class(i); + %[m, Sigma, weights] = extract_params_from_CPD(bnet.CPD{e}); + s = struct(bnet.CPD{e}); % violate privacy of object + m = s.mean; Sigma = s.cov; weights = s.weights; + if length(ps) == 0 + mu(block(i,bs)) = m; + else + mu(block(i,bs)) = m + weights * mu(block(ps,bs)); + end + B(block(ps,bs), block(i,bs)) = weights'; + D(block(i,bs), block(i,bs)) = Sigma; +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@kalman_inf_engine/update_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,9 @@ +function engine = update_engine(engine, newCPDs) +% UPDATE_ENGINE Update the engine to take into account the new parameters (kalman) +% engine = update_engine(engine, newCPDs) + +engine.inf_engine = update_engine(engine.inf_engine, newCPDs); +[engine.trans_mat, engine.trans_cov, engine.obs_mat, engine.obs_cov, engine.init_state, engine.init_cov] = ... + dbn_to_lds(bnet_from_engine(engine)); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@pearl_dbn_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_soft_ev.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/pearl_dbn_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +D/Old//// +D/private////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@pearl_dbn_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@pearl_dbn_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@pearl_dbn_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@pearl_dbn_inf_engine/Old/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +/correct_smooth.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/filter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/filter_evidence_obj_oriented.m/1.1.1.1/Wed May 29 15:59:56 2002// +/smooth_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/smooth_evidence_fast.m/1.1.1.1/Wed May 29 15:59:56 2002// +/wrong_smooth.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@pearl_dbn_inf_engine/Old/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@pearl_dbn_inf_engine/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@pearl_dbn_inf_engine/Old/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@pearl_dbn_inf_engine/Old/correct_smooth.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,244 @@ +function [marginal, msg, loglik] = smooth_evidence(engine, evidence) +% [marginal, msg, loglik] = smooth_evidence(engine, evidence) (pearl_dbn) + +disp('warning: broken'); + +[ss T] = size(evidence); +bnet = bnet_from_engine(engine); +bnet2 = dbn_to_bnet(bnet, T); +ns = bnet2.node_sizes; +hnodes = mysetdiff(1:ss, engine.onodes); +hnodes = hnodes(:)'; + +onodes2 = unroll_set(engine.onodes(:), ss, T); +onodes2 = onodes2(:)'; + +hnodes2 = unroll_set(hnodes(:), ss, T); +hnodes2 = hnodes2(:)'; + +[engine.parent_index, engine.child_index] = mk_pearl_msg_indices(bnet2); + +msg = init_msgs(bnet2.dag, ns, evidence, bnet2.equiv_class, bnet2.CPD); + +verbose = 0; + +niter = 1; +for iter=1:niter + % FORWARD + for t=1:T + if verbose, fprintf('t=%d\n', t); end + % observed leaves send lambda to parents + for i=engine.onodes(:)' + n = i + (t-1)*ss; + ps = parents(bnet2.dag, n); + for p=ps(:)' + j = engine.child_index{p}(n); % n is p's j'th child + if t > 1 + e = bnet.equiv_class(i, 2); + else + e = bnet.equiv_class(i, 1); + end + lam_msg = normalise(compute_lambda_msg(bnet.CPD{e}, n, ps, msg, p)); + msg{p}.lambda_from_child{j} = lam_msg; + if verbose, fprintf('%d sends lambda to %d\n', n, p); disp(lam_msg); end + end + end + + % update pi + for i=hnodes + n = i + (t-1)*ss; + ps = parents(bnet2.dag, n); + if t==1 + e = bnet.equiv_class(i,1); + else + e = bnet.equiv_class(i,2); + end + msg{n}.pi = compute_pi(bnet.CPD{e}, n, ps, msg); + if verbose, fprintf('%d computes pi\n', n); disp(msg{n}.pi); end + end + + % send pi msg to children + for i=hnodes + n = i + (t-1)*ss; + %cs = myintersect(children(bnet2.dag, n), hnodes2); + cs = children(bnet2.dag, n); + for c=cs(:)' + j = engine.parent_index{c}(n); % n is c's j'th parent + pi_msg = normalise(compute_pi_msg(n, cs, msg, c, ns)); + msg{c}.pi_from_parent{j} = pi_msg; + if verbose, fprintf('%d sends pi to %d\n', n, c); disp(pi_msg); end + end + end + end + + % BACKWARD + for t=T:-1:1 + if verbose, fprintf('t = %d\n', t); end + % update lambda + for i=hnodes + n = i + (t-1)*ss; + cs = children(bnet2.dag, n); + msg{n}.lambda = compute_lambda(n, cs, msg, ns); + if verbose, fprintf('%d computes lambda\n', n); disp(msg{n}.lambda); end + end + % send lambda msgs to parents + for i=hnodes + n = i + (t-1)*ss; + %ps = myintersect(parents(bnet2.dag, n), hnodes2); + ps = parents(bnet2.dag, n); + for p=ps(:)' + j = engine.child_index{p}(n); % n is p's j'th child + if t > 1 + e = bnet.equiv_class(i, 2); + else + e = bnet.equiv_class(i, 1); + end + lam_msg = normalise(compute_lambda_msg(bnet.CPD{e}, n, ps, msg, p)); + msg{p}.lambda_from_child{j} = lam_msg; + if verbose, fprintf('%d sends lambda to %d\n', n, p); disp(lam_msg); end + end + end + end + +end + + +marginal = cell(ss,T); +lik = zeros(1,ss*T); +for t=1:T + for i=1:ss + n = i + (t-1)*ss; + [bel, lik(n)] = normalise(msg{n}.pi .* msg{n}.lambda); + marginal{i,t} = bel; + end +end + +loglik = sum(log(lik)); + + + +%%%%%%% + +function lambda = compute_lambda(n, cs, msg, ns) +% Pearl p183 eq 4.50 +lambda = prod_lambda_msgs(n, cs, msg, ns); + +%%%%%%% + +function pi_msg = compute_pi_msg(n, cs, msg, c, ns) +% Pearl p183 eq 4.53 and 4.51 +pi_msg = msg{n}.pi .* prod_lambda_msgs(n, cs, msg, ns, c); + +%%%%%%%%% + +function lam = prod_lambda_msgs(n, cs, msg, ns, except) + +if nargin < 5, except = -1; end + +lam = msg{n}.lambda_from_self(:); +lam = ones(ns(n), 1); +for i=1:length(cs) + c = cs(i); + if c ~= except + lam = lam .* msg{n}.lambda_from_child{i}; + end +end + + +%%%%%%%%% + +function msg = init_msgs(dag, ns, evidence, eclass, CPD) +% INIT_MSGS Initialize the lambda/pi message and state vectors (pearl_dbn) +% msg = init_msgs(dag, ns, evidence) + +N = length(dag); +msg = cell(1,N); +observed = ~isemptycell(evidence(:)); + +for n=1:N + ps = parents(dag, n); + msg{n}.pi_from_parent = cell(1, length(ps)); + for i=1:length(ps) + p = ps(i); + msg{n}.pi_from_parent{i} = ones(ns(p), 1); + end + + cs = children(dag, n); + msg{n}.lambda_from_child = cell(1, length(cs)); + for i=1:length(cs) + c = cs(i); + msg{n}.lambda_from_child{i} = ones(ns(n), 1); + end + + msg{n}.lambda = ones(ns(n), 1); + msg{n}.lambda_from_self = ones(ns(n), 1); + msg{n}.pi = ones(ns(n), 1); + + % Initialize the lambdas with any evidence + if observed(n) + v = evidence{n}; + %msg{n}.lambda_from_self = zeros(ns(n), 1); + %msg{n}.lambda_from_self(v) = 1; % delta function + msg{n}.lambda = zeros(ns(n), 1); + msg{n}.lambda(v) = 1; % delta function + end + +end + + +%%%%%%%% + +function msg = init_ev_msgs(engine, evidence, msg) + +[ss T] = size(evidence); +bnet = bnet_from_engine(engine); +pot_type = 'd'; +t = 1; +hnodes = mysetdiff(1:ss, engine.onodes); +for i=engine.onodes(:)' + fam = family(bnet.dag, i); + e = bnet.equiv_class(i, 1); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,1)); + temp = pot_to_marginal(CPDpot); + msg{i}.lambda_from_self = temp.T; +end +for t=2:T + for i=engine.onodes(:)' + fam = family(bnet.dag, i, 2); % extract from slice t + e = bnet.equiv_class(i, 2); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,t-1:t)); + temp = pot_to_marginal(CPDpot); + n = i + (t-1)*ss; + msg{n}.lambda_from_self = temp.T; + end +end + + +%%%%%%%%%%% + +function msg = init_ev_msgs2(engine, evidence, msg) + +[ss T] = size(evidence); +bnet = bnet_from_engine(engine); +pot_type = 'd'; +t = 1; +hnodes = mysetdiff(1:ss, engine.onodes); +for i=engine.onodes(:)' + fam = family(bnet.dag, i); + e = bnet.equiv_class(i, 1); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,1)); + temp = pot_to_marginal(CPDpot); + msg{i}.lambda_from_self = temp.T; +end +for t=2:T + for i=engine.onodes(:)' + fam = family(bnet.dag, i, 2); % extract from slice t + e = bnet.equiv_class(i, 2); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,t-1:t)); + temp = pot_to_marginal(CPDpot); + n = i + (t-1)*ss; + msg{n}.lambda_from_self = temp.T; + end +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@pearl_dbn_inf_engine/Old/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,123 @@ +function [engine, loglik] = enter_evidence(engine, evidence, filter) +% ENTER_EVIDENCE Add the specified evidence to the network (pearl_dbn) +% [engine, loglik] = enter_evidence(engine, evidence, filter) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% If filter = 1, we do filtering, otherwise smoothing (default). + +if nargin < 3, filter = 0; end + +[ss T] = size(evidence); +bnet = bnet_from_engine(engine); +bnet2 = dbn_to_bnet(bnet, T); +ns = bnet2.node_sizes; +hnodes = mysetdiff(1:ss, engine.onodes); +hnodes = hnodes(:)'; + +[engine.parent_index, engine.child_index] = mk_pearl_msg_indices(bnet2); + +msg = init_msgs(bnet2.dag, ns, evidence); +msg = init_ev_msgs(engine, evidence, msg); + +niter = 1; +for iter=1:niter + % FORWARD + for t=1:T + % update pi + for i=1:ss %hnodes + n = i + (t-1)*ss; + ps = parents(bnet2.dag, n); + if t==1 + e = bnet.equiv_class(i,1); + else + e = bnet.equiv_class(i,2); + end + msg{n}.pi = compute_pi(bnet.CPD{e}, n, ps, msg); + %msg{n}.pi = normalise(msg{n}.pi(:) .* msg{n}.lambda_from_self(:)); + end + % send pi msg to children + for i=1:ss % hnodes + n = i + (t-1)*ss; + cs = children(bnet2.dag, n); + for c=cs(:)' + j = engine.parent_index{c}(n); % n is c's j'th parent + msg{c}.pi_from_parent{j} = normalise(compute_pi_msg(n, cs, msg, c, ns)); + end + end + end + + if filter + disp('skipping smoothing'); + break; + end + + % BACKWARD + for t=T:-1:1 + % update lambda + for i=1:ss % hnodes + n = i + (t-1)*ss; + cs = children(bnet2.dag, n); + msg{n}.lambda = compute_lambda(n, cs, msg, ns); + end + % send lambda msgs to parents + for i=1:ss % hnodes + n = i + (t-1)*ss; + ps = parents(bnet2.dag, n); + for p=ps(:)' + j = engine.child_index{p}(n); % n is p's j'th child + if t > 1 + e = bnet.equiv_class(i, 2); + else + e = bnet.equiv_class(i, 1); + end + msg{p}.lambda_from_child{j} = normalise(compute_lambda_msg(bnet.CPD{e}, n, ps, msg, p)); + end + end + end + +end + + +engine.marginal = cell(ss,T); +lik = zeros(1,ss*T); +for t=1:T + for i=1:ss + n = i + (t-1)*ss; + [bel, lik(n)] = normalise(msg{n}.pi .* msg{n}.lambda); + engine.marginal{i,t} = bel; + end +end + +engine.evidence = evidence; % needed by marginal_nodes and marginal_family +engine.msg = msg; % needed by marginal_family +loglik = sum(log(lik)); + + + +%%%%%%% + +function lambda = compute_lambda(n, cs, msg, ns) +% Pearl p183 eq 4.50 +lambda = prod_lambda_msgs(n, cs, msg, ns); + +%%%%%%% + +function pi_msg = compute_pi_msg(n, cs, msg, c, ns) +% Pearl p183 eq 4.53 and 4.51 +pi_msg = msg{n}.pi .* prod_lambda_msgs(n, cs, msg, ns, c); + +%%%%%%%%% + +function lam = prod_lambda_msgs(n, cs, msg, ns, except) + +if nargin < 5, except = -1; end + +lam = msg{n}.lambda_from_self(:); +%lam = ones(ns(n), 1); +for i=1:length(cs) + c = cs(i); + if c ~= except + lam = lam .* msg{n}.lambda_from_child{i}; + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@pearl_dbn_inf_engine/Old/filter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,146 @@ +function [marginal, msg, loglik] = filter_evidence(engine, evidence) + +error('broken'); + +[ss T] = size(evidence); +bnet = bnet_from_engine(engine); +onodes = engine.onodes; +hnodes = mysetdiff(1:ss, onodes); +hnodes = hnodes(:)'; + +ns = bnet.node_sizes(:); +onodes2 = [onodes(:); onodes(:)+ss]; +ns(onodes2) = 1; + +verbose = 1; +if verbose, fprintf('\nnew filtering\n'); end + +pot_type = 'd'; +niter = engine.max_iter; + +% msg(i1,t1,i2,j2) (i1,t1) -> (i2,t2) +%lambda_msg = cell(ss,T,ss,T); +%pi_msg = cell(ss,T,ss,T); + +% intra_lambda_msg(i,j,t) (i,t) -> (j,t), i is child +% inter_lambda_msg(i,j,t) (i,t+1) -> (j,t), i is child +% inter_pi_msg(i,j,t) (i,t-1) -> (j,t), i is parent +intra_lambda_msg = cell(ss,ss,T); +inter_lambda_msg = cell(ss,ss,T); +inter_pi_msg = cell(ss,ss,T); + +lambda = cell(ss,T); +pi = cell(ss,T); + +for t=1:T + for i=1:ss + lambda{i,t} = ones(ns(i), 1); + pi{i,t} = ones(ns(i), 1); + + cs = children(bnet.intra, i); + for c=cs(:)' + intra_lambda_msg{c,i,t} = ones(ns(i),1); + end + + cs = children(bnet.inter, i); + for c=cs(:)' + inter_lambda_msg{c,i,t} = ones(ns(i),1); + end + + ps = parents(bnet.inter, i); + for p=ps(:)' + inter_pi_msg{p,i,t} = ones(ns(i), 1); % not used for t==1 + end + end +end + +% each hidden node absorbs lambda from its observed child (if any) +for t=1:T + for i=hnodes + c = engine.obschild(i); + if c > 0 + if t==1 + fam = family(bnet.dag, c); + e = bnet.equiv_class(c, 1); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,1)); + else + fam = family(bnet.dag, c, 2); % within 2 slice network + e = bnet.equiv_class(c, 2); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,t-1:t)); + end + temp = pot_to_marginal(CPDpot); + lam_msg = normalise(temp.T); + %if verbose, fprintf('(%d,%d) sends lambda to (%d,%d)\n', c,t, i,t); disp(lam_msg); end + intra_lambda_msg{c,i,t} = lam_msg; + end + end +end + +% FORWARD +for t=1:T + % update pi + for i=hnodes + if t==1 + e = bnet.equiv_class(i,1); + temp = struct(bnet.CPD{e}); + pi{i,t} = temp.CPT; + else + e = bnet.equiv_class(i,2); + temp = struct(bnet.CPD{e}); + ps = parents(bnet.inter, i); + dom = [ps i+ss]; + pot = dpot(dom, ns(dom), temp.CPT); + for p=ps(:)' + temp = dpot(p, ns(p), inter_pi_msg{p,i,t}); + pot = multiply_by_pot(pot, temp); + end + pot = marginalize_pot(pot, i+ss); + temp = pot_to_marginal(pot); + pi{i,t} = temp.T; + %if verbose, fprintf('(%d,%d) computes pi\n', i,t); disp(pi{i,t}); end + end + + c = engine.obschild(i); + if c > 0 + pi{i,t} = normalise(pi{i,t} .* intra_lambda_msg{c,i,t}); + end + %if verbose, fprintf('(%d,%d) recomputes pi\n', i,t); disp(pi{i,t}); end + if verbose, fprintf('%d recomputes pi\n', i+(t-1)*ss); disp(pi{i,t}); end + end + + % send pi msg to children + for i=hnodes + cs = children(bnet.inter, i); + for c=cs(:)' + pot = pi{i,t}; + for k=cs(:)' + if k ~= c + pot = pot .* inter_lambda_msg{k,i,t}; + end + end + cs2 = children(bnet.intra, i); + for k=cs2(:)' + pot = pot .* intra_lambda_msg{k,i,t}; + end + pot = normalise(pot); + %if verbose, fprintf('(%d,%d) sends pi to (%d,%d)\n', i,t, c,t+1); disp(pot); end + if verbose, fprintf('%d sends pi to %d\n', i+(t-1)*ss, c+t*ss); disp(pot); end + inter_pi_msg{i,c,t+1} = pot; + end + end +end + + +marginal = cell(ss,T); +for t=1:T + for i=hnodes + %marginal{i,t} = normalise(pi{i,t} .* lambda{i,t}); + marginal{i,t} = normalise(pi{i,t}); + end +end + +loglik = 0; + +msg.inter_pi_msg = inter_pi_msg; +msg.inter_lambda_msg = inter_lambda_msg; +msg.intra_lambda_msg = intra_lambda_msg;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@pearl_dbn_inf_engine/Old/filter_evidence_obj_oriented.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,158 @@ +function [marginal, msg, loglik] = filter_evidence_old(engine, evidence) +% [marginal, msg, loglik] = filter_evidence(engine, evidence) (pearl_dbn) + +[ss T] = size(evidence); +bnet = bnet_from_engine(engine); +bnet2 = dbn_to_bnet(bnet, T); +ns = bnet2.node_sizes; +hnodes = mysetdiff(1:ss, engine.onodes); +hnodes = hnodes(:)'; + +[engine.parent_index, engine.child_index] = mk_pearl_msg_indices(bnet2); + +msg = init_msgs(bnet2.dag, ns, evidence); +msg = init_ev_msgs(engine, evidence, msg); + +verbose = 1; +if verbose, fprintf('\nold filtering\n'); end + +for t=1:T + % update pi + for i=hnodes + n = i + (t-1)*ss; + ps = parents(bnet2.dag, n); + if t==1 + e = bnet.equiv_class(i,1); + else + e = bnet.equiv_class(i,2); + end + msg{n}.pi = compute_pi(bnet.CPD{e}, n, ps, msg); + %if verbose, fprintf('%d computes pi\n', n); disp(msg{n}.pi); end + msg{n}.pi = normalise(msg{n}.pi(:) .* msg{n}.lambda_from_self(:)); + if verbose, fprintf('%d recomputes pi\n', n); disp(msg{n}.pi); end + end + % send pi msg to children + for i=hnodes + n = i + (t-1)*ss; + cs = children(bnet2.dag, n); + for c=cs(:)' + j = engine.parent_index{c}(n); % n is c's j'th parent + pi_msg = normalise(compute_pi_msg(n, cs, msg, c, ns)); + msg{c}.pi_from_parent{j} = pi_msg; + if verbose, fprintf('%d sends pi to %d\n', n,c); disp(pi_msg); end + end + end +end + + +marginal = cell(ss,T); +lik = zeros(1,ss*T); +for t=1:T + for i=1:ss + n = i + (t-1)*ss; + %[bel, lik(n)] = normalise(msg{n}.pi .* msg{n}.lambda); + [bel, lik(n)] = normalise(msg{n}.pi); + marginal{i,t} = bel; + end +end + +loglik = sum(log(lik)); + + + +%%%%%%% + +function lambda = compute_lambda(n, cs, msg, ns) +% Pearl p183 eq 4.50 +lambda = prod_lambda_msgs(n, cs, msg, ns); + +%%%%%%% + +function pi_msg = compute_pi_msg(n, cs, msg, c, ns) +% Pearl p183 eq 4.53 and 4.51 +pi_msg = msg{n}.pi .* prod_lambda_msgs(n, cs, msg, ns, c); + +%%%%%%%%% + +function lam = prod_lambda_msgs(n, cs, msg, ns, except) + +if nargin < 5, except = -1; end + +%lam = msg{n}.lambda_from_self(:); +lam = ones(ns(n), 1); +for i=1:length(cs) + c = cs(i); + if c ~= except + lam = lam .* msg{n}.lambda_from_child{i}; + end +end + + +%%%%%%%%%%% + +function msg = init_msgs(dag, ns, evidence) +% INIT_MSGS Initialize the lambda/pi message and state vectors (pearl_dbn) +% msg = init_msgs(dag, ns, evidence) +% +% We assume all the hidden nodes are discrete. + +N = length(dag); +msg = cell(1,N); +observed = ~isemptycell(evidence(:)); + +for n=1:N + ps = parents(dag, n); + msg{n}.pi_from_parent = cell(1, length(ps)); + for i=1:length(ps) + p = ps(i); + msg{n}.pi_from_parent{i} = ones(ns(p), 1); + end + + cs = children(dag, n); + msg{n}.lambda_from_child = cell(1, length(cs)); + for i=1:length(cs) + c = cs(i); + msg{n}.lambda_from_child{i} = ones(ns(n), 1); + end + + msg{n}.lambda = ones(ns(n), 1); + msg{n}.pi = ones(ns(n), 1); + + msg{n}.lambda_from_self = ones(ns(n), 1); +end + + +%%%%%%%%% + +function msg = init_ev_msgs(engine, evidence, msg) +% Initialize the lambdas with any evidence + +[ss T] = size(evidence); +bnet = bnet_from_engine(engine); +pot_type = 'd'; +t = 1; +hnodes = mysetdiff(1:ss, engine.onodes); +for i=hnodes(:)' + c = engine.obschild(i); + if c > 0 + fam = family(bnet.dag, c); + e = bnet.equiv_class(c, 1); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,1)); + temp = pot_to_marginal(CPDpot); + n = i; + msg{n}.lambda_from_self = temp.T; + end +end +for t=2:T + for i=hnodes(:)' + c = engine.obschild(i); + if c > 0 + fam = family(bnet.dag, c, 2); + e = bnet.equiv_class(c, 2); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,t-1:t)); + temp = pot_to_marginal(CPDpot); + n = i + (t-1)*ss; + msg{n}.lambda_from_self = temp.T; + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@pearl_dbn_inf_engine/Old/smooth_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,181 @@ +function [marginal, msg, loglik] = smooth_evidence(engine, evidence) + +[ss T] = size(evidence); +bnet = bnet_from_engine(engine); +onodes = engine.onodes; +hnodes = mysetdiff(1:ss, onodes); +hnodes = hnodes(:)'; + +ns = bnet.node_sizes(:); +onodes2 = [onodes(:); onodes(:)+ss]; +ns(onodes2) = 1; + +verbose = 0; +pot_type = 'd'; +niter = engine.max_iter; + +if verbose, fprintf('new smooth\n'); end + +% msg(i1,t1,i2,j2) (i1,t1) -> (i2,t2) +%lambda_msg = cell(ss,T,ss,T); +%pi_msg = cell(ss,T,ss,T); + +% intra_lambda_msg(i,j,t) (i,t) -> (j,t), i is child +% inter_lambda_msg(i,j,t) (i,t+1) -> (j,t), i is child +% inter_pi_msg(i,j,t) (i,t-1) -> (j,t), i is parent +intra_lambda_msg = cell(ss,ss,T); +inter_lambda_msg = cell(ss,ss,T); +inter_pi_msg = cell(ss,ss,T); + +lambda = cell(ss,T); +pi = cell(ss,T); + +for t=1:T + for i=1:ss + lambda{i,t} = ones(ns(i), 1); + pi{i,t} = ones(ns(i), 1); + + cs = children(bnet.intra, i); + for c=cs(:)' + intra_lambda_msg{c,i,t} = ones(ns(i),1); + end + + cs = children(bnet.inter, i); + for c=cs(:)' + inter_lambda_msg{c,i,t} = ones(ns(i),1); + end + + ps = parents(bnet.inter, i); + for p=ps(:)' + inter_pi_msg{p,i,t} = ones(ns(i), 1); % not used for t==1 + end + end +end + + +% each hidden node absorbs lambda from its observed child (if any) +for t=1:T + for i=hnodes + c = engine.obschild(i); + if c > 0 + if t==1 + fam = family(bnet.dag, c); + e = bnet.equiv_class(c, 1); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,1)); + else + fam = family(bnet.dag, c, 2); % within 2 slice network + e = bnet.equiv_class(c, 2); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,t-1:t)); + end + temp = pot_to_marginal(CPDpot); + lam_msg = normalise(temp.T); + intra_lambda_msg{c,i,t} = lam_msg; + end + end +end + +for iter=1:engine.max_iter + % FORWARD + for t=1:T + % update pi + for i=hnodes + if t==1 + e = bnet.equiv_class(i,1); + CPD = struct(bnet.CPD{e}); + pi{i,t} = CPD.CPT; + else + e = bnet.equiv_class(i,2); + CPD = struct(bnet.CPD{e}); + ps = parents(bnet.inter, i); + dom = [ps i+ss]; + pot = dpot(dom, ns(dom), CPD.CPT); + for p=ps(:)' + temp = dpot(p, ns(p), inter_pi_msg{p,i,t}); + pot = multiply_by_pot(pot, temp); + end + pot = marginalize_pot(pot, i+ss); + temp = pot_to_marginal(pot); + pi{i,t} = temp.T; + end + if verbose, fprintf('%d updates pi\n', i+(t-1)*ss); disp(pi{i,t}); end + end + + % send pi msg to children + for i=hnodes + cs = children(bnet.inter, i); + for c=cs(:)' + pot = pi{i,t}; + for k=cs(:)' + if k ~= c + pot = pot .* inter_lambda_msg{k,i,t}; + end + end + cs2 = children(bnet.intra, i); + for k=cs2(:)' + pot = pot .* intra_lambda_msg{k,i,t}; + end + inter_pi_msg{i,c,t+1} = normalise(pot); + if verbose, fprintf('%d sends pi to %d\n', i+(t-1)*ss, c+t*ss); disp(inter_pi_msg{i,c,t+1}); end + end + end + end + + if verbose, fprintf('backwards\n'); end + % BACKWARD + for t=T:-1:1 + % update lambda + for i=hnodes + pot = ones(ns(i), 1); + cs = children(bnet.inter, i); + for c=cs(:)' + pot = pot .* inter_lambda_msg{c,i,t}; + end + cs = children(bnet.intra, i); + for c=cs(:)' + pot = pot .* intra_lambda_msg{c,i,t}; + end + lambda{i,t} = normalise(pot); + if verbose, fprintf('%d computes lambda\n', i+(t-1)*ss); disp(lambda{i,t}); end + end + + % send lambda msgs to hidden parents in prev slcie + for i=hnodes + ps = parents(bnet.inter, i); + if t > 1 + e = bnet.equiv_class(i, 2); + CPD = struct(bnet.CPD{e}); + fam = [ps i+ss]; + for p=ps(:)' + pot = dpot(fam, ns(fam), CPD.CPT); + temp = dpot(i+ss, ns(i), lambda{i,t}); + pot = multiply_by_pot(pot, temp); + for k=ps(:)' + if k ~= p + temp = dpot(k, ns(k), inter_pi_msg{k,i,t}); + pot = multiply_by_pot(pot, temp); + end + end + pot = marginalize_pot(pot, p); + temp = pot_to_marginal(pot); + inter_lambda_msg{i,p,t-1} = normalise(temp.T); + if verbose, fprintf('%d sends lambda to %d\n', i+(t-1)*ss, p+(t-2)*ss); disp(inter_lambda_msg{i,p,t-1}); end + end + end + end + end +end + + + +marginal = cell(ss,T); +for t=1:T + for i=hnodes + marginal{i,t} = normalise(pi{i,t} .* lambda{i,t}); + end +end + +loglik = 0; + +msg.inter_pi_msg = inter_pi_msg; +msg.inter_lambda_msg = inter_lambda_msg; +msg.intra_lambda_msg = intra_lambda_msg;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@pearl_dbn_inf_engine/Old/smooth_evidence_fast.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,179 @@ +function [marginal, msg, loglik] = smooth_evidence_fast(engine, evidence) + +[ss T] = size(evidence); +bnet = bnet_from_engine(engine); +onodes = engine.onodes; +hnodes = mysetdiff(1:ss, onodes); +hnodes = hnodes(:)'; + +ns = bnet.node_sizes(:); +onodes2 = [onodes(:); onodes(:)+ss]; +ns(onodes2) = 1; + +verbose = 0; +pot_type = 'd'; +niter = engine.max_iter; + +if verbose, fprintf('new smooth\n'); end + +% msg(i1,t1,i2,j2) (i1,t1) -> (i2,t2) +%lambda_msg = cell(ss,T,ss,T); +%pi_msg = cell(ss,T,ss,T); + +% intra_lambda_msg(i,j,t) (i,t) -> (j,t), i is child +% inter_lambda_msg(i,j,t) (i,t+1) -> (j,t), i is child +% inter_pi_msg(i,j,t) (i,t-1) -> (j,t), i is parent +intra_lambda_msg = cell(ss,ss,T); +inter_lambda_msg = cell(ss,ss,T); +inter_pi_msg = cell(ss,ss,T); + +lambda = cell(ss,T); +pi = cell(ss,T); + +for t=1:T + for i=1:ss + lambda{i,t} = ones(ns(i), 1); + pi{i,t} = ones(ns(i), 1); + + cs = children(bnet.intra, i); + for c=cs(:)' + intra_lambda_msg{c,i,t} = ones(ns(i),1); + end + + cs = children(bnet.inter, i); + for c=cs(:)' + inter_lambda_msg{c,i,t} = ones(ns(i),1); + end + + ps = parents(bnet.inter, i); + for p=ps(:)' + inter_pi_msg{p,i,t} = ones(ns(i), 1); % not used for t==1 + end + end +end + + +% each hidden node absorbs lambda from its observed child (if any) +for t=1:T + for i=hnodes + c = engine.obschild(i); + if c > 0 + if t==1 + fam = family(bnet.dag, c); + e = bnet.equiv_class(c, 1); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,1)); + else + fam = family(bnet.dag, c, 2); % within 2 slice network + e = bnet.equiv_class(c, 2); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,t-1:t)); + end + temp = pot_to_marginal(CPDpot); + lam_msg = normalise(temp.T); + intra_lambda_msg{c,i,t} = lam_msg; + end + end +end + +for iter=1:engine.max_iter + % FORWARD + for t=1:T + % update pi + for i=hnodes + if t==1 + e = bnet.equiv_class(i,1); + temp = struct(bnet.CPD{e}); + pi{i,t} = temp.CPT; + else + e = bnet.equiv_class(i,2); + CPD = struct(bnet.CPD{e}); + ps = parents(bnet.inter, i); + temp = CPD.CPT; + for p=ps(:)' + temp(:) = temp(:) .* inter_pi_msg{p,i,t}(engine.mult_parent_ndx{i,p}); + end + dom = [ps i+ss]; + pot = dpot(dom, ns(dom), temp); + pot = marginalize_pot(pot, i+ss); + temp = pot_to_marginal(pot); + pi{i,t} = temp.T; + end + if verbose, fprintf('%d updates pi\n', i+(t-1)*ss); disp(pi{i,t}); end + end + + % send pi msg to children + for i=hnodes + cs = children(bnet.inter, i); + for c=cs(:)' + pot = pi{i,t}; + for k=cs(:)' + if k ~= c + pot = pot .* inter_lambda_msg{k,i,t}; + end + end + cs2 = children(bnet.intra, i); + for k=cs2(:)' + pot = pot .* intra_lambda_msg{k,i,t}; + end + inter_pi_msg{i,c,t+1} = normalise(pot); + if verbose, fprintf('%d sends pi to %d\n', i+(t-1)*ss, c+t*ss); disp(inter_pi_msg{i,c,t+1}); end + end + end + end + + if verbose, fprintf('backwards\n'); end + % BACKWARD + for t=T:-1:1 + % update lambda + for i=hnodes + pot = ones(ns(i), 1); + cs = children(bnet.inter, i); + for c=cs(:)' + pot = pot .* inter_lambda_msg{c,i,t}; + end + cs = children(bnet.intra, i); + for c=cs(:)' + pot = pot .* intra_lambda_msg{c,i,t}; + end + lambda{i,t} = normalise(pot); + if verbose, fprintf('%d computes lambda\n', i+(t-1)*ss); disp(lambda{i,t}); end + end + + % send lambda msgs to hidden parents in prev slcie + for i=hnodes + ps = parents(bnet.inter, i); + if t > 1 + e = bnet.equiv_class(i, 2); + CPD = struct(bnet.CPD{e}); + for p=ps(:)' + temp = CPD.CPT(:) .* lambda{i,t}(engine.mult_self_ndx{i}); + for k=ps(:)' + if k ~= p + temp(:) = temp(:) .* inter_pi_msg{k,i,t}(engine.mult_parent_ndx{i,k}); + end + end + fam = [ps i+ss]; + pot = dpot(fam, ns(fam), temp); + pot = marginalize_pot(pot, p); + temp = pot_to_marginal(pot); + inter_lambda_msg{i,p,t-1} = normalise(temp.T); + if verbose, fprintf('%d sends lambda to %d\n', i+(t-1)*ss, p+(t-2)*ss); disp(inter_lambda_msg{i,p,t-1}); end + end + end + end + end +end + + + +marginal = cell(ss,T); +for t=1:T + for i=hnodes + marginal{i,t} = normalise(pi{i,t} .* lambda{i,t}); + end +end + +loglik = 0; + +msg.inter_pi_msg = inter_pi_msg; +msg.inter_lambda_msg = inter_lambda_msg; +msg.intra_lambda_msg = intra_lambda_msg;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@pearl_dbn_inf_engine/Old/wrong_smooth.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,210 @@ +function [marginal, msg, loglik] = smooth_evidence(engine, evidence) +% [marginal, msg, loglik] = smooth_evidence(engine, evidence) (pearl_dbn) + +disp('warning: pearl_dbn smoothing is broken'); + +[ss T] = size(evidence); +bnet = bnet_from_engine(engine); +bnet2 = dbn_to_bnet(bnet, T); +ns = bnet2.node_sizes; +hnodes = mysetdiff(1:ss, engine.onodes); +hnodes = hnodes(:)'; + +onodes2 = unroll_set(engine.onodes(:), ss, T); +onodes2 = onodes2(:)'; + +hnodes2 = unroll_set(hnodes(:), ss, T); +hnodes2 = hnodes2(:)'; + +[engine.parent_index, engine.child_index] = mk_pearl_msg_indices(bnet2); + +msg = init_msgs(bnet2.dag, ns, evidence, bnet2.equiv_class, bnet2.CPD); + +verbose = 0; +pot_type = 'd'; +niter = 1; +for iter=1:niter + % FORWARD + for t=1:T + if verbose, fprintf('t=%d\n', t); end + + % each hidden node absorbs lambda from its observed child (if any) + for i=hnodes + c = engine.obschild(i); + if c > 0 + if t==1 + fam = family(bnet.dag, c); + e = bnet.equiv_class(c, 1); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,1)); + else + fam = family(bnet.dag, 2); % within 2 slice network + e = bnet.equiv_class(c, 2); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,t-1:t)); + end + temp = pot_to_marginal(CPDpot); + n = i + (t-1)*ss; + lam_msg = normalise(temp.T); + j = engine.child_index{n}(c+(t-1)*ss); + assert(j==1); + msg{n}.lambda_from_child{j} = lam_msg; + if verbose, fprintf('%d sends lambda to %d\n', c + (t-1)*ss, n); disp(lam_msg); end + end + end + + % update pi + for i=hnodes + n = i + (t-1)*ss; + ps = parents(bnet2.dag, n); + if t==1 + e = bnet.equiv_class(i,1); + else + e = bnet.equiv_class(i,2); + end + msg{n}.pi = compute_pi(bnet.CPD{e}, n, ps, msg); + if verbose, fprintf('%d computes pi\n', n); disp(msg{n}.pi); end + end + + % send pi msg to children in next slice + for i=hnodes + n = i + (t-1)*ss; + %cs = myintersect(children(bnet2.dag, n), hnodes2); + cs = children(bnet2.dag, n); + for c=cs(:)' + j = engine.parent_index{c}(n); % n is c's j'th parent + pi_msg = normalise(compute_pi_msg(n, cs, msg, c, ns)); + msg{c}.pi_from_parent{j} = pi_msg; + if verbose, fprintf('%d sends pi to %d\n', n, c); disp(pi_msg); end + end + end + end + + % BACKWARD + for t=T:-1:1 + if verbose, fprintf('t = %d\n', t); end + + % update lambda + for i=hnodes + n = i + (t-1)*ss; + cs = children(bnet2.dag, n); + msg{n}.lambda = compute_lambda(n, cs, msg, ns); + if verbose, fprintf('%d computes lambda\n', n); disp(msg{n}.lambda); end + end + + % send lambda msgs to hidden parents in prev slcie + for i=hnodes + n = i + (t-1)*ss; + %ps = myintersect(parents(bnet2.dag, n), hnodes2); + ps = parents(bnet2.dag, n); + for p=ps(:)' + j = engine.child_index{p}(n); % n is p's j'th child + if t > 1 + e = bnet.equiv_class(i, 2); + else + e = bnet.equiv_class(i, 1); + end + lam_msg = normalise(compute_lambda_msg(bnet.CPD{e}, n, ps, msg, p)); + msg{p}.lambda_from_child{j} = lam_msg; + if verbose, fprintf('%d sends lambda to %d\n', n, p); disp(lam_msg); end + end + end + + % send pi msg to observed children + if 0 + for i=hnodes + n = i + (t-1)*ss; + cs = myintersect(children(bnet2.dag, n), onodes2); + %cs = children(bnet2.dag, n); + for c=cs(:)' + j = engine.parent_index{c}(n); % n is c's j'th parent + pi_msg = normalise(compute_pi_msg(n, cs, msg, c, ns)); + msg{c}.pi_from_parent{j} = pi_msg; + if verbose, fprintf('%d sends pi to %d\n', n, c); disp(pi_msg); end + end + end + end + + end +end + + +marginal = cell(ss,T); +lik = zeros(1,ss*T); +for t=1:T + for i=hnodes + n = i + (t-1)*ss; + [bel, lik(n)] = normalise(msg{n}.pi .* msg{n}.lambda); + marginal{i,t} = bel; + end +end + +loglik = 0; +%loglik = sum(log(lik)); + + + +%%%%%%% + +function lambda = compute_lambda(n, cs, msg, ns) +% Pearl p183 eq 4.50 +lambda = prod_lambda_msgs(n, cs, msg, ns); + +%%%%%%% + +function pi_msg = compute_pi_msg(n, cs, msg, c, ns) +% Pearl p183 eq 4.53 and 4.51 +pi_msg = msg{n}.pi .* prod_lambda_msgs(n, cs, msg, ns, c); + +%%%%%%%%% + +function lam = prod_lambda_msgs(n, cs, msg, ns, except) + +if nargin < 5, except = -1; end + +%lam = msg{n}.lambda_from_self(:); +lam = ones(ns(n), 1); +for i=1:length(cs) + c = cs(i); + if c ~= except + lam = lam .* msg{n}.lambda_from_child{i}; + end +end + + +%%%%%%%%% + +function msg = init_msgs(dag, ns, evidence, eclass, CPD) +% INIT_MSGS Initialize the lambda/pi message and state vectors (pearl_dbn) +% msg = init_msgs(dag, ns, evidence) + +N = length(dag); +msg = cell(1,N); +observed = ~isemptycell(evidence(:)); + +for n=1:N + ps = parents(dag, n); + msg{n}.pi_from_parent = cell(1, length(ps)); + for i=1:length(ps) + p = ps(i); + msg{n}.pi_from_parent{i} = ones(ns(p), 1); + end + + cs = children(dag, n); + msg{n}.lambda_from_child = cell(1, length(cs)); + for i=1:length(cs) + c = cs(i); + msg{n}.lambda_from_child{i} = ones(ns(n), 1); + end + + msg{n}.lambda = ones(ns(n), 1); + msg{n}.pi = ones(ns(n), 1); + + % Initialize the lambdas with any evidence + if observed(n) + v = evidence{n}; + msg{n}.lambda = zeros(ns(n), 1); + msg{n}.lambda(v) = 1; % delta function + msg{n}.lambda = []; + end + +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@pearl_dbn_inf_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,35 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (loopy_dbn) +% [engine, loglik] = enter_evidence(engine, evidence, ....) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product (not yet supported), else sum-product [0] +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) + +maximize = 0; +filter = 0; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + for i=1:2:nargs + switch args{i}, + case 'maximize', maximize = args{i+1}; + case 'filter', filter = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +assert(~maximize); +assert(~filter); + +[engine.marginal, engine.msg, loglik] = enter_soft_ev(engine, evidence); +engine.evidence = evidence; % needed by marginal_nodes and marginal_family
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@pearl_dbn_inf_engine/enter_soft_ev.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,137 @@ +function [marginal, msg, loglik] = enter_soft_ev(engine, evidence) +% [marginal, msg, loglik] = smooth_evidence(engine, evidence) (pearl_dbn) + + +[ss T] = size(evidence); +bnet = bnet_from_engine(engine); +bnet2 = dbn_to_bnet(bnet, T); +ns = bnet2.node_sizes; +hnodes = mysetdiff(1:ss, engine.onodes); +hnodes = hnodes(:)'; + +onodes2 = unroll_set(engine.onodes(:), ss, T); +onodes2 = onodes2(:)'; + +hnodes2 = unroll_set(hnodes(:), ss, T); +hnodes2 = hnodes2(:)'; + +[engine.parent_index, engine.child_index] = mk_pearl_msg_indices(bnet2); + +rand_init = 0; +use_ev = 0; +msg = init_pearl_msgs(bnet2.dag, ns, evidence, rand_init, use_ev); +msg = init_pearl_dbn_ev_msgs(bnet, evidence, engine); + +verbose = 0; +pot_type = 'd'; +niter = engine.max_iter; + +if verbose, fprintf('old smooth\n'); end + +for iter=1:niter + % FORWARD + for t=1:T + if verbose, fprintf('t=%d\n', t); end + + % update pi + for i=hnodes + n = i + (t-1)*ss; + ps = parents(bnet2.dag, n); + if t==1 + e = bnet.equiv_class(i,1); + else + e = bnet.equiv_class(i,2); + end + msg{n}.pi = compute_pi(bnet.CPD{e}, n, ps, msg); + if verbose, fprintf('%d computes pi\n', n); disp(msg{n}.pi); end + end + + % send pi msg to children + for i=hnodes + n = i + (t-1)*ss; + %cs = myintersect(children(bnet2.dag, n), hnodes2); + cs = children(bnet2.dag, n); % must use all children to get index right + for c=cs(:)' + j = engine.parent_index{c}(n); % n is c's j'th parent + pi_msg = normalise(compute_pi_msg(n, cs, msg, c, ns)); + msg{c}.pi_from_parent{j} = pi_msg; + if verbose, fprintf('%d sends pi to %d\n', n, c); disp(pi_msg); end + end + end + end + + % BACKWARD + for t=T:-1:1 + if verbose, fprintf('t = %d\n', t); end + + % update lambda + for i=hnodes + n = i + (t-1)*ss; + cs = children(bnet2.dag, n); + msg{n}.lambda = compute_lambda(n, cs, msg, ns); + if verbose, fprintf('%d computes lambda\n', n); disp(msg{n}.lambda); end + end + + % send lambda msgs to hidden parents in prev slcie + for i=hnodes + n = i + (t-1)*ss; + ps = parents(bnet2.dag, n); + for p=ps(:)' + j = engine.child_index{p}(n); % n is p's j'th child + if t > 1 + e = bnet.equiv_class(i, 2); + else + e = bnet.equiv_class(i, 1); + end + lam_msg = normalise(compute_lambda_msg(bnet.CPD{e}, n, ps, msg, p)); + msg{p}.lambda_from_child{j} = lam_msg; + if verbose, fprintf('%d sends lambda to %d\n', n, p); disp(lam_msg); end + end + end + + end +end + + +marginal = cell(ss,T); +lik = zeros(1,ss*T); +for t=1:T + for i=hnodes + n = i + (t-1)*ss; + [bel, lik(n)] = normalise(msg{n}.pi .* msg{n}.lambda); + marginal{i,t} = bel; + end +end + +loglik = 0; +%loglik = sum(log(lik)); + + + +%%%%%%% + +function lambda = compute_lambda(n, cs, msg, ns) +% Pearl p183 eq 4.50 +lambda = prod_lambda_msgs(n, cs, msg, ns); + +%%%%%%% + +function pi_msg = compute_pi_msg(n, cs, msg, c, ns) +% Pearl p183 eq 4.53 and 4.51 +pi_msg = msg{n}.pi .* prod_lambda_msgs(n, cs, msg, ns, c); + +%%%%%%%%% + +function lam = prod_lambda_msgs(n, cs, msg, ns, except) + +if nargin < 5, except = -1; end + +%lam = msg{n}.lambda_from_self(:); +lam = ones(ns(n), 1); +for i=1:length(cs) + c = cs(i); + if c ~= except + lam = lam .* msg{n}.lambda_from_child{i}; + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@pearl_dbn_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,18 @@ +function marginal = marginal_nodes(engine, nodes, t) +% MARGINAL_NODES Compute the marginal on the specified query nodes (pearl_dbn) +% marginal = marginal_nodes(engine, i, t) +% returns Pr(X(i,t) | Y(1:T)), where X(i,t) is the i'th node in the t'th slice. +% If enter_evidence used filtering instead of smoothing, this will return Pr(X(i,t) | Y(1:t)). + +if nargin < 3, t = 1; end +assert(length(nodes)==1); +i = nodes(end); +if ~myismember(i, engine.onodes) + marginal.T = engine.marginal{i,t}; +else + marginal.T = 1; % observed +end + +% we convert the domain to the unrolled numbering system +% so that update_ess extracts the right evidence. +marginal.domain = nodes+(t-1)*engine.ss;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@pearl_dbn_inf_engine/pearl_dbn_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,65 @@ +function engine = pearl_dbn_inf_engine(bnet, varargin) +% LOOPY_DBN_INF_ENGINE Loopy Pearl version of forwards-backwards +% engine = loopy_dbn_inf_engine(bnet, ...) +% +% Optional arguments +% 'max_iter' - specifies the max num. forward-backward passes to perform [1] +% 'tol' - as in loopy_pearl [1e-3] +% 'momentum' - as in loopy_pearl [0] + +error('pearl_dbn does not work yet') + +max_iter = 1; +tol = 1e-3; +momentum = 0; + +if nargin >= 2 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'max_iter', max_iter = args{i+1}; + case 'tol', tol = args{i+1}; + case 'momentum', momentum = args{i+1}; + end + end +end + + +engine.max_iter = max_iter; +engine.tol = tol; +engine.momentum = momentum; +engine.pearl_engine = []; +engine.T = []; +engine.ss = length(bnet.intra); + +engine.marginal = []; +engine.evidence = []; +engine.msg = []; +engine.parent_index = []; +engine.child_index = []; +%[engine.parent_index, engine.child_index] = mk_pearl_msg_indices(bnet); % need to unroll first + +ss = length(bnet.intra); +engines.ss = ss; +onodes = bnet.observed; +hnodes = mysetdiff(1:ss, onodes); +obschild = zeros(1,ss); +for i=hnodes(:)' + %ocs = myintersect(children(bnet.dag, i), onodes); + ocs = children(bnet.intra, i); + assert(length(ocs) <= 1); + if length(ocs)==1 + obschild(i) = ocs(1); + end +end +engine.obschild = obschild; + +engine.mult_self_ndx = []; +engine.mult_parent_ndx = []; +engine.marg_self_ndx = []; +engine.marg_parent_ndx = []; + + +engine = class(engine, 'loopy_dbn_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@pearl_dbn_inf_engine/private/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +/init_pearl_dbn_ev_msgs.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@pearl_dbn_inf_engine/private/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@pearl_dbn_inf_engine/private
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@pearl_dbn_inf_engine/private/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@pearl_dbn_inf_engine/private/init_pearl_dbn_ev_msgs.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,28 @@ +function msg = init_pearl_dbn_ev_msgs(bnet, evidence, engine) + +[ss T] = size(evidence); +pot_type = 'd'; + +% each hidden node absorbs lambda from its observed child (if any) +for t=1:T + for i=hnodes + c = engine.obschild(i); + if c > 0 + if t==1 + fam = family(bnet.dag, c); + e = bnet.equiv_class(c, 1); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,1)); + else + fam = family(bnet.dag, c, 2); % within 2 slice network + e = bnet.equiv_class(c, 2); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,t-1:t)); + end + temp = pot_to_marginal(CPDpot); + n = i + (t-1)*ss; + lam_msg = normalise(temp.T); + j = engine.child_index{n}(c+(t-1)*ss); + assert(j==1); + msg{n}.lambda_from_child{j} = lam_msg; + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@pearl_unrolled_dbn_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/pearl_unrolled_dbn_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/update_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@pearl_unrolled_dbn_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@pearl_unrolled_dbn_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@pearl_unrolled_dbn_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@pearl_unrolled_dbn_inf_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,41 @@ +function [engine, loglik, niter] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (loopy_unrolled_dbn) +% [engine, loglik, niter] = enter_evidence(engine, evidence, ....) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product (not yet supported), else sum-product [0] +% filename - as in loopy_pearl +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) + +maximize = 0; +filename = engine.filename; + +if nargin >= 2 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'maximize', maximize = args{i+1}; + case 'filename', filename = args{i+1}; + end + end +end + + +[ss T] = size(evidence); +if T ~= engine.T + bnetT = dbn_to_bnet(bnet_from_engine(engine), T); + engine.unrolled_engine = pearl_inf_engine(bnetT, 'protocol', engine.protocol, ... + 'max_iter', engine.max_iter_per_slice * T, ... + 'tol', engine.tol, 'momentum', engine.momentum); + engine.T = T; +end +[engine.unrolled_engine, loglik, niter] = enter_evidence(engine.unrolled_engine, evidence(:), ... + 'maximize', maximize, 'filename', filename); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@pearl_unrolled_dbn_inf_engine/marginal_family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function marginal = marginal_family(engine, i, t, add_ev) +% MARGINAL_FAMILY Compute the marginal on the specified family (jtree_unrolled_dbn) +% marginal = marginal_family(engine, i, t) + +if nargin < 3, t = 1; end +if nargin < 4, add_ev = 0; end +assert(~add_ev); + +%marginal = marginal_family(engine.unrolled_engine, i + (t-1)*engine.ss, add_ev); +marginal = marginal_family(engine.unrolled_engine, i + (t-1)*engine.ss); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@pearl_unrolled_dbn_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +function marginal = marginal_nodes(engine, nodes, t, add_ev) +% MARGINAL_NODES Compute the marginal on the specified query nodes (loopy_unrolled_dbn) +% marginal = marginal_nodes(engine, nodes, t) +% +% 't' specifies the time slice of the earliest node in 'nodes'. +% 'nodes' must occur in some clique. +% +% Example: +% Consider a DBN with 2 nodes per slice. +% Then t=2, nodes=[1 3] refers to node 1 in slice 2 and node 1 in slice 3, +% i.e., nodes 3 and 5 in the unrolled network, + +if nargin < 3, t = 1; end +if nargin < 4, add_ev = 0; end + +marginal = marginal_nodes(engine.unrolled_engine, nodes + (t-1)*engine.ss, add_ev);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@pearl_unrolled_dbn_inf_engine/pearl_unrolled_dbn_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,39 @@ +function engine = pearl_unrolled_dbn_inf_engine(bnet, varargin) +% LOOPY_DBN_INF_ENGINE Loopy Pearl version of forwards-backwards +% engine = loopy_unrolld_dbn_inf_engine(bnet, ...) +% +% Optional arguments +% 'max_iter' - specifies the max num. forward-backward passes to perform PER SLICE [2] +% 'tol' - as in loopy_pearl [1e-3] +% 'momentum' - as in loopy_pearl [0] +% protocol - tree or parallel [parallel] +% filename - as in pearl [ '' ] + +max_iter_per_slice = 2; +tol = 1e-3; +momentum = 0; +protocol = 'parallel'; +filename = ''; + +args = varargin; +for i=1:2:length(args) + switch args{i}, + case 'max_iter', max_iter_per_slice = args{i+1}; + case 'tol', tol = args{i+1}; + case 'momentum', momentum = args{i+1}; + case 'protocol', protocol = args{i+1}; + case 'filename', filename = args{i+1}; + end +end + +engine.filename = filename; +engine.max_iter_per_slice = max_iter_per_slice; +engine.tol = tol; +engine.momentum = momentum; +engine.unrolled_engine = []; +engine.T = -1; +engine.ss = length(bnet.intra); +engine.protocol = protocol; + +engine = class(engine, 'pearl_unrolled_dbn_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@pearl_unrolled_dbn_inf_engine/update_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +function engine = update_engine(engine, newCPDs) +% UPDATE_ENGINE Update the engine to take into account the new parameters (pearl_unrolled_dbn) +% engine = update_engine(engine, newCPDs) + +engine.inf_engine = update_engine(engine.inf_engine, newCPDs); +engine.unrolled_engine = update_engine(engine.unrolled_engine, newCPDs);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@stable_ho_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +/enter_evidence.m/1.1.1.1/Wed Feb 19 09:52:12 2003// +/marginal_family.m/1.1.1.1/Wed Feb 19 09:52:12 2003// +/marginal_nodes.m/1.1.1.1/Wed Feb 19 09:52:12 2003// +/stable_ho_inf_engine.m/1.1.1.1/Fri Mar 14 09:45:34 2003// +/test_ho_inf_enginge.m/1.1.1.1/Wed Feb 19 09:52:12 2003// +/update_engine.m/1.1.1.1/Wed Feb 19 09:52:12 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@stable_ho_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@stable_ho_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@stable_ho_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@stable_ho_inf_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,43 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (jtree_unrolled_dbn) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product instead of sum-product [0] +% filter - if 1, does filtering (not supported), else smoothing [0] +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) + +maximize = 0; +filter = 0; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + for i=1:2:nargs + switch args{i}, + case 'maximize', maximize = args{i+1}; + case 'filter', filter = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +if filter + error('jtree_unrolled_dbn does not support filtering') +end + +if size(evidence,2) ~= engine.nslices + error(['engine was created assuming there are ' num2str(engine.nslices) ... + ' slices, but evidence has ' num2str(size(evidence,2))]) +end + +[engine.unrolled_engine, loglik] = enter_evidence(engine.unrolled_engine, evidence, 'maximize', maximize); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@stable_ho_inf_engine/marginal_family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function marginal = marginal_family(engine, i, t, add_ev) +% MARGINAL_FAMILY Compute the marginal on the specified family (jtree_unrolled_dbn) +% marginal = marginal_family(engine, i, t) + +if nargin < 3, t = 1; end +if nargin < 4, add_ev = 0; end +assert(~add_ev); + +%marginal = marginal_family(engine.unrolled_engine, i + (t-1)*engine.ss, add_ev); +marginal = marginal_family(engine.unrolled_engine, i + (t-1)*engine.ss); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@stable_ho_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +function marginal = marginal_nodes(engine, nodes, t, add_ev) +% MARGINAL_NODES Compute the marginal on the specified query nodes (loopy_unrolled_dbn) +% marginal = marginal_nodes(engine, nodes, t) +% +% 't' specifies the time slice of the earliest node in 'nodes'. +% 'nodes' must occur in some clique. +% +% Example: +% Consider a DBN with 2 nodes per slice. +% Then t=2, nodes=[1 3] refers to node 1 in slice 2 and node 1 in slice 3, +% i.e., nodes 3 and 5 in the unrolled network, + +if nargin < 3, t = 1; end +if nargin < 4, add_ev = 0; end + +marginal = marginal_nodes(engine.unrolled_engine, nodes + (t-1)*engine.ss, add_ev);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@stable_ho_inf_engine/stable_ho_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,67 @@ +function engine = dv_unrolled_dbn_inf_engine(bnet, T, varargin) +% JTREE_UNROLLED_DBN_INF_ENGINE Unroll the DBN for T time-slices and apply jtree to the resulting static net +% engine = jtree_unrolled_dbn_inf_engine(bnet, T, ...) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% useC - 1 means use jtree_C_inf_engine instead of jtree_inf_engine [0] +% constrained - 1 means we constrain ourselves to eliminate slice t before t+1 [1] +% +% e.g., engine = jtree_unrolled_inf_engine(bnet, 'useC', 1); + +% set default params +N = length(bnet.intra); +useC = 0; +constrained = 1; + +if nargin >= 3 + args = varargin; + nargs = length(args); + if isstr(args{1}) + for i=1:2:nargs + switch args{i}, + case 'useC', useC = args{i+1}; + case 'constrained', constrained = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end + else + error(['invalid argument name ' args{1}]); + end +end + +bnet2 = hodbn_to_bnet(bnet, T); +ss = length(bnet.intra); +engine.ss = ss; + +% If constrained_order = 1 we constrain ourselves to eliminate slice t before t+1. +% This prevents cliques containing nodes from far-apart time-slices. +if constrained + stages = num2cell(unroll_set(1:ss, ss, T), 1); +else + stages = { 1:length(bnet2.dag) }; +end +if useC + %jengine = jtree_C_inf_engine(bnet2, 'stages', stages); + %function is not implemented + assert(0) +else + jengine = stab_cond_gauss_inf_engine(bnet2); +end + +engine.unrolled_engine = jengine; +% we don't inherit from jtree_inf_engine, because that would only store bnet2, +% and we would lose access to the DBN-specific fields like intra/inter + +engine.nslices = T; +engine = class(engine, 'stable_ho_inf_engine', inf_engine(bnet)); + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@stable_ho_inf_engine/test_ho_inf_enginge.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,87 @@ +function [engine,engine2] = test_ho_inf_enginge(order,T) + +assert(order >= 1) +% Model a SISO system, i. e. all node are one-dimensional +% The nodes are numbered as follows +% u(t) = 1 input +% y(t) = 2 model output +% z(t) = 3 noise +% q(t) = 4 observed output = noise + model output + +ns = [1 1 1 1]; + +% Model a linear system, i.e. there are no discrete nodes +dn = []; + +% Modeling of connections within a time slice +intra = zeros(4); +intra(2,4) = 1; % Connection y(t) -> q(t) +intra(3,4) = 1; % Connection z(t) -> q(t) + +% Connections to the next time slice +inter = zeros(4,4,order); +inter(1,2,1) = 1; % u(t) -> y(t+1); +inter(2,2,1) = 1; %y(t) -> y(t+1); +inter(3,3,1) = 1; %z(t) -> z(t+1); + +if order >= 2 + inter(1,2,2) = 1; % u(t) -> y(t+2); + inter(2,2,2) = 1; % y(t) -> y(t+2); +end + +for i = 3: order + inter(:,:,i) = inter(:,:,i-1); %u(t) -> y(t+i) y(t) -> y(t) +i +end; + + +% Compution of a higer order Markov Model +bnet = mk_higher_order_dbn(intra,inter,ns,'discrete',dn); +bnet2 = mk_dbn(intra,inter(:,:,1),ns,'discrete',dn) + + +%Calculation of the number of nodes with different parameters +%There is one input and one output nodes 2 +%There are two different disturbance node 2 +%There are order +1 nodes for y 1 + order +numOfNodes = 5 + order; + +% First input node +bnet.CPD{1} = gaussian_CPD(bnet,1,'mean',0); +bnet2.CPD{1} = gaussian_CPD(bnet,1,'mean',0); +% Modeled output +bnet.CPD{2} = gaussian_CPD(bnet,2,'mean',0); +bnet2.CPD{2} = gaussian_CPD(bnet,2,'mean',0); +%Disturbance +bnet.CPD{3} = gaussian_CPD(bnet,3,'mean',0); +bnet2.CPD{3} = gaussian_CPD(bnet,3,'mean',0); + +%Qutput +bnet.CPD{4} = gaussian_CPD(bnet,4,'mean',0); +bnet2.CPD{4} = gaussian_CPD(bnet,4,'mean',0); + + +%Output node in the second time-slice +%Remember that node number 6 is an example for +%the fifth equivalence class +bnet.CPD{5} = gaussian_CPD(bnet,6,'mean',0); +bnet2.CPD{5} = gaussian_CPD(bnet,6,'mean',0); + +%Disturbance node in the second time slice +bnet.CPD{6} = gaussian_CPD(bnet,7,'mean',0); +bnet2.CPD{6} = gaussian_CPD(bnet,7,'mean',0); + +% Modeling of the remaining nodes for y +for i = 7:numOfNodes + bnet.CPD{i} = gaussian_CPD(bnet,(i - 6)*4 + 7,'mean',0); +end + +% Generation of the inference engine +engine = dv_unrolled_dbn_inf_engine(bnet,T); +engine2 = jtree_unrolled_dbn_inf_engine(bnet,T); + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/@stable_ho_inf_engine/update_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +function engine = update_engine(engine, newCPDs) +% UPDATE_ENGINE Update the engine to take into account the new parameters (jtree_unrolled_dbn) +% engine = update_engine(engine, newCPDs) + +engine.inf_engine = update_engine(engine.inf_engine, newCPDs); +engine.unrolled_engine = update_engine(engine.unrolled_engine, newCPDs); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +/dummy/1.1.1.1/Sat Jan 18 22:22:28 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +A D/@bk_ff_hmm_inf_engine//// +A D/@bk_inf_engine//// +A D/@cbk_inf_engine//// +A D/@ff_inf_engine//// +A D/@frontier_inf_engine//// +A D/@hmm_inf_engine//// +A D/@jtree_dbn_inf_engine//// +A D/@jtree_unrolled_dbn_inf_engine//// +A D/@kalman_inf_engine//// +A D/@pearl_dbn_inf_engine//// +A D/@pearl_unrolled_dbn_inf_engine//// +A D/@stable_ho_inf_engine////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/dynamic/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@filter_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +/bnet_from_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/filter_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@filter_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/online/@filter_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@filter_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@filter_engine/bnet_from_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function bnet = bnet_from_engine(engine) +% BNET_FROM_ENGINE Return the bnet structure stored inside the engine (smoother_engine) +% bnet = bnet_from_engine(engine) + +bnet = bnet_from_engine(engine.tbn_engine);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@filter_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,14 @@ +function [engine, LL] = enter_evidence(engine, ev, t) +% ENTER_EVIDENCE Call the online filter +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i} = [] if if X(i) is hidden, and otherwise contains its observed value (scalar or column vector) + +engine.old_f = engine.f; +if t==1 + [engine.f, LL] = fwd1(engine.tbn_engine, ev, 1); +else + [engine.f, LL] = fwd(engine.tbn_engine, engine.old_f, ev, t); +end +engine.b = backT(engine.tbn_engine, engine.f, t); +engine.t = t;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@filter_engine/filter_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,10 @@ +function engine = filter_engine(tbn_engine) +% FILTER_ENGINE Create an engine which does online filtering +% function engine = filter_engine(tbn_engine) + +engine.tbn_engine = tbn_engine; +engine.f = []; % space to store filtered message +engine.old_f = []; +engine.b = []; % space to store smoothed message +engine.t = []; +engine = class(engine, 'filter_engine');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@filter_engine/marginal_family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function marginal = marginal_family(engine, i, t, add_ev) +% MARGINAL_FAMILY Compute the joint distribution on a set of family (filter_engine) +% function marginal = marginal_family(engine, i, t, add_ev) + +if nargin < 4, add_ev = 0; end + +if t ~= engine.t + error('mixed up time stamps') +end + +marginal = marginal_family(engine.tbn_engine, engine.b, i, t, add_ev);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@filter_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,10 @@ +function marginal = marginal_nodes(engine, nodes, t, add_ev) +% MARGINAL_NODES Compute the joint distribution on a set of nodes (filter_engine) +% function marginal = marginal_nodes(engine, nodes, t, add_ev) + +if nargin < 4, add_ev = 0; end + +if t ~= engine.t + error('mixed up time stamps') +end +marginal = marginal_nodes(engine.tbn_engine, engine.b, nodes, t, add_ev);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@hmm_2TBN_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,9 @@ +/back.m/1.1.1.1/Wed May 29 15:59:56 2002// +/backT.m/1.1.1.1/Wed May 29 15:59:56 2002// +/fwd.m/1.1.1.1/Wed May 29 15:59:56 2002// +/fwd1.m/1.1.1.1/Wed May 29 15:59:56 2002// +/hmm_2TBN_inf_engine.m/1.1.1.1/Thu Nov 14 20:03:50 2002// +/marginal_family.m/1.1.1.1/Thu Nov 14 20:05:36 2002// +/marginal_nodes.m/1.1.1.1/Thu Nov 14 20:02:46 2002// +/update_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@hmm_2TBN_inf_engine/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +A D/private////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@hmm_2TBN_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/online/@hmm_2TBN_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@hmm_2TBN_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@hmm_2TBN_inf_engine/back.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,21 @@ +function b = back(engine, bfuture, f, t) + +if f.t ~= t + error('mixed up time stamps') +end + +b.t = t; +b.obslik = f.obslik; +bb_future = bfuture.beta .* bfuture.obslik; +if engine.maximize + B = repmat(bb_future(:)', length(bfuture.beta), 1); + b.beta = normalise(max(engine.transprob .* B, [], 2)); +else + b.beta = normalise((engine.transprob * bb_future)); +end +b.gamma = normalise(f.alpha .* b.beta); +if t > 1 + bb_t = b.beta .* b.obslik; + b.xi = normalise((engine.transprob .* (f.past_alpha * bb_t'))); % t-1,t +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@hmm_2TBN_inf_engine/backT.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function b = backT(engine, f, t) + +b.t = t; +b.obslik = f.obslik; +Q = length(f.alpha); +b.beta = ones(Q,1); +b.gamma = f.alpha; +if t > 1 + bb_t = b.obslik; + b.xi = normalise((engine.transprob .* (f.past_alpha * bb_t'))); % T-1,T +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@hmm_2TBN_inf_engine/fwd.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,17 @@ +function [f, logscale] = fwd(engine, fpast, ev, t) +% Forwards pass. + +f.obslik = mk_hmm_obs_lik_vec(engine, ev); +transmat = engine.transprob; +f.past_alpha = fpast.alpha; +if engine.maximize + Q = length(fpast.alpha); + A = repmat(fpast.alpha, [1 Q]); + m = max(transmat .* A, [], 1); + [f.alpha, scale] = normalise(m(:) .* f.obslik); +else + [f.alpha, scale] = normalise((transmat' * fpast.alpha) .* f.obslik); +end +logscale = log(scale); +%f.xi = normalise((fpast.alpha * obslik') .* transmat); % t-1,t +f.t = t;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@hmm_2TBN_inf_engine/fwd1.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function [f, logscale] = fwd1(engine, ev, t) +% Forwards pass for slice 1. + +if t ~= 1 + error('mixed up time stamps') +end +prior = engine.startprob(:); +f.obslik = mk_hmm_obs_lik_vec(engine, ev); +[f.alpha, lik] = normalise(prior .* f.obslik); +logscale = log(lik); +f.t = t;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@hmm_2TBN_inf_engine/hmm_2TBN_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,86 @@ +function engine = hmm_2TBN_inf_engine(bnet, varargin) +% HMM_2TBN_INF_ENGINE Inference engine for DBNs which uses the forwards-backwards algorithm. +% engine = hmm_2TBN_inf_engine(bnet, ...) +% +% The DBN is converted to an HMM with a single meganode, but the observed nodes remain factored. +% This can be faster than jtree if the num. hidden nodes is low, because of lower constant factors. +% +% All hidden nodes must be discrete. +% All observed nodes are assumed to be leaves. +% The parents of each observed leaf are assumed to be a subset of the hidden nodes within the same slice. +% The only exception is if bnet is an AR-HMM, where the parents are assumed to be self in the +% previous slice (continuous), plus all the discrete nodes in the current slice. + + +%% Optional arguments +%% ndx_type - 'B', 'D', or 'SD', used in marginal_family [ 'SD' ] + +ndx_type = 'SD'; +ss = bnet.nnodes_per_slice; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + for i=1:2:nargs + switch args{i}, + %case 'ndx_type', ndx_type = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +% Stuff to do with speeding up marginal_family +%engine.ndx_type = ndx_type; + +[int, engine.persist, engine.transient] = compute_interface_nodes(bnet.intra, bnet.inter); +engine.persist_bitv = zeros(1, ss); +engine.persist_bitv(engine.persist) = 1; + + +ns = bnet.node_sizes(:); +ns(bnet.observed) = 1; +ns(bnet.observed+ss) = 1; +engine.eff_node_sizes = ns; + +% for n=1:ss +% dom = 1:(2*ss); % domain of xi(:,:,1) +% fam = family(bnet.dag, n+ss); +% engine.marg_fam2_ndx_id(n) = add_ndx(dom, fam, ns, ndx_type); + +% dom = 1:ss; % domain of gamma(:,:,1) +% fam = family(bnet.dag, n); +% engine.marg_fam1_ndx_id(n) = add_ndx(dom, fam, ns, ndx_type); + +% engine.marg_singleton_ndx_id(n) = add_ndx(dom, n, ns, ndx_type); +% end + +for o=bnet.observed(:)' + %if bnet.equiv_class(o,1) ~= bnet.equiv_class(o,2) + % error(['observed node ' num2str(o) ' is not tied']) + %end + cs = children(bnet.dag, o); + if ~isempty(cs) + error(['observed node ' num2str(o) ' is not allowed children']) + end +end + +[engine.startprob, engine.transprob, engine.obsprob] = dbn_to_hmm(bnet); + +% This is where we will store the results between enter_evidence and marginal_nodes +engine.one_slice_marginal = []; +engine.two_slice_marginal = []; + +ss = length(bnet.intra); +engine.maximize = []; +engine.evidence = []; +engine.node_sizes = []; + +% avoid the need to do bnet_from_engine, which is slow +engine.slice_size = ss; +engine.parents = bnet.parents; + +engine.bel = []; +engine = class(engine, 'hmm_2TBN_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@hmm_2TBN_inf_engine/marginal_family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,35 @@ +function marginal = marginal_family(engine, b, i, t, add_ev) +% MARGINAL_FAMILY Compute the marginal on the specified family (hmm_2TBN) +% marginal = marginal_family(engine, b, i, t, add_ev) + +ns = engine.eff_node_sizes(:); +ss = engine.slice_size; + +if t==1 % | ~engine.persist_bitv(i) + bigT = b.gamma; + ps = engine.parents{i}; + dom = [ps i]; + %id = engine.marg_fam1_ndx_id(i); + bigdom = 1:ss; + bigsz = ns(bigdom); + bigdom = bigdom + (t-1)*ss; +else % some parents are in previous slice + bigT = b.xi; % (t-1,t) + ps = engine.parents{i+ss}; + dom = [ps i+ss] + (t-2)*ss; + %id = engine.marg_fam2_ndx_id(i); + bigdom = 1:(2*ss); % domain of xi(:,:,t) + bigsz = ns(bigdom); + bigdom = bigdom + (t-2)*ss; +end +marginal.domain = dom; + +%ndx = get_ndx(id, engine.ndx_type); +%marginal.T = marg_table_ndx(bigT, engine.maximize, ndx, engine.ndx_type); +%global SD_NDX +%ndx = SD_NDX{id}; +%marginal.T = marg_table_ndxSD(bigT, engine.maximize, ndx); +marginal.T = marg_table(bigT, bigdom, bigsz, dom, engine.maximize); + +assert(~add_ev); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@hmm_2TBN_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,27 @@ +function marginal = marginal_nodes(engine, b, nodes, t, add_ev) +% MARGINAL_NODES Compute the marginal on the specified nodes (hmm_2TBN) +% marginal = marginal_nodes(engine, b, nodes, t, add_ev) +% +% nodes must be a singleton set + +assert(length(nodes)==1) +ss = engine.slice_size; + +i = nodes(1); +bigT = b.gamma; +dom = i + (t-1)*ss; + +%id = engine.marg_singleton_ndx_id(i); +%global SD_NDX +%ndx = SD_NDX{id}; +%marginal.T = marg_table_ndxSD(bigT, engine.maximize, ndx); + +ns = engine.eff_node_sizes(:); +bigdom = 1:ss; +marginal.T = marg_table(bigT, bigdom + (t-1)*ss, ns(bigdom), dom, engine.maximize); + +marginal.domain = dom; +assert(~add_ev); +%if add_ev +% marginal = add_ev_to_dmarginal(marginal, engine.evidence, engine.node_sizes); +%end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@hmm_2TBN_inf_engine/private/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +/mk_hmm_obs_lik_vec.m/1.1.1.1/Sun May 4 21:47:44 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@hmm_2TBN_inf_engine/private/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/online/@hmm_2TBN_inf_engine/private
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@hmm_2TBN_inf_engine/private/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@hmm_2TBN_inf_engine/private/mk_hmm_obs_lik_vec.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,53 @@ +function obslik = mk_hmm_obs_lik_vec(engine, evidence) + +% P(o1,o2| h) = P(o1|h) * P(o2|h) where h = Q1,Q2,... + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +onodes = bnet.observed; +hnodes = mysetdiff(1:ss, onodes); +ns = bnet.node_sizes(:); +ns(onodes) = 1; + +Q = length(engine.startprob); +obslik = ones(Q, 1); + +for i=1:length(onodes) + o = onodes(i); + %data = cell2num(evidence(o,1)); + data = evidence{o,1}; + if myismember(o, bnet.dnodes) + %obslik_i = eval_pdf_cond_multinomial(data, engine.obsprob{i}.CPT); + obslik_i = multinomial_prob(data, engine.obsprob{i}.CPT); + else + if bnet.auto_regressive(o) + error('can''t handle AR nodes') + end + %% calling mk_ghmm_obs_lik, which calls gaussian_prob, is slow, so we inline it + %% and use the pre-computed inverse matrix + %obslik_i = mk_ghmm_obs_lik(data, engine.obsprob{i}.mu, engine.obsprob{i}.Sigma); + x = data(:); + m = engine.obsprob{i}.mu; + Qi = size(m, 2); + obslik_i = size(Qi, 1); + invC = engine.obsprob{i}.inv_Sigma; + denom = engine.obsprob{i}.denom; + for j=1:Qi + numer = exp(-0.5 * (x-m(:,j))' * invC(:,:,j) * (x-m(:,j))); + obslik_i(j) = numer / denom(j); + end + end + % convert P(o|ps) into P(o|h) by multiplying onto a (h,o) potential of all 1s + ps = bnet.parents{o}; + dom = [ps o]; + obspot_i = dpot(dom, ns(dom), obslik_i); + dom = [hnodes o]; + obspot = dpot(dom, ns(dom)); + obspot = multiply_by_pot(obspot, obspot_i); + % compute p(oi|h) * p(oj|h) + S = struct(obspot); + obslik = obslik .* S.T(:); +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@hmm_2TBN_inf_engine/update_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +function engine = update_engine(engine, newCPDs) +% UPDATE_ENGINE Update the engine to take into account the new parameters (hmm) +% engine = update_engine(engine, newCPDs) + +%engine.inf_engine.bnet.CPD = newCPDs; +engine.inf_engine = update_engine(engine.inf_engine, newCPDs); +[engine.startprob, engine.transprob, engine.obsprob] = dbn_to_hmm(bnet_from_engine(engine)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_2TBN_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +/back.m/1.1.1.1/Mon Jun 17 23:34:12 2002// +/back1.m/1.1.1.1/Mon Jun 17 23:34:26 2002// +/back1_mpe.m/1.1.1.1/Mon Jun 17 23:49:40 2002// +/backT.m/1.1.1.1/Mon Jun 17 23:34:20 2002// +/backT_mpe.m/1.1.1.1/Mon Jun 17 23:38:56 2002// +/back_mpe.m/1.1.1.1/Sun Jul 21 00:32:52 2002// +/fwd.m/1.1.1.1/Mon Jun 17 23:46:06 2002// +/fwd1.m/1.1.1.1/Mon Jun 17 23:46:20 2002// +/jtree_2TBN_inf_engine.m/1.1.1.1/Thu Nov 14 16:31:58 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/set_fields.m/1.1.1.1/Sun Jul 21 01:25:30 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_2TBN_inf_engine/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_2TBN_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/online/@jtree_2TBN_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_2TBN_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_2TBN_inf_engine/Old/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +/jtree_2TBN_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_2TBN_inf_engine/Old/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/online/@jtree_2TBN_inf_engine/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_2TBN_inf_engine/Old/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_2TBN_inf_engine/Old/jtree_2TBN_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,116 @@ +function engine = jtree_2TBN_inf_engine(bnet, varargin) +% JTREE_ONLINE_INF_ENGINE Online Junction tree inference algorithm for DBNs. +% engine = jtree_online_inf_engine(bnet, ...) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% clusters - specifies variables that must be grouped in the 1.5 slice DBN +% maximize - 1 means do max-product, 0 means sum-product [0] +% +% The same nodes must be observed in every slice. + +ss = length(bnet.intra); +clusters = {}; +engine.maximize = 0; + +args = varargin; +nargs = length(args); +for i=1:2:length(args) + switch args{i}, + case 'clusters', clusters = args{i+1}; + case 'maximize', engine.maximize = args{i+1}; + otherwise, error(['unrecognized argument ' args{i}]) + end +end + +engine.evidence = []; +engine.node_sizes = []; + +%int = compute_interface_nodes(bnet.intra, bnet.inter); +int = []; + +if 1 +% include nodes with any outgoing arcs +for u=1:ss + if any(bnet.inter(u,:)) + int = [int u]; + end +end +end + +if 0 +% include nodes with any incoming arcs +incoming = []; +for u=1:ss + if any(bnet.inter(:,u)) + int = [int u]; + incoming = [incoming u]; + end +end +% include nodes which are parents of nodes with incoming +for u=1:ss + cs = children(bnet.intra, u); + if ~isempty(cs) & mysubset(cs, incoming) + int = [int u]; + end +end +int = unique(int); +end % if + +int +engine.interface = int; +engine.nonint = mysetdiff(1:ss, int); + +onodes = bnet.observed; + +% Create a "1.5 slice" jtree, containing the interface nodes of slice 1 +% and all the nodes of slice 2 +% To keep the node numbering the same, we simply disconnect the non-interface nodes +% from slice 1, and set their size to 1. +% We do this to speed things up, and so that the likelihood is computed correctly - we do not need to do +% this if we just want to compute marginals (i.e., we can include nodes whose potentials will +% be left as all 1s). +intra15 = bnet.intra; +for i=engine.nonint(:)' + intra15(:,i) = 0; + intra15(i,:) = 0; +end +dag15 = [intra15 bnet.inter; + zeros(ss) bnet.intra]; +ns = bnet.node_sizes(:); +%ns(engine.nonint) = 1; % disconnected nodes get size 1 +obs_nodes = [onodes(:) onodes(:)+ss]; +bnet15 = mk_bnet(dag15, ns, 'discrete', bnet.dnodes, 'equiv_class', bnet.equiv_class(:), ... + 'observed', obs_nodes(:)); + +% use unconstrained elimination, +% but force there to be a clique containing both interfaces +clusters(end+1:end+2) = {int, int+ss}; +engine.jtree_engine = jtree_inf_engine(bnet15, 'clusters', clusters, 'root', int+ss); +jtree_engine = struct(engine.jtree_engine); % violate object privacy + +engine.in_clq = clq_containing_nodes(engine.jtree_engine, int); +engine.out_clq = clq_containing_nodes(engine.jtree_engine, int+ss); +engine.clq_ass_to_node = jtree_engine.clq_ass_to_node; +engine.root = jtree_engine.root_clq; + +% Also create an engine just for slice 1 +bnet1 = mk_bnet(bnet.intra1, bnet.node_sizes_slice, 'discrete', myintersect(bnet.dnodes,1:ss), ... + 'equiv_class', bnet.equiv_class(:,1), 'observed', onodes); +for i=1:max(bnet1.equiv_class) + bnet1.CPD{i} = bnet.CPD{i}; +end +engine.jtree_engine1 = jtree_inf_engine(bnet1, 'clusters', {int}, 'root', int); +jtree_engine1 = struct(engine.jtree_engine1); % violate object privacy +engine.int_clq1 = clq_containing_nodes(engine.jtree_engine1, int); +engine.clq_ass_to_node1 = jtree_engine1.clq_ass_to_node; +engine.root1 = jtree_engine1.root_clq; + +engine.observed = [onodes onodes+ss]; +engine.observed1 = onodes; +engine.pot_type = determine_pot_type(bnet, onodes); +engine.slice_size = bnet.nnodes_per_slice; + +engine = class(engine, 'jtree_2TBN_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_2TBN_inf_engine/back.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,27 @@ +function b = back(engine, bfuture, f, t) + +if f.t ~= t + error('mixed up time stamps') +end +if t==1 + b = back1(engine, bfuture, f, t); + return; +end + +bnet = bnet_from_engine(engine); +ss = bnet.nnodes_per_slice; + +int = engine.interface; +D = engine.in_clq; +C = engine.out_clq; +phiD = marginalize_pot(bfuture.clpot{D}, int, engine.maximize); +phiD = set_domain_pot(phiD, int+ss); % shift to slice 2 +phiC = marginalize_pot(f.clpot{C}, int+ss, engine.maximize); +ratio = divide_by_pot(phiD, phiC); +f.clpot{C} = multiply_by_pot(f.clpot{C}, ratio); + +[b.clpot, seppot] = distribute_evidence(engine.jtree_engine, f.clpot, f.seppot); +for c=1:length(b.clpot) + [b.clpot{c}, ll(c)] = normalize_pot(b.clpot{c}); +end +b.t = t;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_2TBN_inf_engine/back1.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,21 @@ +function b = back1(engine, bfuture, f, t) + +if t ~= 1 + error('mixed up time stamps') +end +bnet = bnet_from_engine(engine); +ss = bnet.nnodes_per_slice; + +int = engine.interface; +D = engine.in_clq; % from J2 +C = engine.int_clq1; % from J1 +phiD = marginalize_pot(bfuture.clpot{D}, int, engine.maximize); +phiC = marginalize_pot(f.clpot{C}, int, engine.maximize); +ratio = divide_by_pot(phiD, phiC); +f.clpot{C} = multiply_by_pot(f.clpot{C}, ratio); + +[b.clpot, seppot] = distribute_evidence(engine.jtree_engine1, f.clpot, f.seppot); +for c=1:length(b.clpot) + [b.clpot{c}, ll(c)] = normalize_pot(b.clpot{c}); +end +b.t = t;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_2TBN_inf_engine/back1_mpe.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,22 @@ +function [b, mpe] = back1_mpe(engine, bfuture, f, ev1, t) + +if t ~= 1 + error('mixed up time stamps') +end +bnet = bnet_from_engine(engine); +ss = bnet.nnodes_per_slice; +maximize = 1; + +int = engine.interface; +D = engine.in_clq; % from J2 +C = engine.int_clq1; % from J1 +phiD = marginalize_pot(bfuture.clpot{D}, int, maximize); +phiC = marginalize_pot(f.clpot{C}, int, maximize); +ratio = divide_by_pot(phiD, phiC); +f.clpot{C} = multiply_by_pot(f.clpot{C}, ratio); + +[mpe, b.clpot] = find_max_config(engine.jtree_engine1, f.clpot, f.seppot, ev1); +for c=1:length(b.clpot) + [b.clpot{c}, ll(c)] = normalize_pot(b.clpot{c}); +end +b.t = t;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_2TBN_inf_engine/backT.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function b = backT(engine, f, t) + +if t==1 + [b.clpot, seppot] = distribute_evidence(engine.jtree_engine1, f.clpot, f.seppot); +else + [b.clpot, seppot] = distribute_evidence(engine.jtree_engine, f.clpot, f.seppot); +end +for c=1:length(b.clpot) + [b.clpot{c}, ll(c)] = normalize_pot(b.clpot{c}); +end +b.t = t;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_2TBN_inf_engine/backT_mpe.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +function [b, mpe] = backT_mpe(engine, f, ev2, t) + +bnet = bnet_from_engine(engine); +ss = bnet.nnodes_per_slice; + +if t==1 + % ev2 is just the evidence on slice 1 + [mpe, b.clpot] = find_max_config(engine.jtree_engine1, f.clpot, f.seppot, ev2); +else + [mpe, b.clpot] = find_max_config(engine.jtree_engine, f.clpot, f.seppot, ev2); + mpe = mpe((1:ss)+ss); % extract values for slice 2 +end +for c=1:length(b.clpot) + [b.clpot{c}, ll(c)] = normalize_pot(b.clpot{c}); +end +b.t = t;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_2TBN_inf_engine/back_mpe.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,29 @@ +function [b, mpe] = back_mpe(engine, bfuture, f, ev2, t) + +if f.t ~= t + error('mixed up time stamps') +end +if t==1 + error('should call back1_mpe') +end + +maximize = 1; +bnet = bnet_from_engine(engine); +ss = bnet.nnodes_per_slice; +clear bnet % added by matthiasm to save + +int = engine.interface; +D = engine.in_clq; +C = engine.out_clq; +phiD = marginalize_pot(bfuture.clpot{D}, int, maximize); +phiD = set_domain_pot(phiD, int+ss); % shift to slice 2 +phiC = marginalize_pot(f.clpot{C}, int+ss, maximize); +ratio = divide_by_pot(phiD, phiC); +f.clpot{C} = multiply_by_pot(f.clpot{C}, ratio); + +[mpe, b.clpot] = find_max_config(engine.jtree_engine, f.clpot, f.seppot, ev2); +mpe = mpe((1:ss)+ss); % extract values for slice 2 +for c=1:length(b.clpot) + [b.clpot{c}, ll(c)] = normalize_pot(b.clpot{c}); +end +b.t = t;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_2TBN_inf_engine/fwd.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,45 @@ +function [f, logscale] = fwd(engine, fpast, ev, t) +% Forwards pass. + +bnet = bnet_from_engine(engine); +ss = bnet.nnodes_per_slice; + +ev2 = cell(ss, 2); +ev2(:,1) = fpast.evidence; +ev2(:,2) = ev; + +CPDpot = cell(1,ss); +for n=1:ss + fam = family(bnet.dag, n, 2); + e = bnet.equiv_class(n, 2); + CPDpot{n} = convert_to_pot(bnet.CPD{e}, engine.pot_type, fam(:), ev2); +end +clear bnet % inserted by matthias mauch to save memory +f.evidence = ev; +f.t = t; + +% get prior +int = engine.interface; +if fpast.t==1 + prior = marginalize_pot(fpast.clpot{engine.int_clq1}, int, engine.maximize); +else + prior = marginalize_pot(fpast.clpot{engine.out_clq}, int+ss, engine.maximize); + prior = set_domain_pot(prior, int); % shift back to slice 1 +end + +pots = [ {prior} CPDpot ]; +slice1 = 1:ss; +slice2 = slice1 + ss; +CPDclqs = engine.clq_ass_to_node(slice2); +D = engine.in_clq; +clqs = [D CPDclqs]; + +[f.clpot, f.seppot] = init_pot(engine.jtree_engine, clqs, pots, engine.pot_type, engine.observed); +[f.clpot, f.seppot] = collect_evidence(engine.jtree_engine, f.clpot, f.seppot); +for c=1:length(f.clpot) + [f.clpot{c}, ll(c)] = normalize_pot(f.clpot{c}); +end +logscale = ll(engine.root); + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_2TBN_inf_engine/fwd1.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,26 @@ +function [f, logscale] = fwd1(engine, ev, t) +% Forwards pass for slice 1. + +bnet = bnet_from_engine(engine); +ss = bnet.nnodes_per_slice; + +CPDpot = cell(1,ss); +for n=1:ss + fam = family(bnet.dag, n, 1); + e = bnet.equiv_class(n, 1); + CPDpot{n} = convert_to_pot(bnet.CPD{e}, engine.pot_type, fam(:), ev); +end +f.t = t; +f.evidence = ev; +clear bnet % by matthias to save memory +% pots = CPDpot; +slice1 = 1:ss; +CPDclqs = engine.clq_ass_to_node1(slice1); + +[f.clpot, f.seppot] = init_pot(engine.jtree_engine1, CPDclqs, CPDpot, engine.pot_type, engine.observed1); +[f.clpot, f.seppot] = collect_evidence(engine.jtree_engine1, f.clpot, f.seppot); +for c=1:length(f.clpot) + [f.clpot{c}, ll(c)] = normalize_pot(f.clpot{c}); +end +logscale = ll(engine.root1); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_2TBN_inf_engine/fwdMM.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,50 @@ +function f = fwdMM(engine, fpast, ev, t) +% Forwards pass. + +bnet = bnet_from_engine(engine); +ss = bnet.nnodes_per_slice; + +ev2 = cell(ss, 2); +ev2(:,1) = fpast.evidence; +ev2(:,2) = ev; +CPDpot = cell(1,ss); +for n=1:ss + fam = family(bnet.dag, n, 2); + e = bnet.equiv_class(n, 2); + CPDpot{n} = convert_to_pot(bnet.CPD{e}, engine.pot_type, fam(:), ev2); +end +clear bnet % by matthias mauch to save memory + +f.evidence = ev; +f.t = t; + +% get prior +int = engine.interface; +if fpast.t==1 + prior = marginalize_pot(fpast.clpot{engine.int_clq1}, int, engine.maximize); +else + prior = marginalize_pot(fpast.clpot{engine.out_clq}, int+ss, engine.maximize); + prior = set_domain_pot(prior, int); % shift back to slice 1 +end + +pots = [ {prior} CPDpot ]; +slice1 = 1:ss; +slice2 = slice1 + ss; +CPDclqs = engine.clq_ass_to_node(slice2); +D = engine.in_clq; +clqs = [D CPDclqs]; + +[f.clpot, f.seppot] = init_pot(engine.jtree_engine, clqs, pots, engine.pot_type, engine.observed); +[f.clpot, f.seppot] = collect_evidence(engine.jtree_engine, f.clpot, f.seppot); +for c=1:length(f.clpot) + if isa(f.clpot{c}, 'struct') + domain = f.clpot{c}.domain; + sizes = f.clpot{c}.sizes; + T = f.clpot{c}.T; + f.clpot{c} = dpot(domain, sizes, T); + end +% [f.clpot{c}, ll(c)] = normalize_pot(f.clpot{c}); + f.clpot{c} = normalize_pot(f.clpot{c}); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_2TBN_inf_engine/jtree_2TBN_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,69 @@ +function engine = jtree_2TBN_inf_engine(bnet, varargin) +% JTREE_ONLINE_INF_ENGINE Online Junction tree inference algorithm for DBNs. +% engine = jtree_online_inf_engine(bnet, ...) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% clusters - specifies variables that must be grouped in the 1.5 slice DBN +% +% The same nodes must be observed in every slice. +% +% This uses the forwards interface of slice t-1 plus all of slice t. +% By contrast, jtree_dbn uses all of slice t-1 plus the backwards interface of slice t. +% See my thesis for details. + + +clusters = {}; + +args = varargin; +nargs = length(args); +for i=1:2:length(args) + switch args{i}, + case 'clusters', clusters = args{i+1}; + otherwise, error(['unrecognized argument ' args{i}]) + end +end + +engine.maximize = 0; +engine.evidence = []; +engine.node_sizes = []; + +int = compute_fwd_interface(bnet.intra, bnet.inter); +engine.interface = int; +ss = length(bnet.intra); +engine.nonint = mysetdiff(1:ss, int); +onodes = bnet.observed; + +bnet15 = mk_slice_and_half_dbn(bnet, int); + +% use unconstrained elimination, +% but force there to be a clique containing both interfaces +clusters(end+1:end+2) = {int, int+ss}; +engine.jtree_engine = jtree_inf_engine(bnet15, 'clusters', clusters, 'root', int+ss); +jtree_engine = struct(engine.jtree_engine); % violate object privacy + +engine.in_clq = clq_containing_nodes(engine.jtree_engine, int); +engine.out_clq = clq_containing_nodes(engine.jtree_engine, int+ss); +engine.clq_ass_to_node = jtree_engine.clq_ass_to_node; +engine.root = jtree_engine.root_clq; + +% Also create an engine just for slice 1 +bnet1 = mk_bnet(bnet.intra1, bnet.node_sizes_slice, 'discrete', myintersect(bnet.dnodes,1:ss), ... + 'equiv_class', bnet.equiv_class(:,1), 'observed', onodes); +for i=1:max(bnet1.equiv_class) + bnet1.CPD{i} = bnet.CPD{i}; +end +engine.jtree_engine1 = jtree_inf_engine(bnet1, 'clusters', {int}, 'root', int); +jtree_engine1 = struct(engine.jtree_engine1); % violate object privacy +engine.int_clq1 = clq_containing_nodes(engine.jtree_engine1, int); +engine.clq_ass_to_node1 = jtree_engine1.clq_ass_to_node; +engine.root1 = jtree_engine1.root_clq; + +engine.observed = [onodes onodes+ss]; +engine.observed1 = onodes; +engine.pot_type = determine_pot_type(bnet, onodes); +engine.slice_size = bnet.nnodes_per_slice; + +engine = class(engine, 'jtree_2TBN_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_2TBN_inf_engine/marginal_family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +function m = marginal_family(engine, b, i, t, add_ev) +% MARGINAL_FAMILY Compute the marginal on the specified family (jtree_2TBN) +% marginal = marginal_family(engine, b, i, t, add_ev) + +bnet = bnet_from_engine(engine); +if t==1 + m = marginal_nodes(engine, b, family(bnet.dag, i), t, add_ev, 1); +else + ss = length(bnet.intra); + fam = family(bnet.dag, i+ss); + m = marginal_nodes(engine, b, fam, t, add_ev, 1); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_2TBN_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,32 @@ +function marginal = marginal_nodes(engine, b, nodes, t, add_ev, is_fam) +% function marginal = marginal_nodes(engine, b, nodes, t, add_ev, is_fam) (jtree_2TBN) + +if nargin < 6, is_fam = 0; end +ss = engine.slice_size; + +if ~is_fam & (t > 1) & all(nodes<=ss) + nodes = nodes + ss; +end + +if t==1 + c = clq_containing_nodes(engine.jtree_engine1, nodes, is_fam); +else + c = clq_containing_nodes(engine.jtree_engine, nodes, is_fam); +end +if c == -1 + error(['no clique contains ' nodes]) +end +bigpot = b.clpot{c}; +pot = marginalize_pot(bigpot, nodes, engine.maximize); +marginal = pot_to_marginal(pot); + +% we convert the domain to the unrolled numbering system +% so that add_ev_to_dmarginal (maybe called in update_ess) extracts the right evidence. +if t > 1 + marginal.domain = nodes+(t-2)*engine.slice_size; +end +assert(~add_ev); + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_2TBN_inf_engine/set_fields.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +function engine = set_fields(engine, varargin) +% SET_FIELDS Set the fields for a generic engine +% engine = set_fields(engine, name/value pairs) +% +% e.g., engine = set_fields(engine, 'maximize', 1) + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'maximize', + engine.maximize = args{i+1}; + engine.jtree_engine = set_fields(engine.jtree_engine, 'maximize', args{i+1}); + engine.jtree_engine1 = set_fields(engine.jtree_engine1, 'maximize', args{i+1}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_sparse_2TBN_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,10 @@ +/back.m/1.1.1.1/Wed May 29 15:59:56 2002// +/back1.m/1.1.1.1/Wed May 29 15:59:56 2002// +/backT.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/fwd.m/1.1.1.1/Wed May 29 15:59:56 2002// +/fwd1.m/1.1.1.1/Wed May 29 15:59:56 2002// +/jtree_sparse_2TBN_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_sparse_2TBN_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/online/@jtree_sparse_2TBN_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_sparse_2TBN_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_sparse_2TBN_inf_engine/back.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,27 @@ +function b = back(engine, bfuture, f, t) + +if f.t ~= t + error('mixed up time stamps') +end +if t==1 + b = back1(engine, bfuture, f, t); + return; +end + +bnet = bnet_from_engine(engine); +ss = bnet.nnodes_per_slice; + +int = engine.interface; +D = engine.in_clq; +C = engine.out_clq; +phiD = marginalize_pot(bfuture.clpot{D}, int, engine.maximize); +phiD = set_domain_pot(phiD, int+ss); % shift to slice 2 +phiC = marginalize_pot(f.clpot{C}, int+ss, engine.maximize); +ratio = divide_by_pot(phiD, phiC); +f.clpot{C} = multiply_by_pot(f.clpot{C}, ratio); + +[b.clpot, seppot] = distribute_evidence(engine.jtree_engine, f.clpot, f.seppot); +for c=1:length(b.clpot) + [b.clpot{c}, ll(c)] = normalize_pot(b.clpot{c}); +end +b.t = t;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_sparse_2TBN_inf_engine/back1.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,21 @@ +function b = back1(engine, bfuture, f, t) + +if t ~= 1 + error('mixed up time stamps') +end +bnet = bnet_from_engine(engine); +ss = bnet.nnodes_per_slice; + +int = engine.interface; +D = engine.in_clq; % from J2 +C = engine.int_clq1; % from J1 +phiD = marginalize_pot(bfuture.clpot{D}, int, engine.maximize); +phiC = marginalize_pot(f.clpot{C}, int, engine.maximize); +ratio = divide_by_pot(phiD, phiC); +f.clpot{C} = multiply_by_pot(f.clpot{C}, ratio); + +[b.clpot, seppot] = distribute_evidence(engine.jtree_engine1, f.clpot, f.seppot); +for c=1:length(b.clpot) + [b.clpot{c}, ll(c)] = normalize_pot(b.clpot{c}); +end +b.t = t;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_sparse_2TBN_inf_engine/backT.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function b = backT(engine, f, t) + +if t==1 + [b.clpot, seppot] = distribute_evidence(engine.jtree_engine1, f.clpot, f.seppot); +else + [b.clpot, seppot] = distribute_evidence(engine.jtree_engine, f.clpot, f.seppot); +end +for c=1:length(b.clpot) + [b.clpot{c}, ll(c)] = normalize_pot(b.clpot{c}); +end +b.t = t;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_sparse_2TBN_inf_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,22 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (jtree_online) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product instead of sum-product [0] +% + +engine.maximize = 0; +args = varargin; +for i=1:2:length(args) + switch args{i} + case 'maximize', engine.maximize = args{i+1}; + otherwise, error(['unrecognized argument ' args{i}]) + end +end + +[engine, loglik] = offline_smoother(engine, evidence);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_sparse_2TBN_inf_engine/fwd.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,49 @@ +function [f, logscale] = fwd(engine, fpast, ev, t) +% Forwards pass. + +bnet = bnet_from_engine(engine); +ss = bnet.nnodes_per_slice; + +ev2 = cell(ss, 2); +ev2(:,1) = fpast.evidence; +ev2(:,2) = ev; +CPDpot = cell(1,ss); +for n=1:ss + fam = family(bnet.dag, n, 2); + e = bnet.equiv_class(n, 2); + CPDpot{n} = convert_to_pot(bnet.CPD{e}, engine.pot_type, fam(:), ev2); +end +clear bnet % by matthias mauch to save memory + +f.evidence = ev; +f.t = t; + +% get prior +int = engine.interface; +if fpast.t==1 + prior = marginalize_pot(fpast.clpot{engine.int_clq1}, int, engine.maximize); +else + prior = marginalize_pot(fpast.clpot{engine.out_clq}, int+ss, engine.maximize); + prior = set_domain_pot(prior, int); % shift back to slice 1 +end + +pots = [ {prior} CPDpot ]; +slice1 = 1:ss; +slice2 = slice1 + ss; +CPDclqs = engine.clq_ass_to_node(slice2); +D = engine.in_clq; +clqs = [D CPDclqs]; + +[f.clpot, f.seppot] = init_pot(engine.jtree_engine, clqs, pots, engine.pot_type, engine.observed); +[f.clpot, f.seppot] = collect_evidence(engine.jtree_engine, f.clpot, f.seppot); +for c=1:length(f.clpot) + if isa(f.clpot{c}, 'struct') + domain = f.clpot{c}.domain; + sizes = f.clpot{c}.sizes; + T = f.clpot{c}.T; + f.clpot{c} = dpot(domain, sizes, T); + end + [f.clpot{c}, ll(c)] = normalize_pot(f.clpot{c}); +end +logscale = ll(engine.root); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_sparse_2TBN_inf_engine/fwd1.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,32 @@ +function [f, logscale] = fwd1(engine, ev, t) +% Forwards pass for slice 1. + +bnet = bnet_from_engine(engine); +ss = bnet.nnodes_per_slice; + +CPDpot = cell(1,ss); +for n=1:ss + fam = family(bnet.dag, n, 1); + e = bnet.equiv_class(n, 1); + CPDpot{n} = convert_to_pot(bnet.CPD{e}, engine.pot_type, fam(:), ev); +end +f.evidence = ev; +f.t = t; + +pots = CPDpot; +slice1 = 1:ss; +CPDclqs = engine.clq_ass_to_node1(slice1); + +[f.clpot, f.seppot] = init_pot(engine.jtree_engine1, CPDclqs, CPDpot, engine.pot_type, engine.observed1); +[f.clpot, f.seppot] = collect_evidence(engine.jtree_engine1, f.clpot, f.seppot); +for c=1:length(f.clpot) + if isa(f.clpot{c}, 'struct') + domain = f.clpot{c}.domain; + sizes = f.clpot{c}.sizes; + T = f.clpot{c}.T; + f.clpot{c} = dpot(domain, sizes, T); + end + [f.clpot{c}, ll(c)] = normalize_pot(f.clpot{c}); +end +logscale = ll(engine.root1); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_sparse_2TBN_inf_engine/jtree_sparse_2TBN_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,95 @@ +function engine = jtree_sparse_2TBN_inf_engine(bnet, varargin) +% JTREE_ONLINE_INF_ENGINE Online Junction tree inference algorithm for DBNs. +% engine = jtree_online_inf_engine(bnet, ...) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% clusters - specifies variables that must be grouped in the 1.5 slice DBN +% maximize - 1 means do max-product, 0 means sum-product [0] +% +% The same nodes must be observed in every slice. + +ss = length(bnet.intra); +clusters = {}; +engine.maximize = 0; + +args = varargin; +nargs = length(args); +for i=1:2:length(args) + switch args{i}, + case 'clusters', clusters = args{i+1}; + case 'maximize', engine.maximize = args{i+1}; + otherwise, error(['unrecognized argument ' args{i}]) + end +end + +engine.evidence = []; +engine.node_sizes = []; + +int = []; +% include nodes with any outgoing arcs +for u=1:ss + if any(bnet.inter(u,:)) + int = [int u]; + end +end + +engine.interface = int; +engine.nonint = mysetdiff(1:ss, int); + +onodes = bnet.observed; + +% Create a "1.5 slice" jtree, containing the interface nodes of slice 1 +% and all the nodes of slice 2 +% To keep the node numbering the same, we simply disconnect the non-interface nodes +% from slice 1, and set their size to 1. +% We do this to speed things up, and so that the likelihood is computed correctly - we do not need to do +% this if we just want to compute marginals (i.e., we can include nodes whose potentials will +% be left as all 1s). +intra15 = bnet.intra; +for i=engine.nonint(:)' + intra15(:,i) = 0; + intra15(i,:) = 0; + assert(~any(bnet.inter(i,:))) +end +dag15 = [intra15 bnet.inter; + zeros(ss) bnet.intra]; +ns = bnet.node_sizes(:); +ns(engine.nonint) = 1; % disconnected nodes get size 1 +obs_nodes = [onodes(:) onodes(:)+ss]; +bnet15 = mk_bnet(dag15, ns, 'discrete', bnet.dnodes, 'equiv_class', bnet.equiv_class(:), ... + 'observed', obs_nodes(:)); + +% use unconstrained elimination, +% but force there to be a clique containing both interfaces +clusters(end+1:end+2) = {int, int+ss}; +%engine.jtree_engine = jtree_inf_engine(bnet15, 'clusters', clusters, 'root', int+ss); +engine.jtree_engine = jtree_sparse_inf_engine(bnet15, 'clusters', clusters, 'root', int+ss); +jtree_engine = struct(engine.jtree_engine); % violate object privacy + +engine.in_clq = clq_containing_nodes(engine.jtree_engine, int); +engine.out_clq = clq_containing_nodes(engine.jtree_engine, int+ss); +engine.clq_ass_to_node = jtree_engine.clq_ass_to_node; +engine.root = jtree_engine.root_clq; + +% Also create an engine just for slice 1 +bnet1 = mk_bnet(bnet.intra1, bnet.node_sizes_slice, 'discrete', myintersect(bnet.dnodes,1:ss), ... + 'equiv_class', bnet.equiv_class(:,1), 'observed', onodes); +for i=1:max(bnet1.equiv_class) + bnet1.CPD{i} = bnet.CPD{i}; +end +%engine.jtree_engine1 = jtree_inf_engine(bnet1, 'clusters', {int}, 'root', int); +engine.jtree_engine1 = jtree_sparse_inf_engine(bnet1, 'clusters', {int}, 'root', int); +jtree_engine1 = struct(engine.jtree_engine1); % violate object privacy +engine.int_clq1 = clq_containing_nodes(engine.jtree_engine1, int); +engine.clq_ass_to_node1 = jtree_engine1.clq_ass_to_node; +engine.root1 = jtree_engine1.root_clq; + +engine.observed = [onodes onodes+ss]; +engine.observed1 = onodes; +engine.pot_type = determine_pot_type(bnet, onodes); +engine.slice_size = bnet.nnodes_per_slice; + +engine = class(engine, 'jtree_sparse_2TBN_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_sparse_2TBN_inf_engine/marginal_family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +function m = marginal_family(engine, b, i, t, add_ev) +% MARGINAL_FAMILY Compute the marginal on the specified family (jtree_2TBN) +% marginal = marginal_family(engine, b, i, t, add_ev) + +bnet = bnet_from_engine(engine); +if t==1 + m = marginal_nodes(engine, b, family(bnet.dag, i), t, add_ev, 1); +else + ss = length(bnet.intra); + fam = family(bnet.dag, i+ss); + m = marginal_nodes(engine, b, fam, t, add_ev, 1); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@jtree_sparse_2TBN_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,32 @@ +function marginal = marginal_nodes(engine, b, nodes, t, add_ev, is_fam) +% function marginal = marginal_nodes(engine, b, nodes, t, add_ev, is_fam) (jtree_2TBN) + +if nargin < 6, is_fam = 0; end +ss = engine.slice_size; + +if ~is_fam & (t > 1) & all(nodes<=ss) + nodes = nodes + ss; +end + +if t==1 + c = clq_containing_nodes(engine.jtree_engine1, nodes, is_fam); +else + c = clq_containing_nodes(engine.jtree_engine, nodes, is_fam); +end +if c == -1 + error(['no clique contains ' nodes]) +end +bigpot = b.clpot{c}; +pot = marginalize_pot(bigpot, nodes, engine.maximize); +marginal = pot_to_marginal(pot); + +% we convert the domain to the unrolled numbering system +% so that add_ev_to_dmarginal (maybe called in update_ess) extracts the right evidence. +if t > 1 + marginal.domain = nodes+(t-2)*engine.slice_size; +end +assert(~add_ev); + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@smoother_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +/bnet_from_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_evidence.m/1.1.1.1/Mon Jun 17 23:46:46 2002// +/find_mpe.m/1.1.1.1/Mon Jun 17 23:50:16 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/smoother_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/update_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@smoother_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/online/@smoother_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@smoother_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@smoother_engine/bnet_from_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function bnet = bnet_from_engine(engine) +% BNET_FROM_ENGINE Return the bnet structure stored inside the engine (smoother_engine) +% bnet = bnet_from_engine(engine) + +bnet = bnet_from_engine(engine.tbn_engine);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@smoother_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,21 @@ +function [engine, LL] = enter_evidence(engine, ev) +% ENTER_EVIDENCE Call the offline smoother +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% + +T = size(ev, 2); +f = cell(1,T); +b = cell(1,T); % b{t}.clpot{c} +ll = zeros(1,T); +[f{1}, ll(1)] = fwd1(engine.tbn_engine, ev(:,1), 1); +for t=2:T + [f{t}, ll(t)] = fwd(engine.tbn_engine, f{t-1}, ev(:,t), t); +end +LL = sum(ll); +b{T} = backT(engine.tbn_engine, f{T}, T); +for t=T-1:-1:1 + b{t} = back(engine.tbn_engine, b{t+1}, f{t}, t); +end +engine.b = b;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@smoother_engine/find_mpe.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,33 @@ +function mpe = find_mpe(engine, ev) +% FIND_MPE Find the most probable explanation (Viterbi) +% mpe = enter_evidence(engine, evidence, ...) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% + +mpe = cell(size(ev)); +engine.tbn_engine = set_fields(engine.tbn_engine, 'maximize', 1); + +T = size(ev, 2); +f = cell(1,T); +b = cell(1,T); % b{t}.clpot{c} +ll = zeros(1,T); +[f{1}, ll(1)] = fwd1(engine.tbn_engine, ev(:,1), 1); +for t=2:T + [f{t}, ll(t)] = fwd(engine.tbn_engine, f{t-1}, ev(:,t), t); +end + +if T==1 + [b{1}, mpe(:,1)] = backT_mpe(engine.tbn_engine, f{1}, ev(:,1), 1); +else + [b{T}, mpe(:,T)] = backT_mpe(engine.tbn_engine, f{T}, ev(:,T-1:T), T); + for t=T-1:-1:2 + [b{t}, mpe(:,t)] = back_mpe(engine.tbn_engine, b{t+1}, f{t}, ev(:,t-1:t), t); + end + t = 1; + [b{t}, mpe(:,t)] = back1_mpe(engine.tbn_engine, b{t+1}, f{t}, ev(:,1), t); +end +engine.b = b; + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@smoother_engine/find_mpeMM.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,34 @@ +function mpe = find_mpeMM(engine, ev) +% FIND_MPE Find the most probable explanation (Viterbi) +% mpe = enter_evidence(engine, evidence, ...) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% + +mpe = cell(size(ev)); +engine.tbn_engine = set_fields(engine.tbn_engine, 'maximize', 1); +disp('forward step') +T = size(ev, 2); +f = cell(1,T); +% b = cell(1,T); % b{t}.clpot{c} + +f{1} = fwd1(engine.tbn_engine, ev(:,1), 1); +for t=2:T +% [f{t}, ll(t)] = fwd(engine.tbn_engine, f{t-1}, ev(:,t), t); + f{t} = fwdMM(engine.tbn_engine, f{t-1}, ev(:,t), t); +end +disp('backward step') +if T==1 + [b, mpe(:,1)] = backT_mpe(engine.tbn_engine, f{1}, ev(:,1), 1); +else + [b, mpe(:,T)] = backT_mpe(engine.tbn_engine, f{T}, ev(:,T-1:T), T); + for t=T-1:-1:2 + [b, mpe(:,t)] = back_mpe(engine.tbn_engine, b, f{t}, ev(:,t-1:t), t); + end + t = 1; + [b, mpe(:,t)] = back1_mpe(engine.tbn_engine, b, f{t}, ev(:,1), t); +end +engine.b = b; + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@smoother_engine/marginal_family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +function marginal = marginal_family(engine, i, t, add_ev) +% MARGINAL_FAMILY Compute the joint distribution on a set of family (smoother_engine) +% function marginal = marginal_family(engine, i, t, add_ev) + +if nargin < 4, add_ev = 0; end +marginal = marginal_family(engine.tbn_engine, engine.b{t}, i, t, add_ev);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@smoother_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +function marginal = marginal_nodes(engine, nodes, t, add_ev) +% MARGINAL_NODES Compute the joint distribution on a set of nodes (smoother_engine) +% function marginal = marginal_nodes(engine, nodes, t, add_ev) + +if nargin < 4, add_ev = 0; end + +marginal = marginal_nodes(engine.tbn_engine, engine.b{t}, nodes, t, add_ev);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@smoother_engine/smoother_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +function engine = smoother_engine(tbn_engine) +% SMOOTHER_ENGINE Create an engine which does offline (fixed-interval) smoothing in O(T) space/time +% function engine = smoother_engine(tbn_engine) +% +% tbn_engine is any 2TBN inference engine which supports the following methods: +% fwd, fwd1, back, backT, back, marginal_nodes and marginal_family. + +engine.tbn_engine = tbn_engine; +engine.b = []; % space to store smoothed messages +engine = class(engine, 'smoother_engine'); +%engine = class(engine, 'smoother_engine', inf_engine(bnet_from_engine(tbn_engine))); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/@smoother_engine/update_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function engine = update_engine(engine, newCPDs) +% UPDATE_ENGINE Update the engine to take into account the new parameters (smoother_engine). +% engine = update_engine(engine, newCPDs) + +engine.tbn_engine = update_engine(engine.tbn_engine, newCPDs);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +/dummy/1.1.1.1/Sat Jan 18 22:22:38 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +A D/@filter_engine//// +A D/@hmm_2TBN_inf_engine//// +A D/@jtree_2TBN_inf_engine//// +A D/@jtree_sparse_2TBN_inf_engine//// +A D/@smoother_engine////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/online
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/online/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_fg_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +/belprop_fg_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/find_mpe.m/1.1.1.1/Thu Jun 20 00:02:12 2002// +/loopy_converged.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/set_params.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_fg_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@belprop_fg_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_fg_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_fg_inf_engine/belprop_fg_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,47 @@ +function engine = belprop_fg_inf_engine(fg, varargin) +% BELPROP_FG_INF_ENGINE Make a belief propagation inference engine for factor graphs +% engine = belprop_fg_inf_engine(factor_graph, ...) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default in brackets] +% e.g., engine = belprop_inf_engine(fg, 'tol', 1e-2, 'max_iter', 10) +% +% max_iter - max. num. iterations [ 2*num_nodes ] +% momentum - weight assigned to old message in convex combination (useful for damping oscillations) [0] +% tol - tolerance used to assess convergence [1e-3] +% maximize - 1 means use max-product, 0 means use sum-product [0] +% +% This uses potential objects, like belprop_inf_engine, and hence is quite slow. + +engine = init_fields; +engine = class(engine, 'belprop_fg_inf_engine'); + +% set params to default values +N = length(fg.G); +engine.max_iter = 2*N; +engine.momentum = 0; +engine.tol = 1e-3; +engine.maximize = 0; + +% parse optional arguments +engine = set_params(engine, varargin); + +engine.fgraph = fg; + +% store results computed by enter_evidence here +engine.marginal_nodes = cell(1, fg.nvars); +engine.evidence = []; + + +%%%%%%%%%%%% + +function engine = init_fields() + +engine.fgraph = []; +engine.max_iter = []; +engine.momentum = []; +engine.tol = []; +engine.maximize = []; +engine.marginal_nodes = []; +engine.evidence = []; +engine.niter = [];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_fg_inf_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,126 @@ +function [engine, ll, niter] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Propagate evidence using belief propagation +% [engine, ll, niter] = enter_evidence(engine, evidence, ...) +% +% The log-likelihood is not computed; ll = 0. +% niter contains the number of iterations used +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - 1 means use max-product, 0 means use sum-product [0] +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) + +ll = 0; +maximize = 0; + +if nargin >= 3 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'maximize', maximize = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +verbose = 0; + +ns = engine.fgraph.node_sizes; +onodes = find(~isemptycell(evidence)); +hnodes = find(isemptycell(evidence)); +cnodes = engine.fgraph.cnodes; +pot_type = determine_pot_type(engine.fgraph, onodes); + +% prime each local kernel with evidence (if any) +nfactors = engine.fgraph.nfactors; +nvars = engine.fgraph.nvars; +factors = cell(1,nfactors); +for f=1:nfactors + K = engine.fgraph.factors{engine.fgraph.equiv_class(f)}; + factors{f} = convert_to_pot(K, pot_type, engine.fgraph.dom{f}(:), evidence); +end + +% initialise msgs +msg_var_to_fac = cell(nvars, nfactors); +for x=1:nvars + for f=engine.fgraph.dep{x} + msg_var_to_fac{x,f} = mk_initial_pot(pot_type, x, ns, cnodes, onodes); + end +end +msg_fac_to_var = cell(nfactors, nvars); +dom = cell(1, nfactors); +for f=1:nfactors + %hdom{f} = myintersect(engine.fgraph.dom{f}, hnodes); + dom{f} = engine.fgraph.dom{f}(:)'; + for x=dom{f} + msg_fac_to_var{f,x} = mk_initial_pot(pot_type, x, ns, cnodes, onodes); + %msg_fac_to_var{f,x} = marginalize_pot(factors{f}, x); + end +end + + + +converged = 0; +iter = 1; +var_prod = cell(1, nvars); +fac_prod = cell(1, nfactors); + +while ~converged & (iter <= engine.max_iter) + if verbose, fprintf('iter %d\n', iter); end + + % absorb + old_var_prod = var_prod; + for x=1:nvars + var_prod{x} = mk_initial_pot(pot_type, x, ns, cnodes, onodes); + for f=engine.fgraph.dep{x} + var_prod{x} = multiply_by_pot(var_prod{x}, msg_fac_to_var{f,x}); + end + end + for f=1:nfactors + fac_prod{f} = mk_initial_pot(pot_type, dom{f}, ns, cnodes, onodes); + for x=dom{f} + fac_prod{f} = multiply_by_pot(fac_prod{f}, msg_var_to_fac{x,f}); + end + end + + % send msgs to neighbors + old_msg_var_to_fac = msg_var_to_fac; + old_msg_fac_to_var = msg_fac_to_var; + converged = 1; + for x=1:nvars + %if verbose, disp(['var ' num2str(x) ' sending to fac ' num2str(engine.fgraph.dep{x})]); end + for f=engine.fgraph.dep{x} + temp = divide_by_pot(var_prod{x}, old_msg_fac_to_var{f,x}); + msg_var_to_fac{x,f} = normalize_pot(temp); + if ~approxeq_pot(msg_var_to_fac{x,f}, old_msg_var_to_fac{x,f}, engine.tol), converged = 0; end + end + end + for f=1:nfactors + %if verbose, disp(['fac ' num2str(f) ' sending to var ' num2str(dom{f})]); end + for x=dom{f} + temp = divide_by_pot(fac_prod{f}, old_msg_var_to_fac{x,f}); + temp2 = multiply_by_pot(factors{f}, temp); + temp3 = marginalize_pot(temp2, x, maximize); + msg_fac_to_var{f,x} = normalize_pot(temp3); + if ~approxeq_pot(msg_fac_to_var{f,x}, old_msg_fac_to_var{f,x}, engine.tol), converged = 0; end + end + end + + if iter==1 + converged = 0; + end + iter = iter + 1; +end + +niter = iter - 1; +engine.niter = niter; + +for x=1:nvars + engine.marginal_nodes{x} = normalize_pot(var_prod{x}); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_fg_inf_engine/find_mpe.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,49 @@ +function mpe = find_mpe(engine, evidence, varargin) +% FIND_MPE Find the most probable explanation of the data (belprop_fg) +% function mpe = find_mpe(engine, evidence,...) +% +% evidence{i} = [] if X(i) is hidden, and otherwise contains its observed value (scalar or column vector). +% +% This finds the marginally most likely value for each hidden node, +% and may give the wrong results even if the graph is acyclic, +% unless you set break_ties = 1. +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% break_ties is optional. If 1, we will force ties to be broken consistently +% by calling enter_evidence N times. (see Jensen96, p106) Default = 1. + +break_ties = 1; + +% parse optional params +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'break_ties', break_ties = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end +end + +engine = enter_evidence(engine, evidence, 'maximize', 1); + +observed = ~isemptycell(evidence); +evidence = evidence(:); % hack to handle unrolled DBNs +N = length(evidence); +mpe = cell(1,N); +for i=1:N + m = marginal_nodes(engine, i); + % observed nodes are all set to 1 inside the inference engine, so we must undo this + if observed(i) + mpe{i} = evidence{i}; + else + mpe{i} = argmax(m.T); + if break_ties + evidence{i} = mpe{i}; + [engine, ll] = enter_evidence(engine, evidence, 'maximize', 1); + end + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_fg_inf_engine/loopy_converged.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +function niter = loopy_converged(engine) +% LOOPY_CONVERGED Did loopy belief propagation converge? 0 means no, eles we return the num. iterations. +% function niter = loopy_converged(engine) +% +% We use a simple heuristic: we say convergence occurred if the number of iterations +% used was less than the maximum allowed. + +if engine.niter == engine.max_iter + niter = 0; +else + niter = engine.niter; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_fg_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +function marginal = marginal_nodes(engine, query) +% MARGINAL_NODES Compute the marginal on the specified query nodes (belprop) +% marginal = marginal_nodes(engine, query) + +assert(length(query)==1); +marginal = pot_to_marginal(engine.marginal_nodes{query});
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_fg_inf_engine/set_params.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,24 @@ +function engine = set_params(engine, varargin) +% SET_PARAMS Set the parameters (fields) for a belprop_inf_engine object +% engine = set_params(engine, name/value pairs) +% +% The following optional arguments can be specified in the form of name/value pairs: +% e.g., engine = set_params(engine, 'tol', 1e-2, 'max_iter', 10) +% +% max_iter - max. num. loopy iterations +% momentum - weight assigned to old message in convex combination +% tol - tolerance used to assess convergence +% maximize - 1 means use max-product, 0 means use sum-product + +args = varargin{1}; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'max_iter', engine.max_iter = args{i+1}; + case 'momentum', engine.momentum = args{i+1}; + case 'tol', engine.tol = args{i+1}; + case 'maximize', engine.maximize = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +/belprop_inf_engine.m/1.1.1.1/Tue Dec 31 19:00:06 2002// +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/find_mpe.m/1.1.1.1/Wed Jun 19 22:08:40 2002// +/loopy_converged.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_inf_engine/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +A D/Old//// +A D/private////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@belprop_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_inf_engine/Old/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +/belprop_gdl_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/belprop_inf_engine_nostr.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_evidence1.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_domain.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_inf_engine/Old/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@belprop_inf_engine/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_inf_engine/Old/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_inf_engine/Old/belprop_gdl_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,67 @@ +function engine = belprop_gdl_inf_engine(gdl, varargin) +% BELPROP_GDL_INF_ENGINE Make a belief propagation inference engine for a GDL graph +% engine = belprop_gdl_inf_engine(gdl_graph, ...) +% +% If the GDL graph is a tree, this will give exact results. +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default in brackets] +% e.g., engine = belprop_inf_engine(gdl, 'tol', 1e-2, 'max_iter', 10) +% +% protocol - 'tree' means send messages up then down the tree, +% 'parallel' means use synchronous updates ['parallel'] +% max_iter - max. num. iterations [ 2*num_nodes ] +% momentum - weight assigned to old message in convex combination (useful for damping oscillations) [0] +% tol - tolerance used to assess convergence [1e-3] +% maximize - 1 means use max-product, 0 means use sum-product [0] + + +engine = init_fields; +engine = class(engine, 'belprop_gdl_inf_engine'); + +% set default params +N = length(gdl.G); +engine.protocol = 'parallel'; +engine.max_iter = 2*N; +engine.momentum = 0; +engine.tol = 1e-3; +engine.maximize = 0; + +engine = set_params(engine, varargin); + +engine.gdl = gdl; + +if strcmp(engine.protocol, 'tree') + % Make a rooted tree, so there is a fixed message passing order. + root = N; + [engine.tree, engine.preorder, engine.postorder, height, cyclic] = mk_rooted_tree(gdl.G, root); + assert(~cyclic); +end + +% store results computed by enter_evidence here +ndoms = length(gdl.doms); +nvars = length(gdl.vars); +engine.marginal_domains = cell(1, ndoms); + +% to compute the marginal on each variable, we need to know which domain to marginalize +% and we want to choose the lightest. We compute the weight once we have seen the evidence. +engine.dom_weight = []; +engine.evidence = []; + + +%%%%%%%%% + +function engine = init_fields() + +engine.protocol = []; +engine.gdl = []; +engine.max_iter = []; +engine.momentum = []; +engine.tol = []; +engine.maximize = []; +engine.marginal_domains = []; +engine.evidence = []; +engine.tree = []; +engine.preorder = []; +engine.postorder = []; +engine.dom_weight = [];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_inf_engine/Old/belprop_inf_engine_nostr.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,31 @@ +function engine = belprop_inf_engine(fg, max_iter, momentum, tol, maximize) + +if nargin < 2, max_iter = length(fg.G); end +if nargin < 3, momentum = 0; end +if nargin < 4, tol = 1e-3; end +if nargin < 5, maximize = 0; end + +engine.fgraph = fg; +engine.max_iter = max_iter; +engine.momentum = momentum; +engine.tol = tol; +engine.maximize = maximize; + +% store results computed by enter_evidence here +ndoms = length(fg.doms); +nvars = length(fg.vars); +engine.marginal_domains = cell(1, ndoms); + +% to compute the marginal on each variable, we need to know which domain to marginalize +% so we represent each domain as a bit vector, and compute its (pre-evidence) weight +engine.dom_weight = []; + +% engine.dom_bitv = sparse(ndoms, nvars); +% ns = fg.node_sizes; +% for i=1:ndoms +% engine.dom_bitv(i, fg.doms{i}) = 1; +% engine.dom_weight(i) = prod(ns(fg.doms{i})); +% end + + +engine = class(engine, 'belprop_inf_engine');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_inf_engine/Old/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,80 @@ +function engine = enter_evidence(engine, evidence) + +doms = engine.fg.doms; +ndoms = length(doms); +ns = engine.fg.node_sizes; +obs = find(~isemptycell(evidence)); +cobs = myintersect(obs, engine.fg.cnodes); +dobs = myintersect(obs, engine.fg.dnodes); +ns(cobs) = 0; +ns(dobs) = 1; + +% prime each local kernel with evidence (if any) +local_kernel = cell(1, ndoms); +for i=1:length(engine.fg.kernels_of_type) + u = engine.fg.kernels_of_type{i}; + local_kernel(u) = kernel_to_dpots(engine.fg.kernels{i}, evidence, engine.fg.domains_of_type{i}); +end + +% initialise all msgs to 1s +nedges = engine.fg.nedges; +msg = cell(1, nedges); +for i=1:nedges + msg{i} = dpot(engine.fg.sepset{i}, ns(engine.fg.sepset{i})); +end + +prod_of_msg = cell(1, ndoms); +bel = cell(1, ndoms); +old_bel = cell(1, ndoms); + +converged = 0; +iter = 1; +while ~converged & (iter <= engine.max_iter) + + % each node multiplies all its incoming msgs + for i=1:ndoms + prod_of_msg{i} = dpot(doms{i}, ns(doms{i})); + nbrs = engine.fg.nbrs{i}; + for j=1:length(nbrs) + ndx = engine.fg.edge_ndx(j,i); + prod_of_msg{i} = multiply_by_pot(prod_of_msg{i}, msg{ndx}); + end + end + old_msg = msg; + + % each node computes its local belief + for i=1:ndoms + bel{i} = normalize_pot(multiply_pots(prod_of_msg{i}, local_kernel{i})); + end + + % converged? + converged = 1; + for i=1:ndoms + if ~approxeq(bel{i}, old_bel{i}, engine.tol) + converged = 0; + break; + end + end + + if ~converged + % each node sends a msg to each of its neighbors + for i=1:ndoms + nbrs = engine.fg.nbrs{i}; + for j=1:length(nbrs) + % multiply all incoming msgs except from j + temp = prod_of_msg{i}; + ndx = engine.fg.edge_ndx(j,i); + temp = divide_by_pot(temp, old_msg{ndx}); + % send msg from i to j + temp = multiply_by_pot(temp, local_kernel{i}); + ndx = engine.fg.edge_ndx(i,j); + msg{ndx} = normalize_pot(marginalize_pot(temp, engine.fg.sepset{ndx})); + end + end + end + + iter = iter + 1; +end + + +engine.marginal = bel;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_inf_engine/Old/enter_evidence1.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,94 @@ +function engine = enter_evidence(engine, evidence) + +doms = engine.fgraph.doms; +ndoms = length(doms); +ns = engine.fgraph.node_sizes; +obs = find(~isemptycell(evidence)); +cobs = myintersect(obs, engine.fgraph.cnodes); +dobs = myintersect(obs, engine.fgraph.dnodes); +ns(cobs) = 0; +ns(dobs) = 1; + +% recompute the weight of each domain now that we know what nodes are observed +for i=1:ndoms + engine.dom_weight(i) = prod(ns(engine.fgraph.doms{i})); +end + +% prime each local kernel with evidence (if any) +local_kernel = cell(1, ndoms); +for i=1:length(engine.fgraph.kernels_of_type) + u = engine.fgraph.kernels_of_type{i}; + local_kernel(u) = kernel_to_dpots(engine.fgraph.kernels{i}, evidence, engine.fgraph.domains_of_type{i}); +end + +% initialise all msgs to 1s +msg = cell(ndoms, ndoms); +for i=1:ndoms + nbrs = engine.fgraph.nbrs{i}; + for j=nbrs(:)' + dom = engine.fgraph.sepset{i,j}; + msg{i,j} = dpot(dom, ns(dom)); + end +end + +prod_of_msg = cell(1, ndoms); +bel = cell(1, ndoms); +old_bel = cell(1, ndoms); + +converged = 0; +iter = 1; +while ~converged & (iter <= engine.max_iter) + + % each node multiplies all its incoming msgs + for i=1:ndoms + prod_of_msg{i} = dpot(doms{i}, ns(doms{i})); + nbrs = engine.fgraph.nbrs{i}; + for j=nbrs(:)' + prod_of_msg{i} = multiply_by_pot(prod_of_msg{i}, msg{j,i}); + end + end + + % each node computes its local belief + old_bel = bel; + for i=1:ndoms + bel{i} = normalize_pot(multiply_pots(prod_of_msg{i}, local_kernel{i})); + end + + % converged? + if iter==1 + converged = 0; + else + converged = 1; + for i=1:ndoms + belT = get_params(bel{i}, 'table'); + old_belT = get_params(old_bel{i}, 'table'); + if ~approxeq(belT, old_belT, engine.tol) + converged = 0; + break; + end + end + end + + if ~converged + old_msg = msg; + % each node sends a msg to each of its neighbors + for i=1:ndoms + nbrs = engine.fgraph.nbrs{i}; + for j=nbrs(:)' + % multiply all incoming msgs except from j + temp = prod_of_msg{i}; + temp = divide_by_pot(temp, old_msg{j,i}); + % send msg from i to j + temp = multiply_by_pot(temp, local_kernel{i}); + msg{i,j} = normalize_pot(marginalize_pot(temp, engine.fgraph.sepset{i,j})); + end + end + end + + iter = iter + 1 +end + +engine.marginal_domains = bel; +%for i=1:ndoms + %engine.marginal_domains{i} = get_params(bel{i}, 'table'); +%end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_inf_engine/Old/marginal_domain.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function marginal = marginal_domain(engine, i) +% MARGINAL_DOMAIN Return the marginal on the specified domain (belprop) +% marginal = marginal_domain(engine, i) + +marginal = pot_to_marginal(engine.marginal_domains{i});
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_inf_engine/belprop_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,90 @@ +function engine = belprop_inf_engine(bnet, varargin) +% BELPROP_INF_ENGINE Make a loopy belief propagation inference engine +% engine = belprop_inf_engine(bnet, ...) +% +% This is like pearl_inf_engine, except it uses potential objects, +% instead of lambda/pi structs. Hence it is slower. +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default in brackets] +% +% protocol - 'tree' means send messages up then down the tree, +% 'parallel' means use synchronous updates ['parallel'] +% max_iter - max. num. iterations [ 2*num_nodes ] +% momentum - weight assigned to old message in convex combination (useful for damping oscillations) [0] +% tol - tolerance used to assess convergence [1e-3] +% maximize - 1 means use max-product, 0 means use sum-product [0] +% filename - name of file to write beliefs to after each iteration within enter_evidence [ [] ] +% +% e.g., engine = belprop_inf_engine(bnet, 'maximize', 1, 'max_iter', 10) + +% gdl = general distributive law +engine.gdl = bnet_to_gdl(bnet); + +% set default params +N = length(engine.gdl.G); +engine.protocol = 'parallel'; +engine.max_iter = 2*N; +engine.momentum = 0; +engine.tol = 1e-3; +engine.maximize = 0; +engine.filename = []; +engine.fid = []; + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'max_iter', engine.max_iter = args{i+1}; + case 'momentum', engine.momentum = args{i+1}; + case 'tol', engine.tol = args{i+1}; + case 'protocol', engine.protocol = args{i+1}; + case 'filename', engine.filename = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end +end + + +if strcmp(engine.protocol, 'tree') + % Make a rooted tree, so there is a fixed message passing order. + root = N; + [engine.tree, engine.preorder, engine.postorder, height, cyclic] = mk_rooted_tree(engine.gdl.G, root); + assert(~cyclic); +end + +% store results computed by enter_evidence here +engine.marginal_domains = cell(1, N); + +engine.niter = []; + +engine = class(engine, 'belprop_inf_engine', inf_engine(bnet)); + +%%%%%%%%% + +function gdl = bnet_to_gdl(bnet) + +gdl.G = mk_undirected(bnet.dag); +N = length(bnet.dag); +gdl.doms = cell(1,N); +for i=1:N + gdl.doms{i} = family(bnet.dag, i); +end + +% Compute a bit vector representation of the set of domains +% dom_bitv(i,j) = 1 iff variable j occurs in domain i +gdl.dom_bitv = zeros(N, N); +for i=1:N + gdl.dom_bitv(i, gdl.doms{i}) = 1; +end + +% compute the interesection of the domains on either side of each edge (separating set) +gdl.sepset = cell(N, N); +gdl.nbrs = cell(1,N); +for i=1:N + nbrs = neighbors(gdl.G, i); + gdl.nbrs{i} = nbrs; + for j = nbrs(:)' + gdl.sepset{i,j} = myintersect(gdl.doms{i}, gdl.doms{j}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_inf_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,86 @@ +function [engine, ll, niter] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Propagate evidence using belief propagation +% [engine, ll, niter] = enter_evidence(engine, evidence, ...) +% +% The log-likelihood is not computed; ll = 0. +% niter contains the number of iterations used (if engine.protocol = 'parallel') +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - 1 means use max-product, 0 means use sum-product [0] +% exclude - list of nodes whose potential will not be included in the joint [ [] ] +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) + +ll = 0; +exclude = []; +maximize = 0; + +if nargin >= 3 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'exclude', exclude = args{i+1}; + case 'maximize', maximize = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +engine.maximize = maximize; + +if ~isempty(engine.filename) + engine.fid = fopen(engine.filename, 'w'); + if engine.fid == 0 + error(['can''t open ' engine.filename]); + end +else + engine.fid = []; +end + +gdl = engine.gdl; +bnet = bnet_from_engine(engine); + +ndoms = length(gdl.doms); +ns = bnet.node_sizes; +onodes = find(~isemptycell(evidence)); +pot_type = determine_pot_type(bnet, onodes); + +% prime each local kernel with evidence (if any) +local_kernel = cell(1, ndoms); +for i=1:ndoms + if myismember(i, exclude) + local_kernel{i} = mk_initial_pot(pot_type, gdl.doms{i}, ns, bnet.cnodes, onodes); + else + e = bnet.equiv_class(i); + local_kernel{i} = convert_to_pot(bnet.CPD{e}, pot_type, gdl.doms{i}(:), evidence); + end +end + +% initialise all msgs to 1s +msg = cell(ndoms, ndoms); +for i=1:ndoms + nbrs = gdl.nbrs{i}; + for j=nbrs(:)' + dom = gdl.sepset{i,j}; + msg{i,j} = mk_initial_pot(pot_type, dom, ns, bnet.cnodes, onodes); + end +end + +switch engine.protocol + case 'parallel', + [engine.marginal_domains, niter] = parallel_protocol(engine, evidence, pot_type, local_kernel, msg); + case 'tree', + engine.marginal_domains = serial_protocol(engine, evidence, pot_type, local_kernel, msg); + niter = 1; +end +engine.niter = niter; + +%fprintf('just finished %d iterations of belprop\n', niter); + +if ~isempty(engine.filename) + fclose(engine.fid); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_inf_engine/find_mpe.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,49 @@ +function mpe = find_mpe(engine, evidence, varargin) +% FIND_MPE Find the most probable explanation of the data (belprop) +% function mpe = find_mpe(engine, evidence,...) +% +% evidence{i} = [] if X(i) is hidden, and otherwise contains its observed value (scalar or column vector). +% +% This finds the marginally most likely value for each hidden node, +% and may give the wrong results even if the graph is acyclic, +% unless you set break_ties = 1. +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% break_ties is optional. If 1, we will force ties to be broken consistently +% by calling enter_evidence N times. (see Jensen96, p106) Default = 1. + +break_ties = 1; + +% parse optional params +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'break_ties', break_ties = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end +end + +engine = enter_evidence(engine, evidence, 'maximize', 1); + +observed = ~isemptycell(evidence); +evidence = evidence(:); % hack to handle unrolled DBNs +N = length(evidence); +mpe = cell(1,N); +for i=1:N + m = marginal_nodes(engine, i); + % observed nodes are all set to 1 inside the inference engine, so we must undo this + if observed(i) + mpe{i} = evidence{i}; + else + mpe{i} = argmax(m.T); + if break_ties + evidence{i} = mpe{i}; + [engine, ll] = enter_evidence(engine, evidence, 'maximize', 1); + end + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_inf_engine/loopy_converged.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function niter = loopy_converged(engine) +% LOOPY_CONVERGED Did loopy belief propagation converge? 0 means no, eles we return the num. iterations. +% function niter = loopy_converged(engine) +% +% We use a simple heuristic: we say convergence occurred if the number of iterations +% used was less than the maximum allowed. + +if engine.niter == engine.max_iter + niter = 0; +else + niter = engine.niter; +end +%conv = (strcmp(engine.protocol, 'tree') | (engine.niter < engine.max_iter));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_inf_engine/marginal_family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +function [marginal, pot] = marginal_family(engine, query) +% MARGINAL_NODES Compute the marginal on the family of the specified query node (belprop) +% [marginal, pot] = marginal_family(engine, query) + +pot = engine.marginal_domains{query}; +marginal = pot_to_marginal(pot);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,14 @@ +function [marginal, pot] = marginal_nodes(engine, query) +% MARGINAL_NODES Compute the marginal on the specified query nodes (belprop) +% [marginal, pot] = marginal_nodes(engine, query) +% +% query must be a subset of a family + +if isempty(query) + big_pot = engine.marginal_domains{1}; % pick an arbitrary domain +else + big_pot = engine.marginal_domains{query(end)}; +end +pot = marginalize_pot(big_pot, query); +marginal = pot_to_marginal(pot); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_inf_engine/private/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,4 @@ +/junk/1.1.1.1/Wed May 29 15:59:56 2002// +/parallel_protocol.m/1.1.1.1/Wed May 29 15:59:56 2002// +/tree_protocol.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_inf_engine/private/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@belprop_inf_engine/private
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_inf_engine/private/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_inf_engine/private/junk Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,68 @@ +fgraph +fgraph%fgraph%fgraph%fgraph%fgraph%fgraph%fgraph%fgraph%fgraph%fgraph%fgraph%fgraph +fgraph +fgraphffgraphufgraphnfgraphcfgraphtfgraphifgraphofgraphnfgraph fgraph[fgraphbfgraphefgraphlfgraph,fgraph fgraphifgraphtfgraphefgraphrfgraph]fgraph fgraph=fgraph fgraphpfgraphafgraphrfgraphafgraphlfgraphlfgraphefgraphlfgraph_fgraphpfgraphrfgraphofgraphtfgraphofgraphcfgraphofgraphlfgraph(fgraphefgraphnfgraphgfgraphifgraphnfgraphefgraph,fgraph fgraphefgraphvfgraphifgraphdfgraphefgraphnfgraphcfgraphefgraph,fgraph fgraphpfgraphofgraphtfgraph_fgraphtfgraphyfgraphpfgraphefgraph,fgraph fgraphlfgraphofgraphcfgraphafgraphlfgraph_fgraphkfgraphefgraphrfgraphnfgraphefgraphlfgraph,fgraph fgraphmfgraphsfgraphgfgraph)fgraph +fgraph +fgraphdfgraphofgraphmfgraphsfgraph fgraph=fgraph 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_inf_engine/private/parallel_protocol.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,86 @@ +function [bel, niter] = parallel_protocol(engine, evidence, pot_type, local_kernel, msg) + +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes; +onodes = find(~isemptycell(evidence)); + +ndoms = length(engine.gdl.doms); +prod_of_msg = cell(1, ndoms); +bel = cell(1, ndoms); +old_bel = cell(1, ndoms); + +converged = 0; +iter = 1; +while ~converged & (iter <= engine.max_iter) + + % each node multiplies all its incoming msgs and computes its local belief + old_bel = bel; + for i=1:ndoms + prod_of_msg{i} = mk_initial_pot(pot_type, engine.gdl.doms{i}, ns, bnet.cnodes, onodes); + nbrs = engine.gdl.nbrs{i}; + for j=nbrs(:)' + prod_of_msg{i} = multiply_by_pot(prod_of_msg{i}, msg{j,i}); + end + bel{i} = normalize_pot(multiply_by_pot(local_kernel{i}, prod_of_msg{i})); + end + + if ~isempty(engine.fid) + for i=1:ndoms + tmp = pot_to_marginal(bel{i}); + %fprintf(engine.fid, '%9.7f ', tmp.T(1)); + fprintf(engine.fid, '%9.7f ', tmp.U(1)); + end + %fprintf(engine.fid, ' U '); + %for i=1:ndoms + % tmp = pot_to_marginal(bel{i}); + % fprintf(engine.fid, '%9.7f ', tmp.U(1)); + %end + fprintf(engine.fid, '\n'); + end + + % converged? + if iter==1 + converged = 0; + else + converged = 1; + for i=1:ndoms + if ~approxeq_pot(bel{i}, old_bel{i}, engine.tol) + converged = 0; + break; + end + end + end + + if ~converged + old_msg = msg; + % each node sends a msg to each of its neighbors + for i=1:ndoms + nbrs = engine.gdl.nbrs{i}; + for j=nbrs(:)' + % multiply all incoming msgs except from j + temp = prod_of_msg{i}; + temp = divide_by_pot(temp, old_msg{j,i}); + % send msg from i to j + temp = multiply_by_pot(temp, local_kernel{i}); + temp2 = marginalize_pot(temp, engine.gdl.sepset{i,j}, engine.maximize); + msg{i,j} = normalize_pot(temp2); + end + end + end + + iter = iter + 1; +end + + +niter = iter-1; + +if 0 +for i=1:ndoms + prod_of_msg{i} = mk_initial_pot(pot_type, engine.gdl.doms{i}, ns, bnet.cnodes, onodes); + nbrs = engine.gdl.nbrs{i}; + for j=nbrs(:)' + prod_of_msg{i} = multiply_by_pot(prod_of_msg{i}, msg{j,i}); + end + bel{i} = normalize_pot(multiply_by_pot(local_kernel{i}, prod_of_msg{i})); +end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_inf_engine/private/tree_protocol.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,48 @@ +function bel = tree_protocol(engine, evidence, pot_type, local_kernel, msg) + +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes; +onodes = find(~isemptycell(evidence)); + +ndoms = length(engine.gdl.doms); +prod_of_msg = cell(1, ndoms); +bel = cell(1, ndoms); + +% collect to root (node to parents) +for n=engine.postorder + % absorb msgs from children + prod_of_msg{n} = mk_initial_pot(pot_type, engine.gdl.doms{n}, ns, bnet.cnodes, onodes); + for c=children(engine.tree, n) + prod_of_msg{n} = multiply_by_pot(prod_of_msg{n}, msg{c,n}); + end + % send msg to parents + for p=parents(engine.tree, n) + if iter==1 + temp = prod_of_msg{n}; + else + temp = divide_by_pot(prod_of_msg{n}, old_msg{p,n}); + end + temp = multiply_by_pot(temp, local_kernel{n}); + temp2 = marginalize_pot(temp, engine.gdl.sepset{n,p}, engine.maximize); + %fprintf('%d sends %d\n', n, p); + msg{n,p} = normalize_pot(temp2); + end +end + +% distribute from root (node to children) +for n=engine.preorder + % absorb from parents + %prod_of_msg{n} = mk_initial_pot(pot_type, doms{n}, ns, cnodes, onodes); + for p=parents(engine.tree, n) + prod_of_msg{n} = multiply_by_pot(prod_of_msg{n}, msg{p,n}); + end + bel{n} = normalize_pot(multiply_pots(prod_of_msg{n}, local_kernel{n})); + % send msg to children + for c=children(engine.tree, n) + temp = divide_by_pot(prod_of_msg{n}, msg{c,n}); + temp = multiply_by_pot(temp, local_kernel{n}); + temp2 = marginalize_pot(temp, engine.gdl.sepset{n,c}, engine.maximize); + %fprintf('%d sends %d\n', n, c); + msg{n,c} = normalize_pot(temp2); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_mrf2_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +/belprop_mrf2_inf_engine.m/1.1.1.1/Fri Jan 3 22:01:56 2003// +/bp_mrf2.m/1.1.1.1/Mon Jan 5 01:23:34 2004// +/enter_soft_evidence.m/1.1.1.1/Thu Jan 2 17:29:54 2003// +/find_mpe.m/1.1.1.1/Thu Jan 2 17:49:18 2003// +/marginal_nodes.m/1.1.1.1/Tue Dec 31 21:24:30 2002// +/set_params.m/1.1.1.1/Thu Jan 2 17:28:56 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_mrf2_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@belprop_mrf2_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_mrf2_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_mrf2_inf_engine/belprop_mrf2_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,46 @@ +function engine = belprop_mrf2_inf_engine(mrf2, varargin) +% BELPROP_MRF2_INF_ENGINE Belief propagation for MRFs with discrete pairwise potentials +% engine = belprop_mrf2_inf_engine(mrf2, ...) +% +% This is like belprop_inf_engine, except it is designed for mrf2, so is much faster. +% +% [ ... ] = belprop_mrf2_inf_engine(..., 'param1',val1, 'param2',val2, ...) +% allows you to specify optional parameters as name/value pairs. +% Parameters modifying behavior of enter_evidence are below [default value in brackets] +% +% max_iter - max. num. iterations [ 5*nnodes] +% momentum - weight assigned to old message in convex combination +% (useful for damping oscillations) [0] +% tol - tolerance used to assess convergence [1e-3] +% verbose - 1 means print error at every iteration [0] +% +% Parameters can be changed later using set_params + + +% The advantages of pairwise potentials are +% (1) we can compute messages using vector-matrix multiplication +% (2) we can easily specify the parameters: one potential per edge +% In contrast, potentials on larger cliques are more complicated to deal with. + + +nnodes = length(mrf2.adj_mat); + +[engine.max_iter, engine.momentum, engine.tol, engine.verbose] = ... + process_options(varargin, 'max_iter', [], 'momentum', 0, 'tol', 1e-3, ... + 'verbose', 0); + +if isempty(engine.max_iter) % no user supplied value, so compute default + engine.max_iter = 5*nnodes; + %if acyclic(mrf2.adj_mat, 0) --- can be very slow! + % engine.max_iter = nnodes; + %else + % engine.max_iter = 5*nnodes; + %end +end + +engine.bel = cell(1, nnodes); % store results of enter_evidence here +engine.mrf2 = mrf2; + +engine = class(engine, 'belprop_mrf2_inf_engine'); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_mrf2_inf_engine/bp_mrf2.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,209 @@ +function [new_bel, niter, new_msg, edge_id, nstates] = bp_mrf2_general(adj_mat, pot, local_evidence, varargin) +% BP_MRF2_GENERAL Belief propagation on an MRF with pairwise potentials +% function [bel, niter] = bp_mrf2_general(adj_mat, pot, local_evidence, varargin) +% +% Input: +% adj_mat(i,j) = 1 iff there is an edge between nodes i and j +% pot(ki,kj,i,j) or pot{i,j}(ki,kj) = potential on edge between nodes i,j +% If the potentials on all edges are the same, +% you can just pass in 1 array, pot(ki,kj) +% local_evidence(state, node) or local_evidence{i}(k) = Pr(observation at node i | Xi=k) +% +% Use cell arrays if the hidden nodes do not all have the same number of values. +% +% Output: +% bel(k,i) or bel{i}(k) = P(Xi=k|evidence) +% niter contains the number of iterations used +% +% [ ... ] = bp_mrf2(..., 'param1',val1, 'param2',val2, ...) +% allows you to specify optional parameters as name/value pairs. +% Parameters names are below [default value in brackets] +% +% max_iter - max. num. iterations [ 5*nnodes] +% momentum - weight assigned to old message in convex combination +% (useful for damping oscillations) - currently ignored i[0] +% tol - tolerance used to assess convergence [1e-3] +% maximize - 1 means use max-product, 0 means use sum-product [0] +% verbose - 1 means print error at every iteration [0] +% +% fn - name of function to call at end of every iteration [ [] ] +% fnargs - we call feval(fn, bel, iter, fnargs{:}) [ [] ] + +nnodes = length(adj_mat); + +[max_iter, momentum, tol, maximize, verbose, fn, fnargs] = ... + process_options(varargin, 'max_iter', 5*nnodes, 'momentum', 0, ... + 'tol', 1e-3, 'maximize', 0, 'verbose', 0, ... + 'fn', [], 'fnargs', []); + +if iscell(local_evidence) + use_cell = 1; +else + use_cell = 0; + [nstates nnodes] = size(local_evidence); +end + +if iscell(pot) + tied_pot = 0; +else + tied_pot = (ndims(pot)==2); +end + + +% give each edge a unique number +ndx = find(adj_mat); +nedges = length(ndx); +edge_id = zeros(1, nnodes*nnodes); +edge_id(ndx) = 1:nedges; +edge_id = reshape(edge_id, nnodes, nnodes); + +% initialise messages +if use_cell + prod_of_msgs = cell(1, nnodes); + old_bel = cell(1, nnodes); + nstates = zeros(1, nnodes); + old_msg = cell(1, nedges); + for i=1:nnodes + nstates(i) = length(local_evidence{i}); + prod_of_msgs{i} = local_evidence{i}; + old_bel{i} = local_evidence{i}; + end + for i=1:nnodes + nbrs = find(adj_mat(:,i)); + for j=nbrs(:)' + old_msg{edge_id(i,j)} = normalise(ones(nstates(j),1)); + end + end +else + prod_of_msgs = local_evidence; + old_bel = local_evidence; + %old_msg = zeros(nstates, nnodes, nnodes); + old_msg = zeros(nstates, nedges); + m = normalise(ones(nstates,1)); + for i=1:nnodes + nbrs = find(adj_mat(:,i)); + for j=nbrs(:)' + old_msg(:, edge_id(i,j)) = m; + %old_msg(:,i,j) = m; + end + end +end + + +converged = 0; +iter = 1; + +while ~converged & (iter <= max_iter) + + % each node sends a msg to each of its neighbors + for i=1:nnodes + nbrs = find(adj_mat(i,:)); + for j=nbrs(:)' + if tied_pot + pot_ij = pot; + else + if iscell(pot) + pot_ij = pot{i,j}; + else + pot_ij = pot(:,:,i,j); + end + end + pot_ij = pot_ij'; % now pot_ij(xj, xi) + % so pot_ij * msg(xi) = sum_xi pot(xj,xi) msg(xi) = f(xj) + + if 1 + % Compute temp = product of all incoming msgs except from j + % by dividing out old msg from j from the product of all msgs sent to i + if use_cell + temp = prod_of_msgs{i}; + m = old_msg{edge_id(j,i)}; + else + temp = prod_of_msgs(:,i); + m = old_msg(:, edge_id(j,i)); + end + if any(m==0) + fprintf('iter=%d, send from i=%d to j=%d\n', iter, i, j); + keyboard + end + m = m + (m==0); % valid since m(k)=0 => temp(k)=0, so can replace 0's with anything + temp = temp ./ m; + temp_div = temp; + end + + if 1 + % Compute temp = product of all incoming msgs except from j in obvious way + if use_cell + %temp = ones(nstates(i),1); + temp = local_evidence{i}; + for k=nbrs(:)' + if k==j, continue, end; + temp = temp .* old_msg{edge_id(k,i)}; + end + else + %temp = ones(nstates,1); + temp = local_evidence(:,i); + for k=nbrs(:)' + if k==j, continue, end; + temp = temp .* old_msg(:, edge_id(k,i)); + end + end + end + %assert(approxeq(temp, temp_div)) + assert(approxeq(normalise(pot_ij * temp), normalise(pot_ij * temp_div))) + + if maximize + newm = max_mult(pot_ij, temp); % bottleneck + else + newm = pot_ij * temp; + end + newm = normalise(newm); + if use_cell + new_msg{edge_id(i,j)} = newm; + else + new_msg(:, edge_id(i,j)) = newm; + end + end % for j + end % for i + old_prod_of_msgs = prod_of_msgs; + + % each node multiplies all its incoming msgs and computes its local belief + if use_cell + for i=1:nnodes + nbrs = find(adj_mat(:,i)); + prod_of_msgs{i} = local_evidence{i}; + for j=nbrs(:)' + prod_of_msgs{i} = prod_of_msgs{i} .* new_msg{edge_id(j,i)}; + end + new_bel{i} = normalise(prod_of_msgs{i}); + end + err = abs(cat(1,new_bel{:}) - cat(1, old_bel{:})); + else + for i=1:nnodes + nbrs = find(adj_mat(:,i)); + prod_of_msgs(:,i) = local_evidence(:,i); + for j=nbrs(:)' + prod_of_msgs(:,i) = prod_of_msgs(:,i) .* new_msg(:,edge_id(j,i)); + end + new_bel(:,i) = normalise(prod_of_msgs(:,i)); + end + err = abs(new_bel(:) - old_bel(:)); + end + converged = all(err < tol); + if verbose, fprintf('error at iter %d = %f\n', iter, sum(err)); end + if ~isempty(fn) + if isempty(fnargs) + feval(fn, new_bel); + else + feval(fn, new_bel, iter, fnargs{:}); + end + end + + iter = iter + 1; + old_msg = new_msg; + old_bel = new_bel; +end % while + +niter = iter-1; + +fprintf('converged in %d iterations\n', niter); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_mrf2_inf_engine/enter_soft_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +function [engine, ll, niter] = enter_soft_evidence(engine, local_evidence) +% ENTER_SOFT_EVIDENCE Propagate evidence using belief propagation +% [engine, ll, niter] = enter_soft_evidence(engine, local_evidence) +% +% local_evidence{i}(j) = Pr(observation at node i | S(i)=j) +% +% The log-likelihood is not computed; ll = 0. +% niter contains the number of iterations used + +ll = 0; +mrf2 = engine.mrf2; +[bel, niter] = bp_mrf2(mrf2.adj_mat, mrf2.pot, local_evidence, ... + 'max_iter', engine.max_iter, 'momentum', engine.momentum, ... + 'tol', engine.tol, 'maximize', 0, 'verbose', engine.verbose); +engine.bel = bel;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_mrf2_inf_engine/find_mpe.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +function mpe = find_mpe(engine, local_evidence) +% FIND_MPE Find the most probable explanation of the data +% function mpe = find_mpe(engine, local_evidence +% +% local_evidence{i}(j) = Pr(observation at node i | S(i)=j) +% +% This finds the marginally most likely value for each hidden node. +% It may give inconsistent results if there are ties. + +[mpe, niter] = bp_mpe_mrf2(engine.mrf2.adj_mat, engine.mrf2.pot, local_evidence, ... + 'max_iter', engine.max_iter, 'momentum', engine.momentum, ... + 'tol', engine.tol);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_mrf2_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,10 @@ +function marginal = marginal_nodes(engine, query) +% MARGINAL_NODES Compute the marginal on the specified query nodes (belprop) +% marginal = marginal_nodes(engine, query) +% +% query must be a single node + +if length(query)>1 + error('can only handle single node marginals') +end +marginal = engine.bel{query};
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@belprop_mrf2_inf_engine/set_params.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +function engine = set_params(engine, varargin) +% SET_PARAMS Modify parameters of the inference engine +% engine = set_params(engine, 'param1',val1, 'param2',val2, ...) +% +% Parameter names are listed below. +% +% max_iter - max. num. iterations +% momentum - weight assigned to old message in convex combination +% (useful for damping oscillations) +% tol - tolerance used to assess convergence +% verbose - 1 means print error at every iteration [0] + +[engine.max_iter, engine.momentum, engine.tol, engine.verbose] = ... + process_options('max_iter', engine.max_iter, 'momentum', engine.momentum, ... + 'tol', engine.tol, 'verbose', engine.verbose);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@cond_gauss_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,4 @@ +/cond_gauss_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@cond_gauss_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@cond_gauss_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@cond_gauss_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@cond_gauss_inf_engine/cond_gauss_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,23 @@ +function engine = cond_gauss_inf_engine(bnet) +% COND_GAUSS_INF_ENGINE Conditional Gaussian inference engine +% engine = cond_gauss_inf_engine(bnet) +% +% Enumerates all the discrete roots, and runs jtree on the remaining Gaussian nodes. + +dnodes = mysetdiff(1:length(bnet.dag), bnet.cnodes); + +%onodes = dnodes; % all the discrete ndoes will be observed +%engine.sub_engine = jtree_inf_engine(bnet, onodes); +bnet2 = bnet; +bnet2.observed = dnodes; +engine.sub_engine = jtree_inf_engine(bnet2); + +% This is where we will store the results between enter_evidence and marginal_nodes +engine.T = []; +engine.mu = []; +engine.Sigma = []; +engine.joint_dmarginal = []; +engine.onodes = []; % needed for marginal_nodes +engine.evidence = []; % needed for marginal_nodes add_ev + +engine = class(engine, 'cond_gauss_inf_engine', inf_engine(bnet));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@cond_gauss_inf_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,57 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (cond_gauss) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i} = [] if if X(i) is hidden, and otherwise contains its observed value (scalar or column vector) + +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes(:); +observed = ~isemptycell(evidence); +onodes = find(observed); +hnodes = find(isemptycell(evidence)); +engine.evidence = evidence; + +% check there are no C->D links where C is hidden +pot_type = determine_pot_type(bnet, onodes); + +dhid = myintersect(hnodes, bnet.dnodes); +S = prod(ns(dhid)); +T = zeros(S,1); + +N = length(bnet.dag); +mu = cell(1,N); +Sigma = cell(1,N); +cobs = myintersect(bnet.cnodes, onodes); +chid = myintersect(bnet.cnodes, hnodes); +ens = ns; +ens(cobs) = 0; +for j=chid(:)' + mu{j} = zeros(ens(j), S); + Sigma{j} = zeros(ens(j), ens(j), S); +end + +for i=1:S + dvals = ind2subv(ns(dhid), i); + evidence(dhid) = num2cell(dvals); + [sub_engine, loglik] = enter_evidence(engine.sub_engine, evidence); + for j=chid(:)' + m = marginal_nodes(sub_engine, j); + mu{j}(:,i) = m.mu; + Sigma{j}(:,:,i) = m.Sigma; + end + T(i) = exp(loglik); +end + +[T, lik] = normalise(T); +loglik = log(lik); + +engine.T = T; +engine.mu = mu; +engine.Sigma = Sigma; + +dnodes = bnet.dnodes; +dobs = myintersect(dnodes, onodes); +ens(dobs) = 1; +engine.joint_dmarginal = dpot(dnodes, ens(dnodes), myreshape(engine.T, ens(dnodes))); + +engine.onodes = onodes;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@cond_gauss_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,36 @@ +function marginal = marginal_nodes(engine, query, add_ev) +% MARGINAL_NODES Compute the marginal on the specified query nodes (cond_gauss) +% marginal = marginal_nodes(engine, query, add_ev) +% +% 'query' must be a singleton set +% add_ev is an optional argument; if 1, we will "inflate" the marginal of observed nodes +% to their original size, adding 0s to the positions which contradict the evidence + +if nargin < 3, add_ev = 0; end + +if length(query) ~= 1 + error('cond_gauss_inf_engine can only handle marginal queries on single nodes') +end +j = query; +bnet = bnet_from_engine(engine); + +if myismember(j, bnet.cnodes) + if ~myismember(j, engine.onodes) + [m, C] = collapse_mog(engine.mu{j}, engine.Sigma{j}, engine.T); + marginal.mu = m; + marginal.Sigma = C; + marginal.T = 1.0; % single mixture component + else + marginal.mu = engine.evidence{j}; + k = bnet.node_sizes(j); + marginal.Sigma = zeros(k,k); + marginal.T = 1.0; % since P(E|E)=1 + end +else + marginal = pot_to_marginal(marginalize_pot(engine.joint_dmarginal, j)); + if add_ev + marginal = add_ev_to_dmarginal(marginal, engine.evidence, bnet.node_sizes); + end +end + +marginal.domain = query;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@enumerative_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,4 @@ +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enumerative_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@enumerative_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@enumerative_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@enumerative_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@enumerative_inf_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,10 @@ +function [engine, loglik] = enter_evidence(engine, evidence) +% ENTER_EVIDENCE Add the specified evidence to the network (enumerative_inf) +% [engine, loglik] = enter_evidence(engine, evidence) +% +% evidence{i} = [] if if X(i) is hidden, and otherwise contains its observed value (scalar or column vector) + +engine.evidence = evidence; +if nargout == 2 + [m, loglik] = marginal_nodes(engine, []); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@enumerative_inf_engine/enumerative_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function engine = enumerative_inf_engine(bnet) +% ENUMERATIVE_INF_ENGINE Inference engine for fully discrete BNs that uses exhaustive enumeration. +% engine = enumerative_inf_engine(bnet) + + +assert(isempty(bnet.cnodes)); + +% This is where we store stuff between enter_evidence and marginal_nodes +engine.evidence = []; + +engine = class(engine, 'enumerative_inf_engine', inf_engine(bnet));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@enumerative_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,41 @@ +function [marginal, loglik] = marginal_nodes(engine, query) +% MARGINAL_NODES Compute the marginal on the specified query nodes (enumerative_inf) +% [marginal, loglik] = marginal_nodes(engine, query) + + +if isempty(query) & nargout < 2 + marginal.T = 1; + marginal.domain = []; + return; +end + +evidence = engine.evidence; +bnet = bnet_from_engine(engine); +assert(isempty(bnet.cnodes)); +n = length(bnet.dag); +observed = ~isemptycell(evidence); +vals = cat(1,evidence{observed}); +vals = vals(:)'; +ns = bnet.node_sizes; + +sz = ns(query); +T = 0*myones(sz); +p = 0; +for i=1:prod(ns) + inst = ind2subv(ns, i); % i'th instantiation + if isempty(vals) | inst(observed) == vals % agrees with evidence + prob = exp(log_lik_complete(bnet, num2cell(inst(:)))); + p = p + prob; + v = inst(query); + j = subv2ind(sz, v); + T(j) = T(j) + prob; + end +end + +[T, lik] = normalise(T); +lik = p; +loglik = log(lik); + +Tsmall = shrink_obs_dims_in_table(T, query, evidence); +marginal.domain = query; +marginal.T = Tsmall;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gaussian_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,4 @@ +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/gaussian_inf_engine.m/1.1.1.1/Fri May 14 01:13:26 2004// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +D/private////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gaussian_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@gaussian_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gaussian_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gaussian_inf_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,46 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (gaussian_inf_engine) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i} = [] if if X(i) is hidden, and otherwise contains its observed value (scalar or column vector) + +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes; +O = find(~isemptycell(evidence)); +H = find(isemptycell(evidence)); +vals = cat(1, evidence{O}); + +% Compute Pr(H|o) +[Hmu, HSigma, loglik] = condition_gaussian(engine.mu, engine.Sigma, H, O, vals(:), ns); + +engine.Hmu = Hmu; +engine.HSigma = HSigma; +engine.hnodes = H; + +%%%%%%%% + +function [mu2, Sigma2, loglik] = condition_gaussian(mu, Sigma, X, Y, y, ns) +% CONDITION_GAUSSIAN Compute Pr(X|Y=y) where X and Y are jointly Gaussian. +% [mu2, Sigma2, ll] = condition_gaussian(mu, Sigma, X, Y, y, ns) + +if isempty(y) + mu2 = mu; + Sigma2 = Sigma; + loglik = 0; + return; +end + +use_log = 1; + +if length(Y)==length(mu) % instantiating every variable + mu2 = y; + Sigma2 = zeros(length(y)); + loglik = gaussian_prob(y, mu, Sigma, use_log); + return; +end + +[muX, muY, SXX, SXY, SYX, SYY] = partition_matrix_vec(mu, Sigma, X, Y, ns); +K = SXY*inv(SYY); +mu2 = muX + K*(y-muY); +Sigma2 = SXX - K*SYX; +loglik = gaussian_prob(y, muY, SYY, use_log);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gaussian_inf_engine/gaussian_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,25 @@ +function engine = gaussian_inf_engine(bnet) +% GAUSSIAN_INF_ENGINE Computes the joint multivariate Gaussian corresponding to the bnet +% engine = gaussian_inf_engine(bnet) +% +% For details on how to compute the joint Gaussian from the bnet, see +% - "Gaussian Influence Diagrams", R. Shachter and C. R. Kenley, Management Science, 35(5):527--550, 1989. +% Once we have the Gaussian, we can apply the standard formulas for conditioning and marginalization. + +assert(isequal(bnet.cnodes, 1:length(bnet.dag))); + +[W, D, mu] = extract_params_from_gbn(bnet); +U = inv(eye(size(W)) - W')'; +Sigma = U' * D * U; + +engine.mu = mu; +engine.Sigma = Sigma; +%engine.logp = log(normal_coef(Sigma)); + +% This is where we will store the results between enter_evidence and marginal_nodes +engine.Hmu = []; +engine.HSigma = []; +engine.hnodes = []; + +engine = class(engine, 'gaussian_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gaussian_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +function marginal = marginal_nodes(engine, query) +% MARGINAL_NODES Compute the marginal on the specified query nodes (gaussian) +% marginal = marginal_nodes(engine, query) + +% Compute sum_{Hsum} Pr(Hkeep, Hsum | o) +H = engine.hnodes; +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes; +Hkeep = myintersect(H, query); +Hsum = mysetdiff(H, Hkeep); + +[marginal.mu, marginal.Sigma] = marginalize_gaussian(engine.Hmu, engine.HSigma, Hkeep, Hsum, ns); +marginal.domain = query; +marginal.T = 1; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gaussian_inf_engine/private/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +/extract_params_from_gbn.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gaussian_inf_engine/private/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@gaussian_inf_engine/private
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gaussian_inf_engine/private/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gaussian_inf_engine/private/extract_params_from_gbn.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,38 @@ +function [B,D,mu] = extract_params_from_gbn(bnet) +% Extract all the local parameters of each Gaussian node, and collect them into global matrices. +% [B,D,mu] = extract_params_from_gbn(bnet) +% +% B(i,j) is a block matrix that contains the transposed weight matrix from node i to node j. +% D(i,i) is a block matrix that contains the noise covariance matrix for node i. +% mu(i) is a block vector that contains the shifted noise mean for node i. + +% In Shachter's model, the mean of each node in the global gaussian is +% the same as the node's local unconditional mean. +% In Alag's model (which we use), the global mean gets shifted. + + +num_nodes = length(bnet.dag); +bs = bnet.node_sizes(:); % bs = block sizes +N = sum(bs); % num scalar nodes + +B = zeros(N,N); +D = zeros(N,N); +mu = zeros(N,1); + +for i=1:num_nodes % in topological order + ps = parents(bnet.dag, i); + e = bnet.equiv_class(i); + %[m, Sigma, weights] = extract_params_from_CPD(bnet.CPD{e}); + s = struct(bnet.CPD{e}); % violate privacy of object + m = s.mean; Sigma = s.cov; weights = s.weights; + if length(ps) == 0 + mu(block(i,bs)) = m; + else + mu(block(i,bs)) = m + weights * mu(block(ps,bs)); + end + B(block(ps,bs), block(i,bs)) = weights'; + D(block(i,bs), block(i,bs)) = Sigma; +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gibbs_sampling_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,4 @@ +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/gibbs_sampling_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +D/private////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gibbs_sampling_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@gibbs_sampling_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gibbs_sampling_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gibbs_sampling_inf_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,29 @@ +function [engine, loglik] = enter_evidence(engine, evidence) +% ENTER_EVIDENCE Add the specified evidence to the network (gibbs_sampling_inf_engine) +% [engine, loglik] = enter_evidence(engine, evidence) +% +% evidence{i} = [] if if X(i) is hidden, and otherwise contains its observed value +% +% loglik is not computed... we just return a 0 value + +bnet = bnet_from_engine(engine); + +engine.hnodes = find(isemptycell(evidence)); +engine.onodes = mysetdiff(1:length(evidence), engine.hnodes); + +engine.evidence = zeros(engine.slice_size, 1); + +% Reset all counts since they are no longer valid +engine.marginal_counts = {}; +%engine.state = sample_bnet (bnet, 1, 0); +engine.state = cell2num(sample_bnet(bnet)); + +% For speed, we use a normal (not cell) array. We're making use of +% the current restriction to discrete nodes. +for i = engine.onodes + engine.evidence(i) = evidence{i}; +end + +loglik = 0; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gibbs_sampling_inf_engine/gibbs_sampling_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,104 @@ +function engine = gibbs_sampling_inf_engine(bnet, varargin) +% GIBBS_SAMPLING_INF_ENGINE +% +% engine = gibbs_sampling_inf_engine(bnet, ...) +% +% Optional parameters [default in brackets] +% 'burnin' - How long before you start using the samples [100]. +% 'gap' - how often you use the samples in the estimate [1]. +% 'T' - number of samples [1000] +% i.e, number of node flips (so, for +% example if there are 10 nodes in the bnet, and T is 1000, each +% node will get flipped 100 times (assuming a deterministic schedule)) +% The total running time is proportional to burnin + T*gap. +% +% 'order' - if the sampling schedule is deterministic, use this +% parameter to specify the order in which nodes are sampled. +% Order is allowed to include multiple copies of nodes, which is +% useful if you want to, say, focus sampling on particular nodes. +% Default is to use a deterministic schedule that goes through the +% nodes in order. +% +% 'sampling_dist' - when using a stochastic sampling method, at +% each step the node to sample is chosen according to this +% distribution (may be unnormalized) +% +% The sampling_dist and order parameters shouldn't both be used, +% and this will cause an assert. +% +% +% Written by "Bhaskara Marthi" <bhaskara@cs.berkeley.edu> Feb 02. + + +engine.burnin = 100; +engine.gap = 1; +engine.T = 1000; +use_default_order = 1; +engine.deterministic = 1; +engine.order = {}; +engine.sampling_dist = {}; + +if nargin >= 2 + args = varargin; + nargs = length(args); + for i = 1:2:nargs + switch args{i} + case 'burnin' + engine.burnin = args{i+1}; + case 'gap' + engine.gap = args{i+1}; + case 'T' + engine.T = args{i+1}; + case 'order' + assert (use_default_order); + use_default_order = 0; + engine.order = args{i+1}; + case 'sampling_dist' + assert (use_default_order); + use_default_order = 0; + engine.deterministic = 0; + engine.sampling_dist = args{i+1}; + otherwise + error(['unrecognized parameter to gibbs_sampling_inf_engine']); + end + end +end + +engine.slice_size = size(bnet.dag, 2); +if (use_default_order) + engine.order = 1:engine.slice_size; +end +engine.hnodes = []; +engine.onodes = []; +engine.evidence = []; +engine.state = []; +engine.marginal_counts = {}; + +% Precompute the strides for each CPT +engine.strides = compute_strides(bnet); + +% Precompute graphical information +engine.families = compute_families(bnet); +engine.children = compute_children(bnet); + +% For convenience, store the CPTs as tables rather than objects +engine.CPT = get_cpts(bnet); + +engine = class(engine, 'gibbs_sampling_inf_engine', inf_engine(bnet)); + + + + + + + + + + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gibbs_sampling_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,135 @@ +function [marginal, engine] = marginal_nodes(engine, nodes, varargin); +% MARGINAL_NODES Compute the marginal on the specified query nodes +% (gibbs_sampling_engine) +% [marginal, engine] = marginal_nodes(engine, nodes, ...) +% +% returns Pr(X(nodes) | X(observedNodes)) +% +% The engine is also modified, and so it is returned as well, since +% Matlab doesn't support passing by reference(!) So +% if you want to, for example, incrementally run gibbs for a few 100 +% steps at a time, you should use the returned value. +% +% Optional arguments : +% +% 'reset_counts' is 1 if you want to reset the counts made in the +% past, and 0 otherwise (if the current query nodes are different +% from the previous query nodes, or if marginal_nodes has not been +% called before, reset_counts should be set to 1). +% By default it is 1. + + +reset_counts = 1; + +if (nargin > 3) + args = varargin; + nargs = length(args); + for i = 1:2:nargs + switch args{i} + case 'reset_counts' + reset_counts = args{i+1}; + otherwise + error(['Incorrect argument to gibbs_sampling_engine/' ... + ' marginal_nodes']); + end + end +end + +% initialization stuff +bnet = bnet_from_engine(engine); +slice_size = engine.slice_size; +hnodes = engine.hnodes; +onodes = engine.onodes; +nonqnodes = mysetdiff(1:slice_size, nodes); +gap = engine.gap; +burnin = engine.burnin; +T_max = engine.T; +ns = bnet.node_sizes(nodes); + + +% Cache the strides for the marginal table +marg_strides = [1 cumprod(ns(1:end-1))]; + +% Reset counts if necessary +if (reset_counts == 1) + %state = sample_bnet(bnet, 1, 0); + %state = cell2num(sample_bnet(bnet, 'evidence', num2cell(engine.evidence))); + state = cell2num(sample_bnet(bnet)); + state(onodes) = engine.evidence(onodes); + if (length(ns) == 1) + marginal_counts = zeros(ns(1),1); + else + marginal_counts = zeros(ns); + end + +% Otherwise, use the counts that have been stored in the engine +else + state = engine.state; + state(onodes, :) = engine.evidence(onodes, :); + marginal_counts = engine.marginal_counts; +end + +if (engine.deterministic == 1) + pos = 1; + order = engine.order; + orderSize = length(engine.order); +else + sampling_dist = normalise(engine.sampling_dist); +end + + +for t = 1:(T_max*gap+burnin) + + % First, select node m to sample + if (engine.deterministic == 1) + m = engine.order(pos); + pos = pos+1; + if (pos > orderSize) + pos = 1; + end + else + m = my_sample_discrete(sampling_dist); + end + + + % If the node is observed, then don't bother resampling + if (myismember(m, onodes)) + continue; + end + + % Next, compute the posterior + post = compute_posterior (bnet, state, m, engine.strides, engine.families, ... + engine.children, engine.CPT); + state(m) = my_sample_discrete(post); + + % Now update our monte carlo estimate of the posterior + % distribution on the query node + if ((mod(t-burnin, gap) == 0) & (t > burnin)) + + vals = state(nodes); + index = 1+marg_strides*(vals-1); + marginal_counts(index) = marginal_counts(index)+1; + end +end + +% Store results for future computation. Note that we store +% unnormalized counts +engine.state = state; +engine.marginal_counts = marginal_counts; + +marginal.T = normalise(marginal_counts); + + + + + + + + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gibbs_sampling_inf_engine/private/CPT.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function c = CPT(bnet, i) +% CPT Helper function avoid having to type in +% CPD_to_CPT(bnet.CPD{i}) every time + +c = CPD_to_CPT(bnet.CPD{i}); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gibbs_sampling_inf_engine/private/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +/CPT.m/1.1.1.1/Wed May 29 15:59:56 2002// +/compute_children.m/1.1.1.1/Wed May 29 15:59:56 2002// +/compute_families.m/1.1.1.1/Wed May 29 15:59:56 2002// +/compute_families_dbn.m/1.1.1.1/Wed May 29 15:59:56 2002// +/compute_posterior.c/1.1.1.1/Wed May 29 15:59:56 2002// +/compute_posterior_dbn.m/1.1.1.1/Wed May 29 15:59:56 2002// +/compute_strides.m/1.1.1.1/Wed May 29 15:59:56 2002// +/get_cpts.m/1.1.1.1/Wed May 29 15:59:56 2002// +/get_slice_dbn.c/1.1.1.1/Wed May 29 15:59:56 2002// +/get_slice_dbn.m/1.1.1.1/Wed May 29 15:59:56 2002// +/my_sample_discrete.m/1.1.1.1/Wed May 29 15:59:56 2002// +/sample_single_discrete.c/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gibbs_sampling_inf_engine/private/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@gibbs_sampling_inf_engine/private
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gibbs_sampling_inf_engine/private/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gibbs_sampling_inf_engine/private/compute_children.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +function c = compute_children(bnet) +% COMPUTE_CHILDREN +% precomputes the children of nodes in a bnet +% +% The return value is a cell array for now + +ss = size(bnet.dag, 1); +c = cell(ss, 1); +for i = 1:ss + c{i} = children(bnet.dag, i); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gibbs_sampling_inf_engine/private/compute_families.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +function families = compute_families(bnet) +% COMPUTE_FAMILIES +% precomputes the families of nodes in a bnet +% +% The return value is a cell array for now + +ss = size(bnet.dag, 1); +families = cell(ss, 1); +for i = 1:ss + families{i} = family(bnet.dag, i); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gibbs_sampling_inf_engine/private/compute_families_dbn.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function families = compute_families_dbn(bnet) +% COMPUTE_FAMILIES +% precomputes the families of nodes in a dbn +% +% The return value is a cell array for now + +ss = size(bnet.intra, 1); +families = cell(ss, 2); +for i = 1:ss + families{i, 1} = family(bnet.dag, i, 1); + families{i, 2} = family(bnet.dag, i, 2); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gibbs_sampling_inf_engine/private/compute_posterior.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,107 @@ +#include "mex.h" + +/* Helper function that extracts a one-dimensional slice from a cpt */ +/* +void multiplySlice(mxArray *bnet, mxArray *state, int i, int nsi, int j, + mxArray *strides, mxArray *fam, mxArray *cpts, + double *y) +*/ +void multiplySlice(const mxArray *bnet, const mxArray *state, int i, int nsi, int j, + const mxArray *strides, const mxArray *fam, const mxArray *cpts, + double *y) +{ + mxArray *ec, *cpt, *family; + double *ecElts, *cptElts, *famElts, *strideElts, *ev; + int c1, k, famSize, startInd, strideStride, pos, stride; + + strideStride = mxGetM(strides); + strideElts = mxGetPr(strides); + + ev = mxGetPr(state); + + /* Get the CPT */ + ec = mxGetField (bnet, 0, "equiv_class"); + ecElts = mxGetPr(ec); + k = (int) ecElts[j-1]; + cpt = mxGetCell (cpts, k-1); + cptElts = mxGetPr (cpt); + + /* Get the family vector for this cpt */ + family = mxGetCell (fam, j-1); + famSize = mxGetNumberOfElements (family); + famElts = mxGetPr (family); + + /* Figure out starting position and stride */ + startInd = 0; + for (c1 = 0, pos = k-1; c1 < famSize; c1++, pos +=strideStride) { + if (famElts[c1] != i) { + startInd += strideElts[pos]*(ev[(int)famElts[c1]-1]-1); + } + else { + stride = strideElts[pos]; + } + } + + for (c1 = 0, pos = startInd; c1 < nsi; c1++, pos+=stride) { + y[c1] *= cptElts[pos]; + } +} + + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray + *prhs[]) +{ + double *pi, *nsElts, *y, *childrenElts; + mxArray *ns, *children; + double sum; + int i, nsi, c1, numChildren; + + pi = mxGetPr(prhs[2]); + i = (int) pi[0]; + + ns = mxGetField(prhs[0], 0, "node_sizes"); + nsElts = mxGetPr(ns); + nsi = (int) nsElts[i-1]; + + /* Initialize the posterior */ + plhs[0] = mxCreateDoubleMatrix (1, nsi, mxREAL); + y = mxGetPr(plhs[0]); + for (c1 = 0; c1 < nsi; c1++) { + y[c1] = 1; + } + + /* Multiply in the cpt of the node i */ + multiplySlice(prhs[0], prhs[1], i, nsi, i, prhs[3], prhs[4], + prhs[6], y); + + + /* Multiply in cpts of children of i */ + children = mxGetCell (prhs[5], i-1); + numChildren = mxGetNumberOfElements (children); + childrenElts = mxGetPr (children); + + for (c1 = 0; c1 < numChildren; c1++) { + int j; + j = (int) childrenElts[c1]; + multiplySlice (prhs[0], prhs[1], i, nsi, j, prhs[3], prhs[4], + prhs[6], y); + } + + sum = 0; + /* normalize! */ + for (c1 = 0; c1 < nsi; c1++) { + sum += y[c1]; + } + + for (c1 = 0; c1 < nsi; c1++) { + y[c1] /= sum; + } +} + + + + + + + +
Binary file _FullBNT/BNT/inference/static/@gibbs_sampling_inf_engine/private/compute_posterior.mexmaci has changed
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gibbs_sampling_inf_engine/private/compute_posterior_dbn.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,59 @@ +function post = compute_posterior_dbn(bnet, state, i, n, strides, families, ... + CPT) +% COMPUTE_POSTERIOR +% +% post = compute_posterior(bnet, state, i, n, strides, families, +% cpts) +% +% Compute the posterior distribution on node X_i^n of a DBN, +% conditional on evidence in the cell array state +% +% strides is the cached result of compute_strides(bnet) +% families is the cached result of compute_families(bnet) +% cpt is the cached result of get_cpts(bnet) +% +% post is a one-dimensional table + + + +% First multiply in the cpt of the node itself +post = get_slice_dbn(bnet, state, i, n, i, n, strides, families, CPT); +post = post(:); + +% Then multiply in CPTs of children that are in this slice +for j = children(bnet.intra, i) + slice = get_slice_dbn(bnet, state, j, n, i, n, strides, families, CPT); + post = post.*slice(:); +end + +% Finally, if necessary, multiply in CPTs of children in the next +% slice +if (n < size(state,2)) + for j = children(bnet.inter, i) + slice = get_slice_dbn(bnet, state, j, n+1, i, n, strides, families, ... + CPT); + post = post.*slice(:); + end +end + +post = normalise(post); + + + + + + + + + + + + + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gibbs_sampling_inf_engine/private/compute_strides.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,27 @@ +function strides = compute_strides(bnet) +% COMPUTE_STRIDES For each CPT and each variable in that CPT, +% returns the stride of that variable. So in future, we can +% quickly extract a slice of the CPT. +% +% The return value is a 2d array, where strides(i,j) contains the +% stride of the jth variable in the ith CPT. Cell arrays would +% have saved space but they are slower. +% + +num_cpts = size(bnet.CPD, 2); +max_cpt_dim = 1 + max(sum(bnet.dag)); +strides = zeros(num_cpts, max_cpt_dim); + +for i = 1:num_cpts + c = CPT(bnet, i); + siz = size(CPT(bnet, i)); + + % Deal with the special case of a 1-d array separately + if siz(2) == 1 + dim = 1; + else + dim = size(siz, 2); + end + + strides(i, 1:dim ) = [1 cumprod(siz(1:dim-1))]; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gibbs_sampling_inf_engine/private/get_cpts.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +function c = get_cpts(bnet) +% Get all the cpts in tabular form + +cpds = bnet.CPD; +c = cell(size(cpds)); +for i = 1:length(c) + c{i} = CPT(bnet, i); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gibbs_sampling_inf_engine/private/get_slice_dbn.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,116 @@ +#include "mex.h" + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray + *prhs[]) +{ + double *pn, *pi, *pj, *pm, *y, *ecElts, *pcpt, *famElts, *strideElts, + *ev, *nsElts; + int i, k, j, m, n; + mxArray *ec, *cpt, *fam, *ns; + int c1, famSize, nsj; + int strideStride, startInd, stride, pos, numNodes; + + const int BNET = 0; + const int STATE = 1; + const int STRIDES = 6; + const int FAMILIES = 7; + const int CPT = 8; + + pn = mxGetPr(prhs[3]); + n = (int) pn[0]; + pi = mxGetPr(prhs[2]); + i = (int) pi[0]; + pj = mxGetPr(prhs[4]); + j = (int) pj[0]; + pm = mxGetPr(prhs[5]); + m = (int) pm[0]; + ev = mxGetPr(prhs[STATE]); + ns = mxGetField (prhs[BNET], 0, "node_sizes"); + nsElts = mxGetPr (ns); + numNodes = mxGetM(ns); + + strideStride = mxGetM(prhs[STRIDES]); + strideElts = mxGetPr(prhs[STRIDES]); + + + + /* Treat the case n = 1 separately */ + if (pn[0] == 1) { + + /* Get the appropriate CPT */ + ec = mxGetField (prhs[BNET], 0, "eclass1"); + ecElts = mxGetPr(ec); + k = (int) ecElts[i-1]; + cpt = mxGetCell (prhs[8], k-1); + pcpt = mxGetPr(cpt); + + nsj = (int) nsElts[j-1]; + + /* Get the correct family vector */ + /* (Note : MEX is painful) */ + fam = mxGetCell (prhs[FAMILIES], i - 1); + famSize = mxGetNumberOfElements(fam); + famElts = mxGetPr(fam); + + + /* Figure out starting position and stride */ + startInd = 0; + for (c1 = 0, pos = k-1; c1 < famSize; c1++, pos+=strideStride) { + if (famElts[c1] != j) { + startInd += strideElts[pos]*(ev[(int)famElts[c1]-1]-1); + } + else { + stride = strideElts[pos]; + } + } + + plhs[0] = mxCreateDoubleMatrix (1, nsj, mxREAL); + y = mxGetPr(plhs[0]); + for (c1 = 0, pos = startInd; c1 < nsj; c1++, pos+=stride) { + y[c1] = pcpt[pos]; + } + } + + /* Handle the case n > 1 */ + else { + + /* Get the appropriate CPT */ + ec = mxGetField (prhs[BNET], 0, "eclass2"); + ecElts = mxGetPr(ec); + k = (int) ecElts[i-1]; + cpt = mxGetCell (prhs[8], k-1); + pcpt = mxGetPr(cpt); + + /* Figure out size of slice */ + if (m == 1) { + nsj = (int) nsElts[j-1]; + } + else { + nsj = (int) nsElts[j-1+numNodes]; + } + + /* Figure out family */ + fam = mxGetCell (prhs[FAMILIES], i - 1 + numNodes); + famSize = mxGetNumberOfElements(fam); + famElts = mxGetPr(fam); + + startInd = 0; + for (c1 = 0, pos = k-1; c1 < famSize; c1++, pos+=strideStride) { + int f = (int) famElts[c1]; + + if (((f == j+numNodes) && (m == n)) || ((f == j) && (m == + n-1))) { + stride = strideElts[pos]; + } + else { + startInd += strideElts[pos] * (ev[f-1+((n-2)*numNodes)]-1); + } + } + + plhs[0] = mxCreateDoubleMatrix(1,nsj, mxREAL); + y = mxGetPr(plhs[0]); + for (c1 = 0, pos = startInd; c1 < nsj; c1++, pos+=stride) { + y[c1] = pcpt[pos]; + } + } +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gibbs_sampling_inf_engine/private/get_slice_dbn.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,87 @@ +function slice = get_slice_dbn(bnet, state, i, n, j, m, strides, families, ... + CPT) +% slice = get_slice(bnet, state, i, n, j, m, strides, families, cpt) +% +% GET_SLICE get one-dimensional slice of the CPT for node X_i^n +% that corresponds to the different values of X_j^m, where all +% other nodes have values given by state. +% strides is the result of +% calling compute_strides(bnet) +% families is the result of calling compute_families(bnet) +% cpts is the result of calling get_cpts(bnet) +% +% slice is a 1-d array + + +if (n == 1) + + k = bnet.eclass1(i); + c = CPT{k}; + + % Figure out evidence on family + fam = families{i, 1}; + ev = state(fam, 1); + + % Remove evidence on node j + pos = find(fam == j); + ev(pos) = 1; + dim = size(ev, 1); + + % Compute initial index and stride + start_ind = 1+strides(k, 1:dim)*(ev-1); + stride = strides(k, pos); + + % Compute the slice + slice = c(start_ind:stride:start_ind+(bnet.node_sizes(j, 1)-1)*stride); + +else + + k = bnet.eclass2(i); + c = CPT{k}; + + fam = families{i, 2}; + ss = length(bnet.intra); + + % Divide the family into nodes in this time step and nodes in the + % previous time step + this_time_step = fam(find(fam > ss)); + prev_time_step = fam(find(fam <= ss)); + + % Normalize the node numbers + this_time_step = this_time_step - ss; + + % Get the evidence + this_step_ev = state(this_time_step, n); + prev_step_ev = state(prev_time_step, n-1); + + % Remove the evidence for X_j^m + if (m == n) + pos = find(this_time_step == j); + this_step_ev(pos) = 1; + pos = pos + size(prev_time_step, 2); + else + assert (m == n-1); + pos = find(prev_time_step == j); + prev_step_ev(pos) = 1; + end + + % Combine the two time steps + ev = [prev_step_ev; this_step_ev]; + dim = size(ev, 1); + + + % Compute starting index and stride + start_ind = 1 + strides(k, 1:dim)*(ev-1); + stride = strides(k, pos); + + % Compute slice + if (m == 1) + q = 1; + else + q = 2; + end + slice = c(start_ind:stride:start_ind+(bnet.node_sizes(j, q)-1)*stride); +end + + +
Binary file _FullBNT/BNT/inference/static/@gibbs_sampling_inf_engine/private/get_slice_dbn.mexmaci has changed
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gibbs_sampling_inf_engine/private/my_sample_discrete.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +function M = my_sample_discrete(prob) +% A faster version that calls a c subfunction. Will update one +% day to have r and c parameters as well + +R = rand (1,1); +M = sample_single_discrete(R, prob); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@gibbs_sampling_inf_engine/private/sample_single_discrete.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,22 @@ +#include "mex.h" + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray + *prhs[]) +{ + double *y, *pr, *dist; + int k, distSize; + double r, cumSum; + + plhs[0] = mxCreateDoubleMatrix(1,1, mxREAL); + y = mxGetPr (plhs[0]); + + pr = mxGetPr (prhs[0]); + r = pr[0]; + + dist = mxGetPr (prhs[1]); + distSize = mxGetNumberOfElements (prhs[1]); + + for (k = 0, cumSum = 0; (k < distSize) && (r >= cumSum); cumSum += dist[k], k++); + + y[0] = k; +}
Binary file _FullBNT/BNT/inference/static/@gibbs_sampling_inf_engine/private/sample_single_discrete.mexmaci has changed
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@global_joint_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +/enter_evidence.m/1.1.1.1/Mon Jun 7 19:05:42 2004// +/find_mpe.m/1.1.1.1/Wed Jun 19 21:56:32 2002// +/global_joint_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Mon Jun 7 19:04:48 2004// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@global_joint_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@global_joint_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@global_joint_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@global_joint_inf_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,42 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (global_joint) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i} = [] if if X(i) is hidden, and otherwise contains its observed value. +% +% Warning: Computing the log likelihood requires marginalizing all the nodes and can be slow. +% +% The list below gives optional arguments [default value in brackets]. +% +% exclude - list of nodes whose potential will not be included in the joint [ [] ] +% +% e.g., engine = enter_evidence(engine, ev, 'exclude', 3) + +exclude = []; +maximize = 0; + +if nargin >= 3 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'exclude', exclude = args{i+1}; + case 'maximize', maximize = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +assert(~maximize) +bnet = bnet_from_engine(engine); +N = length(bnet.node_sizes); +%[engine.jpot, loglik] = compute_joint_pot(bnet, mysetdiff(1:N, exclude), evidence, 1:N); +[engine.jpot] = compute_joint_pot(bnet, mysetdiff(1:N, exclude), evidence, 1:N); +% jpot should not be normalized, otherwise it gives wrong resutls for limids like asia_dt1 +if nargout == 2 + [m] = marginal_nodes(engine, []); + [T, lik] = normalize(m.T); + loglik = log(lik); +end +%[engine.jpot loglik] = normalize_pot(engine.jpot);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@global_joint_inf_engine/find_mpe.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,28 @@ +function [mpe, ll] = find_mpe(engine, evidence) +% FIND_MPE_GLOBAL Compute the most probable explanation(s) from the global joint +% [mpe, ll] = find_mpe(engine, evidence) +% +% mpe(k,i) is the most probable value of node i in the k'th global mode (cell array) +% +% We assume all nodes are discrete + +%engine = global_joint_inf_engine(bnet); +bnet = bnet_from_engine(engine); +engine = enter_evidence(engine, evidence); +S1 = struct(engine); % violate object privacy +S2 = struct(S1.jpot); % joint potential +prob = max(S2.T(:)); +modes = find(S2.T(:) == prob); + +ens = bnet.node_sizes; +onodes = find(~isemptycell(evidence)); +ens(onodes) = 1; +mpe = ind2subv(ens, modes); +for k=1:length(modes) + for i=onodes(:)' + mpe(k,i) = evidence{i}; + end +end +ll = log(prob); + +mpe = num2cell(mpe);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@global_joint_inf_engine/global_joint_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +function engine = global_joint_inf_engine(bnet) +% GLOBAL_JOINT_INF_ENGINE Construct the global joint distribution as a potential +% engine = global_joint_inf_engine(bnet) +% +% Warning: this has size exponential in the number of discrete hidden variables + +engine.jpot = []; +engine = class(engine, 'global_joint_inf_engine', inf_engine(bnet));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@global_joint_inf_engine/marginal_family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +function [m, pot] = marginal_family(engine, i) +% MARGINAL_FAMILY Compute the marginal on i's family (global_inf_engine) +% [m, pot] = marginal_family(engine, i) +% + +bnet = bnet_from_engine(engine); +[m, pot] = marginal_nodes(engine, family(bnet.dag, i));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@global_joint_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +function [m, pot] = marginal_nodes(engine, query) +% MARGINAL_NODES Compute the marginal on the specified set of nodes (global_joint) +% [m, pot] = marginal_nodes(engine, query) + +pot = marginalize_pot(engine.jpot, query); +m = pot_to_marginal(pot); +%[m.T, lik] = normalize(m.T); +%loglik = log(lik);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,14 @@ +/cliques_from_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/clq_containing_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/collect_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/distribute_evidence.m/1.1.1.1/Mon Jun 17 21:00:08 2002// +/enter_evidence.m/1.1.1.1/Mon Jun 17 20:59:30 2002// +/enter_soft_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/find_max_config.m/1.1.1.1/Mon Jun 17 23:14:52 2002// +/find_mpe.m/1.1.1.1/Mon Jun 17 23:14:08 2002// +/init_pot.m/1.1.1.1/Sun Jun 16 19:34:56 2002// +/jtree_inf_engine.m/1.1.1.1/Fri Oct 31 22:37:48 2003// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/set_fields.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_inf_engine/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@jtree_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_inf_engine/Old/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +/collect_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/distribute_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_soft_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_inf_engine/Old/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@jtree_inf_engine/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_inf_engine/Old/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_inf_engine/Old/collect_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,29 @@ +function engine = collect_evidence(engine, root) + +if isempty(engine.postorder{root}) + % this is the first time we have collected to this root + % memoize the order + [jtree, preorder, postorder] = mk_rooted_tree(engine.jtree, root); + postorder_parents = cell(1,length(postorder)); + for n=postorder(1:end-1) + postorder_parents{n} = parents(jtree, n); + end + engine.postorder{root} = postorder; + engine.postorder_parents{root} = postorder_parents; +else + postorder = engine.postorder{root}; + postorder_parents = engine.postorder_parents{root}; +end + +C = length(engine.clpot); +seppot = cell(C, C); +% separators are implicitely initialized to 1s + +% collect to root (node to parents) +for n=postorder(1:end-1) + for p=postorder_parents{n} + %clpot{p} = divide_by_pot(clpot{n}, seppot{p,n}); % dividing by 1 is redundant + engine.seppot{p,n} = marginalize_pot(engine.clpot{n}, engine.separator{p,n}, engine.maximize); + engine.clpot{p} = multiply_by_pot(engine.clpot{p}, engine.seppot{p,n}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_inf_engine/Old/distribute_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,26 @@ +function engine = distribute_evidence(engine, root) + +if isempty(engine.preorder{root}) + % this is the first time we have distributed from this root + % memoize the order + [jtree, preorder, postorder] = mk_rooted_tree(engine.jtree, root); + preorder_children = cell(1,length(preorder)); + for n=preorder + preorder_children{n} = children(jtree, n); + end + engine.preorder{root} = preorder; + engine.preorder_children{root} = preorder_children; +else + preorder = engine.preorder{root}; + preorder_children = engine.preorder_children{root}; +end + + +% distribute from root (node to children) +for n=preorder(:)' + for c=preorder_children{n}(:)' + engine.clpot{c} = divide_by_pot(engine.clpot{c}, engine.seppot{n,c}); + engine.seppot{n,c} = marginalize_pot(engine.clpot{n}, engine.separator{n,c}, engine.maximize); + engine.clpot{c} = multiply_by_pot(engine.clpot{c}, engine.seppot{n,c}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_inf_engine/Old/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,107 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (jtree) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i} = [] if X(i) is hidden, and otherwise contains its observed value (scalar or column vector). +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product instead of sum-product [0] +% soft - a cell array of soft/virtual evidence; +% soft{i} is a prob. distrib. over i's values, or [] [ cell(1,N) ] +% +% e.g., engine = enter_evidence(engine, ev, 'soft', soft_ev) +% +% For backwards compatibility with BNT2, you can also specify the parameters in the following order +% engine = enter_evidence(engine, ev, soft_ev) + +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes(:); +N = length(bnet.dag); + +% set default params +exclude = []; +soft_evidence = cell(1,N); +maximize = 0; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + if iscell(args{1}) + soft_evidence = args{1}; + else + for i=1:2:nargs + switch args{i}, + case 'soft', soft_evidence = args{i+1}; + case 'maximize', maximize = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end + end +end + +engine.maximize = maximize; + +onodes = find(~isemptycell(evidence)); +hnodes = find(isemptycell(evidence)); +pot_type = determine_pot_type(bnet, onodes); + if strcmp(pot_type, 'cg') + check_for_cd_arcs(onodes, bnet.cnodes, bnet.dag); +end + + +hard_nodes = 1:N; +soft_nodes = find(~isemptycell(soft_evidence)); +S = length(soft_nodes); +if S > 0 + assert(pot_type == 'd'); + assert(mysubset(soft_nodes, bnet.dnodes)); +end + +% Evaluate CPDs with evidence, and convert to potentials +pot = cell(1, N+S); +for n=1:N + fam = family(bnet.dag, n); + e = bnet.equiv_class(n); + pot{n} = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), evidence); +end + +for i=1:S + n = soft_nodes(i); + pot{N+i} = dpot(n, ns(n), soft_evidence{n}); +end + +%clqs = engine.clq_ass_to_node([hard_nodes soft_nodes]); +%[clpot, loglik] = enter_soft_evidence(engine, clqs, pot, onodes, pot_type); +%engine.clpot = clpot; % save the results for marginal_nodes + + +clique = engine.clq_ass_to_node([hard_nodes soft_nodes]); +potential = pot; + + +% Set the clique potentials to all 1s +C = length(engine.cliques); +for i=1:C + engine.clpot{i} = mk_initial_pot(pot_type, engine.cliques{i}, ns, bnet.cnodes, onodes); +end + +% Multiply on specified potentials +for i=1:length(clique) + c = clique(i); + engine.clpot{c} = multiply_by_pot(engine.clpot{c}, potential{i}); +end + +root = 1; % arbitrary +engine = collect_evidence(engine, root); +engine = distribute_evidence(engine, root); + +ll = zeros(1, C); +for i=1:C + [engine.clpot{i}, ll(i)] = normalize_pot(engine.clpot{i}); +end +loglik = ll(1); % we can extract the likelihood from any clique +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_inf_engine/Old/enter_soft_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,19 @@ +function [clpot, loglik] = enter_soft_evidence(engine, clique, potential, onodes, pot_type) +% ENTER_SOFT_EVIDENCE Add the specified potentials to the network (jtree) +% [clpot, loglik] = enter_soft_evidence(engine, clique, potential, onodes, pot_type, maximize) +% +% We multiply potential{i} onto clique(i) before propagating. +% We return all the modified clique potentials. + +[clpot, seppot] = init_pot(engine, clique, potential, pot_type, onodes); +[clpot, seppot] = collect_evidence(engine, clpot, seppot); +[clpot, seppot] = distribute_evidence(engine, clpot, seppot); + +C = length(clpot); +ll = zeros(1, C); +for i=1:C + [clpot{i}, ll(i)] = normalize_pot(clpot{i}); +end +loglik = ll(1); % we can extract the likelihood from any clique + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_inf_engine/cliques_from_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function cliques = cliques_from_engine(engine) +% CLIQUES_FROM_ENGINE Return the cliques stored inside the inf. engine (jtree) +% cliques = cliques_from_engine(engine) + +cliques = engine.cliques;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_inf_engine/clq_containing_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,24 @@ +function c = clq_containing_nodes(engine, nodes, fam) +% CLQ_CONTAINING_NODES Find the lightest clique (if any) that contains the set of nodes +% c = clq_containing_nodes(engine, nodes, family) +% +% If the optional 'family' argument is specified, it means nodes = family(nodes(end)). +% (This is useful since clq_ass_to_node is not accessible to outsiders.) +% Returns c=-1 if there is no such clique. + +if nargin < 3, fam = 0; else fam = 1; end + +if length(nodes)==1 + c = engine.clq_ass_to_node(nodes(1)); +%elseif fam +% c = engine.clq_ass_to_node(nodes(end)); +else + B = engine.cliques_bitv; + w = engine.clique_weight; + clqs = find(all(B(:,nodes), 2)); % all selected columns must be 1 + if isempty(clqs) + c = -1; + else + c = clqs(argmin(w(clqs))); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_inf_engine/collect_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +function [clpot, seppot] = collect_evidence(engine, clpot, seppot) +% COLLECT_EVIDENCE Do message passing from leaves to root (children then parents) +% [clpot, seppot] = collect_evidence(engine, clpot, seppot) + +for n=engine.postorder %postorder(1:end-1) + for p=engine.postorder_parents{n} + %clpot{p} = divide_by_pot(clpot{n}, seppot{p,n}); % dividing by 1 is redundant + seppot{p,n} = marginalize_pot(clpot{n}, engine.separator{p,n}, engine.maximize); + clpot{p} = multiply_by_pot(clpot{p}, seppot{p,n}); + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_inf_engine/distribute_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function [clpot, seppot] = distribute_evidence(engine, clpot, seppot) +% DISTRIBUTE_EVIDENCE Do message passing from root to leaves (parents then children) +% [clpot, seppot] = distribute_evidence(engine, clpot, seppot) + +for n=engine.preorder + for c=engine.preorder_children{n} + clpot{c} = divide_by_pot(clpot{c}, seppot{n,c}); + seppot{n,c} = marginalize_pot(clpot{n}, engine.separator{n,c}, engine.maximize); + clpot{c} = multiply_by_pot(clpot{c}, seppot{n,c}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_inf_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,88 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (jtree) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i} = [] if X(i) is hidden, and otherwise contains its observed value (scalar or column vector). +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% soft - a cell array of soft/virtual evidence; +% soft{i} is a prob. distrib. over i's values, or [] [ cell(1,N) ] +% +% e.g., engine = enter_evidence(engine, ev, 'soft', soft_ev) + +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes(:); +N = length(bnet.dag); + +engine.evidence = evidence; % store this for marginal_nodes with add_ev option +engine.maximize = 0; + +% set default params +exclude = []; +soft_evidence = cell(1,N); + +% parse optional params +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'soft', soft_evidence = args{i+1}; + case 'maximize', engine.maximize = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end +end + +onodes = find(~isemptycell(evidence)); +hnodes = find(isemptycell(evidence)); +pot_type = determine_pot_type(bnet, onodes); + if strcmp(pot_type, 'cg') + check_for_cd_arcs(onodes, bnet.cnodes, bnet.dag); +end + +if is_mnet(bnet) + pot = engine.user_pot; + clqs = engine.nums_ass_to_user_clqs; +else + % Evaluate CPDs with evidence, and convert to potentials + pot = cell(1, N); + for n=1:N + fam = family(bnet.dag, n); + e = bnet.equiv_class(n); + if isempty(bnet.CPD{e}) + error(['must define CPD ' num2str(e)]) + else + pot{n} = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), evidence); + end + end + clqs = engine.clq_ass_to_node(1:N); +end + +% soft evidence +soft_nodes = find(~isemptycell(soft_evidence)); +S = length(soft_nodes); +if S > 0 + assert(pot_type == 'd'); + assert(mysubset(soft_nodes, bnet.dnodes)); +end +for i=1:S + n = soft_nodes(i); + pot{end+1} = dpot(n, ns(n), soft_evidence{n}); +end +clqs = [clqs engine.clq_ass_to_node(soft_nodes)]; + + +[clpot, seppot] = init_pot(engine, clqs, pot, pot_type, onodes); +[clpot, seppot] = collect_evidence(engine, clpot, seppot); +[clpot, seppot] = distribute_evidence(engine, clpot, seppot); + +C = length(clpot); +ll = zeros(1, C); +for i=1:C + [clpot{i}, ll(i)] = normalize_pot(clpot{i}); +end +loglik = ll(1); % we can extract the likelihood from any clique + +engine.clpot = clpot;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_inf_engine/enter_soft_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,21 @@ +function [clpot, loglik] = enter_soft_evidence(engine, clique, potential, onodes, pot_type) +% ENTER_SOFT_EVIDENCE Add the specified potentials to the network (jtree) +% [clpot, loglik] = enter_soft_evidence(engine, clique, potential, onodes, pot_type, maximize) +% +% We multiply potential{i} onto clique(i) before propagating. +% We return all the modified clique potentials. + +% only used by BK! + +[clpot, seppot] = init_pot(engine, clique, potential, pot_type, onodes); +[clpot, seppot] = collect_evidence(engine, clpot, seppot); +[clpot, seppot] = distribute_evidence(engine, clpot, seppot); + +C = length(clpot); +ll = zeros(1, C); +for i=1:C + [clpot{i}, ll(i)] = normalize_pot(clpot{i}); +end +loglik = ll(1); % we can extract the likelihood from any clique + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_inf_engine/find_max_config.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,35 @@ +function [mpe, clpot, seppot] = find_max_config(engine, clpot, seppot, evidence) +% FIND_MAX_CONFIG Backwards pass of Viterbi fro jtree +% function [mpe, clpot, seppot] = find_max_config(engine, clpot, seppot, evidence) +% See Cowell99 p98 + +bnet = bnet_from_engine(engine); +nnodes = length(bnet.dag); +mpe = cell(1, nnodes); +maximize = 1; + +c = engine.root_clq; +pot = struct(clpot{c}); % violate object privacy +dom = pot.domain; +[indices, clpot{c}] = find_most_prob_entry(clpot{c}); +mpe(dom) = num2cell(indices); + +for n=engine.preorder + for c=engine.preorder_children{n} + clpot{c} = divide_by_pot(clpot{c}, seppot{n,c}); + seppot{n,c} = marginalize_pot(clpot{n}, engine.separator{n,c}, maximize); + clpot{c} = multiply_by_pot(clpot{c}, seppot{n,c}); + + pot = struct(clpot{c}); % violate object privacy + dom = pot.domain; + [indices, clpot{c}] = find_most_prob_entry(clpot{c}); + mpe(dom) = num2cell(indices); + end +end + +obs_nodes = find(~isemptycell(evidence)); +% indices for observed nodes will be 1 - need to overwrite these +mpe(obs_nodes) = evidence(obs_nodes); + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_inf_engine/find_mpe.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,71 @@ +function mpe = find_mpe(engine, evidence, varargin) +% FIND_MPE Find the most probable explanation of the data (assignment to the hidden nodes) +% function mpe = find_mpe(engine, evidence,...) +% +% evidence{i} = [] if X(i) is hidden, and otherwise contains its observed value (scalar or column vector). +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% soft - a cell array of soft/virtual evidence; +% soft{i} is a prob. distrib. over i's values, or [] [ cell(1,N) ] +% + +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes(:); +N = length(bnet.dag); + +engine.evidence = evidence; + +% set default params +exclude = []; +soft_evidence = cell(1,N); + +% parse optional params +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'soft', soft_evidence = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end +end +engine.maximize = 1; + +onodes = find(~isemptycell(evidence)); +hnodes = find(isemptycell(evidence)); +pot_type = determine_pot_type(bnet, onodes); + if strcmp(pot_type, 'cg') + check_for_cd_arcs(onodes, bnet.cnodes, bnet.dag); +end + +hard_nodes = 1:N; +soft_nodes = find(~isemptycell(soft_evidence)); +S = length(soft_nodes); +if S > 0 + assert(pot_type == 'd'); + assert(mysubset(soft_nodes, bnet.dnodes)); +end + +% Evaluate CPDs with evidence, and convert to potentials +pot = cell(1, N+S); +for n=1:N + fam = family(bnet.dag, n); + e = bnet.equiv_class(n); + if isempty(bnet.CPD{e}) + error(['must define CPD ' num2str(e)]) + else + pot{n} = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), evidence); + end +end + +for i=1:S + n = soft_nodes(i); + pot{N+i} = dpot(n, ns(n), soft_evidence{n}); +end +clqs = engine.clq_ass_to_node([hard_nodes soft_nodes]); + +[clpot, seppot] = init_pot(engine, clqs, pot, pot_type, onodes); +[clpot, seppot] = collect_evidence(engine, clpot, seppot); +mpe = find_max_config(engine, clpot, seppot, evidence); % instead of distribute evidence
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_inf_engine/init_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,20 @@ +function [clpot, seppot] = init_pot(engine, clqs, pots, pot_type, onodes) +% INIT_POT Initialise potentials with evidence (jtree_inf) +% function [clpot, seppot] = init_pot(engine, clqs, pots, pot_type, onodes) + +cliques = engine.cliques; +bnet = bnet_from_engine(engine); +% Set the clique potentials to all 1s +C = length(cliques); +clpot = cell(1,C); +for i=1:C + clpot{i} = mk_initial_pot(pot_type, cliques{i}, bnet.node_sizes(:), bnet.cnodes(:), onodes); +end + +% Multiply on specified potentials +for i=1:length(clqs) + c = clqs(i); + clpot{c} = multiply_by_pot(clpot{c}, pots{i}); +end + +seppot = cell(C,C); % implicitely initialized to 1
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_inf_engine/jtree_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,134 @@ +function engine = jtree_inf_engine(bnet, varargin) +% JTREE_INF_ENGINE Junction tree inference engine +% engine = jtree_inf_engine(bnet, ...) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% clusters - a cell array of sets of nodes we want to ensure are in the same clique (in addition to families) [ {} ] +% root - the root of the junction tree will be a clique that contains this set of nodes [N] +% stages - stages{t} is a set of nodes we want to eliminate before stages{t+1}, ... [ {1:N} ] +% +% e.g., engine = jtree_inf_engine(bnet, 'maximize', 1); +% +% For more details on the junction tree algorithm, see +% - "Probabilistic networks and expert systems", Cowell, Dawid, Lauritzen and Spiegelhalter, Springer, 1999 +% - "Inference in Belief Networks: A procedural guide", C. Huang and A. Darwiche, +% Intl. J. Approximate Reasoning, 15(3):225-263, 1996. + + +% set default params +N = length(bnet.dag); +clusters = {}; +root = N; +stages = { 1:N }; +maximize = 0; + +if nargin >= 2 + args = varargin; + nargs = length(args); + if ~isstr(args{1}) + error('the interface to jtree has changed; now, onodes is not allowed and all optional params must be passed by name') + end + for i=1:2:nargs + switch args{i}, + case 'clusters', clusters = args{i+1}; + case 'root', root = args{i+1}; + case 'stages', stages = args{i+1}; + case 'maximize', maximize = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +engine = init_fields; +engine = class(engine, 'jtree_inf_engine', inf_engine(bnet)); + +engine.maximize = maximize; + +onodes = bnet.observed; + +%[engine.jtree, dummy, engine.cliques, B, w, elim_order, moral_edges, fill_in_edges, strong] = ... +% dag_to_jtree(bnet, onodes, stages, clusters); + +porder = determine_elim_constraints(bnet, onodes); +strong = ~isempty(porder); +ns = bnet.node_sizes(:); +ns(onodes) = 1; % observed nodes have only 1 possible value +[engine.jtree, root2, engine.cliques, B, w] = ... + graph_to_jtree(moralize(bnet.dag), ns, porder, stages, clusters); + + +engine.cliques_bitv = B; +engine.clique_weight = w; +C = length(engine.cliques); +engine.clpot = cell(1,C); + +% Compute the separators between connected cliques. +[is,js] = find(engine.jtree > 0); +engine.separator = cell(C,C); +for k=1:length(is) + i = is(k); j = js(k); + engine.separator{i,j} = find(B(i,:) & B(j,:)); % intersect(cliques{i}, cliques{j}); +end + +% A node can be a member of many cliques, but is assigned to exactly one, to avoid +% double-counting its CPD. We assign node i to clique c if c is the "lightest" clique that +% contains i's family, so it can accomodate its CPD. + +engine.clq_ass_to_node = zeros(1, N); +for i=1:N + %c = clq_containing_nodes(engine, family(bnet.dag, i)); + clqs_containing_family = find(all(B(:,family(bnet.dag, i)), 2)); % all selected columns must be 1 + c = clqs_containing_family(argmin(w(clqs_containing_family))); + engine.clq_ass_to_node(i) = c; +end + +% Make the jtree rooted, so there is a fixed message passing order. +if strong + % the last clique is guaranteed to be a strong root + engine.root_clq = length(engine.cliques); +else + % jtree_dbn_inf_engine requires the root to contain the interface. + % This may conflict with the strong root requirement! *********** BUG ************* + engine.root_clq = clq_containing_nodes(engine, root); + if engine.root_clq <= 0 + error(['no clique contains ' num2str(root)]); + end +end + +[engine.jtree, engine.preorder, engine.postorder] = mk_rooted_tree(engine.jtree, engine.root_clq); + +% collect +engine.postorder_parents = cell(1,length(engine.postorder)); +for n=engine.postorder(:)' + engine.postorder_parents{n} = parents(engine.jtree, n); +end +% distribute +engine.preorder_children = cell(1,length(engine.preorder)); +for n=engine.preorder(:)' + engine.preorder_children{n} = children(engine.jtree, n); +end + + + +%%%%%%%% + +function engine = init_fields() + +engine.jtree = []; +engine.cliques = []; +engine.separator = []; +engine.cliques_bitv = []; +engine.clique_weight = []; +engine.clpot = []; +engine.clq_ass_to_node = []; +engine.root_clq = []; +engine.preorder = []; +engine.postorder = []; +engine.preorder_children = []; +engine.postorder_parents = []; +engine.maximize = []; +engine.evidence = []; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_inf_engine/marginal_family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function marginal = marginal_family(engine, i, add_ev) +% MARGINAL_FAMILY Compute the marginal on the specified family (jtree) +% marginal = marginal_family(engine, i) + +if nargin < 3, add_ev = 0; end +assert(~add_ev); + +bnet = bnet_from_engine(engine); +fam = family(bnet.dag, i); +c = engine.clq_ass_to_node(i); +marginal = pot_to_marginal(marginalize_pot(engine.clpot{c}, fam));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,22 @@ +function marginal = marginal_nodes(engine, query, add_ev) +% MARGINAL_NODES Compute the marginal on the specified query nodes (jtree) +% marginal = marginal_nodes(engine, query, add_ev) +% +% 'query' must be a subset of some clique; an error will be raised if not. +% add_ev is an optional argument; if 1, we will "inflate" the marginal of observed nodes +% to their original size, adding 0s to the positions which contradict the evidence + +if nargin < 3, add_ev = 0; end + +c = clq_containing_nodes(engine, query); +if c == -1 + error(['no clique contains ' num2str(query)]); +end +marginal = pot_to_marginal(marginalize_pot(engine.clpot{c}, query, engine.maximize)); + +if add_ev + bnet = bnet_from_engine(engine); + %marginal = add_ev_to_dmarginal(marginal, engine.evidence, bnet.node_sizes); + marginal = add_evidence_to_gmarginal(marginal, engine.evidence, bnet.node_sizes, bnet.cnodes); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_inf_engine/set_fields.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function engine = set_fields(engine, varargin) +% SET_FIELDS Set the fields for a generic engine +% engine = set_fields(engine, name/value pairs) +% +% e.g., engine = set_fields(engine, 'maximize', 1) + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'maximize', engine.maximize = args{i+1}; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_limid_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/jtree_limid_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_limid_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@jtree_limid_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_limid_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_limid_inf_engine/Old/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,3 @@ +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes_SS.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_limid_inf_engine/Old/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@jtree_limid_inf_engine/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_limid_inf_engine/Old/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_limid_inf_engine/Old/marginal_family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,59 @@ +function [m, pot] = marginal_family(engine, query) +% MARGINAL_NODES Compute the marginal on the family of the specified node (jtree_limid) +% [m, pot] = marginal_family(engine, query) +% +% query should be a single decision node, or [] (to compute global max expected utility) + +bnet = bnet_from_engine(engine); +if isempty(query) + compute_meu = 1; + d = bnet.decision_nodes(1); % pick an arbitrary root to collect to + fam = []; % marginalize root pot down to a point +else + compute_meu = 0; + d = query; + assert(myismember(d, bnet.decision_nodes)); + fam = family(bnet.dag, d); +end + +clpot = init_clpot(bnet, engine.cliques, engine.clq_ass_to_node, engine.evidence, engine.exclude); + +% collect to root (clique containing d) +C = length(engine.cliques); +seppot = cell(C, C); % separators are implicitely initialized to 1s +for n=engine.postorder{d}(1:end-1) + for p=parents(engine.rooted_jtree{d}, n) + %clpot{p} = divide_by_pot(clpot{n}, seppot{p,n}); % dividing by 1 is redundant + seppot{p,n} = marginalize_pot(clpot{n}, engine.separator{p,n}); + clpot{p} = multiply_by_pot(clpot{p}, seppot{p,n}); + end +end + +root = engine.clq_ass_to_node(d); +assert(root == engine.postorder{d}(end)); +pot = marginalize_pot(clpot{root}, fam); +m = pot_to_marginal(pot); + +%%%%%%%%%%% + + +function clpot = init_clpot(bnet, cliques, clq_ass_to_node, evidence, exclude) + +% Set the clique potentials to all 1s +C = length(cliques); +clpot = cell(1, C); +ns = bnet.node_sizes; +for i=1:C + clpot{i} = upot(cliques{i}, ns(cliques{i})); +end + +N = length(bnet.dag); +nodes = mysetdiff(1:N, exclude); + +for n=nodes(:)' + fam = family(bnet.dag, n); + e = bnet.equiv_class(n); + c = clq_ass_to_node(n); + pot = convert_to_pot(bnet.CPD{e}, 'u', ns, fam, evidence); + clpot{c} = multiply_by_pot(clpot{c}, pot); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_limid_inf_engine/Old/marginal_nodes_SS.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,52 @@ +function [pot, MEU] = marginal_nodes(engine, d) + +C = length(cliques); +%clpot = init_clpot(limid, cliques, d, clq_ass_to_node); +clpot = init_clpot(limid, cliques, [], clq_ass_to_node); + +% collect to root +if 1 + % HUGIN + seppot = cell(C, C); % separators are implicitely initialized to 1s + for n=postorder{di}(1:end-1) + for p=parents(rooted_jtree{di}, n) + %clpot{p} = divide_by_pot(clpot{n}, seppot{p,n}); % dividing by 1 is redundant + seppot{p,n} = marginalize_pot(clpot{n}, separator{p,n}); + clpot{p} = multiply_by_pot(clpot{p}, seppot{p,n}); + end + end +else + % Shafer-Shenoy + msg = cell(C,C); + for n=postorder{di}(1:end-1) + for c=children(rooted_jtree{di}, n) + clpot{n} = multiply_by_pot(clpot{n}, msg{c,n}); + end + p = parents(rooted_jtree{di}, n); + %msg{n,p} = marginalize_pot(clpot{n}, cliques{p}); + msg{n,p} = marginalize_pot(clpot{n}, separator{n,p}); + end + root = clq_ass_to_node(d); + n=postorder{di}(end); + assert(n == root); + for c=children(rooted_jtree{di}, n) + clpot{n} = multiply_by_pot(clpot{n}, msg{c,n}); + end +end + +fam = family(limid.dag, d); +pot = marginalize_pot(clpot{root}, fam); + +%%%%%%% +jpot = compute_joint_pot_limid(limid); +pot2 = marginalize_pot(jpot, fam); +assert(approxeq_pot(pot, pot2)) +%%%%%% + +[policy, score] = extract_policy(pot); + +e = limid.equiv_class(d); +limid.CPD{e} = set_params(limid.CPD{e}, 'policy', policy); + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_limid_inf_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,28 @@ +function engine = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (jtree_limid) +% engine = enter_evidence(engine, evidence, ...) +% +% evidence{i} = [] if if X(i) is hidden, and otherwise contains its observed value. +% +% The list below gives optional arguments [default value in brackets]. +% +% exclude - list of nodes whose potential will not be included in the joint [ [] ] +% +% e.g., engine = enter_evidence(engine, ev, 'exclude', 3) + +exclude = []; + +if nargin >= 3 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'exclude', exclude = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +engine.exclude = exclude; +engine.evidence = evidence;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_limid_inf_engine/jtree_limid_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,52 @@ +function engine = jtree_limid_inf_engine(bnet) +% JTREE_LIMID_INF_ENGINE Make a junction tree engine for use by solve_limid +% engine = jtree_limid_inf_engine(bnet) +% +% This engine is designed to compute marginals on decision nodes + + +MG = moralize(bnet.dag); +% We do not remove the utility nodes, because that complicates the book-keeping. +% Leaving them in will not introduce any un-necessary triangulation arcs, because they are always leaves. +% Also, since utility nodes have size 1, they do not increase the size of the potentials. + +ns = bnet.node_sizes; +elim_order = best_first_elim_order(MG, ns); +[MTG, engine.cliques] = triangulate(MG, elim_order); +[engine.jtree, root, B, w] = cliques_to_jtree(engine.cliques, ns); + +% A node can be a member of many cliques, but is assigned to exactly one, to avoid +% double-counting its CPD. We assign node i to clique c if c is the "lightest" clique that +% contains i's family, so it can accomodate its CPD. +N = length(bnet.dag); +engine.clq_ass_to_node = zeros(1, N); +for i=1:N + clqs_containing_family = find(all(B(:,family(bnet.dag, i)), 2)); % all selected columns must be 1 + c = clqs_containing_family(argmin(w(clqs_containing_family))); + engine.clq_ass_to_node(i) = c; +end + + +% Compute the separators between connected cliques. +[is,js] = find(engine.jtree > 0); +num_cliques = length(engine.cliques); +engine.separator = cell(num_cliques, num_cliques); +for k=1:length(is) + i = is(k); j = js(k); + engine.separator{i,j} = find(B(i,:) & B(j,:)); % intersect(cliques{i}, cliques{j}); +end + + +% create |D| different rooted jtree's +engine.rooted_jtree = cell(1, N); +engine.preorder = cell(1, N); +engine.postorder = cell(1, N); +for d=bnet.decision_nodes(:)' + root = engine.clq_ass_to_node(d); + [engine.rooted_jtree{d}, engine.preorder{d}, engine.postorder{d}] = mk_rooted_tree(engine.jtree, root); +end + +engine.exclude = []; +engine.evidence = []; + +engine = class(engine, 'jtree_limid_inf_engine', inf_engine(bnet));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_limid_inf_engine/marginal_family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,52 @@ +function [m, pot] = marginal_family(engine, query) +% MARGINAL_NODES Compute the marginal on the family of the specified node (jtree_limid) +% [m, pot] = marginal_family(engine, query) +% +% query should be a single decision node + +bnet = bnet_from_engine(engine); +d = query; +assert(myismember(d, bnet.decision_nodes)); +fam = family(bnet.dag, d); + +clpot = init_clpot(bnet, engine.cliques, engine.clq_ass_to_node, engine.evidence, engine.exclude); + +% collect to root (clique containing d) +C = length(engine.cliques); +seppot = cell(C, C); % separators are implicitely initialized to 1s +for n=engine.postorder{d}(1:end-1) + for p=parents(engine.rooted_jtree{d}, n) + %clpot{p} = divide_by_pot(clpot{n}, seppot{p,n}); % dividing by 1 is redundant + seppot{p,n} = marginalize_pot(clpot{n}, engine.separator{p,n}); + clpot{p} = multiply_by_pot(clpot{p}, seppot{p,n}); + end +end + +root = engine.clq_ass_to_node(d); +assert(root == engine.postorder{d}(end)); +pot = marginalize_pot(clpot{root}, fam); +m = pot_to_marginal(pot); + +%%%%%%%%%%% + + +function clpot = init_clpot(bnet, cliques, clq_ass_to_node, evidence, exclude) + +% Set the clique potentials to all 1s +C = length(cliques); +clpot = cell(1, C); +ns = bnet.node_sizes; +for i=1:C + clpot{i} = upot(cliques{i}, ns(cliques{i})); +end + +N = length(bnet.dag); +nodes = mysetdiff(1:N, exclude); + +for n=nodes(:)' + fam = family(bnet.dag, n); + e = bnet.equiv_class(n); + c = clq_ass_to_node(n); + pot = convert_to_pot(bnet.CPD{e}, 'u', fam(:), evidence); + clpot{c} = multiply_by_pot(clpot{c}, pot); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_limid_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,17 @@ +function [m, pot] = marginal_nodes(engine, query) +% MARGINAL_NODES Compute the marginal on the specified nodes (jtree_limid) +% [m, pot] = marginal_nodes(engine, query) +% +% query should be a subset of a family of a decision node + +if isempty(query) + bnet = bnet_from_engine(engine); + d = bnet.decision_nodes(1); % pick an arbitrary decision node + [dummy, big_pot] = marginal_family(engine, d); +else + [dummy, big_pot] = marginal_family(engine, query); +end +pot = marginalize_pot(big_pot, query); +m = pot_to_marginal(pot); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_mnet_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,4 @@ +/enter_evidence.m/1.1.1.1/Mon Jun 17 20:30:02 2002// +/find_mpe.m/1.1.1.1/Mon Jun 17 20:29:40 2002// +/jtree_mnet_inf_engine.m/1.1.1.1/Sat Jan 18 22:13:32 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_mnet_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@jtree_mnet_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_mnet_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_mnet_inf_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,82 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (jtree) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i} = [] if X(i) is hidden, and otherwise contains its observed value (scalar or column vector). +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% soft - a cell array of soft/virtual evidence; +% soft{i} is a prob. distrib. over i's values, or [] [ cell(1,N) ] +% +% e.g., engine = enter_evidence(engine, ev, 'soft', soft_ev) + +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes(:); +N = length(bnet.dag); + +engine.evidence = evidence; % store this for marginal_nodes with add_ev option +engine.maximize = 0; + +% set default params +exclude = []; +soft_evidence = cell(1,N); + +% parse optional params +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'soft', soft_evidence = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end +end + +onodes = find(~isemptycell(evidence)); +hnodes = find(isemptycell(evidence)); +pot_type = determine_pot_type(bnet, onodes); + if strcmp(pot_type, 'cg') + check_for_cd_arcs(onodes, bnet.cnodes, bnet.dag); +end + +% Evaluate CPDs with evidence, and convert to potentials +pot = cell(1, N); +for n=1:N + fam = family(bnet.dag, n); + e = bnet.equiv_class(n); + if isempty(bnet.CPD{e}) + error(['must define CPD ' num2str(e)]) + else + pot{n} = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), evidence); + end +end +clqs = engine.clq_ass_to_node(1:N); + +% soft evidence +soft_nodes = find(~isemptycell(soft_evidence)); +S = length(soft_nodes); +if S > 0 + assert(pot_type == 'd'); + assert(mysubset(soft_nodes, bnet.dnodes)); +end +for i=1:S + n = soft_nodes(i); + pot{end+1} = dpot(n, ns(n), soft_evidence{n}); +end +clqs = [clqs engine.clq_ass_to_node(soft_nodes)]; + + +[clpot, seppot] = init_pot(engine, clqs, pot, pot_type, onodes); +[clpot, seppot] = collect_evidence(engine, clpot, seppot); +[clpot, seppot] = distribute_evidence(engine, clpot, seppot); + +C = length(clpot); +ll = zeros(1, C); +for i=1:C + [clpot{i}, ll(i)] = normalize_pot(clpot{i}); +end +loglik = ll(1); % we can extract the likelihood from any clique + +engine.clpot = clpot;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_mnet_inf_engine/find_mpe.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,71 @@ +function mpe = find_mpe(engine, evidence, varargin) +% FIND_MPE Find the most probable explanation of the data (assignment to the hidden nodes) +% function mpe = find_mpe(engine, evidence,...) +% +% evidence{i} = [] if X(i) is hidden, and otherwise contains its observed value (scalar or column vector). +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% soft - a cell array of soft/virtual evidence; +% soft{i} is a prob. distrib. over i's values, or [] [ cell(1,N) ] +% + +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes(:); +N = length(bnet.dag); + +engine.evidence = evidence; + +% set default params +exclude = []; +soft_evidence = cell(1,N); + +% parse optional params +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'soft', soft_evidence = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end +end +engine.maximize = 1; + +onodes = find(~isemptycell(evidence)); +hnodes = find(isemptycell(evidence)); +pot_type = determine_pot_type(bnet, onodes); + if strcmp(pot_type, 'cg') + check_for_cd_arcs(onodes, bnet.cnodes, bnet.dag); +end + +hard_nodes = 1:N; +soft_nodes = find(~isemptycell(soft_evidence)); +S = length(soft_nodes); +if S > 0 + assert(pot_type == 'd'); + assert(mysubset(soft_nodes, bnet.dnodes)); +end + +% Evaluate CPDs with evidence, and convert to potentials +pot = cell(1, N+S); +for n=1:N + fam = family(bnet.dag, n); + e = bnet.equiv_class(n); + if isempty(bnet.CPD{e}) + error(['must define CPD ' num2str(e)]) + else + pot{n} = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), evidence); + end +end + +for i=1:S + n = soft_nodes(i); + pot{N+i} = dpot(n, ns(n), soft_evidence{n}); +end +clqs = engine.clq_ass_to_node([hard_nodes soft_nodes]); + +[clpot, seppot] = init_pot(engine, clqs, pot, pot_type, onodes); +[clpot, seppot] = collect_evidence(engine, clpot, seppot); +mpe = find_max_config(engine, clpot, seppot);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_mnet_inf_engine/jtree_mnet_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,101 @@ +function engine = jtree_mnet_inf_engine(model, varargin) +% JTREE_MNET_INF_ENGINE Junction tree inference engine for Markov nets +% engine = jtree_inf_engine(mnet, ...) +% + +% set default params +N = length(mnet.graph); +root = N; + +engine = init_fields; +engine = class(engine, 'jtree_mnet_inf_engine', inf_engine(bnet)); + +onodes = bnet.observed; +if is_mnet(bnet) + MG = bnet.graph; +else + error('should be a mnet') +end + +%[engine.jtree, dummy, engine.cliques, B, w, elim_order, moral_edges, fill_in_edges, strong] = ... +% dag_to_jtree(bnet, onodes, stages, clusters); + +porder = determine_elim_constraints(bnet, onodes); +strong = ~isempty(porder); +ns = bnet.node_sizes(:); +ns(onodes) = 1; % observed nodes have only 1 possible value +[engine.jtree, root2, engine.cliques, B, w] = ... + graph_to_jtree(MG, ns, porder, stages, clusters); + +engine.cliques_bitv = B; +engine.clique_weight = w; +C = length(engine.cliques); +engine.clpot = cell(1,C); + +% Compute the separators between connected cliques. +[is,js] = find(engine.jtree > 0); +engine.separator = cell(C,C); +for k=1:length(is) + i = is(k); j = js(k); + engine.separator{i,j} = find(B(i,:) & B(j,:)); % intersect(cliques{i}, cliques{j}); +end + +% A node can be a member of many cliques, but is assigned to exactly one, to avoid +% double-counting its CPD. We assign node i to clique c if c is the "lightest" clique that +% contains i's family, so it can accomodate its CPD. + +engine.clq_ass_to_node = zeros(1, N); +for i=1:N + %c = clq_containing_nodes(engine, family(bnet.dag, i)); + clqs_containing_family = find(all(B(:,family(bnet.dag, i)), 2)); % all selected columns must be 1 + c = clqs_containing_family(argmin(w(clqs_containing_family))); + engine.clq_ass_to_node(i) = c; +end + +% Make the jtree rooted, so there is a fixed message passing order. +if strong + % the last clique is guaranteed to be a strong root + engine.root_clq = length(engine.cliques); +else + % jtree_dbn_inf_engine requires the root to contain the interface. + % This may conflict with the strong root requirement! *********** BUG ************* + engine.root_clq = clq_containing_nodes(engine, root); + if engine.root_clq <= 0 + error(['no clique contains ' num2str(root)]); + end +end + +[engine.jtree, engine.preorder, engine.postorder] = mk_rooted_tree(engine.jtree, engine.root_clq); + +% collect +engine.postorder_parents = cell(1,length(engine.postorder)); +for n=engine.postorder(:)' + engine.postorder_parents{n} = parents(engine.jtree, n); +end +% distribute +engine.preorder_children = cell(1,length(engine.preorder)); +for n=engine.preorder(:)' + engine.preorder_children{n} = children(engine.jtree, n); +end + + + +%%%%%%%% + +function engine = init_fields() + +engine.jtree = []; +engine.cliques = []; +engine.separator = []; +engine.cliques_bitv = []; +engine.clique_weight = []; +engine.clpot = []; +engine.clq_ass_to_node = []; +engine.root_clq = []; +engine.preorder = []; +engine.postorder = []; +engine.preorder_children = []; +engine.postorder_parents = []; +engine.maximize = []; +engine.evidence = []; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_sparse_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +/cliques_from_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/clq_containing_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/collect_evidence.c/1.1.1.1/Wed May 29 15:59:56 2002// +/distribute_evidence.c/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_soft_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/init_pot.c/1.1.1.1/Wed May 29 15:59:56 2002// +/jtree_sparse_inf_engine.m/1.1.1.1/Sat Jan 18 22:11:32 2003// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/set_fields.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_sparse_inf_engine/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +A D/old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_sparse_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@jtree_sparse_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_sparse_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_sparse_inf_engine/cliques_from_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function cliques = cliques_from_engine(engine) +% CLIQUES_FROM_ENGINE Return the cliques stored inside the inf. engine (jtree) +% cliques = cliques_from_engine(engine) + +cliques = engine.cliques;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_sparse_inf_engine/clq_containing_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,24 @@ +function c = clq_containing_nodes(engine, nodes, fam) +% CLQ_CONTAINING_NODES Find the lightest clique (if any) that contains the set of nodes +% c = clq_containing_nodes(engine, nodes, family) +% +% If the optional 'family' argument is specified, it means nodes = family(nodes(end)). +% (This is useful since clq_ass_to_node is not accessible to outsiders.) +% Returns c=-1 if there is no such clique. + +if nargin < 3, fam = 0; else fam = 1; end + +if length(nodes)==1 + c = engine.clq_ass_to_node(nodes(1)); +%elseif fam +% c = engine.clq_ass_to_node(nodes(end)); +else + B = engine.cliques_bitv; + w = engine.clique_weight; + clqs = find(all(B(:,nodes), 2)); % all selected columns must be 1 + if isempty(clqs) + c = -1; + else + c = clqs(argmin(w(clqs))); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_sparse_inf_engine/collect_evidence.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,634 @@ +/* C mex for collect_evidence.c in @jtree_sparse_inf_engine directory */ +/* File enter_evidence.m in directory @jtree_sparse_inf_engine call it*/ + +/******************************************/ +/* collect_evidence has 3 input & 2 output*/ +/* engine */ +/* clpot */ +/* seppot */ +/* */ +/* clpot */ +/* seppot */ +/******************************************/ + +#include <math.h> +#include <stdlib.h> +#include "mex.h" + +int compare(const void* src1, const void* src2){ + int i1 = *(int*)src1 ; + int i2 = *(int*)src2 ; + return i1-i2 ; +} + +void ind_subv(int index, const int *cumprod, int n, int *bsubv){ + int i; + + for (i = n-1; i >= 0; i--) { + bsubv[i] = ((int)floor(index / cumprod[i])); + index = index % cumprod[i]; + } +} + +int subv_ind(const int n, const int *cumprod, const int *subv){ + int i, index=0; + + for(i=0; i<n; i++){ + index += subv[i] * cumprod[i]; + } + return index; +} + +void compute_fixed_weight(int *weight, const double *pbSize, const int *dmask, const int *bCumprod, const int ND, const int diffdim){ + int i, j; + int *eff_cumprod, *subv, *diffsize, *diff_cumprod; + + subv = malloc(diffdim * sizeof(int)); + eff_cumprod = malloc(diffdim * sizeof(int)); + diffsize = malloc(diffdim * sizeof(int)); + diff_cumprod = malloc(diffdim * sizeof(int)); + for(i=0; i<diffdim; i++){ + eff_cumprod[i] = bCumprod[dmask[i]]; + diffsize[i] = (int)pbSize[dmask[i]]; + } + diff_cumprod[0] = 1; + for(i=0; i<diffdim-1; i++){ + diff_cumprod[i+1] = diff_cumprod[i] * diffsize[i]; + } + for(i=0; i<ND; i++){ + ind_subv(i, diff_cumprod, diffdim, subv); + weight[i] = 0; + for(j=0; j<diffdim; j++){ + weight[i] += eff_cumprod[j] * subv[j]; + } + } + free(eff_cumprod); + free(subv); + free(diffsize); + free(diff_cumprod); +} + +void reset_nzmax(mxArray *spArray, const int old_nzmax, const int new_nzmax){ + double *ptr; + void *newptr; + int *ir, *jc; + int nbytes; + + if(new_nzmax == old_nzmax) return; + nbytes = new_nzmax * sizeof(*ptr); + ptr = mxGetPr(spArray); + newptr = mxRealloc(ptr, nbytes); + mxSetPr(spArray, newptr); + nbytes = new_nzmax * sizeof(*ir); + ir = mxGetIr(spArray); + newptr = mxRealloc(ir, nbytes); + mxSetIr(spArray, newptr); + jc = mxGetJc(spArray); + jc[0] = 0; + jc[1] = new_nzmax; + mxSetNzmax(spArray, new_nzmax); +} + +mxArray* convert_ill_table_to_sparse(const double *Table, const int *sequence, const int nzCounts, const int N){ + mxArray *spTable; + int i, temp, *irs, *jcs, count=0; + double *sr; + + spTable = mxCreateSparse(N, 1, nzCounts, mxREAL); + sr = mxGetPr(spTable); + irs = mxGetIr(spTable); + jcs = mxGetJc(spTable); + + jcs[0] = 0; + jcs[1] = nzCounts; + + for(i=0; i<nzCounts; i++){ + irs[i] = sequence[count]; + count++; + temp = sequence[count]; + sr[i] = Table[temp]; + count++; + } + return spTable; +} + +void multiply_null_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, count1, match, temp, bdim, sdim, diffdim, NB, NS, ND, NZB, NZS, bindex, sindex, nzCounts=0; + int *samemask, *diffmask, *sir, *sjc, *bCumprod, *sCumprod, *ssubv, *sequence, *weight; + double *bigTable, *pbDomain, *psDomain, *pbSize, *psSize, *spr, *bpr; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + if(sdim == 0){ + pTemp = mxCreateSparse(NB, 1, NB, mxREAL); + mxSetField(bigPot, 0, "T", pTemp); + bpr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + sjc[0] = 0; + sjc[1] = NB; + for(i=0; i<NB; i++){ + bpr[i] = *spr; + sir[i] = i; + } + return; + } + + NS = 1; + for(i=0; i<sdim; i++){ + NS *= (int)psSize[i]; + } + ND = NB / NS; + + if(ND == 1){ + pTemp1 = mxGetField(smallPot, 0, "T"); + pTemp = mxDuplicateArray(pTemp1); + mxSetField(bigPot, 0, "T", pTemp); + return; + } + + + NZB = ND * NZS; + + diffdim = bdim - sdim; + sequence = malloc(NZB * 2 * sizeof(int)); + bigTable = malloc(NZB * sizeof(double)); + samemask = malloc(sdim * sizeof(int)); + diffmask = malloc(diffdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + weight = malloc(ND * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + count1 = 0; + for(i=0; i<bdim; i++){ + match = 0; + for(j=0; j<sdim; j++){ + if(pbDomain[i] == psDomain[j]){ + samemask[count] = i; + match = 1; + count++; + break; + } + } + if(match == 0){ + diffmask[count1] = i; + count1++; + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + count = 0; + compute_fixed_weight(weight, pbSize, diffmask, bCumprod, ND, diffdim); + for(i=0; i<NZS; i++){ + sindex = sir[i]; + ind_subv(sindex, sCumprod, sdim, ssubv); + temp = 0; + for(j=0; j<sdim; j++){ + temp += ssubv[j] * bCumprod[samemask[j]]; + } + for(j=0; j<ND; j++){ + bindex = weight[j] + temp; + bigTable[nzCounts] = spr[i]; + sequence[count] = bindex; + count++; + sequence[count] = nzCounts; + nzCounts++; + count++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + qsort(sequence, nzCounts, sizeof(int) * 2, compare); + pTemp = convert_ill_table_to_sparse(bigTable, sequence, nzCounts, NB); + mxSetField(bigPot, 0, "T", pTemp); + + free(sequence); + free(bigTable); + free(samemask); + free(diffmask); + free(bCumprod); + free(sCumprod); + free(weight); + free(ssubv); +} + +void multiply_spPot_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, bdim, sdim, NB, NZB, NZS, position, bindex, sindex, nzCounts=0; + int *mask, *result, *bir, *sir, *rir, *bjc, *sjc, *rjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr, *rpr; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + pTemp = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + if(sdim == 0){ + for(i=0; i<NZB; i++){ + bpr[i] *= *spr; + } + return; + } + + pTemp1 = mxCreateSparse(NB, 1, NZB, mxREAL); + rpr = mxGetPr(pTemp1); + rir = mxGetIr(pTemp1); + rjc = mxGetJc(pTemp1); + rjc[0] = 0; + rjc[1] = NZB; + + mask = malloc(sdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sir, NZS, sizeof(int), compare); + if(result){ + position = result - sir; + rpr[nzCounts] = bpr[i] * spr[position]; + rir[nzCounts] = bindex; + nzCounts++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + reset_nzmax(pTemp1, NZB, nzCounts); + mxSetField(bigPot, 0, "T", pTemp1); + + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +} + +mxArray* marginal_null_to_spPot(const mxArray *bigPot, const mxArray *sDomain, const int maximize){ + int i, j, count, bdim, sdim, NB, NS, ND; + int *mask, *sir, *sjc; + double *pbDomain, *psDomain, *pbSize, *psSize, *spr; + mxArray *pTemp, *smallPot; + const char *field_names[] = {"domain", "T", "sizes"}; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + psDomain = mxGetPr(sDomain); + sdim = mxGetNumberOfElements(sDomain); + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + + smallPot = mxCreateStructMatrix(1, 1, 3, field_names); + pTemp = mxDuplicateArray(sDomain); + mxSetField(smallPot, 0, "domain", pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + if(sdim == 0){ + pTemp = mxCreateSparse(1, 1, 1, mxREAL); + mxSetField(smallPot, 0, "T", pTemp); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + *spr = 0; + *sir = 0; + sjc[0] = 0; + sjc[1] = 1; + if(maximize) *spr = 1; + else *spr = NB; + + pTemp = mxCreateDoubleMatrix(1, 1, mxREAL); + *mxGetPr(pTemp) = 1; + mxSetField(smallPot, 0, "sizes", pTemp); + return smallPot; + } + + mask = malloc(sdim * sizeof(int)); + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + pTemp = mxCreateDoubleMatrix(1, count, mxREAL); + psSize = mxGetPr(pTemp); + NS = 1; + for(i=0; i<count; i++){ + psSize[i] = pbSize[mask[i]]; + NS *= (int)psSize[i]; + } + mxSetField(smallPot, 0, "sizes", pTemp); + + ND = NB / NS; + + pTemp = mxCreateSparse(NS, 1, NS, mxREAL); + mxSetField(smallPot, 0, "T", pTemp); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + if(maximize){ + for(i=0; i<NS; i++){ + spr[i] = 1; + sir[i] = i; + } + } + else{ + for(i=0; i<NS; i++){ + spr[i] = ND; + sir[i] = i; + } + } + sjc[0] = 0; + sjc[1] = NS; + + free(mask); + return smallPot; +} + +mxArray* marginal_spPot_to_spPot(const mxArray *bigPot, const mxArray *sDomain, const int maximize){ + int i, j, count, bdim, sdim, NB, NS, NZB, position, bindex, sindex, nzCounts=0; + int *mask, *sequence, *result, *bir, *bjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *sTable, *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr; + mxArray *pTemp, *smallPot; + const char *field_names[] = {"domain", "T", "sizes"}; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + psDomain = mxGetPr(sDomain); + sdim = mxGetNumberOfElements(sDomain); + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + + pTemp = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + + smallPot = mxCreateStructMatrix(1, 1, 3, field_names); + pTemp = mxDuplicateArray(sDomain); + mxSetField(smallPot, 0, "domain", pTemp); + + if(sdim == 0){ + pTemp = mxCreateSparse(1, 1, 1, mxREAL); + mxSetField(smallPot, 0, "T", pTemp); + spr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + *spr = 0; + *bir = 0; + bjc[0] = 0; + bjc[1] = 1; + if(maximize){ + for(i=0; i<NZB; i++){ + *spr = (*spr < bpr[i])? bpr[i] : *spr; + } + } + else{ + for(i=0; i<NZB; i++){ + *spr += bpr[i]; + } + } + + pTemp = mxCreateDoubleMatrix(1, 1, mxREAL); + *mxGetPr(pTemp) = 1; + mxSetField(smallPot, 0, "sizes", pTemp); + return smallPot; + } + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + mask = malloc(sdim * sizeof(int)); + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + pTemp = mxCreateDoubleMatrix(1, count, mxREAL); + psSize = mxGetPr(pTemp); + NS = 1; + for(i=0; i<count; i++){ + psSize[i] = pbSize[mask[i]]; + NS *= (int)psSize[i]; + } + mxSetField(smallPot, 0, "sizes", pTemp); + + + sTable = malloc(NZB * sizeof(double)); + sequence = malloc(NZB * 2 * sizeof(double)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + for(i=0; i<NZB; i++)sTable[i] = 0; + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + count = 0; + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sequence, nzCounts, sizeof(int)*2, compare); + if(result){ + position = (result - sequence) / 2; + if(maximize) + sTable[position] = (sTable[position] < bpr[i]) ? bpr[i] : sTable[position]; + else sTable[position] += bpr[i]; + } + else { + if(maximize) + sTable[nzCounts] = (sTable[nzCounts] < bpr[i]) ? bpr[i] : sTable[nzCounts]; + else sTable[nzCounts] += bpr[i]; + sequence[count] = sindex; + count++; + sequence[count] = nzCounts; + nzCounts++; + count++; + } + } + + qsort(sequence, nzCounts, sizeof(int) * 2, compare); + pTemp = convert_ill_table_to_sparse(sTable, sequence, nzCounts, NS); + mxSetField(smallPot, 0, "T", pTemp); + + free(sTable); + free(sequence); + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); + + return smallPot; +} + + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, n, p, np, pn, loop, loops, nCliques, temp, maximize; + int *collect_order; + double *pr, *pr1; + mxArray *pTemp, *pTemp1, *pPostP, *pClpot, *pSeppot, *pSeparator; + + pTemp = mxGetField(prhs[0], 0, "cliques"); + nCliques = mxGetNumberOfElements(pTemp); + loops = nCliques - 1; + pTemp = mxGetField(prhs[0], 0, "maximize"); + maximize = (int)mxGetScalar(pTemp); + pSeparator = mxGetField(prhs[0], 0, "separator"); + + collect_order = malloc(2 * loops * sizeof(int)); + + pTemp = mxGetField(prhs[0], 0, "postorder"); + pr = mxGetPr(pTemp); + pPostP = mxGetField(prhs[0], 0, "postorder_parents"); + for(i=0; i<loops; i++){ + temp = (int)pr[i] - 1; + pTemp = mxGetCell(pPostP, temp); + pr1 = mxGetPr(pTemp); + collect_order[i] = (int)pr1[0] - 1; + collect_order[i+loops] = temp; + } + + plhs[0] = mxDuplicateArray(prhs[1]); + plhs[1] = mxDuplicateArray(prhs[2]); + + for(loop=0; loop<loops; loop++){ + p = collect_order[loop]; + n = collect_order[loop+loops]; + np = p * nCliques + n; + pn = n * nCliques + p; + pClpot = mxGetCell(plhs[0], n); + pTemp1 = mxGetField(pClpot, 0, "T"); + pTemp = mxGetCell(pSeparator, pn); + if(pTemp1) + pSeppot = marginal_spPot_to_spPot(pClpot, pTemp, maximize); + else pSeppot = marginal_null_to_spPot(pClpot, pTemp, maximize); + mxSetCell(plhs[1], pn, pSeppot); + + pClpot = mxGetCell(plhs[0], p); + pTemp1 = mxGetField(pClpot, 0, "T"); + if(pTemp1) + multiply_spPot_by_spPot(pClpot, pSeppot); + else multiply_null_by_spPot(pClpot, pSeppot); + } + free(collect_order); +} + + + + + +
Binary file _FullBNT/BNT/inference/static/@jtree_sparse_inf_engine/collect_evidence.mexmaci has changed
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_sparse_inf_engine/distribute_evidence.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,618 @@ +/* C mex for distribute_evidence.c in @jtree_sparse_inf_engine directory*/ +/* File enter_evidence.m in directory @jtree_sparse_inf_engine call it */ + +/*********************************************/ +/* distribute_evidence has 3 input & 2 output*/ +/* engine */ +/* clpot */ +/* seppot */ +/* */ +/* clpot */ +/* seppot */ +/*********************************************/ + +#include <math.h> +#include <stdlib.h> +#include "mex.h" + +int compare(const void* src1, const void* src2){ + int i1 = *(int*)src1 ; + int i2 = *(int*)src2 ; + return i1-i2 ; +} + +void ind_subv(int index, const int *cumprod, int n, int *bsubv){ + int i; + + for (i = n-1; i >= 0; i--) { + bsubv[i] = ((int)floor(index / cumprod[i])); + index = index % cumprod[i]; + } +} + +int subv_ind(const int n, const int *cumprod, const int *subv){ + int i, index=0; + + for(i=0; i<n; i++){ + index += subv[i] * cumprod[i]; + } + return index; +} + +void reset_nzmax(mxArray *spArray, const int old_nzmax, const int new_nzmax){ + double *ptr; + void *newptr; + int *ir, *jc; + int nbytes; + + if(new_nzmax == old_nzmax) return; + nbytes = new_nzmax * sizeof(*ptr); + ptr = mxGetPr(spArray); + newptr = mxRealloc(ptr, nbytes); + mxSetPr(spArray, newptr); + nbytes = new_nzmax * sizeof(*ir); + ir = mxGetIr(spArray); + newptr = mxRealloc(ir, nbytes); + mxSetIr(spArray, newptr); + jc = mxGetJc(spArray); + jc[0] = 0; + jc[1] = new_nzmax; + mxSetNzmax(spArray, new_nzmax); +} + +void compute_fixed_weight(int *weight, const double *pbSize, const int *dmask, const int *bCumprod, const int ND, const int diffdim){ + int i, j; + int *eff_cumprod, *subv, *diffsize, *diff_cumprod; + + subv = malloc(diffdim * sizeof(int)); + eff_cumprod = malloc(diffdim * sizeof(int)); + diffsize = malloc(diffdim * sizeof(int)); + diff_cumprod = malloc(diffdim * sizeof(int)); + for(i=0; i<diffdim; i++){ + eff_cumprod[i] = bCumprod[dmask[i]]; + diffsize[i] = (int)pbSize[dmask[i]]; + } + diff_cumprod[0] = 1; + for(i=0; i<diffdim-1; i++){ + diff_cumprod[i+1] = diff_cumprod[i] * diffsize[i]; + } + for(i=0; i<ND; i++){ + ind_subv(i, diff_cumprod, diffdim, subv); + weight[i] = 0; + for(j=0; j<diffdim; j++){ + weight[i] += eff_cumprod[j] * subv[j]; + } + } + free(eff_cumprod); + free(subv); + free(diffsize); + free(diff_cumprod); +} + +mxArray* convert_ill_table_to_sparse(const double *Table, const int *sequence, const int nzCounts, const int N){ + mxArray *spTable; + int i, temp, *irs, *jcs, count=0; + double *sr; + + spTable = mxCreateSparse(N, 1, nzCounts, mxREAL); + sr = mxGetPr(spTable); + irs = mxGetIr(spTable); + jcs = mxGetJc(spTable); + + jcs[0] = 0; + jcs[1] = nzCounts; + + for(i=0; i<nzCounts; i++){ + irs[i] = sequence[count]; + count++; + temp = sequence[count]; + sr[i] = Table[temp]; + count++; + } + return spTable; +} + +void multiply_spPot_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, bdim, sdim, NB, NZB, NZS, position, bindex, sindex, nzCounts=0; + int *mask, *result, *bir, *sir, *rir, *bjc, *sjc, *rjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr, *rpr; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + pTemp = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + if(sdim == 0){ + for(i=0; i<NZB; i++){ + bpr[i] *= *spr; + } + return; + } + + pTemp1 = mxCreateSparse(NB, 1, NZB, mxREAL); + rpr = mxGetPr(pTemp1); + rir = mxGetIr(pTemp1); + rjc = mxGetJc(pTemp1); + rjc[0] = 0; + rjc[1] = NZB; + + mask = malloc(sdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sir, NZS, sizeof(int), compare); + if(result){ + position = result - sir; + rpr[nzCounts] = bpr[i] * spr[position]; + rir[nzCounts] = bindex; + nzCounts++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + reset_nzmax(pTemp1, NZB, nzCounts); + mxSetField(bigPot, 0, "T", pTemp1); + + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +} + +void marginal_spPot_to_spPot(const mxArray *bigPot, mxArray *smallPot, const int maximize){ + int i, j, count, bdim, sdim, NB, NS, NZB, position, bindex, sindex, nzCounts=0; + int *mask, *sequence, *result, *bir, *bjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *sTable, *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr; + mxArray *pTemp; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + NS = 1; + for(i=0; i<sdim; i++){ + NS *= (int)psSize[i]; + } + + pTemp = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + + if(sdim == 0){ + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + *spr = 0; + if(maximize){ + for(i=0; i<NZB; i++){ + *spr = (*spr < bpr[i])? bpr[i] : *spr; + } + } + else{ + for(i=0; i<NZB; i++){ + *spr += bpr[i]; + } + } + return; + } + + mask = malloc(sdim * sizeof(int)); + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + + sTable = malloc(NZB * sizeof(double)); + sequence = malloc(NZB * 2 * sizeof(double)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + for(i=0; i<NZB; i++){ + sTable[i] = 0; + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + count = 0; + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sequence, nzCounts, sizeof(int)*2, compare); + if(result){ + position = (result - sequence) / 2; + if(maximize) + sTable[position] = (sTable[position] < bpr[i]) ? bpr[i] : sTable[position]; + else sTable[position] += bpr[i]; + } + else { + if(maximize) + sTable[nzCounts] = (sTable[nzCounts] < bpr[i]) ? bpr[i] : sTable[nzCounts]; + else sTable[nzCounts] += bpr[i]; + sequence[count] = sindex; + count++; + sequence[count] = nzCounts; + nzCounts++; + count++; + } + } + + pTemp = mxGetField(smallPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + qsort(sequence, nzCounts, sizeof(int) * 2, compare); + pTemp = convert_ill_table_to_sparse(sTable, sequence, nzCounts, NS); + mxSetField(smallPot, 0, "T", pTemp); + + free(sTable); + free(sequence); + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +} + +void divide_null_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, count1, match, temp, bdim, sdim, diffdim, NB, NS, ND, NZB, NZS, bindex, sindex; + int *samemask, *diffmask, *rir, *rjc, *sir, *sjc, *bCumprod, *sCumprod, *ssubv, *weight; + double *pbDomain, *psDomain, *pbSize, *psSize, *rpr, *spr, value; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + if(sdim == 0){ + pTemp1 = mxGetField(bigPot, 0, "T"); + if(pTemp1)mxDestroyArray(pTemp1); + pTemp = mxCreateSparse(NB, 1, NB, mxREAL); + mxSetField(bigPot, 0, "T", pTemp); + rpr = mxGetPr(pTemp); + rir = mxGetIr(pTemp); + rjc = mxGetJc(pTemp); + rjc[0] = 0; + rjc[1] = NB; + value = *spr; + if(value == 0) value = 1; + for(i=0; i<NB; i++){ + rpr[i] = 1 / value; + rir[i] = i; + } + return; + } + + NS = 1; + for(i=0; i<sdim; i++){ + NS *= (int)psSize[i]; + } + ND = NB / NS; + + pTemp = mxCreateSparse(NB, 1, NB, mxREAL); + rpr = mxGetPr(pTemp); + rir = mxGetIr(pTemp); + rjc = mxGetJc(pTemp); + rjc[0] = 0; + rjc[1] = NB; + for(i=0; i<NB; i++){ + rpr[i] = 1; + rir[i] = i; + } + + NZB = ND * NZS; + + diffdim = bdim - sdim; + samemask = malloc(sdim * sizeof(int)); + diffmask = malloc(diffdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + weight = malloc(ND * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + count1 = 0; + for(i=0; i<bdim; i++){ + match = 0; + for(j=0; j<sdim; j++){ + if(pbDomain[i] == psDomain[j]){ + samemask[count] = i; + match = 1; + count++; + break; + } + } + if(match == 0){ + diffmask[count1] = i; + count1++; + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + count = 0; + compute_fixed_weight(weight, pbSize, diffmask, bCumprod, ND, diffdim); + for(i=0; i<NZS; i++){ + sindex = sir[i]; + ind_subv(sindex, sCumprod, sdim, ssubv); + temp = 0; + for(j=0; j<sdim; j++){ + temp += ssubv[j] * bCumprod[samemask[j]]; + } + for(j=0; j<ND; j++){ + bindex = weight[j] + temp; + rpr[bindex] = 1 / (spr[i]); + } + } + + pTemp1 = mxGetField(bigPot, 0, "T"); + if(pTemp1)mxDestroyArray(pTemp1); + mxSetField(bigPot, 0, "T", pTemp); + + free(samemask); + free(diffmask); + free(bCumprod); + free(sCumprod); + free(weight); + free(ssubv); +} + +void divide_spPot_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, bdim, sdim, NB, NZB, NZS, position, bindex, sindex; + int *mask, *result, *bir, *sir, *bjc, *sjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr, value; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + pTemp1 = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp1); + bir = mxGetIr(pTemp1); + bjc = mxGetJc(pTemp1); + NZB = bjc[1]; + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + if(sdim == 0){ + value = *spr; + if(value == 0)value = 1; + for(i=0; i<NZB; i++){ + bpr[i] /= value; + } + return; + } + + mask = malloc(sdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sir, NZS, sizeof(int), compare); + if(result){ + position = result - sir; + bpr[i] /= spr[position]; + } + } + + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +} + + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, loop, loops, nCliques, temp, count, parent, child, maximize, *distribute_order; + double *pr, *pr1; + mxArray *pTemp, *pPreCh, *pClpot, *pSeppot; + + pTemp = mxGetField(prhs[0], 0, "cliques"); + nCliques = mxGetNumberOfElements(pTemp); + loops = nCliques - 1; + pTemp = mxGetField(prhs[0], 0, "maximize"); + maximize = (int)mxGetScalar(pTemp); + + distribute_order = malloc(2 * loops * sizeof(int)); + pTemp = mxGetField(prhs[0], 0, "preorder"); + pr = mxGetPr(pTemp); + pPreCh = mxGetField(prhs[0], 0, "preorder_children"); + count = 0; + for(i=0; i<nCliques; i++){ + temp = (int)pr[i] - 1; + pTemp = mxGetCell(pPreCh, temp); + pr1 = mxGetPr(pTemp); + loop = mxGetNumberOfElements(pTemp); + for(j=0; j<loop; j++){ + distribute_order[count] = temp; + distribute_order[count + loops] = (int)pr1[j] - 1; + count++; + } + } + + plhs[0] = mxDuplicateArray(prhs[1]); + plhs[1] = mxDuplicateArray(prhs[2]); + + for(loop=0; loop<loops; loop++){ + parent = distribute_order[loop]; + child = distribute_order[loop+loops]; + i = nCliques * child + parent; + pClpot = mxGetCell(plhs[0], child); + pTemp = mxGetField(pClpot, 0, "T"); + pSeppot = mxGetCell(plhs[1], i); + if(pTemp){ + if(mxIsEmpty(pTemp)) + divide_null_by_spPot(pClpot, pSeppot); + else + divide_spPot_by_spPot(pClpot, pSeppot); + } + else divide_null_by_spPot(pClpot, pSeppot); + + pClpot = mxGetCell(plhs[0], parent); + marginal_spPot_to_spPot(pClpot, pSeppot, maximize); + mxSetCell(plhs[1], i, pSeppot); + + pClpot = mxGetCell(plhs[0], child); + multiply_spPot_by_spPot(pClpot, pSeppot); + } + free(distribute_order); +}
Binary file _FullBNT/BNT/inference/static/@jtree_sparse_inf_engine/distribute_evidence.mexmaci has changed
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_sparse_inf_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,100 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (jtree) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i} = [] if X(i) is hidden, and otherwise contains its observed value (scalar or column vector). +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product instead of sum-product [0] +% soft - a cell array of soft/virtual evidence; +% soft{i} is a prob. distrib. over i's values, or [] [ cell(1,N) ] +% +% e.g., engine = enter_evidence(engine, ev, 'soft', soft_ev) +% +% For backwards compatibility with BNT2, you can also specify the parameters in the following order +% engine = enter_evidence(engine, ev, soft_ev) + +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes(:); +N = length(bnet.dag); + +engine.evidence = evidence; % store this for marginal_nodes with add_ev option + +% set default params +exclude = []; +soft_evidence = cell(1,N); +maximize = 0; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + if iscell(args{1}) + soft_evidence = args{1}; + else + for i=1:2:nargs + switch args{i}, + case 'soft', soft_evidence = args{i+1}; + case 'maximize', maximize = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end + end +end + +engine.maximize = maximize; + +onodes = find(~isemptycell(evidence)); +hnodes = find(isemptycell(evidence)); +pot_type = determine_pot_type(bnet, onodes); + if strcmp(pot_type, 'cg') + check_for_cd_arcs(onodes, bnet.cnodes, bnet.dag); +end + +hard_nodes = 1:N; +soft_nodes = find(~isemptycell(soft_evidence)); +S = length(soft_nodes); +if S > 0 + assert(pot_type == 'd'); + assert(mysubset(soft_nodes, bnet.dnodes)); +end + +% Evaluate CPDs with evidence, and convert to potentials +pot = cell(1, N+S); +for n=1:N + fam = family(bnet.dag, n); + e = bnet.equiv_class(n); + if isempty(bnet.CPD{e}) + error(['must define CPD ' num2str(e)]) + else + pot{n} = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), evidence); + end +end + +for i=1:S + n = soft_nodes(i); + pot{N+i} = dpot(n, ns(n), soft_evidence{n}); +end +clqs = engine.clq_ass_to_node([hard_nodes soft_nodes]); + +[clpot, seppot] = init_pot(engine, clqs, pot, pot_type, onodes); +[clpot, seppot] = collect_evidence(engine, clpot, seppot); +[clpot, seppot] = distribute_evidence(engine, clpot, seppot); +C = length(clpot); +ll = zeros(1, C); +for i=1:C + domain = clpot{i}.domain; + sizes = clpot{i}.sizes; + T = clpot{i}.T; + clpot{i} = dpot(domain, sizes, T); +end + +for i=1:C + [clpot{i}, ll(i)] = normalize_pot(clpot{i}); +end +loglik = ll(1); % we can extract the likelihood from any clique + +engine.clpot = clpot;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_sparse_inf_engine/enter_soft_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,19 @@ +function [clpot, loglik] = enter_soft_evidence(engine, clique, potential, onodes, pot_type) +% ENTER_SOFT_EVIDENCE Add the specified potentials to the network (jtree) +% [clpot, loglik] = enter_soft_evidence(engine, clique, potential, onodes, pot_type, maximize) +% +% We multiply potential{i} onto clique(i) before propagating. +% We return all the modified clique potentials. + +[clpot, seppot] = init_pot(engine, clique, potential, pot_type, onodes); +[clpot, seppot] = collect_evidence(engine, clpot, seppot); +[clpot, seppot] = distribute_evidence(engine, clpot, seppot); + +C = length(clpot); +ll = zeros(1, C); +for i=1:C + [clpot{i}, ll(i)] = normalize_pot(clpot{i}); +end +loglik = ll(1); % we can extract the likelihood from any clique + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_sparse_inf_engine/init_pot.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,624 @@ +/* C mex init_pot for in @jtree_sparse_inf_engine directory */ +/* The file enter_evidence.m in directory @jtree_sparse_inf_engine call it*/ + +/**************************************/ +/* init_pot.c has 5 input & 2 output */ +/* engine */ +/* clqs */ +/* pots */ +/* pot_type */ +/* onodes */ +/* */ +/* clpot */ +/* seppot */ +/**************************************/ +#include <math.h> +#include <stdlib.h> +#include "mex.h" + +int compare(const void* src1, const void* src2){ + int i1 = *(int*)src1 ; + int i2 = *(int*)src2 ; + return i1-i2 ; +} + +void ind_subv(int index, const int *cumprod, int n, int *bsubv){ + int i; + + for (i = n-1; i >= 0; i--) { + bsubv[i] = ((int)floor(index / cumprod[i])); + index = index % cumprod[i]; + } +} + +int subv_ind(const int n, const int *cumprod, const int *subv){ + int i, index=0; + + for(i=0; i<n; i++){ + index += subv[i] * cumprod[i]; + } + return index; +} + +void compute_fixed_weight(int *weight, const double *pbSize, const int *dmask, const int *bCumprod, const int ND, const int diffdim){ + int i, j; + int *eff_cumprod, *subv, *diffsize, *diff_cumprod; + + subv = malloc(diffdim * sizeof(int)); + eff_cumprod = malloc(diffdim * sizeof(int)); + diffsize = malloc(diffdim * sizeof(int)); + diff_cumprod = malloc(diffdim * sizeof(int)); + for(i=0; i<diffdim; i++){ + eff_cumprod[i] = bCumprod[dmask[i]]; + diffsize[i] = (int)pbSize[dmask[i]]; + } + diff_cumprod[0] = 1; + for(i=0; i<diffdim-1; i++){ + diff_cumprod[i+1] = diff_cumprod[i] * diffsize[i]; + } + for(i=0; i<ND; i++){ + ind_subv(i, diff_cumprod, diffdim, subv); + weight[i] = 0; + for(j=0; j<diffdim; j++){ + weight[i] += eff_cumprod[j] * subv[j]; + } + } + free(eff_cumprod); + free(subv); + free(diffsize); + free(diff_cumprod); +} + +void reset_nzmax(mxArray *spArray, const int old_nzmax, const int new_nzmax){ + double *ptr; + void *newptr; + int *ir, *jc; + int nbytes; + + if(new_nzmax == old_nzmax) return; + nbytes = new_nzmax * sizeof(*ptr); + ptr = mxGetPr(spArray); + newptr = mxRealloc(ptr, nbytes); + mxSetPr(spArray, newptr); + nbytes = new_nzmax * sizeof(*ir); + ir = mxGetIr(spArray); + newptr = mxRealloc(ir, nbytes); + mxSetIr(spArray, newptr); + jc = mxGetJc(spArray); + jc[0] = 0; + jc[1] = new_nzmax; + mxSetNzmax(spArray, new_nzmax); +} + +mxArray* convert_ill_table_to_sparse(const double *bigTable, const int *sequence, const int nzCounts, const int NB){ + mxArray *spTable; + int i, temp, *irs, *jcs, count=0; + double *sr; + + spTable = mxCreateSparse(NB, 1, nzCounts, mxREAL); + sr = mxGetPr(spTable); + irs = mxGetIr(spTable); + jcs = mxGetJc(spTable); + + jcs[0] = 0; + jcs[1] = nzCounts; + + for(i=0; i<nzCounts; i++){ + irs[i] = sequence[count]; + count++; + temp = sequence[count]; + sr[i] = bigTable[temp]; + count++; + } + return spTable; +} + +void multiply_null_by_fuPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, NB, NS, siz_b, siz_s, ndim, nzCounts=0; + int *mask, *sx, *sy, *cpsy, *subs, *s, *cpsy2, *bir, *bjc; + double *pbDomain, *psDomain, *pbSize, *psSize, *spr, *bpr, value; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + siz_b = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + siz_s = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<siz_b; i++){ + NB *= (int)pbSize[i]; + } + NS = 1; + for(i=0; i<siz_s; i++){ + NS *= (int)psSize[i]; + } + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + + pTemp1 = mxCreateSparse(NB, 1, NB, mxREAL); + bpr = mxGetPr(pTemp1); + bir = mxGetIr(pTemp1); + bjc = mxGetJc(pTemp1); + bjc[0] = 0; + bjc[1] = NB; + + if(NS == 1){ + value = *spr; + for(i=0; i<NB; i++){ + bpr[i] = value; + bir[i] = i; + } + nzCounts = NB; + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + reset_nzmax(pTemp1, NB, nzCounts); + mxSetField(bigPot, 0, "T", pTemp1); + return; + } + + if(NS == NB){ + for(i=0; i<NB; i++){ + if(spr[i] != 0){ + bpr[nzCounts] = spr[i]; + bir[nzCounts] = i; + nzCounts++; + } + } + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + reset_nzmax(pTemp1, NB, nzCounts); + mxSetField(bigPot, 0, "T", pTemp1); + return; + } + + mask = malloc(siz_s * sizeof(int)); + count = 0; + for(i=0; i<siz_s; i++){ + for(j=0; j<siz_b; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + ndim = siz_b; + sx = (int *)malloc(sizeof(int)*ndim); + sy = (int *)malloc(sizeof(int)*ndim); + for(i=0; i<ndim; i++){ + sx[i] = (int)pbSize[i]; + sy[i] = 1; + } + for(i=0; i<count; i++){ + sy[mask[i]] = sx[mask[i]]; + } + + s = (int *)malloc(sizeof(int)*ndim); + *(cpsy = (int *)malloc(sizeof(int)*ndim)) = 1; + subs = (int *)malloc(sizeof(int)*ndim); + cpsy2 = (int *)malloc(sizeof(int)*ndim); + for(i = 0; i < ndim; i++){ + subs[i] = 0; + s[i] = sx[i] - 1; + } + + for(i = 0; i < ndim-1; i++){ + cpsy[i+1] = cpsy[i]*sy[i]--; + cpsy2[i] = cpsy[i]*sy[i]; + } + cpsy2[ndim-1] = cpsy[ndim-1]*(--sy[ndim-1]); + + for(j=0; j<NB; j++){ + if(*spr != 0){ + bpr[nzCounts] = *spr; + bir[nzCounts] = j; + nzCounts++; + } + for(i = 0; i < ndim; i++){ + if(subs[i] == s[i]){ + subs[i] = 0; + if(sy[i]) + spr -= cpsy2[i]; + } + else{ + subs[i]++; + if(sy[i]) + spr += cpsy[i]; + break; + } + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + reset_nzmax(pTemp1, NB, nzCounts); + mxSetField(bigPot, 0, "T", pTemp1); + + free(sx); + free(sy); + free(s); + free(cpsy); + free(subs); + free(cpsy2); + free(mask); +} + +void multiply_null_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, count1, match, temp, bdim, sdim, diffdim, NB, NS, ND, NZB, NZS, bindex, sindex, nzCounts=0; + int *samemask, *diffmask, *sir, *sjc, *bCumprod, *sCumprod, *ssubv, *sequence, *weight; + double *bigTable, *pbDomain, *psDomain, *pbSize, *psSize, *spr; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + NS = 1; + for(i=0; i<sdim; i++){ + NS *= (int)psSize[i]; + } + ND = NB / NS; + + if(ND == 1){ + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + pTemp1 = mxGetField(smallPot, 0, "T"); + pTemp = mxDuplicateArray(pTemp1); + mxSetField(bigPot, 0, "T", pTemp); + return; + } + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + NZB = ND * NZS; + + diffdim = bdim - sdim; + sequence = malloc(NZB * 2 * sizeof(int)); + bigTable = malloc(NZB * sizeof(double)); + samemask = malloc(sdim * sizeof(int)); + diffmask = malloc(diffdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + weight = malloc(ND * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + count1 = 0; + for(i=0; i<bdim; i++){ + match = 0; + for(j=0; j<sdim; j++){ + if(pbDomain[i] == psDomain[j]){ + samemask[count] = i; + match = 1; + count++; + break; + } + } + if(match == 0){ + diffmask[count1] = i; + count1++; + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + count = 0; + compute_fixed_weight(weight, pbSize, diffmask, bCumprod, ND, diffdim); + for(i=0; i<NZS; i++){ + sindex = sir[i]; + ind_subv(sindex, sCumprod, sdim, ssubv); + temp = 0; + for(j=0; j<sdim; j++){ + temp += ssubv[j] * bCumprod[samemask[j]]; + } + for(j=0; j<ND; j++){ + bindex = weight[j] + temp; + bigTable[nzCounts] = spr[i]; + sequence[count] = bindex; + count++; + sequence[count] = nzCounts; + nzCounts++; + count++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + qsort(sequence, nzCounts, sizeof(int) * 2, compare); + pTemp = convert_ill_table_to_sparse(bigTable, sequence, nzCounts, NB); + mxSetField(bigPot, 0, "T", pTemp); + + free(sequence); + free(bigTable); + free(samemask); + free(diffmask); + free(bCumprod); + free(sCumprod); + free(weight); + free(ssubv); +} + +void multiply_spPot_by_fuPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, bdim, sdim, NB, NZB, bindex, sindex, nzCounts=0; + int *mask, *bir, *bjc, *rir, *rjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr, *rpr, value; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + pTemp = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + + pTemp1 = mxCreateSparse(NB, 1, NZB, mxREAL); + rpr = mxGetPr(pTemp1); + rir = mxGetIr(pTemp1); + rjc = mxGetJc(pTemp1); + rjc[0] = 0; + rjc[1] = NZB; + + mask = malloc(sdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + value = spr[sindex]; + if(value != 0){ + rpr[nzCounts] = bpr[i] * value; + rir[nzCounts] = bindex; + nzCounts++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + reset_nzmax(pTemp1, NZB, nzCounts); + mxSetField(bigPot, 0, "T", pTemp1); + + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +} + +void multiply_spPot_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, bdim, sdim, NB, NZB, NZS, position, bindex, sindex, nzCounts=0; + int *mask, *result, *bir, *sir, *rir, *bjc, *sjc, *rjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr, *rpr; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + pTemp = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + pTemp1 = mxCreateSparse(NB, 1, NZB, mxREAL); + rpr = mxGetPr(pTemp1); + rir = mxGetIr(pTemp1); + rjc = mxGetJc(pTemp1); + rjc[0] = 0; + rjc[1] = NZB; + + mask = malloc(sdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sir, NZS, sizeof(int), compare); + if(result){ + position = result - sir; + rpr[nzCounts] = bpr[i] * spr[position]; + rir[nzCounts] = bindex; + nzCounts++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + reset_nzmax(pTemp1, NZB, nzCounts); + mxSetField(bigPot, 0, "T", pTemp1); + + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +} + + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, c, loop, nNodes, nCliques, ndomain, ns_num, nOnodes, dims[2]; + double *pClqs, *pr, *pt, *pSize, *eff_ns; + mxArray *pTemp, *pTemp1, *pStruct, *pCliques, *pBigpot, *pSmallpot; + const char *field_names[] = {"domain", "T", "sizes"}; + + nNodes = mxGetNumberOfElements(prhs[1]); + pCliques = mxGetField(prhs[0], 0, "cliques"); + nCliques = mxGetNumberOfElements(pCliques); + pTemp = mxGetField(prhs[0], 0, "actual_node_sizes"); + ns_num = mxGetNumberOfElements(pTemp); + pSize = mxGetPr(pTemp); + + eff_ns = (double *)malloc(ns_num * sizeof(double)); + for(i=0; i<ns_num; i++) eff_ns[i] = pSize[i]; + nOnodes = mxGetNumberOfElements(prhs[4]); + pr = mxGetPr(prhs[4]); + for(i=0; i<nOnodes; i++) eff_ns[(int)pr[i] - 1] = 1; + + plhs[0] = mxCreateCellArray(1, &nCliques); + for(i=0; i<nCliques; i++){ + pStruct = mxCreateStructMatrix(1, 1, 3, field_names); + mxSetCell(plhs[0], i, pStruct); + pTemp = mxGetCell(pCliques, i); + ndomain = mxGetNumberOfElements(pTemp); + pt = mxGetPr(pTemp); + pTemp1 = mxDuplicateArray(pTemp); + mxSetField(pStruct, 0, "domain", pTemp1); + + pTemp = mxCreateDoubleMatrix(1, ndomain, mxREAL); + mxSetField(pStruct, 0, "sizes", pTemp); + pr = mxGetPr(pTemp); + for(j=0; j<ndomain; j++){ + pr[j] = eff_ns[(int)pt[j]-1]; + } + } + + pClqs = mxGetPr(prhs[1]); + for(loop=0; loop<nNodes; loop++){ + c = (int)pClqs[loop] - 1; + pSmallpot = mxGetCell(prhs[2], loop); + pTemp = mxGetField(pSmallpot, 0, "T"); + pBigpot = mxGetCell(plhs[0], c); + pTemp1 = mxGetField(pBigpot, 0, "T"); + if(pTemp1){ + if(mxIsSparse(pTemp)) + multiply_spPot_by_spPot(pBigpot, pSmallpot); + else multiply_spPot_by_fuPot(pBigpot, pSmallpot); + } + else{ + if(mxIsSparse(pTemp)) + multiply_null_by_spPot(pBigpot, pSmallpot); + else multiply_null_by_fuPot(pBigpot, pSmallpot); + } + } + + free(eff_ns); + dims[0] = nCliques; + dims[1] = nCliques; + plhs[1] = mxCreateCellArray(2, dims); +} + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_sparse_inf_engine/jtree_sparse_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,126 @@ +function engine = jtree_sparse_inf_engine(bnet, varargin) +% JTREE_SPARSE_INF_ENGINE Junction tree inference engine when CPTs and Potentials are sparse +% engine = jtree_sparse_inf_engine(bnet, ...) +% It differs from jtree_inf_engine with all CPTs and potentials are 1D sparse arrays. +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% clusters - a cell array of sets of nodes we want to ensure are in the same clique (in addition to families) [ {} ] +% root - the root of the junction tree will be a clique that contains this set of nodes [N] +% stages - stages{t} is a set of nodes we want to eliminate before stages{t+1}, ... [ {1:N} ] +% +% e.g., engine = jtree_inf_engine(bnet, 'maximize', 1); +% +% For more details on the junction tree algorithm, see +% - "Probabilistic networks and expert systems", Cowell, Dawid, Lauritzen and Spiegelhalter, Springer, 1999 +% - "Inference in Belief Networks: A procedural guide", C. Huang and A. Darwiche, +% Intl. J. Approximate Reasoning, 15(3):225-263, 1996. + + +% set default params +N = length(bnet.dag); +clusters = {}; +root = N; +stages = { 1:N }; + +if nargin >= 2 + args = varargin; + nargs = length(args); + if ~isstr(args{1}) + error('the interface to jtree has changed; now, onodes is not allowed and all optional params must be passed by name') + end + for i=1:2:nargs + switch args{i}, + case 'clusters', clusters = args{i+1}; + case 'root', root = args{i+1}; + case 'stages', stages = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +engine = init_fields; +engine = class(engine, 'jtree_sparse_inf_engine', inf_engine(bnet)); + +onodes = bnet.observed; +%[engine.jtree, dummy, engine.cliques, B, w] = dag_to_jtree(bnet, onodes, stages, clusters); + +porder = determine_elim_constraints(bnet, onodes); +strong = ~isempty(porder); +ns = bnet.node_sizes(:); +ns(onodes) = 1; % observed nodes have only 1 possible value +[engine.jtree, root2, engine.cliques, B, w] = ... + graph_to_jtree(moralize(bnet.dag), ns, porder, stages, clusters); + +engine.cliques_bitv = B; +engine.clique_weight = w; +C = length(engine.cliques); +engine.clpot = cell(1,C); + +% Compute the separators between connected cliques. +[is,js] = find(engine.jtree > 0); +engine.separator = cell(C,C); +for k=1:length(is) + i = is(k); j = js(k); + engine.separator{i,j} = find(B(i,:) & B(j,:)); % intersect(cliques{i}, cliques{j}); +end + +% A node can be a member of many cliques, but is assigned to exactly one, to avoid +% double-counting its CPD. We assign node i to clique c if c is the "lightest" clique that +% contains i's family, so it can accomodate its CPD. + +engine.clq_ass_to_node = zeros(1, N); +for i=1:N + %c = clq_containing_nodes(engine, family(bnet.dag, i)); + clqs_containing_family = find(all(B(:,family(bnet.dag, i)), 2)); % all selected columns must be 1 + c = clqs_containing_family(argmin(w(clqs_containing_family))); + engine.clq_ass_to_node(i) = c; +end + +% Make the jtree rooted, so there is a fixed message passing order. +engine.root_clq = clq_containing_nodes(engine, root); +if engine.root_clq <= 0 + error(['no clique contains ' num2str(root)]); +end + +[engine.jtree, engine.preorder, engine.postorder] = mk_rooted_tree(engine.jtree, engine.root_clq); + +% collect +engine.postorder_parents = cell(1,length(engine.postorder)); +for n=engine.postorder(:)' + engine.postorder_parents{n} = parents(engine.jtree, n); +end +% distribute +engine.preorder_children = cell(1,length(engine.preorder)); +for n=engine.preorder(:)' + engine.preorder_children{n} = children(engine.jtree, n); +end + +ns = bnet.node_sizes; +engine.actual_node_sizes = ns; + + +%%%%%%%% + +function engine = init_fields() + +engine.jtree = []; +engine.cliques = []; +engine.separator = []; +engine.cliques_bitv = []; +engine.clique_weight = []; +engine.clpot = []; +engine.clq_ass_to_node = []; +engine.root_clq = []; +engine.preorder = []; +engine.postorder = []; +engine.preorder_children = []; +engine.postorder_parents = []; +engine.maximize = []; +engine.evidence = []; +engine.actual_node_sizes = []; + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_sparse_inf_engine/marginal_family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function marginal = marginal_family(engine, i, add_ev) +% MARGINAL_FAMILY Compute the marginal on the specified family (jtree) +% marginal = marginal_family(engine, i) + +if nargin < 3, add_ev = 0; end +assert(~add_ev); + +bnet = bnet_from_engine(engine); +fam = family(bnet.dag, i); +c = engine.clq_ass_to_node(i); +marginal = pot_to_marginal(marginalize_pot(engine.clpot{c}, fam));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_sparse_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,22 @@ +function marginal = marginal_nodes(engine, query, add_ev) +% MARGINAL_NODES Compute the marginal on the specified query nodes (jtree) +% marginal = marginal_nodes(engine, query, add_ev) +% +% 'query' must be a subset of some clique; an error will be raised if not. +% add_ev is an optional argument; if 1, we will "inflate" the marginal of observed nodes +% to their original size, adding 0s to the positions which contradict the evidence + +if nargin < 3, add_ev = 0; end + +c = clq_containing_nodes(engine, query); +if c == -1 + error(['no clique contains ' num2str(query)]); +end +marginal = pot_to_marginal(marginalize_pot(engine.clpot{c}, query, engine.maximize)); + +if add_ev + bnet = bnet_from_engine(engine); + %marginal = add_ev_to_dmarginal(marginal, engine.evidence, bnet.node_sizes); + marginal = add_evidence_to_gmarginal(marginal, engine.evidence, bnet.node_sizes, bnet.cnodes); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_sparse_inf_engine/old/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +/collect_evidence.c/1.1.1.1/Wed May 29 15:59:56 2002// +/distribute_evidence.c/1.1.1.1/Wed May 29 15:59:56 2002// +/init_pot.c/1.1.1.1/Wed May 29 15:59:56 2002// +/init_pot1.c/1.1.1.1/Wed May 29 15:59:56 2002// +/init_pot1.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_sparse_inf_engine/old/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@jtree_sparse_inf_engine/old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_sparse_inf_engine/old/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_sparse_inf_engine/old/collect_evidence.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,635 @@ +/* C mex for collect_evidence.c in @jtree_sparse_inf_engine directory */ +/* File enter_evidence.m in directory @jtree_sparse_inf_engine call it*/ + +/******************************************/ +/* collect_evidence has 3 input & 2 output*/ +/* engine */ +/* clpot */ +/* seppot */ +/* */ +/* clpot */ +/* seppot */ +/******************************************/ + +#include <math.h> +#include <search.h> +#include "mex.h" + +int compare(const void* src1, const void* src2){ + int i1 = *(int*)src1 ; + int i2 = *(int*)src2 ; + return i1-i2 ; +} + +void ind_subv(int index, const int *cumprod, int n, int *bsubv){ + int i; + + for (i = n-1; i >= 0; i--) { + bsubv[i] = ((int)floor(index / cumprod[i])); + index = index % cumprod[i]; + } +} + +int subv_ind(const int n, const int *cumprod, const int *subv){ + int i, index=0; + + for(i=0; i<n; i++){ + index += subv[i] * cumprod[i]; + } + return index; +} + +void compute_fixed_weight(int *weight, const double *pbSize, const int *dmask, const int *bCumprod, const int ND, const int diffdim){ + int i, j; + int *eff_cumprod, *subv, *diffsize, *diff_cumprod; + + subv = malloc(diffdim * sizeof(int)); + eff_cumprod = malloc(diffdim * sizeof(int)); + diffsize = malloc(diffdim * sizeof(int)); + diff_cumprod = malloc(diffdim * sizeof(int)); + for(i=0; i<diffdim; i++){ + eff_cumprod[i] = bCumprod[dmask[i]]; + diffsize[i] = (int)pbSize[dmask[i]]; + } + diff_cumprod[0] = 1; + for(i=0; i<diffdim-1; i++){ + diff_cumprod[i+1] = diff_cumprod[i] * diffsize[i]; + } + for(i=0; i<ND; i++){ + ind_subv(i, diff_cumprod, diffdim, subv); + weight[i] = 0; + for(j=0; j<diffdim; j++){ + weight[i] += eff_cumprod[j] * subv[j]; + } + } + free(eff_cumprod); + free(subv); + free(diffsize); + free(diff_cumprod); +} + +mxArray* convert_table_to_sparse(const double *bT, const int *index, const int nzCounts, const int N){ + mxArray *spTable; + int i, *irs, *jcs; + double *sr; + + spTable = mxCreateSparse(N, 1, nzCounts, mxREAL); + sr = mxGetPr(spTable); + irs = mxGetIr(spTable); + jcs = mxGetJc(spTable); + + jcs[0] = 0; + jcs[1] = nzCounts; + + for(i=0; i<nzCounts; i++){ + sr[i] = bT[i]; + irs[i] = index[i]; + } + return spTable; +} + +mxArray* convert_ill_table_to_sparse(const double *Table, const int *sequence, const int nzCounts, const int N){ + mxArray *spTable; + int i, temp, *irs, *jcs, count=0; + double *sr; + + spTable = mxCreateSparse(N, 1, nzCounts, mxREAL); + sr = mxGetPr(spTable); + irs = mxGetIr(spTable); + jcs = mxGetJc(spTable); + + jcs[0] = 0; + jcs[1] = nzCounts; + + for(i=0; i<nzCounts; i++){ + irs[i] = sequence[count]; + count++; + temp = sequence[count]; + sr[i] = Table[temp]; + count++; + } + return spTable; +} + +void multiply_null_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, count1, match, temp, bdim, sdim, diffdim, NB, NS, ND, NZB, NZS, bindex, sindex, nzCounts=0; + int *samemask, *diffmask, *sir, *sjc, *bCumprod, *sCumprod, *ssubv, *sequence, *weight; + double *bigTable, *pbDomain, *psDomain, *pbSize, *psSize, *spr, *bpr; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + if(sdim == 0){ + pTemp = mxCreateSparse(NB, 1, NB, mxREAL); + mxSetField(bigPot, 0, "T", pTemp); + bpr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + sjc[0] = 0; + sjc[1] = NB; + for(i=0; i<NB; i++){ + bpr[i] = *spr; + sir[i] = i; + } + return; + } + + NS = 1; + for(i=0; i<sdim; i++){ + NS *= (int)psSize[i]; + } + ND = NB / NS; + + if(ND == 1){ + pTemp1 = mxGetField(smallPot, 0, "T"); + pTemp = mxDuplicateArray(pTemp1); + mxSetField(bigPot, 0, "T", pTemp); + return; + } + + + NZB = ND * NZS; + + diffdim = bdim - sdim; + sequence = malloc(NZB * 2 * sizeof(int)); + bigTable = malloc(NZB * sizeof(double)); + samemask = malloc(sdim * sizeof(int)); + diffmask = malloc(diffdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + weight = malloc(ND * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + count1 = 0; + for(i=0; i<bdim; i++){ + match = 0; + for(j=0; j<sdim; j++){ + if(pbDomain[i] == psDomain[j]){ + samemask[count] = i; + match = 1; + count++; + break; + } + } + if(match == 0){ + diffmask[count1] = i; + count1++; + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + count = 0; + compute_fixed_weight(weight, pbSize, diffmask, bCumprod, ND, diffdim); + for(i=0; i<NZS; i++){ + sindex = sir[i]; + ind_subv(sindex, sCumprod, sdim, ssubv); + temp = 0; + for(j=0; j<sdim; j++){ + temp += ssubv[j] * bCumprod[samemask[j]]; + } + for(j=0; j<ND; j++){ + bindex = weight[j] + temp; + bigTable[nzCounts] = spr[i]; + sequence[count] = bindex; + count++; + sequence[count] = nzCounts; + nzCounts++; + count++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + qsort(sequence, nzCounts, sizeof(int) * 2, compare); + pTemp = convert_ill_table_to_sparse(bigTable, sequence, nzCounts, NB); + mxSetField(bigPot, 0, "T", pTemp); + + free(sequence); + free(bigTable); + free(samemask); + free(diffmask); + free(bCumprod); + free(sCumprod); + free(weight); + free(ssubv); +} + +void multiply_spPot_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, bdim, sdim, NB, NZB, NZS, position, bindex, sindex, nzCounts=0; + int *mask, *index, *result, *bir, *sir, *bjc, *sjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *bigTable, *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr, value; + mxArray *pTemp; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + pTemp = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + if(sdim == 0){ + for(i=0; i<NZB; i++){ + bpr[i] *= *spr; + } + return; + } + + bigTable = malloc(NZB * sizeof(double)); + index = malloc(NZB * sizeof(double)); + mask = malloc(sdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + for(i=0; i<NZB; i++){ + bigTable[i] = 0; + } + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + for(i=0; i<NZB; i++){ + value = bpr[i]; + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sir, NZS, sizeof(int), compare); + if(result){ + position = result - sir; + value *= spr[position]; + bigTable[nzCounts] = value; + index[nzCounts] = bindex; + nzCounts++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + pTemp = convert_table_to_sparse(bigTable, index, nzCounts, NB); + mxSetField(bigPot, 0, "T", pTemp); + + free(bigTable); + free(index); + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +} + +mxArray* marginal_null_to_spPot(const mxArray *bigPot, const mxArray *sDomain, const int maximize){ + int i, j, count, bdim, sdim, NB, NS, ND; + int *mask, *sir, *sjc; + double *pbDomain, *psDomain, *pbSize, *psSize, *spr; + mxArray *pTemp, *smallPot; + const char *field_names[] = {"domain", "T", "sizes"}; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + psDomain = mxGetPr(sDomain); + sdim = mxGetNumberOfElements(sDomain); + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + + smallPot = mxCreateStructMatrix(1, 1, 3, field_names); + pTemp = mxDuplicateArray(sDomain); + mxSetField(smallPot, 0, "domain", pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + if(sdim == 0){ + pTemp = mxCreateSparse(1, 1, 1, mxREAL); + mxSetField(smallPot, 0, "T", pTemp); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + *spr = 0; + *sir = 0; + sjc[0] = 0; + sjc[1] = 1; + if(maximize) *spr = 1; + else *spr = NB; + + pTemp = mxCreateDoubleMatrix(1, 1, mxREAL); + *mxGetPr(pTemp) = 1; + mxSetField(smallPot, 0, "sizes", pTemp); + return smallPot; + } + + mask = malloc(sdim * sizeof(int)); + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + pTemp = mxCreateDoubleMatrix(1, count, mxREAL); + psSize = mxGetPr(pTemp); + NS = 1; + for(i=0; i<count; i++){ + psSize[i] = pbSize[mask[i]]; + NS *= (int)psSize[i]; + } + mxSetField(smallPot, 0, "sizes", pTemp); + + ND = NB / NS; + + pTemp = mxCreateSparse(NS, 1, NS, mxREAL); + mxSetField(smallPot, 0, "T", pTemp); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + if(maximize){ + for(i=0; i<NS; i++){ + spr[i] = 1; + sir[i] = i; + } + } + else{ + for(i=0; i<NS; i++){ + spr[i] = ND; + sir[i] = i; + } + } + sjc[0] = 0; + sjc[1] = NS; + + free(mask); + return smallPot; +} + +mxArray* marginal_spPot_to_spPot(const mxArray *bigPot, const mxArray *sDomain, const int maximize){ + int i, j, count, bdim, sdim, NB, NS, NZB, position, bindex, sindex, nzCounts=0; + int *mask, *sequence, *result, *bir, *bjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *sTable, *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr; + mxArray *pTemp, *smallPot; + const char *field_names[] = {"domain", "T", "sizes"}; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + psDomain = mxGetPr(sDomain); + sdim = mxGetNumberOfElements(sDomain); + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + + pTemp = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + + smallPot = mxCreateStructMatrix(1, 1, 3, field_names); + pTemp = mxDuplicateArray(sDomain); + mxSetField(smallPot, 0, "domain", pTemp); + + if(sdim == 0){ + pTemp = mxCreateSparse(1, 1, 1, mxREAL); + mxSetField(smallPot, 0, "T", pTemp); + spr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + *spr = 0; + *bir = 0; + bjc[0] = 0; + bjc[1] = 1; + if(maximize){ + for(i=0; i<NZB; i++){ + *spr = (*spr < bpr[i])? bpr[i] : *spr; + } + } + else{ + for(i=0; i<NZB; i++){ + *spr += bpr[i]; + } + } + + pTemp = mxCreateDoubleMatrix(1, 1, mxREAL); + *mxGetPr(pTemp) = 1; + mxSetField(smallPot, 0, "sizes", pTemp); + return smallPot; + } + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + mask = malloc(sdim * sizeof(int)); + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + pTemp = mxCreateDoubleMatrix(1, count, mxREAL); + psSize = mxGetPr(pTemp); + NS = 1; + for(i=0; i<count; i++){ + psSize[i] = pbSize[mask[i]]; + NS *= (int)psSize[i]; + } + mxSetField(smallPot, 0, "sizes", pTemp); + + + sTable = malloc(NZB * sizeof(double)); + sequence = malloc(NZB * 2 * sizeof(double)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + for(i=0; i<NZB; i++)sTable[i] = 0; + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + count = 0; + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sequence, nzCounts, sizeof(int)*2, compare); + if(result){ + position = (result - sequence) / 2; + if(maximize) + sTable[position] = (sTable[position] < bpr[i]) ? bpr[i] : sTable[position]; + else sTable[position] += bpr[i]; + } + else { + if(maximize) + sTable[nzCounts] = (sTable[nzCounts] < bpr[i]) ? bpr[i] : sTable[nzCounts]; + else sTable[nzCounts] += bpr[i]; + sequence[count] = sindex; + count++; + sequence[count] = nzCounts; + nzCounts++; + count++; + } + } + + qsort(sequence, nzCounts, sizeof(int) * 2, compare); + pTemp = convert_ill_table_to_sparse(sTable, sequence, nzCounts, NS); + mxSetField(smallPot, 0, "T", pTemp); + + free(sTable); + free(sequence); + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); + + return smallPot; +} + + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, n, p, np, pn, loop, loops, nCliques, temp, maximize; + int *collect_order; + double *pr, *pr1; + mxArray *pTemp, *pTemp1, *pPostP, *pClpot, *pSeppot, *pSeparator; + + pTemp = mxGetField(prhs[0], 0, "cliques"); + nCliques = mxGetNumberOfElements(pTemp); + loops = nCliques - 1; + pTemp = mxGetField(prhs[0], 0, "maximize"); + maximize = (int)mxGetScalar(pTemp); + pSeparator = mxGetField(prhs[0], 0, "separator"); + + collect_order = malloc(2 * loops * sizeof(int)); + + pTemp = mxGetField(prhs[0], 0, "postorder"); + pr = mxGetPr(pTemp); + pPostP = mxGetField(prhs[0], 0, "postorder_parents"); + for(i=0; i<loops; i++){ + temp = (int)pr[i] - 1; + pTemp = mxGetCell(pPostP, temp); + pr1 = mxGetPr(pTemp); + collect_order[i] = (int)pr1[0] - 1; + collect_order[i+loops] = temp; + } + + plhs[0] = mxDuplicateArray(prhs[1]); + plhs[1] = mxDuplicateArray(prhs[2]); + + for(loop=0; loop<loops; loop++){ + p = collect_order[loop]; + n = collect_order[loop+loops]; + np = p * nCliques + n; + pn = n * nCliques + p; + pClpot = mxGetCell(plhs[0], n); + pTemp1 = mxGetField(pClpot, 0, "T"); + pTemp = mxGetCell(pSeparator, pn); + if(pTemp1) + pSeppot = marginal_spPot_to_spPot(pClpot, pTemp, maximize); + else pSeppot = marginal_null_to_spPot(pClpot, pTemp, maximize); + mxSetCell(plhs[1], pn, pSeppot); + + pClpot = mxGetCell(plhs[0], p); + pTemp1 = mxGetField(pClpot, 0, "T"); + if(pTemp1) + multiply_spPot_by_spPot(pClpot, pSeppot); + else multiply_null_by_spPot(pClpot, pSeppot); + } + free(collect_order); +} + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_sparse_inf_engine/old/distribute_evidence.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,613 @@ +/* C mex for distribute_evidence.c in @jtree_sparse_inf_engine directory*/ +/* File enter_evidence.m in directory @jtree_sparse_inf_engine call it */ + +/*********************************************/ +/* distribute_evidence has 3 input & 2 output*/ +/* engine */ +/* clpot */ +/* seppot */ +/* */ +/* clpot */ +/* seppot */ +/*********************************************/ + +#include "mex.h" + +#include <math.h> +#include <search.h> +#include "mex.h" + +int compare(const void* src1, const void* src2){ + int i1 = *(int*)src1 ; + int i2 = *(int*)src2 ; + return i1-i2 ; +} + +void ind_subv(int index, const int *cumprod, int n, int *bsubv){ + int i; + + for (i = n-1; i >= 0; i--) { + bsubv[i] = ((int)floor(index / cumprod[i])); + index = index % cumprod[i]; + } +} + +int subv_ind(const int n, const int *cumprod, const int *subv){ + int i, index=0; + + for(i=0; i<n; i++){ + index += subv[i] * cumprod[i]; + } + return index; +} + +void compute_fixed_weight(int *weight, const double *pbSize, const int *dmask, const int *bCumprod, const int ND, const int diffdim){ + int i, j; + int *eff_cumprod, *subv, *diffsize, *diff_cumprod; + + subv = malloc(diffdim * sizeof(int)); + eff_cumprod = malloc(diffdim * sizeof(int)); + diffsize = malloc(diffdim * sizeof(int)); + diff_cumprod = malloc(diffdim * sizeof(int)); + for(i=0; i<diffdim; i++){ + eff_cumprod[i] = bCumprod[dmask[i]]; + diffsize[i] = (int)pbSize[dmask[i]]; + } + diff_cumprod[0] = 1; + for(i=0; i<diffdim-1; i++){ + diff_cumprod[i+1] = diff_cumprod[i] * diffsize[i]; + } + for(i=0; i<ND; i++){ + ind_subv(i, diff_cumprod, diffdim, subv); + weight[i] = 0; + for(j=0; j<diffdim; j++){ + weight[i] += eff_cumprod[j] * subv[j]; + } + } + free(eff_cumprod); + free(subv); + free(diffsize); + free(diff_cumprod); +} + +mxArray* convert_table_to_sparse(const double *bT, const int *index, const int nzCounts, const int N){ + mxArray *spTable; + int i, *irs, *jcs; + double *sr; + + spTable = mxCreateSparse(N, 1, nzCounts, mxREAL); + sr = mxGetPr(spTable); + irs = mxGetIr(spTable); + jcs = mxGetJc(spTable); + + jcs[0] = 0; + jcs[1] = nzCounts; + + for(i=0; i<nzCounts; i++){ + sr[i] = bT[i]; + irs[i] = index[i]; + } + return spTable; +} + +mxArray* convert_ill_table_to_sparse(const double *Table, const int *sequence, const int nzCounts, const int N){ + mxArray *spTable; + int i, temp, *irs, *jcs, count=0; + double *sr; + + spTable = mxCreateSparse(N, 1, nzCounts, mxREAL); + sr = mxGetPr(spTable); + irs = mxGetIr(spTable); + jcs = mxGetJc(spTable); + + jcs[0] = 0; + jcs[1] = nzCounts; + + for(i=0; i<nzCounts; i++){ + irs[i] = sequence[count]; + count++; + temp = sequence[count]; + sr[i] = Table[temp]; + count++; + } + return spTable; +} + +void multiply_spPot_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, bdim, sdim, NB, NZB, NZS, position, bindex, sindex, nzCounts=0; + int *mask, *index, *result, *bir, *sir, *bjc, *sjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *bigTable, *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr, value; + mxArray *pTemp; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + pTemp = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + if(sdim == 0){ + for(i=0; i<NZB; i++){ + bpr[i] *= *spr; + } + return; + } + + bigTable = malloc(NZB * sizeof(double)); + index = malloc(NZB * sizeof(double)); + mask = malloc(sdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + for(i=0; i<NZB; i++){ + value = bpr[i]; + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sir, NZS, sizeof(int), compare); + if(result){ + position = result - sir; + value *= spr[position]; + bigTable[nzCounts] = value; + index[nzCounts] = bindex; + nzCounts++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + pTemp = convert_table_to_sparse(bigTable, index, nzCounts, NB); + mxSetField(bigPot, 0, "T", pTemp); + + free(bigTable); + free(index); + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +} + +void marginal_spPot_to_spPot(const mxArray *bigPot, mxArray *smallPot, const int maximize){ + int i, j, count, bdim, sdim, NB, NS, NZB, position, bindex, sindex, nzCounts=0; + int *mask, *sequence, *result, *bir, *bjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *sTable, *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr; + mxArray *pTemp; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + NS = 1; + for(i=0; i<sdim; i++){ + NS *= (int)psSize[i]; + } + + pTemp = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + + if(sdim == 0){ + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + *spr = 0; + if(maximize){ + for(i=0; i<NZB; i++){ + *spr = (*spr < bpr[i])? bpr[i] : *spr; + } + } + else{ + for(i=0; i<NZB; i++){ + *spr += bpr[i]; + } + } + return; + } + + mask = malloc(sdim * sizeof(int)); + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + + sTable = malloc(NZB * sizeof(double)); + sequence = malloc(NZB * 2 * sizeof(double)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + for(i=0; i<NZB; i++){ + sTable[i] = 0; + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + count = 0; + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sequence, nzCounts, sizeof(int)*2, compare); + if(result){ + position = (result - sequence) / 2; + if(maximize) + sTable[position] = (sTable[position] < bpr[i]) ? bpr[i] : sTable[position]; + else sTable[position] += bpr[i]; + } + else { + if(maximize) + sTable[nzCounts] = (sTable[nzCounts] < bpr[i]) ? bpr[i] : sTable[nzCounts]; + else sTable[nzCounts] += bpr[i]; + sequence[count] = sindex; + count++; + sequence[count] = nzCounts; + nzCounts++; + count++; + } + } + + pTemp = mxGetField(smallPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + qsort(sequence, nzCounts, sizeof(int) * 2, compare); + pTemp = convert_ill_table_to_sparse(sTable, sequence, nzCounts, NS); + mxSetField(smallPot, 0, "T", pTemp); + + free(sTable); + free(sequence); + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +} + +void divide_null_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, count1, match, temp, bdim, sdim, diffdim, NB, NS, ND, NZB, NZS, bindex, sindex; + int *samemask, *diffmask, *rir, *rjc, *sir, *sjc, *bCumprod, *sCumprod, *ssubv, *weight; + double *pbDomain, *psDomain, *pbSize, *psSize, *rpr, *spr, value; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + if(sdim == 0){ + pTemp = mxCreateSparse(NB, 1, NB, mxREAL); + mxSetField(bigPot, 0, "T", pTemp); + rpr = mxGetPr(pTemp); + rir = mxGetIr(pTemp); + rjc = mxGetJc(pTemp); + rjc[0] = 0; + rjc[1] = NB; + value = *spr; + if(value == 0) value = 1; + for(i=0; i<NB; i++){ + rpr[i] = 1 / value; + rir[i] = i; + } + return; + } + + NS = 1; + for(i=0; i<sdim; i++){ + NS *= (int)psSize[i]; + } + ND = NB / NS; + + + pTemp = mxCreateSparse(NB, 1, NB, mxREAL); + rpr = mxGetPr(pTemp); + rir = mxGetIr(pTemp); + rjc = mxGetJc(pTemp); + rjc[0] = 0; + rjc[1] = NB; + for(i=0; i<NB; i++){ + rpr[i] = 1; + rir[i] = i; + } + + NZB = ND * NZS; + + diffdim = bdim - sdim; + samemask = malloc(sdim * sizeof(int)); + diffmask = malloc(diffdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + weight = malloc(ND * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + count1 = 0; + for(i=0; i<bdim; i++){ + match = 0; + for(j=0; j<sdim; j++){ + if(pbDomain[i] == psDomain[j]){ + samemask[count] = i; + match = 1; + count++; + break; + } + } + if(match == 0){ + diffmask[count1] = i; + count1++; + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + count = 0; + compute_fixed_weight(weight, pbSize, diffmask, bCumprod, ND, diffdim); + for(i=0; i<NZS; i++){ + sindex = sir[i]; + ind_subv(sindex, sCumprod, sdim, ssubv); + temp = 0; + for(j=0; j<sdim; j++){ + temp += ssubv[j] * bCumprod[samemask[j]]; + } + for(j=0; j<ND; j++){ + bindex = weight[j] + temp; + rpr[bindex] = 1 / (spr[i]); + } + } + + pTemp1 = mxGetField(bigPot, 0, "T"); + if(pTemp1)mxDestroyArray(pTemp1); + mxSetField(bigPot, 0, "T", pTemp); + + free(samemask); + free(diffmask); + free(bCumprod); + free(sCumprod); + free(weight); + free(ssubv); +} + +void divide_spPot_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, bdim, sdim, NB, NZB, NZS, position, bindex, sindex; + int *mask, *result, *bir, *sir, *bjc, *sjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr, value; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + pTemp1 = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp1); + bir = mxGetIr(pTemp1); + bjc = mxGetJc(pTemp1); + NZB = bjc[1]; + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + if(sdim == 0){ + value = *spr; + if(value == 0)value = 1; + for(i=0; i<NZB; i++){ + bpr[i] /= value; + } + return; + } + + mask = malloc(sdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sir, NZS, sizeof(int), compare); + if(result){ + position = result - sir; + bpr[i] /= spr[position]; + } + } + + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +} + + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, loop, loops, nCliques, temp, count, parent, child, maximize, *distribute_order; + double *pr, *pr1; + mxArray *pTemp, *pPreCh, *pClpot, *pSeppot; + + pTemp = mxGetField(prhs[0], 0, "cliques"); + nCliques = mxGetNumberOfElements(pTemp); + loops = nCliques - 1; + pTemp = mxGetField(prhs[0], 0, "maximize"); + maximize = (int)mxGetScalar(pTemp); + + distribute_order = malloc(2 * loops * sizeof(int)); + pTemp = mxGetField(prhs[0], 0, "preorder"); + pr = mxGetPr(pTemp); + pPreCh = mxGetField(prhs[0], 0, "preorder_children"); + count = 0; + for(i=0; i<nCliques; i++){ + temp = (int)pr[i] - 1; + pTemp = mxGetCell(pPreCh, temp); + pr1 = mxGetPr(pTemp); + loop = mxGetNumberOfElements(pTemp); + for(j=0; j<loop; j++){ + distribute_order[count] = temp; + distribute_order[count + loops] = (int)pr1[j] - 1; + count++; + } + } + + plhs[0] = mxDuplicateArray(prhs[1]); + plhs[1] = mxDuplicateArray(prhs[2]); + + for(loop=0; loop<loops; loop++){ + parent = distribute_order[loop]; + child = distribute_order[loop+loops]; + i = nCliques * child + parent; + pClpot = mxGetCell(plhs[0], child); + pTemp = mxGetField(pClpot, 0, "T"); + pSeppot = mxGetCell(plhs[1], i); + if(pTemp) + divide_spPot_by_spPot(pClpot, pSeppot); + else divide_null_by_spPot(pClpot, pSeppot); + + pClpot = mxGetCell(plhs[0], parent); + marginal_spPot_to_spPot(pClpot, pSeppot, maximize); + mxSetCell(plhs[1], i, pSeppot); + + pClpot = mxGetCell(plhs[0], child); + multiply_spPot_by_spPot(pClpot, pSeppot); + } + free(distribute_order); +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_sparse_inf_engine/old/init_pot.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,637 @@ +/* C mex init_pot for in @jtree_sparse_inf_engine directory */ +/* The file enter_evidence.m in directory @jtree_sparse_inf_engine call it*/ + +/**************************************/ +/* init_pot.c has 6 input & 2 output */ +/* engine */ +/* clqs */ +/* pots */ +/* pot_type */ +/* onodes */ +/* ndx */ +/* */ +/* clpot */ +/* seppot */ +/**************************************/ +#include <math.h> +#include <search.h> +#include "mex.h" + +int compare(const void* src1, const void* src2){ + int i1 = *(int*)src1 ; + int i2 = *(int*)src2 ; + return i1-i2 ; +} + +void ind_subv(int index, const int *cumprod, int n, int *bsubv){ + int i; + + for (i = n-1; i >= 0; i--) { + bsubv[i] = ((int)floor(index / cumprod[i])); + index = index % cumprod[i]; + } +} + +int subv_ind(const int n, const int *cumprod, const int *subv){ + int i, index=0; + + for(i=0; i<n; i++){ + index += subv[i] * cumprod[i]; + } + return index; +} + +void compute_fixed_weight(int *weight, const double *pbSize, const int *dmask, const int *bCumprod, const int ND, const int diffdim){ + int i, j; + int *eff_cumprod, *subv, *diffsize, *diff_cumprod; + + subv = malloc(diffdim * sizeof(int)); + eff_cumprod = malloc(diffdim * sizeof(int)); + diffsize = malloc(diffdim * sizeof(int)); + diff_cumprod = malloc(diffdim * sizeof(int)); + for(i=0; i<diffdim; i++){ + eff_cumprod[i] = bCumprod[dmask[i]]; + diffsize[i] = (int)pbSize[dmask[i]]; + } + diff_cumprod[0] = 1; + for(i=0; i<diffdim-1; i++){ + diff_cumprod[i+1] = diff_cumprod[i] * diffsize[i]; + } + for(i=0; i<ND; i++){ + ind_subv(i, diff_cumprod, diffdim, subv); + weight[i] = 0; + for(j=0; j<diffdim; j++){ + weight[i] += eff_cumprod[j] * subv[j]; + } + } + free(eff_cumprod); + free(subv); + free(diffsize); + free(diff_cumprod); +} + +mxArray* convert_to_sparse(const double *table, const int NB, const int counts){ + mxArray *spTable; + int i, k, *ir, *jc; + double *sr; + + spTable = mxCreateSparse(NB, 1, counts, mxREAL); + sr = mxGetPr(spTable); + ir = mxGetIr(spTable); + jc = mxGetJc(spTable); + + k = 0; + jc[0] = 0; + jc[1] = counts; + for(i=0; i<NB; i++){ + if(table[i] != 0.0){ + sr[k] = table[i]; + ir[k] = i; + k++; + } + } + + return spTable; +} + +mxArray* convert_table_to_sparse(const double *bT, const int *index, const int nzCounts, const int NB){ + mxArray *spTable; + int i, *irs, *jcs; + double *sr; + + spTable = mxCreateSparse(NB, 1, nzCounts, mxREAL); + sr = mxGetPr(spTable); + irs = mxGetIr(spTable); + jcs = mxGetJc(spTable); + + jcs[0] = 0; + jcs[1] = nzCounts; + + for(i=0; i<nzCounts; i++){ + sr[i] = bT[i]; + irs[i] = index[i]; + } + return spTable; +} + +mxArray* convert_ill_table_to_sparse(const double *bigTable, const int *sequence, const int nzCounts, const int NB){ + mxArray *spTable; + int i, temp, *irs, *jcs, count=0; + double *sr; + + spTable = mxCreateSparse(NB, 1, nzCounts, mxREAL); + sr = mxGetPr(spTable); + irs = mxGetIr(spTable); + jcs = mxGetJc(spTable); + + jcs[0] = 0; + jcs[1] = nzCounts; + + for(i=0; i<nzCounts; i++){ + irs[i] = sequence[count]; + count++; + temp = sequence[count]; + sr[i] = bigTable[temp]; + count++; + } + return spTable; +} + +void multiply_null_by_fuPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, NB, NS, siz_b, siz_s, ndim, nzCounts=0; + int *mask, *sx, *sy, *cpsy, *subs, *s, *cpsy2, *jc; + double *pbDomain, *psDomain, *pbSize, *psSize, *bTable, *sTable, value; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + siz_b = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + siz_s = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<siz_b; i++){ + NB *= (int)pbSize[i]; + } + NS = 1; + for(i=0; i<siz_s; i++){ + NS *= (int)psSize[i]; + } + + pTemp = mxGetField(smallPot, 0, "T"); + sTable = mxGetPr(pTemp); + bTable = malloc(NB * sizeof(double)); + for(i=0; i<NB; i++){ + bTable[i] = 0; + } + + if(NS == 1){ + value = *sTable; + for(i=0; i<NB; i++){ + bTable[i] = value; + } + nzCounts = NB; + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + pTemp = convert_to_sparse(bTable, NB, NB); + mxSetField(bigPot, 0, "T", pTemp); + free(bTable); + return; + } + + if(NS == NB){ + for(i=0; i<NB; i++){ + bTable[i] = sTable[i]; + if(sTable[i] != 0) nzCounts++; + } + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + pTemp = convert_to_sparse(bTable, NB, nzCounts); + mxSetField(bigPot, 0, "T", pTemp); + free(bTable); + return; + } + + mask = malloc(siz_s * sizeof(int)); + count = 0; + for(i=0; i<siz_s; i++){ + for(j=0; j<siz_b; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + ndim = siz_b; + sx = (int *)malloc(sizeof(int)*ndim); + sy = (int *)malloc(sizeof(int)*ndim); + for(i=0; i<ndim; i++){ + sx[i] = (int)pbSize[i]; + sy[i] = 1; + } + for(i=0; i<count; i++){ + sy[mask[i]] = sx[mask[i]]; + } + + s = (int *)malloc(sizeof(int)*ndim); + *(cpsy = (int *)malloc(sizeof(int)*ndim)) = 1; + subs = (int *)malloc(sizeof(int)*ndim); + cpsy2 = (int *)malloc(sizeof(int)*ndim); + for(i = 0; i < ndim; i++){ + subs[i] = 0; + s[i] = sx[i] - 1; + } + + for(i = 0; i < ndim-1; i++){ + cpsy[i+1] = cpsy[i]*sy[i]--; + cpsy2[i] = cpsy[i]*sy[i]; + } + cpsy2[ndim-1] = cpsy[ndim-1]*(--sy[ndim-1]); + + for(j=0; j<NB; j++){ + bTable[j] = *sTable; + if(*sTable != 0.0) nzCounts++; + for(i = 0; i < ndim; i++){ + if(subs[i] == s[i]){ + subs[i] = 0; + if(sy[i]) + sTable -= cpsy2[i]; + } + else{ + subs[i]++; + if(sy[i]) + sTable += cpsy[i]; + break; + } + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + pTemp = convert_to_sparse(bTable, NB, nzCounts); + mxSetField(bigPot, 0, "T", pTemp); + pTemp1 = mxGetField(bigPot, 0, "T"); + jc = mxGetJc(pTemp1); + + free(sx); + free(sy); + free(s); + free(cpsy); + free(subs); + free(cpsy2); + free(mask); + free(bTable); +} + +void multiply_null_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, count1, match, temp, bdim, sdim, diffdim, NB, NS, ND, NZB, NZS, bindex, sindex, nzCounts=0; + int *samemask, *diffmask, *sir, *sjc, *bCumprod, *sCumprod, *ssubv, *sequence, *weight; + double *bigTable, *pbDomain, *psDomain, *pbSize, *psSize, *spr; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + NS = 1; + for(i=0; i<sdim; i++){ + NS *= (int)psSize[i]; + } + ND = NB / NS; + + if(ND == 1){ + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + pTemp1 = mxGetField(smallPot, 0, "T"); + pTemp = mxDuplicateArray(pTemp1); + mxSetField(bigPot, 0, "T", pTemp); + return; + } + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + NZB = ND * NZS; + + diffdim = bdim - sdim; + sequence = malloc(NZB * 2 * sizeof(int)); + bigTable = malloc(NZB * sizeof(double)); + samemask = malloc(sdim * sizeof(int)); + diffmask = malloc(diffdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + weight = malloc(ND * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + count1 = 0; + for(i=0; i<bdim; i++){ + match = 0; + for(j=0; j<sdim; j++){ + if(pbDomain[i] == psDomain[j]){ + samemask[count] = i; + match = 1; + count++; + break; + } + } + if(match == 0){ + diffmask[count1] = i; + count1++; + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + count = 0; + compute_fixed_weight(weight, pbSize, diffmask, bCumprod, ND, diffdim); + for(i=0; i<NZS; i++){ + sindex = sir[i]; + ind_subv(sindex, sCumprod, sdim, ssubv); + temp = 0; + for(j=0; j<sdim; j++){ + temp += ssubv[j] * bCumprod[samemask[j]]; + } + for(j=0; j<ND; j++){ + bindex = weight[j] + temp; + bigTable[nzCounts] = spr[i]; + sequence[count] = bindex; + count++; + sequence[count] = nzCounts; + nzCounts++; + count++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + qsort(sequence, nzCounts, sizeof(int) * 2, compare); + pTemp = convert_ill_table_to_sparse(bigTable, sequence, nzCounts, NB); + mxSetField(bigPot, 0, "T", pTemp); + + free(sequence); + free(bigTable); + free(samemask); + free(diffmask); + free(bCumprod); + free(sCumprod); + free(weight); + free(ssubv); +} + +void multiply_spPot_by_fuPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, bdim, sdim, NB, NZB, bindex, sindex, nzCounts=0; + int *mask, *index, *bir, *bjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *bigTable, *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr, value; + mxArray *pTemp; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + pTemp = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + + bigTable = malloc(NZB * sizeof(double)); + index = malloc(NZB * sizeof(double)); + mask = malloc(sdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + for(i=0; i<NZB; i++){ + bigTable[i] = 0; + } + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + value = spr[sindex]; + if(value != 0){ + bigTable[nzCounts] = bpr[i] * value; + index[nzCounts] = bindex; + nzCounts++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + pTemp = convert_table_to_sparse(bigTable, index, nzCounts, NB); + mxSetField(bigPot, 0, "T", pTemp); + + free(bigTable); + free(index); + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +} + +void multiply_spPot_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, bdim, sdim, NB, NZB, NZS, position, bindex, sindex, nzCounts=0; + int *mask, *index, *result, *bir, *sir, *bjc, *sjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *bigTable, *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr, value; + mxArray *pTemp; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + pTemp = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + bigTable = malloc(NZB * sizeof(double)); + index = malloc(NZB * sizeof(double)); + mask = malloc(sdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + for(i=0; i<NZB; i++){ + bigTable[i] = 0; + } + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + for(i=0; i<NZB; i++){ + value = bpr[i]; + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sir, NZS, sizeof(int), compare); + if(result){ + position = result - sir; + value *= spr[position]; + bigTable[nzCounts] = value; + index[nzCounts] = bindex; + nzCounts++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + pTemp = convert_table_to_sparse(bigTable, index, nzCounts, NB); + mxSetField(bigPot, 0, "T", pTemp); + + free(bigTable); + free(index); + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +} + + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, c, loop, nNodes, nCliques, ndomain, dims[2]; + double *pClqs, *pr, *pt, *pSize; + mxArray *pTemp, *pTemp1, *pStruct, *pCliques, *pBigpot, *pSmallpot; + const char *field_names[] = {"domain", "T", "sizes"}; + + nNodes = mxGetNumberOfElements(prhs[1]); + pCliques = mxGetField(prhs[0], 0, "cliques"); + nCliques = mxGetNumberOfElements(pCliques); + pTemp = mxGetField(prhs[0], 0, "eff_node_sizes"); + pSize = mxGetPr(pTemp); + + plhs[0] = mxCreateCellArray(1, &nCliques); + for(i=0; i<nCliques; i++){ + pStruct = mxCreateStructMatrix(1, 1, 3, field_names); + mxSetCell(plhs[0], i, pStruct); + pTemp = mxGetCell(pCliques, i); + ndomain = mxGetNumberOfElements(pTemp); + pt = mxGetPr(pTemp); + pTemp1 = mxDuplicateArray(pTemp); + mxSetField(pStruct, 0, "domain", pTemp1); + + pTemp = mxCreateDoubleMatrix(1, ndomain, mxREAL); + mxSetField(pStruct, 0, "sizes", pTemp); + pr = mxGetPr(pTemp); + for(j=0; j<ndomain; j++){ + pr[j] = pSize[(int)pt[j]-1]; + } + } + + pClqs = mxGetPr(prhs[1]); + for(loop=0; loop<nNodes; loop++){ + c = (int)pClqs[loop] - 1; + pSmallpot = mxGetCell(prhs[2], loop); + pTemp = mxGetField(pSmallpot, 0, "T"); + pBigpot = mxGetCell(plhs[0], c); + pTemp1 = mxGetField(pBigpot, 0, "T"); + if(pTemp1){ + if(mxIsSparse(pTemp)) + multiply_spPot_by_spPot(pBigpot, pSmallpot); + else multiply_spPot_by_fuPot(pBigpot, pSmallpot); + } + else{ + if(mxIsSparse(pTemp)) + multiply_null_by_spPot(pBigpot, pSmallpot); + else multiply_null_by_fuPot(pBigpot, pSmallpot); + } + } + + dims[0] = nCliques; + dims[1] = nCliques; + plhs[1] = mxCreateCellArray(2, dims); +} + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_sparse_inf_engine/old/init_pot1.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,636 @@ +/* C mex init_pot for in @jtree_sparse_inf_engine directory */ +/* The file enter_evidence.m in directory @jtree_sparse_inf_engine call it*/ + +/**************************************/ +/* init_pot.c has 6 input & 2 output */ +/* engine */ +/* clqs */ +/* pots */ +/* pot_type */ +/* onodes */ +/* ndx */ +/* */ +/* clpot */ +/* seppot */ +/**************************************/ +#include <math.h> +#include <search.h> +#include "mex.h" + +int compare(const void* src1, const void* src2){ + int i1 = *(int*)src1 ; + int i2 = *(int*)src2 ; + return i1-i2 ; +} + +void ind_subv(int index, const int *cumprod, int n, int *bsubv){ + int i; + + for (i = n-1; i >= 0; i--) { + bsubv[i] = ((int)floor(index / cumprod[i])); + index = index % cumprod[i]; + } +} + +int subv_ind(const int n, const int *cumprod, const int *subv){ + int i, index=0; + + for(i=0; i<n; i++){ + index += subv[i] * cumprod[i]; + } + return index; +} + +void compute_fixed_weight(int *weight, const double *pbSize, const int *dmask, const int *bCumprod, const int ND, const int diffdim){ + int i, j; + int *eff_cumprod, *subv, *diffsize, *diff_cumprod; + + subv = malloc(diffdim * sizeof(int)); + eff_cumprod = malloc(diffdim * sizeof(int)); + diffsize = malloc(diffdim * sizeof(int)); + diff_cumprod = malloc(diffdim * sizeof(int)); + for(i=0; i<diffdim; i++){ + eff_cumprod[i] = bCumprod[dmask[i]]; + diffsize[i] = (int)pbSize[dmask[i]]; + } + diff_cumprod[0] = 1; + for(i=0; i<diffdim-1; i++){ + diff_cumprod[i+1] = diff_cumprod[i] * diffsize[i]; + } + for(i=0; i<ND; i++){ + ind_subv(i, diff_cumprod, diffdim, subv); + weight[i] = 0; + for(j=0; j<diffdim; j++){ + weight[i] += eff_cumprod[j] * subv[j]; + } + } + free(eff_cumprod); + free(subv); + free(diffsize); + free(diff_cumprod); +} + +void reset_nzmax(mxArray *spArray, const int old_nzmax, const int new_nzmax){ + double *ptr; + void *newptr; + int *ir, *jc; + int nbytes; + + if(new_nzmax == old_nzmax) return; + nbytes = new_nzmax * sizeof(*ptr); + ptr = mxGetPr(spArray); + newptr = mxRealloc(ptr, nbytes); + mxSetPr(spArray, newptr); + nbytes = new_nzmax * sizeof(*ir); + ir = mxGetIr(spArray); + newptr = mxRealloc(ir, nbytes); + mxSetIr(spArray, newptr); + jc = mxGetJc(spArray); + jc[0] = 0; + jc[1] = new_nzmax; + mxSetNzmax(spArray, new_nzmax); +} + +mxArray* convert_table_to_sparse(const double *bT, const int *index, const int nzCounts, const int NB){ + mxArray *spTable; + int i, *irs, *jcs; + double *sr; + + spTable = mxCreateSparse(NB, 1, nzCounts, mxREAL); + sr = mxGetPr(spTable); + irs = mxGetIr(spTable); + jcs = mxGetJc(spTable); + + jcs[0] = 0; + jcs[1] = nzCounts; + + for(i=0; i<nzCounts; i++){ + sr[i] = bT[i]; + irs[i] = index[i]; + } + return spTable; +} + +mxArray* convert_ill_table_to_sparse(const double *bigTable, const int *sequence, const int nzCounts, const int NB){ + mxArray *spTable; + int i, temp, *irs, *jcs, count=0; + double *sr; + + spTable = mxCreateSparse(NB, 1, nzCounts, mxREAL); + sr = mxGetPr(spTable); + irs = mxGetIr(spTable); + jcs = mxGetJc(spTable); + + jcs[0] = 0; + jcs[1] = nzCounts; + + for(i=0; i<nzCounts; i++){ + irs[i] = sequence[count]; + count++; + temp = sequence[count]; + sr[i] = bigTable[temp]; + count++; + } + return spTable; +} + +void multiply_null_by_fuPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, NB, NS, siz_b, siz_s, ndim, nzCounts=0; + int *mask, *sx, *sy, *cpsy, *subs, *s, *cpsy2, *bir, *bjc; + double *pbDomain, *psDomain, *pbSize, *psSize, *spr, *bpr, value; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + siz_b = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + siz_s = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<siz_b; i++){ + NB *= (int)pbSize[i]; + } + NS = 1; + for(i=0; i<siz_s; i++){ + NS *= (int)psSize[i]; + } + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + + pTemp1 = mxCreateSparse(NB, 1, NB, mxREAL); + bpr = mxGetPr(pTemp1); + bir = mxGetIr(pTemp1); + bjc = mxGetJc(pTemp1); + bjc[0] = 0; + bjc[1] = NB; + + if(NS == 1){ + value = *spr; + for(i=0; i<NB; i++){ + bpr[i] = value; + bir[i] = i; + } + nzCounts = NB; + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + reset_nzmax(pTemp1, NB, nzCounts); + mxSetField(bigPot, 0, "T", pTemp1); + return; + } + + if(NS == NB){ + for(i=0; i<NB; i++){ + if(spr[i] != 0){ + bpr[nzCounts] = spr[i]; + bir[nzCounts] = i; + nzCounts++; + } + } + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + reset_nzmax(pTemp1, NB, nzCounts); + mxSetField(bigPot, 0, "T", pTemp1); + return; + } + + mask = malloc(siz_s * sizeof(int)); + count = 0; + for(i=0; i<siz_s; i++){ + for(j=0; j<siz_b; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + ndim = siz_b; + sx = (int *)malloc(sizeof(int)*ndim); + sy = (int *)malloc(sizeof(int)*ndim); + for(i=0; i<ndim; i++){ + sx[i] = (int)pbSize[i]; + sy[i] = 1; + } + for(i=0; i<count; i++){ + sy[mask[i]] = sx[mask[i]]; + } + + s = (int *)malloc(sizeof(int)*ndim); + *(cpsy = (int *)malloc(sizeof(int)*ndim)) = 1; + subs = (int *)malloc(sizeof(int)*ndim); + cpsy2 = (int *)malloc(sizeof(int)*ndim); + for(i = 0; i < ndim; i++){ + subs[i] = 0; + s[i] = sx[i] - 1; + } + + for(i = 0; i < ndim-1; i++){ + cpsy[i+1] = cpsy[i]*sy[i]--; + cpsy2[i] = cpsy[i]*sy[i]; + } + cpsy2[ndim-1] = cpsy[ndim-1]*(--sy[ndim-1]); + + for(j=0; j<NB; j++){ + if(*spr != 0){ + bpr[nzCounts] = *spr; + bir[nzCounts] = j; + nzCounts++; + } + for(i = 0; i < ndim; i++){ + if(subs[i] == s[i]){ + subs[i] = 0; + if(sy[i]) + spr -= cpsy2[i]; + } + else{ + subs[i]++; + if(sy[i]) + spr += cpsy[i]; + break; + } + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + reset_nzmax(pTemp1, NB, nzCounts); + mxSetField(bigPot, 0, "T", pTemp1); + + free(sx); + free(sy); + free(s); + free(cpsy); + free(subs); + free(cpsy2); + free(mask); +} + +void multiply_null_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, count1, match, temp, bdim, sdim, diffdim, NB, NS, ND, NZB, NZS, bindex, sindex, nzCounts=0; + int *samemask, *diffmask, *sir, *sjc, *bCumprod, *sCumprod, *ssubv, *sequence, *weight; + double *bigTable, *pbDomain, *psDomain, *pbSize, *psSize, *spr; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + NS = 1; + for(i=0; i<sdim; i++){ + NS *= (int)psSize[i]; + } + ND = NB / NS; + + if(ND == 1){ + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + pTemp1 = mxGetField(smallPot, 0, "T"); + pTemp = mxDuplicateArray(pTemp1); + mxSetField(bigPot, 0, "T", pTemp); + return; + } + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + NZB = ND * NZS; + + diffdim = bdim - sdim; + sequence = malloc(NZB * 2 * sizeof(int)); + bigTable = malloc(NZB * sizeof(double)); + samemask = malloc(sdim * sizeof(int)); + diffmask = malloc(diffdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + weight = malloc(ND * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + count1 = 0; + for(i=0; i<bdim; i++){ + match = 0; + for(j=0; j<sdim; j++){ + if(pbDomain[i] == psDomain[j]){ + samemask[count] = i; + match = 1; + count++; + break; + } + } + if(match == 0){ + diffmask[count1] = i; + count1++; + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + count = 0; + compute_fixed_weight(weight, pbSize, diffmask, bCumprod, ND, diffdim); + for(i=0; i<NZS; i++){ + sindex = sir[i]; + ind_subv(sindex, sCumprod, sdim, ssubv); + temp = 0; + for(j=0; j<sdim; j++){ + temp += ssubv[j] * bCumprod[samemask[j]]; + } + for(j=0; j<ND; j++){ + bindex = weight[j] + temp; + bigTable[nzCounts] = spr[i]; + sequence[count] = bindex; + count++; + sequence[count] = nzCounts; + nzCounts++; + count++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + qsort(sequence, nzCounts, sizeof(int) * 2, compare); + pTemp = convert_ill_table_to_sparse(bigTable, sequence, nzCounts, NB); + mxSetField(bigPot, 0, "T", pTemp); + + free(sequence); + free(bigTable); + free(samemask); + free(diffmask); + free(bCumprod); + free(sCumprod); + free(weight); + free(ssubv); +} + +void multiply_spPot_by_fuPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, bdim, sdim, NB, NZB, bindex, sindex, nzCounts=0; + int *mask, *index, *bir, *bjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *bigTable, *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr, value; + mxArray *pTemp; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + pTemp = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + + bigTable = malloc(NZB * sizeof(double)); + index = malloc(NZB * sizeof(double)); + mask = malloc(sdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + value = spr[sindex]; + if(value != 0){ + bigTable[nzCounts] = bpr[i] * value; + index[nzCounts] = bindex; + nzCounts++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + pTemp = convert_table_to_sparse(bigTable, index, nzCounts, NB); + mxSetField(bigPot, 0, "T", pTemp); + + free(bigTable); + free(index); + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +} + +void multiply_spPot_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, bdim, sdim, NB, NZB, NZS, position, bindex, sindex, nzCounts=0; + int *mask, *index, *result, *bir, *sir, *bjc, *sjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *bigTable, *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr, value; + mxArray *pTemp; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + pTemp = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + bigTable = malloc(NZB * sizeof(double)); + index = malloc(NZB * sizeof(double)); + mask = malloc(sdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + for(i=0; i<NZB; i++){ + bigTable[i] = 0; + } + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + for(i=0; i<NZB; i++){ + value = bpr[i]; + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sir, NZS, sizeof(int), compare); + if(result){ + position = result - sir; + value *= spr[position]; + bigTable[nzCounts] = value; + index[nzCounts] = bindex; + nzCounts++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + pTemp = convert_table_to_sparse(bigTable, index, nzCounts, NB); + mxSetField(bigPot, 0, "T", pTemp); + + free(bigTable); + free(index); + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +} + + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, c, loop, nNodes, nCliques, ndomain, dims[2]; + double *pClqs, *pr, *pt, *pSize; + mxArray *pTemp, *pTemp1, *pStruct, *pCliques, *pBigpot, *pSmallpot; + const char *field_names[] = {"domain", "T", "sizes"}; + + nNodes = mxGetNumberOfElements(prhs[1]); + pCliques = mxGetField(prhs[0], 0, "cliques"); + nCliques = mxGetNumberOfElements(pCliques); + pTemp = mxGetField(prhs[0], 0, "eff_node_sizes"); + pSize = mxGetPr(pTemp); + + plhs[0] = mxCreateCellArray(1, &nCliques); + for(i=0; i<nCliques; i++){ + pStruct = mxCreateStructMatrix(1, 1, 3, field_names); + mxSetCell(plhs[0], i, pStruct); + pTemp = mxGetCell(pCliques, i); + ndomain = mxGetNumberOfElements(pTemp); + pt = mxGetPr(pTemp); + pTemp1 = mxDuplicateArray(pTemp); + mxSetField(pStruct, 0, "domain", pTemp1); + + pTemp = mxCreateDoubleMatrix(1, ndomain, mxREAL); + mxSetField(pStruct, 0, "sizes", pTemp); + pr = mxGetPr(pTemp); + for(j=0; j<ndomain; j++){ + pr[j] = pSize[(int)pt[j]-1]; + } + } + + pClqs = mxGetPr(prhs[1]); + for(loop=0; loop<nNodes; loop++){ + c = (int)pClqs[loop] - 1; + pSmallpot = mxGetCell(prhs[2], loop); + pTemp = mxGetField(pSmallpot, 0, "T"); + pBigpot = mxGetCell(plhs[0], c); + pTemp1 = mxGetField(pBigpot, 0, "T"); + if(pTemp1){ + if(mxIsSparse(pTemp)) + multiply_spPot_by_spPot(pBigpot, pSmallpot); + else multiply_spPot_by_fuPot(pBigpot, pSmallpot); + } + else{ + if(mxIsSparse(pTemp)) + multiply_null_by_spPot(pBigpot, pSmallpot); + else multiply_null_by_fuPot(pBigpot, pSmallpot); + } + } + + dims[0] = nCliques; + dims[1] = nCliques; + plhs[1] = mxCreateCellArray(2, dims); +} + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_sparse_inf_engine/old/init_pot1.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,20 @@ +function [clpot, seppot] = init_pot(engine, clqs, pots, pot_type, onodes, ndx) +% INIT_POT Initialise potentials with evidence (jtree_inf) +% function [clpot, seppot] = init_pot(engine, clqs, pots, pot_type, onodes) + +cliques = engine.cliques; +bnet = bnet_from_engine(engine); +% Set the clique potentials to all 1s +C = length(cliques); +clpot = cell(1,C); +for i=1:C + clpot{i} = mk_initial_pot(pot_type, cliques{i}, bnet.node_sizes(:), bnet.cnodes(:), onodes); +end + +% Multiply on specified potentials +for i=1:length(clqs) + c = clqs(i); + clpot{c} = multiply_by_pot(clpot{c}, pots{i}); +end + +seppot = cell(C,C); % implicitely initialized to 1
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@jtree_sparse_inf_engine/set_fields.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function engine = set_fields(engine, varargin) +% SET_FIELDS Set the fields for a generic engine +% engine = set_fields(engine, name/value pairs) +% +% e.g., engine = set_fields(engine, 'maximize', 1) + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'maximize', engine.maximize = args{i+1}; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@likelihood_weighting_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,4 @@ +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/likelihood_weighting_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@likelihood_weighting_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@likelihood_weighting_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@likelihood_weighting_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@likelihood_weighting_inf_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,39 @@ +function [engine, ll] = enter_evidence(engine, evidence, nsamples) +% ENTER_EVIDENCE Add the specified evidence to the network (likelihood_weighting) +% [engine, ll] = enter_evidence(engine, evidence, nsamples) +% evidence{i} = [] if if X(i) is hidden, and otherwise contains its observed value (scalar or column vector) +% +% If nsamples is not specified, the value specified when the engine was created will be used. +% ll (log-likelihood) is set to []. + +ll = []; +if nargin < 3, nsamples = engine.nsamples; end + +bnet = bnet_from_engine(engine); +N = length(bnet.dag); +samples = cell(nsamples, N); +weights = zeros(1, nsamples); + +ns = bnet.node_sizes; +original_evidence = evidence; +observed = ~isemptycell(original_evidence); +for s=1:nsamples + evidence = original_evidence(:); % must be a column vector + w = 1; + for i=1:N + ps = parents(bnet.dag, i); + e = bnet.equiv_class(i); + if observed(i) + p = exp(log_prob_node(bnet.CPD{e}, evidence(i), evidence(ps))); + w = w * p; + else + x = sample_node(bnet.CPD{e}, evidence(ps)); + evidence{i} = x; + end + end + samples(s,:) = evidence; + weights(s) = w; +end + +engine.samples = samples; +engine.weights = weights;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@likelihood_weighting_inf_engine/likelihood_weighting_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,25 @@ +function engine = likelihood_weighting_inf_engine(bnet, varargin) +% LIKELIHOOD_WEIGHTING_INF_ENGINE +% engine = likelihood_weighting_inf_engine(bnet, ...) +% +% Optional arguments [defaults] +% nsamples - [500] + +nsamples = 500; + +if nargin >= 2 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'nsamples', nsamples= args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +engine.nsamples = nsamples; +engine.samples = []; +engine.weights = []; +engine = class(engine, 'likelihood_weighting_inf_engine', inf_engine(bnet));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@likelihood_weighting_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,53 @@ +function marginal = marginal_nodes(engine, nodes) +% MARGINAL_NODES Compute the marginal on the specified query nodes (likelihood_weighting) +% marginal = marginal_nodes(engine, nodes) + +bnet = bnet_from_engine(engine); +ddom = myintersect(nodes, bnet.dnodes); +cdom = myintersect(nodes, bnet.cnodes); +nsamples = size(engine.samples, 1); +ns = bnet.node_sizes; + +%w = normalise(engine.weights); +w = engine.weights; +if mysubset(nodes, ddom) + T = 0*myones(ns(nodes)); + P = prod(ns(nodes)); + indices = ind2subv(ns(nodes), 1:P); + samples = reshape(cat(1, engine.samples{:,nodes}), nsamples, length(nodes)); + for j = 1:P + rows = find_rows(samples, indices(j,:)); + T(j) = sum(w(rows)); + end + T = normalise(T); + marginal.T = T; +elseif subset(nodes, cdom) + samples = reshape(cat(1, engine.samples{:,nodes}), nsamples*sum(ns(nodes)), length(nodes)); + [marginal.mu, marginal.Sigma] = wstats(samples', normalise(w)); +else + error('can''t handle mixed marginals yet'); +end + +marginal.domain = nodes; + +%%%%%%%%% + +function rows = find_rows(M, v) +% FINDROWS Find rows which are equal to a specified vector +% rows = findrows(M, v) +% Each row of M is a sample + +temp = abs(M - repmat(v, size(M, 1), 1)); +rows = find(sum(temp,2) == 0); + +%%%%%%%% + +function [mu, Sigma] = wstats(X, w) + +% Computes the weighted mean and weighted covariance matrix for a given +% set of observations X(:,i), and a set of normalised weights w(i). +% Each column of X is a sample. + +d = X - repmat(X * w', 1, size(X, 2)); +mu = sum(X .* repmat(w, size(X, 1), 1), 2); +Sigma = d * diag(w) * d';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@pearl_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +/bethe_free_energy.m/1.1.1.1/Sun Jul 6 20:57:18 2003// +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/loopy_converged.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_family.m/1.1.1.1/Fri Oct 18 20:05:16 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/pearl_inf_engine.m/1.1.1.1/Sat Jan 11 18:53:28 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@pearl_inf_engine/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +A D/private////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@pearl_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@pearl_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@pearl_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@pearl_inf_engine/bethe_free_energy.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,50 @@ +function loglik = bethe_free_energy(engine, evidence) +% BETHE_FREE_ENERGY Compute Bethe free energy approximation to the log likelihood +% loglik = bethe_free_energy(engine, evidence) +% +% The Bethe free energy is given by an exact energy term and an approximate entropy term. +% Energy +% E = -sum_f sum_i b(f,i) ln theta(f,i) +% where b(f,i) = approximate Pr(family f = i) +% and theta(f,i) = Pr(f = i) +% Entropy +% S = H1 - H2 +% H1 = sum_f sum_p H(b(f)) +% where b(f) = belief on family f, H(.) = entropy +% H2 = sum_n (q(n)-1) H(b(n)) +% where q(n) = num. neighbors of n +% +% This function was written by Yair Weiss, 8/22/01. + +hidden = find(isemptycell(evidence)); +bnet = bnet_from_engine(engine); +N = length(bnet.dag); + +add_ev = 1; +E=0;H1=0;H2=0; +loglik=0; +for n=1:N + ps=parents(bnet.dag,n); + if (length(ps)==0) % root node + qi=length(children(bnet.dag,n))-1; + else + qi=length(children(bnet.dag,n)); + end + bf = marginal_family(engine, n, add_ev); + bf = bf.T(:); + e = bnet.equiv_class(n); + T = CPD_to_CPT(bnet.CPD{e}); + T = T(:); + E = E-sum(log(T+(T==0)).*bf); + + if length(ps) > 0 + % root nodes don't count as fmailies + H1 = H1+sum(log(bf+(bf==0)).*bf); + end + + bi = marginal_nodes(engine, n, add_ev); + bi = bi.T(:); + H2 = H2+qi*sum(log(bi+(bi==0)).*bi); +end +loglik=E+H1-H2; +loglik=-loglik;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@pearl_inf_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,153 @@ +function [engine, loglik, niter] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (pearl) +% [engine, loglik, num_iter] = enter_evidence(engine, evidence, ...) +% evidence{i} = [] if if X(i) is hidden, and otherwise contains its observed value (scalar or column vector) +% +% The following optional arguments can be specified in the form of name/value pa irs: +% [default value in brackets] +% +% maximize - if 1, does max-product instead of sum-product [0] +% 'filename' - msgs will be printed to this file, so you can assess convergence while it runs [engine.filename] +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) +% +% For discrete nodes, loglik is the negative Bethe free energy evaluated at the final beliefs. +% For Gaussian nodes, loglik is currently always 0. +% +% 'num_iter' returns the number of iterations used. + +maximize = 0; +filename = engine.filename; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + for i=1:2:nargs + switch args{i}, + case 'maximize', maximize = args{i+1}; + case 'filename', filename = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + + +if maximize + error('can''t handle max-prop yet') +end + +engine.maximize = maximize; +engine.filename = filename; +engine.bel = []; % reset if necessary + +bnet = bnet_from_engine(engine); +N = length(bnet.dag); +ns = bnet.node_sizes(:); + +observed_bitv = ~isemptycell(evidence); +disconnected = find(engine.disconnected_nodes_bitv); +if ~all(observed_bitv(disconnected)) + error(['The following discrete nodes must be observed: ' num2str(disconnected)]) +end +msg = init_pearl_msgs(engine.msg_type, engine.msg_dag, ns, evidence); + +niter = 1; +switch engine.protocol + case 'parallel', [msg, niter] = parallel_protocol(engine, evidence, msg); + case 'tree', msg = tree_protocol(engine, evidence, msg); + otherwise, + error(['unrecognized protocol ' engine.protocol]) +end +engine.niter = niter; + +engine.marginal = cell(1,N); +nodes = find(~engine.disconnected_nodes_bitv); +for n=nodes(:)' + engine.marginal{n} = compute_bel(engine.msg_type, msg{n}.pi, msg{n}.lambda); +end + +engine.evidence = evidence; % needed by marginal_nodes and marginal_family +engine.msg = msg; % needed by marginal_family + +if (nargout >= 2) + if (engine.msg_type == 'd') + loglik = bethe_free_energy(engine, evidence); + else + loglik = 0; + end +end + + + +%%%%%%%%%%% + +function msg = init_pearl_msgs(msg_type, dag, ns, evidence) +% INIT_MSGS Initialize the lambda/pi message and state vectors +% msg = init_msgs(dag, ns, evidence) +% + +N = length(dag); +msg = cell(1,N); +observed = ~isemptycell(evidence); +lam_msg = 1; + +for n=1:N + ps = parents(dag, n); + msg{n}.pi_from_parent = cell(1, length(ps)); + for i=1:length(ps) + p = ps(i); + msg{n}.pi_from_parent{i} = mk_msg(msg_type, ns(p)); + end + + cs = children(dag, n); + msg{n}.lambda_from_child = cell(1, length(cs)); + for i=1:length(cs) + c = cs(i); + msg{n}.lambda_from_child{i} = mk_msg(msg_type, ns(n), lam_msg); + end + + msg{n}.lambda = mk_msg(msg_type, ns(n), lam_msg); + msg{n}.pi = mk_msg(msg_type, ns(n)); + + if observed(n) + msg{n}.lambda_from_self = mk_msg_with_evidence(msg_type, ns(n), evidence{n}); + else + msg{n}.lambda_from_self = mk_msg(msg_type, ns(n), lam_msg); + end +end + + + +%%%%%%%%% + +function msg = mk_msg(msg_type, sz, is_lambda_msg) + +if nargin < 3, is_lambda_msg = 0; end + +switch msg_type + case 'd', msg = ones(sz, 1); + case 'g', + if is_lambda_msg + msg.precision = zeros(sz, sz); + msg.info_state = zeros(sz, 1); + else + msg.Sigma = zeros(sz, sz); + msg.mu = zeros(sz,1); + end +end + +%%%%%%%%%%%% + +function msg = mk_msg_with_evidence(msg_type, sz, val) + +switch msg_type + case 'd', + msg = zeros(sz, 1); + msg(val) = 1; + case 'g', + %msg.observed_val = val(:); + msg.precision = inf; + msg.mu = val(:); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@pearl_inf_engine/loopy_converged.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function niter = loopy_converged(engine) +% LOOPY_CONVERGED Did loopy belief propagation converge? 0 means no, eles we return the num. iterations. +% function niter = loopy_converged(engine) +% +% We use a simple heuristic: we say convergence occurred if the number of iterations +% used was less than the maximum allowed. + +if engine.niter == engine.max_iter + niter = 0; +else + niter = engine.niter; +end +%conv = (strcmp(engine.protocol, 'tree') | (engine.niter < engine.max_iter));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@pearl_inf_engine/marginal_family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,80 @@ +function m = marginal_family(engine, n, add_ev) +% MARGINAL_FAMILY Compute the marginal on i's family (loopy) +% m = marginal_family(engine, n, add_ev) + +if nargin < 3, add_ev = 0; end + +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes; +ps = parents(bnet.dag, n); +dom = [ps n]; +CPD = bnet.CPD{bnet.equiv_class(n)}; + +switch engine.msg_type + case 'd', + % The method is similar to the following HMM equation: + % xi(i,j,t) = normalise( alpha(i,t) * transmat(i,j) * obsmat(j,t+1) * beta(j,t+1) ) + % where xi(i,j,t) = Pr(Q(t)=i, Q(t+1)=j | y(1:T)) + % beta == lambda, alpha == pi, alpha from each parent = pi msg + % In general, if A,B are parents of C, + % P(A,B,C) = P(C|A,B) pi_msg(A->C) pi_msg(B->C) lambda(C) + % where lambda(C) = P(ev below and including C|C) = prod incoming lamba_msg(children->C) + % and pi_msg(X->C) = P(X|ev above) etc + + T = dpot(dom, ns(dom), CPD_to_CPT(CPD)); + for j=1:length(ps) + p = ps(j); + pi_msg = dpot(p, ns(p), engine.msg{n}.pi_from_parent{j}); + T = multiply_by_pot(T, pi_msg); + end + lambda = dpot(n, ns(n), engine.msg{n}.lambda); + T = multiply_by_pot(T, lambda); + T = normalize_pot(T); + m = pot_to_marginal(T); + if ~add_ev + m.T = shrink_obs_dims_in_table(m.T, dom, engine.evidence); + end + case 'g', + if engine.disconnected_nodes_bitv(n) + m.T = 1; + m.domain = dom; + if add_ev + m = add_ev_to_dmarginal(m, engine.evidence, ns) + end + return; + end + + [m, C, W] = gaussian_CPD_params_given_dps(CPD, dom, engine.evidence); + cdom = myintersect(dom, bnet.cnodes); + pot = linear_gaussian_to_cpot(m, C, W, dom, ns, cdom, engine.evidence); + % linear_gaussian_to_cpot will set the effective size of observed nodes to 0, + % so we need to do this explicitely for the messages, too, + % so they are all the same size. + obs_bitv = ~isemptycell(engine.evidence); + ps = parents(engine.msg_dag, n); + for j=1:length(ps) + p = ps(j); + msg = engine.msg{n}.pi_from_parent{j}; + if obs_bitv(p) + pi_msg = mpot(p, 0); + else + pi_msg = mpot(p, ns(p), 0, msg.mu, msg.Sigma); + end + pot = multiply_by_pot(pot, mpot_to_cpot(pi_msg)); + end + msg = engine.msg{n}.lambda; + if obs_bitv(n) + lambda = cpot(n, 0); + else + lambda = cpot(n, ns(n), 0, msg.info_state, msg.precision); + end + pot = multiply_by_pot(pot, lambda); + m = pot_to_marginal(pot); + if add_ev + m = add_evidence_to_gmarginal(m, engine.evidence, bnet.node_sizes, bnet.cnodes); + end +end + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@pearl_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,43 @@ +function marginal = marginal_nodes(engine, query, add_ev) +% MARGINAL_NODES Compute the marginal on the specified query nodes (loopy) +% marginal = marginal_nodes(engine, query, add_ev) +% +% 'query' must be a single node. +% add_ev is an optional argument; if 1, observed nodes will be set to their original size, +% otherwise they will be treated like points. + +if nargin < 3, add_ev = 0; end + +if length(query) > 1 + error('can only compute marginal on single nodes or families') +end +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes(:); + +switch engine.msg_type + case 'd', + T = engine.marginal{query}; + if ~add_ev + marginal.T = shrink_obs_dims_in_table(T, query, engine.evidence); + else + marginal.T = T; + end + marginal.domain = query; + + case 'g', + if engine.disconnected_nodes_bitv(query) + marginal.T = 1; + marginal.domain = query; + if add_ev + marginal = add_ev_to_dmarginal(marginal, engine.evidence, ns) + end + return; + end + + marginal = engine.marginal{query}; + marginal.domain = query; + if ~add_ev + marginal = shrink_obs_dims_in_gaussian(marginal, query, engine.evidence, ns); + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@pearl_inf_engine/pearl_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,158 @@ +function engine = pearl_inf_engine(bnet, varargin) +% PEARL_INF_ENGINE Pearl's algorithm (belief propagation) +% engine = pearl_inf_engine(bnet, ...) +% +% If the graph has no loops (undirected cycles), you should use the tree protocol, +% and the results will be exact. +% Otherwise, you should use the parallel protocol, and the results may be approximate. +% +% Optional arguments [default in brackets] +% 'protocol' - tree or parallel ['parallel'] +% +% Optional arguments for the loopy case +% 'max_iter' - specifies the max num. iterations to perform [2*num nodes] +% 'tol' - convergence criterion on messages [1e-3] +% 'momentum' - msg = (m*old + (1-m)*new). [m=0] +% 'filename' - msgs will be printed to this file, so you can assess convergence while it runs [[]] +% 'storebel' - 1 means save engine.bel{n,t} for every iteration t and hidden node n [0] +% +% If there are discrete and cts nodes, we assume all the discretes are observed. In this +% case, you must use the parallel protocol, and the evidence pattern must be fixed. + + +N = length(bnet.dag); +protocol = 'parallel'; +max_iter = 2*N; +% We use N+2 for the following reason: +% In N iterations, we get the exact answer for a tree. +% In the N+1st iteration, we notice that the results are the same as before, and terminate. +% In loopy_converged, we see that N+1 < max = N+2, and declare convergence. +tol = 1e-3; +momentum = 0; +filename = []; +storebel = 0; + +args = varargin; +for i=1:2:length(args) + switch args{i}, + case 'protocol', protocol = args{i+1}; + case 'max_iter', max_iter = args{i+1}; + case 'tol', tol = args{i+1}; + case 'momentum', momentum = args{i+1}; + case 'filename', filename = args{i+1}; + case 'storebel', storebel = args{i+1}; + end +end + +engine.filename = filename; +engine.storebel = storebel; +engine.bel = []; + +if strcmp(protocol, 'tree') + % We first send messages up to the root (pivot node), and then back towards the leaves. + % If the bnet is a singly connected graph (no loops), choosing a root induces a directed tree. + % Peot and Shachter discuss ways to pick the root so as to minimize the work, + % taking into account which nodes have changed. + % For simplicity, we always pick the root to be the last node in the graph. + % This means the first pass is equivalent to going forward in time in a DBN. + + engine.root = N; + [engine.adj_mat, engine.preorder, engine.postorder, loopy] = ... + mk_rooted_tree(bnet.dag, engine.root); + % engine.adj_mat might have different edge orientations from bnet.dag + if loopy + error('can only apply tree protocol to loop-less graphs') + end +else + engine.root = []; + engine.adj_mat = []; + engine.preorder = []; + engine.postorder = []; +end + +engine.niter = []; +engine.protocol = protocol; +engine.max_iter = max_iter; +engine.tol = tol; +engine.momentum = momentum; +engine.maximize = []; + +%onodes = find(~isemptycell(evidence)); +onodes = bnet.observed; +engine.msg_type = determine_pot_type(bnet, onodes, 1:N); % needed also by marginal_nodes +if strcmp(engine.msg_type, 'cg') + error('messages must be discrete or Gaussian') +end +[engine.msg_dag, disconnected_nodes] = mk_msg_dag(bnet, engine.msg_type, onodes); +engine.disconnected_nodes_bitv = zeros(1,N); +engine.disconnected_nodes_bitv(disconnected_nodes) = 1; + + +% this is where we store stuff between enter_evidence and marginal_nodes +engine.marginal = cell(1,N); +engine.evidence = []; +engine.msg = []; + +[engine.parent_index, engine.child_index] = mk_loopy_msg_indices(engine.msg_dag); + +engine = class(engine, 'pearl_inf_engine', inf_engine(bnet)); + + +%%%%%%%%% + +function [dag, disconnected_nodes] = mk_msg_dag(bnet, msg_type, onodes) + +% If we are using Gaussian msgs, all discrete nodes must be observed; +% they are then disconnected from the graph, so we don't try to send +% msgs to/from them: their observed value simply serves to index into +% the right set of parameters for the Gaussian nodes (which use CPD.ps +% instead of parents(dag), and hence are unaffected by this "surgery"). + +disconnected_nodes = []; +switch msg_type + case 'd', dag = bnet.dag; + case 'g', + disconnected_nodes = bnet.dnodes; + dag = bnet.dag; + for i=disconnected_nodes(:)' + ps = parents(bnet.dag, i); + cs = children(bnet.dag, i); + if ~isempty(ps), dag(ps, i) = 0; end + if ~isempty(cs), dag(i, cs) = 0; end + end +end + + +%%%%%%%%%% +function [parent_index, child_index] = mk_loopy_msg_indices(dag) +% MK_LOOPY_MSG_INDICES Compute "port numbers" for message passing +% [parent_index, child_index] = mk_loopy_msg_indices(bnet) +% +% child_index{n}(c) = i means c is n's i'th child, i.e., i = find_equiv_posns(c, children(n)) +% child_index{n}(c) = 0 means c is not a child of n. +% parent_index{n}{p} is defined similarly. +% We need to use these indices since the pi_from_parent/ lambda_from_child cell arrays +% cannot be sparse, and hence cannot be indexed by the actual number of the node. +% Instead, we use the number of the "port" on which the message arrived. + +N = length(dag); +child_index = cell(1,N); +parent_index = cell(1,N); +for n=1:N + cs = children(dag, n); + child_index{n} = sparse(1,N); + for i=1:length(cs) + c = cs(i); + child_index{n}(c) = i; + end + ps = parents(dag, n); + parent_index{n} = sparse(1,N); + for i=1:length(ps) + p = ps(i); + parent_index{n}(p) = i; + end +end + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@pearl_inf_engine/private/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +/compute_bel.m/1.1.1.1/Wed May 29 15:59:56 2002// +/parallel_protocol.m/1.1.1.1/Sun Aug 21 20:00:12 2005// +/prod_lambda_msgs.m/1.1.1.1/Wed May 29 15:59:56 2002// +/tree_protocol.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@pearl_inf_engine/private/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@pearl_inf_engine/private
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@pearl_inf_engine/private/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@pearl_inf_engine/private/compute_bel.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,24 @@ +function bel = compute_bel(msg_type, pi, lambda) + +switch msg_type, + case 'd', bel = normalise(pi .* lambda); + case 'g', + if isinf(lambda.precision) % ignore pi because lambda is completely certain (observed) + bel.mu = lambda.mu; + bel.Sigma = zeros(length(bel.mu)); % infinite precision => 0 variance + elseif all(pi.Sigma==0) % ignore lambda because pi is completely certain (delta fn prior) + bel.Sigma = pi.Sigma; + bel.mu = pi.mu; + elseif all(isinf(pi.Sigma)) % ignore pi because pi is completely uncertain + bel.Sigma = inv(lambda.precision); + bel.mu = bel.Sigma * lambda.info_state; + elseif all(lambda.precision == 0) % ignore lambda because lambda is completely uncertain + bel.Sigma = pi.Sigma; + bel.mu = pi.mu; + else % combine both pi and lambda + pi_precision = inv(pi.Sigma); + bel.Sigma = inv(pi_precision + lambda.precision); + bel.mu = bel.Sigma*(pi_precision * pi.mu + lambda.info_state); + end + otherwise, error(['unrecognized msg type ' msg_type]) +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@pearl_inf_engine/private/parallel_protocol.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,114 @@ +function [msg, niter] = parallel_protocol(engine, evidence, msg) + +bnet = bnet_from_engine(engine); +N = length(bnet.dag); +ns = bnet.node_sizes(:); + +if ~isempty(engine.filename) + fid = fopen(engine.filename, 'w'); + if fid == 0 + error(['could not open ' engine.filename ' for writing']) + end +else + fid = []; +end + +converged = 0; +iter = 1; +hidden = find(isemptycell(evidence)); +bel = cell(1,N); +old_bel = cell(1,N); +%nodes = mysetdiff(1:N, engine.disconnected_nodes); +nodes = find(~engine.disconnected_nodes_bitv); +while ~converged & (iter <= engine.max_iter) + % Everybody updates their state in parallel + for n=nodes(:)' + cs_msg = children(engine.msg_dag, n); + %msg{n}.lambda = compute_lambda(n, cs, msg); + msg{n}.lambda = prod_lambda_msgs(n, cs_msg, msg, engine.msg_type); + ps_orig = parents(bnet.dag, n); + msg{n}.pi = CPD_to_pi(bnet.CPD{bnet.equiv_class(n)}, engine.msg_type, n, ps_orig, msg, evidence); + end + + changed = 0; + if ~isempty(fid) + fprintf(fid, 'ITERATION %d\n', iter); + end + for n=hidden(:)' % this will not contain any disconnected nodes + old_bel{n} = bel{n}; + bel{n} = compute_bel(engine.msg_type, msg{n}.pi, msg{n}.lambda); + if ~isempty(fid) + fprintf(fid, 'node %d: %s\n', n, bel_to_str(bel{n}, engine.msg_type)); + end + if engine.storebel + engine.bel{n,iter} = bel{n}; + end + if (iter == 1) | ~approxeq_bel(bel{n}, old_bel{n}, engine.tol, engine.msg_type) + changed = 1; + end + end + %converged = ~changed; + converged = ~changed & (iter > 1); % Sonia Leach changed this + + if ~converged + % Everybody sends to all their neighbors in parallel + for n=nodes(:)' + % lambda msgs to parents + ps_msg = parents(engine.msg_dag, n); + ps_orig = parents(bnet.dag, n); + for p=ps_msg(:)' + j = engine.child_index{p}(n); % n is p's j'th child + old_msg = msg{p}.lambda_from_child{j}(:); + new_msg = CPD_to_lambda_msg(bnet.CPD{bnet.equiv_class(n)}, engine.msg_type, n, ps_orig, ... + msg, p, evidence); + lam_msg = convex_combination_msg(old_msg, new_msg, engine.momentum, engine.msg_type); + msg{p}.lambda_from_child{j} = lam_msg; + end + + % pi msgs to children + cs_msg = children(engine.msg_dag, n); + for c=cs_msg(:)' + j = engine.parent_index{c}(n); % n is c's j'th parent + old_msg = msg{c}.pi_from_parent{j}(:); + %new_msg = compute_pi_msg(n, cs, msg, c)); + new_msg = compute_bel(engine.msg_type, msg{n}.pi, prod_lambda_msgs(n, cs_msg, msg, engine.msg_type, c)); + pi_msg = convex_combination_msg(old_msg, new_msg, engine.momentum, engine.msg_type); + msg{c}.pi_from_parent{j} = pi_msg; + end + end + iter = iter + 1; + end +end + +if fid > 0, fclose(fid); end +%niter = iter - 1; +niter = iter; + +%%%%%%%%%% + +function str = bel_to_str(bel, type) + +switch type + case 'd', str = sprintf('%9.4f ', bel(:)'); + case 'g', str = sprintf('%9.4f ', bel.mu(:)'); +end + + +%%%%%%% + +function a = approxeq_bel(bel1, bel2, tol, type) + +switch type + case 'd', a = approxeq(bel1, bel2, tol); + case 'g', a = approxeq(bel1.mu, bel2.mu, tol) & approxeq(bel1.Sigma, bel2.Sigma, tol); +end + + +%%%%%%% + +function msg = convex_combination_msg(old_msg, new_msg, old_weight, type) + +switch type + case 'd', msg = old_weight * old_msg + (1-old_weight)*new_msg; + case 'g', msg = new_msg; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@pearl_inf_engine/private/prod_lambda_msgs.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,29 @@ +function lam = prod_lambda_msgs(n, cs, msg, msg_type, except) + +if nargin < 5, except = -1; end + +lam = msg{n}.lambda_from_self; +switch msg_type + case 'd', + for i=1:length(cs) + c = cs(i); + if c ~= except + lam = lam .* msg{n}.lambda_from_child{i}; + end + end + case 'g', + if isinf(lam.precision) % isfield(lam, 'observed_val') + return; % pass on the observed msg + end + for i=1:length(cs) + c = cs(i); + if c ~= except + m = msg{n}.lambda_from_child{i}; + lam.precision = lam.precision + m.precision; + lam.info_state = lam.info_state + m.info_state; + end + end +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@pearl_inf_engine/private/tree_protocol.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,71 @@ +function msg = tree_protocol(engine, evidence, msg) + +bnet = bnet_from_engine(engine); +N = length(bnet.dag); + +% Send messages from leaves to root +for i=1:N-1 + n = engine.postorder(i); + above = parents(engine.adj_mat, n); + msg = send_msgs_to_some_neighbors(n, msg, above, bnet, engine.child_index, engine.parent_index, ... + engine.msg_type, evidence); +end + +% Process root +n = engine.root; +cs = children(bnet.dag, n); +%msg{n}.lambda = compute_lambda(n, cs, msg, engine.msg_type); +msg{n}.lambda = prod_lambda_msgs(n, cs, msg, engine.msg_type); +ps = parents(bnet.dag, n); +msg{n}.pi = CPD_to_pi(bnet.CPD{bnet.equiv_class(n)}, engine.msg_type, n, ps, msg, evidence); + +% Send messages from root to leaves +for i=1:N + n = engine.preorder(i); + below = children(engine.adj_mat, n); + msg = send_msgs_to_some_neighbors(n, msg, below, bnet, engine.child_index, engine.parent_index, ... + engine.msg_type, evidence); +end + + +%%%%%%%%%% + +function msg = send_msgs_to_some_neighbors(n, msg, valid_nbrs, bnet, child_index, parent_index, ... + msg_type, evidence) + +verbose = 0; + +ns = bnet.node_sizes; +dag = bnet.dag; +e = bnet.equiv_class(n); +CPD = bnet.CPD{e}; + + +cs = children(dag, n); +%msg{n}.lambda = compute_lambda(n, cs, msg); +msg{n}.lambda = prod_lambda_msgs(n, cs, msg, msg_type); +if verbose, fprintf('%d computes lambda\n', n); display(msg{n}.lambda); end + +ps = parents(dag, n); +msg{n}.pi = CPD_to_pi(CPD, msg_type, n, ps, msg, evidence); +if verbose, fprintf('%d computes pi\n', n); display(msg{n}.pi); end + +ps2 = myintersect(parents(dag, n), valid_nbrs); +for p=ps2(:)' + lam_msg = CPD_to_lambda_msg(CPD, msg_type, n, ps, msg, p, evidence); + j = child_index{p}(n); % n is p's j'th child + msg{p}.lambda_from_child{j} = lam_msg; + if verbose, fprintf('%d sends lambda to %d\n', n, p); display(lam_msg); end +end + +cs2 = myintersect(cs, valid_nbrs); +for c=cs2(:)' + %pi_msg = compute_pi_msg(n, cs, msg, c); + pi_msg = compute_bel(msg_type, msg{n}.pi, prod_lambda_msgs(n, cs, msg, msg_type, c)); + j = parent_index{c}(n); % n is c's j'th parent + msg{c}.pi_from_parent{j} = pi_msg; + if verbose, fprintf('%d sends pi to %d\n', n, c); display(pi_msg); end +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@quickscore_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,4 @@ +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/quickscore_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +D/private////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@quickscore_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@quickscore_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@quickscore_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@quickscore_inf_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,19 @@ +function engine = enter_evidence(engine, pos, neg) +% ENTER_EVIDENCE Add evidence to the QMR network +% engine = enter_evidence(engine, pos, neg) +% +% pos = list of leaves that have positive observations +% neg = list of leaves that have negative observations + +% Extract params for the observed findings +obs = myunion(pos, neg); +%inhibit_obs = engine.inhibit(obs, :); +inhibit_obs = engine.inhibit(:,obs)'; +leak_obs = engine.leak(obs); + +% Find what nodes correspond to the original observed leaves +pos2 = find_equiv_posns(pos, obs); +neg2 = find_equiv_posns(neg, obs); +engine.post = quickscore(pos2, neg2, inhibit_obs, engine.prior, leak_obs); +%engine.post = C_quickscore(pos2, neg2, inhibit_obs, engine.prior, leak_obs); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@quickscore_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function m = marginal_nodes(engine, query) +% MARGINAL_NODES Compute the marginal on the specified query nodes (quickscore) +% marginal = marginal_nodes(engine, query) +% +% 'query' must be a single disease (root) node. + +assert(length(query)==1); +p = engine.post(query); +m.T = [1-p p]'; +m.domain = query; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@quickscore_inf_engine/private/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +/C_quickscore.c/1.1.1.1/Wed May 29 15:59:56 2002// +/nr.h/1.1.1.1/Wed May 29 15:59:56 2002// +/nrutil.c/1.1.1.1/Wed May 29 15:59:56 2002// +/nrutil.h/1.1.1.1/Wed May 29 15:59:56 2002// +/quickscore.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@quickscore_inf_engine/private/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@quickscore_inf_engine/private
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@quickscore_inf_engine/private/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@quickscore_inf_engine/private/C_quickscore.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,164 @@ +/* To compile, type "mex C_quickscore.c" */ + +#include <stdio.h> +#include "nrutil.h" +#include "nrutil.c" +#include <math.h> +#include "mex.h" + +#define MAX(X,Y) (X)>(Y)?(X):(Y) + +int two_to_the(int n) +{ + return 1 << n; +} + +void int2bin(int num, int nbits, int bits[]) +{ + int i, mask; + mask = 1 << (nbits-1); /* mask = 0010...0 , where the 1 is in col nbits (rightmost = col 1) */ + for (i = 0; i < nbits; i++) { + bits[i] = ((num & mask) == 0) ? 0 : 1; + num <<= 1; + } +} + + +void quickscore(int ndiseases, int nfindings, const double *fpos, int npos, const double *fneg, int nneg, + const double *inhibit, const double *prior, const double *leak, double *prob) +{ + double *Pon, *Poff, **Uon, **Uoff, **post, *pterm, *ptermOff, *ptermOn, temp, p, myp; + int *bits, nsubsets, *fmask; + int f, d, i, j, si, size_subset, sign; + + Pon = dvector(0, ndiseases); + Poff = dvector(0, ndiseases); + Pon[0] = 1; + Poff[0] = 0; + for (i=1; i <= ndiseases; i++) { + Pon[i] = prior[i-1]; + Poff[i] = 1-Pon[i]; + } + + Uon = dmatrix(0, nfindings-1, 0, ndiseases); + Uoff = dmatrix(0, nfindings-1, 0, ndiseases); + d = 0; + for (f=0; f < nfindings; f++) { + Uon[f][d] = leak[f]; + Uoff[f][d] = leak[f]; + } + for (f=0; f < nfindings; f++) { + for (d=1; d <= ndiseases; d++) { + Uon[f][d] = inhibit[f + nfindings*(d-1)]; + Uoff[f][d] = 1; + } + } + + post = dmatrix(0, ndiseases, 0, 1); + for (d = 0; d <= ndiseases; d++) { + post[d][0] = 0; + post[d][1] = 0; + } + + bits = ivector(0, npos-1); + fmask = ivector(0, nfindings-1); + pterm = dvector(0, ndiseases); + ptermOff = dvector(0, ndiseases); + ptermOn = dvector(0, ndiseases); + + nsubsets = two_to_the(npos); + + for (si = 0; si < nsubsets; si++) { + int2bin(si, npos, bits); + for (i=0; i < nfindings; i++) fmask[i] = 0; + for (i=0; i < nneg; i++) fmask[(int)fneg[i]-1] = 1; + size_subset = 0; + for (i=0; i < npos; i++) { + if (bits[i]) { + size_subset++; + fmask[(int)fpos[i]-1] = 1; + } + } + p = 1; + for (d=0; d <= ndiseases; d++) { + temp = 1; + for (j = 0; j < nfindings; j++) { + if (fmask[j]) temp *= Uoff[j][d]; + } + ptermOff[d] = temp; + + temp = 1; + for (j = 0; j < nfindings; j++) { + if (fmask[j]) temp *= Uon[j][d]; + } + ptermOn[d] = temp; + + pterm[d] = Poff[d]*ptermOff[d] + Pon[d]*ptermOn[d]; + p *= pterm[d]; + } + sign = (int) pow(-1, size_subset); + for (d=0; d <= ndiseases; d++) { + myp = p / pterm[d]; + post[d][0] += sign*(myp * ptermOff[d]); + post[d][1] += sign*(myp * ptermOn[d]); + } + } /* next si */ + + + for (d=0; d <= ndiseases; d++) { + post[d][0] *= Poff[d]; + post[d][1] *= Pon[d]; + } + for (d=0; d <= ndiseases; d++) { + temp = post[d][0] + post[d][1]; + post[d][0] /= temp; + post[d][1] /= temp; + if (d>0) { prob[d-1] = post[d][1]; } + } + + + free_dvector(Pon, 0, ndiseases); + free_dvector(Poff, 0, ndiseases); + free_dmatrix(Uon, 0, nfindings-1, 0, ndiseases); + free_dmatrix(Uoff, 0, nfindings-1, 0, ndiseases); + free_dmatrix(post, 0, ndiseases, 0, 1); + free_ivector(bits, 0, npos-1); + free_ivector(fmask, 0, nfindings-1); + free_dvector(pterm, 0, ndiseases); + free_dvector(ptermOff, 0, ndiseases); + free_dvector(ptermOn, 0, ndiseases); +} + + +void mexFunction( + int nlhs, mxArray *plhs[], + int nrhs, const mxArray *prhs[] + ) +{ + double *fpos, *fneg, *inhibit, *prior, *leak, *prob; + int npos, nneg, ndiseases, nfindings; + double *p; + + /* read the input args */ + fpos = mxGetPr(prhs[0]); + npos = MAX(mxGetM(prhs[0]), mxGetN(prhs[0])); + + fneg = mxGetPr(prhs[1]); + nneg = MAX(mxGetM(prhs[1]), mxGetN(prhs[1])); + + inhibit = mxGetPr(prhs[2]); /* inhibit(finding, disease) */ + nfindings = mxGetM(prhs[2]); + ndiseases = mxGetN(prhs[2]); + + prior = mxGetPr(prhs[3]); + + leak = mxGetPr(prhs[4]); + + + /* set the output pointers */ + plhs[0] = mxCreateDoubleMatrix(1, ndiseases, mxREAL); + prob = mxGetPr(plhs[0]); + + quickscore(ndiseases, nfindings, fpos, npos, fneg, nneg, inhibit, prior, leak, prob); +} +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@quickscore_inf_engine/private/nr.h Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,536 @@ +/* CAUTION: This is the ANSI C (only) version of the Numerical Recipes + utility file nr.h. Do not confuse this file with the same-named + file nr.h that is supplied in the 'misc' subdirectory. + *That* file is the one from the book, and contains both ANSI and + traditional K&R versions, along with #ifdef macros to select the + correct version. *This* file contains only ANSI C. */ + +#ifndef _NR_H_ +#define _NR_H_ + +#ifndef _FCOMPLEX_DECLARE_T_ +typedef struct FCOMPLEX {float r,i;} fcomplex; +#define _FCOMPLEX_DECLARE_T_ +#endif /* _FCOMPLEX_DECLARE_T_ */ + +#ifndef _ARITHCODE_DECLARE_T_ +typedef struct { + unsigned long *ilob,*iupb,*ncumfq,jdif,nc,minint,nch,ncum,nrad; +} arithcode; +#define _ARITHCODE_DECLARE_T_ +#endif /* _ARITHCODE_DECLARE_T_ */ + +#ifndef _HUFFCODE_DECLARE_T_ +typedef struct { + unsigned long *icod,*ncod,*left,*right,nch,nodemax; +} huffcode; +#define _HUFFCODE_DECLARE_T_ +#endif /* _HUFFCODE_DECLARE_T_ */ + +#include <stdio.h> + +void addint(double **uf, double **uc, double **res, int nf); +void airy(float x, float *ai, float *bi, float *aip, float *bip); +void amebsa(float **p, float y[], int ndim, float pb[], float *yb, + float ftol, float (*funk)(float []), int *iter, float temptr); +void amoeba(float **p, float y[], int ndim, float ftol, + float (*funk)(float []), int *iter); +float amotry(float **p, float y[], float psum[], int ndim, + float (*funk)(float []), int ihi, float fac); +float amotsa(float **p, float y[], float psum[], int ndim, float pb[], + float *yb, float (*funk)(float []), int ihi, float *yhi, float fac); +void anneal(float x[], float y[], int iorder[], int ncity); +double anorm2(double **a, int n); +void arcmak(unsigned long nfreq[], unsigned long nchh, unsigned long nradd, + arithcode *acode); +void arcode(unsigned long *ich, unsigned char **codep, unsigned long *lcode, + unsigned long *lcd, int isign, arithcode *acode); +void arcsum(unsigned long iin[], unsigned long iout[], unsigned long ja, + int nwk, unsigned long nrad, unsigned long nc); +void asolve(unsigned long n, double b[], double x[], int itrnsp); +void atimes(unsigned long n, double x[], double r[], int itrnsp); +void avevar(float data[], unsigned long n, float *ave, float *var); +void balanc(float **a, int n); +void banbks(float **a, unsigned long n, int m1, int m2, float **al, + unsigned long indx[], float b[]); +void bandec(float **a, unsigned long n, int m1, int m2, float **al, + unsigned long indx[], float *d); +void banmul(float **a, unsigned long n, int m1, int m2, float x[], float b[]); +void bcucof(float y[], float y1[], float y2[], float y12[], float d1, + float d2, float **c); +void bcuint(float y[], float y1[], float y2[], float y12[], + float x1l, float x1u, float x2l, float x2u, float x1, + float x2, float *ansy, float *ansy1, float *ansy2); +void beschb(double x, double *gam1, double *gam2, double *gampl, + double *gammi); +float bessi(int n, float x); +float bessi0(float x); +float bessi1(float x); +void bessik(float x, float xnu, float *ri, float *rk, float *rip, + float *rkp); +float bessj(int n, float x); +float bessj0(float x); +float bessj1(float x); +void bessjy(float x, float xnu, float *rj, float *ry, float *rjp, + float *ryp); +float bessk(int n, float x); +float bessk0(float x); +float bessk1(float x); +float bessy(int n, float x); +float bessy0(float x); +float bessy1(float x); +float beta(float z, float w); +float betacf(float a, float b, float x); +float betai(float a, float b, float x); +float bico(int n, int k); +void bksub(int ne, int nb, int jf, int k1, int k2, float ***c); +float bnldev(float pp, int n, long *idum); +float brent(float ax, float bx, float cx, + float (*f)(float), float tol, float *xmin); +float brent_arg(float ax, float bx, float cx, + float (*f)(float, void*), float tol, float *xmin, void *arg); +void broydn(float x[], int n, int *check, + void (*vecfunc)(int, float [], float [])); +void bsstep(float y[], float dydx[], int nv, float *xx, float htry, + float eps, float yscal[], float *hdid, float *hnext, + void (*derivs)(float, float [], float [])); +void caldat(long julian, int *mm, int *id, int *iyyy); +void chder(float a, float b, float c[], float cder[], int n); +float chebev(float a, float b, float c[], int m, float x); +void chebft(float a, float b, float c[], int n, float (*func)(float)); +void chebpc(float c[], float d[], int n); +void chint(float a, float b, float c[], float cint[], int n); +float chixy(float bang); +void choldc(float **a, int n, float p[]); +void cholsl(float **a, int n, float p[], float b[], float x[]); +void chsone(float bins[], float ebins[], int nbins, int knstrn, + float *df, float *chsq, float *prob); +void chstwo(float bins1[], float bins2[], int nbins, int knstrn, + float *df, float *chsq, float *prob); +void cisi(float x, float *ci, float *si); +void cntab1(int **nn, int ni, int nj, float *chisq, + float *df, float *prob, float *cramrv, float *ccc); +void cntab2(int **nn, int ni, int nj, float *h, float *hx, float *hy, + float *hygx, float *hxgy, float *uygx, float *uxgy, float *uxy); +void convlv(float data[], unsigned long n, float respns[], unsigned long m, + int isign, float ans[]); +void copy(double **aout, double **ain, int n); +void correl(float data1[], float data2[], unsigned long n, float ans[]); +void cosft(float y[], int n, int isign); +void cosft1(float y[], int n); +void cosft2(float y[], int n, int isign); +void covsrt(float **covar, int ma, int ia[], int mfit); +void crank(unsigned long n, float w[], float *s); +void cyclic(float a[], float b[], float c[], float alpha, float beta, + float r[], float x[], unsigned long n); +void daub4(float a[], unsigned long n, int isign); +float dawson(float x); +float dbrent(float ax, float bx, float cx, + float (*f)(float), float (*df)(float), float tol, float *xmin); +void ddpoly(float c[], int nc, float x, float pd[], int nd); +int decchk(char string[], int n, char *ch); +void derivs(float x, float y[], float dydx[]); +float df1dim(float x); +void dfour1(double data[], unsigned long nn, int isign); +void dfpmin(float p[], int n, float gtol, int *iter, float *fret, + float (*func)(float []), void (*dfunc)(float [], float [])); +float dfridr(float (*func)(float), float x, float h, float *err); +void dftcor(float w, float delta, float a, float b, float endpts[], + float *corre, float *corim, float *corfac); +void dftint(float (*func)(float), float a, float b, float w, + float *cosint, float *sinint); +void difeq(int k, int k1, int k2, int jsf, int is1, int isf, + int indexv[], int ne, float **s, float **y); +void dlinmin(float p[], float xi[], int n, float *fret, + float (*func)(float []), void (*dfunc)(float [], float[])); +double dpythag(double a, double b); +void drealft(double data[], unsigned long n, int isign); +void dsprsax(double sa[], unsigned long ija[], double x[], double b[], + unsigned long n); +void dsprstx(double sa[], unsigned long ija[], double x[], double b[], + unsigned long n); +void dsvbksb(double **u, double w[], double **v, int m, int n, double b[], + double x[]); +void dsvdcmp(double **a, int m, int n, double w[], double **v); +void eclass(int nf[], int n, int lista[], int listb[], int m); +void eclazz(int nf[], int n, int (*equiv)(int, int)); +float ei(float x); +void eigsrt(float d[], float **v, int n); +float elle(float phi, float ak); +float ellf(float phi, float ak); +float ellpi(float phi, float en, float ak); +void elmhes(float **a, int n); +float erfcc(float x); +float erff(float x); +float erffc(float x); +void eulsum(float *sum, float term, int jterm, float wksp[]); +float evlmem(float fdt, float d[], int m, float xms); +float expdev(long *idum); +float expint(int n, float x); +float f1(float x); +float f1dim(float x); +float f1dim_arg(float x, void *arg); +float f2(float y); +float f3(float z); +float factln(int n); +float factrl(int n); +void fasper(float x[], float y[], unsigned long n, float ofac, float hifac, + float wk1[], float wk2[], unsigned long nwk, unsigned long *nout, + unsigned long *jmax, float *prob); +void fdjac(int n, float x[], float fvec[], float **df, + void (*vecfunc)(int, float [], float [])); +void fgauss(float x, float a[], float *y, float dyda[], int na); +void fill0(double **u, int n); +void fit(float x[], float y[], int ndata, float sig[], int mwt, + float *a, float *b, float *siga, float *sigb, float *chi2, float *q); +void fitexy(float x[], float y[], int ndat, float sigx[], float sigy[], + float *a, float *b, float *siga, float *sigb, float *chi2, float *q); +void fixrts(float d[], int m); +void fleg(float x, float pl[], int nl); +void flmoon(int n, int nph, long *jd, float *frac); +float fmin(float x[]); +void four1(float data[], unsigned long nn, int isign); +void fourew(FILE *file[5], int *na, int *nb, int *nc, int *nd); +void fourfs(FILE *file[5], unsigned long nn[], int ndim, int isign); +void fourn(float data[], unsigned long nn[], int ndim, int isign); +void fpoly(float x, float p[], int np); +void fred2(int n, float a, float b, float t[], float f[], float w[], + float (*g)(float), float (*ak)(float, float)); +float fredin(float x, int n, float a, float b, float t[], float f[], float w[], + float (*g)(float), float (*ak)(float, float)); +void frenel(float x, float *s, float *c); +void frprmn(float p[], int n, float ftol, int *iter, float *fret, + float (*func)(float []), void (*dfunc)(float [], float [])); +void frprmn_arg(float p[], int n, float ftol, int *iter, float *fret, + float (*func)(float [], void*), void (*dfunc)(float [], float [], void*), void* arg); +void ftest(float data1[], unsigned long n1, float data2[], unsigned long n2, + float *f, float *prob); +float gamdev(int ia, long *idum); +float gammln(float xx); +float gammp(float a, float x); +float gammq(float a, float x); +float gasdev(long *idum); +void gaucof(int n, float a[], float b[], float amu0, float x[], float w[]); +void gauher(float x[], float w[], int n); +void gaujac(float x[], float w[], int n, float alf, float bet); +void gaulag(float x[], float w[], int n, float alf); +void gauleg(float x1, float x2, float x[], float w[], int n); +void gaussj(float **a, int n, float **b, int m); +void gcf(float *gammcf, float a, float x, float *gln); +float golden(float ax, float bx, float cx, float (*f)(float), float tol, + float *xmin); +void gser(float *gamser, float a, float x, float *gln); +void hpsel(unsigned long m, unsigned long n, float arr[], float heap[]); +void hpsort(unsigned long n, float ra[]); +void hqr(float **a, int n, float wr[], float wi[]); +void hufapp(unsigned long index[], unsigned long nprob[], unsigned long n, + unsigned long i); +void hufdec(unsigned long *ich, unsigned char *code, unsigned long lcode, + unsigned long *nb, huffcode *hcode); +void hufenc(unsigned long ich, unsigned char **codep, unsigned long *lcode, + unsigned long *nb, huffcode *hcode); +void hufmak(unsigned long nfreq[], unsigned long nchin, unsigned long *ilong, + unsigned long *nlong, huffcode *hcode); +void hunt(float xx[], unsigned long n, float x, unsigned long *jlo); +void hypdrv(float s, float yy[], float dyyds[]); +fcomplex hypgeo(fcomplex a, fcomplex b, fcomplex c, fcomplex z); +void hypser(fcomplex a, fcomplex b, fcomplex c, fcomplex z, + fcomplex *series, fcomplex *deriv); +unsigned short icrc(unsigned short crc, unsigned char *bufptr, + unsigned long len, short jinit, int jrev); +unsigned short icrc1(unsigned short crc, unsigned char onech); +unsigned long igray(unsigned long n, int is); +void iindexx(unsigned long n, long arr[], unsigned long indx[]); +void indexx(unsigned long n, float arr[], unsigned long indx[]); +void interp(double **uf, double **uc, int nf); +int irbit1(unsigned long *iseed); +int irbit2(unsigned long *iseed); +void jacobi(float **a, int n, float d[], float **v, int *nrot); +void jacobn(float x, float y[], float dfdx[], float **dfdy, int n); +long julday(int mm, int id, int iyyy); +void kendl1(float data1[], float data2[], unsigned long n, float *tau, float *z, + float *prob); +void kendl2(float **tab, int i, int j, float *tau, float *z, float *prob); +void kermom(double w[], double y, int m); +void ks2d1s(float x1[], float y1[], unsigned long n1, + void (*quadvl)(float, float, float *, float *, float *, float *), + float *d1, float *prob); +void ks2d2s(float x1[], float y1[], unsigned long n1, float x2[], float y2[], + unsigned long n2, float *d, float *prob); +void ksone(float data[], unsigned long n, float (*func)(float), float *d, + float *prob); +void kstwo(float data1[], unsigned long n1, float data2[], unsigned long n2, + float *d, float *prob); +void laguer(fcomplex a[], int m, fcomplex *x, int *its); +void lfit(float x[], float y[], float sig[], int ndat, float a[], int ia[], + int ma, float **covar, float *chisq, void (*funcs)(float, float [], int)); +void linbcg(unsigned long n, double b[], double x[], int itol, double tol, + int itmax, int *iter, double *err); +void linmin(float p[], float xi[], int n, float *fret, + float (*func)(float [])); +void linmin_arg(float p[], float xi[], int n, float *fret, + float (*func)(float [], void*), void *arg); +void lnsrch(int n, float xold[], float fold, float g[], float p[], float x[], + float *f, float stpmax, int *check, float (*func)(float [])); +void load(float x1, float v[], float y[]); +void load1(float x1, float v1[], float y[]); +void load2(float x2, float v2[], float y[]); +void locate(float xx[], unsigned long n, float x, unsigned long *j); +void lop(double **out, double **u, int n); +void lubksb(float **a, int n, int *indx, float b[]); +void ludcmp(float **a, int n, int *indx, float *d); +void machar(int *ibeta, int *it, int *irnd, int *ngrd, + int *machep, int *negep, int *iexp, int *minexp, int *maxexp, + float *eps, float *epsneg, float *xmin, float *xmax); +void matadd(double **a, double **b, double **c, int n); +void matsub(double **a, double **b, double **c, int n); +void medfit(float x[], float y[], int ndata, float *a, float *b, float *abdev); +void memcof(float data[], int n, int m, float *xms, float d[]); +int metrop(float de, float t); +void mgfas(double **u, int n, int maxcyc); +void mglin(double **u, int n, int ncycle); +float midexp(float (*funk)(float), float aa, float bb, int n); +float midinf(float (*funk)(float), float aa, float bb, int n); +float midpnt(float (*func)(float), float a, float b, int n); +float midsql(float (*funk)(float), float aa, float bb, int n); +float midsqu(float (*funk)(float), float aa, float bb, int n); +void miser(float (*func)(float []), float regn[], int ndim, unsigned long npts, + float dith, float *ave, float *var); +void mmid(float y[], float dydx[], int nvar, float xs, float htot, + int nstep, float yout[], void (*derivs)(float, float[], float[])); +void mnbrak(float *ax, float *bx, float *cx, float *fa, float *fb, + float *fc, float (*func)(float)); +void mnbrak_arg(float *ax, float *bx, float *cx, float *fa, float *fb, + float *fc, float (*func)(float, void*), void *arg); +void mnewt(int ntrial, float x[], int n, float tolx, float tolf); +void moment(float data[], int n, float *ave, float *adev, float *sdev, + float *var, float *skew, float *curt); +void mp2dfr(unsigned char a[], unsigned char s[], int n, int *m); +void mpadd(unsigned char w[], unsigned char u[], unsigned char v[], int n); +void mpdiv(unsigned char q[], unsigned char r[], unsigned char u[], + unsigned char v[], int n, int m); +void mpinv(unsigned char u[], unsigned char v[], int n, int m); +void mplsh(unsigned char u[], int n); +void mpmov(unsigned char u[], unsigned char v[], int n); +void mpmul(unsigned char w[], unsigned char u[], unsigned char v[], int n, + int m); +void mpneg(unsigned char u[], int n); +void mppi(int n); +void mprove(float **a, float **alud, int n, int indx[], float b[], + float x[]); +void mpsad(unsigned char w[], unsigned char u[], int n, int iv); +void mpsdv(unsigned char w[], unsigned char u[], int n, int iv, int *ir); +void mpsmu(unsigned char w[], unsigned char u[], int n, int iv); +void mpsqrt(unsigned char w[], unsigned char u[], unsigned char v[], int n, + int m); +void mpsub(int *is, unsigned char w[], unsigned char u[], unsigned char v[], + int n); +void mrqcof(float x[], float y[], float sig[], int ndata, float a[], + int ia[], int ma, float **alpha, float beta[], float *chisq, + void (*funcs)(float, float [], float *, float [], int)); +void mrqmin(float x[], float y[], float sig[], int ndata, float a[], + int ia[], int ma, float **covar, float **alpha, float *chisq, + void (*funcs)(float, float [], float *, float [], int), float *alamda); +void newt(float x[], int n, int *check, + void (*vecfunc)(int, float [], float [])); +void odeint(float ystart[], int nvar, float x1, float x2, + float eps, float h1, float hmin, int *nok, int *nbad, + void (*derivs)(float, float [], float []), + void (*rkqs)(float [], float [], int, float *, float, float, + float [], float *, float *, void (*)(float, float [], float []))); +void orthog(int n, float anu[], float alpha[], float beta[], float a[], + float b[]); +void pade(double cof[], int n, float *resid); +void pccheb(float d[], float c[], int n); +void pcshft(float a, float b, float d[], int n); +void pearsn(float x[], float y[], unsigned long n, float *r, float *prob, + float *z); +void period(float x[], float y[], int n, float ofac, float hifac, + float px[], float py[], int np, int *nout, int *jmax, float *prob); +void piksr2(int n, float arr[], float brr[]); +void piksrt(int n, float arr[]); +void pinvs(int ie1, int ie2, int je1, int jsf, int jc1, int k, + float ***c, float **s); +float plgndr(int l, int m, float x); +float poidev(float xm, long *idum); +void polcoe(float x[], float y[], int n, float cof[]); +void polcof(float xa[], float ya[], int n, float cof[]); +void poldiv(float u[], int n, float v[], int nv, float q[], float r[]); +void polin2(float x1a[], float x2a[], float **ya, int m, int n, + float x1, float x2, float *y, float *dy); +void polint(float xa[], float ya[], int n, float x, float *y, float *dy); +void powell(float p[], float **xi, int n, float ftol, int *iter, float *fret, + float (*func)(float [])); +void predic(float data[], int ndata, float d[], int m, float future[], int nfut); +float probks(float alam); +void psdes(unsigned long *lword, unsigned long *irword); +void pwt(float a[], unsigned long n, int isign); +void pwtset(int n); +float pythag(float a, float b); +void pzextr(int iest, float xest, float yest[], float yz[], float dy[], + int nv); +float qgaus(float (*func)(float), float a, float b); +void qrdcmp(float **a, int n, float *c, float *d, int *sing); +float qromb(float (*func)(float), float a, float b); +float qromo(float (*func)(float), float a, float b, + float (*choose)(float (*)(float), float, float, int)); +void qroot(float p[], int n, float *b, float *c, float eps); +void qrsolv(float **a, int n, float c[], float d[], float b[]); +void qrupdt(float **r, float **qt, int n, float u[], float v[]); +float qsimp(float (*func)(float), float a, float b); +float qtrap(float (*func)(float), float a, float b); +float quad3d(float (*func)(float, float, float), float x1, float x2); +void quadct(float x, float y, float xx[], float yy[], unsigned long nn, + float *fa, float *fb, float *fc, float *fd); +void quadmx(float **a, int n); +void quadvl(float x, float y, float *fa, float *fb, float *fc, float *fd); +float ran0(long *idum); +float ran1(long *idum); +float ran2(long *idum); +float ran3(long *idum); +float ran4(long *idum); +void rank(unsigned long n, unsigned long indx[], unsigned long irank[]); +void ranpt(float pt[], float regn[], int n); +void ratint(float xa[], float ya[], int n, float x, float *y, float *dy); +void ratlsq(double (*fn)(double), double a, double b, int mm, int kk, + double cof[], double *dev); +double ratval(double x, double cof[], int mm, int kk); +float rc(float x, float y); +float rd(float x, float y, float z); +void realft(float data[], unsigned long n, int isign); +void rebin(float rc, int nd, float r[], float xin[], float xi[]); +void red(int iz1, int iz2, int jz1, int jz2, int jm1, int jm2, int jmf, + int ic1, int jc1, int jcf, int kc, float ***c, float **s); +void relax(double **u, double **rhs, int n); +void relax2(double **u, double **rhs, int n); +void resid(double **res, double **u, double **rhs, int n); +float revcst(float x[], float y[], int iorder[], int ncity, int n[]); +void reverse(int iorder[], int ncity, int n[]); +float rf(float x, float y, float z); +float rj(float x, float y, float z, float p); +void rk4(float y[], float dydx[], int n, float x, float h, float yout[], + void (*derivs)(float, float [], float [])); +void rkck(float y[], float dydx[], int n, float x, float h, + float yout[], float yerr[], void (*derivs)(float, float [], float [])); +void rkdumb(float vstart[], int nvar, float x1, float x2, int nstep, + void (*derivs)(float, float [], float [])); +void rkqs(float y[], float dydx[], int n, float *x, + float htry, float eps, float yscal[], float *hdid, float *hnext, + void (*derivs)(float, float [], float [])); +void rlft3(float ***data, float **speq, unsigned long nn1, + unsigned long nn2, unsigned long nn3, int isign); +float rofunc(float b); +void rotate(float **r, float **qt, int n, int i, float a, float b); +void rsolv(float **a, int n, float d[], float b[]); +void rstrct(double **uc, double **uf, int nc); +float rtbis(float (*func)(float), float x1, float x2, float xacc); +float rtflsp(float (*func)(float), float x1, float x2, float xacc); +float rtnewt(void (*funcd)(float, float *, float *), float x1, float x2, + float xacc); +float rtsafe(void (*funcd)(float, float *, float *), float x1, float x2, + float xacc); +float rtsec(float (*func)(float), float x1, float x2, float xacc); +void rzextr(int iest, float xest, float yest[], float yz[], float dy[], int nv); +void savgol(float c[], int np, int nl, int nr, int ld, int m); +void score(float xf, float y[], float f[]); +void scrsho(float (*fx)(float)); +float select(unsigned long k, unsigned long n, float arr[]); +float selip(unsigned long k, unsigned long n, float arr[]); +void shell(unsigned long n, float a[]); +void shoot(int n, float v[], float f[]); +void shootf(int n, float v[], float f[]); +void simp1(float **a, int mm, int ll[], int nll, int iabf, int *kp, + float *bmax); +void simp2(float **a, int n, int l2[], int nl2, int *ip, int kp, float *q1); +void simp3(float **a, int i1, int k1, int ip, int kp); +void simplx(float **a, int m, int n, int m1, int m2, int m3, int *icase, + int izrov[], int iposv[]); +void simpr(float y[], float dydx[], float dfdx[], float **dfdy, + int n, float xs, float htot, int nstep, float yout[], + void (*derivs)(float, float [], float [])); +void sinft(float y[], int n); +void slvsm2(double **u, double **rhs); +void slvsml(double **u, double **rhs); +void sncndn(float uu, float emmc, float *sn, float *cn, float *dn); +double snrm(unsigned long n, double sx[], int itol); +void sobseq(int *n, float x[]); +void solvde(int itmax, float conv, float slowc, float scalv[], + int indexv[], int ne, int nb, int m, float **y, float ***c, float **s); +void sor(double **a, double **b, double **c, double **d, double **e, + double **f, double **u, int jmax, double rjac); +void sort(unsigned long n, float arr[]); +void sort2(unsigned long n, float arr[], float brr[]); +void sort3(unsigned long n, float ra[], float rb[], float rc[]); +void spctrm(FILE *fp, float p[], int m, int k, int ovrlap); +void spear(float data1[], float data2[], unsigned long n, float *d, float *zd, + float *probd, float *rs, float *probrs); +void sphbes(int n, float x, float *sj, float *sy, float *sjp, float *syp); +void splie2(float x1a[], float x2a[], float **ya, int m, int n, float **y2a); +void splin2(float x1a[], float x2a[], float **ya, float **y2a, int m, int n, + float x1, float x2, float *y); +void spline(float x[], float y[], int n, float yp1, float ypn, float y2[]); +void splint(float xa[], float ya[], float y2a[], int n, float x, float *y); +void spread(float y, float yy[], unsigned long n, float x, int m); +void sprsax(float sa[], unsigned long ija[], float x[], float b[], + unsigned long n); +void sprsin(float **a, int n, float thresh, unsigned long nmax, float sa[], + unsigned long ija[]); +void sprspm(float sa[], unsigned long ija[], float sb[], unsigned long ijb[], + float sc[], unsigned long ijc[]); +void sprstm(float sa[], unsigned long ija[], float sb[], unsigned long ijb[], + float thresh, unsigned long nmax, float sc[], unsigned long ijc[]); +void sprstp(float sa[], unsigned long ija[], float sb[], unsigned long ijb[]); +void sprstx(float sa[], unsigned long ija[], float x[], float b[], + unsigned long n); +void stifbs(float y[], float dydx[], int nv, float *xx, + float htry, float eps, float yscal[], float *hdid, float *hnext, + void (*derivs)(float, float [], float [])); +void stiff(float y[], float dydx[], int n, float *x, + float htry, float eps, float yscal[], float *hdid, float *hnext, + void (*derivs)(float, float [], float [])); +void stoerm(float y[], float d2y[], int nv, float xs, + float htot, int nstep, float yout[], + void (*derivs)(float, float [], float [])); +void svbksb(float **u, float w[], float **v, int m, int n, float b[], + float x[]); +void svdcmp(float **a, int m, int n, float w[], float **v); +void svdfit(float x[], float y[], float sig[], int ndata, float a[], + int ma, float **u, float **v, float w[], float *chisq, + void (*funcs)(float, float [], int)); +void svdvar(float **v, int ma, float w[], float **cvm); +void toeplz(float r[], float x[], float y[], int n); +void tptest(float data1[], float data2[], unsigned long n, float *t, float *prob); +void tqli(float d[], float e[], int n, float **z); +float trapzd(float (*func)(float), float a, float b, int n); +void tred2(float **a, int n, float d[], float e[]); +void tridag(float a[], float b[], float c[], float r[], float u[], + unsigned long n); +float trncst(float x[], float y[], int iorder[], int ncity, int n[]); +void trnspt(int iorder[], int ncity, int n[]); +void ttest(float data1[], unsigned long n1, float data2[], unsigned long n2, + float *t, float *prob); +void tutest(float data1[], unsigned long n1, float data2[], unsigned long n2, + float *t, float *prob); +void twofft(float data1[], float data2[], float fft1[], float fft2[], + unsigned long n); +void vander(double x[], double w[], double q[], int n); +void vegas(float regn[], int ndim, float (*fxn)(float [], float), int init, + unsigned long ncall, int itmx, int nprn, float *tgral, float *sd, + float *chi2a); +void voltra(int n, int m, float t0, float h, float *t, float **f, + float (*g)(int, float), float (*ak)(int, int, float, float)); +void wt1(float a[], unsigned long n, int isign, + void (*wtstep)(float [], unsigned long, int)); +void wtn(float a[], unsigned long nn[], int ndim, int isign, + void (*wtstep)(float [], unsigned long, int)); +void wwghts(float wghts[], int n, float h, + void (*kermom)(double [], double ,int)); +int zbrac(float (*func)(float), float *x1, float *x2); +void zbrak(float (*fx)(float), float x1, float x2, int n, float xb1[], + float xb2[], int *nb); +float zbrent(float (*func)(float), float x1, float x2, float tol); +void zrhqr(float a[], int m, float rtr[], float rti[]); +float zriddr(float (*func)(float), float x1, float x2, float xacc); +void zroots(fcomplex a[], int m, fcomplex roots[], int polish); + +#endif /* _NR_H_ */
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@quickscore_inf_engine/private/nrutil.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,321 @@ +/* CAUTION: This is the ANSI C (only) version of the Numerical Recipes + utility file nrutil.c. Do not confuse this file with the same-named + file nrutil.c that is supplied in the 'misc' subdirectory. + *That* file is the one from the book, and contains both ANSI and + traditional K&R versions, along with #ifdef macros to select the + correct version. *This* file contains only ANSI C. */ + +#include <stdio.h> +#include <stddef.h> +#include <stdlib.h> +#define NR_END 1 +#define FREE_ARG char* + +void nrerror(char error_text[]) +/* Numerical Recipes standard error handler */ +{ + fprintf(stderr,"Numerical Recipes run-time error...\n"); + fprintf(stderr,"%s\n",error_text); + fprintf(stderr,"...now exiting to system...\n"); + exit(1); +} + +float *vector(long nl, long nh) +/* allocate a float vector with subscript range v[nl..nh] */ +{ + float *v; + + v=(float *)malloc((size_t) ((nh-nl+1+NR_END)*sizeof(float))); + if (!v) nrerror("allocation failure in vector()"); + return v-nl+NR_END; +} + +int *ivector(long nl, long nh) +/* allocate an int vector with subscript range v[nl..nh] */ +{ + int *v; + + v=(int *)malloc((size_t) ((nh-nl+1+NR_END)*sizeof(int))); + if (!v) nrerror("allocation failure in ivector()"); + return v-nl+NR_END; +} + +unsigned char *cvector(long nl, long nh) +/* allocate an unsigned char vector with subscript range v[nl..nh] */ +{ + unsigned char *v; + + v=(unsigned char *)malloc((size_t) ((nh-nl+1+NR_END)*sizeof(unsigned char))); + if (!v) nrerror("allocation failure in cvector()"); + return v-nl+NR_END; +} + +unsigned long *lvector(long nl, long nh) +/* allocate an unsigned long vector with subscript range v[nl..nh] */ +{ + unsigned long *v; + + v=(unsigned long *)malloc((size_t) ((nh-nl+1+NR_END)*sizeof(long))); + if (!v) nrerror("allocation failure in lvector()"); + return v-nl+NR_END; +} + +double *dvector(long nl, long nh) +/* allocate a double vector with subscript range v[nl..nh] */ +{ + double *v; + + v=(double *)malloc((size_t) ((nh-nl+1+NR_END)*sizeof(double))); + if (!v) nrerror("allocation failure in dvector()"); + return v-nl+NR_END; +} + +float **matrix(long nrl, long nrh, long ncl, long nch) +/* allocate a float matrix with subscript range m[nrl..nrh][ncl..nch] */ +{ + long i, nrow=nrh-nrl+1,ncol=nch-ncl+1; + float **m; + + /* allocate pointers to rows */ + m=(float **) malloc((size_t)((nrow+NR_END)*sizeof(float*))); + if (!m) nrerror("allocation failure 1 in matrix()"); + m += NR_END; + m -= nrl; + + /* allocate rows and set pointers to them */ + m[nrl]=(float *) malloc((size_t)((nrow*ncol+NR_END)*sizeof(float))); + if (!m[nrl]) nrerror("allocation failure 2 in matrix()"); + m[nrl] += NR_END; + m[nrl] -= ncl; + + for(i=nrl+1;i<=nrh;i++) m[i]=m[i-1]+ncol; + + /* return pointer to array of pointers to rows */ + return m; +} + +double **dmatrix(long nrl, long nrh, long ncl, long nch) +/* allocate a double matrix with subscript range m[nrl..nrh][ncl..nch] */ +{ + long i, nrow=nrh-nrl+1,ncol=nch-ncl+1; + double **m; + + /* allocate pointers to rows */ + m=(double **) malloc((size_t)((nrow+NR_END)*sizeof(double*))); + if (!m) nrerror("allocation failure 1 in matrix()"); + m += NR_END; + m -= nrl; + + /* allocate rows and set pointers to them */ + m[nrl]=(double *) malloc((size_t)((nrow*ncol+NR_END)*sizeof(double))); + if (!m[nrl]) nrerror("allocation failure 2 in matrix()"); + m[nrl] += NR_END; + m[nrl] -= ncl; + + for(i=nrl+1;i<=nrh;i++) m[i]=m[i-1]+ncol; + + /* return pointer to array of pointers to rows */ + return m; +} + +int **imatrix(long nrl, long nrh, long ncl, long nch) +/* allocate a int matrix with subscript range m[nrl..nrh][ncl..nch] */ +{ + long i, nrow=nrh-nrl+1,ncol=nch-ncl+1; + int **m; + + /* allocate pointers to rows */ + m=(int **) malloc((size_t)((nrow+NR_END)*sizeof(int*))); + if (!m) nrerror("allocation failure 1 in matrix()"); + m += NR_END; + m -= nrl; + + + /* allocate rows and set pointers to them */ + m[nrl]=(int *) malloc((size_t)((nrow*ncol+NR_END)*sizeof(int))); + if (!m[nrl]) nrerror("allocation failure 2 in matrix()"); + m[nrl] += NR_END; + m[nrl] -= ncl; + + for(i=nrl+1;i<=nrh;i++) m[i]=m[i-1]+ncol; + + /* return pointer to array of pointers to rows */ + return m; +} + +float **submatrix(float **a, long oldrl, long oldrh, long oldcl, long oldch, + long newrl, long newcl) +/* point a submatrix [newrl..][newcl..] to a[oldrl..oldrh][oldcl..oldch] */ +{ + long i,j,nrow=oldrh-oldrl+1,ncol=oldcl-newcl; + float **m; + + /* allocate array of pointers to rows */ + m=(float **) malloc((size_t) ((nrow+NR_END)*sizeof(float*))); + if (!m) nrerror("allocation failure in submatrix()"); + m += NR_END; + m -= newrl; + + /* set pointers to rows */ + for(i=oldrl,j=newrl;i<=oldrh;i++,j++) m[j]=a[i]+ncol; + + /* return pointer to array of pointers to rows */ + return m; +} + +float **convert_matrix(float *a, long nrl, long nrh, long ncl, long nch) +/* allocate a float matrix m[nrl..nrh][ncl..nch] that points to the matrix +declared in the standard C manner as a[nrow][ncol], where nrow=nrh-nrl+1 +and ncol=nch-ncl+1. The routine should be called with the address +&a[0][0] as the first argument. */ +{ + long i,j,nrow=nrh-nrl+1,ncol=nch-ncl+1; + float **m; + + /* allocate pointers to rows */ + m=(float **) malloc((size_t) ((nrow+NR_END)*sizeof(float*))); + if (!m) nrerror("allocation failure in convert_matrix()"); + m += NR_END; + m -= nrl; + + /* set pointers to rows */ + m[nrl]=a-ncl; + for(i=1,j=nrl+1;i<nrow;i++,j++) m[j]=m[j-1]+ncol; + /* return pointer to array of pointers to rows */ + return m; +} + +double **convert_dmatrix(double *a, long nrl, long nrh, long ncl, long nch) +/* allocate a float matrix m[nrl..nrh][ncl..nch] that points to the matrix +declared in the standard C manner as a[nrow][ncol], where nrow=nrh-nrl+1 +and ncol=nch-ncl+1. The routine should be called with the address +&a[0][0] as the first argument. */ +{ + long i,j,nrow=nrh-nrl+1,ncol=nch-ncl+1; + double **m; + + /* allocate pointers to rows */ + m=(double **) malloc((size_t) ((nrow+NR_END)*sizeof(double*))); + if (!m) nrerror("allocation failure in convert_dmatrix()"); + m += NR_END; + m -= nrl; + + /* set pointers to rows */ + m[nrl]=a-ncl; + for(i=1,j=nrl+1;i<nrow;i++,j++) m[j]=m[j-1]+ncol; + /* return pointer to array of pointers to rows */ + return m; +} + +float ***f3tensor(long nrl, long nrh, long ncl, long nch, long ndl, long ndh) +/* allocate a float 3tensor with range t[nrl..nrh][ncl..nch][ndl..ndh] */ +{ + long i,j,nrow=nrh-nrl+1,ncol=nch-ncl+1,ndep=ndh-ndl+1; + float ***t; + + /* allocate pointers to pointers to rows */ + t=(float ***) malloc((size_t)((nrow+NR_END)*sizeof(float**))); + if (!t) nrerror("allocation failure 1 in f3tensor()"); + t += NR_END; + t -= nrl; + + /* allocate pointers to rows and set pointers to them */ + t[nrl]=(float **) malloc((size_t)((nrow*ncol+NR_END)*sizeof(float*))); + if (!t[nrl]) nrerror("allocation failure 2 in f3tensor()"); + t[nrl] += NR_END; + t[nrl] -= ncl; + + /* allocate rows and set pointers to them */ + t[nrl][ncl]=(float *) malloc((size_t)((nrow*ncol*ndep+NR_END)*sizeof(float))); + if (!t[nrl][ncl]) nrerror("allocation failure 3 in f3tensor()"); + t[nrl][ncl] += NR_END; + t[nrl][ncl] -= ndl; + + for(j=ncl+1;j<=nch;j++) t[nrl][j]=t[nrl][j-1]+ndep; + for(i=nrl+1;i<=nrh;i++) { + t[i]=t[i-1]+ncol; + t[i][ncl]=t[i-1][ncl]+ncol*ndep; + for(j=ncl+1;j<=nch;j++) t[i][j]=t[i][j-1]+ndep; + } + + /* return pointer to array of pointers to rows */ + return t; +} + +void free_vector(float *v, long nl, long nh) +/* free a float vector allocated with vector() */ +{ + free((FREE_ARG) (v+nl-NR_END)); +} + +void free_ivector(int *v, long nl, long nh) +/* free an int vector allocated with ivector() */ +{ + free((FREE_ARG) (v+nl-NR_END)); +} + +void free_cvector(unsigned char *v, long nl, long nh) +/* free an unsigned char vector allocated with cvector() */ +{ + free((FREE_ARG) (v+nl-NR_END)); +} + +void free_lvector(unsigned long *v, long nl, long nh) +/* free an unsigned long vector allocated with lvector() */ +{ + free((FREE_ARG) (v+nl-NR_END)); +} + +void free_dvector(double *v, long nl, long nh) +/* free a double vector allocated with dvector() */ +{ + free((FREE_ARG) (v+nl-NR_END)); +} + +void free_matrix(float **m, long nrl, long nrh, long ncl, long nch) +/* free a float matrix allocated by matrix() */ +{ + free((FREE_ARG) (m[nrl]+ncl-NR_END)); + free((FREE_ARG) (m+nrl-NR_END)); +} + +void free_dmatrix(double **m, long nrl, long nrh, long ncl, long nch) +/* free a double matrix allocated by dmatrix() */ +{ + free((FREE_ARG) (m[nrl]+ncl-NR_END)); + free((FREE_ARG) (m+nrl-NR_END)); +} + +void free_imatrix(int **m, long nrl, long nrh, long ncl, long nch) +/* free an int matrix allocated by imatrix() */ +{ + free((FREE_ARG) (m[nrl]+ncl-NR_END)); + free((FREE_ARG) (m+nrl-NR_END)); +} + +void free_submatrix(float **b, long nrl, long nrh, long ncl, long nch) +/* free a submatrix allocated by submatrix() */ +{ + free((FREE_ARG) (b+nrl-NR_END)); +} + +void free_convert_matrix(float **b, long nrl, long nrh, long ncl, long nch) +/* free a matrix allocated by convert_matrix() */ +{ + free((FREE_ARG) (b+nrl-NR_END)); +} + +void free_convert_dmatrix(double **b, long nrl, long nrh, long ncl, long nch) +/* free a matrix allocated by convert_matrix() */ +{ + free((FREE_ARG) (b+nrl-NR_END)); +} + +void free_f3tensor(float ***t, long nrl, long nrh, long ncl, long nch, + long ndl, long ndh) +/* free a float f3tensor allocated by f3tensor() */ +{ + free((FREE_ARG) (t[nrl][ncl]+ndl-NR_END)); + free((FREE_ARG) (t[nrl]+ncl-NR_END)); + free((FREE_ARG) (t+nrl-NR_END)); +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@quickscore_inf_engine/private/nrutil.h Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,79 @@ +/* CAUTION: This is the ANSI C (only) version of the Numerical Recipes + utility file nrutil.h. Do not confuse this file with the same-named + file nrutil.h that is supplied in the 'misc' subdirectory. + *That* file is the one from the book, and contains both ANSI and + traditional K&R versions, along with #ifdef macros to select the + correct version. *This* file contains only ANSI C. */ + +#ifndef _NR_UTILS_H_ +#define _NR_UTILS_H_ + +static float sqrarg; +#define SQR(a) ((sqrarg=(a)) == 0.0 ? 0.0 : sqrarg*sqrarg) + +static double dsqrarg; +#define DSQR(a) ((dsqrarg=(a)) == 0.0 ? 0.0 : dsqrarg*dsqrarg) + +static double dmaxarg1,dmaxarg2; +#define DMAX(a,b) (dmaxarg1=(a),dmaxarg2=(b),(dmaxarg1) > (dmaxarg2) ?\ + (dmaxarg1) : (dmaxarg2)) + +static double dminarg1,dminarg2; +#define DMIN(a,b) (dminarg1=(a),dminarg2=(b),(dminarg1) < (dminarg2) ?\ + (dminarg1) : (dminarg2)) + +static float maxarg1,maxarg2; +#define FMAX(a,b) (maxarg1=(a),maxarg2=(b),(maxarg1) > (maxarg2) ?\ + (maxarg1) : (maxarg2)) + +static float minarg1,minarg2; +#define FMIN(a,b) (minarg1=(a),minarg2=(b),(minarg1) < (minarg2) ?\ + (minarg1) : (minarg2)) + +static long lmaxarg1,lmaxarg2; +#define LMAX(a,b) (lmaxarg1=(a),lmaxarg2=(b),(lmaxarg1) > (lmaxarg2) ?\ + (lmaxarg1) : (lmaxarg2)) + +static long lminarg1,lminarg2; +#define LMIN(a,b) (lminarg1=(a),lminarg2=(b),(lminarg1) < (lminarg2) ?\ + (lminarg1) : (lminarg2)) + +static int imaxarg1,imaxarg2; +#define IMAX(a,b) (imaxarg1=(a),imaxarg2=(b),(imaxarg1) > (imaxarg2) ?\ + (imaxarg1) : (imaxarg2)) + +static int iminarg1,iminarg2; +#define IMIN(a,b) (iminarg1=(a),iminarg2=(b),(iminarg1) < (iminarg2) ?\ + (iminarg1) : (iminarg2)) + +#define SIGN(a,b) ((b) >= 0.0 ? fabs(a) : -fabs(a)) + +void nrerror(char error_text[]); +float *vector(long nl, long nh); +int *ivector(long nl, long nh); +unsigned char *cvector(long nl, long nh); +unsigned long *lvector(long nl, long nh); +double *dvector(long nl, long nh); +float **matrix(long nrl, long nrh, long ncl, long nch); +double **dmatrix(long nrl, long nrh, long ncl, long nch); +int **imatrix(long nrl, long nrh, long ncl, long nch); +float **submatrix(float **a, long oldrl, long oldrh, long oldcl, long oldch, + long newrl, long newcl); +float **convert_matrix(float *a, long nrl, long nrh, long ncl, long nch); +double **convert_dmatrix(double *a, long nrl, long nrh, long ncl, long nch); +float ***f3tensor(long nrl, long nrh, long ncl, long nch, long ndl, long ndh); +void free_vector(float *v, long nl, long nh); +void free_ivector(int *v, long nl, long nh); +void free_cvector(unsigned char *v, long nl, long nh); +void free_lvector(unsigned long *v, long nl, long nh); +void free_dvector(double *v, long nl, long nh); +void free_matrix(float **m, long nrl, long nrh, long ncl, long nch); +void free_dmatrix(double **m, long nrl, long nrh, long ncl, long nch); +void free_imatrix(int **m, long nrl, long nrh, long ncl, long nch); +void free_submatrix(float **b, long nrl, long nrh, long ncl, long nch); +void free_convert_matrix(float **b, long nrl, long nrh, long ncl, long nch); +void free_convert_dmatrix(double **b, long nrl, long nrh, long ncl, long nch); +void free_f3tensor(float ***t, long nrl, long nrh, long ncl, long nch, + long ndl, long ndh); + +#endif /* _NR_UTILS_H_ */
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@quickscore_inf_engine/private/quickscore.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,76 @@ +function prob = quickscore(fpos, fneg, inhibit, prior, leak) +% QUICKSCORE Heckerman's algorithm for BN2O networks. +% prob = quickscore(fpos, fneg, inhibit, prior, leak) +% +% Consider a BN2O (Binary Node 2-layer Noisy-or) network such as QMR with +% dieases on the top and findings on the bottom. (We assume all findings are observed, +% since hidden leaves can be marginalized away.) +% This algorithm takes O(2^|fpos|) time to compute the marginal on all the diseases. +% +% Inputs: +% fpos = the positive findings (a vector of numbers in {1, ..., Nfindings}) +% fneg = the negative findings (a vector of numbers in {1, ..., Nfindings}) +% inhibit(i,j) = inhibition prob. for finding i, disease j, or 1.0 if j is not a parent. +% prior(j) = prior prob. disease j is ON. We assume prior(off) = 1-prior(on). +% leak(i) = inhibition prob. for the leak node for finding i +% +% Output: +% prob(d) = Pr(disease d = on | ev) +% +% For details, see +% - Heckerman, "A tractable inference algorithm for diagnosing multiple diseases", UAI89. +% - Rish and Dechter, "On the impact of causal independence", UCI tech report, 1998. +% +% Note that this algorithm is numerically unstable, since it adds a large number of positive and +% negative terms and hopes that some of them exactly cancel. +% +% For matlab experts, use 'mex' to compile C_quickscore, which has identical behavior to this function. + +[nfindings ndiseases] = size(inhibit); + +% make the first disease be always on, for the leak term +Pon = [1 prior(:)']; +Poff = 1-Pon; +Uon = [leak(:) inhibit]; % U(f,d) = Pr(f=0|d=1) +Uoff = [leak(:) ones(nfindings, ndiseases)]; % Uoff(f,d) = Pr(f=0|d=0) +ndiseases = ndiseases + 1; + +npos = length(fpos); +post = zeros(ndiseases, 2); +% post(d,1) = alpha Pr(d=off), post(d,2) = alpha Pr(d=m) + +FP = length(fpos); +%allbits = logical(dec2bitv(0:(2^FP - 1), FP)); +allbits = logical(ind2subv(2*ones(1,FP), 1:(2^FP))-1); + +for si=1:2^FP + bits = allbits(si,:); + fprime = fpos(bits); + fmask = zeros(1, nfindings); + fmask(fneg)=1; + fmask(fprime)=1; + fmask = logical(fmask); + p = 1; + pterm = zeros(1, ndiseases); + ptermOff = zeros(1, ndiseases); + ptermOn = zeros(1, ndiseases); + for d=1:ndiseases + ptermOff(d) = prod(Uoff(fmask,d)); + ptermOn(d) = prod(Uon(fmask,d)); + pterm(d) = Poff(d)*ptermOff(d) + Pon(d)*ptermOn(d); + end + p = prod(pterm); + sign = (-1)^(length(fprime)); + for d=1:ndiseases + myp = p / pterm(d); + post(d,1) = post(d,1) + sign*(myp * ptermOff(d)); + post(d,2) = post(d,2) + sign*(myp * ptermOn(d)); + end +end + +post(:,1) = post(:,1) .* Poff(:); +post(:,2) = post(:,2) .* Pon(:); +post = mk_stochastic(post); +prob = post(2:end,2)'; % skip the leak term + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@quickscore_inf_engine/quickscore_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,38 @@ +function engine = quickscore_inf_engine(inhibit, leak, prior) +% QUICKSCORE_INF_ENGINE Exact inference for the QMR network +% engine = quickscore_inf_engine(inhibit, leak, prior) +% +% We create an inference engine for QMR-like networks. +% QMR is a bipartite graph, where the top layer contains hidden disease nodes, +% and the bottom later contains observed finding nodes. +% The diseases have Bernoulli CPDs, the findings noisy-or CPDs. +% The original QMR (Quick Medical Reference) network has specific parameter values which we are not +% allowed to release, for commercial reasons. +% +% inhibit(f,d) = inhibition probability on f->d arc for disease d, finding f +% If inhibit(f,d) = 1, there is effectively no arc from d->f +% leak(j) = inhibition prob. on leak node -> finding j arc +% prior(i) = prob. disease i is on +% +% We use exact inference, which takes O(2^P) time, where P is the number of positive findings. +% For details, see +% - Heckerman, "A tractable inference algorithm for diagnosing multiple diseases", UAI 89. +% - Rish and Dechter, "On the impact of causal independence", UCI tech report, 1998. +% Note that this algorithm is numerically unstable, since it adds a large number of positive and +% negative terms and hopes that some of them exactly cancel. +% +% For an interesting variational approximation, see +% - Jaakkola and Jordan, "Variational probabilistic inference and the QMR-DT network", JAIR 10, 1999. +% +% See also +% - "Loopy belief propagation for approximate inference: an empirical study", +% K. Murphy, Y. Weiss and M. Jordan, UAI 99. + +engine.inhibit = inhibit; +engine.leak = leak; +engine.prior = prior; + +% store results here between enter_evidence and marginal_nodes +engine.post = []; + +engine = class(engine, 'quickscore_inf_engine'); % not a child of the inf_engine class!
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@stab_cond_gauss_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +/README/1.1.1.1/Sun May 11 15:39:50 2003// +/clq_containing_nodes.m/1.1.1.1/Wed May 29 11:59:46 2002// +/enter_evidence.m/1.1.1.1/Wed Mar 12 10:38:00 2003// +/marginal_difclq_nodes.m/1.1.1.1/Fri Feb 21 11:20:32 2003// +/marginal_nodes.m/1.1.1.1/Fri Feb 21 11:13:10 2003// +/marginal_singleclq_nodes.m/1.1.1.1/Wed Jan 29 11:23:58 2003// +/problems.txt/1.1.1.1/Wed May 29 11:59:46 2002// +/push.m/1.1.1.1/Mon Feb 10 15:38:04 2003// +/push_pot_toclique.m/1.1.1.1/Wed May 29 11:59:46 2002// +/stab_cond_gauss_inf_engine.m/1.1.1.1/Fri Mar 28 17:12:42 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@stab_cond_gauss_inf_engine/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@stab_cond_gauss_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@stab_cond_gauss_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@stab_cond_gauss_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@stab_cond_gauss_inf_engine/Old/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +/initialize_engine.m/1.1.1.1/Wed May 29 11:59:46 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@stab_cond_gauss_inf_engine/Old/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@stab_cond_gauss_inf_engine/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@stab_cond_gauss_inf_engine/Old/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@stab_cond_gauss_inf_engine/Old/initialize_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,65 @@ +function [engine, loglik] = initialize_engine(engine) +%initialize +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes(:); +N = length(bnet.dag); + +pot_type = 'scg' +check_for_cd_arcs([], bnet.cnodes, bnet.dag); + +% Evaluate CPDs with evidence, and convert to potentials +pot = cell(1, N); +C = length(engine.cliques); +inited = zeros(1, C); +clpot = cell(1, C); +evidence = cell(1, N); +for n=1:N + fam = family(bnet.dag, n); + e = bnet.equiv_class(n); + pot{n} = CPD_to_scgpot(bnet.CPD{e}, fam, ns, bnet.cnodes, evidence); + cindex = engine.clq_ass_to_node(n); + if inited(cindex) + %clpot{cindex} = direct_combine_pots(clpot{cindex}, pot{n}); + clpot{cindex} = direct_combine_pots(pot{n}, clpot{cindex}); + else + clpot{cindex} = pot{n}; + inited(cindex) = 1; + end +end + +for i=1:C + if inited(i) == 0 + clpot{i} = scgpot([], [], [], []); + end +end + +seppot = cell(C, C); +% separators are is not need to initialize + +% collect to root (node to parents) +for n=engine.postorder(1:end-1) + for p=parents(engine.jtree, n) + [margpot, comppot] = complement_pot(clpot{n}, engine.separator{p,n}); + margpot = marginalize_pot(clpot{n}, engine.separator{p,n}); + clpot{n} = comppot; + %seppot{p, n} = margpot; + clpot{p} = combine_pots(clpot{p}, margpot); + %clpot{p} = combine_pots(margpot, clpot{p}); + end +end + +temppot = clpot; +%temppot = clpot{engine.root}; +for n=engine.preorder + for c=children(engine.jtree, n) + seppot{n,c} = marginalize_pot(temppot{n}, engine.separator{n,c}); + %seppot{n,c} = marginalize_pot(clpot{n}, engine.separator{n,c}); + %clpot{c} = direct_combine_pots(clpot{c}, seppot{n,c}); + temppot{c} = direct_combine_pots(temppot{c}, seppot{n,c}); + end +end + +engine.clpot = clpot; +engine.seppot = seppot; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@stab_cond_gauss_inf_engine/README Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +% Stable conditional Gaussian inference +% Originally written by Huang, Shan <shan.huang@intel.com> 2001 +% Fixed by Rainer Deventer 2003 + + +@techreport{Lauritzen99, + author = "S. Lauritzen and F. Jensen", + title = "Stable Local Computation with Conditional {G}aussian Distributions", + year = 1999, + number = "R-99-2014", + institution = "Dept. Math. Sciences, Aalborg Univ." +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@stab_cond_gauss_inf_engine/clq_containing_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,24 @@ +function c = clq_containing_nodes(engine, nodes, fam) +% CLQ_CONTAINING_NODES Find the lightest clique (if any) that contains the set of nodes +% c = clq_containing_nodes(engine, nodes, family) +% +% If the optional 'family' argument is specified, it means nodes = family(nodes(end)). +% (This is useful since clq_ass_to_node is not accessible to outsiders.) +% Returns c=-1 if there is no such clique. + +if nargin < 3, fam = 0; else fam = 1; end + +if length(nodes)==1 + c = engine.clq_ass_to_node(nodes(1)); +elseif fam + c = engine.clq_ass_to_node(nodes(end)); +else + B = engine.cliques_bitv; + w = engine.clique_weight; + clqs = find(all(B(:,nodes), 2)); % all selected columns must be 1 + if isempty(clqs) + c = -1; + else + c = clqs(argmin(w(clqs))); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@stab_cond_gauss_inf_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,260 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE enter evidence to engine including discrete and continuous evidence +% [engine, ll] = enter_evidence(engine, evidence) +% +% ll is always 0, which is wrong. + +if ~isempty(engine.evidence) + bnet = bnet_from_engine(engine); + engine = stab_cond_gauss_inf_engine(bnet); + engine.evidence = evidence; +else + engine.evidence = evidence; + bnet = bnet_from_engine(engine); +end + +engine.evidence = evidence; +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes(:); +observed = ~isemptycell(evidence); +onodes = find(observed); +hnodes = find(isemptycell(evidence)); +cobs = myintersect(bnet.cnodes, onodes); +dobs = myintersect(bnet.dnodes, onodes); + +engine = incorporate_dis_evidence(engine, dobs, evidence); +l = length(cobs); +for i = 1:l + node = cobs(i); + engine = incorporate_singleconts_evidence(engine, node, evidence); +end +clpot = engine.clpot; + +clq_num = length(engine.cliques); +for n=engine.postorder(1:end-1) + for p=parents(engine.jtree, n) + [margpot, comppot] = complement_pot(clpot{n}, engine.separator{p,n}); + clpot{n} = comppot; + clpot{p} = combine_pots(clpot{p}, margpot); + end +end + +temppot = clpot; +for n=engine.preorder + for c=children(engine.jtree, n) + seppot{n,c} = marginalize_pot(temppot{n}, engine.separator{n,c}); + temppot{c} = direct_combine_pots(temppot{c}, seppot{n,c}); + end +end +engine.clpot = clpot; +engine.seppot = seppot; + +[pot,loglik]=normalize_pot(clpot{engine.root}); + +%%%%%%%%%%%%%%%%%% +function engine = incorporate_dis_evidence(engine, donodes, evidence) +l = length(donodes); +for i=donodes(:)' + node = i; + clqid = engine.clq_ass_to_node(node); + pot = struct(engine.clpot{clqid}); + ns = zeros(1, max(pot.domain)); + ns(pot.ddom) = pot.dsizes; + ns(pot.cheaddom) = pot.cheadsizes; + ns(pot.ctaildom) = pot.ctailsizes; + ddom = pot.ddom; + + potcarray = cell(1, pot.dsize); + for j =1:pot.dsize + tpotc = struct(pot.scgpotc{j}); + potcarray{j} = scgcpot(tpotc.cheadsize, tpotc.ctailsize, 0, tpotc.A, tpotc.B, tpotc.C); + end + + if length(ns(ddom)) == 1 + matrix = pot.scgpotc; + else + matrix = reshape(pot.scgpotc,ns(ddom)); + potcarray = reshape(potcarray, ns(ddom)); + end + + map = find_equiv_posns(node, ddom); + vals = cat(1, evidence{node}); + index = mk_multi_index(length(ddom), map, vals); + potcarray(index{:}) = matrix(index{:}); + potcarray = potcarray(:); + %keyboard; + engine.clpot{clqid} = scgpot(pot.ddom, pot.cheaddom, pot.ctaildom, ns, potcarray); +end + +%%%%%%%%%%%%%%%%%% +function engine = incorporate_singleconts_evidence(engine, node, evidence) +%incorporate_singleconts_evidence incorporate evidence of 1 continuous node +B = engine.cliques_bitv; +clqs_containnode = find(all(B(:,node), 2)); % all selected columns must be 1 +% Every continuous node necessarily apears as head in exactly one clique, +% which is the clique where it appears closest to the strong root. In all other +% clique potentials where it appears, it must be a tail node. +clq_ev_as_head = []; +for i = clqs_containnode(:)' + pot = struct(engine.clpot{i}); + if myismember(node, pot.cheaddom) + clq_ev_as_head = [clq_ev_as_head i]; + break; + end +end + +% If we will incorporate the evidence node which is head of a potential we must rearrange +% the juntion tree by push operation until the tail of the include potential is empty +if ~isempty(clq_ev_as_head) + assert(1 == length(clq_ev_as_head)); + i = clq_ev_as_head; + pot = struct(engine.clpot{i}); + while ~isempty(pot.ctaildom) + [engine, clqtoroot] = push(engine, i, node); + i = clqtoroot; + pot = struct(engine.clpot{i}); + end + B = engine.cliques_bitv; + clqs_containnode = find(all(B(:,node), 2)); +end + +for i = clqs_containnode(:)' + pot = struct(engine.clpot{i}); + if myismember(node, pot.cheaddom) + engine.clpot{i} = incoporate_evidence_headnode(engine.clpot{i}, node, evidence); + else + %assert(myismember(node, pot.ctaildom)); + engine.clpot{i} = incoporate_evidence_tailnode(engine.clpot{i}, node, evidence); + end +end + +%%%%%%%%%%%%%%%%%% +function newscgpot = incoporate_evidence_tailnode(pot, node, evidence) +%ENTER_EVIDENCE_TAILNODE enter the evidence of 1 tailnode of the scgpot +newscgpot = pot; +pot = struct(pot); +%if isempty(pot.ctaildom) +if ~myismember(node, pot.ctaildom) + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % In this case there is no real dependency of the head nodes % + % on the tail. The potential should be returned unchanged % + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + return; +end +%newscgpot = scgpot([], [], [], []); +assert(myismember(node, pot.ctaildom)); +ni = block(find_equiv_posns(node, pot.ctaildom), pot.ctailsizes); + +ctaildom = mysetdiff(pot.ctaildom, node); +cheaddom = pot.cheaddom; +ddom = pot.ddom; +domain = mysetdiff(pot.domain, node); +dsize = pot.dsize; +ns = zeros(1, max(pot.domain)); +ns(pot.ddom) = pot.dsizes; +ns(pot.cheaddom) = pot.cheadsizes; +ns(pot.ctaildom) = pot.ctailsizes; +cheadsizes = pot.cheadsizes; +cheadsize = pot.cheadsize; +ctailsizes = ns(ctaildom); +ctailsize = sum(ns(ctaildom)); + +potarray = cell(1, dsize); +for i=1:dsize + potc = struct(pot.scgpotc{i}); + B = potc.B; + A = potc.A + B(:, ni)*evidence{node}; + B(:, ni) = []; + potarray{i} = scgcpot(cheadsize, ctailsize, potc.p, A, B, potc.C); +end + +newscgpot = scgpot(ddom, cheaddom, ctaildom, ns, potarray); + +%%%%%%%%%%%%%%%% +function newscgpot = incoporate_evidence_headnode(pot, node, evidence) +%ENTER_EVIDENCE_HEADNODE +pot = struct(pot); +y2 = evidence{node}; +assert(myismember(node, pot.cheaddom)); +assert(isempty(pot.ctaildom)); +ddom = pot.ddom; +cheaddom = mysetdiff(pot.cheaddom, node); +ctaildom = pot.ctaildom; +dsize = pot.dsize; +domain = mysetdiff(pot.domain, node); + +ns = zeros(1, max(pot.domain)); +ns(pot.ddom) = pot.dsizes; +ns(pot.cheaddom) = pot.cheadsizes; +ns(pot.ctaildom) = pot.ctailsizes; +ctailsizes = ns(ctaildom); +ctailsize = sum(ctailsizes); +cheadsizes = ns(cheaddom); +cheadsize = sum(cheadsizes); +onodesize = ns(node); + +p = zeros(1,dsize); +A1 = zeros(cheadsize, dsize); +A2 = zeros(onodesize, dsize); +C11 = zeros(cheadsize, cheadsize, dsize); +C12 = zeros(cheadsize, onodesize, dsize); +C21 = zeros(onodesize, cheadsize, dsize); +C22 = zeros(onodesize, onodesize, dsize); +ZM = zeros(onodesize, onodesize); + +n1i = block(find_equiv_posns(cheaddom, pot.cheaddom), pot.cheadsizes); +n2i = block(find_equiv_posns(node, pot.cheaddom), pot.cheadsizes); + +indic = 0; +for i=1:dsize + potc = struct(pot.scgpotc{i}); + p(i) = potc.p; + if ~isempty(n1i) + A1(:,i) = potc.A(n1i); + end + if ~isempty(n2i) + A2(:,i) = potc.A(n2i); + end + C11(:,:,i) = potc.C(n1i, n1i); + C12(:,:,i) = potc.C(n1i, n2i); + C21(:,:,i) = potc.C(n2i, n1i); + C22(:,:,i) = potc.C(n2i, n2i); + if isequal(0, C22(:,:,i)) & isequal(evidence{node}, A2(:, i)) + indic = i; + end +end + +np = zeros(1,dsize); +nA = zeros(cheadsize, dsize); +nC = zeros(cheadsize, cheadsize, dsize); + +if indic + np(:) = 0; + np(indic) = p(indic); + nA = A1; + nC = C11; +else + for i=1:dsize + if isequal(0, C22(:,:,i)) + p(i) = 0; + nA(:, i) = A1(:, i); + nC(:,:,i) = C11(:,:,i); + else + sq = (y2 - A2(:,i))' * inv(C22(:,:,i)) * (y2 - A2(:,i)); + ex = exp(-0.5*sq); + %np(i) = p(i) * ex / ( (2 * pi)^(-onodesize/2) * sqrt(det(C22(:,:,i))) ); + np(i) = p(i) * ex / ( (2 * pi)^(onodesize/2) * sqrt(det(C22(:,:,i))) ); + nA(:,i) = A1(:,i) + C12(:,:,i) * inv(C22(:,:,i)) * (y2 - A2(:,i)); + tmp1 = C12(:,:,i) * inv(C22(:,:,i)) * C21(:,:,i); + nC(:,:,i) = C11(:,:,i) - tmp1; + end + end +end + +scpot = cell(1, dsize); +W = zeros(cheadsize,ctailsize); +for i=1:dsize + scpot{i} = scgcpot(cheadsize, ctailsize, np(i), nA(:,i), W, nC(:,:,i)); +end +ns(node) = 0; +newscgpot = scgpot(ddom, cheaddom, ctaildom, ns, scpot);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@stab_cond_gauss_inf_engine/marginal_difclq_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,55 @@ +function marginal = marginal_difclq_nodes(engine, query_nodes) +% MARGINAL_DIFCLQ_NODES get the marginal distribution of nodes which is not in a single clique +% marginal = marginal_difclq_nodes(engine, query_nodes) + +keyboard +num_clique = length(engine.cliques); +B = engine.cliques_bitv; +clqs_containnodes = []; +for i=1:length(query_nodes) + node = query_nodes(i); + tnodes = find(all(B(:, node), 2)); + clqs_containnodes = myunion(clqs_containnodes, tnodes); +end +% get all cliques contains query nodes + +% get the minimal sub tree in junction which contains these cliques and the node closest to the root of jtree +[subtree, nroot_node] = min_subtree_conti_nodes(engine.jtree, engine.root, clqs_containnodes); +if ~mysubset(query_nodes, engine.cliques{nroot_node}); + % if query nodes is not all memers of the clique closest to the root clique performe push operation + engine = push_tree(engine, subtree, query_nodes, nroot_node); +end + +if ~(nroot_node == engine.root) + % if the clique closest to the root clique is not the root clique we must direct combine the + % potential with the potential stored in separator toward to root + p = parents(engine.jtree, nroot_node); + tpot = direct_combine_pots(engine.clpot{nroot_node}, engine.seppot{p, nroot_node}); +else + tpot = engine.clpot{nroot_node}; +end + +pot = marginalize_pot(tpot, query_nodes); +marginal = pot_to_marginal(pot); +marginal.T = normalise(marginal.T); + + + +function engine = push_tree(engine, tree, query_nodes, inode) +% PUSH_TREE recursive perform push opeartion on tree +% engine = push_tree(engine, tree, query_nodes, inode) + +cs = children(tree, inode); +for i = 1:length(cs) + node = cs(i); + push_tree(engine, tree, query_nodes, node); + push_dom = myintersect(engine.cliques{node}, query_nodes); + [engine, clqtoroot] = push(engine, node, push_dom); +end + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@stab_cond_gauss_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,77 @@ +function marginal = marginal_nodes(engine, query, add_ev) +% MARGINAL_NODES Compute the marginal on the specified query nodes (stab_cond_gauss) +% marginal = marginal_nodes(engine, query, add_ev) +% +% 'query' must be a singleton set. +% add_ev is an optional argument; if 1, we will "inflate" the marginal of observed nodes +% to their original size, adding 0s to the positions which contradict the evidence + +if nargin < 3, add_ev = 0; end +if isempty(engine.evidence) + hquery = query; +else + hquery = []; + for i = query + if isempty(engine.evidence{i}) + hquery = [hquery i]; + end + end +end + +bnet = bnet_from_engine(engine); + +nclq = length(engine.cliques); +clique = 0; +for i = 1:nclq + if mysubset(hquery, engine.cliques{i}) + pot = struct(engine.clpot{i}); + %if mysubset(hquery, pot.cheaddom) | mysubset(hquery, pot.ddom) + if mysubset(hquery, pot.domain) + clique = i; + break; + end + end +end + +if isempty(hquery) + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % If all requested variables are observed, no query is necessary % + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + marginal.mu = []; + marginal.Sigma = []; + marginal.T = 1.0; + marginal.domain = query; +else + if clique == 0 + marginal = marginal_difclq_nodes(engine, hquery); + else + marginal = marginal_singleclq_nodes(engine, clique, hquery); + end + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % Change the format of output, so that it is identical to the % + % format obtained by the same request for the junction-tree % + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + marginal.domain = query; + bnet = bnet_from_engine(engine); + dquery = myintersect(bnet.dnodes,hquery); + ns = bnet.node_sizes(dquery); + if length(ns) == 0 + marginal.T = 1; + else + if length(ns) == 1 + ns = [1 ns]; + end + marginal.T = reshape(marginal.T,ns); + end +end +if add_ev + bnet = bnet_from_engine(engine); + %marginal = add_ev_to_dmarginal(marginal, engine.evidence, bnet.node_sizes); + marginal = add_evidence_to_gmarginal(marginal, engine.evidence, bnet.node_sizes, bnet.cnodes); +end + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@stab_cond_gauss_inf_engine/marginal_singleclq_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,30 @@ +function marginal = marginal_singleclq_nodes(engine, i, query) +% MARGINAL_SINGLECLQ_NODES get the marginal distribution of nodes which is in a single clique +% marginal = marginal_singleclq_nodes(engine, i, query) + +pot = struct(engine.clpot{i}); +if isempty(pot.ctaildom) + if i ~= engine.root + p = parents(engine.jtree, i); + tpot = direct_combine_pots(engine.clpot{i}, engine.seppot{p, i}); + else + tpot = engine.clpot{i}; + end + pot = marginalize_pot(tpot, query); + + marginal = pot_to_marginal(pot); + marginal.T = normalise(marginal.T); +else + [engine, clqtoroot] = push(engine, i, query); + if clqtoroot == engine.root + tpot = engine.clpot{clqtoroot}; + else + p = parents(engine.jtree, clqtoroot); + tpot = direct_combine_pots(engine.clpot{clqtoroot}, engine.seppot{p, clqtoroot}); + end + pot = marginalize_pot(tpot, query); + + marginal = pot_to_marginal(pot); + marginal.T = normalise(marginal.T); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@stab_cond_gauss_inf_engine/problems.txt Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,76 @@ +PROBLEMS WITH STAB_COND_GAUSS_INF_ENGINE + + +- enter_evidence always returns ll=0 + (I set ll=0 since it is not computed) + +- fails on scg_3node, probably because the engine needs to be +re-initialized every time before enter_evidence is called, not just +when the engine is constructed. + +??? Error using ==> assert +assertion violated: + +K>> dbstack +dbstack +> In /home/eecs/murphyk/matlab/BNT/HMM/assert.m at line 9 + In /home/eecs/murphyk/matlab/BNT/examples/static/SCG/scg_3node.m at line 45 + + + +- crashes on scg3 + +Error in ==> /home/eecs/murphyk/matlab/BNT/inference/static/@stab_cond_gauss_inf_engine/stab_cond_gauss_inf_engine.m +On line 77 ==> clpot{cindex} = direct_combine_pots(pot{n}, clpot{cindex}); + +K>> dbstack +dbstack +> In /home/eecs/murphyk/matlab/BNT/inference/static/@stab_cond_gauss_inf_engine/stab_cond_gauss_inf_engine.m at line 77 + In /home/eecs/murphyk/matlab/BNT/examples/static/SCG/scg3.m at line 41 +K>> + + + + + +- fails on scg1 and scg2 + +Warning: One or more output arguments not assigned during call to 'min_subtree_conti_nodes (nearsest_node2)'. +Warning in ==> /home/eecs/murphyk/matlab/BNT/graph/min_subtree_conti_nodes.m (nearsest_node2) +On line 60 ==> nea_node = nearsest_node2(tree, nodes, n); + +K>> dbstack +dbstack +> In /home/eecs/murphyk/matlab/BNT/graph/min_subtree_conti_nodes.m (nearsest_node2) at line 60 + In /home/eecs/murphyk/matlab/BNT/graph/min_subtree_conti_nodes.m (nearest_node) at line 50 + In /home/eecs/murphyk/matlab/BNT/graph/min_subtree_conti_nodes.m at line 11 + In /home/eecs/murphyk/matlab/BNT/inference/static/@stab_cond_gauss_inf_engine/marginal_difclq_nodes.m at line 17 + In /home/eecs/murphyk/matlab/BNT/inference/static/@stab_cond_gauss_inf_engine/marginal_nodes.m at line 23 + In /home/eecs/murphyk/matlab/BNT/examples/static/SCG/scg1.m at line 42 + + + + + +- This code fragment, from BNT/graph/min_subtree_conti_nodes, is clearly redundant + +function nea_node = nearest_node(tree, root, nodes) +%get the nearest node to the root in the tree +nea_node = nearsest_node2(tree, nodes, root); + +function nea_node = nearsest_node2(tree, nodes, inode) +if myismember(inode, nodes) + nea_node = inode; + return; +end +cs = children(tree, inode); +for i = 1:length(cs) + n = cs(i); + nea_node = nearsest_node2(tree, nodes, n); +end + + +- Some names are badly chosen. 'nearsest' is a mis-spelling. 'min_subtree_conti_nodes' should be +'min_subtree_containing_nodes' or 'min_subtree_con_nodes'. + +- In general, the code needs some heavy polishing.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@stab_cond_gauss_inf_engine/push.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,38 @@ +function [engine, clqtoroot] = push(engine, clq, pushdom) +%PUSH_POT push the variables in putshdom which is subset of clq to the clique toword the root and get new engine +%pushdom is pushed variables set +%clq is the index of the clique that pushdom belongs to + +clqdom = engine.cliques{clq}; +assert( mysubset(pushdom, clqdom)); +clqtoroot = parents(engine.jtree, clq); +%sepdom = engine.separator{clq, clqtoroot}; +sepdom = engine.separator{clqtoroot, clq}; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculate the strong marginal of the union of pushdom and and the separatordomain and % +% the corresponding complement % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%[margpot, comppot] = complement_pot(engine.clpot{clq}, pushdom); +newsepdom = myunion(pushdom,sepdom); +[margpot,comppot] = complement_pot(engine.clpot{clq}, newsepdom); +engine.clpot{clqtoroot} = direct_combine_pots(engine.clpot{clqtoroot}, margpot); +engine.clpot{clq} = comppot; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculation of the new separator and separatorpotential of the junction tree % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +engine.seppot{clqtoroot, clq} = direct_combine_pots(engine.seppot{clqtoroot, clq}, margpot); +engine.separator{clqtoroot, clq} = myunion(engine.separator{clqtoroot, clq}, pushdom); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Add pushdomain to the clique towards the root % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +engine.cliques{clqtoroot} = myunion(engine.cliques{clqtoroot}, pushdom); + +num_cliques = length(engine.cliques); +B = sparse(num_cliques, 1); +for i=1:num_cliques + B(i, engine.cliques{i}) = 1; +end +engine.cliques_bitv = B;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@stab_cond_gauss_inf_engine/push_pot_toclique.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +function engine = push_pot_toclique(engine, clqtarget, clq, nodes) +% PUSH_POT push the variables in putshdom which is subset of clq to the target clique toword the root and get new engine +% engine = push_pot_toclique(engine, clqtarget, clq, nodes) +[engine, clqtoroot] = push_pot(engine, clq, nodes) +while clqtoroot ~= clqtarget + [engine, clqtoroot] = push_pot(engine, clqtoroot, nodes) +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@stab_cond_gauss_inf_engine/stab_cond_gauss_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,178 @@ +function engine = stab_cond_gauss_inf_engine(bnet) +% STAB_COND_GAUSS_INF_ENGINE Junction tree using stable CG potentials +% engine = cond_gauss_inf_engine(bnet) +% +% This class was written by Shan Huang (shan.huang@intel.com) 2001 +% and fixed by Rainer Deventer deventer@informatik.uni-erlangen.de March 2003 +N = length(bnet.dag); +clusters = {}; +root = N; +stages = { 1:N }; +onodes = []; +engine = init_fields; +engine.evidence = []; +engine = class(engine, 'stab_cond_gauss_inf_engine', inf_engine(bnet)); + +ns = bnet.node_sizes(:); +ns(onodes) = 1; % observed nodes have only 1 possible value + +%[engine.jtree, dummy, engine.cliques, B, w, elim_order, moral_edges, fill_in_edges, strong] = ... +% dag_to_jtree(bnet, onodes, stages, clusters); + + +partial_order = determine_elim_constraints(bnet, onodes); +strong = ~isempty(partial_order); +stages = {}; +clusters = {}; +[engine.jtree, dummy_root, engine.cliques, B, w, elim_order] = + graph_to_jtree(moralize(bnet.dag), ns, partial_order, stages, clusters); + + +engine.cliques_bitv = B; +engine.clique_weight = w; +C = length(engine.cliques); +engine.clpot = cell(1,C); + +% A node can be a member of many cliques, but is assigned to exactly one, to avoid +% double-counting its CPD. We assign node i to clique c if c is the "lightest" clique that +% contains i's family, so it can accomodate its CPD. + +engine.clq_ass_to_node = zeros(1, N); +num_cliques = length(engine.cliques); +for i=1:N + clqs_containing_family = find(all(B(:,family(bnet.dag, i)), 2)); % all selected columns must be 1 + c = clqs_containing_family(argmin(w(clqs_containing_family))); + engine.clq_ass_to_node(i) = c; +end + +% Compute the separators between connected cliques. +[is,js] = find(engine.jtree > 0); +engine.separator = cell(num_cliques, num_cliques); +for k=1:length(is) + i = is(k); j = js(k); + engine.separator{i,j} = find(B(i,:) & B(j,:)); % intersect(cliques{i}, cliques{j}); +end +%keyboard; +engine.seppot = cell(C,C); + +pot_type = 'scg'; +check_for_cd_arcs([], bnet.cnodes, bnet.dag); + +% Make the jtree rooted, so there is a fixed message passing order. +if strong + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % Start the search for the strong root at the clique with the % + % highest number. % + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + root = length(engine.cliques); + root_found = 0; + + while ((~root_found) & (root >= 1)) + root_found = test_strong_root(engine.jtree,engine.cliques,bnet.dnodes,root); + if ~root_found + root = root - 1; + end + end + assert(root > 0) + engine.root = root; + % the last clique is guaranteed to be a strong root + %engine.root = length(engine.cliques); +else + % jtree_dbn_inf_engine requires the root to contain the interface. + % This may conflict with the strong root requirement! *********** BUG ************* + engine.root = clq_containing_nodes(engine, root); + if engine.root <= 0 + error(['no clique contains ' num2str(root)]); + end +end + +[engine.jtree, engine.preorder, engine.postorder] = mk_rooted_tree(engine.jtree, engine.root); + +% Evaluate CPDs with evidence, and convert to potentials +pot = cell(1, N); +inited = zeros(1, C); +clpot = cell(1, C); +evidence = cell(1, N); +for n=1:N + fam = family(bnet.dag, n); + e = bnet.equiv_class(n); + %pot{n} = CPD_to_scgpot(bnet.CPD{e}, fam, ns, bnet.cnodes, evidence); + pot{n} = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), evidence); + cindex = engine.clq_ass_to_node(n); + if inited(cindex) + clpot{cindex} = direct_combine_pots(pot{n}, clpot{cindex}); + else + clpot{cindex} = pot{n}; + inited(cindex) = 1; + end +end + +for i=1:C + if inited(i) == 0 + clpot{i} = scgpot([], [], [], []); + end +end + +seppot = cell(C, C); +% separators are is not need to initialize + +% collect to root (node to parents) +% Unlike the HUGIN architecture the complements are stored in the cliques during COLLECT +% and the separators are not playing a specific role during this process +for n=engine.postorder(1:end-1) + for p=parents(engine.jtree, n) + if ~isempty(engine.separator{p,n}) + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % The empty case might happen for unlinked nodes, i.e. the DAG is not % + % a single tree, but a forest % + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + [margpot, comppot] = complement_pot(clpot{n}, engine.separator{p,n}); + clpot{n} = comppot; + clpot{p} = combine_pots(clpot{p}, margpot); + end + end +end + +% distribute message from root +% We have not to store the weak clique marginals and keep the original complement potentials. +% This is a minor variation of HUGIN architecture. +temppot = clpot; +for n=engine.preorder + for c=children(engine.jtree, n) + seppot{n,c} = marginalize_pot(temppot{n}, engine.separator{n,c}); + temppot{c} = direct_combine_pots(temppot{c}, seppot{n,c}); + end +end + +engine.clpot = clpot; +engine.seppot = seppot; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% init_fields() % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function engine = init_fields() + +engine.evidence = []; +engine.jtree = []; +engine.cliques = []; +engine.cliques_bitv = []; +engine.clique_weight = []; +engine.preorder = []; +engine.postorder = []; +engine.root = []; +engine.clq_ass_to_node = []; +engine.separator = []; +engine.clpot =[]; +engine.seppot = []; + + + + + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@var_elim_inf_engine/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +/enter_evidence.m/1.1.1.1/Wed Jun 19 22:05:04 2002// +/find_mpe.m/1.1.1.1/Wed Jun 19 22:11:42 2002// +/marginal_nodes.m/1.1.1.1/Thu Sep 30 03:09:00 2004// +/var_elim_inf_engine.m/1.1.1.1/Wed Jun 19 22:04:50 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@var_elim_inf_engine/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@var_elim_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@var_elim_inf_engine/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@var_elim_inf_engine/enter_evidence.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (var_elim) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i} = [] if if X(i) is hidden, and otherwise contains its observed value (scalar or column vector) + +% we could pre-process the evidence here, to prevent repeated work, but we don't. +engine.evidence = evidence; + +if nargout == 2 + [m, loglik] = marginal_nodes(engine, [1]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@var_elim_inf_engine/find_mpe.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,163 @@ +function mpe = find_mpe(engine, new_evidence, max_over) +% FIND_MPE Find the most probable explanation of the data (assignment to the hidden nodes) +% function mpe = find_mpe(engine, evidence, order) +% +% PURPOSE: +% CALC_MPE Computes the most probable explanation to the network nodes +% given the evidence. +% +% [mpe, ll] = calc_mpe(engine, new_evidence, max_over) +% +% INPUT: +% bnet - the bayesian network +% new_evidence - optional, if specified - evidence to be incorporated [cell(1,n)] +% max_over - optional, if specified determines the variable elimination order [1:n] +% +% OUTPUT: +% mpe - the MPE assignmet for the net variables (or [] if no satisfying assignment) +% ll - log assignment probability. +% +% Notes: +% 1. Adapted from '@var_elim_inf_engine\marginal_nodes' for MPE by Ron Zohar, 8/7/01 +% 2. Only discrete potentials are supported at this time. +% 3. Complexity: O(nw*) where n is the number of nodes and w* is the induced tree width. +% 4. Implementation based on: +% - R. Dechter, "Bucket Elimination: A Unifying Framework for Probabilistic Inference", +% UA1 96, pp. 211-219. + +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes; +n = length(bnet.dag); +evidence = cell(1,n); +if (nargin<2) + new_evidence = evidence; +end + +onodes = find(~isemptycell(new_evidence)); % observed nodes +hnodes = find(isemptycell(new_evidence)); % hidden nodes +pot_type = determine_pot_type(bnet, onodes); + +if pot_type ~= 'd' + error('only disrete potentials supported at this time') +end + +for i=1:n + fam = family(bnet.dag, i); + CPT{i} = convert_to_pot(bnet.CPD{bnet.equiv_class(i)}, pot_type, fam(:), evidence); +end + +% handle observed nodes: set impossible cases' probability to zero +% rather than prun matrix (this makes backtracking easier) + +for ii=onodes + lIdx = 1:ns(ii); + lIdx = setdiff(lIdx, new_evidence{ii}); + + sCPT=struct(CPT{ii}); % violate object privacy + + sargs = ''; + for jj=1:(length(sCPT.domain)-1) + sargs = [sargs, ':,']; + end + for jj=lIdx + eval(['sCPT.T(', sargs, num2str(jj), ')=0;']); + end + CPT{ii}=dpot(sCPT.domain, sCPT.sizes, sCPT.T); +end + +B = cell(1,n); +for b=1:n + B{b} = mk_initial_pot(pot_type, [], [], [], []); +end + +if (nargin<3) + max_over = (1:n); +end +order = max_over; % no attempt to optimize this + + +% Initialize the buckets with the CPDs assigned to them +for i=1:n + b = bucket_num(domain_pot(CPT{i}), order); + B{b} = multiply_pots(B{b}, CPT{i}); +end + +% Do backward phase +max_over = max_over(length(max_over):-1:1); % reverse +maximize = 1; +for i=max_over(1:end-1) + % max-ing over variable i which occurs in bucket j + j = bucket_num(i, order); + rest = mysetdiff(domain_pot(B{j}), i); + %temp = marginalize_pot_max(B{j}, rest); + temp = marginalize_pot(B{j}, rest, maximize); + b = bucket_num(domain_pot(temp), order); + % fprintf('maxing over bucket %d (var %d), putting result into bucket %d\n', j, i, b); + sB=struct(B{b}); % violate object privacy + if ~isempty(sB.domain) + B{b} = multiply_pots(B{b}, temp); + else + B{b} = temp; + end +end +result = B{1}; +marginal = pot_to_marginal(result); +[prob, mpe] = max(marginal.T); + +% handle impossible cases +if ~(prob>0) + mpe = []; + ll = -inf; + %warning('evidence has zero probability') + return +end + +ll = log(prob); + +% Do forward phase +for ii=2:n + marginal = pot_to_marginal(B{ii}); + mpeidx = []; + for jj=order(1:length(mpe)) + %assert(ismember(jj, marginal.domain)) %%% bug + temp = find_equiv_posns(jj, marginal.domain); + mpeidx = [mpeidx, temp] ; + if isempty(temp) + mpeidx = [mpeidx, Inf] ; + end + end + [mpeidxsorted sortedtompe] = sort(mpeidx) ; + + % maximize the matrix obtained from assigning values from previous buckets. + % this is done by building a string and using eval. + + kk=1; + sargs = '('; + for jj=1:length(marginal.domain) + if (jj~=1) + sargs = [sargs, ',']; + end + if (mpeidxsorted(kk)==jj) + sargs = [sargs, num2str(mpe(sortedtompe(kk)))]; + if (kk<length(mpe)) + kk = kk+1 ; + end + else + sargs = [sargs, ':']; + end + end + sargs = [sargs, ')'] ; + eval(['[val, loc] = max(marginal.T', sargs, ');']) + mpe = [mpe loc]; +end +[I,J] = sort(order); +mpe = mpe(J); + +mpe = num2cell(mpe); + +%%%%%%%%% + +function b = bucket_num(domain, order) + +b = max(find_equiv_posns(domain, order)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@var_elim_inf_engine/marginal_nodes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,79 @@ +function [marginal, loglik] = marginal_nodes(engine, query, add_ev) +% MARGINAL_NODES Compute the marginal on the specified query nodes (var_elim) +% [marginal, loglik] = marginal_nodes(engine, query) + +if nargin < 3, add_ev = 0; end + +assert(length(query)>=1); + +evidence = engine.evidence; + +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes; +n = length(bnet.dag); + +onodes = find(~isemptycell(evidence)); +hnodes = find(isemptycell(evidence)); +pot_type = determine_pot_type(bnet, onodes); + +% Fold the evidence into the CPTs - this could be done in 'enter_evidence' +CPT = cell(1,n); +for i=1:n + fam = family(bnet.dag, i); + CPT{i} = convert_to_pot(bnet.CPD{bnet.equiv_class(i)}, pot_type, fam(:), evidence); +end + + + +sum_over = mysetdiff(1:n, query); +order = [query sum_over]; % no attempt to optimize this + +% Initialize the buckets with the product of the CPTs assigned to them +B = cell(1,n+1); +for b=1:n+1 + B{b} = mk_initial_pot(pot_type, [], [], [], []); +end +for i=1:n + b = bucket_num(domain_pot(CPT{i}), order); + B{b} = multiply_pots(B{b}, CPT{i}); +end + +% Do the marginalization +sum_over = sum_over(length(sum_over):-1:1); % reverse +for i=sum_over(:)' + % summing over variable i which occurs in bucket j + j = bucket_num(i, order); + rest = mysetdiff(domain_pot(B{j}), i); + % minka + if ~isempty(rest) + temp = marginalize_pot(B{j}, rest); + b = bucket_num(domain_pot(temp), order); + %fprintf('summing over bucket %d (var %d), putting result into bucket %d\n', j, i, b); + B{b} = multiply_pots(B{b}, temp); + end +end + +% Combine all the remaining buckets into one +result = B{1}; +for i=2:length(query) + if ~isempty(domain_pot(B{i})) + result = multiply_pots(result, B{i}); + end +end +[result, loglik] = normalize_pot(result); + + +marginal = pot_to_marginal(result); +% minka: from jtree_inf_engine +if add_ev + bnet = bnet_from_engine(engine); + %marginal = add_ev_to_dmarginal(marginal, engine.evidence, bnet.node_sizes); + marginal = add_evidence_to_gmarginal(marginal, engine.evidence, bnet.node_sizes, bnet.cnodes); +end + +%%%%%%%%% + +function b = bucket_num(domain, order) + +b = max(find_equiv_posns(domain, order)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/@var_elim_inf_engine/var_elim_inf_engine.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +function engine = var_elim_inf_engine(bnet, varargin) +% VAR_ELIM_INF_ENGINE Variable elimination inference engine +% engine = var_elim_inf_engine(bnet) +% +% For details on variable elimination, see +% - R. Dechter, "Bucket Elimination: A Unifying Framework for Probabilistic Inference", UA1 96, pp. 211-219. +% - Z. Li and B. D'Ambrosio, "Efficient inference in Bayes networks as a combinatorial +% optimization problem", Intl. J. Approximate Reasoning, 11(1):55-81, 1994 +% - R. McEliece and S. M. Aji, "The Generalized Distributive Law", IEEE Trans. Inform. Theory, 46(2), 2000 + + +% This is where we will store the results between enter_evidence and marginal_nodes +engine.evidence = []; + +engine = class(engine, 'var_elim_inf_engine', inf_engine(bnet));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +/dummy/1.1.1.1/Sat Jan 18 22:22:46 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,17 @@ +A D/@belprop_fg_inf_engine//// +A D/@belprop_inf_engine//// +A D/@belprop_mrf2_inf_engine//// +A D/@cond_gauss_inf_engine//// +A D/@enumerative_inf_engine//// +A D/@gaussian_inf_engine//// +A D/@gibbs_sampling_inf_engine//// +A D/@global_joint_inf_engine//// +A D/@jtree_inf_engine//// +A D/@jtree_limid_inf_engine//// +A D/@jtree_mnet_inf_engine//// +A D/@jtree_sparse_inf_engine//// +A D/@likelihood_weighting_inf_engine//// +A D/@pearl_inf_engine//// +A D/@quickscore_inf_engine//// +A D/@stab_cond_gauss_inf_engine//// +A D/@var_elim_inf_engine////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/inference/static/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/installC_BNT.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,32 @@ +BNT_HOME = '/Users/matthiasmauch/Documents/_workspace/_MATLAB/_Toolboxes/FullBNT-1.0.4'; % edit this + +d = fullfile(BNT_HOME, 'BNT'); +%PC = (strncmp(computer,'PC',2)); + +cd(sprintf('%s/potentials/Tables', d)) +mex marg_table.c % used by @dpot/marginalize_pot.m +mex marg_sparse_table.c %used by sparse jtree +mex mult_by_table.c +mex mult_by_sparse_table.c +mex divide_by_table.c +mex divide_by_sparse_table.c +mex rep_mult.c + +% Written by Wei Hu +cd(sprintf('%s/CPDs/@discrete_CPD', d)) +mex convert_to_sparse_table.c + +% Written by Wei Hu +cd(sprintf('%s/inference/static/@jtree_sparse_inf_engine', d)) +mex init_pot.c +mex collect_evidence.c +mex distribute_evidence.c + +% written by Bhaskara Marthi +cd(sprintf('%s/inference/static/@gibbs_sampling_inf_engine/private', d)) +mex compute_posterior.c +mex get_slice_dbn.c +mex sample_single_discrete.c + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/installC_graph.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +% These C functions were written by Ilya Shpitser. + +if isunix + mex -c elim.c; + mex -c cell.c; + mex -c map.c; + mex -DUNIX best_first_elim_order.c elim.o cell.o map.o; + mex -DUNIX triangulate.c elim.o cell.o map.o; +else + mex -c elim.c; + mex -c cell.c; + mex -c map.c; + mex best_first_elim_order.c elim.obj cell.obj map.obj; + mex triangulate.c elim.obj cell.obj map.obj; +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/learning/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,23 @@ +/CovMat.m/1.1.1.1/Sun Jul 28 23:09:42 2002// +/bayes_update_params.m/1.1.1.1/Wed May 29 15:59:56 2002// +/bic_score_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/compute_cooling_schedule.m/1.1.1.1/Wed May 29 15:59:56 2002// +/dirichlet_score_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/kpm_learn_struct_mcmc.m/1.1.1.1/Wed May 29 15:59:56 2002// +/learn_params.m/1.1.1.1/Thu Jun 10 01:28:08 2004// +/learn_params_dbn.m/1.1.1.1/Sun Feb 2 00:23:38 2003// +/learn_params_dbn_em.m/1.1.1.1/Mon Aug 18 21:50:34 2003// +/learn_params_em.m/1.1.1.1/Wed May 29 15:59:56 2002// +/learn_struct_K2.m/1.1.1.1/Thu Sep 26 08:39:16 2002// +/learn_struct_dbn_reveal.m/1.1.1.1/Wed May 29 15:59:56 2002// +/learn_struct_mcmc.m/1.1.1.1/Fri Sep 5 14:06:40 2003// +/learn_struct_pdag_ic_star.m/1.1.1.1/Wed May 29 15:59:56 2002// +/learn_struct_pdag_pc.m/1.2/Mon Feb 12 01:15:13 2007// +/learn_struct_pdag_pc_constrain.m/1.1.1.1/Sun Jul 28 23:09:38 2002// +/mcmc_sample_to_hist.m/1.1.1.1/Wed May 29 15:59:56 2002// +/mk_schedule.m/1.1.1.1/Wed May 29 15:59:56 2002// +/mk_tetrad_data_file.m/1.1.1.1/Wed May 29 15:59:56 2002// +/score_dags.m/1.1.1.1/Mon Apr 4 01:39:46 2005// +/score_dags_old.m/1.1.1.1/Wed May 29 15:59:56 2002// +/score_family.m/1.1.1.1/Thu Jun 10 01:33:14 2004// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/learning/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/learning
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/learning/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/learning/CovMat.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,39 @@ +function [CovMatrix, obs, varfields] = CovMat(filename,row_cols) +%[CovMatrix, obs, varfields] = CovMat(filename,row_cols) +%% generates a Covariance Matrix from a file of data consisting of N columns of M data rows +%% filename string name (with path and extension) of file to open +%% row_cols Number_of_converstions_per_row (turns into [3 inf]) +%% Return +%% CovMatrix Covariance matrix +%% obs Number of observations read in +%% varfields Labels of the variables see filename structure below +%% +%% Filename structure: +%% Comma separated, starting with the variable labels, then the data in rows. +%% filename test.txt consists of: +%% +%% Earthquake,Burglar,Radio,Alarm,Call +%% 1,2,3,4,5 +%% 11,22,33,44,55 +%% . . . +%% +%% Example call: +%% [cvmat numdat lables] = CovMat('test.txt',5); +%% +%% Returns Covariance matrix, number of date rows and variable field names +%% Gary R. Bradski 7/2002 + +fmtstr = '%f'; +for i = 2:row_cols + fmtstr = strcat(fmtstr,',%f'); +end + +%% load data +fidCov = fopen(filename,'r'); + +varfields = fgetl(fidCov); +Corx = fscanf(fidCov,fmtstr,[row_cols inf]); +Corx= Corx'; +[obs bla] = size(Corx); +CovMatrix = cov(Corx); +fclose(fidCov); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/learning/bayes_update_params.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,38 @@ +function bnet = bayes_update_params(bnet, cases, clamped) +% BAYES_UPDATE_PARAMS Bayesian parameter updating given completely observed data +% bnet = bayes_update_params(bnet, cases, clamped) +% +% If there is a missing data, you must use EM. +% cases(i,m) is the value assigned to node i in case m (this can also be a cell array). +% clamped(i,m) = 1 if node i was set by intervention in case m (default: clamped = zeros). +% Clamped nodes are not updated. +% If there is a single case, clamped is a list of the clamped nodes, not a bit vector. + + +%if iscell(cases), usecell = 1; else usecell = 0; end + +n = length(bnet.dag); +ncases = size(cases, 2); +if n ~= size(cases, 1) + error('data must be of size nnodes * ncases'); +end + +if ncases == 1 % clamped is a list of nodes + if nargin < 3, clamped = []; end + clamp_set = clamped; + clamped = zeros(n,1); + clamped(clamp_set) = 1; +else % each row of clamped is a bit vector + if nargin < 3, clamped = zeros(n,ncases); end +end + +for i=1:n + e = bnet.equiv_class(i); + if adjustable_CPD(bnet.CPD{e}) + u = find(clamped(i,:)==0); + ps = parents(bnet.dag, i); + bnet.CPD{e} = bayes_update_params(bnet.CPD{e}, cases(i,u), cases(ps,u)); + end +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/learning/bic_score_family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,23 @@ +function [S, LL] = bic_score(counts, CPT, ncases) +% BIC_SCORE Bayesian Information Criterion score for a single family +% [S, LL] = bic_score(counts, CPT, ncases) +% +% S is a large sample approximation to the log marginal likelihood, +% which can be computed using dirichlet_score. +% +% S = \log [ prod_j _prod_k theta_ijk ^ N_ijk ] - 0.5*d*log(ncases) +% where counts encode N_ijk, theta_ijk is the MLE comptued from counts, +% and d is the num of free parameters. + +%CPT = mk_stochastic(counts); +tiny = exp(-700); +LL = sum(log(CPT(:) + tiny) .* counts(:)); +% CPT(i) = 0 iff counts(i) = 0 so it is okay to add tiny + +ns = mysize(counts); +ns_ps = ns(1:end-1); +ns_self = ns(end); +nparams = prod([ns_ps (ns_self-1)]); +% sum-to-1 constraint reduces the effective num. vals of the node by 1 + +S = LL - 0.5*nparams*log(ncases);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/learning/compute_cooling_schedule.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function temp_schedule = compute_cooling_schedule(init_temp, final_temp, anneal_rate) + +temp_schedule = []; +i = 1; +temp_schedule(i)=init_temp; +while temp_schedule(i) > final_temp + i = i + 1; + temp_schedule(i)=temp_schedule(i-1)*anneal_rate; +end + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/learning/dirichlet_score_family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,42 @@ +function LL = dirichlet_score_family(counts, prior) +% DIRICHLET_SCORE Compute the log marginal likelihood of a single family +% LL = dirichlet_score(counts, prior) +% +% counts(a, b, ..., z) is the number of times parent 1 = a, parent 2 = b, ..., child = z +% prior is an optional multidimensional array of the same shape as counts. +% It defaults to a uniform prior. +% +% We marginalize out the parameters: +% LL = log \int \prod_m P(x(i,m) | x(Pa_i,m), theta_i) P(theta_i) d(theta_i) + + +% LL = log[ prod_j gamma(alpha_ij)/gamma(alpha_ij + N_ij) * +% prod_k gamma(alpha_ijk + N_ijk)/gamma(alpha_ijk) ] +% Call the prod_k term U and the prod_j term V. +% We reshape all quantities into (j,k) matrices +% This formula was first derived by Cooper and Herskovits, 1992. +% See also "Learning Bayesian Networks", Heckerman, Geiger and Chickering, MLJ 95. + +ns = mysize(counts); +ns_ps = ns(1:end-1); +ns_self = ns(end); + +if nargin < 2, prior = normalise(myones(ns)); end + + +if 1 + prior = reshape(prior(:), [prod(ns_ps) ns_self]); + counts = reshape(counts, [prod(ns_ps) ns_self]); + %U = prod(gamma(prior + counts) ./ gamma(prior), 2); % mult over k + LU = sum(gammaln(prior + counts) - gammaln(prior), 2); + alpha_ij = sum(prior, 2); % sum over k + N_ij = sum(counts, 2); + %V = gamma(alpha_ij) ./ gamma(alpha_ij + N_ij); + LV = gammaln(alpha_ij) - gammaln(alpha_ij + N_ij); + %L = prod(U .* V); + LL = sum(LU + LV); +else + CPT = mk_stochastic(prior + counts); + LL = sum(log(CPT(:) .* counts(:))); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/learning/kpm_learn_struct_mcmc.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,265 @@ +function [sampled_graphs, accept_ratio, num_edges] = learn_struct_mcmc(data, ns, varargin) +% LEARN_STRUCT_MCMC Monte Carla Markov Chain search over DAGs assuming fully observed data +% [sampled_graphs, accept_ratio, num_edges] = learn_struct_mcmc(data, ns, ...) +% +% data(i,m) is the value of node i in case m. +% ns(i) is the number of discrete values node i can take on. +% +% sampled_graphs{m} is the m'th sampled graph. +% accept_ratio(t) = acceptance ratio at iteration t +% num_edges(t) = number of edges in model at iteration t +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% scoring_fn - 'bayesian' or 'bic' [ 'bayesian' ] +% Currently, only networks with all tabular nodes support Bayesian scoring. +% type - type{i} is the type of CPD to use for node i, where the type is a string +% of the form 'tabular', 'noisy_or', 'gaussian', etc. [ all cells contain 'tabular' ] +% params - params{i} contains optional arguments passed to the CPD constructor for node i, +% or [] if none. [ all cells contain {'prior', 1}, meaning use uniform Dirichlet priors ] +% discrete - the list of discrete nodes [ 1:N ] +% clamped - clamped(i,m) = 1 if node i is clamped in case m [ zeros(N, ncases) ] +% nsamples - number of samples to draw from the chain after burn-in [ 100*N ] +% burnin - number of steps to take before drawing samples [ 5*N ] +% init_dag - starting point for the search [ zeros(N,N) ] +% +% e.g., samples = learn_struct_mcmc(data, ns, 'nsamples', 1000); +% +% This interface is not backwards compatible with BNT2, +% but is designed to be compatible with the other learn_struct_xxx routines. +% +% Note: We currently assume a uniform structural prior. + +[n ncases] = size(data); + + +% set default params +type = cell(1,n); +params = cell(1,n); +for i=1:n + type{i} = 'tabular'; + %params{i} = { 'prior', 1 }; + params{i} = { 'prior_type', 'dirichlet', 'dirichlet_weight', 1 }; +end +scoring_fn = 'bayesian'; +discrete = 1:n; +clamped = zeros(n, ncases); +nsamples = 100*n; +burnin = 5*n; +dag = zeros(n); + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'nsamples', nsamples = args{i+1}; + case 'burnin', burnin = args{i+1}; + case 'init_dag', dag = args{i+1}; + case 'scoring_fn', scoring_fn = args{i+1}; + case 'type', type = args{i+1}; + case 'discrete', discrete = args{i+1}; + case 'clamped', clamped = args{i+1}; + case 'params', if isempty(args{i+1}), params = cell(1,n); else params = args{i+1}; end + end +end + +% We implement the fast acyclicity check described by P. Giudici and R. Castelo, +% "Improving MCMC model search for data mining", submitted to J. Machine Learning, 2001. +use_giudici = 1; +if use_giudici + [nbrs, ops, nodes] = mk_nbrs_of_digraph(dag); + A = init_ancestor_matrix(dag); +else + [nbrs, ops, nodes] = mk_nbrs_of_dag(dag); + A = []; +end + +num_accepts = 1; +num_rejects = 1; +T = burnin + nsamples; +accept_ratio = zeros(1, T); +num_edges = zeros(1, T); +sampled_graphs = cell(1, nsamples); +%sampled_bitv = zeros(nsamples, n^2); + +for t=1:T + [dag, nbrs, ops, nodes, A, accept] = take_step(dag, nbrs, ops, nodes, ns, data, clamped, A, ... + scoring_fn, discrete, type, params); + num_edges(t) = sum(dag(:)); + num_accepts = num_accepts + accept; + num_rejects = num_rejects + (1-accept); + accept_ratio(t) = num_accepts/num_rejects; + if t > burnin + sampled_graphs{t-burnin} = dag; + %sampled_bitv(t-burnin, :) = dag(:)'; + end +end + + +%%%%%%%%% + + +function [new_dag, new_nbrs, new_ops, new_nodes, A, accept] = ... + take_step(dag, nbrs, ops, nodes, ns, data, clamped, A, ... + scoring_fn, discrete, type, params) + + +use_giudici = ~isempty(A); +if use_giudici + [new_dag, op, i, j] = pick_digraph_nbr(dag, nbrs, ops, nodes, A); + %assert(acyclic(new_dag)); + [new_nbrs, new_ops, new_nodes] = mk_nbrs_of_digraph(new_dag); +else + d = sample_discrete(normalise(ones(1, length(nbrs)))); + new_dag = nbrs{d}; + op = ops{d}; + i = nodes(d, 1); j = nodes(d, 2); + [new_nbrs, new_ops, new_nodes] = mk_nbrs_of_dag(new_dag); +end + +bf = bayes_factor(dag, new_dag, op, i, j, ns, data, clamped, scoring_fn, discrete, type, params); + +%R = bf * (new_prior / prior) * (length(nbrs) / length(new_nbrs)); +R = bf * (length(nbrs) / length(new_nbrs)); +u = rand(1,1); +if u > min(1,R) % reject the move + accept = 0; + new_dag = dag; + new_nbrs = nbrs; + new_ops = ops; + new_nodes = nodes; +else + accept = 1; + if use_giudici + A = update_ancestor_matrix(A, op, i, j, new_dag); + end +end + + +%%%%%%%%% + +function bfactor = bayes_factor(old_dag, new_dag, op, i, j, ns, data, clamped, scoring_fn, discrete, type, params) + +u = find(clamped(j,:)==0); +LLnew = score_family(j, parents(new_dag, j), type{j}, scoring_fn, ns, discrete, data(:,u), params{j}); +LLold = score_family(j, parents(old_dag, j), type{j}, scoring_fn, ns, discrete, data(:,u), params{j}); +bf1 = exp(LLnew - LLold); + +if strcmp(op, 'rev') % must also multiply in the changes to i's family + u = find(clamped(i,:)==0); + LLnew = score_family(i, parents(new_dag, i), type{i}, scoring_fn, ns, discrete, data(:,u), params{i}); + LLold = score_family(i, parents(old_dag, i), type{i}, scoring_fn, ns, discrete, data(:,u), params{i}); + bf2 = exp(LLnew - LLold); +else + bf2 = 1; +end +bfactor = bf1 * bf2; + + +%%%%%%%% Giudici stuff follows %%%%%%%%%% + + +function [new_dag, op, i, j] = pick_digraph_nbr(dag, digraph_nbrs, ops, nodes, A) + +legal = 0; +while ~legal + d = sample_discrete(normalise(ones(1, length(digraph_nbrs)))); + i = nodes(d, 1); j = nodes(d, 2); + switch ops{d} + case 'add', + if A(i,j)==0 + legal = 1; + end + case 'del', + legal = 1; + case 'rev', + ps = mysetdiff(parents(dag, j), i); + % if any(A(ps,i)) then there is a path i -> parent of j -> j + % so reversing i->j would create a cycle + legal = ~any(A(ps, i)); + end +end +%new_dag = digraph_nbrs{d}; +new_dag = digraph_nbrs(:,:,d); +op = ops{d}; +i = nodes(d, 1); j = nodes(d, 2); + + +%%%%%%%%%%%%%% + + +function A = update_ancestor_matrix(A, op, i, j, dag) + +switch op + case 'add', + A = do_addition(A, op, i, j, dag); + case 'del', + A = do_removal(A, op, i, j, dag); + case 'rev', + A = do_removal(A, op, i, j, dag); + A = do_addition(A, op, j, i, dag); +end + + +%%%%%%%%%%%% + +function A = do_addition(A, op, i, j, dag) + +A(j,i) = 1; % i is an ancestor of j +anci = find(A(i,:)); +if ~isempty(anci) + A(j,anci) = 1; % all of i's ancestors are added to Anc(j) +end +ancj = find(A(j,:)); +descj = find(A(:,j)); +if ~isempty(ancj) + for k=descj(:)' + A(k,ancj) = 1; % all of j's ancestors are added to each descendant of j + end +end + +%%%%%%%%%%% + +function A = do_removal(A, op, i, j, dag) + +% find all the descendants of j, and put them in topological order +%descj = find(A(:,j)); +R = reachability_graph(dag); +descj = find(R(j,:)); +order = topological_sort(dag); +descj_topnum = order(descj); +[junk, perm] = sort(descj_topnum); +descj = descj(perm); +% Update j and all its descendants +A = update_row(A, j, dag); +for k = descj(:)' + A = update_row(A, k, dag); +end + +%%%%%%%%% + +function A = update_row(A, j, dag) + +% We compute row j of A +A(j, :) = 0; +ps = parents(dag, j); +if ~isempty(ps) + A(j, ps) = 1; +end +for k=ps(:)' + anck = find(A(k,:)); + if ~isempty(anck) + A(j, anck) = 1; + end +end + +%%%%%%%% + +function A = init_ancestor_matrix(dag) + +order = topological_sort(dag); +A = zeros(length(dag)); +for j=order(:)' + A = update_row(A, j, dag); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/learning/learn_params.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,26 @@ +function bnet = learn_params(bnet, data) +% LEARN_PARAMS Find the maximum likelihood params for a fully observed model +% bnet = learn_params(bnet, data) +% +% data(i,m) is the value of node i in case m (can be a cell array) +% +% We set bnet.CPD{i} to its ML/MAP estimate. +% +% Currently we assume no param tying + +% AND THAT EACH DATA POINT IS A SCALAR - no longer assumed + +%if iscell(data) +% data=cell2num(data); +%end +[n ncases] = size(data); +for j=1:n + e = bnet.equiv_class(j); + assert(e==j); + if adjustable_CPD(bnet.CPD{e}) + fam = family(bnet.dag,j); + %bnet.CPD{j} = learn_params(bnet.CPD{j}, data(fam,:)); + bnet.CPD{j} = learn_params(bnet.CPD{j}, fam, data, bnet.node_sizes, bnet.cnodes); + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/learning/learn_params_dbn.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,36 @@ +function bnet = learn_params_dbn(bnet, data) +% LEARN_PARAM_DBN Estimate params of a DBN for a fully observed model +% bnet = learn_params_dbn(bnet, data) +% +% data(i,t) is the value of node i in slice t (can be a cell array) +% We currently assume there is a single time series +% +% We set bnet.CPD{i} to its ML/MAP estimate. +% +% Currently we assume each node in the first 2 slices has its own CPD (no param tying); +% all nodes in slices >2 share their params with slice 2 as usual. + +[ss T] = size(data); + +% slice 1 +for j=1:ss + if adjustable_CPD(bnet.CPD{j}) + fam = family(bnet.dag,j); + bnet.CPD{j} = learn_params(bnet.CPD{j}, data(fam,1)); + end +end + + +% slices 2:T +% data2(:,t) contains [data(:,t-1); data(:,t)]. +% Then we extract out the rows corresponding to the parents in the current and previous slice. +data2 = [data(:,1:T-1); + data(:,2:T)]; +for j=1:ss + j2 = j+ss; + if adjustable_CPD(bnet.CPD{j2}) + fam = family(bnet.dag,j2); + bnet.CPD{j2} = learn_params(bnet.CPD{j2}, data2(fam,:)); + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/learning/learn_params_dbn_em.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,179 @@ +function [bnet, LL, engine] = learn_params_dbn_em(engine, evidence, varargin) +% LEARN_PARAMS_DBN Set the parameters in a DBN to their ML/MAP values using batch EM. +% [bnet, LLtrace, engine] = learn_params_dbn_em(engine, data, ...) +% +% data{l}{i,t} = value of node i in slice t of time-series l, or [] if hidden. +% Suppose you have L time series, each of length T, in an O*T*L array D, +% where O is the num of observed scalar nodes, and N is the total num nodes per slice. +% Then you can create data as follows, where onodes is the index of the observable nodes: +% data = cell(1,L); +% for l=1:L +% data{l} = cell(N, T); +% data{l}(onodes,:) = num2cell(D(:,:,l)); +% end +% Of course it is possible for different sets of nodes to be observed in +% each slice/ sequence, and for each sequence to be a different length. +% +% LLtrace is the learning curve: the vector of log-likelihood scores at each iteration. +% +% Optional arguments [default] +% +% max_iter - specifies the maximum number of iterations [100] +% thresh - specifies the thresold for stopping EM [1e-3] +% We stop when |f(t) - f(t-1)| / avg < threshold, +% where avg = (|f(t)| + |f(t-1)|)/2 and f is log lik. +% verbose - display loglik at each iteration [1] +% anneal - 1 means do deterministic annealing (only for entropic priors) [0] +% anneal_rate - geometric cooling rate [0.8] +% init_temp - initial annealing temperature [10] +% final_temp - final annealing temperature [1e-3] +% + +max_iter = 100; +thresh = 1e-3; +anneal = 0; +anneal_rate = 0.8; +init_temp = 10; +final_temp = 1e-3; +verbose = 1; + +for i=1:2:length(varargin) + switch varargin{i} + case 'max_iter', max_iter = varargin{i+1}; + case 'thresh', thresh = varargin{i+1}; + case 'anneal', anneal = varargin{i+1}; + case 'anneal_rate', anneal_rate = varargin{i+1}; + case 'init_temp', init_temp = varargin{i+1}; + case 'final_temp', final_temp = varargin{i+1}; + otherwise, error(['unrecognized argument' varargin{i}]) + end +end + +% take 1 EM step at each temperature value, then when temp=0, run to convergence +% When using an entropic prior, Z = 1-T, so +% T=2 => Z=-1 (max entropy) +% T=1 => Z=0 (max likelihood) +% T=0 => Z=1 (min entropy / max structure) +num_iter = 1; +LL = []; +if anneal + temperature = init_temp; + while temperature > final_temp + [engine, loglik, logpost] = EM_step(engine, evidence, temperature); + if verbose + fprintf('EM iteration %d, loglik = %8.4f, logpost = %8.4f, temp=%8.4f\n', ... + num_iter, loglik, logpost, temperature); + end + num_iter = num_iter + 1; + LL = [LL loglik]; + temperature = temperature * anneal_rate; + end + temperature = 0; + previous_loglik = loglik; + previous_logpost = logpost; +else + temperature = 0; + previous_loglik = -inf; + previous_logpost = -inf; +end + +converged = 0; +while ~converged & (num_iter <= max_iter) + [engine, loglik, logpost] = EM_step(engine, evidence, temperature); + if verbose + %fprintf('EM iteration %d, loglik = %8.4f, logpost = %8.4f\n', ... + % num_iter, loglik, logpost); + fprintf('EM iteration %d, loglik = %8.4f\n', num_iter, loglik); + end + num_iter = num_iter + 1; + [converged, decreased] = em_converged(loglik, previous_loglik, thresh); + %[converged, decreased] = em_converged(logpost, previous_logpost, thresh); + previous_loglik = loglik; + previous_logpost = logpost; + LL = [LL loglik]; +end + +bnet = bnet_from_engine(engine); + +%%%%%%%%% + +function [engine, loglik, logpost] = EM_step(engine, cases, temp) + +bnet = bnet_from_engine(engine); % engine contains the old params that are used for the E step +ss = length(bnet.intra); +CPDs = bnet.CPD; % these are the new params that get maximized +num_CPDs = length(CPDs); + +% log P(theta|D) = (log P(D|theta) + log P(theta)) - log(P(D)) +% where log P(D|theta) = sum_cases log P(case|theta) +% and log P(theta) = sum_CPDs log P(CPD) - only count once even if tied! +% logpost = log P(theta,D) (un-normalized) +% This should be negative, and increase at every step. + +adjustable = zeros(1,num_CPDs); +logprior = zeros(1, num_CPDs); +for e=1:num_CPDs + adjustable(e) = adjustable_CPD(CPDs{e}); +end +adj = find(adjustable); + +for e=adj(:)' + logprior(e) = log_prior(CPDs{e}); + CPDs{e} = reset_ess(CPDs{e}); +end + +loglik = 0; +for l=1:length(cases) + evidence = cases{l}; + if ~iscell(evidence) + error('training data must be a cell array of cell arrays') + end + [engine, ll] = enter_evidence(engine, evidence); + assert(~isnan(ll)) + loglik = loglik + ll; + T = size(evidence, 2); + + % We unroll ns etc because in update_ess, we refer to nodes by their unrolled number + % so that they extract evidence from the right place. + % (The CPD should really store its own version of ns and cnodes...) + ns = repmat(bnet.node_sizes_slice(:), [1 T]); + cnodes = unroll_set(bnet.cnodes_slice, ss, T); + + %hidden_bitv = repmat(bnet.hidden_bitv(1:ss), [1 T]); + hidden_bitv = zeros(ss, T); + hidden_bitv(isemptycell(evidence))=1; + % hidden_bitv(i) = 1 means node i is hidden. + % We pass this in, rather than using isemptycell(evidence(dom)), because + % isemptycell is very slow. + + t = 1; + for i=1:ss + e = bnet.equiv_class(i,1); + if adjustable(e) + fmarg = marginal_family(engine, i, t); + CPDs{e} = update_ess(CPDs{e}, fmarg, evidence, ns(:), cnodes(:), hidden_bitv(:)); + end + end + + for i=1:ss + e = bnet.equiv_class(i,2); + if adjustable(e) + for t=2:T + fmarg = marginal_family(engine, i, t); + CPDs{e} = update_ess(CPDs{e}, fmarg, evidence, ns(:), cnodes(:), hidden_bitv(:)); + end + end + end +end + +logpost = loglik + sum(logprior(:)); + +for e=adj(:)' + CPDs{e} = maximize_params(CPDs{e}, temp); +end + +engine = update_engine(engine, CPDs); + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/learning/learn_params_em.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,88 @@ +function [bnet, LL, engine] = learn_params_em(engine, evidence, max_iter, thresh) +% LEARN_PARAMS_EM Set the parameters of each adjustable node to their ML/MAP values using batch EM. +% [bnet, LLtrace, engine] = learn_params_em(engine, data, max_iter, thresh) +% +% data{i,l} is the value of node i in case l, or [] if hidden. +% Suppose you have L training cases in an O*L array, D, where O is the num observed +% scalar nodes, and N is the total num nodes. +% Then you can create 'data' as follows, where onodes is the index of the observable nodes: +% data = cell(N, L); +% data(onodes,:) = num2cell(D); +% Of course it is possible for different sets of nodes to be observed in each case. +% +% We return the modified bnet and engine. +% To see the learned parameters for node i, use the construct +% s = struct(bnet.CPD{i}); % violate object privacy +% LLtrace is the learning curve: the vector of log-likelihood scores at each iteration. +% +% max_iter specifies the maximum number of iterations. Default: 10. +% +% thresh specifies the thresold for stopping EM. Default: 1e-3. +% We stop when |f(t) - f(t-1)| / avg < threshold, +% where avg = (|f(t)| + |f(t-1)|)/2 and f is log lik. + +if nargin < 3, max_iter = 10; end +if nargin < 4, thresh = 1e-3; end + +verbose = 1; + +loglik = 0; +previous_loglik = -inf; +converged = 0; +num_iter = 1; +LL = []; + +while ~converged & (num_iter <= max_iter) + [engine, loglik] = EM_step(engine, evidence); + if verbose, fprintf('EM iteration %d, ll = %8.4f\n', num_iter, loglik); end + num_iter = num_iter + 1; + converged = em_converged(loglik, previous_loglik, thresh); + previous_loglik = loglik; + LL = [LL loglik]; +end +if verbose, fprintf('\n'); end + +bnet = bnet_from_engine(engine); + +%%%%%%%%% + +function [engine, loglik] = EM_step(engine, cases) + +bnet = bnet_from_engine(engine); % engine contains the old params that are used for the E step +CPDs = bnet.CPD; % these are the new params that get maximized +num_CPDs = length(CPDs); +adjustable = zeros(1,num_CPDs); +for e=1:num_CPDs + adjustable(e) = adjustable_CPD(CPDs{e}); +end +adj = find(adjustable); +n = length(bnet.dag); + +for e=adj(:)' + CPDs{e} = reset_ess(CPDs{e}); +end + +loglik = 0; +ncases = size(cases, 2); +for l=1:ncases + evidence = cases(:,l); + [engine, ll] = enter_evidence(engine, evidence); + loglik = loglik + ll; + hidden_bitv = zeros(1,n); + hidden_bitv(isemptycell(evidence))=1; + for i=1:n + e = bnet.equiv_class(i); + if adjustable(e) + fmarg = marginal_family(engine, i); + CPDs{e} = update_ess(CPDs{e}, fmarg, evidence, bnet.node_sizes, bnet.cnodes, hidden_bitv); + end + end +end + +for e=adj(:)' + CPDs{e} = maximize_params(CPDs{e}); +end + +engine = update_engine(engine, CPDs); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/learning/learn_params_em2.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,91 @@ +function [bnet, LL, engine] = learn_params_em(engine, evidence, max_iter, thresh) +% LEARN_PARAMS_EM Set the parameters of each adjustable node to their ML/MAP values using batch EM. +% [bnet, LLtrace, engine] = learn_params_em(engine, data, max_iter, thresh) +% +% data{i,l} is the value of node i in case l, or [] if hidden. +% Suppose you have L training cases in an O*L array, D, where O is the num observed +% scalar nodes, and N is the total num nodes. +% Then you can create 'data' as follows, where onodes is the index of the observable nodes: +% data = cell(N, L); +% data(onodes,:) = num2cell(D); +% Of course it is possible for different sets of nodes to be observed in each case. +% +% We return the modified bnet and engine. +% To see the learned parameters for node i, use the construct +% s = struct(bnet.CPD{i}); % violate object privacy +% LLtrace is the learning curve: the vector of log-likelihood scores at each iteration. +% +% max_iter specifies the maximum number of iterations. Default: 10. +% +% thresh specifies the thresold for stopping EM. Default: 1e-3. +% We stop when |f(t) - f(t-1)| / avg < threshold, +% where avg = (|f(t)| + |f(t-1)|)/2 and f is log lik. + +if nargin < 3, max_iter = 10; end +if nargin < 4, thresh = 1e-3; end + +verbose = 0; + +loglik = 0; +previous_loglik = -inf; +converged = 0; +num_iter = 1; +LL = []; + +while ~converged & (num_iter <= max_iter) + [engine, loglik] = EM_step(engine, evidence); + if verbose, fprintf('EM iteration %d, ll = %8.4f\n', num_iter, loglik); end + num_iter = num_iter + 1; + converged = em_converged(loglik, previous_loglik, thresh); + previous_loglik = loglik; + LL = [LL loglik]; +end +if verbose, fprintf('\n'); end + +bnet = bnet_from_engine(engine); + +%%%%%%%%% + +function [engine, loglik] = EM_step(engine, cases) + +bnet = bnet_from_engine(engine); % engine contains the old params that are used for the E step +CPDs = bnet.CPD; % these are the new params that get maximized +num_CPDs = length(CPDs); +adjustable = zeros(1,num_CPDs); +for e=1:num_CPDs + adjustable(e) = adjustable_CPD(CPDs{e}); +end +adj = find(adjustable); +n = length(bnet.dag); + +for e=adj(:)' + CPDs{e} = reset_ess(CPDs{e}); +end + +loglik = 0; +ncases = size(cases, 2); +for l=1:ncases + evidence = cases(:,l); + [engine, ll] = enter_evidence(engine, evidence); + loglik = loglik + ll; + hidden_bitv = zeros(1,n); + hidden_bitv(isemptycell(evidence))=1; + for i=1:n + e = bnet.equiv_class(i); + if adjustable(e) + fmarg = marginal_family(engine, i); + CPDs{e} = update_ess(CPDs{e}, fmarg, evidence, bnet.node_sizes, bnet.cnodes, hidden_bitv); + end + end +end + +for e=adj(:)' + CPDs{e} = maximize_params(CPDs{e}); +end +one_mean = get_field(CPDs{1},'mean'); +one_cov = get_field(CPDs{1},'cov'); +CPDs{1} = gaussian_CPD(bnet,1,'mean',max(one_mean,0),'cov',one_cov,'cov_type', 'diag', ... + 'clamp_cov',1); +engine = update_engine(engine, CPDs); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/learning/learn_struct_K2.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,104 @@ +function dag = learn_struct_K2(data, ns, order, varargin) +% LEARN_STRUCT_K2 Greedily learn the best structure compatible with a fixed node ordering +% best_dag = learn_struct_K2(data, node_sizes, order, ...) +% +% data(i,m) = value of node i in case m (can be a cell array). +% node_sizes(i) is the size of node i. +% order(i) is the i'th node in the topological ordering. +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% max_fan_in - this the largest number of parents we allow per node [N] +% scoring_fn - 'bayesian' or 'bic' [ 'bayesian' ] +% Currently, only networks with all tabular nodes support Bayesian scoring. +% type - type{i} is the type of CPD to use for node i, where the type is a string +% of the form 'tabular', 'noisy_or', 'gaussian', etc. [ all cells contain 'tabular' ] +% params - params{i} contains optional arguments passed to the CPD constructor for node i, +% or [] if none. [ all cells contain {'prior', 1}, meaning use uniform Dirichlet priors ] +% discrete - the list of discrete nodes [ 1:N ] +% clamped - clamped(i,m) = 1 if node i is clamped in case m [ zeros(N, ncases) ] +% verbose - 'yes' means display output while running [ 'no' ] +% +% e.g., dag = learn_struct_K2(data, ns, order, 'scoring_fn', 'bic', 'params', []) +% +% To be backwards compatible with BNT2, you can also specify arguments as follows +% dag = learn_struct_K2(data, node_sizes, order, max_fan_in) +% +% This algorithm is described in +% - Cooper and Herskovits, "A Bayesian method for the induction of probabilistic +% networks from data", Machine Learning Journal 9:308--347, 1992 + +[n ncases] = size(data); + +% set default params +type = cell(1,n); +params = cell(1,n); +for i=1:n + type{i} = 'tabular'; + %params{i} = { 'prior', 1 }; + params{i} = { 'prior_type', 'dirichlet', 'dirichlet_weight', 1 }; +end +scoring_fn = 'bayesian'; +discrete = 1:n; +clamped = zeros(n, ncases); + +max_fan_in = n; +verbose = 0; + +args = varargin; +nargs = length(args); +if length(args) > 0 + if isstr(args{1}) + for i=1:2:nargs + switch args{i}, + case 'verbose', verbose = strcmp(args{i+1}, 'yes'); + case 'max_fan_in', max_fan_in = args{i+1}; + case 'scoring_fn', scoring_fn = args{i+1}; + case 'type', type = args{i+1}; + case 'discrete', discrete = args{i+1}; + case 'clamped', clamped = args{i+1}; + case 'params', if isempty(args{i+1}), params = cell(1,n); else params = args{i+1}; end + end + end + else + max_fan_in = args{1}; + end +end + +dag = zeros(n,n); + +for i=1:n + ps = []; + j = order(i); + u = find(clamped(j,:)==0); + score = score_family(j, ps, type{j}, scoring_fn, ns, discrete, data(:,u), params{j}); + if verbose, fprintf('\nnode %d, empty score %6.4f\n', j, score); end + done = 0; + while ~done & (length(ps) <= max_fan_in) + pps = mysetdiff(order(1:i-1), ps); % potential parents + nps = length(pps); + pscore = zeros(1, nps); + for pi=1:nps + p = pps(pi); + pscore(pi) = score_family(j, [ps p], type{j}, scoring_fn, ns, discrete, data(:,u), params{j}); + if verbose, fprintf('considering adding %d to %d, score %6.4f\n', p, j, pscore(pi)); end + end + [best_pscore, best_p] = max(pscore); + best_p = pps(best_p); + if best_pscore > score + score = best_pscore; + ps = [ps best_p]; + if verbose, fprintf('* adding %d to %d, score %6.4f\n', best_p, j, best_pscore); end + else + done = 1; + end + end + if ~isempty(ps) % need this check for matlab 5.2 + dag(ps, j) = 1; + end +end + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/learning/learn_struct_dbn_reveal.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,101 @@ +function inter = learn_struct_dbn_reveal(seqs, ns, max_fan_in, penalty) +% LEARN_STRUCT_DBN_REVEAL Learn inter-slice adjacency matrix given fully observable discrete time series +% inter = learn_struct_dbn_reveal(seqs, node_sizes, max_fan_in, penalty) +% +% seqs{l}{i,t} = value of node i in slice t of time-series l. +% If you have a single time series in an N*T array D, use +% seqs = { num2cell(D) }. +% If you have L time series, each of length T, in an N*T*L array D, use +% seqs= cell(1,L); for l=1:L, seqs{l} = num2cell(D(:,:,l)); end +% or, in vectorized form, +% seqs = squeeze(num2cell(num2cell(D),[1 2])); +% Currently the data is assumed to be discrete (1,2,...) +% +% node_sizes(i) is the number of possible values for node i +% max_fan_in is the largest number of parents we allow per node (default: N) +% penalty is weight given to the complexity penalty (default: 0.5) +% A penalty of 0.5 gives the BIC score. +% A penalty of 0 gives the ML score. +% Maximizing likelihood is equivalent to maximizing mutual information between parents and child. +% +% inter(i,j) = 1 iff node in slice t connects to node j in slice t+1 +% +% The parent set for each node in slice 2 is computed by evaluating all subsets of nodes in slice 1, +% and picking the largest scoring one. This takes O(n^k) time per node, where n is the num. nodes +% per slice, and k <= n is the max fan in. +% Since all the nodes are observed, we do not need to use an inference engine. +% And since we are only learning the inter-slice matrix, we do not need to check for cycles. +% +% This algorithm is described in +% - "REVEAL: A general reverse engineering algorithm for inference of genetic network +% architectures", Liang et al. PSB 1998 +% - "Extended dependency analysis of large systems", +% Roger Conant, Intl. J. General Systems, 1988, vol 14, pp 97-141 +% - "Learning the structure of DBNs", Friedman, Murphy and Russell, UAI 1998. + +n = length(ns); + +if nargin < 3, max_fan_in = n; end +if nargin < 4, penalty = 0.5; end + +inter = zeros(n,n); + +if ~iscell(seqs) + data{1} = seqs; +end + +nseq = length(seqs); +nslices = 0; +data = cell(1, nseq); +for l=1:nseq + nslices = nslices + size(seqs{l}, 2); + data{l} = cell2num(seqs{l})'; % each row is a case +end +ndata = nslices - nseq; % subtract off the initial slice of each sequence + +% We concatenate the sequences as in the following example. +% Let there be 2 sequences of lengths 4 and 5, with n nodes per slice, +% and let i be the target node. +% Then we construct following matrix D +% +% s{1}{1,1} ... s{1}{1,3} s{2}{1,1} ... s{2}{1,4} +% .... +% s{1}{n,1} ... s{1}{n,3} s{2}{n,1} ... s{2}{n,4} +% s{1}{i,2} ... s{1}{i,4} s{2}{i,2} ... s{2}{i,5} +% +% D(1:n, i) is the i'th input and D(n+1, i) is the i'th output. +% +% We concatenate each sequence separately to avoid treating the transition +% from the end of one sequence to the beginning of another as a "normal" transition. + + +for i=1:n + D = []; + for l=1:nseq + T = size(seqs{l}, 2); + A = cell2num(seqs{l}(:, 1:T-1)); + B = cell2num(seqs{l}(i, 2:T)); + C = [A;B]; + D = [D C]; + end + SS = subsets(1:n, max_fan_in, 1); % skip the empty set + nSS = length(SS); + bic_score = zeros(1, nSS); + ll_score = zeros(1, nSS); + target = n+1; + ns2 = [ns ns(i)]; + for h=1:nSS + ps = SS{h}; + dom = [ps target]; + counts = compute_counts(D(dom, :), ns2(dom)); + CPT = mk_stochastic(counts); + [bic_score(h), ll_score(h)] = bic_score_family(counts, CPT, ndata); + end + if penalty == 0 + h = argmax(ll_score); + else + h = argmax(bic_score); + end + ps = SS{h}; + inter(ps, i) = 1; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/learning/learn_struct_mcmc.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,284 @@ +function [sampled_graphs, accept_ratio, num_edges] = learn_struct_mcmc(data, ns, varargin) +% MY_LEARN_STRUCT_MCMC Monte Carlo Markov Chain search over DAGs assuming fully observed data +% [sampled_graphs, accept_ratio, num_edges] = learn_struct_mcmc(data, ns, ...) +% +% data(i,m) is the value of node i in case m. +% ns(i) is the number of discrete values node i can take on. +% +% sampled_graphs{m} is the m'th sampled graph. +% accept_ratio(t) = acceptance ratio at iteration t +% num_edges(t) = number of edges in model at iteration t +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% scoring_fn - 'bayesian' or 'bic' [ 'bayesian' ] +% Currently, only networks with all tabular nodes support Bayesian scoring. +% type - type{i} is the type of CPD to use for node i, where the type is a string +% of the form 'tabular', 'noisy_or', 'gaussian', etc. [ all cells contain 'tabular' ] +% params - params{i} contains optional arguments passed to the CPD constructor for node i, +% or [] if none. [ all cells contain {'prior', 1}, meaning use uniform Dirichlet priors ] +% discrete - the list of discrete nodes [ 1:N ] +% clamped - clamped(i,m) = 1 if node i is clamped in case m [ zeros(N, ncases) ] +% nsamples - number of samples to draw from the chain after burn-in [ 100*N ] +% burnin - number of steps to take before drawing samples [ 5*N ] +% init_dag - starting point for the search [ zeros(N,N) ] +% +% e.g., samples = my_learn_struct_mcmc(data, ns, 'nsamples', 1000); +% +% Modified by Sonia Leach (SML) 2/4/02, 9/5/03 + + + +[n ncases] = size(data); + + +% set default params +type = cell(1,n); +params = cell(1,n); +for i=1:n + type{i} = 'tabular'; + %params{i} = { 'prior', 1}; + params{i} = { 'prior_type', 'dirichlet', 'dirichlet_weight', 1 }; +end +scoring_fn = 'bayesian'; +discrete = 1:n; +clamped = zeros(n, ncases); +nsamples = 100*n; +burnin = 5*n; +dag = zeros(n); + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'nsamples', nsamples = args{i+1}; + case 'burnin', burnin = args{i+1}; + case 'init_dag', dag = args{i+1}; + case 'scoring_fn', scoring_fn = args{i+1}; + case 'type', type = args{i+1}; + case 'discrete', discrete = args{i+1}; + case 'clamped', clamped = args{i+1}; + case 'params', if isempty(args{i+1}), params = cell(1,n); else params = args{i+1}; end + end +end + +% We implement the fast acyclicity check described by P. Giudici and R. Castelo, +% "Improving MCMC model search for data mining", submitted to J. Machine Learning, 2001. + +% SML: also keep descendant matrix C +use_giudici = 1; +if use_giudici + [nbrs, ops, nodes, A] = mk_nbrs_of_digraph(dag); +else + [nbrs, ops, nodes] = mk_nbrs_of_dag(dag); + A = []; +end + +num_accepts = 1; +num_rejects = 1; +T = burnin + nsamples; +accept_ratio = zeros(1, T); +num_edges = zeros(1, T); +sampled_graphs = cell(1, nsamples); +%sampled_bitv = zeros(nsamples, n^2); + +for t=1:T + [dag, nbrs, ops, nodes, A, accept] = take_step(dag, nbrs, ops, ... + nodes, ns, data, clamped, A, ... + scoring_fn, discrete, type, params); + num_edges(t) = sum(dag(:)); + num_accepts = num_accepts + accept; + num_rejects = num_rejects + (1-accept); + accept_ratio(t) = num_accepts/num_rejects; + if t > burnin + sampled_graphs{t-burnin} = dag; + %sampled_bitv(t-burnin, :) = dag(:)'; + end +end + + +%%%%%%%%% + + +function [new_dag, new_nbrs, new_ops, new_nodes, A, accept] = ... + take_step(dag, nbrs, ops, nodes, ns, data, clamped, A, ... + scoring_fn, discrete, type, params, prior_w) + + +use_giudici = ~isempty(A); +if use_giudici + [new_dag, op, i, j, new_A] = pick_digraph_nbr(dag, nbrs, ops, nodes,A); % updates A + [new_nbrs, new_ops, new_nodes] = mk_nbrs_of_digraph(new_dag, new_A); +else + d = sample_discrete(normalise(ones(1, length(nbrs)))); + new_dag = nbrs{d}; + op = ops{d}; + i = nodes(d, 1); j = nodes(d, 2); + [new_nbrs, new_ops, new_nodes] = mk_nbrs_of_dag(new_dag); +end + +bf = bayes_factor(dag, new_dag, op, i, j, ns, data, clamped, scoring_fn, discrete, type, params); + +%R = bf * (new_prior / prior) * (length(nbrs) / length(new_nbrs)); +R = bf * (length(nbrs) / length(new_nbrs)); +u = rand(1,1); +if u > min(1,R) % reject the move + accept = 0; + new_dag = dag; + new_nbrs = nbrs; + new_ops = ops; + new_nodes = nodes; +else + accept = 1; + if use_giudici +A = new_A; % new_A already updated in pick_digraph_nbr + end +end + + +%%%%%%%%% + +function bfactor = bayes_factor(old_dag, new_dag, op, i, j, ns, data, clamped, scoring_fn, discrete, type, params) + +u = find(clamped(j,:)==0); +LLnew = score_family(j, parents(new_dag, j), type{j}, scoring_fn, ns, discrete, data(:,u), params{j}); +LLold = score_family(j, parents(old_dag, j), type{j}, scoring_fn, ns, discrete, data(:,u), params{j}); +bf1 = exp(LLnew - LLold); + +if strcmp(op, 'rev') % must also multiply in the changes to i's family + u = find(clamped(i,:)==0); + LLnew = score_family(i, parents(new_dag, i), type{i}, scoring_fn, ns, discrete, data(:,u), params{i}); + LLold = score_family(i, parents(old_dag, i), type{i}, scoring_fn, ns, discrete, data(:,u), params{i}); + bf2 = exp(LLnew - LLold); +else + bf2 = 1; +end +bfactor = bf1 * bf2; + + +%%%%%%%% Giudici stuff follows %%%%%%%%%% + + +% SML: This now updates A as it goes from digraph it choses +function [new_dag, op, i, j, new_A] = pick_digraph_nbr(dag, digraph_nbrs, ops, nodes, A) + +d = sample_discrete(normalise(ones(1, length(digraph_nbrs)))); +%d = myunidrnd(length(digraph_nbrs),1,1); +i = nodes(d, 1); j = nodes(d, 2); +new_dag = digraph_nbrs(:,:,d); +op = ops{d}; +new_A = update_ancestor_matrix(A, op, i, j, new_dag); + + +%%%%%%%%%%%%%% + + +function A = update_ancestor_matrix(A, op, i, j, dag) + +switch op +case 'add', + A = do_addition(A, op, i, j, dag); +case 'del', + A = do_removal(A, op, i, j, dag); +case 'rev', + A = do_removal(A, op, i, j, dag); + A = do_addition(A, op, j, i, dag); +end + + +%%%%%%%%%%%% + +function A = do_addition(A, op, i, j, dag) + +A(j,i) = 1; % i is an ancestor of j +anci = find(A(i,:)); +if ~isempty(anci) + A(j,anci) = 1; % all of i's ancestors are added to Anc(j) +end +ancj = find(A(j,:)); +descj = find(A(:,j)); +if ~isempty(ancj) + for k=descj(:)' + A(k,ancj) = 1; % all of j's ancestors are added to each descendant of j + end +end + +%%%%%%%%%%% +function A = do_removal(A, op, i, j, dag) + +% find all the descendants of j, and put them in topological order + +% SML: originally Kevin had the next line commented and the %* lines +% being used but I think this is equivalent and much less expensive +% I assume he put it there for debugging and never changed it back...? +descj = find(A(:,j)); +%* R = reachability_graph(dag); +%* descj = find(R(j,:)); + +order = topological_sort(dag); + +% SML: originally Kevin used the %* line but this was extracting the +% wrong things to sort +%* descj_topnum = order(descj); +[junk, perm] = sort(order); %SML:node i is perm(i)-TH in order +descj_topnum = perm(descj); %SML:descj(i) is descj_topnum(i)-th in order + +% SML: now re-sort descj by rank in descj_topnum +[junk, perm] = sort(descj_topnum); +descj = descj(perm); + +% Update j and all its descendants +A = update_row(A, j, dag); +for k = descj(:)' + A = update_row(A, k, dag); +end + +%%%%%%%%%%% + +function A = old_do_removal(A, op, i, j, dag) + +% find all the descendants of j, and put them in topological order +% SML: originally Kevin had the next line commented and the %* lines +% being used but I think this is equivalent and much less expensive +% I assume he put it there for debugging and never changed it back...? +descj = find(A(:,j)); +%* R = reachability_graph(dag); +%* descj = find(R(j,:)); + +order = topological_sort(dag); +descj_topnum = order(descj); +[junk, perm] = sort(descj_topnum); +descj = descj(perm); +% Update j and all its descendants +A = update_row(A, j, dag); +for k = descj(:)' + A = update_row(A, k, dag); +end + +%%%%%%%%% + +function A = update_row(A, j, dag) + +% We compute row j of A +A(j, :) = 0; +ps = parents(dag, j); +if ~isempty(ps) + A(j, ps) = 1; +end +for k=ps(:)' + anck = find(A(k,:)); + if ~isempty(anck) + A(j, anck) = 1; + end +end + +%%%%%%%% + +function A = init_ancestor_matrix(dag) + +order = topological_sort(dag); +A = zeros(length(dag)); +for j=order(:)' + A = update_row(A, j, dag); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/learning/learn_struct_pdag_ic_star.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,155 @@ +function [pdag, G] = learn_struct_pdag_ic_star(cond_indep, n, k, varargin) +% LEARN_STRUCT_PDAG_IC_STAR Learn a partially oriented DAG (pattern) with latent +% variables using the IC* algorithm +% P = learn_struct_pdag_ic_star(cond_indep, n, k, ...) +% +% n is the number of nodes. +% k is an optional upper bound on the fan-in (default: n) +% cond_indep is a boolean function that will be called as follows: +% feval(cond_indep, x, y, S, ...) +% where x and y are nodes, and S is a set of nodes (positive integers), +% and ... are any optional parameters passed to this function. +% +% The output P is an adjacency matrix, in which +% P(i,j) = -1 if there is either a latent variable L such that i <-L-> j +% OR there is a directed edge from i->j. +% P(i,j) = -2 if there is a marked directed i-*>j edge. +% P(i,j) = P(j,i) = 1 if there is and undirected edge i--j +% P(i,j) = P(j,i) = 2 if there is a latent variable L such that i<-L->j. +% +% The IC* algorithm learns a latent structure associated with a set of observed +% variables. +% The latent structure revealed is the projection in which every latent variable is +% 1) a root node +% 2) linked to exactly two observed variables. +% Latent variables in the projection are represented using a bidirectional graph, +% and thus remain implicit. +% +% See Pearl, "Causality: Models, Reasoning, and Inference", 2000, p52 for more details. +% Written by Tamar Kushnir, 2000 + +sep = cell(n,n); +ord = 0; +done = 0; +G = ones(n,n); +G = setdiag(G,0); +while ~done + done = 1; + [X,Y] = find(G); + for i=1:length(X) + x = X(i); y = Y(i); + nbrs = mysetdiff(myunion(neighbors(G, x), neighbors(G,y)), [x y]); + if length(nbrs) >= ord & G(x,y) ~= 0 + done = 0; + SS = subsets(nbrs, ord, ord); % all subsets of size ord + for si=1:length(SS) + S = SS{si}; + if feval(cond_indep, x, y, S, varargin{:}) + G(x,y) = 0; + G(y,x) = 0; + sep{x,y} = myunion(sep{x,y}, S); + sep{y,x} = myunion(sep{y,x}, S); + break; % no need to check any more subsets + end + end + end + end + ord = ord + 1; +end + +% Create the minimal pattern, +% i.e., the only directed edges are V structures. +pdag = G; +[X, Y] = find(G); +% We want to generate all unique triples x,y,z +% where y is a common neighbor to x and z +for i=1:length(X) + x = X(i); + y = Y(i); + Z = find(G(y,:)); + Z = mysetdiff(Z, x); + for z=Z(:)' + if G(x,z)==0 & ~ismember(y, sep{x,z}) & ~ismember(y, sep{z,x}) + pdag(x,y) = -1; pdag(y,x) = 0; + pdag(z,y) = -1; pdag(y,z) = 0; + end + end +end + +% Convert the minimal pattern to a complete one using the following rules: +% Rule 1: +% if a and b are non-adjacent nodes with a common neighbor c, +% if a->c and not b->c then c-*>b (marked arrow). +% Rule 2: +% if a and b are adjacent and there is a directed path (marked links) from a to b +% then a->b (add arrowhead). +%Pearl (2000) + +arrowin = [-1 -2 2]; +old_pdag = zeros(n); +iter = 0; +while ~isequal(pdag, old_pdag) + iter = iter + 1; + old_pdag = pdag; + % rule 1 + [X, Y] = find(pdag); + for i=1:length(X) + x = X(i); + y = Y(i); + Z = find(pdag(y,:)); + Z = mysetdiff(Z, x); + for z=Z(:)' + if G(x,z)==0 & ismember(pdag(x,y),arrowin) & ~ismember(pdag(z,y),arrowin) + pdag(y,z) = -2; pdag(z,y) = 0; + end + end + end + % rule 2 + [X, Y] = find(G); + %check all adjacent nodes because if pdag(x,y) = -1 + %and pdag(y,x) = 0 there could still be an bidirected edge between x & y. + for i=1:length(X) + x = X(i); + y = Y(i); + if ~ismember(pdag(x,y), arrowin) %x->y doesn't exist yet + %find marked path from x to y + add_arrow = marked_path(x,y,pdag); + if add_arrow + if pdag(y,x)==-1 %bidirected edge + pdag(x,y) = 2; pdag(y,x) = 2; + else + pdag(x,y) = -1;pdag(y,x) = 0; + end + end + end + end +end + + +%%%%%%%%%%%%% + +function t = marked_path(x,y,L) +% MARKED_PATH is a boolean function which returns 1 if a marked path +% between nodes x and y exists in the partially directed latent structure L. +% +% t = marked_path(x,y,L) +% +% x and y are the starting and ending nodes in the path, respectively. +% L is a latent structure (partially directed graph with possible latent variables). +% +% Rule 2 of IC* algorithm (see Pearl, 2000) + +t=0; + +%find set of marked links from x +marked = find(L(x,:)==-2); +if ismember(y,marked) + t=1; %marked path found +else + for m=marked(:)' + t = marked_path(m,y,L); + if t==1 + break; %stop when marked path found + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/learning/learn_struct_pdag_pc.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,129 @@ +function [pdag, G] = learn_struct_pdag_pc(cond_indep, n, k, varargin) +% LEARN_STRUCT_PDAG_PC Learn a partially oriented DAG (pattern) using the PC algorithm +% P = learn_struct_pdag_pc(cond_indep, n, k, ...) +% +% n is the number of nodes. +% k is an optional upper bound on the fan-in (default: n) +% cond_indep is a boolean function that will be called as follows: +% feval(cond_indep, x, y, S, ...) +% where x and y are nodes, and S is a set of nodes (positive integers), +% and ... are any optional parameters passed to this function. +% +% The output P is an adjacency matrix, in which +% P(i,j) = -1 if there is an i->j edge. +% P(i,j) = P(j,i) = 1 if there is an undirected edge i <-> j +% +% The PC algorithm does structure learning assuming all variables are observed. +% See Spirtes, Glymour and Scheines, "Causation, Prediction and Search", 1993, p117. +% This algorithm may take O(n^k) time if there are n variables and k is the max fan-in, +% but this is quicker than the Verma-Pearl IC algorithm, which is always O(n^n). + + +sep = cell(n,n); +ord = 0; +done = 0; +G = ones(n,n); +G=setdiag(G,0); +while ~done + done = 1; + [X,Y] = find(G); + for i=1:length(X) + x = X(i); y = Y(i); + %nbrs = mysetdiff(myunion(neighbors(G, x), neighbors(G,y)), [x y]); + nbrs = mysetdiff(neighbors(G, y), x); % bug fix by Raanan Yehezkel <raanany@ee.bgu.ac.il> 6/27/04 + if length(nbrs) >= ord & G(x,y) ~= 0 + done = 0; + %SS = subsets(nbrs, ord, ord); % all subsets of size ord + SS = subsets1(nbrs, ord); + for si=1:length(SS) + S = SS{si}; + if feval(cond_indep, x, y, S, varargin{:}) + %if isempty(S) + % fprintf('%d indep of %d ', x, y); + %else + % fprintf('%d indep of %d given ', x, y); fprintf('%d ', S); + %end + %fprintf('\n'); + + % diagnostic + %[CI, r] = cond_indep_fisher_z(x, y, S, varargin{:}); + %fprintf(': r = %6.4f\n', r); + + G(x,y) = 0; + G(y,x) = 0; + sep{x,y} = myunion(sep{x,y}, S); + sep{y,x} = myunion(sep{y,x}, S); + break; % no need to check any more subsets + end + end + end + end + ord = ord + 1; +end + + +% Create the minimal pattern, +% i.e., the only directed edges are V structures. +pdag = G; +[X, Y] = find(G); +% We want to generate all unique triples x,y,z +% This code generates x,y,z and z,y,x. +for i=1:length(X) + x = X(i); + y = Y(i); + Z = find(G(y,:)); + Z = mysetdiff(Z, x); + for z=Z(:)' + if G(x,z)==0 & ~ismember(y, sep{x,z}) & ~ismember(y, sep{z,x}) + %fprintf('%d -> %d <- %d\n', x, y, z); + pdag(x,y) = -1; pdag(y,x) = 0; + pdag(z,y) = -1; pdag(y,z) = 0; + end + end +end + +% Convert the minimal pattern to a complete one, +% i.e., every directed edge in P is compelled +% (must be directed in all Markov equivalent models), +% and every undirected edge in P is reversible. +% We use the rules of Pearl (2000) p51 (derived in Meek (1995)) + +old_pdag = zeros(n); +iter = 0; +while ~isequal(pdag, old_pdag) + iter = iter + 1; + old_pdag = pdag; + % rule 1 + [A,B] = find(pdag==-1); % a -> b + for i=1:length(A) + a = A(i); b = B(i); + C = find(pdag(b,:)==1 & G(a,:)==0); % all nodes adj to b but not a + if ~isempty(C) + pdag(b,C) = -1; pdag(C,b) = 0; + %fprintf('rule 1: a=%d->b=%d and b=%d-c=%d implies %d->%d\n', a, b, b, C, b, C); + end + end + % rule 2 + [A,B] = find(pdag==1); % unoriented a-b edge + for i=1:length(A) + a = A(i); b = B(i); + if any( (pdag(a,:)==-1) & (pdag(:,b)==-1)' ); + pdag(a,b) = -1; pdag(b,a) = 0; + %fprintf('rule 2: %d -> %d\n', a, b); + end + end + % rule 3 + [A,B] = find(pdag==1); % a-b + for i=1:length(A) + a = A(i); b = B(i); + C = find( (pdag(a,:)==1) & (pdag(:,b)==-1)' ); + % C contains nodes c s.t. a-c->ba + G2 = setdiag(G(C, C), 1); + if any(G2(:)==0) % there are 2 different non adjacent elements of C + pdag(a,b) = -1; pdag(b,a) = 0; + %fprintf('rule 3: %d -> %d\n', a, b); + end + end +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/learning/learn_struct_pdag_pc_constrain.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,177 @@ +function [pdag, G] = dn_learn_struct_pdag_pc_constrain(adj, cond_indep, n, k, varargin) +% LEARN_STRUCT_PDAG_PC Learn a partially oriented DAG (pattern) using the PC algorithm +% Pdag = learn_struct_pdag_pc_constrain(adj, cond_indep, n, k, ...) +% +% adj = adjacency matrix learned from dependency network P(i,j) = 1 => i--j; 0 => i j +% n is the number of nodes. +% k is an optional upper bound on the fan-in (default: n) +% cond_indep is a boolean function that will be called as follows: +% feval(cond_indep, x, y, S, ...) +% where x and y are nodes, and S is a set of nodes (positive integers), +% and ... are any optional parameters passed to this function. +% +%Output +% pdag Partially directed graph +% G Resulting adjacency graph prior to setting direction arrows +% +% The output P is an adjacency matrix, in which +% P(i,j) = -1 if there is an i->j edge. +% P(i,j) = P(j,i) = 1 if there is an undirected edge i <-> j +% +% The PC algorithm does structure learning assuming all variables are observed. +% See Spirtes, Glymour and Scheines, "Causation, Prediction and Search", 1993, p117. +% This algorithm may take O(n^k) time if there are n variables and k is the max fan-in, +% but this is quicker than the Verma-Pearl IC algorithm, which is always O(n^n). +% +%% Example +%% Given data in a comma separated, filename starting with the variable labels, then the data in rows. +%% filename test.txt consists of: +%% +%% Earthquake,Burglar,Radio,Alarm,Call +%% 1,2,2,2,1 +%% 1,1,2,1,2 +%% . . . +%[CovMatrix, obs, varfields] = CovMat('test.txt',5); +% +%dn = zeros(5,5); +%dn(1,2) = 1; % This was the known Markov blanket of the system that generated test.txt +%dn(2,1) = 1; +%dn(2,4) = 1; +%dn(4,2) = 1; +%dn(1,3) = 1; +%dn(3,1) = 1; +%dn(1,4) = 1; +%dn(4,1) = 1; +%dn(4,5) = 1; +%dn(5,4) = 1; +%dn(3,5) = 1; %loop r->c +%dn(5,3) = 1; %loop c-r +%dn(3,4) = 1; +%dn(4,3) = 1; +% +%max_fan_in = 4; +%alpha = 0.05; +% +%[pdag G] = learn_struct_pdag_pc_constrain(dn,'cond_indep_fisher_z', 5, max_fan_in, CovMatrix, obs, alpha); +%% +%% +%% Gary Bradski, 7/2002 Modified this to take an adjacency matrix from a dependency network. + + +sep = cell(n,n); +ord = 0; +done = 0; +G = ones(n,n); +G=setdiag(G,0); + +while ~done + done = 1; + [X,Y] = find(G); + for i=1:length(X) + x = X(i); y = Y(i); +% nbrs = mysetdiff(myunion(neighbors(G, x), neighbors(G,y)), [x y]);%parents, children, but not self + nbrs = mysetdiff(myunion(neighbors(adj, x), neighbors(adj,y)), [x y]);%parents, children, but not self + + if length(nbrs) >= ord & G(x,y) ~= 0 + done = 0; + SS = subsets(nbrs, ord, ord); % all subsets of size ord + for si=1:length(SS) + S = SS{si}; + %if (feval(dsep,x,y,S,adj)) | (feval(cond_indep, x, y, S, varargin{:})) + if feval(cond_indep, x, y, S, varargin{:}) + %if isempty(S) + % fprintf('%d indep of %d ', x, y); + %else + % fprintf('%d indep of %d given ', x, y); fprintf('%d ', S); + %end + %fprintf('\n'); + + % diagnostic + %[CI, r] = cond_indep_fisher_z(x, y, S, varargin{:}); + %fprintf(': r = %6.4f\n', r); + + G(x,y) = 0; + G(y,x) = 0; + adj(x,y) = 0; %make sure found cond. independencies are marked out + adj(y,x) = 0; + sep{x,y} = myunion(sep{x,y}, S); + sep{y,x} = myunion(sep{y,x}, S); + break; % no need to check any more subsets + end + end + end + end + ord = ord + 1; +end + + + + +% Create the minimal pattern, +% i.e., the only directed edges are V structures. + +pdag = G; +[X, Y] = find(G); +% We want to generate all unique triples x,y,z +% This code generates x,y,z and z,y,x. +for i=1:length(X) + x = X(i); + y = Y(i); + Z = find(G(y,:)); + Z = mysetdiff(Z, x); + for z=Z(:)' + if G(x,z)==0 & ~ismember(y, sep{x,z}) & ~ismember(y, sep{z,x}) + %fprintf('%d -> %d <- %d\n', x, y, z); + pdag(x,y) = -1; pdag(y,x) = 0; + pdag(z,y) = -1; pdag(y,z) = 0; + end + end +end + +% Convert the minimal pattern to a complete one, +% i.e., every directed edge in P is compelled +% (must be directed in all Markov equivalent models), +% and every undirected edge in P is reversible. +% We use the rules of Pearl (2000) p51 (derived in Meek (1995)) + +old_pdag = zeros(n); +iter = 0; +while ~isequal(pdag, old_pdag) + iter = iter + 1; + old_pdag = pdag; + % rule 1 + [A,B] = find(pdag==-1); % a -> b + for i=1:length(A) + a = A(i); b = B(i); + C = find(pdag(b,:)==1 & G(a,:)==0); % all nodes adj to b but not a + if ~isempty(C) + pdag(b,C) = -1; pdag(C,b) = 0; + %fprintf('rule 1: a=%d->b=%d and b=%d-c=%d implies %d->%d\n', a, b, b, C, b, C); + end + end + % rule 2 + [A,B] = find(pdag==1); % unoriented a-b edge + for i=1:length(A) + a = A(i); b = B(i); + if any( (pdag(a,:)==-1) & (pdag(:,b)==-1)' ); + pdag(a,b) = -1; pdag(b,a) = 0; + %fprintf('rule 2: %d -> %d\n', a, b); + end + end + % rule 3 + [A,B] = find(pdag==1); % a-b + for i=1:length(A) + a = A(i); b = B(i); + C = find( (G(a,:)==1) & (pdag(:,b)==-1)' ); + % C contains nodes c s.t. a-c->ba + G2 = setdiag(G(C, C), 1); + if any(G2(:)==0) % there are 2 different non adjacent elements of C + pdag(a,b) = -1; pdag(b,a) = 0; + %fprintf('rule 3: %d -> %d\n', a, b); + end + end +end + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/learning/mcmc_sample_to_hist.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,36 @@ +function mcmc_post = mcmc_sample_to_hist(sampled_graphs, dags) +% MCMC_SAMPLE_TO_HIST Convert a set of sampled dags into a histogram over dags +% hist = mcmc_sample_to_hist(sampled_graphs, dags) +% +% sampled_graphs{m} is the m'th sampled dag +% dags{i} is the i'th dag in the hypothesis space +% hist(i) = Pr(model i | data) + +ndags = length(dags); +nsamples = length(sampled_graphs); +nnodes = length(dags{1}); +% sampled_bitv(m, :) is the m'th sampled graph represented as a vector of n^2 bits, computed +% by stacking the columns of the adjacency matrix vertically. +sampled_bitvs = zeros(nsamples, nnodes*nnodes); +for m=1:nsamples + sampled_bitvs(m, :) = sampled_graphs{m}(:)'; +end + +[ugraphs, I, J] = unique(sampled_bitvs, 'rows'); % each row of ugraphs is a unique bit vector +sampled_indices = subv2ind(2*ones(1,nnodes*nnodes), ugraphs+1); +counts = hist(J, 1:size(ugraphs,1)); % counts(i) = number of times graphs(i,:) occurs in the sample + +mcmc_post = zeros(1, ndags); +for i=1:ndags + bitv = dags{i}(:)'; + % Find the samples that corresponds to this graph by converting the graphs to bitvectors and + % then to integers. + ndx = subv2ind(2*ones(1,nnodes*nnodes), bitv+1); + locn = find(ndx == sampled_indices); + if ~isempty(locn) + mcmc_post(i) = counts(locn); + end +end +mcmc_post = normalise(mcmc_post); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/learning/mk_schedule.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,10 @@ +function schedule = mk_schedule(init_temp, final_temp, anneal_rate) + +init_temp = 10; final_temp = 1e-2; anneal_rate = 0.8; +schedule = []; +temp=init_temp; +schedule = [schedule temp]; +while temp > final_temp + temp = temp * anneal_rate; + schedule = [schedule temp]; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/learning/mk_tetrad_data_file.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,25 @@ +function mk_tetrad_data_file(filename, samples, sig) +% MK_TETRAD_DATA_FILE Make a file containing raw discrete data for input to TETRAD +% mk_tetrad_data_file(filename, samples, sig) +% +% samples(i,j) is the value for case i, variable j +% The resulting file can be used for the 'build' part of Tetrad. +% For details on tetrad, see hss.cmu.edu/html/departments/philosophy/TETRAD/tetrad.html + +[nsamples N] = size(samples); + +fid = fopen(filename, 'w'); +fprintf(fid, '/Raw\n'); +fprintf(fid, '%d\n', nsamples); +for i=1:N + fprintf(fid, 'x%d ', i); +end +fprintf(fid, '\n'); +for i=1:nsamples + fprintf(fid, '%d ', samples(i,:)-1); % tetrad counts from 0 + fprintf(fid, '\n'); +end +%fprintf(fid, '/Knowledge\n'); +%fprintf(fid, 'Significance %4.2f\n', sig); +fclose(fid); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/learning/score_dags.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,67 @@ +function score = score_dags(data, ns, dags, varargin) +% SCORE_DAGS Compute the score of one or more DAGs +% score = score_dags(data, ns, dags, varargin) +% +% data{i,m} = value of node i in case m (can be a cell array). +% node_sizes(i) is the number of size of node i. +% dags{g} is the g'th dag +% score(g) is the score of the i'th dag +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% scoring_fn - 'bayesian' or 'bic' [ 'bayesian' ] +% Currently, only networks with all tabular nodes support Bayesian scoring. +% type - type{i} is the type of CPD to use for node i, where the type is a string +% of the form 'tabular', 'noisy_or', 'gaussian', etc. [ all cells contain 'tabular' ] +% params - params{i} contains optional arguments passed to the CPD constructor for node i, +% or [] if none. [ all cells contain {'prior', 1}, meaning use uniform Dirichlet priors ] +% discrete - the list of discrete nodes [ 1:N ] +% clamped - clamped(i,m) = 1 if node i is clamped in case m [ zeros(N, ncases) ] +% +% e.g., score = score_dags(data, ns, mk_all_dags(n), 'scoring_fn', 'bic', 'params', []); +% +% If the DAGs have a lot of families in common, we can cache the sufficient statistics, +% making this potentially more efficient than scoring the DAGs one at a time. +% (Caching is not currently implemented, however.) + +[n ncases] = size(data); + +% set default params +type = cell(1,n); +params = cell(1,n); +for i=1:n + type{i} = 'tabular'; + params{i} = { 'prior_type', 'dirichlet', 'dirichlet_weight', 1 }; +end +scoring_fn = 'bayesian'; +discrete = 1:n; + +u = [1:ncases]'; % DWH +isclamped = 0; %DWH +clamped = zeros(n, ncases); + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'scoring_fn', scoring_fn = args{i+1}; + case 'type', type = args{i+1}; + case 'discrete', discrete = args{i+1}; + case 'clamped', clamped = args{i+1}, isclamped = 1; %DWH + case 'params', if isempty(args{i+1}), params = cell(1,n); else params = args{i+1}; end + end +end + +NG = length(dags); +score = zeros(1, NG); +for g=1:NG + dag = dags{g}; + for j=1:n + if isclamped %DWH + u = find(clamped(j,:)==0); + end + ps = parents(dag, j); + score(g) = score(g) + score_family(j, ps, type{j}, scoring_fn, ns, discrete, data(:,u), params{j}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/learning/score_dags_old.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,62 @@ +function score = score_dags(data, ns, dags, varargin) +% SCORE_DAGS Compute the score of one or more DAGs +% score = score_dags(data, ns, dags, varargin) +% +% data{i,m} = value of node i in case m (can be a cell array). +% node_sizes(i) is the number of size of node i. +% dags{g} is the g'th dag +% score(g) is the score of the i'th dag +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% scoring_fn - 'bayesian' or 'bic' [ 'bayesian' ] +% Currently, only networks with all tabular nodes support Bayesian scoring. +% type - type{i} is the type of CPD to use for node i, where the type is a string +% of the form 'tabular', 'noisy_or', 'gaussian', etc. [ all cells contain 'tabular' ] +% params - params{i} contains optional arguments passed to the CPD constructor for node i, +% or [] if none. [ all cells contain {'prior', 1}, meaning use uniform Dirichlet priors ] +% discrete - the list of discrete nodes [ 1:N ] +% clamped - clamped(i,m) = 1 if node i is clamped in case m [ zeros(N, ncases) ] +% +% e.g., score = score_dags(data, ns, mk_all_dags(n), 'scoring_fn', 'bic', 'params', []); +% +% If the DAGs have a lot of families in common, we can cache the sufficient statistics, +% making this potentially more efficient than scoring the DAGs one at a time. +% (Caching is not currently implemented, however.) + +[n ncases] = size(data); + +% set default params +type = cell(1,n); +params = cell(1,n); +for i=1:n + type{i} = 'tabular'; + params{i} = { 'prior_type', 'dirichlet', 'dirichlet_weight', 1 }; +end +scoring_fn = 'bayesian'; +discrete = 1:n; +clamped = zeros(n, ncases); + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'scoring_fn', scoring_fn = args{i+1}; + case 'type', type = args{i+1}; + case 'discrete', discrete = args{i+1}; + case 'clamped', clamped = args{i+1}; + case 'params', if isempty(args{i+1}), params = cell(1,n); else params = args{i+1}; end + end +end + +NG = length(dags); +score = zeros(1, NG); +for g=1:NG + dag = dags{g}; + for j=1:n + u = find(clamped(j,:)==0); + ps = parents(dag, j); + score(g) = score(g) + score_family(j, ps, type{j}, scoring_fn, ns, discrete, data(:,u), params{j}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/learning/score_family.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,57 @@ +function score = score_family(j, ps, node_type, scoring_fn, ns, discrete, data, args) +% SCORE_FAMILY_COMPLETE Compute the score of a node and its parents given completely observed data +% score = score_family(j, ps, node_type, scoring_fn, ns, discrete, data, args) +% +% data(i,m) is the value of node i in case m (can be a cell array) +% args is a cell array containing optional arguments passed to the constructor, +% or is [] if none +% +% We create a whole Bayes net which only connects parents to node, +% where node has a CPD of the specified type (with default parameters). +% We then evaluate its score ('bic' or 'bayesian') + +% We should use a cache to avoid unnecessary computation. +% In particular, log_marginal_prob_node for tabular CPDs calls gammaln +% and compute_counts, both of which are slow. + +[n ncases] = size(data); +dag = zeros(n,n); +% SML added to sort ps b/c mk_bnet, learn_params use sorted ps to make +% CPTs +% Kevin had: if ~isempty(ps), dag(ps, j) = 1; end +if ~isempty(ps), dag(ps, j) = 1;, ps = sort(ps);, end + +bnet = mk_bnet(dag, ns, 'discrete', discrete); +%bnet.CPD{j} = xxx_CPD(bnet, j); +%eval(sprintf('bnet.CPD{j} = %s_CPD(bnet, j);', node_type)); +fname = sprintf('%s_CPD', node_type); +%fprintf('score CPD %d\n', j); +if isempty(args) + bnet.CPD{j} = feval(fname, bnet, j); +else + bnet.CPD{j} = feval(fname, bnet, j, args{:}); +end +switch scoring_fn + case 'bic', + fam = [ps j]; + %score = BIC_score_CPD(bnet.CPD{j}, fam, data, ns, bnet.cnodes); + %bnet.CPD{j} = learn_params(bnet.CPD{j}, fam, data, ns, bnet.cnodes); + + % SML 03/16/04 had to special case gaussian b/c generic_CPD/learn_params + % no longer supported because of simple interface to learn_params + % introduced by KPM for tabular nodes below: + % KPM 9 June 04 - tabular nodes have changed back! + if 1 % (isempty(find(j==discrete))) + bnet.CPD{j} = learn_params(bnet.CPD{j}, fam, data, ns, bnet.cnodes); + else + bnet.CPD{j} = learn_params(bnet.CPD{j}, data(fam, :)); + end + L = log_prob_node(bnet.CPD{j}, data(j,:), data(ps,:)); + S = struct(bnet.CPD{j}); % violate object privacy + score = L - 0.5*S.nparams*log(ncases); + case 'bayesian', + %score = bayesian_score_CPD(bnet.CPD{j}, data(fam, :)); + score = log_marg_prob_node(bnet.CPD{j}, data(j,:), data(ps,:)); + otherwise, + error(['unrecognized scoring fn ' scoring_fn]); +end
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IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING +WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR +REDISTRIBUTE THE LIBRARY AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, +INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING +OUT OF THE USE OR INABILITY TO USE THE LIBRARY (INCLUDING BUT NOT LIMITED TO +LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR +THIRD PARTIES OR A FAILURE OF THE LIBRARY TO OPERATE WITH ANY OTHER +SOFTWARE), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE +POSSIBILITY OF SUCH DAMAGES. + +END OF TERMS AND CONDITIONS + +This library is free software; you can redistribute it and/or +modify it under the terms of the GNU Library General Public +License version 2 as published by the Free Software Foundation. + +This library is distributed in the hope that it will be useful, +but WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cgpot/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +/cg_can_to_mom.m/1.1.1.1/Wed May 29 15:59:58 2002// +/cg_mom_to_can.m/1.1.1.1/Wed May 29 15:59:58 2002// +/cgpot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/display.m/1.1.1.1/Wed May 29 15:59:58 2002// +/divide_by_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/domain_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/enter_cts_evidence_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/enter_discrete_evidence_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/marginalize_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/multiply_by_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/multiply_pots.m/1.1.1.1/Wed May 29 15:59:58 2002// +/normalize_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/pot_to_marginal.m/1.1.1.1/Wed May 29 15:59:58 2002// +/set_domain_pot.m/1.1.1.1/Wed Jul 30 13:38:24 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cgpot/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cgpot/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/potentials/@cgpot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cgpot/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cgpot/Old/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,4 @@ +/normalize_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/set_domain_pot.m/1.1.1.1/Wed Jul 30 13:38:08 2003// +/simple_marginalize_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cgpot/Old/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/potentials/@cgpot/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cgpot/Old/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cgpot/Old/normalize_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,24 @@ +function [pot, loglik] = normalize_pot(pot) +% NORMALIZE_POT Convert the CG potential Pr(X,E) into Pr(X|E) and return log Pr(E). +% [pot, loglik] = normalize_pot(pot) + +% Marginalize down to [], so that the normalizing constant becomes Pr(E) +temp = marginalize_pot(cg_can_to_mom(pot), []); +%loglik = temp.mom{1}.logp; +[temp2, loglik] = normalize_pot(temp.mom{1}); + +% Adjust scale factor to reflect the fact that the pot now represents Pr(X | E) instead of Pr(X,E). + +scale = -loglik; +if 1 +switch pot.subtype + case 'm' + for i=1:pot.dsize + pot.mom{i} = rescale_pot(pot.mom{i}, scale); + end + case 'c' + for i=1:pot.dsize + pot.can{i} = rescale_pot(pot.can{i}, scale); + end +end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cgpot/Old/set_domain_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,9 @@ +function pot = set_domain_pot(pot, domain) +% SET_DOMAIN_POT Change the domain of a potential (cgpot) +% pot = set_domain_pot(pot, domain) + +delta = domain(1) - pot.domain(1); +assert(all(domain == pot.domain + delta)); +pot.domain = pot.domain + delta; +pot.ddom = pot.ddom + delta; +pot.cdom = pot.cdom + delta;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cgpot/Old/simple_marginalize_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,86 @@ +function smallpot = marginalize_pot(bigpot, keep) +% MARGINALIZE_POT Marginalize a cgpot onto a smaller domain. +% smallpot = marginalize_pot(bigpot, keep) + +sumover = mysetdiff(bigpot.domain, keep); +csumover = myintersect(sumover, bigpot.cdom); +dsumover = myintersect(sumover, bigpot.ddom); +dkeep = myintersect(keep, bigpot.ddom); +ckeep = myintersect(keep, bigpot.cdom); +%ns = sparse(1, max(bigpot.domain)); % must be full, so I is an integer +ns = zeros(1, max(bigpot.domain)); +ns(bigpot.ddom) = bigpot.dsizes; +ns(bigpot.cdom) = bigpot.csizes; + +% sum(ns(csumover))==0 is like isempty(csumover) but handles observed nodes. +% Similarly, prod(ns(dsumover))==1 is like isempty(dsumover) + +% Marginalize the cts parts. +% If we are in canonical form, we stay that way, since moment form might not exist. +% Besides, we would like to minimize the number of conversions. +if sum(ns(csumover)) > 0 + if bigpot.subtype == 'm' + for i=1:bigpot.dsize + bigpot.mom{i} = marginalize_pot(bigpot.mom{i}, ckeep); + end + else + for i=1:bigpot.dsize + bigpot.can{i} = marginalize_pot(bigpot.can{i}, ckeep); + end + end +end + +% If we are not marginalizing over any discrete nodes, we are done. +if prod(ns(dsumover))==1 + smallpot = cgpot(dkeep, ckeep, ns, bigpot.can, bigpot.mom, bigpot.subtype); + return; +end + +% To marginalize the discrete parts, we must be in moment form. +bigpot = cg_can_to_mom(bigpot); + +I = prod(ns(dkeep)); +J = prod(ns(dsumover)); +C = sum(ns(ckeep)); + +% Reshape bigpot into the form mu1(:,j,i), where i is in dkeep, j is in dsumover +T1 = zeros(I,J); +mu1 = zeros(C,J,I); +Sigma1 = zeros(C,C,J,I); +sum_map = find_equiv_posns(dsumover, bigpot.ddom); +keep_map = find_equiv_posns(dkeep, bigpot.ddom); +iv = zeros(1, length(bigpot.ddom)); % index vector +for i=1:I + keep_iv = ind2subv(ns(dkeep), i); + iv(keep_map) = keep_iv; + for j=1:J + sum_iv = ind2subv(ns(dsumover), j); + iv(sum_map) = sum_iv; + k = subv2ind(ns(bigpot.ddom), iv); + mom = struct(bigpot.mom{k}); % violate object privacy + T1(i,j) = exp(mom.logp); + if C > 0 % so mu1 and Sigma1 are non-empty + mu1(:,j,i) = mom.mu; + Sigma1(:,:,j,i) = mom.Sigma; + end + end +end + +% Collapse the mixture of Gaussians +coef = mk_stochastic(T1); % coef must be convex combination +T2 = sum(T1,2); +T2 = T2 + (T2==0)*eps; +%if C > 0, disp('collapsing onto '); disp(leep); end +mu = []; +Sigma = []; +mom = cell(1,I); +for i=1:I + if C > 0 + [mu, Sigma] = collapse_mog(mu1(:,:,i), Sigma1(:,:,:,i), coef(i,:)); + end + logp = log(T2(i)); + mom{i} = mpot(ckeep, ns(ckeep), logp, mu, Sigma); +end + +smallpot = cgpot(dkeep, ckeep, ns, [], mom, 'm'); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cgpot/cg_can_to_mom.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,10 @@ +function pot = cg_can_to_mom(pot) +% CG_CAN_TO_MOM Convert a CG potential from canonical to moment form, if necessary. +% pot = cg_can_to_mom(pot) + +if pot.subtype ~= 'm' + for i=1:pot.dsize + pot.mom{i} = cpot_to_mpot(pot.can{i}); + end + pot.subtype = 'm'; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cgpot/cg_mom_to_can.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,10 @@ +function pot = cg_mom_to_can(pot) +% CG_MOM_TO_CAN Convert a CG potential from moment to canonical form, if necessary. +% pot = cg_mom_to_can(pot) + +if pot.subtype ~= 'c' + for i=1:pot.dsize + pot.can{i} = mpot_to_cpot(pot.mom{i}); + end + pot.subtype = 'c'; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cgpot/cgpot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,38 @@ +function pot = cgpot(ddom, cdom, node_sizes, can, mom, subtype) +% CPOT Make a canonical CG potential. +% function pot = cgpot(ddom, cdom, node_sizes, can, mom, subtype) +% +% node_sizes(i) is the size of the i'th node. +% can and mom default to 0s. +% subtype defaults to 'c'. + +if nargin < 6, subtype = 'c'; end + +pot.ddom = ddom; +pot.cdom = cdom; +node_sizes = node_sizes(:)'; % row vectors print better +pot.domain = myunion(ddom, cdom); +pot.dsizes = node_sizes(pot.ddom); +pot.dsize = prod(node_sizes(pot.ddom)); +pot.csizes = node_sizes(pot.cdom); +pot.csize = sum(node_sizes(pot.cdom)); +pot.subtype = subtype; + +if nargin < 4 + can = cell(1, pot.dsize); + for i=1:pot.dsize + can{i} = cpot(cdom, node_sizes(cdom)); + end +end +pot.can = can; + +if nargin < 5 + mom = cell(1, pot.dsize); + for i=1:pot.dsize + mom{i} = mpot(cdom, node_sizes(cdom)); + end +end +pot.mom = mom; + +pot = class(pot, 'cgpot'); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cgpot/display.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,4 @@ +function display(pot) + +disp('conditional Gaussian potential object'); +disp(struct(pot));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cgpot/divide_by_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,19 @@ +function bigpot = divide_by_pot(bigpot, smallpot) +% DIVIDE_BY_POT bigpot /= smallpot for cgpot +% bigpot = divide_by_pot(bigpot, smallpot) +% +% smallpot's domain must be a subset of bigpot's domain. + +bigpot = cg_mom_to_can(bigpot); +smallpot = cg_mom_to_can(smallpot); + +mask = find_equiv_posns(smallpot.ddom, bigpot.ddom); +for i=1:bigpot.dsize + if isempty(smallpot.ddom) + src = 1; + else + sub = ind2subv(bigpot.dsizes, i); + src = subv2ind(smallpot.dsizes, sub(mask)); + end + bigpot.can{i} = divide_by_pot(bigpot.can{i}, smallpot.can{src}); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cgpot/domain_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function dom = domain_pot(pot) +% DOMAIN_POT Return the domain of this cgpot. +% dom = domain_pot(pot) + +dom = pot.domain;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cgpot/enter_cts_evidence_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +function pot = enter_cts_evidence_pot(pot, Y, y) +% function pot = enter_cts_evidence_pot(pot, Y, y) cgpot + + +pot = cg_mom_to_can(pot); +for i=1:pot.dsize + pot.can{i} = enter_cts_evidence_pot(pot.can{i}, Y, y); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cgpot/enter_discrete_evidence_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +function pot = enter_discrete_evidence_pot(pot, Y, y) + +%ns = sparse(1, max(pot.domain)); +ns = zeros(1, max(pot.domain)); +ns(pot.ddom) = pot.dsizes; +ns(pot.cdom) = pot.csizes; + +ddom = pot.ddom; +S = prod(ns(ddom)); +sub = ind2subv(ns(ddom), 1:S); +mask = find_equiv_posns(Y, ddom); +sub(mask) = y; +ndx = subv2ind(ns(ddom), sub); + +pot.can = pot.can(ndx); +pot.mom = pot.mom(ndx);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cgpot/marginalize_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,153 @@ +function smallpot = marginalize_pot(bigpot, keep, maximize, useC) +% MARGINALIZE_POT Marginalize a cgpot onto a smaller domain. +% smallpot = marginalize_pot(bigpot, keep, maximize, useC) +% +% If maximize = 1, we raise an error. +% useC is ignored. + +if nargin < 3, maximize = 0; end +assert(~maximize); + + +sumover = mysetdiff(bigpot.domain, keep); +csumover = myintersect(sumover, bigpot.cdom); +dsumover = myintersect(sumover, bigpot.ddom); +dkeep = myintersect(keep, bigpot.ddom); +ckeep = myintersect(keep, bigpot.cdom); +%ns = sparse(1, max(bigpot.domain)); % must be full, so I is an integer +ns = zeros(1, max(bigpot.domain)); +ns(bigpot.ddom) = bigpot.dsizes; +ns(bigpot.cdom) = bigpot.csizes; + +% sum(ns(csumover))==0 is like isempty(csumover) but handles observed nodes. +% Similarly, prod(ns(dsumover))==1 is like isempty(dsumover) + +% Marginalize the cts parts. +% If we are in canonical form, we stay that way, since moment form might not exist. +% Besides, we would like to minimize the number of conversions. +if sum(ns(csumover)) > 0 + if bigpot.subtype == 'm' + for i=1:bigpot.dsize + bigpot.mom{i} = marginalize_pot(bigpot.mom{i}, ckeep); + end + else + for i=1:bigpot.dsize + bigpot.can{i} = marginalize_pot(bigpot.can{i}, ckeep); + end + end +end + +% If we are not marginalizing over any discrete nodes, we are done. +if prod(ns(dsumover))==1 + smallpot = cgpot(dkeep, ckeep, ns, bigpot.can, bigpot.mom, bigpot.subtype); + return; +end + +% To marginalize the discrete parts, we partition the cts parts into those that depend +% on dkeep (i) and those that depend on on dsumover (j). + +I = prod(ns(dkeep)); +J = prod(ns(dsumover)); +C = sum(ns(ckeep)); +sum_map = find_equiv_posns(dsumover, bigpot.ddom); +keep_map = find_equiv_posns(dkeep, bigpot.ddom); +iv = zeros(1, length(bigpot.ddom)); % index vector + +% If in canonical form, marginalize if possible, else convert to moment form. +if 0 & bigpot.subtype == 'c' + p1 = zeros(I,J); + h1 = zeros(C,J,I); + K1 = zeros(C,C,J,I); + for i=1:I + keep_iv = ind2subv(ns(dkeep), i); + iv(keep_map) = keep_iv; + for j=1:J + sum_iv = ind2subv(ns(dsumover), j); + iv(sum_map) = sum_iv; + k = subv2ind(ns(bigpot.ddom), iv); + can = struct(bigpot.can{k}); % violate object privacy + p1(i,j) = exp(can.g); + if C > 0 % so mu1 and Sigma1 are non-empty + h1(:,j,i) = can.h; + K1(:,:,j,i) = can.K; + end + end + end + + % If the cts parts do not depend on j, we can just marginalize the weighting coefficient g. + jdepends = 0; + for i=1:I + for j=2:J + if ~approxeq(h1(:,j,i), h1(:,1,i)) | ~approxeq(K1(:,:,j,i), K1(:,:,1,i)) + jdepends = 1; + break + end + end + end + + if ~jdepends + %g2 = log(sum(p1, 2)); + g2 = zeros(I,1); + for i=1:I + s = sum(p1(i,:)); + if s > 0 + g2(i) = log(s); + end + end + h2 = h1; + K2 = K1; + can = cell(1,I); + j = 1; % arbitrary + for i=1:I + can{i} = cpot(ckeep, ns(ckeep), g2(i), h2(:,j,i), K2(:,:,j,i)); + end + smallpot = cgpot(dkeep, ckeep, ns, can, [], 'c'); + return; + else + % Since the cts parts depend on j, we must convert to moment form + bigpot = cg_can_to_mom(bigpot); + end +end + + +% Marginalize in moment form +bigpot = cg_can_to_mom(bigpot); + +% Now partition the moment components. +T1 = zeros(I,J); +mu1 = zeros(C,J,I); +Sigma1 = zeros(C,C,J,I); +for i=1:I + keep_iv = ind2subv(ns(dkeep), i); + iv(keep_map) = keep_iv; + for j=1:J + sum_iv = ind2subv(ns(dsumover), j); + iv(sum_map) = sum_iv; + k = subv2ind(ns(bigpot.ddom), iv); + mom = struct(bigpot.mom{k}); % violate object privacy + T1(i,j) = exp(mom.logp); + if C > 0 % so mu1 and Sigma1 are non-empty + mu1(:,j,i) = mom.mu; + Sigma1(:,:,j,i) = mom.Sigma; + end + end +end + +% Collapse the mixture of Gaussians +coef = mk_stochastic(T1); % coef must be convex combination +T2 = sum(T1,2); +T2 = T2 + (T2==0)*eps; +%if C > 0, disp('collapsing onto '); disp(leep); end +mu = []; +Sigma = []; +mom = cell(1,I); +for i=1:I + if C > 0 + [mu, Sigma] = collapse_mog(mu1(:,:,i), Sigma1(:,:,:,i), coef(i,:)); + end + logp = log(T2(i)); + mom{i} = mpot(ckeep, ns(ckeep), logp, mu, Sigma); +end + +smallpot = cgpot(dkeep, ckeep, ns, [], mom, 'm'); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cgpot/multiply_by_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,19 @@ +function bigpot = multiply_by_pot(bigpot, smallpot, varargin) +% MULTIPLY_BY_POT bigpot *= smallpot for cgpot +% bigpot = multiply_by_pot(bigpot, smallpot) +% +% smallpot's domain must be a subset of bigpot's domain. + +bigpot = cg_mom_to_can(bigpot); +smallpot = cg_mom_to_can(smallpot); + +mask = find_equiv_posns(smallpot.ddom, bigpot.ddom); +for i=1:bigpot.dsize + if isempty(smallpot.ddom) + src = 1; + else + sub = ind2subv(bigpot.dsizes, i); + src = subv2ind(smallpot.dsizes, sub(mask)); + end + bigpot.can{i} = multiply_by_pot(bigpot.can{i}, smallpot.can{src}); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cgpot/multiply_pots.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +function T = multiply_pots(T1, T2) +% MULTIPLY_POTS Multiply a pair of dpots together pointwise (cgpot) +% T = multiply_pots(pots) + +ddom = myunion(T1.ddom, T2.ddom); +cdom = myunion(T1.cdom, T2.cdom); +dom = myunion(ddom, cdom); +ns = zeros(1, max(dom)); +ns(T1.ddom) = T1.dsizes; +ns(T2.ddom) = T2.dsizes; +ns(T1.cdom) = T1.csizes; +ns(T2.cdom) = T2.csizes; + +T = cgpot(ddom, cdom, ns); +T = multiply_by_pot(T, T1); +T = multiply_by_pot(T, T2);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cgpot/normalize_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,24 @@ +function [pot, loglik] = normalize_pot(pot) +% NORMALIZE_POT Convert the CG potential Pr(X,E) into Pr(X|E) and return log Pr(E). +% [pot, loglik] = normalize_pot(pot) + +% Marginalize down to [], so that the normalizing constant becomes Pr(E) +temp = cg_can_to_mom(marginalize_pot(pot, [])); +%loglik = temp.mom{1}.logp; +[temp2, loglik] = normalize_pot(temp.mom{1}); + +% Adjust scale factor to reflect the fact that the pot now represents Pr(X | E) instead of Pr(X,E). + +scale = -loglik; +if 1 +switch pot.subtype + case 'm' + for i=1:pot.dsize + pot.mom{i} = rescale_pot(pot.mom{i}, scale); + end + case 'c' + for i=1:pot.dsize + pot.can{i} = rescale_pot(pot.can{i}, scale); + end +end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cgpot/pot_to_marginal.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,27 @@ +function m = pot_to_marginal(pot) +% POT_TO_MARGINAL Convert a cgpot to a marginal structure. +% m = pot_to_marginal(pot) + +pot = cg_can_to_mom(pot); +m.domain = pot.domain; +n = pot.csize; +d = length(pot.mom); +if n==0 + m.mu = []; + m.Sigma = []; +else + m.mu = zeros(n, d); + m.Sigma = zeros(n, n, d); +end +m.T = 0*myones(pot.dsizes); +for i=1:pot.dsize + s = struct(pot.mom{i}); % violate privacy of object + if n > 0 + m.mu(:,i) = s.mu; + m.Sigma(:,:,i) = s.Sigma; + end + m.T(i) = exp(s.logp); +end +if isvector(m.T) + m.T = m.T(:)'; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cgpot/set_domain_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,21 @@ +function pot = set_domain_pot(pot, domain) +% SET_DOMAIN_POT Change the domain of a potential (cgpot) +% pot = set_domain_pot(pot, domain) + +delta = domain(1) - pot.domain(1); +assert(all(domain == pot.domain + delta)); +pot.domain = pot.domain + delta; +pot.ddom = pot.ddom + delta; +pot.cdom = pot.cdom + delta; +cdomain = pot.cdom; +n = prod(pot.dsizes); +if(pot.subtype == 'm') + for i = 1: n + pot.mom{i} = set_domain_pot(pot.mom{i}, cdomain); + end +end +if(pot.subtype == 'c') + for i = 1: n + pot.can{i} = set_domain_pot(pot.can{i}, cdomain); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cpot/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,14 @@ +/cpot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/cpot_to_mpot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/display.m/1.1.1.1/Wed May 29 15:59:58 2002// +/divide_by_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/domain_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/enter_cts_evidence_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/marginalize_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/multiply_by_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/multiply_pots.m/1.1.1.1/Wed May 29 15:59:58 2002// +/normalize_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/pot_to_marginal.m/1.1.1.1/Wed May 29 15:59:58 2002// +/rescale_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/set_domain_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cpot/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cpot/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/potentials/@cpot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cpot/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cpot/Old/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,3 @@ +/cpot_to_mpot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/normalize_pot.convert.m/1.1.1.1/Wed May 29 15:59:58 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cpot/Old/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/potentials/@cpot/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cpot/Old/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cpot/Old/cpot_to_mpot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,25 @@ +function mom = cpot_to_mpot(can) +% CPOT_TO_MPOT Convert a canonical potential to moment form. +% mom = cpot_to_mpot(can) + +[logp, mu, Sigma] = canonical_to_moment(can.g, can.h, can.K); +mom = mpot(can.domain, can.sizes, logp, mu, Sigma); + +%%%%%%% + +function [logp, mu, Sigma] = canonical_to_moment(g, h, K) +% CANONICAL_TO_MOMENT Convert canonical characteristics to moment form. +% [logp, mu, Sigma] = canonical_to_moment(g, h, K) + +if det(K)==0 + Sigma = inf*size(K); +else + Sigma = inv(K); +end +mu = Sigma*h; +n = length(mu); +if isempty(mu) + logp = g - 0.5*(log(det(K)) - n*log(2*pi)); +else + logp = g - 0.5*(log(det(K)) - n*log(2*pi) - mu'*K*mu); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cpot/Old/normalize_pot.convert.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,9 @@ +function [mom2, loglik] = normalize_pot(can) +% NORMALIZE_POT Convert the canonical potential Pr(X,E) into moment potential Pr(X|E) and return log Pr(E). +% [mom, loglik] = normalize_pot(can) + +mom = cpot_to_mpot(can); +mom = struct(mom); % violate privacy of object +loglik = mom.logp; +%mom.logp = 0; % now represents Pr(X | E) instead of Pr(X, E). +mom2 = mpot(mom.domain, mom.sizes, 0, mom.mu, mom.Sigma);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cpot/cpot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,17 @@ +function pot = cpot(members, sizes, g, h, K) +% CPOT Make a canonical Gaussian potential. +% pot = cpot(members, sizes, g, h, K) +% +% All params default to 0 if omitted. + +n = sum(sizes); +if nargin < 3, g = 0; end +if nargin < 4, h = zeros(n,1); end +if nargin < 5, K = zeros(n,n); end + +pot.domain = members; +pot.sizes = sizes(:)'; +pot.g = g; +pot.h = h; +pot.K = K; +pot = class(pot, 'cpot');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cpot/cpot_to_mpot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,29 @@ +function mom = cpot_to_mpot(can) +% CPOT_TO_MPOT Convert a canonical potential to moment form. +% mom = cpot_to_mpot(can) + +[logp, mu, Sigma] = canonical_to_moment(can.g, can.h, can.K); +mom = mpot(can.domain, can.sizes, logp, mu, Sigma); + +%%%%%%% + +function [logp, mu, Sigma] = canonical_to_moment(g, h, K) +% CANONICAL_TO_MOMENT Convert canonical characteristics to moment form. +% [logp, mu, Sigma] = canonical_to_moment(g, h, K) + +n = length(K); +if isempty(K) + logp = g - 0.5*(log(det(K)) - n*log(2*pi)); + Sigma = []; + mu = []; +else + if det(K)==0 + Sigma = inf*ones(n,n); + mu = zeros(n,1); % if the precision is zero, the mean is arbitrary + logp = g; % the scaling factor for the uniform distribution is 1 + else + Sigma = inv(K); + mu = Sigma*h; + logp = g - 0.5*(log(det(K)) - n*log(2*pi) - mu'*K*mu); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cpot/display.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,4 @@ +function display(pot) + +disp('canonical potential object'); +disp(struct(pot));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cpot/divide_by_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function bigpot = divide_by_pot(bigpot, smallpot) +% DIVIDE_BY_POT bigpot /= smallpot for cpot +% bigpot = divide_by_pot(bigpot, smallpot) +% +% smallpot's domain must be a subset of bigpot's domain. + +bigpot.g = bigpot.g - smallpot.g; +if sum(smallpot.sizes) > 0 + mask = find_equiv_posns(smallpot.domain, bigpot.domain); + u = block(mask, bigpot.sizes); + bigpot.h(u) = bigpot.h(u) - smallpot.h; + bigpot.K(u, u) = bigpot.K(u, u) - smallpot.K; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cpot/domain_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function dom = domain_pot(pot) +% DOMAIN_POT Return the domain of this cpot. +% dom = domain_pot(pot) + +dom = pot.domain;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cpot/enter_cts_evidence_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +function pot = enter_cts_evidence_pot(pot, Y, y) +% function pot = enter_cts_evidence_pot(pot, Y, y) (cpot) + +ns = sparse(1, max(pot.domain)); +ns(pot.domain) = pot.sizes; + +X = mysetdiff(pot.domain, Y); +[hx, hy, KXX, KXY, KYX, KYY] = partition_matrix_vec(pot.h, pot.K, X, Y, ns); +pot.g = pot.g + hy'*y - 0.5*y'*KYY*y; +if ~isempty(X) + pot.h = hx - KXY*y; + pot.K = KXX; +end + +pot.sizes(find_equiv_posns(Y,pot.domain)) = 0;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cpot/marginalize_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,31 @@ +function smallpot = marginalize_pot(bigpot, keep, maximize, useC) +% MARGINALIZE_POT Marginalize a cpot onto a smaller domain. +% smallpot = marginalize_pot(bigpot, keep, maximize, useC) +% +% The maximize argument is ignored - maxing out a Gaussian is the same as summing it out, +% since the mode and mean are equal. +% The useC argument is ignored. + +node_sizes = sparse(1, max(bigpot.domain)); +node_sizes(bigpot.domain) = bigpot.sizes; +sum_over = mysetdiff(bigpot.domain, keep); + +if sum(node_sizes(sum_over))==0 % isempty(sum_over) + %smallpot = bigpot; + smallpot = cpot(keep, node_sizes(keep), bigpot.g, bigpot.h, bigpot.K); +else + [h1, h2, K11, K12, K21, K22] = partition_matrix_vec(bigpot.h, bigpot.K, sum_over, keep, node_sizes); + n = length(h1); + K11inv = inv(K11); + g = bigpot.g + 0.5*(n*log(2*pi) - log(det(K11)) + h1'*K11inv*h1); + if length(h2) > 0 % ~isempty(keep) % we are are actually keeping something + A = K21*K11inv; + h = h2 - A*h1; + K = K22 - A*K12; + else + h = []; + K = []; + end + smallpot = cpot(keep, node_sizes(keep), g, h, K); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cpot/multiply_by_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function bigpot = multiply_by_pot(bigpot, smallpot, varargin) +% MULTIPLY_BY_POT bigpot *= smallpot for cpot +% bigpot = multiply_by_pot(bigpot, smallpot) +% +% smallpot's domain must be a subset of bigpot's domain. + +bigpot.g = bigpot.g + smallpot.g; +if sum(smallpot.sizes) > 0 + mask = find_equiv_posns(smallpot.domain, bigpot.domain); + u = block(mask, bigpot.sizes); + bigpot.h(u) = bigpot.h(u) + smallpot.h; + bigpot.K(u, u) = bigpot.K(u, u) + smallpot.K; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cpot/multiply_pots.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function T = multiply_pots(T1, T2) +% MULTIPLY_POTS Multiply a pair of dpots together pointwise (cpot) +% T = multiply_pots(pots) + +dom = myunion(T1.domain, T2.domain); +ns = sparse(1, max(dom)); +ns(T1.domain) = T1.sizes; +ns(T2.domain) = T2.sizes; +T = cpot(dom, ns(dom)); +T = multiply_by_pot(T, T1); +T = multiply_by_pot(T, T2);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cpot/normalize_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,10 @@ +function [pot, loglik] = normalize_pot(pot) +% NORMALIZE_POT Convert the canonical potential Pr(X,E) into Pr(X|E) and return log Pr(E). +% [pot, loglik] = normalize_pot(pot) + +mom = cpot_to_mpot(pot); % move the normalizing constant out of g, to reveal the coefficient +%loglik = scaling_factor_pot(mom); +%loglik = mom.logp; +[temp, loglik] = normalize_pot(mom); +pot.g = pot.g - loglik; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cpot/pot_to_marginal.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +function m = pot_to_marginal(pot) +% POT_TO_MARGINAL Convert a cpot to a marginal structure. +% m = pot_to_marginal(pot) + +mom = cpot_to_mpot(pot); +m = pot_to_marginal(mom);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cpot/rescale_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function pot = rescale_pot(pot, s) +% RESCALE_POT Add a constant to the cpot scale factor. +% pot = rescale_pot(pot, s) + +pot.g = pot.g + s;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@cpot/set_domain_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function pot = set_domain_pot(pot, domain) +% SET_DOMAIN_POT Change the domain of a potential (dpot) +% pot = set_domain_pot(pot, domain) + +pot.domain = domain;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@dpot/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,17 @@ +/approxeq_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/display.m/1.1.1.1/Wed May 29 15:59:58 2002// +/divide_by_pot.m/1.1.1.1/Thu Aug 5 15:25:08 2004// +/domain_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/dpot.m/1.1.1.1/Tue Oct 1 19:04:44 2002// +/dpot_to_table.m/1.1.1.1/Wed May 29 15:59:58 2002// +/find_most_prob_entry.m/1.1.1.1/Sun Jun 16 19:06:20 2002// +/get_fields.m/1.1.1.1/Wed May 29 15:59:58 2002// +/marginalize_pot.m/1.1.1.1/Wed Aug 4 19:59:14 2004// +/multiply_by_pot.m/1.1.1.1/Wed Aug 4 19:59:14 2004// +/multiply_pots.m/1.1.1.1/Wed May 29 15:59:58 2002// +/normalize_pot.m/1.1.1.1/Wed Aug 4 15:54:48 2004// +/pot_to_marginal.m/1.1.1.1/Wed May 29 15:59:58 2002// +/set_domain_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/subsasgn.m/1.1.1.1/Wed Apr 27 18:34:48 2005// +/subsref.m/1.1.1.1/Wed Apr 27 18:34:48 2005// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@dpot/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/potentials/@dpot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@dpot/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@dpot/approxeq_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function p = approxeq_pot(A, B, tol) + +if nargin < 3, tol = 1e-3; end + +p = approxeq(A.T, B.T, tol);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@dpot/display.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,4 @@ +function display(pot) + +disp('discrete potential object'); +disp(struct(pot));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@dpot/divide_by_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,14 @@ +function Tbig = divide_by_pot(Tbig, Tsmall) +% DIVIDE_BY_POT Tbig /= Tsmall +% Tbig = divide_by_pot(Tbig, Tsmall) +% +% Tsmall's domain must be a subset of Tbig's domain. + +%process sparse dpot, we do not concern only one of the two pots is sparse +if issparse(Tbig.T) & issparse(Tsmall.T) + Tbig.T = divide_by_sparse_table(Tbig.T, Tbig.domain, Tbig.sizes, Tsmall.T, Tsmall.domain, Tsmall.sizes); +else + Tbig.T = divide_by_table(Tbig.T, Tbig.domain, Tbig.sizes, Tsmall.T, Tsmall.domain, Tsmall.sizes); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@dpot/domain_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function dom = domain_pot(pot) +% DOMAIN_POT Return the domain of this dpot. +% dom = domain_pot(pot) + +dom = pot.domain;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@dpot/dpot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,26 @@ +function pot = dpot(domain, sizes, T) +% DPOT Make a discrete (sparse) potential. +% pot = dpot(domain, sizes, T, spar) +% +% sizes(i) is the size of the i'th domain element. +% T defaults to all 1s. + +%assert(length(sizes) == length(domain)); + +pot.domain = domain(:)'; % so we can see it when we display +if nargin < 3 + pot.T = myones(sizes); + %pot.T = ones(1,prod(sizes)); % 1D vector +else + if isempty(T) + pot.T = []; + else + if issparse(T) + pot.T = T; + else + pot.T = myreshape(T, sizes); + end + end +end +pot.sizes = sizes(:)'; +pot = class(pot, 'dpot');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@dpot/dpot_to_table.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,3 @@ +function T = dpot_to_table(pot) + +T = pot.T;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@dpot/find_most_prob_entry.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,10 @@ +function [indices, pot] = find_most_prob_entry(pot) +% function [indices, pot] = find_most_prob_entry(pot) +% function [indices, pot] = find_most_prob_entry(pot) +% Find the indices of the argmax, and set all other enties to 0. + +%indices = argmax(pot.T); +[m i] = max(pot.T(:)); +indices = ind2subv(pot.sizes, i); +pot.T = 0*myones(pot.sizes); +pot.T(i) = m;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@dpot/get_fields.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function val = get_params(pot, name) +% GET_PARAMS Accessor function for a field (dpot) +% val = get_params(pot, name) +% +% e.g., get_params(pot, 'table') or 'domain' + +switch name + case 'table', val = pot.T; + case 'domain', val = pot.domain; + otherwise, + error(['invalid field name ' name]); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@dpot/marginalize_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,17 @@ +function smallpot = marginalize_pot(bigpot, onto, maximize) +% MARGINALIZE_POT Marginalize a dpot onto a smaller domain. +% smallpot = marginalize_pot(bigpot, onto, maximize) +% +% 'onto' must be in ascending order. + +if nargin < 3, maximize = 0; end + +ns = zeros(1, max(bigpot.domain)); +ns(bigpot.domain) = bigpot.sizes; +%assert(isequal(bigpot.sizes, mysize(bigpot.T))); % may fail if there are trailing dimensions of size 1 +if issparse(bigpot.T) + smallT = marg_sparse_table(bigpot.T, bigpot.domain, bigpot.sizes, onto, maximize); +else + smallT = marg_table(bigpot.T, bigpot.domain, bigpot.sizes, onto, maximize); +end +smallpot = dpot(onto, ns(onto), smallT);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@dpot/multiply_by_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function Tbig = multiply_by_pot(Tbig, Tsmall) +% MULTIPLY_BY_POT Tbig *= Tsmall +% Tbig = multiply_by_pot(Tbig, Tsmall) +% +% Tsmall's domain must be a subset of Tbig's domain. + +%process sparse dpot, we do not consider only one of the two pots is sparse +if issparse(Tbig.T) & issparse(Tsmall.T) + Tbig.T = mult_by_sparse_table(Tbig.T, Tbig.domain, Tbig.sizes, Tsmall.T, Tsmall.domain, Tsmall.sizes); +else + Tbig.T = mult_by_table(Tbig.T, Tbig.domain, Tbig.sizes, Tsmall.T, Tsmall.domain, Tsmall.sizes); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@dpot/multiply_pots.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +function T = multiply_pots(T1, T2) +% MULTIPLY_POTS Multiply a pair of dpots together pointwise. +% T = multiply_pots(pots) + +dom = myunion(T1.domain, T2.domain); +%ns = sparse(1, max(dom)); % causes problems in myreshape on NT +ns = zeros(1, max(dom)); +ns(T1.domain) = T1.sizes; +ns(T2.domain) = T2.sizes; +T = dpot(dom, ns(dom)); +T = multiply_by_pot(T, T1); +T = multiply_by_pot(T, T2);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@dpot/normalize_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +function [pot, loglik] = normalize_pot(pot) +% NORMALIZE_POT Convert the discrete potential Pr(X,E) into Pr(X|E) and return log Pr(E). +% [pot, loglik] = normalize_pot(pot) + +if isempty(pot.T) %add to process sparse + loglik = 0; + return; +end +[pot.T, lik] = normalise(pot.T); +loglik = log(lik + (lik==0)*eps); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@dpot/pot_to_marginal.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +function m = pot_to_marginal(pot) +% POT_TO_MARGINAL Convert a dpot to a marginal structure. +% m = pot_to_marginal(pot) + +m.domain = pot.domain; +m.T = pot.T; +m.mu = []; +m.Sigma = []; + +%if isvector(m.T) +% m.T = m.T(:); +%end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@dpot/set_domain_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function pot = set_domain_pot(pot, domain) +% SET_DOMAIN_POT Change the domain of a potential (dpot) +% pot = set_domain_pot(pot, domain) + +pot.domain = domain;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@dpot/subsasgn.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,3 @@ +function B = subsasgn(A, S, B) + +B = builtin('subsasgn', A, S, B);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@dpot/subsref.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,3 @@ +function B = subsref(A, S) + +B = builtin('subsref', A, S);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@mpot/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,9 @@ +/display.m/1.1.1.1/Wed May 29 15:59:58 2002// +/marginalize_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/mpot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/mpot_to_cpot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/normalize_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/pot_to_marginal.m/1.1.1.1/Wed May 29 15:59:58 2002// +/rescale_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/set_domain_pot.m/1.1.1.1/Wed Jul 30 13:37:52 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@mpot/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/potentials/@mpot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@mpot/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@mpot/display.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,4 @@ +function display(pot) + +disp('moment Gaussian potential object'); +disp(struct(pot));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@mpot/marginalize_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,27 @@ +function smallpot = marginalize_pot(bigpot, keep, maximize, useC) +% MARGINALIZE_POT Marginalize a mpot onto a smaller domain. +% smallpot = marginalize_pot(bigpot, keep, maximize, useC) +% +% The maximize argument is ignored - maxing out a Gaussian is the same as summing it out, +% since the mode and mean are equal. +% The useC argument is ignored. + + +node_sizes = sparse(1, max(bigpot.domain)); +node_sizes(bigpot.domain) = bigpot.sizes; +sum_over = mysetdiff(bigpot.domain, keep); + +[logp, mu, Sigma] = marginalize_gaussian(bigpot.logp, bigpot.mu, bigpot.Sigma, ... + keep, sum_over, node_sizes); +smallpot = mpot(keep, node_sizes(keep), logp, mu, Sigma); + +%%%%%% + +function [logpX, muX, SXX] = marginalize_gaussian(logp, mu, Sigma, X, Y, ns) +% MARGINALIZE_GAUSSIAN Compute Pr(X) from Pr(X,Y) where X and Y are jointly Gaussian. +% [logpX, muX, SXX] = marginalize_gaussian(logp, mu, Sigma, X, Y, ns) +% +% sizes(i) is the size of the i'th block in domain. + +[muX, muY, SXX, SXY, SYX, SYY] = partition_matrix_vec(mu, Sigma, X, Y, ns); +logpX = logp; % Lauritzen (1996) p161
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@mpot/mpot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,17 @@ +function pot = mpot(members, sizes, logp, mu, Sigma) +% MPOT Make a moment Gaussian potential. +% pot = mpot(members, sizes, logp, mu, Sigma) +% +% All params default to 0 if omitted. + +n = sum(sizes); +if nargin < 3, logp = 0; end +if nargin < 4, mu = zeros(n,1); end +if nargin < 5, Sigma = zeros(n,n); end + +pot.domain = members; +pot.sizes = sizes; +pot.logp = logp; +pot.mu = mu; +pot.Sigma = Sigma;zeros(n,n); +pot = class(pot, 'mpot');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@mpot/mpot_to_cpot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,21 @@ +function can = mpot_to_cpot(mom) +% MPOT_TO_CPOT Convert a moment potential to canonical form. +% mom = mpot_to_cpot(can) + +[g, h, K] = moment_to_canonical(mom.logp, mom.mu, mom.Sigma); +can = cpot(mom.domain, mom.sizes, g, h, K); + +%%%%%%%%%%% + +function [g, h, K] = moment_to_canonical(logp, mu, Sigma) +% MOMENT_TO_CANONICAL Convert moment characteristics to canonical form. +% [g, h, K] = moment_to_canonical(logp, mu, Sigma) + +K = inv(Sigma); +h = K*mu; +n = length(K); +if isempty(mu) + g = logp + 0.5*(log(det(K)) - n*log(2*pi)); +else + g = logp + 0.5*(log(det(K)) - n*log(2*pi) - mu'*K*mu); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@mpot/normalize_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +function [pot, loglik] = normalize_pot(pot) +% NORMALIZE_POT Convert the moment potential Pr(X,E) into Pr(X|E) and return log Pr(E). +% [pot, loglik] = normalize_pot(pot) + +loglik = pot.logp; +pot.logp = 0;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@mpot/pot_to_marginal.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function m = pot_to_marginal(pot) +% POT_TO_MARGINAL Convert a mpot to a marginal structure. +% m = pot_to_marginal(pot) + +m.domain = pot.domain; +m.T = exp(pot.logp); +m.mu = pot.mu; +m.Sigma = pot.Sigma; + +if isvector(m.T) + m.T = m.T(:)'; +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@mpot/rescale_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function pot = rescale_pot(pot, s) +% RESCALE_POT Add a constant to the mpot scale factor. +% pot = rescale_pot(pot, s) + +pot.logp = pot.logp + s;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@mpot/set_domain_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function pot = set_domain_pot(pot, domain) +% SET_DOMAIN_POT Change the domain of a potential (mpot) +% pot = set_domain_pot(pot, domain) + +pot.domain = domain;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@scgcpot/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +/marginalize_pot.m/1.1.1.1/Sun May 19 22:11:08 2002// +/normalize_pot.m/1.1.1.1/Sun May 19 22:11:08 2002// +/reduce_pot.m/1.1.1.1/Tue Mar 11 17:37:02 2003// +/rescale_pot.m/1.1.1.1/Sun May 19 22:11:08 2002// +/scgcpot.m/1.1.1.1/Sun May 19 22:11:08 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@scgcpot/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/potentials/@scgcpot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@scgcpot/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@scgcpot/marginalize_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +function smallpot = marginalize_pot(bigpot, keepdom, sumoverdom, nodesizes) +% MARGINALIZE_POT Marginalize a mpot onto a smaller domain. +% smallpot = marginalize_pot(bigpot, keep) + +keepsize = sum(nodesizes(keepdom)); +[A1, A2, B1, B2, C11, C12, C21, C22] = partition_matrix_vec_3(bigpot.A, bigpot.B, bigpot.C, keepdom, sumoverdom, nodesizes); +smallpot = scgcpot(keepsize, bigpot.ctailsize, bigpot.p, A1, B1, C11); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@scgcpot/normalize_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +function [pot, loglik] = normalize_pot(pot) +% NORMALIZE_POT Convert the element of stable conditional gaussian potential Pr(X,E) into Pr(X|E) and return log Pr(E). +% [pot, loglik] = normalize_pot(pot) + +loglik = log(pot.p); +pot.p = 1;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@scgcpot/reduce_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,32 @@ +function [reduced_pot,successful] = reduce(pot,tailnodes) +% Executes the reduce operation defined in +% Stable Local Computation with Conditional Gaussian Distributions +% Steffen L. Lauritzen +% Frank Jensen +% September 1999 +% The potential pot is reduced if B contains any zero columns +% The test are restricted to the positions in tailnodes. +% Any columns successfully deleted are entered in the array successful + +if nargin < 2 + tailnodes = 1:pot.ctailsize; +end + +successful = []; + +% Look for all columns beeing equal to zero +for i = tailnodes + if ~any(pot.B(:,i)) + successful = [successful i]; + end +end + +remain = mysetdiff(1:pot.ctailsize,successful); + +% Erase the zero-columns and decrease the tailsize +pot.B = pot.B(:,remain); +pot.ctailsize = pot.ctailsize - length(successful); + +% Return the reduced potential +reduced_pot = pot; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@scgcpot/rescale_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function pot = rescale_pot(pot, s) +% RESCALE_POT Add a constant to the mpot scale factor. +% pot = rescale_pot(pot, s) + +pot.p = pot.p*s;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@scgcpot/scgcpot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,49 @@ +function pot = scgcpot(cheadsize, ctailsize, p, A, B, C) +% SCGCPOT Make a base object of stable conditional gaussian potential. +% pot = scgcpot(cheadsize, ctailsize, p, A, B, C) +% +% cheadsize is the demension of head nodes. +% ctailsize is the demension of tail nodes. +% r = cheadsize, s = ctailsize +% p is discrete probability. +% A is table of r*1 vectors; +% B is r*s matrices +% C is r*r positive semidefinite symmetric matrices + +if nargin < 3 + p = 1; +end +if nargin < 4 + A = zeros(cheadsize,1); +end +if nargin < 5 + B = zeros(cheadsize,ctailsize); +end +if nargin < 6 + C = zeros(cheadsize,cheadsize); +end + +if isempty(A) + A = zeros(cheadsize,1); +end +if isempty(B) + B = zeros(cheadsize,ctailsize); +end +if isempty(C) + C = zeros(cheadsize,cheadsize); +end + +pot.cheadsize = cheadsize; +pot.ctailsize = ctailsize; + +pot.p = p; +pot.A = A; +pot.B = B; +pot.C = C; +%if cheadsize == 0 +% pot.A = []; +%end +%if ctailsize == 0 +% pot.B = []; +%end +pot = class(pot, 'scgcpot');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@scgpot/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +/README/1.1.1.1/Thu Mar 20 15:07:16 2003// +/combine_pots.m/1.1.1.1/Tue Mar 11 17:49:28 2003// +/complement_pot.m/1.1.1.1/Wed May 21 13:49:34 2003// +/direct_combine_pots.m/1.1.1.1/Sun May 19 22:11:08 2002// +/extension_pot.m/1.1.1.1/Fri Jan 24 12:52:34 2003// +/marginalize_pot.m/1.1.1.1/Tue Mar 11 17:06:08 2003// +/normalize_pot.m/1.1.1.1/Wed May 21 13:49:44 2003// +/pot_to_marginal.m/1.1.1.1/Sun May 19 22:11:08 2002// +/recursive_combine_pots.m/1.1.1.1/Wed May 21 13:49:48 2003// +/reduce_pot.m/1.1.1.1/Tue Mar 11 18:07:12 2003// +/scgpot.m/1.1.1.1/Tue Mar 11 14:04:48 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@scgpot/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/potentials/@scgpot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@scgpot/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@scgpot/README Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +% Stable conditional Gaussian inference +% Written by Rainer Deventer + + +@techreport{Lauritzen99, + author = "S. Lauritzen and F. Jensen", + title = "Stable Local Computation with Conditional {G}aussian Distributions", + year = 1999, + number = "R-99-2014", + institution = "Dept. Math. Sciences, Aalborg Univ." +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@scgpot/combine_pots.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,24 @@ +function pot = combine_pots(pot1, pot2) +% COMBINE_POTS combine two potentials +% pot = combine_pots(pot1, pot2) + +% Reduce both potentials before trying to combine them. +% Cf. "Stable Local computation with Conditional Gaussian Distributions", page 9 +% Consider again two potentials with minimal tail + +% Guarantee minimal tails. If pot1 or pot2 are minimal, they are not changed +pot1 = reduce_pot(pot1); +pot2 = reduce_pot(pot2); + +%if the intersect set of these two potentials' head conts. combination is undifined +if ~isempty( myintersect(pot1.cheaddom, pot2.cheaddom) ) + return; +end + +if isempty( myintersect(pot1.domain, pot2.cheaddom) ) | isempty( myintersect(pot2.domain, pot1.cheaddom)) + % if satisfy the condition of directed combine + pot = direct_combine_pots(pot1, pot2); +else + % perform recursive combine + pot = recursive_combine_pots(pot1, pot2); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@scgpot/complement_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,221 @@ +function [margpot, comppot] = complement_pot(pot, keep) +% COMPLEMENT_POT complement means decompose of a potential into its strong marginal and +% its complement corresponds exactly to the decomposition of a probability distribution +% into its marginal and conditional +% [margpot, comppot] = complement_pot(pot, keep) + +% keep can only include continuous head nodes and discrete nodes +% margpot is the stable CG potential of keep nodes +% comppot is the stable CG potential of others in corresponds exactly to +% the discomposition of a probability distribution of its marginal and conditional + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculation of the marginal requires integration over % +% all variables in csumover. Thus cheadkeep contains all % +% continuous variables in the marginal potential % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%keyboard; +csumover = mysetdiff(pot.cheaddom, keep); +cheadkeep = mysetdiff(pot.cheaddom, csumover); + +nodesizes = zeros(1, max(pot.domain)); +nodesizes(pot.ddom) = pot.dsizes; +nodesizes(pot.cheaddom) = pot.cheadsizes; +nodesizes(pot.ctaildom) = pot.ctailsizes; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Description of the variables in the marginal domain % +% For the calculation of a strong marginal first integration % +% over all continuous variables in the head takes place. % +% The calculation of the marginal over the head variables % +% might result in a smaller or empty tail % +% If there are no head variables, and therefore no tail % +% variables, left marginalisation over discrete variables % +% may take place % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +margdom = mysetdiff(pot.domain,keep); +% margddom = pot.ddom; +margcheaddom = cheadkeep; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Marginalisation over discrete variables is only allowed when % +% the tail is empty % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +margddom = myintersect(pot.ddom,keep); % Discrete domain of marginal +margctaildom = myintersect(pot.ctaildom,keep); % Tail domain +assert(isempty(mysetdiff(pot.ddom,margddom)) | isempty(margctaildom)) + + +%margctaildom = pot.ctaildom; +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Even if marginalisation over continuous variables is only defined % +% for head variables, the marginalisation over haed-variables might % +% result in a smaller tail % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +margctaildom = myintersect(pot.ctaildom,keep); + +margcheadsizes = nodesizes(margcheaddom); +margcheadsize = sum(margcheadsizes); +margctailsizes = nodesizes(margctaildom); +margctailsize = sum(margctailsizes); + +compdom = pot.domain; +compddom = pot.ddom; +compcheaddom = csumover; +compctaildom = myunion(pot.ctaildom, cheadkeep); +compcheadsizes = nodesizes(compcheaddom); +compcheadsize = sum(compcheadsizes); +compctailsizes = nodesizes(compctaildom); +compctailsize = sum(compctailsizes); + +dkeep = myintersect(pot.ddom, keep); +%if dom is only contain discrete node +if isempty(pot.cheaddom) + dsumover = mysetdiff(pot.ddom, dkeep); + + if isempty(dsumover) + margpot = pot; + comppot = scgpot([], [], [], []); + return; + end + + + I = prod(nodesizes(dkeep)); + J = prod(nodesizes(dsumover)); + sum_map = find_equiv_posns(dsumover, pot.ddom); + keep_map = find_equiv_posns(dkeep, pot.ddom); + iv = zeros(1, length(pot.ddom)); % index vector + p1 = zeros(I,J); + for i=1:I + keep_iv = ind2subv(nodesizes(dkeep), i); + iv(keep_map) = keep_iv; + for j=1:J + sum_iv = ind2subv(nodesizes(dsumover), j); + iv(sum_map) = sum_iv; + k = subv2ind(nodesizes(pot.ddom), iv); + potc = struct(pot.scgpotc{k}); % violate object privacy + p1(i,j) = potc.p; + end + end + p2 = sum(p1,2); + p2 = p2 + (p2==0)*eps; + + margscpot = cell(1, I); + compscpot = cell(1, I*J); + iv = zeros(1, length(pot.ddom)); % index vector + for i=1:I + margscpot{i} = scgcpot(0, 0, p2(i)); + keep_iv = ind2subv(nodesizes(dkeep), i); + iv(keep_map) = keep_iv; + for j=1:J + sum_iv = ind2subv(nodesizes(dsumover), j); + iv(sum_map) = sum_iv; + k = subv2ind(nodesizes(pot.ddom), iv); + q = p1(i,j)/p2(i); + compscpot{k} = scgcpot(0, 0, q); + end + end + + margpot = scgpot(dkeep, [], [], nodesizes, margscpot); + comppot = scgpot(pot.ddom, [], [], nodesizes,compscpot); + return; +end +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% head of the potential is not empty % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +dsize = pot.dsize; +compscpot = cell(1, dsize); + +fmaskh = find_equiv_posns(margcheaddom, compctaildom); +fmaskt = find_equiv_posns(margctaildom, compctaildom); + +fh = block(fmaskh, compctailsizes); +ft = block(fmaskt, compctailsizes); + + +if ~isempty(margcheaddom) + for i=1:dsize + potc = struct(pot.scgpotc{i}); + q = 1; + p = potc.p; + [A1, A2, B1, B2, C11, C12, C21, C22] = partition_matrix_vec_3(potc.A, potc.B, potc.C, margcheaddom, compcheaddom, nodesizes); + + if ~isempty(margcheaddom) + margscpot{i} = scgcpot(margcheadsize, margctailsize, p, A1, B1, C11); + else + margscpot{i} = scgcpot(margcheadsize, margctailsize, p); + end + + if ~isempty(compcheaddom) + if ~isempty(margcheaddom) + E = A2 - C21*pinv(C11)*A1; + tmp1 = C21*pinv(C11); + tmp2 = B2 - C21*pinv(C11)*B1; + F = zeros(compcheadsize, compctailsize); + F(:, fh) = tmp1; + F(:, ft) = tmp2; + G = C22 - C21*pinv(C11)*C12; + else + E = A2; + F = B2; + G = C22; + end + compscpot{i} = scgcpot(compcheadsize, compctailsize, q, E, F, G); + else + compscpot{i} = scgcpot(compcheadsize, 0, q); + end + if isempty(margcheaddom) + margpot = scgpot(margddom, [], [], nodesizes, margscpot); + else + margpot = scgpot(margddom, margcheaddom, margctaildom, nodesizes, margscpot); + end + end +else + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % Marginalisation took place over all head variables. % + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % Calculate the strong marginal % + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + margpot = marginalize_pot(pot,keep); + mPot = struct(margpot); + for i =1:dsize + potc = struct(pot.scgpotc{i}); + % Get the probability of the original potential % + q = potc.p; + + % Get the configuration defined by the index i% + config = ind2subv(pot.dsizes,i); + + % Calculate the corresponding configuration in the marginal potential + if isempty(margpot.dsizes) + % keep == [] + indMargPot = 1; + else + equivPos = find_equiv_posns(dkeep,pot.ddom); + indMargPot = subv2ind(margpot.dsizes,config(equivPos)); + end + % Figure out the corresponding marginal potential + mPotC = struct(mPot.scgpotc{indMargPot}); + p = mPotC.p; + if p == 0 + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % The following assignment is correct as p is only zero if q is also zero % + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + compscpot{i} = scgcpot(compcheadsize,compctailsize,0,potc.A,potc.B,potc.C); + else + compscpot{i} = scgcpot(compcheadsize,compctailsize,q/p,potc.A,potc.B,potc.C); + end + end +end +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Put all components in one potential % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +if isempty(compcheaddom) + comppot = scgpot(compddom, [], [], nodesizes,compscpot); +else + comppot = scgpot(compddom, compcheaddom, compctaildom, nodesizes,compscpot); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@scgpot/direct_combine_pots.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,161 @@ +function pot = direct_combine_pots(pot1, pot2) +% DIRECTED_COMBINE_POTS The combination operation corresponds to ordinary composition of conditional distributions. +% In some sense is similar to that of forming disjoint union of set. +% pot = direct_combine_pots(pot1, pot2) + +% directed combine can be performed under the conditon that the head node set of pot1 is disjoint from the domain of +% pot2 or vice versa. if the last conditon was satisfied we exchange the pot1 and pot2 firstly then perform the operation. +% If neither of them was satified the directed combine is undifined. + + +if isempty( myintersect(pot1.domain, pot2.cheaddom) ) + pot1 = pot1; + pot2 = pot2; +elseif isempty( myintersect(pot2.domain, pot1.cheaddom)) + temppot = pot1; + pot1 = pot2; + pot2 = temppot; +else + assert(0); + return; +end + +domain = myunion(pot1.domain, pot2.domain); +nodesizes = zeros(1,max(domain)); +nodesizes(pot2.ctaildom) = pot2.ctailsizes; +nodesizes(pot2.cheaddom) = pot2.cheadsizes; +nodesizes(pot2.ddom) = pot2.dsizes; +nodesizes(pot1.ctaildom) = pot1.ctailsizes; +nodesizes(pot1.cheaddom) = pot1.cheadsizes; +nodesizes(pot1.ddom) = pot1.dsizes; + +dom_u = mysetdiff(pot2.ctaildom, pot1.cheaddom); +if ~isempty(dom_u) & ~mysubset(dom_u, pot1.ctaildom) + pot1 = extension_pot(pot1, [], [], dom_u, nodesizes(dom_u)); +end + +dom_u = myunion(pot1.cheaddom, pot1.ctaildom); +if ~isempty(dom_u) & ~mysubset(dom_u, pot2.ctaildom) + pot2 = extension_pot(pot2, [], [], dom_u, nodesizes(dom_u)); +end + + +cheaddom = myunion(pot1.cheaddom, pot2.cheaddom); +ctaildom = mysetdiff(myunion(pot1.ctaildom, pot2.ctaildom), cheaddom); +cdom = myunion(cheaddom, ctaildom); +ddom = mysetdiff(domain, cdom); +dsizes = nodesizes(ddom); +dsize = prod(nodesizes(ddom)); +cheadsizes = nodesizes(cheaddom); +cheadsize = sum(nodesizes(cheaddom)); +ctailsizes = nodesizes(ctaildom); +ctailsize = sum(nodesizes(ctaildom)); + +r1 = pot1.cheadsize; +s1 = pot1.ctailsize; +scpot = cell(1, dsize); +mask1 = []; +mask2 = []; +if ~isempty(pot1.ddom) + mask1 = find_equiv_posns(pot1.ddom, ddom); +end +if ~isempty(pot2.ddom) + mask2 = find_equiv_posns(pot2.ddom, ddom); +end +cmask1 = []; +cmask2 = []; +if ~isempty(pot1.cheaddom) + cmask1 = find_equiv_posns(pot1.cheaddom, cheaddom); +end +if ~isempty(pot2.cheaddom) + cmask2 = find_equiv_posns(pot2.cheaddom, cheaddom); +end + +u1 = block(cmask1, cheadsizes); +u2 = block(cmask2, cheadsizes); + +fmaskh = find_equiv_posns(pot1.cheaddom, pot2.ctaildom); +fmaskt = find_equiv_posns(pot1.ctaildom, pot2.ctaildom); + +fh = block(fmaskh, pot2.ctailsizes); +ft = block(fmaskt, pot2.ctailsizes); + +for i=1:dsize + sub = ind2subv(dsizes, i); + sub1 = sub(mask1); + sub2 = sub(mask2); + ind1 = subv2ind(pot1.dsizes, sub1); + ind2 = subv2ind(pot2.dsizes, sub2); + + if isempty(ind1) + ind1 = 1; + end + if isempty(ind2) + ind2 = 1; + end + potc1 = struct(pot1.scgpotc{ind1}); + potc2 = struct(pot2.scgpotc{ind2}); + p = potc1.p; + q = potc2.p; + ro = p*q; + + A = potc1.A; + B = potc1.B; + C = potc1.C; + + E = potc2.A; + F = potc2.B; + G = potc2.C; + + F1 = F(:, fh); + F2 = F(:, ft); + + if ~isempty(F1) + K1 = F1*A; + K2 = F1*B; + FCF = F1*C*F1'; + FC = F1*C; + CFT = C*F1'; + else + K1 = zeros(size(E)); + K2 = zeros(size(F2)); + FCF = zeros(size(G)); + FC = zeros(size(C, 1), size(G, 2)); + CFT = zeros(size(G, 2), size(C, 1)); + end + + + U = zeros(cheadsize,1); + W = zeros(cheadsize,cheadsize); + V = zeros(cheadsize,ctailsize); + + if cheadsize > 0 + U(u1) = A; + U(u2) = E + K1; + W(u1, u1) = C; + W(u2, u2) = G + FCF; + W(u1, u2) = CFT; + W(u2, u1) = FC; + else + U = zeros(cheadsize,1); + W = zeros(cheadsize,cheadsize); + end + if cheadsize > 0 | ctailsize > 0 + if ~isempty(u1) + V(u1, :) = B; + else + V(u1, :) = zeros(potc1.cheadsize, ctailsize); + end + if ~isempty(u2) + V(u2, :) = F2 + K2; + else + V(u2, :) = zeros(potc2.cheadsize, ctailsize); + end + else + V = zeros(cheadsize,ctailsize); + end + + scpot{i} = scgcpot(cheadsize, ctailsize, ro, U, V, W); +end + +pot = scgpot(ddom, cheaddom, ctaildom, nodesizes, scpot);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@scgpot/extension_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,55 @@ +function pot = extension_pot(oldpot, ddom_u, dsizes, ctaildom_u, csizes) +% EXTENSION_POT Extense a stable CG potential. +% pot = extension_pot(oldpot, ddom_u, ctaildom_u, dsizes, csizes) +% ddom_u Added discrete nodes +% ctaildom_u Added continuous tail nodes +% csizes is the size of the tail nodes. + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% A CG potential can be extended by adding discrete variables to its % +% domain of continuous variables to its tail % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +ddom = myunion(oldpot.ddom, ddom_u); +ctaildom = myunion(oldpot.ctaildom, ctaildom_u); +cheaddom = oldpot.cheaddom; +udom = myunion(ddom_u, ctaildom_u); +domain = myunion(oldpot.domain, udom); + +ns = zeros(1,max(domain)); +ns(ddom_u) = dsizes; +ns(ctaildom_u) = csizes; +ns(oldpot.ddom) = oldpot.dsizes; +ns(oldpot.cheaddom) = oldpot.cheadsizes; +ns(oldpot.ctaildom) = oldpot.ctailsizes; + +dsizes = ns(ddom); +dsize = prod(ns(ddom)); +cheadsizes = ns(cheaddom); +cheadsize = sum(ns(cheaddom)); +ctailsizes = ns(ctaildom); +ctailsize = sum(ns(ctaildom)); + +BZ = zeros(cheadsize, ctailsize); +potarray = cell(1, dsize); +mask = find_equiv_posns(oldpot.ddom, ddom); + +tmask = find_equiv_posns(oldpot.ctaildom, ctaildom); +tu = block(tmask, ctailsizes); + +for i=1:dsize + sub1 = ind2subv(dsizes, i); + sub2 = sub1(mask); + ind = subv2ind(oldpot.dsizes, sub2); + if isempty(ind) + ind = 1; + end + potc = struct(oldpot.scgpotc{ind}); + p = potc.p; + B = BZ; + if ~isempty(B) + B(:, tu) = potc.B; + end + potarray{i} = scgcpot(cheadsize, ctailsize, p, potc.A, B, potc.C); +end + +pot = scgpot(ddom, cheaddom, ctaildom, ns,potarray);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@scgpot/marginalize_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,92 @@ +function smallpot = marginalize_pot(bigpot, keep) +% MARGINALIZE_POT Marginalize a cgpot onto a smaller domain. +% smallpot = marginalize_pot(bigpot, keep) + +sumover = mysetdiff(bigpot.domain, keep); +cdom = myunion(bigpot.cheaddom, bigpot.ctaildom); +csumover = myintersect(sumover, bigpot.cheaddom); +dsumover = myintersect(sumover, bigpot.ddom); + +dkeep = myintersect(keep, bigpot.ddom); +ckeep = myintersect(keep, bigpot.cheaddom); +cheaddom = myintersect(keep, bigpot.cheaddom); + +assert(isempty(myintersect(csumover,bigpot.ctaildom))); +ns = zeros(1, max(bigpot.domain)); +ns(bigpot.ddom) = bigpot.dsizes; +ns(bigpot.cheaddom) = bigpot.cheadsizes; +ns(bigpot.ctaildom) = bigpot.ctailsizes; + + +if sum(ns(csumover)) > 0 + for i=1:bigpot.dsize + bigpot.scgpotc{i} = marginalize_pot(bigpot.scgpotc{i}, ckeep, csumover, ns); + end +end + +if (isequal(csumover, cheaddom)) + bigpot.ctaildom = []; +end +% If we are not marginalizing over any discrete nodes, we are done. +if prod(ns(dsumover))==1 + smallpot = scgpot(dkeep, cheaddom, bigpot.ctaildom, ns, bigpot.scgpotc); + return; +end + +if (~isempty(bigpot.ctaildom)) + assert(0); + return; +end + +I = prod(ns(dkeep)); +J = prod(ns(dsumover)); +C = sum(ns(ckeep)); +sum_map = find_equiv_posns(dsumover, bigpot.ddom); +keep_map = find_equiv_posns(dkeep, bigpot.ddom); +iv = zeros(1, length(bigpot.ddom)); % index vector + +p1 = zeros(I,J); +A1 = zeros(C,J,I); +C1 = zeros(C,C,J,I); +for i=1:I + keep_iv = ind2subv(ns(dkeep), i); + iv(keep_map) = keep_iv; + for j=1:J + sum_iv = ind2subv(ns(dsumover), j); + iv(sum_map) = sum_iv; + k = subv2ind(ns(bigpot.ddom), iv); + pot = struct(bigpot.scgpotc{k}); % violate object privacy + p1(i,j) = pot.p; + if C > 0 % so mu1 and Sigma1 are non-empty + A1(:,j,i) = pot.A; + C1(:,:,j,i) = pot.C; + end + end +end + +% Collapse the mixture of Gaussians +coef = mk_stochastic(p1); % coef must be convex combination +%keyboard +p2 = sum(p1,2); +if (all(p2 == 0)) + p2 = p2 + (p2==0)*eps; +end +A = []; +S = []; + +pot = cell(1,I); +ctailsize = sum(ns(bigpot.ctaildom)); +tB = zeros(C, ctailsize); +for i=1:I + if C > 0 + [A, S] = collapse_mog(A1(:,:,i), C1(:,:,:,i), coef(i,:)); + end + p = p2(i); + pot{i} = scgcpot(C, ctailsize, p, A, tB, S); +end + +smallpot = scgpot(dkeep, ckeep, bigpot.ctaildom, ns, pot); + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@scgpot/normalize_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +function [pot, loglik] = normalize_pot(pot) +% NORMALIZE_POT Convert the SCG potential Pr(X,E) into Pr(X|E) and return log Pr(E). +% [pot, loglik] = normalize_pot(pot) + +% Marginalize down to [], so that the normalizing constant becomes Pr(E) +temp = marginalize_pot(pot, []); +[temp2, loglik] = normalize_pot(temp.scgpotc{1}); + +% Adjust scale factor to reflect the fact that the pot now represents Pr(X | E) instead of Pr(X,E). + +scale = -loglik; +if 1 + for i=1:pot.dsize + pot.scgpotc{i} = rescale_pot( pot.scgpotc{i}, scale); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@scgpot/pot_to_marginal.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,29 @@ +function m = pot_to_marginal(pot) +% POT_TO_MARGINAL Convert a scgpot to a marginal structure. +% m = pot_to_marginal(pot) + +assert(isempty(pot.ctaildom)) +m.domain = pot.domain; +n = pot.cheadsize; +d = pot.dsize; + +if n==0 + m.mu = []; + m.Sigma = []; +else + m.mu = zeros(n, d); + m.Sigma = zeros(n, n, d); +end +%m.T = 0*myones(pot.dsizes); +m.T = 0*myones(pot.dsize); +for i=1:pot.dsize + potc = struct(pot.scgpotc{i}); % violate privacy of object + if n > 0 + m.mu(:,i) = potc.A; + m.Sigma(:,:,i) = potc.C; + end + m.T(i) = potc.p; +end +if isvector(m.T) + m.T = m.T(:)'; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@scgpot/recursive_combine_pots.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,40 @@ +function pot = recursive_combine_pots(pot1, pot2) +% RECURSIVE_COMBINE_POTS recursive combine two potentials +% pot = recursive_combine_pots(pot1, pot2) + +pot1 = reduce_pot(pot1); +pot2 = reduce_pot(pot2); +% Recursion is stopped, if recusive-combination is defined by direct combination, +% i.e. if the domain of one potential is disjoint from the head of the other. +if (isempty(myintersect(pot1.domain,pot2.cheaddom))|... + isempty(myintersect(pot1.cheaddom,pot2.domain))) + pot = direct_combine_pots(pot1,pot2); +else + % Test wether one of the set-differences is not empty + % as defined in Lauritzen99 "Stable Local Computation with Conditional Gaussian Distributions" + % on page 9 + D12 = mysetdiff(pot1.cheaddom, pot2.domain); + D21 = mysetdiff(pot2.cheaddom, pot1.domain); + if (isempty(D12) & isempty(D21)) + assert(0,'Recursive combination is not defined'); + end + + if ~isempty(D12) + % Calculate the complementary potential for the set + % D1\D12 as defined in Lauritzen 99, page 9 + keep = mysetdiff(pot1.domain,D12); + [margpot, comppot] = complement_pot(pot1,keep); + margpot = reduce_pot(margpot); + comppot = reduce_pot(comppot); + pot = direct_combine_pots( recursive_combine_pots(margpot, pot2), comppot); + elseif ~isempty(D21) + keep = mysetdiff(pot2.domain,D21); + [margpot, comppot] = complement_pot(pot2,D21); + margpot = reduce_pot(margpot); + comppot = reduce_pot(comppot); + pot = direct_combine_pots( recursive_combine_pots(pot1, margpot), comppot); + end +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@scgpot/reduce_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,53 @@ +function [reduced_pot,successful] = reduce_pot(pot,tailnodes) +% Executes the reduce operation defined in +% Stable Local Computation with Conditional Gaussian Distributions +% Steffen L. Lauritzen +% Frank Jensen +% September 1999 +% The potential pot is reduced if B contains any zero columns +% The test are restricted to the positions in tailnodes. +% Any columns successfully deleted are entered in the array successful +if nargin < 2 + tailnodes = pot.ctaildom; +end + +successful = []; +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Keep track of remaining tailnodes % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +rem_tailnodes = pot.ctaildom; +for i = tailnodes + pos = find(i==rem_tailnodes); + successful_red = [pos]; + red_scgcpot = cell(1,pot.dsize); + j = 1; + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % Test whether all components of pot.scgpotc can be reduced % + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + while ((j <= pot.dsize) & ~isempty(successful_red)) + [cpot,successful_red] = reduce_pot(pot.scgpotc{j},pos); + red_scgcpot{j} = cpot; + j = j + 1; + end + + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % If i is a reducible tailnode, then reduce the potential % + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + if ~isempty(successful_red) + successful = [successful i]; + pot.scgpotc = red_scgcpot; + rem_tailnodes = mysetdiff(rem_tailnodes,i); + end; +end + +pot.ctaildom = rem_tailnodes; +positions = find_equiv_posns(rem_tailnodes,pot.ctaildom); +pot.ctailsizes = pot.ctailsizes(positions); +pot.ctailsize = sum(pot.ctailsizes); +pot.domain = mysetdiff(pot.domain,successful); +reduced_pot = pot; + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@scgpot/scgpot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,30 @@ +function pot = scgpot(ddom, cheaddom, ctaildom, node_sizes, scgpotc) +% SCGPOT Make a stable CG potential. +% pot = scgpot(ddom, cheaddom, ctaildom, node_sizes, scgpotc) +% +% ddom is discrete nodes contains in the potential +% cheaddom is head nodes constains in the potential +% ctaildom is tail nodes contains in the potential +% node_sizes(i) is the size of the i'th node. +% scgpotc is list of scgcpot objects. + +pot.ddom = ddom; +pot.cheaddom = cheaddom; +pot.ctaildom = ctaildom; +pot.domain = myunion(ddom, myunion(cheaddom, ctaildom)); +pot.dsizes = node_sizes(pot.ddom); +pot.dsize = prod(node_sizes(pot.ddom)); +pot.cheadsizes = node_sizes(pot.cheaddom); +pot.cheadsize = sum(node_sizes(pot.cheaddom)); +pot.ctailsizes = node_sizes(pot.ctaildom); +pot.ctailsize = sum(node_sizes(pot.ctaildom)); + +if nargin < 5 + scgpotc = cell(1, pot.dsize); + for i=1:pot.dsize + scgpotc{i} = scgcpot(pot.cheadsize, pot.ctailsize); + end +end +pot.scgpotc = scgpotc; + +pot = class(pot, 'scgpot');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@upot/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,10 @@ +/approxeq_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/display.m/1.1.1.1/Wed May 29 15:59:58 2002// +/divide_by_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/marginalize_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/multiply_by_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/normalize_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/pot_to_marginal.m/1.1.1.1/Wed May 29 15:59:58 2002// +/upot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/upot_to_opt_policy.m/1.1.1.1/Wed May 29 15:59:58 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@upot/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/potentials/@upot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@upot/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@upot/approxeq_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function p = approxeq_pot(A, B, tol) + +if nargin < 3, tol = 1e-3; end + +p = approxeq(A.p, B.p, tol) & approxeq(A.u, B.u, tol);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@upot/display.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,4 @@ +function display(pot) + +disp('utility potential object'); +disp(struct(pot));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@upot/divide_by_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function Tbig = divide_by_pot(Tbig, Tsmall) +% DIVIDE_BY_POT Tbig /= Tsmall +% Tbig = divide_by_pot(Tbig, Tsmall) +% +% Tsmall's domain must be a subset of Tbig's domain. + +smallp = extend_domain_table(Tsmall.p, Tsmall.domain, Tsmall.sizes, Tbig.domain, Tbig.sizes); +smallp = smallp + (smallp==0); +Tbig.p = Tbig.p ./ smallp; + +smallu = extend_domain_table(Tsmall.u, Tsmall.domain, Tsmall.sizes, Tbig.domain, Tbig.sizes); +Tbig.u = Tbig.u - smallu; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@upot/marginalize_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,18 @@ +function smallpot = marginalize_pot(bigpot, onto, maximize) +% MARGINALIZE_POT Marginalize a upot onto a smaller domain. +% smallpot = marginalize_pot(bigpot, onto, maximize) +% +% The maximize argument is ignored + +numer = marg_table(bigpot.p .* bigpot.u, bigpot.domain, bigpot.sizes, onto); +denom = marg_table(bigpot.p, bigpot.domain, bigpot.sizes, onto); + +p = denom; +% replace 0s by 1s before dividing. This is valid since demon(i) = 0 => numer(i) = 0 +denom = denom + (denom == 0); +u = numer ./ denom; + +ns = zeros(1, max(bigpot.domain)); +ns(bigpot.domain) = bigpot.sizes; + +smallpot = upot(onto, ns(onto), p, u);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@upot/multiply_by_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +function Tbig = multiply_by_pot(Tbig, Tsmall) +% MULTIPLY_BY_POT Tbig *= Tsmall +% Tbig = multiply_by_pot(Tbig, Tsmall) +% +% Tsmall's domain must be a subset of Tbig's domain. + +smallp = extend_domain_table(Tsmall.p, Tsmall.domain, Tsmall.sizes, Tbig.domain, Tbig.sizes); +Tbig.p = Tbig.p .* smallp; + +smallu = extend_domain_table(Tsmall.u, Tsmall.domain, Tsmall.sizes, Tbig.domain, Tbig.sizes); +Tbig.u = Tbig.u + smallu; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@upot/normalize_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function [pot, loglik] = normalize_pot(pot) +% NORMALIZE_POT Convert the probability part of a utility potential +% [pot, loglik] = normalize_pot(pot) + +[pot.p, lik] = normalise(pot.p); +%pot.u = pot.u - sum(pot.u(:)); +%pot.u = pot.u ./ sum(pot.u(:)); % same as normalise(pot.u) +%pot.u = normalise(pot.u); +%pot.u = pot.u / 726.8121; +pot.u = pot.u / 10; +loglik = log(lik + (lik==0)*eps); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@upot/pot_to_marginal.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,9 @@ +function m = pot_to_marginal(pot) +% POT_TO_MARGINAL Convert a upot to a structure. +% m = pot_to_marginal(pot) + +m.domain = pot.domain; +m.T = pot.p; +m.U = pot.u; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@upot/upot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +function pot = upot(domain, sizes, p, u) +% UPOT Make a discrete utility potential. +% pot = upot(domain, sizes, p, u) +% +% sizes(i) is the size of the i'th domain element. +% p defaults to all 1s, u defaults to all 0s. + +if nargin < 3, p = myones(sizes); end +if nargin < 4, u = 0*myones(sizes); end + +pot.domain = domain; +pot.p = myreshape(p, sizes); +pot.u = myreshape(u, sizes); +pot.sizes = sizes(:)'; +pot = class(pot, 'upot');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/@upot/upot_to_opt_policy.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,25 @@ +function [policy, EU] = upot_to_opt_policy(pot) +% UPOT_TO_OPT_POLICY Compute an optimal deterministic policy given a utility potential +% [policy, EU] = upot_to_opt_policy(pot) +% +% policy(a,b, ..., z) = P(do z | a, b, ..), which will be a delta function +% EU is the contraction of this potential, i.e., P .* U + +sz = pot.sizes; % mysize(pot.p); +if isempty(sz) + EU = pot.u; + policy = []; + return; +end + +parent_size = prod(sz(1:end-1)); +self_size = sz(end); +C = pot.p .* pot.u; % contraction +C = reshape(C, parent_size, self_size); +policy = zeros(parent_size, self_size); +for i=1:parent_size + act = argmax(C(i,:)); + policy(i, act) = 1; +end +policy = myreshape(policy, sz); +EU = sum(C(:));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/CPD_to_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,21 @@ +function pot = CPD_to_pot(pot_type, CPD, domain, ns, cnodes, evidence) +% CPD_TO_POT Convert a CPD to a potential of the specified form, incorporating any evidence +% pot = CPD_to_pot(pot_type, CPD, domain, node_sizes, cnodes, evidence) +% +% pot_type is one of 'd', 'g', or 'cg'. +% domain is the domain of CPD. +% node_sizes(i) is the size of node i. +% cnodes = the cts nodes +% evidence{i} is the evidence on the i'th node. + +switch pot_type + case 'd', + pot = CPD_to_dpot(CPD, domain, ns, cnodes, evidence); + case 'g', + pot = CPD_to_cpot(CPD, domain, ns, cnodes, evidence); + case 'cg', + pot = CPD_to_cgpot(CPD, domain, ns, cnodes, evidence); + otherwise, + error(['can''t handle pot_type ' pot_type]); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +/CPD_to_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/README/1.1.1.1/Wed May 29 15:59:58 2002// +/check_for_cd_arcs.m/1.1.1.1/Wed May 29 15:59:58 2002// +/determine_pot_type.m/1.1.1.1/Wed May 29 15:59:58 2002// +/genops.c/1.1.1.1/Sat Jul 28 15:43:40 2001// +/mk_initial_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,9 @@ +A D/@cgpot//// +A D/@cpot//// +A D/@dpot//// +A D/@mpot//// +A D/@scgcpot//// +A D/@scgpot//// +A D/@upot//// +A D/Old//// +A D/Tables////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/potentials
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/Old/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +/comp_eff_node_sizes.m/1.1.1.1/Wed May 29 15:59:58 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/Old/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/potentials/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/Old/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/Old/comp_eff_node_sizes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function ens = comp_eff_node_sizes(ns, cnodes, ev, domain) + +dnodes = mysetdiff(1:length(ns), cnodes); +odom = domain(~isemptycell(evidence(domain))); +cdom = myintersect(cnodes, domain); +ddom = myintersect(dnodes, domain); +cobs = myintersect(cdom, odom); +dobs = myintersect(ddom, odom); +ens = ns; +ens(cobs) = 0; +ens(dobs) = 1;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/README Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,100 @@ +The following kinds of potentials are supported +- dpot: discrete +- upot: utility +- mpot: Gaussian in moment form +- cpot: Gaussian in canonical form +- cgpot: conditional (mixture) Gaussian, a list of mpots/cpot +- scgpot: stable conditional Gaussian, a list of scgcpots +- scgcpot: just used by scgpot + +Many of these are described in the following book + +@book{Cowell99, + author = "R. G. Cowell and A. P. Dawid and S. L. Lauritzen and D. J. Spiegelhalter", + title = "Probabilistic Networks and Expert Systems", + year = 1999, + publisher = "Springer" +} + +CPD_to_pot converts P(Z|A,B,...) to phi(A,B,...,Z). + +A table is like a dpot, except it is a structure, not an object. +Code that uses tables is faster but less flexible. + + ----------- + +A potential is a joint probability distribution on a set of nodes, +which we call the potential's domain (which is always sorted). +A potential supports the operations of multiplication and +marginalization. + +If the nodes are discrete, the potential can be represented as a table +(multi-dimensional array). If the nodes are Gaussian, the potential +can be represented as a quadratic form. If there are both discrete and +Gaussian nodes, we use a table of quadratic forms. For details on the +Gaussian case, see below. + +For discrete potentials, the 'sizes' field specifies the number of +values each node in the domain can take on. For continuous potentials, +the 'sizes' field specifies the block-size of each node. + +If some of the nodes are observed, extra complications arise. We +handle the discrete and continuous cases differently. Suppose the +domain is [X Y], with sizes [6 2], where X is observed to have value x. +In the discrete case, the potential will have many zeros in it +(T(X,:) will be 0 for all X ~= x), which can be inefficient. Instead, +we set sizes to [1 2], to indicate that X has only one possible value +(namely x). For continuous nodes, we set sizes = [0 2], to indicate that X no +longer appears in the mean vector or covariance matrix (we must avoid +0s in Sigma, lest it be uninvertible). When a potential is created, we +assume the sizes of the nodes have been adjusted to include the +evidence. This is so that the evidence can be incorporated at the +outset, and thereafter the inference algorithms can ignore it. + + ------------ + +A Gaussian potential can be represented in terms of its +moment characteristics (mu, Sigma, logp), or in terms of its canonical +characteristics (g, h, K). Although the moment characteristics are +more familiar, it turns out that canonical characteristics are +more convenient for the junction tree algorithm, for the same kinds of +reasons why backwards inference in an LDS uses the information form of +the Kalman filter (see Murphy (1998a) for a discussion). + +When working with *conditional* Gaussian potentials, the method proposed +by Lauritzen (1992), and implemented here, requires converting from +canonical to moment form before marginalizing the discrete variables, +and converting back from moment to canonical form before +multiplying/dividing. A new algorithm, due to Lauritzen and Jensen +(1999), works exclusively in moment form, and +hence is more numerically stable. It can also handle 0s in the +covariance matrix, i.e., deterministic relationships between cts +variables. However, it has not yet been implemented, +since it requires major changes to the jtree algorithm. + +In Murphy (1998b) we extend Lauritzen (1992) to handle +vector-valued nodes. This means the vectors and matrices become block +vectors and matrices. This manifests itself in the code as in the +following example. +Suppose we have a potential on nodes dom=[3,4,7] with block sizes=[2,1,3]. +Then nodes 3 and 7 correspond to blocks 1,3 which correspond to indices 1,2,4,5,6. +>> find_equiv_posns([3 7], dom)=[1,3] +>> block([1,3],blocks)=[1,2,4,5,6]. + +For more details, see + +- "Filtering and Smoothing in Linear Dynamical Systems using the Junction Tree Algorithm", + K. Murphy, 1998a. UCB Tech Report. + +- "Inference and learning in hybrid Bayesian networks", + K. Murphy. UCB Technical Report CSD-98-990, 1998b. + +- "Propagation of probabilities, means and variances in mixed + graphical association models", S. L. Lauritzen, 1992, JASA 87(420):1098--1108. + +- "Causal probabilistic networks with both discrete and continuous variables", + K. G. Olesen, 1993. PAMI 3(15). This discusses implementation details. + +- "Stable local computation with Conditional Gaussian distributions", + S. Lauritzen and F. Jensen, 1999. Univ. Aalborg Tech Report R-99-2014. + www.math.auc.dk/research/Reports.html.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/Tables/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,19 @@ +/divide_by_sparse_table.c/1.1.1.1/Wed May 29 15:59:58 2002// +/divide_by_table.c/1.1.1.1/Wed May 29 15:59:58 2002// +/divide_by_table.m/1.1.1.1/Thu Aug 5 15:25:54 2004// +/extend_domain_table.m/1.1.1.1/Wed Aug 4 15:53:26 2004// +/marg_sparse_table.c/1.1.1.1/Wed May 29 15:59:58 2002// +/marg_table.c/1.1.1.1/Wed May 29 15:59:58 2002// +/marg_table.m/1.1.1.1/Wed Aug 4 15:51:48 2004// +/marg_tableC.c/1.1.1.1/Wed Oct 2 15:39:02 2002// +/marg_tableM.m/1.1.1.1/Tue Oct 1 17:39:08 2002// +/mult_by_sparse_table.c/1.1.1.1/Wed May 29 15:59:58 2002// +/mult_by_table.c/1.1.1.1/Tue Oct 1 21:23:22 2002// +/mult_by_table.m/1.1.1.1/Wed Aug 4 15:52:58 2004// +/mult_by_table2.m/1.1.1.1/Wed Oct 2 15:30:32 2002// +/mult_by_tableC.c/1.1.1.1/Tue Oct 1 21:33:50 2002// +/mult_by_tableM.m/1.1.1.1/Wed Oct 2 15:28:48 2002// +/mult_by_table_global.m/1.1.1.1/Sun Sep 29 10:21:30 2002// +/rep_mult.c/1.1.1.1/Wed May 29 15:59:58 2002// +/repmat_and_mult.c/1.1.1.1/Tue Oct 1 21:20:00 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/Tables/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/BNT/potentials/Tables
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/Tables/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/Tables/divide_by_sparse_table.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,126 @@ +/* divide_by_sparse_table.c ../potential/tables*/ + +/******************************************/ +/* 6 input & 1 output */ +/* Big table [0] */ +/* Big domain [1] */ +/* big sizes [2] */ +/* Small table [3] */ +/* small domain [4] */ +/* small sizes [5] */ +/* */ +/* New big table[0] */ +/******************************************/ + +#include <math.h> +#include <stdlib.h> +#include "mex.h" + +int compare(const void* src1, const void* src2){ + int i1 = *(int*)src1 ; + int i2 = *(int*)src2 ; + return i1-i2 ; +} + +void ind_subv(int index, const int *cumprod, int n, int *bsubv){ + int i; + + for (i = n-1; i >= 0; i--) { + bsubv[i] = ((int)floor(index / cumprod[i])); + index = index % cumprod[i]; + } +} + +int subv_ind(const int n, const int *cumprod, const int *subv){ + int i, index=0; + + for(i=0; i<n; i++){ + index += subv[i] * cumprod[i]; + } + return index; +} + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, count, bdim, sdim, NB, NZB, NZS, position, bindex, sindex; + int *mask, *result, *bir, *sir, *bjc, *sjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr, value; + + plhs[0] = mxDuplicateArray(prhs[0]); + pbDomain = mxGetPr(prhs[1]); + bdim = mxGetNumberOfElements(prhs[1]); + psDomain = mxGetPr(prhs[4]); + sdim = mxGetNumberOfElements(prhs[4]); + + pbSize = mxGetPr(prhs[2]); + psSize = mxGetPr(prhs[5]); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + bpr = mxGetPr(plhs[0]); + bir = mxGetIr(plhs[0]); + bjc = mxGetJc(plhs[0]); + NZB = bjc[1]; + + spr = mxGetPr(prhs[3]); + sir = mxGetIr(prhs[3]); + sjc = mxGetJc(prhs[3]); + NZS = sjc[1]; + + if(sdim == 0){ + value = *spr; + if(value == 0)value = 1; + for(i=0; i<NZB; i++){ + bpr[i] /= value; + } + return; + } + + mask = malloc(sdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sir, NZS, sizeof(int), compare); + if(result){ + position = result - sir; + bpr[i] /= spr[position]; + } + } + + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/Tables/divide_by_table.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,120 @@ +/* divide_by_table.c ../potential/tables */ + + +/******************************************/ +/* 6 input & 1 output */ +/* Big table [0] */ +/* Big domain [1] */ +/* big sizes [2] */ +/* Small table [3] */ +/* small domain [4] */ +/* small sizes [5] */ +/* */ +/* New big table[0] */ +/******************************************/ + +#include "mex.h" + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, count, NB, NS, siz_b, siz_s, ndim, temp; + int *mask, *sx, *sy, *cpsy, *subs, *s, *cpsy2; + double *pbDomain, *psDomain, *sp, *zp, *bs, value; + + plhs[0] = mxDuplicateArray(prhs[0]); + zp = mxGetPr(plhs[0]); + + siz_b = mxGetNumberOfElements(prhs[1]); + siz_s = mxGetNumberOfElements(prhs[4]); + pbDomain = mxGetPr(prhs[1]); + psDomain = mxGetPr(prhs[4]); + + NB = mxGetNumberOfElements(prhs[0]); + NS = mxGetNumberOfElements(prhs[3]); + sp = mxGetPr(prhs[3]); + + bs = mxGetPr(prhs[2]); + + if(NS == 1){ + value = *sp; + if(value == 0) value = 1; + for(i=0; i<NB; i++){ + zp[i] /= value; + } + return; + } + + if(NS == NB){ + for(i=0; i<NB; i++){ + value = sp[i]; + if(value == 0) value = 1; + zp[i] /= value; + } + return; + } + + mask = malloc(siz_s * sizeof(int)); + count = 0; + for(i=0; i<siz_s; i++){ + for(j=0; j<siz_b; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + ndim = siz_b; + sx = (int *)malloc(sizeof(int)*ndim); + sy = (int *)malloc(sizeof(int)*ndim); + for(i=0; i<ndim; i++){ + sx[i] = (int)bs[i]; + sy[i] = 1; + } + for(i=0; i<count; i++){ + temp = mask[i]; + sy[temp] = sx[temp]; + } + + s = (int *)malloc(sizeof(int)*ndim); + *(cpsy = (int *)malloc(sizeof(int)*ndim)) = 1; + subs = (int *)malloc(sizeof(int)*ndim); + cpsy2 = (int *)malloc(sizeof(int)*ndim); + for(i = 0; i < ndim; i++){ + subs[i] = 0; + s[i] = sx[i] - 1; + } + + for(i = 0; i < ndim-1; i++){ + cpsy[i+1] = cpsy[i]*sy[i]--; + cpsy2[i] = cpsy[i]*sy[i]; + } + cpsy2[ndim-1] = cpsy[ndim-1]*(--sy[ndim-1]); + + for(j=0; j<NB; j++){ + value = *sp; + if(value == 0) value = 1; + *zp++ /= value; + for(i = 0; i < ndim; i++){ + if(subs[i] == s[i]){ + subs[i] = 0; + if(sy[i]) + sp -= cpsy2[i]; + } + else{ + subs[i]++; + if(sy[i]) + sp += cpsy[i]; + break; + } + } + } + free(sx); + free(sy); + free(s); + free(cpsy); + free(subs); + free(cpsy2); + free(mask); +} +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/Tables/divide_by_table.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +function bigT = divide_by_table(bigT, bigdom, bigsz, smallT, smalldom, smallsz) +% DIVIDE_BY_TABLE +% bigT = divide_by_table(bigT, bigdom, bigsz, smallT, smalldom, smallsz) +% + + +Ts = extend_domain_table(smallT, smalldom, smallsz, bigdom, bigsz); +% Replace 0s by 1s before dividing. This is valid, Ts(i)=0 iff Tbig(i)=0. +Ts = Ts + (Ts==0); +%Tbig.T(:) = Tbig.T(:) ./ Ts(:); +bigT(:) = bigT(:) ./ Ts(:); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/Tables/extend_domain_table.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,24 @@ +function B = extend_domain_table(A, smalldom, smallsz, bigdom, bigsz) +% EXTEND_DOMAIN_TABLE Expand an array so it has the desired size. +% B = extend_domain_table(A, smalldom, smallsz, bigdom, bigsz) +% +% A is the array with domain smalldom and sizes smallsz. +% bigdom is the desired domain, with sizes bigsz. +% +% Example: +% smalldom = [1 3], smallsz = [2 4], bigdom = [1 2 3 4], bigsz = [2 1 4 5], +% so B(i,j,k,l) = A(i,k) for i in 1:2, j in 1:1, k in 1:4, l in 1:5 + +if isequal(size(A), [1 1]) % a scalar + B = A; % * myones(bigsz); + return; +end + +map = find_equiv_posns(smalldom, bigdom); +sz = ones(1, length(bigdom)); +sz(map) = smallsz; +B = myreshape(A, sz); % add dimensions for the stuff not in A +sz = bigsz; +sz(map) = 1; % don't replicate along A's dimensions +B = myrepmat(B, sz(:)'); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/Tables/marg_sparse_table.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,181 @@ +/* marg_sparse_table.c ../potential/tables*/ + +/******************************************/ +/* 5 input & 1 output */ +/* Big sparse table */ +/* Big domain */ +/* Big sizes */ +/* onto */ +/* maximize, if missed, maximize=0 */ +/* */ +/* small sparse table */ +/******************************************/ + +#include <math.h> +#include <stdlib.h> +#include "mex.h" + +int compare(const void* src1, const void* src2){ + int i1 = *(int*)src1 ; + int i2 = *(int*)src2 ; + return i1-i2 ; +} + +void ind_subv(int index, const int *cumprod, int n, int *bsubv){ + int i; + + for (i = n-1; i >= 0; i--) { + bsubv[i] = ((int)floor(index / cumprod[i])); + index = index % cumprod[i]; + } +} + +int subv_ind(const int n, const int *cumprod, const int *subv){ + int i, index=0; + + for(i=0; i<n; i++){ + index += subv[i] * cumprod[i]; + } + return index; +} + +mxArray* convert_table_to_sparse(const double *Table, const int *sequence, const int nzCounts, const int N){ + mxArray *spTable; + int i, temp, *irs, *jcs, count=0; + double *sr; + + spTable = mxCreateSparse(N, 1, nzCounts, mxREAL); + sr = mxGetPr(spTable); + irs = mxGetIr(spTable); + jcs = mxGetJc(spTable); + + jcs[0] = 0; + jcs[1] = nzCounts; + + for(i=0; i<nzCounts; i++){ + irs[i] = sequence[count]; + count++; + temp = sequence[count]; + sr[i] = Table[temp]; + count++; + } + return spTable; +} + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, count, bdim, sdim, NS, NZB, position, bindex, sindex, maximize, nzCounts=0; + int *mask, *sequence, *result, *bir, *bjc, *ssize, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *sTable, *pbDomain, *psDomain, *pbSize, *bpr, *spr; + const char *field_names[] = {"domain", "T", "sizes"}; + + if(nrhs < 5) maximize = 0; + else maximize = (int)mxGetScalar(prhs[4]); + + bdim = mxGetNumberOfElements(prhs[1]); + sdim = mxGetNumberOfElements(prhs[3]); + pbSize = mxGetPr(prhs[2]); + pbDomain = mxGetPr(prhs[1]); + psDomain = mxGetPr(prhs[3]); + bpr = mxGetPr(prhs[0]); + bir = mxGetIr(prhs[0]); + bjc = mxGetJc(prhs[0]); + NZB = bjc[1]; + + if(sdim == 0){ + plhs[0] = mxCreateSparse(1, 1, 1, mxREAL); + spr = mxGetPr(plhs[0]); + bir = mxGetIr(plhs[0]); + bjc = mxGetJc(plhs[0]); + *spr = 0; + *bir = 0; + bjc[0] = 0; + bjc[1] = 1; + if(maximize){ + for(i=0; i<NZB; i++){ + *spr = (*spr < bpr[i])? bpr[i] : *spr; + } + } + else{ + for(i=0; i<NZB; i++){ + *spr += bpr[i]; + } + } + return; + } + + mask = malloc(sdim * sizeof(int)); + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + sTable = malloc(NZB * sizeof(double)); + sequence = malloc(NZB * 2 * sizeof(double)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + ssize = malloc(sdim * sizeof(int)); + + NS = 1; + for(i=0; i<count; i++){ + ssize[i] = (int)pbSize[mask[i]]; + NS *= ssize[i]; + } + + for(i=0; i<NZB; i++)sTable[i] = 0; + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * ssize[i]; + } + + count = 0; + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sequence, nzCounts, sizeof(int)*2, compare); + if(result){ + position = (result - sequence) / 2; + if(maximize) + sTable[position] = (sTable[position] < bpr[i]) ? bpr[i] : sTable[position]; + else sTable[position] += bpr[i]; + } + else { + if(maximize) + sTable[nzCounts] = (sTable[nzCounts] < bpr[i]) ? bpr[i] : sTable[nzCounts]; + else sTable[nzCounts] += bpr[i]; + sequence[count] = sindex; + count++; + sequence[count] = nzCounts; + nzCounts++; + count++; + } + } + + qsort(sequence, nzCounts, sizeof(int) * 2, compare); + plhs[0] = convert_table_to_sparse(sTable, sequence, nzCounts, NS); + + free(sTable); + free(sequence); + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); + free(ssize); +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/Tables/marg_table.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,175 @@ +/* marg_table.c ../potential/tables */ + + +/******************************************/ +/* 5 input & 1 output */ +/* Big table */ +/* Big domain */ +/* Big sizes */ +/* onto */ +/* maximize, if missed, maximize=0 */ +/* */ +/* small table */ +/******************************************/ + +#include "mex.h" + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, count, NB, NS, siz_b, siz_s, ndim, temp, maximize; + int *mask, *sx, *sy, *cpsy, *subs, *s, *cpsy2, *ssize; + double *pb, *ps, *bp, *sp, *pbd; + + + siz_b = mxGetNumberOfElements(prhs[1]); + siz_s = mxGetNumberOfElements(prhs[3]); + pb = mxGetPr(prhs[1]); + ps = mxGetPr(prhs[3]); + + NB = mxGetNumberOfElements(prhs[0]); + bp = mxGetPr(prhs[0]); + + pbd = mxGetPr(prhs[2]); + + if(nrhs < 5) maximize = 0; + else maximize = (int)mxGetScalar(prhs[4]); + + if(siz_s == 0){ + plhs[0] = mxCreateDoubleMatrix(1, 1, mxREAL); + sp = mxGetPr(plhs[0]); + if(maximize){ + for(i=0; i<NB; i++){ + *sp = (*sp < bp[i])? bp[i] : *sp; + } + } + else{ + for(i=0; i<NB; i++){ + *sp += bp[i]; + } + } + return; + } + + mask = malloc(siz_s * sizeof(int)); + ssize = malloc(siz_s * sizeof(int)); + count = 0; + for(i=0; i<siz_s; i++){ + for(j=0; j<siz_b; j++){ + if(ps[i] == pb[j]){ + mask[count] = j; + count++; + break; + } + } + } + + ndim = siz_b; + sx = (int *)malloc(sizeof(int)*ndim); + sy = (int *)malloc(sizeof(int)*ndim); + for(i=0; i<ndim; i++){ + sx[i] = (int)pbd[i]; + sy[i] = 1; + } + for(i=0; i<siz_s; i++){ + temp = mask[i]; + sy[temp] = sx[temp]; + ssize[i] = sx[temp]; + } + + NS = 1; + for(i=0; i<ndim; i++){ + NS *= sy[i]; + } + + plhs[0] = mxCreateNumericArray(siz_s, ssize, mxDOUBLE_CLASS, mxREAL); + sp = mxGetPr(plhs[0]); + + if(NS == 1){ + if(maximize){ + for(i=0; i<NB; i++){ + *sp = (*sp < bp[i])? bp[i] : *sp; + } + } + else{ + for(i=0; i<NB; i++){ + *sp += bp[i]; + } + } + free(mask); + free(sx); + free(sy); + free(ssize); + return; + } + + if(NS == NB){ + for(i=0; i<NB; i++) *sp++ = *bp++; + free(mask); + free(sx); + free(sy); + free(ssize); + return; + } + + s = (int *)malloc(sizeof(int)*ndim); + *(cpsy = (int *)malloc(sizeof(int)*ndim)) = 1; + subs = (int *)malloc(sizeof(int)*ndim); + cpsy2 = (int *)malloc(sizeof(int)*ndim); + for(i = 0; i < ndim; i++){ + subs[i] = 0; + s[i] = sx[i] - 1; + } + + for(i = 0; i < ndim-1; i++){ + cpsy[i+1] = cpsy[i]*sy[i]--; + cpsy2[i] = cpsy[i]*sy[i]; + } + cpsy2[ndim-1] = cpsy[ndim-1]*(--sy[ndim-1]); + + if(maximize){ + for(j=0; j<NB; j++){ + *sp = (*sp < *bp)? *bp : *sp; + bp++; + for(i = 0; i < ndim; i++){ + if(subs[i] == s[i]){ + subs[i] = 0; + if(sy[i]) + sp -= cpsy2[i]; + } + else{ + subs[i]++; + if(sy[i]) + sp += cpsy[i]; + break; + } + } + } + } + else{ + for(j=0; j<NB; j++){ + *sp += *bp++; + for(i = 0; i < ndim; i++){ + if(subs[i] == s[i]){ + subs[i] = 0; + if(sy[i]) + sp -= cpsy2[i]; + } + else{ + subs[i]++; + if(sy[i]) + sp += cpsy[i]; + break; + } + } + } + } + + free(sx); + free(sy); + free(s); + free(cpsy); + free(subs); + free(cpsy2); + free(mask); + free(ssize); +} +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/Tables/marg_table.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,27 @@ +function smallT = marg_table(bigT, bigdom, bigsz, onto, maximize) +% MARG_TABLE Marginalize a table +% smallT = marg_table(bigT, bigdom, bigsz, onto, maximize) + +if nargin < 5, maximize = 0; end + + +smallT = myreshape(bigT, bigsz); % make sure it is a multi-dim array +sum_over = mysetdiff(bigdom, onto); +ndx = find_equiv_posns(sum_over, bigdom); +if maximize + for i=1:length(ndx) + smallT = max(smallT, [], ndx(i)); + end +else + for i=1:length(ndx) + smallT = sum(smallT, ndx(i)); + end +end + + +ns = zeros(1, max(bigdom)); +%ns(bigdom) = mysize(bigT); % ignores trailing dimensions of size 1 +ns(bigdom) = bigsz; + +smallT = squeeze(smallT); % remove all dimensions of size 1 +smallT = myreshape(smallT, ns(onto)); % put back relevant dims of size 1
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/Tables/marg_tableC.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,175 @@ +/* marg_table.c ../potential/tables */ + + +/******************************************/ +/* 5 input & 1 output */ +/* Big table */ +/* Big domain */ +/* Big sizes */ +/* onto */ +/* maximize, if missed, maximize=0 */ +/* */ +/* small table */ +/******************************************/ + +#include "mex.h" + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, count, NB, NS, siz_b, siz_s, ndim, temp, maximize; + int *mask, *sx, *sy, *cpsy, *subs, *s, *cpsy2, *ssize; + double *pb, *ps, *bp, *sp, *pbd; + + + siz_b = mxGetNumberOfElements(prhs[1]); + siz_s = mxGetNumberOfElements(prhs[3]); + pb = mxGetPr(prhs[1]); + ps = mxGetPr(prhs[3]); + + NB = mxGetNumberOfElements(prhs[0]); + bp = mxGetPr(prhs[0]); + + pbd = mxGetPr(prhs[2]); + + if(nrhs < 5) maximize = 0; + else maximize = (int)mxGetScalar(prhs[4]); + + if(siz_s == 0){ + plhs[0] = mxCreateDoubleMatrix(1, 1, mxREAL); + sp = mxGetPr(plhs[0]); + if(maximize){ + for(i=0; i<NB; i++){ + *sp = (*sp < bp[i])? bp[i] : *sp; + } + } + else{ + for(i=0; i<NB; i++){ + *sp += bp[i]; + } + } + return; + } + + mask = malloc(siz_s * sizeof(int)); + ssize = malloc(siz_s * sizeof(int)); + count = 0; + for(i=0; i<siz_s; i++){ + for(j=0; j<siz_b; j++){ + if(ps[i] == pb[j]){ + mask[count] = j; + count++; + break; + } + } + } + + ndim = siz_b; + sx = (int *)malloc(sizeof(int)*ndim); + sy = (int *)malloc(sizeof(int)*ndim); + for(i=0; i<ndim; i++){ + sx[i] = (int)pbd[i]; + sy[i] = 1; + } + for(i=0; i<siz_s; i++){ + temp = mask[i]; + sy[temp] = sx[temp]; + ssize[i] = sx[temp]; + } + + NS = 1; + for(i=0; i<ndim; i++){ + NS *= sy[i]; + } + + plhs[0] = mxCreateNumericArray(siz_s, ssize, mxDOUBLE_CLASS, mxREAL); + sp = mxGetPr(plhs[0]); + + if(NS == 1){ + if(maximize){ + for(i=0; i<NB; i++){ + *sp = (*sp < bp[i])? bp[i] : *sp; + } + } + else{ + for(i=0; i<NB; i++){ + *sp += bp[i]; + } + } + free(mask); + free(sx); + free(sy); + free(ssize); + return; + } + + if(NS == NB){ + for(i=0; i<NB; i++) *sp++ = *bp++; + free(mask); + free(sx); + free(sy); + free(ssize); + return; + } + + s = (int *)malloc(sizeof(int)*ndim); + *(cpsy = (int *)malloc(sizeof(int)*ndim)) = 1; + subs = (int *)malloc(sizeof(int)*ndim); + cpsy2 = (int *)malloc(sizeof(int)*ndim); + for(i = 0; i < ndim; i++){ + subs[i] = 0; + s[i] = sx[i] - 1; + } + + for(i = 0; i < ndim-1; i++){ + cpsy[i+1] = cpsy[i]*sy[i]--; + cpsy2[i] = cpsy[i]*sy[i]; + } + cpsy2[ndim-1] = cpsy[ndim-1]*(--sy[ndim-1]); + + if(maximize){ + for(j=0; j<NB; j++){ + *sp = (*sp < *bp)? *bp : *sp; + bp++; + for(i = 0; i < ndim; i++){ + if(subs[i] == s[i]){ + subs[i] = 0; + if(sy[i]) + sp -= cpsy2[i]; + } + else{ + subs[i]++; + if(sy[i]) + sp += cpsy[i]; + break; + } + } + } + } + else{ + for(j=0; j<NB; j++){ + *sp += *bp++; + for(i = 0; i < ndim; i++){ + if(subs[i] == s[i]){ + subs[i] = 0; + if(sy[i]) + sp -= cpsy2[i]; + } + else{ + subs[i]++; + if(sy[i]) + sp += cpsy[i]; + break; + } + } + } + } + + free(sx); + free(sy); + free(s); + free(cpsy); + free(subs); + free(cpsy2); + free(mask); + free(ssize); +} +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/Tables/marg_tableM.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,30 @@ +function smallT = marg_tableM(bigT, bigdom, bigsz, onto, maximize) +% MARG_TABLE Marginalize a table +% smallT = marg_table(bigT, bigdom, bigsz, onto, maximize) + + % marg_tableM is the same as marg_table.m, but we are sure + % it is not marg_table.c + +if nargin < 5, maximize = 0; end + + +smallT = myreshape(bigT, bigsz); % make sure it is a multi-dim array +sum_over = mysetdiff(bigdom, onto); +ndx = find_equiv_posns(sum_over, bigdom); +if maximize + for i=1:length(ndx) + smallT = max(smallT, [], ndx(i)); + end +else + for i=1:length(ndx) + smallT = sum(smallT, ndx(i)); + end +end + + +ns = zeros(1, max(bigdom)); +%ns(bigdom) = mysize(bigT); % ignores trailing dimensions of size 1 +ns(bigdom) = bigsz; + +smallT = squeeze(smallT); % remove all dimensions of size 1 +smallT = myreshape(smallT, ns(onto)); % put back relevant dims of size 1
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/Tables/mult_by_sparse_table.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,155 @@ +/* mult_by_sparse_table.c ../potential/tables*/ + + +/******************************************/ +/* 6 input & 1 output */ +/* Big table [0] */ +/* Big domain [1] */ +/* big sizes [2] */ +/* Small table [3] */ +/* small domain [4] */ +/* small sizes [5] */ +/* */ +/* New big table[0] */ +/******************************************/ + +#include <math.h> +#include <stdlib.h> +#include "mex.h" + +int compare(const void* src1, const void* src2){ + int i1 = *(int*)src1 ; + int i2 = *(int*)src2 ; + return i1-i2 ; +} + +void ind_subv(int index, const int *cumprod, int n, int *bsubv){ + int i; + + for (i = n-1; i >= 0; i--) { + bsubv[i] = ((int)floor(index / cumprod[i])); + index = index % cumprod[i]; + } +} + +int subv_ind(const int n, const int *cumprod, const int *subv){ + int i, index=0; + + for(i=0; i<n; i++){ + index += subv[i] * cumprod[i]; + } + return index; +} + +void reset_nzmax(mxArray *spArray, const int old_nzmax, const int new_nzmax){ + double *ptr; + void *newptr; + int *ir, *jc; + int nbytes; + + if(new_nzmax == old_nzmax) return; + nbytes = new_nzmax * sizeof(*ptr); + ptr = mxGetPr(spArray); + newptr = mxRealloc(ptr, nbytes); + mxSetPr(spArray, newptr); + nbytes = new_nzmax * sizeof(*ir); + ir = mxGetIr(spArray); + newptr = mxRealloc(ir, nbytes); + mxSetIr(spArray, newptr); + jc = mxGetJc(spArray); + jc[0] = 0; + jc[1] = new_nzmax; + mxSetNzmax(spArray, new_nzmax); +} + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, count, bdim, sdim, NB, NZB, NZS, position, bindex, sindex, nzCounts=0; + int *mask, *result, *bir, *sir, *rir, *bjc, *sjc, *rjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr, *rpr; + + pbDomain = mxGetPr(prhs[1]); + bdim = mxGetNumberOfElements(prhs[1]); + psDomain = mxGetPr(prhs[4]); + sdim = mxGetNumberOfElements(prhs[4]); + + pbSize = mxGetPr(prhs[2]); + psSize = mxGetPr(prhs[5]); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + bpr = mxGetPr(prhs[0]); + bir = mxGetIr(prhs[0]); + bjc = mxGetJc(prhs[0]); + NZB = bjc[1]; + + spr = mxGetPr(prhs[3]); + sir = mxGetIr(prhs[3]); + sjc = mxGetJc(prhs[3]); + NZS = sjc[1]; + + plhs[0] = mxDuplicateArray(prhs[0]); + rpr = mxGetPr(plhs[0]); + rir = mxGetIr(plhs[0]); + rjc = mxGetJc(plhs[0]); + rjc[0] = 0; + rjc[1] = NZB; + + if(sdim == 0){ + for(i=0; i<NZB; i++){ + rpr[i] *= *spr; + } + return; + } + + mask = malloc(sdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sir, NZS, sizeof(int), compare); + if(result){ + position = result - sir; + rpr[nzCounts] = bpr[i] * spr[position]; + rir[nzCounts] = bindex; + nzCounts++; + } + } + + reset_nzmax(plhs[0], NZB, nzCounts); + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/Tables/mult_by_table.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,114 @@ +/* mult_by_table.c ../potential/tables */ + + +/******************************************/ +/* 6 input & 1 output */ +/* Big table [0] */ +/* Big domain [1] */ +/* big sizes [2] */ +/* Small table [3] */ +/* small domain [4] */ +/* small sizes [5] */ +/* */ +/* New big table[0] */ +/******************************************/ + +#include "mex.h" + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, count, NB, NS, siz_b, siz_s, ndim, temp; + int *mask, *sx, *sy, *cpsy, *subs, *s, *cpsy2; + double *pbDomain, *psDomain, *sp, *zp, *bs; + + plhs[0] = mxDuplicateArray(prhs[0]); + zp = mxGetPr(plhs[0]); + + siz_b = mxGetNumberOfElements(prhs[1]); + siz_s = mxGetNumberOfElements(prhs[4]); + pbDomain = mxGetPr(prhs[1]); + psDomain = mxGetPr(prhs[4]); + + NB = mxGetNumberOfElements(prhs[0]); + NS = mxGetNumberOfElements(prhs[3]); + sp = mxGetPr(prhs[3]); + + bs = mxGetPr(prhs[2]); + + if(NS == 1){ + for(i=0; i<NB; i++){ + zp[i] *= *sp; + } + return; + } + + if(NS == NB){ + for(i=0; i<NB; i++){ + zp[i] *= sp[i]; + } + return; + } + + mask = malloc(siz_s * sizeof(int)); + count = 0; + for(i=0; i<siz_s; i++){ + for(j=0; j<siz_b; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + ndim = siz_b; + sx = (int *)malloc(sizeof(int)*ndim); + sy = (int *)malloc(sizeof(int)*ndim); + for(i=0; i<ndim; i++){ + sx[i] = (int)bs[i]; + sy[i] = 1; + } + for(i=0; i<count; i++){ + temp = mask[i]; + sy[temp] = sx[temp]; + } + + s = (int *)malloc(sizeof(int)*ndim); + *(cpsy = (int *)malloc(sizeof(int)*ndim)) = 1; + subs = (int *)malloc(sizeof(int)*ndim); + cpsy2 = (int *)malloc(sizeof(int)*ndim); + for(i = 0; i < ndim; i++){ + subs[i] = 0; + s[i] = sx[i] - 1; + } + + for(i = 0; i < ndim-1; i++){ + cpsy[i+1] = cpsy[i]*sy[i]--; + cpsy2[i] = cpsy[i]*sy[i]; + } + cpsy2[ndim-1] = cpsy[ndim-1]*(--sy[ndim-1]); + + for(j=0; j<NB; j++){ + *zp++ *= *sp; + for(i = 0; i < ndim; i++){ + if(subs[i] == s[i]){ + subs[i] = 0; + if(sy[i]) + sp -= cpsy2[i]; + } + else{ + subs[i]++; + if(sy[i]) + sp += cpsy[i]; + break; + } + } + } + free(sx); + free(sy); + free(s); + free(cpsy); + free(subs); + free(cpsy2); + free(mask); +} +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/Tables/mult_by_table.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +function bigT = mult_by_table(bigT, bigdom, bigsz, smallT, smalldom, smallsz) +% MULT_BY_TABLE +% bigT = mult_by_table(bigT, bigdom, bigsz, smallT, smalldom, smallsz) +% + +Ts = extend_domain_table(smallT, smalldom, smallsz, bigdom, bigsz); +bigT(:) = bigT(:) .* Ts(:); % must have bigT(:) on LHS to preserve shape
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/Tables/mult_by_table2.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,30 @@ +function bigT = mult_by_table2(bigT, bigdom, bigsz, smallT, smalldom, smallsz) +% MULT_BY_TABLE +% bigT = mult_by_table(bigT, bigdom, bigsz, smallT, smalldom, smallsz) +% + +%Ts = extend_domain_table(smallT, smalldom, smallsz, bigdom, bigsz); +%bigT(:) = bigT(:) .* Ts(:); % must have bigT(:) on LHS to preserve shape + +% extend_domain_table has a lot of overhead for small tables, +% since it calls myreshape and myrepmat, which check for 1 dimensional case. +% Here, we check up front. + +if length(bigdom)==1 % vector + bigT = bigT .* smallT; % smallT can be scalar or vector +else + if (length(bigsz) == length(smallsz)) & all(bigsz == smallsz) + bigT = bigT .* smallT; + else + map = find_equiv_posns(smalldom, bigdom); + sz = ones(1, length(bigdom)); + sz(map) = smallsz; + smallT = reshape(smallT, sz); % add dimensions of size 1 for missing domain + % we can use reshape instead of myreshape, because we know length(sz)>1 + sz = bigsz; + sz(map) = 1; % don't replicate along small domain, which is shared + % we can use repmat instead of myrepmat, because we know length(sz)>1 + smallT = repmat(smallT, sz(:)'); + bigT(:) = bigT(:) .* smallT(:); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/Tables/mult_by_tableC.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,114 @@ +/* mult_by_table.c ../potential/tables */ + + +/******************************************/ +/* 6 input & 1 output */ +/* Big table [0] */ +/* Big domain [1] */ +/* big sizes [2] */ +/* Small table [3] */ +/* small domain [4] */ +/* small sizes [5] */ +/* */ +/* New big table[0] */ +/******************************************/ + +#include "mex.h" + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, count, NB, NS, siz_b, siz_s, ndim, temp; + int *mask, *sx, *sy, *cpsy, *subs, *s, *cpsy2; + double *pbDomain, *psDomain, *sp, *zp, *bs; + + plhs[0] = mxDuplicateArray(prhs[0]); + zp = mxGetPr(plhs[0]); + + siz_b = mxGetNumberOfElements(prhs[1]); + siz_s = mxGetNumberOfElements(prhs[4]); + pbDomain = mxGetPr(prhs[1]); + psDomain = mxGetPr(prhs[4]); + + NB = mxGetNumberOfElements(prhs[0]); + NS = mxGetNumberOfElements(prhs[3]); + sp = mxGetPr(prhs[3]); + + bs = mxGetPr(prhs[2]); + + if(NS == 1){ + for(i=0; i<NB; i++){ + zp[i] *= *sp; + } + return; + } + + if(NS == NB){ + for(i=0; i<NB; i++){ + zp[i] *= sp[i]; + } + return; + } + + mask = malloc(siz_s * sizeof(int)); + count = 0; + for(i=0; i<siz_s; i++){ + for(j=0; j<siz_b; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + ndim = siz_b; + sx = (int *)malloc(sizeof(int)*ndim); + sy = (int *)malloc(sizeof(int)*ndim); + for(i=0; i<ndim; i++){ + sx[i] = (int)bs[i]; + sy[i] = 1; + } + for(i=0; i<count; i++){ + temp = mask[i]; + sy[temp] = sx[temp]; + } + + s = (int *)malloc(sizeof(int)*ndim); + *(cpsy = (int *)malloc(sizeof(int)*ndim)) = 1; + subs = (int *)malloc(sizeof(int)*ndim); + cpsy2 = (int *)malloc(sizeof(int)*ndim); + for(i = 0; i < ndim; i++){ + subs[i] = 0; + s[i] = sx[i] - 1; + } + + for(i = 0; i < ndim-1; i++){ + cpsy[i+1] = cpsy[i]*sy[i]--; + cpsy2[i] = cpsy[i]*sy[i]; + } + cpsy2[ndim-1] = cpsy[ndim-1]*(--sy[ndim-1]); + + for(j=0; j<NB; j++){ + *zp++ *= *sp; + for(i = 0; i < ndim; i++){ + if(subs[i] == s[i]){ + subs[i] = 0; + if(sy[i]) + sp -= cpsy2[i]; + } + else{ + subs[i]++; + if(sy[i]) + sp += cpsy[i]; + break; + } + } + } + free(sx); + free(sy); + free(s); + free(cpsy); + free(subs); + free(cpsy2); + free(mask); +} +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/Tables/mult_by_tableM.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +function bigT = mult_by_table(bigT, bigdom, bigsz, smallT, smalldom, smallsz) +% MULT_BY_TABLE +% bigT = mult_by_table(bigT, bigdom, bigsz, smallT, smalldom, smallsz) +% + +Ts = extend_domain_table(smallT, smalldom, smallsz, bigdom, bigsz); +bigT(:) = bigT(:) .* Ts(:); % must have bigT(:) on LHS to preserve shape
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/Tables/mult_by_table_global.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +function mult_by_table_global(bigT, bigdom, bigsz, smallT, smalldom, smallsz) + +% all arguments are read only +global NEWBIGT_GLOBAL + +Ts = extend_domain_table(smallT, smalldom, smallsz, bigdom, bigsz); +NEWBIGT_GLOBAL = bigT(:) .* Ts(:);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/Tables/rep_mult.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,92 @@ +/* rep_mult.c repmat first two operands to the size provided by */ +/* the third operand, then perform point multiply */ +/* 3 input, 1 output */ +/* C = rep_mult(A, B, sizes) */ + +#include "mex.h" + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) +{ + double *xp, *yp, *zp, *pSizes; + int xnd, ynd, numElements = 1; + const int *xdim, *ydim; + int i, j, ndim; + int *s, *sx, *sy, *cpsx, *cpsy; + int *subs, *s1, *cpsx2, *cpsy2; + + if (nrhs != 3) + mexErrMsgTxt("Incorrect number of inputs."); + + if (nlhs > 1) + mexErrMsgTxt("Too many output arguments."); + + xnd = mxGetNumberOfDimensions(prhs[0]); + ynd = mxGetNumberOfDimensions(prhs[1]); + xdim = mxGetDimensions(prhs[0]); + ydim = mxGetDimensions(prhs[1]); + ndim = mxGetNumberOfElements(prhs[2]); + + pSizes = mxGetPr(prhs[2]); + + sx = (int *)malloc(sizeof(int)*ndim); + sy = (int *)malloc(sizeof(int)*ndim); + s = (int *)malloc(sizeof(int)*ndim); + s1 = (int *)malloc(sizeof(int)*ndim); + *(cpsx = (int *)malloc(sizeof(int)*ndim)) = 1; + *(cpsy = (int *)malloc(sizeof(int)*ndim)) = 1; + subs = (int *)malloc(sizeof(int)*ndim); + cpsx2 = (int *)malloc(sizeof(int)*ndim); + cpsy2 = (int *)malloc(sizeof(int)*ndim); + for(i=0; i<ndim; i++){ + subs[i] = 0; + sx[i] = (i < xnd) ? xdim[i] : 1; + sy[i] = (i < ynd) ? ydim[i] : 1; + s[i] = (int)pSizes[i]; + s1[i] = s[i] - 1; + numElements *= s[i]; + } + + for(i=0; i<ndim-1; i++){ + cpsx[i+1] = cpsx[i]*sx[i]--; + cpsy[i+1] = cpsy[i]*sy[i]--; + cpsx2[i] = cpsx[i]*sx[i]; + cpsy2[i] = cpsy[i]*sy[i]; + } + cpsx2[ndim-1] = cpsx[ndim-1]*(--sx[ndim-1]); + cpsy2[ndim-1] = cpsy[ndim-1]*(--sy[ndim-1]); + + plhs[0] = mxCreateNumericArray(ndim, s, mxDOUBLE_CLASS, mxREAL); + zp = mxGetPr(plhs[0]); + xp = mxGetPr(prhs[0]); + yp = mxGetPr(prhs[1]); + + for(j=0; j<numElements; j++){ + *zp++ = *xp * *yp; + for(i=0; i<ndim; i++){ + if(subs[i] == s1[i]){ + subs[i] = 0; + if(sx[i]) + xp -= cpsx2[i]; + if(sy[i]) + yp -= cpsy2[i]; + } + else{ + subs[i]++; + if(sx[i]) + xp += cpsx[i]; + if(sy[i]) + yp += cpsy[i]; + break; + } + } + } + free(sx); + free(sy); + free(s); + free(s1); + free(cpsx); + free(cpsy); + free(subs); + free(cpsx2); + free(cpsy2); +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/Tables/repmat_and_mult.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,97 @@ +/**************************************************** +A = mult_by_array(big, small) +implicitely copies small |big|/|small| times +and then does element-wise multiplication. + +i.e., +C = repmat(small(:), 1, length(big(:))/length(small(:))) +A = reshape(big(:) .* C(:), size(big)) + +However, this C version avoids the expense of the repmat. + +Written by wei.hu@intel.com, 28 Jan 2002. +/****************************************************/ + + +#include "mex.h" + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) +{ + double *sp, *zp; + int i, j, NB, NS, xnd, ynd, ndim; + const int *xdim, *ydim; + int *s, *sx, *sy, *cpsy, *subs, *cpsy2; + + if (nrhs != 2) + mexErrMsgTxt("Incorrect number of inputs."); + + if (nlhs > 1) + mexErrMsgTxt("Too many output arguments."); + + plhs[0] = mxDuplicateArray(prhs[0]); + zp = mxGetPr(plhs[0]); + sp = mxGetPr(prhs[1]); + + xnd = mxGetNumberOfDimensions(prhs[0]); + ynd = mxGetNumberOfDimensions(prhs[1]); + xdim = mxGetDimensions(prhs[0]); + ydim = mxGetDimensions(prhs[1]); + ndim = xnd; + + NB = mxGetNumberOfElements(prhs[0]); + NS = mxGetNumberOfElements(prhs[1]); + + if(NS == 1){ + for(i=0; i<NB; i++){ + *zp++ *= *sp; + } + return; + } + + if(NS == NB){ + for(i=0; i<NB; i++){ + *zp++ *= *sp++; + } + return; + } + + sx = (int *)malloc(sizeof(int)*ndim); + sy = (int *)malloc(sizeof(int)*ndim); + s = (int *)malloc(sizeof(int)*ndim); + *(cpsy = (int *)malloc(sizeof(int)*ndim)) = 1; + subs = (int *)malloc(sizeof(int)*ndim); + cpsy2 = (int *)malloc(sizeof(int)*ndim); + for(i=0; i<ndim; i++){ + subs[i] = 0; + sx[i] = xdim[i]; + sy[i] = (i < ynd) ? ydim[i] : 1; + s[i] = sx[i] - 1; + } + + for (i = 0; i < ndim-1; i++){ + cpsy[i+1] = cpsy[i]*sy[i]--; + cpsy2[i] = cpsy[i]*sy[i]; + } + cpsy2[ndim-1] = cpsy[ndim-1]*(--sy[ndim-1]); + + for(j=0; j<NB; j++){ + *zp++ *= *sp; + for(i=0; i<ndim; i++){ + if(subs[i] == s[i]){ + subs[i] = 0; + if(sy[i]) sp -= cpsy2[i]; + } + else{ + subs[i]++; + if(sy[i]) sp += cpsy[i]; + break; + } + } + } + free(sx); + free(sy); + free(s); + free(cpsy); + free(subs); + free(cpsy2); +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/check_for_cd_arcs.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,23 @@ +function check_for_cd_arcs(onodes, cnodes, dag) +% CHECK_FOR_CD_ARCS Raise an error if there are any C->D links where the C node is hidden. +% check_for_cd_arcs(onodes, cnodes, dag) +% +% We cannot convert the logistic/softmax function (C->D CPD) to a Gaussian potential +% unless we use the variational approximation discussed in +% "A variational approximation for Bayesian networks with discrete and continuous latent +% variables", K. Murphy, UAI 1999. + +n = length(dag); +hnodes = mysetdiff(1:n, onodes); +chid = myintersect(cnodes, hnodes); +dnodes = mysetdiff(1:n, cnodes); +for i=chid(:)' + dcs = myintersect(children(dag, i), dnodes); + if ~isempty(dcs) + error(['hidden cts node ' num2str(i) ' has a discrete child']); + end +end + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/determine_pot_type.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,25 @@ +function pot_type = determine_pot_type(model, onodes, nodes) +% DETERMINE_POT_TYPE Determine the type of potential based on the evidence pattern. +% pot_type = determine_pot_type(model, onodes, nodes) +% +% If there are any utility nodes, pot_type = 'u' +% else +% If all hidden nodes are discrete, pot_type = 'd'. +% If all hidden nodes are continuous, pot_type = 'g' (Gaussian). +% If some hidden nodes are discrete, and some cts, pot_type = 'cg' (conditional Gaussian). +% +% nodes defaults to all nodes in graph + +nnodes = length(model.node_sizes); +if nargin < 3, nodes = 1:nnodes; end + +hnodes = mysetdiff(nodes, onodes); +if isfield(model, 'limid') %~isempty(model.utility_nodes) + pot_type = 'u'; +elseif isempty(myintersect(model.cnodes, hnodes)) + pot_type = 'd'; +elseif mysubset(hnodes, model.cnodes) + pot_type = 'g'; +else + pot_type = 'cg'; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/genops.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,707 @@ +/* + + GENOPS.C + Generalized arithmetic operators overloading built-in functions. + + written by Douglas M. Schwarz + schwarz@servtech.com + 26 December 1998 + Last modified: 2 April 1999 + + Copyright 1998-1999 by Douglas M. Schwarz. All rights reserved. + +*/ + + +/* + +Build MEX file by entering the appropriate command at the MATLAB prompt +(-D<name> option is equivalent to #define <name> in source file): + +mex genops.c -DPLUS_MEX -output plus +mex genops.c -DMINUS_MEX -output minus +mex genops.c -DTIMES_MEX -output times +mex genops.c -DRDIVIDE_MEX -output rdivide +mex genops.c -DLDIVIDE_MEX -output ldivide +mex genops.c -DPOWER_MEX -output power +mex genops.c -DEQ_MEX -output eq +mex genops.c -DNE_MEX -output ne +mex genops.c -DLT_MEX -output lt +mex genops.c -DGT_MEX -output gt +mex genops.c -DLE_MEX -output le +mex genops.c -DGE_MEX -output ge + +*/ + +/* This file has been formatted for a tab equal to 4 spaces. */ + +#if defined(EQ_MEX) || defined(NE_MEX) || defined(LT_MEX) || defined(GT_MEX) \ + || defined(LE_MEX) || defined(GE_MEX) +#define RELOP_MEX +#endif + +#include "mex.h" +#include "matrix.h" +#ifdef POWER_MEX +#include <math.h> +#define PI 3.141592653589793 +#endif + +bool allequal(int, const int *, const int *); +void removeZeroImag(double *, double *, int, const int *, int, mxArray **); + +#define xMat prhs[0] +#define yMat prhs[1] +#define zMat plhs[0] + +#define min(A,B) ((A) < (B) ? (A) : (B)) +#define max(A,B) ((A) > (B) ? (A) : (B)) + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) +{ + double *xrp, *xip, *yrp, *yip; +#ifndef RELOP_MEX + double *zr, *zi, *zip; +#endif + double *zrp, *zrend; + int xnd, ynd, numElements = 1; + const int *xdim, *ydim; + bool xcmplx, ycmplx; + mxClassID yclass; + int *s, ndim, *sx, *sy, i, *cpsx, *cpsy; + int *subs, *s1, *cpsx2, *cpsy2; + int ix = 0, iy = 0; + mxArray *args[3], *result[1]; +#if defined(RDIVIDE_MEX) || defined(LDIVIDE_MEX) + double denom; +#endif +#ifdef POWER_MEX + double mag, theta, phi, magx; + int flops = 0; +#endif + + + if (nrhs != 2) + mexErrMsgTxt("Incorrect number of inputs."); + + if (nlhs > 1) + mexErrMsgTxt("Too many output arguments."); + + xnd = mxGetNumberOfDimensions(xMat); + ynd = mxGetNumberOfDimensions(yMat); + xdim = mxGetDimensions(xMat); + ydim = mxGetDimensions(yMat); + + yclass = mxGetClassID(yMat); + +/* If the built-in function in MATLAB can handle the arguments + then use that. */ + if (yclass != mxDOUBLE_CLASS || + (xnd == 2 && xdim[0] == 1 && xdim[1] == 1) || + (ynd == 2 && ydim[0] == 1 && ydim[1] == 1) || + (xnd == ynd && allequal(xnd,xdim,ydim))) + { +#ifdef PLUS_MEX + args[0] = mxCreateString("plus"); +#elif defined(MINUS_MEX) + args[0] = mxCreateString("minus"); +#elif defined(TIMES_MEX) + args[0] = mxCreateString("times"); +#elif defined(RDIVIDE_MEX) + args[0] = mxCreateString("rdivide"); +#elif defined(LDIVIDE_MEX) + args[0] = mxCreateString("ldivide"); +#elif defined(POWER_MEX) + args[0] = mxCreateString("power"); +#elif defined(EQ_MEX) + args[0] = mxCreateString("eq"); +#elif defined(NE_MEX) + args[0] = mxCreateString("ne"); +#elif defined(LT_MEX) + args[0] = mxCreateString("lt"); +#elif defined(GT_MEX) + args[0] = mxCreateString("gt"); +#elif defined(LE_MEX) + args[0] = mxCreateString("le"); +#elif defined(GE_MEX) + args[0] = mxCreateString("ge"); +#endif + args[1] = (mxArray *)xMat; + args[2] = (mxArray *)yMat; + mexCallMATLAB(1, result, 3, args, "builtin"); + mxDestroyArray(args[0]); + zMat = result[0]; + } + else /* X and Y are both N-D and different dimensionality. */ + { + ndim = max(xnd,ynd); + sx = (int *)mxMalloc(sizeof(int)*ndim); + sy = (int *)mxMalloc(sizeof(int)*ndim); + s = (int *)mxMalloc(sizeof(int)*ndim); + s1 = (int *)mxMalloc(sizeof(int)*ndim); + *(cpsx = (int *)mxMalloc(sizeof(int)*ndim)) = 1; + *(cpsy = (int *)mxMalloc(sizeof(int)*ndim)) = 1; + subs = (int *)mxMalloc(sizeof(int)*ndim); + cpsx2 = (int *)mxMalloc(sizeof(int)*ndim); + cpsy2 = (int *)mxMalloc(sizeof(int)*ndim); + for (i = 0; i < ndim; i++) + { + subs[i] = 0; + sx[i] = (i < xnd) ? xdim[i] : 1; + sy[i] = (i < ynd) ? ydim[i] : 1; + if (sx[i] == sy[i]) + s[i] = sx[i]; + else if (sx[i] == 1) + s[i] = sy[i]; + else if (sy[i] == 1) + s[i] = sx[i]; + else + { + mxFree(sx); + mxFree(sy); + mxFree(s); + mxFree(s1); + mxFree(cpsx); + mxFree(cpsy); + mxFree(subs); + mxFree(cpsx2); + mxFree(cpsy2); + mexErrMsgTxt("Array dimensions are not appropriate."); + } + s1[i] = s[i] - 1; + numElements *= s[i]; + } + + for (i = 0; i < ndim-1; i++) + { + cpsx[i+1] = cpsx[i]*sx[i]--; + cpsy[i+1] = cpsy[i]*sy[i]--; + cpsx2[i] = cpsx[i]*sx[i]; + cpsy2[i] = cpsy[i]*sy[i]; + } + cpsx2[ndim-1] = cpsx[ndim-1]*(--sx[ndim-1]); + cpsy2[ndim-1] = cpsy[ndim-1]*(--sy[ndim-1]); + + xcmplx = mxIsComplex(xMat); + ycmplx = mxIsComplex(yMat); + + if (!xcmplx && !ycmplx) /* X and Y both N-D, both real. */ + { +#ifdef POWER_MEX + zMat = mxCreateNumericArray(ndim, s, mxDOUBLE_CLASS, mxCOMPLEX); + zrp = zr = mxGetPr(zMat); + zip = zi = mxGetPi(zMat); +#elif defined(RELOP_MEX) + zMat = mxCreateNumericArray(ndim, s, mxDOUBLE_CLASS, mxREAL); + mxSetLogical(zMat); + zrp = mxGetPr(zMat); +#else + zMat = mxCreateNumericArray(ndim, s, mxDOUBLE_CLASS, mxREAL); + zrp = mxGetPr(zMat); +#endif + xrp = mxGetPr(xMat); + yrp = mxGetPr(yMat); + zrend = zrp + numElements; + while (zrp < zrend) + { +#ifdef PLUS_MEX + *zrp++ = *xrp + *yrp; +#elif defined(MINUS_MEX) + *zrp++ = *xrp - *yrp; +#elif defined(TIMES_MEX) + *zrp++ = *xrp * *yrp; +#elif defined(RDIVIDE_MEX) + *zrp++ = *xrp / *yrp; +#elif defined(LDIVIDE_MEX) + *zrp++ = *yrp / *xrp; +#elif defined(POWER_MEX) + if (*xrp < 0.0 && *yrp != floor(*yrp)) + { + mag = pow(-*xrp,*yrp); + theta = PI * *yrp; + *zrp++ = mag*cos(theta); + *zip++ = mag*sin(theta); + flops += 18; + } + else + { + *zrp++ = pow(*xrp,*yrp); + *zip++ = 0.0; + flops++; + } +#elif defined(EQ_MEX) + *zrp++ = (*xrp == *yrp); +#elif defined(NE_MEX) + *zrp++ = (*xrp != *yrp); +#elif defined(LT_MEX) + *zrp++ = (*xrp < *yrp); +#elif defined(GT_MEX) + *zrp++ = (*xrp > *yrp); +#elif defined(LE_MEX) + *zrp++ = (*xrp <= *yrp); +#elif defined(GE_MEX) + *zrp++ = (*xrp >= *yrp); +#endif + for (i = 0; i < ndim; i++) + { + if (subs[i] == s1[i]) + { + subs[i] = 0; + if (sx[i]) + xrp -= cpsx2[i]; + if (sy[i]) + yrp -= cpsy2[i]; + } + else + { + subs[i]++; + if (sx[i]) + xrp += cpsx[i]; + if (sy[i]) + yrp += cpsy[i]; + break; + } + } + } +#ifdef POWER_MEX + mexAddFlops(flops); + removeZeroImag(zr, zi, ndim, (const int *)s, numElements, &zMat); +#elif !defined(RELOP_MEX) + mexAddFlops(numElements); +#endif + } + else if (!xcmplx && ycmplx) /* X and Y both N-D, X real, Y complex. */ + { +#ifdef POWER_MEX + zMat = mxCreateNumericArray(ndim, s, mxDOUBLE_CLASS, mxCOMPLEX); + zrp = zr = mxGetPr(zMat); + zip = zi = mxGetPi(zMat); +#elif defined(RELOP_MEX) + zMat = mxCreateNumericArray(ndim, s, mxDOUBLE_CLASS, mxREAL); + mxSetLogical(zMat); + zrp = mxGetPr(zMat); +#else + zMat = mxCreateNumericArray(ndim, s, mxDOUBLE_CLASS, mxCOMPLEX); + zrp = mxGetPr(zMat); + zip = mxGetPi(zMat); +#endif + xrp = mxGetPr(xMat); + yrp = mxGetPr(yMat); + yip = mxGetPi(yMat); + zrend = zrp + numElements; + while (zrp < zrend) + { +#ifdef PLUS_MEX + *zrp++ = *xrp + *yrp; + *zip++ = *yip; +#elif defined(MINUS_MEX) + *zrp++ = *xrp - *yrp; + *zip++ = -*yip; +#elif defined(TIMES_MEX) + *zrp++ = *xrp * *yrp; + *zip++ = *xrp * *yip; +#elif defined(RDIVIDE_MEX) + denom = *yrp * *yrp + *yip * *yip; + *zrp++ = (*xrp * *yrp)/denom; + *zip++ = (-*xrp * *yip)/denom; +#elif defined(LDIVIDE_MEX) + *zrp++ = *yrp / *xrp; + *zip++ = *yip / *xrp; +#elif defined(POWER_MEX) + if (*yip == 0.0) + { + if (*xrp < 0.0 && *yrp != floor(*yrp)) + { + mag = pow(-*xrp,*yrp); + theta = PI * *yrp; + *zrp++ = mag*cos(theta); + *zip++ = mag*sin(theta); + flops += 18; + } + else + { + *zrp++ = pow(*xrp,*yrp); + *zip++ = 0.0; + flops++; + } + } + else + { + if (*xrp < 0.0) + { + mag = pow(-*xrp,*yrp)*exp(-PI * *yip); + theta = *yip * log(-*xrp) + PI * *yrp; + *zrp++ = mag*cos(theta); + *zip++ = mag*sin(theta); + flops += 18; + } + else + { + mag = pow(*xrp,*yrp); + theta = *yip * log(*xrp); + *zrp++ = mag*cos(theta); + *zip++ = mag*sin(theta); + flops += 13; + } + } +#elif defined(EQ_MEX) + *zrp++ = (*xrp == *yrp) && (*yip == 0.0); +#elif defined(NE_MEX) + *zrp++ = (*xrp != *yrp) || (*yip != 0.0); +#elif defined(LT_MEX) + *zrp++ = (*xrp < *yrp); +#elif defined(GT_MEX) + *zrp++ = (*xrp > *yrp); +#elif defined(LE_MEX) + *zrp++ = (*xrp <= *yrp); +#elif defined(GE_MEX) + *zrp++ = (*xrp >= *yrp); +#endif + for (i = 0; i < ndim; i++) + { + if (subs[i] == s1[i]) + { + subs[i] = 0; + if (sx[i]) + xrp -= cpsx2[i]; + if (sy[i]) + { + yrp -= cpsy2[i]; + yip -= cpsy2[i]; + } + } + else + { + subs[i]++; + if (sx[i]) + xrp += cpsx[i]; + if (sy[i]) + { + yrp += cpsy[i]; + yip += cpsy[i]; + } + break; + } + } + } +#if defined(PLUS_MEX) || defined(MINUS_MEX) + mexAddFlops(2*numElements); +#elif defined(TIMES_MEX) || defined(RDIVIDE_MEX) || defined(LDIVIDE_MEX) + mexAddFlops(6*numElements); +#elif defined(POWER_MEX) + mexAddFlops(flops); + removeZeroImag(zr, zi, ndim, (const int *)s, numElements, &zMat); +#endif + } + else if (xcmplx && !ycmplx) /* X and Y both N-D, X complex, Y real. */ + { +#ifdef POWER_MEX + zMat = mxCreateNumericArray(ndim, s, mxDOUBLE_CLASS, mxCOMPLEX); + zrp = zr = mxGetPr(zMat); + zip = zi = mxGetPi(zMat); +#elif defined(RELOP_MEX) + zMat = mxCreateNumericArray(ndim, s, mxDOUBLE_CLASS, mxREAL); + mxSetLogical(zMat); + zrp = mxGetPr(zMat); +#else + zMat = mxCreateNumericArray(ndim, s, mxDOUBLE_CLASS, mxCOMPLEX); + zrp = mxGetPr(zMat); + zip = mxGetPi(zMat); +#endif + xrp = mxGetPr(xMat); + xip = mxGetPi(xMat); + yrp = mxGetPr(yMat); + zrend = zrp + numElements; + while (zrp < zrend) + { +#ifdef PLUS_MEX + *zrp++ = *xrp + *yrp; + *zip++ = *xip; +#elif defined(MINUS_MEX) + *zrp++ = *xrp - *yrp; + *zip++ = *xip; +#elif defined(TIMES_MEX) + *zrp++ = *xrp * *yrp; + *zip++ = *xip * *yrp; +#elif defined(RDIVIDE_MEX) + *zrp++ = *xrp / *yrp; + *zip++ = *xip / *yrp; +#elif defined(LDIVIDE_MEX) + denom = *xrp * *xrp + *xip * *xip; + *zrp++ = (*xrp * *yrp)/denom; + *zip++ = (-*xip * *yrp)/denom; +#elif defined(POWER_MEX) + if (*xip == 0.0) + { + if (*xrp < 0.0 && *yrp != floor(*yrp)) + { + mag = pow(-*xrp,*yrp); + theta = PI * *yrp; + *zrp++ = mag*cos(theta); + *zip++ = mag*sin(theta); + flops += 18; + } + else + { + *zrp++ = pow(*xrp,*yrp); + *zip++ = 0.0; + flops++; + } + } + else + { + mag = pow(*xrp * *xrp + *xip * *xip,0.5 * *yrp); + theta = *yrp*atan2(*xip,*xrp); + *zrp++ = mag*cos(theta); + *zip++ = mag*sin(theta); + flops += 18; + } +#elif defined(EQ_MEX) + *zrp++ = (*xrp == *yrp) && (*xip == 0.0); +#elif defined(NE_MEX) + *zrp++ = (*xrp != *yrp) || (*xip != 0.0); +#elif defined(LT_MEX) + *zrp++ = (*xrp < *yrp); +#elif defined(GT_MEX) + *zrp++ = (*xrp > *yrp); +#elif defined(LE_MEX) + *zrp++ = (*xrp <= *yrp); +#elif defined(GE_MEX) + *zrp++ = (*xrp >= *yrp); +#endif + for (i = 0; i < ndim; i++) + { + if (subs[i] == s1[i]) + { + subs[i] = 0; + if (sx[i]) + { + xrp -= cpsx2[i]; + xip -= cpsx2[i]; + } + if (sy[i]) + yrp -= cpsy2[i]; + } + else + { + subs[i]++; + if (sx[i]) + { + xrp += cpsx[i]; + xip += cpsx[i]; + } + if (sy[i]) + yrp += cpsy[i]; + break; + } + } + } +#if defined(PLUS_MEX) || defined(MINUS_MEX) + mexAddFlops(2*numElements); +#elif defined(TIMES_MEX) || defined(RDIVIDE_MEX) || defined(LDIVIDE_MEX) + mexAddFlops(6*numElements); +#elif defined(POWER_MEX) + mexAddFlops(flops); + removeZeroImag(zr, zi, ndim, (const int *)s, numElements, &zMat); +#endif + } + else if (xcmplx && ycmplx) /* X and Y both N-D, both complex. */ + { +#if defined(RELOP_MEX) + zMat = mxCreateNumericArray(ndim, s, mxDOUBLE_CLASS, mxREAL); + mxSetLogical(zMat); + zrp = mxGetPr(zMat); +#else + zMat = mxCreateNumericArray(ndim, s, mxDOUBLE_CLASS, mxCOMPLEX); + zrp = zr = mxGetPr(zMat); + zip = zi = mxGetPi(zMat); +#endif + xrp = mxGetPr(xMat); + xip = mxGetPi(xMat); + yrp = mxGetPr(yMat); + yip = mxGetPi(yMat); + zrend = zrp + numElements; + while (zrp < zrend) + { +#ifdef PLUS_MEX + *zrp++ = *xrp + *yrp; + *zip++ = *xip + *yip; +#elif defined(MINUS_MEX) + *zrp++ = *xrp - *yrp; + *zip++ = *xip - *yip; +#elif defined(TIMES_MEX) + *zrp++ = *xrp * *yrp - *xip * *yip; + *zip++ = *xip * *yrp + *xrp * *yip; +#elif defined(RDIVIDE_MEX) + denom = *yrp * *yrp + *yip * *yip; + *zrp++ = (*xrp * *yrp + *xip * *yip)/denom; + *zip++ = (*xip * *yrp - *xrp * *yip)/denom; +#elif defined(LDIVIDE_MEX) + denom = *xrp * *xrp + *xip * *xip; + *zrp++ = (*xrp * *yrp + *xip * *yip)/denom; + *zip++ = (*xrp * *yip - *xip * *yrp)/denom; +#elif defined(POWER_MEX) + if (*xip == 0.0 && *yip == 0.0) + { + if (*xrp < 0.0 && *yrp != floor(*yrp)) + { + mag = pow(-*xrp,*yrp); + theta = PI * *yrp; + *zrp++ = mag*cos(theta); + *zip++ = mag*sin(theta); + flops += 18; + } + else + { + *zrp++ = pow(*xrp,*yrp); + *zip++ = 0.0; + flops++; + } + } + else if (*xip == 0.0) + { + if (*xrp < 0.0) + { + mag = pow(-*xrp,*yrp)*exp(-PI * *yip); + theta = *yip * log(-*xrp) + PI * *yrp; + *zrp++ = mag*cos(theta); + *zip++ = mag*sin(theta); + flops += 18; + } + else + { + mag = pow(*xrp,*yrp); + theta = *yip * log(*xrp); + *zrp++ = mag*cos(theta); + *zip++ = mag*sin(theta); + flops += 13; + } + } + else if (*yip == 0.0) + { + mag = pow(*xrp * *xrp + *xip * *xip,0.5 * *yrp); + theta = *yrp * atan2(*xip,*xrp); + *zrp++ = mag*cos(theta); + *zip++ = mag*sin(theta); + flops += 18; + } + else + { + magx = sqrt(*xrp * *xrp + *xip * *xip); + phi = atan2(*xip,*xrp); + mag = pow(magx,*yrp)*exp(-*yip * phi); + theta = *yip * log(magx) + *yrp * phi; + *zrp++ = mag*cos(theta); + *zip++ = mag*sin(theta); + flops += 18; + } +#elif defined(EQ_MEX) + *zrp++ = (*xrp == *yrp) && (*xip == *yip); +#elif defined(NE_MEX) + *zrp++ = (*xrp != *yrp) || (*xip != *yip); +#elif defined(LT_MEX) + *zrp++ = (*xrp < *yrp); +#elif defined(GT_MEX) + *zrp++ = (*xrp > *yrp); +#elif defined(LE_MEX) + *zrp++ = (*xrp <= *yrp); +#elif defined(GE_MEX) + *zrp++ = (*xrp >= *yrp); +#endif + for (i = 0; i < ndim; i++) + { + if (subs[i] == s1[i]) + { + subs[i] = 0; + if (sx[i]) + { + xrp -= cpsx2[i]; + xip -= cpsx2[i]; + } + if (sy[i]) + { + yrp -= cpsy2[i]; + yip -= cpsy2[i]; + } + } + else + { + subs[i]++; + if (sx[i]) + { + xrp += cpsx[i]; + xip += cpsx[i]; + } + if (sy[i]) + { + yrp += cpsy[i]; + yip += cpsy[i]; + } + break; + } + } + } +#if defined(PLUS_MEX) || defined(MINUS_MEX) + mexAddFlops(2*numElements); +#elif defined(TIMES_MEX) || defined(RDIVIDE_MEX) || defined(LDIVIDE_MEX) + mexAddFlops(6*numElements); +#elif defined(POWER_MEX) + mexAddFlops(flops); +#endif +#ifndef RELOP_MEX + removeZeroImag(zr, zi, ndim, (const int *)s, numElements, &zMat); +#endif + } + } +} + + +/*********************************************************** +* * +* Tests to see if the vectors xdim and ydim are equal. * +* * +***********************************************************/ +bool allequal(int ndim, const int *xdim, const int *ydim) +{ + int i; + bool result = true; + + for (i = 0; i < ndim; i++) + result = result && (xdim[i] == ydim[i]); + + return(result); +} + + +/****************************************************************************** +* * +* Tests to see if every imaginary element is identically zero and, if so, * +* creates a new array which is real and copies the real elements to it. * +* * +******************************************************************************/ +void removeZeroImag(double *zr, double *zi, int ndim, const int *s, + int numElements, mxArray *plhs[]) +{ + double *zrend, *ziend, *zip, *z1p, *z2p; + bool allImZero = true; + mxArray *temp; + + zip = zi; + ziend = zi + numElements; + while (zip < ziend) + { + allImZero = allImZero && (*zip++ == 0.0); + if (!allImZero) + return; + } + + temp = mxCreateNumericArray(ndim, s, mxDOUBLE_CLASS, mxREAL); + z1p = zr; + z2p = mxGetPr(temp); + zrend = z1p + numElements; + while (z1p < zrend) + *z2p++ = *z1p++; + mxDestroyArray(plhs[0]); + plhs[0] = temp; + return; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/potentials/mk_initial_pot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,29 @@ +function pot = mk_initial_pot(pot_type, dom, ns, cnodes, onodes) +% MK_INITIAL_POT A "initial" potential is one which has not had any evidence entered into it. +% pot = mk_initial_pot(pot_type, domain, node_sizes, cnodes, onodes) +% +% pot_type is one of 'd', 'g', 'cg' or 'u' +% domain is the set of nodes to be included in the potential. +% node_sizes(i) is the size of node i. + +switch pot_type + case 'd', + ns(onodes) = 1; + pot = dpot(dom, ns(dom)); + case 'u', + ns(onodes) = 1; + pot = upot(dom, ns(dom)); + case 'g', + ns(onodes) = 0; + pot = cpot(dom, ns(dom)); + case 'cg', + dnodes = mysetdiff(1:length(ns), cnodes); + ddom = myintersect(dnodes, dom); + cdom = myintersect(cnodes, dom); + dobs = myintersect(dnodes, onodes); + cobs = myintersect(cnodes, onodes); + ns(dobs) = 1; + ns(cobs) = 0; + pot = cgpot(ddom, cdom, ns); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/test_BNT.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,108 @@ +% If all is well, all of these scripts should run without errors. + + +% bnets +cg1 +cg2 +discrete1 +fa1 +gaussian1 +gaussian2 +if exist('@gibbs_sampling_inf_engine/private/compute_posterior','file') + % only exists if installC has been run + gibbs_test1 +end +learn1 +lw1 +mfa1 +mixexp1 +mixexp2 +mixexp3 +mog1 +mpe1 +mpe2 +qmr1 +qmr2 +sample1 +softev1 +softmax1 +sprinkler1 + + +% belief propagation +belprop_polytree_discrete +belprop_polytree_gauss % alag +belprop_polytree_cg +belprop_loop1_discrete +belprop_loop1_gauss +belprop_loopy_discrete +belprop_loopy_gauss +belprop_loopy_cg % like cg1 + + +% factor graphs +%fg1 failed since marginals were not exact + +fg2 +fg3 +fg_mrf1 +fg_mrf2 + + +% Structure learning +bic1 +cooper_yoo +k2demo1 +mcmc1 +model_select1 +pc1 +%pc2 failed due to numerical problems in KPMstats/cond_indep_fisher_z + + + + +% limids +asia_dt1 +id1 +oil1 +pigs1 + + +% dbns +arhmm1 +bat1 +bkff1 +chmm1 +dhmm1 +filter_test1 +ghmm1 +kalman1 +kjaerulff1 +loopy_dbn1 +mhmm1 +mildew1 +reveal1 +viterbi1 +water1 + + +% HHMMs +abcd_hhmm +sample_square_hhmm_discrete +%learn_square_hhmm_cts +sample_motif_hhmm + +%sparse jtree engine & ndx 2TBN engine +if exist('@jtree_sparse_inf_engine/init_pot','file') + % only exists if installC has been run + discrete2 + discrete3 + filter_test1 + water2 +end + +%find . -path '*.m' -exec wc -l {} \; | ~/count.pl + +% we cannot use tic;toc to time test_BNT, since functions within this script +% reset the tic;toc timer. Hence we use the following: +%clock0=clock; cpu0 = cputime; test_BNT; cpu=cputime-cpu0; elapsed=etime(clock, clock0)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/BNT/uninstallC_BNT.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,17 @@ +dirs = {'potentials/Tables', ... + 'CPDs/@discrete_CPD', ... + 'inference/static/@jtree_sparse_inf_engine', ... + 'inference/static/@gibbs_sampling_inf_engine/private'}; + +BNT_HOME = '/home/ai2/murphyk/matlab/FullBNT'; % edit this +%global BNT_HOME + +for d=1:length(dirs) + f = fullfile(BNT_HOME, 'BNT', dirs{d}); + fprintf('removing Cmex files from %s\n', f); + cd(f) + delete *.mex* + delete *.dll + delete *.obj + delete *.o +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,80 @@ +/KLgauss.m/1.1.1.1/Tue Apr 26 02:29:16 2005// +/README.txt/1.1.1.1/Tue Apr 26 02:29:18 2005// +/beta_sample.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/chisquared_histo.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/chisquared_prob.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/chisquared_readme.txt/1.1.1.1/Tue Apr 26 02:29:18 2005// +/chisquared_table.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/clg_Mstep.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/clg_Mstep_simple.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/clg_prob.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/condGaussToJoint.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/cond_indep_fisher_z.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/condgaussTrainObserved.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/condgauss_sample.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/convertBinaryLabels.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/cwr_demo.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/cwr_em.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/cwr_predict.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/cwr_prob.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/cwr_readme.txt/1.1.1.1/Tue Apr 26 02:29:18 2005// +/cwr_test.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/dirichlet_sample.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/dirichletpdf.m/1.1.1.1/Sun May 22 23:32:18 2005// +/dirichletrnd.m/1.1.1.1/Sun May 22 23:32:12 2005// +/distchck.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/eigdec.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/est_transmat.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/fit_paritioned_model_testfn.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/fit_partitioned_model.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/gamma_sample.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/gaussian_prob.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/gaussian_sample.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/histCmpChi2.m/1.1.1.1/Tue May 3 20:18:16 2005// +/linear_regression.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/logist2.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/logist2Apply.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/logist2ApplyRegularized.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/logist2Fit.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/logist2FitRegularized.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/logistK.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/logistK_eval.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/marginalize_gaussian.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/matrix_T_pdf.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/matrix_normal_pdf.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/mc_stat_distrib.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/mixgauss_Mstep.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/mixgauss_classifier_apply.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/mixgauss_classifier_train.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/mixgauss_em.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/mixgauss_init.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/mixgauss_prob.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/mixgauss_prob_test.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/mixgauss_sample.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/mkPolyFvec.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/mk_unit_norm.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/multinomial_prob.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/multinomial_sample.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/multipdf.m/1.1.1.1/Sun May 22 23:32:42 2005// +/multirnd.m/1.1.1.1/Sun May 22 23:32:38 2005// +/normal_coef.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/partial_corr_coef.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/parzen.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/parzenC.c/1.1.1.1/Tue Apr 26 02:29:20 2005// +/parzenC.dll/1.1.1.1/Tue Apr 26 02:29:20 2005// +/parzenC.mexglx/1.1.1.1/Tue Apr 26 02:29:20 2005// +/parzenC_test.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/parzen_fit_select_unif.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/pca.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/rndcheck.m/1.1.1.1/Tue Apr 26 02:29:22 2005// +/sample.m/1.1.1.1/Tue Apr 26 02:29:22 2005// +/sample_discrete.m/1.1.1.1/Tue Apr 26 02:29:22 2005// +/sample_gaussian.m/1.1.1.1/Tue Apr 26 02:29:22 2005// +/standardize.m/1.1.1.1/Wed May 4 04:35:36 2005// +/student_t_logprob.m/1.1.1.1/Tue Apr 26 02:29:22 2005// +/student_t_prob.m/1.1.1.1/Tue Apr 26 02:29:22 2005// +/test_dir.m/1.1.1.1/Sun May 22 23:32:20 2005// +/unidrndKPM.m/1.1.1.1/Tue May 31 18:19:24 2005// +/unif_discrete_sample.m/1.1.1.1/Tue Apr 26 02:29:22 2005// +/weightedRegression.m/1.1.1.1/Tue Apr 26 02:29:22 2005// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/KPMstats
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/KLgauss.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,9 @@ +function kl = KLgauss(P, Q) +%The following computes D(P||Q), the KL divergence between two zero-mean +%Gaussians with covariance P and Q: +% klDiv = -0.5*(log(det(P*inv(Q))) + trace(eye(N)-P*inv(Q))); + +R = P*inv(Q); +kl = -0.5*(log(det(R))) + trace(eye(length(P))-R); + +%To get MI, just set P=cov(X,Y) and Q=blockdiag(cov(X),cov(Y)).
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/README.txt Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,4 @@ +KPMstats is a directory of miscellaneous statistics functions written by +Kevin Patrick Murphy and various other people (see individual file headers). + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/beta_sample.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,76 @@ +function r = betarnd(a,b,m,n); +%BETARND Random matrices from beta distribution. +% R = BETARND(A,B) returns a matrix of random numbers chosen +% from the beta distribution with parameters A and B. +% The size of R is the common size of A and B if both are matrices. +% If either parameter is a scalar, the size of R is the size of the other +% parameter. Alternatively, R = BETARND(A,B,M,N) returns an M by N matrix. + +% Reference: +% [1] L. Devroye, "Non-Uniform Random Variate Generation", +% Springer-Verlag, 1986 + +% Copyright (c) 1993-98 by The MathWorks, Inc. +% $Revision: 1.1.1.1 $ $Date: 2005/04/26 02:29:18 $ + +if nargin < 2, + error('Requires at least two input arguments'); +end + +if nargin == 2 + [errorcode rows columns] = rndcheck(2,2,a,b); +end + +if nargin == 3 + [errorcode rows columns] = rndcheck(3,2,a,b,m); +end + +if nargin == 4 + [errorcode rows columns] = rndcheck(4,2,a,b,m,n); +end + +if errorcode > 0 + error('Size information is inconsistent.'); +end + +r = zeros(rows,columns); + +% Use Theorem 4.1, case A (Devroye, page 430) to derive beta +% random numbers as a ratio of gamma random numbers. +if prod(size(a)) == 1 + a1 = a(ones(rows,1),ones(columns,1)); + g1 = gamrnd(a1,1); +else + g1 = gamrnd(a,1); +end +if prod(size(b)) == 1 + b1 = b(ones(rows,1),ones(columns,1)); + g2 = gamrnd(b1,1); +else + g2 = gamrnd(b,1); +end +r = g1 ./ (g1 + g2); + +% Return NaN if b is not positive. +if any(any(b <= 0)); + if prod(size(b) == 1) + tmp = NaN; + r = tmp(ones(rows,columns)); + else + k = find(b <= 0); + tmp = NaN; + r(k) = tmp(ones(size(k))); + end +end + +% Return NaN if a is not positive. +if any(any(a <= 0)); + if prod(size(a) == 1) + tmp = NaN; + r = tmp(ones(rows,columns)); + else + k = find(a <= 0); + tmp = NaN; + r(k) = tmp(ones(size(k))); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/chisquared_histo.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function s = chisquared_histo(h1, h2) +% Measure distance between 2 histograms (small numbers means more similar) +denom = h1 + h2; +denom = denom + (denom==0); +s = sum(((h1 - h2) .^ 2) ./ denom);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/chisquared_prob.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,35 @@ +function P = chisquared_prob(X2,v) +%CHISQUARED_PROB computes the chi-squared probability function. +% P = CHISQUARED_PROB( X2, v ) returns P(X2|v), the probability +% of observing a chi-squared value <= X2 with v degrees of freedom. +% This is the probability that the sum of squares of v unit-variance +% normally-distributed random variables is <= X2. +% X2 and v may be matrices of the same size size, or either +% may be a scalar. +% +% e.g., CHISQUARED_PROB(5.99,2) returns 0.9500, verifying the +% 95% confidence bound for 2 degrees of freedom. This is also +% cross-checked in, e.g., Abramowitz & Stegun Table 26.8 +% +% See also CHISQUARED_TABLE +% +%Peter R. Shaw, WHOI + +% References: Press et al., Numerical Recipes, Cambridge, 1986; +% Abramowitz & Stegun, Handbook of Mathematical Functions, Dover, 1972. + +% Peter R. Shaw, Woods Hole Oceanographic Institution +% Woods Hole, MA 02543 +% (508) 457-2000 ext. 2473 pshaw@whoi.edu +% March, 1990; fixed Oct 1992 for version 4 + +% Computed using the Incomplete Gamma function, +% as given by Press et al. (Recipes) eq. (6.2.17) + +% Following nonsense is necessary from Matlab version 3 -> version 4 +versn_str=version; eval(['versn=' versn_str(1) ';']); +if versn<=3, %sigh + P = gamma(v/2, X2/2); +else + P = gammainc(X2/2, v/2); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/chisquared_readme.txt Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,36 @@ +From ftp://ftp.mathworks.com/pub/contrib/v4/stats/chisquared/ + +Chi-squared probability function. + +Abstract +m-files to compute the Chi-squared probability function, and +the percentage points of the probability function. + +P = CHISQUARED_PROB( X2, v ) returns P(X2|v), the probability +of observing a chi-squared value <= X2 with v degrees of freedom. +This is the probability that the sum of squares of v unit-variance +normally-distributed random variables is <= X2. + +Conversely: +X2 = CHISQUARED_TABLE( P, v ) returns the X2, the value of +chi-squared corresponding to v degrees of freedom and probability P. + +In reference textbooks, what is normally tabulated are the +percentage points of the chi-squared distribution; thus, one +would use CHISQUARED_TABLE rather than interpolate such a table. + +References: Press et al., Numerical Recipes, Cambridge, 1986; +Abramowitz & Stegun, Handbook of Mathematical Functions, +Dover, 1972; Table 26.8 + +Peter R. Shaw +Woods Hole Oceanographic Institution, Woods Hole, MA 02543 +(508) 457-2000 ext. 2473 +pshaw@whoi.edu + +--FILES GENERATED-- +README_chisq +chisquared_prob.m (function) -- chi-squared probability function +chisquared_table.m (function) -- "percentage points" (i.e., inverse) + of chi-squared probability function +chiaux.m (function) -- auxiliary fn. used by chisquared_table
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/chisquared_table.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,63 @@ +function X2 = chisquared_table(P,v) +%CHISQUARED_TABLE computes the "percentage points" of the +%chi-squared distribution, as in Abramowitz & Stegun Table 26.8 +% X2 = CHISQUARED_TABLE( P, v ) returns the value of chi-squared +% corresponding to v degrees of freedom and probability P. +% P is the probability that the sum of squares of v unit-variance +% normally-distributed random variables is <= X2. +% P and v may be matrices of the same size size, or either +% may be a scalar. +% +% e.g., to find the 95% confidence interval for 2 degrees +% of freedom, use CHISQUARED_TABLE( .95, 2 ), yielding 5.99, +% in agreement with Abramowitz & Stegun's Table 26.8 +% +% This result can be checked through the function +% CHISQUARED_PROB( 5.99, 2 ), yielding 0.9500 +% +% The familiar 1.96-sigma confidence bounds enclosing 95% of +% a 1-D gaussian is found through +% sqrt( CHISQUARED_TABLE( .95, 1 )), yielding 1.96 +% +% See also CHISQUARED_PROB +% +%Peter R. Shaw, WHOI +%Leslie Rosenfeld, MBARI + +% References: Press et al., Numerical Recipes, Cambridge, 1986; +% Abramowitz & Stegun, Handbook of Mathematical Functions, Dover, 1972. + +% Peter R. Shaw, Woods Hole Oceanographic Institution +% Woods Hole, MA 02543 pshaw@whoi.edu +% Leslie Rosenfeld, MBARI +% Last revision: Peter Shaw, Oct 1992: fsolve with version 4 + +% ** Calls function CHIAUX ** +% Computed using the Incomplete Gamma function, +% as given by Press et al. (Recipes) eq. (6.2.17) + +[mP,nP]=size(P); +[mv,nv]=size(v); +if mP~=mv | nP~=nv, + if mP==1 & nP==1, + P=P*ones(mv,nv); + elseif mv==1 & nv==1, + v=v*ones(mP,nP); + else + error('P and v must be the same size') + end +end +[m,n]=size(P); X2 = zeros(m,n); +for i=1:m, + for j=1:n, + if v(i,j)<=10, + x0=P(i,j)*v(i,j); + else + x0=v(i,j); + end +% Note: "old" and "new" calls to fsolve may or may not follow +% Matlab version 3.5 -> version 4 (so I'm keeping the old call around...) +% X2(i,j) = fsolve('chiaux',x0,zeros(16,1),[v(i,j),P(i,j)]); %(old call) + X2(i,j) = fsolve('chiaux',x0,zeros(16,1),[],[v(i,j),P(i,j)]); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/clg_Mstep.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,203 @@ +function [mu, Sigma, B] = clg_Mstep(w, Y, YY, YTY, X, XX, XY, varargin) +% MSTEP_CLG Compute ML/MAP estimates for a conditional linear Gaussian +% [mu, Sigma, B] = Mstep_clg(w, Y, YY, YTY, X, XX, XY, varargin) +% +% We fit P(Y|X,Q=i) = N(Y; B_i X + mu_i, Sigma_i) +% and w(i,t) = p(M(t)=i|y(t)) = posterior responsibility +% See www.ai.mit.edu/~murphyk/Papers/learncg.pdf. +% +% See process_options for how to specify the input arguments. +% +% INPUTS: +% w(i) = sum_t w(i,t) = responsibilities for each mixture component +% If there is only one mixture component (i.e., Q does not exist), +% then w(i) = N = nsamples, and +% all references to i can be replaced by 1. +% Y(:,i) = sum_t w(i,t) y(:,t) = weighted observations +% YY(:,:,i) = sum_t w(i,t) y(:,t) y(:,t)' = weighted outer product +% YTY(i) = sum_t w(i,t) y(:,t)' y(:,t) = weighted inner product +% You only need to pass in YTY if Sigma is to be estimated as spherical. +% +% In the regression context, we must also pass in the following +% X(:,i) = sum_t w(i,t) x(:,t) = weighted inputs +% XX(:,:,i) = sum_t w(i,t) x(:,t) x(:,t)' = weighted outer product +% XY(i) = sum_t w(i,t) x(:,t) y(:,t)' = weighted outer product +% +% Optional inputs (default values in []) +% +% 'cov_type' - 'full', 'diag' or 'spherical' ['full'] +% 'tied_cov' - 1 (Sigma) or 0 (Sigma_i) [0] +% 'clamped_cov' - pass in clamped value, or [] if unclamped [ [] ] +% 'clamped_mean' - pass in clamped value, or [] if unclamped [ [] ] +% 'clamped_weights' - pass in clamped value, or [] if unclamped [ [] ] +% 'cov_prior' - added to Sigma(:,:,i) to ensure psd [0.01*eye(d,d,Q)] +% +% If cov is tied, Sigma has size d*d. +% But diagonal and spherical covariances are represented in full size. + +[cov_type, tied_cov, ... + clamped_cov, clamped_mean, clamped_weights, cov_prior, ... + xs, ys, post] = ... + process_options(varargin, ... + 'cov_type', 'full', 'tied_cov', 0, 'clamped_cov', [], 'clamped_mean', [], ... + 'clamped_weights', [], 'cov_prior', [], ... + 'xs', [], 'ys', [], 'post', []); + +[Ysz Q] = size(Y); + +if isempty(X) % no regression + %B = []; + B2 = zeros(Ysz, 1, Q); + for i=1:Q + B(:,:,i) = B2(:,1:0,i); % make an empty array of size Ysz x 0 x Q + end + [mu, Sigma] = mixgauss_Mstep(w, Y, YY, YTY, varargin{:}); + return; +end + + +N = sum(w); +if isempty(cov_prior) + cov_prior = 0.01*repmat(eye(Ysz,Ysz), [1 1 Q]); +end +%YY = YY + cov_prior; % regularize the scatter matrix + +% Set any zero weights to one before dividing +% This is valid because w(i)=0 => Y(:,i)=0, etc +w = w + (w==0); + +Xsz = size(X,1); +% Append 1 to X to get Z +ZZ = zeros(Xsz+1, Xsz+1, Q); +ZY = zeros(Xsz+1, Ysz, Q); +for i=1:Q + ZZ(:,:,i) = [XX(:,:,i) X(:,i); + X(:,i)' w(i)]; + ZY(:,:,i) = [XY(:,:,i); + Y(:,i)']; +end + + +%%% Estimate mean and regression + +if ~isempty(clamped_weights) & ~isempty(clamped_mean) + B = clamped_weights; + mu = clamped_mean; +end +if ~isempty(clamped_weights) & isempty(clamped_mean) + B = clamped_weights; + % eqn 5 + mu = zeros(Ysz, Q); + for i=1:Q + mu(:,i) = (Y(:,i) - B(:,:,i)*X(:,i)) / w(i); + end +end +if isempty(clamped_weights) & ~isempty(clamped_mean) + mu = clamped_mean; + % eqn 3 + B = zeros(Ysz, Xsz, Q); + for i=1:Q + tmp = XY(:,:,i)' - mu(:,i)*X(:,i)'; + %B(:,:,i) = tmp * inv(XX(:,:,i)); + B(:,:,i) = (XX(:,:,i) \ tmp')'; + end +end +if isempty(clamped_weights) & isempty(clamped_mean) + mu = zeros(Ysz, Q); + B = zeros(Ysz, Xsz, Q); + % Nothing is clamped, so we must estimate B and mu jointly + for i=1:Q + % eqn 9 + if rcond(ZZ(:,:,i)) < 1e-10 + sprintf('clg_Mstep warning: ZZ(:,:,%d) is ill-conditioned', i); + % probably because there are too few cases for a high-dimensional input + ZZ(:,:,i) = ZZ(:,:,i) + 1e-5*eye(Xsz+1); + end + %A = ZY(:,:,i)' * inv(ZZ(:,:,i)); + A = (ZZ(:,:,i) \ ZY(:,:,i))'; + B(:,:,i) = A(:, 1:Xsz); + mu(:,i) = A(:, Xsz+1); + end +end + +if ~isempty(clamped_cov) + Sigma = clamped_cov; + return; +end + + +%%% Estimate covariance + +% Spherical +if cov_type(1)=='s' + if ~tied_cov + Sigma = zeros(Ysz, Ysz, Q); + for i=1:Q + % eqn 16 + A = [B(:,:,i) mu(:,i)]; + %s = trace(YTY(i) + A'*A*ZZ(:,:,i) - 2*A*ZY(:,:,i)) / (Ysz*w(i)); % wrong! + s = (YTY(i) + trace(A'*A*ZZ(:,:,i)) - trace(2*A*ZY(:,:,i))) / (Ysz*w(i)); + Sigma(:,:,i) = s*eye(Ysz,Ysz); + + %%%%%%%%%%%%%%%%%%% debug + if ~isempty(xs) + [nx T] = size(xs); + zs = [xs; ones(1,T)]; + yty = 0; + zAAz = 0; + yAz = 0; + for t=1:T + yty = yty + ys(:,t)'*ys(:,t) * post(i,t); + zAAz = zAAz + zs(:,t)'*A'*A*zs(:,t)*post(i,t); + yAz = yAz + ys(:,t)'*A*zs(:,t)*post(i,t); + end + assert(approxeq(yty, YTY(i))) + assert(approxeq(zAAz, trace(A'*A*ZZ(:,:,i)))) + assert(approxeq(yAz, trace(A*ZY(:,:,i)))) + s2 = (yty + zAAz - 2*yAz) / (Ysz*w(i)); + assert(approxeq(s,s2)) + end + %%%%%%%%%%%%%%% end debug + + end + else + S = 0; + for i=1:Q + % eqn 18 + A = [B(:,:,i) mu(:,i)]; + S = S + trace(YTY(i) + A'*A*ZZ(:,:,i) - 2*A*ZY(:,:,i)); + end + Sigma = repmat(S / (N*Ysz), [1 1 Q]); + end +else % Full/diagonal + if ~tied_cov + Sigma = zeros(Ysz, Ysz, Q); + for i=1:Q + A = [B(:,:,i) mu(:,i)]; + % eqn 10 + SS = (YY(:,:,i) - ZY(:,:,i)'*A' - A*ZY(:,:,i) + A*ZZ(:,:,i)*A') / w(i); + if cov_type(1)=='d' + Sigma(:,:,i) = diag(diag(SS)); + else + Sigma(:,:,i) = SS; + end + end + else % tied + SS = zeros(Ysz, Ysz); + for i=1:Q + A = [B(:,:,i) mu(:,i)]; + % eqn 13 + SS = SS + (YY(:,:,i) - ZY(:,:,i)'*A' - A*ZY(:,:,i) + A*ZZ(:,:,i)*A'); + end + SS = SS / N; + if cov_type(1)=='d' + Sigma = diag(diag(SS)); + else + Sigma = SS; + end + Sigma = repmat(Sigma, [1 1 Q]); + end +end + +Sigma = Sigma + cov_prior; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/clg_Mstep_simple.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,52 @@ +function [mu, B] = clg_Mstep_simple(w, Y, YY, YTY, X, XX, XY) +% CLG_MSTEP_SIMPLE Same as CLG_MSTEP, but doesn;t estimate Sigma, so is slightly faster +% function [mu, B] = clg_Mstep_simple(w, Y, YY, YTY, X, XX, XY) +% +% See clg_Mstep for details. +% Unlike clg_Mstep, there are no optional arguments, which are slow to process +% if this function is inside a tight loop. + +[Ysz Q] = size(Y); + +if isempty(X) % no regression + %B = []; + B2 = zeros(Ysz, 1, Q); + for i=1:Q + B(:,:,i) = B2(:,1:0,i); % make an empty array of size Ysz x 0 x Q + end + [mu, Sigma] = mixgauss_Mstep(w, Y, YY, YTY); + return; +end + +N = sum(w); +%YY = YY + cov_prior; % regularize the scatter matrix + +% Set any zero weights to one before dividing +% This is valid because w(i)=0 => Y(:,i)=0, etc +w = w + (w==0); + +Xsz = size(X,1); +% Append 1 to X to get Z +ZZ = zeros(Xsz+1, Xsz+1, Q); +ZY = zeros(Xsz+1, Ysz, Q); +for i=1:Q + ZZ(:,:,i) = [XX(:,:,i) X(:,i); + X(:,i)' w(i)]; + ZY(:,:,i) = [XY(:,:,i); + Y(:,i)']; +end + +mu = zeros(Ysz, Q); +B = zeros(Ysz, Xsz, Q); +for i=1:Q + % eqn 9 + if rcond(ZZ(:,:,i)) < 1e-10 + sprintf('clg_Mstep warning: ZZ(:,:,%d) is ill-conditioned', i); + %probably because there are too few cases for a high-dimensional input + ZZ(:,:,i) = ZZ(:,:,i) + 1e-5*eye(Xsz+1); + end + %A = ZY(:,:,i)' * inv(ZZ(:,:,i)); + A = (ZZ(:,:,i) \ ZY(:,:,i))'; + B(:,:,i) = A(:, 1:Xsz); + mu(:,i) = A(:, Xsz+1); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/clg_prob.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,14 @@ +function p = eval_pdf_clg(X,Y,mu,Sigma,W) +% function p = eval_pdf_clg(X,Y,mu,Sigma,W) +% +% p(c,t) = N(Y(:,t); mu(:,c) + W(:,:,c)*X(:,t), Sigma(:,:,c)) + +[d T] = size(Y); +[d nc] = size(mu); +p = zeros(nc,T); +for c=1:nc + denom = (2*pi)^(d/2)*sqrt(abs(det(Sigma(:,:,c)))); + M = repmat(mu(:,c), 1, T) + W(:,:,c)*X; + mahal = sum(((Y-M)'*inv(Sigma(:,:,c))).*(Y-M)',2); + p(c,:) = (exp(-0.5*mahal) / denom)'; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/condGaussToJoint.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,22 @@ +function [muXY, SigmaXY] = condGaussToJoint(muX, SigmaX, muY, SigmaY, WYgivenX) + +% Compute P(X,Y) from P(X) * P(Y|X) where P(X)=N(X;muX,SigmaX) +% and P(Y|X) = N(Y; WX + muY, SigmaY) + +% For details on how to compute a Gaussian from a Bayes net +% - "Gaussian Influence Diagrams", R. Shachter and C. R. Kenley, Management Science, 35(5):527--550, 1989. + +% size(W) = dy x dx +dx = length(muX); +dy = length(muY); +muXY = [muX(:); WYgivenX*muX(:) + muY]; + +W = [zeros(dx,dx) WYgivenX'; + zeros(dy,dx) zeros(dy,dy)]; +D = [SigmaX zeros(dx,dy); + zeros(dy,dx) SigmaY]; + +U = inv(eye(size(W)) - W')'; +SigmaXY = U' * D * U; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/cond_indep_fisher_z.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,142 @@ +function [CI, r, p] = cond_indep_fisher_z(X, Y, S, C, N, alpha) +% COND_INDEP_FISHER_Z Test if X indep Y given Z using Fisher's Z test +% CI = cond_indep_fisher_z(X, Y, S, C, N, alpha) +% +% C is the covariance (or correlation) matrix +% N is the sample size +% alpha is the significance level (default: 0.05) +% +% See p133 of T. Anderson, "An Intro. to Multivariate Statistical Analysis", 1984 + +if nargin < 6, alpha = 0.05; end + +r = partial_corr_coef(C, X, Y, S); +z = 0.5*log( (1+r)/(1-r) ); +z0 = 0; +W = sqrt(N - length(S) - 3)*(z-z0); % W ~ N(0,1) +cutoff = norminv(1 - 0.5*alpha); % P(|W| <= cutoff) = 0.95 +%cutoff = mynorminv(1 - 0.5*alpha); % P(|W| <= cutoff) = 0.95 +if abs(W) < cutoff + CI = 1; +else % reject the null hypothesis that rho = 0 + CI = 0; +end +p = normcdf(W); +%p = mynormcdf(W); + +%%%%%%%%% + +function p = normcdf(x,mu,sigma) +%NORMCDF Normal cumulative distribution function (cdf). +% P = NORMCDF(X,MU,SIGMA) computes the normal cdf with mean MU and +% standard deviation SIGMA at the values in X. +% +% The size of P is the common size of X, MU and SIGMA. A scalar input +% functions as a constant matrix of the same size as the other inputs. +% +% Default values for MU and SIGMA are 0 and 1 respectively. + +% References: +% [1] M. Abramowitz and I. A. Stegun, "Handbook of Mathematical +% Functions", Government Printing Office, 1964, 26.2. + +% Copyright (c) 1993-98 by The MathWorks, Inc. +% $Revision: 1.1.1.1 $ $Date: 2005/04/26 02:29:18 $ + +if nargin < 3, + sigma = 1; +end + +if nargin < 2; + mu = 0; +end + +[errorcode x mu sigma] = distchck(3,x,mu,sigma); + +if errorcode > 0 + error('Requires non-scalar arguments to match in size.'); +end + +% Initialize P to zero. +p = zeros(size(x)); + +% Return NaN if SIGMA is not positive. +k1 = find(sigma <= 0); +if any(k1) + tmp = NaN; + p(k1) = tmp(ones(size(k1))); +end + +% Express normal CDF in terms of the error function. +k = find(sigma > 0); +if any(k) + p(k) = 0.5 * erfc( - (x(k) - mu(k)) ./ (sigma(k) * sqrt(2))); +end + +% Make sure that round-off errors never make P greater than 1. +k2 = find(p > 1); +if any(k2) + p(k2) = ones(size(k2)); +end + +%%%%%%%% + +function x = norminv(p,mu,sigma); +%NORMINV Inverse of the normal cumulative distribution function (cdf). +% X = NORMINV(P,MU,SIGMA) finds the inverse of the normal cdf with +% mean, MU, and standard deviation, SIGMA. +% +% The size of X is the common size of the input arguments. A scalar input +% functions as a constant matrix of the same size as the other inputs. +% +% Default values for MU and SIGMA are 0 and 1 respectively. + +% References: +% [1] M. Abramowitz and I. A. Stegun, "Handbook of Mathematical +% Functions", Government Printing Office, 1964, 7.1.1 and 26.2.2 + +% Copyright (c) 1993-98 by The MathWorks, Inc. +% $Revision: 1.1.1.1 $ $Date: 2005/04/26 02:29:18 $ + +if nargin < 3, + sigma = 1; +end + +if nargin < 2; + mu = 0; +end + +[errorcode p mu sigma] = distchck(3,p,mu,sigma); + +if errorcode > 0 + error('Requires non-scalar arguments to match in size.'); +end + +% Allocate space for x. +x = zeros(size(p)); + +% Return NaN if the arguments are outside their respective limits. +k = find(sigma <= 0 | p < 0 | p > 1); +if any(k) + tmp = NaN; + x(k) = tmp(ones(size(k))); +end + +% Put in the correct values when P is either 0 or 1. +k = find(p == 0); +if any(k) + tmp = Inf; + x(k) = -tmp(ones(size(k))); +end + +k = find(p == 1); +if any(k) + tmp = Inf; + x(k) = tmp(ones(size(k))); +end + +% Compute the inverse function for the intermediate values. +k = find(p > 0 & p < 1 & sigma > 0); +if any(k), + x(k) = sqrt(2) * sigma(k) .* erfinv(2 * p(k) - 1) + mu(k); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/condgaussTrainObserved.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,27 @@ +function [mu, Sigma] = mixgaussTrainObserved(obsData, hiddenData, nstates, varargin); +% mixgaussTrainObserved Max likelihood estimates of conditional Gaussian from raw data +% function [mu, Sigma] = mixgaussTrainObserved(obsData, hiddenData, nstates, ...); +% +% Input: +% obsData(:,i) +% hiddenData(i) - this is the mixture component label for example i +% Optional arguments - same as mixgauss_Mstep +% +% Output: +% mu(:,q) +% Sigma(:,:,q) - same as mixgauss_Mstep + +[D numex] = size(obsData); +Y = zeros(D, nstates); +YY = zeros(D,D,nstates); +YTY = zeros(nstates,1); +w = zeros(nstates, 1); +for q=1:nstates + ndx = find(hiddenData==q); + w(q) = length(ndx); % each data point has probability 1 of being in this cluster + data = obsData(:,ndx); + Y(:,q) = sum(data,2); + YY(:,:,q) = data*data'; + YTY(q) = sum(diag(data'*data)); +end +[mu, Sigma] = mixgauss_Mstep(w, Y, YY, YTY, varargin{:});
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/condgauss_sample.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function x = mixgauss_sample(mu, Sigma, labels) +% MIXGAUSS_SAMPLE Sample from a mixture of Gaussians given known mixture labels +% function x = mixgauss_sample(mu, Sigma, labels) + +T = length(labels); +[D Q] = size(mu); +x = zeros(D,T); +for q=1:Q + ndx = find(labels==q); + x(:,ndx) = gaussian_sample(mu(:,q)', Sigma(:,:,q), length(ndx))'; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/convertBinaryLabels.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,3 @@ +% labels01 = (labelsPM+1)/2; % maps -1->0, +1->1 +% labelsPM = (2*labels01)-1; % maps 0,1 -> -1,1 +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/cwr_demo.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,124 @@ +% Compare my code with +% http://www.media.mit.edu/physics/publications/books/nmm/files/index.html +% +% cwm.m +% (c) Neil Gershenfeld 9/1/97 +% 1D Cluster-Weighted Modeling example +% +clear all +figure; +seed = 0; +rand('state', seed); +randn('state', seed); +x = (-10:10)'; +y = (x > 0); +npts = length(x); +plot(x,y,'+') +xlabel('x') +ylabel('y') +nclusters = 4; +nplot = 100; +xplot = 24*(1:nplot)'/nplot - 12; + +mux = 20*rand(1,nclusters) - 10; +muy = zeros(1,nclusters); +varx = ones(1,nclusters); +vary = ones(1,nclusters); +pc = 1/nclusters * ones(1,nclusters); +niterations = 5; +eps = 0.01; + + +I = repmat(eye(1,1), [1 1 nclusters]); +O = repmat(zeros(1,1), [1 1 nclusters]); +X = x(:)'; +Y = y(:)'; + +cwr = cwr_em(X, Y, nclusters, 'muX', mux, 'muY', muy, 'SigmaX', I, ... + 'cov_typeX', 'spherical', 'SigmaY', I, 'cov_typeY', 'spherical', ... + 'priorC', pc, 'weightsY', O, 'create_init_params', 0, ... + 'clamp_weights', 1, 'max_iter', niterations, ... + 'cov_priorX', eps*ones(1,1,nclusters), ... + 'cov_priorY', eps*ones(1,1,nclusters)); + + +% Gershenfeld's EM code +for step = 1:niterations + pplot = exp(-(kron(xplot,ones(1,nclusters)) ... + - kron(ones(nplot,1),mux)).^2 ... + ./ (2*kron(ones(nplot,1),varx))) ... + ./ sqrt(2*pi*kron(ones(nplot,1),varx)) ... + .* kron(ones(nplot,1),pc); + plot(xplot,pplot,'k'); + pause(0); + px = exp(-(kron(x,ones(1,nclusters)) ... + - kron(ones(npts,1),mux)).^2 ... + ./ (2*kron(ones(npts,1),varx))) ... + ./ sqrt(2*pi*kron(ones(npts,1),varx)); + py = exp(-(kron(y,ones(1,nclusters)) ... + - kron(ones(npts,1),muy)).^2 ... + ./ (2*kron(ones(npts,1),vary))) ... + ./ sqrt(2*pi*kron(ones(npts,1),vary)); + p = px .* py .* kron(ones(npts,1),pc); + pp = p ./ kron(sum(p,2),ones(1,nclusters)); + pc = sum(pp)/npts; + yfit = sum(kron(ones(npts,1),muy) .* p,2) ... + ./ sum(p,2); + mux = sum(kron(x,ones(1,nclusters)) .* pp) ... + ./ (npts*pc); + varx = eps + sum((kron(x,ones(1,nclusters)) ... + - kron(ones(npts,1),mux)).^2 .* pp) ... + ./ (npts*pc); + muy = sum(kron(y,ones(1,nclusters)) .* pp) ... + ./ (npts*pc); + vary = eps + sum((kron(y,ones(1,nclusters)) ... + - kron(ones(npts,1),muy)).^2 .* pp) ... + ./ (npts*pc); +end + + +% Check equal +cwr_pc = cwr.priorC'; +assert(approxeq(cwr_pc, pc)) +cwr_mux = cwr.muX; +assert(approxeq(mux, cwr_mux)) +cwr_SigmaX = squeeze(cwr.SigmaX)'; +assert(approxeq(varx, cwr_SigmaX)) +cwr_muy = cwr.muY; +assert(approxeq(muy, cwr_muy)) +cwr_SigmaY = squeeze(cwr.SigmaY)'; +assert(approxeq(vary, cwr_SigmaY)) + + +% Prediction + +X = xplot(:)'; +[cwr_mu, Sigma, post] = cwr_predict(cwr, X); +cwr_ystd = squeeze(Sigma)'; + +% pplot(t,c) +pplot = exp(-(kron(xplot,ones(1,nclusters)) ... + - kron(ones(nplot,1),mux)).^2 ... + ./ (2*kron(ones(nplot,1),varx))) ... + ./ sqrt(2*pi*kron(ones(nplot,1),varx)) ... + .* kron(ones(nplot,1),pc); +yplot = sum(kron(ones(nplot,1),muy) .* pplot,2) ... + ./ sum(pplot,2); +ystdplot = sum(kron(ones(nplot,1),(muy.^2+vary)) .* pplot,2) ... + ./ sum(pplot,2) - yplot.^2; + + +% Check equal +assert(approxeq(yplot(:)', cwr_mu(:)')) +assert(approxeq(ystdplot, cwr_ystd)) +assert(approxeq(pplot ./ repmat(sum(pplot,2), 1, nclusters),post') ) + +plot(xplot,yplot,'k'); +hold on +plot(xplot,yplot+ystdplot,'k--'); +plot(xplot,yplot-ystdplot,'k--'); +plot(x,y,'k+'); +axis([-12 12 -1 1.1]); +plot(xplot,.8*pplot/max(max(pplot))-1,'k') +hold off +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/cwr_em.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,161 @@ +function cwr = cwr_em(X, Y, nc, varargin) +% CWR_LEARN Fit the parameters of a cluster weighted regression model using EM +% function cwr = cwr_learn(X, Y, ...) +% +% X(:, t) is the t'th input example +% Y(:, t) is the t'th output example +% nc is the number of clusters +% +% Kevin Murphy, May 2003 + +[max_iter, thresh, cov_typeX, cov_typeY, clamp_weights, ... + muX, muY, SigmaX, SigmaY, weightsY, priorC, create_init_params, ... +cov_priorX, cov_priorY, verbose, regress, clamp_covX, clamp_covY] = process_options(... + varargin, 'max_iter', 10, 'thresh', 1e-2, 'cov_typeX', 'full', ... + 'cov_typeY', 'full', 'clamp_weights', 0, ... + 'muX', [], 'muY', [], 'SigmaX', [], 'SigmaY', [], 'weightsY', [], 'priorC', [], ... + 'create_init_params', 1, 'cov_priorX', [], 'cov_priorY', [], 'verbose', 0, ... + 'regress', 1, 'clamp_covX', 0, 'clamp_covY', 0); + +[nx N] = size(X); +[ny N2] = size(Y); +if N ~= N2 + error(sprintf('nsamples X (%d) ~= nsamples Y (%d)', N, N2)); +end +%if N < nx +% fprintf('cwr_em warning: dim X (%d) > nsamples X (%d)\n', nx, N); +%end +if (N < nx) & regress + fprintf('cwr_em warning: dim X = %d, nsamples X = %d\n', nx, N); +end +if (N < ny) + fprintf('cwr_em warning: dim Y = %d, nsamples Y = %d\n', ny, N); +end +if (nc > N) + error(sprintf('cwr_em: more centers (%d) than data', nc)) +end + +if nc==1 + % No latent variable, so there is a closed-form solution + w = 1/N; + WYbig = Y*w; + WYY = WYbig * Y'; + WY = sum(WYbig, 2); + WYTY = sum(diag(WYbig' * Y)); + cwr.priorC = 1; + cwr.SigmaX = []; + if ~regress + % This is just fitting an unconditional Gaussian + cwr.weightsY = []; + [cwr.muY, cwr.SigmaY] = ... + mixgauss_Mstep(1, WY, WYY, WYTY, ... + 'cov_type', cov_typeY, 'cov_prior', cov_priorY); + % There is a much easier way... + assert(approxeq(cwr.muY, mean(Y'))) + assert(approxeq(cwr.SigmaY, cov(Y') + 0.01*eye(ny))) + else + % This is just linear regression + WXbig = X*w; + WXX = WXbig * X'; + WX = sum(WXbig, 2); + WXTX = sum(diag(WXbig' * X)); + WXY = WXbig * Y'; + [cwr.muY, cwr.SigmaY, cwr.weightsY] = ... + clg_Mstep(1, WY, WYY, WYTY, WX, WXX, WXY, ... + 'cov_type', cov_typeY, 'cov_prior', cov_priorY); + end + if clamp_covY, cwr.SigmaY = SigmaY; end + if clamp_weights, cwr.weightsY = weightsY; end + return; +end + + +if create_init_params + [cwr.muX, cwr.SigmaX] = mixgauss_init(nc, X, cov_typeX); + [cwr.muY, cwr.SigmaY] = mixgauss_init(nc, Y, cov_typeY); + cwr.weightsY = zeros(ny, nx, nc); + cwr.priorC = normalize(ones(nc,1)); +else + cwr.muX = muX; cwr.muY = muY; cwr.SigmaX = SigmaX; cwr.SigmaY = SigmaY; + cwr.weightsY = weightsY; cwr.priorC = priorC; +end + + +if clamp_covY, cwr.SigmaY = SigmaY; end +if clamp_covX, cwr.SigmaX = SigmaX; end +if clamp_weights, cwr.weightsY = weightsY; end + +previous_loglik = -inf; +num_iter = 1; +converged = 0; + +while (num_iter <= max_iter) & ~converged + + % E step + + [likXandY, likYgivenX, post] = cwr_prob(cwr, X, Y); + loglik = sum(log(likXandY)); + % extract expected sufficient statistics + w = sum(post,2); % post(c,t) + WYY = zeros(ny, ny, nc); + WY = zeros(ny, nc); + WYTY = zeros(nc,1); + + WXX = zeros(nx, nx, nc); + WX = zeros(nx, nc); + WXTX = zeros(nc, 1); + WXY = zeros(nx,ny,nc); + %WYY = repmat(reshape(w, [1 1 nc]), [ny ny 1]) .* repmat(Y*Y', [1 1 nc]); + for c=1:nc + weights = repmat(post(c,:), ny, 1); + WYbig = Y .* weights; + WYY(:,:,c) = WYbig * Y'; + WY(:,c) = sum(WYbig, 2); + WYTY(c) = sum(diag(WYbig' * Y)); + + weights = repmat(post(c,:), nx, 1); % weights(nx, nsamples) + WXbig = X .* weights; + WXX(:,:,c) = WXbig * X'; + WX(:,c) = sum(WXbig, 2); + WXTX(c) = sum(diag(WXbig' * X)); + WXY(:,:,c) = WXbig * Y'; + end + + % M step + % Q -> X is called Q->Y in Mstep_clg + [cwr.muX, cwr.SigmaX] = mixgauss_Mstep(w, WX, WXX, WXTX, ... + 'cov_type', cov_typeX, 'cov_prior', cov_priorX); + for c=1:nc + assert(is_psd(cwr.SigmaX(:,:,c))) + end + + if clamp_weights % affects estimate of mu and Sigma + W = cwr.weightsY; + else + W = []; + end + [cwr.muY, cwr.SigmaY, cwr.weightsY] = ... + clg_Mstep(w, WY, WYY, WYTY, WX, WXX, WXY, ... + 'cov_type', cov_typeY, 'clamped_weights', W, ... + 'cov_prior', cov_priorY); + %'xs', X, 'ys', Y, 'post', post); % debug + %a = linspace(min(Y(2,:)), max(Y(2,:)), nc+2); + %cwr.muY(2,:) = a(2:end-1); + + cwr.priorC = normalize(w); + + for c=1:nc + assert(is_psd(cwr.SigmaY(:,:,c))) + end + + if clamp_covY, cwr.SigmaY = SigmaY; end + if clamp_covX, cwr.SigmaX = SigmaX; end + if clamp_weights, cwr.weightsY = weightsY; end + + if verbose, fprintf(1, 'iteration %d, loglik = %f\n', num_iter, loglik); end + num_iter = num_iter + 1; + converged = em_converged(loglik, previous_loglik, thresh); + previous_loglik = loglik; + +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/cwr_predict.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,57 @@ +function [mu, Sigma, weights, mask] = cwr_predict(cwr, X, mask_data) +% CWR_PREDICT cluster weighted regression: predict Y given X +% function [mu, Sigma] = cwr_predict(cwr, X) +% +% mu(:,t) = E[Y|X(:,t)] = sum_c P(c | X(:,t)) E[Y|c, X(:,t)] +% Sigma(:,:,t) = Cov[Y|X(:,t)] +% +% [mu, Sigma, weights, mask] = cwr_predict(cwr, X, mask_data) +% mask(i) = sum_t sum_c p(mask_data(:,i) | X(:,t), c) P(c|X(:,t)) +% This evaluates the predictive density on a set of points +% (This is only sensible if T=1, ie. X is a single vector) + +[nx T] = size(X); +[ny nx nc] = size(cwr.weightsY); +mu = zeros(ny, T); +Sigma = zeros(ny, ny, T); + +if nargout == 4 + comp_mask = 1; + N = size(mask_data,2); + mask = zeros(N,1); +else + comp_mask = 0; +end + +if nc==1 + if isempty(cwr.weightsY) + mu = repmat(cwr.muY, 1, T); + Sigma = repmat(cwr.SigmaY, [1 1 T]); + else + mu = repmat(cwr.muY, 1, T) + cwr.weightsY * X; + Sigma = repmat(cwr.SigmaY, [1 1 T]); + %for t=1:T + % mu(:,t) = cwr.muY + cwr.weightsY*X(:,t); + % Sigma(:,:,t) = cwr.SigmaY; + %end + end + if comp_mask, mask = gaussian_prob(mask_data, mu, Sigma); end + weights = []; + return; +end + + +% likX(c,t) = p(x(:,t) | c) +likX = mixgauss_prob(X, cwr.muX, cwr.SigmaX); +weights = normalize(repmat(cwr.priorC, 1, T) .* likX, 1); +for t=1:T + mut = zeros(ny, nc); + for c=1:nc + mut(:,c) = cwr.muY(:,c) + cwr.weightsY(:,:,c)*X(:,t); + if comp_mask + mask = mask + gaussian_prob(mask_data, mut(:,c), cwr.SigmaY(:,:,c)) * weights(c); + end + end + %w = normalise(cwr.priorC(:) .* likX(:,t)); + [mu(:,t), Sigma(:,:,t)] = collapse_mog(mut, cwr.SigmaY, weights(:,t)); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/cwr_prob.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,39 @@ +function [likXandY, likYgivenX, post] = cwr_prob(cwr, X, Y); +% CWR_EVAL_PDF cluster weighted regression: evaluate likelihood of Y given X +% function [likXandY, likYgivenX, post] = cwr_prob(cwr, X, Y); +% +% likXandY(t) = p(x(:,t), y(:,t)) +% likXgivenY(t) = p(x(:,t)| y(:,t)) +% post(c,t) = p(c | x(:,t), y(:,t)) + +[nx N] = size(X); +nc = length(cwr.priorC); + +if nc == 1 + [mu, Sigma] = cwr_predict(cwr, X); + likY = gaussian_prob(Y, mu, Sigma); + likXandY = likY; + likYgivenX = likY; + post = ones(1,N); + return; +end + + +% likY(c,t) = p(y(:,t) | c) +likY = clg_prob(X, Y, cwr.muY, cwr.SigmaY, cwr.weightsY); + +% likX(c,t) = p(x(:,t) | c) +[junk, likX] = mixgauss_prob(X, cwr.muX, cwr.SigmaX); +likX = squeeze(likX); + +% prior(c,t) = p(c) +prior = repmat(cwr.priorC(:), 1, N); + +post = likX .* likY .* prior; +likXandY = sum(post, 1); +post = post ./ repmat(likXandY, nc, 1); +%loglik = sum(log(lik)); +%loglik = log(lik); + +likX = sum(likX .* prior, 1); +likYgivenX = likXandY ./ likX;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/cwr_readme.txt Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,20 @@ +This directory implements Cluster Weighted Regression, as described in +Neil Gershenfeld, "The nature of mathematical modelling", p182. +(See also http://www.media.mit.edu/physics/publications/books/nmm/files/index.html) + +Written by K. Murphy, 2 May 2003 + +The model is as follows: + +X<--| +| Q +v | +Y<-- + +where Q is a discrete latent mixture variable. + +A mixture of experts has an X->Q arc instead of a Q->X arc; +the X->Q arc is modelled by a softmax, which is slightly harder to fit than a +mixture of Gaussians. + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/cwr_test.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,80 @@ +% Verify that my code gives the same results as the 1D example at +% http://www.media.mit.edu/physics/publications/books/nmm/files/cwm.m + +seed = 0; +rand('state', seed); +randn('state', seed); +x = (-10:10)'; +y = double(x > 0); +npts = length(x); +plot(x,y,'+') + +nclusters = 4; +nplot = 100; +xplot = 24*(1:nplot)'/nplot - 12; + +mux = 20*rand(1,nclusters) - 10; +muy = zeros(1,nclusters); +varx = ones(1,nclusters); +vary = ones(1,nclusters); +pc = 1/nclusters * ones(1,nclusters); + + +I = repmat(eye(1,1), [1 1 nclusters]); +O = repmat(zeros(1,1), [1 1 nclusters]); +X = x(:)'; +Y = y(:)'; + +% Do 1 iteration of EM + +%cwr = cwr_em(X, Y, nclusters, 'muX', mux, 'muY', muy, 'SigmaX', I, 'cov_typeX', 'spherical', 'SigmaY', I, 'cov_typeY', 'spherical', 'priorC', pc, 'weightsY', O, 'init_params', 0, 'clamp_weights', 1, 'max_iter', 1, 'cov_priorX', zeros(1,1,nclusters), 'cov_priorY', zeros(1,1,nclusters)); + +cwr = cwr_em(X, Y, nclusters, 'muX', mux, 'muY', muy, 'SigmaX', I, 'cov_typeX', 'spherical', 'SigmaY', I, 'cov_typeY', 'spherical', 'priorC', pc, 'weightsY', O, 'create_init_params', 0, 'clamp_weights', 1, 'max_iter', 1); + + +% Check this matches Gershenfeld's code + +% E step +% px(t,c) = prob(x(t) | c) +px = exp(-(kron(x,ones(1,nclusters)) ... + - kron(ones(npts,1),mux)).^2 ... + ./ (2*kron(ones(npts,1),varx))) ... + ./ sqrt(2*pi*kron(ones(npts,1),varx)); +py = exp(-(kron(y,ones(1,nclusters)) ... + - kron(ones(npts,1),muy)).^2 ... + ./ (2*kron(ones(npts,1),vary))) ... + ./ sqrt(2*pi*kron(ones(npts,1),vary)); +p = px .* py .* kron(ones(npts,1),pc); +pp = p ./ kron(sum(p,2),ones(1,nclusters)); + +% M step +eps = 0.01; +pc2 = sum(pp)/npts; + +mux2 = sum(kron(x,ones(1,nclusters)) .* pp) ... + ./ (npts*pc2); +varx2 = eps + sum((kron(x,ones(1,nclusters)) ... + - kron(ones(npts,1),mux2)).^2 .* pp) ... + ./ (npts*pc2); +muy2 = sum(kron(y,ones(1,nclusters)) .* pp) ... + ./ (npts*pc2); +vary2 = eps + sum((kron(y,ones(1,nclusters)) ... + - kron(ones(npts,1),muy2)).^2 .* pp) ... + ./ (npts*pc2); + + +denom = (npts*pc2); +% denom(c) = N*pc(c) = w(c) = sum_t pp(c,t) +% since pc(c) = sum_t pp(c,t) / N + +cwr_mux = cwr.muX; +assert(approxeq(mux2, cwr_mux)) +cwr_SigmaX = squeeze(cwr.SigmaX)'; +assert(approxeq(varx2, cwr_SigmaX)) + +cwr_muy = cwr.muY; +assert(approxeq(muy2, cwr_muy)) +cwr_SigmaY = squeeze(cwr.SigmaY)'; +assert(approxeq(vary2, cwr_SigmaY)) + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/dirichlet_sample.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,18 @@ +function theta = dirichlet_sample(alpha, N) +% SAMPLE_DIRICHLET Sample N vectors from Dir(alpha(1), ..., alpha(k)) +% theta = sample_dirichlet(alpha, N) +% theta(i,j) = i'th sample of theta_j, where theta ~ Dir + +% We use the method from p. 482 of "Bayesian Data Analysis", Gelman et al. + +assert(alpha > 0); +k = length(alpha); +theta = zeros(N, k); +scale = 1; % arbitrary +for i=1:k + %theta(:,i) = gamrnd(alpha(i), scale, N, 1); + theta(:,i) = gamma_sample(alpha(i), scale, N, 1); +end +%theta = mk_stochastic(theta); +S = sum(theta,2); +theta = theta ./ repmat(S, 1, k);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/dirichletpdf.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,39 @@ +function p = dirichletpdf(x, alpha) +%DIRICHLETPDF Dirichlet probability density function. +% p = dirichletpdf(x, alpha) returns the probability of vector +% x under the Dirichlet distribution with parameter vector +% alpha. +% +% Author: David Ross + +%------------------------------------------------- +% Check the input +%------------------------------------------------- +error(nargchk(2,2,nargin)); + +% enusre alpha is a vector +if min(size(alpha)) ~= 1 | ndims(alpha) > 2 | length(alpha) == 1 + error('alpha must be a vector'); +end + +% ensure x is is a vector of the same size as alpha +if any(size(x) ~= size(alpha)) + error('x and alpha must be the same size'); +end + + +%------------------------------------------------- +% Main +%------------------------------------------------- +if any(x < 0) + p = 0; +elseif sum(x) ~= 1 + disp(['dirichletpdf warning: sum(x)~=1, but this may be ' ... + 'due to numerical issues']); + p = 0; +else + z = gammaln(sum(alpha)) - sum(gammaln(alpha)); + z = exp(z); + + p = z * prod(x.^(alpha-1)); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/dirichletrnd.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,32 @@ +function x = dirichletrnd(alpha) +%DIRICHLETRND Random vector from a dirichlet distribution. +% x = dirichletrnd(alpha) returns a vector randomly selected +% from the Dirichlet distribution with parameter vector alpha. +% +% The algorithm used is the following: +% For each alpha(i), generate a value s(i) with distribution +% Gamma(alpha(i),1). Now x(i) = s(i) / sum_j s(j). +% +% The above algorithm was recounted to me by Radford Neal, but +% a reference would be appreciated... +% Do the gamma parameters always have to be 1? +% +% Author: David Ross +% $Id: dirichletrnd.m,v 1.1.1.1 2005/05/22 23:32:12 yozhik Exp $ + +%------------------------------------------------- +% Check the input +%------------------------------------------------- +error(nargchk(1,1,nargin)); + +if min(size(alpha)) ~= 1 | length(alpha) < 2 + error('alpha must be a vector of length at least 2'); +end + + +%------------------------------------------------- +% Main +%------------------------------------------------- +gamma_vals = gamrnd(alpha, ones(size(alpha)), size(alpha)); +denom = sum(gamma_vals); +x = gamma_vals / denom; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/distchck.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,173 @@ +function [errorcode,out1,out2,out3,out4] = distchck(nparms,arg1,arg2,arg3,arg4) +%DISTCHCK Checks the argument list for the probability functions. + +% B.A. Jones 1-22-93 +% Copyright (c) 1993-98 by The MathWorks, Inc. +% $Revision: 1.1.1.1 $ $Date: 2005/04/26 02:29:18 $ + +errorcode = 0; + +if nparms == 1 + out1 = arg1; + return; +end + +if nparms == 2 + [r1 c1] = size(arg1); + [r2 c2] = size(arg2); + scalararg1 = (prod(size(arg1)) == 1); + scalararg2 = (prod(size(arg2)) == 1); + if ~scalararg1 & ~scalararg2 + if r1 ~= r2 | c1 ~= c2 + errorcode = 1; + return; + end + end + if scalararg1 + out1 = arg1(ones(r2,1),ones(c2,1)); + else + out1 = arg1; + end + if scalararg2 + out2 = arg2(ones(r1,1),ones(c1,1)); + else + out2 = arg2; + end +end + +if nparms == 3 + [r1 c1] = size(arg1); + [r2 c2] = size(arg2); + [r3 c3] = size(arg3); + scalararg1 = (prod(size(arg1)) == 1); + scalararg2 = (prod(size(arg2)) == 1); + scalararg3 = (prod(size(arg3)) == 1); + + if ~scalararg1 & ~scalararg2 + if r1 ~= r2 | c1 ~= c2 + errorcode = 1; + return; + end + end + + if ~scalararg1 & ~scalararg3 + if r1 ~= r3 | c1 ~= c3 + errorcode = 1; + return; + end + end + + if ~scalararg3 & ~scalararg2 + if r3 ~= r2 | c3 ~= c2 + errorcode = 1; + return; + end + end + + if ~scalararg1 + out1 = arg1; + end + if ~scalararg2 + out2 = arg2; + end + if ~scalararg3 + out3 = arg3; + end + rows = max([r1 r2 r3]); + columns = max([c1 c2 c3]); + + if scalararg1 + out1 = arg1(ones(rows,1),ones(columns,1)); + end + if scalararg2 + out2 = arg2(ones(rows,1),ones(columns,1)); + end + if scalararg3 + out3 = arg3(ones(rows,1),ones(columns,1)); + end + out4 =[]; + +end + +if nparms == 4 + [r1 c1] = size(arg1); + [r2 c2] = size(arg2); + [r3 c3] = size(arg3); + [r4 c4] = size(arg4); + scalararg1 = (prod(size(arg1)) == 1); + scalararg2 = (prod(size(arg2)) == 1); + scalararg3 = (prod(size(arg3)) == 1); + scalararg4 = (prod(size(arg4)) == 1); + + if ~scalararg1 & ~scalararg2 + if r1 ~= r2 | c1 ~= c2 + errorcode = 1; + return; + end + end + + if ~scalararg1 & ~scalararg3 + if r1 ~= r3 | c1 ~= c3 + errorcode = 1; + return; + end + end + + if ~scalararg1 & ~scalararg4 + if r1 ~= r4 | c1 ~= c4 + errorcode = 1; + return; + end + end + + if ~scalararg3 & ~scalararg2 + if r3 ~= r2 | c3 ~= c2 + errorcode = 1; + return; + end + end + + if ~scalararg4 & ~scalararg2 + if r4 ~= r2 | c4 ~= c2 + errorcode = 1; + return; + end + end + + if ~scalararg3 & ~scalararg4 + if r3 ~= r4 | c3 ~= c4 + errorcode = 1; + return; + end + end + + + if ~scalararg1 + out1 = arg1; + end + if ~scalararg2 + out2 = arg2; + end + if ~scalararg3 + out3 = arg3; + end + if ~scalararg4 + out4 = arg4; + end + + rows = max([r1 r2 r3 r4]); + columns = max([c1 c2 c3 c4]); + if scalararg1 + out1 = arg1(ones(rows,1),ones(columns,1)); + end + if scalararg2 + out2 = arg2(ones(rows,1),ones(columns,1)); + end + if scalararg3 + out3 = arg3(ones(rows,1),ones(columns,1)); + end + if scalararg4 + out4 = arg4(ones(rows,1),ones(columns,1)); + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/eigdec.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,59 @@ +function [evals, evec] = eigdec(x, N) +%EIGDEC Sorted eigendecomposition +% +% Description +% EVALS = EIGDEC(X, N computes the largest N eigenvalues of the +% matrix X in descending order. [EVALS, EVEC] = EIGDEC(X, N) also +% computes the corresponding eigenvectors. +% +% See also +% PCA, PPCA +% + +% Copyright (c) Ian T Nabney (1996-2001) + +if nargout == 1 + evals_only = logical(1); +else + evals_only = logical(0); +end + +if N ~= round(N) | N < 1 | N > size(x, 2) + error('Number of PCs must be integer, >0, < dim'); +end + +% Find the eigenvalues of the data covariance matrix +if evals_only + % Use eig function as always more efficient than eigs here + temp_evals = eig(x); +else + % Use eig function unless fraction of eigenvalues required is tiny + if (N/size(x, 2)) > 0.04 + fprintf('netlab pca: using eig\n'); + [temp_evec, temp_evals] = eig(x); + else + options.disp = 0; + fprintf('netlab pca: using eigs\n'); + [temp_evec, temp_evals] = eigs(x, N, 'LM', options); + end + temp_evals = diag(temp_evals); +end + +% Eigenvalues nearly always returned in descending order, but just +% to make sure..... +[evals perm] = sort(-temp_evals); +evals = -evals(1:N); +%evec=temp_evec(:,1:N); +if ~evals_only + if evals == temp_evals(1:N) + % Originals were in order + evec = temp_evec(:, 1:N); + return + else + fprintf('netlab pca: sorting evec\n'); + % Need to reorder the eigenvectors + for i=1:N + evec(:,i) = temp_evec(:,perm(i)); + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/est_transmat.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +function [A,C] = est_transmat(seq) +% ESTIMATE_TRANSMAT Max likelihood of a Markov chain transition matrix +% [A,C] = estimate_transmat(seq) +% +% seq is a vector of positive integers +% +% e.g., seq = [1 2 1 2 3], C(1,2)=2, C(2,1)=1, C(2,3)=1, so +% A(1,:)=[0 1 0], A(2,:) = [0.5 0 0.5], +% all other entries are 0 + +% Use a trick with sparse matrices to count the number of each transition. +% From http://www.mathworks.com/company/newsletter/may03/dna.shtml + +C = full(sparse(seq(1:end-1), seq(2:end),1)); +A = mk_stochastic(C);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/fit_paritioned_model_testfn.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function model = foo(inputs, outputs, varargin) + +model.inputs = inputs; +model.outputs = outputs; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/fit_partitioned_model.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,59 @@ +function [model, partition_size] = fit_partitioned_model(... + inputs, outputs, selectors, sel_sizes, min_size, partition_names, fn_name, varargin) +%function [models, partition_sizes] = fit_partitioned_model(... +% inputs, outputs, selectors, sel_sizes, min_size, partition_names, fn_name, varargin) +% +% Fit models to different subsets (columns) of the input/output data, +% as chosen by the selectors matrix. If there is only output data, set input=[]. +% If there is less than min_size data in partition i, +% we set model{i} = [] +% +% Example: +% selectors = [1 2 1 1 1 +% 1 2 2 1 2] +% sel_sizes = [2 2] so there are 4 models: (1,1), (2,1), (1,2), (2,2) +% We fit model{1} to data from columns 1,4 +% We fit model{2} to no data +% We fit model{3} to data from column 3,5 +% We fit model{4} to data from column 2 (assuming min_size <= 1) +% +% For each partition, we call the specified function with the specified arguments +% as follows: +% model{i} = fn(input(:,cols{i}), output(:,cols{i}), args) +% (We omit input if []) +% partition_size(i) is the amount of data in the i'th partition. +% +% Example use: row 1 of selectors is whether an object is present/absent +% and row 2 is the location. +% +% Demo: +% inputs = 1:5; outputs = 6:10; selectors = as above +% fn = 'fit_partitioned_model_testfn'; +% [model, partition_size] = fit_partitioned_model(inputs, outputs, selectors, [2 2], fn) +% should produce +% model{1}.input = [1 4], model{1}.output = [6 9] +% model{2} = [] +% model{3}.input = [3 5], model{3}.output = [8 10], +% model{4}.input = [2], model{3}.output = [7], +% partition_size = [2 0 2 1] + + +sel_ndx = subv2ind(sel_sizes, selectors'); +Nmodels = prod(sel_sizes); +model = cell(1, Nmodels); +partition_size = zeros(1, Nmodels); +for m=1:Nmodels + ndx = find(sel_ndx==m); + partition_size(m) = length(ndx); + if ~isempty(partition_names) % & (partition_size(m) < min_size) + fprintf('partition %s has size %d, min size = %d\n', ... + partition_names{m}, partition_size(m), min_size); + end + if partition_size(m) >= min_size + if isempty(inputs) + model{m} = feval(fn_name, outputs(:, ndx), varargin{:}); + else + model{m} = feval(fn_name, inputs(:,ndx), outputs(:, ndx), varargin{:}); + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/gamma_sample.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,126 @@ +function r = gamrnd(a,b,m,n); +%GAMRND Random matrices from gamma distribution. +% R = GAMRND(A,B) returns a matrix of random numbers chosen +% from the gamma distribution with parameters A and B. +% The size of R is the common size of A and B if both are matrices. +% If either parameter is a scalar, the size of R is the size of the other +% parameter. Alternatively, R = GAMRND(A,B,M,N) returns an M by N matrix. +% +% Some references refer to the gamma distribution +% with a single parameter. This corresponds to GAMRND +% with B = 1. (See Devroye, pages 401-402.) + +% GAMRND uses a rejection or an inversion method depending on the +% value of A. + +% References: +% [1] L. Devroye, "Non-Uniform Random Variate Generation", +% Springer-Verlag, 1986 + +% B.A. Jones 2-1-93 +% Copyright (c) 1993-98 by The MathWorks, Inc. +% $Revision: 1.1.1.1 $ $Date: 2005/04/26 02:29:18 $ + +if nargin < 2, + error('Requires at least two input arguments.'); +end + + +if nargin == 2 + [errorcode rows columns] = rndcheck(2,2,a,b); +end + +if nargin == 3 + [errorcode rows columns] = rndcheck(3,2,a,b,m); +end + +if nargin == 4 + [errorcode rows columns] = rndcheck(4,2,a,b,m,n); +end + +if errorcode > 0 + error('Size information is inconsistent.'); +end + +% Initialize r to zero. +lth = rows*columns; +r = zeros(lth,1); +a = a(:); b = b(:); + +scalara = (length(a) == 1); +if scalara + a = a*ones(lth,1); +end + +scalarb = (length(b) == 1); +if scalarb + b = b*ones(lth,1); +end + +% If a == 1, then gamma is exponential. (Devroye, page 405). +k = find(a == 1); +if any(k) + r(k) = -b(k) .* log(rand(size(k))); +end + + +k = find(a < 1 & a > 0); +% (Devroye, page 418 Johnk's generator) +if any(k) + c = zeros(lth,1); + d = zeros(lth,1); + c(k) = 1 ./ a(k); + d(k) = 1 ./ (1 - a(k)); + accept = k; + while ~isempty(accept) + u = rand(size(accept)); + v = rand(size(accept)); + x = u .^ c(accept); + y = v .^ d(accept); + k1 = find((x + y) <= 1); + if ~isempty(k1) + e = -log(rand(size(k1))); + r(accept(k1)) = e .* x(k1) ./ (x(k1) + y(k1)); + accept(k1) = []; + end + end + r(k) = r(k) .* b(k); +end + +% Use a rejection method for a > 1. +k = find(a > 1); +% (Devroye, page 410 Best's algorithm) +bb = zeros(size(a)); +c = bb; +if any(k) + bb(k) = a(k) - 1; + c(k) = 3 * a(k) - 3/4; + accept = k; + count = 1; + while ~isempty(accept) + m = length(accept); + u = rand(m,1); + v = rand(m,1); + w = u .* (1 - u); + y = sqrt(c(accept) ./ w) .* (u - 0.5); + x = bb(accept) + y; + k1 = find(x >= 0); + if ~isempty(k1) + z = 64 * (w .^ 3) .* (v .^ 2); + k2 = (z(k1) <= (1 - 2 * (y(k1) .^2) ./ x(k1))); + k3 = k1(find(k2)); + r(accept(k3)) = x(k3); + k4 = k1(find(~k2)); + k5 = k4(find(log(z(k4)) <= (2*(bb(accept(k4)).*log(x(k4)./bb(accept(k4)))-y(k4))))); + r(accept(k5)) = x(k5); + omit = [k3; k5]; + accept(omit) = []; + end + end + r(k) = r(k) .* b(k); +end + +% Return NaN if a or b is not positive. +r(b <= 0 | a <= 0) = NaN; + +r = reshape(r,rows,columns);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/gaussian_prob.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,28 @@ +function p = gaussian_prob(x, m, C, use_log) +% GAUSSIAN_PROB Evaluate a multivariate Gaussian density. +% p = gaussian_prob(X, m, C) +% p(i) = N(X(:,i), m, C) where C = covariance matrix and each COLUMN of x is a datavector + +% p = gaussian_prob(X, m, C, 1) returns log N(X(:,i), m, C) (to prevents underflow). +% +% If X has size dxN, then p has size Nx1, where N = number of examples + +if nargin < 4, use_log = 0; end + +if length(m)==1 % scalar + x = x(:)'; +end +[d N] = size(x); +%assert(length(m)==d); % slow +m = m(:); +M = m*ones(1,N); % replicate the mean across columns +denom = (2*pi)^(d/2)*sqrt(abs(det(C))); +mahal = sum(((x-M)'*inv(C)).*(x-M)',2); % Chris Bregler's trick +if any(mahal<0) + warning('mahal < 0 => C is not psd') +end +if use_log + p = -0.5*mahal - log(denom); +else + p = exp(-0.5*mahal) / (denom+eps); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/gaussian_sample.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,25 @@ +function x = gsamp(mu, covar, nsamp) +%GSAMP Sample from a Gaussian distribution. +% +% Description +% +% X = GSAMP(MU, COVAR, NSAMP) generates a sample of size NSAMP from a +% D-dimensional Gaussian distribution. The Gaussian density has mean +% vector MU and covariance matrix COVAR, and the matrix X has NSAMP +% rows in which each row represents a D-dimensional sample vector. +% +% See also +% GAUSS, DEMGAUSS +% + +% Copyright (c) Ian T Nabney (1996-2001) + +d = size(covar, 1); + +mu = reshape(mu, 1, d); % Ensure that mu is a row vector + +[evec, eval] = eig(covar); + +coeffs = randn(nsamp, d)*sqrt(eval); + +x = ones(nsamp, 1)*mu + coeffs*evec';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/histCmpChi2.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +function d = histCmpChi2(h1, h2) +% Compare two histograms using chi-squared +% function d = histCmpChi2(h1, h2) +% +% d(i,j) = chi^2(h1(i,:), h2(j,:)) = sum_b (h1(i,b)-h2(j,b)^2 / (h1(i,b) + h2(j,b)) + +[N B] = size(h1); +d = zeros(N,N); +for i=1:N + h1i = repmat(h1(i,:), N, 1); + numer = (h1i - h2).^2; + denom = h1i + h2 + eps; % if denom=0, then numer=0 + d(i,:) = sum(numer ./ denom, 2); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/linear_regression.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,68 @@ +function [muY, SigmaY, weightsY] = linear_regression(X, Y, varargin) +% LINEAR_REGRESSION Fit params for P(Y|X) = N(Y; W X + mu, Sigma) +% +% X(:, t) is the t'th input example +% Y(:, t) is the t'th output example +% +% Kevin Murphy, August 2003 +% +% This is a special case of cwr_em with 1 cluster. +% You can also think of it as a front end to clg_Mstep. + +[cov_typeY, clamp_weights, muY, SigmaY, weightsY,... + cov_priorY, regress, clamp_covY] = process_options(... + varargin, ... + 'cov_typeY', 'full', 'clamp_weights', 0, ... + 'muY', [], 'SigmaY', [], 'weightsY', [], ... + 'cov_priorY', [], 'regress', 1, 'clamp_covY', 0); + +[nx N] = size(X); +[ny N2] = size(Y); +if N ~= N2 + error(sprintf('nsamples X (%d) ~= nsamples Y (%d)', N, N2)); +end + +w = 1/N; +WYbig = Y*w; +WYY = WYbig * Y'; +WY = sum(WYbig, 2); +WYTY = sum(diag(WYbig' * Y)); +if ~regress + % This is just fitting an unconditional Gaussian + weightsY = []; + [muY, SigmaY] = ... + mixgauss_Mstep(1, WY, WYY, WYTY, ... + 'cov_type', cov_typeY, 'cov_prior', cov_priorY); + % There is a much easier way... + assert(approxeq(muY, mean(Y'))) + assert(approxeq(SigmaY, cov(Y') + 0.01*eye(ny))) +else + % This is just linear regression + WXbig = X*w; + WXX = WXbig * X'; + WX = sum(WXbig, 2); + WXTX = sum(diag(WXbig' * X)); + WXY = WXbig * Y'; + [muY, SigmaY, weightsY] = ... + clg_Mstep(1, WY, WYY, WYTY, WX, WXX, WXY, ... + 'cov_type', cov_typeY, 'cov_prior', cov_priorY); +end +if clamp_covY, SigmaY = SigmaY; end +if clamp_weights, weightsY = weightsY; end + +if nx==1 & ny==1 & regress + P = polyfit(X,Y); % Y = P(1) X^1 + P(2) X^0 = ax + b + assert(approxeq(muY, P(2))) + assert(approxeq(weightsY, P(1))) +end + +%%%%%%%% Test +if 0 + c1 = randn(2,100); c2 = randn(2,100); + y = c2(1,:); X = [ones(size(c1,2),1) c1']; + b = regress(y(:), X); % stats toolbox + [m,s,w] = linear_regression(c1, y); + assert(approxeq(b(1),m)) + assert(approxeq(b(2), w(1))) + assert(approxeq(b(3), w(2))) +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/logist2.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,115 @@ +function [beta,p,lli] = logist2(y,x,w) +% [beta,p,lli] = logist2(y,x) +% +% 2-class logistic regression. +% +% INPUT +% y Nx1 colum vector of 0|1 class assignments +% x NxK matrix of input vectors as rows +% [w] Nx1 vector of sample weights +% +% OUTPUT +% beta Kx1 column vector of model coefficients +% p Nx1 column vector of fitted class 1 posteriors +% lli log likelihood +% +% Class 1 posterior is 1 / (1 + exp(-x*beta)) +% +% David Martin <dmartin@eecs.berkeley.edu> +% April 16, 2002 + +% Copyright (C) 2002 David R. Martin <dmartin@eecs.berkeley.edu> +% +% This program is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as +% published by the Free Software Foundation; either version 2 of the +% License, or (at your option) any later version. +% + +% This program is distributed in the hope that it will be useful, but +% WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +% General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA +% 02111-1307, USA, or see http://www.gnu.org/copyleft/gpl.html. + +error(nargchk(2,3,nargin)); + +% check inputs +if size(y,2) ~= 1, + error('Input y not a column vector.'); +end +if size(y,1) ~= size(x,1), + error('Input x,y sizes mismatched.'); +end + +% get sizes +[N,k] = size(x); + +% if sample weights weren't specified, set them to 1 +if nargin < 3, + w = 1; +end + +% normalize sample weights so max is 1 +w = w / max(w); + +% initial guess for beta: all zeros +beta = zeros(k,1); + +% Newton-Raphson via IRLS, +% taken from Hastie/Tibshirani/Friedman Section 4.4. +iter = 0; +lli = 0; +while 1==1, + iter = iter + 1; + + % fitted probabilities + p = 1 ./ (1 + exp(-x*beta)); + + % log likelihood + lli_prev = lli; + lli = sum( w .* (y.*log(p+eps) + (1-y).*log(1-p+eps)) ); + + % least-squares weights + wt = w .* p .* (1-p); + + % derivatives of likelihood w.r.t. beta + deriv = x'*(w.*(y-p)); + + % Hessian of likelihood w.r.t. beta + % hessian = x'Wx, where W=diag(w) + % Do it this way to be memory efficient and fast. + hess = zeros(k,k); + for i = 1:k, + wxi = wt .* x(:,i); + for j = i:k, + hij = wxi' * x(:,j); + hess(i,j) = -hij; + hess(j,i) = -hij; + end + end + + % make sure Hessian is well conditioned + if (rcond(hess) < eps), + error(['Stopped at iteration ' num2str(iter) ... + ' because Hessian is poorly conditioned.']); + break; + end; + + % Newton-Raphson update step + step = hess\deriv; + beta = beta - step; + + % termination criterion based on derivatives + tol = 1e-6; + if abs(deriv'*step/k) < tol, break; end; + + % termination criterion based on log likelihood +% tol = 1e-4; +% if abs((lli-lli_prev)/(lli+lli_prev)) < 0.5*tol, break; end; +end; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/logist2Apply.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function p = logist2Apply(beta, x) +% LOGIST2APPLY 2 class logistic regression: compute posterior prob of class 1 +% function p = logist2Apply(beta, x) +% +% x(:,i) - each COLUMN is a test case; we append 1s automatically, if appropriate + +[D Ncases] = size(x); +if length(beta)==D+1 + F = [x; ones(1,Ncases)]; +else + F = x; +end +p = 1./(1+exp(-beta(:)'*F));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/logist2ApplyRegularized.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,3 @@ +function prob = logist2ApplyRegularized(net, features) + +prob = glmfwd(net, features')';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/logist2Fit.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,22 @@ +function [beta, p] = logist2Fit(y, x, addOne, w) +% LOGIST2FIT 2 class logsitic classification +% function beta = logist2Fit(y,x, addOne) +% +% y(i) = 0/1 +% x(:,i) = i'th input - we optionally append 1s to last dimension +% w(i) = optional weight +% +% beta(j)- regression coefficient + +if nargin < 3, addOne = 1; end +if nargin < 4, w = 1; end + +Ncases = size(x,2); +if Ncases ~= length(y) + error(sprintf('size of data = %dx%d, size of labels=%d', size(x,1), size(x,2), length(y))) +end +if addOne + x = [x; ones(1,Ncases)]; +end +[beta, p] = logist2(y(:), x', w(:)); +beta = beta(:);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/logist2FitRegularized.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function [net, niter] = logist2FitRegularized(labels, features, maxIter) + +if nargin < 3, maxIter = 100; end + +[D N] = size(features); +weightPrior = 0.5; +net = glm(D, 1, 'logistic', weightPrior); +options = foptions; +options(14) = maxIter; +[net, options] = glmtrain(net, options, features', labels(:)); +niter = options(14); +%w = logist2Fit(labelsPatches(jValidPatches), features(:, jValidPatches)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/logistK.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,287 @@ +function [beta,post,lli] = logistK(x,y,w,beta) +% [beta,post,lli] = logistK(x,y,beta,w) +% +% k-class logistic regression with optional sample weights +% +% k = number of classes +% n = number of samples +% d = dimensionality of samples +% +% INPUT +% x dxn matrix of n input column vectors +% y kxn vector of class assignments +% [w] 1xn vector of sample weights +% [beta] dxk matrix of model coefficients +% +% OUTPUT +% beta dxk matrix of fitted model coefficients +% (beta(:,k) are fixed at 0) +% post kxn matrix of fitted class posteriors +% lli log likelihood +% +% Let p(i,j) = exp(beta(:,j)'*x(:,i)), +% Class j posterior for observation i is: +% post(j,i) = p(i,j) / (p(i,1) + ... p(i,k)) +% +% See also logistK_eval. +% +% David Martin <dmartin@eecs.berkeley.edu> +% May 3, 2002 + +% Copyright (C) 2002 David R. Martin <dmartin@eecs.berkeley.edu> +% +% This program is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as +% published by the Free Software Foundation; either version 2 of the +% License, or (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, but +% WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +% General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA +% 02111-1307, USA, or see http://www.gnu.org/copyleft/gpl.html. + +% TODO - this code would be faster if x were transposed + +error(nargchk(2,4,nargin)); + +debug = 0; +if debug>0, + h=figure(1); + set(h,'DoubleBuffer','on'); +end + +% get sizes +[d,nx] = size(x); +[k,ny] = size(y); + +% check sizes +if k < 2, + error('Input y must encode at least 2 classes.'); +end +if nx ~= ny, + error('Inputs x,y not the same length.'); +end + +n = nx; + +% make sure class assignments have unit L1-norm +sumy = sum(y,1); +if abs(1-sumy) > eps, + sumy = sum(y,1); + for i = 1:k, y(i,:) = y(i,:) ./ sumy; end +end +clear sumy; + +% if sample weights weren't specified, set them to 1 +if nargin < 3, + w = ones(1,n); +end + +% normalize sample weights so max is 1 +w = w / max(w); + +% if starting beta wasn't specified, initialize randomly +if nargin < 4, + beta = 1e-3*rand(d,k); + beta(:,k) = 0; % fix beta for class k at zero +else + if sum(beta(:,k)) ~= 0, + error('beta(:,k) ~= 0'); + end +end + +stepsize = 1; +minstepsize = 1e-2; + +post = computePost(beta,x); +lli = computeLogLik(post,y,w); + +for iter = 1:100, + %disp(sprintf(' logist iter=%d lli=%g',iter,lli)); + vis(x,y,beta,lli,d,k,iter,debug); + + % gradient and hessian + [g,h] = derivs(post,x,y,w); + + % make sure Hessian is well conditioned + if rcond(h) < eps, + % condition with Levenberg-Marquardt method + for i = -16:16, + h2 = h .* ((1 + 10^i)*eye(size(h)) + (1-eye(size(h)))); + if rcond(h2) > eps, break, end + end + if rcond(h2) < eps, + warning(['Stopped at iteration ' num2str(iter) ... + ' because Hessian can''t be conditioned']); + break + end + h = h2; + end + + % save lli before update + lli_prev = lli; + + % Newton-Raphson with step-size halving + while stepsize >= minstepsize, + % Newton-Raphson update step + step = stepsize * (h \ g); + beta2 = beta; + beta2(:,1:k-1) = beta2(:,1:k-1) - reshape(step,d,k-1); + + % get the new log likelihood + post2 = computePost(beta2,x); + lli2 = computeLogLik(post2,y,w); + + % if the log likelihood increased, then stop + if lli2 > lli, + post = post2; lli = lli2; beta = beta2; + break + end + + % otherwise, reduce step size by half + stepsize = 0.5 * stepsize; + end + + % stop if the average log likelihood has gotten small enough + if 1-exp(lli/n) < 1e-2, break, end + + % stop if the log likelihood changed by a small enough fraction + dlli = (lli_prev-lli) / lli; + if abs(dlli) < 1e-3, break, end + + % stop if the step size has gotten too small + if stepsize < minstepsize, brea, end + + % stop if the log likelihood has decreased; this shouldn't happen + if lli < lli_prev, + warning(['Stopped at iteration ' num2str(iter) ... + ' because the log likelihood decreased from ' ... + num2str(lli_prev) ' to ' num2str(lli) '.' ... + ' This may be a bug.']); + break + end +end + +if debug>0, + vis(x,y,beta,lli,d,k,iter,2); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% class posteriors +function post = computePost(beta,x) + [d,n] = size(x); + [d,k] = size(beta); + post = zeros(k,n); + bx = zeros(k,n); + for j = 1:k, + bx(j,:) = beta(:,j)'*x; + end + for j = 1:k, + post(j,:) = 1 ./ sum(exp(bx - repmat(bx(j,:),k,1)),1); + end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% log likelihood +function lli = computeLogLik(post,y,w) + [k,n] = size(post); + lli = 0; + for j = 1:k, + lli = lli + sum(w.*y(j,:).*log(post(j,:)+eps)); + end + if isnan(lli), + error('lli is nan'); + end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% gradient and hessian +%% These are computed in what seems a verbose manner, but it is +%% done this way to use minimal memory. x should be transposed +%% to make it faster. +function [g,h] = derivs(post,x,y,w) + + [k,n] = size(post); + [d,n] = size(x); + + % first derivative of likelihood w.r.t. beta + g = zeros(d,k-1); + for j = 1:k-1, + wyp = w .* (y(j,:) - post(j,:)); + for ii = 1:d, + g(ii,j) = x(ii,:) * wyp'; + end + end + g = reshape(g,d*(k-1),1); + + % hessian of likelihood w.r.t. beta + h = zeros(d*(k-1),d*(k-1)); + for i = 1:k-1, % diagonal + wt = w .* post(i,:) .* (1 - post(i,:)); + hii = zeros(d,d); + for a = 1:d, + wxa = wt .* x(a,:); + for b = a:d, + hii_ab = wxa * x(b,:)'; + hii(a,b) = hii_ab; + hii(b,a) = hii_ab; + end + end + h( (i-1)*d+1 : i*d , (i-1)*d+1 : i*d ) = -hii; + end + for i = 1:k-1, % off-diagonal + for j = i+1:k-1, + wt = w .* post(j,:) .* post(i,:); + hij = zeros(d,d); + for a = 1:d, + wxa = wt .* x(a,:); + for b = a:d, + hij_ab = wxa * x(b,:)'; + hij(a,b) = hij_ab; + hij(b,a) = hij_ab; + end + end + h( (i-1)*d+1 : i*d , (j-1)*d+1 : j*d ) = hij; + h( (j-1)*d+1 : j*d , (i-1)*d+1 : i*d ) = hij; + end + end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% debug/visualization +function vis (x,y,beta,lli,d,k,iter,debug) + + if debug<=0, return, end + + disp(['iter=' num2str(iter) ' lli=' num2str(lli)]); + if debug<=1, return, end + + if d~=3 | k>10, return, end + + figure(1); + res = 100; + r = abs(max(max(x))); + dom = linspace(-r,r,res); + [px,py] = meshgrid(dom,dom); + xx = px(:); yy = py(:); + points = [xx' ; yy' ; ones(1,res*res)]; + func = zeros(k,res*res); + for j = 1:k, + func(j,:) = exp(beta(:,j)'*points); + end + [mval,ind] = max(func,[],1); + hold off; + im = reshape(ind,res,res); + imagesc(xx,yy,im); + hold on; + syms = {'w.' 'wx' 'w+' 'wo' 'w*' 'ws' 'wd' 'wv' 'w^' 'w<'}; + for j = 1:k, + [mval,ind] = max(y,[],1); + ind = find(ind==j); + plot(x(1,ind),x(2,ind),syms{j}); + end + pause(0.1); + +% eof
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/logistK_eval.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,83 @@ +function [post,lik,lli] = logistK_eval(beta,x,y) +% [post,lik,lli] = logistK_eval(beta,x,y) +% +% Evaluate logistic regression model. +% +% INPUT +% beta dxk model coefficients (as returned by logistK) +% x dxn matrix of n input column vectors +% [y] kxn vector of class assignments +% +% OUTPUT +% post kxn fitted class posteriors +% lik 1xn vector of sample likelihoods +% lli log likelihood +% +% Let p(i,j) = exp(beta(:,j)'*x(:,i)), +% Class j posterior for observation i is: +% post(j,i) = p(i,j) / (p(i,1) + ... p(i,k)) +% The likelihood of observation i given soft class assignments +% y(:,i) is: +% lik(i) = prod(post(:,i).^y(:,i)) +% The log-likelihood of the model given the labeled samples is: +% lli = sum(log(lik)) +% +% See also logistK. +% +% David Martin <dmartin@eecs.berkeley.edu> +% May 7, 2002 + +% Copyright (C) 2002 David R. Martin <dmartin@eecs.berkeley.edu> +% +% This program is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as +% published by the Free Software Foundation; either version 2 of the +% License, or (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, but +% WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +% General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA +% 02111-1307, USA, or see http://www.gnu.org/copyleft/gpl.html. + +error(nargchk(2,3,nargin)); + +% check sizes +if size(beta,1) ~= size(x,1), + error('Inputs beta,x not the same height.'); +end +if nargin > 3 & size(y,2) ~= size(x,2), + error('Inputs x,y not the same length.'); +end + +% get sizes +[d,k] = size(beta); +[d,n] = size(x); + +% class posteriors +post = zeros(k,n); +bx = zeros(k,n); +for j = 1:k, + bx(j,:) = beta(:,j)'*x; +end +for j = 1:k, + post(j,:) = 1 ./ sum(exp(bx - repmat(bx(j,:),k,1)),1); +end +clear bx; + +% likelihood of each sample +if nargout > 1, + y = y ./ repmat(sum(y,1),k,1); % L1-normalize class assignments + lik = prod(post.^y,1); +end + +% total log likelihood +if nargout > 2, + lli = sum(log(lik+eps)); +end; + +% eof
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/marginalize_gaussian.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +function [muX, SXX] = marginalize_gaussian(mu, Sigma, X, Y, ns) +% MARGINALIZE_GAUSSIAN Compute Pr(X) from Pr(X,Y) where X and Y are jointly Gaussian. +% [muX, SXX] = marginalize_gaussian(mu, Sigma, X, Y, ns) + +[muX, muY, SXX, SXY, SYX, SYY] = partition_matrix_vec(mu, Sigma, X, Y, ns); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/matrix_T_pdf.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +function p = matrix_T_pdf(A, M, V, K, n) +% MATRIX_T_PDF Evaluate the density of a matrix under a Matrix-T distribution +% p = matrix_T_pdf(A, M, V, K, n) + +% See "Bayesian Linear Regression", T. Minka, MIT Tech Report, 2001 + +[d m] = size(K); +is = 1:d; +c1 = prod(gamma((n+1-is)/2)) / prod(gamma((n-m+1-is)/2)); +c2 = det(K)^(d/2) / det(pi*V)^(m/2); %% pi or 2pi? +p = c1 * c2 * det((A-M)'*inv(V)*(A-M)*K + eye(m))^(-n/2); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/matrix_normal_pdf.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,9 @@ +function p = matrix_normal_pdf(A, M, V, K) +% MATRIX_NORMAL_PDF Evaluate the density of a matrix under a Matrix-Normal distribution +% p = matrix_normal_pdf(A, M, V, K) + +% See "Bayesian Linear Regression", T. Minka, MIT Tech Report, 2001 + +[d m] = size(K); +c = det(K)^(d/2) / det(2*pi*V)^(m/2); +p = c * exp(-0.5*tr((A-M)'*inv(V)*(A-M)*K));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/mc_stat_distrib.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,26 @@ +function pi = mc_stat_distrib(P) +% MC_STAT_DISTRIB Compute stationary distribution of a Markov chain +% function pi = mc_stat_distrib(P) +% +% Each row of P should sum to one; pi is a column vector + +% Kevin Murphy, 16 Feb 2003 + +% The stationary distribution pi satisfies pi P = pi +% subject to sum_i pi(i) = 1, 0 <= pi(i) <= 1 +% Hence +% (P' 0n (pi = (pi +% 1n 0) 1) 1) +% or P2 pi2 = pi2. +% Naively we can solve this using (P2 - I(n+1)) pi2 = 0(n+1) +% or P3 pi2 = 0(n+1), i.e., pi2 = P3 \ zeros(n+1,1) +% but this is singular (because of the sum-to-one constraint). +% Hence we replace the last row of P' with 1s instead of appending ones to create P2, +% and similarly for pi. + +n = length(P); +P4 = P'-eye(n); +P4(end,:) = 1; +pi = P4 \ [zeros(n-1,1);1]; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/mixgauss_Mstep.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,106 @@ +function [mu, Sigma] = mixgauss_Mstep(w, Y, YY, YTY, varargin) +% MSTEP_COND_GAUSS Compute MLEs for mixture of Gaussians given expected sufficient statistics +% function [mu, Sigma] = Mstep_cond_gauss(w, Y, YY, YTY, varargin) +% +% We assume P(Y|Q=i) = N(Y; mu_i, Sigma_i) +% and w(i,t) = p(Q(t)=i|y(t)) = posterior responsibility +% See www.ai.mit.edu/~murphyk/Papers/learncg.pdf. +% +% INPUTS: +% w(i) = sum_t w(i,t) = responsibilities for each mixture component +% If there is only one mixture component (i.e., Q does not exist), +% then w(i) = N = nsamples, and +% all references to i can be replaced by 1. +% YY(:,:,i) = sum_t w(i,t) y(:,t) y(:,t)' = weighted outer product +% Y(:,i) = sum_t w(i,t) y(:,t) = weighted observations +% YTY(i) = sum_t w(i,t) y(:,t)' y(:,t) = weighted inner product +% You only need to pass in YTY if Sigma is to be estimated as spherical. +% +% Optional parameters may be passed as 'param_name', param_value pairs. +% Parameter names are shown below; default values in [] - if none, argument is mandatory. +% +% 'cov_type' - 'full', 'diag' or 'spherical' ['full'] +% 'tied_cov' - 1 (Sigma) or 0 (Sigma_i) [0] +% 'clamped_cov' - pass in clamped value, or [] if unclamped [ [] ] +% 'clamped_mean' - pass in clamped value, or [] if unclamped [ [] ] +% 'cov_prior' - Lambda_i, added to YY(:,:,i) [0.01*eye(d,d,Q)] +% +% If covariance is tied, Sigma has size d*d. +% But diagonal and spherical covariances are represented in full size. + +[cov_type, tied_cov, clamped_cov, clamped_mean, cov_prior, other] = ... + process_options(varargin,... + 'cov_type', 'full', 'tied_cov', 0, 'clamped_cov', [], 'clamped_mean', [], ... + 'cov_prior', []); + +[Ysz Q] = size(Y); +N = sum(w); +if isempty(cov_prior) + %cov_prior = zeros(Ysz, Ysz, Q); + %for q=1:Q + % cov_prior(:,:,q) = 0.01*cov(Y(:,q)'); + %end + cov_prior = repmat(0.01*eye(Ysz,Ysz), [1 1 Q]); +end +%YY = reshape(YY, [Ysz Ysz Q]) + cov_prior; % regularize the scatter matrix +YY = reshape(YY, [Ysz Ysz Q]); + +% Set any zero weights to one before dividing +% This is valid because w(i)=0 => Y(:,i)=0, etc +w = w + (w==0); + +if ~isempty(clamped_mean) + mu = clamped_mean; +else + % eqn 6 + %mu = Y ./ repmat(w(:)', [Ysz 1]);% Y may have a funny size + mu = zeros(Ysz, Q); + for i=1:Q + mu(:,i) = Y(:,i) / w(i); + end +end + +if ~isempty(clamped_cov) + Sigma = clamped_cov; + return; +end + +if ~tied_cov + Sigma = zeros(Ysz,Ysz,Q); + for i=1:Q + if cov_type(1) == 's' + % eqn 17 + s2 = (1/Ysz)*( (YTY(i)/w(i)) - mu(:,i)'*mu(:,i) ); + Sigma(:,:,i) = s2 * eye(Ysz); + else + % eqn 12 + SS = YY(:,:,i)/w(i) - mu(:,i)*mu(:,i)'; + if cov_type(1)=='d' + SS = diag(diag(SS)); + end + Sigma(:,:,i) = SS; + end + end +else % tied cov + if cov_type(1) == 's' + % eqn 19 + s2 = (1/(N*Ysz))*(sum(YTY,2) + sum(diag(mu'*mu) .* w)); + Sigma = s2*eye(Ysz); + else + SS = zeros(Ysz, Ysz); + % eqn 15 + for i=1:Q % probably could vectorize this... + SS = SS + YY(:,:,i)/N - mu(:,i)*mu(:,i)'; + end + if cov_type(1) == 'd' + Sigma = diag(diag(SS)); + else + Sigma = SS; + end + end +end + +if tied_cov + Sigma = repmat(Sigma, [1 1 Q]); +end +Sigma = Sigma + cov_prior;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/mixgauss_classifier_apply.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function [classHatTest, probPos] = mixgauss_classifier_apply(mixgauss, testFeatures) + +Bpos = mixgauss_prob(testFeatures, mixgauss.pos.mu, mixgauss.pos.Sigma, mixgauss.pos.prior); +Bneg = mixgauss_prob(testFeatures, mixgauss.neg.mu, mixgauss.neg.Sigma, mixgauss.neg.prior); +prior_pos = mixgauss.priorC(1); +prior_neg = mixgauss.priorC(2); +post = normalize([Bpos * prior_pos; Bneg * prior_neg], 1); +probPos = post(1,:)'; +[junk, classHatTest] = max(post); +classHatTest(find(classHatTest==2))=0; +classHatTest = classHatTest(:);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/mixgauss_classifier_train.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,33 @@ +function mixgauss = mixgauss_classifier_train(trainFeatures, trainLabels, nc, varargin) +% function mixgauss = mixgauss_classifier_train(trainFeatures, trainLabels, nclusters, varargin) +% trainFeatures(:,i) for i'th example +% trainLabels should be 0,1 +% To evaluate performance on a tets set, use +% mixgauss = mixgauss_classifier_train(trainFeatures, trainLabels, nc, 'testFeatures', tf, 'testLabels', tl) + +[testFeatures, testLabels, max_iter, thresh, cov_type, mu, Sigma, priorC, method, ... + cov_prior, verbose, prune_thresh] = process_options(... + varargin, 'testFeatures', [], 'testLabels', [], ... + 'max_iter', 10, 'thresh', 0.01, 'cov_type', 'diag', ... + 'mu', [], 'Sigma', [], 'priorC', [], 'method', 'kmeans', ... + 'cov_prior', [], 'verbose', 0, 'prune_thresh', 0); + +Nclasses = 2; % max([trainLabels testLabels]) + 1; + +pos = find(trainLabels == 1); +neg = find(trainLabels == 0); + +if verbose, fprintf('fitting pos\n'); end +[mixgauss.pos.mu, mixgauss.pos.Sigma, mixgauss.pos.prior] = ... + mixgauss_em(trainFeatures(:, pos), nc, varargin{:}); + +if verbose, fprintf('fitting neg\n'); end +[mixgauss.neg.mu, mixgauss.neg.Sigma, mixgauss.neg.prior] = ... + mixgauss_em(trainFeatures(:, neg), nc, varargin{:}); + + +if ~isempty(priorC) + mixgauss.priorC = priorC; +else + mixgauss.priorC = normalize([length(pos) length(neg)]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/mixgauss_em.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,74 @@ +function [mu, Sigma, prior] = mixgauss_em(Y, nc, varargin) +% MIXGAUSS_EM Fit the parameters of a mixture of Gaussians using EM +% function [mu, Sigma, prior] = mixgauss_em(data, nc, varargin) +% +% data(:, t) is the t'th data point +% nc is the number of clusters + +% Kevin Murphy, 13 May 2003 + +[max_iter, thresh, cov_type, mu, Sigma, method, ... + cov_prior, verbose, prune_thresh] = process_options(... + varargin, 'max_iter', 10, 'thresh', 1e-2, 'cov_type', 'full', ... + 'mu', [], 'Sigma', [], 'method', 'kmeans', ... + 'cov_prior', [], 'verbose', 0, 'prune_thresh', 0); + +[ny T] = size(Y); + +if nc==1 + % No latent variable, so there is a closed-form solution + mu = mean(Y')'; + Sigma = cov(Y'); + if strcmp(cov_type, 'diag') + Sigma = diag(diag(Sigma)); + end + prior = 1; + return; +end + +if isempty(mu) + [mu, Sigma, prior] = mixgauss_init(nc, Y, cov_type, method); +end + +previous_loglik = -inf; +num_iter = 1; +converged = 0; + +%if verbose, fprintf('starting em\n'); end + +while (num_iter <= max_iter) & ~converged + % E step + probY = mixgauss_prob(Y, mu, Sigma, prior); % probY(q,t) + [post, lik] = normalize(probY .* repmat(prior, 1, T), 1); % post(q,t) + loglik = log(sum(lik)); + + % extract expected sufficient statistics + w = sum(post,2); % w(c) = sum_t post(c,t) + WYY = zeros(ny, ny, nc); % WYY(:,:,c) = sum_t post(c,t) Y(:,t) Y(:,t)' + WY = zeros(ny, nc); % WY(:,c) = sum_t post(c,t) Y(:,t) + WYTY = zeros(nc,1); % WYTY(c) = sum_t post(c,t) Y(:,t)' Y(:,t) + for c=1:nc + weights = repmat(post(c,:), ny, 1); % weights(:,t) = post(c,t) + WYbig = Y .* weights; % WYbig(:,t) = post(c,t) * Y(:,t) + WYY(:,:,c) = WYbig * Y'; + WY(:,c) = sum(WYbig, 2); + WYTY(c) = sum(diag(WYbig' * Y)); + end + + % M step + prior = normalize(w); + [mu, Sigma] = mixgauss_Mstep(w, WY, WYY, WYTY, 'cov_type', cov_type, 'cov_prior', cov_prior); + + if verbose, fprintf(1, 'iteration %d, loglik = %f\n', num_iter, loglik); end + num_iter = num_iter + 1; + converged = em_converged(loglik, previous_loglik, thresh); + previous_loglik = loglik; + +end + +if prune_thresh > 0 + ndx = find(prior < prune_thresh); + mu(:,ndx) = []; + Sigma(:,:,ndx) = []; + prior(ndx) = []; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/mixgauss_init.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,49 @@ +function [mu, Sigma, weights] = mixgauss_init(M, data, cov_type, method) +% MIXGAUSS_INIT Initial parameter estimates for a mixture of Gaussians +% function [mu, Sigma, weights] = mixgauss_init(M, data, cov_type. method) +% +% INPUTS: +% data(:,t) is the t'th example +% M = num. mixture components +% cov_type = 'full', 'diag' or 'spherical' +% method = 'rnd' (choose centers randomly from data) or 'kmeans' (needs netlab) +% +% OUTPUTS: +% mu(:,k) +% Sigma(:,:,k) +% weights(k) + +if nargin < 4, method = 'kmeans'; end + +[d T] = size(data); +data = reshape(data, d, T); % in case it is data(:, t, sequence_num) + +switch method + case 'rnd', + C = cov(data'); + Sigma = repmat(diag(diag(C))*0.5, [1 1 M]); + % Initialize each mean to a random data point + indices = randperm(T); + mu = data(:,indices(1:M)); + weights = normalise(ones(M,1)); + case 'kmeans', + mix = gmm(d, M, cov_type); + options = foptions; + max_iter = 5; + options(1) = -1; % be quiet! + options(14) = max_iter; + mix = gmminit(mix, data', options); + mu = reshape(mix.centres', [d M]); + weights = mix.priors(:); + for m=1:M + switch cov_type + case 'diag', + Sigma(:,:,m) = diag(mix.covars(m,:)); + case 'full', + Sigma(:,:,m) = mix.covars(:,:,m); + case 'spherical', + Sigma(:,:,m) = mix.covars(m) * eye(d); + end + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/mixgauss_prob.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,133 @@ +function [B, B2] = mixgauss_prob(data, mu, Sigma, mixmat, unit_norm) +% EVAL_PDF_COND_MOG Evaluate the pdf of a conditional mixture of Gaussians +% function [B, B2] = eval_pdf_cond_mog(data, mu, Sigma, mixmat, unit_norm) +% +% Notation: Y is observation, M is mixture component, and both may be conditioned on Q. +% If Q does not exist, ignore references to Q=j below. +% Alternatively, you may ignore M if this is a conditional Gaussian. +% +% INPUTS: +% data(:,t) = t'th observation vector +% +% mu(:,k) = E[Y(t) | M(t)=k] +% or mu(:,j,k) = E[Y(t) | Q(t)=j, M(t)=k] +% +% Sigma(:,:,j,k) = Cov[Y(t) | Q(t)=j, M(t)=k] +% or there are various faster, special cases: +% Sigma() - scalar, spherical covariance independent of M,Q. +% Sigma(:,:) diag or full, tied params independent of M,Q. +% Sigma(:,:,j) tied params independent of M. +% +% mixmat(k) = Pr(M(t)=k) = prior +% or mixmat(j,k) = Pr(M(t)=k | Q(t)=j) +% Not needed if M is not defined. +% +% unit_norm - optional; if 1, means data(:,i) AND mu(:,i) each have unit norm (slightly faster) +% +% OUTPUT: +% B(t) = Pr(y(t)) +% or +% B(i,t) = Pr(y(t) | Q(t)=i) +% B2(i,k,t) = Pr(y(t) | Q(t)=i, M(t)=k) +% +% If the number of mixture components differs depending on Q, just set the trailing +% entries of mixmat to 0, e.g., 2 components if Q=1, 3 components if Q=2, +% then set mixmat(1,3)=0. In this case, B2(1,3,:)=1.0. + + + + +if isvector(mu) & size(mu,2)==1 + d = length(mu); + Q = 1; M = 1; +elseif ndims(mu)==2 + [d Q] = size(mu); + M = 1; +else + [d Q M] = size(mu); +end +[d T] = size(data); + +if nargin < 4, mixmat = ones(Q,1); end +if nargin < 5, unit_norm = 0; end + +%B2 = zeros(Q,M,T); % ATB: not needed allways +%B = zeros(Q,T); + +if isscalar(Sigma) + mu = reshape(mu, [d Q*M]); + if unit_norm % (p-q)'(p-q) = p'p + q'q - 2p'q = n+m -2p'q since p(:,i)'p(:,i)=1 + %avoid an expensive repmat + disp('unit norm') + %tic; D = 2 -2*(data'*mu)'; toc + D = 2 - 2*(mu'*data); + tic; D2 = sqdist(data, mu)'; toc + assert(approxeq(D,D2)) + else + D = sqdist(data, mu)'; + end + clear mu data % ATB: clear big old data + % D(qm,t) = sq dist between data(:,t) and mu(:,qm) + logB2 = -(d/2)*log(2*pi*Sigma) - (1/(2*Sigma))*D; % det(sigma*I) = sigma^d + B2 = reshape(exp(logB2), [Q M T]); + clear logB2 % ATB: clear big old data + +elseif ndims(Sigma)==2 % tied full + mu = reshape(mu, [d Q*M]); + D = sqdist(data, mu, inv(Sigma))'; + % D(qm,t) = sq dist between data(:,t) and mu(:,qm) + logB2 = -(d/2)*log(2*pi) - 0.5*logdet(Sigma) - 0.5*D; + %denom = sqrt(det(2*pi*Sigma)); + %numer = exp(-0.5 * D); + %B2 = numer/denom; + B2 = reshape(exp(logB2), [Q M T]); + +elseif ndims(Sigma)==3 % tied across M + B2 = zeros(Q,M,T); + for j=1:Q + % D(m,t) = sq dist between data(:,t) and mu(:,j,m) + if isposdef(Sigma(:,:,j)) + D = sqdist(data, permute(mu(:,j,:), [1 3 2]), inv(Sigma(:,:,j)))'; + logB2 = -(d/2)*log(2*pi) - 0.5*logdet(Sigma(:,:,j)) - 0.5*D; + B2(j,:,:) = exp(logB2); + else + error(sprintf('mixgauss_prob: Sigma(:,:,q=%d) not psd\n', j)); + end + end + +else % general case + B2 = zeros(Q,M,T); + for j=1:Q + for k=1:M + %if mixmat(j,k) > 0 + B2(j,k,:) = gaussian_prob(data, mu(:,j,k), Sigma(:,:,j,k)); + %end + end + end +end + +% B(j,t) = sum_k B2(j,k,t) * Pr(M(t)=k | Q(t)=j) + +% The repmat is actually slower than the for-loop, because it uses too much memory +% (this is true even for small T). + +%B = squeeze(sum(B2 .* repmat(mixmat, [1 1 T]), 2)); +%B = reshape(B, [Q T]); % undo effect of squeeze in case Q = 1 + +B = zeros(Q,T); +if Q < T + for q=1:Q + %B(q,:) = mixmat(q,:) * squeeze(B2(q,:,:)); % squeeze chnages order if M=1 + B(q,:) = mixmat(q,:) * permute(B2(q,:,:), [2 3 1]); % vector * matrix sums over m + end +else + for t=1:T + B(:,t) = sum(mixmat .* B2(:,:,t), 2); % sum over m + end +end +%t=toc;fprintf('%5.3f\n', t) + +%tic +%A = squeeze(sum(B2 .* repmat(mixmat, [1 1 T]), 2)); +%t=toc;fprintf('%5.3f\n', t) +%assert(approxeq(A,B)) % may be false because of round off error
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/mixgauss_prob_test.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,111 @@ +function test_eval_pdf_cond_mixgauss() + +%Q = 10; M = 100; d = 20; T = 500; +Q = 2; M = 3; d = 4; T = 5; + +mu = rand(d,Q,M); +data = randn(d,T); +%mixmat = mk_stochastic(rand(Q,M)); +mixmat = mk_stochastic(ones(Q,M)); + +% tied scalar +Sigma = 0.01; + +mu = rand(d,M,Q); +weights = mixmat'; +N = M*ones(1,Q); +tic; [B, B2, D] = parzen(data, mu, Sigma, N, weights); toc +tic; [BC, B2C, DC] = parzenC(data, mu, Sigma, N); toc +approxeq(B,BC) +B2C = reshape(B2C,[M Q T]); +approxeq(B2,B2C) +DC = reshape(DC,[M Q T]); +approxeq(D,DC) + + +return + +tic; [B, B2] = eval_pdf_cond_mixgauss(data, mu, Sigma, mixmat); toc +tic; C = eval_pdf_cond_parzen(data, mu, Sigma); toc +approxeq(B,C) + +return; + + +mu = reshape(mu, [d Q*M]); + +data = mk_unit_norm(data); +mu = mk_unit_norm(mu); +tic; D = 2 -2*(data'*mu); toc % avoid an expensive repmat +tic; D2 = sqdist(data, mu); toc +approxeq(D,D2) + + +% D(t,m) = sq dist between data(:,t) and mu(:,m) +mu = reshape(mu, [d Q*M]); +D = dist2(data', mu'); +%denom = (2*pi)^(d/2)*sqrt(abs(det(C))); +denom = (2*pi*Sigma)^(d/2); % sqrt(det(2*pi*Sigma)) +numer = exp(-0.5/Sigma * D'); +B2 = numer / denom; +B2 = reshape(B2, [Q M T]); + +tic; B = squeeze(sum(B2 .* repmat(mixmat, [1 1 T]), 2)); toc + +tic +A = zeros(Q,T); +for q=1:Q + A(q,:) = mixmat(q,:) * squeeze(B2(q,:,:)); % sum over m +end +toc +assert(approxeq(A,B)) + +tic +A = zeros(Q,T); +for t=1:T + A(:,t) = sum(mixmat .* B2(:,:,t), 2); % sum over m +end +toc +assert(approxeq(A,B)) + + + + +mu = reshape(mu, [d Q M]); +B3 = zeros(Q,M,T); +for j=1:Q + for k=1:M + B3(j,k,:) = gaussian_prob(data, mu(:,j,k), Sigma*eye(d)); + end +end +assert(approxeq(B2, B3)) + +logB4 = -(d/2)*log(2*pi*Sigma) - (1/(2*Sigma))*D; % det(sigma*I) = sigma^d +B4 = reshape(exp(logB4), [Q M T]); +assert(approxeq(B4, B3)) + + + + +% tied cov matrix + +Sigma = rand_psd(d,d); +mu = reshape(mu, [d Q*M]); +D = sqdist(data, mu, inv(Sigma))'; +denom = sqrt(det(2*pi*Sigma)); +numer = exp(-0.5 * D); +B2 = numer / denom; +B2 = reshape(B2, [Q M T]); + +mu = reshape(mu, [d Q M]); +B3 = zeros(Q,M,T); +for j=1:Q + for k=1:M + B3(j,k,:) = gaussian_prob(data, mu(:,j,k), Sigma); + end +end +assert(approxeq(B2, B3)) + +logB4 = -(d/2)*log(2*pi) - 0.5*logdet(Sigma) - 0.5*D; +B4 = reshape(exp(logB4), [Q M T]); +assert(approxeq(B4, B3))
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/mixgauss_sample.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,22 @@ +function [data, indices] = mixgauss_sample(mu, Sigma, mixweights, Nsamples) +% mixgauss_sample Sample data from a mixture of Gaussians +% function [data, indices] = mixgauss_sample(mu, Sigma, mixweights, Nsamples) +% +% Model is P(X) = sum_k mixweights(k) N(X; mu(:,k), Sigma(:,:,k)) or Sigma(k) for scalar +% data(:,i) is the i'th sample from P(X) +% indices(i) is the component from which sample i was drawn + +[D K] = size(mu); +data = zeros(D, Nsamples); +indices = sample_discrete(mixweights, 1, Nsamples); +for k=1:K + if ndims(Sigma) < 3 + sig = Sigma(k); + else + sig = Sigma(:,:,k); + end + ndx = find(indices==k); + if length(ndx) > 0 + data(:,ndx) = sample_gaussian(mu(:,k), sig, length(ndx))'; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/mkPolyFvec.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,24 @@ +function p = mkPolyFvec(x) +% MKPOLYFVEC Make feature vector by constructing 2nd order polynomial from input data +% function p = mkPolyFvec(x) +% +% x(:,i) for example i +% p(:,i) = [x(1,i) x(2,i) x(3,i) x(1,i)^2 x(2,i)^2 x(3,i)^2 .. +% x(1,i)*x(2,i) x(1,i)*x(3,i) x(2,i)*x(3,i)]' +% +% Example +% x = [4 5 6]' +% p = [4 5 6 16 25 36 20 24 30]' + +fvec = x; +fvecSq = x.*x; +[D N] = size(x); +fvecCross = zeros(D*(D-1)/2, N); +i = 1; +for d=1:D + for d2=d+1:D + fvecCross(i,:) = x(d,:) .* x(d2,:); + i = i + 1; + end +end +p = [fvec; fvecSq; fvecCross];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/mk_unit_norm.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function B = mk_unit_norm(A) +% MK_UNIT_NORM Make each column be a unit norm vector +% function B = mk_unit_norm(A) +% +% We divide each column by its magnitude + + +[nrows ncols] = size(A); +s = sum(A.^2); +ndx = find(s==0); +s(ndx)=1; +B = A ./ repmat(sqrt(s), [nrows 1]); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/multinomial_prob.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,20 @@ +function B = eval_pdf_cond_multinomial(data, obsmat) +% EVAL_PDF_COND_MULTINOMIAL Evaluate pdf of conditional multinomial +% function B = eval_pdf_cond_multinomial(data, obsmat) +% +% Notation: Y = observation (O values), Q = conditioning variable (K values) +% +% Inputs: +% data(t) = t'th observation - must be an integer in {1,2,...,K}: cannot be 0! +% obsmat(i,o) = Pr(Y(t)=o | Q(t)=i) +% +% Output: +% B(i,t) = Pr(y(t) | Q(t)=i) + +[Q O] = size(obsmat); +T = prod(size(data)); % length(data); +B = zeros(Q,T); + +for t=1:T + B(:,t) = obsmat(:, data(t)); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/multinomial_sample.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,22 @@ +function Y = sample_cond_multinomial(X, M) +% SAMPLE_MULTINOMIAL Sample Y(i) ~ M(X(i), :) +% function Y = sample_multinomial(X, M) +% +% X(i) = i'th sample +% M(i,j) = P(Y=j | X=i) = noisy channel model +% +% e.g., if X is a binary image, +% Y = sample_multinomial(softeye(2, 0.9), X) +% will create a noisy version of X, where bits are flipped with probability 0.1 + +if any(X(:)==0) + error('data must only contain positive integers') +end + +Y = zeros(size(X)); +for i=min(X(:)):max(X(:)) + ndx = find(X==i); + Y(ndx) = sample_discrete(M(i,:), length(ndx), 1); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/multipdf.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,45 @@ +function p = multipdf(x,theta) +%MULTIPDF Multinomial probability density function. +% p = multipdf(x,theta) returns the probabilities of +% vector x, under the multinomial distribution +% with parameter vector theta. +% +% Author: David Ross + +%-------------------------------------------------------- +% Check the arguments. +%-------------------------------------------------------- +error(nargchk(2,2,nargin)); + +% make sure theta is a vector +if ndims(theta) > 2 | all(size(theta) > 1) + error('theta must be a vector'); +end + +% make sure x is of the appropriate size +if ndims(x) > 2 | any(size(x) ~= size(theta)) + error('columns of X must have same length as theta'); +end + + +%-------------------------------------------------------- +% Main... +%-------------------------------------------------------- +p = prod(theta .^ x); +p = p .* factorial(sum(x)) ./ prod(factorial_v(x)); + + +%-------------------------------------------------------- +% Function factorial_v(x): computes the factorial function +% on each element of x +%-------------------------------------------------------- +function r = factorial_v(x) + +if size(x,2) == 1 + x = x'; +end + +r = []; +for y = x + r = [r factorial(y)]; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/multirnd.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,48 @@ +function r = multirnd(theta,k) +%MULTIRND - Random vector from multinomial distribution. +% r = multirnd(theta,k) returns a vector randomly selected +% from the multinomial distribution with parameter vector +% theta, and count k (i.e. sum(r) = k). +% +% Note: if k is unspecified, then it is assumed k=1. +% +% Author: David Ross +% + +%-------------------------------------------------------- +% Check the arguments. +%-------------------------------------------------------- +error(nargchk(1,2,nargin)); + +% make sure theta is a vector +if ndims(theta) > 2 | all(size(theta) > 1) + error('theta must be a vector'); +end + +% if theta is a row vector, convert it to a column vector +if size(theta,1) == 1 + theta = theta'; +end + +% make sure k is a scalar? + +% if the number of samples has not been provided, set +% it to one +if nargin == 1 + k = 1; +end + + +%-------------------------------------------------------- +% Main... +%-------------------------------------------------------- +n = length(theta); +theta_cdf = cumsum(theta); + +r = zeros(n,1); +random_vals = rand(k,1); + +for j = 1:k + index = min(find(random_vals(j) <= theta_cdf)); + r(index) = r(index) + 1; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/normal_coef.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +function c = normal_coef (Sigma) +% NORMAL_COEF Compute the normalizing coefficient for a multivariate gaussian. +% c = normal_coef (Sigma) + +n = length(Sigma); +c = (2*pi)^(-n/2) * det(Sigma)^(-0.5); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/partial_corr_coef.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,28 @@ +function [r, c] = partial_corr_coef(S, i, j, Y) +% PARTIAL_CORR_COEF Compute a partial correlation coefficient +% [r, c] = partial_corr_coef(S, i, j, Y) +% +% S is the covariance (or correlation) matrix for X, Y, Z +% where X=[i j], Y is conditioned on, and Z is marginalized out. +% Let S2 = Cov[X | Y] be the partial covariance matrix. +% Then c = S2(i,j) and r = c / sqrt( S2(i,i) * S2(j,j) ) +% + +% Example: Anderson (1984) p129 +% S = [1.0 0.8 -0.4; +% 0.8 1.0 -0.56; +% -0.4 -0.56 1.0]; +% r(1,3 | 2) = 0.0966 +% +% Example: Van de Geer (1971) p111 +%S = [1 0.453 0.322; +% 0.453 1.0 0.596; +% 0.322 0.596 1]; +% r(2,3 | 1) = 0.533 + +X = [i j]; +i2 = 1; % find_equiv_posns(i, X); +j2 = 2; % find_equiv_posns(j, X); +S2 = S(X,X) - S(X,Y)*inv(S(Y,Y))*S(Y,X); +c = S2(i2,j2); +r = c / sqrt(S2(i2,i2) * S2(j2,j2));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/parzen.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,88 @@ +function [B,B2,dist] = parzen(data, mu, Sigma, N) +% EVAL_PDF_COND_PARZEN Evaluate the pdf of a conditional Parzen window +% function B = eval_pdf_cond_parzen(data, mu, Sigma, N) +% +% B(q,t) = Pr(data(:,t) | Q=q) = sum_{m=1}^{N(q)} w(m,q)*K(data(:,t) - mu(:,m,q); sigma) +% where K() is a Gaussian kernel with spherical variance sigma, +% and w(m,q) = 1/N(q) if m<=N(q) and = 0 otherwise +% where N(q) is the number of mxiture components for q +% +% B2(m,q,t) = K(data(:,t) - mu(:,m,q); sigma) for m=1:max(N) + +% This is like eval_pdf_cond_parzen, except mu is mu(:,m,q) instead of mu(:,q,m) +% and we use 1/N(q) instead of mixmat(q,m) + +if nargout >= 2 + keep_B2 = 1; +else + keep_B2 = 0; +end + +if nargout >= 3 + keep_dist = 1; +else + keep_dist = 0; +end + +[d M Q] = size(mu); +[d T] = size(data); + +M = max(N(:)); + +B = zeros(Q,T); +const1 = (2*pi*Sigma)^(-d/2); +const2 = -(1/(2*Sigma)); +if T*Q*M>20000000 % not enough memory to call sqdist + disp('eval parzen for loop') + if keep_dist, + dist = zeros(M,Q,T); + end + if keep_B2 + B2 = zeros(M,Q,T); + end + for q=1:Q + D = sqdist(mu(:,1:N(q),q), data); % D(m,t) + if keep_dist + dist(:,q,:) = D; + end + tmp = const1 * exp(const2*D); + if keep_B2, + B2(:,q,:) = tmp; + end + if N(q) > 0 + %B(q,:) = (1/N(q)) * const1 * sum(exp(const2*D), 2); + B(q,:) = (1/N(q)) * sum(tmp,1); + end + end +else + %disp('eval parzen vectorized') + dist = sqdist(reshape(mu(:,1:M,:), [d M*Q]), data); % D(mq,t) + dist = reshape(dist, [M Q T]); + B2 = const1 * exp(const2*dist); % B2(m,q,t) + if ~keep_dist + clear dist + end + + % weights(m,q) is the weight of mixture component m for q + % = 1/N(q) if m<=N(q) and = 0 otherwise + % e.g., N = [2 3 1], M = 3, + % weights = [1/2 1/3 1 = 1/2 1/3 1/1 2 3 1 1 1 1 + % 1/2 1/3 0 1/2 1/3 1/1 .* 2 3 1 <= 2 2 2 + % 0 1/3 0] 1/2 1/3 1/1 2 3 1 3 3 3 + + Ns = repmat(N(:)', [M 1]); + ramp = 1:M; + ramp = repmat(ramp(:), [1 Q]); + n = N + (N==0); % avoid 1/0 by replacing with 0* 1/1m where 0 comes from mask + N1 = repmat(1 ./ n(:)', [M 1]); + mask = (ramp <= Ns); + weights = N1 .* mask; + B2 = B2 .* repmat(mask, [1 1 T]); + + % B(q,t) = sum_m B2(m,q,t) * P(m|q) = sum_m B2(m,q,t) * weights(m,q) + B = squeeze(sum(B2 .* repmat(weights, [1 1 T]), 1)); + B = reshape(B, [Q T]); % undo effect of squeeze in case Q = 1 +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/parzenC.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,116 @@ +/* C mex version of parzen.m +[B,B2] = parzen(feat, mu, Sigma, Nproto); +*/ +#include "mex.h" +#include <stdio.h> +#include <math.h> + +#define PI 3.141592654 + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int D, M, Q, T, d, m, q, t; + double *data, *mu, *SigmaPtr, *N, Sigma; + double *B, *dist, *B2, tmp; + const int* dim_mu; + double const1, const2, sum_m, sum_d, diff; + int Dt, DMq, Dm, MQt, Mq; + int dims_B2[3]; + + int ndim_mu, i, save_B2; + + data = mxGetPr(prhs[0]); + mu = mxGetPr(prhs[1]); + SigmaPtr = mxGetPr(prhs[2]); + Sigma = *SigmaPtr; + N = mxGetPr(prhs[3]); + + D = mxGetM(prhs[0]); + T = mxGetN(prhs[0]); + + ndim_mu = mxGetNumberOfDimensions(prhs[1]); + dim_mu = mxGetDimensions(prhs[1]); + D = dim_mu[0]; + M = dim_mu[1]; + /* printf("parzenC: nlhs=%d, D=%d, M=%d, T=%d\n", nlhs, D, M, T); */ + + /* If mu is mu(d,m,o,p), then [d M Q] = size(mu) in matlab sets Q=o*p, + i.e.. the size of all conditioning variabeles */ + Q = 1; + for (i = 2; i < ndim_mu; i++) { + /* printf("dim_mu[%d]=%d\n", i, dim_mu[i]); */ + Q = Q*dim_mu[i]; + } + + /* M = max(N) */ + M = -1000000; + for (i=0; i < Q; i++) { + /* printf("N[%d]=%d\n", i, (int) N[i]); */ + if (N[i] > M) { + M = (int) N[i]; + } + } + + /* printf("parzenC: nlhs=%d, D=%d, Q=%d, M=%d, T=%d\n", nlhs, D, Q, M, T); */ + + plhs[0] = mxCreateDoubleMatrix(Q,T, mxREAL); + B = mxGetPr(plhs[0]); + + if (nlhs >= 2) + save_B2 = 1; + else + save_B2 = 0; + + if (save_B2) { + /* printf("parzenC saving B2\n"); */ + /*plhs[1] = mxCreateDoubleMatrix(M*Q*T,1, mxREAL);*/ + dims_B2[0] = M; + dims_B2[1] = Q; + dims_B2[2] = T; + plhs[1] = mxCreateNumericArray(3, dims_B2, mxDOUBLE_CLASS, mxREAL); + B2 = mxGetPr(plhs[1]); + } else { + /* printf("parzenC not saving B2\n"); */ + } + /* + plhs[2] = mxCreateDoubleMatrix(M*Q*T,1, mxREAL); + dist = mxGetPr(plhs[2]); + */ + const1 = pow(2*PI*Sigma, -D/2.0); + const2 = -(1/(2*Sigma)); + + for (t=0; t < T; t++) { + /* printf("t=%d!\n",t); */ + Dt = D*t; + MQt = M*Q*t; + for (q=0; q < Q; q++) { + sum_m = 0; + DMq = D*M*q; + Mq = M*q; + + for (m=0; m < (int)N[q]; m++) { + sum_d = 0; + Dm = D*m; + for (d=0; d < D; d++) { + /* diff = data(d,t) - mu(d,m,q) */ + /*diff = data[d + D*t] - mu[d + D*m + D*M*q]; */ + diff = data[d + Dt] - mu[d + Dm + DMq]; + sum_d = sum_d + diff*diff; + } + /* dist[m,q,t] = dist[m + M*q + M*Q*t] = dist[m + Mq + MQt] = sum_d */ + tmp = const1 * exp(const2*sum_d); + sum_m = sum_m + tmp; + if (save_B2) + B2[m + Mq + MQt] = tmp; + } + + if (N[q]>0) { + B[q + Q*t] = (1.0/N[q]) * sum_m; + } else { + B[q + Q*t] = 0.0; + } + } + } +} + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/parzenC_test.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,10 @@ +d = 2; M = 3; Q = 4; T = 5; Sigma = 10; +N = sample_discrete(normalize(ones(1,M)), 1, Q); +data = randn(d,T); +mu = randn(d,M,Q); + +[BM, B2M] = parzen(data, mu, Sigma, N); +[B, B2] = parzenC(data, mu, Sigma, N); + +approxeq(B,BM) +approxeq(B2,B2M)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/parzen_fit_select_unif.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,45 @@ +function [mu, N, pick] = parzen_fit_select_unif(data, labels, max_proto, varargin) +% PARZEN_FIT_SELECT_UNIF Fit a parzen density estimator by selecting prototypes uniformly from data +% [mu, N, pick] = parzen_fit_select_unif(data, max_proto, labels, ...) +% +% We partition the data into different subsets based on the labels. +% We then choose up to max_proto columns from each subset, chosen uniformly. +% +% INPUTS +% data(:,t) +% labels(t) - should be in {1,2,..,Q} +% max_proto - max number of prototypes per partition +% +% Optional args +% partition_names{m} - for debugging +% boundary - do not choose prototypes which are within 'boundary' of the label transition +% +% OUTPUTS +% mu(:, m, q) for label q, prototype m for 1 <= m <= N(q) +% N(q) = number of prototypes for label q +% pick{q} = identity of the prototypes + +nclasses = max(labels); +[boundary, partition_names] = process_options(... + varargin, 'boundary', 0, 'partition_names', []); + +[D T] = size(data); +mu = zeros(D, 1, nclasses); % dynamically determine num prototypes (may be less than K) +mean_feat = mean(data,2); +pick = cell(1,nclasses); +for c=1:nclasses + ndx = find(labels==c); + if isempty(ndx) + %fprintf('no training images have label %d (%s)\n', c, partition_names{c}) + fprintf('no training images have label %d\n', c); + nviews = 1; + mu(:,1,c) = mean_feat; + else + foo = linspace(boundary+1, length(ndx-boundary), max_proto); + pick{c} = ndx(unique(floor(foo))); + nviews = length(pick{c}); + %fprintf('picking %d views for class %d=%s\n', nviews, c, class_names{c}); + mu(:,1:nviews,c) = data(:, pick{c}); + end + N(c) = nviews; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/pca.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,42 @@ +function [PCcoeff, PCvec] = pca(data, N) +%PCA Principal Components Analysis +% +% Description +% PCCOEFF = PCA(DATA) computes the eigenvalues of the covariance +% matrix of the dataset DATA and returns them as PCCOEFF. These +% coefficients give the variance of DATA along the corresponding +% principal components. +% +% PCCOEFF = PCA(DATA, N) returns the largest N eigenvalues. +% +% [PCCOEFF, PCVEC] = PCA(DATA) returns the principal components as well +% as the coefficients. This is considerably more computationally +% demanding than just computing the eigenvalues. +% +% See also +% EIGDEC, GTMINIT, PPCA +% + +% Copyright (c) Ian T Nabney (1996-2001) + +if nargin == 1 + N = size(data, 2); +end + +if nargout == 1 + evals_only = logical(1); +else + evals_only = logical(0); +end + +if N ~= round(N) | N < 1 | N > size(data, 2) + error('Number of PCs must be integer, >0, < dim'); +end + +% Find the sorted eigenvalues of the data covariance matrix +if evals_only + PCcoeff = eigdec(cov(data), N); +else + [PCcoeff, PCvec] = eigdec(cov(data), N); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/rndcheck.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,294 @@ +function [errorcode, rows, columns] = rndcheck(nargs,nparms,arg1,arg2,arg3,arg4,arg5) +%RNDCHECK error checks the argument list for the random number generators. + +% B.A. Jones 1-22-93 +% Copyright (c) 1993-98 by The MathWorks, Inc. +% $Revision: 1.1.1.1 $ $Date: 2005/04/26 02:29:22 $ + +sizeinfo = nargs - nparms; +errorcode = 0; + +if nparms == 3 + [r1 c1] = size(arg1); + [r2 c2] = size(arg2); + [r3 c3] = size(arg3); +end + +if nparms == 2 + [r1 c1] = size(arg1); + [r2 c2] = size(arg2); +end + +if sizeinfo == 0 + if nparms == 1 + [rows columns] = size(arg1); + end + + if nparms == 2 + scalararg1 = (prod(size(arg1)) == 1); + scalararg2 = (prod(size(arg2)) == 1); + if ~scalararg1 & ~scalararg2 + if r1 ~= r2 | c1 ~= c2 + errorcode = 1; + return; + end + end + if ~scalararg1 + [rows columns] = size(arg1); + elseif ~scalararg2 + [rows columns] = size(arg2); + else + [rows columns] = size(arg1); + end + end + + if nparms == 3 + scalararg1 = (prod(size(arg1)) == 1); + scalararg2 = (prod(size(arg2)) == 1); + scalararg3 = (prod(size(arg3)) == 1); + + if ~scalararg1 & ~scalararg2 + if r1 ~= r2 | c1 ~= c2 + errorcode = 1; + return; + end + end + + if ~scalararg1 & ~scalararg3 + if r1 ~= r3 | c1 ~= c3 + errorcode = 1; + return; + end + end + + if ~scalararg3 & ~scalararg2 + if r3 ~= r2 | c3 ~= c2 + errorcode = 1; + return; + end + end + if ~scalararg1 + [rows columns] = size(arg1); + elseif ~scalararg2 + [rows columns] = size(arg2); + else + [rows columns] = size(arg3); + end + end +end + +if sizeinfo == 1 + scalararg1 = (prod(size(arg1)) == 1); + if nparms == 1 + if prod(size(arg2)) ~= 2 + errorcode = 2; + return; + end + if ~scalararg1 & arg2 ~= size(arg1) + errorcode = 3; + return; + end + if (arg2(1) < 0 | arg2(2) < 0 | arg2(1) ~= round(arg2(1)) | arg2(2) ~= round(arg2(2))), + errorcode = 4; + return; + end + rows = arg2(1); + columns = arg2(2); + end + + if nparms == 2 + if prod(size(arg3)) ~= 2 + errorcode = 2; + return; + end + scalararg2 = (prod(size(arg2)) == 1); + if ~scalararg1 & ~scalararg2 + if r1 ~= r2 | c1 ~= c2 + errorcode = 1; + return; + end + end + if (arg3(1) < 0 | arg3(2) < 0 | arg3(1) ~= round(arg3(1)) | arg3(2) ~= round(arg3(2))), + errorcode = 4; + return; + end + if ~scalararg1 + if any(arg3 ~= size(arg1)) + errorcode = 3; + return; + end + [rows columns] = size(arg1); + elseif ~scalararg2 + if any(arg3 ~= size(arg2)) + errorcode = 3; + return; + end + [rows columns] = size(arg2); + else + rows = arg3(1); + columns = arg3(2); + end + end + + if nparms == 3 + if prod(size(arg4)) ~= 2 + errorcode = 2; + return; + end + scalararg1 = (prod(size(arg1)) == 1); + scalararg2 = (prod(size(arg2)) == 1); + scalararg3 = (prod(size(arg3)) == 1); + + if (arg4(1) < 0 | arg4(2) < 0 | arg4(1) ~= round(arg4(1)) | arg4(2) ~= round(arg4(2))), + errorcode = 4; + return; + end + + if ~scalararg1 & ~scalararg2 + if r1 ~= r2 | c1 ~= c2 + errorcode = 1; + return; + end + end + + if ~scalararg1 & ~scalararg3 + if r1 ~= r3 | c1 ~= c3 + errorcode = 1; + return; + end + end + + if ~scalararg3 & ~scalararg2 + if r3 ~= r2 | c3 ~= c2 + errorcode = 1; + return; + end + end + if ~scalararg1 + if any(arg4 ~= size(arg1)) + errorcode = 3; + return; + end + [rows columns] = size(arg1); + elseif ~scalararg2 + if any(arg4 ~= size(arg2)) + errorcode = 3; + return; + end + [rows columns] = size(arg2); + elseif ~scalararg3 + if any(arg4 ~= size(arg3)) + errorcode = 3; + return; + end + [rows columns] = size(arg3); + else + rows = arg4(1); + columns = arg4(2); + end + end +end + +if sizeinfo == 2 + if nparms == 1 + scalararg1 = (prod(size(arg1)) == 1); + if ~scalararg1 + [rows columns] = size(arg1); + if rows ~= arg2 | columns ~= arg3 + errorcode = 3; + return; + end + end + if (arg2 < 0 | arg3 < 0 | arg2 ~= round(arg2) | arg3 ~= round(arg3)), + errorcode = 4; + return; + end + rows = arg2; + columns = arg3; + end + + if nparms == 2 + scalararg1 = (prod(size(arg1)) == 1); + scalararg2 = (prod(size(arg2)) == 1); + if ~scalararg1 & ~scalararg2 + if r1 ~= r2 | c1 ~= c2 + errorcode = 1; + return; + end + end + if ~scalararg1 + [rows columns] = size(arg1); + if rows ~= arg3 | columns ~= arg4 + errorcode = 3; + return; + end + elseif ~scalararg2 + [rows columns] = size(arg2); + if rows ~= arg3 | columns ~= arg4 + errorcode = 3; + return; + end + else + if (arg3 < 0 | arg4 < 0 | arg3 ~= round(arg3) | arg4 ~= round(arg4)), + errorcode = 4; + return; + end + rows = arg3; + columns = arg4; + end + end + + if nparms == 3 + scalararg1 = (prod(size(arg1)) == 1); + scalararg2 = (prod(size(arg2)) == 1); + scalararg3 = (prod(size(arg3)) == 1); + + if ~scalararg1 & ~scalararg2 + if r1 ~= r2 | c1 ~= c2 + errorcode = 1; + return; + end + end + + if ~scalararg1 & ~scalararg3 + if r1 ~= r3 | c1 ~= c3 + errorcode = 1; + return; + end + end + + if ~scalararg3 & ~scalararg2 + if r3 ~= r2 | c3 ~= c2 + errorcode = 1; + return; + end + end + + if ~scalararg1 + [rows columns] = size(arg1); + if rows ~= arg4 | columns ~= arg5 + errorcode = 3; + return; + end + elseif ~scalararg2 + [rows columns] = size(arg2); + if rows ~= arg4 | columns ~= arg5 + errorcode = 3; + return; + end + elseif ~scalararg3 + [rows columns] = size(arg3); + if rows ~= arg4 | columns ~= arg5 + errorcode = 3; + return; + end + else + if (arg4 < 0 | arg5 < 0 | arg4 ~= round(arg4) | arg5 ~= round(arg5)), + errorcode = 4; + return; + end + rows = arg4; + columns = arg5; + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/sample.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +function x = sample(p, n) +% SAMPLE Sample from categorical distribution. +% Returns a row vector of integers, sampled according to the probability +% distribution p. +% Uses the stick-breaking algorithm. +% Much faster algorithms are also possible. + +if nargin < 2 + n = 1; +end + +cdf = cumsum(p(:)); +for i = 1:n + x(i) = sum(cdf < rand) + 1; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/sample_discrete.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,40 @@ +function M = sample_discrete(prob, r, c) +% SAMPLE_DISCRETE Like the built in 'rand', except we draw from a non-uniform discrete distrib. +% M = sample_discrete(prob, r, c) +% +% Example: sample_discrete([0.8 0.2], 1, 10) generates a row vector of 10 random integers from {1,2}, +% where the prob. of being 1 is 0.8 and the prob of being 2 is 0.2. + +n = length(prob); + +if nargin == 1 + r = 1; c = 1; +elseif nargin == 2 + c == r; +end + +R = rand(r, c); +M = ones(r, c); +cumprob = cumsum(prob(:)); + +if n < r*c + for i = 1:n-1 + M = M + (R > cumprob(i)); + end +else + % loop over the smaller index - can be much faster if length(prob) >> r*c + cumprob2 = cumprob(1:end-1); + for i=1:r + for j=1:c + M(i,j) = sum(R(i,j) > cumprob2)+1; + end + end +end + + +% Slower, even though vectorized +%cumprob = reshape(cumsum([0 prob(1:end-1)]), [1 1 n]); +%M = sum(R(:,:,ones(n,1)) > cumprob(ones(r,1),ones(c,1),:), 3); + +% convert using a binning algorithm +%M=bindex(R,cumprob);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/sample_gaussian.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,19 @@ +function M = sample_gaussian(mu, Sigma, N) +% SAMPLE_GAUSSIAN Draw N random row vectors from a Gaussian distribution +% samples = sample_gaussian(mean, cov, N) + +if nargin==2 + N = 1; +end + +% If Y = CX, Var(Y) = C Var(X) C'. +% So if Var(X)=I, and we want Var(Y)=Sigma, we need to find C. s.t. Sigma = C C'. +% Since Sigma is psd, we have Sigma = U D U' = (U D^0.5) (D'^0.5 U'). + +mu = mu(:); +n=length(mu); +[U,D,V] = svd(Sigma); +M = randn(n,N); +M = (U*sqrt(D))*M + mu*ones(1,N); % transform each column +M = M'; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/standardize.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,18 @@ +function [S, mu, sigma2] = standardize(M, mu, sigma2) +% function S = standardize(M, mu, sigma2) +% Make each column of M be zero mean, std 1. +% Thus each row is scaled separately. +% +% If mu, sigma2 are omitted, they are computed from M + +M = double(M); +if nargin < 2 + mu = mean(M,2); + sigma2 = std(M,0,2); + sigma2 = sigma2 + eps*(sigma2==0); +end + +[nrows ncols] = size(M); +S = M - repmat(mu(:), [1 ncols]); +S = S ./ repmat(sigma2, [1 ncols]); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/student_t_logprob.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function L = log_student_pdf(X, mu, lambda, alpha) +% LOG_STUDENT_PDF Evaluate the log of the multivariate student-t distribution at a point +% L = log_student_pdf(X, mu, lambda, alpha) +% +% Each column of X is evaluated. +% See Bernardo and Smith p435. + +k = length(mu); +assert(size(X,1) == k); +[k N] = size(X); +logc = gammaln(0.5*(alpha+k)) - gammaln(0.5*alpha) - (k/2)*log(alpha*pi) + 0.5*log(det(lambda)); +middle = (1 + (1/alpha)*(X-mu)'*lambda*(X-mu)); % scalar version +L = logc - ((alpha+k)/2)*log(middle);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/student_t_prob.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,19 @@ +function p = student_t_pdf(X, mu, lambda, alpha) +% STUDENT_T_PDF Evaluate the multivariate student-t distribution at a point +% p = student_t_pdf(X, mu, lambda, alpha) +% +% Each column of X is evaluated. +% See Bernardo and Smith p435. + +k = length(mu); +assert(size(X,1) == k); +[k N] = size(X); +numer = gamma(0.5*(alpha+k)); +denom = gamma(0.5*alpha) * (alpha*pi)^(k/2); +c = (numer/denom) * det(lambda)^(0.5); +p = c*(1 + (1/alpha)*(X-mu)'*lambda*(X-mu))^(-(alpha+k)/2); % scalar version +%m = repmat(mu(:), 1, N); +%exponent = sum((X-m)'*lambda*(X-m), 2); % column vector +%p = c*(1 + (1/alpha)*exponent).^(-(alpha+k)/2); + +keyboard
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/test_dir.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,19 @@ +% # of sample points +n_samples = 1000; + +p = ones(3,1)/3; + +% Low Entropy +alpha = 0.5*p; + +% High Entropy +%alpha = 10*p; + +% draw n_samples random points from the 3-d dirichlet(alpha), +% and plot the results +points = zeros(3,n_samples); +for i = 1:n_samples + points(:,i) = dirichletrnd(alpha); +end + +scatter3(points(1,:)', points(2,:)', points(3,:)', 'r', '.', 'filled'); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/unidrndKPM.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +function R = unidrndKPM(min, max, nr, nc) + +if nargin < 3 + nr = 1; nc = 1; +end + +R = unidrnd(max-min+1, nr, nc) + (min-1);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/unif_discrete_sample.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +function r = unif_discrete_sample(n, nrows, ncols) +% UNIF_DISCRETE_SAMPLE Generate random numbers uniformly from {1,2,..,n} +% function r = unif_discrete_sample(n, nrows, ncols) +% Same as unidrnd in the stats toolbox. + +r = ceil(n .* rand(nrows,ncols));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMstats/weightedRegression.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,58 @@ +function [a, b, error] = weightedRegression(x, z, w) +% [a , b, error] = fitRegression(x, z, w); +% % Weighted scalar linear regression +% +% Find a,b to minimize +% error = sum(w * |z - (a*x + b)|^2) +% and x(i) is a scalar + +if nargin < 3, w = ones(1,length(x)); end + +w = w(:)'; +x = x(:)'; +z = z(:)'; + +W = sum(w); +Y = sum(w .* z); +YY = sum(w .* z .* z); +YTY = sum(w .* z .* z); +X = sum(w .* x); +XX = sum(w .* x .* x); +XY = sum(w .* x .* z); + +[b, a] = clg_Mstep_simple(W, Y, YY, YTY, X, XX, XY); +error = sum(w .* (z - (a*x + b)).^2 ); + +if 0 + % demo + seed = 1; + rand('state', seed); randn('state', seed); + x = -10:10; + N = length(x); + noise = randn(1,N); + aTrue = rand(1,1); + bTrue = rand(1,1); + z = aTrue*x + bTrue + noise; + + w = ones(1,N); + [a, b, err] = weightedRegression(x, z, w); + + b2=regress(z(:), [x(:) ones(N,1)]); + assert(approxeq(b,b2(2))) + assert(approxeq(a,b2(1))) + + % Make sure we go through x(15) perfectly + w(15) = 1000; + [aW, bW, errW] = weightedRegression(x, z, w); + + figure; + plot(x, z, 'ro') + hold on + plot(x, a*x+b, 'bx-') + plot(x, aW*x+bW, 'gs-') + title(sprintf('a=%5.2f, aHat=%5.2f, aWHat=%5.3f, b=%5.2f, bHat=%5.2f, bWHat=%5.3f, err=%5.3f, errW=%5.3f', ... + aTrue, a, aW, bTrue, b, bW, err, errW)) + legend('truth', 'ls', 'wls') + +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,176 @@ +/README.txt/1.1.1.1/Tue Apr 26 02:30:30 2005// +/approx_unique.m/1.1.1.1/Tue Apr 26 02:30:30 2005// +/approxeq.m/1.1.1.1/Tue Apr 26 02:30:30 2005// +/argmax.m/1.1.1.1/Tue Apr 26 02:30:30 2005// +/argmin.m/1.1.1.1/Tue Apr 26 02:30:30 2005// +/asdemo.html/1.1.1.1/Wed Mar 30 19:59:00 2005// +/asdemo.m/1.1.1.1/Sun Mar 27 02:51:24 2005// +/asort.m/1.1.1.1/Fri May 13 20:52:22 2005// +/assert.m/1.1.1.1/Tue Apr 26 02:30:30 2005// +/assignEdgeNums.m/1.1.1.1/Tue Apr 26 02:30:30 2005// +/assign_cols.m/1.1.1.1/Tue Apr 26 02:30:30 2005// +/axis_pct.m/1.1.1.1/Tue Apr 26 02:30:30 2005// +/bipartiteMatchingDemo.m/1.1.1.1/Mon May 9 16:47:32 2005// +/bipartiteMatchingDemoPlot.m/1.1.1.1/Mon May 9 04:45:10 2005// +/bipartiteMatchingHungarian.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/bipartiteMatchingIntProg.m/1.1.1.1/Mon May 9 05:25:10 2005// +/block.m/1.1.1.1/Tue Apr 26 02:30:30 2005// +/cell2matPad.m/1.1.1.1/Tue Jun 28 01:30:44 2005// +/cell2num.m/1.1.1.1/Tue Apr 26 02:30:30 2005// +/centeringMatrix.m/1.1.1.1/Sun May 8 22:48:48 2005// +/checkpsd.m/1.1.1.1/Fri Feb 7 02:25:30 2003// +/chi2inv.m/1.1.1.1/Tue Apr 26 02:30:30 2005// +/choose.m/1.1.1.1/Tue Apr 26 02:30:30 2005// +/collapse_mog.m/1.1.1.1/Tue Apr 26 02:30:30 2005// +/colmult.c/1.1.1.1/Tue Apr 26 02:30:30 2005// +/colmult.mexglx/1.1.1.1/Tue Apr 26 02:30:30 2005// +/computeROC.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/compute_counts.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/conf2mahal.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/cross_entropy.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/dirKPM.m/1.1.1.1/Fri May 13 20:52:22 2005// +/div.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/draw_circle.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/draw_ellipse.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/draw_ellipse_axes.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/em_converged.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/entropy.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/exportfig.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/extend_domain_table.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/factorial.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/filepartsLast.m/1.1.1.1/Mon May 30 22:08:06 2005// +/find_equiv_posns.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/fullfileKPM.m/1.1.1.1/Sat Aug 27 01:08:50 2005// +/genpathKPM.m/1.1.1.1/Wed May 25 19:11:42 2005// +/hash_add.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/hash_del.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/hash_lookup.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/hsvKPM.m/1.1.1.1/Mon May 2 20:19:00 2005// +/hungarian.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/image_rgb.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/imresizeAspect.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/ind2subv.c/1.1.1.1/Tue Apr 26 02:30:32 2005// +/ind2subv.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/initFigures.m/1.1.1.1/Wed Jun 1 04:49:22 2005// +/installC_KPMtools.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/is_psd.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/is_stochastic.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/isemptycell.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/isposdef.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/isscalar.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/isvector.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/junk.c/1.1.1.1/Tue Apr 26 02:30:32 2005// +/loadcell.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/logb.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/logdet.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/logsum.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/logsum_simple.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/logsum_test.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/logsumexp.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/logsumexpv.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/mahal2conf.m/1.1.1.1/Wed Apr 27 17:58:32 2005// +/marg_table.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/marginalize_table.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/matprint.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/max_mult.c/1.1.1.1/Tue Apr 26 02:30:34 2005// +/max_mult.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/mexutil.c/1.1.1.1/Tue Apr 26 02:30:34 2005// +/mexutil.h/1.1.1.1/Tue Apr 26 02:30:34 2005// +/mk_multi_index.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/mk_stochastic.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/mkdirKPM.m/1.1.1.1/Mon May 9 22:20:22 2005// +/montageKPM.m/1.1.1.1/Wed Jun 1 19:39:54 2005// +/montageKPM2.m/1.1.1.1/Wed Jul 6 19:32:54 2005// +/montageKPM3.m/1.1.1.1/Tue Jun 28 01:35:44 2005// +/mult_by_table.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/myintersect.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/myismember.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/myones.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/myplot.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/myrand.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/myrepmat.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/myreshape.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/mysetdiff.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/mysize.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/mysubset.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/mysymsetdiff.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/myunion.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/nchoose2.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/ncols.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/nonmaxsup.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/normalise.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/normaliseC.c/1.1.1.1/Tue Apr 26 02:30:36 2005// +/normaliseC.dll/1.1.1.1/Tue Apr 26 02:30:36 2005// +/normalize.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/nrows.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/num2strcell.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/optimalMatching.m/1.1.1.1/Mon May 9 22:20:22 2005// +/optimalMatchingTest.m/1.1.1.1/Mon May 9 22:20:22 2005// +/partitionData.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/partition_matrix_vec.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/pca_kpm.m/1.1.1.1/Tue Sep 13 05:18:28 2005// +/pca_netlab.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/pick.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/plotBox.m/1.1.1.1/Mon May 30 06:01:56 2005// +/plotColors.m/1.1.1.1/Thu May 26 01:31:22 2005// +/plotROC.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/plotROCkpm.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/plot_axis_thru_origin.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/plot_ellipse.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/plot_matrix.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/plot_polygon.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/plotcov2.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/plotcov2New.m/1.1.1.1/Mon Jul 11 19:07:28 2005// +/plotcov3.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/plotgauss1d.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/plotgauss2d.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/plotgauss2d_old.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/polygon_area.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/polygon_centroid.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/polygon_intersect.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/previewfig.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/process_options.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/rand_psd.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/rectintC.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/rectintLoopC.c/1.1.1.1/Sun Jun 5 18:46:40 2005// +/rectintLoopC.dll/1.1.1.1/Sun Jun 5 18:46:40 2005// +/rectintLoopC.mexglx/1.1.1.1/Tue Apr 26 02:30:38 2005// +/rectintSparse.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/rectintSparseC.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/rectintSparseLoopC.c/1.1.1.1/Tue Apr 26 02:30:38 2005// +/rectintSparseLoopC.dll/1.1.1.1/Tue Apr 26 02:30:38 2005// +/repmatC.c/1.1.1.1/Tue Apr 26 02:30:38 2005// +/repmatC.dll/1.1.1.1/Tue Apr 26 02:30:38 2005// +/repmatC.mexglx/1.1.1.1/Tue Apr 26 02:30:38 2005// +/rgb2grayKPM.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/rnd_partition.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/rotate_xlabel.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/safeStr.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/sampleUniformInts.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/sample_discrete.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/set_xtick_label.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/set_xtick_label_demo.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/setdiag.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/softeye.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/sort_evec.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/splitLongSeqIntoManyShort.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/sprintf_intvec.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/sqdist.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/strmatch_multi.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/strmatch_substr.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/strsplit.m/1.1.1.1/Tue May 3 19:01:46 2005// +/subplot2.m/1.1.1.1/Tue Apr 26 02:30:40 2005// +/subplot3.m/1.1.1.1/Tue Apr 26 02:30:40 2005// +/subsets.m/1.1.1.1/Tue Apr 26 02:30:40 2005// +/subsets1.m/1.1.1.1/Mon May 9 22:20:22 2005// +/subsetsFixedSize.m/1.1.1.1/Mon May 9 02:55:36 2005// +/subv2ind.c/1.1.1.1/Tue Apr 26 02:30:40 2005// +/subv2ind.m/1.1.1.1/Tue Apr 26 02:30:40 2005// +/sumv.m/1.1.1.1/Tue Apr 26 02:30:40 2005// +/suptitle.m/1.1.1.1/Tue Apr 26 02:30:40 2005// +/unaryEncoding.m/1.1.1.1/Tue Apr 26 02:30:40 2005// +/wrap.m/1.1.1.1/Tue Apr 26 02:30:40 2005// +/xticklabel_rotate90.m/1.1.1.1/Tue Apr 26 02:30:40 2005// +/zipload.m/1.1.1.1/Tue Apr 26 02:30:40 2005// +/zipsave.m/1.1.1.1/Tue Apr 26 02:30:40 2005// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +FullBNT/KPMtools
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/README.txt Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,3 @@ +KPMtools is a directory of miscellaneous matlab functions written by +Kevin Patrick Murphy and various other people (see individual file headers). +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/approx_unique.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,41 @@ +function [B, keep] = approx_unique(A, thresh, flag) +% APPROX_UNIQUE Return elements of A that differ from the rest by less than thresh +% B = approx_unique(A, thresh) +% B = approx_unique(A, thresh, 'rows') + +keep = []; + +if nargin < 3 | isempty(flag) + A = sort(A) + B = A(1); + for i=2:length(A) + if ~approxeq(A(i), A(i-1), thresh) + B = [B A(i)]; + keep = [keep i]; + end + end +else +% A = sortrows(A); +% B = A(1,:); +% for i=2:size(A,1) +% if ~approxeq(A(i,:), A(i-1,:), thresh) +% B = [B; A(i,:)]; +% keep = [keep i]; +% end +% end + B = []; + for i=1:size(A,1) + duplicate = 0; + for j=i+1:size(A,1) + if approxeq(A(i,:), A(j,:), thresh) + duplicate = 1; + break; + end + end + if ~duplicate + B = [B; A(i,:)]; + keep = [keep i]; + end + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/approxeq.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,21 @@ +function p = approxeq(a, b, tol, rel) +% APPROXEQ Are a and b approximately equal (to within a specified tolerance)? +% p = approxeq(a, b, thresh) +% 'tol' defaults to 1e-3. +% p(i) = 1 iff abs(a(i) - b(i)) < thresh +% +% p = approxeq(a, b, thresh, 1) +% p(i) = 1 iff abs(a(i)-b(i))/abs(a(i)) < thresh + +if nargin < 3, tol = 1e-2; end +if nargin < 4, rel = 0; end + +a = a(:); +b = b(:); +d = abs(a-b); +if rel + p = ~any( (d ./ (abs(a)+eps)) > tol); +else + p = ~any(d > tol); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/argmax.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function indices = argmax(v) +% ARGMAX Return as a subscript vector the location of the largest element of a multidimensional array v. +% indices = argmax(v) +% +% Returns the first maximum in the case of ties. +% Example: +% X = [2 8 4; 7 3 9]; +% argmax(X) = [2 3], i.e., row 2 column 3 + +[m i] = max(v(:)); +indices = ind2subv(mysize(v), i);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/argmin.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +function indices = argmin(v) +% ARGMIN Return as a subscript vector the location of the smallest element of a multidimensional array v. +% indices = argmin(v) +% +% Returns the first minimum in the case of ties. +% Example: +% X = [2 8 4; 7 3 9]; +% argmin(X) = [1 1], i.e., row 1 column 1 + +[m i] = min(v(:)); +indices = ind2subv(mysize(v), i); +%indices = ind2subv(size(v), i);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/asdemo.html Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,690 @@ +<html xmlns:v="urn:schemas-microsoft-com:vml" +xmlns:o="urn:schemas-microsoft-com:office:office" +xmlns:w="urn:schemas-microsoft-com:office:word" +xmlns:st1="urn:schemas-microsoft-com:office:smarttags" +xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd" +xmlns="http://www.w3.org/TR/REC-html40"> + +<head> +<meta http-equiv=Content-Type content="text/html; charset=utf-8"> +<meta name=ProgId content=Word.Document> +<meta name=Generator content="Microsoft Word 10"> +<meta name=Originator content="Microsoft Word 10"> +<link rel=File-List href="asdemo_files/filelist.xml"> +<title>asdemo</title> +<o:SmartTagType namespaceuri="urn:schemas-microsoft-com:office:smarttags" + name="time"/> +<o:SmartTagType namespaceuri="urn:schemas-microsoft-com:office:smarttags" + name="date"/> +<o:SmartTagType namespaceuri="urn:schemas-microsoft-com:office:smarttags" + name="place"/> +<!--[if gte mso 9]><xml> + <o:DocumentProperties> + <o:Author>Caroline Schwarz</o:Author> + <o:Template>Normal</o:Template> + <o:LastAuthor>us</o:LastAuthor> + <o:Revision>33</o:Revision> + <o:TotalTime>0</o:TotalTime> + <o:Created>2005-03-26T16:20:00Z</o:Created> + <o:LastSaved>2005-03-30T09:58:00Z</o:LastSaved> + <o:Pages>1</o:Pages> + <o:Words>1062</o:Words> + <o:Characters>6055</o:Characters> + <o:Company>office</o:Company> + <o:Lines>50</o:Lines> + <o:Paragraphs>14</o:Paragraphs> + <o:CharactersWithSpaces>7103</o:CharactersWithSpaces> + <o:Version>10.3501</o:Version> + </o:DocumentProperties> + <o:OfficeDocumentSettings> + <o:RelyOnVML/> + <o:AllowPNG/> + <o:TargetScreenSize>1280x1024</o:TargetScreenSize> + </o:OfficeDocumentSettings> +</xml><![endif]--><!--[if gte mso 9]><xml> + <w:WordDocument> + <w:SpellingState>Clean</w:SpellingState> + <w:GrammarState>Clean</w:GrammarState> + <w:Compatibility> + <w:UseFELayout/> + </w:Compatibility> + <w:BrowserLevel>MicrosoftInternetExplorer4</w:BrowserLevel> + </w:WordDocument> +</xml><![endif]--><!--[if !mso]><object + classid="clsid:38481807-CA0E-42D2-BF39-B33AF135CC4D" id=ieooui></object> +<style> +st1\:*{behavior:url(#ieooui) } +</style> +<![endif]--> +<style> +<!--p + {max-width: 600px; + width:expression(document.body.clientWidth > 620 ? 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create some data</span></pre><pre style='margin-left:22.5pt'><b +style='mso-bidi-font-weight:normal'><span style='mso-tab-count:2'>         </span>d = {<o:p></o:p></b></pre><pre +style='margin-left:22.5pt'><span class=comment>%<span style='mso-tab-count: +2'>        </span>strings with one valid alphanumeric number</span></pre><pre +style='margin-left:22.5pt'><span class=comment>%<span style='mso-tab-count: +2'>        </span>sorted <b style='mso-bidi-font-weight:normal'>numerically</b></span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'-<span class=SpellE><span class=GramE>inf</span></span>'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'<span class=GramE>x-3.2e4y</span>'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'<span class=GramE>f-1.4</span>'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'-.1'</span></pre><pre style='margin-left:22.5pt'><span +style='mso-tab-count:3'>                 </span><span class=string>'+ <span +class=GramE>.1d-2</span>'</span></pre><pre style='margin-left:22.5pt'><span +style='mso-tab-count:3'>                 </span><span class=string>'.1'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'f.1'</span></pre><pre style='margin-left:22.5pt'><span +style='mso-tab-count:3'>                 </span><span class=string>'f -+1.4'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'f.2'</span></pre><pre style='margin-left:22.5pt'><span +style='mso-tab-count:3'>                 </span><span class=string>'f.3'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'f.10'</span></pre><pre style='margin-left:22.5pt'><span +style='mso-tab-count:3'>                 </span><span class=string>'f.11'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'+<span class=SpellE>inf</span>'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=GramE><span class=string>' -</span></span><span class=SpellE><span +class=string>nan</span></span><span class=string>'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'+ <span class=SpellE><span class=GramE>nan</span></span>'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'<span class=SpellE><span class=GramE>nan</span></span>'</span></pre><pre +style='margin-left:22.5pt'><span class=comment>%<span style='mso-tab-count: +2'>        </span>strings with many numbers or invalid/ambiguous numbers</span></pre><pre +style='margin-left:22.5pt'><span class=comment>%<span style='mso-tab-count: +2'>        </span>sorted in <span class=SpellE><span class=GramE><b +style='mso-bidi-font-weight:normal'>ascii</b></span></span><b style='mso-bidi-font-weight: +normal'> dictionary order</b></span></pre><pre style='margin-left:22.5pt'><span +style='mso-tab-count:3'>                 </span><span class=GramE><span +class=string>' <span class=SpellE>nan</span></span></span><span class=string> <span +class=SpellE>nan</span>'</span></pre><pre style='margin-left:22.5pt'><span +style='mso-tab-count:3'>                 </span><span class=string>'+ .1e-.2'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'-1 2'</span></pre><pre style='margin-left:22.5pt'><span +style='mso-tab-count:3'>                 </span><span class=string>'Z12e12ez'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'<span class=SpellE><span class=GramE>inf</span></span> -<span +class=SpellE>inf</span>'</span></pre><pre style='margin-left:22.5pt'><span +style='mso-tab-count:3'>                 </span><span class=string>'s.3TT.4'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'z12e12ez'</span></pre><pre style='margin-left:22.5pt'><span +class=comment>%<span style='mso-tab-count:2'>        </span>strings without numbers</span></pre><pre +style='margin-left:22.5pt'><span class=comment>%<span style='mso-tab-count: +2'>        </span>sorted in <span class=SpellE><span class=GramE><b +style='mso-bidi-font-weight:normal'>ascii</b></span></span><b style='mso-bidi-font-weight: +normal'> dictionary order</b></span></pre><pre style='margin-left:22.5pt'><span +style='mso-tab-count:3'>                 </span><span class=string>' . .. '</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'.'</span></pre><pre style='margin-left:22.5pt'><span +style='mso-tab-count:3'>                 </span><span class=string>'...'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=GramE><span class=string>'.b a.'</span></span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'<span class=GramE>a</span> string'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'a. .b'</span></pre><pre style='margin-left:22.5pt'><b +style='mso-bidi-font-weight:normal'><span style='mso-tab-count:2'>         </span>};<o:p></o:p></b></pre><pre +style='margin-left:22.5pt'><span class=comment>%<span style='mso-spacerun:yes'>  </span>... and scramble it...</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:2'>         </span><span +class=GramE>rand(</span><span class=string>'seed'</span>,10);</pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:2'>         </span>d=<span +class=GramE>d(</span><span class=SpellE>randperm</span>(<span class=SpellE>numel</span>(d)));</pre><pre +style='margin-left:22.5pt'><o:p> </o:p></pre><pre style='margin-left:22.5pt'><span +class=comment>% - run <b style='mso-bidi-font-weight:normal'>ASORT</b> with<o:p></o:p></span></pre><pre +style='margin-left:22.5pt'><span class=comment>%<span style='mso-spacerun:yes'>  </span><b +style='mso-bidi-font-weight:normal'>verbose output</b>:<span style='mso-tab-count: +2'>             </span><-v></span></pre><pre style='margin-left:22.5pt'><span +class=comment>%<span style='mso-spacerun:yes'>  </span><span class=GramE><b +style='mso-bidi-font-weight:normal'>keep</b></span><b style='mso-bidi-font-weight: +normal'> additional results</b>:<span style='mso-tab-count:1'>    </span><-d></span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:2'>         </span>o=<span +class=SpellE><span class=GramE>asort</span></span><span class=GramE>(</span>d,<span +class=string>'-v'</span>,<span class=string>'-d'</span>);</pre><pre +style='margin-left:22.5pt'><span class=comment>% - or</span></pre><pre +style='margin-left:22.5pt'><span class=comment>%<span style='mso-tab-count: +2'>        </span>p=<span class=SpellE><span class=GramE>asort</span></span><span +class=GramE>(</span>char(d),</span><span class=string>'-v'</span>,<span +class=string>'-d'</span>);</pre><pre style='margin-left:7.5pt'><i><span +style='color:gray'><o:p> </o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'INPUT'<span style='mso-spacerun:yes'>      </span>'ASCII SORT'<span style='mso-spacerun:yes'>   </span>'NUM SORT'<span style='mso-spacerun:yes'>            </span>'NUM READ'<span style='mso-spacerun:yes'>        </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'...'<span style='mso-spacerun:yes'>        </span><span +class=SpellE><span class=GramE>'</span></span><span class=GramE> -</span><span +class=SpellE>nan</span>'<span style='mso-spacerun:yes'>        </span>'--- NUMERICAL'<span style='mso-spacerun:yes'>       </span>'--- NUMBERS'<span style='mso-spacerun:yes'>     </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'+ .1e-.2'<span style='mso-spacerun:yes'>   </span><span +class=GramE>' .</span> .. '<span style='mso-spacerun:yes'>       </span>'-<span +class=SpellE><span class=GramE>inf</span></span>'<span style='mso-spacerun:yes'>        </span><span style='mso-spacerun:yes'>        </span>[<span style='mso-spacerun:yes'>            </span>-<span +class=SpellE>Inf</span>]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'.1'<span style='mso-spacerun:yes'>         </span>' <span +class=SpellE>nan</span> <span class=SpellE>nan</span>'<span style='mso-spacerun:yes'>     </span>'x-3.2e4y'<span style='mso-spacerun:yes'>            </span>[<span style='mso-spacerun:yes'>          </span>-32000]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'.b a.'<span style='mso-spacerun:yes'>      </span>'+ .1d-2'<span style='mso-spacerun:yes'>      </span>'f-1.4'<span style='mso-spacerun:yes'>               </span>[<span style='mso-spacerun:yes'>            </span>-1.4]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'-<span +class=SpellE>inf</span>'<span style='mso-spacerun:yes'>       </span>'+ .1e-.2'<span style='mso-spacerun:yes'>     </span>'-.1'<span style='mso-spacerun:yes'>                 </span>[<span style='mso-spacerun:yes'>            </span>-0.1]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'f.1'<span style='mso-spacerun:yes'>        </span>'+ <span +class=SpellE><span class=GramE>nan</span></span>'<span style='mso-spacerun:yes'>        </span>'+ .1d-2'<span style='mso-spacerun:yes'>             </span>[<span style='mso-spacerun:yes'>           </span>0.001]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span><span class=GramE>' -</span><span +class=SpellE>nan</span>'<span style='mso-spacerun:yes'>      </span>'+<span +class=SpellE>inf</span>'<span style='mso-spacerun:yes'>         </span>'.1'<span style='mso-spacerun:yes'>                  </span>[<span style='mso-spacerun:yes'>             </span>0.1]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'-1 2'<span style='mso-spacerun:yes'>       </span>'-.1'<span style='mso-spacerun:yes'>          </span>'f.1'<span style='mso-spacerun:yes'>                 </span>[<span style='mso-spacerun:yes'>               </span>1]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'<span +class=SpellE><span class=GramE>nan</span></span>'<span style='mso-spacerun:yes'>        </span>'-1 2'<span style='mso-spacerun:yes'>         </span>'f -+1.4'<span style='mso-spacerun:yes'>             </span>[<span style='mso-spacerun:yes'>             </span>1.4]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'<span +class=GramE>a</span> string'<span style='mso-spacerun:yes'>   </span>'-<span +class=SpellE>inf</span>'<span style='mso-spacerun:yes'>         </span>'f.2'<span style='mso-spacerun:yes'>                 </span>[<span style='mso-spacerun:yes'>               </span>2]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span><span class=GramE>'f.3'<span style='mso-spacerun:yes'>        </span>'.'</span><span style='mso-spacerun:yes'>            </span>'f.3'<span style='mso-spacerun:yes'>                 </span>[<span style='mso-spacerun:yes'>               </span>3]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'+ <span +class=GramE>.1d-2</span>'<span style='mso-spacerun:yes'>    </span>'...'<span style='mso-spacerun:yes'>          </span>'f.10'<span style='mso-spacerun:yes'>  </span><span style='mso-spacerun:yes'>              </span>[<span style='mso-spacerun:yes'>              </span>10]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'a. .b'<span style='mso-spacerun:yes'>      </span>'.1'<span style='mso-spacerun:yes'>           </span>'f.11'<span style='mso-spacerun:yes'>                </span>[<span style='mso-spacerun:yes'>    </span><span style='mso-spacerun:yes'>          </span>11]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'s.3TT.4'<span style='mso-spacerun:yes'>    </span>'.b a.'<span style='mso-spacerun:yes'>    </span><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>   </span>'+<span +class=SpellE>inf</span>'<span style='mso-spacerun:yes'>                </span>[<span style='mso-spacerun:yes'>             </span><span +class=SpellE><span class=GramE>Inf</span></span>]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'>   </span>'+<span +class=SpellE>inf</span>'<span style='mso-spacerun:yes'>       </span>'Z12e12ez'<span style='mso-spacerun:yes'>     </span>' -<span +class=SpellE>nan</span>'<span style='mso-spacerun:yes'>               </span>[<span style='mso-spacerun:yes'>         </span><span style='mso-spacerun:yes'>    </span></span></i><st1:place><span + class=SpellE><i><span style='color:gray'>NaN</span></i></span></st1:place><i><span +style='color:gray'>]<o:p></o:p></span></i></pre><pre><i><span style='color: +gray'> <span style='mso-spacerun:yes'>   </span><span class=GramE>' <span +class=SpellE>nan</span></span> <span class=SpellE>nan</span>'<span style='mso-spacerun:yes'>   </span>'a string'<span style='mso-spacerun:yes'>     </span>'+ <span +class=SpellE>nan</span>'<span style='mso-spacerun:yes'>               </span>[<span style='mso-spacerun:yes'>             </span></span></i><st1:place><span + class=SpellE><i><span style='color:gray'>NaN</span></i></span></st1:place><i><span +style='color:gray'>]<o:p></o:p></span></i></pre><pre><i><span style='color: +gray'><span style='mso-spacerun:yes'>   </span>'<span class=GramE>f-1.4</span>'<span style='mso-spacerun:yes'>      </span>'a. .<span +class=GramE>b</span>'<span style='mso-spacerun:yes'>   </span><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>    </span>'<span +class=SpellE>nan</span>'<span style='mso-spacerun:yes'>                 </span>[<span style='mso-spacerun:yes'>             </span></span></i><st1:place><span + class=SpellE><i><span style='color:gray'>NaN</span></i></span></st1:place><i><span +style='color:gray'>]<o:p></o:p></span></i></pre><pre><i><span style='color: +gray'><span style='mso-spacerun:yes'>   </span>'x-3.2e4y'<span style='mso-spacerun:yes'>   </span>'f -+1.4'<span style='mso-spacerun:yes'>    </span><span style='mso-spacerun:yes'>  </span>'--- ASCII NUMBERS'<span style='mso-spacerun:yes'>   </span>'--- ASCII NUMBERS'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'<span +class=SpellE><span class=GramE>inf</span></span> -<span class=SpellE>inf</span>'<span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>  </span>'f-1.4'<span style='mso-spacerun:yes'>        </span>' <span +class=SpellE>nan</span> <span class=SpellE>nan</span>'<span style='mso-spacerun:yes'>            </span>' <span +class=SpellE>nan</span> <span class=SpellE>nan</span>'<span style='mso-spacerun:yes'>        </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'+ <span +class=SpellE><span class=GramE>nan</span></span>'<span style='mso-spacerun:yes'>      </span>'f.1'<span style='mso-spacerun:yes'>          </span>'+ .1e-.2'<span style='mso-spacerun:yes'>            </span>'+ .1e-.2'<span style='mso-spacerun:yes'>        </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'f.2'<span style='mso-spacerun:yes'>        </span>'f.10'<span style='mso-spacerun:yes'>         </span>'-1 2'<span style='mso-spacerun:yes'>                </span>'-1 2'<span style='mso-spacerun:yes'>            </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'f.11'<span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>     </span><span +class=SpellE>'f.11'</span><span style='mso-spacerun:yes'>         </span>'Z12e12ez'<span style='mso-spacerun:yes'>            </span><span +class=SpellE>'Z12e12ez'</span><span style='mso-spacerun:yes'>        </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'Z12e12ez'<span style='mso-spacerun:yes'>   </span>'f.2'<span style='mso-spacerun:yes'>     </span><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>    </span>'<span +class=SpellE>inf</span> -<span class=SpellE>inf</span>'<span style='mso-spacerun:yes'>            </span>'<span +class=SpellE>inf</span> -<span class=SpellE>inf</span>'<span style='mso-spacerun:yes'>        </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'z12e12ez'<span style='mso-spacerun:yes'>   </span>'f.3'<span style='mso-spacerun:yes'>          </span>'s.3TT.4'<span style='mso-spacerun:yes'>             </span><span +class=SpellE>'s.3TT.4'</span><span style='mso-spacerun:yes'>         </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'f -+1.4'<span style='mso-spacerun:yes'>    </span>'<span +class=SpellE>inf</span> -<span class=SpellE>inf</span>'<span style='mso-spacerun:yes'>     </span>'z12e12ez'<span style='mso-spacerun:yes'>            </span><span +class=SpellE>'z12e12ez'</span><span style='mso-spacerun:yes'>        </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>' . .. '<span style='mso-spacerun:yes'>     </span>'<span +class=SpellE><span class=GramE>nan</span></span>'<span style='mso-spacerun:yes'>          </span>'--- ASCII STRINGS'<span style='mso-spacerun:yes'>   </span>'--- ASCII STRINGS'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'f.10'<span style='mso-spacerun:yes'>       </span>'s.3TT.4'<span style='mso-spacerun:yes'>      </span><span +class=GramE>' .</span> .. '<span style='mso-spacerun:yes'>        </span><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>     </span><span +class=SpellE>'</span> . .. '<span style='mso-spacerun:yes'>          </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'.'<span style='mso-spacerun:yes'>          </span><span +class=GramE>'x-3.2e4y'<span style='mso-spacerun:yes'>     </span>'.'</span><span style='mso-spacerun:yes'>                   </span><span +class=SpellE>'.'</span><span style='mso-spacerun:yes'>               </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'-.1'<span style='mso-spacerun:yes'>    </span><span style='mso-spacerun:yes'>    </span>'z12e12ez'<span style='mso-spacerun:yes'>     </span>'...'<span style='mso-spacerun:yes'>                 </span><span +class=SpellE>'</span>...'<span style='mso-spacerun:yes'>             </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span><span class=GramE>' <span +class=SpellE>'</span></span><span style='mso-spacerun:yes'>          </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span>'.b a.'<span style='mso-spacerun:yes'>               </span>'.b a.'<span style='mso-spacerun:yes'>           </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>   </span><span +class=GramE>' <span class=SpellE>'</span></span><span style='mso-spacerun:yes'>          </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span>'a string'<span style='mso-spacerun:yes'>            </span>'a string'<span style='mso-spacerun:yes'>        </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>   </span><span +class=GramE>' <span class=SpellE>'</span></span><span style='mso-spacerun:yes'>          </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span>'a. .b'<span style='mso-spacerun:yes'>               </span>'a. .<span +class=GramE>b</span>'<span style='mso-spacerun:yes'>           </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><o:p> </o:p></span></i></pre><pre style='margin-left: +22.5pt'><span class=comment>% - show results</span></pre><pre style='margin-left: +22.5pt'><span style='mso-tab-count:2'>         </span><span class=GramE>o</span></pre><pre><i><span +style='color:gray'>o = <o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>          </span><span +class=GramE>magic</span>: 'ASORT'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>     </span><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>     </span><span +class=SpellE><span class=GramE>ver</span></span>: '</span></i><st1:date +Year="2005" Day="30" Month="3"><i><span style='color:gray'>30-Mar-2005</span></i></st1:date><i><span +style='color:gray'> </span></i><st1:time Minute="57" Hour="11"><i><span + style='color:gray'>11:57:07</span></i></st1:time><i><span style='color:gray'>'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>          </span>time: '30-Mar-2005 11:57:17'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>       </span><span +class=GramE>runtime</span>: 0.047<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span><span +class=SpellE>input_class</span>: 'cell'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span><span +class=SpellE>input_msize</span>: [29 1]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span><span +class=SpellE>input_bytes</span>: 2038<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span><span +class=SpellE>strng_class</span>: 'char'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span><span +class=SpellE>strng_msize</span>: [29 8]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span><span +class=SpellE>strng_bytes</span>: 464<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>           </span><span +class=SpellE><span class=GramE>anr</span></span>: {16x1 cell}<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>     </span><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>     </span><span +class=SpellE><span class=GramE>snr</span></span>: {7x1 cell}<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>     </span><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>     </span><span +class=SpellE><span class=GramE>str</span></span>: {6x1 cell}<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>             </span><span +class=GramE>c</span>: [29x12 char]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>             </span><span +class=GramE>t</span>: [29x12 logical]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>             </span><span +class=GramE>n</span>: [16x12 char]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>           </span><span +class=GramE>d</span>: [16x1 double]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><o:p> </o:p></span></i></pre><pre style='margin-left: +22.5pt'><span style='mso-tab-count:2'>         </span><span class=SpellE>o.anr</span></pre><pre><span +class=SpellE><span class=GramE><i><span style='color:gray'>ans</span></i></span></span><i><span +style='color:gray'> = <o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>   </span>'-<span +class=SpellE><span class=GramE>inf</span></span>'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'<span +class=GramE>x-3.2e4y</span>'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'<span +class=GramE>f-1.4</span>'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'-.1'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'+ <span +class=GramE>.1d-2</span>'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'.1'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'f.1'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'f -+1.4'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'f.2'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'f.3'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'f.10'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'f.11'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'>   </span>'+<span +class=SpellE>inf</span>'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span><span class=GramE>' -</span><span +class=SpellE>nan</span>'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>  </span>'+ <span +class=SpellE><span class=GramE>nan</span></span>'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'<span +class=SpellE><span class=GramE>nan</span></span>'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><o:p> </o:p></span></i></pre><pre style='margin-left: +22.5pt'><span class=comment>% - run <b style='mso-bidi-font-weight:normal'>ASORT</b> with <b +style='mso-bidi-font-weight:normal'>no-space</b>/<b style='mso-bidi-font-weight: +normal'>template</b> options</span></pre><pre style='margin-left:22.5pt'><span +class=comment>%<span style='mso-spacerun:yes'>  </span><span class=GramE>NOTE</span> the impact of -w/-t order!</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:2'>         </span>s<span +class=GramE>={</span><span class=string>'ff - 1'</span>,<span class=string>'ff + 1'</span>,<span +class=string>'- 12'</span>};</pre><pre><i><span style='color:gray'><o:p> </o:p></span></i></pre><pre +style='margin-left:22.5pt'><span class=comment>%<span style='mso-spacerun:yes'>  </span>RAW</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:2'>         </span>o=<span +class=SpellE>asort</span>(s,<span class=string>'-v'</span>);</pre><pre><i><span +style='color:gray'><o:p> </o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'INPUT'<span style='mso-spacerun:yes'>    </span>'ASCII SORT'<span style='mso-spacerun:yes'>   </span>'NUM SORT'<span style='mso-spacerun:yes'>            </span>'NUM READ'<span style='mso-spacerun:yes'>        </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'<span +class=GramE>ff</span> - 1'<span style='mso-spacerun:yes'>   </span>'- 12'<span style='mso-spacerun:yes'>         </span>'--- NUMERICAL'<span style='mso-spacerun:yes'>       </span>'--- NUMBERS'<span style='mso-spacerun:yes'>     </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'<span +class=GramE>ff</span> + 1'<span style='mso-spacerun:yes'>   </span>'ff + 1'<span style='mso-spacerun:yes'>       </span>'ff + 1'<span style='mso-spacerun:yes'>              </span>[<span style='mso-spacerun:yes'>               </span>1]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'- 12'<span style='mso-spacerun:yes'>     </span>'ff - 1'<span style='mso-spacerun:yes'>       </span>'ff - 1'<span style='mso-spacerun:yes'>              </span>[<span style='mso-spacerun:yes'>               </span>1]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'>   </span><span class=GramE>' <span +class=SpellE>'</span></span><span style='mso-spacerun:yes'>        </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span><span +class=SpellE>'</span>- 12'<span style='mso-spacerun:yes'>                </span>[<span style='mso-spacerun:yes'>              </span>12]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'>   </span><span class=GramE>' <span +class=SpellE>'</span></span><span style='mso-spacerun:yes'>        </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>   </span><span style='mso-spacerun:yes'>   </span><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>     </span><span +class=SpellE>'</span>--- ASCII NUMBERS'<span style='mso-spacerun:yes'>   </span>'--- ASCII NUMBERS'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'>   </span><span class=GramE>' <span +class=SpellE>'</span></span><span style='mso-spacerun:yes'>        </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span><span +class=SpellE>'</span>--- ASCII STRINGS'<span style='mso-spacerun:yes'>   </span>'--- ASCII STRINGS'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><o:p> </o:p></span></i></pre><pre style='margin-left: +22.5pt'><span class=comment>%<span style='mso-spacerun:yes'>  </span><span +class=GramE>remove</span> <span class=SpellE><b style='mso-bidi-font-weight: +normal'>SPACE</b>s</span></span></pre><pre style='margin-left:22.5pt'><span +style='mso-tab-count:2'>         </span>o=<span class=SpellE>asort</span>(s,<span +class=string>'-v'</span>,<span class=string>'-w'</span>);</pre><pre><i><span +style='color:gray'><o:p> </o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'INPUT'<span style='mso-spacerun:yes'>   </span>'ASCII SORT'<span style='mso-spacerun:yes'>   </span>'NUM SORT'<span style='mso-spacerun:yes'>            </span>'NUM READ'<span style='mso-spacerun:yes'>        </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'ff-1' <span style='mso-spacerun:yes'>    </span>'-12'<span style='mso-spacerun:yes'>          </span>'--- NUMERICAL'<span style='mso-spacerun:yes'>       </span>'--- NUMBERS'<span style='mso-spacerun:yes'>     </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'<span +class=GramE>ff+</span>1'<span style='mso-spacerun:yes'>    </span><span +class=SpellE>'ff+1'</span><span style='mso-spacerun:yes'>         </span>'-12'<span style='mso-spacerun:yes'>                 </span>[<span style='mso-spacerun:yes'>             </span>-12]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'-12'<span style='mso-spacerun:yes'>     </span>'ff-1'<span style='mso-spacerun:yes'>   </span><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>     </span><span +class=SpellE>'ff-1'</span><span style='mso-spacerun:yes'>                </span>[<span style='mso-spacerun:yes'>              </span>-1]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'>   </span><span class=GramE>' <span +class=SpellE>'</span></span><span style='mso-spacerun:yes'>       </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span>'ff+1'<span style='mso-spacerun:yes'>       </span><span style='mso-spacerun:yes'>         </span>[<span style='mso-spacerun:yes'>               </span>1]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'>   </span><span class=GramE>' <span +class=SpellE>'</span></span><span style='mso-spacerun:yes'>       </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span><span +class=SpellE>'</span>--- ASCII NUMBERS'<span style='mso-spacerun:yes'>   </span>'--- ASCII NUMBERS'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'>   </span><span class=GramE>' <span +class=SpellE>'</span></span><span style='mso-spacerun:yes'>       </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span><span +class=SpellE>'</span>--- ASCII STRINGS'<span style='mso-spacerun:yes'>   </span>'--- ASCII STRINGS'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><o:p> </o:p></span></i></pre><pre style='margin-left: +22.5pt'><span class=comment>%<span style='mso-spacerun:yes'>  </span><span +class=GramE>remove</span> <b style='mso-bidi-font-weight:normal'>TEMPLATE</b>(s)</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:2'>         </span>o=<span +class=SpellE>asort</span>(s,<span class=string>'-v'</span>,<span class=string>'-t'</span><span +class=GramE>,{</span><span class=string>'ff'</span>,<span class=string>'1'</span>});</pre><pre><i><span +style='color:gray'><o:p> </o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'INPUT'<span style='mso-spacerun:yes'>   </span>'ASCII SORT'<span style='mso-spacerun:yes'>   </span>'NUM SORT'<span style='mso-spacerun:yes'>            </span>'NUM READ'<span style='mso-spacerun:yes'>        </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span><span class=GramE>' -</span> ' <span style='mso-spacerun:yes'>     </span><span +class=SpellE>'</span> + '<span style='mso-spacerun:yes'>          </span><span +class=SpellE>'</span>--- NUMERICAL'<span style='mso-spacerun:yes'>       </span>'--- NUMBERS'<span style='mso-spacerun:yes'>     </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span><span class=GramE>' +</span> ' <span style='mso-spacerun:yes'>     </span><span +class=SpellE>'</span> - '<span style='mso-spacerun:yes'>          </span><span +class=SpellE>'</span>- 2'<span style='mso-spacerun:yes'>                 </span>[<span style='mso-spacerun:yes'>               </span>2]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'- 2'<span style='mso-spacerun:yes'>     </span>'- 2'<span style='mso-spacerun:yes'>          </span>'--- ASCII NUMBERS'<span style='mso-spacerun:yes'>  </span><span style='mso-spacerun:yes'> </span>'--- ASCII NUMBERS'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'>   </span><span class=GramE>' <span +class=SpellE>'</span></span><span style='mso-spacerun:yes'>       </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span><span +class=SpellE>'</span>--- ASCII STRINGS'<span style='mso-spacerun:yes'>   </span>'--- ASCII STRINGS'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'>   </span>' <span +class=SpellE>'</span><span style='mso-spacerun:yes'>       </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span><span +class=SpellE>'</span> + '<span style='mso-spacerun:yes'>                 </span><span +class=SpellE>'</span> + '<span style='mso-spacerun:yes'>             </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>   </span>' <span +class=SpellE>'</span><span style='mso-spacerun:yes'>       </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span><span +class=SpellE>'</span> - '<span style='mso-spacerun:yes'>                 </span><span +class=SpellE>'</span> - '<span style='mso-spacerun:yes'>             </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><o:p> </o:p></span></i></pre><pre style='margin-left: +22.5pt'><span class=comment>%<span style='mso-spacerun:yes'>  </span><span +class=GramE>remove</span> <b style='mso-bidi-font-weight:normal'>TEMPLATE</b>(s) than <span +class=SpellE><b style='mso-bidi-font-weight:normal'>SPACE</b>s</span></span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:2'>         </span>o=<span +class=SpellE>asort</span>(s,<span class=string>'-v'</span>,<span class=string>'-t'</span>,<span +class=string>'1'</span>,<span class=string>'-w'</span>);</pre><pre><i><span +style='color:gray'><o:p> </o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'INPUT'<span style='mso-spacerun:yes'>   </span>'ASCII SORT'<span style='mso-spacerun:yes'>   </span>'NUM SORT'<span style='mso-spacerun:yes'>            </span>'NUM READ'<span style='mso-spacerun:yes'>        </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'ff-'<span style='mso-spacerun:yes'>     </span>'-2'<span style='mso-spacerun:yes'>           </span>'--- NUMERICAL'<span style='mso-spacerun:yes'>       </span>'--- NUMBERS'<span style='mso-spacerun:yes'>     </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'ff+' <span style='mso-spacerun:yes'>     </span><span +class=SpellE>'ff+'</span><span style='mso-spacerun:yes'>          </span>'-2'<span style='mso-spacerun:yes'>               </span><span style='mso-spacerun:yes'>   </span>[<span style='mso-spacerun:yes'>              </span>-2]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'-2'<span style='mso-spacerun:yes'>      </span>'ff-'<span style='mso-spacerun:yes'>          </span>'--- ASCII NUMBERS'<span style='mso-spacerun:yes'>   </span>'--- ASCII NUMBERS'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'>   </span><span class=GramE>' <span +class=SpellE>'</span></span><span style='mso-spacerun:yes'>       </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span><span +class=SpellE>'</span>--- ASCII STRINGS'<span style='mso-spacerun:yes'>   </span>'--- ASCII STRINGS'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'>   </span><span class=GramE>' <span +class=SpellE>'</span></span><span style='mso-spacerun:yes'>       </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span>'ff+'<span style='mso-spacerun:yes'>                 </span><span +class=SpellE>'ff+'</span><span style='mso-spacerun:yes'>             </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span><span class=GramE>' <span +class=SpellE>'</span></span><span style='mso-spacerun:yes'>   </span><span style='mso-spacerun:yes'>    </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span>'ff-'<span style='mso-spacerun:yes'>                 </span><span +class=SpellE>'ff-'</span><span style='mso-spacerun:yes'>             </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><o:p> </o:p></span></i></pre><pre style='margin-left: +22.5pt'><span class=comment>%<span style='mso-spacerun:yes'>  </span><span +class=GramE>remove</span> <span class=SpellE><b style='mso-bidi-font-weight: +normal'>SPACE</b>s</span> than <b style='mso-bidi-font-weight:normal'>TEMPLATE</b>(s)</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:2'>         </span>o=<span +class=SpellE>asort</span>(s,<span class=string>'-v'</span>,<span class=string>'-w'</span>,<span +class=string>'-t'</span>,<span class=string>'1'</span>);</pre><pre><i><span +style='color:gray'><o:p> </o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'INPUT'<span style='mso-spacerun:yes'>   </span>'ASCII SORT'<span style='mso-spacerun:yes'>   </span>'NUM SORT'<span style='mso-spacerun:yes'>            </span>'NUM READ'<span style='mso-spacerun:yes'>        </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'ff- '<span style='mso-spacerun:yes'>    </span><span +class=SpellE>'</span>- 2'<span style='mso-spacerun:yes'>          </span>'--- NUMERICAL'<span style='mso-spacerun:yes'>  </span><span style='mso-spacerun:yes'>     </span>'--- NUMBERS'<span style='mso-spacerun:yes'>     </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'ff+ '<span style='mso-spacerun:yes'>    </span>'ff+ '<span style='mso-spacerun:yes'>   </span><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>     </span><span +class=SpellE>'</span>- 2'<span style='mso-spacerun:yes'>                 </span>[<span style='mso-spacerun:yes'>               </span>2]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'- 2'<span style='mso-spacerun:yes'>     </span>'ff- '<span style='mso-spacerun:yes'>   </span><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>     </span><span +class=SpellE>'</span>--- ASCII NUMBERS'<span style='mso-spacerun:yes'>   </span>'--- ASCII NUMBERS'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'>   </span><span class=GramE>' <span +class=SpellE>'</span></span><span style='mso-spacerun:yes'>       </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span><span +class=SpellE>'</span>--- ASCII STRINGS'<span style='mso-spacerun:yes'>   </span>'--- ASCII STRINGS'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'>   </span><span class=GramE>' <span +class=SpellE>'</span></span><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>      </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span>'ff+ '<span style='mso-spacerun:yes'>                </span>'ff+ '<span style='mso-spacerun:yes'>            </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>   </span><span +class=GramE>' <span class=SpellE>'</span></span><span style='mso-spacerun:yes'>       </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span>'ff- '<span style='mso-spacerun:yes'>                </span>'ff- '<span style='mso-spacerun:yes'>            </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><o:p> </o:p></span></i></pre> + +<p class=footer style='tab-stops:45.8pt 91.6pt 137.4pt 183.2pt 229.0pt 274.8pt 320.6pt 366.4pt 412.2pt 458.0pt 503.8pt 549.6pt 595.4pt 641.2pt 687.0pt 732.8pt'><br> +Published with MATLAB® 7.0.4<o:p></o:p></p> + +</div> + +<!-- +##### SOURCE BEGIN ##### +% ASORT +% a pedestrian NUMERICAL SORTER of ALPHANUMERIC data + +% - create some data + d = { +% strings with one valid alphanumeric number +% sorted numerically + '-inf' + 'x-3.2e4y' + 'f-1.4' + '-.1' + '+ .1d-2' + '.1' + 'f.1' + 'f -+1.4' + 'f.2' + 'f.3' + 'f.10' + 'f.11' + '+inf' + ' -nan' + '+ nan' + 'nan' +% strings with many numbers or invalid/ambiguous numbers +% sorted in ascii dictionary order + ' nan nan' + '+ .1e-.2' + '-1 2' + 'Z12e12ez' + 'inf -inf' + 's.3TT.4' + 'z12e12ez' +% strings without numbers +% sorted in ascii dictionary order + ' . .. ' + '.' + '...' + '.b a.' + 'a string' + 'a. .b' + }; +% ... and scramble it... + rand('seed',10); + d=d(randperm(numel(d))); + +% - run ASORT with verbose output +% and keep additional results + o=asort(d,'-v','-d'); +% - or +% p=asort(char(d)); + +% - show results + o + o.anr + +% - run ASORT with no-space/template options +% NOTE the impact of -t/-w order! + s={'ff - 1','ff + 1','- 12'}; +% RAW + o=asort(s,'-v'); +% remove SPACEs + o=asort(s,'-v','-w'); +% remove TEMPLATE(s) + o=asort(s,'-v','-t',{'ff','1'}); +% remove TEMPLATE(s) than SPACEs + o=asort(s,'-v','-t','1','-w'); +% remove SPACEs than TEMPLATE(s) + o=asort(s,'-v','-w','-t','1'); + + +##### SOURCE END ##### +--> +</body> + +</html>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/asdemo.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,70 @@ +% ASORT +% a pedestrian NUMERICAL SORTER of ALPHANUMERIC data + +% - create some data + d = { +% strings with one valid alphanumeric number +% sorted numerically + '-inf' + 'x-3.2e4y' + 'f-1.4' + '-.1' + '+ .1d-2' + '.1' + 'f.1' + 'f -+1.4' + 'f.2' + 'f.3' + 'f.10' + 'f.11' + '+inf' + ' -nan' + '+ nan' + 'nan' +% strings with many numbers or invalid/ambiguous numbers +% sorted in ascii dictionary order + ' nan nan' + '+ .1e-.2' + '-1 2' + 'Z12e12ez' + 'inf -inf' + 's.3TT.4' + 'z12e12ez' +% strings without numbers +% sorted in ascii dictionary order + ' . .. ' + '.' + '...' + '.b a.' + 'a string' + 'a. .b' + }; +% ... and scramble it... + rand('seed',10); + d=d(randperm(numel(d))); + +% - run ASORT with +% verbose output: <-v> +% keep additional results: <-d> + o=asort(d,'-v','-d'); +% - or +% p=asort(char(d),'-v','-d'); + +% - show results + o + o.anr + +% - run ASORT with no-space/template options +% NOTE the impact of -w/-t order! + s={'ff - 1','ff + 1','- 12'}; +% RAW + o=asort(s,'-v'); +% remove SPACEs + o=asort(s,'-v','-w'); +% remove TEMPLATE(s) + o=asort(s,'-v','-t',{'ff','1'}); +% remove TEMPLATE(s) than SPACEs + o=asort(s,'-v','-t','1','-w'); +% remove SPACEs than TEMPLATE(s) + o=asort(s,'-v','-w','-t','1'); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/asort.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,376 @@ +%[ANR,SNR,STR] = ASORT(INP,'OPT',...); +% S = ASORT(INP,'OPT',...); +% to sort alphanumeric strings numerically if +% they contain one properly formatted number +% otherwise, ascii dictionary sorting is applied +% +% INP unsorted input: +% - a char array +% - a cell array of strings +% OPT options +% -s - sorting option +% '-s','ascend' [def] +% '-s','descend' +% -st - force output form S [def: nargout dependent] +% -t - replace matching template(s) with one space +% prior to sorting +% '-t','template' +% '-t',{'template1','template2',...} +% -w - remove space(s) prior to sorting +% +% NOTE -t/-w options are processed in the +% order that they appear in +% the command line +% +% -v - verbose output [def: quiet] +% -d - debug mode +% save additional output in S +% .c: lex parser input +% .t: lex parser table +% .n: lex parser output +% .d: numbers read from .n +% +% ANR numerically sorted alphanumeric strings [eg, 'f.-1.5e+2x.x'] +% - contain one number that can be read by +% <strread> | <sscanf> +% SNR ascii dict sorted alphanumeric strings +% http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=7212# +% +% - contain more than one number [eg, 'f.-1.5e +2.x'] +% - contain incomplete|ambiguous numbers [eg, 'f.-1.5e+2.x'] +% STR ascii dict sorted strings +% - contain no numbers [eg, 'a test'] +% +% S structure with fields +% .anr +% .srn +% .str + +% created: +% us 03-Mar-2002 +% modified: +% us 30-Mar-2005 11:57:07 / TMW R14.sp2 + +%-------------------------------------------------------------------------------- +function varargout=asort(inp,varargin) + +varargout(1:nargout)={[]}; +if ~nargin + help(mfilename); + return; +end + +% - common parameters/options +n=[]; +ds=[]; +anr={}; +snr={}; +str={}; +smod='ascend'; % sorting option +tmpl={}; % template(s) +sflg=false; % output mode: structure +tflg=false; % remove template(s) +dflg=false; % debug mode +vflg=false; % verbose output +wflg=false; % remove spaces + +if nargin > 1 + ix=find(strcmp('-s',varargin)); + if ~isempty(ix) && nargin > ix(end)+1 + smod=varargin{ix(end)+1}; + end + ix=find(strcmp('-t',varargin)); + if ~isempty(ix) && nargin > ix(end)+1 + tflg=ix(end); + tmpl=varargin{ix(end)+1}; + end + if find(strcmp('-d',varargin)); + dflg=true; + end + if find(strcmp('-st',varargin)); + sflg=true; + end + if find(strcmp('-v',varargin)); + vflg=true; + end + ix=find(strcmp('-w',varargin)); + if ~isempty(ix) + wflg=ix(end); + end +end +% spec numbers +ntmpl={ + ' inf ' + '+inf ' + '-inf ' + ' nan ' + '+nan ' + '-nan ' + }; +% spec chars +ctmpl={ + '.' % decimal point + 'd' % exponent + 'e' % exponent + }; + +if nargout <= 3 + varargout{1}=inp; +else + disp(sprintf('ASORT> too many output args [%-1d/%-1d]\n',nargout,3)); + help(mfilename); + return; +end +if isempty(inp) + disp(sprintf('ASORT> input is empty')); + return; +end + +ti=clock; +winp=whos('inp'); +switch winp.class + case 'cell' + if ~iscellstr(inp) + disp(sprintf('ASORT> cell is not an array of strings')); + return; + end + inp=inp(:); + [ins,inx]=sort(inp); + case 'char' + % [ins,inx]=sortrows(inp); + inp=cstr(inp); + otherwise + disp(sprintf('ASORT> does not sort input of class <%s>',winp.class)); + return; +end + +inp=inp(:); +inp=setinp(inp,tmpl,[tflg wflg]); +[ins,inx]=sort(inp); +if strcmp(smod,'descend') + ins=ins(end:-1:1,:); + inx=inx(end:-1:1); +end +ins=inp(inx); +c=lower(char(ins)); +wins=whos('c'); +[cr,cc]=size(c); + +% - LEXICAL PARSER +%-------------------------------------------------------------------------------- +% - extend input on either side for search +c=[' '*ones(cr,2) c ' '*ones(cr,2)]; + +% - search for valid alphanumeric items in strings +% numbers/signs +t=(c>='0'&c<='9'); +t=t|c=='-'; +t=t|c=='+'; +[tr,tc]=size(t); +% decimal points +% note: valid numbers with dec points must follow these templates +% nr.nr +% sign.nr +% nr.<SPACE> +% <SPACE>.nr +ix1= t(:,1:end-2) & ... + ~isletter(c(:,1:end-2)) & ... + c(:,2:end-1)=='.'; +t(:,2:end-1)=t(:,2:end-1)|ix1; +ix1= (t(:,3:end) & ... + (~isletter(c(:,3:end)) & ... + ~isletter(c(:,1:end-2))) | ... + (c(:,3:end)=='e' | ... + c(:,3:end)=='d')) & ... + c(:,2:end-1)=='.'; +t(:,2:end-1)=t(:,2:end-1)|ix1; +% t(:,3:end)=t(:,3:end)|ix1; +% signs +t(c=='-')=false; +t(c=='+')=false; +ix1= t(:,3:end) & ... + (c(:,2:end-1)=='-' | ... + c(:,2:end-1)=='+'); +t(:,2:end-1)=t(:,2:end-1)|ix1; +% exponents +ix1= t(:,1:end-2) & ... + (c(:,2:end-1)=='e' | ... + c(:,2:end-1)=='d'); +t(:,2:end-1)=t(:,2:end-1)|ix1; +% spec numbers +c=reshape(c.',1,[]); +t=t'; +ic=[]; +for j=1:numel(ntmpl) + ic=[ic,strfind(c,ntmpl{j})]; +end +ic=sort(ic); +for i=1:numel(ic) + ix=ic(i)+0:ic(i)+4; + t(ix)=true; +end +t=t'; +c=reshape(c.',[tc,tr]).'; +t(c==' ')=false; +%-------------------------------------------------------------------------------- + +% - only allow one number per string +il=~any(t,2); +ib=strfind(reshape(t.',1,[]),[0 1]); +if ~isempty(ib) + ixe=cell(3,1); + n=reshape(char(t.*c).',1,[]); + for i=1:numel(ctmpl) + id=strfind(n,ctmpl{i}); + if ~isempty(id) + [dum,dum,ixu{i},ixe{i}]=dupinx(id,tc); + end + end + in=false(tr,1); + im=in; + % must check for anomalous cases like <'.d'> + id=sort(... + [find(n>='0' & n<='9'),... + strfind(n,'inf'),... + strfind(n,'nan')]); + % [ibu,ibd,ixbu,ixe{i+1}]=dupinx(id,tc); + [ibu,ibd,ixbu,ixbd]=dupinx(id,tc); + in(ixbu)=true; + in(ixbd)=true; + [ibu,ibd,ixbu,ixbd]=dupinx(ib,tc); + im(ixbu)=true; + in=in&im; + in([ixe{:}])=false; + il=~any(t,2); + ia=~(in|il); + + % - read valid strings + n=t(in,:).*c(in,:); + n(n==0)=' '; + n=char(n); + dn=strread(n.','%n'); + if numel(dn) ~= numel(find(in)) + %disp(sprintf('ASORT> unexpected fatal error reading input!')); + if nargout + s.c=c; + s.t=t; + s.n=n; + s.d=dn; + varargout{1}=s; + end + return; + end + + % - sort numbers + [ds,dx]=sort(dn,1,smod); + in=find(in); + anr=ins(in(dx)); + snr=ins(ia); +end +str=ins(il); +to=clock; + +% - prepare output +if nargout < 3 || sflg + s.magic='ASORT'; + s.ver='30-Mar-2005 11:57:07'; + s.time=datestr(clock); + s.runtime=etime(to,ti); + s.input_class=winp.class; + s.input_msize=winp.size; + s.input_bytes=winp.bytes; + s.strng_class=wins.class; + s.strng_msize=wins.size; + s.strng_bytes=wins.bytes; + s.anr=anr; + s.snr=snr; + s.str=str; + if dflg + s.c=c; + s.t=t; + s.n=n; + s.d=ds; + end + varargout{1}=s; +else + s={anr,snr,str}; + for i=1:nargout + varargout{i}=s{i}; + end +end + +if vflg + inp=cstr(inp); + an=[{'--- NUMERICAL'}; anr]; + as=[{'--- ASCII NUMBERS'}; snr]; + at=[{'--- ASCII STRINGS'}; str]; + nn=[{'--- NUMBERS'}; num2cell(ds)]; + ag={' ';' ';' '}; + u=[{'INPUT'}; inp;ag]; + v=[{'ASCII SORT'}; ins;ag]; + w=[{'NUM SORT'}; an;as;at]; + x=[{'NUM READ'}; nn;as;at]; + w=[u,v,w,x]; + disp(w); +end + +return; +%-------------------------------------------------------------------------------- +function c=cstr(s) +% - bottleneck waiting for a good <cellstr> replacement +% it consumes ~75% of <asort>'s processing time! + +c=s; +if ischar(s) + sr=size(s,1); + c=cell(sr,1); + for i=1:sr + c{i}=s(i,:); % no deblanking! + end +end +return; +%-------------------------------------------------------------------------------- +function [idu,idd,ixu,ixd]=dupinx(ix,nc) +% - check for more than one entry/row in a matrix of column size <nc> +% unique indices: idu / ixu +% duplicate indices: idd / ixd + +if isempty(ix) + idu=[]; + idd=[]; + ixu=[]; + ixd=[]; + return; +end +id=fix(ix/nc)+1; +idi=diff(id)~=0; +ide=[true idi]; +idb=[idi true]; +idu=idb & ide; +idd=idb==1 & ide==0; +ixu=id(idu); +ixd=id(idd); +return; +%-------------------------------------------------------------------------------- +function inp=setinp(inp,tmpl,flg) +% - remove space(s) and/or templates + +if isempty(inp) || ~any(flg) + return; +end + +for i=sort(flg) + switch i + case flg(1) + if ischar(tmpl) + tmpl={tmpl}; + end + for i=1:numel(tmpl) + inp=strrep(inp,tmpl{i},' '); + end + case flg(2) + inp=strrep(inp,' ',''); + end +end +return; +%--------------------------------------------------------------------------------
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/assert.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,10 @@ +function assert(pred, str) +% ASSERT Raise an error if the predicate is not true. +% assert(pred, string) + +if nargin<2, str = ''; end + +if ~pred + s = sprintf('assertion violated: %s', str); + error(s); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/assignEdgeNums.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,32 @@ +function [edge_id, nedges] = assignEdgeNums(adj_mat) +% give each edge a unique number +% we number (i,j) for j>i first, in row, column order. +% Then we number the reverse links + +nnodes = length(adj_mat); +edge_id = zeros(nnodes); +e = 1; +for i=1:nnodes + for j=i+1:nnodes + if adj_mat(i,j) + edge_id(i,j) = e; + e = e+1; + end + end +end + +nedges = e-1; +tmp = edge_id; +ndx = find(tmp); +tmp(ndx) = tmp(ndx)+nedges; +edge_id = edge_id + triu(tmp)'; + + +if 0 +ndx = find(adj_mat); +nedges = length(ndx); +nnodes = length(adj_mat); +edge_id = zeros(1, nnodes*nnodes); +edge_id(ndx) = 1:nedges; +edge_id = reshape(edge_id, nnodes, nnodes); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/assign_cols.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,36 @@ +function M = assign_cols(cols, vals, M) +% ASSIGN_COLS Assign values to columns of a matrix +% function M = assign_cols(M, cols, vals, M) +% +% Example: +% M = assign_cols(data, ones(1,N)) +% will construct a 1-of-K encoding of the data, where K=ncols=max(data) and N=nrows=length(data) +% +% Example: +% M = zeros(3,2); +% M = assign_cols([1 2 1], [10 20 30], M) +% is equivalent to +% M(1, 1) = 10 +% M(2, 2) = 20 +% M(3, 1) = 30 +% + +if nargin < 3 + nr = length(cols); + nc = max(cols); + M = zeros(nr, nc); +else + [nr nc] = size(M); +end + +if 0 +for r=1:nr + M(r, cols(r)) = vals(r); +end +end + +if 1 +rows = 1:nr; +ndx = subv2ind([nr nc], [rows(:) cols(:)]); +M(ndx) = vals; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/axis_pct.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,70 @@ +function ax = axis_pct(pct) +% AXIS_PCT Set reasonable axis limits. +% AXIS_PCT(pct) sets axis limits to extend pct% beyond limits of plotted +% objects. Default is 5%. +% Works for linear or log scale. +% Unfortunately, the axes won't change when new points are plotted. + +if nargin < 1 + pct = 0.05; +end +ax = [Inf -Inf Inf -Inf Inf -Inf]; + +% find bounding box of plotted objects +children = get(gca,'children'); +for child = children' + if strcmp(get(child,'type'),'text') + xyz = get(child,'position'); + % need to determine bounding box of the text + c([1 2]) = xyz(1); + c([3 4]) = xyz(2); + c([5 6]) = xyz(3); + else + x = get(child,'xdata'); + c(1) = min(x); + c(2) = max(x); + y = get(child,'ydata'); + c(3) = min(y); + c(4) = max(y); + z = get(child,'zdata'); + if isempty(z) + c([5 6]) = 0; + else + c(5) = min(z); + c(6) = max(z); + end + end + ax([1 3 5]) = min(ax([1 3 5]), c([1 3 5])); + ax([2 4 6]) = max(ax([2 4 6]), c([2 4 6])); +end +if strcmp(get(gca,'xscale'), 'log') + ax([1 2]) = log(ax([1 2])); +end +if strcmp(get(gca,'yscale'), 'log') + ax([3 4]) = log(ax([3 4])); +end +dx = ax(2)-ax(1); +if dx == 0 + dx = 1; +end +dy = ax(4)-ax(3); +if dy == 0 + dy = 1; +end +dz = ax(6)-ax(5); +if dz == 0 + dz = 1; +end +ax = ax + [-dx dx -dy dy -dz dz]*pct; +if strcmp(get(gca,'xscale'), 'log') + ax([1 2]) = exp(ax([1 2])); +end +if strcmp(get(gca,'yscale'), 'log') + ax([3 4]) = exp(ax([3 4])); +end +% clip for 2D +ax = ax(1:length(axis)); +axis(ax); +if nargout < 1 + clear ax +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/bipartiteMatchingDemo.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,112 @@ +% Consider matching sources to detections + +% s1 d2 +% s2 d3 +% d1 + +%a = bipartiteMatchingHungarian([52;0.01]) + +% sources(:,i) = [x y] coords +sources = [0.1 0.7; 0.6 0.4]'; +detections = [0.2 0.2; 0.2 0.8; 0.7 0.1]'; +dst = sqdist(sources, detections); + +% a = [2 3] which means s1-d2, s2-d3 +a = bipartiteMatchingHungarian(dst); +a2 = bipartiteMatchingIntProg(dst); +assert(isequal(a(:),a2(:))) + + +figure(1); clf +bipartiteMatchingDemoPlot(sources, detections, a) + + + + +%%%% Flip roles of sources and detections + +%dst = dst'; +dst = sqdist(detections, sources); +% a = [0 1 2] which means d1-0, d2-s1, d3-s2 +a = bipartiteMatchingHungarian(dst); + +a2 = bipartiteMatchingIntProg(dst); +assert(isequal(a(:),a2(:))) + +figure(2); clf +bipartiteMatchingDemoPlot(detections, sources, a) % swapped args + + + + +%%%%%%%%%% Move s1 nearer to d1 +% d2 +% s2 d3 +% s1 d1 + +sources = [0.1 0.3; 0.6 0.4]'; +detections = [0.2 0.2; 0.2 0.8; 0.7 0.1]'; +dst = sqdist(sources, detections); + +% a = [2 3] which means s1-d2, s2-d3 +a = bipartiteMatchingHungarian(dst); +[a2, ass] = bipartiteMatchingIntProg(dst); +assert(isequal(a(:),a2(:))) + + +figure(3); clf +bipartiteMatchingDemoPlot(sources, detections, a) + + + +%%%%%%%%%% + +% Use random points + +% Generate 2D data from a mixture of 2 Gaussians (from netlab demgmm1) +randn('state', 0); rand('state', 0); +gmix = gmm(2, 2, 'spherical'); +ndat1 = 10; ndat2 = 10; ndata = ndat1+ndat2; +%gmix.centres = [0.3 0.3; 0.7 0.7]; +%gmix.covars = [0.01 0.01]; +gmix.centres = [0.5 0.5; 0.5 0.5]; +gmix.covars = [0.1 0.01]; +[x, label] = gmmsamp(gmix, ndata); + +ndx = find(label==1); +sources = x(ndx,:)'; +ndx = find(label==2); +detections = x(ndx,:)'; +dst = sqdist(sources, detections); + +[a, ass] = bipartiteMatchingIntProg(dst); +[a2] = bipartiteMatchingHungarian(dst); +assert(isequal(a(:), a2(:))) + +figure(4); clf +bipartiteMatchingDemoPlot(sources, detections, a) + +% only match 80% of points +p1 = size(sources, 2); +p2 = size(detections, 2); +nmatch = ceil(0.8*min(p1,p2)); +a2 = bipartiteMatchingIntProg(dst, nmatch); +figure(5); clf +bipartiteMatchingDemoPlot(sources, detections, a2) + + +%%% swap roles + +ndx = find(label==2); +sources = x(ndx,:)'; +ndx = find(label==1); +detections = x(ndx,:)'; +dst = sqdist(sources, detections); + +% only match 80% of points +p1 = size(sources, 2); +p2 = size(detections, 2); +nmatch = ceil(0.8*min(p1,p2)); +a2 = bipartiteMatchingIntProg(dst, nmatch); +figure(6); clf +bipartiteMatchingDemoPlot(sources, detections, a2)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/bipartiteMatchingDemoPlot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,24 @@ +function bipartiteMatchingDemoPlot(sources, detections, a) + +hold on +p1 = size(sources,2); +p2 = size(detections,2); +for i=1:p1 + h=text(sources(1,i), sources(2,i), sprintf('s%d', i)); + set(h, 'color', 'r'); +end +for i=1:p2 + h=text(detections(1,i), detections(2,i), sprintf('d%d', i)); + set(h, 'color', 'b'); +end + +if nargin < 3, return; end + +for i=1:p1 + j = a(i); + if j==0 % i not matched to anything + continue + end + line([sources(1,i) detections(1,j)], [sources(2,i) detections(2,j)]) +end +axis_pct;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/bipartiteMatchingHungarian.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,90 @@ +% MATCH - Solves the weighted bipartite matching (or assignment) +% problem. +% +% Usage: a = match(C); +% +% Arguments: +% C - an m x n cost matrix; the sets are taken to be +% 1:m and 1:n; C(i, j) gives the cost of matching +% items i (of the first set) and j (of the second set) +% +% Returns: +% +% a - an m x 1 assignment vector, which gives the +% minimum cost assignment. a(i) is the index of +% the item of 1:n that was matched to item i of +% 1:m. If item i (of 1:m) was not matched to any +% item of 1:n, then a(i) is zero. + +% Copyright (C) 2002 Mark A. Paskin +% +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, but +% WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +% General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 +% USA. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [a] = optimalMatching(C) + +% Trivial cases: +[p, q] = size(C); +if (p == 0) + a = []; + return; +elseif (q == 0) + a = zeros(p, 1); + return; +end + + +if 0 +% First, reduce the problem by making easy optimal matches. If two +% elements agree that they are the best match, then match them up. +[x, a] = min(C, [], 2); +[y, b] = min(C, [], 1); +u = find(1:p ~= b(a(:))); +a(u) = 0; +v = find(1:q ~= a(b(:))'); +C = C(u, v); +if (isempty(C)) return; end +end + +% Get the (new) size of the two sets, u and v. +[m, n] = size(C); + +%mx = realmax; +mx = 2*max(C(:)); +mn = -2*min(C(:)); +% Pad the affinity matrix to be square +if (m < n) + C = [C; mx * ones(n - m, n)]; +elseif (n < m) + C = [C, mx * ones(m, m - n)]; +end + +% Run the Hungarian method. First replace infinite values by the +% largest (or smallest) finite values. +C(find(isinf(C) & (C > 0))) = mx; +C(find(isinf(C) & (C < 0))) = mn; +%fprintf('running hungarian\n'); +[b, cost] = hungarian(C'); + +% Extract only the real assignments +ap = b(1:m)'; +ap(find(ap > n)) = 0; + +a = ap; +%% Incorporate this sub-assignment into the complete assignment +% k = find(ap); +% a(u(k)) = v(ap(k)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/bipartiteMatchingIntProg.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,69 @@ +function [a,ass] = bipartiteMatchingIntProg(dst, nmatches) +% BIPARTITEMATCHINGINTPROG Use binary integer programming (linear objective) to solve for optimal linear assignment +% function a = bipartiteMatchingIntProg(dst) +% a(i) = best matching column for row i +% +% This gives the same result as bipartiteMatchingHungarian. +% +% function a = bibpartiteMatchingIntProg(dst, nmatches) +% only matches the specified number (must be <= min(size(dst))). +% This can be used to allow outliers in both source and target. +% +% For details, see Marciel & Costeira, "A global solution to sparse correspondence +% problems", PAMI 25(2), 2003 + +if nargin < 2, nmatches = []; end + +[p1 p2] = size(dst); +p1orig = p1; p2orig = p2; +dstorig = dst; + +if isempty(nmatches) % no outliers allowed (modulo size difference) + % ensure matrix is square + m = max(dst(:)); + if p1<p2 + dst = [dst; m*ones(p2-p1, p2)]; + elseif p1>p2 + dst = [dst m*ones(p1, p1-p2)]; + end +end +[p1 p2] = size(dst); + + +c = dst(:); % vectorize cost matrix + +% row-sum: ensure each column sums to 1 +A2 = kron(eye(p2), ones(1,p1)); +b2 = ones(p2,1); + +% col-sum: ensure each row sums to 1 +A3 = kron(ones(1,p2), eye(p1)); +b3 = ones(p1,1); + +if isempty(nmatches) + % enforce doubly stochastic + A = [A2; A3]; + b = [b2; b3]; + Aineq = zeros(1, p1*p2); + bineq = 0; +else + nmatches = min([nmatches, p1, p2]); + Aineq = [A2; A3]; + bineq = [b2; b3]; % row and col sums <= 1 + A = ones(1,p1*p2); + b = nmatches; % total num matches = b (otherwise get degenerate soln) +end + + +ass = bintprog(c, Aineq, bineq, A, b); +ass = reshape(ass, p1, p2); + +a = zeros(1, p1orig); +for i=1:p1orig + ndx = find(ass(i,:)==1); + if ~isempty(ndx) & (ndx <= p2orig) + a(i) = ndx; + end +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/block.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +function sub = block(blocks, block_sizes) +% BLOCK Return a vector of subscripts corresponding to the specified blocks. +% sub = block(blocks, block_sizes) +% +% e.g., block([2 5], [2 1 2 1 2]) = [3 7 8]. + +blocks = blocks(:)'; +block_sizes = block_sizes(:)'; +skip = [0 cumsum(block_sizes)]; +start = skip(blocks)+1; +fin = start + block_sizes(blocks) - 1; +sub = []; +for j=1:length(blocks) + sub = [sub start(j):fin(j)]; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/cell2matPad.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,22 @@ +function data2 = cell2matPad(data) +% data{f}(y,x,b) - each frame can have a different size (can can even be empty) +% data2(y,x,b,f) = zero padded version + +Nframes = length(data); +Nbands = -inf; +nr = -inf; nc = -inf; +for f=1:Nframes + if isempty(data{f}), continue; end + nr = max(nr, size(data{f},1)); + nc = max(nc, size(data{f},2)); + Nbands = max(Nbands, size(data{f},3)); +end +data2 = zeros(nr, nc, Nbands, Nframes); +for f=1:Nframes + if isempty(data{f}), continue; end + data2(1:size(data{f},1), 1:size(data{f},2), :, f) = data{f}; +end +if Nbands == 1 + data2 = squeeze(data2); % reshape(data2, [size(data2,1), size(data2,2), Nframes]); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/cell2num.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,57 @@ +function N = cell2num(C) +% CELL2NUM Convert a 2D cell array to a 2D numeric array +% N = cell2num(C) +% If the cells contain column vectors, they must have the same number of rows in each row of C. +% Each column will be concatenated. +% +% Example 1: +% C = num2cell(rand(2,2)) +% [0.4565] [0.8214] +% [0.0185] [0.4447] +% N = cell2num(C) +% 0.4565 0.8214 +% 0.0185 0.4447 +% +% Example 2: +% C = cell(2, 3); +% for i=1:2 +% for j=1:3 +% C{i,j} = rand(i, 1); +% end +% end +% C = +% [ 0.8998] [ 0.8216] [ 0.6449] +% [2x1 double] [2x1 double] [2x1 double] +% C{2,1} = +% 0.8180 +% 0.6602 +% N=cell2num(C) +% 0.8998 0.8216 0.6449 +% 0.8180 0.3420 0.3412 +% 0.6602 0.2897 0.5341 + + +% error('use cell2mat in matlab 7') + + +if isempty(C) + N = []; + return; +end + +if any(cellfun('isempty', C)) %any(isemptycell(C)) + error('can''t convert cell array with empty cells to matrix') +end + +[nrows ncols] = size(C); +%N = reshape(cat(1, C{:}), [nrows ncols]); % this only works if C only contains scalars +r = 0; +for i=1:nrows + r = r + size(C{i,1}, 1); +end +c = 0; +for j=1:ncols + c = c + size(C{1,j}, 2); +end +N = reshape(cat(1, C{:}), [r c]); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/centeringMatrix.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +N = 3; +x = rand(N,2); % each row is a feature vector +m = mean(x,1); +xc = x-repmat(m, N, 1); + +C = eye(N) - (1/N)*ones(N,N); +xc2 = C*x; +assert(approxeq(xc, xc2))
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/checkpsd.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +function s = checkpsd(s) + +if (any(isnan(s) | isinf(s) | ~isreal(s))) + warning('S contains complex numbers, Inf, or NaN'); +end +% Drop any negative eigenvalues. +[V, D] = eig(full(s)); +d = real(diag(D)); +if (any(d < 0)) + warning(sprintf(['S is not positive semidefinite (min. eig. =' ... + ' %0.5g); projecting.'], min(d))); + d(find(d < 0)) = 0; + D = diag(d); + s = V * D * V'; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/chi2inv.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,36 @@ +function x = chi2inv(p,v); +%CHI2INV Inverse of the chi-square cumulative distribution function (cdf). +% X = CHI2INV(P,V) returns the inverse of the chi-square cdf with V +% degrees of freedom at the values in P. The chi-square cdf with V +% degrees of freedom, is the gamma cdf with parameters V/2 and 2. +% +% The size of X is the common size of P and V. A scalar input +% functions as a constant matrix of the same size as the other input. + +% References: +% [1] M. Abramowitz and I. A. Stegun, "Handbook of Mathematical +% Functions", Government Printing Office, 1964, 26.4. +% [2] E. Kreyszig, "Introductory Mathematical Statistics", +% John Wiley, 1970, section 10.2 (page 144) + +% Copyright 1993-2002 The MathWorks, Inc. +% $Revision: 1.1.1.1 $ $Date: 2005/04/26 02:30:30 $ + +if nargin < 2, + error('Requires two input arguments.'); +end + +[errorcode p v] = distchck(2,p,v); + +if errorcode > 0 + error('Requires non-scalar arguments to match in size.'); +end + +% Call the gamma inverse function. +x = gaminv(p,v/2,2); + +% Return NaN if the degrees of freedom is not positive. +k = (v <= 0); +if any(k(:)) + x(k) = NaN; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/choose.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function c = choose(n,k) +% CHOOSE The number of ways of choosing k things from n +% c = choose(n,k) + +c = factorial(n)/(factorial(k) * factorial(n-k));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/collapse_mog.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,22 @@ +function [new_mu, new_Sigma, new_Sigma2] = collapse_mog(mu, Sigma, coefs) +% COLLAPSE_MOG Collapse a mixture of Gaussians to a single Gaussian by moment matching +% [new_mu, new_Sigma] = collapse_mog(mu, Sigma, coefs) +% +% coefs(i) - weight of i'th mixture component +% mu(:,i), Sigma(:,:,i) - params of i'th mixture component + +% S = sum_c w_c (S_c + m_c m_c' + m m' - 2 m_c m') +% = sum_c w_c (S_c + m_c m_c') + m m' - 2 (sum_c m_c) m' +% = sum_c w_c (S_c + m_c m_c') - m m' + +new_mu = sum(mu * diag(coefs), 2); % weighted sum of columns + +n = length(new_mu); +new_Sigma = zeros(n,n); +new_Sigma2 = zeros(n,n); +for j=1:length(coefs) + m = mu(:,j) - new_mu; + new_Sigma = new_Sigma + coefs(j) * (Sigma(:,:,j) + m*m'); + new_Sigma2 = new_Sigma2 + coefs(j) * (Sigma(:,:,j) + mu(:,j)*mu(:,j)'); +end +%assert(approxeq(new_Sigma, new_Sigma2 - new_mu*new_mu'))
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/colmult.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,62 @@ +#include <stdio.h> +#include "mex.h" + +/* +out = colop(M, v) + +Apply binary operator to a vector v and to each column of M in turn +to produce a matrix the same size as M. + +This is equivalent to + +out = zeros(size(M)); +for col=1:size(M,2) + out(:,col) = op(M(:,col), v); +end + +The code needs to be modified for each different operator 'op'. +eg op = '.*' + +In vectorized form: + +out = M .* repmat(v(:), 1, size(M,2)) + +(This function was formerly called repmat_and_mult.c) + +*/ + +/* M(i,j) = M(i + nrows*j) since Matlab uses Fortran layout. */ + + +#define INMAT(i,j) M[(i)+nrows*(j)] +#define OUTMAT(i,j) out[(i)+nrows*(j)] + +void mexFunction( + int nlhs, mxArray *plhs[], + int nrhs, const mxArray *prhs[] + ) +{ + double *out, *M, *v; + int nrows, ncols, r, c; + + /* read the input args */ + M = mxGetPr(prhs[0]); + nrows = mxGetM(prhs[0]); + ncols = mxGetN(prhs[0]); + + v = mxGetPr(prhs[1]); + + plhs[0] = mxCreateDoubleMatrix(nrows, ncols, mxREAL); + out = mxGetPr(plhs[0]); + + for (c=0; c < ncols; c++) { + for (r=0; r < nrows; r++) { + OUTMAT(r,c) = INMAT(r,c) * v[r]; + /* printf("r=%d, c=%d, M=%f, v=%f\n", r, c, INMAT(r,c), v[r]); */ + } + } + +} + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/computeROC.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,64 @@ +function [FPrate, TPrate, AUC, thresholds] = computeROC(confidence, testClass) +% function [FPrate, TPrate, AUC, thresholds] = computeROC(confidence, testClass) +% +% computeROC computes the data for an ROC curve based on a classifier's confidence output. +% It returns the false positive rate and the true positive rate along with +% the area under the ROC curve, and the list of thresholds. +% +% Inputs: +% - confidence(i) is proportional to the probability that +% testClass(i) is positive +% +% testClass = 0 => target absent +% testClass = 1 => target present +% +% Based on algorithms 2 and 4 from Tom Fawcett's paper "ROC Graphs: Notes and +% Practical Considerations for Data Mining Researchers" (2003) +% http://www.hpl.hp.com/techreports/2003/HPL-2003-4.pdf" +% +% Vlad Magdin, 21 Feb 2005 + +% break ties in scores +S = rand('state'); +rand('state',0); +confidence = confidence + rand(size(confidence))*10^(-10); +rand('state',S) +[thresholds order] = sort(confidence, 'descend'); +testClass = testClass(order); + +%%% -- calculate TP/FP rates and totals -- %%% +AUC = 0; +faCnt = 0; +tpCnt = 0; +falseAlarms = zeros(1,size(thresholds,2)); +detections = zeros(1,size(thresholds,2)); +fPrev = -inf; +faPrev = 0; +tpPrev = 0; + +P = max(size(find(testClass==1))); +N = max(size(find(testClass==0))); + +for i=1:length(thresholds) + if thresholds(i) ~= fPrev + falseAlarms(i) = faCnt; + detections(i) = tpCnt; + + AUC = AUC + polyarea([faPrev faPrev faCnt/N faCnt/N],[0 tpPrev tpCnt/P 0]); + + fPrev = thresholds(i); + faPrev = faCnt/N; + tpPrev = tpCnt/P; + end + + if testClass(i) == 1 + tpCnt = tpCnt + 1; + else + faCnt = faCnt + 1; + end +end + +AUC = AUC + polyarea([faPrev faPrev 1 1],[0 tpPrev 1 0]); + +FPrate = falseAlarms/N; +TPrate = detections/P;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/compute_counts.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,17 @@ +function count = compute_counts(data, sz) +% COMPUTE_COUNTS Count the number of times each combination of discrete assignments occurs +% count = compute_counts(data, sz) +% +% data(i,t) is the value of variable i in case t +% sz(i) : values for variable i are assumed to be in [1:sz(i)] +% +% Example: to compute a transition matrix for an HMM from a sequence of labeled states: +% transmat = mk_stochastic(compute_counts([seq(1:end-1); seq(2:end)], [nstates nstates])); + +assert(length(sz) == size(data, 1)); +P = prod(sz); +indices = subv2ind(sz, data'); % each row of data' is a case +%count = histc(indices, 1:P); +count = hist(indices, 1:P); +count = myreshape(count, sz); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/conf2mahal.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,62 @@ +% CONF2MAHAL - Translates a confidence interval to a Mahalanobis +% distance. Consider a multivariate Gaussian +% distribution of the form +% +% p(x) = 1/sqrt((2 * pi)^d * det(C)) * exp((-1/2) * MD(x, m, inv(C))) +% +% where MD(x, m, P) is the Mahalanobis distance from x +% to m under P: +% +% MD(x, m, P) = (x - m) * P * (x - m)' +% +% A particular Mahalanobis distance k identifies an +% ellipsoid centered at the mean of the distribution. +% The confidence interval associated with this ellipsoid +% is the probability mass enclosed by it. Similarly, +% a particular confidence interval uniquely determines +% an ellipsoid with a fixed Mahalanobis distance. +% +% If X is an d dimensional Gaussian-distributed vector, +% then the Mahalanobis distance of X is distributed +% according to the Chi-squared distribution with d +% degrees of freedom. Thus, the Mahalanobis distance is +% determined by evaluating the inverse cumulative +% distribution function of the chi squared distribution +% up to the confidence value. +% +% Usage: +% +% m = conf2mahal(c, d); +% +% Inputs: +% +% c - the confidence interval +% d - the number of dimensions of the Gaussian distribution +% +% Outputs: +% +% m - the Mahalanobis radius of the ellipsoid enclosing the +% fraction c of the distribution's probability mass +% +% See also: MAHAL2CONF + +% Copyright (C) 2002 Mark A. Paskin +% +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, but +% WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +% General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 +% USA. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function m = conf2mahal(c, d) + +m = chi2inv(c, d); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/cross_entropy.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +function kl = cross_entropy(p, q, symmetric) +% CROSS_ENTROPY Compute the Kullback-Leibler divergence between two discrete prob. distributions +% kl = cross_entropy(p, q, symmetric) +% +% If symmetric = 1, we compute the symmetric version. Default: symmetric = 0; + +tiny = exp(-700); +if nargin < 3, symmetric = 0; end +p = p(:); +q = q(:); +if symmetric + kl = (sum(p .* log((p+tiny)./(q+tiny))) + sum(q .* log((q+tiny)./(p+tiny))))/2; +else + kl = sum(p .* log((p+tiny)./(q+tiny))); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/dirKPM.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,98 @@ +function filenames = dirKPM(dirname, ext, varargin) +% dirKPM Like the built-in dir command, but returns filenames as a cell array instead of a struct +% +% filenames = dirKPM(dirname) +% returns all files, except '.' and '..' +% +% filenames = dirKPM('images', '*.jpg') +% returns files with this extension +% eg filenames{1} = 'foo.jpg' etc +% +% OPTIONAL ARGUMENTS [default in brackets] +% filenames = dirKPM('images', '', param1, val1, param2, val2, ...) +% +% 'fileType'='image' ['all'] means return files with extension .jpg, .png, .bmp +% +% 'prepend'=1 [0] means preprend folder name to filename +% eg filenames{1} = 'images/foo.jpg' +% +% 'doSort'=1 [1] means sort filenames in ascending alphanumerical order (where possible) +% +% 'doRecurse'=1 [0] recursive dir, apply the same dirKPM call on all +% subfolders (decrease MAXDEPTH option to prevent recursion from branching +% too explosively) + +if nargin < 1, dirname = '.'; end + +if nargin < 2, ext = ''; end + +[fileType, prepend, doSort, doRecurse, MAXDEPTH, DEPTH] = process_options(... + varargin, 'fileType', 'all', 'prepend', 0, 'doSort', 1, 'doRecurse', 0,... + 'MAXDEPTH', 3, 'DEPTH', 0); + +tmp = dir(fullfile(dirname, ext)); +[filenames I] = setdiff({tmp.name}, {'.', '..'}); +tmp = tmp(I); + +if doRecurse && sum([tmp.isdir])>0 && DEPTH<MAXDEPTH + for fi=1:length(tmp) + subDirFilenames = {}; + + if tmp(fi).isdir + varargin = change_option( varargin, 'prepend', false ); + varargin = change_option( varargin, 'doSort', false ); + varargin = change_option( varargin, 'DEPTH', DEPTH+1 ); + subDirFilenames = dirKPM( fullfile(dirname,tmp(fi).name), ext, varargin{:} ); + + for sdfi=1:length(subDirFilenames) + subDirFilenames{sdfi} = fullfile(tmp(fi).name, subDirFilenames{sdfi}); + end + end + + + nfilenames = length(filenames); + if length(subDirFilenames)>0 + filenames(nfilenames+1:nfilenames+length(subDirFilenames)) = subDirFilenames; + end + end +end + +nfiles = length(filenames); +if nfiles==0 return; end + +switch fileType + case 'image', + for fi=1:nfiles + good(fi) = isImage(filenames{fi}); + end + filenames = filenames(find(good)); + case 'all', + % no-op + otherwise + error(sprintf('unrecognized file type %s', fileType)); +end + +if doSort +% % sort filenames alphanumerically (if possible) +% DJE, buggy, MUST save tmp.anr/snr/str or else we potentially lose +% filenames +% tmp = asort(filenames, '-s', 'ascend'); +% if ~isempty(tmp.anr) +% filenames = tmp.anr'; +% else +% filenames = tmp.str'; +% end + % if names could not be sorted, return original order + + filenames=sort(filenames); + +end + + +if prepend + nfiles = length(filenames); + for fi=1:nfiles + filenames{fi} = fullfile(dirname, filenames{fi}); + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/div.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function d = div(a,b) +% DIV Integer division +% d = div(a,b) + +d = floor(a / b);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/draw_circle.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,28 @@ +function h = draw_circle(x, r, outline_color, fill_color) +% draw filled circles at centers x with radii r. +% x is a matrix of columns. r is a row vector. + +n = 40; % resolution +radians = [0:(2*pi)/(n-1):2*pi]; +unitC = [sin(radians); cos(radians)]; + +% extend r if necessary +if length(r) < cols(x) + r = [r repmat(r(length(r)), 1, cols(x)-length(r))]; +end + +h = []; +% hold is needed for fill() +held = ishold; +hold on +for i=1:cols(x) + y = unitC*r(i) + repmat(x(:, i), 1, n); + if nargin < 4 + h = [h line(y(1,:), y(2,:), 'Color', outline_color)]; + else + h = [h fill(y(1,:), y(2,:), fill_color, 'EdgeColor', outline_color)]; + end +end +if ~held + hold off +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/draw_ellipse.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,24 @@ +function h = draw_ellipse(x, c, outline_color, fill_color) +% DRAW_ELLIPSE(x, c, outline_color, fill_color) +% Draws ellipses at centers x with covariance matrix c. +% x is a matrix of columns. c is a positive definite matrix. +% outline_color and fill_color are optional. + +n = 40; % resolution +radians = [0:(2*pi)/(n-1):2*pi]; +unitC = [sin(radians); cos(radians)]; +r = chol(c)'; + +if nargin < 3 + outline_color = 'g'; +end + +h = []; +for i=1:cols(x) + y = r*unitC + repmat(x(:, i), 1, n); + if nargin < 4 + h = [h line(y(1,:), y(2,:), 'Color', outline_color)]; + else + h = [h fill(y(1,:), y(2,:), fill_color, 'EdgeColor', outline_color)]; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/draw_ellipse_axes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,19 @@ +function h = draw_ellipse_axes(x, c, linespec) +% DRAW_ELLIPSE_AXES(x, c, linespec) +% Draws the major and minor axes of ellipses. +% Ellipses are centered at x with covariance matrix c. +% x is a matrix of columns. c is a positive definite matrix. +% linespec is optional. + +[v,e] = eig(c); +v = v*sqrt(e); + +h = []; +for j = 1:cols(v) + x1 = repmat(x(1,:),2,1) + repmat([-1;1]*v(1,j),1,cols(x)); + x2 = repmat(x(2,:),2,1) + repmat([-1;1]*v(2,j),1,cols(x)); + h = [h line(x1,x2)]; +end +if nargin > 2 + set_linespec(h,linespec); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/em_converged.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,32 @@ +function [converged, decrease] = em_converged(loglik, previous_loglik, threshold, check_increased) +% EM_CONVERGED Has EM converged? +% [converged, decrease] = em_converged(loglik, previous_loglik, threshold) +% +% We have converged if the slope of the log-likelihood function falls below 'threshold', +% i.e., |f(t) - f(t-1)| / avg < threshold, +% where avg = (|f(t)| + |f(t-1)|)/2 and f(t) is log lik at iteration t. +% 'threshold' defaults to 1e-4. +% +% This stopping criterion is from Numerical Recipes in C p423 +% +% If we are doing MAP estimation (using priors), the likelihood can decrase, +% even though the mode of the posterior is increasing. + +if nargin < 3, threshold = 1e-4; end +if nargin < 4, check_increased = 1; end + +converged = 0; +decrease = 0; + +if check_increased + if loglik - previous_loglik < -1e-3 % allow for a little imprecision + fprintf(1, '******likelihood decreased from %6.4f to %6.4f!\n', previous_loglik, loglik); + decrease = 1; +converged = 0; +return; + end +end + +delta_loglik = abs(loglik - previous_loglik); +avg_loglik = (abs(loglik) + abs(previous_loglik) + eps)/2; +if (delta_loglik / avg_loglik) < threshold, converged = 1; end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/entropy.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,17 @@ +function H = entropy(v, scale) +% ENTROPY Entropy log base 2 +% H = entropy(v) +% If v is a matrix, we compute the entropy of each column +% +% % H = entropy(v,1) means we scale the result so that it lies in [0,1] + +if nargin < 2, scale = 0; end + +v = v + (v==0); +H = -1 * sum(v .* log2(v), 1); % sum the rows + +if scale + n = size(v, 1); + unif = normalise(ones(n,1)); + H = H / entropy(unif); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/exportfig.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,991 @@ +function varargout = exportfig(varargin) +%EXPORTFIG Export a figure. +% EXPORTFIG(H, FILENAME) writes the figure H to FILENAME. H is +% a figure handle and FILENAME is a string that specifies the +% name of the output file. +% +% EXPORTFIG(H, FILENAME, OPTIONS) writes the figure H to FILENAME +% with options initially specified by the structure OPTIONS. The +% field names of OPTIONS must be legal parameters listed below +% and the field values must be legal values for the corresponding +% parameter. Default options can be set in releases prior to R12 +% by storing the OPTIONS structure in the root object's appdata +% with the command +% setappdata(0,'exportfigdefaults', OPTIONS) +% and for releases after R12 by setting the preference with the +% command +% setpref('exportfig', 'defaults', OPTIONS) +% +% EXPORTFIG(...,PARAM1,VAL1,PARAM2,VAL2,...) specifies +% parameters that control various characteristics of the output +% file. Any parameter value can be the string 'auto' which means +% the parameter uses the default factory behavior, overriding +% any other default for the parameter. +% +% Format Paramter: +% 'Format' a string +% specifies the output format. Defaults to 'eps'. For a +% list of export formats type 'help print'. +% 'Preview' one of the strings 'none', 'tiff' +% specifies a preview for EPS files. Defaults to 'none'. +% +% Size Parameters: +% 'Width' a positive scalar +% specifies the width in the figure's PaperUnits +% 'Height' a positive scalar +% specifies the height in the figure's PaperUnits +% 'Bounds' one of the strings 'tight', 'loose' +% specifies a tight or loose bounding box. Defaults to 'tight'. +% 'Reference' an axes handle or a string +% specifies that the width and height parameters +% are relative to the given axes. If a string is +% specified then it must evaluate to an axes handle. +% +% Specifying only one dimension sets the other dimension +% so that the exported aspect ratio is the same as the +% figure's or reference axes' current aspect ratio. +% If neither dimension is specified the size defaults to +% the width and height from the figure's or reference +% axes' size. Tight bounding boxes are only computed for +% 2-D views and in that case the computed bounds enclose all +% text objects. +% +% Rendering Parameters: +% 'Color' one of the strings 'bw', 'gray', 'cmyk' +% 'bw' specifies that lines and text are exported in +% black and all other objects in grayscale +% 'gray' specifies that all objects are exported in grayscale +% 'rgb' specifies that all objects are exported in color +% using the RGB color space +% 'cmyk' specifies that all objects are exported in color +% using the CMYK color space +% 'Renderer' one of 'painters', 'zbuffer', 'opengl' +% specifies the renderer to use +% 'Resolution' a positive scalar +% specifies the resolution in dots-per-inch. +% 'LockAxes' one of 0 or 1 +% specifies that all axes limits and ticks should be fixed +% while exporting. +% +% The default color setting is 'bw'. +% +% Font Parameters: +% 'FontMode' one of the strings 'scaled', 'fixed' +% 'FontSize' a positive scalar +% in 'scaled' mode multiplies with the font size of each +% text object to obtain the exported font size +% in 'fixed' mode specifies the font size of all text +% objects in points +% 'DefaultFixedFontSize' a positive scalar +% in 'fixed' mode specified the default font size in +% points +% 'FontSizeMin' a positive scalar +% specifies the minimum font size allowed after scaling +% 'FontSizeMax' a positive scalar +% specifies the maximum font size allowed after scaling +% 'FontEncoding' one of the strings 'latin1', 'adobe' +% specifies the character encoding of the font +% 'SeparateText' one of 0 or 1 +% specifies that the text objects are stored in separate +% file as EPS with the base filename having '_t' appended. +% +% If FontMode is 'scaled' but FontSize is not specified then a +% scaling factor is computed from the ratio of the size of the +% exported figure to the size of the actual figure. +% +% The default 'FontMode' setting is 'scaled'. +% +% Line Width Parameters: +% 'LineMode' one of the strings 'scaled', 'fixed' +% 'LineWidth' a positive scalar +% 'DefaultFixedLineWidth' a positive scalar +% 'LineWidthMin' a positive scalar +% specifies the minimum line width allowed after scaling +% 'LineWidthMax' a positive scalar +% specifies the maximum line width allowed after scaling +% The semantics of 'Line' parameters are exactly the +% same as the corresponding 'Font' parameters, except that +% they apply to line widths instead of font sizes. +% +% Style Map Parameter: +% 'LineStyleMap' one of [], 'bw', or a function name or handle +% specifies how to map line colors to styles. An empty +% style map means styles are not changed. The style map +% 'bw' is a built-in mapping that maps lines with the same +% color to the same style and otherwise cycles through the +% available styles. A user-specified map is a function +% that takes as input a cell array of line objects and +% outputs a cell array of line style strings. The default +% map is []. +% +% Examples: +% exportfig(gcf,'fig1.eps','height',3); +% Exports the current figure to the file named 'fig1.eps' with +% a height of 3 inches (assuming the figure's PaperUnits is +% inches) and an aspect ratio the same as the figure's aspect +% ratio on screen. +% +% opts = struct('FontMode','fixed','FontSize',10,'height',3); +% exportfig(gcf, 'fig2.eps', opts, 'height', 5); +% Exports the current figure to 'fig2.eps' with all +% text in 10 point fonts and with height 5 inches. +% +% See also PREVIEWFIG, APPLYTOFIG, RESTOREFIG, PRINT. + +% Copyright 2000 Ben Hinkle +% Email bug reports and comments to bhinkle@mathworks.com + +if (nargin < 2) + error('Too few input arguments'); +end + +% exportfig(H, filename, [options,] ...) +H = varargin{1}; +if ~LocalIsHG(H,'figure') + error('First argument must be a handle to a figure.'); +end +filename = varargin{2}; +if ~ischar(filename) + error('Second argument must be a string.'); +end +paramPairs = {varargin{3:end}}; +if nargin > 2 + if isstruct(paramPairs{1}) + pcell = LocalToCell(paramPairs{1}); + paramPairs = {pcell{:}, paramPairs{2:end}}; + end +end +verstr = version; +majorver = str2num(verstr(1)); +defaults = []; +if majorver > 5 + if ispref('exportfig','defaults') + defaults = getpref('exportfig','defaults'); + end +elseif exist('getappdata') + defaults = getappdata(0,'exportfigdefaults'); +end +if ~isempty(defaults) + dcell = LocalToCell(defaults); + paramPairs = {dcell{:}, paramPairs{:}}; +end + +% Do some validity checking on param-value pairs +if (rem(length(paramPairs),2) ~= 0) + error(['Invalid input syntax. Optional parameters and values' ... + ' must be in pairs.']); +end + +auto.format = 'eps'; +auto.preview = 'none'; +auto.width = -1; +auto.height = -1; +auto.color = 'bw'; +auto.defaultfontsize=10; +auto.fontsize = -1; +auto.fontmode='scaled'; +auto.fontmin = 8; +auto.fontmax = 60; +auto.defaultlinewidth = 1.0; +auto.linewidth = -1; +auto.linemode=[]; +auto.linemin = 0.5; +auto.linemax = 100; +auto.fontencoding = 'latin1'; +auto.renderer = []; +auto.resolution = []; +auto.stylemap = []; +auto.applystyle = 0; +auto.refobj = -1; +auto.bounds = 'tight'; +explicitbounds = 0; +auto.lockaxes = 1; +auto.separatetext = 0; +opts = auto; + +% Process param-value pairs +args = {}; +for k = 1:2:length(paramPairs) + param = lower(paramPairs{k}); + if ~ischar(param) + error('Optional parameter names must be strings'); + end + value = paramPairs{k+1}; + + switch (param) + case 'format' + opts.format = LocalCheckAuto(lower(value),auto.format); + if strcmp(opts.format,'preview') + error(['Format ''preview'' no longer supported. Use PREVIEWFIG' ... + ' instead.']); + end + case 'preview' + opts.preview = LocalCheckAuto(lower(value),auto.preview); + if ~strcmp(opts.preview,{'none','tiff'}) + error('Preview must be ''none'' or ''tiff''.'); + end + case 'width' + opts.width = LocalToNum(value, auto.width); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.width) + error('Width must be a numeric scalar > 0'); + end + end + case 'height' + opts.height = LocalToNum(value, auto.height); + if ~ischar(value) | ~strcmp(value,'auto') + if(~LocalIsPositiveScalar(opts.height)) + error('Height must be a numeric scalar > 0'); + end + end + case 'color' + opts.color = LocalCheckAuto(lower(value),auto.color); + if ~strcmp(opts.color,{'bw','gray','rgb','cmyk'}) + error('Color must be ''bw'', ''gray'',''rgb'' or ''cmyk''.'); + end + case 'fontmode' + opts.fontmode = LocalCheckAuto(lower(value),auto.fontmode); + if ~strcmp(opts.fontmode,{'scaled','fixed'}) + error('FontMode must be ''scaled'' or ''fixed''.'); + end + case 'fontsize' + opts.fontsize = LocalToNum(value,auto.fontsize); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.fontsize) + error('FontSize must be a numeric scalar > 0'); + end + end + case 'defaultfixedfontsize' + opts.defaultfontsize = LocalToNum(value,auto.defaultfontsize); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.defaultfontsize) + error('DefaultFixedFontSize must be a numeric scalar > 0'); + end + end + case 'fontsizemin' + opts.fontmin = LocalToNum(value,auto.fontmin); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.fontmin) + error('FontSizeMin must be a numeric scalar > 0'); + end + end + case 'fontsizemax' + opts.fontmax = LocalToNum(value,auto.fontmax); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.fontmax) + error('FontSizeMax must be a numeric scalar > 0'); + end + end + case 'fontencoding' + opts.fontencoding = LocalCheckAuto(lower(value),auto.fontencoding); + if ~strcmp(opts.fontencoding,{'latin1','adobe'}) + error('FontEncoding must be ''latin1'' or ''adobe''.'); + end + case 'linemode' + opts.linemode = LocalCheckAuto(lower(value),auto.linemode); + if ~strcmp(opts.linemode,{'scaled','fixed'}) + error('LineMode must be ''scaled'' or ''fixed''.'); + end + case 'linewidth' + opts.linewidth = LocalToNum(value,auto.linewidth); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.linewidth) + error('LineWidth must be a numeric scalar > 0'); + end + end + case 'defaultfixedlinewidth' + opts.defaultlinewidth = LocalToNum(value,auto.defaultlinewidth); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.defaultlinewidth) + error(['DefaultFixedLineWidth must be a numeric scalar >' ... + ' 0']); + end + end + case 'linewidthmin' + opts.linemin = LocalToNum(value,auto.linemin); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.linemin) + error('LineWidthMin must be a numeric scalar > 0'); + end + end + case 'linewidthmax' + opts.linemax = LocalToNum(value,auto.linemax); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.linemax) + error('LineWidthMax must be a numeric scalar > 0'); + end + end + case 'linestylemap' + opts.stylemap = LocalCheckAuto(value,auto.stylemap); + case 'renderer' + opts.renderer = LocalCheckAuto(lower(value),auto.renderer); + if ~ischar(value) | ~strcmp(value,'auto') + if ~strcmp(opts.renderer,{'painters','zbuffer','opengl'}) + error(['Renderer must be ''painters'', ''zbuffer'' or' ... + ' ''opengl''.']); + end + end + case 'resolution' + opts.resolution = LocalToNum(value,auto.resolution); + if ~ischar(value) | ~strcmp(value,'auto') + if ~(isnumeric(value) & (prod(size(value)) == 1) & (value >= 0)); + error('Resolution must be a numeric scalar >= 0'); + end + end + case 'applystyle' % means to apply the options and not export + opts.applystyle = 1; + case 'reference' + if ischar(value) + if strcmp(value,'auto') + opts.refobj = auto.refobj; + else + opts.refobj = eval(value); + end + else + opts.refobj = value; + end + if ~LocalIsHG(opts.refobj,'axes') + error('Reference object must evaluate to an axes handle.'); + end + case 'bounds' + opts.bounds = LocalCheckAuto(lower(value),auto.bounds); + explicitbounds = 1; + if ~strcmp(opts.bounds,{'tight','loose'}) + error('Bounds must be ''tight'' or ''loose''.'); + end + case 'lockaxes' + opts.lockaxes = LocalToNum(value,auto.lockaxes); + case 'separatetext' + opts.separatetext = LocalToNum(value,auto.separatetext); + otherwise + error(['Unrecognized option ' param '.']); + end +end + +% make sure figure is up-to-date +drawnow; + +allLines = findall(H, 'type', 'line'); +allText = findall(H, 'type', 'text'); +allAxes = findall(H, 'type', 'axes'); +allImages = findall(H, 'type', 'image'); +allLights = findall(H, 'type', 'light'); +allPatch = findall(H, 'type', 'patch'); +allSurf = findall(H, 'type', 'surface'); +allRect = findall(H, 'type', 'rectangle'); +allFont = [allText; allAxes]; +allColor = [allLines; allText; allAxes; allLights]; +allMarker = [allLines; allPatch; allSurf]; +allEdge = [allPatch; allSurf]; +allCData = [allImages; allPatch; allSurf]; + +old.objs = {}; +old.prop = {}; +old.values = {}; + +% Process format +if strncmp(opts.format,'eps',3) & ~strcmp(opts.preview,'none') + args = {args{:}, ['-' opts.preview]}; +end + +hadError = 0; +oldwarn = warning; +try + + % lock axes limits, ticks and labels if requested + if opts.lockaxes + old = LocalManualAxesMode(old, allAxes, 'TickMode'); + old = LocalManualAxesMode(old, allAxes, 'TickLabelMode'); + old = LocalManualAxesMode(old, allAxes, 'LimMode'); + end + + % Process size parameters + figurePaperUnits = get(H, 'PaperUnits'); + oldFigureUnits = get(H, 'Units'); + oldFigPos = get(H,'Position'); + set(H, 'Units', figurePaperUnits); + figPos = get(H,'Position'); + refsize = figPos(3:4); + if opts.refobj ~= -1 + oldUnits = get(opts.refobj, 'Units'); + set(opts.refobj, 'Units', figurePaperUnits); + r = get(opts.refobj, 'Position'); + refsize = r(3:4); + set(opts.refobj, 'Units', oldUnits); + end + aspectRatio = refsize(1)/refsize(2); + if (opts.width == -1) & (opts.height == -1) + opts.width = refsize(1); + opts.height = refsize(2); + elseif (opts.width == -1) + opts.width = opts.height * aspectRatio; + elseif (opts.height == -1) + opts.height = opts.width / aspectRatio; + end + wscale = opts.width/refsize(1); + hscale = opts.height/refsize(2); + sizescale = min(wscale,hscale); + old = LocalPushOldData(old,H,'PaperPositionMode', ... + get(H,'PaperPositionMode')); + set(H, 'PaperPositionMode', 'auto'); + newPos = [figPos(1) figPos(2)+figPos(4)*(1-hscale) ... + wscale*figPos(3) hscale*figPos(4)]; + set(H, 'Position', newPos); + set(H, 'Units', oldFigureUnits); + + % process line-style map + if ~isempty(opts.stylemap) & ~isempty(allLines) + oldlstyle = LocalGetAsCell(allLines,'LineStyle'); + old = LocalPushOldData(old, allLines, {'LineStyle'}, ... + oldlstyle); + newlstyle = oldlstyle; + if ischar(opts.stylemap) & strcmpi(opts.stylemap,'bw') + newlstyle = LocalMapColorToStyle(allLines); + else + try + newlstyle = feval(opts.stylemap,allLines); + catch + warning(['Skipping stylemap. ' lasterr]); + end + end + set(allLines,{'LineStyle'},newlstyle); + end + + % Process rendering parameters + switch (opts.color) + case {'bw', 'gray'} + if ~strcmp(opts.color,'bw') & strncmp(opts.format,'eps',3) + opts.format = [opts.format 'c']; + end + args = {args{:}, ['-d' opts.format]}; + + %compute and set gray colormap + oldcmap = get(H,'Colormap'); + newgrays = 0.30*oldcmap(:,1) + 0.59*oldcmap(:,2) + 0.11*oldcmap(:,3); + newcmap = [newgrays newgrays newgrays]; + old = LocalPushOldData(old, H, 'Colormap', oldcmap); + set(H, 'Colormap', newcmap); + + %compute and set ColorSpec and CData properties + old = LocalUpdateColors(allColor, 'color', old); + old = LocalUpdateColors(allAxes, 'xcolor', old); + old = LocalUpdateColors(allAxes, 'ycolor', old); + old = LocalUpdateColors(allAxes, 'zcolor', old); + old = LocalUpdateColors(allMarker, 'MarkerEdgeColor', old); + old = LocalUpdateColors(allMarker, 'MarkerFaceColor', old); + old = LocalUpdateColors(allEdge, 'EdgeColor', old); + old = LocalUpdateColors(allEdge, 'FaceColor', old); + old = LocalUpdateColors(allCData, 'CData', old); + + case {'rgb','cmyk'} + if strncmp(opts.format,'eps',3) + opts.format = [opts.format 'c']; + args = {args{:}, ['-d' opts.format]}; + if strcmp(opts.color,'cmyk') + args = {args{:}, '-cmyk'}; + end + else + args = {args{:}, ['-d' opts.format]}; + end + otherwise + error('Invalid Color parameter'); + end + if (~isempty(opts.renderer)) + args = {args{:}, ['-' opts.renderer]}; + end + if (~isempty(opts.resolution)) | ~strncmp(opts.format,'eps',3) + if isempty(opts.resolution) + opts.resolution = 0; + end + args = {args{:}, ['-r' int2str(opts.resolution)]}; + end + + % Process font parameters + if ~isempty(opts.fontmode) + oldfonts = LocalGetAsCell(allFont,'FontSize'); + oldfontunits = LocalGetAsCell(allFont,'FontUnits'); + set(allFont,'FontUnits','points'); + switch (opts.fontmode) + case 'fixed' + if (opts.fontsize == -1) + set(allFont,'FontSize',opts.defaultfontsize); + else + set(allFont,'FontSize',opts.fontsize); + end + case 'scaled' + if (opts.fontsize == -1) + scale = sizescale; + else + scale = opts.fontsize; + end + newfonts = LocalScale(oldfonts,scale,opts.fontmin,opts.fontmax); + set(allFont,{'FontSize'},newfonts); + otherwise + error('Invalid FontMode parameter'); + end + old = LocalPushOldData(old, allFont, {'FontSize'}, oldfonts); + old = LocalPushOldData(old, allFont, {'FontUnits'}, oldfontunits); + end + if strcmp(opts.fontencoding,'adobe') & strncmp(opts.format,'eps',3) + args = {args{:}, '-adobecset'}; + end + + % Process line parameters + if ~isempty(opts.linemode) + oldlines = LocalGetAsCell(allMarker,'LineWidth'); + old = LocalPushOldData(old, allMarker, {'LineWidth'}, oldlines); + switch (opts.linemode) + case 'fixed' + if (opts.linewidth == -1) + set(allMarker,'LineWidth',opts.defaultlinewidth); + else + set(allMarker,'LineWidth',opts.linewidth); + end + case 'scaled' + if (opts.linewidth == -1) + scale = sizescale; + else + scale = opts.linewidth; + end + newlines = LocalScale(oldlines, scale, opts.linemin, opts.linemax); + set(allMarker,{'LineWidth'},newlines); + end + end + + % adjust figure bounds to surround axes + if strcmp(opts.bounds,'tight') + if (~strncmp(opts.format,'eps',3) & LocalHas3DPlot(allAxes)) | ... + (strncmp(opts.format,'eps',3) & opts.separatetext) + if (explicitbounds == 1) + warning(['Cannot compute ''tight'' bounds. Using ''loose''' ... + ' bounds.']); + end + opts.bounds = 'loose'; + end + end + warning('off'); + if ~isempty(allAxes) + if strncmp(opts.format,'eps',3) + if strcmp(opts.bounds,'loose') + args = {args{:}, '-loose'}; + end + old = LocalPushOldData(old,H,'Position', oldFigPos); + elseif strcmp(opts.bounds,'tight') + oldaunits = LocalGetAsCell(allAxes,'Units'); + oldapos = LocalGetAsCell(allAxes,'Position'); + oldtunits = LocalGetAsCell(allText,'units'); + oldtpos = LocalGetAsCell(allText,'Position'); + set(allAxes,'units','points'); + apos = LocalGetAsCell(allAxes,'Position'); + oldunits = get(H,'Units'); + set(H,'units','points'); + origfr = get(H,'position'); + fr = []; + for k=1:length(allAxes) + if ~strcmpi(get(allAxes(k),'Tag'),'legend') + axesR = apos{k}; + r = LocalAxesTightBoundingBox(axesR, allAxes(k)); + r(1:2) = r(1:2) + axesR(1:2); + fr = LocalUnionRect(fr,r); + end + end + if isempty(fr) + fr = [0 0 origfr(3:4)]; + end + for k=1:length(allAxes) + ax = allAxes(k); + r = apos{k}; + r(1:2) = r(1:2) - fr(1:2); + set(ax,'Position',r); + end + old = LocalPushOldData(old, allAxes, {'Position'}, oldapos); + old = LocalPushOldData(old, allText, {'Position'}, oldtpos); + old = LocalPushOldData(old, allText, {'Units'}, oldtunits); + old = LocalPushOldData(old, allAxes, {'Units'}, oldaunits); + old = LocalPushOldData(old, H, 'Position', oldFigPos); + old = LocalPushOldData(old, H, 'Units', oldFigureUnits); + r = [origfr(1) origfr(2)+origfr(4)-fr(4) fr(3:4)]; + set(H,'Position',r); + else + args = {args{:}, '-loose'}; + old = LocalPushOldData(old,H,'Position', oldFigPos); + end + end + + % Process text in a separate file if needed + if opts.separatetext & ~opts.applystyle + % First hide all text and export + oldtvis = LocalGetAsCell(allText,'visible'); + set(allText,'visible','off'); + oldax = LocalGetAsCell(allAxes,'XTickLabel',1); + olday = LocalGetAsCell(allAxes,'YTickLabel',1); + oldaz = LocalGetAsCell(allAxes,'ZTickLabel',1); + null = cell(length(oldax),1); + [null{:}] = deal([]); + set(allAxes,{'XTickLabel'},null); + set(allAxes,{'YTickLabel'},null); + set(allAxes,{'ZTickLabel'},null); + print(H, filename, args{:}); + set(allText,{'Visible'},oldtvis); + set(allAxes,{'XTickLabel'},oldax); + set(allAxes,{'YTickLabel'},olday); + set(allAxes,{'ZTickLabel'},oldaz); + % Now hide all non-text and export as eps in painters + [path, name, ext] = fileparts(filename); + tfile = fullfile(path,[name '_t.eps']); + tfile2 = fullfile(path,[name '_t2.eps']); + foundRenderer = 0; + for k=1:length(args) + if strncmp('-d',args{k},2) + args{k} = '-deps'; + elseif strncmp('-zbuffer',args{k},8) | ... + strncmp('-opengl', args{k},6) + args{k} = '-painters'; + foundRenderer = 1; + end + end + if ~foundRenderer + args = {args{:}, '-painters'}; + end + allNonText = [allLines; allLights; allPatch; ... + allImages; allSurf; allRect]; + oldvis = LocalGetAsCell(allNonText,'visible'); + oldc = LocalGetAsCell(allAxes,'color'); + oldaxg = LocalGetAsCell(allAxes,'XGrid'); + oldayg = LocalGetAsCell(allAxes,'YGrid'); + oldazg = LocalGetAsCell(allAxes,'ZGrid'); + [null{:}] = deal('off'); + set(allAxes,{'XGrid'},null); + set(allAxes,{'YGrid'},null); + set(allAxes,{'ZGrid'},null); + set(allNonText,'Visible','off'); + set(allAxes,'Color','none'); + print(H, tfile2, args{:}); + set(allNonText,{'Visible'},oldvis); + set(allAxes,{'Color'},oldc); + set(allAxes,{'XGrid'},oldaxg); + set(allAxes,{'YGrid'},oldayg); + set(allAxes,{'ZGrid'},oldazg); + %hack up the postscript file + fid1 = fopen(tfile,'w'); + fid2 = fopen(tfile2,'r'); + line = fgetl(fid2); + while ischar(line) + if strncmp(line,'%%Title',7) + fprintf(fid1,'%s\n',['%%Title: ', tfile]); + elseif (length(line) < 3) + fprintf(fid1,'%s\n',line); + elseif ~strcmp(line(end-2:end),' PR') & ... + ~strcmp(line(end-1:end),' L') + fprintf(fid1,'%s\n',line); + end + line = fgetl(fid2); + end + fclose(fid1); + fclose(fid2); + delete(tfile2); + + elseif ~opts.applystyle + drawnow; + print(H, filename, args{:}); + end + warning(oldwarn); + +catch + warning(oldwarn); + hadError = 1; +end + +% Restore figure settings +if opts.applystyle + varargout{1} = old; +else + for n=1:length(old.objs) + if ~iscell(old.values{n}) & iscell(old.prop{n}) + old.values{n} = {old.values{n}}; + end + set(old.objs{n}, old.prop{n}, old.values{n}); + end +end + +if hadError + error(deblank(lasterr)); +end + +% +% Local Functions +% + +function outData = LocalPushOldData(inData, objs, prop, values) +outData.objs = {objs, inData.objs{:}}; +outData.prop = {prop, inData.prop{:}}; +outData.values = {values, inData.values{:}}; + +function cellArray = LocalGetAsCell(fig,prop,allowemptycell); +cellArray = get(fig,prop); +if nargin < 3 + allowemptycell = 0; +end +if ~iscell(cellArray) & (allowemptycell | ~isempty(cellArray)) + cellArray = {cellArray}; +end + +function newArray = LocalScale(inArray, scale, minv, maxv) +n = length(inArray); +newArray = cell(n,1); +for k=1:n + newArray{k} = min(maxv,max(minv,scale*inArray{k}(1))); +end + +function gray = LocalMapToGray1(color) +gray = color; +if ischar(color) + switch color(1) + case 'y' + color = [1 1 0]; + case 'm' + color = [1 0 1]; + case 'c' + color = [0 1 1]; + case 'r' + color = [1 0 0]; + case 'g' + color = [0 1 0]; + case 'b' + color = [0 0 1]; + case 'w' + color = [1 1 1]; + case 'k' + color = [0 0 0]; + end +end +if ~ischar(color) + gray = 0.30*color(1) + 0.59*color(2) + 0.11*color(3); +end + +function newArray = LocalMapToGray(inArray); +n = length(inArray); +newArray = cell(n,1); +for k=1:n + color = inArray{k}; + if ~isempty(color) + color = LocalMapToGray1(color); + end + if isempty(color) | ischar(color) + newArray{k} = color; + else + newArray{k} = [color color color]; + end +end + +function newArray = LocalMapColorToStyle(inArray); +inArray = LocalGetAsCell(inArray,'Color'); +n = length(inArray); +newArray = cell(n,1); +styles = {'-','--',':','-.'}; +uniques = []; +nstyles = length(styles); +for k=1:n + gray = LocalMapToGray1(inArray{k}); + if isempty(gray) | ischar(gray) | gray < .05 + newArray{k} = '-'; + else + if ~isempty(uniques) & any(gray == uniques) + ind = find(gray==uniques); + else + uniques = [uniques gray]; + ind = length(uniques); + end + newArray{k} = styles{mod(ind-1,nstyles)+1}; + end +end + +function newArray = LocalMapCData(inArray); +n = length(inArray); +newArray = cell(n,1); +for k=1:n + color = inArray{k}; + if (ndims(color) == 3) & isa(color,'double') + gray = 0.30*color(:,:,1) + 0.59*color(:,:,2) + 0.11*color(:,:,3); + color(:,:,1) = gray; + color(:,:,2) = gray; + color(:,:,3) = gray; + end + newArray{k} = color; +end + +function outData = LocalUpdateColors(inArray, prop, inData) +value = LocalGetAsCell(inArray,prop); +outData.objs = {inData.objs{:}, inArray}; +outData.prop = {inData.prop{:}, {prop}}; +outData.values = {inData.values{:}, value}; +if (~isempty(value)) + if strcmp(prop,'CData') + value = LocalMapCData(value); + else + value = LocalMapToGray(value); + end + set(inArray,{prop},value); +end + +function bool = LocalIsPositiveScalar(value) +bool = isnumeric(value) & ... + prod(size(value)) == 1 & ... + value > 0; + +function value = LocalToNum(value,auto) +if ischar(value) + if strcmp(value,'auto') + value = auto; + else + value = str2num(value); + end +end + +%convert a struct to {field1,val1,field2,val2,...} +function c = LocalToCell(s) +f = fieldnames(s); +v = struct2cell(s); +opts = cell(2,length(f)); +opts(1,:) = f; +opts(2,:) = v; +c = {opts{:}}; + +function c = LocalIsHG(obj,hgtype) +c = 0; +if (length(obj) == 1) & ishandle(obj) + c = strcmp(get(obj,'type'),hgtype); +end + +function c = LocalHas3DPlot(a) +zticks = LocalGetAsCell(a,'ZTickLabel'); +c = 0; +for k=1:length(zticks) + if ~isempty(zticks{k}) + c = 1; + return; + end +end + +function r = LocalUnionRect(r1,r2) +if isempty(r1) + r = r2; +elseif isempty(r2) + r = r1; +elseif max(r2(3:4)) > 0 + left = min(r1(1),r2(1)); + bot = min(r1(2),r2(2)); + right = max(r1(1)+r1(3),r2(1)+r2(3)); + top = max(r1(2)+r1(4),r2(2)+r2(4)); + r = [left bot right-left top-bot]; +else + r = r1; +end + +function c = LocalLabelsMatchTicks(labs,ticks) +c = 0; +try + t1 = num2str(ticks(1)); + n = length(ticks); + tend = num2str(ticks(n)); + c = strncmp(labs(1),t1,length(labs(1))) & ... + strncmp(labs(n),tend,length(labs(n))); +end + +function r = LocalAxesTightBoundingBox(axesR, a) +r = []; +atext = findall(a,'type','text','visible','on'); +if ~isempty(atext) + set(atext,'units','points'); + res=LocalGetAsCell(atext,'extent'); + for n=1:length(atext) + r = LocalUnionRect(r,res{n}); + end +end +if strcmp(get(a,'visible'),'on') + r = LocalUnionRect(r,[0 0 axesR(3:4)]); + oldunits = get(a,'fontunits'); + set(a,'fontunits','points'); + label = text(0,0,'','parent',a,... + 'units','points',... + 'fontsize',get(a,'fontsize'),... + 'fontname',get(a,'fontname'),... + 'fontweight',get(a,'fontweight'),... + 'fontangle',get(a,'fontangle'),... + 'visible','off'); + fs = get(a,'fontsize'); + + % handle y axis tick labels + ry = [0 -fs/2 0 axesR(4)+fs]; + ylabs = get(a,'yticklabels'); + yticks = get(a,'ytick'); + maxw = 0; + if ~isempty(ylabs) + for n=1:size(ylabs,1) + set(label,'string',ylabs(n,:)); + ext = get(label,'extent'); + maxw = max(maxw,ext(3)); + end + if ~LocalLabelsMatchTicks(ylabs,yticks) & ... + strcmp(get(a,'xaxislocation'),'bottom') + ry(4) = ry(4) + 1.5*ext(4); + end + if strcmp(get(a,'yaxislocation'),'left') + ry(1) = -(maxw+5); + else + ry(1) = axesR(3); + end + ry(3) = maxw+5; + r = LocalUnionRect(r,ry); + end + + % handle x axis tick labels + rx = [0 0 0 fs+5]; + xlabs = get(a,'xticklabels'); + xticks = get(a,'xtick'); + if ~isempty(xlabs) + if strcmp(get(a,'xaxislocation'),'bottom') + rx(2) = -(fs+5); + if ~LocalLabelsMatchTicks(xlabs,xticks); + rx(4) = rx(4) + 2*fs; + rx(2) = rx(2) - 2*fs; + end + else + rx(2) = axesR(4); + % exponent is still below axes + if ~LocalLabelsMatchTicks(xlabs,xticks); + rx(4) = rx(4) + axesR(4) + 2*fs; + rx(2) = -2*fs; + end + end + set(label,'string',xlabs(1,:)); + ext1 = get(label,'extent'); + rx(1) = -ext1(3)/2; + set(label,'string',xlabs(size(xlabs,1),:)); + ext2 = get(label,'extent'); + rx(3) = axesR(3) + (ext2(3) + ext1(3))/2; + r = LocalUnionRect(r,rx); + end + set(a,'fontunits',oldunits); + delete(label); +end + +function c = LocalManualAxesMode(old, allAxes, base) +xs = ['X' base]; +ys = ['Y' base]; +zs = ['Z' base]; +oldXMode = LocalGetAsCell(allAxes,xs); +oldYMode = LocalGetAsCell(allAxes,ys); +oldZMode = LocalGetAsCell(allAxes,zs); +old = LocalPushOldData(old, allAxes, {xs}, oldXMode); +old = LocalPushOldData(old, allAxes, {ys}, oldYMode); +old = LocalPushOldData(old, allAxes, {zs}, oldZMode); +set(allAxes,xs,'manual'); +set(allAxes,ys,'manual'); +set(allAxes,zs,'manual'); +c = old; + +function val = LocalCheckAuto(val, auto) +if ischar(val) & strcmp(val,'auto') + val = auto; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/extend_domain_table.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,24 @@ +function B = extend_domain_table(A, smalldom, smallsz, bigdom, bigsz) +% EXTEND_DOMAIN_TABLE Expand an array so it has the desired size. +% B = extend_domain_table(A, smalldom, smallsz, bigdom, bigsz) +% +% A is the array with domain smalldom and sizes smallsz. +% bigdom is the desired domain, with sizes bigsz. +% +% Example: +% smalldom = [1 3], smallsz = [2 4], bigdom = [1 2 3 4], bigsz = [2 1 4 5], +% so B(i,j,k,l) = A(i,k) for i in 1:2, j in 1:1, k in 1:4, l in 1:5 + +if isequal(size(A), [1 1]) % a scalar + B = A; % * myones(bigsz); + return; +end + +map = find_equiv_posns(smalldom, bigdom); +sz = ones(1, length(bigdom)); +sz(map) = smallsz; +B = myreshape(A, sz); % add dimensions for the stuff not in A +sz = bigsz; +sz(map) = 1; % don't replicate along A's dimensions +B = myrepmat(B, sz(:)'); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/factorial.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,9 @@ +function x = factorial(n) +% FACTORIAL Compute n! +% x = factorial(n) + +if n == 0 + x = 1; +else + x = n*factorial(n-1); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/filepartsLast.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +function [last] = filepartsLast(fname) +% filepartsLast Return the last part of a filename (strip off directory and suffix) +% function filepartsLast(fname) +% +% Examples +% filepartsLast('C:/foo/bar') = 'bar' +% filepartsLast('C:/foo/bar.mat') = 'bar' +% filepartsLast('C:/foo/bar.mat.gz') = 'bar.mat' +% filepartsLast('bar.mat') = 'bar' + +[pathstr,name,ext,versn] = fileparts(fname); +last = name;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/find_equiv_posns.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,24 @@ +function p = find_equiv_posns(vsmall, vlarge) +% FIND_EQUIV_POSNS p[i] = the place where vsmall[i] occurs in vlarge. +% p = find_equiv_posns(vsmall, vlarge) +% THE VECTORS ARE ASSUMED TO BE SORTED. +% +% e.g., vsmall=[2,8], vlarge=[2,7,8,4], p=[1,3] +% +% In R/S, this function is called 'match' + +%if ~mysubset(vsmall, vlarge) +% error('small domain must occur in large domain'); +%end + +if isempty(vsmall) | isempty(vlarge) + p = []; + return; +end + +bitvec = sparse(1, max(vlarge)); +%bitvec = zeros(1, max(vlarge)); +bitvec(vsmall) = 1; +p = find(bitvec(vlarge)); + +%p = find(ismember(vlarge, vsmall)); % slower
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/fullfileKPM.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +function f = fullfileKPM(varargin) +% fullfileKPM Concatenate strings with file separator, then convert it to a/b/c +% function f = fullfileKPM(varargin) + +f = fullfile(varargin{:}); +f = strrep(f, '\', '/');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/genpathKPM.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,45 @@ +function p = genpathKPM(d) +% genpathKPM Like built-in genpath, but omits directories whose name is 'Old', 'old' or 'CVS' +% function p = genpathKPM(d) + +if nargin==0, + p = genpath(fullfile(matlabroot,'toolbox')); + if length(p) > 1, p(end) = []; end % Remove trailing pathsep + return +end + +% initialise variables +methodsep = '@'; % qualifier for overloaded method directories +p = ''; % path to be returned + +% Generate path based on given root directory +files = dir(d); +if isempty(files) + return +end + +% Add d to the path even if it is empty. +p = [p d pathsep]; + +% set logical vector for subdirectory entries in d +isdir = logical(cat(1,files.isdir)); +% +% Recursively descend through directories which are neither +% private nor "class" directories. +% +dirs = files(isdir); % select only directory entries from the current listing + +for i=1:length(dirs) + dirname = dirs(i).name; + if ~strcmp( dirname,'.') & ... + ~strcmp( dirname,'..') & ... + ~strncmp( dirname,methodsep,1)& ... + ~strcmp( dirname,'private') & ... + ~strcmp( dirname, 'old') & ... % KPM + ~strcmp( dirname, 'Old') & ... % KPM + ~strcmp( dirname, 'CVS') % KPM + p = [p genpathKPM(fullfile(d,dirname))]; % recursive calling of this function. + end +end + +%------------------------------------------------------------------------------
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/hash_add.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,19 @@ +function hash_add(key, val, fname) +% HASH_ADD Append key,value pair to end of hashtable stored in a file +% function hash_add(key, val, filename) +% +% See hash_lookup for an example + +if ~exist(fname, 'file') + % new hashtable + hashtable.key{1} = key; + hashtable.value{1} = val; +else + %hashtable = importdata(fname); + %hashtable = load(fname, '-mat'); + load(fname, '-mat'); + Nentries = length(hashtable.key); + hashtable.key{Nentries+1} = key; + hashtable.value{Nentries+1} = val; +end +save(fname, 'hashtable', '-mat');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/hash_del.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,26 @@ +function ndx = hash_del(key, fname) +% HASH_DEL Remove all entries that match key from hashtable stored in a file +% ndx = hash_del(key, fname) +% +% Returns indices of matching entries (if any) +% See hash_lookup for an example + +ndx = []; + +if ~exist(fname, 'file') + % new hashtable - no op +else + %hashtable = importdata(fname); + %hashtable = load(fname, '-mat'); + load(fname, '-mat'); + Nentries = length(hashtable.key); + for i=1:Nentries + if isequal(hashtable.key{i}, key) + ndx = [ndx i]; + end + end + hashtable.key(ndx) = []; + hashtable.value(ndx) = []; + save(fname, 'hashtable', '-mat'); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/hash_lookup.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,34 @@ +function [val, found, Nentries] = hash_lookup(key, fname) +% HASH_LOOKUP Lookup a key in a hash table stored in a file using linear search +% function [val, found, Nentries] = hash_lookup(key, filename) +% +% Example: +% If htbl.mat does not exist, +% [val,found,N] = hash_lookup('foo', 'htbl') +% returns found val = [], found = 0, N = 0 +% hash_add('foo', 42, 'htbl') +% hash_add('bar', [1:10], 'htbl') +% [val,found,N] = hash_lookup('foo', 'htbl') +% now returns val = 42, found = 1, N = 2 +% +% Type 'delete htbl' to delete the file/ reset the hashtable + + +val = []; +found = 0; + +if exist(fname, 'file')==0 + % new hashtable + Nentries = 0; +else + %hashtable = importdata(fname); + load(fname); + Nentries = length(hashtable.key); + for i=1:Nentries + if isequal(hashtable.key{i}, key) + val = hashtable.value{i}; + found = 1; + break; + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/hsvKPM.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +function colors = hsvKPM(N) +% hsvKPM Like built-in HSV, except it randomizes the order, so that adjacent colors are dis-similar +% function colors = hsvKPM(N) + +colors = hsv(N); +perm = randperm(N); +colors = colors(perm,:);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/hungarian.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,464 @@ +function [C,T]=hungarian(A) +%HUNGARIAN Solve the Assignment problem using the Hungarian method. +% +%[C,T]=hungarian(A) +%A - a square cost matrix. +%C - the optimal assignment. +%T - the cost of the optimal assignment. + +% Adapted from the FORTRAN IV code in Carpaneto and Toth, "Algorithm 548: +% Solution of the assignment problem [H]", ACM Transactions on +% Mathematical Software, 6(1):104-111, 1980. + +% v1.0 96-06-14. Niclas Borlin, niclas@cs.umu.se. +% Department of Computing Science, Umeå University, +% Sweden. +% All standard disclaimers apply. + +% A substantial effort was put into this code. If you use it for a +% publication or otherwise, please include an acknowledgement or at least +% notify me by email. /Niclas + +[m,n]=size(A); + +if (m~=n) + error('HUNGARIAN: Cost matrix must be square!'); +end + +% Save original cost matrix. +orig=A; + +% Reduce matrix. +A=hminired(A); + +% Do an initial assignment. +[A,C,U]=hminiass(A); + +% Repeat while we have unassigned rows. +while (U(n+1)) + % Start with no path, no unchecked zeros, and no unexplored rows. + LR=zeros(1,n); + LC=zeros(1,n); + CH=zeros(1,n); + RH=[zeros(1,n) -1]; + + % No labelled columns. + SLC=[]; + + % Start path in first unassigned row. + r=U(n+1); + % Mark row with end-of-path label. + LR(r)=-1; + % Insert row first in labelled row set. + SLR=r; + + % Repeat until we manage to find an assignable zero. + while (1) + % If there are free zeros in row r + if (A(r,n+1)~=0) + % ...get column of first free zero. + l=-A(r,n+1); + + % If there are more free zeros in row r and row r in not + % yet marked as unexplored.. + if (A(r,l)~=0 & RH(r)==0) + % Insert row r first in unexplored list. + RH(r)=RH(n+1); + RH(n+1)=r; + + % Mark in which column the next unexplored zero in this row + % is. + CH(r)=-A(r,l); + end + else + % If all rows are explored.. + if (RH(n+1)<=0) + % Reduce matrix. + [A,CH,RH]=hmreduce(A,CH,RH,LC,LR,SLC,SLR); + end + + % Re-start with first unexplored row. + r=RH(n+1); + % Get column of next free zero in row r. + l=CH(r); + % Advance "column of next free zero". + CH(r)=-A(r,l); + % If this zero is last in the list.. + if (A(r,l)==0) + % ...remove row r from unexplored list. + RH(n+1)=RH(r); + RH(r)=0; + end + end + + % While the column l is labelled, i.e. in path. + while (LC(l)~=0) + % If row r is explored.. + if (RH(r)==0) + % If all rows are explored.. + if (RH(n+1)<=0) + % Reduce cost matrix. + [A,CH,RH]=hmreduce(A,CH,RH,LC,LR,SLC,SLR); + end + + % Re-start with first unexplored row. + r=RH(n+1); + end + + % Get column of next free zero in row r. + l=CH(r); + + % Advance "column of next free zero". + CH(r)=-A(r,l); + + % If this zero is last in list.. + if(A(r,l)==0) + % ...remove row r from unexplored list. + RH(n+1)=RH(r); + RH(r)=0; + end + end + + % If the column found is unassigned.. + if (C(l)==0) + % Flip all zeros along the path in LR,LC. + [A,C,U]=hmflip(A,C,LC,LR,U,l,r); + % ...and exit to continue with next unassigned row. + break; + else + % ...else add zero to path. + + % Label column l with row r. + LC(l)=r; + + % Add l to the set of labelled columns. + SLC=[SLC l]; + + % Continue with the row assigned to column l. + r=C(l); + + % Label row r with column l. + LR(r)=l; + + % Add r to the set of labelled rows. + SLR=[SLR r]; + end + end +end + +% Calculate the total cost. +T=sum(orig(logical(sparse(C,1:size(orig,2),1)))); + + +function A=hminired(A) +%HMINIRED Initial reduction of cost matrix for the Hungarian method. +% +%B=assredin(A) +%A - the unreduced cost matris. +%B - the reduced cost matrix with linked zeros in each row. + +% v1.0 96-06-13. Niclas Borlin, niclas@cs.umu.se. + +[m,n]=size(A); + +% Subtract column-minimum values from each column. +colMin=min(A); +A=A-colMin(ones(n,1),:); + +% Subtract row-minimum values from each row. +rowMin=min(A')'; +A=A-rowMin(:,ones(1,n)); + +% Get positions of all zeros. +[i,j]=find(A==0); + +% Extend A to give room for row zero list header column. +A(1,n+1)=0; +for k=1:n + % Get all column in this row. + cols=j(k==i)'; + % Insert pointers in matrix. + A(k,[n+1 cols])=[-cols 0]; +end + + +function [A,C,U]=hminiass(A) +%HMINIASS Initial assignment of the Hungarian method. +% +%[B,C,U]=hminiass(A) +%A - the reduced cost matrix. +%B - the reduced cost matrix, with assigned zeros removed from lists. +%C - a vector. C(J)=I means row I is assigned to column J, +% i.e. there is an assigned zero in position I,J. +%U - a vector with a linked list of unassigned rows. + +% v1.0 96-06-14. Niclas Borlin, niclas@cs.umu.se. + +[n,np1]=size(A); + +% Initalize return vectors. +C=zeros(1,n); +U=zeros(1,n+1); + +% Initialize last/next zero "pointers". +LZ=zeros(1,n); +NZ=zeros(1,n); + +for i=1:n + % Set j to first unassigned zero in row i. + lj=n+1; + j=-A(i,lj); + + % Repeat until we have no more zeros (j==0) or we find a zero + % in an unassigned column (c(j)==0). + + while (C(j)~=0) + % Advance lj and j in zero list. + lj=j; + j=-A(i,lj); + + % Stop if we hit end of list. + if (j==0) + break; + end + end + + if (j~=0) + % We found a zero in an unassigned column. + + % Assign row i to column j. + C(j)=i; + + % Remove A(i,j) from unassigned zero list. + A(i,lj)=A(i,j); + + % Update next/last unassigned zero pointers. + NZ(i)=-A(i,j); + LZ(i)=lj; + + % Indicate A(i,j) is an assigned zero. + A(i,j)=0; + else + % We found no zero in an unassigned column. + + % Check all zeros in this row. + + lj=n+1; + j=-A(i,lj); + + % Check all zeros in this row for a suitable zero in another row. + while (j~=0) + % Check the in the row assigned to this column. + r=C(j); + + % Pick up last/next pointers. + lm=LZ(r); + m=NZ(r); + + % Check all unchecked zeros in free list of this row. + while (m~=0) + % Stop if we find an unassigned column. + if (C(m)==0) + break; + end + + % Advance one step in list. + lm=m; + m=-A(r,lm); + end + + if (m==0) + % We failed on row r. Continue with next zero on row i. + lj=j; + j=-A(i,lj); + else + % We found a zero in an unassigned column. + + % Replace zero at (r,m) in unassigned list with zero at (r,j) + A(r,lm)=-j; + A(r,j)=A(r,m); + + % Update last/next pointers in row r. + NZ(r)=-A(r,m); + LZ(r)=j; + + % Mark A(r,m) as an assigned zero in the matrix . . . + A(r,m)=0; + + % ...and in the assignment vector. + C(m)=r; + + % Remove A(i,j) from unassigned list. + A(i,lj)=A(i,j); + + % Update last/next pointers in row r. + NZ(i)=-A(i,j); + LZ(i)=lj; + + % Mark A(r,m) as an assigned zero in the matrix . . . + A(i,j)=0; + + % ...and in the assignment vector. + C(j)=i; + + % Stop search. + break; + end + end + end +end + +% Create vector with list of unassigned rows. + +% Mark all rows have assignment. +r=zeros(1,n); +rows=C(C~=0); +r(rows)=rows; +empty=find(r==0); + +% Create vector with linked list of unassigned rows. +U=zeros(1,n+1); +U([n+1 empty])=[empty 0]; + + +function [A,C,U]=hmflip(A,C,LC,LR,U,l,r) +%HMFLIP Flip assignment state of all zeros along a path. +% +%[A,C,U]=hmflip(A,C,LC,LR,U,l,r) +%Input: +%A - the cost matrix. +%C - the assignment vector. +%LC - the column label vector. +%LR - the row label vector. +%U - the +%r,l - position of last zero in path. +%Output: +%A - updated cost matrix. +%C - updated assignment vector. +%U - updated unassigned row list vector. + +% v1.0 96-06-14. Niclas Borlin, niclas@cs.umu.se. + +n=size(A,1); + +while (1) + % Move assignment in column l to row r. + C(l)=r; + + % Find zero to be removed from zero list.. + + % Find zero before this. + m=find(A(r,:)==-l); + + % Link past this zero. + A(r,m)=A(r,l); + + A(r,l)=0; + + % If this was the first zero of the path.. + if (LR(r)<0) + ...remove row from unassigned row list and return. + U(n+1)=U(r); + U(r)=0; + return; + else + + % Move back in this row along the path and get column of next zero. + l=LR(r); + + % Insert zero at (r,l) first in zero list. + A(r,l)=A(r,n+1); + A(r,n+1)=-l; + + % Continue back along the column to get row of next zero in path. + r=LC(l); + end +end + + +function [A,CH,RH]=hmreduce(A,CH,RH,LC,LR,SLC,SLR) +%HMREDUCE Reduce parts of cost matrix in the Hungerian method. +% +%[A,CH,RH]=hmreduce(A,CH,RH,LC,LR,SLC,SLR) +%Input: +%A - Cost matrix. +%CH - vector of column of 'next zeros' in each row. +%RH - vector with list of unexplored rows. +%LC - column labels. +%RC - row labels. +%SLC - set of column labels. +%SLR - set of row labels. +% +%Output: +%A - Reduced cost matrix. +%CH - Updated vector of 'next zeros' in each row. +%RH - Updated vector of unexplored rows. + +% v1.0 96-06-14. Niclas Borlin, niclas@cs.umu.se. + +n=size(A,1); + +% Find which rows are covered, i.e. unlabelled. +coveredRows=LR==0; + +% Find which columns are covered, i.e. labelled. +coveredCols=LC~=0; + +r=find(~coveredRows); +c=find(~coveredCols); + +% Get minimum of uncovered elements. +m=min(min(A(r,c))); + +% Subtract minimum from all uncovered elements. +A(r,c)=A(r,c)-m; + +% Check all uncovered columns.. +for j=c + % ...and uncovered rows in path order.. + for i=SLR + % If this is a (new) zero.. + if (A(i,j)==0) + % If the row is not in unexplored list.. + if (RH(i)==0) + % ...insert it first in unexplored list. + RH(i)=RH(n+1); + RH(n+1)=i; + % Mark this zero as "next free" in this row. + CH(i)=j; + end + % Find last unassigned zero on row I. + row=A(i,:); + colsInList=-row(row<0); + if (length(colsInList)==0) + % No zeros in the list. + l=n+1; + else + l=colsInList(row(colsInList)==0); + end + % Append this zero to end of list. + A(i,l)=-j; + end + end +end + +% Add minimum to all doubly covered elements. +r=find(coveredRows); +c=find(coveredCols); + +% Take care of the zeros we will remove. +[i,j]=find(A(r,c)<=0); + +i=r(i); +j=c(j); + +for k=1:length(i) + % Find zero before this in this row. + lj=find(A(i(k),:)==-j(k)); + % Link past it. + A(i(k),lj)=A(i(k),j(k)); + % Mark it as assigned. + A(i(k),j(k))=0; +end + +A(r,c)=A(r,c)+m;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/image_rgb.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,36 @@ +function image_rgb(M) +% Show a matrix of integers as a color image. +% This is like imagesc, except we know what the mapping is from integer to color. +% If entries of M contain integers in {1,2,3}, we map +% this to red/green/blue + +cmap = [1 0 0; % red + 0 1 0; % green + 0 0 1; % blue + 127/255 1 212/255]; % aquamarine +image(M) +set(gcf,'colormap', cmap); + +if 1 + % make dummy handles, one per object type, for the legend + str = {}; + for i=1:size(cmap,1) + dummy_handle(i) = line([0 0.1], [0 0.1]); + set(dummy_handle(i), 'color', cmap(i,:)); + set(dummy_handle(i), 'linewidth', 2); + str{i} = num2str(i); + end + legend(dummy_handle, str, -1); +end + +if 0 +[nrows ncols] = size(M); +img = zeros(nrows, ncols, 3); +for r=1:nrows + for c=1:ncols + q = M(r,c); + img(r,c,q) = 1; + end +end +image(img) +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/imresizeAspect.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,44 @@ +function img = imresizeAspect(img, maxSize) +% function img = imresizeAspect(img, maxSize) +% If image is larger than max size, reduce size, preserving aspect ratio of input. +% +% If size(input) = [y x] and maxSize = [yy xx], +% then size(output) is given by the following (where a=y/x) +% if y<yy & x<xx then [y x] +% else if a<1 then [a*xx xx] +% else [yy yy/a] + +% For why we use bilinear, +% See http://nickyguides.digital-digest.com/bilinear-vs-bicubic.htm + +if isempty(maxSize), return; end + +[y x c] = size(img); +a= y/x; +yy = maxSize(1); xx = maxSize(2); +if y <= yy & x <= xx + % no-op +else + if a < 1 + img = imresize(img, ceil([a*xx xx]), 'bilinear'); + else + img = imresize(img, ceil([yy yy/a]), 'bilinear'); + end + fprintf('resizing from %dx%d to %dx%d\n', y, x, size(img,1), size(img,2)); +end + + +%test +if 0 + maxSize = [240 320]; + %img = imread('C:\Images\Wearables\web_static_office\8.jpg'); + %img = imread('C:\Images\Wearables\web_static_office\billandkirsten.jpg'); + %img = imread('C:\Images\Wearables\web_static_street_april\p5.jpg'); + img = imread('C:\Images\Wearables\Database_static_street\art11.jpg'); + img2 = imresizeAspect(img, maxSize); + figure(1); clf; imshow(img) + figure(2); clf; imshow(img2) + fprintf('%dx%d (%5.3f) to %dx%d (%5.3f)\n', ... + size(img,1), size(img,2), size(img,1)/size(img,2), ... + size(img2,1), size(img2,2), size(img2,1)/size(img2,2)); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/ind2subv.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,22 @@ +function sub = ind2subv(siz,index) +%IND2SUBV Subscript vector from linear index. +% IND2SUBV(SIZ,IND) returns a vector of the equivalent subscript values +% corresponding to a single index into an array of size SIZ. +% If IND is a vector, then the result is a matrix, with subscript vectors +% as rows. + +%sub = ind2subvTest(siz, index); +n = length(siz); +if n==0, sub = index; return; end % added by KPM 17 Nov 07 +cum_size = cumprod(siz(:)'); +prev_cum_size = [1 cum_size(1:end-1)]; +index = index(:) - 1; +sub = rem(repmat(index,1,n),repmat(cum_size,length(index),1)); +sub = floor(sub ./ repmat(prev_cum_size,length(index),1))+1; + +% slow way +%for dim = n:-1:1 +% sub(:,dim) = floor(index/cum_size(dim))+1; +% index = rem(index,cum_size(dim)); +%end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/ind2subvKPM.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,176 @@ +/* C mex version of ind2subv.m in misc directory */ +/* 2 input, 1 output */ +/* siz, ndx */ +/* sub */ + +#include "mex.h" +#include <math.h> + +void rbinary(int num, int n, double *rbits){ + int i, mask; + num = num - 1; + + mask = 1 << (n-1); /* mask = 00100...0 , where the 1 is in column n (rightmost = col 1) */ + for (i = 0; i < n; i++) { + rbits[n-i-1] = ((num & mask) == 0) ? 1 : 2; + num <<= 1; + } +} + +void ind_subv(int num, const double *sizes, int n, double *rbits){ + int i; + int *cumprod; + + cumprod = malloc(n * sizeof(int)); + num = num - 1; + cumprod[0] = 1; + for (i = 0; i < n-1; i++) + cumprod[i+1] = cumprod[i] * (int)sizes[i]; + for (i = n-1; i >= 0; i--) { + rbits[i] = ((int)floor(num / cumprod[i])) + 1; + num = num % cumprod[i]; + } + free(cumprod); +} + + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, k, nCol, nRow, nnRow, binary, count, temp, temp1, start; + double *pSize, *pNdx, *pr; + double ndx; + int *subv, *cumprod, *templai; + + pSize = mxGetPr(prhs[0]); + pNdx = mxGetPr(prhs[1]); + nCol = mxGetNumberOfElements(prhs[0]); + nnRow = mxGetNumberOfElements(prhs[1]); + + nRow = 1; + for(i=0; i<nCol; i++){ + nRow *= (int)pSize[i]; + } + + if(nCol == 0){ + plhs[0] = mxDuplicateArray(prhs[1]); + return; + } + + binary = 2; + for (i = 0; i < nCol; i++){ + if (pSize[i] > 2.0){ + binary = 0; + break; + } + else if((int)pSize[i] == 1){ + binary = 1; + } + } + + if(nnRow == 1){ + ndx = mxGetScalar(prhs[1]); + plhs[0] = mxCreateDoubleMatrix(1, nCol, mxREAL); + pr = mxGetPr(plhs[0]); + if(binary == 2)rbinary((int)ndx, nCol, pr); + else ind_subv((int)ndx, pSize, nCol, pr); + return; + } + + plhs[0] = mxCreateDoubleMatrix(nnRow, nCol, mxREAL); + pr = mxGetPr(plhs[0]); + + subv = malloc(nRow * nCol * sizeof(int)); + + if (binary == 2) { + for(j=0; j<nCol; j++){ + temp = (1 << j); + temp1 = j * nRow; + for(i=0; i<nRow; i++){ + subv[temp1 + i] = ((i & temp) == 0) ? 1 : 2; + } + } + } + else if(binary == 1){ + cumprod = (int *)malloc(nCol * sizeof(int)); + templai = (int *)malloc(nCol * sizeof(int)); + cumprod[0] = 1; + templai[0] = nRow; + for(i=1; i<nCol; i++){ + k = (int)pSize[i-1] - 1; + cumprod[i] = cumprod[i-1] << k; + templai[i] = templai[i-1] >> k; + } + for(j=0; j<nCol; j++){ + temp1 = j * nRow; + if(pSize[j] == 1.0){ + for(i=0; i<nRow; i++){ + subv[temp1 + i] = 1; + } + } + else{ + temp = 1; + count = 0; + for(i=0; i<templai[j]; i++){ + if(temp > 2) temp = 1; + for(k=0; k<cumprod[j]; k++){ + subv[temp1 + count] = temp; + count++; + } + temp++; + } + } + } + free(templai); + free(cumprod); + } + else { + cumprod = (int *)malloc(nCol * sizeof(int)); + templai = (int *)malloc(nCol * sizeof(int)); + cumprod[0] = 1; + for(i=1; i<nCol; i++){ + cumprod[i] = (int)(cumprod[i-1] * pSize[i-1]); + } + templai[0] = nRow; + for(i=1; i<nCol; i++){ + templai[i] = nRow / cumprod[i]; + } + for(j=0; j<nCol; j++){ + temp1 = j * nRow; + if(pSize[j] == 1.0){ + for(i=0; i<nRow; i++){ + subv[temp1 + i] = 1; + } + } + else{ + temp = 1; + count = 0; + for(i=0; i<templai[j]; i++){ + if(temp > (int)pSize[j]) temp = 1; + for(k=0; k<cumprod[j]; k++){ + subv[temp1 + count] = temp; + count++; + } + temp++; + } + } + } + free(cumprod); + free(templai); + } + + count = 0; + for(j=0; j<nCol; j++){ + temp1 = j * nRow; + for(i=0; i<nnRow; i++){ + start = (int)pNdx[i] - 1; + pr[count] = subv[temp1 + start]; + count++; + } + } + free(subv); +} + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/ind2subvKPM.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,57 @@ +function sub = ind2subvKPM(siz, ndx) +% IND2SUBV Like the built-in ind2sub, but returns the answer as a row vector. +% sub = ind2subv(siz, ndx) +% +% siz and ndx can be row or column vectors. +% sub will be of size length(ndx) * length(siz). +% +% Example +% ind2subv([2 2 2], 1:8) returns +% [1 1 1 +% 2 1 1 +% ... +% 2 2 2] +% That is, the leftmost digit toggle fastest. +% +% See also SUBV2IND + +n = length(siz); + + +if n==0 + sub = ndx; + return; +end + +if all(siz==2) + sub = dec2bitv(ndx-1, n); + sub = sub(:,n:-1:1)+1; + return; +end + +cp = [1 cumprod(siz(:)')]; +ndx = ndx(:) - 1; +sub = zeros(length(ndx), n); +for i = n:-1:1 % i'th digit + sub(:,i) = floor(ndx/cp(i))+1; + ndx = rem(ndx,cp(i)); +end + + + +%%%%%%%%%% + +function bits = dec2bitv(d,n) +% DEC2BITV Convert a decimal integer to a bit vector. +% bits = dec2bitv(d,n) is just like the built-in dec2bin, except the answer is a vector, not a string. +% n is an optional minimum length on the bit vector. +% If d is a vector, each row of the output array will be a bit vector. + + +if (nargin<2) + n=1; % Need at least one digit even for 0. +end +d = d(:); + +[f,e]=log2(max(d)); % How many digits do we need to represent the numbers? +bits=rem(floor(d*pow2(1-max(n,e):0)),2);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/ind2subvMinka.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,23 @@ +function sub = ind2subvMinka(siz,index) +%IND2SUBV Subscript vector from linear index. +% IND2SUBV(SIZ,IND) returns a vector of the equivalent subscript values +% corresponding to a single index into an array of size SIZ. +% If IND is a vector, then the result is a matrix, with subscript vectors +% as rows. + +% Written by Tom Minka +% (c) Microsoft Corporation. All rights reserved. + +n = length(siz); +if n==0, sub = index; return; end % added by KPM 17 Nov 07 +cum_size = cumprod(siz(:)'); +prev_cum_size = [1 cum_size(1:end-1)]; +index = index(:) - 1; +sub = rem(repmat(index,1,n),repmat(cum_size,length(index),1)); +sub = floor(sub ./ repmat(prev_cum_size,length(index),1))+1; + +% slow way +%for dim = n:-1:1 +% sub(:,dim) = floor(index/cum_size(dim))+1; +% index = rem(index,cum_size(dim)); +%end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/ind2subvTest.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,10 @@ +function sub = ind2subvTest(siz,index) +%IND2SUBV Subscript vector from linear index. +% IND2SUBV(SIZ,IND) returns a vector of the equivalent subscript values +% corresponding to a single index into an array of size SIZ. +% If IND is a vector, then the result is a matrix, with subscript vectors +% as rows. + +sub = ind2subvMinka(siz, index); +subKPM = ind2subvKPM(siz, index); +assert(isequal(sub, subKPM))
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/initFigures.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,48 @@ +% initFigures +% Position 6 figures on the edges of the screen. +% [xmin ymin w h] where (0,0) = bottom left +% Numbers assume screen resolution is 1024 x 1280 + +global FIGNUM NUMFIGS +FIGNUM = 1; NUMFIGS = 6; + +screenMain = true; % set to false if initializing figures for second screen +%screenMain = false; + +if screenMain + xoff = 0; +else + %xoff = 1280; + xoff = -1280; +end + +% 2 x 3 design +w = 400; h = 300; +xs = [10 450 875] + xoff; +ys = [650 40]; + +if 0 +% 3x3 design +w = 350; h = 250; +xs = [10 380 750]+xoff; +ys = [700 350 10]; +end + + +Nfigs = length(xs)*length(ys); +if screenMain + fig = 1; +else + fig = Nfigs + 1; +end + +for yi=1:length(ys) + for xi=1:length(xs) + figure(fig); + set(gcf, 'position', [xs(xi) ys(yi) w h]); + fig = fig + 1; + end +end + +% To plot something on the next available figure (with wrap around), use +% sfigure(FIGNUM); clf; FIGNUM = wrap(FIGNUM+1, NUMFIGS);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/installC_KPMtools.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,9 @@ +mex ind2subv.c +mex subv2ind.c +mex normalise.c +mex -c mexutil.c +if ~isunix + mex repmatC.c mexutil.obj +else + mex repmatC.c mexutil.o +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/is_psd.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function b = positive_semidefinite(M) +% +% Return true iff v M v' >= 0 for any vector v. +% We do this by checking that all the eigenvalues are non-negative. + +E = eig(M); +if length(find(E>=0)) == length(E) + b = 1; +else + b = 0; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/is_stochastic.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function p = is_stochastic(T) +% IS_STOCHASTIC Is the argument a stochastic matrix, i.e., the sum over the last dimension is 1. +% p = is_stochastic(T) + +p = approxeq(T, mk_stochastic(T));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/isemptycell.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +function E = isemptycell(C) +% ISEMPTYCELL Apply the isempty function to each element of a cell array +% E = isemptycell(C) +% +% This is equivalent to E = cellfun('isempty', C), +% where cellfun is a function built-in to matlab version 5.3 or newer. + +if 0 % all(version('-release') >= 12) + E = cellfun('isempty', C); +else + E = zeros(size(C)); + for i=1:prod(size(C)) + E(i) = isempty(C{i}); + end + E = logical(E); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/isequalKPM.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +function p = isequalKPM(a,b) + +if isempty(a) & isempty(b) + p = 1; +else + p = isequal(a,b); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/isposdef.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,9 @@ +function b = isposdef(a) +% ISPOSDEF Test for positive definite matrix. +% ISPOSDEF(A) returns 1 if A is positive definite, 0 otherwise. +% Using chol is much more efficient than computing eigenvectors. + +% From Tom Minka's lightspeed toolbox + +[R,p] = chol(a); +b = (p == 0);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/isscalar.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function p = isscalar(v) +% ISSCALAR Returns 1 if all dimensions have size 1. +% p = isscalar(v) + +p = (prod(size(v))==1);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/isvector.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,9 @@ +function p = isvector(v) +% ISVECTOR Returns 1 if all but one dimension have size 1. +% p = isvector(v) +% +% Example: isvector(rand(1,2,1)) = 1, isvector(rand(2,2)) = 0. + +s=size(v); +p = (ndims(v)<=2) & (s(1) == 1 | s(2) == 1); +%p = sum( size(v) > 1) <= 1; % Peter Acklam's solution
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/junk.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,67 @@ + + m = mxGetM(prhs[0]); + n = mxGetN(prhs[0]); + pr = mxGetPr(prhs[0]); + pi = mxGetPi(prhs[0]); + cmplx = (pi == NULL ? 0 : 1); + + /* Allocate space for sparse matrix. + * NOTE: Assume at most 20% of the data is sparse. Use ceil + * to cause it to round up. + */ + + percent_sparse = 0.2; + nzmax = (int)ceil((double)m*(double)n*percent_sparse); + + plhs[0] = mxCreateSparse(m,n,nzmax,cmplx); + sr = mxGetPr(plhs[0]); + si = mxGetPi(plhs[0]); + irs = mxGetIr(plhs[0]); + jcs = mxGetJc(plhs[0]); + + /* Copy nonzeros. */ + k = 0; + isfull = 0; + for (j = 0; (j < n); j++) { + int i; + jcs[j] = k; + for (i = 0; (i < m); i++) { + if (IsNonZero(pr[i]) || (cmplx && IsNonZero(pi[i]))) { + + /* Check to see if non-zero element will fit in + * allocated output array. If not, increase + * percent_sparse by 10%, recalculate nzmax, and augment + * the sparse array. + */ + if (k >= nzmax) { + int oldnzmax = nzmax; + percent_sparse += 0.1; + nzmax = (int)ceil((double)m*(double)n*percent_sparse); + + /* Make sure nzmax increases atleast by 1. */ + if (oldnzmax == nzmax) + nzmax++; + + mxSetNzmax(plhs[0], nzmax); + mxSetPr(plhs[0], mxRealloc(sr, nzmax*sizeof(double))); + if (si != NULL) + mxSetPi(plhs[0], mxRealloc(si, nzmax*sizeof(double))); + mxSetIr(plhs[0], mxRealloc(irs, nzmax*sizeof(int))); + + sr = mxGetPr(plhs[0]); + si = mxGetPi(plhs[0]); + irs = mxGetIr(plhs[0]); + } + sr[k] = pr[i]; + if (cmplx) { + si[k] = pi[i]; + } + irs[k] = i; + k++; + } + } + pr += m; + pi += m; + } + jcs[n] = k; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/loadcell.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,153 @@ +function [lc,dflag,dattype]=loadcell(fname,delim,exclusions,options); +%function [lc,dflag,numdata]=loadcell(fname,delim,exclusions); +% +% loadcell loads a cell array with character delimited +% data, which can have variable length lines and content. +% Numeric values are converted from string to double +% unless options is a string containing 'string'. +% +% loadcell is for use with small datasets. It is not optimised +% for large datasets. +% +% fname is the filename to be loaded +% +% delim is/are the relevant delimiter(s). If char(10) is included +% newlines are simply treated as delimiters and a 1-d array is created. +% +% exclusions are the set of characters to be treated as paired +% braces: line ends or delimiters within braces are ignored. +% braces are single characters and any brace can pair with +% any other brace: no type pair checking is done. +% +% options can be omitted or can contain 'string' if no numeric +% conversion is required, 'single' if multiple adjacent seperators +% should not be treated as one, 'free' if all linefeeds should be stripped +% first and 'empty2num' if empty fields are to be treated as numeric +% zeros rather than an empty character set. Combine options using +% concatenation. +% +% lc is a cell array containing the loaded data. +% +% dflag is a set of flags denoting the (i,j) values where data was entered +% dflag(i,j)=1 implies lc(i,j) was loaded from the data, and not just set +% to empty, say, by default. +% +% numdata is an array numdata(i,j)=NaN implies +% lc(i,j) is a string, otherwise it stores the number at i,j. +% This will occur regardless of whether the 'string' option is set. +% +% lc will return -1 if the file is not found or could not be +% opened. +% +% Hint: numdata+(1/dflag-1) provides a concise descriptor for the numeric data +% Inf=not loaded +% NaN=was string or empty set. +% otherwise numeric +% +% EXAMPLE +% +%[a,b]=loadcell('resultsfile',[',' char(9)],'"','single-string'); +% will load file 'resultsfile' into variable a, treating any of tab or +% comma as delimiters. Delimiters or carriage returns lying +% between two double inverted commas will be ignored. Two adjacent delimiters +% will count twice, and all data will be kept as a string. +% +% Note: in space-separated data 'single' would generally be omitted, +% wheras in comma-seperated data it would be included. +% +% Note the exclusion characters will remain in the final data, and any data +% contained within or containing exclusion characters will not be +% converted to numerics. +% +% (c) Amos Storkey 2002 +% v b160702 + +% MATLAB is incapable of loading variable length lines or variable type values +% with a whole file command under the standard library sets. This mfile +% fills that gap. +if (nargin<4) + options=' '; +end; +dflag = []; +%Open file +fid=fopen(fname,'rt'); +%Cannot open: return -1 +if (fid<0) + lc=-1; +else + fullfile=fread(fid,'uchar=>char')'; + %Strip LF if free is set + if ~isempty(findstr(options,'free')) + fullfile=strrep(fullfile,char(10),''); + end; + %Find all delimiters + delimpos=[]; + for s=1:length(delim) + delimpos=[delimpos find(fullfile==delim(s))]; + end + %Find all eol + endpos=find(fullfile==char(10)); + endpos=setdiff(endpos,delimpos); + %find all exclusions + xclpos=[]; + for s=1:length(exclusions); + xclpos=[xclpos find(fullfile==exclusions(s))]; + end + sort(xclpos); + xclpos=[xclpos(1:2:end-1);xclpos(2:2:end)]; + %Combine eol and delimiters + jointpos=union(delimpos,endpos); + t=1; + %Remove delim/eol within exclusion pairs + removedelim=[]; + for s=1:length(jointpos) + if any((jointpos(s)>xclpos(1,:)) & (jointpos(s)<xclpos(2,:))) + removedelim(t)=jointpos(s); + t=t+1; + end; + + end + %and add start point + jointpos=[0 setdiff(jointpos,removedelim)]; + i=1; + j=1; + posind=1; + multflag=isempty(findstr(options,'single')); + stringflag=~isempty(findstr(options,'string')); + emptnum=~isempty(findstr(options,'empty2num')); + %Run through + while (posind<(length(jointpos))) + %Get current field + tempstr=fullfile(jointpos(posind)+1:jointpos(posind+1)-1); + %If empty only continue if adjacent delim count. + if ~(isempty(tempstr) & multflag); + %This ij is set + dflag(i,j)=1; + %Convert to num + tempno=str2double([tempstr]); + %If emptystring convert to zero if emptnum set + if (isempty(tempstr) & emptnum) + tempno=0; + end; + %Set dattype to no (or NaN if not a num + dattype(i,j)=tempno; + %If NaN set lc to string else to num if stringflag not set + if (isnan(tempno) | stringflag) + lc{i,j}=tempstr; + else + lc{i,j}=tempno; + end; + %Next j + j=j+1; + end; + %If eol inc i and reset j + if ismember(jointpos(posind+1),endpos) + i=i+1; + j=1; + end; + %Inc to next delim + posind=posind+1; + end; +end; +%Logicalise dflag +dflag=logical(dflag);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/logb.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function x = logb(y, base) +% logb - logarithm to base b +% function x = logb(y, base) + +x = log(y) ./ log(base);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/logdet.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +function y = logdet(A) +% log(det(A)) where A is positive-definite. +% This is faster and more stable than using log(det(A)). + +% From Tom Minka's lightspeed toolbox + +U = chol(A); +y = 2*sum(log(diag(U)));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/logsum.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,28 @@ +function y=logsum(x,d) +%LOGSUM logsum(x,d)=log(sum(exp(x),d)) +% d gives dimension to sum along + +% Copyright (C) Mike Brookes 1998 +% +% Last modified Mon Oct 12 15:47:25 1998 +% +% VOICEBOX is a MATLAB toolbox for speech processing. Home page is at +% http://www.ee.ic.ac.uk/hp/staff/dmb/voicebox/voicebox.html +% + +if nargin==1 + d=[find(size(x)-1) 1]; + d=d(1); +end +n=size(x,d); +if n<=1, y=x; return; end +s=size(x); +p=[d:ndims(x) 1:d-1]; +z=reshape(permute(x,p),n,prod(s)/n); + +y=max(z); +y=y+log(sum(exp(z-y(ones(n,1),:)))); + +s(d)=1; +y=ipermute(reshape(y,s(p)),p); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/logsum_simple.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,23 @@ +function result = logsum(logv) + +len = length(logv); +if (len<2); + error('Subroutine logsum cannot sum less than 2 terms.'); +end; + +% First two terms +if (logv(2)<logv(1)), + result = logv(1) + log( 1 + exp( logv(2)-logv(1) ) ); +else, + result = logv(2) + log( 1 + exp( logv(1)-logv(2) ) ); +end; + +% Remaining terms +for (i=3:len), + term = logv(i); + if (result<term), + result = term + log( 1 + exp( result-term ) ); + else, + result = result + log( 1 + exp( term-result ) ); + end; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/logsum_test.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +p1 = log(1e-5); +p2 = log(5*1e-6); +p3 = log(sum(exp([p1 p2]))) +p4 = logsumexp([p1 p2],2) +p5 = logsum([p1 p2]) +p6 = logsum([p1 p2])
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/logsumexp.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,24 @@ +function s = logsumexp(a, dim) +% Returns log(sum(exp(a),dim)) while avoiding numerical underflow. +% Default is dim = 1 (rows) or dim=2 for a row vector +% logsumexp(a, 2) will sum across columns instead of rows + +% Written by Tom Minka, modified by Kevin Murphy + +if nargin < 2 + dim = 1; + if ndims(a) <= 2 & size(a,1)==1 + dim = 2; + end +end + +% subtract the largest in each column +[y, i] = max(a,[],dim); +dims = ones(1,ndims(a)); +dims(dim) = size(a,dim); +a = a - repmat(y, dims); +s = y + log(sum(exp(a),dim)); +%i = find(~finite(y)); +%if ~isempty(i) +% s(i) = y(i); +%end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/logsumexpv.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +function s = logsumexpv(a) +% Returns log(sum(exp(a))) while avoiding numerical underflow. +% +% e.g., log(e^a1 + e^a2) = a1 + log(1 + e^(a2-a1)) if a1>a2 +% If a1 ~ a2, and a1>a2, then e^(a2-a1) is exp(small negative number), +% which can be computed without underflow. + +% Same as logsumexp, except we assume a is a vector. +% This avoids a call to repmat, which takes 50% of the time! + +a = a(:)'; % make row vector +m = max(a); +b = a - m*ones(1,length(a)); +s = m + log(sum(exp(b))); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/mahal2conf.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,60 @@ +% MAHAL2CONF - Translates a Mahalanobis distance into a confidence +% interval. Consider a multivariate Gaussian +% distribution of the form +% +% p(x) = 1/sqrt((2 * pi)^d * det(C)) * exp((-1/2) * MD(x, m, inv(C))) +% +% where MD(x, m, P) is the Mahalanobis distance from x +% to m under P: +% +% MD(x, m, P) = (x - m) * P * (x - m)' +% +% A particular Mahalanobis distance k identifies an +% ellipsoid centered at the mean of the distribution. +% The confidence interval associated with this ellipsoid +% is the probability mass enclosed by it. +% +% If X is an d dimensional Gaussian-distributed vector, +% then the Mahalanobis distance of X is distributed +% according to the Chi-squared distribution with d +% degrees of freedom. Thus, the confidence interval is +% determined by integrating the chi squared distribution +% up to the Mahalanobis distance of the measurement. +% +% Usage: +% +% c = mahal2conf(m, d); +% +% Inputs: +% +% m - the Mahalanobis radius of the ellipsoid +% d - the number of dimensions of the Gaussian distribution +% +% Outputs: +% +% c - the confidence interval, i.e., the fraction of +% probability mass enclosed by the ellipsoid with the +% supplied Mahalanobis distance +% +% See also: CONF2MAHAL + +% Copyright (C) 2002 Mark A. Paskin +% +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, but +% WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +% General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 +% USA. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function c = mahal2conf(m, d) + +c = chi2cdf(m, d); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/marg_table.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,27 @@ +function smallT = marg_table(bigT, bigdom, bigsz, onto, maximize) +% MARG_TABLE Marginalize a table +% smallT = marg_table(bigT, bigdom, bigsz, onto, maximize) + +if nargin < 5, maximize = 0; end + + +smallT = myreshape(bigT, bigsz); % make sure it is a multi-dim array +sum_over = mysetdiff(bigdom, onto); +ndx = find_equiv_posns(sum_over, bigdom); +if maximize + for i=1:length(ndx) + smallT = max(smallT, [], ndx(i)); + end +else + for i=1:length(ndx) + smallT = sum(smallT, ndx(i)); + end +end + + +ns = zeros(1, max(bigdom)); +%ns(bigdom) = mysize(bigT); % ignores trailing dimensions of size 1 +ns(bigdom) = bigsz; + +smallT = squeeze(smallT); % remove all dimensions of size 1 +smallT = myreshape(smallT, ns(onto)); % put back relevant dims of size 1
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/marginalize_table.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,50 @@ +function smallT = marginalize_table(bigT, bigdom, bigsz, onto, maximize) +% MARG_TABLE Marginalize a table +% function smallT = marginalize_table(bigT, bigdom, bigsz, onto, maximize) + +% Like marg_table in BNT, except we do not assume the domains are sorted + +if nargin < 5, maximize = 0; end + + +smallT = myreshape(bigT, bigsz); % make sure it is a multi-dim array +sum_over = mysetdiff(bigdom, onto); +ndx = find_equiv_posns(sum_over, bigdom); +if maximize + for i=1:length(ndx) + smallT = max(smallT, [], ndx(i)); + end +else + for i=1:length(ndx) + smallT = sum(smallT, ndx(i)); + end +end + + +ns = zeros(1, max(bigdom)); +%ns(bigdom) = mysize(bigT); % ignores trailing dimensions of size 1 +ns(bigdom) = bigsz; + +% If onto has a different ordering than bigdom, the following +% will produce the wrong results + +%smallT = squeeze(smallT); % remove all dimensions of size 1 +%smallT = myreshape(smallT, ns(onto)); % put back relevant dims of size 1 + +% so permute dimensions to match desired ordering (as specified by onto) + + +% like find_equiv_posns, but keeps ordering +outdom = [onto sum_over]; +for i=1:length(outdom) + j = find(bigdom==outdom(i)); + match(i) = j; +end +outdom = [onto sum_over]; +for i=1:length(outdom) + j = find(bigdom==outdom(i)); + match(i) = j; +end +if match ~= 1 + smallT = permute(smallT, match); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/matprint.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,36 @@ +% MATPRINT - prints a matrix with specified format string +% +% Usage: matprint(a, fmt, fid) +% +% a - Matrix to be printed. +% fmt - C style format string to use for each value. +% fid - Optional file id. +% +% Eg. matprint(a,'%3.1f') will print each entry to 1 decimal place + +% Peter Kovesi +% School of Computer Science & Software Engineering +% The University of Western Australia +% pk @ csse uwa edu au +% http://www.csse.uwa.edu.au/~pk +% +% March 2002 + +function matprint(a, fmt, fid) + + if nargin < 3 + fid = 1; + end + + [rows,cols] = size(a); + + % Construct a format string for each row of the matrix consisting of + % 'cols' copies of the number formating specification + fmtstr = []; + for c = 1:cols + fmtstr = [fmtstr, ' ', fmt]; + end + fmtstr = [fmtstr '\n']; % Add a line feed + + fprintf(fid, fmtstr, a'); % Print the transpose of the matrix because + % fprintf runs down the columns of a matrix. \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/max_mult.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,46 @@ +/* C mex version of max_mult.m in BPMRF2 directory */ +/* gcc -Wall -I/mit/matlab_v6.5/distrib/bin/glnx86 -c max_mult.c */ + +#include <math.h> +#include "mex.h" + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) +{ + int rows,cols,common,m,n,p; + double y1, y2; + double *arr1, *arr2, *arr3; + + + if (nrhs!=2 || nlhs>1) + mexErrMsgTxt("max_mult requires two inputs and one output"); + if (mxIsChar(prhs[0]) || mxIsClass(prhs[0], "sparse") || mxIsComplex(prhs[0]) + || mxIsChar(prhs[1]) || mxIsClass(prhs[1], "sparse") || mxIsComplex(prhs[1])) + mexErrMsgTxt("Inputs must be real, full, and nonstring"); + if (mxGetN(prhs[0])!=mxGetM(prhs[1])) + mexErrMsgTxt("The number of columns of A must be the same as the number of rows of x"); + + + arr1=mxGetPr(prhs[0]); + arr2=mxGetPr(prhs[1]); + p=mxGetN(prhs[0]); + m=mxGetM(prhs[0]); + n=mxGetN(prhs[1]); + plhs[0]=mxCreateDoubleMatrix(m, n, mxREAL); + arr3=mxMalloc(m*n*sizeof(double)); + + for (rows=0; rows<m ; rows++) + for (cols=0; cols<n ; cols++) + { + y1=arr1[rows]*arr2[cols*p]; + for (common=1; common<p; common++) + { + y2=arr1[rows+common*m]*arr2[common+cols*p]; + if (y2>y1) + y1=y2; + } + arr3[rows+cols*m]=y1; + } + + mxSetPr(plhs[0], arr3); + +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/max_mult.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,21 @@ +function y=max_mult(A,x) +% MAX_MULT Like matrix multiplication, but sum gets replaced by max +% function y=max_mult(A,x) y(i) = max_j A(i,j) x(j) + +%X=ones(size(A,1),1) * x(:)'; % X(j,i) = x(i) +%y=max(A.*X, [], 2); + +% This is faster +if size(x,2)==1 + X=x*ones(1,size(A,1)); % X(i,j) = x(i) + y=max(A'.*X)'; +else +%this works for arbitrarily sized A and x (but is ugly, and slower than above) + X=repmat(x, [1 1 size(A,1)]); + B=repmat(A, [1 1 size(x,2)]); + C=permute(B,[2 3 1]); + y=permute(max(C.*X),[3 2 1]); +% this is even slower, as is using squeeze instead of permute +% Y=permute(X, [3 1 2]); +% y=permute(max(Y.*B, [], 2), [1 3 2]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/mexutil.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,43 @@ +#include "mexutil.h" + +/* Functions to create uninitialized arrays. */ + +mxArray *mxCreateNumericArrayE(int ndim, const int *dims, + mxClassID class, mxComplexity ComplexFlag) +{ + mxArray *a; + int i, *dims1 = mxMalloc(ndim*sizeof(int)); + size_t sz = 1; + for(i=0;i<ndim;i++) { + sz *= dims[i]; + dims1[i] = 1; + } + a = mxCreateNumericArray(ndim,dims1,class,ComplexFlag); + sz *= mxGetElementSize(a); + mxSetDimensions(a, dims, ndim); + mxFree(dims1); + mxSetData(a, mxRealloc(mxGetData(a), sz)); + if(ComplexFlag == mxCOMPLEX) { + mxSetPi(a, mxRealloc(mxGetPi(a),sz)); + } + return a; +} +mxArray *mxCreateNumericMatrixE(int m, int n, mxClassID class, + mxComplexity ComplexFlag) +{ + size_t sz = m*n*sizeof(double); + mxArray *a = mxCreateNumericMatrix(1, 1, class, ComplexFlag); + mxSetM(a,m); + mxSetN(a,n); + mxSetPr(a, mxRealloc(mxGetPr(a),sz)); + if(ComplexFlag == mxCOMPLEX) { + mxSetPi(a, mxRealloc(mxGetPi(a),sz)); + } + return a; +} +mxArray *mxCreateDoubleMatrixE(int m, int n, + mxComplexity ComplexFlag) +{ + return mxCreateNumericMatrixE(m,n,mxDOUBLE_CLASS,ComplexFlag); +} +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/mexutil.h Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +#include "mex.h" + +mxArray *mxCreateNumericArrayE(int ndim, const int *dims, + mxClassID class, mxComplexity ComplexFlag); +mxArray *mxCreateNumericMatrixE(int m, int n, mxClassID class, + mxComplexity ComplexFlag); +mxArray *mxCreateDoubleMatrixE(int m, int n, + mxComplexity ComplexFlag);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/mk_multi_index.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,23 @@ +function index = mk_multi_index(n, dims, vals) +% MK_MULTI_INDEX Compute the indices of the submatrix where dims(i)=vals(i). +% index = mk_multi_index(n, dims, vals) +% +% Example: +% index = mk_multi_index(3, [1 3], [3 2]) +% gives index = {3, ':', 2}, which will select out dim 1 = 3 and dim 3 = 2 +% So if A(:,:,1)=[1 2;3 4; 5 6]; A(:,:,2)=[7 8; 9 10; 11 12] +% then A(index{:}) = [11 12]: + +if n==0 + index = { 1 }; + return; +end + +index = cell(1,n); +for i=1:n + index{i} = ':'; +end +for i=1:length(dims) + index{dims(i)} = vals(i); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/mk_stochastic.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,27 @@ +function [T,Z] = mk_stochastic(T) +% MK_STOCHASTIC Ensure the argument is a stochastic matrix, i.e., the sum over the last dimension is 1. +% [T,Z] = mk_stochastic(T) +% +% If T is a vector, it will sum to 1. +% If T is a matrix, each row will sum to 1. +% If T is a 3D array, then sum_k T(i,j,k) = 1 for all i,j. + +% Set zeros to 1 before dividing +% This is valid since S(j) = 0 iff T(i,j) = 0 for all j + +if (ndims(T)==2) & (size(T,1)==1 | size(T,2)==1) % isvector + [T,Z] = normalise(T); +elseif ndims(T)==2 % matrix + Z = sum(T,2); + S = Z + (Z==0); + norm = repmat(S, 1, size(T,2)); + T = T ./ norm; +else % multi-dimensional array + ns = size(T); + T = reshape(T, prod(ns(1:end-1)), ns(end)); + Z = sum(T,2); + S = Z + (Z==0); + norm = repmat(S, 1, ns(end)); + T = T ./ norm; + T = reshape(T, ns); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/mkdirKPM.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,24 @@ +function mkdirKPM(dname) +% function mkdirKPM(dname) +% If directory does not exist, make it +% +% mkdirKPM('foo\bar\baz') makes subdirectories bar and baz +% mkdirKPM('foo\bar\baz.txt') makes subdirectories bar + +% convert foo\bar\baz to pathstr=foo\bar, name=baz +% convert foo\bar\baz.txt to pathstr=foo\bar, name=baz +[pathstr, name, ext, versn] = fileparts(dname); +if ~isempty(ext) % we stripped off something after final period + % convert foo\bar to pathstr=foo, name=bar + % convert foo\bar.bad to pathstr=foo, name=bar.bad + [pathstr, name, ext, versn] = fileparts(pathstr); + name = sprintf('%s%s', name, ext); % in case there is a period in the directory name +end + +dname = fullfile(pathstr, name); +if ~exist(dname, 'dir') + %fprintf('mkdirKPM: making %s, %s \n', pathstr, name); + mkdir(pathstr, name) +else + %fprintf('mkdirKPM: %s already exists\n', dname) +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/montageKPM.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,102 @@ +function h = montageKPM(arg) +% montageKPM is like the built-in montage, but assumes input is MxNxK or filenames +% +% Converts patches (y,x,i) into patches(y,x,1,i) +% Also, adds a black border aroudn them + +if iscell(arg) + h= montageFilenames(arg); +else + nr = size(arg,1); nc = size(arg,2); Npatches = size(arg,3); + patchesColor = reshape(arg, [nr nc 1 Npatches]); + patchesColor = patchesColor ./ max(patchesColor(:)); + + if 1 + %put a black border around them for display purposes + border = 5; + bgColor = ones(1,1,class(patchesColor)); + patchesColorBig = bgColor*ones(nr+2*border, nc+2*border, 1, Npatches, class(patchesColor)); + %patchesColorBig = zeros(nr+2*border, nc+2*border, 1, Npatches, class(patchesColor)); + patchesColorBig(border+1:end-border, border+1:end-border, :, :) = patchesColor; + else + patchesColorBig = patchesColor; + end + montage(patchesColorBig) + +end + +%%%%%%%%%%%%% + +function h = montageFilenames(filenames) + +%[nRows, nCols, nBands, nFrames] = size(a); + +% Estimate nMontageColumns and nMontageRows given the desired ratio of +% Columns to Rows to be one (square montage). +aspectRatio = 1; +nMontageCols = sqrt(aspectRatio * nRows * nFrames / nCols); + +% Make sure montage rows and columns are integers. The order in the adjustment +% matters because the montage image is created horizontally across columns. +nMontageCols = ceil(nMontageCols); +nMontageRows = ceil(nFrames / nMontageCols); + +% Create the montage image. +b = a(1,1); % to inherit type +b(1,1) = 0; % from a +b = repmat(b, [nMontageRows*nRows, nMontageCols*nCols, nBands, 1]); + +rows = 1 : nRows; +cols = 1 : nCols; + +for i = 0:nMontageRows-1 + for j = 0:nMontageCols-1, + k = j + i * nMontageCols + 1; + if k <= nFrames + b(rows + i * nRows, cols + j * nCols, :) = a(:,:,:,k); + else + break; + end + end +end + +if isempty(cm) + hh = imshow(b); +else + hh = imshow(b,cm); +end + +if nargout > 0 + h = hh; +end + +%-------------------------------------------------------------- +%Parse Inputs Function + +function [I,map] = parse_inputs(varargin) + +% initialize variables +map = []; + +iptchecknargin(1,2,nargin,mfilename); +iptcheckinput(varargin{1},{'uint8' 'double' 'uint16' 'logical' 'single' ... + 'int16'},{},mfilename, 'I, BW, or RGB',1); +I = varargin{1}; + +if nargin==2 + if isa(I,'int16') + eid = sprintf('Images:%s:invalidIndexedImage',mfilename); + msg1 = 'An indexed image can be uint8, uint16, double, single, or '; + msg2 = 'logical.'; + error(eid,'%s %s',msg1, msg2); + end + map = varargin{2}; + iptcheckinput(map,{'double'},{},mfilename,'MAP',1); + if ((size(map,1) == 1) && (prod(map) == numel(I))) + % MONTAGE(D,[M N P]) OBSOLETE + eid = sprintf('Images:%s:obsoleteSyntax',mfilename); + msg1 = 'MONTAGE(D,[M N P]) is an obsolete syntax.'; + msg2 = 'Use multidimensional arrays to represent multiframe images.'; + error(eid,'%s\n%s',msg1,msg2); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/montageKPM2.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,69 @@ +function montageKPM2(data) +% data(y,x,b,f) or data(y,x,f) +% can be double - uses imagesc to display, not imshow +% based on imaqmontage + +if ndims(data)==3 + nr = size(data,1); nc = size(data,2); Npatches = size(data,3); Nbands = 1; + data = reshape(data, [nr nc Nbands Npatches]); +else + nr = size(data,1); nc = size(data,2); Nbands = size(data,3); Npatches = size(data,4); +end +nativeVal = data(1, 1); +dataOrig = data; + +%put a black border around them for display purposes +border = 5; +bgColor = min(data(:)); +%bgColor = max(data(:)); +data = bgColor*ones(nr+2*border, nc+2*border, Nbands, Npatches, class(data)); +data(border+1:end-border, border+1:end-border, :, :) = dataOrig; + +[width, height, bands, nFrames] = size(data); + +% Determine the number of axis rows and columns. +axCols = sqrt(nFrames); +if (axCols<1) + % In case we have a slim image. + axCols = 1; +end +axRows = nFrames/axCols; +if (ceil(axCols)-axCols) < (ceil(axRows)-axRows), + axCols = ceil(axCols); + axRows = ceil(nFrames/axCols); +else + axRows = ceil(axRows); + axCols = ceil(nFrames/axRows); +end + +% Size the storage to hold all frames. +storage = repmat(nativeVal, [axRows*width, axCols*height, bands, 1]); + +% Fill the storage up with data pixels. +rows = 1:width; +cols = 1:height; +for i=0:axRows-1, + for j=0:axCols-1, + k = j+i*axCols+1; + if k<=nFrames, + storage(rows+i*width, cols+j*height, :) = data(:,:,:,k); + else + break; + end + end +end + + +% Display the tiled frames nicely and +% pop the window forward. +im = imagesc(storage); + +ax = get(im, 'Parent'); +fig = get(ax, 'Parent'); +set(ax, 'XTick', [], 'YTick', []) +figure(fig) + +% If working with single band images, update the colormap. +if 0 % bands==1, + colormap(gray); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/montageKPM3.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function montageKPM3(data) +% data{f}(y,x,b) - each frame can have a different size (can can even be empty) + +data2 = cell2matPad(data); +montageKPM2(data2)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/mult_by_table.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +function bigT = mult_by_table(bigT, bigdom, bigsz, smallT, smalldom, smallsz) +% MULT_BY_TABLE +% bigT = mult_by_table(bigT, bigdom, bigsz, smallT, smalldom, smallsz) +% + +Ts = extend_domain_table(smallT, smalldom, smallsz, bigdom, bigsz); +bigT(:) = bigT(:) .* Ts(:); % must have bigT(:) on LHS to preserve shape
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/myintersect.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,28 @@ +function C = myintersect(A,B) +% MYINTERSECT Intersection of two sets of positive integers (much faster than built-in intersect) +% C = myintersect(A,B) + +A = A(:)'; B = B(:)'; + +if isempty(A) + ma = 0; +else + ma = max(A); +end + +if isempty(B) + mb = 0; +else + mb = max(B); +end + +if ma==0 | mb==0 + C = []; +else + %bits = sparse(1, max(ma,mb)); + bits = zeros(1, max(ma,mb)); + bits(A) = 1; + C = B(logical(bits(B))); +end + +%sum( bitget( bitand( cliquesb(i), cliquesb(j) ), 1:52 ) );
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/myismember.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,18 @@ +function p = myismember(a,A) +% MYISMEMBER Is 'a' an element of a set of positive integers? (much faster than built-in ismember) +% p = myismember(a,A) + +%if isempty(A) | a < min(A) | a > max(A) % slow + +if length(A)==0 + p = 0; +elseif a < min(A) + p = 0; +elseif a > max(A) + p = 0; +else + bits = zeros(1, max(A)); + bits(A) = 1; + p = bits(a); +end +p = logical(p);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/myones.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function T = myones(sizes) +% MYONES Like the built-in ones, except myones(k) produces a k*1 vector instead of a k*k matrix, +% T = myones(sizes) + +if length(sizes)==0 + T = 1; +elseif length(sizes)==1 + T = ones(sizes, 1); +else + T = ones(sizes(:)'); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/myplot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +colors = ['r' 'b' 'k' 'g' 'c' 'y' 'm' ... + 'r' 'b' 'k' 'g' 'c' 'y' 'm']; +symbols = ['o' 'x' 's' '>' '<' '^' 'v' ... + '*' 'p' 'h' '+' 'd' 'o' 'x']; +for i=1:length(colors) + styles{i} = sprintf('-%s%s', colors(i), symbols(i)); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/myrand.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +function T = myrand(sizes) +% MYRAND Like the built-in rand, except myrand(k) produces a k*1 vector instead of a k*k matrix, +% T = myrand(sizes) + +if length(sizes)==0 + warning('myrand[]'); + T = rand(1,1); +elseif length(sizes)==1 + T = rand(sizes, 1); +else + T = rand(sizes); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/myrepmat.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,9 @@ +function T = myrepmat(T, sizes) +% MYREPMAT Like the built-in repmat, except myrepmat(T,n) == repmat(T,[n 1]) +% T = myrepmat(T, sizes) + +if length(sizes)==1 + T = repmat(T, [sizes 1]); +else + T = repmat(T, sizes(:)'); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/myreshape.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function T = myreshape(T, sizes) +% MYRESHAPE Like the built-in reshape, except myreshape(T,n) == reshape(T,[n 1]) +% T = myreshape(T, sizes) + +if length(sizes)==0 + return; +elseif length(sizes)==1 + T = reshape(T, [sizes 1]); +else + T = reshape(T, sizes(:)'); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/mysetdiff.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,19 @@ +function C = mysetdiff(A,B) +% MYSETDIFF Set difference of two sets of positive integers (much faster than built-in setdiff) +% C = mysetdiff(A,B) +% C = A \ B = { things in A that are not in B } +% +% Original by Kevin Murphy, modified by Leon Peshkin + +if isempty(A) + C = []; + return; +elseif isempty(B) + C = A; + return; +else % both non-empty + bits = zeros(1, max(max(A), max(B))); + bits(A) = 1; + bits(B) = 0; + C = A(logical(bits(A))); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/mysize.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +function sz = mysize(M) +% MYSIZE Like the built-in size, except it returns n if M is a vector of length n, and 1 if M is a scalar. +% sz = mysize(M) +% +% The behavior is best explained by examples +% - M = rand(1,1), mysize(M) = 1, size(M) = [1 1] +% - M = rand(2,1), mysize(M) = 2, size(M) = [2 1] +% - M = rand(1,2), mysize(M) = 2, size(M) = [1 2] +% - M = rand(2,2,1), mysize(M) = [2 2], size(M) = [2 2] +% - M = rand(1,2,1), mysize(M) = 2, size(M) = [1 2] + +if isvector(M) + sz = length(M); +else + sz = size(M); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/mysubset.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function p=mysubset(small,large) +% MYSUBSET Is the small set of +ve integers a subset of the large set? +% p = mysubset(small, large) + +% Surprisingly, this is not built-in. + +if isempty(small) + p = 1; % isempty(large); +else + p = length(myintersect(small,large)) == length(small); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/mysymsetdiff.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,29 @@ +function C = mysymsetdiff(A,B) +% MYSYMSETDIFF Symmetric set difference of two sets of positive integers (much faster than built-in setdiff) +% C = mysetdiff(A,B) +% C = (A\B) union (B\A) = { things that A and B don't have in common } + +if isempty(A) + ma = 0; +else + ma = max(A); +end + +if isempty(B) + mb = 0; +else + mb = max(B); +end + +if ma==0 + C = B; +elseif mb==0 + C = A; +else % both non-empty + m = max(ma,mb); + bitsA = sparse(1, m); + bitsA(A) = 1; + bitsB = sparse(1, m); + bitsB(B) = 1; + C = find(xor(bitsA, bitsB)); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/myunion.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,29 @@ +function C = myunion(A,B) +% MYUNION Union of two sets of positive integers (much faster than built-in union) +% C = myunion(A,B) + +if isempty(A) + ma = 0; +else + ma = max(A); +end + +if isempty(B) + mb = 0; +else + mb = max(B); +end + +if ma==0 & mb==0 + C = []; +elseif ma==0 & mb>0 + C = B; +elseif ma>0 & mb==0 + C = A; +else + %bits = sparse(1, max(ma,mb)); + bits = zeros(1, max(ma,mb)); + bits(A) = 1; + bits(B) = 1; + C = find(bits); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/nchoose2.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,35 @@ +function c = nchoose2(v, f) +%NCHOOSE2 All combinations of N elements taken two at a time. +% +% NCHOOSE2(1:N) or NCHOOSEK(V) where V is a vector of length N, +% produces a matrix with N*(N-1)/2 rows and K columns. Each row of +% the result has K of the elements in the vector V. +% +% NCHOOSE2(N,FLAG) is the same as NCHOOSE2(1:N) but faster. +% +% NCHOOSE2(V) is much faster than NCHOOSEK(V,2). +% +% See also NCHOOSEK, PERMS. + +% Author: Peter J. Acklam +% Time-stamp: 2000-03-03 13:03:59 +% E-mail: jacklam@math.uio.no +% URL: http://www.math.uio.no/~jacklam + + nargs = nargin; + if nargs < 1 + error('Not enough input arguments.'); + elseif nargs == 1 + v = v(:); + n = length(v); + elseif nargs == 2 + n = v; + else + error('Too many input arguments.'); + end + + [ c(:,2), c(:,1) ] = find( tril( ones(n), -1 ) ); + + if nargs == 1 + c = v(c); + end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/ncols.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,4 @@ +function c = cols(x) +% COLS The number of columns. +% COLS is a more readable alternative to size(x,2). +c = size(x,2);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/nonmaxsup.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,42 @@ +% NONMAXSUP - Non-maximal Suppression +% +% Usage: cim = nonmaxsup(im, radius) +% +% Arguments: +% im - image to be processed. +% radius - radius of region considered in non-maximal +% suppression (optional). Typical values to use might +% be 1-3. Default is 1. +% +% Returns: +% cim - image with pixels that are not maximal within a +% square neighborhood zeroed out. + +% Copyright (C) 2002 Mark A. Paskin +% +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, but +% WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +% General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 +% USA. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function cim = nonmaxsup(m, radius) + if (nargin == 1) radius = 1; end + % Extract local maxima by performing a grey scale morphological + % dilation and then finding points in the corner strength image that + % match the dilated image and are also greater than the threshold. + sze = 2 * radius + 1; % Size of mask. + mx = ordfilt2(m, sze^2, ones(sze)); % Grey-scale dilate. + cim = sparse(m .* (m == mx)); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/normalise.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,38 @@ +function [M, z] = normalise(A, dim) +% NORMALISE Make the entries of a (multidimensional) array sum to 1 +% [M, c] = normalise(A) +% c is the normalizing constant +% +% [M, c] = normalise(A, dim) +% If dim is specified, we normalise the specified dimension only, +% otherwise we normalise the whole array. + +if nargin < 2 + z = sum(A(:)); + % Set any zeros to one before dividing + % This is valid, since c=0 => all i. A(i)=0 => the answer should be 0/1=0 + s = z + (z==0); + if ~(s == 1) + M = A / s; + else + M = A; % by matthiasm (saving memory) + end +elseif dim==1 % normalize each column + z = sum(A); + s = z + (z==0); + %M = A ./ (d'*ones(1,size(A,1)))'; + M = A ./ repmatC(s, size(A,1), 1); +else + % Keith Battocchi - v. slow because of repmat + z=sum(A,dim); + s = z + (z==0); + L=size(A,dim); + d=length(size(A)); + v=ones(d,1); + v(dim)=L; + %c=repmat(s,v); + c=repmat(s,v'); + M=A./c; +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/normaliseC.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,29 @@ +/* C mex version of normalise.m in misc directory */ + +#include "mex.h" + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) +{ + double *T, *sum_ptr, sum; + int i, N; + + plhs[0] = mxDuplicateArray(prhs[0]); + T = mxGetPr(plhs[0]); + if(mxIsSparse(plhs[0])) N = mxGetJc(plhs[0])[mxGetN(plhs[0])]; + else N = mxGetNumberOfElements(plhs[0]); + + plhs[1] = mxCreateDoubleMatrix(1, 1, mxREAL); + sum_ptr = mxGetPr(plhs[1]); + + sum = 0; + for (i = 0; i < N; i++) { + sum += *T++; + } + T = mxGetPr(plhs[0]); + if (sum > 0) { + for (i = 0; i < N; i++) { + *T++ /= sum; + } + } + *sum_ptr = sum; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/normalize.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,34 @@ +function [M, z] = normalise(A, dim) +% NORMALISE Make the entries of a (multidimensional) array sum to 1 +% [M, c] = normalise(A) +% c is the normalizing constant +% +% [M, c] = normalise(A, dim) +% If dim is specified, we normalise the specified dimension only, +% otherwise we normalise the whole array. + +if nargin < 2 + z = sum(A(:)); + % Set any zeros to one before dividing + % This is valid, since c=0 => all i. A(i)=0 => the answer should be 0/1=0 + s = z + (z==0); + M = A / s; +elseif dim==1 % normalize each column + z = sum(A); + s = z + (z==0); + %M = A ./ (d'*ones(1,size(A,1)))'; + M = A ./ repmatC(s, size(A,1), 1); +else + % Keith Battocchi - v. slow because of repmat + z=sum(A,dim); + s = z + (z==0); + L=size(A,dim); + d=length(size(A)); + v=ones(d,1); + v(dim)=L; + %c=repmat(s,v); + c=repmat(s,v'); + M=A./c; +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/nrows.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,4 @@ +function r = rows(x) +% ROWS The number of rows. +% ROWS is a more readable alternative to size(x,1). +r = size(x,1);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/num2strcell.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +function c = num2strcell(n, format) +% num2strcell Convert vector of numbers to cell array of strings +% function c = num2strcell(n, format) +% +% If format is omitted, we use +% c{i} = sprintf('%d', n(i)) + +if nargin < 2, format = '%d'; end + +N = length(n); +c = cell(1,N); +for i=1:N + c{i} = sprintf(format, n(i)); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/optimalMatching.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,90 @@ +% MATCH - Solves the weighted bipartite matching (or assignment) +% problem. +% +% Usage: a = match(C); +% +% Arguments: +% C - an m x n cost matrix; the sets are taken to be +% 1:m and 1:n; C(i, j) gives the cost of matching +% items i (of the first set) and j (of the second set) +% +% Returns: +% +% a - an m x 1 assignment vector, which gives the +% minimum cost assignment. a(i) is the index of +% the item of 1:n that was matched to item i of +% 1:m. If item i (of 1:m) was not matched to any +% item of 1:n, then a(i) is zero. + +% Copyright (C) 2002 Mark A. Paskin +% +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, but +% WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +% General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 +% USA. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [a] = optimalMatching(C) + +% Trivial cases: +[p, q] = size(C); +if (p == 0) + a = []; + return; +elseif (q == 0) + a = zeros(p, 1); + return; +end + + +if 0 +% First, reduce the problem by making easy optimal matches. If two +% elements agree that they are the best match, then match them up. +[x, a] = min(C, [], 2); +[y, b] = min(C, [], 1); +u = find(1:p ~= b(a(:))); +a(u) = 0; +v = find(1:q ~= a(b(:))'); +C = C(u, v); +if (isempty(C)) return; end +end + +% Get the (new) size of the two sets, u and v. +[m, n] = size(C); + +%mx = realmax; +mx = 2*max(C(:)); +mn = -2*min(C(:)); +% Pad the affinity matrix to be square +if (m < n) + C = [C; mx * ones(n - m, n)]; +elseif (n < m) + C = [C, mx * ones(m, m - n)]; +end + +% Run the Hungarian method. First replace infinite values by the +% largest (or smallest) finite values. +C(find(isinf(C) & (C > 0))) = mx; +C(find(isinf(C) & (C < 0))) = mn; +%fprintf('running hungarian\n'); +[b, cost] = hungarian(C'); + +% Extract only the real assignments +ap = b(1:m)'; +ap(find(ap > n)) = 0; + +a = ap; +%% Incorporate this sub-assignment into the complete assignment +% k = find(ap); +% a(u(k)) = v(ap(k)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/optimalMatchingTest.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,18 @@ +% Consider matching sources to detections + +% s1 d2 +% s2 d3 +% d1 + +a = optimalMatching([52;0.01]) + +% sources(:,i) = [x y] coords +sources = [0.1 0.7; 0.6 0.4]'; +detections = [0.2 0.2; 0.2 0.8; 0.7 0.1]'; +dst = sqdist(sources, detections) + +% a = [2 3] which means s1-d2, s2-d3 +a = optimalMatching(dst) + +% a = [0 1 2] which means d1-0, d2-s1, d3-s2 +a = optimalMatching(dst')
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/partitionData.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,25 @@ +function varargout = partitionData(Ndata, varargin) +% PARTITIONDATA Partition a vector of indices into random sets +% [a,b,c,...] = partitionData(N, 0.3, 0.2, 0.5, ...) +% +% Examples: +% [a,b,c]=partitionData(105,0.3,0.2,0.5); +% a= 1:30, b=32:52, c=52:105 (last bin gets all the left over) + +Npartitions = length(varargin); +perm = randperm(Ndata); +%perm = 1:Ndata; +ndx = 1; +for i=1:Npartitions + pc(i) = varargin{i}; + Nbin(i) = fix(Ndata*pc(i)); + low(i) = ndx; + if i==Npartitions + high(i) = Ndata; + else + high(i) = low(i)+Nbin(i)-1; + end + varargout{i} = perm(low(i):high(i)); + ndx = ndx+Nbin(i); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/partition_matrix_vec.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,21 @@ +function [m1, m2, K11, K12, K21, K22] = partition_matrix_vec(m, K, n1, n2, bs) +% PARTITION_MATRIX_VEC Partition a vector and matrix into blocks. +% [m1, m2, K11, K12, K21, K22] = partition_matrix_vec(m, K, blocks1, blocks2, bs) +% +% bs(i) = block size of i'th node +% +% Example: +% n1 = [6 8], n2 = [5], bs = [- - - - 2 1 - 2], where - = don't care +% m = [0.1 0.2 0.3 0.4 0.5], K = some 5*5 matrix, +% So E[X5] = [0.1 0.2], E[X6] = [0.3], E[X8] = [0.4 0.5] +% m1 = [0.3 0.4 0.5], m2 = [0.1 0.2]; + +dom = myunion(n1, n2); +n1i = block(find_equiv_posns(n1, dom), bs(dom)); +n2i = block(find_equiv_posns(n2, dom), bs(dom)); +m1 = m(n1i); +m2 = m(n2i); +K11 = K(n1i, n1i); +K12 = K(n1i, n2i); +K21 = K(n2i, n1i); +K22 = K(n2i, n2i);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/pca_kpm.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,32 @@ +function [pc_vec]=pca_kpm(features,N, method); +% PCA_KPM Compute top N principal components using eigs or svd. +% [pc_vec]=pca_kpm(features,N) +% +% features(:,i) is the i'th example - each COLUMN is an observation +% pc_vec(:,j) is the j'th basis function onto which you should project the data +% using pc_vec' * features + +[d ncases] = size(features); +fm=features-repmat(mean(features,2), 1, ncases); + + +if method==1 % d*d < d*ncases + fprintf('pca_kpm eigs\n'); + options.disp = 0; + C = cov(fm'); % d x d matrix + [pc_vec, evals] = eigs(C, N, 'LM', options); +else + % [U,D,V] = SVD(fm), U(:,i)=evec of fm fm', V(:,i) = evec of fm' fm + fprintf('pca_kpm svds\n'); + [U,D,V] = svds(fm', N); + pc_vec = V; +end + +if 0 +X = randn(5,3); +X = X-repmat(mean(X),5,1); +C = X'*X; +C2=cov(X) +[U,D,V]=svd(X); +[V2,D2]=eig(X) +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/pca_netlab.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,42 @@ +function [PCcoeff, PCvec] = pca(data, N) +%PCA Principal Components Analysis +% +% Description +% PCCOEFF = PCA(DATA) computes the eigenvalues of the covariance +% matrix of the dataset DATA and returns them as PCCOEFF. These +% coefficients give the variance of DATA along the corresponding +% principal components. +% +% PCCOEFF = PCA(DATA, N) returns the largest N eigenvalues. +% +% [PCCOEFF, PCVEC] = PCA(DATA) returns the principal components as well +% as the coefficients. This is considerably more computationally +% demanding than just computing the eigenvalues. +% +% See also +% EIGDEC, GTMINIT, PPCA +% + +% Copyright (c) Ian T Nabney (1996-2001) + +if nargin == 1 + N = size(data, 2); +end + +if nargout == 1 + evals_only = logical(1); +else + evals_only = logical(0); +end + +if N ~= round(N) | N < 1 | N > size(data, 2) + error('Number of PCs must be integer, >0, < dim'); +end + +% Find the sorted eigenvalues of the data covariance matrix +if evals_only + PCcoeff = eigdec(cov(data), N); +else + [PCcoeff, PCvec] = eigdec(cov(data), N); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/pick.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,9 @@ +function [i,j] = pick(ndx) +% PICK Pick an entry at random from a vector +% function [i,j] = pick(ndx) +% +% i = ndx(j) for j ~ U(1:length(ndx)) + +dist = normalize(ones(1,length(ndx))); +j = sample_discrete(dist); +i = ndx(j);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/plotBox.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,22 @@ +function [h, ht] =plotBox(box, col, str) +% function h=plotBox(box, col, str) +% +% box = [xlow xhigh ylow yhigh] +% col = color (default - red) +% str = string printed at center (default '') + +if nargin < 2, col = 'r'; end +if nargin < 3, str = ''; end + +box = double(box); % fails on single + +h = plot([box(1) box(2) box(2) box(1) box(1)], [ box(3) box(3) box(4) box(4) box(3)]); +set(h, 'color', col); +set(h, 'linewidth', 2); +if ~isempty(str) + xc = mean(box(1:2)); + yc = mean(box(3:4)); + ht = text(xc, yc, str); +else + ht = []; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/plotColors.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,10 @@ +function styles = plotColors() + +colors = ['r' 'b' 'k' 'g' 'c' 'y' 'm' ... + 'r' 'b' 'k' 'g' 'c' 'y' 'm']; +symbols = ['o' 'x' '+' '>' '<' '^' 'v' ... + '*' 'p' 'h' 's' 'd' 'o' 'x']; +for i=1:length(colors) + styles{i} = sprintf('-%s%s', colors(i), symbols(i)); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/plotROC.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,26 @@ +function [falseAlarmRate, detectionRate, area, th] = plotROC(confidence, testClass, col, varargin) +% function [falseAlarmRate, detectionRate, area, th] = plotroc(confidence, testClass, color) + +if nargin < 3, col = []; end + +[scale01] = process_options(varargin, 'scale01', 1); + +[falseAlarmRate detectionRate area th] = computeROC(confidence, testClass); + +if ~isempty(col) + h=plot(falseAlarmRate, detectionRate, [col '-']); + %set(h, 'linewidth', 2); + ex = 0.05*max(falseAlarmRate); + ey = 0.05; + if scale01 + axis([0-ex max(falseAlarmRate)+ex 0-ey 1+ey]) + else + % zoom in on the top left corner + axis([0-ex max(falseAlarmRate)*0.5+ex 0.5-ey 1+ey]) + end + grid on + ylabel('detection rate') + %xlabel('# false alarms') + xlabel('false alarm rate') +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/plotROCkpm.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,69 @@ +function [falseAlarmRate, detectionRate, area, th] = plotROC(confidence, testClass, col, varargin) +% You pass the scores and the classes, and the function returns the false +% alarm rate and the detection rate for different points across the ROC. +% +% [faR, dR] = plotROC(score, class) +% +% faR (false alarm rate) is uniformly sampled from 0 to 1 +% dR (detection rate) is computed using the scores. +% +% class = 0 => target absent +% class = 1 => target present +% +% score is the output of the detector, or any other measure of detection. +% There is no plot unless you add a third parameter that is the color of +% the graph. For instance: +% [faR, dR] = plotROC(score, class, 'r') +% +% faR, dR are size 1x1250 + +if nargin < 3, col = []; end +[scale01] = process_options(varargin, 'scale01', 1); + +S = rand('state'); +rand('state',0); +confidence = confidence + rand(size(confidence))*10^(-10); +rand('state',S) + +ndxAbs = find(testClass==0); % absent +ndxPres = find(testClass==1); % present + +[th, j] = sort(confidence(ndxAbs)); +th = th(fix(linspace(1, length(th), 1250))); + +cAbs = confidence(ndxAbs); +cPres = confidence(ndxPres); +for t=1:length(th) + if length(ndxPres) == 0 + detectionRate(t) = 0; + else + detectionRate(t) = sum(cPres>=th(t)) / length(ndxPres); + end + if length(ndxAbs) == 0 + falseAlarmRate(t) = 0; + else + falseAlarmRate(t) = sum(cAbs>=th(t)) / length(ndxAbs); + end + + %detectionRate(t) = sum(confidence(ndxPres)>=th(t)) / length(ndxPres); + %falseAlarmRate(t) = sum(confidence(ndxAbs)>=th(t)) / length(ndxAbs); + %detections(t) = sum(confidence(ndxPres)>=th(t)); + %falseAlarms(t) = sum(confidence(ndxAbs)>=th(t)); +end + +area = sum(abs(falseAlarmRate(2:end) - falseAlarmRate(1:end-1)) .* detectionRate(2:end)); + +if ~isempty(col) + h=plot(falseAlarmRate, detectionRate, [col '-']); + %set(h, 'linewidth', 2); + e = 0.05; + if scale01 + axis([0-e 1+e 0-e 1+e]) + else + % zoom in on the top left corner + axis([0-e 0.5+e 0.5-e 1+e]) + end + grid on + ylabel('detection rate') + xlabel('false alarm rate') +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/plot_axis_thru_origin.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,3 @@ +function plot_axis_thru_origin() + +lnx=line(get(gca,'xlim'),[0 0]); lny=line([0 0],get(gca,'ylim'));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/plot_ellipse.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,19 @@ +% PLOT_ELLIPSE +% h=plot_ellipse(x,y,theta,a,b) +% +% This routine plots an ellipse with centre (x,y), axis lengths a,b +% with major axis at an angle of theta radians from the horizontal. + +% +% Author: P. Fieguth +% Jan. 98 +% +%http://ocho.uwaterloo.ca/~pfieguth/Teaching/372/plot_ellipse.m + +function h=plot_ellipse(x,y,theta,a,b) + +np = 100; +ang = [0:np]*2*pi/np; +R = [cos(theta) -sin(theta); sin(theta) cos(theta)]; +pts = [x;y]*ones(size(ang)) + R*[cos(ang)*a; sin(ang)*b]; +h=plot( pts(1,:), pts(2,:) );
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/plot_matrix.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,48 @@ +function plot_matrix(G, bw) +% PLOT_MATRIX Plot a 2D matrix as a grayscale image, and label the axes +% +% plot_matrix(M) +% +% For 0/1 matrices (eg. adjacency matrices), use +% plot_matrix(M,1) + +if nargin < 2, bw = 0; end + +if 0 + imagesc(G) + %image(G) + %colormap([1 1 1; 0 0 0]); % black squares on white background + %colormap(gray) + grid on + n = length(G); + + % shift the grid lines so they don't intersect the squares + set(gca,'xtick',1.5:1:n); + set(gca,'ytick',1.5:1:n); + + % Turn off the confusing labels, which are fractional + % Ideally we could shift the labels to lie between the axis lines... +% set(gca,'xticklabel', []); +% set(gca,'yticklabel', []); +else + % solution provided by Jordan Rosenthal <jr@ece.gatech.edu> + % You can plot the grid lines manually: + % This uses the trick that a point with a value nan does not get plotted. + imagesc(G); + if bw + colormap([1 1 1; 0 0 0]); + end + n = length(G); + x = 1.5:1:n; + x = [ x; x; repmat(nan,1,n-1) ]; + y = [ 0.5 n+0.5 nan ].'; + y = repmat(y,1,n-1); + x = x(:); + y = y(:); + line(x,y,'linestyle',':','color','k'); + line(y,x,'linestyle',':','color','k'); + set(gca,'xtick',1:n) + set(gca,'ytick',1:n) +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/plot_polygon.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,23 @@ +function out=plot_polygon(p, args, close_loop) +% PLOT_POLYGON +% function handle=plot_polygon(p, args, close_loop) +% p(1,i), p(2,i) are the x/y coords of point i. +% If non-empty, args are passed thru to the plot command. +% If close_loop = 1, connect the last point to the first + +% All rights reserved. Documentation updated April 1999. +% Matt Kawski. http://math.la.asu.edu/~kawski +% He calls it pplot + +if nargin < 2, args = []; end +if nargin < 3, close_loop = 0; end + +if close_loop + p = [p p(:,1)]; +end + +if isempty(args) + out=plot(p(1,:),p(2,:)); +else + out=plot(p(1,:),p(2,:),args); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/plotcov2.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,86 @@ +% PLOTCOV2 - Plots a covariance ellipse with major and minor axes +% for a bivariate Gaussian distribution. +% +% Usage: +% h = plotcov2(mu, Sigma[, OPTIONS]); +% +% Inputs: +% mu - a 2 x 1 vector giving the mean of the distribution. +% Sigma - a 2 x 2 symmetric positive semi-definite matrix giving +% the covariance of the distribution (or the zero matrix). +% +% Options: +% 'conf' - a scalar between 0 and 1 giving the confidence +% interval (i.e., the fraction of probability mass to +% be enclosed by the ellipse); default is 0.9. +% 'num-pts' - the number of points to be used to plot the +% ellipse; default is 100. +% +% This function also accepts options for PLOT. +% +% Outputs: +% h - a vector of figure handles to the ellipse boundary and +% its major and minor axes +% +% See also: PLOTCOV3 + +% Copyright (C) 2002 Mark A. Paskin +% +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, but +% WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +% General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 +% USA. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function h = plotcov2(mu, Sigma, varargin) + +if size(Sigma) ~= [2 2], error('Sigma must be a 2 by 2 matrix'); end +if length(mu) ~= 2, error('mu must be a 2 by 1 vector'); end + +[p, ... + n, ... + plot_opts] = process_options(varargin, 'conf', 0.9, ... + 'num-pts', 100); +h = []; +holding = ishold; +if (Sigma == zeros(2, 2)) + z = mu; +else + % Compute the Mahalanobis radius of the ellipsoid that encloses + % the desired probability mass. + k = conf2mahal(p, 2); + % The major and minor axes of the covariance ellipse are given by + % the eigenvectors of the covariance matrix. Their lengths (for + % the ellipse with unit Mahalanobis radius) are given by the + % square roots of the corresponding eigenvalues. + if (issparse(Sigma)) + [V, D] = eigs(Sigma); + else + [V, D] = eig(Sigma); + end + % Compute the points on the surface of the ellipse. + t = linspace(0, 2*pi, n); + u = [cos(t); sin(t)]; + w = (k * V * sqrt(D)) * u; + z = repmat(mu, [1 n]) + w; + % Plot the major and minor axes. + L = k * sqrt(diag(D)); + h = plot([mu(1); mu(1) + L(1) * V(1, 1)], ... + [mu(2); mu(2) + L(1) * V(2, 1)], plot_opts{:}); + hold on; + h = [h; plot([mu(1); mu(1) + L(2) * V(1, 2)], ... + [mu(2); mu(2) + L(2) * V(2, 2)], plot_opts{:})]; +end + +h = [h; plot(z(1, :), z(2, :), plot_opts{:})]; +if (~holding) hold off; end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/plotcov2New.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,119 @@ +% PLOTCOV2 - Plots a covariance ellipsoid with axes for a bivariate +% Gaussian distribution. +% +% Usage: +% [h, s] = plotcov2(mu, Sigma[, OPTIONS]); +% +% Inputs: +% mu - a 2 x 1 vector giving the mean of the distribution. +% Sigma - a 2 x 2 symmetric positive semi-definite matrix giving +% the covariance of the distribution (or the zero matrix). +% +% Options: +% 'conf' - a scalar between 0 and 1 giving the confidence +% interval (i.e., the fraction of probability mass to +% be enclosed by the ellipse); default is 0.9. +% 'num-pts' - if the value supplied is n, then (n + 1)^2 points +% to be used to plot the ellipse; default is 20. +% 'label' - if non-empty, a string that will label the +% ellipsoid (default: []) +% 'plot-axes' - a 0/1 flag indicating if the ellipsoid's axes +% should be plotted (default: 1) +% 'plot-opts' - a cell vector of arguments to be handed to PLOT3 +% to contol the appearance of the axes, e.g., +% {'Color', 'g', 'LineWidth', 1}; the default is {} +% 'fill-color' - a color specifier; is this is not [], the +% covariance ellipse is filled with this color +% (default: []) +% +% Outputs: +% h - a vector of handles on the axis lines +% +% See also: PLOTCOV3 + +% Copyright (C) 2002 Mark A. Paskin +% +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, but +% WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +% General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 +% USA. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [h, s] = plotcov2New(mu, Sigma, varargin) + +h = []; +s = []; + +if size(Sigma) ~= [2 2], error('Sigma must be a 2 by 2 matrix'); end +if length(mu) ~= 2, error('mu must be a 2 by 1 vector'); end + +Sigma = checkpsd(Sigma); + +[p, ... + n, ... + label, ... + plot_axes, ... + plot_opts, ... + fill_color] = process_options(varargin, 'conf', 0.9, ... + 'num-pts', 20, ... + 'label', [], ... + 'plot-axes', 1, ... + 'plot-opts', {}, ... + 'fill-color', []); +holding = ishold; +% Compute the Mahalanobis radius of the ellipsoid that encloses +% the desired probability mass. +k = conf2mahal(p, 2); +% Scale the covariance matrix so the confidence region has unit +% Mahalanobis distance. +Sigma = Sigma * k; +% The axes of the covariance ellipse are given by the eigenvectors of +% the covariance matrix. Their lengths (for the ellipse with unit +% Mahalanobis radius) are given by the square roots of the +% corresponding eigenvalues. +[V, D] = eig(full(Sigma)); +V = real(V); +D = real(D); +D = abs(D); + +% Compute the points on the boundary of the ellipsoid. +t = linspace(0, 2*pi, n); +u = [cos(t(:))'; sin(t(:))']; +w = (V * sqrt(D)) * u; +z = repmat(mu(:), [1 n]) + w; +h = [h; plot(z(1, :), z(2, :), plot_opts{:})]; +if (~isempty(fill_color)) + s = patch(z(1, :), z(2, :), fill_color); +end + +% Plot the axes. +if (plot_axes) + hold on; + L = sqrt(diag(D)); + h = plot([mu(1); mu(1) + L(1) * V(1, 1)], ... + [mu(2); mu(2) + L(1) * V(2, 1)], plot_opts{:}); + h = [h; plot([mu(1); mu(1) + L(2) * V(1, 2)], ... + [mu(2); mu(2) + L(2) * V(2, 2)], plot_opts{:})]; +end + + +if (~isempty(label)) + th = text(mu(1), mu(2), label); + set(th, 'FontSize', 18); + set(th, 'FontName', 'Times'); + set(th, 'FontWeight', 'bold'); + set(th, 'FontAngle', 'italic'); + set(th, 'HorizontalAlignment', 'center'); +end + +if (~holding & plot_axes) hold off; end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/plotcov3.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,109 @@ +% PLOTCOV3 - Plots a covariance ellipsoid with axes for a trivariate +% Gaussian distribution. +% +% Usage: +% [h, s] = plotcov3(mu, Sigma[, OPTIONS]); +% +% Inputs: +% mu - a 3 x 1 vector giving the mean of the distribution. +% Sigma - a 3 x 3 symmetric positive semi-definite matrix giving +% the covariance of the distribution (or the zero matrix). +% +% Options: +% 'conf' - a scalar between 0 and 1 giving the confidence +% interval (i.e., the fraction of probability mass to +% be enclosed by the ellipse); default is 0.9. +% 'num-pts' - if the value supplied is n, then (n + 1)^2 points +% to be used to plot the ellipse; default is 20. +% 'plot-opts' - a cell vector of arguments to be handed to PLOT3 +% to contol the appearance of the axes, e.g., +% {'Color', 'g', 'LineWidth', 1}; the default is {} +% 'surf-opts' - a cell vector of arguments to be handed to SURF +% to contol the appearance of the ellipsoid +% surface; a nice possibility that yields +% transparency is: {'EdgeAlpha', 0, 'FaceAlpha', +% 0.1, 'FaceColor', 'g'}; the default is {} +% +% Outputs: +% h - a vector of handles on the axis lines +% s - a handle on the ellipsoid surface object +% +% See also: PLOTCOV2 + +% Copyright (C) 2002 Mark A. Paskin +% +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, but +% WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +% General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 +% USA. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [h, s] = plotcov3(mu, Sigma, varargin) + +if size(Sigma) ~= [3 3], error('Sigma must be a 3 by 3 matrix'); end +if length(mu) ~= 3, error('mu must be a 3 by 1 vector'); end + +[p, ... + n, ... + plot_opts, ... + surf_opts] = process_options(varargin, 'conf', 0.9, ... + 'num-pts', 20, ... + 'plot-opts', {}, ... + 'surf-opts', {}); +h = []; +holding = ishold; +if (Sigma == zeros(3, 3)) + z = mu; +else + % Compute the Mahalanobis radius of the ellipsoid that encloses + % the desired probability mass. + k = conf2mahal(p, 3); + % The axes of the covariance ellipse are given by the eigenvectors of + % the covariance matrix. Their lengths (for the ellipse with unit + % Mahalanobis radius) are given by the square roots of the + % corresponding eigenvalues. + if (issparse(Sigma)) + [V, D] = eigs(Sigma); + else + [V, D] = eig(Sigma); + end + if (any(diag(D) < 0)) + error('Invalid covariance matrix: not positive semi-definite.'); + end + % Compute the points on the surface of the ellipsoid. + t = linspace(0, 2*pi, n); + [X, Y, Z] = sphere(n); + u = [X(:)'; Y(:)'; Z(:)']; + w = (k * V * sqrt(D)) * u; + z = repmat(mu(:), [1 (n + 1)^2]) + w; + + % Plot the axes. + L = k * sqrt(diag(D)); + h = plot3([mu(1); mu(1) + L(1) * V(1, 1)], ... + [mu(2); mu(2) + L(1) * V(2, 1)], ... + [mu(3); mu(3) + L(1) * V(3, 1)], plot_opts{:}); + hold on; + h = [h; plot3([mu(1); mu(1) + L(2) * V(1, 2)], ... + [mu(2); mu(2) + L(2) * V(2, 2)], ... + [mu(3); mu(3) + L(2) * V(3, 2)], plot_opts{:})]; + h = [h; plot3([mu(1); mu(1) + L(3) * V(1, 3)], ... + [mu(2); mu(2) + L(3) * V(2, 3)], ... + [mu(3); mu(3) + L(3) * V(3, 3)], plot_opts{:})]; +end + +s = surf(reshape(z(1, :), [(n + 1) (n + 1)]), ... + reshape(z(2, :), [(n + 1) (n + 1)]), ... + reshape(z(3, :), [(n + 1) (n + 1)]), ... + surf_opts{:}); + +if (~holding) hold off; end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/plotgauss1d.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,9 @@ +function h = plotgauss1d(mu, sigma2) +% function h = plotgauss1d(mu, sigma^2) +% Example +% plotgauss1d(0,5); hold on; h=plotgauss1d(0,2);set(h,'color','r') + +sigma = sqrt(sigma2); +x = linspace(mu-3*sigma, mu+3*sigma, 100); +p = gaussian_prob(x, mu, sigma2); +h = plot(x, p, '-');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/plotgauss2d.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,130 @@ +function h=plotgauss2d(mu, Sigma) +% PLOTGAUSS2D Plot a 2D Gaussian as an ellipse with optional cross hairs +% h=plotgauss2(mu, Sigma) +% + +h = plotcov2(mu, Sigma); +return; + +%%%%%%%%%%%%%%%%%%%%%%%% + +% PLOTCOV2 - Plots a covariance ellipse with major and minor axes +% for a bivariate Gaussian distribution. +% +% Usage: +% h = plotcov2(mu, Sigma[, OPTIONS]); +% +% Inputs: +% mu - a 2 x 1 vector giving the mean of the distribution. +% Sigma - a 2 x 2 symmetric positive semi-definite matrix giving +% the covariance of the distribution (or the zero matrix). +% +% Options: +% 'conf' - a scalar between 0 and 1 giving the confidence +% interval (i.e., the fraction of probability mass to +% be enclosed by the ellipse); default is 0.9. +% 'num-pts' - the number of points to be used to plot the +% ellipse; default is 100. +% +% This function also accepts options for PLOT. +% +% Outputs: +% h - a vector of figure handles to the ellipse boundary and +% its major and minor axes +% +% See also: PLOTCOV3 + +% Copyright (C) 2002 Mark A. Paskin + +function h = plotcov2(mu, Sigma, varargin) + +if size(Sigma) ~= [2 2], error('Sigma must be a 2 by 2 matrix'); end +if length(mu) ~= 2, error('mu must be a 2 by 1 vector'); end + +[p, ... + n, ... + plot_opts] = process_options(varargin, 'conf', 0.9, ... + 'num-pts', 100); +h = []; +holding = ishold; +if (Sigma == zeros(2, 2)) + z = mu; +else + % Compute the Mahalanobis radius of the ellipsoid that encloses + % the desired probability mass. + k = conf2mahal(p, 2); + % The major and minor axes of the covariance ellipse are given by + % the eigenvectors of the covariance matrix. Their lengths (for + % the ellipse with unit Mahalanobis radius) are given by the + % square roots of the corresponding eigenvalues. + if (issparse(Sigma)) + [V, D] = eigs(Sigma); + else + [V, D] = eig(Sigma); + end + % Compute the points on the surface of the ellipse. + t = linspace(0, 2*pi, n); + u = [cos(t); sin(t)]; + w = (k * V * sqrt(D)) * u; + z = repmat(mu, [1 n]) + w; + % Plot the major and minor axes. + L = k * sqrt(diag(D)); + h = plot([mu(1); mu(1) + L(1) * V(1, 1)], ... + [mu(2); mu(2) + L(1) * V(2, 1)], plot_opts{:}); + hold on; + h = [h; plot([mu(1); mu(1) + L(2) * V(1, 2)], ... + [mu(2); mu(2) + L(2) * V(2, 2)], plot_opts{:})]; +end + +h = [h; plot(z(1, :), z(2, :), plot_opts{:})]; +if (~holding) hold off; end + +%%%%%%%%%%%% + +% CONF2MAHAL - Translates a confidence interval to a Mahalanobis +% distance. Consider a multivariate Gaussian +% distribution of the form +% +% p(x) = 1/sqrt((2 * pi)^d * det(C)) * exp((-1/2) * MD(x, m, inv(C))) +% +% where MD(x, m, P) is the Mahalanobis distance from x +% to m under P: +% +% MD(x, m, P) = (x - m) * P * (x - m)' +% +% A particular Mahalanobis distance k identifies an +% ellipsoid centered at the mean of the distribution. +% The confidence interval associated with this ellipsoid +% is the probability mass enclosed by it. Similarly, +% a particular confidence interval uniquely determines +% an ellipsoid with a fixed Mahalanobis distance. +% +% If X is an d dimensional Gaussian-distributed vector, +% then the Mahalanobis distance of X is distributed +% according to the Chi-squared distribution with d +% degrees of freedom. Thus, the Mahalanobis distance is +% determined by evaluating the inverse cumulative +% distribution function of the chi squared distribution +% up to the confidence value. +% +% Usage: +% +% m = conf2mahal(c, d); +% +% Inputs: +% +% c - the confidence interval +% d - the number of dimensions of the Gaussian distribution +% +% Outputs: +% +% m - the Mahalanobis radius of the ellipsoid enclosing the +% fraction c of the distribution's probability mass +% +% See also: MAHAL2CONF + +% Copyright (C) 2002 Mark A. Paskin + +function m = conf2mahal(c, d) + +m = chi2inv(c, d); % matlab stats toolbox
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/plotgauss2d_old.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,40 @@ +function h=plotgauss2d_old(mu, Sigma, plot_cross) +% PLOTGAUSS2D Plot a 2D Gaussian as an ellipse with optional cross hairs +% h=plotgauss2(mu, Sigma) +% +% h=plotgauss2(mu, Sigma, 1) also plots the major and minor axes +% +% Example +% clf; S=[2 1; 1 2]; plotgauss2d([0;0], S, 1); axis equal + +if nargin < 3, plot_cross = 0; end +[V,D]=eig(Sigma); +lam1 = D(1,1); +lam2 = D(2,2); +v1 = V(:,1); +v2 = V(:,2); +%assert(approxeq(v1' * v2, 0)) +if v1(1)==0 + theta = 0; % horizontal +else + theta = atan(v1(2)/v1(1)); +end +a = sqrt(lam1); +b = sqrt(lam2); +h=plot_ellipse(mu(1), mu(2), theta, a,b); + +if plot_cross + mu = mu(:); + held = ishold; + hold on + minor1 = mu-a*v1; minor2 = mu+a*v1; + hminor = line([minor1(1) minor2(1)], [minor1(2) minor2(2)]); + + major1 = mu-b*v2; major2 = mu+b*v2; + hmajor = line([major1(1) major2(1)], [major1(2) major2(2)]); + %set(hmajor,'color','r') + if ~held + hold off + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/polygon_area.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,22 @@ +function a = polygon_area(x,y) +% AREA Area of a planar polygon. +% AREA(X,Y) Calculates the area of a 2-dimensional +% polygon formed by vertices with coordinate vectors +% X and Y. The result is direction-sensitive: the +% area is positive if the bounding contour is counter- +% clockwise and negative if it is clockwise. +% +% See also TRAPZ. + +% Copyright (c) 1995 by Kirill K. Pankratov, +% kirill@plume.mit.edu. +% 04/20/94, 05/20/95 + + % Make polygon closed ............. +x = [x(:); x(1)]; +y = [y(:); y(1)]; + + % Calculate contour integral Int -y*dx (same as Int x*dy). +lx = length(x); +a = -(x(2:lx)-x(1:lx-1))'*(y(1:lx-1)+y(2:lx))/2; +a = abs(a);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/polygon_centroid.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,79 @@ +function [x0,y0] = centroid(x,y) +% CENTROID Center of mass of a polygon. +% [X0,Y0] = CENTROID(X,Y) Calculates centroid +% (center of mass) of planar polygon with vertices +% coordinates X, Y. +% Z0 = CENTROID(X+i*Y) returns Z0=X0+i*Y0 the same +% as CENTROID(X,Y). + +% Copyright (c) 1995 by Kirill K. Pankratov, +% kirill@plume.mit.edu. +% 06/01/95, 06/07/95 + +% Algorithm: +% X0 = Int{x*ds}/Int{ds}, where ds - area element +% so that Int{ds} is total area of a polygon. +% Using Green's theorem the area integral can be +% reduced to a contour integral: +% Int{x*ds} = -Int{x^2*dy}, Int{ds} = Int{x*dy} along +% the perimeter of a polygon. +% For a polygon as a sequence of line segments +% this can be reduced exactly to a sum: +% Int{x^2*dy} = Sum{ (x_{i}^2+x_{i+1}^2+x_{i}*x_{i+1})* +% (y_{i+1}-y_{i})}/3; +% Int{x*dy} = Sum{(x_{i}+x_{i+1})(y_{i+1}-y_{i})}/2. +% Similarly +% Y0 = Int{y*ds}/Int{ds}, where +% Int{y*ds} = Int{y^2*dx} = +% = Sum{ (y_{i}^2+y_{i+1}^2+y_{i}*y_{i+1})* +% (x_{i+1}-x_{i})}/3. + + % Handle input ...................... +if nargin==0, help centroid, return, end +if nargin==1 + sz = size(x); + if sz(1)==2 % Matrix 2 by n + y = x(2,:); x = x(1,:); + elseif sz(2)==2 % Matrix n by 2 + y = x(:,2); x = x(:,1); + else + y = imag(x); + x = real(x); + end +end + + % Make a polygon closed .............. +x = [x(:); x(1)]; +y = [y(:); y(1)]; + + % Check length ....................... +l = length(x); +if length(y)~=l + error(' Vectors x and y must have the same length') +end + + % X-mean: Int{x^2*dy} ................ +del = y(2:l)-y(1:l-1); +v = x(1:l-1).^2+x(2:l).^2+x(1:l-1).*x(2:l); +x0 = v'*del; + + % Y-mean: Int{y^2*dx} ................ +del = x(2:l)-x(1:l-1); +v = y(1:l-1).^2+y(2:l).^2+y(1:l-1).*y(2:l); +y0 = v'*del; + + % Calculate area: Int{y*dx} .......... +a = (y(1:l-1)+y(2:l))'*del; +tol= 2*eps; +if abs(a)<tol + disp(' Warning: area of polygon is close to 0') + a = a+sign(a)*tol+(~a)*tol; +end + % Multiplier +a = 1/3/a; + + % Divide by area ..................... +x0 = -x0*a; +y0 = y0*a; + +if nargout < 2, x0 = x0+i*y0; end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/polygon_intersect.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,60 @@ +function [is,S] = isintpl(x1,y1,x2,y2) +% ISINTPL Check for intersection of polygons. +% [IS,S] = ISINTPL(X1,Y1,X2,Y2) Accepts coordinates +% X1,Y1 and X2, Y2 of two polygons. Returns scalar +% IS equal to 1 if these polygons intersect each other +% and 0 if they do not. +% Also returns Boolean matrix S of the size length(X1) +% by length(X2) so that {ij} element of which is 1 if +% line segments i to i+1 of the first polygon and +% j to j+1 of the second polygon intersect each other, +% 0 if they don't and .5 if they "touch" each other. + +% Copyright (c) 1995 by Kirill K. Pankratov, +% kirill@plume.mit.edu. +% 06/20/95, 08/25/95 + + + % Handle input ................................... +if nargin==0, help isintpl, return, end +if nargin<4 + error(' Not enough input arguments ') +end + + % Make column vectors and check sizes ............ +x1 = x1(:); y1 = y1(:); +x2 = x2(:); y2 = y2(:); +l1 = length(x1); +l2 = length(x2); +if length(y1)~=l1 | length(y2)~=l2 + error('(X1,Y1) and (X2,Y2) must pair-wise have the same length.') +end + + % Quick exit if empty +if l1<1 | l2<1, is = []; S = []; return, end + + % Check if their ranges are intersecting ......... +lim1 = [min(x1) max(x1)]'; +lim2 = [min(x2) max(x2)]'; +isx = interval(lim1,lim2); % X-ranges +lim1 = [min(y1) max(y1)]'; +lim2 = [min(y2) max(y2)]'; +isy = interval(lim1,lim2); % Y-ranges +is = isx & isy; +S = zeros(l2,l1); + +if ~is, return, end % Early exit if disparate limits + + % Indexing ....................................... +[i11,i12] = meshgrid(1:l1,1:l2); +[i21,i22] = meshgrid([2:l1 1],[2:l2 1]); +i11 = i11(:); i12 = i12(:); +i21 = i21(:); i22 = i22(:); + + % Calculate matrix of intersections .............. +S(:) = iscross([x1(i11) x1(i21)]',[y1(i11) y1(i21)]',... + [x2(i12) x2(i22)]',[y2(i12) y2(i22)]')'; + +S = S'; +is = any(any(S)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/previewfig.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,49 @@ +function f = previewfig(h,varargin) +%PREVIEWFIG Preview a figure to be exported using EXPORTFIG. +% F = PREVIEWFIG(H) creates a preview of H with the default +% EXPORTFIG options and returns the preview's figure handle in F. +% F = PREVIEWFIG(H,OPTIONS) creates a preview with OPTIONS as +% described in EXPORTFIG. +% PREVIEWFIG(...,PARAM1,VAL1,PARAM2,VAL2,...) creates a preview +% with the specified parameter-value pairs to H as described in +% EXPORTFIG. +% +% See also EXPORTFIG, APPLYTOFIG, RESTOREFIG. + +% Copyright 2000 Ben Hinkle +% Email bug reports and comments to bhinkle@mathworks.com + +filename = [tempname, '.png']; +args = {'resolution',0,'format','png'}; +if nargin > 1 + exportfig(h, filename, varargin{:}, args{:}); +else + exportfig(h, filename, args{:}); +end + +X = imread(filename,'png'); +height = size(X,1); +width = size(X,2); +delete(filename); +f = figure( 'Name', 'Preview', ... + 'Menubar', 'none', ... + 'NumberTitle', 'off', ... + 'Visible', 'off'); +image(X); +axis image; +ax = findobj(f, 'type', 'axes'); +axesPos = [0 0 width height]; +set(ax, 'Units', 'pixels', ... + 'Position', axesPos, ... + 'Visible', 'off'); +figPos = get(f,'Position'); +rootSize = get(0,'ScreenSize'); +figPos(3:4) = axesPos(3:4); +if figPos(1) + figPos(3) > rootSize(3) + figPos(1) = rootSize(3) - figPos(3) - 50; +end +if figPos(2) + figPos(4) > rootSize(4) + figPos(2) = rootSize(4) - figPos(4) - 50; +end +set(f, 'Position',figPos, ... + 'Visible', 'on');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/process_options.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,132 @@ +% PROCESS_OPTIONS - Processes options passed to a Matlab function. +% This function provides a simple means of +% parsing attribute-value options. Each option is +% named by a unique string and is given a default +% value. +% +% Usage: [var1, var2, ..., varn[, unused]] = ... +% process_options(args, ... +% str1, def1, str2, def2, ..., strn, defn) +% +% Arguments: +% args - a cell array of input arguments, such +% as that provided by VARARGIN. Its contents +% should alternate between strings and +% values. +% str1, ..., strn - Strings that are associated with a +% particular variable +% def1, ..., defn - Default values returned if no option +% is supplied +% +% Returns: +% var1, ..., varn - values to be assigned to variables +% unused - an optional cell array of those +% string-value pairs that were unused; +% if this is not supplied, then a +% warning will be issued for each +% option in args that lacked a match. +% +% Examples: +% +% Suppose we wish to define a Matlab function 'func' that has +% required parameters x and y, and optional arguments 'u' and 'v'. +% With the definition +% +% function y = func(x, y, varargin) +% +% [u, v] = process_options(varargin, 'u', 0, 'v', 1); +% +% calling func(0, 1, 'v', 2) will assign 0 to x, 1 to y, 0 to u, and 2 +% to v. The parameter names are insensitive to case; calling +% func(0, 1, 'V', 2) has the same effect. The function call +% +% func(0, 1, 'u', 5, 'z', 2); +% +% will result in u having the value 5 and v having value 1, but +% will issue a warning that the 'z' option has not been used. On +% the other hand, if func is defined as +% +% function y = func(x, y, varargin) +% +% [u, v, unused_args] = process_options(varargin, 'u', 0, 'v', 1); +% +% then the call func(0, 1, 'u', 5, 'z', 2) will yield no warning, +% and unused_args will have the value {'z', 2}. This behaviour is +% useful for functions with options that invoke other functions +% with options; all options can be passed to the outer function and +% its unprocessed arguments can be passed to the inner function. + +% Copyright (C) 2002 Mark A. Paskin +% +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, but +% WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +% General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 +% USA. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [varargout] = process_options(args, varargin) + +% Check the number of input arguments +n = length(varargin); +if (mod(n, 2)) + error('Each option must be a string/value pair.'); +end + +% Check the number of supplied output arguments +if (nargout < (n / 2)) + error('Insufficient number of output arguments given'); +elseif (nargout == (n / 2)) + warn = 1; + nout = n / 2; +else + warn = 0; + nout = n / 2 + 1; +end + +% Set outputs to be defaults +varargout = cell(1, nout); +for i=2:2:n + varargout{i/2} = varargin{i}; +end + +% Now process all arguments +nunused = 0; +for i=1:2:length(args) + found = 0; + for j=1:2:n + if strcmpi(args{i}, varargin{j}) + varargout{(j + 1)/2} = args{i + 1}; + found = 1; + break; + end + end + if (~found) + if (warn) + warning(sprintf('Option ''%s'' not used.', args{i})); + args{i} + else + nunused = nunused + 1; + unused{2 * nunused - 1} = args{i}; + unused{2 * nunused} = args{i + 1}; + end + end +end + +% Assign the unused arguments +if (~warn) + if (nunused) + varargout{nout} = unused; + else + varargout{nout} = cell(0); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/rand_psd.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function M = rand_psd(d, d2, k) +% Create a random positive definite matrix of size d by d by k (k defaults to 1) +% M = rand_psd(d, d2, k) default: d2 = d, k = 1 + +if nargin<2, d2 = d; end +if nargin<3, k = 1; end +if d2 ~= d, error('must be square'); end + +M = zeros(d,d,k); +for i=1:k + A = rand(d); + M(:,:,i) = A*A'; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/rectintC.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,22 @@ +function [overlap, normoverlap] = rectintC(A,B) +% +% A(i,:) = [x y w h] +% B(j,:) = [x y w h] +% overlap(i,j) = area of intersection +% normoverlap(i,j) = overlap(i,j) / min(area(i), area(j)) +% +% Same as built-in rectint, but faster and uses less memory (since avoids repmat). + + +leftA = A(:,1); +bottomA = A(:,2); +rightA = leftA + A(:,3); +topA = bottomA + A(:,4); + +leftB = B(:,1)'; +bottomB = B(:,2)'; +rightB = leftB + B(:,3)'; +topB = bottomB + B(:,4)'; + +verbose = 0; +[overlap, normoverlap] = rectintLoopC(leftA, rightA, topA, bottomA, leftB, rightB, topB, bottomB, verbose);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/rectintLoopC.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,71 @@ + +#include "mex.h" +#include <stdio.h> + +#define MAX(x,y) ((x)>(y) ? (x) : (y)) +#define MIN(x,y) ((x)<(y) ? (x) : (y)) + +void mexFunction( + int nlhs, mxArray *plhs[], + int nrhs, const mxArray *prhs[] + ) +{ + int j,k,m,n,nzmax,*irs,*jcs, *irs2, *jcs2; + double *overlap, *overlap2, tmp, areaA, areaB; + double *leftA, *rightA, *topA, *bottomA; + double *leftB, *rightB, *topB, *bottomB; + double *verbose; + + m = MAX(mxGetM(prhs[0]), mxGetN(prhs[0])); + n = MAX(mxGetM(prhs[4]), mxGetN(prhs[4])); + /* printf("A=%d, B=%d\n", m, n); */ + + leftA = mxGetPr(prhs[0]); + rightA = mxGetPr(prhs[1]); + topA = mxGetPr(prhs[2]); + bottomA = mxGetPr(prhs[3]); + + leftB = mxGetPr(prhs[4]); + rightB = mxGetPr(prhs[5]); + topB = mxGetPr(prhs[6]); + bottomB = mxGetPr(prhs[7]); + + verbose = mxGetPr(prhs[8]); + + plhs[0] = mxCreateDoubleMatrix(m,n, mxREAL); + overlap = mxGetPr(plhs[0]); + + plhs[1] = mxCreateDoubleMatrix(m,n, mxREAL); + overlap2 = mxGetPr(plhs[1]); + + k = 0; + for (j = 0; (j < n); j++) { + int i; + for (i = 0; (i < m); i++) { + tmp = (MAX(0, MIN(rightA[i], rightB[j]) - MAX(leftA[i], leftB[j]) )) * + (MAX(0, MIN(topA[i], topB[j]) - MAX(bottomA[i], bottomB[j]) )); + + if (tmp > 0) { + overlap[k] = tmp; + + areaA = (rightA[i]-leftA[i])*(topA[i]-bottomA[i]); + areaB = (rightB[j]-leftB[j])*(topB[j]-bottomB[j]); + overlap2[k] = tmp/MIN(areaA, areaB); + + if (*verbose) { + printf("j=%d,i=%d,overlap=%5.3f, norm=%5.3f\n", j,i, overlap[k], overlap2[k]); + } + } + + k++; + } + } +} + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/rectintSparse.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,59 @@ +function [overlap, normoverlap] = rectintSparse(A,B) +% +% A(i,:) = [x y w h] +% B(j,:) = [x y w h] +% overlap(i,j) = area of intersection +% normoverla(i,j) +% +% Same as built-in rectint, but uses less memory. +% Use rectintSparseC for a faster version. +% + +leftA = A(:,1); +bottomA = A(:,2); +rightA = leftA + A(:,3); +topA = bottomA + A(:,4); + +leftB = B(:,1)'; +bottomB = B(:,2)'; +rightB = leftB + B(:,3)'; +topB = bottomB + B(:,4)'; + +numRectA = size(A,1); +numRectB = size(B,1); + +%out = rectintSparseLoopC(leftA, rightA, topA, bottomA, leftB, rightB, topB, bottomB); + +nnz = ceil(0.2*numRectA*numRectB); % guess of number of non-zeroes +overlap = sparse([], [], [], numRectA, numRectB, nnz); +normoverlap = sparse([], [], [], numRectA, numRectB, nnz); +for j=1:numRectB + for i=1:numRectA + tmp = (max(0, min(rightA(i), rightB(j)) - max(leftA(i), leftB(j)) ) ) .* ... + (max(0, min(topA(i), topB(j)) - max(bottomA(i), bottomB(j)) ) ); + if tmp>0 + overlap(i,j) = tmp; + areaA = (rightA(i)-leftA(i))*(topA(i)-bottomA(i)); + areaB = (rightB(j)-leftB(j))*(topB(j)-bottomB(j)); + normoverlap(i,j) = min(tmp/areaA, tmp/areaB); + end + %fprintf('j=%d, i=%d, overlap=%5.3f, norm=%5.3f\n',... + % j, i, overlap(i,j), normoverlap(i,j)); + end +end + + +if 0 +N = size(bboxDense01,2); % 1000; +rect = bboxToRect(bboxDense01)'; +A = rect(1:2,:); +B = rect(1:N,:); + +tic; out1 = rectint(A, B); toc +tic; out2 = rectintSparse(A, B); toc +tic; out3 = rectintSparseC(A, B); toc +tic; out4 = rectintC(A, B); toc +assert(approxeq(out1, out2)) +assert(approxeq(out1, full(out3))) +assert(approxeq(out1, out4)) +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/rectintSparseC.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,32 @@ +function [overlap, normoverlap] = rectintSparseC(A,B) +% +% function [area, normarea] = rectintSparseC(A,B) +% A(i,:) = [x y w h] +% B(j,:) = [x y w h] +% out(i,j) = area of intersection +% +% Same as built-in rectint, but uses less memory. +% Also, returns area of overlap normalized by area of patch. +% See rectintSparse + +if isempty(A) | isempty(B) + overlap = []; + normoverlap = []; + return; +end + +leftA = A(:,1); +bottomA = A(:,2); +rightA = leftA + A(:,3); +topA = bottomA + A(:,4); + +leftB = B(:,1)'; +bottomB = B(:,2)'; +rightB = leftB + B(:,3)'; +topB = bottomB + B(:,4)'; + +numRectA = size(A,1); +numRectB = size(B,1); + +verbose = 0; +[overlap, normoverlap] = rectintSparseLoopC(leftA, rightA, topA, bottomA, leftB, rightB, topB, bottomB, verbose);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/rectintSparseLoopC.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,147 @@ +/* This is based on +http://www.mathworks.com/access/helpdesk/help/techdoc/matlab_external/ch04cr12.shtml + +See rectintSparse.m for the matlab version of this code. + +*/ + +#include <math.h> /* Needed for the ceil() prototype. */ +#include "mex.h" +#include <stdio.h> + +/* If you are using a compiler that equates NaN to be zero, you + * must compile this example using the flag -DNAN_EQUALS_ZERO. + * For example: + * + * mex -DNAN_EQUALS_ZERO fulltosparse.c + * + * This will correctly define the IsNonZero macro for your C + * compiler. + */ + +#if defined(NAN_EQUALS_ZERO) +#define IsNonZero(d) ((d) != 0.0 || mxIsNaN(d)) +#else +#define IsNonZero(d) ((d) != 0.0) +#endif + +#define MAX(x,y) ((x)>(y) ? (x) : (y)) +#define MIN(x,y) ((x)<(y) ? (x) : (y)) + +void mexFunction( + int nlhs, mxArray *plhs[], + int nrhs, const mxArray *prhs[] + ) +{ + /* Declare variables. */ + int j,k,m,n,nzmax,*irs,*jcs, *irs2, *jcs2; + double *overlap, *overlap2, tmp, areaA, areaB; + double percent_sparse; + double *leftA, *rightA, *topA, *bottomA; + double *leftB, *rightB, *topB, *bottomB; + double *verbose; + + /* Get the size and pointers to input data. */ + m = MAX(mxGetM(prhs[0]), mxGetN(prhs[0])); + n = MAX(mxGetM(prhs[4]), mxGetN(prhs[4])); + /* printf("A=%d, B=%d\n", m, n); */ + + leftA = mxGetPr(prhs[0]); + rightA = mxGetPr(prhs[1]); + topA = mxGetPr(prhs[2]); + bottomA = mxGetPr(prhs[3]); + + leftB = mxGetPr(prhs[4]); + rightB = mxGetPr(prhs[5]); + topB = mxGetPr(prhs[6]); + bottomB = mxGetPr(prhs[7]); + + verbose = mxGetPr(prhs[8]); + + /* Allocate space for sparse matrix. + * NOTE: Assume at most 20% of the data is sparse. Use ceil + * to cause it to round up. + */ + + percent_sparse = 0.01; + nzmax = (int)ceil((double)m*(double)n*percent_sparse); + + plhs[0] = mxCreateSparse(m,n,nzmax,0); + overlap = mxGetPr(plhs[0]); + irs = mxGetIr(plhs[0]); + jcs = mxGetJc(plhs[0]); + + plhs[1] = mxCreateSparse(m,n,nzmax,0); + overlap2 = mxGetPr(plhs[1]); + irs2 = mxGetIr(plhs[1]); + jcs2 = mxGetJc(plhs[1]); + + + /* Assign nonzeros. */ + k = 0; + for (j = 0; (j < n); j++) { + int i; + jcs[j] = k; + jcs2[j] = k; + for (i = 0; (i < m); i++) { + tmp = (MAX(0, MIN(rightA[i], rightB[j]) - MAX(leftA[i], leftB[j]) )) * + (MAX(0, MIN(topA[i], topB[j]) - MAX(bottomA[i], bottomB[j]) )); + + if (*verbose) { + printf("j=%d,i=%d,tmp=%5.3f\n", j,i,tmp); + } + + if (IsNonZero(tmp)) { + + /* Check to see if non-zero element will fit in + * allocated output array. If not, increase + * percent_sparse by 20%, recalculate nzmax, and augment + * the sparse array. + */ + if (k >= nzmax) { + int oldnzmax = nzmax; + percent_sparse += 0.2; + nzmax = (int)ceil((double)m*(double)n*percent_sparse); + + /* Make sure nzmax increases atleast by 1. */ + if (oldnzmax == nzmax) + nzmax++; + printf("reallocating from %d to %d\n", oldnzmax, nzmax); + + mxSetNzmax(plhs[0], nzmax); + mxSetPr(plhs[0], mxRealloc(overlap, nzmax*sizeof(double))); + mxSetIr(plhs[0], mxRealloc(irs, nzmax*sizeof(int))); + overlap = mxGetPr(plhs[0]); + irs = mxGetIr(plhs[0]); + + mxSetNzmax(plhs[1], nzmax); + mxSetPr(plhs[1], mxRealloc(overlap2, nzmax*sizeof(double))); + mxSetIr(plhs[1], mxRealloc(irs2, nzmax*sizeof(int))); + overlap2 = mxGetPr(plhs[1]); + irs2 = mxGetIr(plhs[1]); + } + + overlap[k] = tmp; + irs[k] = i; + + areaA = (rightA[i]-leftA[i])*(topA[i]-bottomA[i]); + areaB = (rightB[j]-leftB[j])*(topB[j]-bottomB[j]); + overlap2[k] = MIN(tmp/areaA, tmp/areaB); + irs2[k] = i; + + k++; + } /* IsNonZero */ + } /* for i */ + } + jcs[n] = k; + jcs2[n] = k; + +} + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/repmatC.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,149 @@ +/* +mex -c mexutil.c +mex repmat.c mexutil.obj +to check for warnings: +gcc -Wall -I/cygdrive/c/MATLAB6p1/extern/include -c repmat.c +*/ +#include "mexutil.h" +#include <string.h> + +/* repeat a block of memory rep times */ +void memrep(char *dest, size_t chunk, int rep) +{ +#if 0 + /* slow way */ + int i; + char *p = dest; + for(i=1;i<rep;i++) { + p += chunk; + memcpy(p, dest, chunk); + } +#else + /* fast way */ + if(rep == 1) return; + memcpy(dest + chunk, dest, chunk); + if(rep & 1) { + dest += chunk; + memcpy(dest + chunk, dest, chunk); + } + /* now repeat using a block twice as big */ + memrep(dest, chunk<<1, rep>>1); +#endif +} + +void repmat(char *dest, const char *src, int ndim, int *destdimsize, + int *dimsize, const int *dims, int *rep) +{ + int d = ndim-1; + int i, chunk; + /* copy the first repetition into dest */ + if(d == 0) { + chunk = dimsize[0]; + memcpy(dest,src,chunk); + } + else { + /* recursively repeat each slice of src */ + for(i=0;i<dims[d];i++) { + repmat(dest + i*destdimsize[d-1], src + i*dimsize[d-1], + ndim-1, destdimsize, dimsize, dims, rep); + } + chunk = destdimsize[d-1]*dims[d]; + } + /* copy the result rep-1 times */ + memrep(dest,chunk,rep[d]); +} + +void mexFunction(int nlhs, mxArray *plhs[], + int nrhs, const mxArray *prhs[]) +{ + const mxArray *srcmat; + int ndim, *dimsize, eltsize; + const int *dims; + int ndimdest, *destdims, *destdimsize; + char *src, *dest; + int *rep; + int i,nrep; + int extra_rep = 1; + int empty; + + if(nrhs < 2) mexErrMsgTxt("Usage: xrepmat(A, [N M ...])"); + srcmat = prhs[0]; + if(mxIsSparse(srcmat)) { + mexErrMsgTxt("Sorry, can't handle sparse matrices yet."); + } + if(mxIsCell(srcmat)) { + mexErrMsgTxt("Sorry, can't handle cell arrays yet."); + } + ndim = mxGetNumberOfDimensions(srcmat); + dims = mxGetDimensions(srcmat); + eltsize = mxGetElementSize(srcmat); + + /* compute dimension sizes */ + dimsize = mxCalloc(ndim, sizeof(int)); + dimsize[0] = eltsize*dims[0]; + for(i=1;i<ndim;i++) dimsize[i] = dimsize[i-1]*dims[i]; + + /* determine repetition vector */ + ndimdest = ndim; + if(nrhs == 2) { + nrep = mxGetN(prhs[1]); + if(nrep > ndimdest) ndimdest = nrep; + rep = mxCalloc(ndimdest, sizeof(int)); + for(i=0;i<nrep;i++) { + double repv = mxGetPr(prhs[1])[i]; + rep[i] = (int)repv; + } + if(nrep == 1) { + /* special behavior */ + nrep = 2; + rep[1] = rep[0]; + } + } + else { + nrep = nrhs-1; + if(nrep > ndimdest) ndimdest = nrep; + rep = mxCalloc(ndimdest, sizeof(int)); + for(i=0;i<nrep;i++) { + rep[i] = (int)*mxGetPr(prhs[i+1]); + } + } + for(i=nrep;i<ndimdest;i++) rep[i] = 1; + + /* compute output size */ + destdims = mxCalloc(ndimdest, sizeof(int)); + for(i=0;i<ndim;i++) destdims[i] = dims[i]*rep[i]; + for(;i<ndimdest;i++) { + destdims[i] = rep[i]; + extra_rep *= rep[i]; + } + destdimsize = mxCalloc(ndim, sizeof(int)); + destdimsize[0] = eltsize*destdims[0]; + for(i=1;i<ndim;i++) destdimsize[i] = destdimsize[i-1]*destdims[i]; + + + /* for speed, array should be uninitialized */ + plhs[0] = mxCreateNumericArray(ndimdest, destdims, mxGetClassID(srcmat), + mxIsComplex(srcmat)?mxCOMPLEX:mxREAL); + + /* if any rep[i] == 0, output should be empty array. + Added by KPM 11/13/02. + */ + empty = 0; + for (i=0; i < nrep; i++) { + if (rep[i]==0) + empty = 1; + } + if (empty) + return; + + src = (char*)mxGetData(srcmat); + dest = (char*)mxGetData(plhs[0]); + repmat(dest,src,ndim,destdimsize,dimsize,dims,rep); + if(ndimdest > ndim) memrep(dest,destdimsize[ndim-1],extra_rep); + if(mxIsComplex(srcmat)) { + src = (char*)mxGetPi(srcmat); + dest = (char*)mxGetPi(plhs[0]); + repmat(dest,src,ndim,destdimsize,dimsize,dims,rep); + if(ndimdest > ndim) memrep(dest,destdimsize[ndim-1],extra_rep); + } +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/rgb2grayKPM.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function g = rgb2grayKPM(rgb) +% function g = rgb2grayKPM(rgb) +% rgb2grayKPM Like the built-in function, but if r is already gray, does not cause an error + +[nr nc ncolors] = size(rgb); +if ncolors > 1 + g = rgb2gray(rgb); +else + g = rgb; +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/rnd_partition.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function [train, test] = rnd_partition(data, train_percent); +% function [train, test] = rnd_partition(data, train_percent); +% +% data(:,i) is the i'th example +% train_percent of these columns get put into train, the rest into test + +N = size(data, 2); +ndx = randperm(N); +k = ceil(N*train_percent); +train_ndx = ndx(1:k); +test_ndx = ndx(k+1:end); +train = data(:, train_ndx); +test = data(:, test_ndx);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/rotate_xlabel.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,37 @@ +function hText = rotate_xlabel(degrees, newlabels) + +% Posted to comp.soft-sys.matlab on 2003-05-01 13:45:36 PST +% by David Borger (borger@ix.netcom.com) + +xtl = get(gca,'XTickLabel'); +set(gca,'XTickLabel',''); +lxtl = length(xtl); +xtl = newlabels; +if 0 % nargin>1 + lnl = length(newlabels); + if lnl~=lxtl + error('Number of new labels must equal number of old'); + end; + xtl = newlabels; +end; + + +hxLabel=get(gca,'XLabel'); +xLP=get(hxLabel,'Position'); +y=xLP(2); +XTick=get(gca,'XTick'); +y=repmat(y,length(XTick),1); +%fs=get(gca,'fontsize'); +fs = 12; +hText=text(XTick,y,xtl,'fontsize',fs); +set(hText,'Rotation',degrees,'HorizontalAlignment','right'); + +% Modifications by KPM + +ylim = get(gca,'ylim'); +height = ylim(2)-ylim(1); +N = length(hText); +for i=1:N + voffset = ylim(2) - 0*height; + set(hText(i), 'position', [i voffset 0]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/safeStr.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function s = safeStr(s) +% Change punctuation characters to they print properly + +s = strrep(s, '\', '/'); +s = strrep(s, '_', '-');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/sampleUniformInts.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +function M = sampleUniformInts(N, r, c) + +% M is an rxc matrix of integers in 1..N + +prob = normalize(ones(N,1)); +M = sample_discrete(prob, r, c);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/sample_discrete.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,40 @@ +function M = sample_discrete(prob, r, c) +% SAMPLE_DISCRETE Like the built in 'rand', except we draw from a non-uniform discrete distrib. +% M = sample_discrete(prob, r, c) +% +% Example: sample_discrete([0.8 0.2], 1, 10) generates a row vector of 10 random integers from {1,2}, +% where the prob. of being 1 is 0.8 and the prob of being 2 is 0.2. + +n = length(prob); + +if nargin == 1 + r = 1; c = 1; +elseif nargin == 2 + c == r; +end + +R = rand(r, c); +M = ones(r, c); +cumprob = cumsum(prob(:)); + +if n < r*c + for i = 1:n-1 + M = M + (R > cumprob(i)); + end +else + % loop over the smaller index - can be much faster if length(prob) >> r*c + cumprob2 = cumprob(1:end-1); + for i=1:r + for j=1:c + M(i,j) = sum(R(i,j) > cumprob2)+1; + end + end +end + + +% Slower, even though vectorized +%cumprob = reshape(cumsum([0 prob(1:end-1)]), [1 1 n]); +%M = sum(R(:,:,ones(n,1)) > cumprob(ones(r,1),ones(c,1),:), 3); + +% convert using a binning algorithm +%M=bindex(R,cumprob);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/set_xtick_label.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,51 @@ +function set_xtick_label(tick_labels, angle, axis_label) +% SET_XTICK_LABEL Print the xtick labels at an angle instead of horizontally +% set_xtick_label(tick_labels, angle, axis_label) +% +% angle default = 90 +% axis_label default = '' +% +% This is derived from Solution Number: 5375 on mathworks.com +% See set_xtick_label_demo for an example + +if nargin < 2, angle = 90; end +if nargin < 3, axis_label = []; end + +% Reduce the size of the axis so that all the labels fit in the figure. +pos = get(gca,'Position'); +%set(gca,'Position',[pos(1), .2, pos(3) .65]) +%set(gca,'Position',[pos(1), 0, pos(3) .45]) +%set(gca,'Position',[pos(1), 0.1, pos(3) 0.5]) + +ax = axis; % Current axis limits +axis(axis); % Fix the axis limits +Yl = ax(3:4); % Y-axis limits + +%set(gca, 'xtick', 1:length(tick_labels)); +set(gca, 'xtick', 0.7:1:length(tick_labels)); +Xt = get(gca, 'xtick'); + +% Place the text labels +t = text(Xt,Yl(1)*ones(1,length(Xt)),tick_labels); +set(t,'HorizontalAlignment','right','VerticalAlignment','top', 'Rotation', angle); + +% Remove the default labels +set(gca,'XTickLabel','') + +% Get the Extent of each text object. This +% loop is unavoidable. +for i = 1:length(t) + ext(i,:) = get(t(i),'Extent'); +end + +% Determine the lowest point. The X-label will be +% placed so that the top is aligned with this point. +LowYPoint = min(ext(:,2)); + +% Place the axis label at this point +if ~isempty(axis_label) + Xl = get(gca, 'Xlim'); + XMidPoint = Xl(1)+abs(diff(Xl))/2; + tl = text(XMidPoint,LowYPoint, axis_label, 'VerticalAlignment','top', ... + 'HorizontalAlignment','center'); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/set_xtick_label_demo.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,52 @@ + +% Generate some test data. Assume that the X-axis represents months. +x = 1:12; +y = 10*rand(1,length(x)); + +% Plot the data. +h = plot(x,y,'+'); + +% Add a title. +title('This is a title') + +% Set the X-Tick locations so that every other month is labeled. +Xt = 1:2:11; +Xl = [1 12]; +set(gca,'XTick',Xt,'XLim',Xl); + +% Add the months as tick labels. +months = ['Jan'; + 'Feb'; + 'Mar'; + 'Apr'; + 'May'; + 'Jun'; + 'Jul'; + 'Aug'; + 'Sep'; + 'Oct'; + 'Nov'; + 'Dec']; + +set_xtick_label(months(1:2:12, :), 90, 'xaxis label'); + + + +if 0 + + +% Generate some test data. Assume that the X-axis represents months. +x = 1:8; +y = 10*rand(1,length(x)); + +% Plot the data. +h = plot(x,y,'+'); + +S = subsets(1:3); +str = cell(1,8); +for i=1:2^3 + str{i} = num2str(S{i}); +end +set_xtick_label(str); + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/setdiag.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,21 @@ +function M = setdiag(M, v) +% SETDIAG Set the diagonal of a matrix to a specified scalar/vector. +% M = set_diag(M, v) + +n = length(M); +if length(v)==1 + v = repmat(v, 1, n); +end + +% e.g., for 3x3 matrix, elements are numbered +% 1 4 7 +% 2 5 8 +% 3 6 9 +% so diagnoal = [1 5 9] + + +J = 1:n+1:n^2; +M(J) = v; + +%M = triu(M,1) + tril(M,-1) + diag(v); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/softeye.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function M = softeye(K, p) +% SOFTEYE Make a stochastic matrix with p on the diagonal, and the remaining mass distributed uniformly +% M = softeye(K, p) +% +% M is a K x K matrix. + +M = p*eye(K); +q = 1-p; +for i=1:K + M(i, [1:i-1 i+1:K]) = q/(K-1); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/sort_evec.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,22 @@ +function [evec, evals] = sort_evec(temp_evec, temp_evals, N) + +if ~isvector(temp_evals) + temp_evals = diag(temp_evals); +end + +% Eigenvalues nearly always returned in descending order, but just +% to make sure..... +[evals perm] = sort(-temp_evals); +evals = -evals(1:N); +if evals == temp_evals(1:N) + % Originals were in order + evec = temp_evec(:, 1:N); + return +else + fprintf('sorting evec\n'); + % Need to reorder the eigenvectors + for i=1:N + evec(:,i) = temp_evec(:,perm(i)); + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/splitLongSeqIntoManyShort.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +function short = splitLongSeqIntoManyShort(long, Tsmall) +% splitLongSeqIntoManyShort Put groups of columns into a cell array of narrower matrices +% function short = splitLongSeqIntoManyShort(long, Tsmall) +% +% long(:,t) +% short{i} = long(:,ndx1:ndx2) where each segment (except maybe the last) is of length Tsmall + +T = length(long); +Nsmall = ceil(T/Tsmall); +short = cell(Nsmall,1); + +t = 1; +for i=1:Nsmall + short{i} = long(:,t:min(T,t+Tsmall-1)); + t = t+Tsmall; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/sprintf_intvec.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,9 @@ +function s = sprintf_intvec(v) +% SPRINTF_INTVEC Print a vector of ints as comma separated string, with no trailing comma +% function s = sprintf_intvec(v) +% +% e.g., sprintf_intvec(1:3) returns '1,2,3' + +s = sprintf('%d,', v); +s = s(1:end-1); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/sqdist.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,29 @@ +function m = sqdist(p, q, A) +% SQDIST Squared Euclidean or Mahalanobis distance. +% SQDIST(p,q) returns m(i,j) = (p(:,i) - q(:,j))'*(p(:,i) - q(:,j)). +% SQDIST(p,q,A) returns m(i,j) = (p(:,i) - q(:,j))'*A*(p(:,i) - q(:,j)). + +% From Tom Minka's lightspeed toolbox + +[d, pn] = size(p); +[d, qn] = size(q); + +if nargin == 2 + + pmag = sum(p .* p, 1); + qmag = sum(q .* q, 1); + m = repmat(qmag, pn, 1) + repmat(pmag', 1, qn) - 2*p'*q; + %m = ones(pn,1)*qmag + pmag'*ones(1,qn) - 2*p'*q; + +else + + if isempty(A) | isempty(p) + error('sqdist: empty matrices'); + end + Ap = A*p; + Aq = A*q; + pmag = sum(p .* Ap, 1); + qmag = sum(q .* Aq, 1); + m = repmat(qmag, pn, 1) + repmat(pmag', 1, qn) - 2*p'*Aq; + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/strmatch_multi.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,30 @@ +function [posns] = strmatch_multi(keys, strs) +% STRMATCH_MULTI Find where each key occurs in list of strings. +% [pos] = strmatch_multi(key, strs) where key is a string and strs is a cell array of strings +% works like the built-in command sequence pos = strmatch(key, strs, 'exact'), +% except that pos is the first occurrence of key in strs; if there is no occurence, pos is 0. +% +% [posns] = strmatch_multi(keys, strs), where keys is a cell array of strings, +% matches each element of keys. It loops over whichever is shorter, keys or strs. + +if ~iscell(keys), keys = {keys}; end +nkeys = length(keys); +posns = zeros(1, nkeys); +if length(keys) < length(strs) + for i=1:nkeys + %pos = strmatch(keys{i}, strs, 'exact'); + ndx = strcmp(keys{i}, strs); % faster + pos = find(ndx); + if ~isempty(pos) + posns(i) = pos(1); + end + end +else + for s=1:length(strs) + %ndx = strmatch(strs{s}, keys, 'exact'); + ndx = strcmp(strs{s}, keys); + ndx = find(ndx); + posns(ndx) = s; + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/strmatch_substr.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,28 @@ +function ndx = strmatch_substr(str, strs) +% STRMATCH_SUBSTR Like strmatch, except str can match any part of strs{i}, not just prefix. +% ndx = strmatch_substr(str, strs) +% +% Example: +% i = strmatch('max', {'max','minimax','maximum'}) +% returns i = [1; 3] since only 1 and 3 begin with max, but +% i = strmatch_substr('max', {'max','minimax','maximum'}) +% returns i = [1;2;3]; +% +% If str is also a cell array, it is like calling strmatch_substr several times +% and concatenating the results. +% Example: +% +% i = strmatch_substr({'foo', 'dog'}, {'foo', 'hoofoo', 'dog'}) +% returns i = [1;2;3] + +ndx = []; +if ~iscell(str), str = {str}; end +for j=1:length(str) + for i=1:length(strs) + %ind = strfind(strs{i}, str{j}); % not supported in 6.0 + ind = findstr(strs{i}, str{j}); + if ~isempty(ind) + ndx = [ndx; i]; + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/strsplit.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,53 @@ +function parts = strsplit(splitstr, str, option) +%STRSPLIT Split string into pieces. +% +% STRSPLIT(SPLITSTR, STR, OPTION) splits the string STR at every occurrence +% of SPLITSTR and returns the result as a cell array of strings. By default, +% SPLITSTR is not included in the output. +% +% STRSPLIT(SPLITSTR, STR, OPTION) can be used to control how SPLITSTR is +% included in the output. If OPTION is 'include', SPLITSTR will be included +% as a separate string. If OPTION is 'append', SPLITSTR will be appended to +% each output string, as if the input string was split at the position right +% after the occurrence SPLITSTR. If OPTION is 'omit', SPLITSTR will not be +% included in the output. + +% Author: Peter J. Acklam +% Time-stamp: 2004-09-22 08:48:01 +0200 +% E-mail: pjacklam@online.no +% URL: http://home.online.no/~pjacklam + + nargsin = nargin; + error(nargchk(2, 3, nargsin)); + if nargsin < 3 + option = 'omit'; + else + option = lower(option); + end + + splitlen = length(splitstr); + parts = {}; + + while 1 + + k = strfind(str, splitstr); + if isempty(k) + parts{end+1} = str; + break + end + + switch option + case 'include' + parts(end+1:end+2) = {str(1:k(1)-1), splitstr}; + case 'append' + parts{end+1} = str(1 : k(1)+splitlen-1); + case 'omit' + parts{end+1} = str(1 : k(1)-1); + otherwise + error(['Invalid option string -- ', option]); + end + + + str = str(k(1)+splitlen : end); + + end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/subplot2.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,17 @@ +function subplot2(nrows, ncols, i, j) +% function subplot2(nrows, ncols, i, j) + + +sz = [nrows ncols]; +%k = sub2ind(sz, i, j) +k = sub2ind(sz(end:-1:1), j, i); +subplot(nrows, ncols, k); + +if 0 + ncols_plot = ceil(sqrt(Nplots)); + nrows_plot = ceil(Nplots/ncols_plot); + Nplots = nrows_plot*ncols_plot; + for p=1:Nplots + subplot(nrows_plot, ncols_plot, p); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/subplot3.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +function fignum = subplot3(nrows, ncols, fignumBase, plotnumBase) +% function subplot3(nrows, ncols, fignumBase, plotnumBase) +% Choose a subplot number, opening a new figure if necessary +% eg nrows=2, ncols = 2, we plot on (fignum, plotnum) = (1,1), (1,2), (1,3), (1,4), (2,1), ... + +nplotsPerFig = nrows*ncols; +fignum = fignumBase + div(plotnumBase-1, nplotsPerFig); +plotnum = wrap(plotnumBase, nplotsPerFig); +figure(fignum); +if plotnum==1, clf; end +subplot(nrows, ncols, plotnum); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/subsets.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,58 @@ +function [T, bitv] = subsets(S, U, L, sorted, N) +% SUBSETS Create a set of all the subsets of S which have cardinality <= U and >= L +% T = subsets(S, U, L) +% U defaults to length(S), L defaults to 0. +% So subsets(S) generates the powerset of S. +% +% Example: +% T = subsets(1:4, 2, 1) +% T{:} = 1, 2, [1 2], 3, [1 3], [2 3], 4, [1 4], [2 4], [3 4] +% +% T = subsets(S, U, L, sorted) +% If sorted=1, return the subsets in increasing size +% +% Example: +% T = subsets(1:4, 2, 1, 1) +% T{:} = 1, 2, 3, 4, [1 2], [1 3], [2 3], [1 4], [2 4], [3 4] +% +% [T, bitv] = subsets(S, U, L, sorted, N) +% Row i of bitv is a bit vector representation of T{i}, +% where bitv has N columns (representing 1:N). +% N defaults to max(S). +% +% Example: +% [T,bitv] = subsets(2:4, 2^3, 0, 0, 5) +% T{:} = [], 2, 3, [2 3], 4, [2 4], [3 4], [2 3 4] +% bitv= +% 0 0 0 0 0 +% 0 1 0 0 0 +% 0 0 1 0 0 +% 0 1 1 0 0 +% 0 0 0 1 0 +% 0 1 0 1 0 +% 0 0 1 1 0 +% 0 1 1 1 0 + +n = length(S); + +if nargin < 2, U = n; end +if nargin < 3, L = 0; end +if nargin < 4, sorted = 0; end +if nargin < 5, N = max(S); end + +bits = ind2subv(2*ones(1,n), 1:2^n)-1; +sm = sum(bits,2); +masks = bits((sm <= U) & (sm >= L), :); +m = size(masks, 1); +T = cell(1, m); +for i=1:m + s = S(find(masks(i,:))); + T{i} = s; +end + +if sorted + T = sortcell(T); +end + +bitv = zeros(m, N); +bitv(:, S) = masks;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/subsets1.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,45 @@ +function sub_s=subsets1(s,k) +% SUBSETS1 creates sub-sets of a specific from a given set +% SS = subsets1(S, k) +% +% S is the given set +% k is the required sub-sets size +% +% Example: +% +% >> ss=subsets1([1:4],3); +% >> ss{:} +% ans = +% 1 2 3 +% ans = +% 1 2 4 +% ans = +% 1 3 4 +% ans = +% 2 3 4 +% +% Written by Raanan Yehezkel, 2004 + +if k<0 % special case + error('subset size must be positive'); +elseif k==0 % special case + sub_s={[]}; +else + l=length(s); + ss={}; + if l>=k + if k==1 % Exit condition + for I=1:l + ss{I}=s(I); + end + else + for I=1:l + ss1=subsets1(s([(I+1):l]),k-1); + for J=1:length(ss1) + ss{end+1}=[s(I),ss1{J}]; + end + end + end + end + sub_s=ss; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/subsetsFixedSize.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,45 @@ +function sub_s=subsets1(s,k) +% SUBSETS1 creates sub-sets of a specific size from a given set +% SS = subsets1(S, k) +% +% S is the given set +% k is the required sub-sets size +% +% Example: +% +% >> ss=subsets1([1:4],3); +% >> ss{:} +% ans = +% 1 2 3 +% ans = +% 1 2 4 +% ans = +% 1 3 4 +% ans = +% 2 3 4 +% +% Written by Raanan Yehezkel, 2004 + +if k<0 % special case + error('subset size must be positive'); +elseif k==0 % special case + sub_s={[]}; +else + l=length(s); + ss={}; + if l>=k + if k==1 % Exit condition + for I=1:l + ss{I}=s(I); + end + else + for I=1:l + ss1=subsets1(s([(I+1):l]),k-1); + for J=1:length(ss1) + ss{end+1}=[s(I),ss1{J}]; + end + end + end + end + sub_s=ss; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/subv2ind.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,17 @@ +function index = subv2ind(siz,sub) +%SUBV2IND Linear index from subscript vector. +% SUBV2IND(SIZ,SUB) returns an equivalent single index corresponding to a +% subscript vector for an array of size SIZ. +% If SUB is a matrix, with subscript vectors as rows, then the result is a +% column vector. +% +% This is the opposite of IND2SUBV, so that +% SUBV2IND(SIZ,IND2SUBV(SIZ,IND)) == IND. +% +% See also IND2SUBV, SUB2IND. + +%index = subv2indTest(siz,sub); +prev_cum_size = [1 cumprod(siz(1:end-1))]; +%index = (sub-1)*prev_cum_size' + 1; +index = sub*prev_cum_size' - sum(prev_cum_size) + 1; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/subv2indKPM.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,89 @@ +/* C mex version of subv2ind*/ +/* 2 inputs, 1 output */ +/* siz, subv */ +/* ndx */ +#include "mex.h" + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, k, nCol, nRow, binary, temp; + double *pSize, *pSubv, *pr; + int *cumprod; + + pSize = mxGetPr(prhs[0]); + pSubv = mxGetPr(prhs[1]); + nCol = mxGetNumberOfElements(prhs[0]); + nRow = mxGetM(prhs[1]); + + + if(mxIsEmpty(prhs[1])){ + plhs[0] = mxCreateDoubleMatrix(0, 0, mxREAL); + return; + } + + if(mxIsEmpty(prhs[0])){ + plhs[0] = mxCreateDoubleMatrix(1, 1, mxREAL); + *mxGetPr(plhs[0]) = 1; + return; + } + + binary = 2; + for (i = 0; i < nCol; i++){ + if (pSize[i] > 2.0){ + binary = 0; + break; + } + else if(pSize[i] == 1.0){ + binary = 1; + } + } + + plhs[0] = mxCreateDoubleMatrix(nRow, 1, mxREAL); + pr = mxGetPr(plhs[0]); + for(i=0; i<nRow; i++){ + pr[i] = 1.0; + } + + if (binary == 2){ + for(j=0; j<nCol; j++){ + temp = j * nRow; + for(i=0; i<nRow; i++){ + pr[i] += ((int)pSubv[temp + i] - 1) << j; + } + } + } + else if(binary == 1){ + cumprod = malloc(nCol * sizeof(int)); + cumprod[0] = 1; + for(i=1; i<nCol; i++){ + k = (int)pSize[i-1] - 1; + cumprod[i] = cumprod[i-1] << k; + } + for(j=0; j<nCol; j++){ + temp = j * nRow; + for(i=0; i<nRow; i++){ + k = (int)pSubv[temp + i] - 1; + if(k)pr[i] += cumprod[j]; + } + } + free(cumprod); + } + else { + cumprod = malloc(nCol * sizeof(int)); + cumprod[0] = 1; + for(i=1; i<nCol; i++){ + k = (int)pSize[i-1]; + cumprod[i] = cumprod[i-1] * k; + } + for(j=0; j<nCol; j++){ + temp = j * nRow; + for(i=0; i<nRow; i++){ + k = (int)pSubv[temp + i] - 1; + pr[i] += cumprod[j] * k; + } + } + free(cumprod); + } +} + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/subv2indKPM.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,66 @@ +function ndx = subv2indKPM(siz, subv) +% SUBV2IND Like the built-in sub2ind, but the subscripts are given as row vectors. +% ind = subv2ind(siz,subv) +% +% siz can be a row or column vector of size d. +% subv should be a collection of N row vectors of size d. +% ind will be of size N * 1. +% +% Example: +% subv = [1 1 1; +% 2 1 1; +% ... +% 2 2 2]; +% subv2ind([2 2 2], subv) returns [1 2 ... 8]' +% i.e., the leftmost digit toggles fastest. +% +% See also IND2SUBV. + + +if isempty(subv) + ndx = []; + return; +end + +if isempty(siz) + ndx = 1; + return; +end + +[ncases ndims] = size(subv); + +%if length(siz) ~= ndims +% error('length of subscript vector and sizes must be equal'); +%end + +if all(siz==2) + %rbits = subv(:,end:-1:1)-1; % read from right to left, convert to 0s/1s + %ndx = bitv2dec(rbits)+1; + twos = pow2(0:ndims-1); + ndx = ((subv-1) * twos(:)) + 1; + %ndx = sum((subv-1) .* twos(ones(ncases,1), :), 2) + 1; % equivalent to matrix * vector + %ndx = sum((subv-1) .* repmat(twos, ncases, 1), 2) + 1; % much slower than ones + %ndx = ndx(:)'; +else + %siz = siz(:)'; + cp = [1 cumprod(siz(1:end-1))]'; + %ndx = ones(ncases, 1); + %for i = 1:ndims + % ndx = ndx + (subv(:,i)-1)*cp(i); + %end + ndx = (subv-1)*cp + 1; +end + +%%%%%%%%%%% + +function d = bitv2dec(bits) +% BITV2DEC Convert a bit vector to a decimal integer +% d = butv2dec(bits) +% +% This is just like the built-in bin2dec, except the argument is a vector, not a string. +% If bits is an array, each row will be converted. + +[m n] = size(bits); +twos = pow2(n-1:-1:0); +d = sum(bits .* twos(ones(m,1),:),2); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/subv2indMinka.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,17 @@ +function index = subv2indMinka(siz,sub) +%SUBV2IND Linear index from subscript vector. +% SUBV2IND(SIZ,SUB) returns an equivalent single index corresponding to a +% subscript vector for an array of size SIZ. +% If SUB is a matrix, with subscript vectors as rows, then the result is a +% column vector. +% +% This is the opposite of IND2SUBV, so that +% SUBV2IND(SIZ,IND2SUBV(SIZ,IND)) == IND. +% +% See also IND2SUBV, SUB2IND. + +% Written by Tom Minka + +prev_cum_size = [1 cumprod(siz(1:end-1))]; +%index = (sub-1)*prev_cum_size' + 1; +index = sub*prev_cum_size' - sum(prev_cum_size) + 1;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/subv2indTest.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +function ndx = subv2indTest(siz,sub) +%SUBV2IND Linear index from subscript vector. +% SUBV2IND(SIZ,SUB) returns an equivalent single index corresponding to a +% subscript vector for an array of size SIZ. +% If SUB is a matrix, with subscript vectors as rows, then the result is a +% column vector. +% +% This is the opposite of IND2SUBV, so that +% SUBV2IND(SIZ,IND2SUBV(SIZ,IND)) == IND. +% +% See also IND2SUBV, SUB2IND. + +ndx = subv2indMinka(siz,sub); +ndx2 = subv2indKPM(siz,sub); +assert(isequalKPM(ndx,ndx2))
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/sumv.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function T2 = sumv(T1, sum_over) +% +% Like the built in sum, but will sum over several dimensions and then squeeze the result. + +T2 = T1; +for i=1:length(sum_over) + if sum_over(i) <= ndims(T2) % prevent summing over non-existent dimensions + T2=sum(T2, sum_over(i)); + end +end +T2 = squeeze(T2);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/suptitle.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,105 @@ +function hout=suptitle(str, fs) +%SUPTITLE Puts a title above all subplots. +% SUPTITLE('text') adds text to the top of the figure +% above all subplots (a "super title"). Use this function +% after all subplot commands. + +% Drea Thomas 6/15/95 drea@mathworks.com + +% Warning: If the figure or axis units are non-default, this +% will break. + +% Parameters used to position the supertitle. + +% Amount of the figure window devoted to subplots +plotregion = .92; + +% Y position of title in normalized coordinates +titleypos = .95; + +% Fontsize for supertitle +if nargin < 2 + fs = get(gcf,'defaultaxesfontsize')+4; +end + +% Fudge factor to adjust y spacing between subplots +fudge=1; + +haold = gca; +figunits = get(gcf,'units'); + +% Get the (approximate) difference between full height (plot + title +% + xlabel) and bounding rectangle. + + if (~strcmp(figunits,'pixels')), + set(gcf,'units','pixels'); + pos = get(gcf,'position'); + set(gcf,'units',figunits); + else, + pos = get(gcf,'position'); + end + ff = (fs-4)*1.27*5/pos(4)*fudge; + + % The 5 here reflects about 3 characters of height below + % an axis and 2 above. 1.27 is pixels per point. + +% Determine the bounding rectange for all the plots + +% h = findobj('Type','axes'); + +% findobj is a 4.2 thing.. if you don't have 4.2 comment out +% the next line and uncomment the following block. + +h = findobj(gcf,'Type','axes'); % Change suggested by Stacy J. Hills + +% If you don't have 4.2, use this code instead +%ch = get(gcf,'children'); +%h=[]; +%for i=1:length(ch), +% if strcmp(get(ch(i),'type'),'axes'), +% h=[h,ch(i)]; +% end +%end + + + + +max_y=0; +min_y=1; + +oldtitle =0; +for i=1:length(h), + if (~strcmp(get(h(i),'Tag'),'suptitle')), + pos=get(h(i),'pos'); + if (pos(2) < min_y), min_y=pos(2)-ff/5*3;end; + if (pos(4)+pos(2) > max_y), max_y=pos(4)+pos(2)+ff/5*2;end; + else, + oldtitle = h(i); + end +end + +if max_y > plotregion, + scale = (plotregion-min_y)/(max_y-min_y); + for i=1:length(h), + pos = get(h(i),'position'); + pos(2) = (pos(2)-min_y)*scale+min_y; + pos(4) = pos(4)*scale-(1-scale)*ff/5*3; + set(h(i),'position',pos); + end +end + +np = get(gcf,'nextplot'); +set(gcf,'nextplot','add'); +if (oldtitle), + delete(oldtitle); +end +ha=axes('pos',[0 1 1 1],'visible','off','Tag','suptitle'); +ht=text(.5,titleypos-1,str);set(ht,'horizontalalignment','center','fontsize',fs); +set(gcf,'nextplot',np); +axes(haold); +if nargout, + hout=ht; +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/unaryEncoding.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +function U = unaryEncoding(data, K) +% unaryEncoding Encode data(s) as a 1-of-K column vector +% function U = unaryEncoding(data, K) +% +% eg. +% If data = [3 2 2] and K=3, +% then U = [0 0 0 +% 0 1 1 +% 1 0 0] + +if nargin < 2, K = max(data); end +N = length(data); +U = zeros(K,N); +ndx = subv2ind([K N], [data(:)'; 1:N]'); +U(ndx) = 1; +U = reshape(U, [K N]);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/wrap.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +function v = wrap(u,N) +% WRAP Wrap a vector of indices around a torus. +% v = wrap(u,N) +% +% e.g., wrap([-1 0 1 2 3 4], 3) = 2 3 1 2 3 1 + +v = mod(u-1,N)+1;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/xticklabel_rotate90.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,68 @@ +function xticklabel_rotate90(XTick,varargin) +%XTICKLABEL_ROTATE90 - Rotate numeric Xtick labels by 90 degrees +% +% Syntax: xticklabel_rotate90(XTick) +% +% Input: XTick - vector array of XTick positions & values (numeric) +% +% Output: none +% +% Example 1: Set the positions of the XTicks and rotate them +% figure; plot([1960:2004],randn(45,1)); xlim([1960 2004]); +% xticklabel_rotate90([1960:2:2004]); +% %If you wish, you may set a few text "Property-value" pairs +% xticklabel_rotate90([1960:2:2004],'Color','m','Fontweight','bold'); +% +% Example 2: %Rotate XTickLabels at their current position +% XTick = get(gca,'XTick'); +% xticklabel_rotate90(XTick); +% +% Other m-files required: none +% Subfunctions: none +% MAT-files required: none +% +% See also: TEXT, SET + +% Author: Denis Gilbert, Ph.D., physical oceanography +% Maurice Lamontagne Institute, Dept. of Fisheries and Oceans Canada +% email: gilbertd@dfo-mpo.gc.ca Web: http://www.qc.dfo-mpo.gc.ca/iml/ +% February 1998; Last revision: 24-Mar-2003 + +if ~isnumeric(XTick) + error('XTICKLABEL_ROTATE90 requires a numeric input argument'); +end + +%Make sure XTick is a column vector +XTick = XTick(:); + +%Set the Xtick locations and set XTicklabel to an empty string +set(gca,'XTick',XTick,'XTickLabel','') + +% Define the xtickLabels +xTickLabels = num2str(XTick); + +% Determine the location of the labels based on the position +% of the xlabel +hxLabel = get(gca,'XLabel'); % Handle to xlabel +xLabelString = get(hxLabel,'String'); + +if ~isempty(xLabelString) + warning('You may need to manually reset the XLABEL vertical position') +end + +set(hxLabel,'Units','data'); +xLabelPosition = get(hxLabel,'Position'); +y = xLabelPosition(2); + +%CODE below was modified following suggestions from Urs Schwarz +y=repmat(y,size(XTick,1),1); +% retrieve current axis' fontsize +fs = get(gca,'fontsize'); + +% Place the new xTickLabels by creating TEXT objects +hText = text(XTick, y, xTickLabels,'fontsize',fs); + +% Rotate the text objects by 90 degrees +set(hText,'Rotation',90,'HorizontalAlignment','right',varargin{:}) + +%------------- END OF CODE --------------
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/zipload.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,47 @@ +%ZIPLOAD Load compressed data file created with ZIPSAVE +% +% [data] = zipload( filename ) +% filename: string variable that contains the name of the +% compressed file (do not include '.zip' extension) +% Use only with files created with 'zipsave' +% pkzip25.exe has to be in the matlab path. This file is a compression utility +% made by Pkware, Inc. It can be dowloaded from: http://www.pkware.com +% Or directly from ftp://ftp.pkware.com/pk250c32.exe, for the Windows 95/NT version. +% This function was tested using 'PKZIP 2.50 Command Line for Windows 9x/NT' +% It is important to use version 2.5 of the utility. Otherwise the command line below +% has to be changed to include the proper options of the compression utility you +% wish to use. +% This function was tested in MATLAB Version 5.3 under Windows NT. +% Fernando A. Brucher - May/25/1999 +% +% Example: +% [loadedData] = zipload('testfile'); +%-------------------------------------------------------------------- + +function [data] = zipload( filename ) + +%--- Decompress data file by calling pkzip (comand line command) --- +% Options used: +% 'extract' = decompress file +% 'silent' = no console output +% 'over=all' = overwrite files + +%eval( ['!pkzip25 -extract -silent -over=all ', filename, '.zip'] ) +eval( ['!pkzip25 -extract -silent -over=all ', filename, '.zip'] ) + + +%--- Load data from decompressed file --- +% try, catch takes care of cases when pkzip fails to decompress a +% valid matlab format file + +try + tmpStruc = load( filename ); + data = tmpStruc.data; +catch, return, end + + +%--- Delete decompressed file --- + +delete( [filename,'.mat'] ) + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_FullBNT/KPMtools/zipsave.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,42 @@ +%ZIPSAVE Save data in compressed format +% +% zipsave( filename, data ) +% filename: string variable that contains the name of the resulting +% compressed file (do not include '.zip' extension) +% pkzip25.exe has to be in the matlab path. This file is a compression utility +% made by Pkware, Inc. It can be dowloaded from: http://www.pkware.com +% This function was tested using 'PKZIP 2.50 Command Line for Windows 9x/NT' +% It is important to use version 2.5 of the utility. Otherwise the command line below +% has to be changed to include the proper options of the compression utility you +% wish to use. +% This function was tested in MATLAB Version 5.3 under Windows NT. +% Fernando A. Brucher - May/25/1999 +% +% Example: +% testData = [1 2 3; 4 5 6; 7 8 9]; +% zipsave('testfile', testData); +% +% Modified by Kevin Murphy, 26 Feb 2004, to use winzip +%------------------------------------------------------------------------ + +function zipsave( filename, data ) + +%--- Save data in a temporary file in matlab format (.mat)--- + +eval( ['save ''', filename, ''' data'] ) + + +%--- Compress data by calling pkzip (comand line command) --- +% Options used: +% 'add' = add compressed files to the resulting zip file +% 'silent' = no console output +% 'over=all' = overwrite files + +%eval( ['!pkzip25 -silent -add -over=all ', filename, '.zip ', filename,'.mat'] ) +eval( ['!zip ', filename, '.zip ', filename,'.mat'] ) + +%--- Delete temporary matlab format file --- + +delete( [filename,'.mat'] ) + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_beattracker/README Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +To perform the analysis call: + +out = getmeasures2(fname,ts); +ts is an optional input parameter to specify the number of beats per bar + +out is a struct with the following data + +out.beats - beat times in seconds +out.measures - bar boundaries in seconds +out.timesig - estimate of the number of beats per bar (will = ts if specified) +out.df - the onset detection function + + +The code is set to downsample the audio file to 11025Hz before processing +This speeds up the calculation of the onset detection function but has +negligible effect on the output of the beat tracker.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_beattracker/adapt_thresh.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,64 @@ +function [dfout,m] = adapt_thresh(df,pre,post); + + + + +df = df(:)'; +% uses juan's adaptive median threshold + +alpha = 9; +thresh=0.033; +fn = @mean; + +if(nargin<2) + pre = 8; + post = 7; +end + +%df = df-min(df); +%alpha norm +%df = df/real(mean(abs(df+eps).^alpha).^(1/alpha)); + + + + +% low pass filtering + +%[b,a]=butter(2,0.28); + +%b = [0.1600 0.3200 0.1600]; +%a = [1.0000 -0.5949 0.2348]; + + +%df=filtfilt(b,a,df); + + +% median threshold + +N=length(df); + +for i=1:min(post,N) + k=min(i+pre,N); + m(i)=feval(fn,df(1:k)); + % m2(i) = feval(@mean,df(1:k)); +end + +if N>(post+pre) + m=[m feval(fn,buffer(df,post+pre+1,post+pre,'nodelay'))]; +end + +% if N>(post+pre) +% m2=[m2 feval(@mean,buffer(df,post+pre+1,post+pre,'nodelay'))]; +% end + +for i=N+(1-pre:0) + j=max(i-post,1); + m(i)=feval(fn,df(j:end)); +% m2(i)=feval(@mean,df(j:end)); +end + +%df = (df- m*thresh); +df = df-m; + +dfout = (df>0).*df; +%dfout = df; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_beattracker/bcfm.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,96 @@ +function [measures,db] = bcfm(bcf,beats,timesig) + + +if timesig == 0 + timesig =4; +end +% extract measures from bcf and timesig. + +for k=1:timesig, + db(k) = mean(bcf(k:timesig:end)); +end + +[val,downbeat] = max(db); + + + +if(downbeat ==timesig), + downbeat = 0; +end + +downbeat = downbeat+1; + +measures = beats(downbeat:timesig:end); + + +if 0 + + % for extracting batchwise measures... + for i=1:222, + meas{1,i} = bcfm(bcfs{i}.spec,anns.beats{i},anns.timesig{i}); + % meas{2,i} = bcfm(bcf{i}.specs,anns.beats{i},anns.timesig{i}); + meas{2,i} = bcfm(bcfs{i}.cq,anns.beats{i},anns.timesig{i}); + % meas{4,i} = bcfm(bcf{i}.cqs,anns.beats{i},anns.timesig{i}); + meas{3,i} = bcfm(bcfs{i}.hpcp,anns.beats{i},anns.timesig{i}); + % meas{6,i} = bcfm(bcf{i}.hpcps,anns.beats{i},anns.timesig{i}); + meas{4,i} = bcfm(bcfs{i}.chroma,anns.beats{i},anns.timesig{i}); + % meas{8,i} = bcfm(bcf{i}.chromas,anns.beats{i},anns.timesig{i}); + meas{5,i} = bcfm(bcfs{i}.mfcc,anns.beats{i},anns.timesig{i}); + % y = bcfs{i}.spec+bcfs{i}.cq+bcfs{i}.hpcp+bcfs{i}.chroma+bcfs{i}.mfcc; + % meas{6,i} = bcfm(y,anns.beats{i},anns.timesig{i}); + +% % voting mechanism.. find which measures are the same... +% zz = zeros(5); +% for k=1:5, +% for j=1:5, +% zz(k,j) = double(~~sum(meas{k,i}==meas{j,i})); +% end +% end +% +% for p=1:5, zz(p,p) = 0; end +% % find which is similar to another +% [val,ind] = max(sum(zz)); +% % and put these in place... +% meas{6,i} = meas{ind,i}; +% + + end + % and their evaluation + for j=1:5 + for i=1:222, + % only need rcl + [m.rcl(j,i), m.rtot(j,i), m.acl(j,i), m.atot(j,i)] = cont_eval(anns.actmeasures{i},meas{j,i},0.1); + end + end +end + +if 0 + + % for extracting batchwise measures... + for i=1:222, + meas{1,i} = bcfm(bcf{i}.spec,beats{i},timesig{i}); + % meas{2,i} = bcfm(bcf{i}.specs,anns.beats{i},anns.timesig{i}); + meas{2,i} = bcfm(bcf{i}.cq,beats{i},timesig{i}); + % meas{4,i} = bcfm(bcf{i}.cqs,anns.beats{i},anns.timesig{i}); + meas{3,i} = bcfm(bcf{i}.hpcp,beats{i},timesig{i}); + % meas{6,i} = bcfm(bcf{i}.hpcps,anns.beats{i},anns.timesig{i}); + meas{4,i} = bcfm(bcf{i}.chroma,beats{i},timesig{i}); + % meas{8,i} = bcfm(bcf{i}.chromas,anns.beats{i},anns.timesig{i}); + end + % and their evaluation + for j=1:4 + for i=1:222, + % only need rcl + [m.rcl(j,i), m.rtot(j,i), m.acl(j,i), m.atot(j,i)] = cont_eval(anns.actmeasures{i},meas{j,i},0.1); + end + end +end + +%new classification approach +if 0 +fname = '95_HipHopFatty'; +[bcf,frame,beats] = getbcfs2(fname); y = kmeans(frame.spec,4); +measures = bcfm(~~abs(diff(y)),beats,4); +figure(1);subplot(311); imagesc(10*log(1+abs(frame.spec'))); axis xy; colormap('hot'); subplot(312); stem(y); axis tight; subplot(313); stem(~~abs(diff(y))); axis tight; +out = playbeats(measures,wavread(fname),44100); wavwrite(out,44100,'~/Desktop/z2.wav'); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_beattracker/bt_parms.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,38 @@ +function p = bt_parms(res) + +if nargin<1 + res = 0.01161; +end + +p.fs = 44100; + +p.timeres = round(p.fs * res); + +p.winlen = round(512^2/p.timeres); + +p.step = round(p.winlen/4); + +p.bwinlen = 512; % always! +p.bstep = 128; % for the beat tracker! + +% step and consistency thresholds from beat tracker +p.stepthresh = round(8*(512/p.timeres)); +p.constthresh = round(p.stepthresh/2); + +% parameter for rayleigh distribution weighting +% p.rayparam = round(43*(512/p.timeres)); +p.rayparam = round(33*(512/p.timeres)); + +% minimum and maximum periodicities for comb filterbank +p.pmax = round(80*(512/p.timeres)); +p.pmin = round(4*(512/p.timeres)); +p.lowest = round(21*(512/p.timeres)); % dixon upper limit of 247 bpm + +%parameters for adaptive movingm mean threshold +p.pre = round(8*(512/p.timeres)); +% p.post = round(7*(512/p.timeres)); +p.post = p.pre-1; + +% factor for converting between beat period and tempo +p.fact = 60*p.fs/p.timeres; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_beattracker/chromaframe.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,112 @@ +function [spec] = chromaframe(x,spark); +% function [spec,mfcc,cq,hpcp,chroma] = chromaframe(x,spark); + +% function to return spectrogram, constq, hpcp and chromagram +% for one single frame of arbitrary length +% with the possible inclusion of an adaptive thresholding stage. + +fmin = 110; +fmax = 880; + +if nargin<2 + spark = sparsekernel(fmin, fmax, 36, 44100/16, 0.054); + spark(1025:end,:) = []; +end + + +% make x a row vector +x = x(:)'; +x2 = x; +fs = 44100; +% downsample by a factor of 16. +%x = x(1:16:end); +% x = resample(x,11025,44100); + x = resample(x,2756,11024); +% x = resample(x,11024,44096); + +len = length(x); + + +% make windowing function +w = hanningz(len); +w = w(:)'; +% w = ones(1,length(x)); % or no windowing... shouldn't be better.. +% take fft +spec = abs(fft(w.*x,2048)); +spec = adapt_thresh(spec(1:1024),16,15); + +% %spec = spec(1:1024); +% +% %cq = adapt_thresh(abs(spec*spark)); +% cq = abs(spec*spark); +% hpcp = zeros(1,36); +% +% for k=1:36 +% hpcp(k) = sum(abs(cq(k:36:end))); +% end +% +% %hpcp = adapt_thresh(hpcp,2,3); +% +% % for k=1:round(abs(log2(fmax/fmin))), +% % l(k) = sum(hpcp(k:3:end)); +% % end +% % +% % [val,ind] = max(l); +% % +% % chroma = hpcp(ind:3:end); +% % +% for i=1:12 % sum bins.. +% chroma(i) = sum(hpcp((i-1)*3+1:3*i)); +% end + + +%chroma = adapt_thresh(chroma,3,2); + +% % modulated complex lapped transform +% % spec = abs(fmclt(x',1024)'); +% % spec = adapt_thresh(spec); +% +% % % try to treat everything as gaussians +% % n = 1:length(cq); +% % mu = sum(n.*cq)/sum(cq); +% % sigma = sqrt(sum((n-mu).^2)/sum(cq)); +% % cq = exp(-0.5 * ((n - mu)./sigma).^2) ./ (sqrt(2*pi) .* sigma); +% % +% % n = 1:length(hpcp); +% % mu = sum(n.*hpcp)/sum(hpcp); +% % sigma = sqrt(sum((n-mu).^2)/sum(hpcp)); +% % hpcp = exp(-0.5 * ((n - mu)./sigma).^2) ./ (sqrt(2*pi) .* sigma); +% % +% % n = 1:length(chroma); +% % mu = sum(n.*chroma)/sum(chroma); +% % sigma = sqrt(sum((n-mu).^2)/sum(chroma)); +% % chroma = exp(-0.5 * ((n - mu)./sigma).^2) ./ (sqrt(2*pi) .* sigma); +% % +% % n = 1:length(spec); +% % mu = sum(n.*spec)/sum(spec); +% % sigma = sqrt(sum((n-mu).^2)/sum(spec)); +% % spec = exp(-0.5 * ((n - mu)./sigma).^2) ./ (sqrt(2*pi) .* sigma); +% +% %x2 = resample(x2,8000,44100); +% %spec = mfcc(x',8000,100); +% %spec = abs(sum(spec'))'; +% +% x2 = x2(1:4:end); +% spec2 = abs(fft(hanning(length(x2))'.*x2,2048)); +% spec2 = spec2(1:1024); +% mlmx = fft2melmx(1024,round(44100/16),40); +% [size(mlmx) size(spec2)]; +% mfcc = 20*log10(1+spec*mlmx'); +% spec = 20*log10(1+spec*mlmx'); + +% spec = adapt_thresh(mfcc,2,3); +% x2 = resample(x2,1378,44096); +% len = length(x2); +% w = hanningz(len); +% w = w(:)'; +% % take fft +% spec = abs(fft(w.*x2,1024)); +% spec = adapt_thresh(spec(1:512),16,15); + spec = spec(1:512); +% mlmx = fft2melmx(512,round(44100),40); +% spec = spec*mlmx'; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_beattracker/dojsdiv2.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,24 @@ +function sd = dojsdiv2(pre,post); + +pre = pre-min(pre); +post = post-min(post); +pre = pre(:)'; +post = post(:)'; +pre = pre+eps; +post = post+eps; +pre = pre/sum(pre); + +pi1 = 0.5; +pi2 = 0.5; + +% normalise to sum to unity +pre = pre/(sum(pre)+eps); +post = post/(sum(post)+eps); + +% combined distribution +a1 = pi1*pre + pi2*post; + +%sd = -sum(a1.*log(a1)) + pi1*sum(pre.*log(pre)) + pi2*sum(post.*log(post)); + + +sd = sum((-a1.*log(a1)) + pi1*(pre.*log(pre)) + pi2*(post.*log(post)));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_beattracker/findmeter.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,62 @@ +function [timesig,three_energy,four_energy] = findmeter_mm(acf,period,thresh); + +% a simple enegy test with current period value, where energy is evaluated +% at small regions at double and four times the period, to be compared with +% energy at 3 and 6 times the beat period, to decide if the time signature +% of the current frame is duple or triple. + +if nargin<3 + thresh = 0; +%default state +end + + +acf = acf/sum(acf); + +period = round(period); + +sw= 2; + + +% % new approach - LESS GOOD than the old approach +% +% numperiods = floor(length(acf)/period); +% num4multiples = floor(numperiods/4); +% % % just check we haven't gone over the end of the acf +% num4multiples = num4multiples- double((period*num4multiples*4+sw) > length(acf)); +% +% % initialize; +% three_energy = 0; +% four_energy = 0; +% +% for i=1:length(num4multiples) +% +% three_energy = three_energy + sum(acf(3*i*period-sw:1:3*i*period+sw)); +% four_energy = four_energy + sum(acf(4*i*period-sw:1:4*i*period+sw)); +% +% end + + +% old approach... + +if(length(acf)<6*period+sw) + + + three_energy = sum(acf(3*period-sw:1:3*period+sw)); + + four_energy = sum(acf(4*period-sw:1:4*period+sw)); + +else + + three_energy = sum(acf(3*period-sw:1:3*period+sw)) + sum(acf(6*period-sw:1:6*period+sw)); + + four_energy = sum(acf(4*period-sw:1:4*period+sw)) + sum(acf(2*period-sw:1:2*period+sw)); + +end + + +if ( (four_energy - three_energy) >= thresh ) + timesig = 4; +else + timesig = 3; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_beattracker/findmeter_mm.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,62 @@ +function [timesig,three_energy,four_energy] = findmeter_mm(acf,period,thresh); + +% a simple enegy test with current period value, where energy is evaluated +% at small regions at double and four times the period, to be compared with +% energy at 3 and 6 times the beat period, to decide if the time signature +% of the current frame is duple or triple. + +if nargin<3 + thresh = 0; +%default state +end + + +acf = acf/sum(acf); + +period = round(period); + +sw= 2; + + +% % new approach - LESS GOOD than the old approach +% +% numperiods = floor(length(acf)/period); +% num4multiples = floor(numperiods/4); +% % % just check we haven't gone over the end of the acf +% num4multiples = num4multiples- double((period*num4multiples*4+sw) > length(acf)); +% +% % initialize; +% three_energy = 0; +% four_energy = 0; +% +% for i=1:length(num4multiples) +% +% three_energy = three_energy + sum(acf(3*i*period-sw:1:3*i*period+sw)); +% four_energy = four_energy + sum(acf(4*i*period-sw:1:4*i*period+sw)); +% +% end + + +% old approach... + +if(length(acf)<6*period+sw) + + + three_energy = sum(acf(3*period-sw:1:3*period+sw)); + + four_energy = sum(acf(4*period-sw:1:4*period+sw)); + +else + + three_energy = sum(acf(3*period-sw:1:3*period+sw)) + sum(acf(6*period-sw:1:6*period+sw)); + + four_energy = sum(acf(4*period-sw:1:4*period+sw)) + sum(acf(2*period-sw:1:2*period+sw)); + +end + + +if ( (four_energy - three_energy) >= thresh ) + timesig = 4; +else + timesig = 3; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_beattracker/ftacf.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function acf = ftacf(x) + + +x = x(:); + +[M,N] = size(x); + +X = fft(x,2^nextpow2(2*M-1)); +acf = ifft(X.*conj(X)); + +acf = acf(1:M)./[M:-1:1]';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_beattracker/get_dfs.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,90 @@ +function df = get_dfs(x,p) + +% function to calculate the following onset detection functions +% df{1} = complex spectral difference +% df{2} = spectral difference +% df{3} = phase deviation +x = mean(x,2); + +% onset analysis step increment +o_step = p.winlen*2; % should be 1024 +% onset analysis winlen +o_winlen = o_step*2; % should be 2048 + +hlfwin = o_winlen/2; % will be half fft size + +% formulate hanningz window function +win = hanningz(o_winlen); +% win = tukeywin(o_winlen,0.5); + +% loop parameters +N = length(x); +pin = 0; +pend = N - o_winlen; + +% vectors to store phase and magnitude calculations +theta1 = zeros(hlfwin,1); +theta2 = zeros(hlfwin,1); +oldmag = zeros(hlfwin,1); + +% output onset detection functions +df = {}; + +% df sample number +k = 0; +while pin<pend + + k=k+1; + % calculate windowed fft frame + segment = x(pin+1:pin+o_winlen); + X = fft(fftshift(win.*segment)); + %X = X.*fftshift(hanningz(length(X))); + X = X(floor(length(X)/2)+1:length(X),:); + % X = X.*[length(X):-1:1]'; + % separate into magnitude + mag = (abs(X)); + +% mag = [0 abs(diff(log(eps+mag)))']'; + % and phase + theta = angle(X); + + % complexsd part + dev=princarg(theta-2*theta1+theta2); +% keyboard + meas=oldmag - (mag.*exp(i.*dev)); + df{1}(k) = sum(sqrt((real(meas)).^2+(imag(meas)).^2)); + +% % spectral difference part +% dev2=sqrt(mag.^2-oldmag.^2); +% df{2}(k) = sum(abs(dev2(mag>0.1))); +% +% +% % phase deviation part +% df{3}(k) = sum(abs(dev((mag>0.1)))); +% % df{3}(k) = sum(abs(dev(end-5:end))); +% +% % energy part +% df{4}(k) = sum(abs(segment)); +% +% % hfc part +% df{5}(k) = sum(mag.*(length(mag):-1:1)'); +% % df{5}(k) = sum(mag.*rand(hlfwin,1)); +% + % update vectors + theta2 = theta1; + theta1 = theta; + oldmag = mag; + pin = pin+o_step; +end + +% now interpolate each detection function by a factor of 2, +% to get resolution of 11.6ms + +for j=1:1,%5 + df{j} = interp(df{j},2); + % also check there are no negative elements + df{j}(df{j}<0) = 0; + % and scale to sum to unity +% df{j} = (df{j}+eps)/sum(df{j}+eps); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_beattracker/getalignment2.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,60 @@ +function [alignment] = getalignment2(dfframe,phwv,period,timesig) + + +period = round(period); + + + +%normalise and linearly weight most recent strongest +%dfframe = dfframe.*([1:length(dfframe)]); + +% reverse +dfframe = dfframe(end:-1:1); + +% output of alignment comb filter +phcf = zeros(1,period); + +numelem = floor((length(dfframe)-0)/period); + +%dfframe = [dfframe' zeros(1,length(dfframe))]'; +%this line is neeeded for using commented part below + +if (~timesig) % initial state + + + + % fit as many as possible + for i=1:period + for b = 1:numelem, + phcf(i) = phcf(i) + dfframe(( b-1)*period+i) * phwv(i); %+ ... +% dfframe(( b-1)*period+i+1) * phwv(i) + ... +% dfframe(( b-1)*period+i+2) * phwv(i);% + ... +% dfframe(( b-1)*period+i+3) * phwv(i) + ... +% dfframe(( b-1)*period+i+4) * phwv(i); + end + end + +else + + numelem = timesig; + for i=1:period + for b = 1:numelem, + % for a = 1-b:b-1 + phcf(i) = phcf(i) + dfframe(( b-1)*period+i) * phwv(i) + ... + dfframe(( b-1)*period+i+1) * phwv(i) + ... + dfframe(( b-1)*period+i+2) * phwv(i) + ... + dfframe(( b-1)*period+i+3) * phwv(i) + ... + dfframe(( b-1)*period+i+4) * phwv(i); + % end + end + end + +end + + +[val,alignment] = max(phcf); +[val2,bestguess] = max(phwv); + +if alignment>=2, +alignment = alignment-1; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_beattracker/getbcfs2.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,88 @@ +function [bcf,frame,beats] = getbcfs2(fname,beats); + + + +if nargin<2 + + [x fs] = wavread(fname); + x = mean(x,2); + if fs~=44100 + x = resample(x,44100,fs); + end + p = bt_parms; + + df = get_dfs(x(1:4:end),bt_parms(0.01161*4)); + [beats,timesig] = newtt_mm(df{1},p); + beats = beatadjust(beats,df{1}); +end + + + +beats = beats(:)'; + + +siz = wavread(fname,'size'); +[x fs] = wavread(fname); +x = mean(x,2); +% if siz(2)>1 +% x = mean(x')'; +% end + +if fs ~= 44100 + x = resample(x,44100,fs); +end + +beats = round(beats); +beats(beats>length(x)) = []; +beats = sort(beats); +%beats = beats - 4096; +%beats(beats<1) = 1; + +% +% spark = sparsekernel(110, 880, 36, 44100/16, 0.054); +% spark(1025:end,:) = []; +spark = 1; + +for k=1:length(beats)-1 +% [spec(k,:),mfcc(k,:), cq(k,:),hpcp(k,:),chroma(k,:)] = chromaframe(x(beats(k):beats(k+1)),spark); + [spec(k,:)] = chromaframe(x(beats(k):beats(k+1)),spark); + +end + + + +for k=1:size(spec,1)-1, + +% bcf.spec(k) = dobeatkl(spec(k,:),spec(k+1,:)); +% bcf.cq(k) = dobeatkl(cq(k,:).^2,cq(k+1,:).^2); +% bcf.hpcp(k) = dobeatkl(hpcp(k,:).^2,hpcp(k+1,:).^2); +% bcf.chroma(k) = dobeatkl(chroma(k,:).^2,chroma(k+1,:).^2); +% bcf.mfcc(k) = dobeatkl(mfcc(k,:).^2,mfcc(k+1,:).^2); + bcf.spec(k) = dojsdiv2(spec(k,:),spec(k+1,:)); +% bcf.cq(k) = dojsdiv2(cq(k,:),cq(k+1,:)); +% bcf.hpcp(k) = dojsdiv2(hpcp(k,:),hpcp(k+1,:)); +% bcf.chroma(k) = dojsdiv2(chroma(k,:),chroma(k+1,:)); +% bcf.mfcc(k) = dojsdiv2(mfcc(k,:),mfcc(k+1,:)); + +end + + +% bcf.spec=adapt_thresh(bcf.spec,2,1); + +frame.spec = spec; + +% % my added bit +% temp = 0*spec; +% for i=1:length(temp)-1 +% temp(i)=spec(i)+0.2/spec(i+1); +% end +% frame.spec = temp; + + + +% frame.cq = cq; +% frame.hpcp = hpcp; +% frame.chroma = chroma; +% frame.mfcc = mfcc; + +% for i=1:222, [c,n] = meterTracker(wavread(files(i+2).name),44100); kc{i} = round(44100*c.measures); kn{i} = round(44100*n.measures); if~mod(i,10),i,end; clear c; clear n; end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_beattracker/getmeasures2.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,38 @@ +function out = getmeasures2(fname,ts); + +if nargin<2 + % optional argument to hand label the timesignature + ts = 0; +end + + +[x fs] = wavread(fname); +x = mean(x,2); + +if fs~=11025, + x = resample(x,11025,fs); +end + +x = x + randn(length(x),1) * 0.0000001; + +df = get_dfs(x,bt_parms(0.01161*4)); +disp 'onset detection function' + +[beats,localscore,timesig] = newtt_hmm_dp(df{1}); +disp 'beats' + +[bcf,frame] = getbcfs2(fname,beats); +if~timesig, timesig = 4; end + +if ts + measures = bcfm(bcf.spec,beats,ts); +else + measures = bcfm(bcf.spec,beats,timesig); +end + + + +out.beats = beats/44100; +out.measures = measures/44100; +out.timesig = timesig; +out.df = df{1};
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_beattracker/getmeasures3 Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,39 @@ +function out = getmeasures4(fname,ts); + +if nargin<2 + % optional argument to hand label the timesignature + ts = 0; +end + +sz = wavread(fn,'size'); + +[x fs] = wavread(fname,round([0.5 0.7]*sz(1))); +x = mean(x,2); + +if fs~=11025, + x = resample(x,11025,fs); +end + +x = x + randn(length(x),1) * 0.0000001; + +df = get_dfs(x,bt_parms(0.01161*4)); +disp 'onset detection function' + +[beats,localscore,timesig] = newtt_hmm_dp(df{1}); +disp 'beats' + +[bcf,frame] = getbcfs2(fname,beats); +if~timesig, timesig = 4; end + +if ts + measures = bcfm(bcf.spec,beats,ts); +else + measures = bcfm(bcf.spec,beats,timesig); +end + + + +out.beats = beats/44100; +out.measures = measures/44100; +out.timesig = timesig; +out.df = df{1};
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_beattracker/getmeasures3.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,38 @@ +function out = getmeasures3(x,fs,ts); + +if nargin<2 + % optional argument to hand label the timesignature + ts = 0; +end + + +% [x fs] = wavread(fname); +% x = mean(x,2); + +if fs~=11025, + x = resample(x,11025,fs); +end + +x = x + randn(length(x),1) * 0.0000001; + +df = get_dfs(x,bt_parms(0.01161*4)); +disp 'onset detection function' + +[beats,localscore,timesig] = newtt_hmm_dp(df{1}); +disp 'beats' + +% [bcf,frame] = getbcfs2(fname,beats); +% if~timesig, timesig = 4; end +% +% if ts +% measures = bcfm(bcf.spec,beats,ts); +% else +% measures = bcfm(bcf.spec,beats,timesig); +% end + + + +out.beats = beats/44100; +% out.measures = measures/44100; +% out.timesig = timesig; +% out.df = df{1};
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_beattracker/getmeasures4.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,59 @@ +function out = getmeasures4(fname,ts); + +if nargin<2 + % optional argument to hand label the timesignature + ts = 0; +end + +sz = wavread(fname,'size'); + +[x fs] = wavread(fname); +x = mean(x,2); + +if fs~=11025, + x = resample(x,11025,fs); +end + +x = x + randn(length(x),1) * 0.0000001; + +df = get_dfs(x,bt_parms(0.01161*4)); +disp 'onset detection function' + +[beats,localscore,timesig] = newtt_hmm_dp(df{1}); +disp 'beats' + +% [bcf,frame] = getbcfs2(fname,beats); +% if~timesig, timesig = 4; end +% +% if ts +% measures = bcfm(bcf.spec,beats,ts); +% else +% measures = bcfm(bcf.spec,beats,timesig); +% end +reduced_fs = 86.12; +% x = resample(x,reduced_fs,11025); +% x = abs(x); +winlength = round(reduced_fs/10/2)*2; +win = gausswin(winlength); +earlybeats = [0 mean([beats(2:end);beats(1:end-1)])]; + + + + +% for iBeat = 1:length(beats) +% beatindex = max(round(beats(iBeat)/(44100/reduced_fs)),winlength/2+1); +% earlybeatindex = max(round(earlybeats(iBeat)/(44100/reduced_fs)),winlength/2+1); +% % beatstrength = sum(x(beatindex-winlength/2+(1:winlength)) .* win); +% beatstrength = sum(localscore(beatindex-winlength/2+(1:winlength))' .* win); +% % earlybeatstrength = sum(x(earlybeatindex-winlength/2+(1:winlength)) +% % .* win); +% earlybeatstrength = sum(localscore(earlybeatindex-winlength/2+(1:winlength))' .* win); +% if earlybeatstrength > 1.5 * beatstrength +% beats(iBeat) = earlybeats(iBeat); +% end +% end + +out.beats = beats/44100; +% out.measures = measures/44100; +% out.timesig = timesig; +% out.df = df{1};
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_beattracker/getperiod.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,56 @@ +function [period,rcf,period2] = getperiod(acf,wv,timesig,pmax,pmin,lowest) + + +% using for loop approach to extract beat period + +pmax = round(pmax); +pmin = round(pmin); + +rcf = zeros(1,pmax); + + +if(~timesig) % timesig unknown, must be general state + numelem = 4; + + for i=pmin:pmax-1, % maximum beat period + for a=1:numelem, % number of comb elements + for b=1-a:a-1, % gs using normalization of comb elements + rcf(i) = rcf(i) + (acf(a*i+b)*wv(i))/(2*a-1); + end + end + end + +else + numelem = timesig; % timesig known must be context dependent state + + for i=pmin:pmax-1, % maximum beat period + for a=1:numelem, % number of comb elements + for b=1-a:a-1, % cds not normalizing comb elements + rcf(i) = rcf(i) + acf(a*i+b)*wv(i); + end + end + end + +end + + +[val, period] = max(rcf); + + + + +period = refineperiod(period,acf,timesig); + + + while(period<lowest) + period = period*2; + end +%period + +if (~sum(abs(wv))) % i.e. using cds_wv before initialised + period = 0; +end + +% not important... just here to prevent an error in the number of outputs +period2 = period*2; +%figure(1); plot(rcf); pause \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_beattracker/hanningz.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +function w=hanningz(n) +w=.5*(1-cos(2*pi*(0:n-1)'/(n)));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_beattracker/newtt_hmm_dp.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,162 @@ +function [beats,localscore,timesig,ppath] = newtt_hmm_dp(df,tempo) + +if nargin<2 + tempo = 0; +end + +beats = []; + +p = bt_parms; + +df = df(:)'; +% +% df = df/alpha_norm(9,df); +df = qnormalise(df,9,2); + +% work out period track with viterbi decoding +[ppath,obs,prob,wv]= tempo_hmm(df,p.step,tempo); + +% ppp=textread('home/studio/Desktop/path.txt'); +% ppp = [ppp(:)' ppp(end)*ones(1,2)]; +% ppath = ppp; + +ppath(ppath<20) = 20; +dp = abs(diff(ppath)); +dp = [0 dp(:)']; +dp(dp>16) = 16; +% plot(dp),pause(0.01); +% now incorporate ellis' dynamic programming, +% but for time-varying period....! + + +tempmat = (ppath'*ones(1,p.step))'; +pd = round(tempmat(:)); +% length(pd) + + +% df = abs(diff(df)); +% prob = 0.5+0.5*prob/max(prob); +% prob = 4*prob; + +% prob = 4*prob/max(prob); + + +% a=ftacf(adapt_thresh(df)); +% a=a(1:512); +% [tmp,r]=getperiod(a,wv,0,128,1,0); +% r=sunity(r); +% val=max(r); +% tightness = 100*val; +tightness= [4]; % want to try and get a tightness out of the signal automatically... + + +alpha=0.8; +mpd = round(median(ppath)); +templt = exp(-0.5*(([-mpd:mpd]/(mpd/32)).^2)); +localscore = conv(templt,df); +% this removes the offset imposed by the filtering... + +localscore = localscore(round(length(templt)/2)+[1:length(df)]); +localscore = localscore/sum(localscore); + + +aacf = ftacf(localscore); +[timesig] = findmeter_mm(aacf(1:512),mpd); + +% % subplot(311); plot(localscore); +% % al = localscore; +% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% %%%% little bootstrap on phase +% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% apin = 0; +% awinlen=512; +% astep = 128; +% apend = length(localscore)-2*awinlen; +% ak = 0; +% while (apin<apend) +% ak = ak+1; +% align = getalignment2(localscore(apin+1:apin+awinlen),ones(1,pd(ak)),pd(ak),0); +% try +% localscore(apin+align-4:apin+align+4) = (1+gausswin(9)').*localscore(apin+align-4:apin+align+4); +% catch +% temp_a = 1; +% end +% apin=apin+astep; +% end +% +% % subplot(312); plot(localscore); +% % subplot(313); plot(al-localscore); +backlink = zeros(1,length(localscore)); +cumscore = zeros(1,length(localscore)); + +starting = 1; +for i = 1:length(localscore) +% pause; +% i +% search range for previous beat + prange = round(-2*pd(i)):-round(pd(i))/2; + +% want to update tightness based on periodicity change.. + % or as andrew does.. whether it's an important beat. + % Skewed window + mu = -pd(i); +% tightness = prob(ceil(i/128)); +% sigma = pd(i)/4; +% txwt = exp(-0.5 * ((prange - mu)./sigma).^2) ./ (sqrt(2*pi) .* sigma); + txwt = exp( -0.5* ( ( ( tightness - 1* log(1+dp(ceil(i/128))) ) * log(prange/-pd(i))).^2) ); +% txwt = -(prange ./ mu.^ 2) .* exp(-prange .^ 2 ./ (2*mu .^ 2)); +% txwt = txwt.^2; +% keyboard + timerange = i + prange; + +% Are we reaching back before time zero? + zpad = max(0, min(1-timerange(1),length(prange))); +% Search over all possible predecessors and apply transition +% weighting + scorecands = txwt .* [zeros(1,zpad),cumscore(timerange(zpad+1:end))]; +% Find best predecessor beat + [vv,xx] = max(scorecands); +% Add on local score + cumscore(i) = alpha*vv + (1-alpha)*localscore(i); + +% special case to catch first onset +% if starting == 1 & localscore(i) > 100*abs(vv) + if starting == 1 & localscore(i) < 0.01*max(localscore); + backlink(i) = -1; + else + backlink(i) = timerange(xx); + % prevent it from resetting, even through a stretch of silence + starting = 0; + end + +end + + +%try +% align = getalignment2(localscore(end-1024:end),ones(1,2*pd(end)),2*pd(end),0); +%catch + align = getalignment2(localscore(end-512:end),ones(1,1*pd(end)),1*pd(end),0); +%end + +b = []; +b = length(df) - align; + +% b = bestendx; + +while backlink(b(end)) > 0 +b = [b,backlink(b(end))]; +end + + +b = fliplr(b); + +% return beat times in secs +beats = (b*512); % seems to be an offset. +beats(beats<1) = []; +% +% otherbeats{kk} = b; +% score(kk) = dfscore(b,df); +% [val,ind] = max(score); +% beats = otherbeats{ind}; +% beats = 512*(length(df)-beats/512); +% beats = sort(beats);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_beattracker/princarg.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,3 @@ +function phase=princarg(phasein) +%phase=princarg(phasein) maps phasein into the [-pi:pi] range + phase=mod(phasein+pi,-2*pi)+pi;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_beattracker/refineperiod.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,65 @@ +function period = refineperiod(period,acf,timesig); + + +if ~timesig, timesig=4; end + +% peaks = zeros(1,timesig); +% ind = period; +% for i=1:timesig +% segment = acf(ind*i-(i-1):ind*i+(i-1)); +% [val,loc] = max(segment); +% peaks(i) = loc + ind*i-(i-1)-1; +% end +% +% +% for i=1:timesig +% peaks(i) = (peaks(i)-1)/i; +% end +% +% period = mean(peaks); +% +% + + + + + +% +% +if ~timesig + timesig =4; +end + +ind = period; + +if (timesig==4) + peak = zeros(1,4); + + peak(1) = ind; + + [val2,ind2] = max(acf(2*ind-1:2*ind+1)); + peak(2) = ind2 + 2*ind-2; + + [val3,ind3] = max(acf(3*ind-2:3*ind+2)); + peak(3) = ind3 + 3*ind-4; + + [val4,ind4] = max(acf(4*ind-3:4*ind+3)); + peak(4) = ind4 + 4*ind-9; + + period = mean([ peak(1) peak(2)/2 peak(3)/3 peak(4)/4 ]); + +else + + peak = zeros(1,3); + + peak(1) = ind; + + [val2,ind2] = max(acf(2*ind-1:2*ind+1)); + peak(2) = ind2 +2*ind-2; + + [val3,ind3] = max(acf(3*ind-2:3*ind+2)); + peak(3) = ind3 + 3*ind-4; + + period = mean([ peak(1) peak(2)/2 peak(3)/3 ]); + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_beattracker/sunity.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,3 @@ +function out =sunity(in); + +out = in/sum(eps+in);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_beattracker/tempo_hmm.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,148 @@ +function [path,obs,prob,wv,tmat,posterior]= tempo_hmm(df,step,tempo,tightness) + + +% if nargin<4 +% step = 128; +% tempo = 0; +% tightness = 8; +% end + +if nargin<3 + step = 128; + tempo = 0; +end + +% df = df/alpha_norm(9,df); +df = qnormalise(df,9,2); +p = bt_parms; +winlen = 512; + + + +df = [df(:)' zeros(1,winlen/2)]; % add half a frame... then fix last 2 values +% try using matlab hmm to do tempo tracking... +df = df(:)'; + +pin = 0; +pend = length(df) - winlen; +n = [1:128]; +% p.rayparam = 33; + +if tempo + mu = p.fact/tempo; + sigma = mu/4; + wv = exp(-0.5 * ((n - mu)./sigma).^2) ./ (sqrt(2*pi) .* sigma); +else + wv = (n ./ p.rayparam .^ 2) .* exp(-n .^ 2 ./ (2*p.rayparam .^ 2)); +end + +wv = sunity(wv); +lenwv = length(wv); +% wv = ~~wv; + +ct = 0; +while(pin<pend) + + + ct = ct+1; + segment = adapt_thresh(df(pin+1:pin+winlen)); +% acf(:,ct) = (ftacf(segment)); + acf(:,ct) = [ ftacf(segment(:))' zeros(1,512-winlen)]'; + + [temp,rcf] = getperiod(acf(:,ct),wv,0,128,1,p.lowest); + temp2 = adapt_thresh(rcf); +% st = sum(temp2); +% temp3 = zeros(1,lenwv); +% tempmat = zeros(lenwv); +% for i=1:lenwv, +% for j=1:lenwv, +% tempmat(i,j) = (temp2(i)+temp2(j)); +% end +% tempmat(i,i) = 0; +% end; +% temp3 = sum(tempmat); %plot(temp3); pause; + temp3=temp2; + temp3 = temp3'.*(acf(1:length(temp3),ct)); +% temp3 = acf(1:length(wv),ct); +% temp(1:10) = 0; +% temp(118:end) = 0; +% temp3(1:10) = 0; + +% for i=1:lenwv, +% temp4(i) = temp2(i)+st; +% end +% keyboard +% obs(:,ct) = sunity(temp3(:)); %% WAS NOT COMMENTED WHEN MATTHEW GAVE IT +% TO ME. + obs(:,ct) = sunity(rcf) + 0.001*rand(1,length(rcf)); +% obs(:,ct) = (temp3(:)); +% keyboard +% obs(:,ct) = sunity(adapt_thresh(rcf(:))); + + + pin = pin+step; +end + + + +tmat = zeros(128); +% make transition matrix +for i=20:118, tmat(:,i) = sunity(normpdf(n,i,8))'; end; +% % [alpha, beta, gamma] = fwdback(wv,tmat,obs); +% figure; mesh(tmat); + +% t = textread('/home/studio/Desktop/tmat.txt'); +% +% tmat = reshape(t,128,128); +% % figure(1); mesh(tmat); +% +% for i=1:size(tmat,1) +% tmat(i,:) = 1/128+filtfilt(gausswin(9),1,tmat(i,:)); +% end +% +% for i=1:size(tmat,2) +% tmat(:,i) = 1/128+filtfilt(gausswin(9),1,tmat(:,i)); +% end +% for i=1:size(tmat,2) +% tmat(:,i) = sunity(tmat(:,i)); +% end +% % figure; mesh(tmat); +% tmat(1:20,:) = 0; +% tmat(:,1:20) = 0; +% tmat(120:128,:) = 0; +% tmat(:,120:128) = 0; + + + +% figure(2); mesh(tmat); + +% keyboard +% [path,posterior] = viterbi_path(wv,tmat,obs); +path = viterbi_path(wv,tmat,obs); +% try tracking backwards.. probably doesn't matter +%% FIXME - forwards backwards? +% [path,posterior] = viterbi_path(wv,tmat,fliplr(obs)); +% path = fliplr(path); +% path + +% obs = obs/sum(sum(obs)); + +% +% for i=1:length(path), +% % prob(i) = sum(obs(path(i)-2:path(i)+2,i)); +% +% prob(i) = sum(obs(path(i)-2:path(i)+2,i)); +% +% % range = max(min(size(obs,1),path(i)-2:path(i)+2),1); +% % prob(i) = sum(obs(range,i)); +% +% %path(i)= refineperiod(path(i),acf(:,i),0); +% end + +path = [path path(end)*ones(1,2)]; +prob = 1; + + +% path = interp(path,2); + +% for i=1:222, [r(1,i),r(2,i),r(3,i),r(4,i)]=cont_eval(anns.beats{i},beats{i},0.175);end; mean(r')
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_beattracker/viterbi_path.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,64 @@ +function [path,p] = viterbi_path(prior, transmat, obslik) +% VITERBI Find the most-probable (Viterbi) path through the HMM state trellis. +% path = viterbi(prior, transmat, obslik) +% +% Inputs: +% prior(i) = Pr(Q(1) = i) +% transmat(i,j) = Pr(Q(t+1)=j | Q(t)=i) +% obslik(i,t) = Pr(y(t) | Q(t)=i) +% +% Outputs: +% path(t) = q(t), where q1 ... qT is the argmax of the above expression. + + +% delta(j,t) = prob. of the best sequence of length t-1 and then going to state j, and O(1:t) +% psi(j,t) = the best predecessor state, given that we ended up in state j at t + +scaled = 1; + +T = size(obslik, 2); +prior = prior(:); +Q = length(prior); + +delta = zeros(Q,T); +psi = zeros(Q,T); +path = zeros(1,T); +scale = ones(1,T); + + +t=1; +delta(:,t) = prior .* obslik(:,t); +if scaled + [delta(:,t), n] = normalise(delta(:,t)); + scale(t) = 1/n; +end +psi(:,t) = 0; % arbitrary value, since there is no predecessor to t=1 +for t=2:T + for j=1:Q + [delta(j,t), psi(j,t)] = max(delta(:,t-1) .* transmat(:,j)); + delta(j,t) = delta(j,t) * obslik(j,t); + end + if scaled + [delta(:,t), n] = normalise(delta(:,t)); + scale(t) = 1/n; + end +end +[p(T), path(T)] = max(delta(:,T)); +for t=T-1:-1:1 + path(t) = psi(path(t+1),t+1); +end + +% keyboard + +% If scaled==0, p = prob_path(best_path) +% If scaled==1, p = Pr(replace sum with max and proceed as in the scaled forwards algo) +% Both are different from p(data) as computed using the sum-product (forwards) algorithm + +if 0 +if scaled + loglik = -sum(log(scale)); + %loglik = prob_path(prior, transmat, obslik, path); +else + loglik = log(p); +end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,36 @@ +/.DS_Store/1.1.1.1/Wed Aug 31 15:49:35 2005// +/addshort2list.m/1.6/Mon Oct 10 16:52:44 2005// +/checklabs.m/1.4/Wed Sep 28 10:35:25 2005// +/checklabsyntax.m/1.6/Wed Sep 28 10:39:46 2005// +/chord2fifthpositions.m/1.1/Mon Oct 3 11:39:41 2005// +/chord2midinotes.m/1.7/Thu Aug 3 12:10:47 2006// +/chord2notes.m/1.9/Mon Oct 31 17:22:10 2005// +/chord2pitchclasses.m/1.1/Mon Oct 3 11:39:17 2005// +/chord2quality.m/1.1/Thu Nov 3 15:12:50 2005// +/contents.m/1.11/Wed Nov 30 15:40:00 2005// +/degree2note.m/1.5/Wed Sep 28 13:52:52 2005// +/degree2semitone.m/1.4/Wed Sep 28 10:50:50 2005// +/degrees2notes.m/1.4/Wed Sep 28 10:45:41 2005// +/degrees2quality.m/1.1/Thu Nov 3 15:13:53 2005// +/degrees2semitones.m/1.5/Mon Oct 3 11:27:47 2005// +/getchordinfo.m/1.5/Mon Oct 31 16:22:10 2005// +/interval2degree.m/1.5/Wed Sep 28 13:14:17 2005// +/interval2semitone.m/1.4/Wed Sep 28 13:15:45 2005// +/labread.m/1.4/Wed Sep 28 13:17:04 2005// +/labwrite.m/1.4/Wed Sep 28 13:18:21 2005// +/mlf2lab.m/1.4/Wed Sep 28 13:26:11 2005// +/note2degree.m/1.1/Fri Sep 30 11:41:14 2005// +/note2fifthinfo.m/1.3/Fri Sep 30 11:50:33 2005// +/note2fifthposition.m/1.3/Fri Sep 30 11:50:51 2005// +/note2pitchclass.m/1.5/Mon Oct 31 16:21:33 2005// +/notes2fifthpositions.m/1.1/Mon Oct 3 11:40:29 2005// +/notes2pitchclasses.m/1.1/Mon Oct 3 11:40:08 2005// +/parsechord.m/1.5/Mon Oct 31 16:23:03 2005// +/parsedegree.m/1.5/Mon Oct 10 15:39:17 2005// +/parsedegreelist.m/1.4/Wed Sep 28 13:38:53 2005// +/parsenote.m/1.4/Wed Sep 28 13:40:10 2005// +/short2quality.m/1.2/Tue Nov 8 14:19:42 2005// +/shorthand2degrees.m/1.6/Tue Aug 1 20:52:51 2006// +/syntaxcheck.m/1.8/Mon Oct 31 16:07:36 2005// +/types.m/1.5/Wed Nov 30 15:33:10 2005// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +chrish/chordtools
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:matthiasm@dspmac1.elec.qmul.ac.uk:/cvsroot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/addshort2list.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,178 @@ +% +%ADDSHORT2LIST Combine degrees of a shorthand with another degree list +% +% [fulldegreelist, success, errormessage] = addshort2list(shorthand, degreelist, {verbose}) +% +% Combine shorthand and degreelist to form full degree list for a chord. +% Takes into account the omit degree character '*' in producing final list. +% +% Success = 1 if shorthand and degreelist combined correctly, 0 otherwise. +% +% If optional argument 'verbose' is 1, function prints any errormessage to +% the screen. +% +% returns: fulldegreelist (string) +% success (boolean) +% errormessage (string) +% +% Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA + +% +function [fulldegreelist, success, errormessage] = addshort2list(shorthand, degreelist, verbose) + +% set verbose default to 0 +if nargin < 3 + verbose = 0; +end + +errormessage = ''; + +% initialise variables +fulldegreelist = ''; +shortintervals = []; +degreeintervals = []; +addlist = []; +success = 1; + +% convert shorthand string to list of degrees +[shortdegrees, ok, errors] = shorthand2degrees(shorthand); + +if ok + % if that works convert the shorthand degrees and degreelist to + % intervals and accidentals + + % add the implied 1 interval to the shorthand list + if isempty(shortdegrees) + rootinterval = '1'; % no comma if shorthand is empty + else + rootinterval = '1,'; % comma shows that there are further degrees after this one + end + + + [shortintervals, ok, errors] = parsedegreelist([rootinterval shortdegrees]); + + if (ok) + + + % this part of the algorithm finds all the present intervals and sorts + % them into ascending numeric order + + % First put the shorthand intervals into the full list + + [ilength,y] = size(shortintervals); + + for index = 1:ilength + % convert interval to a semitone value + semitone = interval2semitone(shortintervals(index,1),shortintervals(index,2)); + + % Using the semitone value as an index will find redundant + % degrees and also sort them in number order. Multiplying the interval by its + % presence value leaves either the interval value or 0. Any element + % in the array with interval of 0 will be discarded later + % We add 1 to the semitone value to handle the '1' or 'unity' + % interval that would return a 0 semitone value + + % record the interval + addlist(semitone+1,1) = shortintervals(index,1) .* shortintervals(index,3); + % record the accidentals as well + addlist(semitone+1,2) = shortintervals(index,2); + + end + else + success = 0; + end + + [degreeintervals, ok2, errors] = parsedegreelist(degreelist); + ok = ok && ok2; + + if ok + % Now add the degrees from the degreelist taking redundant and + % ommitted degrees into account + + [ilength,y] = size(degreeintervals); + + for index = 1:ilength + % convert interval to a semitone value + [semitone, ok, errors] = interval2semitone(degreeintervals(index,1),degreeintervals(index,2)); + + if ok && semitone >= 0 + % record the interval + addlist(semitone+1,1) = degreeintervals(index,1) .* degreeintervals(index,3); + % record the accidentals as well + addlist(semitone+1,2) = degreeintervals(index,2); + + else + success = 0; + end + + end + else + success=0; + end + + + % now create fulldegreelist + if ok + + [ilength,y] = size(addlist); + + for index = 1:ilength + + % if there is a non zero interval in this element, convert + % it to a degree symbol and add it to the output list + if(addlist(index,1) ~=0) + + degreesymbol = interval2degree(addlist(index,1),addlist(index,2)); + + if isempty(fulldegreelist) + % if this is the first degree then start list + fulldegreelist = degreesymbol; + else + % add to list with a separating comma + fulldegreelist = [fulldegreelist ',' degreesymbol]; + end + end + end + else + + success = 0; + + end + +else + + success = 0; + +end + + +if success == 0 + errormessage = [errors sprintf(['Error in addshort2list: unsuccessful '... + 'combination "' shorthand '" and "' degreelist '"\n'])]; + + if verbose ==1 + fprintf(1,errormessage); + end + + +end + + \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/checklabs.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,58 @@ +% +% CHECKLABS check chord syntax in all lab files in a directory +% +% success = checklabs(directory, logfile) +% +% Check the syntax of chord symbols in every lab file in the target +% directory. A log of syntax errors is created in the text file logfile +% +% Success = 1 if all symbols have correct syntax, 0 otherwise. +% +% returns: success (boolean) +% +% +% Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA + +% +function [success] = checklabs(directory, logfile) + +olddir = cd; + +cd(directory); + +files = dir('*.lab'); + +ilength = length(files); + +success = 1; + +fid = fopen(logfile, 'a'); + +for index = 1:ilength + + ok = checklabsyntax(files(index).name,fid); + + success = success && ok; + +end + +fclose(fid); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/checklabsyntax.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,70 @@ +% +% CHECKLABSYNTAX Check the syntax of all chords in a lab file +% +% [success] = checklabsyntax(labfile, {fid}) +% +% Checks syntax of all chord symbols in a lab file. Any incorrect chords +% will produce syntax errors and be quoted with time of chord from the +% labfile given for inspection. +% +% fid is the file id of the output log file. If not given, the error log +% will be printed to the screen +% +% Success = 1 if all symbols have correct syntax, 0 otherwise. +% +% Returns: success (boolean) +% +% +% Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA + +% +function [success] = checklabsyntax(labfile,fid) + +if nargin < 2 + fid = 1; +end + +success = 1; + +[times1,t2,symbols] = labread(labfile); + +ilength = length(symbols); + +% new file +fprintf(fid, ['*** File: ' labfile ' ***\n\n']); + +errormessage = ''; + +for index = 1:ilength + + [ok, errormessage] = syntaxcheck(char(symbols(index))); + + + if ok == 0 + + success = 0; + + fprintf(fid,['Time: %f seconds\n' errormessage '\n\n'], times1(index)); + + end +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/chord2fifthpositions.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,82 @@ +% +%CHORD2FIFTHPOSITIONS Convert chord symbol to pitch positions on line of fifths +% +% [fifthpostions, bass, success, errormessage] = chord2fifthpostions(chordsymbol, {verbose}) +% +% Converts a chord symbol to an array of integer positions on the line of fifths. +% C is the reference position 0. +% +% In the case of the 'no chord' symbol 'N' function returns an empty array +% +% Success = 1 if notes extracted from chordsymbol correctly, 0 otherwise. +% +% If optional argument 'verbose' is 1, function prints any errormessage to +% the screen. +% +% calls: chord2notes +% notes2fifthpositions +% +% returns: fifthpostions (array of integers) +% success (boolean) +% errormessage (string) +% +% +% Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA + +% +function [fifthpostions, bass, success, errormessage] = chord2fifthpostions(chordsymbol, verbose) + +% set verbose default to 0 +if nargin < 2 + verbose = 0; +end + +errormessage = ''; +mainlist = ''; +success = 1; +fifthpostions = []; +bass = ''; + +% get constituent notes +[chordnotes, bassnote, success, errormessage] = chord2notes(chordsymbol); + +if success && ~isempty(chordnotes) +% find fifthpostions of notes +[fifthpostions, success, errormessage] = notes2fifthpositions(chordnotes); +end + +if success && ~isempty(bassnote) +[bass, success, errormessage] = note2fifthposition(char(bassnote)); +end + + + +if success == 0 + errormessage = [errormessage sprintf(['Error in chord2fifthpostions: Couldn''t convert chord"' chordsymbol '"\n'])]; + if verbose ==1 + fprintf(1,errormessage); + end + + +end + + + \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/chord2midinotes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,148 @@ +% +%CHORD2MIDINOTES Generate MIDI note values for a given chord symbol +% +% [midinotes, success, errormessage] = chord2midinotes(chordsymbol, {verbose}) +% +% Obtains a set of midi note values for notes present in chord "chordsymbol". +% The voicing is built on the rootnote with all other degrees occuring +% above in number order unless a bass degree is specified. +% +% In the case of the 'no chord' symbol 'N' function returns midinote -1 +% +% Success = 1 if chord is converted correctly else 0. +% +% If optional argument 'verbose' is 1, function prints any errormessage to +% the screen. +% +% Returns: midinotes (array of integers) +% success (boolean) +% errormessage (string) +% +% +% % Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA + +% +function [midinotes, success,errormessage] = chord2midinotes(chordsymbol, verbose) + +% set verbose default to 0 +if nargin < 2 + verbose = 0; +end + +errormessage = ''; +mainlist = ''; +success = 1; +midinotes = 0; + +% parse the chordsymbol +[rootnote,shorthand,degreelist,bass, success, errormessage] = getchordinfo(chordsymbol); + +if success + + + %if 'no chord' then return -1 + if rootnote == 'N' + + midinotes = -1; + else + + % get root's pitchclass with respect to middle C and calculate absolute + % pitch of root in that octave + if success + + [rootpitchclass, success, errormessage] = note2pitchclass(rootnote); + + % midinote C4 = 60 so add pitchclass to calculate rootpitch + rootpitch = 60 + rootpitchclass; + + end + + + % combine shorthand and degreelist and obtain semitone values for each + % degree + if success + + [mainlist, success, errormessage] = addshort2list(shorthand, degreelist); + + if success + + [semitones,success,errormessage] = degrees2semitones(mainlist); + + % add rootpitch to obtain midinotes + chordpitches = semitones + rootpitch; + + end + + end + + % Now find the bass note + + if success + + % if we haven't got a bass degree then play the rootnote as the + % bassnote + if isempty(bass) + basspitchclass = rootpitchclass; + else + % otherwise convert the bassdegree to a note + [bassnote,success,errormessage] = degree2note(bass, rootnote); + + if success + + [basspitchclass, success, errormessage] = note2pitchclass(char(bassnote)); + + end + end + + if success + % calculate bass note so that it is in the octave below the + % rest of the chord i.e. add to C3 midinote 48 + basspitch = basspitchclass + 48; + + % Put midinote list together + midinotes = [basspitch, rootpitch]; + + for index = 1:length(chordpitches) + if chordpitches(index) ~= rootpitch + midinotes = [midinotes, chordpitches(index)]; + end + end + end + + end + end + +%alter pitches for chords with high roots +if rootpitchclass > 6 + midinotes = midinotes-12; +end + +end + +if success == 0 + errormessage = [errormessage sprintf(['Error in chord2midinotes: Couldn''t convert chord"' chordsymbol '"\n'])]; + if verbose ==1 + fprintf(1,errormessage); + end +end + + + \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/chord2notes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,138 @@ +% +%CHORD2NOTES Convert chord symbol to list of constituent notes +% +% [chordnotes, bassnote, success, errormessage] = chord2notes(chordsymbol, {verbose}) +% +% Converts a chord symbol to a cell array of note strings and a bassnote string. +% +% In the case of the 'no chord' symbol 'N' function returns an empty array +% +% Success = 1 if notes extracted from chordsymbol correctly, 0 otherwise. +% +% If optional argument 'verbose' is 1, function prints any errormessage to +% the screen. +% +% calls: getnoteinfo +% parsenote +% addshort2list +% degree2note +% +% returns: chordnotes (cell array of note strings) +% bassnote (string) +% success (boolean) +% errormessage (string) +% +% +% Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA + +% +function [chordnotes, bassnote, success, errormessage] = chord2notes(chordsymbol, verbose) + +% set verbose default to 0 +if nargin < 2 + verbose = 0; +end + +errormessage = ''; +mainlist = ''; +success = 1; +chordnotes = []; +bassnote = ''; + +% parse the chordsymbol +[rootnote,shorthand,degreelist,bass, success, errormessage] = getchordinfo(chordsymbol); + + + + +%if 'no chord' then return N +if (success == 1) + + + + if rootnote == 'N' + + chordnotes = []; + else + + + % combine shorthand and degreelist and obtain note names for each + % degree + if success + + [mainlist, success, errormessage] = addshort2list(shorthand, degreelist); + + if success + + % convert list of degrees to list of notes + [chordnotes,success,errormessage] = degrees2notes(mainlist,rootnote); + + end + + end + + % Now find the bass note + + if success + + if ~isempty(bass) + + [bassnote,success,errormessage] = degree2note(bass, rootnote); + + if success + + ilength = length(chordnotes); + index = 1; + while index<=ilength + if char(bassnote) == char(chordnotes(index)) + index = ilength +1; + contains = 1; + else + contains = 0; + index = index +1; + end + end + + if contains == 0 + + chordnotes = [bassnote; chordnotes]; + end + end + else + bassnote = rootnote; + end + + + end + end +end + +if success == 0 + errormessage = [errormessage sprintf(['Error in chord2notes: Couldn''t convert chord"' chordsymbol '"\n'])]; + if verbose ==1 + fprintf(1,errormessage); + end + + +end + + + \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/chord2pitchclasses.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,81 @@ +% +%CHORD2PITCHCLASSES Convert chord symbol to array of pitch classes +% +% [pitchclasses, bassclass, success, errormessage] = chord2pitchclasses(chordsymbol, {verbose}) +% +% Converts a chord symbol to an array of integer pitch class values and a +% bass pitch class value. Pitch class values are referenced w.r.t. C = 0. +% +% In the case of the 'no chord' symbol 'N' function returns an empty array +% +% Success = 1 if notes extracted from chordsymbol correctly, 0 otherwise. +% +% If optional argument 'verbose' is 1, function prints any errormessage to +% the screen. +% +% calls: chord2notes +% notes2pitchclasses +% +% returns: pitchclasses (array of integers) +% bassclass (integer) +% success (boolean) +% errormessage (string) +% +% +% Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA + +% +function [pitchclasses, bassclass, success, errormessage] = chord2pitchclasses(chordsymbol, verbose) + +% set verbose default to 0 +if nargin < 2 + verbose = 0; +end + +errormessage = ''; +mainlist = ''; +success = 1; +pitchclasses = []; +bassclass = ''; + +% get constituent notes +[chordnotes, bassnote, success, errormessage] = chord2notes(chordsymbol); + +if success && ~isempty(chordnotes) +% find pitchclasses of notes +[pitchclasses, success, errormessage] = notes2pitchclasses(chordnotes); +end + +if success && ~isempty(bassnote) +[bassclass, success, errormessage] = note2pitchclass(char(bassnote)); +end + +if success == 0 + errormessage = [errormessage sprintf(['Error in chord2pitchclasses: Couldn''t convert chord"' chordsymbol '"\n'])]; + if verbose ==1 + fprintf(1,errormessage); + end + + +end + + + \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/chord2quality.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,85 @@ +% +% CHORD2QUALITY return the chord quality of a given chord symbol +% +% [quality,success, errormessage] = chord2quality(chordsymbol, {verbose}) +% +% Returns the chord quality of a given chord symbol. Quality values are from +% the enumeration: +% +% 0 Major +% 1 Minor +% 2 Diminished +% 3 Augmented +% 4 Suspended +% +% Success = 1 if symbols parsed correctly, 0 otherwise. +% +% If optional argument 'verbose' is 1, function prints any errormessage to +% the screen. +% +% calls: getchordinfo +% short2quality +% degrees2quality +% +% returns: quality (integer) +% success (boolean) +% errormessage (string) +% +% +% Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA + +% + +function [quality, success, errormessage] = chord2quality(chordsymbol,verbose) + +quality = ''; +errormessage = ''; + +if nargin < 2 + verbose = 0; +end + +[rootnote,shorthand,degreelist,bassdegree,success,error] = getchordinfo(chordsymbol); + +if success + + if ~isempty(shorthand) + + [quality, success, error] = short2quality(shorthand); + + else + + [quality, success, error] = degrees2quality(degreelist); + + end + +end + + + +if(success==0) + + errormessage = sprintf([error 'Error in Chord2quality: Chord "' chordsymbol '"\n']); + + if verbose == 1 + fprintf(1, errormessage); + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/contents.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,64 @@ +% C4DM Chord Symbols Toolkit +% +% Version 1.0 November 05 +% +%TYPES +% types - help describes types, structures and enumerations used in toolbox +% +%SYMBOL MANIPULATION FUNCTIONS +% parsechord - Parse chord symbol to its constituent string elements +% parsenote - Parse a note string to a natural plus accidentals +% parsedegree - Parse a degree to an interval plus accidentals +% parsedegreelist - Parse a degree list to intervals and accidentals +% degree2semitone - Convert a degree to equivalent number of semitones +% interval2semitone - Convert interval + accidentals to semitone value +% interval2degree - Convert interval + accidentals to a degree string +% degrees2semitones - Convert a degree list to an array of semitone values +% degree2note - Convert a degree to correctly spelled note w.r.t. a root +% degrees2quality - Return the quality of a given degreelist +% note2pitchclass - Convert a note to a pitchclass value w.r.t middle C +% notes2pitchclasses - Convert a note list to an array pitchclasses +% note2fifthinfo - Find line of fifths natural-position and accidentals +% note2fifthposition - Find note position on the line of fifths w.r.t. C=0 +% notes2fifthpositions - Convert notes list to positions on line of fifths +% note2degree - Convert a note to a degree w.r.t. a given root +% shorthand2degrees - Convert a shorthand string to equivalent degreelist +% short2quality - Return the quality of a given shorthand string +% +% +%HIGHER LEVEL FUNCTIONS +% getchordinfo - Check chord symbol validity and return constituent parts +% syntaxcheck - Check the syntax and validity of a chord symbol +% addshort2list - Combine degrees of a shorthand with another degree list +% chord2quality - Returns the chord quality of a given chord symbol +% chord2notes - Convert chord symbol to list of constituent notes +% chord2midinotes - Generate MIDI note values for a given chord symbol +% chord2pitchclasses - Convert chord symbol to array of pitch classes +% chord2fifthpositions - Convert chord symbol to pitch positions on line of fifths +% +%LAB FILE FUNCTIONS +% labread - Read in times and symbols from a lab file +% labwrite - Write times and symbols to a lab file +% mlf2lab - Convert an mlf transcription file to a lab file +% checklabsyntax - Check the syntax of all chords in a lab file +% checklabs - Check chord syntax in all lab files in a directory +% +% Author: Christopher Harte, September 05 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/degree2note.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,157 @@ +% +%DEGREE2NOTE convert a degree to correctly spelled note w.r.t. a root +% +% [note,success,errormessage] = degree2note(degree, root {verbose}) +% +% Converts a degree to a note (string) with root as reference note +% for degree interval. +% +% Success = 1 if degree is converted correctly, 0 otherwise. +% +% If optional argument 'verbose' is 1, function prints any errormessage to +% the screen. +% +% returns: note (string) +% success (boolean) +% errormessage (string) +% +% See also degrees2notes, parsenote. +% +% +% Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA + +% +function [note,success,errormessage] = degree2note(degree, root,verbose) + +if nargin < 3 + verbose = 0; +end +errormessage = ''; + +error1 = ''; +error2 = ''; + +note = []; + +intervaltranslation = [5,0,2,4,-1,1,3,5]; % scale degree translations on line of fifths + +fifthpositions = {'F','C','G','D','A','E','B'}; %order of naturals on line of fifths + +success = 1; + +% parse the root note +[rootnatural,rootaccs, rsuccess, error1] = parsenote(root); + +%parse the degree +[interval, degreeaccs, present, dsuccess, error2] = parsedegree(degree); + +% if parsing symbols was successful +if(rsuccess && dsuccess); + + switch(rootnatural) % find root natural position on line of fifths + + case 'F' + fifthindex = 0; + case 'C' + fifthindex = 1; + case 'G' + fifthindex = 2; + case 'D' + fifthindex = 3; + case 'A' + fifthindex = 4; + case 'E' + fifthindex = 5; + case 'B' + fifthindex = 6; + + end + + %locate enharmonic root on line of fifths (modulo 6 arithmetic) + + fifthoffset = rootaccs*7; + + fifthindex = fifthindex + fifthoffset; + + + % calculate interval translation on line of fifths (add 1 to account + % for matlab referencing of array elements... + intervaloffset = intervaltranslation(mod(interval,7)+1); + finalposition = fifthindex + intervaloffset; + + + naturalvalue = mod(finalposition,7); + + + % calculate number of accidentals + if finalposition <0 + %if final position is negative then calculate number of flats + % remembering to include the extra first flat (-1) + accidentals = fix((finalposition+1)/7) + degreeaccs -1; + + else + % note is a natural or has a number of sharps + accidentals = fix(finalposition/7) + degreeaccs; + end + + note = fifthpositions(naturalvalue+1); + + if accidentals > 0 + + for i=1:accidentals + + note = strcat(note, '#'); + + end + + elseif accidentals <=0 + + for i=1:abs(accidentals) + + note = strcat(note, 'b'); + + end + end + +else + + success=0; + +end + +if(success==0) % correct degree therefore return success = 1 + % if not an integer then the degree string is incorrect + errormessage = [error1 error2 sprintf(['Error in degree2note: Unrecognised degree "' degree '" or root "' root '"\n'])]; + + if verbose == 1 + fprintf(1,errormessage); + end + +end + + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/degree2semitone.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,76 @@ +% +% DEGREE2SEMITONE convert a degree to equivalent number of semitones +% +% [semitone,success, errormessage] = degree2semitone(degree, {verbose}) +% +% Converts a degree string to equivalent number of semitones. +% +% Success = 1 if symbols parsed correctly, 0 otherwise. +% +% If optional argument 'verbose' is 1, function prints any errormessage to +% the screen. +% +% calls: parsedegree +% interval2semitone +% +% returns: semitone (integer) +% success (boolean) +% errormessage (string) +% +% +% Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA + +% +function [semitone, success, errormessage] = degree2semitone(degree, verbose) + +if nargin <2 + % verbose defaults 0 + verbose = 0; +end + +errormessage = ''; + +ilength = length(degree); + +success=0; +semitone = 0; + +% parse the degree string + +[interval, accidentals, present, ok] = parsedegree(degree); + +if (ok == 1) + + % convert interval and accidentals to equivalent number of semitones + [semitone, ok, errors] = interval2semitone(interval,accidentals); + + success = 1; + +else + + errormessage = sprintf(['Error in degree2semitone: incorrect degree "' degree '"\n']); + +end + +if (verbose ==1) && (success ==0) + fprintf(1,errormessage); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/degrees2notes.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,111 @@ +% +%DEGREES2NOTES Convert a degreelist to an array of notes w.r.t. a root +% +% [notes, success, errormessage] = degrees2notes(degreelist, root, {verbose}) +% +% Converts the degrees in degreelist to correctly spelled notes with +% respect to the root note. +% +% Success = 1 if degrees converted correctly, 0 otherwise. +% +% If optional argument 'verbose' is 1, function prints any errormessage to +% the screen. +% +% returns: notes (cell array of strings) +% success (boolean) +% errormessage (string) +% +% See also degree2note +% +% +%% Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA + +% +function [notes, success, errormessage] = degrees2notes(degreelist, root, verbose) + +% set verbose default to 0 +if nargin < 3 + verbose = 0; +end + +errormessage = ''; + +ilength = length(degreelist); + +index = 1; + +tempindex = 1; + +tempstring = ''; + +success = 1; + +notes = {}; + + +while index <= ilength + + + while (degreelist(index) ~= ',') + + tempstring(tempindex) = degreelist(index); + tempindex = tempindex +1; + index = index + 1; + + if(index > ilength) + break; + end + + if (degreelist(index) == ',') && (index == ilength) + success = 0; + errormessage = sprintf(['Error in degrees2notes: degree list finishes with a comma "' degreelist '"\n']); + end + + + end + + [newnote,ok, error] = degree2note(tempstring,root); + % concatenate error messages if there are any... + errormessage = [errormessage error]; + + if(ok == 1) + tempstring = ''; + tempindex = 1; + notes = [notes; cellstr(newnote)]; + + index = index + 1; + else + + errormessage = [errormessage sprintf(['Error in degrees2notes: incorrect degree in list "' degreelist '"\n'])]; + success = 0; + index = ilength +1; + end + + +end + +if (success == 0) && (verbose == 1) + + fprintf(1,errormessage) + +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/degrees2quality.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,115 @@ +% +%DEGREES2QUALITY convert a degreelist to chord quality enumeration +% +% [quality,success, errormessage] = degrees2quality(degreelist, {verbose}) +% +% Converts a list of degree strings to an integer value denoting the +% chord's quality. Quality values are from the enumeration: +% +% 0 Major +% 1 Minor +% 2 Diminished +% 3 Augmented +% 4 Suspended +% +% Success = 1 if symbols parsed correctly, 0 otherwise. +% +% If optional argument 'verbose' is 1, function prints any errormessage to +% the screen. +% +% calls: degrees2semitones +% +% returns: quality (integer) +% success (boolean) +% errormessage (string) +% +% +% Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA + +% +function [quality,success,errormessage] = degrees2quality(degreelist,verbose) + + + + +quality = ''; +errormessage = ''; + +if nargin < 2 + verbose = 0; +end + +% define templates for the 5 different triad chords (four quality families +% plus suspension) - weights mean maj thirds will have more effect than min +% 3rds which in turn have more effect than 5ths and then 2nds and 4ths + +templates = [[1,0,0,0,6,0,0,4,0,0,0,0]; ... % maj + [1,0,0,5,0,0,0,4,0,0,0,0]; ... % min + [1,0,0,5,0,0,4,0,0,0,0,0]; ... % dim + [1,0,0,0,6,0,0,0,4,0,0,0]; ... % aug + [1,0,2,0,0,2,0,4,0,0,0,0]]; % sus + + +% get the semitone equivalents of the degrees in the degree list +[semitones,success,error] = degrees2semitones(degreelist); + +indexa = 1; + +% initialise a binary vector showing which semitones are present +present = zeros(1,12); + +while indexa <= 3 && indexa <= length(semitones) + + % for each of the first three semitones in the list make its position a + % one in the vector 'present' + + if semitones(indexa) < 12 + + present(semitones(indexa)+1) = 1; + + end + indexa = indexa +1; + +end + +% multiply present by the templates matrix to give a vector of scores for +% the possible qualities +qvector = templates * present'; + + +% find maximum value from the qualities vector +% this function benfits from the max function's picking of the first +% maximum value if there are several equal ones so is predisposed toward +% major if the quality is not obvious from the input. (e.g. C:(1) returns major) +[value,index] = max(qvector); + +% take 1 from index to give correct enumeration +quality = index-1; + + +if(success==0) + + errormessage = sprintf([error 'Error in degrees2quality: incorrect degree in list "' degreelist '"\n']); + + if verbose == 1 + fprintf(1, errormessage); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/degrees2semitones.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,117 @@ +% +%DEGREES2SEMITONES convert a degree list to an array of semitone values +% +% [semitones, success, errormessage] = degrees2semitones(degreelist, {verbose}) +% +% Converts a list of degrees to corresponding semitone intervals +% +% Success = 1 if symbols parsed correctly, 0 otherwise. +% +% If optional argument 'verbose' is 1, function prints any errormessage to +% the screen. +% +% calls degree2semitone +% +% returns: semitones (array of integers) +% success (boolean) +% errormessage (string) +% +% +% Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA +% + +function [semitones, success, errormessage] = degrees2semitones(degreelist, verbose) + +% set verbose default to 0 +if nargin < 2 + verbose = 0; +end + +errormessage = ''; + +ilength = length(degreelist); + +index = 1; + +tempindex = 1; + +tempstring = ''; + +success = 1; + +parindex = 1; + +semitones = []; + + +while index <= ilength + + + while (degreelist(index) ~= ',') + + tempstring(tempindex) = degreelist(index); + tempindex = tempindex +1; + index = index + 1; + + if(index > ilength) + break; + end + + if (degreelist(index) == ',') && (index == ilength) + success = 0; + errormessage = sprintf(['Error in degrees2semitones: degree list finishes with a comma "' degreelist '"\n']); + end + + + end + + [semitones(parindex),ok, error] = degree2semitone(tempstring); + % concatenate error messages if there are any... + errormessage = [errormessage error]; + + if(ok == 1) + tempstring = ''; + tempindex = 1; + parindex = parindex + 1; + index = index + 1; + else + + errormessage = [errormessage sprintf(['Error in degrees2semitones: incorrect degree in list "' degreelist '"\n'])]; + success = 0; + index = ilength +1; + end + + +end + +if (success == 0) && (verbose == 1) + + fprintf(1,errormessage) + +end + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/getchordinfo.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,91 @@ +% +% GETCHORDINFO Check chord symbol validity and return constituent parts +% +% [rootnote, shorthand,degreelist,bassdegree, success, errormessage] = getchordinfo(chordsymbol, {verbose}) +% +% Checks symbol for correct syntax and returns chord information Differs +% from parsechord in that notes, shorthands, degrees and bass values are all +% checked for validity in the process. +% +% Success = 1 if all symbols have correct syntax, 0 otherwise. +% +% If optional argument 'verbose' is 1, function prints any errormessage to +% the screen. +% +% Returns: rootnote (string) +% shorthand (string) +% degreelist (string) +% bassdegree (string) +% success (boolean) true or false for correct syntax +% errormessage (string) +% +% See also: syntaxcheck +% +% Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA + +% +function [rootnote, shorthand,degreelist,bassdegree, success, errormessage] = getchordinfo(chordsymbol, verbose) + +if nargin < 2 + verbose = 0; +end + +errormessage = ''; +rootnote = ''; +shorthand = ''; +degrees = ''; +bassdegreel = ''; + + +success = 0; + +% parse the chord symbol into its constituent parts +[rootnote,shorthand, degreelist,bassdegree, success, errormessage] = parsechord(chordsymbol); + +if success == 1 + + if rootnote ~= 'N' + + % check validity of rootnote + [temp, temp2, success, errormessage] = parsenote(rootnote); + + %check validity of shorthand list + if ((success == 1) && ~(isempty(shorthand))) + [temp, success, errormessage] = shorthand2degrees(shorthand); + end + + % check validity of degreelist + if (success == 1) && ~(isempty(degreelist)) + [temp, success, errormessage] = parsedegreelist(degreelist); + end + + % check validity of bass degree + if (success == 1) && ~(isempty(bassdegree)) + [temp,temp2,temp3, success, errormessage] = parsedegree(bassdegree); + end + + end + +end + +if (success ==0) && (verbose == 1) + fprintf(1,errormessage); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/interval2degree.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,84 @@ +% +% INTERVAL2DEGREE +% +% [degree,success,errormessage] = interval2degree(interval,accidentals,{verbose}) +% +% Converts an interval and accidental pair to a degree string. +% +% Success = 1 if interval converted correctly, 0 otherwise. +% +% If optional argument 'verbose' is 1, function prints any errormessage to +% the screen. +% +% returns: degree (string) +% success (boolean) +% errormessage (string) +% +% Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA + +% +function [degree,success,errormessage] = interval2degree(interval,accidentals, verbose) + +% set verbose default to 0 +if nargin < 3 + verbose = 0; +end + +errormessage = ''; + + +success = 1; +degree = ''; + + +if accidentals >=1 + % then the interval is either a natural or has a number of sharps + if accidentals ~= 0 + for index = 1:accidentals + degree = [degree '#']; + end + end + +else + % then the interval has a number of flats + + for index = 1:abs(accidentals) + + degree = [degree 'b']; + end + +end + +if isnumeric(interval) + + intervalstring = num2str(interval); + degree = [degree intervalstring]; + +else + success = 0; + errormessage = 'Error in interval2degree: incorrect interval'; +end + +if (success == 0) && (verbose == 1) + fprintf(1,errormessage); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/interval2semitone.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,73 @@ +% +% INTERVAL2SEMITONE +% +% [semitone, success, errormessage] = interval2semitone(interval, accidental, {verbose}) +% +% Convert an interval and accidentals pair to a semitone value +% +% Success = 1 if interval converted correctly, 0 otherwise. +% +% If optional argument 'verbose' is 1, function prints any errormessage to +% the screen. +% +% returns: semitone (string) +% success (boolean) +% errormessage (string) +% +% Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA + +% +function [semitone, success, errormessage] = interval2semitone(interval, accidentals, verbose) + +if nargin < 3 + % verbose defaults 0 + verbose = 0; +end + +errormessage = ''; + +% semitone equivalents for intervals +% interval 1,2,3,4,5,6,7 +semitonetranslation = [0,2,4,5,7,9,11]; + +semitone = 0; + +success = 1; + +if (interval > 0) && (interval < 50) + +% semitone value is the number of semitones equivalent to the interval +% added to the number of accidentals (sharps are positive, flats are +% negative) and the number of octaves above the reference note to +% account for extensions + +semitone = semitonetranslation(mod(interval,8) + fix(interval/8)) + accidentals + 12*fix(interval/8); + +else + success = 0; + + errormessage = sprintf('Error in interval2semitone: out of range interval "%d"\n', interval); + + if verbose == 1 + fprintf(1,errormessage); + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/labread.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,39 @@ +% +%LABREAD read lab file +% +% [times1, times2, labels] = labread(labfile); +% +% read times and labels from a wavesurfer lab file +% +% +% returns: times1 (array of segment start-times) +% times2 (array of segment end-times) +% labels (cell array of label strings) +% +% See also labwrite, mlf2lab, checklabsyntax. +% +% Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA +% + +function [times1, times2, labels] = labread(labfile); + +[times1,times2, labels] = textread(labfile, '%f %f %s'); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/labwrite.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,58 @@ +% +%LABWRITE write a labfile to disk +% +% labwrite(times1, times2, labels, labfile, {protect}) +% +% Write time indices and labels to a wavesurfer .lab transcription file +% with file name labfile +% +% If optional argument 'protect' is 1 then if a lab file of the given name +% already exists it will be copied to a backup file with extension '.labx' +% before writing new data to the lab file +% +% See also labread, mlf2lab, checklabsyntax. +% +% Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA +% + +function labwrite(times1, times2, labels, labfile, protect) + +if nargin<5 + protect = 0; +end + +if (protect == 1) + check = copyfile(labfile, [labfile 'x']); +end + +fid = fopen(labfile, 'w'); + +for i = 1:length(times1) + + outtext = ['%f %f ' char(labels(i)) '\n']; + + fprintf(fid, outtext, times1(i), times2(i)); + +end + +fclose(fid); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/mlf2lab.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,54 @@ +% +% MLF2LAB Convert mlf file to lab file +% +% mlf2lab(inputfile, labfile) +% +% Converts a '.mlf' transcription file "inputfile" to a wavesurfer '.lab' +% transcription file "labfile". +% +% See also labread, labwrite, checklabsyntax. +% +% Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA + +% +function mlf2lab(inputfile, labfile) + + +[timestamps, timestamps2, stamps] = textread(inputfile, '%f %f %s'); + + +newtimes = timestamps./10000000; +newtimes2 = timestamps2./10000000; + + +fid = fopen(labfile, 'w'); + +for i = 1:length(newtimes) + + outtext = ['%f %f ' char(stamps(i)) '\n']; + + fprintf(fid, outtext, newtimes(i), newtimes2(i)); + +end + +fclose(fid); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/note2degree.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,136 @@ +% +%NOTE2DEGREE convert a note to a degree w.r.t. a given root +% +% [degree,success,errormessage] = note2degree(note,root,{octave},{verbose}) +% +% Converts a note string to a degree with respect to a given root note. +% Optional value octave gives octave offset to account for extended +% intervals. +% +% Success = 1 if note converted correctly, 0 otherwise. +% +% Optional argument octave defaults to 0. +% +% If optional argument 'verbose' is 1, function prints any errormessage to +% the screen. +% +% calls: note2fifthposition +% +% returns: degree (string) +% success (boolean) +% errormessage (string) +% +% +% Author: Christopher Harte, September 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA + +% +function [degree,success,errormessage] = note2degree(note, root,octave, verbose) + +% set verbose default to 0 +if nargin < 3 + octave = 0; + verbose = 0; + +elseif nargin < 4 + verbose = 0; +end + +errormessage = ''; +success = 1; +degree = ''; + +% interval translations on the line of fifths +fifthtranslations = [1,5,2,6,3,7,4]; + + +% get note and root natural position and accidentals on line of fifths +[noteposition, success1,error1] = note2fifthposition(note); + +[rootposition, success2,error2] = note2fifthposition(root); + +if success1 && success2 + + % take the difference between the two note positions for relative positions + % of notes with respect to one and other + fifthsdifference = noteposition - rootposition + 1; + + % natural difference on line of fifths + fifthsinterval = mod((fifthsdifference-1),7); + + i=0; + + % find number of accidentals apart on line of fifths + if fifthsdifference < 0 % if above 0 then either natural or sharp + + %if final position is negative then calculate number of flats + % remembering to include the extra first flat (-1) + accidentals = fix((fifthsdifference+1)/7) -1; + + else + % note is a natural or has a number of sharps + accidentals = fix(fifthsdifference/7); + + end + + + % put the required number of sharps or flats into the output string + if accidentals > 0 + + for i=1:accidentals + + degree = ['#' degree]; + + end + + elseif accidentals <=0 + + for i=1:abs(accidentals) + + degree = ['b' degree]; + + end + end + + + % find interval value from translation array + interval = fifthtranslations(fifthsinterval+1); + + if octave >= 0 + interval = interval + 7.*octave; + else + success = 0; + errormessage = 'Error in note2degree: Octave argument is negative'; + end + + + + degree = [degree num2str(interval)]; + +else + success = 0; + errormessage = [error1 error2]; +end + +if (success == 0) && (verbose == 1) + fprintf(1,errormessage); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/note2fifthinfo.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,94 @@ +% +%NOTE2FIFTHINFO find line of fifths natural-position and accidentals +% +% [position, accidentals, success,errormessage] = note2fifthinfo(note,{verbose}) +% +% Converts a note string to a natural position on the line of fifths and its +% corresponding number of sharps or flats. +% +% *** NOTE! *** +% Reference for natural positions on line of fifths for this function is F = 0. +% +% Success = 1 if note converted correctly, 0 otherwise. +% +% If optional argument 'verbose' is 1, function prints any errormessage to +% the screen. +% +% calls: parsenote +% +% returns: position (integer) +% accidentals (integer) +% success (boolean) +% errormessage (string) +% +% +% Author: Christopher Harte, September 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA + +% +function [position,accidentals, success, errormessage] = note2fifthinfo(note, verbose) + +% set verbose default to 0 +if nargin < 3 + verbose = 0; +end + +errormessage = ''; +success = 1; +position = ''; +accidentals = ''; + + +[natural, accidentals, success, errormessage] = parsenote(note); + +if success + switch(natural) + case 'F' + position = 0; + + case 'C' + position = 1; + + case 'G' + position = 2; + + case 'D' + position = 3; + + case 'A' + position = 4; + + case 'E' + position = 5; + + case 'B' + position = 6; + + otherwise + errormessage = 'Error in note2fifthinfo: unrecognised natural'; + success=0; + end +end + + +if (success == 0) && (verbose == 1) + fprintf(1,errormessage); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/note2fifthposition.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,62 @@ +% +%NOTE2FIFTHPOSITION find note position on the line of fifths w.r.t. C=0 +% +% [noteposition, success, errormessage] = note2fifthposition(note,{verbose}) +% +% Converts a note string to a natural position on the line of fifths and its +% corresponding number of sharps or flats. Reference on line of fifths is C = 0. +% +% Success = 1 if note converted correctly, 0 otherwise. +% +% If optional argument 'verbose' is 1, function prints any errormessage to +% the screen. +% +% calls: note2fifthinfo +% +% returns: noteposistion (integer) +% success (boolean) +% errormessage (string) +% +% +% Author: Christopher Harte, September 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA + +% +function [noteposition, success, errormessage] = note2fifthposition(note, verbose) + +% set verbose default to 0 +if nargin < 3 + verbose = 0; +end + +errormessage = ''; +success = 1; +noteposition = ''; + +[notenatural, noteaccidentals, success1,errormessage] = note2fifthinfo(note); + +if success + noteposition = notenatural + 7.*noteaccidentals -1; +end + +if (success == 0) && (verbose == 1) + fprintf(1,errormessage); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/note2pitchclass.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,126 @@ +% +%NOTE2PITCHCLASS convert a note to a pitchclass value w.r.t middle C +% +% [pitchclass, success, errormessage] = note2pitchclass(note, {verbose}) +% +% Converts a note string to a pitch class (integer) with C as reference +% pitch class 0 +% +% Success = 1 if symbols parsed correctly, 0 otherwise. +% +% If optional argument 'verbose' is 1, function prints any errormessage to +% the screen. +% +% returns: pitchclass (integer) +% success (boolean) +% errormessage (string) +% +% +% Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA + +% +function [pitchclass, success, errormessage] = note2pitchclass(note, verbose) + +if nargin < 2 + verbose = 0; +end + +errormessage = ''; + +ilength = length(note); + +index = 1; +success = 1; + + +% first char should be a natural name A-G +switch note(index) + + case 'C' % natural C + pitchclass = 0; + index = index + 1; + + case 'D' % natural D + pitchclass = 2; + index = index + 1; + + case 'E' % natural E + pitchclass = 4; + index = index + 1; + + case 'F' % natural F + pitchclass = 5; + index = index + 1; + + case 'G' % natural G + pitchclass = 7; + index = index + 1; + + case 'A' % natural A + pitchclass = 9; + index = index + 1; + + case 'B' % natural B + pitchclass = 11; + index = index + 1; + + otherwise %Unrecognised character + errormessage = sprintf(['Error in Note2PitchClass: Unrecognised note "' note '"\n']); + index = ilength + 1; + pitchclass=-1; + success = 0; + +end + +% any other characters should be either flats or sharps +while index <= ilength + + switch(note(index)) + + case 'b' % FLAT + pitchclass = pitchclass - 1; %decrement pitchclass value + index = index + 1; + + case '#' % SHARP + pitchclass = pitchclass + 1; %increment pitchclass value + index = index + 1; + + otherwise % Unrecognised character + errormessage = sprintf(['Error in Note2PitchClass: Unrecognised note "' note '"\n']); + index = ilength + 1; + pitchclass=-1; + success = 0; + end + +end + +% Use modulo command to make sure that we are back within range 0-12 +if(success == 1) + + pitchclass = mod(pitchclass,12); + +else + if(verbose == 1) + fprintf(1,errormessage); + end +end + + \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/notes2fifthpositions.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,80 @@ +% +%NOTES2FIFTHPOSITIONS convert notes list to positions on line of fifths +% +% [pitchclasses, success, errormessage] = notes2fifthpostions(notes, {verbose}) +% +% Converts a cell array of note strings to an array of integers denoting +% positions on the line of fifths with C as reference position 0. +% +% Success = 1 if symbols converted correctly, 0 otherwise. +% +% If optional argument 'verbose' is 1, function prints any errormessage to +% the screen. +% +% calls: note2pitchclass +% +% returns: pitchclasses (array of integers) +% success (boolean) +% errormessage (string) +% +% +% Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA +% + +function [fifthpositions, success, errormessage] = notes2fifthpostions(notes, verbose) + +% set verbose default to 0 +if nargin < 2 + verbose = 0; +end + +errormessage = ''; + +ilength = length(notes); + +index = 1; + +success = 1; + +fifthpositions = []; + +while index <= ilength + + [fifthpositions(index), success, errormessage] = note2fifthposition(char(notes(index))); + + if success + index=index+1; + else + errormessage = [errormessage sprintf(['Error in notes2fifthpostions: couldn''t convert notes "' notes '"\n'])]; + index = ilength +1; + end + + +end + +if (success == 0) && (verbose == 1) + + fprintf(1,errormessage) + +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/notes2pitchclasses.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,80 @@ +% +%NOTES2PITCHCLASSES convert a note list to an array pitchclasses +% +% [pitchclasses, success, errormessage] = notes2pitchclasses(notes, {verbose}) +% +% Converts a cell array of note strings to an array of pitch class integer +% values. Pitch class values are referenced w.r.t. C = 0. +% +% Success = 1 if symbols converted correctly, 0 otherwise. +% +% If optional argument 'verbose' is 1, function prints any errormessage to +% the screen. +% +% calls note2pitchclass +% +% returns: pitchclasses (array of integers) +% success (boolean) +% errormessage (string) +% +% +% Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA +% + +function [pitchclasses, success, errormessage] = notes2pitchclasses(notes, verbose) + +% set verbose default to 0 +if nargin < 2 + verbose = 0; +end + +errormessage = ''; + +ilength = length(notes); + +index = 1; + +success = 1; + +pitchclasses = []; + +while index <= ilength + + [pitchclasses(index), success, errormessage] = note2pitchclass(char(notes(index))); + + if success + index=index+1; + else + errormessage = [errormessage sprintf(['Error in notes2pitchclasses: couldn''t convert notes "' notes '"\n'])]; + index = ilength +1; + end + + +end + +if (success == 0) && (verbose == 1) + + fprintf(1,errormessage) + +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/parsechord.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,274 @@ +% +%PARSECHORD parse chord symbol to its constituent string elements +% +% [rootnote, shorthand, degreelist, +% bass, success, errormessage] = parsechord(chord, {verbose}) +% +% Parses the chord symbol string "chord" into its separate string elements. +% +% NOTE: This function does not check validity of the chord elements, it +% merely separates them according to the chord syntax. +% To obtain chord elements with included validity check use GETCHORDINFO. +% +% Success = 1 if symbols parsed correctly, 0 otherwise. +% +% If optional argument 'verbose' is 1, function prints any errormessage to +% the screen. +% +% returns: rootnote (note string) +% shorthand (shorthand string) +% degreelist (degree-list string) +% bass (degree string) +% success (boolean) +% errormessage (string) +% +% See also: getchordinfo +% +% Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA + +% +function [rootnote, shorthand, degrees, bass, success, errormessage] = parsechord(chord, verbose) + +% set verbose default to 0 +if nargin <2 + verbose = 0; +end + +ilength = length(chord); + +% initialise variables +errormessage = ''; +rootnote = ''; +shorthand = ''; +degrees = ''; +bass = ''; + +success = 1; +index = 1; + + +% check for 'no chord' symbol +if chord(index) == 'N' + rootnote = chord(index); + index = index +1; + % check to see there are no further characters + if(index<=ilength) + errormessage = sprintf(['Error in parsechord: \nExtra characters after "no chord" symbol "' chord '"\n']); + success = 0; + end +else +% parse thechord symbol + + % the first part of the symbol before a switch character should be the root note + while ((index <=ilength )) + + if (chord(index) == ':') || (chord(index) == '/') || ... + (chord(index) == '(') || (chord(index) == ')') + + break + end + + rootnote(index) = chord(index); + index = index+1; + + if(index > ilength) || (chord(index) == '/') + % if chord is a rootnote on its own or with just a bass note + % then it is a major chord therefore set shorthand to 'maj' + shorthand = 'maj'; + end + end + + % initialise booleans to record which switch characters we have found + colon = 0; + openbracket = 0; + closebracket = 0; + slash = 0; + + % parse the rest of the chord symbol + while(index <= ilength) + + % reset temporary index + tempindex = 1; + + switch(chord(index)) + + case ':' + + % if we find a colon after any switch characters have + % already occured then the symbol is incorrect + if (colon || openbracket || closebracket || slash) + errormessage = sprintf(['Error in parsechord: \nIncorrect character sequence in chord "' chord '"\n']); + success = 0; + index = ilength+1; + else + + % found the first instance of a colon character + colon = 1; + + index = index +1; + + if(index > ilength) + errormessage = sprintf(['Error in parsechord: \nFound ":" at end of chord string "' chord '"\n']); + success = 0; + end + % colon should be followed by a shorthand string or + % a degree list contained in brackets + while (index <= ilength) + + if (chord(index) == ':') || (chord(index) == '/') || ... + (chord(index) == '(') || (chord(index) == ')') + + break + end + + % copy character into shorthand + shorthand(tempindex) = chord(index); + index = index +1; + tempindex = tempindex +1; + + end + + end + + case '(' + + % if we have had a colon but no other switch charaters then + % an open bracket signifies the start of the degree list + if (colon && ~slash && ~closebracket && ~openbracket) + + openbracket = 1; + + index = index +1; + + while (index <= ilength) + + if (chord(index) == ':') || (chord(index) == '/') || ... + (chord(index) == '(') || (chord(index) == ')') + + break + end + + % copy character into degrees + degrees(tempindex) = chord(index); + index = index +1; + tempindex = tempindex +1; + + end + + if(index > ilength) + errormessage = sprintf(['Error in parsechord: \nDegree list brackets not closed in chord "' chord '"\n']); + success = 0; + end + + else + errormessage = sprintf(['Error in parsechord: \nIncorrect character sequence in chord "' chord '"\n']); + success = 0; + index = ilength +1; + end + + case ')' + + + % if we find a closing bracket then we should either be at + % the end of the symbol or there should be a slash to follow + if (colon && openbracket && ~slash && ~closebracket) + + closebracket = 1; + index = index +1; + + else + errormessage = sprintf(['Error in parsechord: \nIncorrect character sequence in chord "' chord '"\n']); + success = 0; + index = ilength +1; + end + + % check to see that the brackets contained something + if isempty(degrees) + errormessage = sprintf(['Error in parsechord: \nBrackets contain no degrees in chord "' chord '"\n']); + success = 0; + index = ilength +1; + end + + + case '/' + % forward slash should be followed by a degree string + + slash = 1; + + % move on to next character to process the expected bass degree + index = index +1; + + % check that we haven't overun the end of the symbol string + if(index > ilength) + errormessage = sprintf(['Error in parsechord: \nNo bass degree "/" at end of chord "' chord '"\n']); + success = 0; + end + + % check that if we have had an open bracket that it also + % had a closed bracket + if(xor(openbracket,closebracket)) + errormessage = sprintf(['Error in parsechord: \nFound "/" before closing bracket in chord "' chord '"\n']); + success = 0; + index = ilength +1; + end + + % check that the previous character was not a ':' + if(chord(index-2) == ':') + errormessage = sprintf(['Error in parsechord: \nFound "/" directly after ":" in chord "' chord '"\n']); + success = 0; + index = ilength +1; + end + + + while( index <= ilength ) + + % if we find a switch character after a slash then + % the symbol is incorrect + if (chord(index) == ':') || (chord(index) == '/') || ... + (chord(index) == '(') || (chord(index) == ')') + + errormessage = sprintf(['Error in parsechord: \nIncorrect character sequence in chord "' chord '"\n']); + success = 0; + index = ilength +1; + + else + + % copy remaining characters into bass + bass(tempindex) = chord(index); + index = index +1; + tempindex = tempindex +1; + end + end + + otherwise + errormessage = sprintf(['Error in parsechord: \nUnrecognised chord "' chord '"\n']); + success = 0; + index = ilength + 1; + end + + end +end + +if (verbose == 1) && (success == 0) + fprintf(1,errormessage); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/parsedegree.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,123 @@ +% +% PARSEDEGREE +% +% [interval, accidentals, success, errormessage] = parsedegree(degree, {verbose}) +% +% Parse a degree to an interval value and a number of accidentals +% if accidentals is positive it denotes number of sharps, +% if it is negative it denotes number of flats. +% If the omit degree character '*' is found then present is returned as 0 +% otherwise it is 1. +% +% Success = 1 if symbols parsed correctly, 0 otherwise. +% +% If optional argument 'verbose' is 1, function prints any errormessage to +% the screen. +% +% returns: interval (integer) +% accidentals (integer) +% present (boolean) +% success (boolean) +% errormessage (string) +% +% Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA + +% +function [interval,accidentals,present,success, errormessage] = parsedegree(degree, verbose) + +if nargin < 2 + verbose = 0; +end + +ilength = length(degree); + +errormessage = ''; +accidentals = 0; +interval = 0; +success=1; +present = 1; + +index = 1; + +% if the input string is not empty +if (isempty(degree) == 0) + + + % check for omit degree '*' + if degree(index) == '*' + present = 0; + index = index +1; + end + + + tempstring = ''; + tempindex = 1; + % parse the degree string + while index <= ilength + + switch degree(index) + + case 'b' % FLAT + accidentals = accidentals - 1; %decrement accidental count + index = index + 1; + + case '#' % SHARP + accidentals = accidentals + 1; %increment accidental count + index = index + 1; + + case {'1','2','3','4','5','6','7','8','9'} + % if neither of the above then remaining string should be + % an integer interval value + tempstring(tempindex) = degree(index); + + tempindex = tempindex+1; + index = index+1; + + otherwise + % unrecognised symbol + success=0; + index = ilength+1; + end + end +else + success=0; +end + +if success + +% convert the interval string to an integer + [interval, success] = str2num(tempstring); + + % check it worked and that the interval is valid + if isempty(interval) || (interval <= 0) + success = 0; + end +end + +if(success==0) % correct degree therefore return success = 1 + % if not an integer then the degree string is incorrect + errormessage = sprintf(['Error in parsedegree: Unrecognised degree "' degree '"\n']); + interval = 0; + if verbose == 1 + fprintf(1, errormessage); + end + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/parsedegreelist.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,121 @@ +% +% PARSEDEGREELIST +% +% [pardegrees, success, errormessage] = parsedegreelist(degreelist, {verbose}) +% +% Parses a comma delimited list of degrees to an array of intervals, their +% associated accidentals and whether or not they are present (presence +% denoted by a '*' in front of a degree in the input degreelist). +% +% Success = 1 if symbols parsed correctly, 0 otherwise. +% +% If optional argument 'verbose' is 1, function prints any errormessage to +% the screen. +% +% calls parsedegree +% +% returns: degrees (array of int: [interval,accidental,present]) +% success (boolean) +% errormessage (string) +% +% +% Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA + +% +function [pardegrees, success, errormessage] = parsedegreelist(degreelist, verbose) + +% set verbose default to 0 +if nargin < 2 + verbose = 0; +end + +errormessage = ''; + +ilength = length(degreelist); + +index = 1; + +tempindex = 1; + +tempstring = ''; + +success = 1; + +parindex = 1; + +pardegrees = []; + + +while index <= ilength + + + while (degreelist(index) ~= ',') + + tempstring(tempindex) = degreelist(index); + tempindex = tempindex +1; + index = index + 1; + + if(index > ilength) + break; + end + + if (degreelist(index) == ',') && (index == ilength) + success = 0; + errormessage = sprintf(['Error in parsedegreelist: degree list finishes with a comma "' degreelist '"\n']); + end + + + end + + [pardegrees(parindex,1),pardegrees(parindex,2),pardegrees(parindex,3),ok, error] = parsedegree(tempstring); + % concatenate error messages if there are any... + errormessage = [errormessage error]; + + if(ok == 1) + tempstring = ''; + tempindex = 1; + parindex = parindex + 1; + index = index + 1; + else + + errormessage = [errormessage sprintf(['Error in parsedegreelist: incorrect degree in list "' degreelist '"\n'])]; + success = 0; + index = ilength +1; + end + + +end + +if (success == 0) && (verbose == 1) + + fprintf(1,errormessage) + +end + + + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/parsenote.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,102 @@ +% +% PARSENOTE +% +% [natural, accidentals, success, errormessage] = parsenote(note, {verbose}) +% +% Parse a note to a natural character value and a number of accidentals +% if accidentals is positive it denotes number of sharps, +% if it is negative it denotes number of flats. +% +% Success = 1 if symbols parsed correctly, 0 otherwise. +% +% If optional argument 'verbose' is 1, function prints any errormessage to +% the screen. +% +% returns: natural (char) +% accidentals (integer) +% success (boolean) +% errormessage (string) +% +% Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA + +% +function [natural,accidentals,success, errormessage] = parsenote(note, verbose) + +if nargin < 2 + verbose = 0; +end + +errormessage = ''; + +ilength = length(note); + +accidentals = 0; +natural = []; +success=1; + +index = 1; + +if (isempty(note)) % if we have been passed an empty string + success=0; + +else % parse the note string + + switch(note(index)) + + case {'A','B','C','D','E','F','G'} + + %first character should be a natural + natural = note(index); + index= index+1; + + %remaining characters should be sharps or flats + while index <= ilength + + switch note(index) + + case 'b' % FLAT + accidentals = accidentals - 1; %decrement accidental count + index = index + 1; + + case '#' % SHARP + accidentals = accidentals + 1; %increment accidental count + index = index + 1; + + otherwise + % if neither of the above then the note is incorrect + success=0; + index = ilength+1; + end + end + + otherwise + success=0; + end +end + +if(success==0) % correct note therefore return success = 1 + % if not an integer then the note string is incorrect + errormessage = sprintf(['Error in parsenote: Unrecognised note "' note '"\n']); + + if verbose ==1 + fprintf(1,errormessage); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/short2quality.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,115 @@ +% +%SHORT2QUALITY return the quality of a given shorthand string +% +% [quality,success, errormessage] = short2quality(shorthand, {verbose}) +% +% Returns the quality of a given shorthand string. Quality values are from +% the enumeration: +% +% 0 Major +% 1 Minor +% 2 Diminished +% 3 Augmented +% 4 Suspended +% +% +% Success = 1 if symbols parsed correctly, 0 otherwise. +% +% If optional argument 'verbose' is 1, function prints any errormessage to +% the screen. +% +% calls: +% +% returns: quality (integer) +% success (boolean) +% errormessage (string) +% +% +% Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA + +% +function [quality,success,errormessage] = short2quality(shorthand,verbose) + +if nargin < 2 + verbose = 0; +end + +success = 1; +errormessage = ''; +quality = ''; + +switch shorthand + + + % triads + case 'maj' + quality = 0; + case 'min' + quality = 1; + case 'dim' + quality = 2; + case 'aug' + quality = 3; + + % sevenths + case 'maj7' + quality = 0; + case 'min7' + quality = 1; + case '7' + quality = 0; + case 'minmaj7' + quality = 1; + case 'dim7' + quality = 2; + case 'hdim7' + quality = 2; + + % sixths + case 'maj6' + quality = 0; + case 'min6' + quality = 1; + + % ninths + + case '9' + quality = 0; + case 'maj9' + quality = 0; + case 'min9' + quality = 1; + + % suspended + case 'sus4' + quality = 4; + case 'sus2' + quality = 4; + + otherwise + success = 0; + errormessage = ['Error in short2quality: unrecognised shorthand: ' shorthand]; +end + + +if (success ==0) && (verbose == 1) + fprintf(1,errormessage); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/shorthand2degrees.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,114 @@ +% +% SHORTHAND2DEGREES +% +% [degreelist, success, errormessage] = shorthand2degrees(shorthand, {verbose}) +% +% Converts a shorthand string to a degree-list +% +% Success = 1 if shorthand is recognised, 0 otherwise. +% +% If optional argument 'verbose' is 1, function prints any errormessage to +% the screen. +% +% returns: degreelist (string) +% success (boolean) +% errormessage (string) +% +% Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA + +% +function [degreelist, success, errormessage] = shorthand2degrees(shorthand, verbose) + +%set verbose to default to 0;; +if nargin <2 + verbose = 0; +end + +degreelist = ''; +errormessage = ''; + +success = 1; + +switch(shorthand) + + % Empty: + case '' + degreelist = ''; %empty degreelist + + % Triad chords: + case 'maj' + degreelist = '1,3,5'; % major + case 'min' + degreelist = '1,b3,5'; % minor + case 'dim' + degreelist = '1,b3,b5'; % diminished + case 'aug' + degreelist = '1,3,#5'; % augmented + + % Seventh chords: + case 'maj7' + degreelist = '1,3,5,7'; %major seventh + case 'min7' + degreelist = '1,b3,5,b7'; % minor seventh + case '7' + degreelist = '1,3,5,b7'; % seventh + case 'dim7' + degreelist = '1,b3,b5,bb7'; % diminished seventh + case 'hdim7' + degreelist = '1,b3,b5,b7'; % half diminished seventh + case 'minmaj7' + degreelist = '1,b3,5,7'; % minor (major seventh) + + % Sixth Chords: + case 'maj6' + degreelist = '1,3,5,6'; % major sixth + case 'min6' + degreelist = '1,b3,5,6'; % minor sixth + + % Ninth chords: + case 'maj9' + degreelist = '1,3,5,7,9'; % major ninth + case 'min9' + degreelist = '1,b3,5,b7,9'; % minor ninth + case '9' + degreelist = '1,3,5,b7,9'; % ninth + + case 'sus2' + degreelist = '1,2,5'; % suspended second + case 'sus4' + degreelist = '1,4,5'; % suspended fourth + case 'fullsus' + degreelist = '1,2,4,5'; % fully suspended chord + + + otherwise + errormessage = sprintf(['Error in shorthand2degrees: Unrecognised shorthand string "' shorthand '"\n']); + if verbose == 1 + fprintf(1,errormessage); + end + success = 0; + +end + + + + + \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/syntaxcheck.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,58 @@ +% +%SYNTAXCHECK Checks symbol for correct syntax. +% +% [success, errormessage] = syntaxcheck(chordsymbol, {verbose}) +% +% Checks symbol for correct syntax and checks for valid note, shorthand +% and interval values. +% +% calls getchordinfo +% +% Success = 1 if symbol has correct syntax, 0 otherwise. +% +% If optional argument 'verbose' is 1, function prints any errormessage to +% the screen. +% +% Returns: chordinfo (struct) +% success (boolean) true or false for correct syntax +% errormessage (string) +% +% See also: getchordinfo +% +% Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA + +% +function [success, errormessage] = syntaxcheck(chordsymbol, verbose) + +if nargin < 2 + verbose = 0; +end + +errormessage = ''; +chordinfo = []; + +success = 0; + +[root,short,degrees,bass, success, errormessage] = getchordinfo(chordsymbol,verbose); + +if (success ==0) && (verbose == 1) + fprintf(1,errormessage); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/01final/01_-_I_Saw_Her_Standing_There.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,61 @@ +0.0000000 2.6122670 N +2.6122670 11.4590700 E +11.4590700 12.9219270 A +12.9219270 17.4434738 E +17.4434738 20.4103620 B +20.4103620 21.9080490 E +21.9080490 23.3709070 E:7/3 +23.3709070 24.8569840 A +24.8569840 26.3430610 A:min/b3 +26.3430610 27.8407480 E +27.8407480 29.3500450 B +29.3500450 35.3059630 E +35.3059630 36.8036500 A +36.8036500 41.2631021 E +41.2631021 44.2456460 B +44.2456460 45.7201130 E +45.7201130 47.2061900 E:7/3 +47.2061900 48.6922670 A +48.6922670 50.1551240 A:min/b3 +50.1551240 51.6528110 E +51.6528110 53.1388880 B +53.1388880 56.1110430 E +56.1110430 65.1319950 A +65.1319950 68.1505890 B +68.1505890 71.1924030 A +71.1924030 74.1993870 E +74.1993870 75.6970740 A +75.6970740 80.2365750 E +80.2365750 83.2087300 B +83.2087300 86.2216932 E +86.2216932 87.7366210 A +87.7366210 89.2575280 A:min/b3 +89.2575280 90.7203850 E +90.7203850 92.1574527 B +92.1574527 104.1066890 E +104.1066890 107.1252830 B +107.1252830 110.1787070 E +110.1787070 113.1240869 A +113.1240869 114.6137180 E +114.6137180 116.0997950 B +116.0997950 118.9449608 E +118.9449608 128.0464620 A +128.0464620 131.0534460 B +131.0534460 134.0372100 A +134.0372100 137.0441950 E +137.0441950 138.4755242 A +138.4755242 143.0581630 E +143.0581630 146.0419270 B +146.0419270 147.5512240 E +147.5512240 149.0605210 E:7/3 +149.0605210 150.5117680 A +150.5117680 152.0210650 A:min/b3 +152.0210650 153.5303620 E +153.5303620 155.0628790 B +155.0628790 159.5327210 E +159.5327210 161.0652380 B +161.0652380 165.5815190 E +165.5815190 167.1140360 B +167.1140360 168.6465530 A +168.6465530 169.7374090 E +169.7374090 171.6871730 E:9
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/01final/02_-_Misery.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,45 @@ +0.0000000 0.4141790 N +0.4141790 4.3656554 F +4.3656554 6.6905920 G +6.6905920 8.7806060 C +8.7806060 9.7172890 A:min +9.7172890 10.6347610 G +10.6347610 12.3762580 C +12.3762580 14.1990240 F +14.1990240 15.9869610 C +15.9869610 19.5976640 F +19.5976640 21.3391600 G +21.3391600 23.1154870 C +23.1154870 24.9382530 A:min +24.9382530 26.7261900 C +26.7261900 28.5257360 F +28.5257360 30.2672330 C +30.2672330 33.9127660 F +33.9127660 35.6895300 G +35.6895300 39.2514740 C +39.2514740 42.8524480 A:min +42.8524480 46.4283210 C +46.4283210 50.0622440 A:min +50.0622440 53.6845570 G +53.6845570 55.4841040 C +55.4841040 57.2372100 F +57.2372100 59.0599770 C +59.0599770 62.6126300 F +62.6126300 64.4121760 G +64.4121760 68.0273920 C +68.0273920 71.5987520 A:min +71.5987520 75.2326750 C +75.2326750 78.8898180 A:min +78.8898180 82.5353510 G +82.5353510 84.3142050 C +84.3142050 86.1228340 F +86.1228340 87.8875510 C +87.8875510 91.5209280 F +91.5209280 93.3560620 G +93.3560620 95.1553960 C +95.1553960 97.0129930 A:min +97.0129930 98.8125390 C +98.8125390 100.6353060 A:min +100.6353060 102.4696820 C +102.4696820 104.2692290 A:min +104.2692290 106.1616550 C
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/01final/03_-_Anna_(Go_To_Him).lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,73 @@ +0.0000000 0.7084620 N +0.7084620 2.9678800 D +2.9678800 5.1890030 B:min +5.1890030 7.4292730 D +7.4292730 9.6695440 B:min +9.6695440 11.8523710 D +11.8523710 14.0351990 B:min +14.0351990 16.2754690 D +16.2754690 18.5348880 B:min +18.5348880 20.6411250 D +20.6411250 22.8622478 B:min +22.8622478 23.6472998 D +23.6472998 25.0067802 B:min +25.0067802 25.8875700 E:min +25.8875700 27.2661980 A +27.2661980 29.4107310 D +29.4107310 31.6127060 B:min +31.6127060 33.8338290 D +33.8338290 36.0358040 B:min +36.0358040 38.2760750 D +38.2760750 40.4206070 B:min +40.4206070 42.6417300 D +42.6417300 44.8820010 B:min +44.8820010 47.0265330 D +47.0265330 49.0561800 B:min +49.0561800 49.9369700 D +49.9369700 51.3155980 B:min +51.3155980 52.1963880 E:min +52.1963880 53.5941640 A +53.5941640 56.7535200 D +56.7535200 57.2513570 D/2 +57.2513570 57.7109000 D/3 +57.7109000 66.4805060 G +66.4805060 73.9097790 D +73.9097790 74.3884690 D/2 +74.3884690 74.9437500 D/3 +74.9437500 83.5027320 G +83.5027320 87.7535020 E:7 +87.7535020 91.9659770 A +91.9659770 94.1679520 D +94.1679520 96.2550420 B:min +96.2550420 98.5144600 D +98.5144600 100.5632540 B:min +100.5632540 101.5589300 D +101.5589300 102.9950010 B:min +102.9950010 103.7992000 E:min +103.7992000 105.1969760 A +105.1969760 108.3563320 D +108.3563320 108.9882030 D/2 +108.9882030 109.5626310 D/3 +109.5626310 118.2556470 G +118.2556470 125.7806580 D +125.7806580 126.3359387 D/2 +126.3359387 126.8720718 D/3 +126.8720718 131.1611370 G +131.1611370 135.5459400 G:min +135.5459400 139.7967090 E:7 +139.7967090 144.2006600 A +144.2006600 146.2111590 D +146.2111590 148.4514300 B:min +148.4514300 150.6151100 D +150.6151100 152.8362330 B:min +152.8362330 153.6595800 D +153.6595800 155.0382080 B:min +155.0382080 155.8615550 E:min +155.8615550 157.2401830 A +157.2401830 159.4804540 D +159.4804540 161.6249860 B:min +161.6249860 163.9418470 D +163.9418470 165.9906420 B:min +165.9906420 168.1734690 D +168.1734690 170.5286250 B:min +170.5286250 174.5304760 D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/01final/04_-_Chains.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,42 @@ +0.000000 0.459543 N +0.459543 7.639897 Bb +7.639897 14.977403 Bb +14.977403 18.646156 Eb:7 +18.646156 22.338129 Bb +22.338129 24.184115 F:9 +24.184115 26.041712 Eb:9 +26.041712 27.910918 Bb +27.910918 29.780124 F +29.780124 37.106020 Bb +37.106020 40.797993 Eb:7 +40.797993 44.466746 Bb +44.466746 46.324342 F:9 +46.324342 48.170328 Eb:9 +48.170328 49.981485 Bb +49.981485 51.839081 Bb:7 +51.839081 55.589104 Eb +55.589104 59.292687 Bb +59.292687 63.042709 Eb +63.042709 66.734682 F +66.734682 74.118628 Bb +74.118628 77.857040 Eb:7 +77.857040 81.525793 Bb +81.525793 83.395000 F:9 +83.395000 85.252596 Eb:9 +85.252596 87.110192 Bb +87.110192 88.944569 Bb:7 +88.944569 92.659761 Eb +92.659761 96.363344 Bb +96.363344 100.113367 Eb +100.113367 103.793730 F +103.793730 111.224115 Bb +111.224115 114.927698 Eb:7 +114.927698 118.642891 Bb +118.642891 120.512097 F:9 +120.512097 122.369693 Eb:9 +122.369693 124.536063 Bb +124.536063 126.050056 F +126.050056 137.230929 Bb +137.230929 139.011657 Eb +139.011657 139.773049 Eb:min +139.773049 141.769966 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/01final/05_-_Boys.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,51 @@ +0.0000000 1.0731190 N +1.0731190 2.5127560 B +2.5127560 4.1033230 A +4.1033230 5.6822800 E +5.6822800 7.2844570 B +7.2844570 13.8905340 E +13.8905340 17.1064980 A +17.1064980 20.4385610 E +20.4385610 22.0987880 B +22.0987880 23.7590150 A +23.7590150 25.5054710 E +25.5054710 27.0678580 B +27.0678580 33.6855460 E +33.6855460 36.9479490 A +36.9479490 40.2567930 E +40.2567930 41.9054090 B +41.9054090 43.6004660 A +43.6004660 45.3032481 E +45.3032481 46.8860900 B +46.8860900 53.6547070 E +53.6547070 57.0099900 A +57.0099900 60.3768830 E +60.3768830 62.0138900 B +62.0138900 63.7205570 A +63.7205570 65.5622730 E +65.5622730 67.0526200 B +67.0526200 73.6006480 E +73.6006480 76.9094910 A +76.9094910 80.2647750 E +80.2647750 82.0062710 B +82.0062710 83.6664980 A +83.6664980 85.4544340 E +85.4544340 87.0566110 B +87.0566110 93.9297180 E +93.9297180 97.3082210 A +97.3082210 100.7099450 E +100.7099450 102.4398310 B +102.4398310 104.1464980 A +104.1464980 105.8996040 E +105.8996040 107.5830510 B +107.5830510 114.3981080 E +114.3981080 117.8230510 A +117.8230510 121.2015550 E +121.2015550 122.9082210 B +122.9082210 124.6148880 A +124.6148880 126.6814480 E +126.6814480 128.0862710 B +128.0862710 134.9245480 E +134.9245480 138.3378810 A +138.3378810 141.7163850 E +141.7163850 143.4930510 B
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/01final/06_-_Ask_Me_Why.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,96 @@ +0.0000000 0.4786900 N +0.4786900 3.0253230 E +3.0253230 3.5139090 E:maj7 +3.5139090 3.9666980 F#:min7 +3.9666980 5.7081940 G#:min7 +5.7081940 7.0201220 F#:min7 +7.0201220 7.4845210 B:7 +7.4845210 10.0619360 E +10.0619360 10.1484390 E:maj7 +10.5031150 11.0023440 F#:min7 +11.0023440 12.7438410 G#:min7 +12.7438410 14.0325480 F#:min7 +14.0325480 14.5085570 B:7 +14.5085570 16.2152240 E +16.2152240 17.9799400 G# +17.9799400 21.4629340 C#:min +21.4629340 23.2392600 A +23.2392600 25.0504170 F#:9 +25.0504170 25.9792150 B +25.9792150 26.4436140 E:maj7 +26.4436140 26.9312330 F#:min7 +26.9312330 28.7075590 G#:min7 +28.7075590 29.9993710 F#:min7 +29.9993710 30.4838860 B:7 +30.4838860 33.1018750 E +33.1018750 33.5605300 E:maj7 +33.5605300 34.0249290 F#:min7 +34.0249290 35.7896460 G#:min7 +35.7896460 37.5311420 F#:min7 +37.5311420 39.3074690 E +39.3074690 41.0837950 G# +41.0837950 44.6364480 C#:min +44.6364480 46.4165250 A +46.4165250 48.2007110 E +48.2007110 50.0466980 E:aug +50.0466980 51.7765840 A +51.7765840 53.5645210 B +53.5645210 55.3524570 E +55.3524570 57.1520040 E:aug +57.1520040 58.9167200 A +58.9167200 60.6930470 B +60.6930470 62.8591610 E +62.8591610 63.3285120 B +63.3285120 63.7696910 E:maj7 +63.7696910 64.2224800 F#:min7 +64.2224800 65.9175370 G#:min7 +65.9175370 67.7054730 A +67.7054730 69.4818000 G#:min7 +69.4818000 71.3045660 A:maj7 +71.3045660 74.0205320 E +74.0205320 74.4624800 E:maj7 +74.4624800 74.9733190 F#:min7 +74.9733190 76.7496460 G#:min7 +76.7496460 78.0964030 F#:min7 +78.0964030 78.5724120 B:7 +78.5724120 81.2659270 E +81.2659270 81.7187160 E:maj7 +81.7187160 82.2179450 F#:min7 +82.2179450 83.9710510 G#:min7 +83.9710510 85.3410290 F#:min7 +85.3410290 85.7822080 B:7 +85.7822080 87.5817540 E +87.5817540 89.3348610 G# +89.3348610 91.1576270 C#:min +91.1576270 92.9107340 C#:min +92.9107340 94.7218900 A:min +94.7218900 96.4982170 F#:9 +96.4982170 97.4453960 B +97.4453960 97.9494640 E:maj7 +97.9494640 98.4254730 F#:min7 +98.4254730 100.2018000 G#:min7 +100.2018000 101.9665160 A +101.9665160 103.8008930 G#:min7 +103.8008930 105.5656090 A +105.5656090 107.3535460 E +107.3535460 109.1414820 E:aug +109.1414820 110.9178090 A +110.9178090 112.6941350 B +112.6941350 114.4820720 E +114.4820720 116.2467880 E:aug +116.2467880 118.0231150 A +118.0231150 119.5961310 B +119.5961310 121.9405290 E +121.9405290 122.4232960 B +122.4232960 122.8644760 E:maj7 +122.8644760 123.2940450 F#:min7 +123.2940450 125.0471510 G#:min7 +125.0471510 126.8234780 A +126.8234780 128.6114140 G#:min7 +128.6114140 130.3943760 A:maj7 +130.3943760 132.2105070 E +132.2105070 133.9984440 A:maj7/5 +133.9984440 135.7747700 E +135.7747700 137.5859270 A:maj7/5 +137.5859270 139.2432030 B:maj6 +139.2432030 143.9829630 G#:min
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/01final/07_-_Please_Please_Me.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,78 @@ +0.0000000 0.4443630 N +0.4443630 0.8665460 B +0.8665460 7.6467720 E +7.6467720 11.0136660 E +11.0136660 11.8728040 A +11.8728040 12.9177020 E +12.9177020 13.3937110 G +13.3937110 13.8465000 A +13.8465000 14.4502190 B +14.4502190 17.8867720 E +17.8867720 18.7459110 A +18.7459110 21.3813760 E +21.3813760 23.0880420 A +23.0880420 24.8063190 F#:min +24.8063190 26.5129860 C#:min/5 +26.5129860 28.2080420 A +28.2080420 29.9030990 E +29.9030990 30.7738470 A +30.7738470 31.6445960 B +31.6445960 33.3860920 E +33.3860920 34.2568400 A +34.2568400 35.1508090 B +35.1508090 38.5293120 E +38.5293120 39.3768400 A +39.3768400 40.4797880 E +40.4797880 40.9321490 G +40.9321490 41.4341470 A +41.4341470 42.0123050 B +42.0123050 45.4488590 E +45.4488590 46.3196070 A +46.3196070 48.8970220 E +48.8970220 50.6152980 A +50.6152980 52.2871350 F#:min +52.2871350 54.0170220 C#:min/5 +54.0170220 55.7352980 A +55.7352980 57.4651850 E +57.4651850 58.3591530 A +58.3591530 59.2299020 B +59.2299020 60.9713980 E +60.9713980 62.6316250 E +62.6316250 64.4079520 A +64.4079520 66.1378380 B +66.1378380 69.6092210 E +69.6092210 71.3274980 A +71.3274980 73.0806050 B +73.0806050 74.8221010 E +74.8221010 75.7276790 A +75.7276790 76.6332580 B +76.6332580 78.3863640 E +78.3863640 79.2571130 A +79.2571130 80.1394710 B +80.1394710 83.6340740 E +83.6340740 84.4583820 A +84.4583820 85.3407410 E +85.3407410 85.9060250 G +85.9060250 86.4981260 A +86.4981260 87.1054570 B +87.1054570 90.5536200 E +90.5536200 91.4243690 A +91.4243690 94.0598340 E +94.0598340 95.8013300 A +95.8013300 97.5312170 F#:min +97.5312170 99.2262740 C#:min/5 +99.2262740 100.9445500 A +100.9445500 102.6976570 E +102.6976570 103.6148450 A +103.6148450 104.4739830 B +104.4739830 106.2154800 E +106.2154800 107.0978380 A +107.0978380 107.9801960 B +107.9801960 109.7216930 E +109.7216930 110.5692210 A +110.5692210 111.4748000 B +111.4748000 112.3919880 E +112.3919880 113.2627360 G +113.2627360 114.1218740 C +114.1218740 114.9926230 B +114.9926230 119.3983060 E
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/01final/08_-_Love_Me_Do.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,72 @@ +0.0000000 0.4659520 N +0.4659520 2.1032320 G +2.1032320 3.7054090 C +3.7054090 5.2727560 G +5.2727560 6.8865430 C +6.8865430 8.5003300 G +8.5003300 10.0908970 C +10.0908970 13.3533000 G +13.3533000 14.9903070 G +14.9903070 16.6157040 C +16.6157040 18.2411010 G +18.2411010 19.8664970 C +19.8664970 21.4802840 G +21.4802840 23.1172910 C +23.1172910 26.3913050 C +26.3913050 28.2101550 C +28.2101550 29.7117580 G +29.7117580 31.3603750 C +31.3603750 32.9393320 G +32.9393320 34.5995590 C +34.5995590 36.3062250 G +36.3062250 37.9432320 C +37.9432320 39.5570190 G +39.5570190 41.2056360 C +41.2056360 42.8310330 G +42.8310330 44.4448190 C +44.4448190 49.4022800 C +49.4022800 51.0276770 G +51.0276770 52.7111230 C +52.7111230 54.3249100 G +54.3249100 55.9851370 G +55.9851370 59.2243210 D +59.2243210 60.8613270 C +60.8613270 62.4867240 G +62.4867240 65.7375180 D +65.7375180 67.3513050 C +67.3513050 69.1522410 G +69.1522410 70.7530280 G +70.7530280 72.3319850 C +72.3319850 73.9689920 G +73.9689920 75.5827790 C +75.5827790 77.1849560 G +77.1849560 78.7987420 C +78.7987420 83.8790750 C +83.8790750 85.4164290 G +85.4164290 87.0534360 C +87.0534360 88.6672230 G +88.6672230 90.2694000 G +90.2694000 93.4737540 D +93.4737540 95.1223700 C +95.1223700 96.7477670 G +96.7477670 99.9521210 D +99.9521210 101.5426880 C +101.5426880 103.1680850 G +103.1680850 108.9051370 G +108.9051370 109.6348420 D +109.6348420 111.2834590 G +111.2834590 112.8740260 C +112.8740260 114.4762030 G +114.4762030 116.0783800 C +116.0783800 117.7037760 G +117.7037760 119.2943430 C +119.2943430 124.3115080 G +124.3115080 125.8307610 G +125.8307610 127.4329370 C +127.4329370 129.0235040 G +129.0235040 130.6140710 C +130.6140710 132.2162480 G +132.2162480 133.8532550 C +133.8532550 135.4089920 G +135.4089920 137.0459990 C +137.0459990 138.6597850 G
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/01final/09_-_P._S._I_Love_You.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,77 @@ +0.000000 0.465069 N +0.465069 1.397949 G +1.397949 2.338358 C#:7 +2.338358 4.114683 D +4.114683 4.973822 G +4.973822 5.844570 C#:7 +5.844570 7.644118 D +7.644118 8.549696 G +8.549696 9.408834 C#:7 +9.408834 11.219991 D +11.219991 12.113959 D +12.113959 12.996317 A +12.996317 14.772644 D +14.772644 16.560579 D +16.560579 18.302076 E:min +18.302076 20.066792 D +20.066792 21.866339 A +21.866339 23.689106 B:min +23.689106 25.453822 A +25.453822 28.147337 Bb +28.147337 28.611736 Bb +28.611736 29.052915 C +29.052915 32.582349 D +32.582349 34.405114 D +34.405114 36.146611 E:min +36.146611 37.934548 D +37.934548 39.757314 A +39.757314 41.545251 B:min +41.545251 43.309967 A +43.309967 46.003482 Bb +46.003482 46.491101 Bb +46.491101 46.932280 C +46.932280 50.461714 D +50.461714 52.238039 G +52.238039 54.037586 D +54.037586 55.860352 G +55.860352 57.636679 D +57.636679 59.436225 G +59.436225 61.200942 D +61.200942 62.118130 D +62.118130 63.046928 A +63.046928 64.788426 D +64.788426 66.646022 D +66.646022 68.375908 E:min +68.375908 70.163844 D +70.163844 71.998221 A +71.998221 73.751328 B:min +73.751328 75.527654 A +75.527654 78.221170 Bb +78.221170 78.662348 Bb +78.662348 79.126747 C +79.126747 82.656181 D +82.656181 84.455727 G +84.455727 86.255274 D +86.255274 88.031600 G +88.031600 89.796317 D +89.796317 91.572643 G +91.572643 93.348969 D +93.348969 94.225395 D +94.225395 95.148516 A +95.148516 96.971283 D +96.971283 98.782439 D +98.782439 100.570375 E:min +100.570375 102.311872 D +102.311872 104.157859 A +104.157859 105.945796 B:min +105.945796 107.687292 A +107.687292 110.427246 Bb +110.427246 110.868426 Bb +110.868426 111.332825 C +111.332825 113.985730 D +113.985730 114.479129 Bb +114.479129 114.908698 C +114.908698 117.590603 D +117.590603 118.066611 Bb +118.066611 118.542620 C +118.542620 121.759660 D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/01final/10_-_Baby_It's_You.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,55 @@ +0.0000000 0.4569370 N +0.4569370 2.6513070 G +2.6513070 4.7527130 E:min +4.7527130 6.9005590 G +6.9005590 9.0019650 E:min +9.0019650 11.1101060 G +11.1101060 13.3360430 E:min +13.3360430 17.5817380 C +17.5817380 21.8542100 G +21.8542100 26.1150710 C +26.1150710 30.2714430 G +30.2714430 34.5787450 E:min +34.5787450 38.7931670 A:min +38.7931670 40.9410120 G +40.9410120 43.1004680 E:min +43.1004680 45.2018740 C +45.2018740 49.4859560 D +49.4859560 51.6570210 G +51.6570210 53.7468170 E:min +53.7468170 55.9294930 G +55.9294930 58.0773390 E:min +58.0773390 62.3498110 C +62.3498110 66.6455020 G +66.6455020 70.8831440 C +70.8831440 75.1323960 G +75.1323960 79.4164770 E:min +79.4164770 83.5960690 A:min +83.5960690 85.7439150 G +85.7439150 87.8685410 E:min +87.8685410 89.9467270 C +89.9467270 94.3120780 D +94.3120780 96.5411940 G +96.5411940 98.6309900 E:min +98.6309900 100.7788360 G +100.7788360 102.9266810 E:min +102.9266810 105.0977470 C +105.0977470 107.1178830 D +107.1178830 109.2657290 G +109.2657290 111.3090850 G +111.3090850 113.4569310 C +113.4569310 115.5467270 D +115.5467270 119.8191980 G +119.8191980 124.1265000 E:min +124.1265000 128.4105820 A:min +128.4105820 130.6048670 G +130.6048670 132.7527130 E:min +132.7527130 134.8541190 C +134.8541190 137.0484050 D +137.0484050 139.2659100 D +139.2659100 141.3789260 G +141.3789260 143.5499920 E:min +143.5499920 145.7326680 G +145.7326680 147.9037340 E:min +147.9037340 150.0515790 G +150.0515790 152.2110350 E:min
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/01final/11_-_Do_You_Want_To_Know_A_Secret.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,113 @@ +0.0000000 0.4978379 N +0.4978379 3.5040128 E:min +3.5040128 4.5864640 A:min +4.5864640 6.9897300 E:min +6.9897300 9.9851040 G +9.9851040 11.2779430 F +11.2779430 14.9541740 B +14.9541740 15.9526330 E +15.9526330 16.4634720 G#:min +16.4634720 16.9394800 G:min +16.9394800 17.8566690 F#:min +17.8566690 18.8319080 B +18.8319080 19.7955360 E +19.7955360 20.3063750 G#:min +20.3063750 20.8056040 G:min +20.8056040 21.7692320 F#:min +21.7692320 22.7328600 B +22.7328600 23.6848770 E +23.6848770 24.1608870 G#:min +24.1608870 24.6368950 G:min +24.6368950 25.6237440 F#:min +25.6237440 26.5409330 C:min6 +26.5409330 27.4929500 E +27.4929500 27.9921790 G#:min +27.9921790 28.4797980 G:min +28.4797980 29.4550360 F#:min +29.4550360 30.4534940 B:7 +30.4534940 31.3939020 E +31.3939020 31.8699120 G#:min +31.8699120 32.3575300 G:min +32.3575300 33.3443780 F#:min +33.3443780 34.3080060 B +34.3080060 36.2468730 A +36.2468730 38.1392990 B +38.1392990 40.0085060 C#:min +40.0085060 41.0417940 F#:min +41.0417940 41.9822020 B +41.9822020 42.9342200 E +42.9342200 43.4450600 G#:min +43.4450600 43.9326780 G +43.9326780 44.8730870 F#:min +44.8730870 45.8367150 B +45.8367150 46.7539020 E +46.7539020 47.2415210 G#:min +47.2415210 47.7291410 G:min +47.7291410 48.7043780 F#:min +48.7043780 49.6563970 B +49.6563970 50.6664640 E +50.6664640 51.1192550 G#:min +51.1192550 51.6068730 G:min +51.6068730 52.5937220 F#:min +52.5937220 53.5225200 C:min6 +53.5225200 54.4977570 E +54.4977570 54.9853770 G#:min +54.9853770 55.4613850 G:min +55.4613850 56.4482340 F#:min +56.4482340 57.4118620 B +57.4118620 58.3522690 E +58.3522690 58.8282790 G#:min +58.8282790 59.3275080 G:min +59.3275080 60.3027470 F#:min +60.3027470 61.2895940 B +61.2895940 63.2400710 A +63.2400710 65.1441060 B +65.1441060 67.0713620 C#:min +67.0713620 68.0466010 F#:min +68.0466010 69.0218400 B +69.0218400 69.9845189 A +69.9845189 70.9723150 F#:min +70.9723150 71.9127240 C#:min +71.9127240 72.8415220 B:min +72.8415220 73.8167590 A +73.8167590 74.7803870 F#:min +74.7803870 75.7207960 C#:min +75.7207960 76.6728140 B:min +76.6728140 78.6232900 F#:min +78.6232900 80.4692760 B +80.4692760 81.4329040 E +81.4329040 81.9205230 G#:min +81.9205230 82.4081430 G:min +82.4081430 83.3601610 F#:min +83.3601610 84.3121800 B:7 +84.3121800 85.2641970 E +85.2641970 85.7169860 G#:min +85.7169860 86.2394360 G:min +86.2394360 87.2146730 F#:min +87.2146730 88.1666920 B +88.1666920 89.1070990 E +89.1070990 89.5831080 G#:min +89.5831080 90.0591190 G:min +90.0591190 91.0343560 F#:min +91.0343560 92.0095940 C:min6 +92.0095940 92.9383920 E +92.9383920 93.4144020 G#:min +93.4144020 93.8904100 G:min +93.8904100 94.8888680 F#:min +94.8888680 95.8408870 B +95.8408870 96.8277340 E +96.8277340 97.2805230 G#:min +97.2805230 97.7681430 G:min +97.7681430 98.7549910 F#:min +98.7549910 99.7534490 B +99.7534490 101.7155350 A +101.7155350 103.6195710 B +103.6195710 105.5468270 C#:min +105.5468270 106.5104550 F#:min +106.5104550 107.4973030 B +107.4973030 109.4245610 C#:min +109.4245610 110.3881890 F#:min +110.3881890 111.3402060 B +111.3402060 113.3022920 C#:min +113.3022920 114.2543100 F#:min +114.2543100 115.2643780 B
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/01final/12_-_A_Taste_Of_Honey.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,59 @@ +0.0000000 0.4403950 N +0.4403950 4.3679580 F#:min +4.3679580 6.1442840 A +6.1442840 8.1760300 E +8.1760300 15.0026970 F#:min +15.0026970 16.8138540 F#:min +16.8138540 18.5785700 F#:minmaj7 +18.5785700 20.3781170 F#:min7 +20.3781170 22.2124930 B +22.2124930 24.0120400 F#:min +24.0120400 25.8231960 F#:minmaj7 +25.8231960 27.6459620 F#:min7 +27.6459620 29.4803390 B +29.4803390 33.0678220 F#:min +33.0678220 34.8905880 A +34.8905880 36.6320850 E +36.6320850 37.8279130 F#:min +37.8279130 38.4780710 B:min +38.4780710 40.1963480 F#:min +40.1963480 41.1483660 F#:min +41.1483660 41.9726740 B:min +41.9726740 43.7025610 F#:min +43.7025610 45.9665070 A +45.9665070 48.1491820 B +48.1491820 50.3318580 F#:min +50.3318580 52.5261440 B +52.5261440 54.7088200 A +54.7088200 56.8914950 E +56.8914950 63.7878220 F#:min +63.7878220 65.5873680 F#:min +65.5873680 67.3869150 F#:minmaj7 +67.3869150 69.1864610 F#:min7 +69.1864610 70.9976180 B +70.9976180 72.8319940 F#:min +72.8319940 74.6315410 F#:minmaj7 +74.6315410 76.4426970 F#:min7 +76.4426970 78.2306340 B +78.2306340 81.8297270 F#:min +81.8297270 83.6524930 A +83.6524930 85.4056000 E +85.4056000 86.3808380 F#:min +86.3808380 87.2051460 B:min +87.2051460 88.9350330 F#:min +88.9350330 89.9567110 F#:min +89.9567110 90.7229690 B:min +90.7229690 92.4296360 F#:min +92.4296360 94.6819710 A +94.6819710 96.9343070 B +96.9343070 99.1634230 F#:min +99.1634230 101.3460980 B +101.3460980 106.1061890 A +106.1061890 111.3771190 E +111.3771190 112.4336270 F#:min +112.4336270 112.9444660 B:min +112.9444660 113.8899910 F#:min +113.8899910 114.5218630 B:min +114.5218630 115.4983910 F#:min +115.4983910 116.1952590 B:min +116.1952590 120.2469980 F#
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/01final/13_-_There's_A_Place.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,61 @@ +0.000000 0.492180 N +0.492180 0.942239 B +0.942239 2.521195 E:maj7 +2.521195 4.193032 A +4.193032 5.795209 E:maj7 +5.795209 8.291354 A +8.291354 9.162102 B +9.162102 10.752669 E +10.752669 12.447726 A +12.447726 14.073123 E +14.073123 15.791400 A +15.791400 17.463236 E +17.463236 19.123463 C#:min +19.123463 22.478746 B +22.478746 24.162193 G#:min +24.162193 25.892080 A +25.892080 27.529087 E:maj7 +27.529087 28.353395 A +28.353395 29.200923 F#:min +29.200923 33.438565 C#:min +33.438565 34.355753 B +34.355753 35.992760 E +35.992760 37.722647 A +37.722647 39.417703 E +39.417703 41.112760 A +41.112760 42.749767 E +42.749767 44.479654 C#:min +44.479654 47.881377 B +47.881377 51.294710 A +51.294710 54.626774 B +54.626774 56.321830 C#:min +56.321830 58.063327 F# +58.063327 59.735164 E +59.735164 61.511490 G# +61.511490 63.194937 C#:min +63.194937 64.936434 F# +64.936434 66.631490 E +66.631490 68.396207 G# +68.396207 70.996842 C#:min +70.996842 71.902420 B +71.902420 73.609087 E +73.609087 75.373803 A +75.373803 77.080470 E +77.080470 78.798746 A +78.798746 80.493803 E +80.493803 82.188860 C#:min +82.188860 85.671853 B +85.671853 87.343690 G#:min +87.343690 89.085186 A +89.085186 90.791853 E:maj7 +90.791853 91.767091 A +91.767091 92.533350 F#:min +92.533350 95.134376 C#:min +95.134376 96.004733 B +96.004733 97.676570 E +97.676570 99.360016 A +99.360016 101.008633 E +101.008633 102.657250 A +102.657250 104.282647 E +104.282647 105.931263 A +105.931263 107.626320 E
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/01final/14_-_Twist_And_Shout.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,115 @@ +0.000000 0.431661 N +0.431661 1.159256 N +1.159256 2.088054 D +2.088054 3.028463 G +3.028463 4.874449 A +4.874449 5.756807 D +5.756807 6.708825 G +6.708825 8.578032 A +8.578032 9.506830 D +9.506830 10.412408 G +10.412408 12.339664 A +12.339664 13.268463 D +13.268463 14.208871 G +14.208871 16.124518 A +16.124518 17.041705 D +17.041705 17.982113 G +17.982113 19.874541 A +19.874541 20.814948 D +20.814948 21.778576 G +21.778576 23.566513 A +23.566513 24.472091 D +24.472091 25.412500 G +25.412500 27.281706 A +27.281706 28.233723 D +28.233723 29.185741 G +29.185741 31.020119 A +31.020119 31.948916 D +31.948916 32.900934 G +32.900934 34.828190 A +34.828190 35.780209 D +35.780209 36.709007 G +36.709007 38.520163 A +38.520163 39.460572 D +39.460572 40.412591 G +40.412591 42.293406 A +42.293406 43.268645 D +43.268645 44.243882 G +44.243882 46.124699 A +46.124699 47.041888 D +47.041888 48.028735 G +48.028735 49.955991 A +49.955991 50.908009 D +50.908009 51.860028 G +51.860028 53.659574 A +53.659574 54.588372 D +54.588372 55.586830 G +55.586830 57.467647 A +57.467647 58.419664 D +58.419664 59.383292 G +59.383292 61.322159 A +61.322159 62.239348 D +62.239348 63.237806 G +63.237806 65.176671 A +65.176671 66.151910 D +66.151910 67.115538 G +67.115538 69.054405 A +69.054405 70.041252 D +70.041252 70.981661 G +70.981661 71.980119 A +71.980119 72.490957 G +72.490957 72.955356 A +72.955356 73.953814 D +73.953814 74.894222 G +74.894222 75.915900 A +75.915900 76.403520 G +76.403520 76.891138 A +76.891138 77.866377 D +77.866377 78.864835 G +78.864835 79.851682 A +79.851682 80.362521 G +80.362521 80.861751 A +80.861751 81.836990 D +81.836990 82.800618 G +82.800618 83.775855 A +83.775855 84.275084 G +84.275084 84.785923 A +84.785923 90.776269 A +90.776269 92.738363 A:7 +92.738363 96.558441 A +96.558441 97.568508 D +97.568508 98.520527 G +98.520527 100.436172 A +100.436172 101.399800 D +101.399800 102.351820 G +102.351820 104.221025 A +104.221025 105.161434 D +105.161434 106.090232 G +106.090232 108.052318 A +108.052318 109.027557 D +109.027557 109.956355 G +109.956355 111.813951 A +111.813951 112.765968 D +112.765968 113.717986 G +113.717986 115.622023 A +115.622023 116.597262 D +116.597262 117.526060 G +117.526060 119.499755 A +119.499755 120.416944 D +120.416944 121.380572 G +121.380572 123.354269 A +123.354269 124.329506 D +124.329506 125.316355 G +125.316355 127.243611 A +127.243611 128.218849 D +128.218849 129.170867 G +129.170867 131.132953 A +131.132953 132.108191 D +132.108191 133.060210 G +133.060210 134.964246 A +134.964246 135.939484 D +135.939484 136.891502 G +136.891502 138.830368 A +138.830368 146.701932 A +146.701932 147.657368 D +147.657368 152.003406 D:9
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/01final/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +/.DS_Store/1.1.1.1/Fri Sep 23 09:24:37 2005// +/01_-_I_Saw_Her_Standing_There.lab/1.2/Mon Jul 24 22:46:01 2006// +/02_-_Misery.lab/1.2/Tue Oct 17 21:52:04 2006// +/03_-_Anna_(Go_To_Him).lab/1.2/Tue Oct 17 21:52:04 2006// +/04_-_Chains.lab/1.1.1.1/Fri Sep 23 09:24:37 2005// +/05_-_Boys.lab/1.2/Thu Jul 27 22:01:34 2006// +/06_-_Ask_Me_Why.lab/1.2/Thu Jul 27 21:43:13 2006// +/07_-_Please_Please_Me.lab/1.2/Thu Jul 27 21:43:44 2006// +/08_-_Love_Me_Do.lab/1.4/Tue Oct 17 21:52:04 2006// +/09_-_P._S._I_Love_You.lab/1.1.1.1/Fri Sep 23 09:24:37 2005// +/10_-_Baby_It's_You.lab/1.2/Thu Jul 27 21:44:53 2006// +/11_-_Do_You_Want_To_Know_A_Secret.lab/1.3/Tue Oct 17 21:52:04 2006// +/12_-_A_Taste_Of_Honey.lab/1.2/Thu Jul 27 21:45:39 2006// +/13_-_There's_A_Place.lab/1.1.1.1/Fri Sep 23 09:24:37 2005// +/14_-_Twist_And_Shout.lab/1.1.1.1/Fri Sep 23 09:24:37 2005// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/01final/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +chrish/transcriptions/01final
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/01final/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:matthiasm@dspmac1.elec.qmul.ac.uk:/cvsroot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/02final/01_-_It_Won't_Be_Long.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,70 @@ +0.0000000 1.0722662 N +1.0722662 1.7232850 F# +1.7232850 5.1667190 C#:min +5.1667190 8.7077620 E +8.7077620 10.4608690 C#:min +10.4608690 11.3548370 A +11.3548370 12.2139750 G:dim7 +12.2139750 13.9670820 E +13.9670820 15.7201890 E +15.7201890 17.4384650 E +17.4384650 19.2031820 C +19.2031820 20.9911180 E +20.9911180 22.7790550 E +22.7790550 24.5669910 C +24.5669910 26.3200980 E +26.3200980 28.1080340 E +28.1080340 31.6374680 C#:min +31.6374680 35.2481700 E +35.2481700 37.0244970 C#:min +37.0244970 37.9300750 A +37.9300750 38.7892140 G:dim7 +38.7892140 42.3650870 E +42.3650870 44.1530230 E +44.1530230 45.9177400 B:aug/3 +45.9177400 47.7521160 B:min/b3 +47.7521160 49.5516630 C#:7 +49.5516630 51.3512090 A +51.3512090 53.1623660 B +53.1623660 54.9386920 F#:min7 +54.9386920 56.6337490 B +56.6337490 58.4216850 E +58.4216850 60.2212320 C +60.2212320 62.0323880 E +62.0323880 63.8203250 E +63.8203250 65.6663110 C +65.6663110 67.4426380 E +67.4426380 69.2537940 E +69.2537940 72.8644970 C#:min +72.8644970 76.4635900 E +76.4635900 78.2515270 C#:min +78.2515270 79.1571050 A +79.1571050 80.0626830 G:dim7 +80.0626830 81.8506190 E +81.8506190 83.6849960 E +83.6849960 85.4613220 E +85.4613220 87.2724790 B:aug/3 +87.2724790 89.1300750 B:min/b3 +89.1300750 90.9876720 C#:7 +90.9876720 92.7523880 A +92.7523880 94.5751550 B +94.5751550 96.3747010 F#:min7 +96.3747010 98.0813680 B +98.0813680 99.8809140 E +99.8809140 101.6688510 C +101.6688510 103.4800070 E +103.4800070 105.2911640 E +105.2911640 107.1255400 C +107.1255400 108.9250870 E +108.9250870 110.7478530 E +110.7478530 114.3701660 C#:min +114.3701660 117.9692590 E +117.9692590 119.8500750 C#:min +119.8500750 120.8980171 A +120.8980171 122.9468115 G:dim7 +122.9468115 124.0296670 E +124.0296670 124.7494860 G:7 +124.7494860 125.7015040 F#:7 +125.7015040 126.6651320 F:7 +126.6651320 130.0122800 E:maj7 +130.0122800 133.7077690 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/02final/02_-_All_I've_Got_To_Do.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,45 @@ +0.0000000 0.4978379 N +0.4978379 3.6571650 E:aug(9,11) +3.6571650 7.7554870 C#:min +7.7554870 11.9234690 E +11.9234690 15.9521310 C#:min +15.9521310 19.9459630 F#:min +19.9459630 21.9660990 A:min +21.9660990 25.9715410 E +25.9715410 30.0466430 C#:min +30.0466430 34.1333560 E +34.1333560 38.1504080 C#:min +38.1504080 42.1326300 F#:min +42.1326300 44.1411560 A:min +44.1411560 48.0885480 E +48.0885480 52.0011110 A +52.0011110 55.9252830 C#:min +55.9252830 57.8989790 A +57.8989790 58.5916903 E +58.5916903 59.9075050 C#:min +59.9075050 61.8812010 A +61.8812010 62.6701314 E +62.6701314 63.8432870 E/5 +63.8432870 65.8866430 E +65.8866430 70.0081850 C#:min +70.0081850 74.1181170 E +74.1181170 78.1467800 C#:min +78.1467800 82.1754420 F#:min +82.1754420 84.1491380 A:min +84.1491380 88.1545800 E +88.1545800 92.0787520 A +92.0787520 96.0261450 C#:min +96.0261450 97.9998410 A +97.9998410 98.7250833 E +98.7250833 99.9619270 C#:min +99.9619270 101.9124030 A +101.9124030 102.7018820 E +102.7018820 103.8977090 E/5 +103.8977090 105.8481850 A +105.8481850 106.6260540 E +106.6260540 107.8451020 E/5 +107.8451020 109.8768480 E +109.8768480 113.9403400 C#:min +113.9403400 118.1083210 E +118.1083210 120.3044411 C#:min +120.3044411 124.4594728 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/02final/03_-_All_My_Loving.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,74 @@ +0.0000000 0.4786903 N +0.4786903 1.3652490 C#:min +1.3652490 2.7932770 F#:min +2.7932770 4.2793530 B +4.2793530 5.8002610 E +5.8002610 7.3443870 C#:min +7.3443870 8.8536840 A +8.8536840 10.3862020 F#:min +10.3862020 11.9535480 D +11.9535480 13.4860660 B +13.4860660 14.9721420 F#:min +14.9721420 16.5278790 B +16.5278790 18.0836160 E +18.0836160 19.6161330 C#:min +19.6161330 21.1486500 A +21.1486500 22.6927770 B +22.6927770 25.8158610 E +25.8158610 27.3483780 F#:min +27.3483780 28.8808950 B +28.8808950 30.3669720 E +30.3669720 31.9227090 C#:min +31.9227090 33.4436170 A +33.4436170 35.0109640 F#:min +35.0109640 36.5667010 D +36.5667010 38.1224380 B +38.1224380 39.6549550 F#:min +39.6549550 41.1990820 B +41.1990820 42.7315990 E +42.7315990 44.2989460 C#:min +44.2989460 45.8546830 A +45.8546830 47.3639800 B +47.3639800 48.9429370 E +48.9429370 50.5218940 E +50.5218940 52.0544110 C#:min +52.0544110 53.5753180 C:aug +53.5753180 56.6751820 E +56.6751820 58.1844790 C#:min +58.1844790 59.7169960 C:aug +59.7169960 61.2959530 E +61.2959530 62.8284700 E +62.8284700 65.8702830 A:7 +65.8702830 68.9004870 E +68.9004870 70.4330050 F#:min +70.4330050 71.9306920 B +71.9306920 73.4980390 E +73.4980390 75.1466560 E +75.1466560 76.5746830 F#:min +76.5746830 78.1420300 B +78.1420300 79.6629360 E +79.6629360 81.2535040 C#:min +81.2535040 82.7744100 A +82.7744100 84.3301480 F#:min +84.3301480 85.8974950 D +85.8974950 87.4416220 B +87.4416220 88.9973580 F#:min +88.9973580 90.5647040 B +90.5647040 92.1320510 E +92.1320510 93.6645680 C#:min +93.6645680 95.2203060 A +95.2203060 96.7412120 B +96.7412120 98.3085590 E +98.3085590 99.8526860 E +99.8526860 101.3968140 C#:min +101.3968140 102.9641600 C:aug +102.9641600 106.0408050 E +106.0408050 107.5965410 C#:min +107.5965410 109.1754980 C:aug +109.1754980 112.2869720 E +112.2869720 115.3636160 C#:min +115.3636160 118.4518700 E +118.4518700 121.5052940 C#:min +121.5052940 123.0958610 E +123.0958610 125.8781010 E +125.8781010 129.3246710 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/02final/04_-_Don't_Bother_Me.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,86 @@ +0.0000000 0.5361331 N +0.5361331 3.3957300 D +3.3957300 6.1937340 E:min +6.1937340 7.5985420 B:min +7.5985420 9.0381790 A +9.0381790 10.4545960 G +10.4545960 11.8710130 E +11.8710130 13.3222600 B:min +13.3222600 14.6922380 A +14.6922380 17.5482920 G +17.5482920 18.9647090 E:min +18.9647090 20.3811270 A +20.3811270 21.8207640 E:min +21.8207640 23.2023510 E:min +23.2023510 24.6187680 B:min +24.6187680 26.0467960 A +26.0467960 27.4283830 G +27.4283830 28.8564100 E +28.8564100 30.2379970 B:min +30.2379970 31.6428050 A +31.6428050 33.0360020 G +33.0360020 34.4872490 G +34.4872490 35.8804460 E:min +35.8804460 37.2620340 A +37.2620340 38.6552310 E:min +38.6552310 40.0832580 E:min +40.0832580 42.8580430 D +42.8580430 45.6212170 E:min +45.6212170 48.4076120 D +48.4076120 51.2172260 E:min +51.2172260 54.0268410 B:min +54.0268410 56.8480650 A:min +56.8480650 59.6112400 C +59.6112400 61.0624870 E:min +61.0624870 62.4440740 E:min +62.4440740 63.8604920 B:min +63.8604920 65.2652990 A +65.2652990 66.6352760 G +66.6352760 68.0516940 E +68.0516940 69.4332810 B:min +69.4332810 70.8845280 A +70.8845280 72.2428950 G +72.2428950 73.7173620 G +73.7173620 75.1337800 E:min +75.1337800 76.5385870 A +76.5385870 77.9201740 E:min +77.9201740 79.2785420 E:min +79.2785420 80.6485190 B:min +80.6485190 82.0417160 A +82.0417160 83.4349130 G +83.4349130 84.8281110 E +84.8281110 86.2096980 B:min +86.2096980 87.6261150 A +87.6261150 89.0077030 G +89.0077030 90.4357300 G +90.4357300 91.8405370 E:min +91.8405370 93.2685640 A +93.2685640 94.6849810 E:min +94.6849810 96.0665690 E:min +96.0665690 98.8529630 D +98.8529630 101.6625780 E:min +101.6625780 104.3909230 D +104.3909230 107.2121470 E:min +107.2121470 110.0217620 B:min +110.0217620 112.8545960 A:min +112.8545960 115.6177710 C +115.6177710 118.4157750 E:min +118.4157750 119.8321920 B:min +119.8321920 121.2253900 A +121.2253900 122.6418070 G +122.6418070 124.0698340 E +124.0698340 125.4282010 B:min +125.4282010 126.8562290 A +126.8562290 128.2726460 G +128.2726460 129.6890630 G +129.6890630 131.1054800 E:min +131.1054800 132.5102880 A +132.5102880 133.7269166 E:min +133.7269166 135.3663420 A +135.3663420 136.4841726 E:min +136.4841726 138.1875670 A +138.1875670 139.3601740 E:min +139.3601740 140.9043010 A +140.9043010 142.1349590 E:min +142.1349590 144.1815123 A +144.1815123 149.0833008 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/02final/05_-_Little_Child.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,67 @@ +0.0000000 0.4978379 N +0.4978379 2.2221070 E +2.2221070 3.6385240 A +3.6385240 6.7267780 E:9 +6.7267780 9.9195220 E +9.9195220 10.7554400 E +10.7554400 11.5216990 A +11.5216990 13.1470960 E +13.1470960 14.7376630 B +14.7376630 16.3050100 A +16.3050100 17.9420160 F#:9 +17.9420160 19.5325830 B +19.5325830 23.5264150 E +23.5264150 24.3042840 A +24.3042840 25.8600210 E +25.8600210 27.4854180 B +27.4854180 29.0643750 A +29.0643750 29.8886830 F#:9 +29.8886830 30.6549410 B +30.6549410 32.2338980 E +32.2338980 33.8360750 E +33.8360750 35.3918120 B +35.3918120 36.9591590 E +36.9591590 38.5148960 E +38.5148960 40.1054630 F# +40.1054630 41.7308600 B +41.7308600 45.7130820 E +45.7130820 46.4909510 A +46.4909510 48.0815170 E +48.0815170 49.6720840 B +49.6720840 51.2626510 A +51.2626510 52.0866170 F#:9 +52.0866170 52.8416080 B +52.8416080 54.3160750 E +54.3160750 60.6319030 E +60.6319030 63.6969370 A +63.6969370 66.8548510 E +66.8548510 68.3873680 B +68.3873680 69.9895450 A +69.9895450 71.5220620 F# +71.5220620 73.0894090 B +73.0894090 74.6616500 E +74.6616500 76.2821520 B +76.2821520 79.4516760 E +79.4516760 81.0886830 F# +81.0886830 82.6792500 B +82.6792500 86.6614720 E +86.6614720 87.4741700 A +87.4741700 89.0531270 E +89.0531270 90.6669140 B +90.6669140 92.2110410 A +92.2110410 92.9667670 F# +92.9667670 93.7667780 B +93.7667780 94.5368710 E +94.5368710 95.3109050 C# +95.3109050 96.1261230 F# +96.1261230 96.9014720 B +96.9014720 97.7345220 E +97.7345220 98.5036490 C# +98.5036490 99.2663310 F# +99.2663310 100.0942160 B +100.0942160 100.8364350 E +100.8364350 101.6847830 C# +101.6847830 102.5207010 F# +102.5207010 103.2637400 B +103.2637400 104.4170380 E +104.4170380 107.6529850 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/02final/06_-_Till_There_Was_You.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,93 @@ +0.0000000 0.4778134 N +0.4778134 1.4716650 F +1.4716650 2.4933430 F#:dim +2.4933430 3.4453610 G:min +3.4453610 4.4322090 C:9 +4.4322090 5.4306670 F +5.4306670 6.4175150 F#:dim +6.4175150 7.3811430 G:min +7.3811430 8.3679910 C:9 +8.3679910 10.3184670 F +10.3184670 12.2805540 F#:dim +12.2805540 14.2426400 G:min +14.2426400 16.2047260 Bb:min +16.2047260 17.1567440 F +17.1567440 17.6443630 A:min +17.6443630 18.1203720 G#:min +18.1203720 19.1188300 G:min +19.1188300 20.1172880 C:9 +20.1172880 22.0445440 F +22.0445440 23.0197830 G:min7 +23.0197830 23.9950210 C:9 +23.9950210 25.9454970 F +25.9454970 27.8727530 F#:dim +27.8727530 29.8464490 G:min +29.8464490 31.7737050 Bb:min +31.7737050 32.7605540 F +32.7605540 33.2481730 A:min +33.2481730 33.7241820 G#:min +33.7241820 34.7342500 G:min +34.7342500 35.6978780 C:9 +35.6978780 37.6483540 F +37.6483540 39.6104400 F:9 +39.6104400 41.5376960 Bb +41.5376960 43.4765630 Bb:min +43.4765630 45.4270390 F +45.4270390 47.3659050 D:9 +47.3659050 48.3643630 G:min +48.3643630 49.3163810 G:minmaj7 +49.3163810 51.2552470 G:7 +51.2552470 53.2869930 C:9 +53.2869930 55.1562000 C:7 +55.1562000 57.1531160 F +57.1531160 59.1500320 F#:dim +59.1500320 61.0656780 G:min +61.0656780 63.0858140 Bb:min +63.0858140 64.0262220 F +64.0262220 64.5370610 A:min +64.5370610 65.0595100 G#:min +65.0595100 66.0115290 G:min +66.0115290 66.9983770 C:9 +66.9983770 68.9372430 F +68.9372430 69.8892610 G:min7 +69.8892610 70.8644990 C:9 +70.8644990 72.7801450 F +72.7801450 74.7538410 F#:dim +74.7538410 76.7275380 G:min +76.7275380 78.7012340 Bb:min +78.7012340 79.6880820 F +79.6880820 80.1757010 A:min +80.1757010 80.6749300 G#:min +80.6749300 81.6385580 G:min +81.6385580 82.6602360 F#:7(#9) +82.6602360 84.6223220 F +84.6223220 86.6076280 F:9 +86.6076280 88.5116650 Bb +88.5116650 90.4853610 Bb:min +90.4853610 92.4242270 F +92.4242270 94.3863130 D:9 +94.3863130 95.4196010 G:min +95.4196010 96.3600090 G:minmaj7 +96.3600090 98.3453150 G:7 +98.3453150 100.3190120 C:9 +100.3190120 102.2346580 C:7 +102.2346580 104.2547940 F +104.2547940 106.2284900 F#:dim +106.2284900 108.2021860 G:min +108.2021860 110.1758820 Bb:min +110.1758820 111.1627300 F +111.1627300 111.6851790 A:min +111.6851790 112.1727980 G#:min +112.1727980 113.1015970 G:min +113.1015970 114.1000550 C:9 +114.1000550 116.0157010 F +116.0157010 117.9429570 F +117.9429570 118.9065850 C:7 +118.9065850 119.8934330 B:7 +119.8934330 121.8555190 C:7/5 +121.8555190 123.7943860 F +123.7943860 125.7216420 C# +125.7216420 127.7069480 F +127.7069480 128.7170160 F:maj7 +128.7170160 131.9729231 F:maj7 +131.9729231 136.5575617 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/02final/07_-_Please_Mister_Postman.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,41 @@ +0.0000000 0.6510188 N +0.6510188 4.4781640 C#:min +4.4781640 8.3675060 F#:min +8.3675060 12.1987990 A +12.1987990 16.0881410 F#:min +16.0881410 19.9774830 D +19.9774830 23.8552160 E +23.8552160 27.7097280 A +27.7097280 31.5990710 F#:min +31.5990710 35.5116330 D +35.5116330 39.3893650 E +39.3893650 43.3135380 A +43.3135380 47.2144900 F#:min +47.2144900 51.1386630 D +51.1386630 55.0744450 E +55.0744450 58.9289570 A +58.9289570 62.8415200 F#:min +62.8415200 66.7889120 D +66.7889120 70.6434240 E +70.6434240 74.5327670 A +74.5327670 78.4337190 F#:min +78.4337190 82.3811110 D +82.3811110 86.3168940 E +86.3168940 90.2410660 A +90.2410660 94.1768480 F#:min +94.1768480 98.1590710 D +98.1590710 102.0832430 E +102.0832430 106.0422450 A +106.0422450 109.9548080 F#:min +109.9548080 113.8441500 D +113.8441500 117.8147620 E +117.8147620 121.7737650 A +121.7737650 125.6631070 F#:min +125.6631070 129.5640590 D +129.5640590 133.5114520 E +133.5114520 137.4124040 A +137.4124040 141.3365760 F#:min +141.3365760 145.2491390 D +145.2491390 149.2313610 E +149.2313610 151.5916378 A +151.5916378 156.2828026 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/02final/08_-_Roll_Over_Beethoven.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,77 @@ +0.0000000 0.4403951 N +0.4403951 1.0148234 A +1.0148234 6.6442211 D +6.6442211 9.4318790 G +9.4318790 12.2414930 D +12.2414930 13.6579110 G +13.6579110 15.0743280 A +15.0743280 16.5255750 D +16.5255750 17.9652120 D/5 +17.9652120 19.3700200 D +19.3700200 20.8444870 G +20.8444870 23.7353710 D +23.7353710 25.2330580 G +25.2330580 26.7075250 G +26.7075250 28.1819920 D +28.1819920 29.6796790 D +29.6796790 31.1425370 G +31.1425370 32.6402240 A +32.6402240 35.6239880 D +35.6239880 37.1100650 D +37.1100650 38.6193620 G +38.6193620 41.6379560 D +41.6379560 44.6565500 G +44.6565500 47.6403140 D +47.6403140 49.1380010 G +49.1380010 50.6589080 A +50.6589080 53.6542830 D +53.6542830 55.1403600 D +55.1403600 56.6380470 G +56.6380470 59.6218110 D +59.6218110 62.6520150 G +62.6520150 65.6706090 D +65.6706090 67.2495660 G +67.2495660 68.6892030 A +68.6892030 71.7077970 D +71.7077970 77.5360060 D +77.5360060 80.4733300 G +80.4733300 83.3642150 D +83.3642150 84.8386820 G +84.8386820 86.2434890 A +86.2434890 89.1924230 D +89.1924230 90.6204500 D +90.6204500 92.0716980 G +92.0716980 95.0206320 D +95.0206320 97.9463460 G +97.9463460 100.8720600 D +100.8720600 102.3581370 G +102.3581370 103.8093850 A +103.8093850 106.7931490 D +106.7931490 108.2560060 D +108.2560060 109.7536930 G +109.7536930 112.7142370 D +112.7142370 115.7328310 G +115.7328310 118.7514250 D +118.7514250 120.2723320 G +120.2723320 121.7700200 A +121.7700200 124.8002240 D +124.8002240 126.3095210 D +126.3095210 127.8420380 G +127.8420380 129.3397250 D +129.3397250 130.8954620 D +130.8954620 133.8792260 G +133.8792260 136.8862100 D +136.8862100 138.3955070 G +138.3955070 139.9280240 A +139.9280240 142.8885680 D +142.8885680 148.8212670 D +148.8212670 151.7702010 G +151.7702010 154.7887950 D +154.7887950 156.2748720 A +156.2748720 157.1243178 A +157.1243178 157.7609490 D +157.7609490 158.5504280 G +158.5504280 159.1309270 A +159.1309270 159.6998150 D +159.6998150 163.4248616 D +163.4248616 167.1203505 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/02final/09_-_Hold_Me_Tight.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,85 @@ +0.0000000 0.4403951 N +0.4403951 2.1569280 F +2.1569280 3.8403750 C +3.8403750 5.5586520 F +5.5586520 7.2653180 Bb +7.2653180 9.0300350 G +9.0300350 10.8063610 C +10.8063610 12.5478580 F +12.5478580 14.2661350 Bb +14.2661350 16.0076310 G +16.0076310 17.7375180 C +17.7375180 19.5138440 F +19.5138440 21.2785610 F:7 +21.2785610 22.9968370 Bb +22.9968370 24.7615540 Bb:min +24.7615540 26.5146610 F +26.5146610 28.3025970 Bb:min +28.3025970 30.0673140 F +30.0673140 31.7972000 C +31.7972000 33.5619170 F +33.5619170 35.2918030 Bb +35.2918030 37.0449100 G +37.0449100 38.8212370 C +38.8212370 40.5859530 F +40.5859530 42.3390600 Bb +42.3390600 44.0921660 G +44.0921660 45.8568830 C +45.8568830 47.6332090 F +47.6332090 49.3979260 F:7 +49.3979260 51.1742520 Bb +51.1742520 52.9157490 Bb:min +52.9157490 54.7036860 F +54.7036860 56.5032320 Bb:min +56.5032320 58.2563390 F +58.2563390 60.0210550 Ab +60.0210550 61.7625520 F +61.7625520 63.5620980 Ab +63.5620980 65.3268150 F +65.3268150 67.0683110 Bb +67.0683110 68.8098080 G:min +68.8098080 70.5629150 G +70.5629150 72.2811910 C +72.2811910 73.9994680 F +73.9994680 75.8106240 Bb +75.8106240 77.5405110 G +77.5405110 79.3168370 C +79.3168370 81.1047740 F +81.1047740 82.8811010 Bb +82.8811010 84.6458170 G +84.6458170 86.4105340 C +86.4105340 88.2100800 F +88.2100800 89.9864070 F:7 +89.9864070 91.7511230 Bb +91.7511230 93.5274500 Bb:min +93.5274500 95.3037760 F +95.3037760 97.1033230 Bb:min +97.1033230 98.8912590 F +98.8912590 100.6791960 Ab +100.6791960 102.4206920 F +102.4206920 104.2202390 Ab +104.2202390 106.0081750 F +106.0081750 107.7380620 Bb +107.7380620 109.5027790 G:min +109.5027790 111.2674950 G +111.2674950 112.9393320 C +112.9393320 114.7156580 F +114.7156580 116.4919850 Bb +116.4919850 118.2683110 G +118.2683110 120.0446380 C +120.0446380 121.8325740 F +121.8325740 123.6089010 Bb +123.6089010 125.3271780 G +125.3271780 127.1151140 C +127.1151140 128.9262710 F +128.9262710 130.6909870 F:7 +130.6909870 132.4789240 Bb +132.4789240 134.2436400 Bb:min +134.2436400 136.0431870 F +136.0431870 137.8659530 Bb:min +137.8659530 139.7119400 F +139.7119400 141.5230960 Ab +141.5230960 143.3806920 F +143.3806920 145.5865880 Ab +145.5865880 149.0367100 F +149.0367100 152.4641330 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/02final/10_-_You_Really_Got_A_Hold_On_Me.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,61 @@ +0.0000000 0.4403951 N +0.4403951 2.0543090 E +2.0543090 5.0380740 A +5.0380740 8.0102280 F#:min +8.0102280 10.9823820 A +10.9823820 13.9777560 F#:min +13.9777560 20.0962140 A +20.0962140 26.2262820 F#:min +26.2262820 29.2913160 A +29.2913160 32.3679600 D +32.3679600 33.8656470 B:7 +33.8656470 35.4213840 E +35.4213840 41.6211120 A +41.6211120 47.8672800 F#:min +47.8672800 54.1482780 A +54.1482780 60.3944450 F#:min +60.3944450 63.5407490 A +63.5407490 66.6406130 D +66.6406130 68.2195700 B:7 +68.2195700 69.7869170 E +69.7869170 76.0795250 A +76.0795250 82.2908630 F#:min +82.2908630 85.4371670 A +85.4371670 88.5834700 D +88.5834700 90.1856470 A +90.1856470 91.8342640 A +91.8342640 93.5757610 A +93.5757610 97.3954430 E +97.3954430 98.9740023 A +98.9740023 99.8219280 F#:min +99.8219280 100.5997970 A/3 +100.5997970 101.3892750 F#:min +101.3892750 102.0858740 E +102.0858740 103.6067810 A +103.6067810 105.1276880 A +105.1276880 105.8939470 B:min/5 +105.8939470 106.6485950 A/5 +106.6485950 107.4264640 F#:min +107.4264640 108.2159420 E +108.2159420 109.7484590 A +109.7484590 115.9830170 A +115.9830170 122.3220650 F#:min +122.3220650 125.4799780 A +125.4799780 128.6378920 D +128.6378920 130.2632890 B:7 +130.2632890 131.8306360 E +131.8306360 138.2161230 A +138.2161230 144.6132210 F#:min +144.6132210 147.8175750 A +147.8175750 150.9870990 D +150.9870990 152.6008850 A +152.6008850 154.3075520 A +154.3075520 155.9445590 A +155.9445590 159.7178010 E +159.7178010 166.1845590 A +166.1845590 172.5236060 F#:min +172.5236060 172.7497482 A +172.7497482 173.9943429 N +173.9943429 175.6234700 A +175.6234700 178.7812457 A:9 +178.7812457 182.7639489 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/02final/11_-_I_Wanna_Be_Your_Man.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,49 @@ +0.0000000 0.4212474 N +0.4212474 1.2562470 N +1.2562470 6.0976070 E +6.0976070 10.9041380 E +10.9041380 15.7687190 E +15.7687190 19.4026410 E +19.4026410 20.6216890 E +20.6216890 21.8407370 E +21.8407370 23.0481740 F# +23.0481740 24.2904420 B +24.2904420 25.4746600 E +25.4746600 26.6820970 C# +26.6820970 27.8779250 F# +27.8779250 29.0737530 B +29.0737530 29.3724356 E +29.3724356 31.5466780 D:maj/2 +31.5466780 36.4228680 E +36.4228680 41.2642290 E +41.2642290 46.1984690 E +46.1984690 51.0746600 E +51.0746600 52.2937070 E +52.2937070 53.4663150 F# +53.4663150 54.7201920 B +54.7201920 55.9392400 E +55.9392400 57.1582880 C# +57.1582880 58.3657250 F# +58.3657250 59.5847730 B +59.5847730 59.8937278 E +59.8937278 62.0228680 D:maj/2 +62.0228680 66.8293990 E +66.8293990 71.5198300 E +71.5198300 76.2334800 E +76.2334800 81.0167910 E +81.0167910 85.8813720 E +85.8813720 88.2846370 E +88.2846370 90.6530720 E +90.6530720 93.0911680 E +93.0911680 95.5060430 E +95.5060430 96.7134800 E +96.7134800 97.9441380 F# +97.9441380 99.1631860 B +99.1631860 100.3938430 E +100.3938430 101.5780610 C# +101.5780610 102.8087190 F# +102.8087190 103.9697160 B +103.9697160 104.2778906 E +104.2778906 106.4542510 D:maj/2 +106.4542510 114.1580580 E +114.1580580 118.7534846 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/02final/12_-_Devil_In_Her_Heart.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,92 @@ +0.0000000 0.4595427 N +0.4595427 1.1871519 N +1.1871519 3.1426620 A:min +3.1426620 5.0699180 D +5.0699180 7.0436150 G +7.0436150 9.1334110 G +9.1334110 11.0954970 A:min +11.0954970 13.0808030 D +13.0808030 15.0196680 G +15.0196680 16.9933650 G +16.9933650 18.9670600 A:min +18.9670600 20.9407580 D +20.9407580 22.8796240 G +22.8796240 23.4601230 G +23.4601230 23.9361320 G +23.9361320 24.4005310 A:min +24.4005310 24.9113690 G/3 +24.9113690 26.8850650 C +26.8850650 28.8471510 C:min +28.8471510 30.7976280 G +30.7976280 31.2968570 G +31.2968570 31.7612560 G +31.7612560 32.2488740 A:min +32.2488740 32.7481030 G/3 +32.7481030 34.7334110 C +34.7334110 36.7187160 C:min +36.7187160 38.7272430 A +38.7272430 40.7009390 D +40.7009390 42.7442940 D +42.7442940 44.7063800 A:min +44.7063800 46.7032960 D +46.7032960 50.6739080 G +50.6739080 52.6476050 A:min +52.6476050 54.6096910 D +54.6096910 56.5717770 G +56.5717770 57.0826170 G +57.0826170 57.5818460 G +57.5818460 58.0462450 A:min +58.0462450 58.5570840 G/3 +58.5570840 60.5540000 C +60.5540000 62.5625260 C:min +62.5625260 64.5478320 G +64.5478320 65.0586710 G +65.0586710 65.5346800 G +65.5346800 66.0106890 A:min +66.0106890 66.5215280 G/3 +66.5215280 68.5300550 C +68.5300550 70.5385810 C:min +70.5385810 72.5238870 A +72.5238870 74.5091920 D +74.5091920 76.5409390 D +76.5409390 78.5262450 A:min +78.5262450 80.5347710 D +80.5347710 84.4473330 G +84.4473330 86.4790790 A:min +86.4790790 88.4527760 D +88.4527760 90.4032520 G +90.4032520 90.9837500 G +90.9837500 91.4249300 G +91.4249300 91.9009390 A:min +91.9009390 92.4001680 G/3 +92.4001680 94.4319140 C +94.4319140 96.4172200 C:min +96.4172200 98.4257460 G +98.4257460 98.9133650 G +98.9133650 99.4009840 G +99.4009840 99.8886030 A:min +99.8886030 100.3762210 G/3 +100.3762210 102.3847480 C +102.3847480 104.3700550 C:min +104.3700550 106.3669710 A +106.3669710 108.3406660 D +108.3406660 110.3724130 D +110.3724130 112.3809380 A:min +112.3809380 114.3430240 D +114.3430240 116.3051100 G +116.3051100 118.2671960 G +118.2671960 120.2757230 A:min +120.2757230 122.2378090 D +122.2378090 124.1998960 G +124.1998960 126.1968120 G +126.1968120 128.1588970 A:min +128.1588970 130.1558130 D +130.1558130 132.1178990 G +132.1178990 134.0683760 G +134.0683760 136.0769020 A:min +136.0769020 138.0273780 D +138.0273780 140.0010740 G +140.0010740 140.5243186 G +140.5243186 141.0343620 D +141.0343620 143.9325934 G:maj6(9) +143.9325934 147.4940490 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/02final/13_-_Not_A_Second_Time.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,69 @@ +0.0000000 0.4595427 N +0.4595427 2.3079480 G +2.3079480 4.1358840 E:min +4.1358840 6.0347510 G +6.0347510 7.8342970 E:min +7.8342970 9.7383330 D +9.7383330 11.6075400 G +11.6075400 13.5347960 D +13.5347960 15.3807820 G +15.3807820 17.2615990 E:min +17.2615990 19.1888550 G +19.1888550 21.0464510 E:min +21.0464510 22.9737080 D +22.9737080 24.8313040 A:min +24.8313040 26.7585600 D +26.7585600 28.6393760 A:min +28.6393760 30.5318030 B:min +30.5318030 32.4126190 G +32.4126190 34.3398750 E:min +34.3398750 36.2671320 A:min +36.2671320 38.1247280 F#:dim/b3 +38.1247280 40.0403740 B:min +40.0403740 41.9328010 D:7/5 +41.9328010 43.8484470 E:min +43.8484470 44.0969486 E:min +44.0969486 45.7408730 N +45.7408730 47.6565190 A:min +47.6565190 49.5605560 B:min +49.5605560 51.4529820 G +51.4529820 53.3686280 E:min +53.3686280 55.2958840 A:min +55.2958840 57.2115310 A:min +57.2115310 59.1271770 B:min +59.1271770 61.0544330 D:7/5 +61.0544330 62.9468590 E:min +62.9468590 64.8276760 N +64.8276760 66.8013720 G +66.8013720 68.6937980 E:min +68.6937980 70.6326640 G +70.6326640 72.5367010 E:min +72.5367010 74.5103970 D +74.5103970 76.4144330 G +76.4144330 78.3532990 D +78.3532990 80.3269960 G +80.3269960 82.2542520 E:min +82.2542520 84.1931180 G +84.1931180 86.1203740 E:min +86.1203740 88.0708500 D +88.0708500 90.0097170 A:min +90.0097170 91.9601930 D +91.9601930 93.8642290 A:min +93.8642290 95.8843650 B:min +95.8843650 97.8000110 G +97.8000110 99.7272680 E:min +99.7272680 101.6777440 A:min +101.6777440 103.5933900 A:min +103.5933900 105.5090360 B:min +105.5090360 107.4479030 D:7/5 +107.4479030 109.4099890 E:min +109.4099890 109.7541074 E:min +109.7541074 111.3024150 N +111.3024150 113.2528910 G +113.2528910 115.2033670 E:min +115.2033670 117.1074040 G +117.1074040 119.0578800 E:min +119.0578800 120.9735260 G +120.9735260 122.8543420 E:min +122.8543420 124.8396490 G +124.8396490 128.1932570 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/02final/14_-_Money.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,92 @@ +0.0000000 0.4595427 N +0.4595427 1.4743661 E:min +1.4743661 2.2785657 A +2.2785657 2.9487321 E:min +2.9487321 3.5231605 A +3.5231605 4.1020400 G +4.1020400 5.0076190 E:min +5.0076190 5.8783670 A +5.8783670 6.5865760 E:min +6.5865760 7.0625850 A +7.0625850 7.7359630 G +7.7359630 9.5355100 B:7(#9) +9.5355100 11.3698860 A:7 +11.3698860 12.2870740 E:min +12.2870740 12.9715707 A +12.9715707 13.4945120 E:min +13.4945120 15.0270290 B:7 +15.0270290 18.0107930 E +18.0107930 18.7190020 A +18.7190020 21.7375960 E +21.7375960 22.4574150 E +22.4574150 26.1261670 A:7 +26.1261670 29.7717000 E +29.7717000 31.6176870 B:7 +31.6176870 33.4404530 A:7 +33.4404530 35.2864400 E:7 +35.2864400 35.5534690 E:7 +35.5534690 37.1324260 B:7 +37.1324260 40.1278000 E +40.1278000 40.8592290 A +40.8592290 44.5628110 E +44.5628110 48.2663940 A:7 +48.2663940 52.0164170 E +52.0164170 53.8856230 B:7 +53.8856230 55.6851700 A:7 +55.6851700 57.7749660 E:7 +57.7749660 59.4235820 B:7 +59.4235820 62.5054133 E +62.5054133 63.1736050 A +63.1736050 66.9700680 E +66.9700680 70.6852600 A:7 +70.6852600 74.4004530 E:7 +74.4004530 76.2696600 B:7 +76.2696600 78.1272560 A:7 +78.1272560 80.2170520 E:7 +80.2170520 81.8656690 B:7 +81.8656690 82.8705856 E +82.8705856 83.7348750 A +83.7348750 84.4314740 E:min +84.4314740 84.8958730 A +84.8958730 85.6156910 G +85.6156910 86.5444890 E:min +86.5444890 87.4500680 A +87.4500680 88.1698860 E:min +88.1698860 88.6226750 A +88.6226750 89.2960540 G +89.2960540 91.1420400 B:7(#9) +91.1420400 93.0228570 A:7(#9) +93.0228570 93.9864850 E:min +93.9864850 94.6414132 A +94.6414132 95.1358730 E:min +95.1358730 96.7496600 B:7 +96.7496600 99.8495230 E +99.8495230 100.5693420 A +100.5693420 104.3541950 E +104.3541950 108.0926070 A +108.0926070 111.7961900 E +111.7961900 113.6653960 B:7 +113.6653960 115.5113830 A:7 +115.5113830 117.6127890 E:7 +117.6127890 119.2033560 B:7 +119.2033560 120.2134240 E +120.2134240 121.0957820 A +121.0957820 122.9765980 E +122.9765980 124.8458050 E +124.8458050 126.6801810 E +126.6801810 130.3837640 A +130.3837640 134.0989570 E +134.0989570 135.9797730 B:7 +135.9797730 137.8489790 A:7 +137.8489790 139.9736050 E:7 +139.9736050 141.5990020 B:7 +141.5990020 145.3838550 E +145.3838550 149.1106570 E +149.1106570 152.8142400 A +152.8142400 156.5642630 E +156.5642630 158.4799090 B:7 +158.4799090 160.2910650 A:7 +160.2910650 162.1951020 E +162.1951020 163.0194100 E/5 +163.0194100 166.0536185 E:7 +166.0536185 167.3382712 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/02final/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +/.DS_Store/1.1.1.1/Fri Sep 23 09:24:37 2005// +/01_-_It_Won't_Be_Long.lab/1.1.1.1/Fri Sep 23 09:24:37 2005// +/02_-_All_I've_Got_To_Do.lab/1.3/Thu Jul 27 21:47:04 2006// +/03_-_All_My_Loving.lab/1.2/Thu Jul 27 22:24:34 2006// +/04_-_Don't_Bother_Me.lab/1.1.1.1/Fri Sep 23 09:24:37 2005// +/05_-_Little_Child.lab/1.2/Thu Jul 27 22:24:34 2006// +/06_-_Till_There_Was_You.lab/1.2/Thu Jul 27 21:47:04 2006// +/07_-_Please_Mister_Postman.lab/1.1.1.1/Fri Sep 23 09:24:37 2005// +/08_-_Roll_Over_Beethoven.lab/1.1.1.1/Fri Sep 23 09:24:37 2005// +/09_-_Hold_Me_Tight.lab/1.2/Thu Jul 27 22:24:34 2006// +/10_-_You_Really_Got_A_Hold_On_Me.lab/1.2/Thu Jul 27 21:47:04 2006// +/11_-_I_Wanna_Be_Your_Man.lab/1.2/Thu Jul 27 21:47:04 2006// +/12_-_Devil_In_Her_Heart.lab/1.1.1.1/Fri Sep 23 09:24:37 2005// +/13_-_Not_A_Second_Time.lab/1.1.1.1/Fri Sep 23 09:24:37 2005// +/14_-_Money.lab/1.2/Thu Jul 27 21:47:04 2006// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/02final/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +chrish/transcriptions/02final
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/02final/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:matthiasm@dspmac1.elec.qmul.ac.uk:/cvsroot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/03final/01_-_A_Hard_Day's_Night.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,101 @@ +0.0000000 1.0148234 N +1.0148234 4.3206570 G:sus4(b7) +4.3206570 5.0172560 G +5.0172560 5.8531740 C +5.8531740 7.5250110 G +7.5250110 9.2316770 F +9.2316770 10.9151240 G +10.9151240 11.7742620 G +11.7742620 12.6682310 C +12.6682310 14.3748970 G +14.3748970 16.0699540 F +16.0699540 17.7882310 G +17.7882310 19.5181170 C +19.5181170 21.2131740 D +21.2131740 22.1071420 G +22.1071420 22.9546710 C +22.9546710 24.6613370 G +24.6613370 25.5320860 G +25.5320860 26.4028340 C +26.4028340 28.0746710 G +28.0746710 29.7697270 F +29.7697270 31.5344440 G +31.5344440 32.3819720 G +32.3819720 33.2411110 C +33.2411110 34.9477770 G +34.9477770 36.6544440 F +36.6544440 38.4075500 G +38.4075500 40.1258270 C +40.1258270 41.8789340 D +41.8789340 42.7264620 G +42.7264620 43.5972100 C +43.5972100 45.3387070 G +45.3387070 47.0685940 B:min +47.0685940 48.7868700 E:min +48.7868700 52.3046930 B:min +52.3046930 53.9765300 G +53.9765300 55.6948070 E:min +55.6948070 57.4014730 C +57.4014730 59.1661900 D:7 +59.1661900 60.0717680 G +60.0717680 60.9076860 C +60.9076860 62.6259630 G +62.6259630 64.3674600 F +64.3674600 66.1205660 G +66.1205660 66.9564850 G +66.9564850 67.8388430 C +67.8388430 69.6267790 G +69.6267790 71.3334460 F +71.3334460 73.0749430 G +73.0749430 74.8464640 C +74.8464640 76.5347160 D +76.5347160 77.4170740 G +77.4170740 78.2878230 C +78.2878230 79.0350420 G +79.0350420 80.0409290 G/5 +80.0409290 80.9116770 G +80.9116770 81.8172560 C +81.8172560 83.6170240 G +83.6170240 85.3118590 F +85.3118590 86.9953060 G +86.9953060 87.8892740 G +87.8892740 88.7484120 C +88.7484120 90.4782990 G +90.4782990 92.2662350 F +92.2662350 93.9729020 G +93.9729020 95.7027890 C +95.7027890 97.4558950 D +97.4558950 98.3034230 G +98.3034230 99.1625620 C +99.1625620 100.9388880 G +100.9388880 102.6455550 B:min +102.6455550 104.3986620 E:min +104.3986620 107.8584350 B:min +107.8584350 109.6115410 G +109.6115410 111.3530380 E:min +111.3530380 113.0364850 C +113.0364850 114.7895910 D:7 +114.7895910 115.6255100 G +115.6255100 116.5194780 C +116.5194780 118.2725840 G +118.2725840 119.9444210 F +119.9444210 121.7323580 G +121.7323580 122.5914960 G +122.5914960 123.4622440 C +123.4622440 125.2037410 G +125.2037410 126.9220180 F +126.9220180 128.6519040 G +128.6519040 130.3934010 C +130.3934010 132.1929470 D +132.1929470 133.0753060 G +133.0753060 133.9344440 C +133.9344440 134.7842970 G +134.7842970 135.6875500 G:sus4/5 +135.6875500 136.6047390 G +136.6047390 137.4638770 C +137.4638770 138.3457530 G +138.3457530 139.2285940 C +139.2285940 140.0993420 G +140.0993420 141.0264180 C +141.0264180 148.7046110 F:maj9(*7) +148.7046110 152.3043610 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/03final/02_-_I_Should_Have_Known_Better.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,136 @@ +0.000000 0.433233 N +0.433233 1.279698 G +1.279698 2.227599 D +2.227599 3.133177 G +3.133177 4.050366 D +4.050366 4.967554 G +4.967554 5.896352 D +5.896352 6.825150 G +6.825150 7.765558 D +7.765558 8.636307 G +8.636307 9.553495 D +9.553495 10.470683 G +10.470683 11.364651 D +11.364651 12.281840 G +12.281840 13.222248 D +13.222248 14.127826 G +14.127826 15.056624 D +15.056624 15.973812 G +15.973812 16.844561 D +16.844561 17.808189 E:min/b3 +17.808189 18.725377 E:min +18.725377 20.524923 C +20.524923 22.394130 D +22.394130 23.311318 G +23.311318 24.228506 D +24.228506 25.157304 G +25.157304 26.097713 D +26.097713 26.968461 G +26.968461 27.862429 D +27.862429 28.791227 G +28.791227 29.708415 D +29.708415 30.613994 G +30.613994 31.507962 D +31.507962 32.436760 G +32.436760 33.342338 D +33.342338 34.236307 G +34.236307 35.141885 D +35.141885 36.046293 E:min/b3 +36.046293 36.976261 E:min +36.976261 38.775808 C +38.775808 40.668234 B +40.668234 42.525830 E:min +42.525830 44.336987 C +44.336987 46.148143 G +46.148143 48.017350 B +48.017350 51.651273 E:min +51.651273 53.508869 G +53.508869 55.320025 G:7 +55.320025 57.189232 C +57.189232 58.988778 D +58.988778 60.857985 G +60.857985 62.715581 E:min +62.715581 64.515127 C +64.515127 66.361114 D +66.361114 67.324742 G +67.324742 68.265150 D +68.265150 69.182338 G +69.182338 70.134357 D +70.134357 71.039935 G +71.039935 71.945513 D +71.945513 72.862701 G +72.862701 73.779889 D +73.779889 74.662248 G +74.662248 75.591046 D +75.591046 76.531454 G +76.531454 77.460252 D +77.460252 78.365830 G +78.365830 79.259799 D +79.259799 80.188597 E:min/b3 +80.188597 81.117395 E:min +81.117395 82.928552 C +82.928552 84.751318 D +84.751318 85.668506 G +85.668506 86.608914 D +86.608914 87.537713 G +87.537713 88.454901 D +88.454901 89.383699 G +89.383699 90.266057 D +90.266057 91.183245 G +91.183245 92.088824 D +92.088824 92.994402 G +92.994402 93.923200 D +93.923200 94.863608 G +94.863608 95.757576 D +95.757576 96.709595 G +96.709595 97.615173 D +97.615173 98.556103 E:min/b3 +98.556103 99.403109 E:min +99.403109 101.225876 C +101.225876 103.025422 D +103.025422 104.012270 G +104.012270 104.941069 D +104.941069 105.811817 G +105.811817 106.752225 D +106.752225 107.611363 G +107.611363 108.551771 D +108.551771 109.492180 G +109.492180 110.386148 D +110.386148 111.314946 G +111.314946 112.197304 D +112.197304 113.114493 G +113.114493 114.043291 D +114.043291 114.948869 G +114.948869 115.854447 D +115.854447 116.771635 E:min/b3 +116.771635 117.688824 E:min +117.688824 119.558030 C +119.558030 121.404016 B:7 +121.404016 123.226783 E:min +123.226783 125.037939 C +125.037939 126.883926 G +126.883926 128.729912 B +128.729912 130.622338 E:min +130.622338 132.410275 E:min +132.410275 134.279481 G +134.279481 136.102248 G:7 +136.102248 137.959844 C +137.959844 139.829050 D +139.829050 141.651817 G +141.651817 143.474583 E:min +143.474583 145.355400 C +145.355400 147.224606 D +147.224606 148.141794 G +148.141794 149.105422 D +149.105422 150.022610 G +150.022610 150.974629 D +150.974629 151.880207 G +151.880207 152.843835 D +152.843835 153.761023 G +153.761023 154.643381 D +154.643381 155.583790 G +155.583790 156.524198 D +156.524198 157.429776 G +157.429776 158.393404 D +158.393404 159.333812 G +159.333812 160.262610 D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/03final/03_-_If_I_Fell.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,81 @@ +0.0000000 0.4403951 N +0.4403951 1.0216030 N +1.0216030 3.2042790 Eb:min +3.2042790 5.4566150 D +5.4566150 7.6973400 Db +7.6973400 9.9148460 Bb:min +9.9148460 12.1787920 Eb:min +12.1787920 14.4775670 D +14.4775670 16.6718530 E:min7 +16.6718530 18.9590180 A +18.9590180 20.0735760 D +20.0735760 21.1068640 E:min +21.1068640 22.8483610 F#:min +22.8483610 23.3592000 F:dim +23.3592000 25.5418750 E:min7 +25.5418750 27.7245510 A +27.7245510 28.8739390 D +28.8739390 29.9072270 E:min +29.9072270 31.5674540 F#:min +31.5674540 32.1247330 F:dim +32.1247330 34.3074080 E:min7 +34.3074080 36.4668640 A +36.4668640 38.7192000 D +38.7192000 39.8105380 G:min +39.8105380 40.8902650 A +40.8902650 41.9583830 D +41.9583830 43.0729410 E:min +43.0729410 44.7215580 F#:min +44.7215580 45.2904470 F:dim +45.2904470 47.4499030 E:min7 +47.4499030 49.6093580 A +49.6093580 50.7703560 D +50.7703560 51.8268640 E:min +51.8268640 53.4406510 F#:min +53.4406510 53.9747100 F:dim +53.9747100 56.2154360 E:min7 +56.2154360 58.4097210 A +58.4097210 60.6388370 D:9 +60.6388370 62.8795630 D:9 +62.8795630 65.0970680 G +65.0970680 67.2565240 G:min +67.2565240 69.5204700 G:min +69.5204700 71.6799250 D +71.6799250 73.9090410 A:7 +73.9090410 74.9423290 D +74.9423290 76.0220570 E:min +76.0220570 77.6474540 F#:min +77.6474540 78.2163430 F:dim +78.2163430 80.4570680 E:min7 +80.4570680 82.6513540 A +82.6513540 83.7659120 D +83.7659120 84.8920800 E:min +84.8920800 86.5290860 F#:min +86.5290860 87.1095850 F:dim +87.1095850 89.2690410 E:min7 +89.2690410 91.4865470 A +91.4865470 93.7040520 D:9 +93.7040520 95.8983380 D:9 +95.8983380 98.0926240 G +98.0926240 100.3449590 G:min +100.3449590 102.5740750 G:min +102.5740750 104.7799710 D +104.7799710 107.0090860 A:7 +107.0090860 108.1236440 D +108.1236440 109.1801520 E:min +109.1801520 110.8171590 F#:min +110.8171590 111.3976580 F:dim +111.3976580 113.5919430 E:min7 +113.5919430 115.8442790 A +115.8442790 116.9936670 D +116.9936670 118.0617850 E:min +118.0617850 119.7684510 F#:min +119.7684510 120.3141200 F:dim +120.3141200 122.5548460 E:min7 +122.5548460 124.7375220 A +124.7375220 127.0014670 D +127.0014670 129.1957530 G:min +129.1957530 131.4713090 D +131.4713090 133.7700840 G:min +133.7700840 137.3458151 D +137.3458151 141.8455037 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/03final/04_-_I'm_Happy_Just_To_Dance_With_You.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,108 @@ +0.0000000 0.3822458 N +0.3822458 2.1542170 C#:min +2.1542170 2.8043760 F#:min +2.8043760 3.8260540 G# +3.8260540 5.6604300 C#:min +5.6604300 6.2989790 F#:min +6.2989790 7.3670970 G# +7.3670970 9.1898630 C#:min +9.1898630 9.8864620 F#:min +9.8864620 10.9778000 G# +10.9778000 11.6691157 A +11.6691157 12.7309096 B +12.7309096 13.4623350 E:maj6 +13.4623350 14.5885030 B +14.5885030 15.2734920 E +15.2734920 16.3764390 G#:min +16.3764390 17.0614280 F#:min +17.0614280 18.1759860 B +18.1759860 18.8609750 E +18.8609750 19.9639220 G#:min +19.9639220 20.6140810 F#:min +20.6140810 21.7634690 B +21.7634690 23.5746250 A +23.5746250 24.2712240 E +24.2712240 25.3857820 C#:min +25.3857820 26.0823800 A +26.0823800 27.1969380 B:aug +27.1969380 27.8980695 E +27.8980695 29.0313150 B +29.0313150 29.7046930 E +29.7046930 30.8076410 G#:min +30.8076410 31.5042400 F#:min +31.5042400 32.6420180 B +32.6420180 33.3386160 E +33.3386160 34.4183440 G#:min +34.4183440 35.1149430 F#:min +35.1149430 36.3107700 B +36.3107700 38.0987070 A +38.0987070 38.7953060 E +38.7953060 39.9098630 C#:min +39.9098630 40.6529020 A +40.6529020 41.7442400 B:aug +41.7442400 43.5902260 E +43.5902260 45.4129930 C#:min +45.4129930 46.1095910 F#:min +46.1095910 47.2125390 G# +47.2125390 49.0120860 C#:min +49.0120860 49.6854640 F#:min +49.6854640 50.8232420 G# +50.8232420 52.6343990 C#:min +52.6343990 53.3193870 F#:min +53.3193870 54.4339450 G# +54.4339450 55.3279130 A +55.3279130 56.2451020 B +56.2451020 56.9184800 E:maj6 +56.9184800 58.0678680 B +58.0678680 58.7180270 E +58.7180270 59.8674140 G#:min +59.8674140 60.5291830 F#:min +60.5291830 61.6785710 B +61.6785710 62.3519500 E +62.3519500 63.4781170 G#:min +63.4781170 64.1747160 F#:min +64.1747160 65.2892740 B +65.2892740 67.1004300 A +67.1004300 67.7738090 E +67.7738090 68.9464170 C#:min +68.9464170 69.8519950 A +69.8519950 70.7691830 B:aug +70.7691830 72.5222900 E +72.5222900 74.3450560 C#:min +74.3450560 75.0068250 F#:min +75.0068250 76.2026530 G# +76.2026530 78.0021990 C#:min +78.0021990 78.6987980 F#:min +78.6987980 79.8481850 G# +79.8481850 81.6477320 C#:min +81.6477320 82.3095010 F#:min +82.3095010 83.4704980 G# +83.4704980 84.4109070 A +84.4109070 85.3280950 B +85.3280950 86.2220630 E:maj6 +86.2220630 87.1508610 B +87.1508610 87.7778000 E +87.7778000 88.9620180 G#:min +88.9620180 89.6237860 F#:min +89.6237860 90.7731740 B +90.7731740 91.4465530 E +91.4465530 92.6075510 G#:min +92.6075510 93.2460990 F#:min +93.2460990 94.4187070 B +94.4187070 96.2414730 A +96.2414730 96.9148520 E +96.9148520 98.0758500 C#:min +98.0758500 98.7608390 A +98.7608390 99.8870060 B:aug +99.8870060 101.7329930 C#:min +101.7329930 102.4412010 F#:min +102.4412010 103.5557590 G# +103.5557590 104.3104080 A +104.3104080 105.4017460 B +105.4017460 107.2709520 C#:min +107.2709520 107.9443310 F#:min +107.9443310 109.0821080 G# +109.0821080 109.7670970 A +109.7670970 110.9397050 B +110.9397050 114.7799274 E:maj6 +114.7799274 118.6342395 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/03final/05_-_And_I_Love_Her.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,61 @@ +0.0000000 0.4212474 N +0.4212474 1.2138090 N +1.2138090 5.1379810 F#:min +5.1379810 9.2246930 E +9.2246930 11.2448290 F#:min +11.2448290 13.2765750 C#:min +13.2765750 15.3431510 F#:min +15.3431510 17.3981170 C#:min +17.3981170 19.4763030 F#:min +19.4763030 21.5777090 C#:min +21.5777090 23.6210650 A +23.6210650 25.6760310 B +25.6760310 29.8788430 E +29.8788430 31.9686390 F#:min +31.9686390 34.0003850 C#:min +34.0003850 36.1017910 F#:min +36.1017910 38.2031970 C#:min +38.2031970 40.2929930 F#:min +40.2929930 42.3595690 C#:min +42.3595690 44.4841950 A +44.4841950 46.5739900 B +46.5739900 50.8000220 E +50.8000220 52.9478680 C#:min +52.9478680 55.0724940 B +55.0724940 57.1506800 C#:min +57.1506800 59.2869160 G#:min +59.2869160 61.3302720 C#:min +61.3302720 63.4200680 G#:min +63.4200680 67.6460990 B +67.6460990 69.8055550 F#:min +69.8055550 71.9301810 C#:min +71.9301810 73.9967570 F#:min +73.9967570 76.1097730 C#:min +76.1097730 78.2343990 F#:min +78.2343990 80.3474140 C#:min +80.3474140 82.4720400 A +82.4720400 84.5850560 B +84.5850560 88.7878680 E +88.7878680 90.9589340 G:min +90.9589340 92.9790700 D:min +92.9790700 95.1849650 G:min +95.1849650 97.2863710 D:min +97.2863710 99.4226070 G:min +99.4226070 101.5356230 D:min +101.5356230 103.6834690 Bb +103.6834690 105.8080950 C +105.8080950 110.0805660 F +110.0805660 112.2284120 G:min +112.2284120 114.3065980 D:min +114.3065980 116.4544440 G:min +116.4544440 118.5326300 D:min +118.5326300 120.6572560 G:min +120.6572560 122.7354420 D:min +122.7354420 124.9065070 Bb +124.9065070 127.0311330 C +127.0311330 131.2803850 F +131.2803850 135.6225170 G:min +135.6225170 139.8137180 F +139.8137180 144.1210200 G:min +144.1210200 147.1493920 D +147.1493920 150.8448810 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/03final/06_-_Tell_Me_Why.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,99 @@ +0.0000000 0.8655100 N +0.8655100 1.5272780 E:min +1.5272780 2.3980270 A +2.3980270 3.0249650 E:min +3.0249650 3.8841040 A +3.8841040 4.5574820 E:min +4.5574820 5.4166210 A +5.4166210 6.0667800 E:min +6.0667800 6.9491380 A +6.9491380 8.4352150 D +8.4352150 9.9677320 B:min +9.9677320 11.5466890 E:min +11.5466890 13.0675960 A +13.0675960 13.8222440 D +13.8222440 14.6001130 B:min +14.6001130 15.1806120 E:min +15.1806120 16.1210200 A +16.1210200 17.6535370 D +17.6535370 19.2092740 B:min +19.2092740 20.7417910 E:min +20.7417910 22.3091380 A +22.3091380 23.0521760 D +23.0521760 23.8184350 B:min +23.8184350 24.4105440 E:min +24.4105440 25.3625620 A +25.3625620 26.8834690 D +26.8834690 28.4043760 B:min +28.4043760 29.8788430 E:min +29.8788430 31.4694100 A +31.4694100 33.0135370 D +33.0135370 34.5344440 B:min +34.5344440 36.0785710 E:min +36.0785710 37.6226980 A +37.6226980 39.1087750 D +39.1087750 40.6761220 B:min +40.6761220 42.2202490 E:min +42.2202490 43.7875960 A +43.7875960 44.5654640 D +44.5654640 45.3433330 B:min +45.3433330 45.9122220 E:min +45.9122220 46.8526300 A +46.8526300 48.3851470 D +48.3851470 49.9873240 B:min +49.9873240 51.4966210 E:min +51.4966210 53.0639680 A +53.0639680 53.8070060 D +53.8070060 54.6661450 B:min +54.6661450 55.2698630 E:min +55.2698630 56.1870520 A +56.1870520 57.8008390 D +57.8008390 59.3449650 B:min +59.3449650 60.8658730 E:min +60.8658730 62.4448290 A +62.4448290 63.9773460 D +63.9773460 65.5214730 B:min +65.5214730 67.0772100 E:min +67.0772100 68.6677770 A +68.6677770 70.2002940 D +70.2002940 71.7560310 B:min +71.7560310 73.2885480 E:min +73.2885480 74.8675050 A +74.8675050 75.6685940 D +75.6685940 76.4464620 B:min +76.4464620 77.0385710 E:min +77.0385710 77.9673690 A +77.9673690 79.5347160 D +79.5347160 81.1252830 B:min +81.1252830 82.6926300 E:min +82.6926300 84.2251470 A +84.2251470 85.8157140 D +85.8157140 87.3366210 D:7 +87.3366210 90.4597050 G +90.4597050 93.6060090 A +93.6060090 96.7058730 B:min +96.7058730 98.2964390 E:min +98.2964390 99.8289560 A +99.8289560 100.6300450 D +100.6300450 101.4195230 B:min +101.4195230 101.6792229 E:min +101.6792229 102.9404300 N +102.9404300 104.4729470 D +104.4729470 106.0751240 B:min +106.0751240 107.6424710 E:min +107.6424710 109.2562580 A +109.2562580 110.0430716 D +110.0430716 110.7887750 B:min +110.7887750 111.4041040 E:min +111.4041040 112.3445120 A +112.3445120 113.8886390 D +113.8886390 115.4792060 B:min +115.4792060 117.0465530 E:min +117.0465530 118.6719500 A +118.6719500 120.2392970 B:min +120.2392970 120.8861124 Bb +120.8861124 121.7718140 Bb:7 +121.7718140 122.3523120 A:sus4 +122.3523120 123.2114510 A:9(11) +123.2114510 125.7877610 D +125.7877610 130.0316126 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/03final/07_-_Can't_Buy_Me_Love.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,65 @@ +0.0000000 0.4595427 N +0.4595427 1.5504980 C +1.5504980 2.8508160 E:min +2.8508160 4.1975730 A:min +4.1975730 5.4978910 E:min +5.4978910 6.8214280 A:min +6.8214280 8.1333560 D:min +8.1333560 9.5163628 G +9.5163628 14.9484120 C:7 +14.9484120 17.6535370 F:7 +17.6535370 20.3934920 C:7 +20.3934920 21.8215190 G +21.8215190 24.6079130 F:7 +24.6079130 26.0127210 C:7 +26.0127210 31.5390700 C:7 +31.5390700 34.3835140 F:7 +34.3835140 37.1002490 C:7 +37.1002490 38.4934460 G +38.4934460 39.9795230 F:7 +39.9795230 41.2914510 F:7 +41.2914510 42.6962580 C:7 +42.6962580 44.1010650 E:min +44.1010650 45.4942630 A:min +45.4942630 48.3038770 C +48.3038770 49.6970740 E:min +49.6970740 51.0786620 A:min +51.0786620 52.4602490 D:min +52.4602490 53.9347160 G +53.9347160 59.5307250 C:7 +59.5307250 62.3287300 F:7 +62.3287300 65.1383440 C:7 +65.1383440 66.5199310 G +66.5199310 67.9943990 F:7 +67.9943990 69.3527660 F:7 +69.3527660 70.8272330 C:7 +70.8272330 72.1856000 C:7 +72.1856000 77.7583900 C:7 +77.7583900 80.5680040 F:7 +80.5680040 83.3776190 C:7 +83.3776190 84.8056460 G +84.8056460 86.2220630 F:7 +86.2220630 87.6152600 C:7 +87.6152600 89.0548970 C:7 +89.0548970 90.3784350 E:min +90.3784350 91.7948520 A:min +91.7948520 94.6044670 C +94.6044670 96.0092740 E:min +96.0092740 97.4140810 A:min +97.4140810 98.8072780 D:min +98.8072780 100.2353060 G +100.2353060 105.8777550 C:7 +105.8777550 108.7221990 F:7 +108.7221990 111.5202040 C:7 +111.5202040 112.9366210 G +112.9366210 114.4110880 F:7 +114.4110880 115.8042850 F:7 +115.8042850 117.2207020 C:7 +117.2207020 118.6022900 E:min +118.6022900 119.9258270 A:min +119.9258270 121.2145350 E:min +121.2145350 122.6425620 A:min +122.6425620 124.0009290 D:min +124.0009290 125.4753960 G +125.4753960 130.1654609 C:7 +130.1654609 132.9035693 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/03final/08_-_Any_Time_At_All.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,79 @@ +0.0000000 0.4403951 N +0.4403951 1.6549880 N +1.6549880 3.1178450 B:min +3.1178450 4.9638320 D +4.9638320 8.3191150 A +8.3191150 9.9793420 B:min +9.9793420 10.8617000 G +10.8617000 11.6976190 A +11.6976190 15.0296820 D +15.0296820 16.7015190 D +16.7015190 18.3733560 F#:min/5 +18.3733560 20.0800220 B:min +20.0800220 21.7402490 G:min/b3 +21.7402490 23.4469160 D/5 +23.4469160 25.0839220 A/3 +25.0839220 26.7673690 D +26.7673690 28.4740360 F#:min/5 +28.4740360 30.1342630 B:min +30.1342630 31.8060990 G:min/b3 +31.8060990 33.4779360 D/5 +33.4779360 35.1381630 A/3 +35.1381630 38.5282760 D +38.5282760 40.0840130 B:min +40.0840130 41.9300000 D +41.9300000 45.3201130 A +45.3201130 47.0035600 B:min +47.0035600 47.8859180 G +47.8859180 48.7218360 A +48.7218360 52.1119500 D +52.1119500 53.8302260 D +53.8302260 55.5368930 F#:min/5 +55.5368930 57.2435600 B:min +57.2435600 58.9153960 G:min/b3 +58.9153960 60.6104530 D/5 +60.6104530 62.2822900 A/3 +62.2822900 63.9773460 D +63.9773460 65.6840130 F#:min/5 +65.6840130 67.3906800 B:min +67.3906800 69.1321760 G:min/b3 +69.1321760 70.7924030 D/5 +70.7924030 72.4758500 A/3 +72.4758500 74.2057360 D +74.2057360 75.9588430 D +75.9588430 77.4217000 B:min +77.4217000 79.3373460 D +79.3373460 82.6810200 A +82.6810200 84.3876870 B:min +84.3876870 85.3048750 G +85.3048750 86.1407930 A +86.1407930 89.5541260 D +89.5541260 90.4248750 A:7/5 +90.4248750 91.2840130 B:min/5 +91.2840130 93.0139000 A:7/b7 +93.0139000 93.8498180 A:7/5 +93.8498180 94.6857360 B:min/5 +94.6857360 95.8467340 A:7/b7 +95.8467340 96.0673240 A +96.0673240 96.3575730 G# +96.3575730 97.4953510 G +97.4953510 97.7623800 G +97.7623800 98.0526300 G# +98.0526300 99.1787980 A +99.1787980 99.4806570 A +99.4806570 99.7825170 G# +99.7825170 101.5124030 G +101.5124030 103.2190700 A +103.2190700 106.6440130 D +106.6440130 108.1533100 B:min +108.1533100 110.0573460 D +110.0573460 113.5171200 A +113.5171200 115.2470060 B:min +115.2470060 116.1177550 G +116.1177550 116.9768930 A +116.9768930 118.7300000 D +118.7300000 120.4134460 D +120.4134460 122.1317230 G +122.1317230 123.8267800 A +123.8267800 129.0166042 D +129.0166042 133.2099311 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/03final/09_-_I'll_Cry_Instead.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,42 @@ +0.0000000 0.4403951 N +0.4403951 3.0249650 G +3.0249650 10.4205210 G +10.4205210 12.9050560 D:7 +12.9050560 17.8392970 C:7 +17.8392970 19.1396140 G +19.1396140 20.2773920 D +20.2773920 22.7851470 G +22.7851470 27.7774370 G +27.7774370 30.2155320 G +30.2155320 32.7000680 D:7 +32.7000680 37.6807480 C:7 +37.6807480 38.9578450 G +38.9578450 40.2001130 D +40.2001130 42.6962580 G +42.6962580 45.2388430 B:min +45.2388430 46.4521048 A +46.4521048 47.7117680 A:7 +47.7117680 50.2195230 D +50.2195230 51.4734010 E:min +51.4734010 52.0887300 A +52.0887300 52.7272780 D +52.7272780 60.1460540 G +60.1460540 62.6770290 D:7 +62.6770290 67.6577090 C:7 +67.6577090 68.8999770 G +68.8999770 70.1190240 D +70.1190240 72.6616090 G +72.6616090 75.1577550 B:min +75.1577550 77.6190700 A +77.6190700 80.0803850 D +80.0803850 81.3574820 E:min +81.3574820 81.9379810 A +81.9379810 82.6113600 D +82.6113600 87.4991600 G +87.4991600 90.0301360 G +90.0301360 92.4798410 D:7 +92.4798410 97.4489110 C:7 +97.4489110 98.6293453 G +98.6293453 99.8521760 D +99.8521760 103.5119861 G +103.5119861 107.6670178 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/03final/10_-_Things_We_Said_Today.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,46 @@ +0.0000000 0.4020998 N +0.4020998 4.0466430 A:min +4.0466430 17.7580270 A:min +17.7580270 19.4995230 C +19.4995230 21.2178000 C:9 +21.2178000 22.9360770 F +22.9360770 24.6775730 Bb +24.6775730 31.6087300 A:min +31.6087300 33.3850560 A:min +33.3850560 47.2938090 A:min +47.2938090 49.0120860 C +49.0120860 50.7187520 C:9 +50.7187520 52.4950790 F +52.4950790 54.1901360 Bb +54.1901360 59.4030150 A:min +59.4030150 61.2373920 A +61.2373920 62.9208390 A +62.9208390 64.6855550 D +64.6855550 66.4270520 B +66.4270520 68.1337180 E:7 +68.1337180 69.8519950 A +69.8519950 71.5934920 D +71.5934920 73.3117680 B +73.3117680 75.0416550 Bb +75.0416550 89.1361670 A:min +89.1361670 90.8660540 C +90.8660540 92.6423800 C:9 +92.6423800 94.3606570 F +94.3606570 96.1369840 Bb +96.1369840 101.2221540 A:min +101.2221540 103.1494100 A +103.1494100 104.8212470 A +104.8212470 106.5975730 D +106.5975730 108.3042400 B +108.3042400 110.0921760 E:7 +110.0921760 111.7524030 A +111.7524030 113.4822900 D +113.4822900 115.2353960 B +115.2353960 116.9304530 Bb +116.9304530 131.0249650 A:min +131.0249650 132.7200220 C +132.7200220 134.5195690 C:9 +134.5195690 136.2610650 F +136.2610650 137.9909520 Bb +137.9909520 153.2191848 A:min +153.2191848 158.7145492 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/03final/11_-_When_I_Get_Home.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,58 @@ +0.0000000 1.2138090 N +1.2138090 4.8129020 A +4.8129020 8.7835140 A +8.7835140 10.6643310 D:9 +10.6643310 12.5103170 G +12.5103170 14.3911330 A:min +14.3911330 16.1790700 G +16.1790700 18.0714960 C +18.0714960 19.9639220 F +19.9639220 21.8447390 C +21.8447390 23.7255550 F +23.7255550 25.6063710 C +25.6063710 27.5104080 F +27.5104080 31.0398410 G +31.0398410 34.8014730 A +34.8014730 38.7720860 A +38.7720860 40.6645120 D:9 +40.6645120 42.5685480 G +42.5685480 44.4377550 A:min +44.4377550 46.2373010 G +46.2373010 48.1181170 C +48.1181170 50.0221540 F +50.0221540 51.8913600 C +51.8913600 53.8070060 F +53.8070060 55.7110430 C +55.7110430 57.6150790 F +57.6150790 61.1574702 G +61.1574702 64.8945350 A +64.8945350 68.9696370 A +68.9696370 70.8504530 D:9 +70.8504530 72.7544890 G +72.7544890 76.6090020 A:min +76.6090020 78.5478680 C +78.5478680 80.4635140 A:min +80.4635140 82.3791600 C +82.3791600 84.2831970 A:min +84.2831970 86.1872330 F +86.1872330 88.0796590 G +88.0796590 90.0185260 F +90.0185260 91.9690020 G +91.9690020 93.9426980 A:min +93.9426980 95.7770740 G +95.7770740 97.7507700 C +97.7507700 99.6896370 F +99.6896370 101.5936730 C +101.5936730 103.4628790 F +103.4628790 105.4597950 C +105.4597950 107.3754420 F +107.3754420 111.0209750 G +111.0209750 114.8174370 A +114.8174370 118.9041490 A +118.9041490 120.7849650 D:9 +120.7849650 122.7354420 G +122.7354420 126.6247840 A +126.6247840 128.5636500 D:9 +128.5636500 130.3399770 G +130.3399770 134.0524260 C +134.0524260 138.1691624 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/03final/12_-_You_Can't_Do_That.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,59 @@ +0.0000000 0.4212474 N +0.4212474 4.0582530 G +4.0582530 7.7966660 G:7 +7.7966660 15.1573920 G:7 +15.1573920 18.8958040 C:7 +18.8958040 22.6458270 G:7 +22.6458270 24.2131740 D:7 +24.2131740 26.2333100 C:7 +26.2333100 28.0909070 G:7 +28.0909070 29.9252830 D:7 +29.9252830 37.3904980 G:7 +37.3904980 41.1405210 C:7 +41.1405210 44.8905440 G:7 +44.8905440 46.6668700 D:7 +46.6668700 48.5128570 C:7 +48.5128570 52.1816090 G:7 +52.1816090 54.0508160 B +54.0508160 55.9200220 E:min +55.9200220 56.8488200 A:min +56.8488200 57.7892290 B:min +57.7892290 59.6352150 G +59.6352150 61.4463710 B +61.4463710 63.3736280 E:min +63.3736280 65.2660540 A:min +65.2660540 66.0439220 B:min +66.0439220 67.2049200 D +67.2049200 74.6469160 G:7 +74.6469160 78.3969380 C:7 +78.3969380 82.1237410 G:7 +82.1237410 83.9232870 D:7 +83.9232870 85.7808840 C:7 +85.7808840 87.6194688 G:7 +87.6194688 89.4612470 D:7 +89.4612470 96.9845120 G:7 +96.9845120 100.6416550 C:7 +100.6416550 104.3452380 G:7 +104.3452380 106.2144440 D:7 +106.2144440 108.0952600 C:7 +108.0952600 111.7756230 G:7 +111.7756230 113.7144890 B +113.7144890 115.4983907 E:min +115.4983907 116.5124940 A:min +116.5124940 117.4412920 B:min +117.4412920 119.3104980 G +119.3104980 121.1913150 B +121.1913150 123.1417910 E:min +123.1417910 125.0226070 A:min +125.0226070 125.7424260 B:min +125.7424260 126.9846930 D +126.9846930 134.5427890 G:7 +134.5427890 138.3044210 C:7 +138.3044210 142.0776640 G:7 +142.0776640 143.8656000 D:7 +143.8656000 145.8044670 C:7 +145.8044670 148.5280201 G +148.5280201 149.0900900 F +149.0900900 150.4236336 F# +150.4236336 152.8170850 G +152.8170850 157.4316593 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/03final/13_-_I'll_Be_Back.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,68 @@ +0.0000000 0.4212474 N +0.4212474 1.1488567 F#:(1,4,b7) +1.1488567 4.9975265 A +4.9975265 7.0536280 A:min +7.0536280 8.8996140 G +8.8996140 10.8733100 F +10.8733100 12.7425170 E +12.7425170 16.5389790 A +16.5389790 18.4778450 A:min +18.4778450 20.3470520 G +20.3470520 22.2510880 F +22.2510880 24.1435140 E +24.1435140 27.9748070 A +27.9748070 31.7712690 F#:min +31.7712690 35.5445120 B:min +35.5445120 36.5197500 E +36.5197500 37.2803990 D +37.2803990 38.4702260 E +38.4702260 39.2016550 D +39.2016550 40.4090920 E +40.4090920 42.3827890 A:min +42.3827890 44.2171650 G +44.2171650 46.1676410 F +46.1676410 48.0368480 E +48.0368480 51.9378000 A +51.9378000 53.8766660 A:min +53.8766660 55.7807020 G +55.7807020 57.6731290 F +57.6731290 59.6003850 E +59.6003850 61.5508610 A +61.5508610 63.4897270 A +63.4897270 67.3674600 B:min +67.3674600 71.1987520 C#:min +71.1987520 73.1376190 F#:min +73.1376190 75.0880950 B +75.0880950 76.9689110 D +76.9689110 77.9557590 E +77.9557590 78.6871880 D +78.6871880 79.8946250 E +79.8946250 80.6608840 D +80.6608840 81.8218820 E +81.8218820 83.7491380 A:min +83.7491380 85.7228340 G +85.7228340 87.6268700 F +87.6268700 89.5657360 E +89.5657360 91.5510430 A +91.5510430 93.4666890 A +93.4666890 95.3823350 A:min +95.3823350 97.3328110 G +97.3328110 99.3297270 F +99.3297270 101.2221540 E +101.2221540 103.1842400 A +103.1842400 105.1463260 A +105.1463260 109.0588880 F#:min +109.0588880 112.9482310 B:min +112.9482310 113.9118590 E +113.9118590 114.6432870 D +114.6432870 115.8275050 E +115.8275050 116.6402040 D +116.6402040 117.8128110 E +117.8128110 119.7748970 A:min +119.7748970 121.7137640 G +121.7137640 123.6874600 F +123.6874600 125.5798860 E +125.5798860 131.2687750 A +131.2687750 135.2161670 A:min +135.2161670 139.0939000 A +139.0939000 140.3136948 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/03final/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +/.DS_Store/1.1.1.1/Fri Sep 23 09:24:37 2005// +/01_-_A_Hard_Day's_Night.lab/1.2/Thu Jul 27 22:30:52 2006// +/02_-_I_Should_Have_Known_Better.lab/1.3/Tue Oct 17 21:53:05 2006// +/03_-_If_I_Fell.lab/1.1.1.1/Fri Sep 23 09:24:37 2005// +/04_-_I'm_Happy_Just_To_Dance_With_You.lab/1.2/Fri Apr 21 15:20:15 2006// +/05_-_And_I_Love_Her.lab/1.1.1.1/Fri Sep 23 09:24:37 2005// +/06_-_Tell_Me_Why.lab/1.1.1.1/Fri Sep 23 09:24:37 2005// +/07_-_Can't_Buy_Me_Love.lab/1.1.1.1/Fri Sep 23 09:24:37 2005// +/08_-_Any_Time_At_All.lab/1.2/Thu Jul 27 21:47:55 2006// +/09_-_I'll_Cry_Instead.lab/1.2/Thu Jul 27 21:47:55 2006// +/10_-_Things_We_Said_Today.lab/1.2/Thu Jul 27 21:47:55 2006// +/11_-_When_I_Get_Home.lab/1.2/Thu Jul 27 21:47:55 2006// +/12_-_You_Can't_Do_That.lab/1.2/Thu Jul 27 21:47:55 2006// +/13_-_I'll_Be_Back.lab/1.4/Tue Oct 17 21:53:05 2006// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/03final/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +chrish/transcriptions/03final
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/03final/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:matthiasm@dspmac1.elec.qmul.ac.uk:/cvsroot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/04final/01_-_No_Reply.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,57 @@ +0.0000000 1.1105610 N +1.1105610 2.3591530 C +2.3591530 4.2316221 F +4.2316221 6.2136650 G +6.2136650 10.0333480 C:maj6 +10.0333480 11.9954340 F +11.9954340 13.9110800 G +13.9110800 17.5200640 C:maj6 +17.5200640 19.4026000 A:min +19.4026000 21.2950260 E:min +21.2950260 23.1294020 F:maj7 +23.1294020 25.3004680 E:min +25.3004680 27.2509440 D:min +27.2509440 29.1665900 G +29.1665900 33.0327130 C:maj6 +33.0327130 34.9367490 F +34.9367490 36.8523950 G +36.8523950 40.7069080 C:maj6 +40.7069080 42.6806040 F +42.6806040 44.5564910 G +44.5564910 48.1721230 C:maj6 +48.1721230 50.0297200 A:min +50.0297200 51.9569760 E:min +51.9569760 53.8145720 F:maj7 +53.8145720 55.9159780 E:min +55.9159780 57.8432340 F +57.8432340 59.7821010 G +59.7821010 63.7062730 C:maj6 +63.7062730 65.6219190 C +65.6219190 67.5143460 E +67.5143460 71.3688580 A +71.3688580 73.2612840 D:min +73.2612840 75.1885410 F +75.1885410 78.9850030 C +78.9850030 80.9006490 C +80.9006490 82.7930760 E +82.7930760 86.6243680 A +86.6243680 88.5516240 D:min +88.5516240 90.4440510 F +90.4440510 94.2985630 C +94.2985630 96.2606490 F +96.2606490 98.1879060 G +98.1879060 102.0656380 C:maj6 +102.0656380 103.9928940 F +103.9928940 105.9433710 G +105.9433710 109.5772930 C:maj6 +109.5772930 111.4465000 A:min +111.4465000 113.3273160 E:min +113.3273160 115.2313520 F:maj7 +115.2313520 117.3443680 E:min +117.3443680 119.3412840 F +119.3412840 121.2104910 G +121.2104910 124.8444140 C:maj6 +124.8444140 126.7368400 A:min +126.7368400 128.6292660 E:min +128.6292660 130.4288130 F:maj7 +130.4288130 133.3631120 C:maj9
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/04final/02_-_I'm_a_Loser.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,116 @@ +0.0000000 0.4403950 N +0.4403950 1.1488567 D +1.1488567 2.8338465 A:min7 +2.8338465 5.0089350 D +5.0089350 6.9314353 A:min7 +6.9314353 8.7696059 D +8.7696059 10.0907910 A:min +10.0907910 10.7790930 F +10.7790930 11.4989120 D +11.4989120 12.7992290 G +12.7992290 14.0763270 D +14.0763270 15.3882540 F +15.3882540 16.7350120 G +16.7350120 18.0585490 G +18.0585490 19.3820870 D +19.3820870 20.6940140 F +20.6940140 22.0407710 G +22.0407710 23.3643090 G +23.3643090 24.6530160 D +24.6530160 25.9997740 F +25.9997740 27.3233110 G +27.3233110 28.6584580 G +28.6584580 29.9936060 D +29.9936060 31.2358730 F +31.2358730 32.5826310 G +32.5826310 33.9409980 A:min7 +33.9409980 35.2761450 D +35.2761450 36.6112930 A:min7 +36.6112930 37.9928800 D +37.9928800 39.3396380 G +39.3396380 40.6515650 E:min +40.6515650 42.0099320 A:min +42.0099320 42.6833110 F +42.6833110 43.3450800 D +43.3450800 44.6453970 G +44.6453970 46.0269840 D +46.0269840 47.3273020 F +47.3273020 48.6740590 G +48.6740590 49.9975970 G +49.9975970 51.3211340 D +51.3211340 52.6446720 F +52.6446720 54.0030390 G +54.0030390 55.3381860 G +55.3381860 56.6385040 D +56.6385040 57.9504310 F +57.9504310 59.3087990 G +59.3087990 60.6555560 G +60.6555560 61.9674830 D +61.9674830 63.2910210 F +63.2910210 64.6493880 G +64.6493880 65.9613160 A:min7 +65.9613160 67.2848530 D +67.2848530 68.6200000 A:min7 +68.6200000 69.9435380 D +69.9435380 71.2786850 G +71.2786850 72.6022230 E:min +72.6022230 73.9257600 A:min +73.9257600 74.6223590 F +74.6223590 75.3073470 D +75.3073470 76.6308850 G +76.6308850 77.9776420 D +77.9776420 79.3360090 F +79.3360090 80.6595470 G +80.6595470 82.0063040 G +82.0063040 83.3530620 D +83.3530620 84.7346490 F +84.7346490 86.0930160 G +86.0930160 87.4049440 A:min +87.4049440 88.7517010 D +88.7517010 90.0868480 A:min +90.0868480 91.4452160 D +91.4452160 92.8500230 G +92.8500230 94.1735600 E:min +94.1735600 95.5087080 A:min +95.5087080 96.0635654 F +96.0635654 96.8786850 D:7 +96.8786850 98.2370530 G +98.2370530 99.5722000 D +99.5722000 100.9653970 F +100.9653970 102.3121550 G +102.3121550 103.6473020 G +103.6473020 105.0056690 D +105.0056690 106.3408170 F +106.3408170 107.6875740 G +107.6875740 109.0575510 G +109.0575510 110.3578690 D +110.3578690 111.7046260 F +111.7046260 113.0513840 G +113.0513840 114.3865310 G +114.3865310 115.7216780 D +115.7216780 117.0219960 F +117.0219960 118.3571430 G +118.3571430 119.6806810 A:min7 +119.6806810 121.0042180 D +121.0042180 122.3161450 A:min7 +122.3161450 123.6396830 D +123.6396830 125.0096600 G +125.0096600 126.3448080 E:min +126.3448080 127.7147850 A:min +127.7147850 128.3881640 F +128.3881640 129.0615420 D +129.0615420 130.4083000 G +130.4083000 131.7086170 D +131.7086170 133.1250340 F +133.1250340 134.3789120 G +134.3789120 135.6560090 G +135.6560090 136.9911570 D +136.9911570 138.3379140 F +138.3379140 139.6846720 G +139.6846720 140.9849890 A:min +140.9849890 142.3317460 D:maj6 +142.3317460 143.6436740 A:min +143.6436740 144.9788210 D:maj6 +144.9788210 146.3720190 G +146.3720190 147.7187760 E:min +147.7187760 149.7853520 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/04final/03_-_Baby's_In_Black.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,83 @@ +0.0000000 0.4212470 N +0.4212470 1.2742550 A +1.2742550 2.1662340 E +2.1662340 3.9532110 A +3.9532110 5.7468920 A +5.7468920 7.5058640 E +7.5058640 9.3054100 D:7 +9.3054100 11.0701270 E +11.0701270 11.9640950 A +11.9640950 12.8000130 D +12.8000130 13.7237310 A +13.7237310 14.5415100 E +14.5415100 16.3294460 A +16.3294460 18.1173830 A +18.1173830 18.9545500 A +18.9545500 19.8821000 A:7/3 +19.8821000 21.6584260 D +21.6584260 23.4115330 D +23.4115330 24.3403310 A +24.3403310 25.2110790 E +25.2110790 26.9990160 A +26.9990160 28.7521220 A +28.7521220 30.4820090 E +30.4820090 32.3047750 D:7 +32.3047750 34.0811020 E +34.0811020 34.9750700 A +34.9750700 35.8109880 D +35.8109880 37.5989250 A +37.5989250 39.3752510 A +39.3752510 41.1747980 A +41.1747980 42.0107160 A +42.0107160 42.8930750 A:7/3 +42.8930750 44.7158410 D +44.7158410 46.4573380 D +46.4573380 47.3861360 A +47.3861360 48.2336640 E +48.2336640 49.9983810 A +49.9983810 51.7979270 F#:min7 +51.7979270 53.5510340 B +53.5510340 55.3748916 D +55.3748916 57.0572470 E +57.0572470 58.9032330 A +58.9032330 60.6795600 E +60.6795600 62.4326660 D:7 +62.4326660 64.2322130 E +64.2322130 65.1610110 A +65.1610110 66.0433690 D +66.0433690 67.8313060 A +67.8313060 69.6656820 A +69.6656820 71.4187890 E +71.4187890 73.1486760 D:7 +73.1486760 74.9366120 E +74.9366120 75.8654100 A +75.8654100 76.7709880 D +76.7709880 78.5357050 A +78.5357050 80.3468610 F#:min7 +80.3468610 82.1347980 B +82.1347980 83.9343440 D +83.9343440 85.6990610 E +85.6990610 87.5334370 A +87.5334370 89.3213740 E +89.3213740 91.0744800 D:7 +91.0744800 92.9669070 E +92.9669070 93.8957050 A +93.8957050 94.7316230 D +94.7316230 96.5195600 A +96.5195600 98.3539360 A +98.3539360 100.2579730 A +100.2579730 101.1287210 A +101.1287210 102.0342990 A:7 +102.0342990 103.8802850 D +103.8802850 105.6798320 D +105.6798320 106.5970200 A +106.5970200 107.4561580 E +107.4561580 109.2789250 A +109.2789250 111.0320320 A +111.0320320 112.8547980 E +112.8547980 114.7123940 D:7 +114.7123940 116.5583810 E +116.5583810 117.4755690 A +117.4755690 118.4043670 D +118.4043670 120.1923040 A +120.1923040 122.9016570 A
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/04final/04_-_Rock_and_Roll_Music.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,55 @@ +0.000000 0.446618 N +0.446618 2.408196 E:7 +2.408196 8.085475 A +8.085475 10.953139 D +10.953139 13.878853 A +13.878853 18.139715 E +18.139715 19.672232 A +19.672232 21.111869 E +21.111869 22.597946 A +22.597946 25.477221 E +25.477221 28.356495 A +28.356495 31.200939 D +31.200939 32.640577 E +32.640577 34.103434 E +34.103434 39.861982 A +39.861982 42.776087 D +42.776087 45.655361 A +45.655361 49.974273 E +49.974273 51.448740 A +51.448740 52.911597 E +52.911597 54.386064 A +54.386064 57.230509 E +57.230509 60.040123 A +60.040123 62.884568 D +62.884568 65.729012 E +65.729012 71.429511 A +71.429511 74.308785 D +74.308785 77.141620 A +77.141620 81.344431 E +81.344431 82.784069 A +82.784069 84.200486 E +84.200486 85.767833 A +85.767833 88.612277 E +88.612277 91.433502 A +91.433502 94.196676 D +94.196676 97.017901 E +97.017901 102.764840 A +102.764840 105.632504 D +105.632504 108.511778 A +108.511778 112.726200 E +112.726200 114.258717 A +114.258717 115.605475 E +115.605475 117.114772 A +117.114772 119.994046 E +119.994046 122.815270 A +122.815270 125.601665 D +125.601665 127.064522 E +127.064522 128.446109 E:7 +128.446109 134.158218 A +134.158218 137.037493 D +137.037493 139.916767 A +139.916767 144.200849 E +144.200849 145.640486 A +145.640486 147.080123 E +147.080123 149.136676 A
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/04final/05_-_I'll_Follow_the_Sun.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,77 @@ +0.000000 0.446041 N +0.446041 1.323560 C +1.323560 2.252358 G +2.252358 3.146326 F +3.146326 4.075124 C +4.075124 5.805011 G +5.805011 7.534897 F:7 +7.534897 9.334444 C +9.334444 11.064331 D +11.064331 11.946689 C +11.946689 12.852267 E:min/5 +12.852267 13.723015 D +13.723015 14.593764 G +14.593764 15.522562 C +15.522562 16.393310 G +16.393310 17.298888 F +17.298888 18.204467 C +18.204467 20.027233 G +20.027233 21.780340 F:7 +21.780340 23.591496 C +23.591496 25.356213 D +25.356213 26.203741 C +26.203741 27.144149 E:min/5 +27.144149 28.049727 D +28.049727 28.908866 G +28.908866 30.731632 C +30.731632 32.566009 C:7 +32.566009 34.365555 D:min +34.365555 36.199931 F:min +36.199931 37.987868 C +37.987868 39.740975 C:7 +39.740975 41.552131 D:min +41.552131 43.351678 F:min +43.351678 45.139614 C +45.139614 46.962380 D:min +46.962380 48.773537 G +48.773537 50.538253 F:7 +50.538253 52.361020 C +52.361020 54.102517 D +54.102517 55.019705 C +55.019705 55.913673 E:min/5 +55.913673 56.830861 D +56.830861 57.748049 G +57.748049 58.676848 C +58.676848 59.570816 G +59.570816 60.464784 F +60.464784 61.381972 C +61.381972 63.181519 G +63.181519 64.957845 F:7 +64.957845 66.745782 C +66.745782 68.498888 D +68.498888 69.427687 C +69.427687 70.321655 E:min/5 +70.321655 71.215623 D +71.215623 72.097981 G +72.097981 73.932358 C +73.932358 75.743514 C:7 +75.743514 77.566281 D:min +77.566281 79.389047 F:min +79.389047 81.188594 C +81.188594 82.953310 C:7 +82.953310 84.776077 D:min +84.776077 86.598843 F:min +86.598843 88.410000 C +88.410000 90.244376 D:min +90.244376 92.032312 G +92.032312 93.843469 F:7 +93.843469 95.666235 C +95.666235 97.419342 D +97.419342 98.301700 C +98.301700 99.218888 E:min/5 +99.218888 100.089637 D +100.089637 100.995215 G +100.995215 101.924013 C +101.924013 102.829591 G +102.829591 103.723560 F +103.723560 106.537309 C
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/04final/06_-_Mr._Moonlight.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,99 @@ +0.0000000 0.4403951 N +0.4403951 5.6653020 F# +5.6653020 6.5692830 F# +6.5692830 7.4980810 F#:sus4 +7.4980810 8.4552950 F# +8.4552950 9.3324570 F#:sus4 +9.3324570 10.2960850 F# +10.2960850 11.2016640 F#:sus4 +11.2016640 12.1304620 F# +12.1304620 13.0824800 F#:sus4 +13.0824800 14.0112780 F# +14.0112780 14.9284660 F#:sus4 +14.9284660 15.8804840 F# +15.8804840 16.8208930 F# +16.8208930 20.5476950 B +20.5476950 21.5229330 F# +21.5229330 22.4517320 F#:sus4 +22.4517320 23.3457000 F# +23.3457000 24.3093280 F#:sus4 +24.3093280 31.8325930 D#:min +31.8325930 35.5942260 B +35.5942260 36.5578540 F# +36.5578540 37.4866520 F#:sus4 +37.4866520 39.4255180 F# +39.4255180 43.1639310 B +43.1639310 45.0215280 F# +45.0215280 46.9255640 D#:7 +46.9255640 48.8063800 G# +48.8063800 50.8149060 C#:7 +50.8149060 51.7901440 F# +51.7901440 52.7305520 F#:sus4 +52.7305520 53.6825710 F# +53.6825710 54.5997590 F#:sus4 +54.5997590 55.5866070 F# +55.5866070 56.5037950 F#:sus4 +56.5037950 57.5022530 F# +57.5022530 58.4194410 F#:sus4 +58.4194410 59.3830690 F# +59.3830690 60.3583080 F#:sus4 +60.3583080 61.2987160 F# +61.2987160 62.2507340 F#:sus4 +62.2507340 63.1795320 F# +63.1795320 64.1779900 F#:sus4 +64.1779900 65.1067880 F# +65.1067880 66.0704160 F#:sus4 +66.0704160 67.0224350 F# +67.0224350 67.9744530 F#:sus4 +67.9744530 69.9481490 F# +69.9481490 73.7446110 B +73.7446110 75.6486480 F# +75.6486480 77.5875140 D#:7 +77.5875140 79.4683300 G# +79.4683300 81.4768560 C#:7 +81.4768560 82.4404840 F# +82.4404840 83.3808930 F#:sus4 +83.3808930 84.3329110 F# +84.3329110 85.2849290 F#:sus4 +85.2849290 100.5520490 F# +100.5520490 102.4676950 B +102.4676950 104.3369020 B +104.3369020 106.2409380 F# +106.2409380 108.0985340 D#:7 +108.0985340 110.0374010 G# +110.0374010 112.0343170 C#:7 +112.0343170 112.9747250 F# +112.9747250 113.9383530 F#:sus4 +113.9383530 114.9019810 F# +114.9019810 115.8307790 F#:sus4 +115.8307790 116.7827970 F# +116.7827970 117.7232060 F#:sus4 +117.7232060 118.6752240 F# +118.6752240 119.6388520 F#:sus4 +119.6388520 120.5676500 F# +120.5676500 121.5196680 F#:sus4 +121.5196680 122.4600760 F# +122.4600760 123.4353140 F#:sus4 +123.4353140 124.3989430 F# +124.3989430 125.3857910 F#:sus4 +125.3857910 126.3029790 F# +126.3029790 127.2549970 F#:sus4 +127.2549970 128.2186250 F# +128.2186250 129.1474230 F#:sus4 +129.1474230 131.0862890 F# +131.0862890 134.8014820 B +134.8014820 136.7055180 F# +136.7055180 138.6095550 D#:7 +138.6095550 140.5019810 G# +140.5019810 142.5221170 C#:7 +142.5221170 143.5321850 F# +143.5321850 144.4493730 F#:sus4 +144.4493730 145.3549520 F# +145.3549520 146.3534100 F#:sus4 +146.3534100 147.3170380 F# +147.3170380 148.2690560 F#:sus4 +148.2690560 149.2210740 F# +149.2210740 150.1847020 F#:sus4 +150.1847020 151.1483300 F# +151.1483300 152.1583980 F#:sus4 +152.1583980 153.0755860 F#
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/04final/07_-_Kansas_City-_Hey,_Hey,_Hey,_Hey.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,61 @@ +0.000000 0.439592 N +0.439592 1.108536 D +1.108536 2.841419 G:7 +2.841419 4.606135 C:7 +4.606135 6.599499 G:7 +6.599499 6.812031 C +6.812031 7.102281 C# +7.102281 8.158789 D:7 +8.158789 15.298925 G +15.298925 18.956067 C +18.956067 22.589990 G +22.589990 24.401147 D +24.401147 26.189083 C +26.189083 28.202000 G +28.202000 28.461467 C +28.461467 28.800769 C# +28.800769 29.788176 D +29.788176 37.079242 G +37.079242 40.713165 C +40.713165 44.370308 G +44.370308 46.169854 D +46.169854 47.981011 C +47.981011 50.094027 G +50.094027 50.268176 C +50.268176 50.593256 C# +50.593256 51.614934 D:7 +51.614934 58.882779 G +58.882779 62.493482 C +62.493482 66.150625 G +66.150625 68.019832 D +68.019832 69.830988 C +69.830988 71.632681 G +71.632681 73.499741 D +73.499741 80.802417 G +80.802417 84.447950 C +84.447950 88.105092 G +88.105092 89.904639 D +89.904639 91.750625 C +91.750625 93.567570 G +93.567570 95.419378 D:7 +95.419378 102.768494 G +102.768494 106.460467 C +106.460467 110.129219 G +110.129219 111.986816 D +111.986816 113.786362 C +113.786362 115.642172 G +115.642172 115.921597 D +115.921597 116.154798 C +116.154798 116.421827 C# +116.421827 117.358642 D +117.358642 124.723496 G +124.723496 128.426544 C +128.426544 132.106906 G +132.106906 133.918063 D +133.918063 135.729219 C +135.729219 137.577061 G +137.577061 137.868975 D +137.868975 138.088131 C +138.088131 138.364684 C# +138.364684 139.397972 D +139.397972 144.682448 G
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/04final/08_-_Eight_Days_a_Week.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,101 @@ +0.000000 0.414094 N +0.414094 1.329503 D +1.329503 1.770682 D:maj6 +1.770682 2.258301 D +2.258301 3.117439 E/b7 +3.117439 3.593448 E:maj6/b7 +3.593448 3.999798 E/b7 +3.999798 4.893766 G/5 +4.893766 5.323335 G:maj6/5 +5.323335 5.764514 G/5 +5.764514 6.623653 D +6.623653 7.064832 D:maj6 +7.064832 7.494401 D +7.494401 9.212677 D +9.212677 10.919344 E +10.919344 12.660841 G +12.660841 14.425557 D +14.425557 16.143834 D +16.143834 17.908550 E +17.908550 19.638437 G +19.638437 21.310274 D +21.310274 23.133040 B:min +23.133040 24.828097 G +24.828097 26.534764 B:min +26.534764 28.241430 E +28.241430 29.948097 D +29.948097 31.654764 E +31.654764 33.349820 G +33.349820 35.114537 D +35.114537 36.867643 D +36.867643 38.574310 E +38.574310 40.315807 G +40.315807 42.045693 D +42.045693 43.763970 D +43.763970 45.505467 E +45.505467 47.223743 G +47.223743 48.930410 D +48.930410 50.671906 B:min +50.671906 52.378573 G +52.378573 54.027190 B:min +54.027190 55.780296 E +55.780296 57.521793 D +57.521793 59.205240 E +59.205240 60.888687 G +60.888687 62.630183 D +62.630183 66.101566 A +66.101566 69.665829 B:min +69.665829 73.125603 E +73.125603 74.843879 G +74.843879 76.608596 A:7 +76.608596 78.292043 D +78.292043 80.010319 E +80.010319 81.728596 G +81.728596 83.481702 D +83.481702 85.223199 D +85.223199 86.906646 E +86.906646 88.648142 G +88.648142 90.389639 D +90.389639 92.096306 B:min +92.096306 93.814582 G +93.814582 95.544469 B:min +95.544469 97.274355 E +97.274355 98.981022 D +98.981022 100.699299 E +100.699299 102.417575 G +102.417575 104.170682 D +104.170682 107.618845 A +107.618845 111.183108 B:min +111.183108 114.642882 E +114.642882 116.384378 G +116.384378 118.114265 A:7 +118.114265 119.832541 D +119.832541 121.550818 E +121.550818 123.303925 G +123.303925 125.068641 D +125.068641 126.786918 D +126.786918 128.563244 E +128.563244 130.339571 G +130.339571 132.081067 D +132.081067 133.776124 B:min +133.776124 135.506011 G +135.506011 137.189457 B:min +137.189457 138.919344 E +138.919344 140.660841 D +140.660841 142.379117 E +142.379117 144.085784 G +144.085784 145.815671 D +145.815671 147.510727 G +147.510727 149.240614 D +149.240614 150.970501 G +150.970501 152.711997 D +152.711997 153.582745 D +153.582745 154.058755 D:maj6 +154.058755 154.511544 D +154.511544 155.370682 E/b7 +155.370682 155.858301 E:maj6/b7 +155.858301 156.311090 E/b7 +156.311090 157.158618 G/5 +157.158618 157.599798 G:maj6/5 +157.599798 158.075807 G/5 +158.075807 161.214817 D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/04final/09_-_Words_of_Love.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,101 @@ +0.000000 0.426857 N +0.426857 0.656213 E +0.656213 2.557446 A +2.557446 3.509465 D +3.509465 4.461483 E +4.461483 6.319079 A +6.319079 7.282707 D +7.282707 8.269555 E +8.269555 10.161982 A +10.161982 11.137220 D +11.137220 12.089238 E +12.089238 14.062934 A +14.062934 15.014952 D +15.014952 15.966970 E +15.966970 17.894226 A +17.894226 18.881075 D +18.881075 19.856313 E +19.856313 21.771959 A +21.771959 22.747197 D +22.747197 23.734045 E +23.734045 25.672911 A +25.672911 26.636539 D +26.636539 27.623387 E +27.623387 29.550644 A +29.550644 30.537492 D +30.537492 31.512730 E +31.512730 33.463206 A +33.463206 34.450054 D +34.450054 35.436902 E +35.436902 37.364158 A +37.364158 38.362617 D +38.362617 39.361075 E +39.361075 41.299941 A +41.299941 42.321619 D +42.321619 43.296857 E +43.296857 45.247333 A +45.247333 46.269011 D +46.269011 47.197809 E +47.197809 49.194725 A +49.194725 50.158353 D +50.158353 51.145202 E +51.145202 53.142118 A +53.142118 54.140576 D +54.140576 55.115814 E +55.115814 57.077900 A +57.077900 58.064748 D +58.064748 59.063206 E +59.063206 61.066477 A +61.066477 62.025456 D +62.025456 62.964158 E +62.964158 64.914635 A +64.914635 65.901483 D +65.901483 66.888331 E +66.888331 68.838807 A +68.838807 69.790825 D +69.790825 70.800893 E +70.800893 72.751369 A +72.751369 73.726607 D +73.726607 74.725065 E +74.725065 76.752649 A +76.752649 77.745879 D +77.745879 78.660848 E +78.660848 80.611324 A +80.611324 81.621392 D +81.621392 82.585020 E +82.585020 84.547106 A +84.547106 85.522344 D +85.522344 86.520802 E +86.520802 88.482889 A +88.482889 89.492957 D +89.492957 90.479805 E +90.479805 92.453501 A +92.453501 93.417129 D +93.417129 94.427197 E +94.427197 96.412503 A +96.412503 97.387741 D +97.387741 98.409419 E +98.409419 100.406335 A +100.406335 101.393183 D +101.393183 102.356812 E +102.356812 104.353728 A +104.353728 105.352186 D +105.352186 106.350644 E +106.350644 108.347560 A +108.347560 109.311188 D +109.311188 110.309646 E +110.309646 112.318172 A +112.318172 113.305020 D +113.305020 114.303478 E +114.303478 116.323614 A +116.323614 117.322072 D +117.322072 118.285700 E +118.285700 120.282617 A +120.282617 121.292685 D +121.292685 122.256313 E +122.256313 124.241619 A +124.241619 125.251687 D +125.251687 126.226925 E +126.226925 128.258671 A +128.258671 129.245519 D +129.245519 133.024226 E
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/04final/10_-_Honey_Don't.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,64 @@ +0.000000 0.440395 N +0.440395 1.900495 E/5 +1.900495 3.398182 E/#4 +3.398182 4.837819 E/4 +4.837819 7.659045 E +7.659045 10.526708 E +10.526708 13.405983 C +13.405983 16.320087 E +16.320087 19.199361 C +19.199361 22.125076 B:9 +22.125076 25.015960 E +25.015960 30.820949 E +30.820949 33.711833 A +33.711833 36.625937 E +36.625937 39.516821 B:9 +39.516821 42.407706 E +42.407706 45.368251 E +45.368251 48.259135 C +48.259135 51.138410 E +51.138410 54.075733 C +54.075733 57.001447 B:9 +57.001447 59.927161 E +59.927161 65.836641 E +65.836641 68.715914 A +68.715914 71.630020 E +71.630020 74.590564 B:9 +74.590564 77.504667 E +77.504667 80.465211 E +80.465211 83.390927 C +83.390927 86.339861 E +86.339861 88.021569 C +88.021569 89.277185 B +89.277185 92.226118 E +92.226118 95.186663 E +95.186663 98.135598 A +98.135598 101.084531 E +101.084531 104.068296 B:9 +104.068296 106.959181 E +106.959181 109.931335 E +109.931335 112.880268 C +112.880268 115.829202 E +115.829202 118.801356 C +118.801356 121.738681 B:9 +121.738681 124.722446 E +124.722446 127.694600 E +127.694600 130.620314 E +130.620314 133.546029 A +133.546029 136.564623 E +136.564623 139.525167 B:9 +139.525167 142.508930 E +142.508930 145.504304 E +145.504304 148.406800 C +148.406800 151.367344 E +151.367344 152.663904 C +152.663904 154.293058 B:9 +154.293058 157.265211 E +157.265211 160.190926 E +160.190926 163.151470 A +163.151470 166.112014 E +166.112014 169.060948 B:9 +169.060948 170.624363 E +170.624363 171.232014 B:7 +171.232014 171.677482 E +171.677482 176.119728 E
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/04final/11_-_Every_Little_Thing.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,96 @@ +0.000000 0.420489 N +0.420489 1.921537 A +1.921537 2.385936 D +2.385936 3.744303 E +3.744303 5.694779 A +5.694779 6.194008 D +6.194008 7.645256 E +7.645256 9.618952 A +9.618952 10.129791 G +10.129791 11.062412 D +11.062412 11.312776 D/7 +11.312776 11.569428 A/3 +11.569428 12.567886 B:min +12.567886 13.508294 D/5 +13.508294 14.448702 E/3 +14.448702 15.423940 A +15.423940 17.444076 A +17.444076 17.908476 D +17.908476 19.382943 E +19.382943 21.321809 A +21.321809 21.797818 G +21.797818 22.756186 D +22.756186 23.019645 D/7 +23.019645 23.272285 A/3 +23.272285 24.270743 B:min +24.270743 25.234371 D/5 +25.234371 26.232829 E/3 +26.232829 27.184847 A +27.184847 29.112104 A +29.112104 31.050970 G +31.050970 32.989836 G +32.989836 34.882262 A +34.882262 36.809519 A +36.809519 38.736775 G +38.736775 40.664031 G +40.664031 42.556457 A +42.556457 44.530154 A +44.530154 45.017773 D +45.017773 46.445800 E +46.445800 48.442716 A +48.442716 48.941945 G +48.941945 49.899886 D +49.899886 50.165672 D/7 +50.165672 50.418143 A/3 +50.418143 51.414870 B:min +51.414870 52.378498 D/5 +52.378498 53.342126 E/3 +53.342126 54.346294 A +54.346294 56.314280 A +56.314280 56.848339 D +56.848339 58.346027 E +58.346027 60.331333 A +60.331333 60.830562 G +60.830562 61.796236 D +61.796236 62.048724 D/7 +62.048724 62.305029 A/3 +62.305029 63.326707 B:min +63.326707 64.290335 D/5 +64.290335 65.288793 E/3 +65.288793 66.275641 A +66.275641 68.168067 A +68.168067 70.153373 G +70.153373 72.103850 G +72.103850 74.007886 A +74.007886 75.969972 A +75.969972 77.920448 G +77.920448 79.824485 G +79.824485 81.737345 A +81.737345 83.655777 A +83.655777 84.166616 D +84.166616 85.617863 E +85.617863 87.498680 A +87.498680 87.986299 G +87.986299 89.140901 D +89.140901 89.414326 A/3 +89.414326 90.366344 B:min +90.366344 91.329972 D/5 +91.329972 92.270380 E/3 +92.270380 93.234008 A +93.234008 95.196095 A +95.196095 97.134961 G +97.134961 99.027387 G +99.027387 101.001083 A +101.001083 102.916729 A +102.916729 104.820766 G +104.820766 106.736412 G +106.736412 108.617228 A +108.617228 110.544485 A +110.544485 111.043714 D +111.043714 112.553011 E +112.553011 114.468657 A +114.468657 115.002716 D +115.002716 116.535233 E +116.535233 118.497319 A +118.497319 118.984938 D +118.984938 119.817442 E
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/04final/12_-_I_Don't_Want_to_Spoil_the_Party.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,93 @@ +0.0000000 0.4371360 N +0.4371360 1.6989590 G +1.6989590 2.3723380 G/b7 +2.3723380 3.0108870 E:min7 +3.0108870 4.2415440 D:sus4 +4.2415440 4.8452630 F:maj9 +4.8452630 5.4838120 C/3 +5.4838120 6.7260790 D:7 +6.7260790 7.3297980 D/2 +7.3297980 7.6330930 A:min/6 +7.6330930 7.9683470 A:min +7.9683470 10.4528820 G +10.4528820 12.9258070 G +12.9258070 15.3755120 G +15.3755120 17.8600480 G +17.8600480 20.3329730 D +20.3329730 21.5868500 E:min +21.5868500 22.8291180 B +22.8291180 24.0713850 A:min +24.0713850 25.3252630 D +25.3252630 26.5675310 G +26.5675310 27.8214080 F:7 +27.8214080 29.0404560 G +29.0404560 30.2943330 G +30.2943330 37.7131090 G +37.7131090 40.1628140 D +40.1628140 41.4166920 E:min +41.4166920 42.6241290 B +42.6241290 43.9012270 A:min +43.9012270 45.1318840 D +45.1318840 46.3509320 G +46.3509320 47.6396390 F:7 +47.6396390 50.1357840 G +50.1357840 52.6203200 G +52.6203200 53.8858070 E:min +53.8858070 55.1512950 A +55.1512950 56.3819520 C +56.3819520 57.6010000 D +57.6010000 60.1551950 G +60.1551950 61.3742430 E:min +61.3742430 62.6513400 A +62.6513400 63.9052180 C +63.9052180 65.1126550 D +65.1126550 72.6939700 G +72.6939700 75.2017260 D:7(#9) +75.2017260 76.4672130 E:min +76.4672130 77.7443110 B +77.7443110 79.0097980 A:min +79.0097980 80.2752860 D +80.2752860 81.4943330 G +81.4943330 82.7830410 F:7 +82.7830410 85.2327460 G +85.2327460 90.2830860 G +90.2830860 92.8488910 G +92.8488910 94.0973480 D:9 +94.0973480 95.3450360 D:7(#9) +95.3450360 96.6337440 E:min +96.6337440 97.9224510 B +97.9224510 99.1414990 A:min +99.1414990 100.3953760 D +100.3953760 101.6956940 G +101.6956940 102.9844010 F:7 +102.9844010 105.5385960 G +105.5385960 108.0695710 G +108.0695710 109.3466690 E:min +109.3466690 110.6353760 A +110.6353760 111.8544240 C +111.8544240 113.1083010 D +113.1083010 115.6508870 G +115.6508870 116.9047640 E:min +116.9047640 118.1818620 A +118.1818620 119.4241290 C +119.4241290 120.6431770 D +120.6431770 128.2128820 G +128.2128820 129.4391590 D:7(#9) +129.4391590 130.7670770 D:9 +130.7670770 132.0441750 E:min +132.0441750 133.3096620 B +133.3096620 134.5635400 A:min +134.5635400 135.8290270 D +135.8290270 137.0945150 G +137.0945150 138.3832220 F:7 +138.3832220 140.8793670 G +140.8793670 142.2145150 G +142.2145150 142.8184320 G:7 +142.8184320 143.4683920 E:min/b3 +143.4683920 144.7106600 D:7 +144.7106600 145.3375990 D:7 +145.3375990 146.0341970 D:7 +146.0341970 147.2996850 D +147.2996850 147.9382330 D +147.9382330 148.5535620 D:7 +148.5535620 151.6317630 G
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/04final/13_-_What_You're_Doing.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,72 @@ +0.0000000 0.4212470 N +0.4212470 7.5250110 N +7.5250110 9.3709970 D +9.3709970 11.1241040 G +11.1241040 12.9352600 D +12.9352600 14.7464170 G +14.7464170 16.5575730 D +16.5575730 18.4035600 G +18.4035600 20.3192060 D +20.3192060 22.1419720 G +22.1419720 24.0111790 B:min +24.0111790 27.6799310 G +27.6799310 29.5607480 D +29.5607480 31.4299540 G +31.4299540 33.3456000 D +33.3456000 35.1683670 G +35.1683670 37.0724030 D +37.0724030 38.9067790 G +38.9067790 40.7875960 B:min +40.7875960 44.4911790 G +44.4911790 46.3709760 D +46.3709760 48.2179810 G +48.2179810 50.0639680 B:min +50.0639680 51.8983440 G +51.8983440 53.7211110 B:min +53.7211110 57.4014730 E +57.4014730 61.1282760 A +61.1282760 62.9974820 D +62.9974820 64.8086390 G +64.8086390 66.6778450 D +66.6778450 68.5586620 G +68.5586620 70.4394780 B:min +70.4394780 74.1314510 G +74.1314510 76.0238770 D +76.0238770 77.8582530 G +77.8582530 79.7506800 D +79.7506800 81.5618360 G +81.5618360 83.4542620 D +83.4542620 85.2770290 G +85.2770290 87.1024833 B:min +87.1024833 88.9690020 G +88.9690020 90.8498180 G +90.8498180 92.7074140 D +92.7074140 94.5882310 G +94.5882310 96.4226070 B:min +96.4226070 98.2453740 G +98.2453740 100.0797500 B:min +100.0797500 103.8181630 E:min +103.8181630 107.5914050 A +107.5914050 109.4722220 D +109.4722220 111.2833780 G +111.2833780 113.1758040 D +113.1758040 115.0566210 G +115.0566210 116.9258270 B:min +116.9258270 118.7718130 G +118.7718130 120.6410200 G +120.6410200 122.5218360 D +122.5218360 124.3329930 G +124.3329930 126.2718590 D +126.2718590 128.1642850 G +128.1642850 130.0218820 D +130.0218820 131.9143080 G +131.9143080 132.4823219 D +132.4823219 133.7652118 D:(1) +133.7652118 135.5677080 N +135.5677080 139.2402040 A:(1) +139.2402040 141.0978000 D +141.0978000 142.9321760 G:7 +142.9321760 144.7665530 D +144.7665530 146.5893190 G:7 +146.5893190 148.4701350 D +148.4701350 150.3045120 G
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/04final/14_-_Everybody's_Trying_to_Be_My_Baby.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,57 @@ +0.000000 0.442301 N +0.442301 7.705280 E:7 +7.705280 10.549725 A +10.549725 13.370949 E +13.370949 14.752537 B:9 +14.752537 16.192174 A +16.192174 18.932128 E +18.932128 24.632627 E:7 +24.632627 27.488682 A +27.488682 30.298296 E +30.298296 31.714713 B:9 +31.714713 33.107911 A +33.107911 35.882695 E +35.882695 38.634260 E +38.634260 41.478704 E +41.478704 44.311539 A +44.311539 47.109543 E +47.109543 48.491131 B:9 +48.491131 49.919158 A +49.919158 52.728772 E +52.728772 58.382831 E:7 +58.382831 61.157616 A +61.157616 64.036890 E +64.036890 65.418478 B:9 +65.418478 66.858115 A +66.858115 69.621289 E +69.621289 72.396074 E:7(#9) +72.396074 75.182469 E:7(#9) +75.182469 78.026913 A:7 +78.026913 80.836527 E:7(#9) +80.836527 82.264555 B:9 +82.264555 83.669362 A:9 +83.669362 85.039339 E +85.039339 86.432537 E +86.432537 89.242151 E:7 +89.242151 92.121425 E:7 +92.121425 94.942650 A:9 +94.942650 97.775484 E +97.775484 99.215122 B:9 +99.215122 100.585099 A +100.585099 103.394713 E:7 +103.394713 109.013942 E:7 +109.013942 111.858387 A +111.858387 114.691221 E +114.691221 116.107639 B:9 +116.107639 117.489226 A +117.489226 120.322060 E +120.322060 125.918069 E:7 +125.918069 128.785734 A +128.785734 131.595348 E +131.595348 133.034986 B:9 +133.034986 134.486233 A +134.486233 135.949090 E +135.949090 136.489038 B:7 +136.489038 136.970768 E +136.970768 139.699113 E:7 +139.699113 142.630556 E:9
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/04final/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +/.DS_Store/1.1.1.1/Fri Sep 23 09:24:37 2005// +/01_-_No_Reply.lab/1.2/Thu Jul 27 21:48:40 2006// +/02_-_I'm_a_Loser.lab/1.3/Tue Oct 17 21:53:33 2006// +/03_-_Baby's_In_Black.lab/1.3/Tue Oct 17 21:53:34 2006// +/04_-_Rock_and_Roll_Music.lab/1.1.1.1/Fri Sep 23 09:24:37 2005// +/05_-_I'll_Follow_the_Sun.lab/1.1.1.1/Fri Sep 23 09:24:37 2005// +/06_-_Mr._Moonlight.lab/1.3/Thu Jul 27 22:35:20 2006// +/07_-_Kansas_City-_Hey,_Hey,_Hey,_Hey.lab/1.1.1.1/Fri Sep 23 09:24:37 2005// +/08_-_Eight_Days_a_Week.lab/1.2/Fri Jul 21 11:46:26 2006// +/09_-_Words_of_Love.lab/1.1.1.1/Fri Sep 23 09:24:37 2005// +/10_-_Honey_Don't.lab/1.1.1.1/Fri Sep 23 09:24:37 2005// +/11_-_Every_Little_Thing.lab/1.1.1.1/Fri Sep 23 09:24:37 2005// +/12_-_I_Don't_Want_to_Spoil_the_Party.lab/1.2/Thu Jul 27 21:48:40 2006// +/13_-_What_You're_Doing.lab/1.2/Thu Jul 27 21:48:40 2006// +/14_-_Everybody's_Trying_to_Be_My_Baby.lab/1.1.1.1/Fri Sep 23 09:24:37 2005// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/04final/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +chrish/transcriptions/04final
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/04final/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:matthiasm@dspmac1.elec.qmul.ac.uk:/cvsroot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/05final/01_-_Help!.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,59 @@ +0.0000000 1.0531186 N +1.0531186 3.5938540 B:min +3.5938540 6.0900000 G +6.0900000 8.6558040 E +8.6558040 11.1403400 A +11.1403400 13.6597050 A +13.6597050 16.1094100 C#:min +16.1094100 18.6868250 F#:min +18.6868250 19.3718140 D +19.3718140 19.9871420 G +19.9871420 21.2526300 A +21.2526300 23.7603850 A +23.7603850 26.2565300 C#:min +26.2565300 28.7642850 F#:min +28.7642850 29.3912240 D +29.3912240 29.9949430 G +29.9949430 31.2139900 A +31.2139900 36.2178910 B:min +36.2178910 41.2566210 G +41.2566210 46.3650110 E +46.3650110 48.8959860 A +48.8959860 51.3921310 A +51.3921310 53.9463260 A +53.9463260 56.4889110 C#:min +56.4889110 59.0082760 F#:min +59.0082760 59.6584350 D +59.6584350 60.2737640 G +60.2737640 61.5276410 A +61.5276410 64.1398860 A +64.1398860 66.6476410 C#:min +66.6476410 69.1437860 F#:min +69.1437860 69.7475050 D +69.7475050 70.3396140 G +70.3396140 71.5354420 A +71.5354420 76.5973920 B:min +76.5973920 81.7057820 G +81.7057820 86.7677320 E +86.7677320 89.1704253 A +89.1704253 91.8645120 A +91.8645120 94.3374370 A +94.3374370 96.8800220 C#:min +96.8800220 99.3993870 F#:min +99.3993870 100.0495460 D +100.0495460 100.6532650 G +100.6532650 101.9768020 A +101.9768020 104.5309970 A +104.5309970 107.1316320 C#:min +107.1316320 109.7206570 F#:min +109.7206570 110.3359860 D +110.3359860 110.9745350 G +110.9745350 112.1703620 A +112.1703620 117.2207020 B:min +117.2207020 122.2594330 G +122.2594330 127.3910430 E +127.3910430 129.9336280 A +129.9336280 132.4646030 F#:min +132.4646030 133.7184800 A +133.7184800 136.9437153 A:maj6 +136.9437153 140.9072708 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/05final/02_-_The_Night_Before.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,106 @@ +0.0000000 0.7001660 N +0.7001660 3.4961450 D +3.4961450 6.3405890 F +6.3405890 9.1385940 G +9.1385940 11.8523713 A:7 +11.8523713 13.2949650 D +13.2949650 14.7113830 C +14.7113830 16.1510200 G +16.1510200 17.5906570 A +17.5906570 18.9838540 D +18.9838540 20.4234920 C +20.4234920 21.8282990 G +21.8282990 23.2563260 A +23.2563260 24.6379130 B:min +24.6379130 26.0659410 G:min +26.0659410 27.4707480 B:min +27.4707480 28.9219950 G:min +28.9219950 30.2919720 D +30.2919720 31.7432190 G +31.7432190 33.1015870 D +33.1015870 33.6704760 F +33.6704760 34.1697050 F +34.1697050 34.5528340 G +34.5528340 36.0040810 D +36.0040810 37.3856680 C +37.3856680 38.7672560 G +38.7672560 40.1604530 A +40.1604530 41.5884800 D +41.5884800 43.0281170 C +43.0281170 44.4097050 G +44.4097050 45.8493420 A +45.8493420 47.2889790 B:min +47.2889790 48.7286160 G:min +48.7286160 50.0985940 B:min +50.0985940 51.5846710 G:min +51.5846710 53.0010880 D +53.0010880 54.3478450 G +54.3478450 55.7990920 D +55.7990920 57.2039000 D +57.2039000 58.6203170 A:min +58.6203170 60.0135140 D +60.0135140 62.7999090 G +62.7999090 64.1931060 B:min +64.1931060 65.5863030 E +65.5863030 68.3186767 A +68.3186767 69.7891150 D +69.7891150 71.1590920 C +71.1590920 72.5755100 G +72.5755100 73.9687070 A +73.9687070 75.3851240 D +75.3851240 76.7783210 C +76.7783210 78.1715190 G +78.1715190 79.6343760 A +79.6343760 81.0740130 B:min +81.0740130 82.5020400 G:min +82.5020400 83.9416780 B:min +83.9416780 85.4161450 G:min +85.4161450 86.7745120 D +86.7745120 88.2141490 G +88.2141490 89.6653960 D +89.6653960 90.2226750 F +90.2226750 90.6522440 F +90.6522440 91.0237640 G +91.0237640 92.3937410 D +92.3937410 93.8333780 C +93.8333780 95.2265750 G +95.2265750 96.6081630 A +96.6081630 98.0826300 D +98.0826300 99.4874370 C +99.4874370 100.9154640 G +100.9154640 102.3551020 A +102.3551020 103.8643990 B:min +103.8643990 105.2111560 G:min +105.2111560 106.6159630 B:min +106.6159630 108.0439900 G:min +108.0439900 109.4487980 D +109.4487980 110.8187750 G +110.8187750 112.2468020 D +112.2468020 113.6400000 D +113.6400000 115.0564170 A:min +115.0564170 116.4612240 D +116.4612240 117.8544210 G +117.8544210 119.2127890 G +119.2127890 120.6059860 B:min +120.6059860 122.0224030 E +122.0224030 123.4272100 A +123.4272100 124.7855780 A +124.7855780 126.1439450 D +126.1439450 127.5603620 C +127.5603620 128.9883900 G +128.9883900 130.4280270 A +130.4280270 131.8560540 D +131.8560540 133.2840810 C +133.2840810 134.6656680 G +134.6656680 136.0588660 A +136.0588660 137.4636730 B:min +137.4636730 138.9149200 G:min +138.9149200 140.3197270 B:min +140.3197270 141.8058040 G:min +141.8058040 143.2222220 D +143.2222220 144.6270290 G +144.6270290 146.0898860 D +146.0898860 147.5527430 F +147.5527430 148.9343310 D +148.9343310 152.5215760 D +152.5215760 156.4276880 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/05final/03_-_You've_Got_To_Hide_Your_Love_Away.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,108 @@ +0.0000000 0.4786903 N +0.4786903 2.4560770 G +2.4560770 3.4429250 G +3.4429250 4.3949430 D +4.3949430 5.3701810 F +5.3701810 6.3454190 G +6.3454190 8.3423350 C +8.3423350 9.2363030 F +9.2363030 10.1999310 C +10.1999310 11.1751700 G +11.1751700 12.1620180 D +12.1620180 13.1256460 F +13.1256460 14.1008840 G +14.1008840 16.0629700 C +16.0629700 17.0149880 F +17.0149880 18.0134460 C +18.0134460 19.9407020 D +19.9407020 20.8927210 G +20.8927210 21.8563490 D +21.8563490 22.8431970 F +22.8431970 23.8416550 G +23.8416550 25.7805210 C +25.7805210 26.7093190 F +26.7093190 27.6845570 C +27.6845570 28.6481850 G +28.6481850 29.6350340 D +29.6350340 30.5986620 F +30.5986620 31.5855100 G +31.5855100 33.5359860 C +33.5359860 34.4996140 F +34.4996140 35.4864620 C +35.4864620 36.4733100 D +36.4733100 37.4369380 D/b7 +37.4369380 38.4353960 D/6 +38.4353960 39.3990240 D/5 +39.3990240 41.3611110 G +41.3611110 43.3115870 C +43.3115870 44.2636050 D:sus4 +44.2636050 45.2272330 D +45.2272330 46.2024710 D:maj(2) +46.2024710 47.1544890 D +47.1544890 49.1514050 G +49.1514050 51.0786620 C +51.0786620 52.0422900 D:sus4 +52.0422900 53.0175280 D +53.0175280 53.9695460 D:maj(2) +53.9695460 54.9912240 D +54.9912240 56.0245120 G +56.0245120 56.9881400 D +56.9881400 57.9169380 F +57.9169380 58.9037860 G +58.9037860 60.8426530 C +60.8426530 61.8527210 F +61.8527210 62.8163490 C +62.8163490 63.7799770 G +63.7799770 64.7668250 D +64.7668250 65.7420630 F +65.7420630 66.7056910 G +66.7056910 68.6909970 C +68.6909970 69.6546250 F +69.6546250 70.6182530 C +70.6182530 72.5687300 D +72.5687300 73.5555780 G +73.5555780 74.5656460 D +74.5656460 75.5060540 F +75.5060540 76.5277320 G +76.5277320 78.5130380 C +78.5130380 79.4418360 F +79.4418360 80.3938540 C +80.3938540 81.3807020 G +81.3807020 82.3907700 D +82.3907700 83.2963490 F +83.2963490 84.3180270 G +84.3180270 86.3497730 C +86.3497730 87.3017910 F +87.3017910 88.2538090 C +88.2538090 89.2058270 D +89.2058270 90.1810650 D/b7 +90.1810650 91.1795230 D/6 +91.1795230 92.1199310 D/5 +92.1199310 94.1168480 G +94.1168480 96.0441040 C +96.0441040 96.9845120 D:sus4 +96.9845120 97.9249200 D +97.9249200 98.9233780 D:maj(2) +98.9233780 99.8753960 D +99.8753960 101.8607020 G +101.8607020 103.8111790 C +103.8111790 104.7980270 D:sus4 +104.7980270 105.7500450 D +105.7500450 106.7020630 D:maj(2) +106.7020630 107.7353510 D +107.7353510 108.6757590 G +108.6757590 109.6161670 D +109.6161670 110.6030150 F +110.6030150 111.6130830 G +111.6130830 113.6100000 C +113.6100000 114.5504080 F +114.5504080 115.5140360 C +115.5140360 116.5124940 G +116.5124940 117.5225620 D +117.5225620 118.4629700 F +118.4629700 119.4614280 G +119.4614280 121.4931740 C +121.4931740 122.4568020 F +122.4568020 123.4088200 C +123.4088200 126.9103670 G +126.9103670 131.2760224 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/05final/04_-_I_Need_You.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,106 @@ +0.0000000 0.4786903 N +0.4786903 0.8616425 A +0.8616425 2.5657799 A:sus2 +2.5657799 3.1019130 A:sus4 +3.1019130 3.9885940 A +3.9885940 5.6720400 A +5.6720400 7.2760923 D +7.2760923 7.7547825 A +7.7547825 8.9802297 A:sus2 +8.9802297 9.5738056 A:sus4 +9.5738056 10.9661900 A +10.9661900 12.6960770 A +12.6960770 14.2266751 D +14.2266751 14.7053654 A +14.7053654 15.9116649 A:sus2 +15.9116649 16.3712076 A:sus4 +16.3712076 17.8392970 A +17.8392970 19.6388430 F#:min +19.6388430 21.3339000 C#:min +21.3339000 23.0870060 F#:min +23.0870060 24.6238280 B:min +24.6238280 25.0067802 A +25.0067802 26.4237035 A:sus2 +26.4237035 27.0172794 A:sus4 +27.0172794 28.3695460 A +28.3695460 30.0994330 A +30.0994330 31.6318537 D +31.6318537 32.0531011 A +32.0531011 33.4125815 A:sus2 +33.4125815 33.8338290 A:sus4 +33.8338290 35.3239220 A +35.3239220 37.0654190 A +37.0654190 38.6207318 D +38.6207318 39.0802745 A +39.0802745 40.4206072 A:sus2 +40.4206072 40.8227071 A:sus4 +40.8227071 42.3247390 A +42.3247390 44.0197950 F#:min +44.0197950 45.7612920 C#:min +45.7612920 47.5027890 F#:min +47.5027890 49.1327703 B:min +49.1327703 49.5731654 A +49.5731654 50.8943506 A:sus2 +50.8943506 51.3155980 A:sus4 +51.3155980 52.7504980 A +52.7504980 54.5268250 D +54.5268250 56.2915410 E +56.2915410 59.8093650 A +59.8093650 61.5160310 D +61.5160310 63.2923580 E +63.2923580 65.1035140 B +65.1035140 68.5865070 E +68.5865070 70.3976640 A +70.3976640 71.9375753 D +71.9375753 72.3588227 A +72.3588227 73.7720565 A:sus2 +73.7720565 74.1815979 A:sus4 +74.1815979 75.6918140 A +75.6918140 77.4797500 A +77.4797500 79.0278377 D +79.0278377 79.4373791 A +79.4373791 80.7615629 A:sus2 +80.7615629 81.1858715 A:sus4 +81.1858715 82.7274600 A +82.7274600 84.4457360 F#:min +84.4457360 86.1988430 C#:min +86.1988430 87.9751700 F#:min +87.9751700 89.5667029 B:min +89.5667029 89.9625929 A +89.9625929 91.3532530 A:sus2 +91.3532530 91.8510909 A:sus4 +91.8510909 93.3389790 A +93.3389790 95.1269160 D +95.1269160 96.8684120 E +96.8684120 100.4094550 A +100.4094550 102.2090020 D +102.2090020 103.9969380 E +103.9969380 105.7964850 B +105.7964850 109.3607480 E +109.3607480 111.1719040 A +111.1719040 112.7603972 D +112.7603972 113.1972414 A +113.1972414 114.5218626 A:sus2 +114.5218626 114.9431100 A:sus4 +114.9431100 116.5124940 A +116.5124940 118.3120400 A +118.3120400 119.8864175 D +119.8864175 120.3232616 A +120.3232616 121.5681835 A:sus2 +121.5681835 122.0468738 A:sus4 +122.0468738 123.6410200 A +123.6410200 125.3244670 F#:min +125.3244670 127.2168930 C#:min +127.2168930 128.9351700 F#:min +128.9351700 130.5481451 B:min +130.5481451 130.9576865 A +130.9576865 132.4183841 A:sus2 +132.4183841 132.8825311 A:sus4 +132.8825311 134.3338090 A +134.3338090 137.7819720 F#:min +137.7819720 141.3694550 D +141.3694550 141.9603894 A +141.9603894 143.3121852 A:sus2 +143.3121852 143.9933100 A:sus4 +143.9933100 147.5323443 A +147.5323443 151.2661285 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/05final/05_-_Another_Girl.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,82 @@ +0.0000000 0.4403951 N +0.4403951 1.3020376 N +1.3020376 2.7347160 A +2.7347160 4.1163030 D +4.1163030 5.4398410 A +5.4398410 6.7401580 D +6.7401580 8.1101360 A +8.1101360 9.4104530 G +9.4104530 10.7804300 A +10.7804300 12.0923580 D +12.0923580 13.4275050 A +13.4275050 14.7510430 G +14.7510430 16.0397500 A +16.0397500 17.3748970 D +17.3748970 21.3687300 D +21.3687300 22.7387070 E +22.7387070 24.1086840 A +24.1086840 25.4438320 D +25.4438320 26.8021990 A +26.8021990 28.1025170 D +28.1025170 29.5073240 A +29.5073240 30.8192510 G +30.8192510 32.1311790 A +32.1311790 33.4663260 D +33.4663260 34.8130830 A +34.8130830 36.1482310 G +36.1482310 37.5298180 A +37.5298180 38.8997950 D +38.8997950 42.9052380 D +42.9052380 44.2287750 E +44.2287750 45.6103620 A +45.6103620 46.9571200 D +46.9571200 48.2806570 A +48.2806570 49.6041950 D +49.6041950 51.0206120 C +51.0206120 52.3905890 G +52.3905890 53.7025170 C +53.7025170 55.1073240 G +55.1073240 56.4540810 C +56.4540810 57.7776190 E +57.7776190 59.1592060 A +59.1592060 60.4827430 E +60.4827430 61.8643310 A +61.8643310 63.2110880 G +63.2110880 64.5926750 A +64.5926750 65.9510430 D +65.9510430 67.3094100 A +67.3094100 68.6561670 G +68.6561670 70.0493650 A +70.0493650 71.3845120 D +71.3845120 75.4363940 D +75.4363940 76.8412010 E +76.8412010 78.2460090 A +78.2460090 79.5463260 D +79.5463260 80.9163030 A +80.9163030 82.2514510 D +82.2514510 83.6794780 C +83.6794780 85.0146250 G +85.0146250 86.4078230 C +86.4078230 87.7778000 G +87.7778000 89.1361670 C +89.1361670 90.4829250 E +90.4829250 91.8529020 A +91.8529020 93.2228790 E +93.2228790 94.5812470 A +94.5812470 95.9628340 G +95.9628340 97.3444210 A +97.3444210 98.7143990 D +98.7143990 100.0843760 A +100.0843760 101.4543530 G +101.4543530 102.8359410 A +102.8359410 104.2175280 D +104.2175280 108.3274600 D +108.3274600 109.7670970 E +109.7670970 111.1486840 A +111.1486840 112.5186620 D +112.5186620 113.8654190 A +113.8654190 115.2237860 D +115.2237860 116.6169840 A +116.6169840 117.9869610 D +117.9869610 123.5403874 A +123.5403874 127.5613858 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/05final/06_-_You're_Going_to_Lose_That_Girl.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,75 @@ +0.0000000 0.4978379 N +0.4978379 1.2950790 B +1.2950790 3.0365750 E +3.0365750 4.9522220 C#:min +4.9522220 6.8330380 F#:min9 +6.8330380 8.7719040 B +8.7719040 10.5830610 E +10.5830610 12.4058270 G# +12.4058270 14.2285940 F#:min +14.2285940 16.0978000 B +16.0978000 17.8509070 E +17.8509070 19.6852830 G# +19.6852830 21.4964390 F#:min +21.4964390 23.3308160 B +23.3308160 25.1651920 E +25.1651920 26.9647390 C#:min +26.9647390 28.7642850 F#:min9 +28.7642850 30.5522220 B +30.5522220 32.3865980 E +32.3865980 34.2093650 G# +34.2093650 36.0089110 F#:min +36.0089110 37.8200680 B +37.8200680 39.6080040 E +39.6080040 41.4075510 G# +41.4075510 43.1838770 F#:min +43.1838770 45.0879130 B +45.0879130 46.8874600 E +46.8874600 48.7334460 C#:min +48.7334460 50.5213830 F#:min9 +50.5213830 52.3441490 B +52.3441490 54.1901360 F#:min +54.1901360 55.9432420 D +55.9432420 57.8240580 G +57.8240580 59.6119950 C +59.6119950 63.2459180 G +63.2459180 65.0454640 G +65.0454640 66.8334010 C +66.8334010 68.6793870 F +68.6793870 70.4789340 E +70.4789340 72.2784800 G# +72.2784800 74.0431970 F#:min +74.0431970 75.9472330 B +75.9472330 77.7467800 E +77.7467800 79.5695460 G# +79.5695460 81.3574820 F#:min +81.3574820 83.2150790 B +83.2150790 85.0726750 E +85.0726750 86.9302720 C#:min +86.9302720 88.7182080 F#:min9 +88.7182080 90.5525850 B +90.5525850 92.3405210 F#:min +92.3405210 94.1400680 D +94.1400680 95.9976640 G +95.9976640 97.8088200 C +97.8088200 101.4079130 G +101.4079130 103.2539000 G +103.2539000 105.0070060 C +105.0070060 106.8529930 F +106.8529930 108.6757590 E +108.6757590 110.4985260 G# +110.4985260 112.3212920 F#:min +112.3212920 114.1440580 B +114.1440580 115.9436050 E +115.9436050 117.8128110 G# +117.8128110 119.6123580 F#:min +119.6123580 121.4699540 B +121.4699540 123.2695010 E +123.2695010 125.1387070 C#:min +125.1387070 126.9498630 F#:min9 +126.9498630 128.8190700 B +128.8190700 130.6882760 F#:min +130.6882760 131.5706340 D +131.5706340 132.4762130 A +132.4762130 135.8905967 E +135.8905967 140.0647759 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/05final/07_-_Ticket_To_Ride.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,53 @@ +0.0000000 0.4212474 N +0.4212474 4.0466430 A +4.0466430 7.9475960 A +7.9475960 19.5691830 A +19.5691830 21.4848290 B:min +21.4848290 23.4004760 E +23.4004760 25.3393420 F#:min +25.3393420 27.1853280 D +27.1853280 28.9152150 F#:min +28.9152150 30.9817910 G:maj7 +30.9817910 32.8742170 F#:min +32.8742170 34.8130830 E +34.8130830 38.7256460 A +38.7256460 50.2775730 A +50.2775730 52.2512690 B:min +52.2512690 54.1785260 E +54.1785260 56.1057820 F#:min +56.1057820 57.9633780 D +57.9633780 59.7280950 F#:min +59.7280950 61.8527210 G:maj7 +61.8527210 63.7799770 F#:min +63.7799770 65.7304530 E +65.7304530 69.6197950 A +69.6197950 75.4828340 D:7 +75.4828340 77.4100900 E +77.4100900 83.2150790 D:7 +83.2150790 87.0579810 E +87.0579810 98.9001580 A +98.9001580 100.8854640 B:min +100.8854640 102.8939900 E +102.8939900 104.9025170 F#:min +104.9025170 106.8181630 D +106.8181630 108.5828790 F#:min +108.5828790 110.7307250 G:maj7 +110.7307250 112.7276410 F#:min +112.7276410 114.6665070 E +114.6665070 118.6255100 A +118.6255100 124.4537180 D:7 +124.4537180 126.4506340 E +126.4506340 132.1859630 D:7 +132.1859630 136.0869160 E +136.0869160 147.9523120 A +147.9523120 149.9143990 B:min +149.9143990 151.9113150 E +151.9113150 153.8617910 F#:min +153.8617910 155.8238770 D +155.8238770 157.5421540 F#:min +157.5421540 159.7480490 G:maj7 +159.7480490 161.6753060 F#:min +161.6753060 163.6606120 E +163.6606120 167.5731740 A +167.5731740 187.7040326 A +187.7040326 192.3186069 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/05final/08_-_Act_Naturally.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,64 @@ +0.0000000 0.4403951 N +0.4403951 1.4460090 G +1.4460090 2.5837860 D +2.5837860 3.8260540 D:7 +3.8260540 6.4150790 G +6.4150790 8.9344440 G +8.9344440 11.4654190 C +11.4654190 14.0544440 G +14.0544440 16.6086390 D +16.6086390 19.1512240 G +19.1512240 21.7402490 C +21.7402490 24.3176640 D +24.3176640 25.6063710 G +25.6063710 26.9215413 G +26.9215413 29.5305440 D +29.5305440 32.0963490 G +32.0963490 34.6737640 D +34.6737640 37.2163490 G +37.2163490 39.8169840 D +39.8169840 42.3943990 G +42.3943990 43.7179360 A +43.7179360 44.9834240 A +44.9834240 47.5608390 D +47.5608390 50.1730830 G +50.1730830 52.7156680 C +52.7156680 55.3046930 G +55.3046930 57.8937180 D +57.8937180 60.4363030 G +60.4363030 63.0717680 C +63.0717680 65.6724030 D +65.6724030 66.9843310 G +66.9843310 68.2498180 G +68.2498180 70.8388430 D +70.8388430 73.3930380 G +73.3930380 75.9588430 D +75.9588430 78.5130380 G +78.5130380 81.0904530 G +81.0904530 83.6562580 C +83.6562580 86.3033330 G +86.3033330 88.8923580 D +88.8923580 91.5278230 G +91.5278230 94.0936280 C +94.0936280 96.6942630 D +96.6942630 99.2948970 G +99.2948970 101.8839220 D +101.8839220 104.5077770 G +104.5077770 107.0851920 D +107.0851920 109.6974370 G +109.6974370 112.2400220 D +112.2400220 114.8522670 G +114.8522670 117.4412920 A +117.4412920 120.0651470 D +120.0651470 122.6541720 G +122.6541720 125.2199770 C +125.2199770 127.7857820 G +127.7857820 130.3864170 D +130.3864170 132.9986620 G +132.9986620 135.6109070 C +135.6109070 138.2115410 D +138.2115410 140.8237860 G +140.8237860 143.4476410 D +143.4476410 144.8060090 G +144.8060090 148.5088724 G +148.5088724 153.0468563 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/05final/09_-_It's_Only_Love.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,63 @@ +0.0000000 0.4595427 N +0.4595427 2.4909070 C +2.4909070 4.5342630 A:min +4.5342630 6.6124480 C +6.6124480 8.7254640 A:min +8.7254640 9.7703620 C +9.7703620 10.8849200 E:min/5 +10.8849200 11.9182080 Bb +11.9182080 12.9631060 F +12.9631060 15.0645120 G +15.0645120 17.1659180 G:aug +17.1659180 18.2804760 C +18.2804760 19.3253740 E:min/5 +19.3253740 20.4167120 Bb +20.4167120 21.4964390 F +21.4964390 23.5630150 G +23.5630150 25.6876410 G:aug +25.6876410 27.8006570 F +27.8006570 29.9485030 G +29.9485030 32.0847390 C +32.0847390 34.1977550 A:min +34.1977550 36.3223800 Bb +36.3223800 38.4818360 G +38.4818360 40.6064620 C +40.6064620 42.7543080 A:min +42.7543080 44.8208840 Bb +44.8208840 46.9571200 G +46.9571200 49.0817460 F +49.0817460 51.2412010 G +51.2412010 52.3209290 C +52.3209290 53.4006570 E:min/5 +53.4006570 54.4339450 Bb +54.4339450 55.4788430 F +55.4788430 57.5570290 G +57.5570290 59.6584350 G:aug +59.6584350 60.7381630 C +60.7381630 61.8062810 E:min/5 +61.8062810 62.8743990 Bb +62.8743990 63.9192970 F +63.9192970 66.0323120 G +66.0323120 68.1569380 G:aug +68.1569380 70.2467340 F +70.2467340 72.4178000 G +72.4178000 74.5424260 C +74.5424260 76.6786620 A:min +76.6786620 78.7800680 Bb +78.7800680 80.9163030 G +80.9163030 82.9712690 C +82.9712690 85.1423350 A:min +85.1423350 87.2437410 Bb +87.2437410 89.3799770 G +89.3799770 91.5394330 F +91.5394330 93.6176190 G +93.6176190 95.7886840 F +95.7886840 97.8784800 G +97.8784800 100.0379360 C +100.0379360 102.0929020 A:min +102.0929020 104.2291380 C +104.2291380 106.3305440 A:min +106.3305440 108.4319500 C +108.4319500 110.5449650 A:min +110.5449650 115.0197005 C +115.0197005 118.6385990 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/05final/10_-_You_Like_Me_Too_Much.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,88 @@ +0.000000 0.529543 N +0.529543 1.550839 G +1.550839 4.488163 G +4.488163 6.043900 Bb +6.043900 7.773786 D:7(#9) +7.773786 10.049342 G +10.049342 12.742857 A:min +12.742857 14.147664 C +14.147664 15.459591 G +15.459591 16.876009 A:min +16.876009 18.199546 A:min +18.199546 19.557913 C +19.557913 20.951111 G +20.951111 22.286258 B:min +22.286258 23.679455 B:min7 +23.679455 26.442630 D:7 +26.442630 27.800997 G +27.800997 29.170975 C +29.170975 31.876099 D +31.876099 33.234467 A:min +33.234467 34.616054 A:min7 +34.616054 35.951201 C:maj7 +35.951201 37.309569 G +37.309569 38.691156 A:min7 +38.691156 40.049523 A:min7 +40.049523 41.384671 C:maj7 +41.384671 42.789478 G:7 +42.789478 44.147845 B:min7 +44.147845 45.506213 B:min7 +45.506213 48.222947 D:7 +48.222947 49.616145 G +49.616145 50.916462 C +50.916462 53.656417 D:7 +53.656417 56.361541 E:min +56.361541 59.031836 A +59.031836 60.413424 B:min +60.413424 61.736961 A +61.736961 62.271020 E +62.271020 63.106938 A +63.106938 63.594557 A +63.594557 64.453696 D:7 +64.453696 67.158820 A:min7 +67.158820 68.517188 C:maj7 +68.517188 69.898775 G:7 +69.898775 71.280362 A:min7 +71.280362 72.627120 A:min7 +72.627120 73.962267 C:maj7 +73.962267 75.320634 G:7 +75.320634 78.025759 B:min7 +78.025759 80.765714 D:7 +80.765714 82.054421 G +82.054421 83.436009 C +83.436009 86.199183 D:7 +86.199183 91.632653 G +91.632653 94.279727 C +94.279727 97.031292 G +97.031292 99.748027 D +99.748027 102.418321 D:7 +102.418321 103.788299 G +103.788299 105.146666 C +105.146666 107.863401 D:7 +107.863401 110.545306 E:min +110.545306 113.215600 A +113.215600 114.573968 B:min +114.573968 115.967165 A +115.967165 116.512834 E +116.512834 117.325532 A +117.325532 117.836371 A +117.836371 118.660680 D +118.660680 121.389024 A:min7 +121.389024 122.759002 C:maj7 +122.759002 124.082539 G:7 +124.082539 126.834104 A:min7 +126.834104 128.169251 C:maj7 +128.169251 129.539229 G:7 +129.539229 132.255963 B:min7 +132.255963 134.972698 D:7 +134.972698 136.307845 G +136.307845 137.701043 C +137.701043 140.382947 D:7 +140.382947 141.764535 G:7 +141.764535 143.180952 C +143.180952 145.920907 D +145.920907 148.672471 G +148.672471 150.042448 Bb +150.042448 151.458866 D:7(#9) +151.458866 154.380599 G +154.380599 158.707959 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/05final/11_-_Tell_Me_What_You_See.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,108 @@ +0.0000000 0.4978379 N +0.4978379 4.1511330 G +4.1511330 4.9870520 G +4.9870520 5.8926300 C +5.8926300 6.7749880 D +6.7749880 7.6689560 G +7.6689560 8.5048750 G +8.5048750 9.3640130 C +9.3640130 11.1403400 G +11.1403400 12.0226980 G +12.0226980 12.8702260 C +12.8702260 13.7177550 D +13.7177550 14.6233330 G +14.6233330 15.4940810 C +15.4940810 16.3416090 D +16.3416090 18.1295460 G +18.1295460 19.8594330 C +19.8594330 21.6009290 G +21.6009290 23.3540360 C +23.3540360 25.1419720 G +25.1419720 26.8486390 C +26.8486390 28.5901360 G +28.5901360 29.4841040 C +29.4841040 30.3548520 D +30.3548520 32.1195690 G +32.1195690 33.0135370 G +33.0135370 33.8610650 C +33.8610650 34.7318140 D +34.7318140 35.6373920 G +35.6373920 36.4849200 G +36.4849200 37.3324480 C +37.3324480 39.0739450 G +39.0739450 39.9446930 G +39.9446930 40.8038320 C +40.8038320 41.7094100 D +41.7094100 42.6033780 G +42.6033780 43.4160770 C +43.4160770 44.2868250 D +44.2868250 46.0631510 G +46.0631510 47.8046480 C +47.8046480 49.5461450 G +49.5461450 51.2644210 C +51.2644210 53.0175280 G +53.0175280 54.7706340 C +54.7706340 56.5005210 G +56.5005210 57.3480490 C +57.3480490 58.2652380 D +58.2652380 60.0067340 G +60.0067340 63.5245570 G:7 +63.5245570 67.1004300 C +67.1004300 68.8303170 G +68.8303170 70.5369840 D +70.5369840 74.1012470 G +74.1012470 75.0184350 G +75.0184350 75.8543530 C +75.8543530 76.7599310 D +76.7599310 77.7003400 G +77.7003400 78.5594780 G +78.5594780 79.4302260 C +79.4302260 81.2297730 G +81.2297730 82.1469610 G +82.1469610 82.9944890 C +82.9944890 83.8536280 D +83.8536280 84.7475960 G +84.7475960 85.6531740 C +85.6531740 86.5007020 D +86.5007020 88.2770290 G +88.2770290 90.0649650 C +90.0649650 91.8645120 G +91.8645120 93.5827890 C +93.5827890 95.3707250 G +95.3707250 97.1238320 C +97.1238320 98.8885480 G +98.8885480 99.7941260 C +99.7941260 100.6880950 D +100.6880950 102.4644210 G +102.4644210 106.0402940 G:7 +106.0402940 109.5697270 C +109.5697270 111.3228340 G +111.3228340 113.0875510 D +113.0875510 116.5937640 G +116.5937640 117.4645120 G +117.4645120 118.3468700 C +118.3468700 119.2176190 D +119.2176190 120.1464170 G +120.1464170 121.0287750 G +121.0287750 121.9227430 C +121.9227430 123.6874600 G +123.6874600 124.5698180 G +124.5698180 125.4637860 C +125.4637860 126.3693650 D +126.3693650 127.2285030 G +127.2285030 128.1224710 C +128.1224710 129.0164390 D +129.0164390 130.7811560 G +130.7811560 132.5574820 C +132.5574820 134.3105890 G +134.3105890 136.0404760 C +136.0404760 137.8400220 G +137.8400220 139.5699090 C +139.5699090 141.3578450 G +141.3578450 142.2169840 C +142.2169840 143.1109520 D +143.1109520 144.8872780 G +144.8872780 148.4515410 G:7 +148.4515410 152.2828340 C +152.2828340 155.3062744 G +155.3062744 159.5570441 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/05final/12_-_I've_Just_Seen_a_Face.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,65 @@ +0.0000000 0.4403951 N +0.4403951 1.4552185 A +1.4552185 5.5791600 F#:min +5.5791600 6.5892290 D +6.5892290 7.5992970 G:maj6/5 +7.5992970 9.6542630 D +9.6542630 10.6411110 E/5 +10.6411110 11.6279590 D/5 +11.6279590 15.6682310 A +15.6682310 19.7317230 F#:min +19.7317230 21.7982990 D +21.7982990 22.7619270 E +22.7619270 23.7836050 A +23.7836050 27.7658270 A +27.7658270 31.7596590 F#:min +31.7596590 33.8146250 D +33.8146250 34.7898630 E +34.7898630 35.7302720 A +35.7302720 37.7387980 E +37.7387980 39.7241040 D +39.7241040 40.7225620 A +40.7225620 41.7094100 D +41.7094100 43.7063260 A +43.7063260 47.7698180 A +47.7698180 51.7404300 F#:min +51.7404300 53.7837860 D +53.7837860 54.6855776 E +54.6855776 55.7110430 A +55.7110430 57.7079590 E +57.7079590 59.7048750 D +59.7048750 60.6568930 A +60.6568930 61.6553510 D +61.6553510 63.6174370 A +63.6174370 67.6228790 A +67.6228790 71.5934920 F#:min +71.5934920 73.5787980 D +73.5787980 74.5308160 E +74.5308160 75.5408840 A +75.5408840 77.5378000 E +77.5378000 79.5463260 D +79.5463260 80.5563940 A +80.5563940 81.5316320 D +81.5316320 83.5169380 A +83.5169380 87.4643310 A +87.4643310 91.4465530 F#:min +91.4465530 93.4666890 D +93.4666890 94.4883670 E +94.4883670 95.4519950 A +95.4519950 97.4256910 E +97.4256910 99.4458270 D +99.4458270 100.4210650 A +100.4210650 101.4427430 D +101.4427430 103.3816090 A +103.3816090 105.3436960 E +105.3436960 107.3638320 D +107.3638320 108.3622900 A +108.3622900 109.3491380 D +109.3491380 111.2996140 A +111.2996140 113.2733100 E +113.2733100 115.2470060 D +115.2470060 116.2802940 A +116.2802940 118.3513848 D +118.3513848 119.2756680 E +119.2756680 123.1765828 A +123.1765828 127.0061051 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/05final/13_-_Yesterday.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,97 @@ +0.0000000 0.4595427 N +0.4595427 5.3469610 F:maj(*3) +5.3469610 7.8431060 F:maj(*3) +7.8431060 9.0621540 E:min +9.0621540 10.2347610 A:7 +10.2347610 12.2084580 D:min +12.2084580 12.7773460 D:min/b7 +12.7773460 13.9963940 Bb:maj7 +13.9963940 15.2502720 C:7 +15.2502720 17.6303170 F +17.6303170 18.8377550 D:min +18.8377550 20.0800220 G +20.0800220 20.7185710 Bb +20.7185710 22.5761670 F +22.5761670 25.0450754 F +25.0450754 26.2681400 A:sus4/5 +26.2681400 27.4871880 A +27.4871880 29.3563940 D:min +29.3563940 29.9368930 A:min/b3 +29.9368930 31.1675510 Bb:maj +31.1675510 32.3865980 C +32.3865980 32.8955960 Bb/5 +32.8955960 33.6869160 F +33.6869160 34.8246930 F +34.8246930 36.0901810 D:min7 +36.0901810 37.2627890 G:7 +37.2627890 37.9013370 Bb +37.9013370 39.8402040 F +39.8402040 41.0940810 E:min7(4) +41.0940810 42.3015190 A +42.3015190 42.9516780 D:min +42.9516780 43.5553960 D:min/b7 +43.5553960 44.1823350 Bb +44.1823350 44.8092740 Bb/7 +44.8092740 45.9934920 G +45.9934920 47.2241490 C +47.2241490 49.7086840 F +49.7086840 50.9393420 E:min7(4) +50.9393420 52.1351700 A +52.1351700 52.7504980 D:min +52.7504980 53.4238770 D:min/b7 +53.4238770 54.0508160 Bb +54.0508160 54.6313150 Bb/7 +54.6313150 55.8851920 G:min +55.8851920 57.1274600 C +57.1274600 59.7164850 F +59.7164850 62.1081400 F +62.1081400 63.3271880 A:sus4/5 +63.3271880 64.6739450 A +64.6739450 66.6012010 D:min +66.6012010 67.2397500 A:min/b3 +67.2397500 68.4820180 Bb +68.4820180 69.7358950 C +69.7358950 70.2908807 Bb/5 +70.2908807 71.0710430 F +71.0710430 72.2552600 F +72.2552600 73.5091380 D:min7 +73.5091380 74.7514050 G:7 +74.7514050 75.4596140 Bb +75.4596140 77.3172100 F +77.3172100 78.6175280 E:min7(4) +78.6175280 79.8017460 A +79.8017460 80.4402940 D:min +80.4402940 81.1368930 D:min/b7 +81.1368930 81.7754420 Bb +81.7754420 82.3907700 Bb/7 +82.3907700 83.7026980 G:min +83.7026980 84.8172560 C +84.8172560 85.9727743 F +85.9727743 87.2785710 F:7 +87.2785710 88.4976190 E:min7(4) +88.4976190 89.7747160 A +89.7747160 90.3319950 D:min +90.3319950 90.9821540 D:min/b7 +90.9821540 91.5858730 Bb +91.5858730 92.2128110 Bb/7 +92.2128110 93.4434690 G:min +93.4434690 94.6741260 C +94.6741260 97.2747610 F +97.2747610 99.8405660 F +99.8405660 101.0596140 E:min7(4) +101.0596140 102.3599310 A +102.3599310 103.6718590 D:min +103.6718590 104.9025170 D:min +104.9025170 106.0867340 Bb:maj7 +106.0867340 107.3406120 C:maj6 +107.3406120 107.9159367 Bb:maj7/5 +107.9159367 109.8251470 F +109.8251470 111.1022440 D:min +111.1022440 112.3096820 G +112.3096820 113.0178910 Bb +113.0178910 114.8870970 F +114.8870970 116.2802940 F/5 +116.2802940 117.7663710 G/3 +117.7663710 118.6487300 Bb +118.6487300 123.1191400 F +123.1191400 127.1592860 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/05final/14_-_Dizzy_Miss_Lizzie.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,59 @@ +0.0000000 0.4020998 N +0.4020998 1.8291380 N +1.8291380 8.6674140 A +8.6674140 12.1039680 D +12.1039680 15.5869610 A +15.5869610 17.2820180 E +17.2820180 19.0002940 D +19.0002940 20.7766210 A +20.7766210 22.4020180 E +22.4020180 29.3099540 A +29.3099540 32.7697270 D +32.7697270 36.2875510 A +36.2875510 38.0522670 E +38.0522670 39.8053740 D +39.8053740 41.8255100 A +41.8255100 43.2999770 E +43.2999770 50.2195230 A +50.2195230 53.6676870 D +53.6676870 57.2435600 A +57.2435600 58.9966660 E +58.9966660 60.7729930 D +60.7729930 62.5493190 A +62.5493190 64.2908160 E +64.2908160 71.3729020 A +71.3729020 74.9371650 D +74.9371650 78.4201580 A +78.4201580 80.2313150 E +80.2313150 81.9728110 D +81.9728110 83.7723580 A +83.7723580 85.4674140 E +85.4674140 92.5146710 A +92.5146710 95.9744440 D +95.9744440 99.5503170 A +99.5503170 101.2918140 E +101.2918140 103.0913600 D +103.0913600 104.8909070 A +104.8909070 106.6091830 E +106.6091830 113.7144890 A +113.7144890 117.2787520 D +117.2787520 120.8081850 A +120.8081850 122.6077320 E +122.6077320 124.3608390 D +124.3608390 126.2210530 A +126.2210530 128.2966210 E +128.2966210 134.8678680 A +134.8678680 138.4321310 D +138.4321310 141.9615640 A +141.9615640 143.7727210 E +143.7727210 145.4909970 D +145.4909970 147.3021540 A +147.3021540 148.9391600 E +148.9391600 156.1373460 A +156.1373460 159.7248290 D +159.7248290 163.3007020 A +163.3007020 165.0770290 E +165.0770290 166.8185260 D +166.8185260 168.6993420 A +168.6993420 171.9455485 A:maj6 +171.9455485 174.0517858 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/05final/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +/.DS_Store/1.1.1.1/Fri Sep 23 09:24:38 2005// +/01_-_Help!.lab/1.2/Tue Oct 17 21:54:13 2006// +/02_-_The_Night_Before.lab/1.3/Tue Oct 17 21:54:13 2006// +/03_-_You've_Got_To_Hide_Your_Love_Away.lab/1.2/Tue Oct 17 21:54:13 2006// +/04_-_I_Need_You.lab/1.3/Tue Oct 17 21:54:13 2006// +/05_-_Another_Girl.lab/1.1.1.1/Fri Sep 23 09:24:38 2005// +/06_-_You're_Going_to_Lose_That_Girl.lab/1.3/Tue Aug 1 22:51:53 2006// +/07_-_Ticket_To_Ride.lab/1.1.1.1/Fri Sep 23 09:24:39 2005// +/08_-_Act_Naturally.lab/1.1.1.1/Fri Sep 23 09:24:39 2005// +/09_-_It's_Only_Love.lab/1.1.1.1/Fri Sep 23 09:24:39 2005// +/10_-_You_Like_Me_Too_Much.lab/1.2/Wed Aug 2 00:41:06 2006// +/11_-_Tell_Me_What_You_See.lab/1.1.1.1/Fri Sep 23 09:24:39 2005// +/12_-_I've_Just_Seen_a_Face.lab/1.2/Wed Aug 2 00:41:06 2006// +/13_-_Yesterday.lab/1.1.1.1/Fri Sep 23 09:24:39 2005// +/14_-_Dizzy_Miss_Lizzie.lab/1.1.1.1/Fri Sep 23 09:24:39 2005// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/05final/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +chrish/transcriptions/05final
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/05final/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:matthiasm@dspmac1.elec.qmul.ac.uk:/cvsroot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/06final/01_-_Drive_My_Car.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,84 @@ +0.0000000 1.1297091 N +1.1297091 3.0444702 N +3.0444702 5.2081502 D +5.2081502 7.1581170 D +7.1581170 9.0505440 G +9.0505440 11.0358500 D +11.0358500 12.9979360 G +12.9979360 14.9832420 D +14.9832420 16.9337180 G +16.9337180 20.7417910 A:min7(*5,b6) +20.7417910 22.6806570 B:min +22.6806570 24.6543530 G:7 +24.6543530 26.5816090 B:min +26.5816090 28.5088660 G:7 +28.5088660 30.4941720 B:min +30.4941720 31.4345800 E:7 +31.4345800 32.4214280 A +32.4214280 33.4314960 D +33.4314960 34.4067340 G +34.4067340 36.3456000 A +36.3456000 38.3076870 D +38.3076870 40.3046030 G +40.3046030 42.2782990 D +42.2782990 44.2519950 G +44.2519950 46.2256910 D +46.2256910 48.1761670 G +48.1761670 52.0771200 A:min7(*5,b6) +52.0771200 54.0275960 B:min +54.0275960 55.9316320 G:7 +55.9316320 57.9517680 B:min +57.9517680 59.8790240 G:7 +59.8790240 61.8411110 B:min +61.8411110 62.8395690 E:7 +62.8395690 63.8264170 A +63.8264170 64.8364850 D +64.8364850 65.8117230 G +65.8117230 67.7854190 A:7(#9) +67.7854190 69.7475050 D +69.7475050 71.6863710 G +71.6863710 73.6368480 D +73.6368480 75.6221540 G +75.6221540 77.5842400 D +77.5842400 79.5231060 G +79.5231060 83.4240580 A:min(*5) +83.4240580 85.4093650 B:min +85.4093650 87.3017910 G:7 +87.3017910 89.3103170 B:min +89.3103170 91.2724030 G:7 +91.2724030 93.2460990 B:min +93.2460990 94.1981170 E:7 +94.1981170 95.1965750 A +95.1965750 96.1950340 D +96.1950340 97.1586620 G +97.1586620 99.0859180 A +99.0859180 101.0712240 D +101.0712240 103.0333100 G +103.0333100 104.9605660 D +104.9605660 106.9807020 G +106.9807020 108.9195690 D +108.9195690 110.8816550 G +110.8816550 114.7826070 A:min7(*5,b6) +114.7826070 116.7214730 B:min +116.7214730 118.6603400 G:7 +118.6603400 120.6572560 B:min +120.6572560 122.5961220 G:7 +122.5961220 124.5814280 B:min +124.5814280 125.5450560 E:7 +125.5450560 126.5551240 A +126.5551240 127.5187520 D +127.5187520 128.4939900 G +128.4939900 130.4792970 A:7(#9) +130.4792970 131.5009750 D +131.5009750 132.4646030 G +132.4646030 134.4150790 A:7(#9) +134.4150790 135.4251470 D +135.4251470 136.4003850 G +136.4003850 138.3740810 A:7(#9) +138.3740810 139.3609290 D +139.3609290 140.2781170 G +140.2781170 142.2518140 A:7(#9) +142.2518140 143.2386620 D +143.2386620 144.1906800 G +144.1906800 146.1759860 A:7(#9) +146.1759860 149.7917624 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/06final/02_-_Norwegian_Wood_(This_Bird_Has_Flown).lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,45 @@ +0.0000000 0.4212474 N +0.4212474 5.3469610 E +5.3469610 6.2989790 D +6.2989790 8.3191150 E +8.3191150 13.2185260 E +13.2185260 14.1937640 D +14.1937640 16.1674600 E +16.1674600 21.0900900 E +21.0900900 22.0304980 D +22.0304980 24.0041950 E +24.0041950 28.9500450 E +28.9500450 29.9601130 D +29.9601130 31.9686390 E +31.9686390 35.9856910 E:min +35.9856910 39.9795230 A +39.9795230 43.9269160 E:min +43.9269160 45.9122220 F#:min +45.9122220 47.8859180 B +47.8859180 52.9594780 E +52.9594780 53.9927660 D +53.9927660 55.9664620 E +55.9664620 61.0400220 E +61.0400220 62.0036500 D +62.0036500 64.0934460 E +64.0934460 69.1670060 E +69.1670060 70.1306340 D +70.1306340 72.1623800 E +72.1623800 77.2707700 E +77.2707700 78.2692290 D +78.2692290 80.3241950 E +80.3241950 84.4457360 E:min +84.4457360 88.4743990 A +88.4743990 92.5262810 E:min +92.5262810 94.5348070 F#:min +94.5348070 96.5897730 B +96.5897730 101.6517230 E +101.6517230 102.7082310 D +102.7082310 104.7515870 E +104.7515870 109.9064170 E +109.9064170 110.9280950 D +110.9280950 112.9946710 E +112.9946710 118.0682310 E +118.0682310 119.1131290 D +119.1131290 122.6404497 E +122.6404497 125.2828201 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/06final/03_-_You_Won't_See_Me.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,94 @@ +0.0000000 0.4595427 N +0.4595427 1.2254190 D +1.2254190 4.4413830 A +4.4413830 6.4382990 A +6.4382990 8.4584350 B +8.4584350 10.5482310 D +10.5482310 12.5225377 A +12.5225377 14.5652830 A +14.5652830 16.5621990 B +16.5621990 18.6519950 D +18.6519950 20.6489110 A +20.6489110 22.7038770 A:7 +22.7038770 24.7356230 D +24.7356230 26.7905890 D:min +26.7905890 28.8107250 A +28.8107250 30.8308610 A +30.8308610 32.8742170 B +32.8742170 33.6985260 D +33.6985260 36.8216090 A +36.8216090 37.6575280 D +37.6575280 40.8851020 A +40.8851020 42.9168480 A +42.9168480 45.0066430 B +45.0066430 47.0732190 D +47.0732190 49.1281850 A +49.1281850 51.1715410 A +51.1715410 53.2265070 B +53.2265070 55.3163030 D +55.3163030 57.3480490 A +57.3480490 59.4378450 A:7 +59.4378450 61.4928110 D +61.4928110 63.5593870 D:min +63.5593870 65.6491830 A +65.6491830 67.6693190 A +67.6693190 69.7707250 B +69.7707250 70.5834240 D +70.5834240 73.8342170 A +73.8342170 74.6353060 D +74.6353060 77.9209290 A +77.9209290 80.0107250 B:min +80.0107250 82.0656910 B:hdim7/b3 +82.0656910 84.1322670 D:dim7 +84.1322670 86.2685030 A +86.2685030 90.3087750 B +90.3087750 92.4101810 E:sus4(b7) +92.4101810 94.5115870 E +94.5115870 96.6246030 A +96.6246030 98.7143990 B +98.7143990 100.8390240 D +100.8390240 102.8475510 A +102.8475510 104.9489560 A +104.9489560 107.0155320 B +107.0155320 109.1517680 D +109.1517680 111.2183440 A +111.2183440 113.3313600 A:7 +113.3313600 115.4443760 D +115.4443760 117.5457820 D:min +117.5457820 119.6587980 A +119.6587980 121.8182530 A +121.8182530 123.8848290 B +123.8848290 124.6743080 D +124.6743080 128.0295910 A +128.0295910 128.7958500 D +128.7958500 132.1163030 A +132.1163030 134.2177090 B:min +134.2177090 136.2610650 B:hdim7/b3 +136.2610650 138.3624710 D:dim7 +138.3624710 140.4754870 A +140.4754870 144.6899090 B +144.6899090 146.8261450 E:sus4(b7) +146.8261450 148.8578910 E +148.8578910 150.9709070 A +150.9709070 153.0955320 B:7 +153.0955320 155.2085480 D +155.2085480 157.3099540 A +157.3099540 159.4113600 A +159.4113600 161.5359860 B:7 +161.5359860 163.6606120 D +163.6606120 165.7155780 A +165.7155780 167.7821540 A:7 +167.7821540 169.9532190 D +169.9532190 172.0430150 D:min +172.0430150 174.2256910 A +174.2256910 176.3735370 A +176.3735370 178.4749430 B:7 +178.4749430 179.2876410 D +179.2876410 182.6661450 A +182.6661450 183.3859630 D +183.3859630 186.8225170 A +186.8225170 188.9007020 A +188.9007020 190.9904980 B:7 +190.9904980 193.0919040 D +193.0919040 197.2482760 A +197.2482760 202.1796266 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/06final/04_-_Nowhere_Man.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,74 @@ +0.0000000 0.4403951 N +0.4403951 2.5373460 E +2.5373460 4.5110430 B +4.5110430 6.5079590 A +6.5079590 8.5048750 E +8.5048750 10.6643310 A:maj6 +10.6643310 12.6728570 A:min +12.6728570 16.6550790 E +16.6550790 18.6287750 E +18.6287750 20.6605210 B +20.6605210 22.6109970 A +22.6109970 24.5730830 E +24.5730830 26.5700000 A:maj6 +26.5700000 28.5204760 A:min +28.5204760 32.4446480 E +32.4446480 34.3835140 G#:min +34.3835140 36.4036500 A +36.4036500 38.3541260 G#:min +38.3541260 40.3510430 A +40.3510430 42.3711790 G#:min +42.3711790 44.3100450 A +44.3100450 46.2721310 A +46.2721310 48.1993870 B +48.1993870 50.1963030 E +50.1963030 52.1700000 B +52.1700000 54.1320860 A +54.1320860 56.1290020 E +56.1290020 58.1026980 A:maj6 +58.1026980 60.0880040 A:min +60.0880040 64.0121760 E +64.0121760 66.0439220 E +66.0439220 68.0060090 B +68.0060090 69.9680950 A +69.9680950 71.9650110 E +71.9650110 73.9735370 A:maj6 +73.9735370 75.8775730 A:min +75.8775730 79.8597950 E +79.8597950 81.8102720 G#:min +81.8102720 83.7839680 A +83.7839680 85.7576640 G#:min +85.7576640 87.8126300 A +87.8126300 89.7747160 G#:min +89.7747160 91.7716320 A +91.7716320 93.7801580 A +93.7801580 95.6841950 B +95.6841950 97.6578910 E +97.6578910 99.6548070 B +99.6548070 101.5936730 A +101.5936730 103.5905890 E +103.5905890 105.5526750 A:maj6 +105.5526750 107.5612010 A:min +107.5612010 111.5202040 E +111.5202040 113.4474600 G#:min +113.4474600 115.4327660 A +115.4327660 117.3948520 G#:min +117.3948520 119.4265980 A +119.4265980 121.4002940 G#:min +121.4002940 123.3739900 A +123.3739900 125.3825170 A +125.3825170 127.3097730 B +127.3097730 129.2718590 E +129.2718590 131.2919950 B +131.2919950 133.2424710 A +133.2424710 135.2742170 E +135.2742170 137.2479130 A:maj6 +137.2479130 139.2680490 A:min +139.2680490 143.1573920 E +143.1573920 145.1194780 A:maj6 +145.1194780 147.1163940 A:min +147.1163940 151.0870060 E +151.0870060 153.0839220 A:maj6 +153.0839220 155.0460090 A:min +155.0460090 161.3377719 E +161.3377719 163.9801423 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/06final/05_-_Think_For_Yourself.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,63 @@ +0.0000000 0.4212474 N +0.4212474 3.5125850 G +3.5125850 3.7912240 G +3.7912240 4.0930830 G# +4.0930830 5.9158500 A:min +5.9158500 7.7734460 D:min +7.7734460 9.6194330 Bb +9.6194330 11.4538090 C +11.4538090 15.1225620 G +15.1225620 16.9569380 A:min +16.9569380 18.8261450 D:min +18.8261450 20.6837410 Bb +20.6837410 22.5297270 C +22.5297270 24.3641040 G +24.3641040 26.2449200 A:min +26.2449200 29.9020630 C +29.9020630 33.5243760 G +33.5243760 35.3007020 Eb/5 +35.3007020 37.1234690 D +37.1234690 40.8270520 G +40.8270520 42.6846480 A:min +42.6846480 44.5074140 D:min +44.5074140 46.3766210 Bb +46.3766210 48.1993870 C +48.1993870 51.9378000 G +51.9378000 53.8186160 A:min +53.8186160 55.7110430 D:min +55.7110430 57.5802490 Bb +57.5802490 59.4494550 C +59.4494550 61.3302720 G +61.3302720 63.2226980 A:min +63.2226980 66.8914510 C +66.8914510 70.5485940 G +70.5485940 72.3365300 Eb/5 +72.3365300 74.1709070 D +74.1709070 77.8860990 G +77.8860990 79.7088660 A:min +79.7088660 81.5896820 D:min +81.5896820 83.4008390 Bb +83.4008390 85.2932650 C +85.2932650 89.0316780 G +89.0316780 90.8776640 A:min +90.8776640 92.7700900 D:min +92.7700900 94.6160770 Bb +94.6160770 96.5201130 C +96.5201130 98.4125390 G +98.4125390 100.3165750 A:min +100.3165750 104.0549880 C +104.0549880 107.6540810 G +107.6540810 109.4536280 Eb/5 +109.4536280 111.2763940 D +111.2763940 114.9451470 G +114.9451470 118.6022900 C +118.6022900 122.2246030 G +122.2246030 124.0589790 Eb/5 +124.0589790 125.9165750 D +125.9165750 127.7528619 C:7 +127.7528619 129.5504980 G +129.5504980 131.3732650 Eb/5 +131.3732650 133.1960310 D +133.1960310 135.0187980 C:7 +135.0187980 136.4003850 G +136.4003850 139.1456906 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/06final/06_-_The_Word.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,65 @@ +0.0000000 0.4020998 N +0.4020998 1.3020376 G +1.3020376 5.3469610 D:7(#9) +5.3469610 13.3230150 D:7(#9) +13.3230150 17.2936280 G:7 +17.2936280 21.2642400 D:7(#9) +21.2642400 21.9724480 A:sus4 +21.9724480 23.1914960 A +23.1914960 23.8184350 G:sus4 +23.8184350 24.9910430 G +24.9910430 28.7642850 D:7(#9) +28.7642850 30.7031510 D +30.7031510 32.6768480 C +32.6768480 34.6505440 F +34.6505440 36.6474600 G +36.6474600 44.6235140 D:7(#9) +44.6235140 48.5825170 G:7 +48.5825170 52.5531290 D:7(#9) +52.5531290 53.3077770 A:sus4 +53.3077770 54.4107250 A +54.4107250 55.1537640 G:sus4 +55.1537640 56.2683210 G +56.2683210 60.0880040 D:7(#9) +60.0880040 62.0384800 D +62.0384800 63.9773460 C +63.9773460 65.9162130 F +65.9162130 67.9363490 G +67.9363490 75.9007930 D:7(#9) +75.9007930 79.8365750 G:7 +79.8365750 83.8304080 D:7(#9) +83.8304080 84.5850560 A:sus4 +84.5850560 85.6664125 A +85.6664125 86.4310430 G:sus4 +86.4310430 87.6500900 G +87.6500900 91.3768930 D:7(#9) +91.3768930 93.2693190 D +93.2693190 95.2546250 C +95.2546250 97.2399310 F +97.2399310 99.2136280 G +99.2136280 107.2129020 D:7(#9) +107.2129020 111.1835140 G:7 +111.1835140 115.1309070 D:7(#9) +115.1309070 115.8739450 A:sus4 +115.8739450 117.0697730 A +117.0697730 117.8592510 G:sus4 +117.8592510 119.0318590 G +119.0318590 122.9560310 D:7(#9) +122.9560310 124.8368480 D +124.8368480 126.8105440 C:maj(9) +126.8105440 128.7842400 F:maj6 +128.7842400 130.7347160 G +130.7347160 134.7285480 D +134.7285480 138.7804300 D:7(#9) +138.7804300 142.7278230 G:7 +142.7278230 146.6868250 D:7(#9) +146.6868250 147.4995230 A:sus4 +147.4995230 148.5676410 A +148.5676410 149.3687300 G:sus4 +149.3687300 150.5065070 G +150.5065070 154.3378000 D:7(#9) +154.3378000 156.2418360 D +156.2418360 158.1923120 C:maj(9) +158.1923120 160.1892290 F:maj6 +160.1892290 162.0236050 G +162.0236050 163.6163377 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/06final/07_-_Michelle.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,94 @@ +0.0000000 0.4212474 N +0.4212474 1.4153749 F:min/5 +1.4153749 2.3748070 E:aug +2.3748070 3.3848750 F:min7 +3.3848750 4.3717230 F:min6/5 +4.3717230 5.4570692 C#:maj7/3 +5.4570692 5.9166119 Bb:min/5 +5.9166119 6.4385684 C#/3 +6.4385684 8.4700450 C +8.4700450 10.5714510 F +10.5714510 12.6148070 Bb:min7 +12.6148070 14.6813830 Eb:maj6 +14.6813830 16.7363490 B:dim7 +16.7363490 17.7696370 C +17.7696370 18.8029250 B:dim7 +18.8029250 20.8695010 C +20.8695010 22.9962810 F +22.9962810 24.9910430 Bb:min7 +24.9910430 27.0343990 Eb:maj6 +27.0343990 29.0313150 B:dim7 +29.0313150 30.0762130 C +30.0762130 31.0862810 B:dim7 +31.0862810 33.1644670 C +33.1644670 37.2743990 F:min +37.2743990 39.2829250 Ab:7 +39.2829250 41.3843310 Db +41.3843310 43.3580270 C +43.3580270 45.3781630 F:min +45.3781630 46.3882310 F:min/5 +46.3882310 47.3750790 E:aug +47.3750790 48.4083670 F:min7 +48.4083670 49.4068250 F:min6/5 +49.4068250 51.4501810 C#:maj7/3 +51.4501810 53.5631970 C +53.5631970 55.5136730 F +55.5136730 57.5918590 Bb:min7 +57.5918590 59.5771650 Eb:maj6 +59.5771650 61.6669610 B:dim7 +61.6669610 62.6189790 C +62.6189790 63.6174370 B:dim7 +63.6174370 65.6724030 C +65.6724030 69.7242850 F:min +69.7242850 71.6863710 Ab:7 +71.6863710 73.7529470 Db +73.7529470 75.7498630 C +75.7498630 77.7932190 F:min +77.7932190 78.8032870 F:min/5 +78.8032870 79.8017460 E:aug +79.8017460 80.8466430 F:min7 +80.8466430 81.8334920 F:min6/5 +81.8334920 83.8884580 C#:maj7/3 +83.8884580 85.9085940 C +85.9085940 87.9055100 F +87.9055100 89.9488660 Bb:min7 +89.9488660 91.9690020 Eb:maj6 +91.9690020 93.9891380 B:dim7 +93.9891380 94.9992060 C +94.9992060 95.9976640 B:dim7 +95.9976640 98.0410200 C +98.0410200 102.0580720 F:min +102.0580720 104.0898180 Ab:7 +104.0898180 106.1099540 Db +106.1099540 108.1881400 C +108.1881400 110.2082760 F:min +110.2082760 111.2284734 F:min/5 +111.2284734 112.2168020 E:aug +112.2168020 113.2268700 F:min7 +113.2268700 114.1904980 F:min6/5 +114.1904980 116.2570740 C#:maj7/3 +116.2570740 118.3700900 C +118.3700900 120.4134460 F +120.4134460 122.5961220 Bb:min7 +122.5961220 124.7323580 Eb:maj6 +124.7323580 126.8685940 B:dim7 +126.8685940 127.9715410 C +127.9715410 128.9816090 B:dim7 +128.9816090 131.1991150 C +131.1991150 132.3020630 F:min/5 +132.3020630 133.3817910 E:aug +133.3817910 134.5079590 F:min7 +134.5079590 135.5528570 F:min6/5 +135.5528570 137.7007020 C#:maj7/3 +137.7007020 139.8369380 C +139.8369380 141.9731740 F +141.9731740 144.2139000 Bb:min7 +144.2139000 146.4314050 Eb:maj6 +146.4314050 148.6140810 B:dim7 +148.6140810 149.7286390 C +149.7286390 150.8083670 B:dim7 +150.8083670 153.0258730 C +153.0258730 155.1737180 F +155.1737180 157.3680040 Bb:min7 +157.3680040 159.5042400 Eb:maj6 +159.5042400 162.2377097 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/06final/08_-_What_Goes_On.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,61 @@ +0.0000000 0.4595427 N +0.4595427 1.4808390 B +1.4808390 2.6766660 E +2.6766660 3.8841040 B +3.8841040 6.3570290 E +6.3570290 13.7177550 E +13.7177550 16.1790700 A +16.1790700 23.6326750 E +23.6326750 26.1404300 A +26.1404300 27.3594780 B:sus4 +27.3594780 28.6133560 B +28.6133560 31.0514510 E +31.0514510 33.5708160 E +33.5708160 36.0321310 A:min +36.0321310 38.5282760 E +38.5282760 41.0360310 A:min +41.0360310 42.3131290 A:min +42.3131290 43.5553960 B +43.5553960 46.0167120 E +46.0167120 48.5476870 B +48.5476870 55.9084120 E +55.9084120 58.3929470 A +58.3929470 65.6956230 E +65.6956230 68.2614280 A +68.2614280 69.5385260 B:sus4 +69.5385260 70.7691830 B +70.7691830 73.2304980 E +73.2304980 75.7862451 E +75.7862451 78.1988441 A:min +78.1988441 80.7263288 E +80.7263288 83.1918590 A:min +83.1918590 84.4341260 A:min +84.4341260 85.6531740 B +85.6531740 88.1144890 E +88.1144890 90.5874140 B +90.5874140 97.9829700 E +97.9829700 100.4326750 A +100.4326750 107.7934010 E +107.7934010 110.2779360 A +110.2779360 111.4505440 B +111.4505440 112.7160310 B:7 +112.7160310 115.2005660 E +115.2005660 117.6851020 E +117.6851020 120.1696370 A:min +120.1696370 122.6213021 E +122.6213021 125.1387070 A:min +125.1387070 126.3345350 A:min +126.3345350 127.6348520 B +127.6348520 130.0613370 E +130.0613370 132.5226530 B +132.5226530 139.9298180 E +139.9298180 142.4143530 A +142.4143530 149.8099090 E +149.8099090 152.3408840 A +152.3408840 153.5367120 B:sus4 +153.5367120 154.7789790 B +154.7789790 157.2286840 E +157.2286840 159.7364390 E:7 +159.7364390 161.4547160 E +161.4547160 165.8757558 E +165.8757558 170.3945920 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/06final/09_-_Girl.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,94 @@ +0.0000000 0.4403951 N +0.4403951 1.1297091 N +1.1297091 2.3520180 C:min +2.3520180 3.5942850 G:7 +3.5942850 6.1252600 C:min +6.1252600 8.6794550 F:min +8.6794550 9.8985030 Eb +9.8985030 11.1291600 G +11.1291600 12.3946480 C:min +12.3946480 13.6485260 G:7 +13.6485260 16.1098410 C:min +16.1098410 18.6524260 F:min +18.6524260 21.1253510 C:min +21.1253510 22.3211790 Eb +22.3211790 23.5634460 G:min/5 +23.5634460 24.8405440 Ab/3 +24.8405440 26.0595910 Bb +26.0595910 27.2902490 Eb +27.2902490 28.5441260 G:min/5 +28.5441260 29.8096140 Ab/3 +29.8096140 31.0054420 Bb +31.0054420 32.2593190 C:min +32.2593190 33.5015870 G:7 +33.5015870 36.0325620 C:min +36.0325620 38.5287070 F:min +38.5287070 39.8290240 Eb +39.8290240 41.0248520 G +41.0248520 42.2903400 C:min +42.2903400 43.4977770 G:7 +43.4977770 46.0287520 C:min +46.0287520 48.4784580 F:min +48.4784580 51.0442630 C:min +51.0442630 52.2749200 Eb +52.2749200 53.5171880 G:min/5 +53.5171880 54.7942850 Ab/3 +54.7942850 56.0249430 Bb +56.0249430 57.3020400 Eb +57.3020400 58.5326980 G:min/5 +58.5326980 59.8097950 Ab/3 +59.8097950 61.0520630 Bb +61.0520630 63.5133780 F:min +63.5133780 65.9979130 C +65.9979130 68.4243990 F:min +68.4243990 70.9205440 C +70.9205440 73.4166890 F:min +73.4166890 75.8315640 C +75.8315640 78.2812690 F:min +78.2812690 80.6961450 Ab +80.6961450 81.9134807 Eb +81.9134807 83.1922900 G:min/5 +83.1922900 84.4345570 Ab/3 +84.4345570 85.6652150 Bb +85.6652150 86.9074820 Eb +86.9074820 88.1497500 G:min/5 +88.1497500 89.3920180 Ab/3 +89.3920180 90.5762350 Bb +90.5762350 91.8881630 C:min +91.8881630 93.1072100 G:7 +93.1072100 95.6033560 C:min +95.6033560 98.0762810 F:min +98.0762810 99.3417680 Eb +99.3417680 100.5492060 G +100.5492060 101.7914730 C:min +101.7914730 103.0337410 G:7 +103.0337410 105.4950560 C:min +105.4950560 108.0144210 F:min +108.0144210 110.5337860 C:min +110.5337860 111.8108840 Eb +111.8108840 112.9951020 G:min/5 +112.9951020 114.2605890 Ab/3 +114.2605890 115.4912470 Bb +115.4912470 116.7335140 Eb +116.7335140 117.9525620 G:min/5 +117.9525620 119.1948290 Ab/3 +119.1948290 120.4254870 Bb +120.4254870 121.6677550 C:min +121.6677550 122.8635820 G:7 +122.8635820 125.3481170 C:min +125.3481170 127.8674820 F:min +127.8674820 129.0981400 Eb +129.0981400 130.3868480 G +130.3868480 131.6639450 C:min +131.6639450 132.8829930 G:7 +132.8829930 135.3907480 C:min +135.3907480 137.8636730 F:min +137.8636730 140.3830380 C:min +140.3830380 141.6717460 Eb +141.6717460 142.9140130 G:min/5 +142.9140130 144.1330610 Ab/3 +144.1330610 145.3637180 Bb +145.3637180 146.5827660 Eb +146.5827660 147.8134240 G:min/5 +147.8134240 148.9976410 Ab/3 +148.9976410 153.3149229 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/06final/10_-_I'm_Looking_Through_You.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,135 @@ +0.0000000 0.4020998 N +0.4020998 1.7246480 Ab/5 +1.7246480 3.1294550 Eb +3.1294550 3.8028340 Ab +3.8028340 4.4646030 Db +4.4646030 5.1728110 Ab +5.1728110 5.8229700 Db +5.8229700 6.5311790 Ab +6.5311790 7.1929470 Db +7.1929470 7.9243760 Ab +7.9243760 8.5977550 Db +8.5977550 10.0141720 Bb:min +10.0141720 11.3957590 F:min +11.3957590 12.7657360 Eb +12.7657360 13.4799183 Ab +13.4799183 14.1937640 Db +14.1937640 15.5869610 Bb:min +15.5869610 16.9685480 F:min +16.9685480 18.3617460 Eb +18.3617460 19.7781630 F:min +19.7781630 21.1829700 Bb:min +21.1829700 21.8795690 Ab +21.8795690 22.5529470 Db +22.5529470 23.9461450 Eb +23.9461450 24.6311330 Ab +24.6311330 25.3509520 Db +25.3509520 26.7325390 Bb:min +26.7325390 28.1025170 Db +28.1025170 28.7875050 Ab +28.7875050 29.4957140 Db +29.4957140 30.2039220 Ab +30.2039220 30.8773010 Db +30.8773010 31.5739000 Ab +31.5739000 32.2704980 Db +32.2704980 32.9147436 Ab +32.9147436 33.5849100 Db +33.5849100 35.0568930 Bb:min +35.0568930 36.4617000 F:min +36.4617000 37.8084580 Eb +37.8084580 38.5282760 Ab +38.5282760 39.2132650 Db +39.2132650 40.6180720 Bb:min +40.6180720 41.9764390 F:min +41.9764390 43.3928570 Eb +43.3928570 44.7512240 F:min +44.7512240 46.1676410 Bb:min +46.1676410 46.8410200 Ab +46.8410200 47.5608390 Db +47.5608390 48.9888660 Eb +48.9888660 49.6854640 Ab +49.6854640 50.3704530 Db +50.3704530 51.7520400 Bb:min +51.7520400 53.1568480 Db +53.1568480 53.8534460 Ab +53.8534460 54.5732650 Db +54.5732650 55.2698630 Ab +55.2698630 55.9664620 Db +55.9664620 56.6746710 Ab +56.6746710 57.3596590 Db +57.3596590 58.7644670 Ab +58.7644670 61.5856910 Db +61.5856910 64.3953060 Ab +64.3953060 67.1700900 Db +67.1700900 68.5516780 Eb:sus4 +68.5516780 69.9564850 Eb +69.9564850 70.6995230 Ab +70.6995230 71.4077320 Db +71.4077320 72.8009290 Bb:min +72.8009290 74.1825170 F:min +74.1825170 75.5989340 Eb +75.5989340 76.3768020 Ab +76.3768020 77.0385710 Db +77.0385710 78.4317680 Bb:min +78.4317680 79.8249650 F:min +79.8249650 81.2297730 Eb +81.2297730 82.6345800 F:min +82.6345800 84.0626070 Bb:min +84.0626070 84.7708160 Ab +84.7708160 85.4558040 Db +85.4558040 86.8490020 Eb +86.8490020 87.5688200 Ab +87.5688200 88.2538090 Db +88.2538090 89.6586160 Bb:min +89.6586160 91.0866430 Db +91.0866430 91.7716320 Ab +91.7716320 92.5262810 Db +92.5262810 93.2112690 Ab +93.2112690 93.9310880 Db +93.9310880 94.6044670 Ab +94.6044670 95.3010650 Db +95.3010650 96.7407020 Ab +96.7407020 99.5038770 Db +99.5038770 102.2786620 Ab +102.2786620 105.0766660 Db +105.0766660 106.4466430 Eb:sus4 +106.4466430 107.8282310 Eb +107.8282310 108.5480490 Ab +108.5480490 109.2562580 Db +109.2562580 110.6726750 Bb:min +110.6726750 112.0542630 F:min +112.0542630 113.4242400 Eb +113.4242400 114.1440580 Ab +114.1440580 114.8522670 Db +114.8522670 116.2106340 Bb:min +116.2106340 117.6270520 F:min +117.6270520 119.0550790 Eb +119.0550790 120.4482760 F:min +120.4482760 121.8414730 Bb:min +121.8414730 122.5729020 Ab +122.5729020 123.2578910 Db +123.2578910 124.6162580 Eb +124.6162580 125.3592970 Ab +125.3592970 126.0675050 Db +126.0675050 127.4490920 Bb:min +127.4490920 128.8539000 Db +128.8539000 129.5853280 Ab +129.5853280 130.3167570 Db +130.3167570 131.0017460 Ab +131.0017460 131.6983440 Db +131.6983440 132.3717230 Ab +132.3717230 133.1147610 Db +133.1147610 133.8113600 Ab +133.8113600 134.5311790 Db +134.5311790 135.2045570 Ab +135.2045570 135.8663260 Db +135.8663260 136.5861450 Ab +136.5861450 137.2711330 Db +137.2711330 138.0025620 Ab +138.0025620 138.6991600 Db +138.6991600 139.4189790 Ab +139.4189790 140.0923580 Db +140.0923580 140.8237860 Ab +140.8237860 141.4855550 Db +141.4855550 142.2053740 Ab +142.2053740 147.5706395 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/06final/11_-_In_My_Life.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,80 @@ +0.0000000 0.4403951 N +0.4403951 2.4700418 A +2.4700418 5.0451020 E +5.0451020 7.1420590 A +7.1420590 9.6078230 E +9.6078230 10.7572100 A +10.7572100 11.9298180 E +11.9298180 12.8237860 F#:min +12.8237860 14.2750340 A:7 +14.2750340 15.4360310 D +15.4360310 16.5854190 D:min +16.5854190 18.8958040 A +18.8958040 20.0684120 A +20.0684120 21.2410200 E +21.2410200 22.0769380 F#:min +22.0769380 23.5397950 A:7 +23.5397950 24.7305934 D +24.7305934 25.9082310 D:min +25.9082310 28.2302260 A +28.2302260 30.5522220 F#:min +30.5522220 32.8858270 D +32.8858270 35.1729930 G +35.1729930 37.4833780 A +37.4833780 39.8169840 F#:min +39.8169840 42.1273690 B +42.1273690 44.4029250 D:min +44.4029250 46.7017000 A +46.7017000 49.0585260 A +49.0585260 51.4153510 E +51.4153510 52.5763490 A +52.5763490 53.7489560 E +53.7489560 54.5964850 F#:min +54.5964850 56.0593420 A:7 +56.0593420 57.2319500 D +57.2319500 58.4393870 D:min +58.4393870 60.8426530 A +60.8426530 61.9804300 A +61.9804300 63.1646480 E +63.1646480 63.9889560 F#:min +63.9889560 65.4866430 A:7 +65.4866430 66.6244210 D +66.6244210 67.8318590 D:min +67.8318590 70.1770740 A +70.1770740 72.5222900 F#:min +72.5222900 74.7862350 D +74.7862350 77.0966210 G +77.0966210 79.3837860 A +79.3837860 81.7638320 F#:min +81.7638320 84.1090470 B +84.1090470 86.3846030 D:min +86.3846030 88.6601580 A +88.6601580 89.8327660 A +89.8327660 91.0402040 E +91.0402040 91.9225620 F#:min +91.9225620 93.3621990 A:7 +93.3621990 94.5348070 D +94.5348070 95.7074140 D:min +95.7074140 98.0294100 A +98.0294100 99.1671880 A +99.1671880 100.3397950 E +100.3397950 101.1989340 F#:min +101.1989340 102.6037410 A:7 +102.6037410 103.7763490 D +103.7763490 104.9257360 D:min +104.9257360 107.2477320 A +107.2477320 109.5581170 F#:min +109.5581170 111.8336730 D +111.8336730 114.1440580 G +114.1440580 116.4312240 A +116.4312240 118.7764390 F#:min +118.7764390 121.0752150 B +121.0752150 123.3391600 D:min +123.3391600 125.6379360 A +125.6379360 127.6812920 A +127.6812920 130.3399770 E +130.3399770 134.9026980 D:min +134.9026980 137.1898630 A +137.1898630 139.8485480 E +139.8485480 143.7985601 A +143.7985601 147.8578537 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/06final/12_-_Wait.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,117 @@ +0.0000000 0.4020998 N +0.4020998 1.1325390 F#:min +1.1325390 1.8291380 F#:min +1.8291380 3.0481850 B/5 +3.0481850 3.7447840 B:min/5 +3.7447840 4.8825620 F#:min +4.8825620 5.5443310 C# +5.5443310 6.7749880 F#:min +6.7749880 7.5064170 F#:min7 +7.5064170 8.7022440 B/5 +8.7022440 9.4220630 B:min/5 +9.4220630 10.6062810 F#:min +10.6062810 11.3377090 C# +11.3377090 12.4870970 F#:min +12.4870970 13.2069160 A +13.2069160 14.2982530 D:maj7 +14.2982530 15.0180720 A +15.0180720 16.1558500 D:maj7 +16.1558500 16.8640580 A +16.8640580 17.9902260 D:maj7 +17.9902260 18.9306340 A +18.9306340 19.8826530 C#:7 +19.8826530 21.7286390 F#:min +21.7286390 22.4716780 F#:min7 +22.4716780 23.6326750 B/5 +23.6326750 24.3873240 B:min/5 +24.3873240 25.5599310 F#:min +25.5599310 26.2565300 C#:min +26.2565300 27.4523580 F#:min +27.4523580 28.1953960 F#:min7 +28.1953960 29.3563940 B/5 +29.3563940 30.0994330 B:min/5 +30.0994330 31.2256000 F#:min +31.2256000 31.9686390 C#:7 +31.9686390 33.0948070 F#:min +33.0948070 33.7681850 A +33.7681850 34.8827430 D:maj7 +34.8827430 35.6141720 A +35.6141720 36.7171200 D:maj7 +36.7171200 37.4021080 A +37.4021080 38.5863260 D:maj7 +38.5863260 39.5035140 A +39.5035140 40.3974820 C#:7 +40.3974820 42.2434690 F#:min +42.2434690 44.0894550 B:maj(*3) +44.0894550 45.9122220 E +45.9122220 47.7582080 A +47.7582080 49.6506340 F#:min +49.6506340 51.4501810 B:maj(*3) +51.4501810 53.2961670 E +53.2961670 55.1653740 A +55.1653740 55.6878230 C#:sus4 +55.6878230 57.1739000 C# +57.1739000 57.8240580 F#:min7 +57.8240580 59.0082760 B/5 +59.0082760 59.7048750 B:min/5 +59.7048750 60.8658730 F#:min +60.8658730 61.5973010 C# +61.5973010 62.7931290 F#:min +62.7931290 63.5129470 F#:min +63.5129470 64.7319950 B/5 +64.7319950 65.4402040 B:min/5 +65.4402040 66.6592510 F#:min +66.6592510 67.3326300 C# +67.3326300 68.4820180 F#:min +68.4820180 69.1902260 A +69.1902260 70.3047840 D:maj7 +70.3047840 70.9897730 A +70.9897730 72.1507700 D:maj7 +72.1507700 72.8473690 A +72.8473690 74.0199770 D:maj7 +74.0199770 74.9836050 A +74.9836050 75.8891830 C#:7 +75.8891830 77.7932190 F#:min +77.7932190 79.6740360 B:maj(*3) +79.6740360 81.5664620 E +81.5664620 83.4588880 A +83.4588880 85.3513150 F#:min +85.3513150 87.2437410 B:maj(*3) +87.2437410 89.1245570 E +89.1245570 91.0169840 A +91.0169840 91.4929930 C#:sus4 +91.4929930 92.9326300 C# +92.9326300 93.6872780 F#:min +93.6872780 94.8250560 B/5 +94.8250560 95.5797050 B:min/5 +95.5797050 96.7523120 F#:min +96.7523120 97.4373010 C#:min +97.4373010 98.6795690 F#:min +98.6795690 99.3877770 F#:min +99.3877770 100.5719950 B/5 +100.5719950 101.2918140 B:min/5 +101.2918140 102.4760310 F#:min +102.4760310 103.2074600 C# +103.2074600 104.3568480 F#:min +104.3568480 105.0186160 A +105.0186160 106.1796140 D:maj7 +106.1796140 106.8762130 A +106.8762130 107.9791600 D:maj7 +107.9791600 108.7454190 A +108.7454190 109.9180270 D:maj7 +109.9180270 110.8584350 A +110.8584350 111.7756230 C#:7 +111.7756230 113.6448290 F#:min +113.6448290 114.3530380 F#:min +114.3530380 115.5720860 B/5 +115.5720860 116.2570740 B:min/5 +116.2570740 117.4412920 F#:min +117.4412920 118.1030610 C# +118.1030610 119.3685480 F#:min +119.3685480 120.0767570 F#:min +120.0767570 121.2609750 B/5 +121.2609750 121.9575730 B:min/5 +121.9575730 123.1998410 F#:min +123.1998410 125.3941260 C#:7 +125.3941260 131.4483509 F#:min +131.4483509 136.5990583 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/06final/13_-_If_I_Needed_Someone.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,36 @@ +0.0000000 0.3829522 N +0.3829522 7.7966660 A +7.7966660 15.2386620 A +15.2386620 18.9074140 G:maj/9 +18.9074140 30.0181630 A +30.0181630 33.6869160 G:maj/9 +33.6869160 37.4369380 A +37.4369380 39.2597050 E:min +39.2597050 42.9516780 F# +42.9516780 44.8557140 B:min +44.8557140 46.7249200 E:min +46.7249200 48.6173460 F# +48.6173460 50.4981630 B:min +50.4981630 52.3557590 E +52.3557590 59.9370740 A +59.9370740 63.6870970 G:maj/9 +63.6870970 67.4371200 A +67.4371200 74.9603850 A +74.9603850 78.6987980 G:maj/9 +78.6987980 82.4488200 A +82.4488200 90.0301360 A +90.0301360 93.8149880 G:maj/9 +93.8149880 97.6114510 A +97.6114510 99.5154870 E:min +99.5154870 103.3119500 F# +103.3119500 105.2624260 B:min +105.2624260 107.1780720 E:min +107.1780720 109.0472780 F# +109.0472780 110.9629250 B:min +110.9629250 112.9017910 E +112.9017910 120.5759860 A +120.5759860 124.3840580 G:maj/9 +124.3840580 128.2153510 A +128.2153510 135.8431060 A +135.8431060 139.6243808 A +139.6243808 143.4921983 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/06final/14_-_Run_For_Your_Life.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,59 @@ +0.0000000 0.4112470 N +0.4112470 8.5877550 D +8.5877550 11.2696590 D +11.2696590 13.9747840 B:min +13.9747840 16.6915190 D +16.6915190 19.3966430 B:min +19.3966430 20.7898410 B:min +20.7898410 22.1365980 E +22.1365980 23.4949650 B:min +23.4949650 24.8417230 E +24.8417230 26.2233100 B:min +26.2233100 26.8966890 E:min/b3 +26.8966890 27.6165070 F# +27.6165070 30.2751920 B:min +30.2751920 35.6854420 D +35.6854420 38.3905660 D +38.3905660 41.1189110 B:min +41.1189110 43.8240360 D +43.8240360 46.5291600 B:min +46.5291600 47.8991380 B:min +47.8991380 49.2575050 E +49.2575050 50.6042630 B:min +50.6042630 51.9626300 E +51.9626300 53.3674370 B:min +53.3674370 54.0175960 E:min/b3 +54.0175960 54.6909750 F# +54.6909750 57.3960990 B:min +57.3960990 62.8992290 D:maj6 +62.8992290 65.5346930 G +65.5346930 68.2049880 D +68.2049880 70.9565530 A +70.9565530 73.6848970 D +73.6848970 76.3784120 D +76.3784120 79.0951470 B:min +79.0951470 81.8118820 D +81.8118820 84.5634460 B:min +84.5634460 85.9450340 B:min +85.9450340 87.2917910 E +87.2917910 88.6733780 B:min +88.6733780 90.0549650 E +90.0549650 91.4017230 B:min +91.4017230 92.0751020 E:min/b3 +92.0751020 92.7717000 F# +92.7717000 95.4768250 B:min +95.4768250 100.9270586 D +100.9270586 103.5805890 D +103.5805890 106.2624940 B:min +106.2624940 109.0024480 D +109.0024480 111.7307930 B:min +111.7307930 113.1123800 B:min +113.1123800 114.4823580 E +114.4823580 115.8291150 B:min +115.8291150 117.2107020 E +117.2107020 118.6039000 B:min +118.6039000 119.2772780 E:min/b3 +119.2772780 119.9622670 F# +119.9622670 122.6906120 B:min +122.6906120 137.2500771 D +137.2500771 138.5521146 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/06final/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +/.DS_Store/1.1.1.1/Fri Sep 23 09:24:39 2005// +/01_-_Drive_My_Car.lab/1.3/Tue Oct 17 21:54:46 2006// +/02_-_Norwegian_Wood_(This_Bird_Has_Flown).lab/1.1.1.1/Fri Sep 23 09:24:39 2005// +/03_-_You_Won't_See_Me.lab/1.3/Tue Oct 17 21:54:46 2006// +/04_-_Nowhere_Man.lab/1.1.1.1/Fri Sep 23 09:24:39 2005// +/05_-_Think_For_Yourself.lab/1.1.1.1/Fri Sep 23 09:24:39 2005// +/06_-_The_Word.lab/1.2/Sun Jul 30 21:51:52 2006// +/07_-_Michelle.lab/1.3/Tue Oct 17 21:54:46 2006// +/08_-_What_Goes_On.lab/1.1.1.1/Fri Sep 23 09:24:39 2005// +/09_-_Girl.lab/1.2/Sun Jul 30 21:51:52 2006// +/10_-_I'm_Looking_Through_You.lab/1.2/Sun Jul 30 21:51:52 2006// +/11_-_In_My_Life.lab/1.3/Tue Oct 17 21:54:46 2006// +/12_-_Wait.lab/1.2/Sun Jul 30 21:51:52 2006// +/13_-_If_I_Needed_Someone.lab/1.1.1.1/Fri Sep 23 09:24:39 2005// +/14_-_Run_For_Your_Life.lab/1.1.1.1/Fri Sep 23 09:24:39 2005// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/06final/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +chrish/transcriptions/06final
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/06final/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:matthiasm@dspmac1.elec.qmul.ac.uk:/cvsroot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/07final/01_-_Taxman.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,43 @@ +0.0000000 6.1463832 N +6.1463832 12.5608830 D:7 +12.5608830 13.9586086 D:7(#9) +13.9586086 19.6081390 D:7 +19.6081390 20.9474866 D:7(#9) +20.9474866 23.0446920 D:7 +23.0446920 26.5973450 C +26.5973450 28.4085020 G:7 +28.4085020 35.5138080 D:7 +35.5138080 36.8605650 D:7(#9) +36.8605650 42.5958940 D:7 +42.5958940 43.9078210 D:7(#9) +43.9078210 46.1021070 D:7 +46.1021070 49.6315400 C +49.6315400 51.4775270 G:7 +51.4775270 60.3823790 D:7 +60.3823790 62.1703160 C:9 +62.1703160 67.3421486 D:7 +67.3421486 71.0171180 C:9 +71.0171180 85.2973910 D:7 +85.2973910 88.9313130 C +88.9313130 90.8005200 G:7 +90.8005200 98.0335360 D:7 +98.0335360 99.3761021 D:7(#9) +99.3761021 105.2897710 D:7 +105.2897710 106.6365290 D:7(#9) +106.6365290 108.9004740 D:7 +108.9004740 112.5227870 C +112.5227870 114.3919940 G:7 +114.3919940 118.0955760 D:7 +118.0955760 121.7178890 D:7 +121.7178890 123.1110870 D:7(#9) +123.1110870 128.9973450 D:7 +128.9973450 130.4021520 D:7(#9) +130.4021520 132.5499980 D:7 +132.5499980 136.1839210 C +136.1839210 138.0531270 G:7 +138.0531270 139.8758940 D:7 +139.8758940 143.6259170 F:7 +143.6259170 147.2017900 D:7 +147.2017900 148.5663153 D:7(#9) +148.5663153 154.9807650 D:7 +154.9807650 158.7528444 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/07final/02_-_Eleanor_Rigby.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,55 @@ +0.0000000 0.3829522 N +0.3829522 3.8492740 C +3.8492740 7.3438770 E:min +7.3438770 10.7920400 C +10.7920400 14.2402040 E:min +14.2402040 19.5575730 E:min +19.5575730 22.2162580 C +22.2162580 23.1218360 E:min +23.1218360 27.4193792 E:min +27.4193792 28.3114960 E:min/b7 +28.3114960 30.9237410 C +30.9237410 31.7828790 E:min +31.7828790 33.5475960 E:min7 +33.5475960 35.3123120 E:min6 +35.3123120 37.0189790 C/3 +37.0189790 38.7953060 E:min +38.7953060 40.5948520 E:min7 +40.5948520 42.2666890 E:min6 +42.2666890 43.9733560 C/3 +43.9733560 45.7729020 E:min +45.7729020 51.0438320 E:min +51.0438320 53.6096370 C +53.6096370 54.5268250 E:min +54.5268250 59.7280950 E:min +59.7280950 62.3403400 C +62.3403400 63.2226980 E:min +63.2226980 64.9409750 E:min7 +64.9409750 66.6824710 E:min6 +66.6824710 68.3891380 C/3 +68.3891380 70.1422440 E:min +70.1422440 71.9069610 E:min7 +71.9069610 73.6252380 E:min6 +73.6252380 75.3435140 C/3 +75.3435140 77.0966210 E:min +77.0966210 80.5447840 C +80.5447840 83.9697270 E:min +83.9697270 87.3830610 C +87.3830610 90.8312240 E:min +90.8312240 96.0208840 E:min +96.0208840 98.6215190 C +98.6215190 99.4574370 E:min +99.4574370 104.6122670 E:min +104.6122670 107.2709520 C +107.2709520 108.1533100 E:min +108.1533100 109.9180270 E:min7 +109.9180270 111.6711330 E:min6 +111.6711330 113.4358500 C/3 +113.4358500 115.2121760 E:min +115.2121760 116.9652830 E:min7 +116.9652830 118.6719500 E:min6 +118.6719500 120.4366660 C/3 +120.4366660 121.8745453 E:min +121.8745453 122.3340879 F#:min7 +122.3340879 123.5978303 E:min +123.5978303 127.5805334 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/07final/03_-_I'm_Only_Sleeping.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,84 @@ +0.0000000 0.4127210 N +0.4127210 2.9088660 Eb:min +2.9088660 5.1495910 Ab:min +5.1495910 6.3105890 F# +6.3105890 7.4599770 B +7.4599770 8.6209750 F# +8.6209750 9.6890920 Bb:7 +9.6890920 12.0459180 Eb:min +12.0459180 14.3679130 Ab:min +14.3679130 15.5521310 F# +15.5521310 16.7131290 B +16.7131290 17.8509070 F# +17.8509070 21.3687300 B +21.3687300 23.6675050 Gb +23.6675050 25.9430610 Ab:min +25.9430610 28.2534460 Bb:min +28.2534460 30.5522220 Ab:min +30.5522220 35.2310430 B:maj7 +35.2310430 37.5994780 Eb:min +37.5994780 39.8402040 Ab:min +39.8402040 41.0128110 F# +41.0128110 42.1970290 B +42.1970290 43.3231970 F# +43.3231970 44.4725850 Bb:7 +44.4725850 46.8061900 Eb:min +46.8061900 49.1514050 Ab:min +49.1514050 50.2891830 F# +50.2891830 51.4501810 B +51.4501810 52.6227890 F# +52.6227890 56.1057820 B +56.1057820 58.3581170 Gb +58.3581170 60.6917230 Ab:min +60.6917230 62.9788880 Bb:min7 +62.9788880 65.3124940 Ab:min +65.3124940 70.0377550 B:maj7 +70.0377550 74.6585260 Eb:min +74.6585260 77.0153510 Db:min +77.0153510 79.2676870 Eb:7 +79.2676870 81.5896820 Ab:min +81.5896820 83.9116780 E +83.9116780 86.2685030 Eb:7 +86.2685030 88.5556680 Ab:min +88.5556680 89.7282760 Gb +89.7282760 90.8892740 B +90.8892740 92.0386620 Gb +92.0386620 93.1996590 Bb:7 +93.1996590 95.5332650 Eb:min +95.5332650 97.8552600 Ab:min +97.8552600 99.0162580 F# +99.0162580 100.1772560 B +100.1772560 101.3266430 F# +101.3266430 104.8444670 B +104.8444670 107.2477320 Gb +107.2477320 109.5116780 Ab:min +109.5116780 111.8917230 Bb:min +111.8917230 114.1556680 Ab:min +114.1556680 116.5124940 B:maj7 +116.5124940 118.8460990 Ab:min7 +118.8460990 123.5249200 Eb:min +123.5249200 125.9049650 Db:min +125.9049650 128.1573010 Eb:7 +128.1573010 130.5605660 Ab:min +130.5605660 132.7503884 E +132.7503884 135.2393870 Eb:7 +135.2393870 137.5381630 Ab:min +137.5381630 138.6759410 Gb +138.6759410 139.8601580 B +139.8601580 141.0095460 Gb +141.0095460 142.1937640 Bb:7 +142.1937640 144.5389790 Eb:min +144.5389790 146.8377550 Ab:min +146.8377550 147.9755320 F# +147.9755320 149.1713600 B +149.1713600 150.3439680 F# +150.3439680 153.8617910 B +153.8617910 156.2534460 Gb +156.2534460 158.5754420 Ab:min +158.5754420 160.9206570 Bb:min +160.9206570 163.2310430 Ab:min +163.2310430 165.5646480 B:maj7 +165.5646480 165.9714939 Ab:min7 +165.9714939 167.9330830 Eb:min +167.9330830 176.3494991 Eb:min +176.3494991 181.2895828 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/07final/04_-_Love_You_To.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,37 @@ +0.0000000 0.4403951 N +0.4403951 4.9592313 N +4.9592313 35.8347610 C:min(*b3) +35.8347610 58.2652380 C:min(*b3) +58.2652380 59.7213993 Bb +59.7213993 60.2383848 C:min(*b3) +60.2383848 61.6089110 Bb +61.6089110 62.1429700 C:min(*b3) +62.1429700 63.4781170 Bb +63.4781170 63.9773460 C:min(*b3) +63.9773460 65.3705440 Bb +65.3705440 88.2770290 C:min(*b3) +88.2770290 89.7166660 Bb +89.7166660 90.3001344 C:min(*b3) +90.3001344 91.6090920 Bb +91.6090920 92.1431510 C:min(*b3) +92.1431510 93.4782990 Bb +93.4782990 94.0239680 C:min(*b3) +94.0239680 95.3707250 Bb +95.3707250 114.9048148 C:min(*b3) +114.9048148 116.3383440 Bb +116.3383440 116.8840130 C:min(*b3) +116.8840130 118.2539900 Bb +118.2539900 118.7996590 C:min(*b3) +118.7996590 120.1696370 Bb +120.1696370 120.6920860 C:min(*b3) +120.6920860 122.0040130 Bb +122.0040130 144.7176454 C:min(*b3) +144.7176454 146.2340360 Bb +146.2340360 146.7797050 C:min(*b3) +146.7797050 148.1032420 Bb +148.1032420 148.6140810 C:min(*b3) +148.6140810 149.9840580 Bb +149.9840580 150.4716780 C:min(*b3) +150.4716780 151.8997050 Bb +151.8997050 176.5984181 C:min(*b3) +176.5984181 180.9449258 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/07final/05_-_Here,_There_And_Everywhere.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,98 @@ +0.0000000 0.4243310 N +0.4243310 2.0729470 G +2.0729470 3.9635555 B:min +3.9635555 6.8098180 Bb +6.8098180 7.6689560 A:sus4 +7.6689560 8.3887750 A:min +8.3887750 10.1070520 D +10.1070520 11.5699090 G +11.5699090 13.0792060 A:min7 +13.0792060 14.5652830 G/3 +14.5652830 15.9817000 C +15.9817000 17.3865070 G +17.3865070 18.8841950 A:min7 +18.8841950 20.3238320 G/3 +20.3238320 21.8099090 C +21.8099090 23.2843760 F#:min +23.2843760 24.6891830 B:7 +24.6891830 26.1288200 F#:min +26.1288200 27.5220180 B:7 +27.5220180 29.0313150 E:min +29.0313150 30.4129020 A:min +30.4129020 31.8873690 A:min7 +31.8873690 33.3502260 D:7 +33.3502260 34.8130830 G +34.8130830 36.2643310 A:min7 +36.2643310 37.6923580 G/3 +37.6923580 39.1087750 C +39.1087750 40.5832420 G +40.5832420 42.0344890 A:min7 +42.0344890 43.5205660 G/3 +43.5205660 44.9485940 C +44.9485940 46.3882310 F#:min +46.3882310 47.8046480 B:7 +47.8046480 49.2675050 F#:min +49.2675050 50.7187520 B:7 +50.7187520 52.1351700 E:min +52.1351700 53.5980270 A:min +53.5980270 55.0608840 A:min7 +55.0608840 55.8152866 D +55.8152866 56.4889110 F +56.4889110 57.9401580 Bb +57.9401580 59.4378450 G:min +59.4378450 60.8542630 C:min +60.8542630 62.2590700 D:7 +62.2590700 65.1151240 G:min +65.1151240 66.5663710 C:min +66.5663710 67.9943990 D +67.9943990 69.4456460 G +69.4456460 70.9085030 A:min7 +70.9085030 72.3713600 G/3 +72.3713600 73.7877770 C +73.7877770 75.2041950 G +75.2041950 76.6438320 A:min7 +76.6438320 78.1066890 G/3 +78.1066890 79.5231060 C +79.5231060 80.9511330 F#:min +80.9511330 82.3907700 B:7 +82.3907700 83.8304080 F#:min +83.8304080 85.2932650 B +85.2932650 86.7329020 E:min +86.7329020 88.2189790 A:min +88.2189790 89.6702260 A:min7 +89.6702260 90.3552150 D +90.3552150 91.0866430 F +91.0866430 92.5262810 Bb +92.5262810 93.9775280 G:min +93.9775280 95.4752150 C:min +95.4752150 96.9032420 D:7 +96.9032420 99.8057360 G:min +99.8057360 101.2221540 C:min +101.2221540 102.6385710 D:7 +102.6385710 104.1014280 G +104.1014280 105.5526750 A:min7 +105.5526750 107.0271420 G/3 +107.0271420 108.5016090 C +108.5016090 109.9644670 G +109.9644670 111.4041040 A:min7 +111.4041040 112.8901810 G/3 +112.8901810 114.3298180 C +114.3298180 115.7926750 F#:min +115.7926750 117.2323120 B:7 +117.2323120 118.6719500 F#:min +118.6719500 120.1812470 B:7 +120.1812470 121.5860540 E:min +121.5860540 123.0489110 A:min +123.0489110 124.5349880 A:min7 +124.5349880 125.2199770 D:7 +125.2199770 125.9397950 D +125.9397950 127.4258730 G +127.4258730 128.8074600 A:min7 +128.8074600 130.3283670 G/3 +130.3283670 131.7912240 C +131.7912240 133.3005210 G +133.3005210 134.7285480 A:min7 +134.7285480 136.2146250 G/3 +136.2146250 137.7123120 C +137.7123120 141.4242563 G +141.4242563 145.7133212 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/07final/06_-_Yellow_Submarine.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,107 @@ +0.0000000 0.4595427 N +0.4595427 0.9932190 F# +0.9932190 2.5953960 C# +2.5953960 3.1875050 B +3.1875050 4.9057820 F# +4.9057820 5.4514510 D#:min +5.4514510 7.1348970 G#:min +7.1348970 7.7037860 G#:min +7.7037860 9.3756230 C# +9.3756230 9.9212920 B +9.9212920 11.5466890 C# +11.5466890 12.0575280 B +12.0575280 13.7758040 F# +13.7758040 14.3098630 D#:min +14.3098630 15.8772100 G#:min +15.8772100 16.4344890 G#:min +16.4344890 18.0714960 C# +18.0714960 18.6403850 B +18.6403850 20.2425620 C# +20.2425620 20.8114510 B +20.8114510 22.4020180 F# +22.4020180 22.9709070 D#:min +22.9709070 24.5614730 G#:min +24.5614730 25.0955320 G#:min +25.0955320 26.7325390 C# +26.7325390 27.2549880 B +27.2549880 28.8803850 C# +28.8803850 29.4492740 B +29.4492740 31.0746710 F# +31.0746710 31.6435600 D#:min +31.6435600 33.2225170 G#:min +33.2225170 33.7797950 G#:min +33.7797950 35.9740810 C# +35.9740810 38.1037463 F# +38.1037463 42.4872780 C# +42.4872780 44.6002940 F# +44.6002940 46.7249200 F# +46.7249200 51.0438320 C# +51.0438320 53.1684580 F# +53.1684580 54.7938540 C# +54.7938540 55.3279130 B +55.3279130 56.9881400 F# +56.9881400 57.4989790 D#:min +57.4989790 59.1127660 G#:min +59.1127660 59.6352150 G#:min +59.6352150 61.3302720 C# +61.3302720 61.8991600 B +61.8991600 63.4548970 C# +63.4548970 64.0470060 B +64.0470060 65.6259630 F# +65.6259630 68.7016290 F# +68.7016290 69.9564850 C# +69.9564850 70.5137640 B +70.5137640 72.6964390 F# +72.6964390 74.8675050 C# +74.8675050 76.9805210 C# +76.9805210 79.1167570 F# +79.1167570 81.2413830 F# +81.2413830 83.3543990 C# +83.3543990 85.5022440 C# +85.5022440 87.6500900 F# +87.6500900 89.2290470 C# +89.2290470 89.8211560 B +89.8211560 91.4581630 F# +91.4581630 92.0038320 D#:min +92.0038320 93.5827890 G#:min +93.5827890 94.1865070 G#:min +94.1865070 95.8699540 C# +95.8699540 96.4388430 B +96.4388430 98.1222900 C# +98.1222900 98.6447390 B +98.6447390 100.2701360 F# +100.2701360 100.8887634 D#:min +100.8887634 102.4295910 G#:min +102.4295910 102.9868700 G#:min +102.9868700 104.6819270 C# +104.6819270 105.2275960 B +105.2275960 106.8762130 C# +106.8762130 107.4102720 B +107.4102720 109.0821080 F# +109.0821080 109.6161670 D#:min +109.6161670 111.2183440 G#:min +111.2183440 111.7640130 G#:min +111.7640130 113.3661900 C# +113.3661900 113.9350790 B +113.9350790 115.5140360 C# +115.5140360 116.0597050 B +116.0597050 117.7083210 F# +117.7083210 118.1843310 D#:min +118.1843310 119.8445570 G#:min +119.8445570 120.3786160 G#:min +120.3786160 122.5496820 C# +122.5496820 124.7787980 F# +124.7787980 129.0396590 C# +129.0396590 131.1642850 F# +131.1642850 133.3237410 F# +133.3237410 137.6194330 C# +137.6194330 139.7556680 F# +139.7556680 141.9151240 F# +141.9151240 144.0629700 C# +144.0629700 146.1992060 C# +146.1992060 148.3586620 F# +148.3586620 150.4600680 F# +150.4600680 152.6659630 C# +152.6659630 154.7673690 C# +154.7673690 156.7997881 F# +156.7997881 159.8634059 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/07final/07_-_She_Said_She_Said.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,94 @@ +0.0000000 0.4403951 N +0.4403951 2.5489560 Bb +2.5489560 4.7896820 Bb +4.7896820 6.8794780 Bb +6.8794780 8.0056460 Bb +8.0056460 9.0969840 Ab +9.0969840 11.3377090 Eb +11.3377090 12.3826070 Bb +12.3826070 13.5203850 Ab +13.5203850 15.6682310 Eb +15.6682310 16.7827890 Bb +16.7827890 17.9089560 Ab +17.9089560 20.1264620 Eb +20.1264620 20.9623800 Bb +20.9623800 22.2975280 Ab +22.2975280 23.1450560 Eb +23.1450560 24.5150340 Bb +24.5150340 25.3741720 Bb +25.3741720 26.7325390 Ab +26.7325390 27.5916780 Eb +27.5916780 28.9848750 Bb +28.9848750 30.1458730 Bb +30.1458730 31.2023800 Ab:maj(9) +31.2023800 33.3966660 Eb +33.3966660 34.4880040 Bb +34.4880040 35.6025620 Ab:maj(9) +35.6025620 37.7968480 Eb +37.7968480 38.8881850 Bb +38.8881850 40.0027430 Ab:maj(9) +40.0027430 42.2086390 Eb +42.2086390 43.0445570 Bb +43.0445570 44.3564850 Ab +44.3564850 45.2040130 Eb +45.2040130 46.5856000 Bb +46.5856000 47.4447390 Bb +47.4447390 48.7914960 Ab +48.7914960 49.7319040 Eb +49.7319040 51.1018820 Bb +51.1018820 52.2048290 Bb +52.2048290 53.3193870 Ab +53.3193870 55.5949430 Bb +55.5949430 57.3248290 Ab +57.3248290 59.0431060 Bb +59.0431060 60.7613830 F:min +60.7613830 64.1398860 Bb +64.1398860 65.8813830 Eb +65.8813830 69.2947160 Bb +69.2947160 71.0013830 Eb +71.0013830 72.1623800 Bb +72.1623800 73.2885480 Ab +73.2885480 75.5524940 Eb +75.5524940 76.7018820 Bb +76.7018820 77.8628790 Ab +77.8628790 80.1036050 Eb +80.1036050 81.2529930 Bb +81.2529930 82.4139900 Ab +82.4139900 84.6198860 Eb +84.6198860 85.4790240 Bb +85.4790240 86.8606120 Ab +86.8606120 87.7081400 Eb +87.7081400 89.0897270 Bb +89.0897270 89.8908160 Bb +89.8908160 91.3072330 Ab +91.3072330 92.1895910 Eb +92.1895910 93.6176190 Bb +93.6176190 94.7670060 Bb +94.7670060 95.8931740 Ab +95.8931740 98.1455100 Bb +98.1455100 99.9102260 Ab +99.9102260 101.6749430 Bb +101.6749430 103.3816090 F:min +103.3816090 106.8065530 Bb +106.8065530 108.5016090 Eb +108.5016090 111.9265530 Bb +111.9265530 113.5983900 Eb +113.5983900 114.7324863 Bb +114.7324863 115.9784350 Ab +115.9784350 118.2307700 Eb +118.2307700 119.3685480 Bb +119.3685480 120.4947160 Ab +120.4947160 122.7818820 Eb +122.7818820 123.9196590 Bb +123.9196590 125.1038770 Ab +125.1038770 127.3794330 Eb +127.3794330 128.2153510 Bb +128.2153510 129.6201580 Ab +129.6201580 130.4444670 Eb +130.4444670 131.9073240 Bb +131.9073240 132.7664620 Bb +132.7664620 134.1828790 Ab +134.1828790 135.0768480 Eb +135.0768480 136.4932650 Bb +136.4932650 152.1086233 Bb +152.1086233 156.8955261 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/07final/08_-_Good_Day_Sunshine.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,59 @@ +0.0000000 0.4020998 N +0.4020998 8.4352150 E +8.4352150 9.8864620 B +9.8864620 12.4522670 F# +12.4522670 13.9615640 B +13.9615640 16.5041490 F# +16.5041490 20.5444210 E:7 +20.5444210 21.6125390 A +21.6125390 22.6226070 F#:7 +22.6226070 24.6311330 B:7 +24.6311330 26.6512690 E:7 +26.6512690 28.7410650 A +28.7410650 29.7511330 A +29.7511330 30.7612010 F#:7 +30.7612010 32.8045570 B:7 +32.8045570 34.8479130 E:7 +34.8479130 36.9028790 A +36.9028790 38.4470060 B +38.4470060 41.0012010 F# +41.0012010 42.5337180 B +42.5337180 45.0763030 F# +45.0763030 49.1630150 E:7 +49.1630150 50.1846930 A +50.1846930 51.2295910 F#:7 +51.2295910 53.2381170 B:7 +53.2381170 55.2582530 E:7 +55.2582530 57.3596590 A +57.3596590 58.3929470 D +58.3929470 59.3914050 B:7 +59.3914050 61.5160310 E:7 +61.5160310 63.6290470 A:7 +63.6290470 65.6956230 D +65.6956230 67.2745800 B +67.2745800 69.8055550 F# +69.8055550 71.3845120 B +71.3845120 73.8690470 F# +73.8690470 78.0021990 E:7 +78.0021990 79.0354870 A +79.0354870 80.0687750 F#:7 +80.0687750 82.0656910 B:7 +82.0656910 84.1206570 E:7 +84.1206570 86.2104530 A +86.2104530 87.2553510 A +87.2553510 88.2886390 F#:7 +88.2886390 90.3087750 B:7 +90.3087750 92.3405210 E:7 +92.3405210 94.4651470 A +94.4651470 96.0092740 B +96.0092740 98.5518590 F# +98.5518590 100.0959860 B +100.0959860 102.6269610 F# +102.6269610 106.2028340 E:7 +106.2028340 107.7353510 B +107.7353510 110.2082760 F# +110.2082760 111.7407930 B +111.7407930 114.2601580 F# +114.2601580 117.7315410 E:7 +117.7315410 125.4551486 F:7 +125.4551486 129.3038184 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/07final/09_-_And_Your_Bird_Can_Sing.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,43 @@ +0.0000000 0.4978379 N +0.4978379 7.6921760 E +7.6921760 14.9368020 E +14.9368020 16.7595690 F#:min +16.7595690 18.5591150 A +18.5591150 22.2162580 E +22.2162580 29.4144440 E +29.4144440 31.1907700 F#:min +31.1907700 32.9903170 A +32.9903170 36.6474600 E +36.6474600 38.4353960 G#:min +38.4353960 40.2001130 G:aug +40.2001130 42.0460990 B/5 +42.0460990 43.8340360 F:dim +43.8340360 45.6451920 E +45.6451920 49.2558950 F#:min +49.2558950 51.0902720 B +51.0902720 58.3000680 E +58.3000680 60.0996140 F#:min +60.0996140 61.8875510 A +61.8875510 65.5330830 E +65.5330830 67.3326300 G#:min +67.3326300 69.1321760 G:aug +69.1321760 70.9665530 B/5 +70.9665530 72.7544890 F:dim +72.7544890 74.6120860 E +74.6120860 78.2227890 F#:min +78.2227890 80.0223350 B +80.0223350 80.9627430 E +80.9627430 81.8915410 E/7 +81.8915410 82.8087300 E/6 +82.8087300 83.7026980 E/5 +83.7026980 87.2901810 E +87.2901810 89.0897270 F#:min +89.0897270 90.8892740 A +90.8892740 94.5115870 E +94.5115870 101.6517230 E +101.6517230 103.4628790 F#:min +103.4628790 105.2275960 A +105.2275960 108.8963490 E +108.8963490 114.2949880 E +114.2949880 117.7199310 A/5 +117.7199310 121.5681835 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/07final/10_-_For_No_One.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,68 @@ +0.0000000 0.4212474 N +0.4212474 1.9336280 B +1.9336280 3.4661450 B/7 +3.4661450 4.9290020 G#:min +4.9290020 6.4499090 B/5 +6.4499090 7.9940360 E +7.9940360 9.4801130 A:7 +9.4801130 12.4870970 B +12.4870970 13.9383440 B +13.9383440 15.4128110 B/7 +15.4128110 16.9104980 G#:min +16.9104980 18.3849650 B/5 +18.3849650 19.8826530 E +19.8826530 21.3687300 A:7 +21.3687300 24.2828340 B +24.2828340 25.7340810 C#:min +25.7340810 27.1853280 G#:7 +27.1853280 30.0413830 C#:min +30.0413830 31.4461900 C#:min +31.4461900 32.8974370 G#:7 +32.8974370 37.1582990 C#:min +37.1582990 37.9361670 F#:sus4 +37.9361670 38.7256460 F# +38.7256460 40.2813830 B +40.2813830 41.7790700 B/7 +41.7790700 43.2535370 G#:min +43.2535370 44.7512240 B/5 +44.7512240 46.2489110 E +46.2489110 47.7233780 A:7 +47.7233780 50.7535820 B +50.7535820 52.1816090 B +52.1816090 53.6328570 B/7 +53.6328570 55.1073240 G#:min +55.1073240 56.5817910 B/5 +56.5817910 58.0678680 E +58.0678680 59.5655550 A:7 +59.5655550 62.5260990 B +62.5260990 63.9425170 C#:min +63.9425170 65.3473240 G#:7 +65.3473240 68.2265980 C#:min +68.2265980 69.6197950 C#:min +69.6197950 71.0129930 G#:7 +71.0129930 75.3202940 C#:min +75.3202940 76.1213830 F#:sus4 +76.1213830 76.8644210 F# +76.8644210 78.3737180 B +78.3737180 79.8714050 B/7 +79.8714050 81.3458730 G#:min +81.3458730 82.8203400 B/5 +82.8203400 84.2831970 E +84.2831970 85.7808840 A:7 +85.7808840 88.7646480 B +88.7646480 90.2391150 B +90.2391150 91.7484120 B/7 +91.7484120 93.1532190 G#:min +93.1532190 94.6392970 B/5 +94.6392970 96.0905440 E +96.0905440 97.6336695 A:7 +97.6336695 100.4791150 B +100.4791150 101.9651920 C#:min +101.9651920 103.3583900 G#:7 +103.3583900 106.0203232 C#:min +106.0203232 107.6192510 C#:min +107.6192510 109.0008390 G#:7 +109.0008390 113.3429700 C#:min +113.3429700 114.3495341 F#:sus4 +114.3495341 116.8578711 F# +116.8578711 121.1852313 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/07final/11_-_Doctor_Robert.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,33 @@ +0.0000000 0.4212474 N +0.4212474 6.1480490 A +6.1480490 17.6999770 A +17.6999770 26.4887300 F# +26.4887300 27.9864170 E +27.9864170 29.4260540 F# +29.4260540 32.3285480 B +32.3285480 43.9849650 A +43.9849650 52.7272780 F# +52.7272780 54.1785260 E +54.1785260 55.6297730 F# +55.6297730 58.6251470 B +58.6251470 61.6205210 B +61.6205210 63.0717680 E/5 +63.0717680 64.6042850 B +64.6042850 67.5416090 B +67.5416090 70.4441040 E/5 +70.4441040 85.1655550 A +85.1655550 93.9659180 F# +93.9659180 95.3475050 E +95.3475050 96.8219720 F# +96.8219720 99.7941260 B +99.7941260 102.7843769 B +102.7843769 104.2987980 E/5 +104.2987980 105.8197050 B +105.8197050 108.7570290 B +108.7570290 111.7640130 E/5 +111.7640130 126.5899540 A +126.5899540 129.3804089 F# +129.3804089 130.1080181 B +130.1080181 130.5867084 E/5 +130.5867084 131.8121555 B +131.8121555 134.7417400 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/07final/12_-_I_Want_To_Tell_You.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,66 @@ +0.0000000 2.0679420 A +2.0679420 2.9553060 D:sus4/5 +2.9553060 3.7337842 D/5 +3.7337842 5.8345800 A +5.8345800 6.8098180 D:sus4/5 +6.8098180 7.4948070 D/5 +7.4948070 9.6890920 A +9.6890920 10.6411110 D:sus4/5 +10.6411110 11.4305890 D/5 +11.4305890 13.5319950 A +13.5319950 14.5072330 D:sus4/5 +14.5072330 15.3199310 D/5 +15.3199310 22.1582080 A +22.1582080 25.0258730 B +25.0258730 32.5143080 E:maj(b9) +32.5143080 34.4531740 A +34.4531740 35.3935820 D:sus4/5 +35.3935820 36.0785710 D/5 +36.0785710 42.9052380 A +42.9052380 45.7380720 B +45.7380720 53.2381170 E:maj(b9) +53.2381170 55.1305440 A +55.1305440 56.0361220 D:sus4/5 +56.0361220 56.9417000 D/5 +56.9417000 58.8225170 B:min +58.8225170 60.6568930 B:dim +60.6568930 62.5725390 A +62.5725390 64.4533560 B +64.4533560 66.3922220 B:min +66.3922220 68.2149880 B:dim +68.2149880 69.0857360 A +69.0857360 71.9766210 A:sus4 +71.9766210 78.5130380 A +78.5130380 81.3458730 B +81.3458730 88.7878680 E:maj(b9) +88.7878680 90.6919040 A +90.6919040 91.5742630 D:sus4/5 +91.5742630 92.5262810 D/5 +92.5262810 94.3838770 B:min +94.3838770 96.2995230 B:dim +96.2995230 98.1687300 A +98.1687300 100.0611560 B +100.0611560 102.0000220 B:min +102.0000220 103.8808390 B:dim +103.8808390 104.6587070 A +104.6587070 107.6192510 A:sus4 +107.6192510 114.1788880 A +114.1788880 117.0001130 B +117.0001130 124.5001580 E:maj(b9) +124.5001580 126.4622440 A +126.4622440 127.3910430 D:sus4/5 +127.3910430 128.3662810 D/5 +128.3662810 130.2703170 A +130.2703170 131.2803850 D:sus4/5 +131.2803850 132.1743530 D/5 +132.1743530 134.1480490 A +134.1480490 135.0768480 D:sus4/5 +135.0768480 136.0288660 D/5 +136.0288660 137.9561220 A +137.9561220 138.8617000 D:sus4/5 +138.8617000 139.7324480 D/5 +139.7324480 141.7525850 A +141.7525850 142.7046030 D:sus4/5 +142.7046030 143.4940810 D/5 +143.4940810 145.5954870 A +145.5954870 149.6960243 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/07final/13_-_Got_To_Get_You_Into_My_Life.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,81 @@ +0.0000000 0.4403951 N +0.4403951 1.7246480 G +1.7246480 3.5040128 F:maj(9)/9 +3.5040128 7.4831970 G +7.4831970 11.1055100 G +11.1055100 14.4375730 F:maj(9)/9 +14.4375730 17.9553960 G +17.9553960 21.4616090 F:maj(9)/9 +21.4616090 22.5297270 B:min +22.5297270 23.4004760 B:min/7 +23.4004760 24.2944440 B:min/b7 +24.2944440 25.1535820 B:min/6 +25.1535820 26.0011110 B:min +26.0011110 26.8718590 B:min/7 +26.8718590 27.7658270 B:min/b7 +27.7658270 28.6017460 B:min/6 +28.6017460 29.4957140 C +29.4957140 30.3780720 C/7 +30.3780720 31.2488200 A:min +31.2488200 32.0847390 D +32.0847390 35.5329020 G +35.5329020 38.9114050 G +38.9114050 42.4176190 F:maj(9)/9 +42.4176190 45.9934920 G +45.9934920 49.5113150 F:maj(9)/9 +49.5113150 50.5446030 B:min +50.5446030 51.4037410 B:min/7 +51.4037410 52.3093190 B:min/b7 +52.3093190 52.9826980 B:min/6 +52.9826980 54.0624260 B:min +54.0624260 54.9563940 B:min/7 +54.9563940 55.8503620 B:min/b7 +55.8503620 56.6978910 B:min/6 +56.6978910 57.5918590 C +57.5918590 58.4626070 C/7 +58.4626070 59.3333560 A:min +59.3333560 60.1808840 D +60.1808840 63.6290470 G +63.6290470 65.3821540 G +65.3821540 68.8419270 C +68.8419270 70.6182530 D +70.6182530 74.1012470 G +74.1012470 77.6887300 G +77.6887300 81.0091830 F:maj(9)/9 +81.0091830 84.5502260 G +84.5502260 88.2189790 F:maj(9)/9 +88.2189790 89.0897270 B:min +89.0897270 90.0069160 B:min/7 +90.0069160 90.8660540 B:min/b7 +90.8660540 91.7484120 B:min/6 +91.7484120 92.6191600 B:min +92.6191600 93.4899090 B:min/7 +93.4899090 94.3722670 B:min/b7 +94.3722670 95.2546250 B:min/6 +95.2546250 96.1021540 C +96.1021540 97.0077320 C/7 +97.0077320 97.8784800 A:min +97.8784800 98.7492290 D +98.7492290 102.1857820 G +102.1857820 103.9272780 G +103.9272780 107.3986620 C +107.3986620 109.1633780 D +109.1633780 112.8089110 G +112.8089110 114.2485480 G +114.2485480 114.9622576 F:maj/9 +114.9622576 115.5366860 C:sus4/4 +115.5366860 116.0345239 C/3 +116.0345239 118.1982039 G +118.1982039 121.6441040 C +121.6441040 123.4088200 D +123.4088200 126.8105440 G +126.8105440 131.6054640 G +131.6054640 133.7997500 G:sus4(2) +133.7997500 135.1000680 G +135.1000680 137.3059630 G:sus4(2) +137.3059630 139.0706800 G +139.0706800 140.7773460 G:sus4(2) +140.7773460 142.0428340 G +142.0428340 144.2255100 G:sus4(2) +144.2255100 146.1537163 G +146.1537163 150.5385192 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/07final/14_-_Tomorrow_Never_Knows.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,37 @@ +0.0000000 0.7276092 N +0.7276092 19.0931740 C +19.0931740 22.8896370 Bb:maj(9)/9 +22.8896370 26.6628790 C +26.6628790 34.1861450 C +34.1861450 37.9593870 Bb:maj(9)/9 +37.9593870 41.7094100 C +41.7094100 49.2791150 C +49.2791150 53.0291380 Bb:maj(9)/9 +53.0291380 56.7907700 C +56.7907700 65.2167637 C +65.2167637 66.2315871 Bb:maj/9 +66.2315871 66.7485726 C +66.7485726 67.6293627 Bb:maj/9 +67.6293627 80.9943954 C +80.9943954 83.1197802 Bb:maj/9 +83.1197802 87.6500900 C +87.6500900 94.6857360 C +94.6857360 98.4938090 Bb:maj(9)/9 +98.4938090 109.9876870 C +109.9876870 113.8421990 Bb:maj(9)/9 +113.8421990 117.6618820 C +117.6618820 125.3128570 C +125.3128570 129.0977090 Bb:maj(9)/9 +129.0977090 132.9986620 C +132.9986620 140.6728570 C +140.6728570 144.5389790 Bb:maj(9)/9 +144.5389790 148.3238320 C +148.3238320 152.1667340 Bb:maj(9)/9 +152.1667340 155.9748070 C +155.9748070 159.7712690 Bb:maj(9)/9 +159.7712690 163.6141720 C +163.6141720 167.4338540 Bb:maj(9)/9 +167.4338540 171.3812470 C +171.3812470 175.0666092 Bb:maj(9)/9 +175.0666092 176.2920563 C +176.2920563 177.1154036 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/07final/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +/.DS_Store/1.1.1.1/Fri Sep 23 09:24:39 2005// +/01_-_Taxman.lab/1.2/Sun Jul 30 21:52:25 2006// +/02_-_Eleanor_Rigby.lab/1.2/Tue Oct 17 21:55:13 2006// +/03_-_I'm_Only_Sleeping.lab/1.1.1.1/Fri Sep 23 09:24:39 2005// +/04_-_Love_You_To.lab/1.4/Tue Oct 17 21:55:13 2006// +/05_-_Here,_There_And_Everywhere.lab/1.2/Tue Nov 8 15:05:23 2005// +/06_-_Yellow_Submarine.lab/1.2/Sun Jul 30 21:52:25 2006// +/07_-_She_Said_She_Said.lab/1.1.1.1/Fri Sep 23 09:24:39 2005// +/08_-_Good_Day_Sunshine.lab/1.1.1.1/Fri Sep 23 09:24:39 2005// +/09_-_And_Your_Bird_Can_Sing.lab/1.1.1.1/Fri Sep 23 09:24:39 2005// +/10_-_For_No_One.lab/1.2/Sun Jul 30 21:52:25 2006// +/11_-_Doctor_Robert.lab/1.1.1.1/Fri Sep 23 09:24:39 2005// +/12_-_I_Want_To_Tell_You.lab/1.1.1.1/Fri Sep 23 09:24:39 2005// +/13_-_Got_To_Get_You_Into_My_Life.lab/1.1.1.1/Fri Sep 23 09:24:39 2005// +/14_-_Tomorrow_Never_Knows.lab/1.1.1.1/Fri Sep 23 09:24:39 2005// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/07final/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +chrish/transcriptions/07final
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/07final/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:matthiasm@dspmac1.elec.qmul.ac.uk:/cvsroot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/08final/01_-_Sgt._Pepper's_Lonely_Hearts_Club_Band.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,58 @@ +0.0000000 12.3502092 N +12.3502092 17.5026070 A:7 +17.5026070 20.0684120 C:7 +20.0684120 21.6821990 G +21.6821990 22.5993870 D +22.5993870 23.9113150 G:7 +23.9113150 25.2000220 A:7 +25.2000220 26.5467800 C:7 +26.5467800 27.7658270 G:7 +27.7658270 29.0313150 G:7 +29.0313150 30.3316320 A:7 +30.3316320 31.6319500 C:7 +31.6319500 32.8393870 G:7 +32.8393870 35.4284120 A:7 +35.4284120 37.9361670 C:7 +37.9361670 39.2248750 G +39.2248750 40.5019720 C +40.5019720 42.9516780 G +42.9516780 45.5290920 C:7 +45.5290920 48.0600680 F +48.0600680 50.5910430 C +50.5910430 55.6646030 D +55.6646030 56.9997500 G +56.9997500 58.3000680 Bb:maj6 +58.3000680 59.5655550 C +59.5655550 60.8658730 G +60.8658730 63.4432870 C +63.4432870 65.9162130 G +65.9162130 67.1933100 G +67.1933100 68.4355780 Bb:maj6 +68.4355780 69.6430150 C +69.6430150 70.8852830 G +70.8852830 73.3930380 A +73.3930380 75.9007930 D +75.9007930 78.4085480 C +78.4085480 80.8234240 G +80.8234240 83.0409290 A:7 +83.0409290 83.6446480 C:sus4 +83.6446480 84.2019270 C/9 +84.2019270 85.7460540 G +85.7460540 88.1957590 C +88.1957590 90.5990240 F:7 +90.5990240 93.0719500 C +93.0719500 97.9365300 D +97.9365300 99.3065070 G:7 +99.3065070 100.5371650 A:7 +100.5371650 101.7910430 C:7 +101.7910430 103.0100900 G:7 +103.0100900 104.2291380 G:7 +104.2291380 105.4017460 A:7 +105.4017460 106.6788430 C:7 +106.6788430 107.8862810 G:7 +107.8862810 110.3592060 A:7 +110.3592060 112.7276410 C:7 +112.7276410 113.9931290 G +113.9931290 115.2121760 C:7 +115.2121760 117.6502720 G +117.6502720 122.3915308 C
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/08final/02_-_With_A_Little_Help_From_My_Friends.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,99 @@ +0.0000000 2.2402705 C +2.2402705 4.3833330 D +4.3833330 8.8183440 E +8.8183440 9.9677320 E +9.9677320 11.0706800 B/3 +11.0706800 12.1968480 F#:min/5 +12.1968480 13.3346250 F#:min +13.3346250 14.4027430 F#:min +14.4027430 15.4940810 B +15.4940810 17.7115870 E +17.7115870 18.7797050 E +18.7797050 19.8710430 B +19.8710430 22.0653280 F#:min +22.0653280 23.1450560 F#:min +23.1450560 24.2247840 B +24.2247840 26.4306800 E +26.4306800 27.4523580 D +27.4523580 28.5785260 A +28.5785260 30.7495910 E +30.7495910 31.8409290 D +31.8409290 32.9206570 A +32.9206570 35.0801130 E +35.0801130 37.2163490 A +37.2163490 39.4106340 E +39.4106340 43.5786160 B +43.5786160 44.6467340 E +44.6467340 45.7032420 B +45.7032420 47.8859180 F#:min +47.8859180 48.9424260 F#:min +48.9424260 50.0337640 B +50.0337640 52.2048290 E +52.2048290 53.2845570 E +53.2845570 54.3294550 B +54.3294550 56.4889110 F#:min +56.4889110 57.5454190 F#:min +57.5454190 58.5787070 B +58.5787070 60.7381630 E +60.7381630 61.8178910 D +61.8178910 62.8511790 A +62.8511790 65.0106340 E +65.0106340 66.0787520 D +66.0787520 67.1700900 A +67.1700900 69.2482760 E +69.2482760 71.3845120 A +71.3845120 73.5671880 E +73.5671880 75.6713594 C#:min +75.6713594 77.8396590 F# +77.8396590 78.9193870 E +78.9193870 79.9642850 D +79.9642850 82.1237410 A +82.1237410 84.2483670 C#:min +84.2483670 86.3613830 F# +86.3613830 87.3830610 E +87.3830610 88.4627890 D +88.4627890 90.6454640 A +90.6454640 91.7135820 E +91.7135820 92.7933100 B +92.7933100 94.9411560 F#:min +94.9411560 95.9976640 F#:min +95.9976640 97.0541720 B +97.0541720 99.2136280 E +99.2136280 100.2933560 E +100.2933560 101.3730830 B +101.3730830 103.4860990 F#:min +103.4860990 104.5309970 F#:min +104.5309970 105.6107250 B +105.6107250 107.7353510 E +107.7353510 108.7918590 D +108.7918590 109.8367570 A +109.8367570 111.9962130 E +111.9962130 113.0527210 D +113.0527210 114.1324480 A +114.1324480 116.2686840 E +116.2686840 118.4397500 A +118.4397500 120.5992060 E +120.5992060 122.7170402 C#:min +122.7170402 124.8716780 F# +124.8716780 125.9165750 E +125.9165750 126.9730830 D +126.9730830 129.1441490 A +129.1441490 131.2687750 C#:min +131.2687750 133.3817910 F# +133.3817910 134.4382990 E +134.4382990 135.4948070 D +135.4948070 137.6542630 A +137.6542630 138.6875510 D +138.6875510 139.7672780 A +139.7672780 141.8802940 E +141.8802940 142.9716320 D +142.9716320 144.0049200 A +144.0049200 146.2108160 E +146.2108160 148.3006120 A +148.3006120 150.4484580 E +150.4484580 152.5266430 D +152.5266430 154.6396590 A +154.6396590 156.7642850 C/5 +156.7642850 158.8889110 D:9/5 +158.8889110 162.9461713 E +162.9461713 163.9035518 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/08final/03_-_Lucy_In_The_Sky_With_Diamonds.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,120 @@ +0.0000000 0.2872142 N +0.2872142 1.7446480 A/5 +1.7446480 3.1958950 A:7/b7 +3.1958950 4.7284120 F#:min7 +4.7284120 6.1448290 D:min/b3 +6.1448290 7.5728570 A +7.5728570 9.0376725 A:7/b7 +9.0376725 10.4637410 F#:min7 +10.4637410 11.8569380 F:aug +11.8569380 13.2153060 A/5 +13.2153060 14.6897730 A:7/b7 +14.6897730 16.1061900 F#:min7 +16.1061900 18.7880950 F +18.7880950 20.2045120 A +20.2045120 21.5628790 A:7/b7 +21.5628790 22.9560770 F#:min7 +22.9560770 24.3608840 F:aug +24.3608840 25.6844210 A +25.6844210 27.0776190 A:7/b7 +27.0776190 29.7943530 F#:min7 +29.7943530 31.1062810 D:min +31.1062810 32.4530380 D:min/b7 +32.4530380 35.1117230 Bb +35.1117230 37.7123580 C:maj(9) +37.7123580 40.2897730 F:maj6 +40.2897730 42.8207480 Bb +42.8207480 45.2820630 C:maj(9) +45.2820630 47.7201580 G +47.7201580 50.5065530 D +50.5065530 51.8533100 G +51.8533100 53.1768480 C +53.1768480 55.7194330 D +55.7194330 57.0545800 G +57.0545800 58.3316780 C +58.3316780 60.8046030 D +60.8046030 62.0933100 G +62.0933100 63.3820180 C +63.3820180 65.9013830 D +65.9013830 68.4671880 D +68.4671880 69.8603850 A +69.8603850 71.1490920 A:7/b7 +71.1490920 72.4958500 F#:min7 +72.4958500 73.9354870 F:aug +73.9354870 75.2706340 A/5 +75.2706340 76.5709520 A:7/b7 +76.5709520 77.8480490 F#:min7 +77.8480490 80.5067340 F +80.5067340 81.7954420 A +81.7954420 83.1189790 A:7/b7 +83.1189790 84.5005660 F#:min7 +84.5005660 85.7544440 F:aug +85.7544440 87.0895910 A +87.0895910 88.4247390 A:7/b7 +88.4247390 91.0253740 F#:min +91.0253740 92.3605210 D:min +92.3605210 93.6376190 D:min/b7 +93.6376190 96.1918140 Bb +96.1918140 98.6763490 C:maj(9) +98.6763490 101.1028340 F:maj6 +101.1028340 103.5060990 Bb +103.5060990 105.9325850 C:maj(9) +105.9325850 108.3474600 G +108.3474600 111.0641950 D +111.0641950 112.3412920 G +112.3412920 113.6880490 C +113.6880490 116.2306340 D +116.2306340 117.5309520 G +117.5309520 118.8080490 C +118.8080490 121.3622440 D +121.3622440 122.6161220 G +122.6161220 123.8816090 C +123.8816090 126.4009750 D +126.4009750 128.9319500 D +128.9319500 130.3715870 A +130.3715870 131.6602940 A:7/b7 +131.6602940 133.0070520 F#:min7 +133.0070520 134.3421990 F:aug +134.3421990 135.7237860 A +135.7237860 137.0705440 A:7/b7 +137.0705440 138.4056910 F#:min +138.4056910 140.9366660 F +140.9366660 142.2834240 A/5 +142.2834240 143.5489110 A:7/b7 +143.5489110 144.8608390 F#:min7 +144.8608390 146.1959860 F:aug +146.1959860 147.5079130 A/5 +147.5079130 148.8198410 A:7/b7 +148.8198410 151.3972560 F#:min +151.3972560 153.9514510 D:maj(*3) +153.9514510 155.1704980 G +155.1704980 156.4475960 C +156.4475960 158.9205210 D +158.9205210 160.1627890 G +160.1627890 161.4282760 C +161.4282760 163.8083210 D +163.8083210 165.0970290 G +165.0970290 166.3392970 C +166.3392970 168.7425620 D +168.7425620 171.2154870 D +171.2154870 173.7116320 A +173.7116320 174.9539000 G +174.9539000 176.1729470 C +176.1729470 178.5762130 D +178.5762130 179.8533100 G +179.8533100 181.0491380 C +181.0491380 183.5104530 D +183.5104530 184.7295010 G +184.7295010 185.9717680 C +185.9717680 188.4098630 D +188.4098630 190.8711790 D +190.8711790 193.3557140 A +193.3557140 194.5979810 G +194.5979810 195.8286390 C +195.8286390 198.2435140 D +198.2435140 199.4625620 G +199.4625620 200.7048290 C +200.7048290 203.1429250 D +203.1429250 204.3735820 G +204.3735820 205.5810200 C +205.5810200 208.3451574 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/08final/04_-_Getting_Better.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,74 @@ +0.0000000 0.4403951 N +0.4403951 4.6387520 F:maj(9)/5 +4.6387520 6.6937180 C +6.6937180 8.7719040 F:maj(9)/5 +8.7719040 24.8401130 G +24.8401130 26.8370290 C +26.8370290 28.8107250 D:min7(4)/5 +28.8107250 30.8192510 E:min7 +30.8192510 32.8161670 F:maj(9)/6 +32.8161670 34.8479130 C +34.8479130 36.7867800 D:min7(4)/5 +36.7867800 38.8069160 E:min7 +38.8069160 40.7922220 F:maj(9) +40.7922220 60.4246930 G +60.4246930 62.3983900 C +62.3983900 64.3836960 D:min7(4)/b7 +64.3836960 66.2877320 E:min7 +66.2877320 68.2265980 F:maj(9)/6 +68.2265980 70.3163940 C +70.3163940 72.1739900 D:min7(4)/b7 +72.1739900 74.1244670 E:min7 +74.1244670 76.1329930 F:maj(9) +76.1329930 76.5857820 F +76.5857820 77.1082310 C/3 +77.1082310 77.5610200 F +77.5610200 78.0648108 C/3 +78.0648108 78.5362580 F +78.5362580 79.0238770 C/3 +79.0238770 79.5231060 F +79.5231060 80.0223350 C/4 +80.0223350 82.0192510 C +82.0192510 83.9581170 D:min7(4)/b7 +83.9581170 85.9085940 E:min7/b3 +85.9085940 87.8590700 F:maj(9) +87.8590700 89.8095460 C +89.8095460 91.8064620 D:min7(4)/b7 +91.8064620 93.7685480 E:min7/b3 +93.7685480 95.7422440 F:maj(9) +95.7422440 117.3135820 G +117.3135820 119.2130273 C +119.2130273 121.1680950 D:min7(4)/b7 +121.1680950 123.1534010 E:min7/b3 +123.1534010 125.0690470 F:maj(9) +125.0690470 127.0543530 C +127.0543530 128.9932190 D:min7(4)/b7 +128.9932190 130.9785260 E:min7/b3 +130.9785260 132.9406120 F:maj(9) +132.9406120 133.4494506 F +133.4494506 133.9375403 C/3 +133.9375403 134.3970830 F +134.3970830 134.9140685 C/3 +134.9140685 135.3712337 F +135.3712337 135.8714491 C/5 +135.8714491 136.3539450 F/3 +136.3539450 136.9054201 G/3 +136.9054201 138.8036500 C +138.8036500 140.7541260 D:min7(4)/b7 +140.7541260 142.7046030 E:min7/b3 +142.7046030 144.6782990 F:maj(9) +144.6782990 146.6519950 C +146.6519950 148.6605210 D:min7(4)/b7 +148.6605210 150.5877770 E:min7/b3 +150.5877770 152.5614730 F:maj(9) +152.5614730 152.9678230 F +152.9678230 153.5251020 C/3 +153.5251020 154.0011110 F +154.0011110 154.5119500 C/3 +154.5119500 154.9531290 F +154.9531290 155.4523580 C/3 +155.4523580 155.9764408 F +155.9764408 156.4392060 G/3 +156.4392060 157.9869400 C +157.9869400 167.0054649 G:(1) +167.0054649 167.7713693 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/08final/05_-_Fixing_A_Hole.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,99 @@ +0.0000000 0.3314510 N +0.3314510 1.7594780 F +1.7594780 2.9785260 C:aug +2.9785260 4.3273601 F:min7 +4.3273601 6.7053280 Bb:7 +6.7053280 7.8547160 F +7.8547160 9.0041040 C:aug +9.0041040 10.1534920 F:min7 +10.1534920 11.2796590 F:min6/5 +11.2796590 13.5552150 F:min7 +13.5552150 15.7776316 F:min6 +15.7776316 18.0714960 F:min7 +18.0714960 20.3238320 F:min6 +20.3238320 22.5761670 F:min7 +22.5761670 24.8401130 F:min6 +24.8401130 25.9430610 F +25.9430610 27.0343990 C:aug +27.0343990 28.1025170 F:min7 +28.1025170 29.2402940 F:min6/5 +29.2402940 31.4694100 F:min7 +31.4694100 33.6172560 F:min6 +33.6172560 35.8115410 F:min7 +35.8115410 37.9477770 F:min6 +37.9477770 40.0956230 F:min7 +40.0956230 42.3247390 F:min6 +42.3247390 43.4392970 F +43.4392970 44.5306340 C:9(*3) +44.5306340 45.5871420 F +45.5871420 46.6436500 C:9(*3) +46.6436500 47.7233780 F +47.7233780 48.7566660 C:9(*3) +48.7566660 50.9277320 F +50.9277320 52.0190700 C +52.0190700 53.0871880 G +53.0871880 54.1553060 C +54.1553060 55.1769840 G +55.1769840 56.2799310 C +56.2799310 57.3596590 G +57.3596590 59.4958950 C +59.4958950 60.6336730 F +60.6336730 61.6901810 C:aug +61.6901810 62.7931290 F:min7 +62.7931290 63.8612470 F:min6/5 +63.8612470 66.0090920 F:min7 +66.0090920 68.0988880 F:min6 +68.0988880 70.2931740 F:min7 +70.2931740 72.3713600 F:min6 +72.3713600 74.5308160 F:min7 +74.5308160 76.6090020 F:min6 +76.6090020 77.7003400 F +77.7003400 78.7800680 C:aug +78.7800680 79.8365750 F:min7 +79.8365750 80.8698630 F:min6 +80.8698630 82.9596590 F:min7 +82.9596590 85.0378450 F:min6 +85.0378450 87.0928110 F:min7 +87.0928110 89.1477770 F:min6 +89.1477770 91.1679130 F:min7 +91.1679130 93.2577090 F:min6 +93.2577090 94.3026070 F +94.3026070 95.3707250 C:9(*3) +95.3707250 96.4156230 F +96.4156230 97.4489110 C:9(*3) +97.4489110 98.4821990 F +98.4821990 99.5038770 C:9(*3) +99.5038770 101.5936730 F +101.5936730 102.6269610 C +102.6269610 103.6718590 G +103.6718590 104.7051470 C +104.7051470 105.6919950 G +105.6919950 106.7368930 C +106.7368930 107.7469610 G +107.7469610 109.8019270 C +109.8019270 110.8816550 F +110.8816550 111.9497730 C:aug +111.9497730 112.9946710 F:min7 +112.9946710 114.0627890 F:min6 +114.0627890 116.1409750 F:min7 +116.1409750 118.2191600 F:min6 +118.2191600 120.3553960 F:min7 +120.3553960 122.4684120 F:min6/b3 +122.4684120 124.5582080 F:min7/b3 +124.5582080 126.5783440 F:min6/b3 +126.5783440 127.6812920 F +127.6812920 128.7378000 C:aug +128.7378000 129.7710880 F:min7 +129.7710880 130.8624260 F:min6/5 +130.8624260 132.9638320 F:min7 +132.9638320 135.0304080 F:min6 +135.0304080 137.0969840 F:min7 +137.0969840 139.1867800 F:min6 +139.1867800 141.1836960 F:min7 +141.1836960 143.2502720 F:min6 +143.2502720 145.3748970 F:min7 +145.3748970 147.3950340 F:min6 +147.3950340 149.4383900 F:min7 +149.4383900 151.5049650 F:min6 +151.5049650 153.5251020 F:min7 +153.5251020 156.4742787 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/08final/06_-_She's_Leaving_Home.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,75 @@ +0.0000000 0.4475510 N +0.4475510 5.9158500 E +5.9158500 7.2858270 E +7.2858270 8.6906340 B:min +8.6906340 10.0373920 A/3 +10.0373920 11.4073690 A +11.4073690 12.8005660 C#:min/5 +12.8005660 14.2169840 C#:min +14.2169840 16.9917680 F# +16.9917680 19.8246030 A:maj/9 +19.8246030 22.5993870 B +22.5993870 25.3509520 A:maj/9 +25.3509520 28.1953960 B +28.1953960 29.5769840 E +29.5769840 31.0050110 B:min +31.0050110 32.3865980 F#:min +32.3865980 33.8262350 F#:min/5 +33.8262350 36.6293802 C#:min/5 +36.6293802 39.5615640 F# +39.5615640 42.3711790 A:maj/9 +42.3711790 45.1111330 B +45.1111330 47.9207480 A:maj/9 +47.9207480 50.7535820 B +50.7535820 64.7784350 E +64.7784350 67.5300000 B:min/b3 +67.5300000 70.3976640 C#:min +70.3976640 73.2072780 F#:9 +73.2072780 76.1678230 C#:min +76.1678230 77.6422900 F#:9 +77.6422900 79.2096370 E +79.2096370 80.6492740 B:min/b3 +80.6492740 82.1585710 F#:min +82.1585710 83.5982080 F#:min/b3 +83.5982080 86.5239220 C#:min +86.5239220 89.4264170 F# +89.4264170 92.2940810 A:maj/9 +92.2940810 95.1617460 B +95.1617460 97.9945800 A:maj/9 +97.9945800 100.9319040 B +100.9319040 102.3599310 E +102.3599310 103.7763490 B:min/b3 +103.7763490 106.7949430 F#:min +106.7949430 109.6742170 C#:min +109.6742170 112.4954420 F#:9 +112.4954420 115.2934460 A:maj/9 +115.2934460 118.2191600 B +118.2191600 121.1797050 A:maj/9 +121.1797050 124.1750790 B +124.1750790 138.0954420 E +138.0954420 140.9166660 B:min/b3 +140.9166660 143.8539900 C#:min +143.8539900 146.6636050 F#:9 +146.6636050 149.5196590 C#:min +149.5196590 150.9825170 F#:maj(9) +150.9825170 152.5150340 E +152.5150340 153.9546710 B:min +153.9546710 155.4175280 F#:min +155.4175280 156.8107250 F#:min/5 +156.8107250 159.6667800 C#:min/5 +159.6667800 162.5344440 F# +162.5344440 165.3672780 A:maj/9 +165.3672780 168.1885030 B +168.1885030 171.1142170 A:maj/9 +171.1142170 174.0167120 B +174.0167120 187.7745350 E +187.7745350 190.5493190 B:min/b3 +190.5493190 193.4291683 C#:min +193.4291683 196.2614280 F#:9 +196.2614280 199.1058730 C#:min +199.1058730 201.9619270 F#:9 +201.9619270 204.9108610 C#:min +204.9108610 207.8017460 F# +207.8017460 210.9828790 A +210.9828790 213.8405218 E +213.8405218 214.9510833 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/08final/07_-_Being_For_The_Benefit_Of_Mr._Kite!.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,113 @@ +0.0000000 0.5172100 N +0.5172100 2.6418360 Bb +2.6418360 4.7664620 A +4.7664620 5.8694100 D:min +5.8694100 6.9375280 G +6.9375280 8.0288660 C:min +8.0288660 9.1782530 G:aug +9.1782530 10.2579810 Bb +10.2579810 11.3377090 D:min +11.3377090 13.5436050 G +13.5436050 15.7030610 G:aug +15.7030610 16.7943990 C:min +16.7943990 17.9205660 G:aug +17.9205660 18.9654640 Bb +18.9654640 20.0684120 D:min +20.0684120 22.2394780 A +22.2394780 23.3424260 D:min +23.3424260 24.4105440 D:min7/b7 +24.4105440 25.4438320 G:min/b3 +25.4438320 26.5003400 A +26.5003400 28.6249650 D:min +28.6249650 29.6814730 G:min +29.6814730 30.8076410 A +30.8076410 32.8626070 D:min +32.8626070 33.9075050 G:min +33.9075050 34.9988430 A +34.9988430 36.0437410 D:min +36.0437410 37.1350790 G +37.1350790 38.2612470 C:min +38.2612470 39.3409750 G:aug +39.3409750 40.4207020 Bb +40.4207020 41.4772100 D:min +41.4772100 43.6018360 G +43.6018360 45.7496820 G:aug +45.7496820 46.8178000 C:min +46.8178000 47.8046480 G:aug +47.8046480 48.9308160 Bb +48.9308160 49.9292740 D:min +49.9292740 52.0887300 A +52.0887300 53.1568480 D:min +53.1568480 54.2017460 D:min7/b7 +54.2017460 55.2582530 G:min/b3 +55.2582530 56.3612010 A +56.3612010 58.4742170 D:min +58.4742170 59.5191150 G:min +59.5191150 60.5756230 A +60.5756230 61.7598410 D:min +61.7598410 62.9092290 A:aug/#5 +62.9092290 64.0353960 C +64.0353960 65.1267340 E:min +65.1267340 67.3558500 A +67.3558500 69.5733560 A:aug +69.5733560 70.6646930 D:min +70.6646930 71.7676410 A:aug/#5 +71.7676410 72.8705890 C +72.8705890 73.9038770 E:min +73.9038770 76.0865530 B +76.0865530 78.2924480 E:min +78.2924480 79.3477008 A:min/b3 +79.3477008 80.4402940 B +80.4402940 82.5533100 E:min +82.5533100 83.6562580 A:min/b3 +83.6562580 84.7127660 B +84.7127660 86.8838320 E:min +86.8838320 89.1361670 G +89.1361670 90.2623350 C:min +90.2623350 91.3652830 G:aug +91.3652830 92.4334010 Bb +92.4334010 93.5131290 D:min +93.5131290 95.6493650 G +95.6493650 97.7972100 G:aug +97.7972100 98.8537180 C:min +98.8537180 99.9218360 G:aug +99.9218360 101.0596140 Bb +101.0596140 102.0929020 D:min +102.0929020 104.2523580 A +104.2523580 105.3436960 D:min +105.3436960 106.4234240 D:min7/b7 +106.4234240 107.5031510 G:min/b3 +107.5031510 108.5132190 A +108.5132190 110.6610650 D:min +110.6610650 111.7407930 G:min/b3 +111.7407930 112.8437410 A +112.8437410 114.9103170 D:min +114.9103170 115.9668250 G:min/b3 +115.9668250 117.0001130 A +117.0001130 118.0798410 D:min +118.0798410 119.1943990 A:aug +119.1943990 120.2857360 C +120.2857360 121.3654640 E:min +121.3654640 123.5713600 A +123.5713600 125.6959860 A:aug +125.6959860 126.7873240 D:min +126.7873240 127.8902720 A:aug +127.8902720 128.9700000 C +128.9700000 130.0729470 E:min +130.0729470 132.1975730 B +132.1975730 133.2656910 E:min +133.2656910 134.3221990 E:min/b3 +134.3221990 135.4019270 A:min/b3 +135.4019270 136.4816550 B +136.4816550 138.5598410 E:min +138.5598410 139.6511790 A:min/b3 +139.6511790 140.7192970 B +140.7192970 142.9019720 E:min +142.9019720 143.9584800 A:min/b3 +143.9584800 145.0265980 B +145.0265980 146.0831060 E:min +146.0831060 147.1860540 E:min7/b7 +147.1860540 148.2890020 A:min/b3 +148.2890020 149.4151700 B +149.4151700 154.7509937 E:min +154.7509937 156.9721166 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/08final/08_-_Within_You_Without_You.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,16 @@ +0.0000000 1.2062995 N +1.2062995 31.4694100 C# +31.4694100 60.3915657 C# +60.3915657 64.1398860 F#/5 +64.1398860 118.9258131 C# +118.9258131 127.0061051 F#/5 +127.0061051 130.8159860 C# +130.8159860 135.5644670 F#/5 +135.5644670 136.0629252 C#:sus4 +136.0629252 273.4853307 C# +273.4853307 281.2401133 F#/5 +281.2401133 284.9616550 C# +284.9616550 289.5940360 F#/5 +289.5940360 290.0288668 C#:sus4 +290.0288668 304.4661656 C# +304.4661656 304.9831511 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/08final/09_-_When_I'm_Sixty-Four.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,77 @@ +0.0000000 0.3446570 N +0.3446570 3.7796140 Db +3.7796140 4.6503620 Gb +4.6503620 5.5211110 Ab +5.5211110 7.5180270 Db +7.5180270 10.7339900 Db +10.7339900 16.0049200 Db +16.0049200 17.7696370 Ab +17.7696370 22.9476870 Ab +22.9476870 24.6427430 Db +24.6427430 28.0792970 Db +28.0792970 29.8091830 Db:7 +29.8091830 31.4926300 Gb +31.4926300 32.3749880 Gb +32.3749880 33.2457360 E:dim7/b3 +33.2457360 34.1048750 Db/5 +34.1048750 34.9059630 Bb:7 +34.9059630 35.7651020 Eb:9 +35.7651020 36.6126300 Ab +36.6126300 38.4005660 Db +38.4005660 41.7558500 Bb:min +41.7558500 43.4276870 Ab +43.4276870 45.1343530 Bb:min +45.1343530 48.5128570 Bb:min +48.5128570 51.9610200 F +51.9610200 55.3627430 Bb:min +55.3627430 58.7180270 Eb:min +58.7180270 60.4363030 Gb +60.4363030 62.0965300 Ab +62.0965300 63.8148070 Db +63.8148070 67.2978000 Ab +67.2978000 72.3713600 Db +72.3713600 74.0664170 Ab +74.0664170 79.2096370 Ab:7 +79.2096370 80.8814730 Db +80.8814730 84.2135370 Db +84.2135370 85.9550340 Db:7 +85.9550340 87.5920400 Gb +87.5920400 88.4976190 Gb +88.4976190 89.3335370 E:dim7/b3 +89.3335370 90.1810650 Db/5 +90.1810650 91.0053740 Bb:7 +91.0053740 91.8877320 Eb:9 +91.8877320 92.7120400 Ab +92.7120400 94.4883670 Db +94.4883670 97.8320400 Bb:min +97.8320400 99.5270970 Ab +99.5270970 101.2337640 Bb:min +101.2337640 104.5774370 Bb:min +104.5774370 107.9327210 F +107.9327210 111.3112240 Bb:min +111.3112240 114.6665070 Eb:min +114.6665070 116.3383440 Gb +116.3383440 118.0682310 Ab +118.0682310 119.7865070 Db +119.7865070 123.2114510 Ab +123.2114510 128.3430610 Db +128.3430610 130.0148970 Ab +130.0148970 135.1348970 Ab +135.1348970 136.8067340 Db +136.8067340 140.1736280 Db +140.1736280 141.8222440 Db:7 +141.8222440 143.5056910 Gb +143.5056910 144.3648290 Gb +144.3648290 145.2123580 E:dim7/b3 +145.2123580 146.0482760 Db/5 +146.0482760 146.8725850 Bb:7 +146.8725850 147.7433330 Eb:9 +147.7433330 148.5676410 Ab +148.5676410 150.3439680 Db +150.3439680 153.7573010 Db +153.7573010 154.6048290 Gb +154.6048290 155.4291380 Ab +155.4291380 155.8232599 Db +155.8232599 156.3210978 Ab +156.3210978 156.7997881 Db +156.7997881 157.6231354 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/08final/10_-_Lovely_Rita.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,90 @@ +0.0000000 0.2680666 N +0.2680666 3.0714050 B +3.0714050 5.7649200 A +5.7649200 8.5048750 E +8.5048750 11.2332190 B +11.2332190 13.9731740 B +13.9731740 16.7015190 A +16.7015190 19.4414730 E +19.4414730 22.1698180 B +22.1698180 23.5281850 E +23.5281850 24.2480040 D +24.2480040 24.9213830 A +24.9213830 26.2913600 E +26.2913600 27.6613370 B +27.6613370 29.0080950 C#:min +29.0080950 30.4245120 F# +30.4245120 33.1992970 B +33.1992970 34.6157140 E +34.6157140 35.9624710 A +35.9624710 37.3324480 D +37.3324480 38.7140360 G +38.7140360 40.0956230 E +40.0956230 41.4772100 B +41.4772100 42.8471880 E +42.8471880 44.2403850 A +44.2403850 45.5987520 D +45.5987520 46.9222900 G +46.9222900 48.3387070 E +48.3387070 49.7086840 B +49.7086840 50.3820630 E +50.3820630 51.0902720 C#:min +51.0902720 51.7636500 F#:min +51.7636500 52.4602490 B +52.4602490 53.8766660 E +53.8766660 54.5964850 D +54.5964850 55.2698630 A +55.2698630 56.6398410 E +56.6398410 57.9749880 B +57.9749880 59.3565750 C#:min +59.3565750 60.7149430 F# +60.7149430 62.1339983 B +62.1339983 63.5361670 A/9 +63.5361670 64.8945350 E/5 +64.8945350 66.3109520 B +66.3109520 69.0625170 A/9 +69.0625170 71.7560310 B +71.7560310 73.1027890 E +73.1027890 73.8109970 D +73.8109970 74.4611560 A +74.4611560 75.8659630 E +75.8659630 77.1778910 B +77.1778910 78.5594780 C#:min +78.5594780 79.9410650 F# +79.9410650 82.7390700 B +82.7390700 84.0742170 E +84.0742170 85.3977550 A +85.3977550 86.7561220 D +86.7561220 88.1377090 G +88.1377090 89.4960770 E +89.4960770 90.8776640 B +90.8776640 92.2244210 E +92.2244210 93.6060090 A +93.6060090 94.9411560 D +94.9411560 96.3227430 G +96.3227430 97.7275510 E +97.7275510 99.0626980 B +99.0626980 99.7360770 E +99.7360770 100.4675050 C#:min +100.4675050 101.1408840 F#:min +101.1408840 101.8258730 B +101.8258730 103.2306800 E +103.2306800 103.8924480 D +103.8924480 104.6122670 A +104.6122670 105.9822440 E +105.9822440 107.3406120 B +107.3406120 108.7454190 C#:min +108.7454190 110.0689560 F# +110.0689560 112.7973010 B +112.7973010 115.5256460 A +115.5256460 118.2191600 E +118.2191600 120.9591150 B +120.9591150 123.6874600 B +123.6874600 126.3925850 A +126.3925850 129.0977090 E +129.0977090 131.7447840 B +131.7447840 153.2383324 A:(1,2,4) +153.2383324 158.5986620 A:min7(*b3) +158.5986620 159.9105890 D:min7(*b3) +159.9105890 161.3952148 A:(1) +161.3952148 162.0462336 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/08final/11_-_Good_Morning_Good_Morning.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,137 @@ +0.0000000 3.5997509 N +3.5997509 4.6155320 A +4.6155320 5.6604300 D +5.6604300 6.7053280 A +6.7053280 7.7618360 D +7.7618360 8.7602940 A +8.7602940 9.7703620 D +9.7703620 10.0524959 A +10.0524959 11.7324480 A/5 +11.7324480 12.4638770 A:min +12.4638770 13.2069160 E:min +13.2069160 15.6450110 G +15.6450110 16.1797315 A +16.1797315 16.6550790 E:(1) +16.6550790 17.4213370 A +17.4213370 18.1411560 E:min +18.1411560 20.5676410 G +20.5676410 22.5181170 A +22.5181170 25.0142630 D +25.0142630 26.9763490 E +26.9763490 27.7542170 A +27.7542170 28.4856460 E:min +28.4856460 29.9833330 G +29.9833330 30.9701810 A +30.9701810 31.9221990 D +31.9221990 33.8726750 A +33.8726750 34.6041040 A +34.6041040 35.3007020 E:min +35.3007020 37.7852380 G +37.7852380 38.3309070 A +38.3309070 38.8417460 E:(1) +38.8417460 39.5731740 A +39.5731740 40.2813830 E:min +40.2813830 42.7659180 G +42.7659180 43.7522916 A +43.7522916 44.7280040 D +44.7280040 45.7148520 A +45.7148520 46.7249200 D +46.7249200 47.7117680 A +47.7117680 48.6986160 D +48.6986160 49.7319040 A +49.7319040 50.7303620 D +50.7303620 51.7172100 A +51.7172100 52.6808390 D +52.6808390 53.7025170 A +53.7025170 54.6661450 E +54.6661450 55.4207930 A +55.4207930 56.1290020 E:min +56.1290020 58.5903170 G +58.5903170 59.1127660 A +59.1127660 59.6468250 E:(1) +59.6468250 60.4130830 A +60.4130830 61.0632420 E:min +61.0632420 63.5709970 G +63.5709970 65.4982530 A +65.4982530 68.0408390 D +68.0408390 69.9564850 E +69.9564850 70.7111330 A +70.7111330 71.5006120 E:min +71.5006120 72.9402490 G +72.9402490 73.9503170 A +73.9503170 74.8907250 D +74.8907250 76.8760310 A +76.8760310 77.6190700 A +77.6190700 78.3504980 E:min +78.3504980 80.8350340 G +80.8350340 81.3390524 A +81.3390524 81.8177426 A/5 +81.8177426 82.6113600 A +82.6113600 83.3079590 E:min +83.3079590 85.7344440 G +85.7344440 86.7445120 A +86.7445120 87.7081400 D +87.7081400 88.6833780 A +88.6833780 89.6934460 D +89.6934460 90.6222440 A +90.6222440 91.6090920 D +91.6090920 92.5959410 A +92.5959410 93.5711790 D +93.5711790 94.5696370 A +94.5696370 95.5216550 D +95.5216550 96.6246030 A +96.6246030 97.5185710 E +97.5185710 98.2964390 A +98.2964390 99.0162580 E:min +99.0162580 101.4427430 G +101.4427430 101.8844392 A +101.8844392 102.4876410 E:(1) +102.4876410 103.2306800 A +103.2306800 103.9504980 E:min +103.9504980 106.3769840 G +106.3769840 108.3042400 A +108.3042400 110.7771650 D +110.7771650 112.7160310 E +112.7160310 113.4822900 A +113.4822900 114.2253280 E:min +114.2253280 115.7230150 G +115.7230150 116.6750340 A +116.6750340 117.6270520 D +117.6270520 119.6239680 A +119.6239680 120.5411560 A +120.5411560 121.5396140 D +121.5396140 122.5496820 A +122.5496820 123.4436500 D +123.4436500 124.4188880 A +124.4188880 125.4405660 D +125.4405660 126.4506340 A +126.4506340 127.3678230 D +127.3678230 128.2966210 A +128.2966210 129.2950790 D +129.2950790 130.0148970 A +130.0148970 131.1875050 D +131.1875050 132.2091830 A +132.2091830 133.1960310 D +133.1960310 133.8578000 A +133.8578000 135.0768480 D +135.0768480 135.8082760 A +135.8082760 136.8531740 D +136.8531740 137.7471420 A +137.7471420 139.0242400 D +139.0242400 140.0226980 A +140.0226980 141.0327660 D +141.0327660 141.7525850 A +141.7525850 143.0412920 D +143.0412920 143.7959410 A +143.7959410 144.7595690 D +144.7595690 145.6999770 A +145.6999770 146.9074140 D +146.9074140 147.7201130 A +147.7201130 148.6721310 D +148.6721310 149.6589790 A +149.6589790 150.7851470 D +150.7851470 151.6442850 A +151.6442850 152.6256088 D +152.6256088 153.6212846 A +153.6212846 154.5595176 D +154.5595176 161.2228863 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/08final/12_-_Sgt._Pepper's_Lonely_Hearts_Club_Band_(Reprise).lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,40 @@ +0.0000000 0.5172100 N +0.5172100 1.1488567 G:(1) +1.1488567 12.5335370 N +12.5335370 20.5328110 F +20.5328110 21.5428790 F +21.5428790 22.5645570 Ab +22.5645570 23.5862350 Bb +23.5862350 24.6079130 F +24.6079130 26.6512690 Bb +26.6512690 28.5785260 F +28.5785260 29.6118140 F +29.6118140 30.6218820 Ab +30.6218820 31.6667800 Bb +31.6667800 32.7000680 F +32.7000680 34.7550340 G +34.7550340 36.6939000 C +36.6939000 38.7604760 Bb +38.7604760 40.8038320 F +40.8038320 42.8239680 G +42.8239680 44.8208840 D +44.8208840 45.8425620 G +45.8425620 46.8526300 Bb +46.8526300 47.8626980 C +47.8626980 48.9075960 G +48.9075960 50.9509520 C +50.9509520 52.9594780 G +52.9594780 54.0159860 G +54.0159860 55.0144440 Bb +55.0144440 56.0361220 C +56.0361220 57.0694100 G +57.0694100 59.0895460 A +59.0895460 61.0864620 D +61.0864620 63.1414280 C +63.1414280 65.1499540 G +65.1499540 67.1817000 A +67.1817000 71.1987520 C:maj(2) +71.1987520 73.2421080 G +73.2421080 75.2738540 Bb +75.2738540 77.2707700 C +77.2707700 78.8690105 G
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/08final/13_-_A_Day_In_The_Life.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,120 @@ +0.0000000 1.9147611 G +1.9147611 3.3699796 B:min +3.3699796 6.5195690 E:min7 +6.5195690 12.8480471 C +12.8480471 14.4259630 G +14.4259630 15.9236500 B:min/5 +15.9236500 17.4909970 E:min7 +17.4909970 18.9886840 E:min7/b7 +18.9886840 20.5560310 C +20.5560310 22.1233780 E:min/5 +22.1233780 25.2232420 A:min +25.2232420 26.7441490 G +26.7441490 28.3695460 B:min +28.3695460 29.9252830 E:min7 +29.9252830 31.4461900 E:min7/b7 +31.4461900 33.0599770 C +33.0599770 34.5924940 F +34.5924940 36.1482310 E:min +36.1482310 37.6807480 E:min7/b7 +37.6807480 39.2945350 C +39.2945350 40.8154420 F +40.8154420 42.3827890 E:min +42.3827890 43.5437860 C +43.5437860 43.9501360 D +43.9501360 45.5174820 G +45.5174820 47.0035600 B:min/5 +47.0035600 50.0337640 E:min7/b7 +50.0337640 51.6591600 C +51.6591600 53.1452380 E:min/5 +53.1452380 56.3495910 A:min +56.3495910 57.8704980 G +57.8704980 59.4494550 B:min +59.4494550 61.3186620 E:min7 +61.3186620 62.4100000 E:min7/b7 +62.4100000 64.0353960 C +64.0353960 65.5563030 F +65.5563030 67.1584800 E:min +67.1584800 68.6561670 E:min7/b7 +68.6561670 71.6979810 C +71.6979810 73.2537180 G +73.2537180 74.7862350 B:min/5 +74.7862350 76.2723120 E:min7 +76.2723120 77.8048290 E:min7/b7 +77.8048290 79.4070060 C +79.4070060 80.9046930 E:min/5 +80.9046930 82.8783900 A:min +82.8783900 83.9813370 A:min/5 +83.9813370 85.5370740 G +85.5370740 87.0231510 B:min +87.0231510 88.5672780 E:min7 +88.5672780 90.0881850 E:min7/b7 +90.0881850 91.6787520 C +91.6787520 93.1996590 F +93.1996590 94.7670060 E:min +94.7670060 96.2646930 E:min7/b7 +96.2646930 100.8970740 C +100.8970740 102.3947610 F#:(1,4,b7) +102.3947610 103.9040580 E:min/b3 +103.9040580 105.4365750 A:min(2) +105.4365750 106.9690920 E:min(*3)/5 +106.9690920 108.5132190 C#:dim7(*b5) +108.5132190 110.1037860 E:min(*5)/b7 +110.1037860 111.6827430 E:min(*5) +111.6827430 113.2617000 E:min(*5) +113.2617000 135.6799729 N +135.6799729 145.9437860 E +145.9437860 148.8578910 D +148.8578910 150.3207480 E +150.3207480 151.7371650 B +151.7371650 153.1768020 E +153.1768020 156.1141260 B +156.1141260 160.4910880 E +160.4910880 163.4168020 D +163.4168020 164.8216090 E +164.8216090 166.3076870 B +166.3076870 167.7008840 E +167.7008840 169.1521310 B +169.1521310 172.1126750 C +172.1126750 174.9687300 G +174.9687300 177.9176640 D +177.9176640 180.8782080 A +180.8782080 183.8039220 E +183.8039220 186.6599770 C +186.6599770 189.6089110 G +189.6089110 192.4649650 D +192.4649650 195.4487300 A +195.4487300 196.4936280 E:(1) +196.4936280 196.8767570 D:(1) +196.8767570 197.9448750 C:(1) +197.9448750 198.3396140 D:(1) +198.3396140 199.8721310 G +199.8721310 201.3233780 B:min/5 +201.3233780 202.8558950 E:min7 +202.8558950 204.3187520 E:min7/b7 +204.3187520 205.7816090 C +205.7816090 207.2096370 E:min/5 +207.2096370 209.1368930 A:min +209.1368930 210.2166210 F#:hdim7 +210.2166210 211.6562580 G +211.6562580 213.1075050 B:min +213.1075050 214.6168020 E:min7 +214.6168020 216.0680490 E:min7/b7 +216.0680490 217.5541260 C +217.5541260 218.9937640 F +218.9937640 220.4914510 E:min +220.4914510 221.9426980 E:min7/b7 +221.9426980 224.8568020 C +224.8568020 226.3544890 C +226.3544890 227.7825170 F#:(1,4,b5) +227.7825170 229.2453740 E:min/b3 +229.2453740 230.7198410 A:min(2) +230.7198410 232.1362580 E:min(*b3)/5 +232.1362580 233.5642850 C:maj7(*5) +233.5642850 234.9923120 E:min7(*5)/b7 +234.9923120 241.3364915 E:(1,2,5,b6) +241.3364915 259.3160984 N +259.3160984 261.1542691 E:(1) +261.1542691 303.6236707 E +303.6236707 306.8596170 N +306.8596170 333.7045679 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/08final/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +/.DS_Store/1.1.1.1/Fri Sep 23 09:24:39 2005// +/01_-_Sgt._Pepper's_Lonely_Hearts_Club_Band.lab/1.1.1.1/Fri Sep 23 09:24:39 2005// +/02_-_With_A_Little_Help_From_My_Friends.lab/1.2/Tue Oct 17 21:55:42 2006// +/03_-_Lucy_In_The_Sky_With_Diamonds.lab/1.2/Sun Jul 30 21:53:00 2006// +/04_-_Getting_Better.lab/1.3/Tue Oct 17 21:55:42 2006// +/05_-_Fixing_A_Hole.lab/1.2/Sun Jul 30 21:53:00 2006// +/06_-_She's_Leaving_Home.lab/1.2/Sun Jul 30 21:53:00 2006// +/07_-_Being_For_The_Benefit_Of_Mr._Kite!.lab/1.2/Sun Jul 30 21:53:00 2006// +/08_-_Within_You_Without_You.lab/1.2/Tue Oct 17 21:55:42 2006// +/09_-_When_I'm_Sixty-Four.lab/1.2/Sun Jul 30 21:53:00 2006// +/10_-_Lovely_Rita.lab/1.2/Fri Jul 21 11:47:33 2006// +/11_-_Good_Morning_Good_Morning.lab/1.1.1.1/Fri Sep 23 09:24:40 2005// +/12_-_Sgt._Pepper's_Lonely_Hearts_Club_Band_(Reprise).lab/1.1.1.1/Fri Sep 23 09:24:40 2005// +/13_-_A_Day_In_The_Life.lab/1.4/Fri Jul 21 11:49:09 2006// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/08final/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +chrish/transcriptions/08final
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/08final/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:matthiasm@dspmac1.elec.qmul.ac.uk:/cvsroot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/09final/01_-_Magical_Mystery_Tour.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,74 @@ +0.0000000 1.1105614 N +1.1105614 2.6470970 D +2.6470970 4.1099540 A +4.1099540 9.7872330 E +9.7872330 12.7245570 E +12.7245570 14.1409750 G +14.1409750 15.5922220 A +15.5922220 18.4831060 E +18.4831060 19.9111330 G +19.9111330 21.3972100 A +21.3972100 24.2184350 E +24.2184350 25.6696820 G +25.6696820 27.1093190 A +27.1093190 29.9653740 E +29.9653740 31.4398410 G +31.4398410 32.8794780 A +32.8794780 34.3075050 D +34.3075050 35.7819720 D/b7 +35.7819720 37.2912690 G/3 +37.2912690 38.7192970 G:min(9)/b3 +38.7192970 40.1821540 D/5 +40.1821540 41.6450110 A +41.6450110 44.4430150 E +44.4430150 45.8246030 G +45.8246030 47.2294100 A +47.2294100 50.0506340 E +50.0506340 51.4438320 G +51.4438320 52.8834690 A +52.8834690 55.7163030 E +55.7163030 57.2139900 G +57.2139900 58.6187980 A +58.6187980 61.4168020 E +61.4168020 62.9028790 G +62.9028790 64.3192970 A +64.3192970 65.8169840 D +65.8169840 67.2798410 D/b7 +67.2798410 68.7659180 G/3 +68.7659180 70.2287750 G:min(9)/b3 +70.2287750 71.6800220 D/5 +71.6800220 73.0848290 A +73.0848290 75.8944440 B +75.8944440 78.7737180 F#:min7 +78.7737180 81.6413830 B +81.6413830 83.9633780 F#:min7 +83.9633780 84.4742170 G#:(1) +84.4742170 85.8093650 A +85.8093650 87.2722220 B +87.2722220 91.3241040 E +91.3241040 93.1817000 G +93.1817000 95.0160770 A +95.0160770 98.6035600 E +98.6035600 100.4495460 G +100.4495460 102.2258730 A +102.2258730 105.7901360 E +105.7901360 107.6129020 G +107.6129020 109.3892290 A +109.3892290 113.1044210 E +113.1044210 114.8923580 G +114.8923580 116.7383440 A +116.7383440 118.7004300 D +118.7004300 120.6973460 D/b7 +120.6973460 122.6710430 G/3 +122.6710430 124.7027890 Bb +124.7027890 126.6880950 D/5 +126.6880950 128.6501810 A +128.6501810 130.6122670 D +130.6122670 132.5859630 D/b7 +132.5859630 134.4900000 G/3 +134.4900000 136.3940360 Bb +136.3940360 138.3445120 D/5 +138.3445120 140.2601580 A +140.2601580 147.9703317 D +147.9703317 166.4204130 D:min +166.4204130 171.9349250 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/09final/02_-_The_Fool_On_The_Hill.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,116 @@ +0.0000000 0.4403951 N +0.4403951 4.0350340 D:maj6 +4.0350340 7.4831970 D:maj6 +7.4831970 10.8849200 G:maj6/3 +10.8849200 14.2285940 D +14.2285940 17.6419270 G:maj6/3 +17.6419270 19.3718140 E:min +19.3718140 21.0623723 A +21.0623723 22.7473621 D:maj6 +22.7473621 24.4132043 B:min7 +24.4132043 26.0359410 E:min +26.0359410 27.7890470 A +27.7890470 28.6448264 D:min +28.6448264 29.4608840 G:min/5 +29.4608840 31.1095010 D:min +31.1095010 34.4531740 Bb/3 +34.4531740 36.1250110 C/5 +36.1250110 37.7504080 C +37.7504080 39.3990240 D:min +39.3990240 40.9895910 D:min7 +40.9895910 44.4224580 D:maj6 +44.4224580 47.7001580 D:maj6 +47.7001580 50.9857820 G:maj6/3 +50.9857820 54.2946250 D +54.2946250 57.5802490 G:maj6/3 +57.5802490 59.2288660 E:min +59.2288660 60.9007020 A +60.9007020 62.5377090 D:maj6 +62.5377090 64.2559860 B:min7 +64.2559860 65.9278230 E:min +65.9278230 67.6228790 A +67.6228790 68.4144148 D:min +68.4144148 69.2598860 G:min/5 +69.2598860 70.8852830 D:min +70.8852830 74.2289560 Bb/3 +74.2289560 75.8891830 C/5 +75.8891830 77.5378000 C +77.5378000 79.1980270 D:min +79.1980270 80.5922955 D:min7 +80.5922955 83.9929470 D:maj6 +83.9929470 84.7708160 D:maj6 +84.7708160 85.6067340 D:maj6/5 +85.6067340 86.4194330 D:maj6 +86.4194330 87.2669610 D:maj6/5 +87.2669610 88.1028790 G:maj6/5 +88.1028790 88.9387980 G:maj6/2 +88.9387980 89.7398860 G:maj6/5 +89.7398860 90.5641950 G:maj6/2 +90.5641950 91.3768930 D +91.3768930 92.2360310 D/5 +92.2360310 93.1067800 D +93.1067800 93.9194780 D/5 +93.9194780 94.7205660 G:maj6/5 +94.7205660 95.5564850 G:maj6/2 +95.5564850 96.4040130 G:maj6/5 +96.4040130 97.1934920 G:maj6/2 +97.1934920 98.8769380 E:min +98.8769380 100.6300450 A +100.6300450 102.2206120 D:maj6 +102.2206120 103.8924480 B:min7 +103.8924480 105.5875050 E:min +105.5875050 107.2361220 A +107.2361220 108.0691176 D:min +108.0691176 108.9195690 G:min/5 +108.9195690 110.5914050 D:min +110.5914050 113.9582990 Bb/3 +113.9582990 115.6301360 C/5 +115.6301360 117.2207020 C +117.2207020 118.8809290 D:min +118.8809290 120.4250560 D:min7 +120.4250560 123.7339000 D:maj6 +123.7339000 124.5349880 D:maj6 +124.5349880 125.3825170 D:maj6/5 +125.3825170 126.1952150 D:maj6 +126.1952150 127.0427430 D:maj6/5 +127.0427430 127.8786620 G:maj6/5 +127.8786620 128.6797500 G:maj6/2 +128.6797500 129.5504980 G:maj6/5 +129.5504980 130.3631970 G:maj6/2 +130.3631970 131.2107250 D +131.2107250 132.0118140 D/5 +132.0118140 132.8012920 D +132.8012920 133.6604300 D/5 +133.6604300 134.4847390 G:maj6/5 +134.4847390 137.0041040 G:maj6 +137.0041040 138.6527210 E:min +138.6527210 140.3129470 A +140.3129470 141.9383440 D:maj6 +141.9383440 143.6334010 B:min7 +143.6334010 145.2936280 E:min +145.2936280 146.9190240 A +146.9190240 147.7429680 D:min +147.7429680 148.6024710 G:min/5 +148.6024710 150.2626980 D:min +150.2626980 153.5715410 Bb/3 +153.5715410 155.1737180 C/5 +155.1737180 156.7410650 C +156.7410650 158.3664620 D:min +158.3664620 159.9802490 D:min7 +159.9802490 163.3239220 D:maj6 +163.3239220 164.1366210 D:maj6 +164.1366210 164.9609290 D:maj6/5 +164.9609290 165.7736280 D:maj6 +165.7736280 166.6792060 D:maj6/5 +166.6792060 167.5035140 G:maj6/5 +167.5035140 168.3278230 G:maj6/2 +168.3278230 169.1173010 G:maj6/5 +169.1173010 169.9532190 G:maj6/2 +169.9532190 170.7775280 D +170.7775280 171.6366660 D/5 +171.6366660 172.4377550 D +172.4377550 173.2620630 D/5 +173.2620630 174.0283210 G:maj6/5 +174.0283210 174.9339000 G:maj6/2 +174.9339000 175.7349880 G:maj6/5 +175.7349880 180.1790214 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/09final/03_-_Flying.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,26 @@ +0.0000000 0.4020998 N +0.4020998 10.8850110 C +10.8850110 15.9353510 F:7 +15.9353510 21.1482310 C +21.1482310 23.7140360 G +23.7140360 26.3146710 F +26.3146710 28.8991960 C +28.8991960 31.5275510 G:7 +31.5275510 39.5083410 C +39.5083410 41.8952600 C:7 +41.8952600 47.0268700 F +47.0268700 52.1468700 C +52.1468700 54.7126750 G +54.7126750 57.2784800 F +57.2784800 59.8026390 C +59.8026390 62.4100900 G +62.4100900 70.0958950 C +70.0958950 72.6384800 C:7 +72.6384800 77.7817000 F +77.7817000 82.8784800 C +82.8784800 85.4675050 G +85.4675050 88.0913600 F +88.0913600 98.2054520 C +98.2054520 110.9392810 A:min +110.9392810 130.0651910 F#:dim +130.0651910 136.5076020 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/09final/04_-_Blue_Jay_Way.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,23 @@ +0.0000000 1.6658422 N +1.6658422 22.2686718 C +22.2686718 24.2596140 D#:dim7 +24.2596140 28.1721760 C +28.1721760 30.6451020 D#:dim7 +30.6451020 34.6621540 C +34.6621540 43.5437860 D#:dim7 +43.5437860 48.6540801 C +48.6540801 68.5484481 C +68.5484481 70.5485940 D#:dim7 +70.5485940 74.1825170 C +74.1825170 76.2723120 D#:dim7 +76.2723120 79.8263911 C +79.8263911 87.6617000 D#:dim7 +87.6617000 110.9029641 C +110.9029641 112.7392510 D#:dim7 +112.7392510 116.2222440 C +116.2222440 118.3004300 D#:dim7 +118.3004300 121.7137640 C +121.7137640 129.1209290 D#:dim7 +129.1209290 133.7652118 C +133.7652118 231.9924573 C +231.9924573 236.6836220 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/09final/05_-_Your_Mother_Should_Know.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,74 @@ +0.0000000 0.6127236 N +0.6127236 4.8060504 A:min +4.8060504 6.8208110 A:min +6.8208110 8.9036392 F:maj7 +8.9036392 10.9375480 A/5 +10.9375480 12.8519270 D:min +12.8519270 14.8488430 G:7 +14.8488430 15.8240810 C +15.8240810 16.8109290 C/7 +16.8109290 18.7497950 A:7 +18.7497950 20.7699310 D:7 +20.7699310 22.7784580 G:7 +22.7784580 24.8450340 C +24.8450340 26.9464390 E +26.9464390 28.9781850 A:min +28.9781850 31.0563710 F:maj7 +31.0563710 33.1113370 A +33.1113370 35.1779130 D:min +35.1779130 37.2444890 G:7 +37.2444890 38.2197270 C +38.2197270 39.2181850 C/7 +39.2181850 41.3195910 A:7 +41.3195910 43.3397270 D:7 +43.3397270 45.3714730 G:7 +45.3714730 47.4496590 C +47.4496590 50.5727430 E +50.5727430 52.6509290 A:min +52.6509290 54.7407250 F:maj7 +54.7407250 56.7492510 D:min/b3 +56.7492510 58.7345570 G:9(*3) +58.7345570 60.8127430 C +60.8127430 62.8212690 E +62.8212690 64.8530150 A:min +64.8530150 66.9079810 F:maj7 +66.9079810 68.9629470 A +68.9629470 70.9946930 D:min +70.9946930 72.9916090 G:7 +72.9916090 74.0597270 C +74.0597270 75.0233560 C/7 +75.0233560 77.0899310 A +77.0899310 79.0752380 D:7 +79.0752380 81.1185940 G:7 +81.1185940 83.1619500 C +83.1619500 85.2401360 A +85.2401360 87.2602720 D:7 +87.2602720 89.2687980 G:7 +89.2687980 91.3469840 C +91.3469840 94.4584580 E +94.4584580 96.5598630 A:min +96.5598630 98.5567800 F:maj7 +98.5567800 100.5072560 D:min/b3 +100.5072560 102.5854420 G:9(*3) +102.5854420 104.6055780 C +104.6055780 106.6721540 E +106.6721540 108.6806800 A:min +108.6806800 110.7008160 F:maj7 +110.7008160 112.7557820 A/5 +112.7557820 114.7875280 D:min +114.7875280 116.8424940 G:7 +116.8424940 117.8061220 C +117.8061220 118.8278000 C/7 +118.8278000 120.8711560 A +120.8711560 122.8564620 D:7 +122.8564620 124.9346480 G:7 +124.9346480 126.9431740 C +126.9431740 128.9865300 A +128.9865300 131.0298860 D:7 +131.0298860 133.0616320 G:7 +133.0616320 135.0933780 C +135.0933780 137.1715640 A +137.1715640 139.1684800 D:7 +139.1684800 141.2234460 G:7 +141.2234460 145.2005980 C +145.2005980 149.6236960 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/09final/06_-_I_Am_The_Walrus.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,122 @@ +0.0000000 0.4595427 N +0.4595427 4.7332420 B +4.7332420 6.1496590 B +6.1496590 7.5776870 A +7.5776870 8.9592740 G +8.9592740 10.3873010 F +10.3873010 13.3014050 E +13.3014050 16.0297500 E:7 +16.0297500 18.9206340 D +18.9206340 21.7882990 D:7 +21.7882990 23.2163260 A +23.2163260 24.6443530 A/b7 +24.6443530 26.0839900 C +26.0839900 27.5700680 D +27.5700680 28.9632650 A +28.9632650 30.4145120 A/b7 +30.4145120 33.3170060 C +33.3170060 36.1730610 D +36.1730610 39.0291150 A +39.0291150 40.4223120 A +40.4223120 41.8619500 A/b7 +41.8619500 43.3015870 F#:min7 +43.3015870 43.9981850 F +43.9981850 44.6715640 G +44.6715640 46.1228110 A +46.1228110 47.5392290 A/b7 +47.5392290 50.3488430 F +50.3488430 55.9332420 B +55.9332420 58.7776870 C +58.7776870 61.5989110 D +61.5989110 64.3736960 E +64.3736960 65.7901130 A +65.7901130 67.2297500 A/b7 +67.2297500 68.6809970 C +68.6809970 70.0858040 D +70.0858040 71.5254420 A +71.5254420 72.9534690 A/b7 +72.9534690 75.7746930 C +75.7746930 78.6307480 D +78.6307480 81.4519720 A +81.4519720 87.0944210 D:maj(11) +87.0944210 89.9156460 A +89.9156460 92.7484800 E +92.7484800 95.5697050 D +95.5697050 97.0209520 A +97.0209520 98.4141490 A/b7 +98.4141490 99.8421760 F#:min7 +99.8421760 100.5271650 F +100.5271650 101.1657140 G +101.1657140 102.6053510 A +102.6053510 103.9637180 A/b7 +103.9637180 106.7965530 F +106.7965530 112.2764620 B +112.2764620 115.0976870 C +115.0976870 117.8608610 D +117.8608610 120.6124260 E +120.6124260 123.5729700 N +123.5729700 124.8268480 B +124.8268480 126.2432650 A +126.2432650 127.6364620 G +127.6364620 128.9832190 F +128.9832190 131.8508840 E +131.8508840 133.2324710 B +133.2324710 134.6604980 A +134.6604980 136.0072560 G +136.0072560 137.3772330 F +137.3772330 138.8052600 E +138.8052600 140.1868480 F +140.1868480 145.6783670 B +145.6783670 148.4415410 C +148.4415410 151.1118360 D +151.1118360 153.8285710 E +153.8285710 156.5336960 D +156.5336960 157.9501130 A +157.9501130 159.3549200 A/b7 +159.3549200 160.7365070 C +160.7365070 162.1761450 D +162.1761450 163.5461220 A +163.5461220 164.9044890 A/b7 +164.9044890 167.7257140 C +167.7257140 170.4656680 D +170.4656680 173.2404530 A +173.2404530 174.7033100 A +174.7033100 176.0152380 A/b7 +176.0152380 177.3619950 F#:min7 +177.3619950 178.0702040 F +178.0702040 178.6855320 G +178.6855320 180.0671200 A +180.0671200 181.4487070 A/b7 +181.4487070 184.1654420 F +184.1654420 189.5524710 B +189.5524710 192.3272560 C +192.3272560 195.0556000 D +195.0556000 197.7491150 E +197.7491150 200.3845800 D +200.3845800 203.1013150 C +203.1013150 205.7367800 B +205.7367800 208.4070740 A +208.4070740 211.0889790 G +211.0889790 213.7592740 F +213.7592740 216.4411790 E:7 +216.4411790 219.0882530 D:maj(9) +219.0882530 221.7933780 C:maj(#11) +221.7933780 224.5217230 G/3 +224.5217230 227.2500680 A +227.2500680 229.9087520 G +229.9087520 232.6719270 F +232.6719270 235.3538320 E:7 +235.3538320 238.0009070 D:maj(9) +238.0009070 240.6595910 C:maj(#11) +240.6595910 243.2950560 G/3 +243.2950560 245.9305210 A +245.9305210 248.5543760 G +248.5543760 251.1666210 F +251.1666210 253.7904760 E:7 +253.7904760 256.5072100 D:maj(9) +256.5072100 259.2471650 C:maj(#11) +259.2471650 261.9755100 G/3 +261.9755100 264.6806340 A +264.6806340 267.4089790 G +267.4089790 270.1489340 F +270.1489340 272.7843990 E:7
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/09final/07_-_Hello_Goodbye.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,105 @@ +0.0000000 0.5361331 N +0.5361331 3.0597950 F:maj6 +3.0597950 5.5443310 C +5.5443310 7.9940360 G +7.9940360 10.4553510 A:min +10.4553510 12.9166660 G +12.9166660 15.3547610 A:min +15.3547610 17.8160770 G +17.8160770 19.0815640 G +19.0815640 20.3238320 D:min7/4 +20.3238320 21.5428790 F:maj/9 +21.5428790 22.8083670 C +22.8083670 24.0506340 C/7 +24.0506340 25.3161220 A:min +25.3161220 26.5235600 C/5 +26.5235600 27.7774370 F +27.7774370 29.0080950 Ab +29.0080950 30.2503620 C +30.2503620 31.4694100 C/5 +31.4694100 32.6884580 A:min +32.6884580 33.9307250 C/5 +33.9307250 35.1613830 F +35.1613830 36.3688200 Bb +36.3688200 38.8185260 C +38.8185260 41.2682310 F:maj6 +41.2682310 43.7295460 C +43.7295460 46.1792510 G +46.1792510 48.6057360 A:min +48.6057360 51.0206120 G +51.0206120 53.4935370 A:min +53.4935370 55.8851920 G +55.8851920 57.0810200 G +57.0810200 58.3000680 D:min7/4 +58.3000680 59.5539450 F:maj/9 +59.5539450 60.7745179 C +60.7745179 62.0617000 C/7 +62.0617000 63.2807480 A:min +63.2807480 64.4881850 C/5 +64.4881850 65.7188430 F +65.7188430 66.9495010 Ab +66.9495010 68.1569380 C +68.1569380 69.3875960 C/7 +69.3875960 70.5950340 A:min +70.5950340 71.8024710 C/5 +71.8024710 72.9982990 F +72.9982990 74.2289560 Bb +74.2289560 76.6902720 C +76.6902720 79.0354870 F:maj6 +79.0354870 81.4039220 C +81.4039220 83.7955780 G +83.7955780 86.2220630 A:min +86.2220630 88.6369380 G +88.6369380 91.0634240 A:min +91.0634240 93.4202490 G +93.4202490 94.6509070 G +94.6509070 95.8235140 D:min7/4 +95.8235140 97.0541720 F:maj/9 +97.0541720 98.2848290 C +98.2848290 99.4922670 C/7 +99.4922670 100.6880950 A:min +100.6880950 101.9187520 C/5 +101.9187520 103.1261900 F +103.1261900 104.3336280 Ab +104.3336280 105.5642850 C +105.5642850 106.7949430 C/7 +106.7949430 108.0023800 A:min +108.0023800 109.1982080 C/5 +109.1982080 110.3940360 F +110.3940360 111.6014730 Bb +111.6014730 114.0047390 C +114.0047390 116.4196140 F:maj6 +116.4196140 118.8228790 C +118.8228790 121.2029250 G +121.2029250 123.5597500 A:min +123.5597500 125.9514050 G +125.9514050 128.3430610 A:min +128.3430610 130.7231060 G +130.7231060 131.9653740 G +131.9653740 133.0683210 D:min7/4 +133.0683210 134.2873690 F:maj/9 +134.2873690 135.4948070 C +135.4948070 136.7022440 C/7 +136.7022440 137.9096820 A:min +137.9096820 139.1403400 C/5 +139.1403400 140.3361670 F +140.3361670 141.5784350 Ab +141.5784350 142.7626530 C +142.7626530 143.9933100 C/7 +143.9933100 145.1775280 A:min +145.1775280 146.4197950 C/5 +146.4197950 147.6040130 F +147.6040130 148.7998410 Bb +148.7998410 150.0188880 C +150.0188880 151.2147160 C/7 +151.2147160 152.4105440 A:min +152.4105440 153.5947610 C/5 +153.5947610 154.8021990 F +154.8021990 155.9631970 Ab +155.9631970 157.2054640 Ab +157.2054640 158.4129020 Ab/7 +158.4129020 159.6435600 Ab/b7 +159.6435600 160.8393870 F:7 +160.8393870 165.6343080 C +165.6343080 205.5304586 C +205.5304586 211.4470704 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/09final/08_-_Strawberry_Fields_Forever.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,109 @@ +0.0000000 0.3600496 N +0.3600496 1.7478680 F +1.7478680 3.0365750 A:min/5 +3.0365750 4.4878230 A:dim/b5 +4.4878230 5.1728110 Eb +5.1728110 5.8578000 A:dim7/b3 +5.8578000 7.2742170 G:min/5 +7.2742170 8.5745350 F/5 +8.5745350 9.2479130 Eb/5 +9.2479130 9.9909520 Bb +9.9909520 15.1225620 Bb +15.1225620 20.3006120 F:min +20.3006120 25.5599310 D:dim +25.5599310 26.9415190 Eb +26.9415190 29.6698630 G +29.6698630 31.8060990 Eb +31.8060990 34.4183440 Bb +34.4183440 35.6722220 F +35.6722220 36.9957590 F:maj7 +36.9957590 38.9346250 F:7 +38.9346250 39.5963940 F#:dim +39.5963940 40.9083210 G:min +40.9083210 42.2550790 F +42.2550790 44.8673240 Eb +44.8673240 46.1792510 Eb +46.1792510 47.5143990 F +47.5143990 48.2226070 Bb +48.2226070 48.8379360 F/3 +48.8379360 49.5345350 G:min +49.5345350 50.1963030 F +50.1963030 51.5430610 Eb +51.5430610 52.8782080 F +52.8782080 54.2597950 Eb +54.2597950 55.5717230 Bb +55.5717230 60.6220630 Bb +60.6220630 64.2908160 F:min +64.2908160 65.5098630 F:min/b7 +65.5098630 70.2931740 D:dim +70.2931740 71.1523120 Eb +71.1523120 71.5006120 F +71.5006120 73.9270970 G +73.9270970 75.8195230 Eb +75.8195230 80.8118140 Bb +80.8118140 82.3095010 F +82.3095010 83.5401580 A:min/5 +83.5401580 84.7127660 F:7/b7 +84.7127660 85.3629250 Eb +85.3629250 85.9666430 Eb:dim +85.9666430 87.3714510 G:min/5 +87.3714510 88.6369380 G:min/b7 +88.6369380 90.0185260 Eb +90.0185260 90.5525850 Eb/3 +90.5525850 91.1098630 F:(1) +91.1098630 92.3869610 Eb +92.3869610 93.6176190 F +93.6176190 95.0108160 Bb +95.0108160 95.6609750 Eb/3 +95.6609750 96.2763030 Eb/2 +96.2763030 97.4605210 Eb +97.4605210 98.7260090 F +98.7260090 99.9218360 Eb +99.9218360 101.1757140 Bb +101.1757140 106.1796140 Bb +106.1796140 109.7903170 F:min +109.7903170 110.9629250 F:min/b7 +110.9629250 115.7810650 D:dim +115.7810650 116.6634240 Eb +116.6634240 117.0117230 F +117.0117230 119.3801580 G +119.3801580 121.3074140 Eb +121.3074140 126.4506340 Bb +126.4506340 127.8554420 F +127.8554420 129.1093190 A:min/5 +129.1093190 130.3980270 A:dim/b3 +130.3980270 131.0133560 Eb +131.0133560 131.6286840 Eb:dim +131.6286840 132.9986620 G:min/5 +132.9986620 134.2757590 G:min/b7 +134.2757590 135.5528570 Eb +135.5528570 136.1333560 G:(1) +136.1333560 136.7602940 F:(1) +136.7602940 138.0838320 Eb +138.0838320 139.3377090 F +139.3377090 140.5567570 Bb +140.5567570 141.2533560 G:(1) +141.2533560 141.8570740 F:(1) +141.8570740 143.0761220 Eb +143.0761220 144.3183900 F +144.3183900 145.5026070 Eb +145.5026070 146.6868250 Bb +146.6868250 151.8532650 Bb +151.8532650 155.2433780 F:min +155.2433780 156.7758950 F:min7 +156.7758950 161.6056460 G/5 +161.6056460 162.5460540 Eb +162.5460540 162.8711330 F +162.8711330 165.3556680 G +165.3556680 167.3177550 Eb +167.3177550 168.4903620 Bb +168.4903620 169.7674600 G:min +169.7674600 171.7411560 Eb +171.7411560 173.5058730 Bb +173.5058730 174.4811110 Eb +174.4811110 175.5143990 F +175.5143990 177.9989340 Eb +177.9989340 180.4370290 Bb +180.4370290 183.5880896 Bb/b7 +183.5880896 214.1056742 Bb +214.1056742 250.5061666 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/09final/09_-_Penny_Lane.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,142 @@ +0.0000000 0.4403951 N +0.4403951 1.6898180 B +1.6898180 2.2091879 B/7 +2.2091879 2.7463260 B/6 +2.7463260 3.2803850 B/5 +3.2803850 3.8492740 E:maj6 +3.8492740 4.4065530 C#:min7 +4.4065530 4.9406120 F#:sus4 +4.9406120 5.4862810 F# +5.4862810 6.0203400 B +6.0203400 6.5776190 B/7 +6.5776190 7.1232870 B/6 +7.1232870 7.6341260 B/5 +7.6341260 9.7471420 B:min +9.7471420 11.8717680 G#:hdim7 +11.8717680 14.0080040 G:maj7 +14.0080040 15.0529020 F#:sus4 +15.0529020 16.1210200 F# +16.1210200 17.1891380 F#:sus4 +17.1891380 18.2572560 F# +18.2572560 18.8029250 B +18.8029250 19.3485940 B/7 +19.3485940 19.8826530 B/6 +19.8826530 20.4515410 B/5 +20.4515410 20.9972100 E:maj6 +20.9972100 21.5312690 C#:min7 +21.5312690 22.0537180 F#:sus4 +22.0537180 22.6109970 F# +22.6109970 23.1334460 B +23.1334460 23.6558950 B/7 +23.6558950 24.1551240 B/6 +24.1551240 24.6891830 B/5 +24.6891830 26.8370290 B:min +26.8370290 28.8919950 G#:hdim7 +28.8919950 31.0050110 G:maj7 +31.0050110 32.0380906 F#:sus4 +32.0380906 33.1412470 F# +33.1412470 35.3239220 E +35.3239220 37.4253280 A +37.4253280 39.4919040 A/3 +39.4919040 43.6598860 D +43.6598860 45.7729020 A +45.7729020 47.8510880 A/3 +47.8510880 49.9408840 D +49.9408840 52.1003400 F# +52.1003400 52.6343990 B +52.6343990 53.1568480 B/7 +53.1568480 53.6909070 B/6 +53.6909070 54.2249650 B/5 +54.2249650 54.7358040 E:maj6 +54.7358040 55.2698630 C#:min7 +55.2698630 55.8039220 F#:sus4 +55.8039220 56.3263710 F# +56.3263710 56.8488200 B +56.8488200 57.3712690 B/7 +57.3712690 57.9285480 B/6 +57.9285480 58.4277770 B/5 +58.4277770 60.5872330 B:min +60.5872330 62.6654190 G#:hdim7 +62.6654190 64.8016550 G:maj7 +64.8016550 65.8349430 F#:sus4 +65.8349430 66.9262810 F# +66.9262810 67.9711790 F#:sus4 +67.9711790 69.0392970 F# +69.0392970 69.5965750 B +69.5965750 70.1190240 B/7 +70.1190240 70.5950340 B/6 +70.5950340 71.1174820 B/5 +71.1174820 71.6515410 E:maj6 +71.6515410 72.1972100 C#:min7 +72.1972100 72.7312690 F#:sus4 +72.7312690 73.2769380 F# +73.2769380 73.7877770 B +73.7877770 74.3334460 B/7 +74.3334460 74.8094550 B/6 +74.8094550 75.3319040 B/5 +75.3319040 77.4913600 B:min +77.4913600 79.5463260 G#:hdim7 +79.5463260 81.6477320 G:maj7 +81.6477320 82.7042400 F#:sus4 +82.7042400 83.8071880 F# +83.8071880 85.9666430 E +85.9666430 88.1144890 A +88.1144890 90.1926750 A/3 +90.1926750 94.3954870 D +94.3954870 96.4852830 A +96.4852830 98.5982990 A/3 +98.5982990 100.6880950 D +100.6880950 102.8707700 F# +102.8707700 103.4164390 B +103.4164390 103.9156680 B/7 +103.9156680 104.4381170 B/6 +104.4381170 104.9489560 B/5 +104.9489560 105.4830150 E:maj6 +105.4830150 106.0170740 C#:min7 +106.0170740 106.5975730 F#:sus4 +106.5975730 107.0968020 F# +107.0968020 107.6192510 B +107.6192510 108.1417000 B/7 +108.1417000 108.6873690 B/6 +108.6873690 109.1982080 B/5 +109.1982080 111.3344440 B:min +111.3344440 113.4242400 G#:hdim7 +113.4242400 115.5256460 G:maj7 +115.5256460 116.5821540 F#:sus4 +116.5821540 117.6618820 F# +117.6618820 118.7416090 F#:sus4 +118.7416090 119.7516780 F# +119.7516780 120.2973460 B +120.2973460 120.8430150 B/7 +120.8430150 121.3770740 B/6 +121.3770740 121.8763030 B/5 +121.8763030 122.4103620 E:maj6 +122.4103620 122.9560310 C#:min7 +122.9560310 123.4784800 F#:sus4 +123.4784800 123.9893190 F# +123.9893190 124.5117680 B +124.5117680 125.0226070 B/7 +125.0226070 125.5566660 B/6 +125.5566660 126.0791150 B/5 +126.0791150 128.2385710 B:min +128.2385710 130.2935370 G#:hdim7 +130.2935370 132.4413830 G:maj7 +132.4413830 133.4630610 F#:sus4 +133.4630610 134.5195690 F# +134.5195690 136.6674140 E +136.6674140 138.7804300 A +138.7804300 140.9050560 A/3 +140.9050560 145.0962580 D +145.0962580 147.2789340 A +147.2789340 149.3455100 A/3 +149.3455100 151.4996831 D +151.4996831 153.6179810 F# +153.6179810 155.6961670 B +155.6961670 157.8440130 B/3 +157.8440130 161.9887750 E +161.9887750 164.1830610 B +164.1830610 166.1799770 B/3 +166.1799770 168.2813830 E +168.2813830 170.3595690 E/5 +170.3595690 178.4284184 B +178.4284184 183.3528637 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/09final/10_-_Baby_You're_A_Rich_Man.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,81 @@ +0.0000000 0.4595427 N +0.4595427 0.7276092 G +0.7276092 3.0497950 G/5 +3.0497950 5.6272100 C/9 +5.6272100 8.2046250 G +8.2046250 10.7239900 C/5 +10.7239900 13.2433560 G:7 +13.2433560 15.7162810 C/5 +15.7162810 18.2240360 G:7 +18.2240360 20.6853510 C/5 +20.6853510 23.2511560 G +23.2511560 25.7356910 C/5 +25.7356910 28.2202260 G:7 +28.2202260 30.7127684 G +30.7127684 33.1660770 F/9 +33.1660770 34.4315640 F +34.4315640 35.6622220 G +35.6622220 38.1351470 C +38.1351470 40.6661220 G +40.6661220 43.0577770 F/9 +43.0577770 44.3000450 F +44.3000450 45.4842630 G +45.4842630 48.0732870 C +48.0732870 50.6042630 G +50.6042630 53.0307480 C +53.0307480 55.5036730 G +55.5036730 57.9765980 G +57.9765980 60.4611330 F/9 +60.4611330 61.7614510 F +61.7614510 62.9921080 G +62.9921080 65.3837640 C +65.3837640 67.8915190 G +67.8915190 70.2831740 F/9 +70.2831740 71.5951020 F +71.5951020 72.7909290 G +72.7909290 75.2754640 C +75.2754640 77.7251700 G +77.7251700 80.1980950 C +80.1980950 82.7174600 G +82.7174600 85.2136050 C +85.2136050 86.3978230 Bb +86.3978230 87.6865300 G/3 +87.6865300 90.1130150 C +90.1130150 92.5743310 G +92.5743310 95.0588660 C +95.0588660 97.5201810 G +97.5201810 100.0047160 C +100.0047160 102.4892510 G +102.4892510 104.9853960 C +104.9853960 107.4931510 G +107.4931510 109.9776870 C/5 +109.9776870 112.4157820 G +112.4157820 117.3268020 G +117.3268020 118.6039000 F +118.6039000 119.8345570 G +119.8345570 122.2494330 C +122.2494330 127.1604530 G +127.1604530 128.4839900 F +128.4839900 129.6798180 G +129.6798180 132.0946930 C +132.0946930 134.5443990 G +134.5443990 137.0289340 C +137.0289340 139.5250790 G +139.5250790 141.9631740 C +141.9631740 143.1822220 Bb +143.1822220 144.4128790 G/3 +144.4128790 146.8741950 C +146.8741950 149.3587300 G +149.3587300 151.8200450 C +151.8200450 154.2233100 G +154.2233100 156.7194550 C +156.7194550 159.1807700 G +159.1807700 161.5956460 C +161.5956460 164.0221310 G +164.0221310 166.3905660 C +166.3905660 168.7473920 G +168.7473920 171.1158270 C +171.1158270 173.4958730 G +173.4958730 175.8062580 C +175.8062580 178.7620981 G +178.7620981 183.6638866 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/09final/11_-_All_You_Need_Is_Love.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,139 @@ +0.0000000 0.4403951 N +0.4403951 1.0148234 D +1.0148234 2.3531970 G +2.3531970 3.5838540 D +3.5838540 6.1264390 G +6.1264390 6.7882080 C +6.7882080 8.7154640 D +8.7154640 9.9809520 G +9.9809520 11.2232190 D/3 +11.2232190 12.6048070 E:min +12.6048070 13.1969160 E:min/b7 +13.1969160 14.4159630 G +14.4159630 15.6698410 D/3 +15.6698410 16.9817680 E:min +16.9817680 17.5738770 E:min/b7 +17.5738770 18.7813150 A:min +18.7813150 19.9771420 G +19.9771420 21.2078000 D/3 +21.2078000 22.4848970 F#:dim/b7 +22.4848970 23.7039450 D +23.7039450 24.9346030 D/b7 +24.9346030 26.1536500 G/3 +26.1536500 26.7573690 D +26.7573690 27.9531970 G +27.9531970 29.1722440 D/3 +29.1722440 30.4725620 E:min +30.4725620 31.0646710 E:min/b7 +31.0646710 32.2372780 G +32.2372780 33.4447160 D/3 +33.4447160 34.6985940 E:min +34.6985940 35.2674820 E:min/b7 +35.2674820 36.4052600 A:min +36.4052600 37.6126980 G +37.6126980 38.8201360 D/3 +38.8201360 40.0624030 D:7/2 +40.0624030 41.2582310 D +41.2582310 42.4540580 D/b7 +42.4540580 43.6614960 G/3 +43.6614960 44.2768250 D +44.2768250 45.4610430 G +45.4610430 46.6452600 D/3 +46.6452600 47.8759180 E:min +47.8759180 48.4796370 E:min/b7 +48.4796370 49.6290240 G +49.6290240 50.8364620 D/3 +50.8364620 52.0787300 E:min +52.0787300 52.6824480 E:min/b7 +52.6824480 53.8666660 A:min +53.8666660 55.0160540 G +55.0160540 56.0259104 D/3 +56.0259104 57.3728790 D:7/2 +57.3728790 58.5919270 D +58.5919270 59.8458040 D/b7 +59.8458040 61.0532420 G +61.0532420 61.5978652 D +61.5978652 62.7482990 G +62.7482990 63.9789560 A +63.9789560 66.3938320 D +66.3938320 67.5432190 G +67.5432190 68.6926070 A +68.6926070 71.0726530 D +71.0726530 72.1988200 G +72.1988200 73.3714280 B +73.3714280 74.5440360 E:min +74.5440360 75.7050340 G:maj7/5 +75.7050340 76.8892510 C +76.8892510 78.0618590 D +78.0618590 79.2344670 G +79.2344670 80.4070740 G +80.4070740 81.6145120 D/3 +81.6145120 82.7406800 E:min +82.7406800 83.3443990 E:min/b7 +83.3443990 84.4937860 G +84.4937860 85.6780040 D/3 +85.6780040 86.8854420 E:min +86.8854420 87.4427210 E:min/b7 +87.4427210 88.5804980 A:min +88.5804980 89.6950560 G +89.6950560 90.8908840 D/3 +90.8908840 92.0170520 D:7/2 +92.0170520 93.1664390 D +93.1664390 94.3158270 D/b7 +94.3158270 95.5116550 G +95.5116550 96.0573240 D +96.0573240 97.1834920 G +97.1834920 98.3444890 A +98.3444890 100.6780950 D +100.6780950 101.8274820 G +101.8274820 103.0000900 A +103.0000900 105.2988660 D +105.2988660 106.4482530 G +106.4482530 107.5860310 B +107.5860310 108.7238090 E:min +108.7238090 109.8267570 E:min/b7 +109.8267570 111.0225850 C +111.0225850 112.1835820 D +112.1835820 113.3097500 G +113.3097500 114.4823580 G +114.4823580 115.6317460 D/3 +115.6317460 116.8507930 E:min +116.8507930 117.3848520 E:min/b7 +117.3848520 118.5574600 G +118.5574600 119.7184580 D/3 +119.7184580 120.9258950 E:min +120.9258950 121.4483440 E:min/b7 +121.4483440 122.5861220 A:min +122.5861220 123.6542400 G +123.6542400 124.8268480 D/3 +124.8268480 126.0226750 D:7 +126.0226750 127.1140130 D +127.1140130 128.3330610 D/b7 +128.3330610 129.4592290 G +129.4592290 130.0281170 D +130.0281170 131.1775050 G +131.1775050 132.3385030 A +132.3385030 134.6837180 D +134.6837180 135.8331060 G +135.8331060 136.9244440 A +136.9244440 139.2348290 D +139.2348290 140.3609970 G +140.3609970 141.4871650 B +141.4871650 143.7627210 E:min +143.7627210 144.9121080 C +144.9121080 146.0614960 D +146.0614960 147.1992740 G +147.1992740 148.3486620 G +148.3486620 149.5212690 A +149.5212690 151.7852150 D +151.7852150 152.9578230 G +152.9578230 154.0259410 A +154.0259410 156.3363260 D +156.3363260 157.4741040 G +157.4741040 158.5770520 B +158.5770520 159.7496590 E:min +159.7496590 160.8293870 E:min/b7 +160.8293870 161.9671650 C +161.9671650 163.1049430 D +163.1049430 224.7546602 G +224.7546602 228.2203779 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/09final/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +/01_-_Magical_Mystery_Tour.lab/1.3/Tue Oct 17 21:56:17 2006// +/02_-_The_Fool_On_The_Hill.lab/1.2/Tue Oct 17 21:56:17 2006// +/03_-_Flying.lab/1.3/Tue Oct 17 21:56:17 2006// +/04_-_Blue_Jay_Way.lab/1.3/Tue Oct 17 21:56:17 2006// +/05_-_Your_Mother_Should_Know.lab/1.2/Wed Aug 2 00:42:11 2006// +/06_-_I_Am_The_Walrus.lab/1.2/Wed Aug 2 00:42:11 2006// +/07_-_Hello_Goodbye.lab/1.2/Wed Aug 2 00:42:11 2006// +/08_-_Strawberry_Fields_Forever.lab/1.2/Wed Aug 2 00:42:11 2006// +/09_-_Penny_Lane.lab/1.1/Wed Jun 21 16:35:09 2006// +/10_-_Baby_You're_A_Rich_Man.lab/1.1/Wed Jun 21 16:35:09 2006// +/11_-_All_You_Need_Is_Love.lab/1.3/Tue Oct 17 21:56:17 2006// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/09final/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +chrish/transcriptions/09final
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/09final/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:matthiasm@dspmac1.elec.qmul.ac.uk:/cvsroot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD1final/CD1_-_01_-_Back_in_the_USSR.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,98 @@ +0.000000 10.006758 N +10.006758 11.787486 E +11.787486 16.706689 E:7 +16.706689 18.366916 A +18.366916 20.015532 D +20.015532 21.722199 C +21.722199 23.417256 D +23.417256 25.112312 A +25.112312 26.760929 D +26.760929 28.432766 C +28.432766 30.127823 D +30.127823 31.799659 A +31.799659 33.471496 C +33.471496 36.803560 D +36.803560 38.730816 A +38.730816 38.928185 D +38.928185 39.148775 D# +39.148775 40.135623 E +40.135623 41.865510 A +41.865510 43.514126 D +43.514126 45.220793 C +45.220793 46.904240 D +46.904240 48.622517 A +48.622517 50.282743 D +50.282743 52.001020 C +52.001020 53.696077 D +53.696077 55.367913 A +55.367913 57.074580 C +57.074580 62.891179 D +62.891179 65.015804 A +65.015804 65.364104 A/2 +65.364104 65.770453 A/b3 +65.770453 66.200022 A/3 +66.200022 69.520476 D +69.520476 71.679931 A +71.679931 72.097891 A/2 +72.097891 72.504240 A/b3 +72.504240 72.922199 A/3 +72.922199 73.781337 D +73.781337 74.582426 A/3 +74.582426 75.418344 C +75.418344 76.300702 B +76.300702 77.949319 E +77.949319 79.690816 D +79.690816 81.594852 A +81.594852 81.850272 D +81.850272 82.070861 D# +82.070861 83.034489 E +83.034489 84.717936 A +84.717936 86.436213 D +86.436213 88.108049 C +88.108049 89.826326 D +89.826326 91.498991 A +91.498991 93.170000 D +93.170000 94.865056 C +94.865056 96.560113 D +96.560113 98.231950 A +98.231950 99.938616 C +99.938616 101.564013 D +101.564013 103.282290 D +103.282290 104.954126 A +104.954126 105.395306 A +105.395306 105.824875 A/2 +105.824875 106.208004 A/b3 +106.208004 106.625963 A/3 +106.625963 110.027687 D +110.027687 112.152312 A +112.152312 112.593492 A/2 +112.593492 113.023061 A/3b +113.023061 113.417800 A/3 +113.417800 114.276938 D +114.276938 115.147687 A/3 +115.147687 115.995215 C +115.995215 116.831133 B +116.831133 118.514580 E +118.514580 120.209637 D +120.209637 122.125283 A +122.125283 122.322653 D +122.322653 122.554852 D# +122.554852 123.588140 E +123.588140 125.294807 A +125.294807 126.978253 D +126.978253 128.673310 C +128.673310 130.403197 D +130.403197 132.075034 A +132.075034 133.770090 D +133.770090 135.476757 C +135.476757 137.218253 D +137.218253 138.913310 A +138.913310 140.619977 C +140.619977 142.315034 D +142.315034 143.975260 D +143.975260 145.890907 A +145.890907 146.146326 D +146.146326 146.320476 D# +146.320476 147.307324 E +147.307324 160.315506 A +160.315506 163.264238 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD1final/CD1_-_02_-_Dear_Prudence.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,131 @@ +0.0000000 1.7478680 N +1.7478680 3.3732650 D +3.3732650 5.0218820 C/9 +5.0218820 6.6124480 G/5 +6.6124480 8.2262350 A/4 +8.2262350 11.5002490 C/9 +11.5002490 13.1488660 D/5 +13.1488660 14.7626530 D/b7 +14.7626530 16.4344890 G:maj7/3 +16.4344890 18.0947160 G:minmaj7/b3 +18.0947160 19.6852830 D/5 +19.6852830 21.3571200 D:7 +21.3571200 23.0289560 G:maj7/3 +23.0289560 24.6543530 G:minmaj7/5 +24.6543530 26.2449200 D/5 +26.2449200 27.8819270 D:7 +27.8819270 29.5256165 G:maj7/3 +29.5256165 31.1327210 G:minmaj7/5 +31.1327210 32.7929470 D/5 +32.7929470 34.4299540 D:7 +34.4299540 36.0901810 G:maj7/3 +36.0901810 37.7620180 G:minmaj7/5 +37.7620180 39.2829250 D/5 +39.2829250 40.8851020 D:7 +40.8851020 42.4060090 G:maj7/3 +42.4060090 44.0778450 G:minmaj7/5 +44.0778450 45.7032420 D/5 +45.7032420 47.3170290 D/b7 +47.3170290 48.9308160 G:maj7/3 +48.9308160 50.5097730 G:minmaj7/b3 +50.5097730 52.1932190 D/5 +52.1932190 53.7837860 D/b7 +53.7837860 55.3975730 C +55.3975730 56.9881400 G +56.9881400 58.6831970 D +58.6831970 60.2853740 D/b7 +60.2853740 61.9339900 G:maj7/3 +61.9339900 63.5477770 G:minmaj7/b3 +63.5477770 65.1499540 D/5 +65.1499540 66.7289110 D/b7 +66.7289110 68.3078680 G:maj7/3 +68.3078680 69.9100450 G:minmaj7/b3 +69.9100450 71.4541720 D/5 +71.4541720 73.0447390 D/b7 +73.0447390 74.6353060 G:maj7/3 +74.6353060 76.1910430 G:minmaj7/b3 +76.1910430 77.7816090 D/5 +77.7816090 79.4186160 D/b7 +79.4186160 81.0091830 G:maj7/3 +81.0091830 82.5649200 G:minmaj7/b3 +82.5649200 84.1903170 D/5 +84.1903170 85.7344440 D/b7 +85.7344440 87.3482310 G:maj7/3 +87.3482310 88.8923580 G:minmaj7/b3 +88.8923580 90.4597050 D +90.4597050 92.0734920 D/b7 +92.0734920 93.6872780 G:maj7/3 +93.6872780 95.3010650 G:minmaj7/b3 +95.3010650 98.4705890 D +98.4705890 100.0959860 C +100.0959860 101.6865530 G +101.6865530 108.1649200 D +108.1649200 114.5271880 D +114.5271880 120.7733560 D +120.7733560 122.3058730 F +122.3058730 123.9080490 G# +123.9080490 125.4637860 G +125.4637860 127.1240130 D +127.1240130 128.6565300 D/b7 +128.6565300 130.3283670 G:maj7/3 +130.3283670 131.9305440 G:minmaj7/b3 +131.9305440 133.5443310 D/5 +133.5443310 135.1813370 D/b7 +135.1813370 136.7138540 G:maj7/3 +136.7138540 138.3740810 G:minmaj7/b3 +138.3740810 139.9182080 D/5 +139.9182080 141.5087750 D/b7 +141.5087750 143.1806120 G:maj7/3 +143.1806120 144.7015190 G:minmaj7/b3 +144.7015190 146.2920860 D/5 +146.2920860 147.8942630 D/b7 +147.8942630 149.4500000 G:maj7/3 +149.4500000 151.0637860 G:minmaj7/b3 +151.0637860 152.6427430 D/5 +152.6427430 154.1636500 D/b7 +154.1636500 155.8470970 G:maj7/3 +155.8470970 157.3563940 G:minmaj7/b3 +157.3563940 158.9121310 D +158.9121310 160.4214280 D/b7 +160.4214280 162.0119950 G:maj7/3 +162.0119950 163.6141720 G:minmaj7/b3 +163.6141720 166.7836960 D +166.7836960 168.4207020 C +168.4207020 169.9764390 G +169.9764390 171.5434487 D +171.5434487 173.0414730 D/b7 +173.0414730 174.5275510 G:maj7/3 +174.5275510 175.9787980 G:minmaj7/b3 +175.9787980 177.4648750 D +177.4648750 178.9045120 D/b7 +178.9045120 180.3673690 G:maj7/3 +180.3673690 181.7721760 G:minmaj7/b3 +181.7721760 183.2234240 D +183.2234240 184.6398410 D/b7 +184.6398410 186.0330380 G:maj7/3 +186.0330380 187.5075050 G:minmaj7/b3 +187.5075050 188.9355320 D +188.9355320 190.4448290 D/b7 +190.4448290 191.8031970 G:maj7/3 +191.8031970 193.2312240 G:minmaj7/b3 +193.2312240 194.6940810 D +194.6940810 196.0872780 D/b7 +196.0872780 197.5617460 G:maj7/3 +197.5617460 198.9665530 G:minmaj7/b3 +198.9665530 201.8458270 D +201.8458270 204.7367120 D/b7 +204.7367120 207.5231060 G:maj7/3 +207.5231060 210.3095010 G:minmaj7/b3 +210.3095010 213.2468250 D +213.2468250 214.7445120 C +214.7445120 216.3118590 G +216.3118590 219.5394330 D +219.5394330 221.0835600 D +221.0835600 222.6857360 D:sus4(9) +222.6857360 224.3343530 D +224.3343530 226.0526300 C/9 +226.0526300 227.7012470 G/5 +227.7012470 229.4659630 A/4 +229.4659630 233.0647235 C/9 +233.0647235 234.7114180 D +234.7114180 236.3581126 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD1final/CD1_-_03_-_Glass_Onion.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,69 @@ +0.0000000 1.3812690 N +1.3812690 3.3433560 A:min +3.3433560 5.3170520 F:7 +5.3170520 7.3487980 A:min +7.3487980 9.3573240 F:7 +9.3573240 11.3194100 A:min +11.3194100 13.3859860 G:min7 +13.3859860 15.4061220 C +15.4061220 17.4378680 G:min7 +17.4378680 19.1793650 C +19.1793650 21.1414510 F:7 +21.1414510 23.1151470 D:7 +23.1151470 25.0656230 F:7 +25.0656230 27.0741490 D:7 +27.0741490 29.0478450 F:7 +29.0478450 31.2421310 G +31.2421310 33.2158270 A:min +33.2158270 35.2591830 F:7 +35.2591830 37.2444890 A:min +37.2444890 39.2530150 F:7 +39.2530150 41.2267120 A:min +41.2267120 43.2352380 G:min7 +43.2352380 45.2553740 C +45.2553740 47.3103400 G:min7 +47.3103400 49.0518360 C +49.0518360 51.0835820 F:7 +51.0835820 52.9992290 D:7 +52.9992290 55.0193650 F:7 +55.0193650 56.9930610 D:7 +56.9930610 59.1525170 F:7 +59.1525170 61.1262130 G +61.1262130 65.0852150 A:min +65.0852150 68.9977770 F/3 +68.9977770 72.8987300 F#:dim/b3 +72.8987300 76.7880720 A:min7 +76.7880720 78.7617680 F:7 +78.7617680 79.5966197 G +79.5966197 80.7470740 N +80.7470740 82.8067418 A:min +82.8067418 84.7409070 F:7 +84.7409070 86.7971936 A:min +86.7971936 88.7231290 F:7 +88.7231290 90.7316550 A:min +90.7316550 92.7517910 G:min7 +92.7517910 94.7719270 C +94.7719270 96.7804530 G:min7 +96.7804530 98.4522900 C +98.4522900 100.4956460 F:7 +100.4956460 102.3880720 D:7 +102.3880720 104.3733780 F:7 +104.3733780 106.3819040 D:7 +106.3819040 108.3091600 F:7 +108.3091600 109.0264982 G +109.0264982 112.4887520 F:7(b9) +112.4887520 113.2201810 B:dim7/b9 +113.2201810 113.9400000 Bb:dim7/5 +113.9400000 114.7643080 C:dim7 +114.7643080 118.6768700 C:dim7/2 +118.6768700 119.4895690 Bb:dim7/7 +119.4895690 120.3370970 D:dim7 +120.3370970 121.3703850 A:dim7 +121.3703850 125.6196370 F:7(b9) +125.6196370 126.6297050 B:dim7/b9 +126.6297050 127.6629930 Bb:dim7/5 +127.6629930 128.7195010 C:dim7 +128.7195010 133.6305210 C:dim7/2 +133.6305210 134.6521990 B:dim7/b9 +134.6521990 135.7783670 Bb:dim7/5 +135.7783670 137.9202434 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD1final/CD1_-_04_-_Ob-La-Di,_Ob-La-Da.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,103 @@ +0.0000000 1.3786280 F +1.3786280 1.6049880 A:min/5 +1.6049880 1.8139680 A:dim/b5 +1.8139680 2.0926070 D:(1) +2.0926070 10.6375510 Bb +10.6375510 12.7737860 Bb +12.7737860 17.0114280 F +17.0114280 21.3071200 Bb +21.3071200 23.4085260 Eb +23.4085260 24.4998630 Bb +24.4998630 25.5331510 F +25.5331510 27.6809970 Bb +27.6809970 29.8056230 Bb +29.8056230 30.8621310 F +30.8621310 31.9418590 G:min +31.9418590 33.0099770 Bb +33.0099770 34.0432650 F +34.0432650 36.1562810 Bb +36.1562810 38.2925170 Bb +38.2925170 39.3374140 F +39.3374140 40.4055320 G:min +40.4055320 41.4388200 Bb +41.4388200 42.5069380 F +42.5069380 44.6083440 Bb +44.6083440 46.7097500 Bb +46.7097500 50.8545120 F +50.8545120 55.0689340 Bb +55.0689340 57.1935600 Eb +57.1935600 58.2732870 Bb +58.2732870 59.2833560 F +59.2833560 61.4195910 Bb +61.4195910 63.5209970 Bb +63.5209970 64.5891150 F +64.5891150 65.6456230 G:min +65.6456230 66.6905210 Bb +66.6905210 67.7818590 F +67.7818590 69.8716550 Bb +69.8716550 71.9614510 Bb +71.9614510 73.0411790 F +73.0411790 74.0976870 G:min +74.0976870 75.1541950 Bb +75.1541950 76.1990920 F +76.1990920 78.3237180 Bb +78.3237180 82.4800900 Eb +82.4800900 83.5482080 Bb +83.5482080 84.5814960 Bb:sus4(9) +84.5814960 85.6496140 Bb +85.6496140 86.6945120 Bb:sus2(b7) +86.6945120 90.9321540 Eb +90.9321540 93.0335600 Bb/5 +93.0335600 95.1349650 F +95.1349650 97.2479810 Bb +97.2479810 101.4972330 F +101.4972330 105.7232650 Bb +105.7232650 107.8362810 Eb +107.8362810 108.9043990 Bb +108.9043990 109.9609070 F +109.9609070 112.0971420 Bb +112.0971420 114.2217680 Bb +114.2217680 115.2621956 F +115.2621956 116.3347840 G:min +116.3347840 117.3912920 Bb +117.3912920 118.4594100 F +118.4594100 120.5840360 Bb +120.5840360 122.7086620 Bb +122.7086620 123.7767800 F +123.7767800 124.8100680 G:min +124.8100680 125.9246250 Bb +125.9246250 126.9463030 F +126.9463030 129.0593190 Bb +129.0593190 133.3317910 Eb +133.3317910 134.3999090 Bb +134.3999090 135.4680270 Bb:sus4(9) +135.4680270 136.5825850 Bb +136.5825850 137.5810430 Bb:sus2(b7) +137.5810430 141.7606340 Eb +141.7606340 143.8620400 Bb/5 +143.8620400 145.9982760 F +145.9982760 148.1345120 Bb +148.1345120 152.4534240 F +152.4534240 156.6678450 Bb +156.6678450 158.7924710 Eb +158.7924710 159.8489790 Bb +159.8489790 160.9054870 F +160.9054870 163.0301130 Bb +163.0301130 165.1779590 Bb +165.1779590 166.2770344 F +166.2770344 167.3217069 G:min +167.3217069 168.3823120 Bb +168.3823120 169.4272100 F +169.4272100 171.5518360 Bb +171.5518360 173.6880720 Bb +173.6880720 174.7561900 F +174.7561900 175.8243080 G:min +175.8243080 176.8808160 Bb +176.8808160 177.9373240 F +177.9373240 182.1865750 G:min +182.1865750 182.4303850 F +182.4303850 182.7206340 F/b7 +182.7206340 182.9760540 F/6 +182.9760540 183.2779130 F/5 +183.2779130 187.2140118 Bb +187.2140118 188.8027845 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD1final/CD1_-_05_-_Wild_Honey_Pie.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,24 @@ +0.0000000 0.0873864 N +0.0873864 2.7579360 G:7 +2.7579360 5.4398410 F:7 +5.4398410 6.7517680 E:7 +6.7517680 8.0636960 Eb:7 +8.0636960 9.3756230 D:7 +9.3756230 14.8323120 G:7 +14.8323120 17.4793870 G:7 +17.4793870 20.1845120 F:7 +20.1845120 21.4964390 E:7 +21.4964390 22.7851470 Eb:7 +22.7851470 24.1086840 D:7 +24.1086840 29.4957140 G:7 +29.4957140 32.2472780 G:7 +32.2472780 34.8711330 F:7 +34.8711330 36.1714510 E:7 +36.1714510 37.5182080 Eb:7 +37.5182080 38.8069160 D:7 +38.8069160 41.5933100 G:7 +41.5933100 44.2636050 F:7 +44.2636050 46.9687300 G:7 +46.9687300 49.6390240 F:7 +49.6390240 52.4710986 G:7 +52.4710986 52.8755002 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD1final/CD1_-_06_-The_Continuing_Story_of_Bungalow_Bill.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,156 @@ +0.0000000 0.1148857 N +0.1148857 8.5015394 E:min(2) +8.5015394 9.4418140 C +9.4418140 10.4983210 G +10.4983210 11.5664390 C +11.5664390 12.6113370 F:min +12.6113370 13.6678450 C +13.6678450 14.6430830 F:min +14.6430830 15.6995910 G +15.6995910 16.7444890 A +16.7444890 17.7777770 E +17.7777770 18.8110650 A +18.8110650 19.8907930 D:min +19.8907930 20.9008610 A +20.9008610 21.9225390 D:min +21.9225390 24.0703850 E +24.0703850 25.7073920 A:min +25.7073920 27.3560090 C +27.3560090 29.9468639 F +29.9468639 30.6532420 G +30.6532420 32.2205890 A:min +32.2205890 33.8692060 C +33.8692060 36.2840810 F +36.2840810 37.0967800 G +37.0967800 38.7105660 E +38.7105660 40.2779130 G +40.2779130 41.9729700 A:min +41.9729700 44.9777387 F:min +44.9777387 46.0117097 N +46.0117097 46.9884800 C +46.9884800 48.0449880 G +48.0449880 49.1014960 C +49.1014960 50.1463940 F:min +50.1463940 51.2145120 C +51.2145120 52.2361900 F:min +52.2361900 53.2810880 G +53.2810880 54.2911560 A +54.2911560 55.3708840 E +55.3708840 56.3693420 A +56.3693420 57.4606800 D:min +57.4606800 58.5636280 A +58.5636280 59.5272560 D:min +59.5272560 61.6402720 E +61.6402720 63.3353280 A:min +63.3353280 65.0071650 C +65.0071650 67.4568700 F +67.4568700 68.2115190 G +68.2115190 69.8833560 A:min +69.8833560 71.4623120 C +71.4623120 73.9468480 F +73.9468480 74.7479360 G +74.7479360 76.4546030 E +76.4546030 77.9987300 G +77.9987300 79.6589560 A:min +79.6589560 82.9283041 F:min +82.9283041 83.8965980 N +83.8965980 84.9182760 C +84.9182760 85.9747840 G +85.9747840 87.0196820 C +87.0196820 88.0761900 F:min +88.0761900 89.1326980 C +89.1326980 90.1659860 F:min +90.1659860 91.2224940 G +91.2224940 92.2673920 A +92.2673920 93.3239000 E +93.3239000 94.3571880 A +94.3571880 95.3904760 D:min +95.3904760 96.4469840 A +96.4469840 97.5034920 D:min +97.5034920 98.0375510 E +98.0375510 98.5716090 E/b7 +98.5716090 99.0940580 E/b6 +99.0940580 99.5932870 E/5 +99.5932870 101.2535140 A:min +101.2535140 102.9137410 C +102.9137410 105.5027660 F +105.5027660 106.2109750 G +106.2109750 107.9060310 A:min +107.9060310 109.5430380 C +109.5430380 111.9927430 F +111.9927430 112.7473920 G +112.7473920 114.4540580 E +114.4540580 116.1026750 G +116.1026750 117.7164620 A:min +117.7164620 121.1086408 F:min +121.1086408 121.6058040 N +121.6058040 122.5346030 C +122.5346030 123.5911110 G +123.5911110 124.6476190 C +124.6476190 125.6576870 F:min +125.6576870 126.7606340 C +126.7606340 127.8055320 F:min +127.8055320 128.8388200 G +128.8388200 129.8837180 A +129.8837180 130.9286160 E +130.9286160 131.9967340 A +131.9967340 133.0184120 D:min +133.0184120 134.0865300 A +134.0865300 135.0733780 D:min +135.0733780 136.1414960 E +136.1414960 137.1631740 C +137.1631740 138.2080720 G +138.2080720 139.2761900 C +139.2761900 140.2862580 F:min +140.2862580 141.3659860 C +141.3659860 142.3876640 F:min +142.3876640 143.4557820 G +143.4557820 144.4890700 A +144.4890700 145.5339680 E +145.5339680 146.5672560 A +146.5672560 147.6237640 D:min +147.6237640 148.6222220 A +148.6222220 149.6903400 D:min +149.6903400 150.7352380 E +150.7352380 151.7685260 C +151.7685260 152.8250340 G +152.8250340 153.9047610 C +153.9047610 154.9264390 F:min +154.9264390 155.9713370 C +155.9713370 157.0046250 F:min +157.0046250 158.0959630 G +158.0959630 159.1176410 A +159.1176410 160.1973690 E +160.1973690 161.2538770 A +161.2538770 162.3103850 D:min +162.3103850 163.3668930 A +163.3668930 164.3769610 D:min +164.3769610 165.5495690 E +165.5495690 166.4667570 C +166.4667570 167.4884350 G +167.4884350 168.5449430 C +168.5449430 169.5782310 F:min +169.5782310 170.6231290 C +170.6231290 171.5051535 F:min +171.5051535 172.6316550 G +172.6316550 173.6649430 A +173.6649430 174.7098410 E +174.7098410 175.7895690 A +175.7895690 176.7996370 D:min +176.7996370 177.8329250 A +177.8329250 178.8778230 D:min +178.8778230 180.0272100 E +180.0272100 181.1069380 C +181.1069380 182.2679360 G +182.2679360 183.3360540 C +183.3360540 184.0558730 F:min +184.0558730 185.0078910 C +185.0078910 185.9482990 F:min +185.9482990 187.8572134 G +187.8572134 188.8337416 A +188.8337416 189.6867120 E +189.6867120 190.6619500 A +190.6619500 191.4978680 D:min +191.4978680 192.5195460 A +192.5195460 193.7037640 D:min +193.7037640 194.0418919 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD1final/CD1_-_07_-_While_My_Guitar_Gently_Weeps.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,141 @@ +0.000000 2.232828 A:min +2.232828 4.253764 A:min/b7 +4.253764 6.401609 A:min/6 +6.401609 8.491405 F +8.491405 10.639251 A:min +10.639251 12.717437 G +12.717437 14.818843 D +14.818843 16.943469 E +16.943469 19.079705 A:min +19.079705 21.192721 A:min/b7 +21.192721 23.270907 A:min/6 +23.270907 25.383922 F +25.383922 27.520158 A:min +27.520158 29.679614 G +29.679614 31.862290 D +31.862290 33.975306 E +33.975306 36.076712 A:min +36.076712 38.143287 A:min/b7 +38.143287 40.175034 A:min/6 +40.175034 42.218390 F +42.218390 44.354625 A:min +44.354625 46.479251 G +46.479251 48.534217 C +48.534217 50.682063 E +50.682063 52.748639 A +52.748639 54.873265 C#:min +54.873265 56.963061 F#:min +56.963061 59.064467 C#:min +59.064467 63.336938 B:min +63.336938 64.428276 E +64.428276 65.473174 E/2 +65.473174 66.494852 E/3 +66.494852 67.528140 E/4 +67.528140 69.664376 A +69.664376 71.719342 C#:min +71.719342 73.878798 F#:min +73.878798 75.968594 C#:min +75.968594 80.252675 B:min +80.252675 81.262743 E +81.262743 82.330861 E/2 +82.330861 83.364149 E/3 +83.364149 84.409047 E/4 +84.409047 86.522063 A:min +86.522063 88.565419 A:min/b7 +88.565419 90.666825 A:min/6 +90.666825 92.721791 F +92.721791 94.823197 A:min +94.823197 96.959433 G +96.959433 99.130498 D +99.130498 101.231904 E +101.231904 103.263650 A:min +103.263650 105.353446 A:min/b7 +105.353446 107.466462 A:min/6 +107.466462 109.544648 F +109.544648 111.611224 A:min +111.611224 113.666190 G +113.666190 115.755986 C +115.755986 117.776122 E +117.776122 119.831088 A:min +119.831088 121.886054 A:min/b7 +121.886054 123.952630 A:min/6 +123.952630 126.007596 F +126.007596 128.050952 A:min +128.050952 130.071088 G +130.071088 132.126054 D +132.126054 134.146190 E +134.146190 136.212766 A:min +136.212766 138.290952 A:min/b7 +138.290952 140.369138 A:min/6 +140.369138 142.377664 F +142.377664 144.479070 A:min +144.479070 146.557256 G +146.557256 148.554172 C +148.554172 150.632358 E +150.632358 152.722154 A +152.722154 154.765510 C#:min +154.765510 156.843696 F#:min +156.843696 158.968321 C#:min +158.968321 163.147913 B:min +163.147913 164.169591 E +164.169591 165.237709 E/2 +165.237709 166.270997 E/3 +166.270997 167.223015 E/4 +167.223015 169.289591 A +169.289591 171.379387 C#:min +171.379387 173.515623 F#:min +173.515623 175.628639 C#:min +175.628639 179.808231 B:min +179.808231 180.864739 E +180.864739 181.886417 E/2 +181.886417 182.954535 E/3 +182.954535 183.941383 E/4 +183.941383 185.961519 A:min +185.961519 188.062925 A:min/b7 +188.062925 190.059841 A:min/6 +190.059841 192.149637 F +192.149637 194.216213 A:min +194.216213 196.294399 G +196.294399 198.430634 D +198.430634 200.497210 E +200.497210 202.528956 A:min +202.528956 204.607142 A:min/b7 +204.607142 206.743378 A:min/6 +206.743378 208.798344 F +208.798344 210.899750 A:min +210.899750 213.001156 G +213.001156 215.114172 C +215.114172 217.180748 E +217.180748 219.235714 A:min +219.235714 221.267460 A:min/b7 +221.267460 223.380476 A:min/6 +223.380476 225.400612 F +225.400612 227.455578 A:min +227.455578 229.533764 G +229.533764 231.611950 D +231.611950 233.608866 E +233.608866 235.756712 A:min +235.756712 237.765238 A:min/b7 +237.765238 239.831814 A:min/6 +239.831814 241.898390 F +241.898390 243.906916 A:min +243.906916 245.985102 G +245.985102 248.051678 C +248.051678 250.083424 E +250.083424 252.138390 A:min +252.138390 254.158526 A:min/b7 +254.158526 256.236712 A:min/6 +256.236712 258.326507 F +258.326507 260.381473 A:min +260.381473 262.506099 G +262.506099 264.549455 D +264.549455 266.557981 E +266.557981 268.647777 A:min +268.647777 270.691133 A:min/b7 +270.691133 272.757709 A:min/6 +272.757709 274.754625 F +274.754625 276.844421 A:min +276.844421 278.876167 G +278.876167 280.989183 C +280.989183 283.166579 E +283.166579 285.340240 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD1final/CD1_-_08_-_Happiness_is_a_Warm_Gun.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,77 @@ +0.0000000 0.2106237 N +0.2106237 0.7084616 E:(1) +0.7084616 2.4909070 A:min7 +2.4909070 4.1859630 A:min6 +4.1859630 5.8926300 E:min(9) +5.8926300 7.6109070 E:min +7.6109070 9.2827430 A:min7 +9.2827430 10.9778000 A:min6 +10.9778000 12.6380270 E:min(9) +12.6380270 14.3563030 E:min +14.3563030 19.5807930 D:min +19.5807930 23.0173460 A:min +23.0173460 26.4539000 D:min +26.4539000 29.9252830 A:min +29.9252830 33.4547160 D:min +33.4547160 36.8680490 A:min +36.8680490 41.1753510 D:min +41.1753510 44.6931740 A:min +44.6931740 53.7837860 A:7 +53.7837860 56.3728110 C:7 +56.3728110 58.9618360 A:min +58.9618360 67.9943990 A:7 +67.9943990 70.6414730 C:7 +70.6414730 73.1821700 A:min +73.1821700 74.4031060 A +74.4031060 76.6670520 C +76.6670520 77.8048290 A +77.8048290 80.4286840 G +80.4286840 81.5664620 A +81.5664620 83.8282418 C +83.8282418 84.8869160 A +84.8869160 87.4875510 G +87.4875510 88.6137180 A +88.6137180 90.8776640 C +90.8776640 91.9573920 A +91.9573920 94.5115870 G +94.5115870 96.1950340 C +96.1950340 97.7739900 A:min +97.7739900 99.3529470 F +99.3529470 100.9086840 G +100.9086840 102.5573010 C +102.5573010 104.1826980 A:min +104.1826980 105.8313150 F +105.8313150 107.4799310 G +107.4799310 108.7201364 C +108.7201364 110.0225170 A:min +110.0225170 111.1951240 F +111.1951240 112.4954420 G +112.4954420 113.6796590 C +113.6796590 114.8870970 A:min +114.8870970 116.1061450 F +116.1061450 117.3019720 G +117.3019720 118.5674600 C +118.5674600 119.7516780 A:min +119.7516780 121.0055550 F +121.0055550 122.2362130 G +122.2362130 123.9080490 C +123.9080490 125.6379360 A:min +125.6379360 127.3097730 F +127.3097730 128.9583900 G +128.9583900 130.6534460 C +130.6534460 132.3020630 A:min +132.3020630 134.0087300 F +134.0087300 135.6921760 G +135.6921760 140.4285805 F:min +140.4285805 141.5965848 N +141.5965848 142.1901607 C:(1) +142.1901607 143.8539900 C +143.8539900 145.5142170 A:min +145.5142170 147.2324940 F +147.2324940 148.9043310 G +148.9043310 150.6458270 C +150.6458270 152.3060540 A:min +152.3060540 154.0243310 F +154.0243310 155.6729470 G +155.6729470 160.8399341 C +160.8399341 163.4248616 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD1final/CD1_-_09_-_Martha_My_Dear.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,89 @@ +0.0000000 0.3446570 N +0.3446570 2.9770060 Eb +2.9770060 3.6155550 G:min/5 +3.6155550 4.2657140 G:min +4.2657140 4.9158730 G:min/b7 +4.9158730 5.5544210 C/3 +5.5544210 6.2394100 C:7/3 +6.2394100 8.9445350 F +8.9445350 10.2564620 Bb +10.2564620 11.5451700 Ab:maj9 +11.5451700 12.9499770 Bb:7 +12.9499770 14.1458040 Ab:maj7 +14.1458040 15.4809520 Bb:7 +15.4809520 16.7348290 Ab:maj7 +16.7348290 19.4167340 Bb:7 +19.4167340 22.0638090 Eb +22.0638090 22.6907480 G:min/5 +22.6907480 23.3176870 G:min +23.3176870 23.9446250 G:min/b7 +23.9446250 24.6063940 C/3 +24.6063940 25.2681630 C:7/3 +25.2681630 27.9384580 F +27.9384580 29.2968250 Bb +29.2968250 30.6087520 Ab:maj9 +30.6087520 31.9322900 Bb:7 +31.9322900 33.1977770 Ab:maj7 +33.1977770 34.5097050 Bb:7 +34.5097050 35.7287520 Ab:maj7 +35.7287520 38.3874370 Bb:7 +38.3874370 41.0809520 D:min7 +41.0809520 43.7976870 G:min7 +43.7976870 49.2311560 F:maj6 +49.2311560 50.5779130 G:min/4 +50.5779130 51.9246710 C +51.9246710 53.2830380 G:min/4 +53.2830380 53.9680270 C +53.9680270 54.6414050 A +54.6414050 57.3813600 D:min +57.3813600 60.0516550 G:min7 +60.0516550 61.4216320 D:min +61.4216320 64.1151470 G:9 +64.1151470 65.4619040 D:min +65.4619040 68.1786390 G:9 +68.1786390 70.8489340 C:7 +70.8489340 73.5540580 Bb:maj7 +73.5540580 76.2475730 D:min +76.2475730 78.9294780 G:min7 +78.9294780 80.3342850 Eb +80.3342850 83.0974600 Eb +83.0974600 83.7824480 G:min/5 +83.7824480 84.4558270 G:min +84.4558270 85.1408160 G:min/b7 +85.1408160 85.7909750 C/3 +85.7909750 86.4643530 C:7/3 +86.4643530 89.1462580 F +89.1462580 90.5394550 Bb +90.5394550 91.8281630 Ab:maj9 +91.8281630 93.2213600 Bb:7 +93.2213600 94.4636280 Ab:maj7 +94.4636280 95.8336050 Bb:7 +95.8336050 97.1107020 Ab:maj7 +97.1107020 99.8506570 Bb:7 +99.8506570 102.5557820 D:min +102.5557820 105.3305660 G:min7 +105.3305660 110.8336960 F:maj6 +110.8336960 112.2152830 G:min/4 +112.2152830 113.5736500 C +113.5736500 114.9552380 G:min/4 +114.9552380 115.6170060 C +115.6170060 116.2903850 A +116.2903850 119.0651700 D:min +119.0651700 121.7935140 G:min7 +121.7935140 123.1634920 Eb +123.1634920 125.8570060 Eb +125.8570060 126.5536050 G:min/5 +126.5536050 127.2269840 G:min +127.2269840 127.8887520 G:min/b7 +127.8887520 128.5273010 C/3 +128.5273010 129.2006800 C:7/3 +129.2006800 131.9522440 F +131.9522440 133.2990020 Bb +133.2990020 134.6341490 Ab:maj9 +134.6341490 135.9344670 Bb:7 +135.9344670 137.2463940 Ab:maj7 +137.2463940 138.5699310 Bb:7 +138.5699310 139.8238090 Ab:maj7 +139.8238090 142.5405440 Bb:7 +142.5405440 147.8578537 Eb +147.8578537 148.7220786 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD1final/CD1_-_10_-_I'm_So_Tired.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,52 @@ +0.0000000 0.1723285 N +0.1723285 1.5157590 E +1.5157590 3.3153060 A +3.3153060 5.0684120 G# +5.0684120 6.8911790 D +6.8911790 8.7139450 E +8.7139450 10.4786620 A +10.4786620 12.2782080 F#:min +12.2782080 14.0661450 D +14.0661450 15.8192510 E +15.8192510 17.5143080 A +17.5143080 19.2790240 E:aug +19.2790240 21.0089110 F#:min +21.0089110 24.5383440 D:min +24.5383440 26.2566210 A +26.2566210 28.0909970 G# +28.0909970 29.7976640 D +29.7976640 31.6320400 E +31.6320400 33.3735370 A +33.3735370 35.1846930 F#:min +35.1846930 36.8681400 D +36.8681400 38.6444670 E +38.6444670 40.3859630 A +40.3859630 42.0229700 E:aug +42.0229700 43.6715870 F#:min +43.6715870 47.0152600 D:min +47.0152600 53.7142170 A +53.7142170 60.2390240 E +60.2390240 63.4433780 D +63.4433780 67.0773010 A +67.0773010 68.8187980 A +68.8187980 70.5254640 G# +70.5254640 72.2205210 D +72.2205210 73.9271880 E +73.9271880 75.6222440 A +75.6222440 77.3173010 F#:min +77.3173010 79.0239680 D +79.0239680 80.7770740 E +80.7770740 82.4605210 A +82.4605210 84.1555780 E:aug +84.1555780 85.7809750 F#:min +85.7809750 87.4471403 D:min7 +87.4471403 89.1710880 D:min +89.1710880 95.6958950 A +95.6958950 102.1742630 E +102.1742630 105.4250560 D +105.4250560 108.6294100 A +108.6294100 111.8221540 D +111.8221540 115.0032870 A +115.0032870 118.1379810 D +118.1379810 122.8127782 A +122.8127782 123.2723209 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD1final/CD1_-_11_-_Black_Bird.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,130 @@ +0.0000000 0.0778704 N +0.0778704 0.7627210 G +0.7627210 1.3664390 A:min7 +1.3664390 1.9817680 G/3 +1.9817680 4.6056230 G +4.6056230 5.2557820 G +5.2557820 5.8943310 A:min7 +5.8943310 6.5096590 G/3 +6.5096590 9.1102940 G +9.1102940 9.7488430 C +9.7488430 10.4106120 C#:dim +10.4106120 11.0375510 D +11.0375510 11.6877090 D#:dim +11.6877090 12.9764170 E:min +12.9764170 14.3115640 Eb:aug +14.3115640 14.9385030 D +14.9385030 15.5654420 C#:dim +15.5654420 16.8889790 C +16.8889790 18.1892970 C:min +18.1892970 19.4663940 G/3 +19.4663940 20.7667120 A:7 +20.7667120 22.0205890 D:sus4(b7) +22.0205890 23.2744670 G +23.2744670 23.9014050 C +23.9014050 24.5980040 G/3 +24.5980040 25.8518820 A:7 +25.8518820 27.1405890 D:sus4(b7) +27.1405890 28.4292970 G +28.4292970 29.0678450 G +29.0678450 29.7180040 A:min7 +29.7180040 30.3449430 G/3 +30.3449430 32.9339680 G +32.9339680 33.5841260 C +33.5841260 34.2110650 C#:dim +34.2110650 34.8728340 D +34.8728340 35.4997730 D#:dim +35.4997730 36.7536500 E:min +36.7536500 38.0771880 Eb:aug +38.0771880 38.7273460 D +38.7273460 39.3658950 C#:dim +39.3658950 40.6313830 C +40.6313830 41.9317000 C:min +41.9317000 43.1971880 G/3 +43.1971880 44.4394550 A:7 +44.4394550 45.6701130 D:sus4(b7) +45.6701130 46.9588200 G +46.9588200 47.6205890 F +47.6205890 48.2591380 C/3 +48.2591380 48.8860770 D:min +48.8860770 49.5014050 C +49.5014050 50.7668930 Bb +50.7668930 52.0556000 C +52.0556000 52.6941490 F +52.6941490 53.3443080 C/3 +53.3443080 53.9828570 D:min +53.9828570 54.5749650 C +54.5749650 55.8985030 Bb +55.8985030 57.1407700 A:7 +57.1407700 58.3830380 D:sus4(b7) +58.3830380 58.9867570 G +58.9867570 59.5904760 A:min7 +59.5904760 60.1941950 G/3 +60.1941950 62.8064390 G +62.8064390 63.4798180 C +63.4798180 64.0835370 C#:dim +64.0835370 64.7336960 D +64.7336960 65.3606340 D#:dim +65.3606340 66.6377320 E:min +66.6377320 67.9496590 Eb:aug +67.9496590 68.5765980 D +68.5765980 69.2383670 C#:dim +69.2383670 70.5154640 C +70.5154640 71.7925620 C:min +71.7925620 73.0696590 G/3 +73.0696590 74.3119270 A:7 +74.3119270 75.5774140 D:sus4(b7) +75.5774140 76.9009520 G +76.9009520 77.5278910 F +77.5278910 78.1548290 C/3 +78.1548290 78.7933780 D:min +78.7933780 79.3970970 C +79.3970970 80.6858040 Bb +80.6858040 81.9629020 C +81.9629020 82.6478910 F +82.6478910 83.2632190 C/3 +83.2632190 83.9133780 D:min +83.9133780 84.5170970 C +84.5170970 85.7825850 Bb +85.7825850 87.0480720 A:7 +87.0480720 88.2787300 D:sus4(b7) +88.2787300 88.9056680 G +88.9056680 89.5326070 A:min7 +89.5326070 90.1131060 G/3 +90.1131060 101.7463030 G +101.7463030 102.3500220 G +102.3500220 102.9653510 A:min7 +102.9653510 103.6503400 G/3 +103.6503400 104.3121080 C +104.3121080 104.9970970 G/3 +104.9970970 105.6704760 G:dim/b3 +105.6704760 106.9359630 D:sus4(b7) +106.9359630 107.5629020 G +107.5629020 108.2014510 A:min7 +108.2014510 108.8283900 G/3 +108.8283900 111.4174140 G +111.4174140 112.0443530 C +112.0443530 112.6596820 C#:dim +112.6596820 113.3214510 D +113.3214510 113.9483900 D#:dim +113.9483900 115.1790470 E:min +115.1790470 116.4793650 Eb:aug +116.4793650 117.1295230 D +117.1295230 117.7100220 C#:dim +117.7100220 118.9871200 C +118.9871200 120.3106570 C:min +120.3106570 121.5297050 G/3 +121.5297050 122.8416320 A:7 +122.8416320 124.0722900 D:sus4(b7) +124.0722900 125.3377770 G +125.3377770 125.9298860 C +125.9298860 126.5568250 G/3 +126.5568250 127.8223120 A:7 +127.8223120 129.1226300 D:sus4(b7) +129.1226300 130.3184580 G +130.3184580 130.9570060 C +130.9570060 131.5839450 G/3 +131.5839450 132.9190920 A:7 +132.9190920 134.3239000 D:sus4(b7) +134.3239000 137.8208733 G +137.8208733 138.2825178 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD1final/CD1_-_12_-_Piggies.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,93 @@ +0.0000000 0.0168490 N +0.0168490 1.4227890 Ab +1.4227890 2.8508160 Eb +2.8508160 4.1163030 Ab +4.1163030 5.4746710 Eb +5.4746710 6.8098180 Ab +6.8098180 8.1565750 Eb +8.1565750 9.4917230 Ab +9.4917230 10.8152600 Eb +10.8152600 12.1387980 Ab +12.1387980 13.4507250 Eb +13.4507250 14.8090920 F:min +14.8090920 16.1790700 Bb +16.1790700 17.5374370 F:min +17.5374370 18.8493650 Bb +18.8493650 19.5459630 Eb +19.5459630 20.2193420 F:dim +20.2193420 20.9043310 F#:dim +20.9043310 21.6009290 G:dim +21.6009290 22.9476870 Ab +22.9476870 24.3292740 Eb +24.3292740 25.5947610 Ab +25.5947610 26.9299090 Eb +26.9299090 28.2766660 Ab +28.2766660 29.5653740 Eb +29.5653740 30.9237410 Ab +30.9237410 32.2472780 Eb +32.2472780 33.5708160 Ab +33.5708160 34.8595230 Eb +34.8595230 36.2295010 F:min +36.2295010 37.5530380 Bb +37.5530380 38.8649650 F:min +38.8649650 40.1768930 Bb +40.1768930 40.8270520 Eb +40.8270520 41.4656000 F:dim +41.4656000 42.1041490 F#:dim +42.1041490 42.8007480 G:dim +42.8007480 44.1126750 Ab +44.1126750 45.4594330 Eb +45.4594330 46.6900900 Ab +46.6900900 48.0020180 C +48.0020180 49.3255550 Bb:min +49.3255550 50.6142630 C +50.6142630 51.9261900 Db +51.9261900 53.1568480 Ab +53.1568480 58.3000680 Eb +58.3000680 59.5655550 Bb:min +59.5655550 60.8658730 C +60.8658730 62.1429700 Db +62.1429700 65.9742630 Eb +65.9742630 67.2281400 Ab +67.2281400 68.4820180 Eb +68.4820180 69.7475050 Ab +69.7475050 71.0710430 Eb +71.0710430 72.3597500 Ab +72.3597500 73.6484580 Eb +73.6484580 74.9255550 F:min +74.9255550 76.2607020 Bb +76.2607020 77.5145800 F:min +77.5145800 78.8032870 Bb +78.8032870 79.4302260 Eb +79.4302260 80.0687750 F:dim +80.0687750 80.6957140 F#:dim +80.6957140 81.3807020 G:dim +81.3807020 82.6345800 Ab +82.6345800 83.9232870 Eb +83.9232870 85.1887750 Ab +85.1887750 86.5007020 Eb +86.5007020 87.7778000 Ab +87.7778000 89.0316780 Eb +89.0316780 90.3203850 Ab +90.3203850 91.6207020 Eb +91.6207020 92.8745800 Ab +92.8745800 94.1632870 Eb +94.1632870 95.4287750 F:min +95.4287750 96.7407020 Bb +96.7407020 98.0061900 F:min +98.0061900 99.3065070 Bb +99.3065070 99.9682760 Eb +99.9682760 100.5719950 F:dim +100.5719950 101.2685940 F#:dim +101.2685940 101.9071420 G:dim +101.9071420 103.1958500 Ab +103.1958500 104.4845570 Eb +104.4845570 105.7848750 Ab:min +105.7848750 107.1316320 Eb +107.1316320 108.3855100 Ab:min +108.3855100 109.6393870 Eb +109.6393870 110.9397050 Bb +110.9397050 114.3180365 Eb +114.3180365 117.8279727 A +117.8279727 121.0829974 E +121.0829974 124.2399792 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD1final/CD1_-_13_-_Rocky_Raccoon.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,95 @@ +0.0000000 6.3971200 A:min7 +6.3971200 9.4157140 A:min7 +9.4157140 10.8553510 D:sus4 +10.8553510 12.3182080 D +12.3182080 15.3832420 G:7 +15.3832420 16.8460990 C +16.8460990 18.3553960 C/7 +18.3553960 21.4204300 A:min7 +21.4204300 22.9529470 D:sus4 +22.9529470 24.4390240 D +24.4390240 27.4576190 G:7 +27.4576190 28.9901360 C +28.9901360 30.4878230 C/7 +30.4878230 33.5876870 A:min7 +33.5876870 35.0853740 D:sus4 +35.0853740 36.5482310 D +36.5482310 39.6248750 G:7 +39.6248750 41.1690020 C +41.1690020 42.6782990 C/7 +42.6782990 45.7201130 A:min7 +45.7201130 47.2990700 D:sus4 +47.2990700 48.7619270 D +48.7619270 51.8037410 G:7 +51.8037410 53.3826980 C +53.3826980 54.8687750 C/7 +54.8687750 57.8873690 A:min7 +57.8873690 59.4314960 D:sus4 +59.4314960 60.9524030 D +60.9524030 63.9709970 G:7 +63.9709970 65.4802940 C +65.4802940 67.0244210 C/7 +67.0244210 70.0546250 A:min7 +70.0546250 71.5523120 D:sus4 +71.5523120 73.0964390 D +73.0964390 76.1730830 G:7 +76.1730830 77.7172100 C +77.7172100 79.2497270 C/7 +79.2497270 82.3844210 A:min7 +82.3844210 83.8821080 D:sus4 +83.8821080 85.4146250 D +85.4146250 88.4912690 G:7 +88.4912690 90.0237860 C +90.0237860 91.5563030 C/7 +91.5563030 94.5748970 A:min7 +94.5748970 96.0958040 D:sus4 +96.0958040 97.6631510 D +97.6631510 100.5888660 G:7 +100.5888660 102.1329930 C +102.1329930 103.6771200 C/7 +103.6771200 106.6841040 A:min7 +106.6841040 108.1701810 D:sus4 +108.1701810 109.7259180 D +109.7259180 112.7445120 G:7 +112.7445120 114.2421990 C +114.2421990 115.7282760 C/7 +115.7282760 118.5843310 A:min7 +118.5843310 121.5448750 D:7 +121.5448750 124.3660990 G:7 +124.3660990 127.2802040 C +127.2802040 130.1362580 A:min7 +130.1362580 132.9923120 D:7 +132.9923120 135.8019270 G:7 +135.8019270 138.6812010 C +138.6812010 141.6997950 A:min7 +141.6997950 144.6951700 D +144.6951700 147.7253740 G:7 +147.7253740 149.2230610 C +149.2230610 150.6975280 C/7 +150.6975280 153.6812920 A:min7 +153.6812920 155.1789790 D:sus4 +155.1789790 156.6070060 D +156.6070060 159.4746710 G:7 +159.4746710 160.9607480 C +160.9607480 162.4352150 C/7 +162.4352150 165.4073690 A:min7 +165.4073690 166.9166660 D:sus4 +166.9166660 168.3911330 D +168.3911330 171.3865070 G:7 +171.3865070 172.8958040 C +172.8958040 174.3702720 C/7 +174.3702720 177.2843760 A:min7 +177.2843760 178.7704530 D:sus4 +178.7704530 180.2100900 D +180.2100900 183.2286840 G:7 +183.2286840 184.7379810 C +184.7379810 186.1195690 C/7 +186.1195690 188.9872330 A:min7 +188.9872330 191.8084580 D:7 +191.8084580 194.6412920 G +194.6412920 196.0577090 C +196.0577090 197.4973460 C/7 +197.4973460 200.2953510 A:min7 +200.2953510 203.1281850 D:7 +203.1281850 205.9378000 G:7 +205.9378000 212.7782599 C
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD1final/CD1_-_14_-_Don't_Pass_Me_By.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,42 @@ +0.0000000 9.8610198 N +9.8610198 11.2480490 C +11.2480490 22.5213370 C +22.5213370 28.1405660 F +28.1405660 33.7249650 G +33.7249650 37.8929470 F +37.8929470 43.6282760 C +43.6282760 49.1314050 C +49.1314050 54.7158040 F +54.7158040 60.2769840 G +60.2769840 64.5146250 F +64.5146250 70.2035140 C +70.2035140 75.8807930 C +75.8807930 81.5580720 F +81.5580720 87.2469610 C +87.2469610 92.9706800 G +92.9706800 98.6015190 F +98.6015190 104.3020180 C +104.3020180 109.9328570 C +109.9328570 115.5056460 F +115.5056460 121.1016550 G +121.1016550 125.3509070 F +125.3509070 130.9701360 C +130.9701360 136.6125850 C +136.6125850 142.2434240 F +142.2434240 147.9090920 C +147.9090920 153.5283210 G +153.5283210 159.1359410 F +159.1359410 164.8016090 C +164.8016090 170.3511790 C/5 +170.3511790 175.9123580 C +175.9123580 181.6360770 C +181.6360770 187.4062350 F +187.4062350 194.5695910 C +194.5695910 200.2584800 G +200.2584800 205.9473690 F +205.9473690 211.7059180 C +211.7059180 214.5851920 F +214.5851920 217.4876870 G +217.4876870 218.2307250 C:sus4 +218.2307250 218.9853740 C +218.9853740 230.3649103 C
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD1final/CD1_-_15_-_Why_Don't_We_Do_It_In_The_Road.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,21 @@ +0.0000000 10.2056768 N +10.2056768 18.0035370 D +18.0035370 20.6157820 D:7 +20.6157820 25.7009520 G +25.7009520 30.7977320 D:7 +30.7977320 33.3403170 A +33.3403170 35.9409520 G +35.9409520 40.9796820 D:7 +40.9796820 51.3473920 D:7 +51.3473920 56.3745120 G +56.3745120 61.4248520 D:7 +61.4248520 64.0138770 A +64.0138770 66.5216320 G +66.5216320 71.6068020 D:7 +71.6068020 81.7539220 D:7 +81.7539220 86.9319720 G +86.9319720 91.9823120 D:7 +91.9823120 94.5248970 A +94.5248970 97.0790920 G +97.0790920 99.2803641 D:7 +99.2803641 101.3866013 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD1final/CD1_-_16_-_I_Will.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,83 @@ +0.0000000 0.4978379 N +0.4978379 0.7850521 E:(1) +0.7850521 2.2506570 F +2.2506570 3.4464850 D:min +3.4464850 4.6190920 G:min7 +4.6190920 5.8033100 C +5.8033100 6.9526980 F +6.9526980 8.1485260 D:min +8.1485260 9.2979130 A:min +9.2979130 10.4705210 F:7 +10.4705210 11.6315190 Bb +11.6315190 12.7809070 C +12.7809070 13.9767340 D:min +13.9767340 15.1725620 F +15.1725620 16.3451700 Bb +16.3451700 17.4597270 C +17.4597270 18.6787750 F +18.6787750 19.8629930 D:min +19.8629930 21.0239900 G:min7 +21.0239900 22.1733780 C +22.1733780 23.3575960 F +23.3575960 24.4953740 D:min +24.4953740 25.6795910 G:min7 +25.6795910 26.8638090 C +26.8638090 27.9667570 F +27.9667570 29.1625850 D:min +29.1625850 30.3468020 A:min +30.3468020 31.4497500 F:7 +31.4497500 32.6455780 Bb +32.6455780 33.7485260 C +33.7485260 34.9211330 D:min +34.9211330 36.1285710 F +36.1285710 37.2663490 Bb +37.2663490 38.4389560 C +38.4389560 39.6115640 F +39.6115640 40.7841720 F:7 +40.7841720 41.9451700 Bb +41.9451700 43.1409970 A:min +43.1409970 45.4746030 D:min +45.4746030 46.6239900 G:min +46.6239900 47.7617680 C +47.7617680 48.9459860 F +48.9459860 50.1069840 F:7 +50.1069840 51.2563710 Bb +51.2563710 52.4173690 A:min +52.4173690 54.7393650 D:min +54.7393650 57.0497500 G +57.0497500 59.3717460 C +59.3717460 60.4979130 F +60.4979130 61.6705210 D:min +61.6705210 62.8199090 G:min7 +62.8199090 63.9692970 C +63.9692970 65.1419040 F +65.1419040 66.2912920 D:min +66.2912920 67.4522900 A:min +67.4522900 68.6132870 F:7 +68.6132870 69.7394550 Bb +69.7394550 70.9236730 C +70.9236730 71.4925620 D:min +71.4925620 72.1078910 Bb:min/b3 +72.1078910 73.2688880 F/5 +73.2688880 74.4298860 Bb +74.4298860 75.5560540 C +75.5560540 76.1713830 D:min +76.1713830 76.7634920 Bb:min/b3 +76.7634920 77.9244890 F/5 +77.9244890 79.0622670 Bb +79.0622670 80.2116550 C +80.2116550 80.8153740 D:min +80.8153740 81.4423120 Bb:min/b3 +81.4423120 81.9995910 F/5 +81.9995910 82.5568700 B:min7 +82.5568700 83.7178680 G:min +83.7178680 84.8672560 C +84.8672560 89.5344670 Db +89.5344670 94.0623580 F +94.0623580 95.1885260 Bb +95.1885260 96.3611330 A:min +96.3611330 98.6715190 D:min +98.6715190 99.7976870 G:min +99.7976870 100.9819040 C +100.9819040 103.7151670 F +103.7151670 106.0320280 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD1final/CD1_-_17_-_Julia.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,86 @@ +0.0000000 0.4212474 N +0.4212474 2.2470970 D +2.2470970 4.0466430 B:min7 +4.0466430 7.5992970 F#:min +7.5992970 9.3291830 D +9.3291830 11.0474600 B:min7 +11.0474600 12.8702260 F#:min +12.8702260 14.6001130 A +14.6001130 16.4693190 D +16.4693190 18.1875960 B:min7 +18.1875960 20.0335820 A:min7 +20.0335820 21.7402490 A:min9 +21.7402490 25.3625620 B +25.3625620 27.1513127 G:9 +27.1513127 28.9384350 G:min7 +28.9384350 30.7147610 D +30.7147610 32.4214280 B:min7 +32.4214280 34.2093650 F#:min +34.2093650 35.9973010 A +35.9973010 37.8084580 D +37.8084580 39.6080040 B:min7 +39.6080040 41.4191600 A:min7 +41.4191600 43.2070970 A:min9 +43.2070970 46.7945800 B +46.7945800 48.5709070 G:9 +48.5709070 50.3936730 G:min7 +50.3936730 52.1583900 D +52.1583900 53.8882760 B:min7 +53.8882760 55.6413830 F#:min +55.6413830 57.4641490 A +57.4641490 59.2753060 D +59.2753060 61.0051920 D +61.0051920 64.5346250 C#:min +64.5346250 68.1453280 D +68.1453280 71.7328110 B:min7 +71.7328110 73.4885318 F#:min7 +73.4885318 75.2738540 F#:min6 +75.2738540 77.0269610 D/3 +77.0269610 78.7568480 F#:min +78.7568480 80.5796140 D +80.5796140 82.4023800 B:min7 +82.4023800 84.1154560 A:min7 +84.1154560 85.9898630 A:min9 +85.9898630 89.5773460 B +89.5773460 91.3768930 G:7 +91.3768930 93.0951700 G:min7 +93.0951700 94.9063260 D +94.9063260 96.6246030 B:min7 +96.6246030 98.4009290 F#:min +98.4009290 100.2585260 A +100.2585260 102.0348520 D +102.0348520 103.8111790 D +103.8111790 105.5178450 D +105.5178450 107.3057820 B:min7 +107.3057820 110.8932650 F#:min +110.8932650 112.6115410 D +112.6115410 114.4459180 B:min7 +114.4459180 116.3151240 F#:min +116.3151240 118.0566210 A +118.0566210 119.8213370 D +119.8213370 121.6092740 B:min7 +121.6092740 123.4088200 A:min7 +123.4088200 125.2199770 A:min9 +125.2199770 128.7145800 B +128.7145800 130.5721760 G:7 +130.5721760 132.3485030 G:min7 +132.3485030 134.1248290 D +134.1248290 135.9288919 B:min7 +135.9288919 137.7471420 F#:min +137.7471420 139.5234690 A +139.5234690 141.3230150 D +141.3230150 143.1457820 B:min7 +143.1457820 144.9453280 A:min7 +144.9453280 146.6984350 A:min9 +146.6984350 150.2975280 B +150.2975280 152.0854640 G:9 +152.0854640 153.8850110 G:min7 +153.8850110 155.6497270 D +155.6497270 157.4260540 B:min7 +157.4260540 159.2023800 F#:min +159.2023800 160.9554870 D +160.9554870 162.7318140 F#:min +162.7318140 164.4849200 D +164.4849200 166.1821176 F#:min +166.1821176 167.9446930 A +167.9446930 173.4773574 D:maj7
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD1final/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,18 @@ +/CD1_-_01_-_Back_in_the_USSR.lab/1.2/Tue Oct 17 21:57:01 2006// +/CD1_-_02_-_Dear_Prudence.lab/1.2/Tue Oct 17 21:57:01 2006// +/CD1_-_03_-_Glass_Onion.lab/1.2/Tue Oct 17 21:57:01 2006// +/CD1_-_04_-_Ob-La-Di,_Ob-La-Da.lab/1.2/Wed Aug 2 00:42:50 2006// +/CD1_-_05_-_Wild_Honey_Pie.lab/1.1/Mon Jun 26 18:07:15 2006// +/CD1_-_06_-The_Continuing_Story_of_Bungalow_Bill.lab/1.2/Wed Aug 2 00:42:50 2006// +/CD1_-_07_-_While_My_Guitar_Gently_Weeps.lab/1.2/Wed Aug 2 00:42:50 2006// +/CD1_-_08_-_Happiness_is_a_Warm_Gun.lab/1.1/Mon Jun 26 18:07:15 2006// +/CD1_-_09_-_Martha_My_Dear.lab/1.2/Wed Aug 2 00:42:50 2006// +/CD1_-_10_-_I'm_So_Tired.lab/1.1/Mon Jun 26 18:07:15 2006// +/CD1_-_11_-_Black_Bird.lab/1.1/Mon Jun 26 18:07:15 2006// +/CD1_-_12_-_Piggies.lab/1.1/Mon Jun 26 18:07:15 2006// +/CD1_-_13_-_Rocky_Raccoon.lab/1.1/Mon Jun 26 18:07:15 2006// +/CD1_-_14_-_Don't_Pass_Me_By.lab/1.1/Mon Jun 26 18:07:15 2006// +/CD1_-_15_-_Why_Don't_We_Do_It_In_The_Road.lab/1.1/Mon Jun 26 18:07:15 2006// +/CD1_-_16_-_I_Will.lab/1.2/Wed Aug 2 00:42:50 2006// +/CD1_-_17_-_Julia.lab/1.1/Mon Jun 26 18:07:15 2006// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD1final/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +chrish/transcriptions/10CD1final
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD1final/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:matthiasm@dspmac1.elec.qmul.ac.uk:/cvsroot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD2final/CD2_-_01_-_Birthday.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,55 @@ +0.0000000 1.8343990 N +1.8343990 8.5681850 A:7 +8.5681850 11.9931290 D:7 +11.9931290 15.4412920 A:7 +15.4412920 18.8894550 E:7 +18.8894550 22.3260090 A:7 +22.3260090 29.1758950 A:7 +29.1758950 32.6240580 D:7 +32.6240580 36.0490020 A:7 +36.0490020 39.5319950 E:7 +39.5319950 43.3693394 A:7 +43.3693394 56.6683210 N +56.6683210 70.5190240 E +70.5190240 72.2721310 C +72.2721310 74.0252380 G:7 +74.0252380 75.7202940 C +75.7202940 77.5198410 G:7 +77.5198410 79.2497270 C +79.2497270 81.0144440 G:7 +81.0144440 82.7327210 C +82.7327210 83.1772230 G:7 +83.1772230 83.6266890 G:7/b2 +83.6266890 84.0580132 G:7/2 +84.0580132 84.4858270 G:7/b3 +84.4858270 86.2969840 E/5 +86.2969840 87.9572100 E +87.9572100 94.9115870 A:7 +94.9115870 98.3945800 D:7 +98.3945800 101.8659630 A:7 +101.8659630 105.3025170 E:7 +105.3025170 108.7274600 A:7 +108.7274600 111.3280950 A +111.3280950 112.2568930 G:(1) +112.2568930 114.8110880 A +114.8110880 115.6586160 G:(1) +115.6586160 117.3536730 C +117.3536730 119.0487300 G:7 +119.0487300 120.8018360 C +120.8018360 122.5549430 G:7 +122.5549430 124.2732190 C +124.2732190 126.0147160 G:7 +126.0147160 127.7678230 C +127.7678230 128.1932570 C/5 +128.1932570 128.6145044 G:7/b2 +128.6145044 129.0166042 G:7/2 +129.0166042 129.4628790 G:7/b3 +129.4628790 131.1695460 E/5 +131.1695460 132.8529930 E +132.8529930 139.8189790 A:7 +139.8189790 143.3368020 D:7 +143.3368020 146.7617460 A:7 +146.7617460 150.1750790 E:7 +150.1750790 155.0956507 A:7 +155.0956507 162.6781047 A +162.6781047 162.8887284 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD2final/CD2_-_02_-_Yer_Blues.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,84 @@ +0.0000000 3.4365750 N +3.4365750 12.6897270 E +12.6897270 17.2756680 A +17.2756680 22.0357590 E +22.0357590 24.3577550 G +24.3577550 27.6782080 B +27.6782080 28.3193170 E +28.3193170 28.8972560 E/3 +28.8972560 30.0350340 A +30.0350340 31.1728110 E/5 +31.1728110 32.3221990 B +32.3221990 41.7611110 E +41.7611110 46.4283210 A +46.4283210 51.2000220 E +51.2000220 53.5800680 G +53.5800680 56.9237410 B +56.9237410 57.6900000 E +57.6900000 58.1311790 E/3 +58.1311790 59.3270060 A +59.3270060 60.5112240 E/5 +60.5112240 61.6838320 B +61.6838320 63.4743311 E +63.4743311 64.1103170 B:7 +64.1103170 65.9563030 E +65.9563030 66.5251920 B:7 +66.5251920 70.9227519 E +70.9227519 75.7551240 A +75.7551240 80.5384350 E +80.5384350 82.9068700 G +82.9068700 86.1924940 B +86.1924940 87.0051920 E +87.0051920 87.4579810 E/3 +87.4579810 88.6189790 A +88.6189790 89.7915870 E/5 +89.7915870 90.9177550 B +90.9177550 92.8084715 E +92.8084715 93.3326300 B:7 +93.3326300 95.1902260 E +95.1902260 95.7591150 B:7 +95.7591150 100.5308160 E +100.5308160 105.1051470 A +105.1051470 109.8071880 E +109.8071880 112.1640130 G +112.1640130 115.3915870 B +115.3915870 116.1578450 E +116.1578450 116.5758040 E/3 +116.5758040 117.7600220 A +117.7600220 118.8978000 E/5 +118.8978000 120.1052380 B +120.1052380 121.9976640 E +121.9976640 122.5665530 B:7 +122.5665530 124.3893190 E +124.3893190 125.0046480 B:7 +125.0046480 129.7763490 E +129.7763490 134.2694100 A +134.2694100 138.8553510 E +138.8553510 141.1309070 G +141.1309070 143.4296820 B +143.4296820 145.6936280 E +145.6936280 147.9459630 B +147.9459630 156.7347160 E +156.7347160 161.1581170 A +161.1581170 165.5582990 E +165.5582990 167.7641950 G +167.7641950 170.0165300 B +170.0165300 172.2572560 E +172.2572560 174.4399310 B +174.4399310 183.3563940 E +183.3563940 187.7449650 A +187.7449650 192.2264170 E +192.2264170 194.4671420 G +194.4671420 196.7775280 B +196.7775280 207.3774370 E +207.3774370 212.0794780 A +212.0794780 216.8047390 E +216.8047390 219.1267340 G +219.1267340 222.3995040 B +222.3995040 223.1079657 E +223.1079657 223.5385260 E/3 +223.5385260 224.7227430 A +224.7227430 225.8953510 E/5 +225.8953510 226.9866890 B +226.9866890 238.8090068 E +238.8090068 241.0910514 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD2final/CD2_-_03_-_Mother_Nature's_Son.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,121 @@ +0.0000000 0.3638046 N +0.3638046 2.1636801 B:min +2.1636801 4.0018507 D/5 +4.0018507 5.9357595 G#:hdim7 +5.9357595 8.4249489 E:9 +8.4249489 9.8284120 D +9.8284120 11.2796590 D:sus4 +11.2796590 12.7076870 D:maj(9) +12.7076870 14.1124940 D +14.1124940 19.7085030 D +19.7085030 21.1597500 D +21.1597500 24.0622440 G/5 +24.0622440 25.5018820 D +25.5018820 26.8950790 B:min +26.8950790 28.3811560 D/5 +28.3811560 29.7859630 G#:hdim7 +29.7859630 31.2372100 E:9 +31.2372100 31.8989790 A +31.8989790 32.6304080 D/5 +32.6304080 33.3386160 A +33.3386160 34.0352150 D/5 +34.0352150 34.7434240 A +34.7434240 35.4632420 D/5 +35.4632420 36.2062810 A +36.2062810 36.9144890 D/5 +36.9144890 38.3309070 D +38.3309070 39.7589340 F/6 +39.7589340 41.1173010 G/5 +41.1173010 42.5801580 D +42.5801580 43.9385260 D +43.9385260 45.3781630 F/6 +45.3781630 46.7481400 G/5 +46.7481400 48.1413370 D +48.1413370 49.6158040 D +49.6158040 52.4370290 G/5 +52.4370290 53.8186160 D +53.8186160 55.2466430 B:min +55.2466430 56.6398410 D/5 +56.6398410 58.0562580 G#:hdim7 +58.0562580 59.4726750 E:9 +59.4726750 60.1808840 A +60.1808840 60.9007020 D/5 +60.9007020 61.6089110 A +61.6089110 62.3171200 D/5 +62.3171200 63.0717680 A +63.0717680 63.7335370 D/5 +63.7335370 64.3953060 A +64.3953060 65.1731740 D/5 +65.1731740 66.6128110 D +66.6128110 68.0060090 F/6 +68.0060090 69.3411560 G/5 +69.3411560 70.8040130 D +70.8040130 72.3249200 D +72.3249200 73.6484580 D:sus4(2) +73.6484580 75.0300450 G/5 +75.0300450 76.4696820 D +76.4696820 77.8860990 D +77.8860990 79.2792970 D:sus4(2) +79.2792970 80.6957140 G/5 +80.6957140 82.1005210 D +82.1005210 83.5053280 D:maj7 +83.5053280 86.2917230 D:7 +86.2917230 87.6849200 G/5 +87.6849200 89.0432870 G:min/5 +89.0432870 91.9341720 D +91.9341720 93.3970290 D +93.3970290 96.2182530 G/5 +96.2182530 97.7043310 D +97.7043310 99.1439680 B:min +99.1439680 100.5836050 D/5 +100.5836050 101.9419720 G#:hdim7 +101.9419720 103.3816090 E:9 +103.3816090 104.1246480 A +104.1246480 104.7980270 D/5 +104.7980270 105.5178450 A +105.5178450 106.1796140 D/5 +106.1796140 106.8994330 A +106.8994330 107.5728110 D/5 +107.5728110 108.3158500 A +108.3158500 108.9776190 D/5 +108.9776190 110.4056460 D +110.4056460 111.7872330 F/6 +111.7872330 113.1804300 G/5 +113.1804300 114.6316780 D +114.6316780 116.0364850 D +116.0364850 117.4645120 D:sus4(2) +117.4645120 118.8809290 G/5 +118.8809290 120.2857360 D +120.2857360 121.7021540 D +121.7021540 123.0605210 D:sus4(2) +123.0605210 124.5233780 G/5 +124.5233780 125.9281850 D +125.9281850 127.3213830 D:maj7 +127.3213830 130.1426070 D:7 +130.1426070 131.5706340 G/5 +131.5706340 132.9638320 G:min/5 +132.9638320 135.7966660 D +135.7966660 137.2014730 D +137.2014730 140.0459180 G/5 +140.0459180 141.5087750 D +141.5087750 142.9251920 B:min +142.9251920 144.2835600 D/5 +144.2835600 145.6999770 G#:hdim7 +145.6999770 147.1047840 E:9 +147.1047840 147.8246030 A +147.8246030 148.5560310 D/5 +148.5560310 149.2642400 A +149.2642400 149.9492290 D/5 +149.9492290 150.6690470 A +150.6690470 151.3656460 D/5 +151.3656460 152.1086840 A +152.1086840 152.7820630 D/5 +152.7820630 154.2217000 D +154.2217000 155.5800680 F/6 +155.5800680 156.9732650 G/5 +156.9732650 158.3896820 D +158.3896820 159.8060990 D +159.8060990 161.2457360 F/6 +161.2457360 162.6273240 G/5 +162.6273240 166.7182507 D:7 +166.7182507 167.9819930 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD2final/CD2_-_04_-_Everybody's_Got_Something_To_Hide_Except_Me_and_M.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,117 @@ +0.0000000 0.6627240 N +0.6627240 1.0645800 E +1.0645800 1.5754190 A/3 +1.5754190 2.1210880 A:maj(*5) +2.1210880 2.5970970 N +2.5970970 3.1079360 E +3.1079360 3.6419950 A/3 +3.6419950 4.1063940 A:maj(*5) +4.1063940 4.5940130 N +4.5940130 5.1396820 E +5.1396820 5.6389110 A/3 +5.6389110 6.1033100 A:maj(*5) +6.1033100 6.6025390 N +6.6025390 7.1249880 E +7.1249880 7.6358270 A/3 +7.6358270 8.0886160 A:maj(*5) +8.0886160 8.4136960 N +8.4136960 9.1567340 E +9.1567340 10.3293420 A +10.3293420 11.0491600 E +11.0491600 12.3030380 A +12.3030380 13.0576870 E +13.0576870 14.3347840 A +14.3347840 15.1010430 E +15.1010430 16.3317000 A +16.3317000 17.0747390 E +17.0747390 18.2937860 A +18.2937860 19.0600450 E +19.0600450 20.3023120 A +20.3023120 24.3890240 A +24.3890240 28.5105660 D +28.5105660 32.7249880 B +32.7249880 33.4796370 E +33.4796370 34.2923350 D +34.2923350 35.7102949 G +35.7102949 36.3705210 N +36.3705210 37.1716090 E +37.1716090 37.8914280 G +37.8914280 40.0741040 D +40.0741040 41.3362190 E +41.3362190 42.0826300 A +42.0826300 43.3132870 E +43.3132870 44.1027660 A +44.1027660 45.3798630 E +45.3798630 46.1345120 A +46.1345120 47.4000000 E +47.4000000 48.1546480 A +48.1546480 48.9092970 E +48.9092970 50.1515640 A +50.1515640 50.9642630 E +50.9642630 52.1717000 A +52.1717000 53.0772780 E +53.0772780 54.1686160 A +54.1686160 54.9116550 E +54.9116550 56.1655320 A +56.1655320 56.9666210 E +56.9666210 58.2204980 A +58.2204980 58.9983670 E +58.9983670 60.2174140 A +60.2174140 64.3273460 A +64.3273460 68.4372780 D +68.4372780 72.6168700 B +72.6168700 73.3947390 E +73.3947390 74.1726070 D +74.1726070 75.8096660 G:maj6 +75.8096660 76.2507930 N +76.2507930 76.9938320 E +76.9938320 77.7368700 G +77.7368700 79.4686300 D:maj6 +79.4686300 79.8847160 N +79.8847160 81.1153740 E +81.1153740 81.9396820 A +81.9396820 83.1819500 E +83.1819500 83.9482080 A +83.9482080 85.1672560 E +85.1672560 85.9567340 A +85.9567340 87.1525620 E +87.1525620 87.9420400 A +87.9420400 88.6850790 E +88.6850790 89.9853960 A +89.9853960 90.7400450 E +90.7400450 91.9823120 A +91.9823120 92.7601810 E +92.7601810 93.9560090 A +93.9560090 94.7570970 E +94.7570970 95.9993650 A +95.9993650 96.7888430 E +96.7888430 98.0195010 A +98.0195010 98.8205890 E +98.8205890 100.0512470 A +100.0512470 104.1727890 A +104.1727890 108.3175510 D +108.3175510 112.4623120 B +112.4623120 113.2517910 E +113.2517910 114.0296590 D +114.0296590 115.4218003 G:maj6 +115.4218003 116.1078450 N +116.1078450 116.9089340 E +116.9089340 117.6055320 G +117.6055320 119.7301580 D:maj6 +119.7301580 120.3998750 E:(1) +120.3998750 123.7175170 N +123.7175170 125.8718360 E +125.8718360 132.1992740 D +132.1992740 132.6737870 E +132.6737870 134.1845800 A +134.1845800 134.6721990 E +134.6721990 135.9028570 A +135.9028570 136.6342850 E +136.6342850 137.9113830 A +137.9113830 138.6544210 E +138.6544210 139.8850790 A +139.8850790 140.6629470 E +140.6629470 141.9168250 A +141.9168250 142.6714730 E +142.6714730 143.9100830 A +143.9100830 144.8384770 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD2final/CD2_-_05_-_Sexy_Sadie.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,117 @@ +0.000000 0.980790 D +0.980790 2.414897 C +2.414897 3.900975 D +3.900975 5.421882 G +5.421882 6.942789 F# +6.942789 8.533356 F +8.533356 9.996213 D +9.996213 11.540340 G +11.540340 13.107687 F# +13.107687 16.265600 B:min +16.265600 17.786507 C +17.786507 19.365464 D +19.365464 20.944421 G +20.944421 22.500158 F# +22.500158 24.090725 C +24.090725 25.646462 D +25.646462 27.248639 G +27.248639 28.839206 F# +28.839206 30.406553 F +30.406553 32.020340 D +32.020340 33.587687 G +33.587687 35.155034 F# +35.155034 36.757210 B:min +36.757210 38.370997 B:min7 +38.370997 39.961564 C +39.961564 41.552131 D +41.552131 43.142698 G +43.142698 44.779705 F# +44.779705 46.300612 C +46.300612 47.891179 D +47.891179 49.516575 G +49.516575 51.072312 F# +51.072312 52.674489 F +52.674489 54.230226 D +54.230226 55.820793 G +55.820793 57.422970 A:min +57.422970 58.990317 B:min +58.990317 60.557664 C +60.557664 62.136621 G +62.136621 63.680748 A:min +63.680748 65.248095 B:min +65.248095 66.838662 C +66.838662 68.406009 A +68.406009 69.926916 Ab +69.926916 71.471043 G +71.471043 73.050000 F# +73.050000 76.231133 B:min +76.231133 77.833310 C +77.833310 79.389047 D +79.389047 81.049274 G +81.049274 82.581791 F# +82.581791 84.183968 C +84.183968 85.751315 D +85.751315 87.365102 G +87.365102 88.944058 F# +88.944058 90.557845 F +90.557845 92.160022 D +92.160022 93.762199 G +93.762199 95.375986 F# +95.375986 98.568730 B:min +98.568730 100.159297 C +100.159297 101.703424 D +101.703424 103.375260 G +103.375260 104.942607 F# +104.942607 106.568004 C +106.568004 108.158571 D +108.158571 109.656258 G +109.656258 111.339705 F# +111.339705 112.930272 F +112.930272 114.567278 D +114.567278 116.192675 G +116.192675 117.771632 A:min +117.771632 119.385419 B:min +119.385419 121.010816 C +121.010816 122.601383 G +122.601383 124.191950 A:min +124.191950 125.736077 B:min +125.736077 127.361473 C +127.361473 128.870770 A +128.870770 130.484557 Ab +130.484557 132.040294 G +132.040294 133.700521 F# +133.700521 136.881655 B:min +136.881655 138.460612 C +138.460612 140.062789 D +140.062789 141.641746 G +141.641746 143.278752 F# +143.278752 144.846099 C +144.846099 146.494716 D +146.494716 148.096893 G +148.096893 149.710680 F# +149.710680 151.394126 F +151.394126 153.007913 D +153.007913 154.644920 G +154.644920 156.281927 F# +156.281927 159.567551 B:min +159.567551 161.146507 C +161.146507 162.783514 D +162.783514 164.397301 G +164.397301 166.022698 F# +166.022698 167.601655 C +167.601655 169.250272 D +169.250272 170.875668 G +170.875668 172.477845 F# +172.477845 174.103242 F +174.103242 175.705419 D +175.705419 177.295986 G +177.295986 178.909773 F# +178.909773 181.986417 B:min +181.986417 183.681473 C +183.681473 185.202380 D +185.202380 186.781337 G +186.781337 188.337074 F# +188.337074 189.892811 C +189.892811 191.460158 D +191.460158 193.097165 G +193.097165 195.386487 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD2final/CD2_-_06_-_Helter_Skelter.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,47 @@ +0.0000000 0.1654307 N +0.1654307 0.5885710 E:(1,5) +0.5885710 6.5212690 E:(1,b7)/b7 +6.5212690 9.4353740 C#:(1,b3) +9.4353740 12.2449880 C +12.2449880 15.0894330 G +15.0894330 18.0035370 E +18.0035370 32.2954190 E +32.2954190 35.1979130 G +35.1979130 37.9959180 A +37.9959180 40.9100220 E +40.9100220 43.7544670 A +43.7544670 46.6105210 E +46.6105210 49.4665750 A +49.4665750 52.2994100 E +52.2994100 69.5170060 E +69.5170060 72.3614510 G +72.3614510 75.2175050 A +75.2175050 78.0503400 E +78.0503400 80.9063940 A +80.9063940 83.8321080 E +83.8321080 86.6649430 A +86.6649430 89.4745570 E +89.4745570 95.2214960 E +95.2214960 97.9382310 A +97.9382310 100.8291150 E +100.8291150 103.7432190 A +103.7432190 106.7037640 E +106.7037640 109.5482080 E +109.5482080 112.4623120 C#:(1,b3)/b3 +112.4623120 115.2835370 C/3 +115.2835370 118.1163710 G +118.1163710 132.7101130 E +132.7101130 135.5661670 G +135.5661670 138.3641720 A +138.3641720 141.3014960 E +141.3014960 144.2852600 A +144.2852600 147.0368250 E +147.0368250 149.9160990 A +149.9160990 152.7257140 E +152.7257140 179.6512570 E +179.6512570 181.7533470 N +181.7533470 191.6655780 E:9 +191.6655780 220.1796820 E +220.1796820 224.4503470 N +224.4503470 262.2470190 E +262.2470190 269.7408340 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD2final/CD2_-_07_-_Long_Long_Long.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,72 @@ +0.000000 0.413805 N +0.413805 1.946938 G:min +1.946938 3.560725 G:(1,b3,4)/b3 +3.560725 5.139682 G:min +5.139682 6.869569 F +6.869569 10.073922 C +10.073922 11.699319 Bb +11.699319 13.394376 A:min +13.394376 14.566984 G:min +14.566984 15.124263 C:9(*3) +15.124263 16.749659 F +16.749659 18.409886 Bb +18.409886 20.058503 F +20.058503 21.811609 C +21.811609 23.506666 C/5 +23.506666 26.989659 G:min +26.989659 28.603446 F +28.603446 30.194013 C +30.194013 33.723446 G:min +33.723446 35.395283 F +35.395283 38.843060 C +38.843060 42.024580 C:7 +42.024580 43.731247 Bb +43.731247 45.345034 A:min +45.345034 47.074920 G:min(4) +47.074920 48.851247 F +48.851247 50.592743 Bb +50.592743 52.345850 F +52.345850 54.084559 C +54.084559 55.852063 C/5 +55.852063 59.369886 G:min +59.369886 61.053333 F +61.053333 62.771609 C +62.771609 66.103673 G:min +66.103673 67.868390 F +67.868390 74.660226 C +74.660226 76.332063 Bb +76.332063 78.050340 F +78.050340 79.838276 C +79.838276 84.865396 G:min +84.865396 86.572063 Bb +86.572063 88.290340 F +88.290340 90.066666 C +90.066666 91.843648 G:min +91.843648 93.526439 Bb +93.526439 100.271836 C +100.271836 101.932063 Bb +101.932063 103.673560 A:min +103.673560 105.473106 G:min +105.473106 107.319092 F +107.319092 109.048979 Bb +109.048979 110.860136 F +110.860136 114.436009 C +114.436009 118.058321 G:min +118.058321 119.764988 F +119.764988 121.367165 C +121.367165 124.838548 G:min +124.838548 126.533605 F +126.533605 128.379591 C +128.379591 131.827755 G:min +131.827755 133.592471 F +133.592471 135.403628 C +135.403628 139.002721 G:min +139.002721 140.628117 F +140.628117 142.381224 C +142.381224 145.945487 G:min +145.945487 147.675374 F +147.675374 158.190121 C +158.190121 158.936878 C:(1) +158.936878 159.702782 C:(1,5) +159.702782 181.779978 C:sus4 +181.779978 184.345758 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD2final/CD2_-_08_-_Revolution_1.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,88 @@ +0.0000000 0.4020998 N +0.4020998 2.4125990 A +2.4125990 3.2550939 N +3.2550939 14.0734942 A +14.0734942 16.2174600 E:7 +16.2174600 22.5216780 A +22.5216780 27.5371880 D +27.5371880 32.6223580 A +32.6223580 38.8917460 A +38.8917460 43.9420860 D +43.9420860 48.8298860 E +48.8298860 51.3260310 B:min +51.3260310 53.8453960 E +53.8453960 56.3647610 B:min +56.3647610 56.7827210 G +56.7827210 57.3088003 A +57.3088003 60.0219040 F# +60.0219040 62.5064390 E +62.5064390 64.9909750 A +64.9909750 67.3594100 D +67.3594100 69.8323350 A +69.8323350 72.3400900 D +72.3400900 74.7433560 A +74.7433560 77.1814510 D +77.1814510 82.1505210 E +82.1505210 88.2109290 A +88.2109290 93.0987300 D/5 +93.0987300 97.9052600 A +97.9052600 104.0121080 A +104.0121080 108.8882990 D/5 +108.8882990 113.7180490 E +113.7180490 116.1097050 B:min +116.1097050 118.5245800 E +118.5245800 121.0439450 B:min +121.0439450 121.4735140 G +121.4735140 122.0772330 A +122.0772330 124.7243080 F# +124.7243080 126.9998630 E +126.9998630 129.3682990 A +129.3682990 131.7599540 D +131.7599540 134.0587300 A +134.0587300 136.4619950 D +136.4619950 138.8420400 A +138.8420400 141.2336960 D +141.2336960 145.9821760 E +145.9821760 151.9845350 A +151.9845350 156.7678450 D/5 +156.7678450 161.5975960 A +161.5975960 167.5535140 A +167.5535140 172.2787750 D/5 +172.2787750 173.5156527 E +173.5156527 174.7052600 E:sus2(b7) +174.7052600 175.8546480 E:maj6 +175.8546480 177.0156460 E:7 +177.0156460 179.3956910 B:min +179.3956910 181.8221760 E +181.8221760 184.2254420 B:min +184.2254420 184.6434010 G +184.6434010 185.2239000 A +185.2239000 187.8477550 F# +187.8477550 190.1581400 E +190.1581400 192.5614050 A +192.5614050 194.9066210 D +194.9066210 197.2518360 A +197.2518360 199.6202720 D +199.6202720 201.9887070 A +201.9887070 204.3339220 D +204.3339220 209.8370520 E +209.8370520 212.1938770 A +212.1938770 214.5274820 D/5 +214.5274820 216.8959180 A +216.8959180 219.2759630 D/5 +219.2759630 221.6095690 A +221.6095690 223.8967340 D/5 +223.8967340 226.2187300 A +226.2187300 228.6916550 D/5 +228.6916550 230.9672100 A +230.9672100 233.3356460 D/5 +233.3356460 235.6924710 A +235.6924710 238.0260770 D/5 +238.0260770 240.2900220 A +240.2900220 242.6120180 D/5 +242.6120180 244.9456230 A +244.9456230 247.2908390 D/5 +247.2908390 249.6244440 A +249.6244440 252.0160990 D/5 +252.0160990 254.3380950 A +254.3380950 255.6397571 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD2final/CD2_-_09_-_Honey_Pie.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,95 @@ +0.0000000 0.3829522 N +0.3829522 1.4610990 E:min +1.4610990 2.4363370 A +2.4363370 5.0741170 A:min/4 +5.0741170 7.1732080 C:min +7.1732080 11.2599200 G +11.2599200 12.1778807 E:min +12.1778807 13.0130270 A +13.0130270 17.3668834 A:min/4 +17.3668834 19.4449540 C:min +19.4449540 23.6855951 G +23.6855951 28.9651360 A:9 +28.9651360 39.1819160 D +39.1819160 42.5720290 G +42.5720290 44.3019160 Eb +44.3019160 45.9621420 E +45.9621420 47.6804190 A +47.6804190 49.3638660 D +49.3638660 51.0240920 G +51.0240920 51.8484010 Eb +51.8484010 52.7075390 D +52.7075390 56.0976530 G +56.0976530 57.7578790 Eb +57.7578790 59.4529360 E +59.4529360 61.1596030 A +61.1596030 62.8314390 D +62.8314390 64.5264960 G +64.5264960 65.3391950 F#/5 +65.3391950 66.2099430 F/5 +66.2099430 67.9050000 E:min/5 +67.9050000 69.6116660 A:7/3 +69.6116660 71.6782420 G/3 +71.6782420 72.1078110 G +72.1078110 72.5373800 F#:dim/b3 +72.5373800 72.9553400 B:dim +72.9553400 74.6387860 C +74.6387860 76.2990130 E/5 +76.2990130 78.0637300 A:min +78.0637300 78.3075390 D +78.3075390 78.7487180 D/2 +78.7487180 79.6775170 D/3 +79.6775170 83.0328000 G +83.0328000 84.6698070 Eb +84.6698070 86.3416430 E +86.3416430 87.9902600 A +87.9902600 89.7201470 D +89.7201470 91.3571540 G +91.3571540 92.1930720 Eb +92.1930720 93.0289900 D +93.0289900 96.3146140 G +96.3146140 97.9748410 Eb +97.9748410 99.6002380 E +99.6002380 101.2024140 A +101.2024140 102.8626410 D +102.8626410 104.5344780 G +104.5344780 105.3820060 Eb +105.3820060 106.2411450 D +106.2411450 109.5615980 G +109.5615980 111.1637750 Eb +111.1637750 112.7427320 E +112.7427320 114.4145690 A +114.4145690 116.0167460 D +116.0167460 117.6885820 G +117.6885820 118.5128910 F#/5 +118.5128910 119.3488090 F/5 +119.3488090 121.0670860 E:min/5 +121.0670860 122.6808730 A:7/3 +122.6808730 124.8287180 G/3 +124.8287180 125.2466780 G +125.2466780 125.6181970 F#:dim/b3 +125.6181970 126.0825960 B:dim +126.0825960 127.7776530 C +127.7776530 129.4030490 E/5 +129.4030490 131.1097160 A:min +131.1097160 131.3767460 D +131.3767460 131.7830950 D/2 +131.7830950 132.7351130 D/3 +132.7351130 135.9859070 G +135.9859070 137.6577430 Eb +137.6577430 139.3063600 E +139.3063600 140.9898070 A +140.9898070 142.6848630 D +142.6848630 144.3450900 G +144.3450900 145.1577890 Eb +145.1577890 146.0401470 D +146.0401470 149.3257700 G +149.3257700 150.9279470 Eb +150.9279470 152.5185140 E +152.5185140 154.1671310 A +154.1671310 155.8389680 D +155.8389680 157.4875850 G +157.4875850 158.1609630 Eb +158.1609630 159.0897610 D +159.0897610 160.1314724 G +160.1314724 161.2994767 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD2final/CD2_-_10_-_Savoy_Truffle.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,82 @@ +0.0000000 1.4169232 N +1.4169232 6.7686390 E:7 +6.7686390 12.7013370 E +12.7013370 16.4978000 F# +16.4978000 20.6657820 A +20.6657820 24.4970740 G +24.4970740 28.6302260 B +28.6302260 29.6402940 E:min +29.6402940 30.6271420 C/3 +30.6271420 31.6023800 C#:dim/b3 +31.6023800 32.5776190 C/3 +32.5776190 34.5745350 C:maj7 +34.5745350 35.6911473 G +35.6911473 36.5714510 N +36.5714510 42.0397500 E:7 +42.0397500 47.9956680 E +47.9956680 51.7921310 F# +51.7921310 55.9833330 A +55.9833330 59.7565750 G +59.7565750 63.9709970 B +63.9709970 64.9578450 E:min +64.9578450 65.9446930 C/3 +65.9446930 66.9547610 C#:dim/b3 +66.9547610 67.9067800 C/3 +67.9067800 69.9269160 C:maj7 +69.9269160 71.0184899 G +71.0184899 71.9006120 N +71.9006120 73.8975280 E:min +73.8975280 75.8480040 A +75.8480040 76.5562130 A:sus4(2) +76.5562130 77.6591600 A +77.6591600 78.4021990 G +78.4021990 79.5515870 B +79.5515870 81.5485030 E:min +81.5485030 83.5686390 A +83.5686390 84.3000680 A:sus4(2) +84.3000680 85.4610650 A +85.4610650 86.1808840 G +86.1808840 87.2838320 B +87.2838320 95.1670060 E +95.1670060 99.1260090 F# +99.1260090 103.0734010 A +103.0734010 107.1252830 G +107.1252830 111.0262350 B +111.0262350 112.0130830 E:min +112.0130830 112.9883210 C/3 +112.9883210 113.9983900 C#:dim/b3 +113.9983900 114.9504080 C/3 +114.9504080 116.9357140 C:maj7 +116.9357140 117.9875802 G +117.9875802 118.9674600 N +118.9674600 120.9411560 E:min +120.9411560 122.9148520 A +122.9148520 123.7159410 A:sus4(2) +123.7159410 124.7956680 A +124.7956680 125.5619270 G +125.5619270 126.7229250 B +126.7229250 128.6966210 E:min +128.6966210 130.6935370 A +130.6935370 131.4365750 E:sus4(2) +131.4365750 132.5859630 E +132.5859630 133.3290020 G +133.3290020 134.5828790 B +134.5828790 142.5473240 E +142.5473240 146.3205660 F# +146.3205660 150.5233780 A +150.5233780 154.3082310 G +154.3082310 158.4994330 B +158.4994330 159.5095010 E:min +159.5095010 160.4731290 C/3 +160.4731290 161.4715870 C#:dim/b3 +161.4715870 162.4119950 C/3 +162.4119950 164.4089110 C:maj7 +164.4089110 165.4545083 G +165.4545083 166.3709970 N +166.3709970 167.4042850 E:min +167.4042850 168.3795230 C/3 +168.3795230 169.3199310 C#:dim/b3 +169.3199310 170.3067800 C/3 +170.3067800 172.3153060 C:maj7 +172.3153060 173.4390622 G +173.4390622 174.8942807 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD2final/CD2_-_11_-_Cry_Baby_Cry.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,99 @@ +0.0000000 0.3829522 N +0.3829522 1.9004980 G +1.9004980 3.4214050 A:min +3.4214050 4.9887520 F +4.9887520 6.5328790 G +6.5328790 8.0305660 E:min +8.0305660 9.5979130 A +9.5979130 11.2000900 F +11.2000900 12.8254870 E:min +12.8254870 14.3696140 Eb:aug +14.3696140 15.9485710 G/5 +15.9485710 17.4694780 C#:hdim7 +17.4694780 18.9555550 C:7 +18.9555550 20.5925620 G +20.5925620 22.1947390 E:min +22.1947390 23.7272560 Eb:aug +23.7272560 25.2365530 G/5 +25.2365530 26.8387300 C#:hdim7 +26.8387300 28.3480270 C:7 +28.3480270 29.8921540 G +29.8921540 31.4362810 A:min +31.4362810 33.0036280 F +33.0036280 34.5361450 G +34.5361450 36.0686620 E:min +36.0686620 37.6127890 A +37.6127890 39.1801360 F +39.1801360 40.7126530 G +40.7126530 42.2567800 E:min +42.2567800 43.7892970 Eb:aug +43.7892970 45.3102040 G/5 +45.3102040 46.8775510 C#:hdim7 +46.8775510 48.4100680 C:7 +48.4100680 49.9541950 G +49.9541950 51.5563710 E:min +51.5563710 53.0772780 Eb:aug +53.0772780 54.6794550 G/5 +54.6794550 56.1887520 C#:hdim7 +56.1887520 57.7328790 C:7 +57.7328790 59.3002260 G +59.3002260 60.8211330 A:min +60.8211330 62.4000900 F +62.4000900 63.9790470 G +63.9790470 65.4999540 E:min +65.4999540 67.0440810 A +67.0440810 68.5998180 F +68.5998180 70.2136050 G +70.2136050 71.8273920 E:min +71.8273920 73.4063490 Eb:aug +73.4063490 75.0085260 G/5 +75.0085260 76.5990920 C#:hdim7 +76.5990920 78.1664390 C:7 +78.1664390 79.7221760 G +79.7221760 81.3011330 E:min +81.3011330 82.8917000 Eb:aug +82.8917000 84.5519270 G/5 +84.5519270 86.1889340 C#:hdim7 +86.1889340 87.6634010 C:7 +87.6634010 89.1959180 G +89.1959180 90.7980950 A:min +90.7980950 92.4002720 F +92.4002720 93.9443990 G +93.9443990 95.5581850 E:min +95.5581850 97.1255320 A +97.1255320 98.7160990 F +98.7160990 100.3182760 G +100.3182760 101.9552830 E:min +101.9552830 103.5458500 Eb:aug +103.5458500 105.1364170 G/5 +105.1364170 106.7618140 C#:hdim7 +106.7618140 108.2943310 C:7 +108.2943310 109.9081170 G +109.9081170 111.5335140 E:min +111.5335140 113.1589110 Eb:aug +113.1589110 114.8075280 G/5 +114.8075280 116.4213150 C#:hdim7 +116.4213150 117.9654420 C:7 +117.9654420 119.5908390 G +119.5908390 121.1697950 A:min +121.1697950 122.7487520 F +122.7487520 124.3277090 G +124.3277090 125.9066660 E:min +125.9066660 127.4624030 A +127.4624030 129.0529700 F +129.0529700 130.6203170 G +130.6203170 132.1180040 A:min +132.1180040 133.7434010 F +133.7434010 135.2875280 G +135.2875280 136.8897050 E:min +136.8897050 138.4454420 A +138.4454420 140.0476190 F +140.0476190 141.6149650 G +141.6149650 143.1358730 A:min +143.1358730 144.7380490 F +144.7380490 146.2937860 G +146.2937860 147.8727430 E:min +147.8727430 149.4400900 A +149.4400900 151.0074370 F +151.0074370 154.7126985 E:min +154.7126985 181.8065683 F:min
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD2final/CD2_-_12_-_Revolution_9.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,91 @@ +0.0000000 9.2865914 N +9.2865914 10.5694814 E:min/3 +10.5694814 14.2649703 B:min +14.2649703 16.1414362 E:min +16.1414362 18.3617460 B:min +18.3617460 20.5560310 C# +20.5560310 23.5898570 C# +23.5898570 25.1408135 B:min +25.1408135 31.7658870 N +31.7658870 39.9993598 C#:min +39.9993598 59.1278234 Bb +59.1278234 69.6398619 N +69.6398619 75.1735216 C#:min +75.1735216 77.7967443 B:min +77.7967443 79.0221914 N +79.0221914 80.5157051 E:min6 +80.5157051 82.4496138 B:min +82.4496138 83.8473894 N +83.8473894 85.9919219 Eb +85.9919219 87.6003212 N +87.6003212 89.2087206 Ab:min +89.2087206 120.2852935 N +120.2852935 121.3384122 D:min +121.3384122 122.3340879 C +122.3340879 123.5597500 G +123.5597500 124.6318013 C +124.6318013 125.7615103 G +125.7615103 129.0931947 N +129.0931947 134.0141308 E +134.0141308 136.7522392 N +136.7522392 138.8010336 B:min +138.8010336 140.7732375 E:min +140.7732375 143.9708886 C +143.9708886 146.0482760 E:min +146.0482760 149.7402490 C +149.7402490 158.8308610 A:(1) +158.8308610 168.6904546 N +168.6904546 169.8967541 F +169.8967541 172.4816817 Eb +172.4816817 182.8596869 N +182.8596869 183.5681485 F +183.5681485 184.6787100 Eb +184.6787100 185.9615999 N +185.9615999 191.2807480 N +191.2807480 192.0505403 C +192.0505403 192.6058210 F +192.6058210 192.9504780 C +192.9504780 193.5057588 N +193.5057588 195.2290438 G +195.2290438 195.6502912 N +195.6502912 196.6459670 C +196.6459670 196.9523288 F +196.9523288 197.4693143 C +197.4693143 197.8522665 N +197.8522665 199.8244704 G +199.8244704 200.4563416 N +200.4563416 201.3179841 C +201.3179841 201.9115601 F +201.9115601 202.2370695 C +202.2370695 202.7157597 N +202.7157597 204.2475686 G +204.2475686 228.4501492 N +228.4501492 230.2117294 B:min +230.2117294 232.7392141 F#:min/5 +232.7392141 235.5922082 B:min +235.5922082 239.7663874 N +239.7663874 246.9658892 C#:min +246.9658892 283.6729470 N +283.6729470 307.4340454 N +307.4340454 312.7379337 D:(1) +312.7379337 338.1468137 D:(1,5) +338.1468137 349.9417422 N +349.9417422 351.4735511 C:maj7 +351.4735511 352.6989982 C +352.6989982 353.5031979 N +353.5031979 354.9775640 C +354.9775640 355.8200589 N +355.8200589 357.4859010 C:maj7 +357.4859010 403.1912490 N +403.1912490 404.9719768 B:min +404.9719768 409.1078608 F#:min +409.1078608 412.5352832 B:min +412.5352832 413.3586305 B +413.3586305 416.2307722 N +416.2307722 421.9367603 A +421.9367603 435.0720216 N +435.0720216 439.0738723 B +439.0738723 443.5735610 B:maj7 +443.5735610 445.4883221 E:min +445.4883221 447.8626259 B:maj7 +447.8626259 502.1652512 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD2final/CD2_-_13_-_Good_Night.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,103 @@ +0.0000000 0.4707700 N +0.4707700 2.0613370 G:maj7 +2.0613370 3.9421540 C/5 +3.9421540 5.7417000 G:maj7 +5.7417000 7.5760770 C/5 +7.5760770 9.2595230 G:maj7 +9.2595230 10.9661900 C/5 +10.9661900 12.7889560 G:maj7 +12.7889560 14.7278230 C/5 +14.7278230 16.6782990 G +16.6782990 18.5010650 B:min7 +18.5010650 20.3818820 A:min7 +20.3818820 21.3571200 D:sus4 +21.3571200 22.2859180 D +22.2859180 24.2596140 G +24.2596140 26.2217000 B:min7 +26.2217000 27.9748070 A:min7 +27.9748070 29.7163030 C/5 +29.7163030 31.5739000 B:min +31.5739000 33.4663260 A:min +33.4663260 35.1729930 C/5 +35.1729930 37.0654190 D/3 +37.0654190 38.9230150 G +38.9230150 40.8154420 B:min7 +40.8154420 44.5422440 A:min7 +44.5422440 46.5159410 B:min +46.5159410 48.4548070 A:min +48.4548070 50.3588430 C +50.3588430 52.0422900 D +52.0422900 53.8186160 G:maj7 +53.8186160 55.6529930 C:maj(#4)/5 +55.6529930 57.4060990 G:maj7 +57.4060990 59.1824260 C:maj(#4)/5 +59.1824260 60.9819720 G +60.9819720 62.8047390 C/5 +62.8047390 64.4649650 G +64.4649650 66.3109520 C/5 +66.3109520 68.0640580 G +68.0640580 69.9680950 B:min7 +69.9680950 71.6399310 A:min7 +71.6399310 73.4859180 C/5 +73.4859180 75.3319040 B:min +75.3319040 76.9921310 A:min +76.9921310 78.8381170 C/5 +78.8381170 80.6841040 D/3 +80.6841040 82.5765300 G +82.5765300 84.4225170 B:min7 +84.4225170 88.0100000 A:min7 +88.0100000 89.8327660 B:min +89.8327660 91.7251920 A:min +91.7251920 93.5247390 C/5 +93.5247390 95.4055550 D/3 +95.4055550 97.1586620 G:maj7 +97.1586620 99.0859180 C:maj(#4)/5 +99.0859180 100.8041950 G:maj7 +100.8041950 102.5921310 C:maj(#4)/5 +102.5921310 104.4497270 G +104.4497270 106.2376640 C/5 +106.2376640 107.9791600 G +107.9791600 109.8483670 C/5 +109.8483670 111.6014730 G +111.6014730 113.3429700 A:min +113.3429700 115.0496370 A +115.0496370 116.7911330 D:min +116.7911330 118.5674600 G +118.5674600 120.2973460 C/5 +120.2973460 122.1085030 D/3 +122.1085030 123.0721310 C +123.0721310 124.0009290 D +124.0009290 125.9514050 G +125.9514050 127.7973920 B:min7 +127.7973920 129.5040580 A:min7 +129.5040580 131.3384350 C/5 +131.3384350 132.9290020 B:min +132.9290020 134.7517680 A:min +134.7517680 136.5280950 C/5 +136.5280950 138.2812010 D/3 +138.2812010 140.1620180 G +140.1620180 141.9731740 B:min7 +141.9731740 145.5374370 A:min7 +145.5374370 147.3021540 B:min +147.3021540 149.1133100 A:min +149.1133100 150.9360770 C/5 +150.9360770 152.8052830 D/3 +152.8052830 154.6396590 G:maj7 +154.6396590 156.4740360 C:maj(#4)/5 +156.4740360 158.2039220 G:maj7 +158.2039220 160.1776190 C:maj(#4)/5 +160.1776190 161.9539450 G:maj7 +161.9539450 163.7883210 C/5 +163.7883210 165.5530380 G:maj7 +165.5530380 167.5035140 C/5 +167.5035140 169.3378910 G +169.3378910 171.2419270 B:min7 +171.2419270 173.2504530 A:min7 +173.2504530 175.1544890 C/5 +175.1544890 176.9888660 G +176.9888660 179.0554420 B:min7 +179.0554420 181.1336280 A:min7 +181.1336280 182.3294550 D:sus4 +182.3294550 184.1638320 D +184.1638320 189.4273176 G +189.4273176 191.6484405 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD2final/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,14 @@ +/CD2_-_01_-_Birthday.lab/1.2/Tue Oct 17 21:58:19 2006// +/CD2_-_02_-_Yer_Blues.lab/1.2/Wed Aug 2 00:43:45 2006// +/CD2_-_03_-_Mother_Nature's_Son.lab/1.1/Sun Jul 2 19:20:54 2006// +/CD2_-_04_-_Everybody's_Got_Something_To_Hide_Except_Me_and_M.lab/1.2/Wed Aug 2 00:43:45 2006// +/CD2_-_05_-_Sexy_Sadie.lab/1.2/Wed Aug 2 00:43:45 2006// +/CD2_-_06_-_Helter_Skelter.lab/1.2/Wed Aug 2 00:43:45 2006// +/CD2_-_07_-_Long_Long_Long.lab/1.2/Wed Aug 2 00:43:45 2006// +/CD2_-_08_-_Revolution_1.lab/1.1/Sun Jul 2 19:20:54 2006// +/CD2_-_09_-_Honey_Pie.lab/1.2/Wed Aug 2 00:43:45 2006// +/CD2_-_10_-_Savoy_Truffle.lab/1.2/Wed Aug 2 00:43:45 2006// +/CD2_-_11_-_Cry_Baby_Cry.lab/1.1/Sun Jul 2 19:20:54 2006// +/CD2_-_12_-_Revolution_9.lab/1.1/Sun Jul 2 19:20:54 2006// +/CD2_-_13_-_Good_Night.lab/1.1/Sun Jul 2 19:20:54 2006// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD2final/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +chrish/transcriptions/10CD2final
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/10CD2final/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:matthiasm@dspmac1.elec.qmul.ac.uk:/cvsroot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/11final/01_-_Come_Together.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,36 @@ +0.0000000 1.1722660 N +1.1722660 12.5852380 D:min +12.5852380 24.3693650 D:min +24.3693650 30.2091830 A +30.2091830 34.6039950 G:7 +34.6039950 36.0373920 N +36.0373920 47.4151700 D:min +47.4151700 59.1412470 D:min +59.1412470 64.9694550 A +64.9694550 70.7121281 G:7 +70.7121281 72.9339000 B:min +72.9339000 73.6188880 A +73.6188880 75.0585260 G +75.0585260 76.5213830 A +76.5213830 87.9223800 D:min +87.9223800 99.5671880 D:min +99.5671880 105.4186160 A +105.4186160 111.2584350 G:7 +111.2584350 113.4062810 B:min +113.4062810 114.1144890 A +114.1144890 115.5773460 G +115.5773460 117.0750340 A +117.0750340 122.6362130 D:min +122.6362130 134.2229700 D:min +134.2229700 145.7516780 A +145.7516780 151.5218360 D:min +151.5218360 163.3175730 D:min +163.3175730 169.1806120 A +169.1806120 175.0204300 G:7 +175.0204300 177.1798860 B:min +177.1798860 177.9229250 A +177.9229250 179.3625620 G +179.3625620 180.8602490 A +180.8602490 192.2728570 D:min +192.2728570 256.2758900 D:min +256.2758900 260.6798410 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/11final/02_-_Something.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,99 @@ +0.0000000 1.2637423 N +1.2637423 3.1228450 F +3.1228450 4.0748630 Eb +4.0748630 5.0733210 G/5 +5.0733210 8.8465640 C +8.8465640 12.5037070 E:min/5 +12.5037070 16.1840700 C:7 +16.1840700 18.8775850 F +18.8775850 19.7599430 C/3 +19.7599430 23.4403060 D +23.4403060 24.4706471 G +24.4706471 25.3211220 G:sus4(2)/2 +25.3211220 27.1903280 G/3 +27.1903280 29.0247050 A:min +29.0247050 30.8242510 Ab:aug +30.8242510 32.6005780 C/5 +32.6005780 34.4117340 D:7 +34.4117340 36.2345010 F +36.2345010 37.1865190 Eb +37.1865190 38.1269270 G/5 +38.1269270 41.8885600 C +41.8885600 45.5457020 E:min/5 +45.5457020 49.1680150 C:7 +49.1680150 51.8731400 F +51.8731400 52.7903280 C/3 +52.7903280 56.4126410 D +56.4126410 57.3066090 G +57.3066090 58.2586280 G:sus4(2)/2 +58.2586280 60.0233440 G/3 +60.0233440 61.8228910 A:min +61.8228910 63.6340470 Ab:aug +63.6340470 65.3987640 C/5 +65.3987640 67.2331400 D:7 +67.2331400 69.1023460 F +69.1023460 70.0802570 Eb +70.0802570 71.0184899 G/5 +71.0184899 74.6286960 A +74.6286960 76.3701920 A +76.3701920 78.0456632 C#:min/5 +78.0456632 79.7138660 F#:min +79.7138660 81.4437520 A/5 +81.4437520 83.1039790 D +83.1039790 84.7874260 G +84.7874260 85.4749364 A +85.4749364 86.1833980 A/7 +86.1833980 86.8152692 A/b7 +86.8152692 87.4279927 A/6 +87.4279927 87.8875354 A/b6 +87.8875354 88.2820290 A/5 +88.2820290 89.9306460 A +89.9306460 91.6140920 C#:min/5 +91.6140920 93.2859290 F#:min +93.2859290 94.9809860 A/5 +94.9809860 96.6412130 D +96.6412130 98.3478790 G +98.3478790 99.0328680 C +99.0328680 99.6714170 C/7 +99.6714170 100.2867460 C/6 +100.2867460 100.9601240 C/5 +100.9601240 101.3780830 C/3 +101.3780830 101.8308730 C/2 +101.8308730 105.4880150 C +105.4880150 108.9942290 E:min/5 +108.9942290 112.6281510 C:7 +112.6281510 115.2403960 F +115.2403960 116.0879250 C/3 +116.0879250 119.7102380 D +119.7102380 120.6274260 G +120.6274260 121.4517340 G:sus4(2)/2 +121.4517340 123.2977210 G/3 +123.2977210 125.0392170 A:min +125.0392170 126.8271540 Ab:aug +126.8271540 128.6034800 C/5 +128.6034800 130.2985370 D:7 +130.2985370 132.1561330 F +132.1561330 133.0384920 Eb +133.0384920 133.8976300 G/5 +133.8976300 137.5779930 C +137.5779930 141.2351360 E:min/5 +141.2351360 144.8110090 C:7 +144.8110090 147.5161330 F +147.5161330 148.3984920 C/3 +148.3984920 152.0091950 D +152.0091950 152.9147730 G +152.9147730 153.8319084 G:sus4(2) +153.8319084 155.6082870 G/3 +155.6082870 157.3730040 A:min +157.3730040 159.1145010 Ab:aug +159.1145010 160.9488770 C/5 +160.9488770 162.7484240 D:7 +162.7484240 164.5828000 F +164.5828000 165.4767680 Eb +165.4767680 166.3475170 G/5 +166.3475170 169.8885600 A +169.8885600 171.7113260 F +171.7113260 172.6517340 Eb +172.6517340 173.7546820 G/5 +173.7546820 178.8386886 C +178.8386886 182.8979821 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/11final/03_-_Maxwell's_Silver_Hammer.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,96 @@ +0.0000000 0.3829522 N +0.3829522 1.3299090 D +1.3299090 2.2703170 F#:min/5 +2.2703170 4.1395230 B +4.1395230 7.8313730 E:min +7.8313730 11.5002490 A +11.5002490 13.3114050 D +13.3114050 15.1341720 A +15.1341720 16.0745800 D +16.0745800 16.9569380 F#:min/5 +16.9569380 18.8261450 B +18.8261450 22.4484580 E:min +22.4484580 26.1520400 A +26.1520400 27.9748070 D +27.9748070 29.8207930 A +29.8207930 33.4779360 E:7 +33.4779360 37.0770290 A +37.0770290 40.7341720 D +40.7341720 44.3448750 E:7 +44.3448750 47.9555780 A:7 +47.9555780 48.8727660 E:min +48.8727660 49.7783440 A +49.7783440 51.5778910 D +51.5778910 52.5879590 D +52.5879590 53.4935370 F#/5 +53.4935370 54.4339450 B:min +54.4339450 55.3975730 D:7/5 +55.3975730 57.2783900 G +57.2783900 59.0314960 D +59.0314960 59.9602940 D +59.9602940 60.8774820 F#:min/5 +60.8774820 62.7118590 B +62.7118590 66.3457820 E:min +66.3457820 70.0029250 A +70.0029250 71.7908610 D +71.7908610 73.6368480 A +73.6368480 74.5424260 D +74.5424260 75.4712240 F#:min/5 +75.4712240 77.2707700 B +77.2707700 80.9975730 E:min +80.9975730 84.6779360 A +84.6779360 86.5123120 D +86.5123120 88.3350790 A +88.3350790 91.9922220 E:7 +91.9922220 95.5332650 A +95.5332650 99.1904080 D +99.1904080 102.7430610 E:7 +102.7430610 106.3653740 A:7 +106.3653740 108.1765300 E:min +108.1765300 109.9528570 A +109.9528570 113.5751700 D +113.5751700 117.1742630 E:7 +117.1742630 120.8430150 A:7 +120.8430150 121.7602040 E:min +121.7602040 122.6541720 A +122.6541720 124.5117680 D +124.5117680 125.4405660 D +125.4405660 126.3693650 F#/5 +126.3693650 127.2981630 B:min +127.2981630 128.2385710 D:7/5 +128.2385710 129.8973944 G +129.8973944 131.8608840 G/5 +131.8608840 132.8477320 D +132.8477320 133.7300900 F#:min/5 +133.7300900 135.5528570 B +135.5528570 139.1983900 E:min +139.1983900 142.8555320 A +142.8555320 144.7363490 D +144.7363490 146.5126750 A +146.5126750 147.4530830 D +147.4530830 148.3702720 F#:min/5 +148.3702720 150.1930380 B +150.1930380 153.8385710 E:min +153.8385710 157.4957140 A +157.4957140 159.3417000 D +159.3417000 161.1760770 A +161.1760770 164.7983900 E:7 +164.7983900 168.3858730 A +168.3858730 172.0430150 D +172.0430150 175.5608390 E:7 +175.5608390 179.1367120 A:7 +179.1367120 180.9943080 E:min +180.9943080 182.7822440 A +182.7822440 186.3232870 D +186.3232870 189.8178910 E:7 +189.8178910 193.3937640 A:7 +193.3937640 194.2877320 E:min +194.2877320 195.1933100 A +195.1933100 197.0509070 D +197.0509070 197.9448750 D +197.9448750 198.8620630 F#/5 +198.8620630 199.8140810 B:min +199.8140810 200.7544890 D:7/5 +200.7544890 202.5656460 G +202.5656460 204.7262589 D +204.7262589 207.1580055 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/11final/04_-_Oh!_Darling.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,68 @@ +0.0000000 0.4403951 N +0.4403951 2.7955512 E:aug +2.7955512 7.1803542 A +7.1803542 11.3028790 E +11.3028790 15.4476410 F#:min +15.4476410 19.6272330 D +19.6272330 21.6705890 B:min +21.6705890 23.7952150 E +23.7952150 25.8501810 B:min +25.8501810 28.0096370 E +28.0096370 29.9252830 A +29.9252830 31.8989790 D +31.8989790 33.8262350 A +33.8262350 35.9624710 E +35.9624710 40.1420630 A +40.1420630 44.2868250 E +44.2868250 48.4664170 F#:min +48.4664170 52.5879590 D +52.5879590 54.6661450 B:min +54.6661450 56.7559410 E +56.7559410 58.8689560 B:min +58.8689560 60.9355320 E +60.9355320 62.9324480 A +62.9324480 64.8713150 D +64.8713150 66.7869610 A +66.7869610 68.7142170 A:7 +68.7142170 72.7777090 D +72.7777090 76.9340810 F:7 +76.9340810 85.1423350 A +85.1423350 93.3621990 B +93.3621990 95.4171650 E +95.4171650 97.4373010 F:7 +97.4373010 99.1654784 E +99.1654784 101.5820630 E:aug +101.5820630 105.9125850 A +105.9125850 110.1270060 E +110.1270060 114.3414280 F#:min +114.3414280 118.4861900 D +118.4861900 120.5875960 B:min +120.5875960 122.7470520 E +122.7470520 124.8484580 B:min +124.8484580 126.9614730 E +126.9614730 128.9467800 A +128.9467800 130.9320860 D +130.9320860 132.8825620 A +132.8825620 134.8214280 A:7 +134.8214280 138.9894100 D +138.9894100 143.0645120 F:7 +143.0645120 151.3308160 A +151.3308160 159.5739000 B +159.5739000 161.6172560 E +161.6172560 163.6141720 F:7 +163.6141720 165.7504080 E +165.7504080 168.0491830 E:aug +168.0491830 172.2636050 A +172.2636050 176.4896370 E +176.4896370 180.6227890 F#:min +180.6227890 184.8836500 D +184.8836500 186.9850560 B:min +186.9850560 189.1096820 E +189.1096820 191.1762580 B:min +191.1762580 193.2776640 E +193.2776640 195.2745800 A +195.2745800 197.2831060 D +197.2831060 198.3976640 A +198.3976640 199.0478230 Bb:7 +199.0478230 204.2858639 A:7 +204.2858639 206.5452820 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/11final/05_-_Octopus's_Garden.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,100 @@ +0.0000000 0.4212474 N +0.4212474 1.9452380 E +1.9452380 2.2354870 B:9 +2.2354870 4.4413830 E +4.4413830 4.7548520 E/5 +4.7548520 5.0451020 D +5.0451020 5.3934010 D# +5.3934010 6.6588880 E +6.6588880 7.9359860 C#:min +7.9359860 9.2014730 A +9.2014730 10.5017910 B +10.5017910 12.4987070 E +12.4987070 13.1488660 E/7 +13.1488660 15.1225620 C#:min +15.1225620 15.7959410 C#:min/b7 +15.7959410 18.4197950 A +18.4197950 21.0668700 B +21.0668700 23.0289560 E +23.0289560 23.6675050 E/7 +23.6675050 25.6644210 C#:min +25.6644210 26.3145800 C#:min/b7 +26.3145800 28.9384350 A +28.9384350 31.5739000 B +31.5739000 36.1250110 C#:min +36.1250110 36.7867800 C#:min/b7 +36.7867800 39.4338540 A +39.4338540 42.0344890 B +42.0344890 43.9965750 E +43.9965750 44.6119040 E/7 +44.6119040 46.5391600 C#:min +46.5391600 47.1777090 C#:min/b7 +47.1777090 48.5244670 A +48.5244670 49.7783440 B +49.7783440 52.4602490 E +52.4602490 54.3875050 E +54.3875050 55.0260540 E/7 +55.0260540 57.0345800 C#:min +57.0345800 57.6615190 C#:min/b7 +57.6615190 60.3318140 A +60.3318140 62.9324480 B +62.9324480 64.9177550 E +64.9177550 65.5446930 E/7 +65.5446930 67.5719199 C#:min +67.5719199 68.1801580 C#:min/b7 +68.1801580 70.8040130 A +70.8040130 73.3698180 B +73.3698180 77.9557590 C#:min +77.9557590 78.5943080 C#:min/b7 +78.5943080 81.2065530 A +81.2065530 83.8187980 B +83.8187980 85.7228340 E +85.7228340 86.3962130 E/7 +86.3962130 88.3466890 C#:min +88.3466890 88.9620180 C#:min/b7 +88.9620180 90.3087750 A +90.3087750 91.5510430 B +91.5510430 94.1748970 E +94.1748970 96.7871420 A +96.7871420 99.3413370 F#:min +99.3413370 101.9303620 D +101.9303620 104.4845570 E +104.4845570 107.0271420 A +107.0271420 109.6045570 F#:min +109.6045570 110.8932650 D +110.8932650 112.1935820 E +112.1935820 113.4822900 A +113.4822900 114.7477770 B +114.7477770 117.3600220 E +117.3600220 119.6342748 C#:min +119.6342748 119.9722670 C#:min/b7 +119.9722670 122.5845120 A +122.5845120 125.1735370 B +125.1735370 127.1401383 E +127.1401383 127.7973920 E/7 +127.7973920 129.7250659 C#:min +129.7250659 130.3864170 C#:min/b7 +130.3864170 132.9754420 A +132.9754420 135.5992970 B +135.5992970 140.1155780 C#:min +140.1155780 140.7657360 C#:min/b7 +140.7657360 143.4476410 A +143.4476410 146.0250560 B +146.0250560 147.9535917 E +147.9535917 148.6140810 E/7 +148.6140810 150.4619288 C#:min +150.4619288 151.2031060 C#:min/b7 +151.2031060 152.4685940 A +152.4685940 153.7340810 B +153.7340810 155.6317838 C#:min +155.6317838 156.2882760 C#:min/b7 +156.2882760 157.5885940 A +157.5885940 158.8424710 B +158.8424710 160.7348970 C#:min +160.7348970 161.3734460 C#:min/b7 +161.3734460 162.6621540 A +162.6621540 163.9392510 B +163.9392510 165.5762580 E +165.5762580 165.9593870 B +165.9593870 168.3075024 E +168.3075024 171.1030537 N
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/11final/07_-_Here_Comes_The_Sun.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,131 @@ +0.0000000 0.3662810 N +0.3662810 3.6054640 A +3.6054640 5.4630610 D +5.4630610 7.2626070 E +7.2626070 10.9661900 A +10.9661900 12.5915870 D +12.5915870 14.7046030 E +14.7046030 18.3269160 A +18.3269160 20.1729020 D +20.1729020 21.9956680 B +21.9956680 23.8532650 A +23.8532650 24.4802040 D:maj6 +24.4802040 25.1651920 A:maj(9)/3 +25.1651920 25.8385710 B:min +25.8385710 26.5003400 A +26.5003400 26.9879590 E:sus4(b7) +26.9879590 27.4523580 E:7 +27.4523580 31.2023800 A +31.2023800 33.0599770 D +33.0599770 34.9291830 E +34.9291830 38.6559860 A +38.6559860 40.5251920 D +40.5251920 42.3827890 E +42.3827890 46.1328110 A +46.1328110 48.0368480 D +48.0368480 49.9060540 B +49.9060540 51.7636500 A +51.7636500 52.4370290 D:maj6 +52.4370290 53.1104080 A:maj(9)/3 +53.1104080 53.8302260 B:min +53.8302260 54.4919950 A +54.4919950 55.0028340 E:sus4(7) +55.0028340 55.4672330 E:7 +55.4672330 57.3248290 A +57.3248290 59.1243760 E +59.1243760 62.8743990 A +62.8743990 64.7436050 D +64.7436050 66.6360310 E +66.6360310 70.3744440 A +70.3744440 72.2552600 D +72.2552600 74.1360770 E +74.1360770 77.9325390 A +77.9325390 79.7901360 D +79.7901360 81.6709520 B +81.6709520 83.4937180 A +83.4937180 84.2135370 D:maj6 +84.2135370 84.8985260 A:maj(9)/3 +84.8985260 85.5951240 B:min +85.5951240 86.2801130 A +86.2801130 86.7561220 E:sus4(b7) +86.7561220 87.2437410 E:7 +87.2437410 89.0897270 A +89.0897270 90.7267340 E +90.7267340 91.3652830 C +91.3652830 92.0734920 G +92.0734920 92.7468700 D/3 +92.7468700 93.2112690 D +93.2112690 95.0224260 A +95.0224260 96.5897730 E +96.5897730 97.2747610 C +97.2747610 97.9249200 G +97.9249200 98.6215190 D/3 +98.6215190 99.1091380 D +99.1091380 100.9086840 A +100.9086840 102.4644210 E +102.4644210 103.1261900 C +103.1261900 103.8227890 G +103.8227890 104.5077770 D/3 +104.5077770 104.9837860 D +104.9837860 106.8065530 A +106.8065530 108.3390700 E +108.3390700 109.0240580 C +109.0240580 109.6974370 G +109.6974370 110.3940360 D/3 +110.3940360 110.8468250 D +110.8468250 112.6812010 A +112.6812010 114.2369380 E +114.2369380 114.9103170 C +114.9103170 115.5488660 G +115.5488660 116.2454640 D/3 +116.2454640 116.7098630 D +116.7098630 118.5558500 A +118.5558500 120.0767570 E +120.0767570 120.7269160 C +120.7269160 121.4351240 G +121.4351240 122.1085030 D/3 +122.1085030 122.5612920 D +122.5612920 124.4188880 A +124.4188880 126.2068250 E +126.2068250 127.8090020 E:sus4 +127.8090020 129.6201580 E +129.6201580 131.6286840 E:7 +131.6286840 135.4135370 A +135.4135370 137.2595230 D +137.2595230 139.1055100 E +139.1055100 142.8439220 A +142.8439220 144.7247390 D +144.7247390 146.5939450 E +146.5939450 150.3323580 A +150.3323580 152.2363940 D +152.2363940 154.1056000 B +154.1056000 155.9748070 A +155.9748070 156.6597950 D:maj6 +156.6597950 157.3912240 A:maj(9)/3 +157.3912240 158.0413830 B:min +158.0413830 158.6915410 A +158.6915410 159.1791600 E:sus4(b7) +159.1791600 159.6551700 E:7 +159.6551700 163.3703620 A +163.3703620 165.1815190 D +165.1815190 167.1203850 B +167.1203850 169.0128110 A +169.0128110 169.6397500 D:maj6 +169.6397500 170.3595690 A:maj(9)/3 +170.3595690 171.0445570 B:min +171.0445570 171.7527660 A +171.7527660 172.2055550 E:sus4(b7) +172.2055550 172.6815640 E:7 +172.6815640 174.5507700 A +174.5507700 175.2473690 D:maj6 +175.2473690 175.9555780 A:maj(9)/3 +175.9555780 176.6637860 B:min +176.6637860 177.3023350 A +177.3023350 177.7667340 E:sus4(b7) +177.7667340 178.2543530 E:7 +178.2543530 178.9857820 A:min7 +178.9857820 179.7056000 G/2 +179.7056000 180.4254190 D/5 +180.4254190 181.0291380 D +181.0291380 183.5910890 A +183.5910890 185.5266208 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/11final/08_-_Because.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,49 @@ +0.0000000 0.3829522 N +0.3829522 6.5311790 C#:min +6.5311790 9.6194330 D#:hdim7 +9.6194330 12.6844670 G# +12.6844670 15.7146710 A +15.7146710 18.7448750 C#:min +18.7448750 22.8896370 A:9 +22.8896370 24.8517230 A:7(*5,13) +24.8517230 27.7542170 D +27.7542170 30.8773010 D:dim +30.8773010 36.9260990 C#:min +36.9260990 39.9098630 D#:hdim7 +39.9098630 42.9400680 G# +42.9400680 45.9586620 A +45.9586620 48.9656460 C#:min +48.9656460 51.9842400 A:9 +51.9842400 54.8751240 A:7(13) +54.8751240 57.9053280 D +57.9053280 60.8774820 D:dim +60.8774820 66.9262810 C#:min +66.9262810 69.9100450 D#:hdim7 +69.9100450 72.8125390 G# +72.8125390 75.8079130 A +75.8079130 78.7916780 C#:min +78.7916780 81.7870520 A:9 +81.7870520 84.7708160 A:7(13) +84.7708160 87.8126300 D +87.8126300 90.7267340 D:dim +90.7267340 96.7639220 F# +96.7639220 102.7662810 G#:7 +102.7662810 108.6525390 C#:min +108.6525390 111.6246930 D#:hdim7 +111.6246930 114.6200680 G# +114.6200680 117.5341720 A +117.5341720 120.4947160 C#:min +120.4947160 123.4320400 A:9 +123.4320400 126.2997050 A:7(13) +126.2997050 129.3182990 D +129.3182990 132.2788430 D:dim7 +132.2788430 138.1186620 C#:min +138.1186620 140.9863260 D#:hdim7 +140.9863260 143.9120400 G# +143.9120400 146.8145350 A +146.8145350 149.7054190 C#:min +149.7054190 152.6891830 A:9 +152.6891830 155.5916780 A:7(13) +155.5916780 158.4941720 D +158.4941720 164.4396850 D:dim7 +164.4396850 165.5188163 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/11final/09_-_You_Never_Give_Me_Your_Money.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,126 @@ +0.0000000 3.0976547 A:min7 +3.0976547 4.1279130 D:min9 +4.1279130 5.9855100 D:min +5.9855100 8.8880040 G:7 +8.8880040 9.9096820 C:sus4 +9.9096820 11.7904980 C +11.7904980 14.6813830 F:maj7 +14.6813830 15.7030610 B:dim +15.7030610 17.5142170 E +17.5142170 23.2959860 A:min +23.2959860 26.2449200 A:min7 +26.2449200 27.3503231 D:min9 +27.3503231 29.1474140 D:min +29.1474140 32.0847390 G:7 +32.0847390 33.1528570 C:sus4 +33.1528570 35.0104530 C +35.0104530 37.9129470 F:maj7 +37.9129470 38.9578450 B:dim +38.9578450 40.8270520 E +40.8270520 46.5972100 A:min +46.5972100 49.5577550 A:min7 +49.5577550 50.5678230 D:min9 +50.5678230 52.4486390 D:min +52.4486390 55.3511330 G:7 +55.3511330 56.4540810 C:sus4 +56.4540810 58.2536280 C +58.2536280 61.1677320 F:maj7 +61.1677320 62.2358500 B:dim +62.2358500 64.1166660 E +64.1166660 68.4956484 A:min +68.4956484 69.8171650 G +69.8171650 71.2568020 C +71.2568020 72.6383900 E +72.6383900 74.0083670 A:min +74.0083670 75.3551240 C:7 +75.3551240 76.7251020 F +76.7251020 78.0718590 G +78.0718590 80.7421540 C +80.7421540 82.1005210 C +82.1005210 83.4472780 E +83.4472780 84.8172560 A:min +84.8172560 86.2104530 C:7 +86.2104530 87.5920400 F +87.5920400 88.9504080 G +88.9504080 91.6787520 C +91.6787520 94.3722670 Bb +94.3722670 97.1354420 F +97.1354420 99.9102260 C +99.9102260 102.6501810 Bb +102.6501810 105.3669160 F +105.3669160 108.0952600 C +108.0952600 110.7655550 Bb +110.7655550 113.4590700 F +113.4590700 116.2222440 C +116.2222440 118.8809290 Bb +118.8809290 121.5860540 F +121.5860540 124.3027890 C +124.3027890 126.9846930 Bb +126.9846930 129.6665980 F +129.6665980 132.3368930 C +132.3368930 134.9839680 D +134.9839680 136.3191150 Eb +136.3191150 137.6194330 G +137.6194330 138.5830610 C +138.5830610 140.1968480 A +140.1968480 141.2533560 Eb +141.2533560 142.8439220 C +142.8439220 143.8656000 Gb +143.8656000 145.4329470 Eb +145.4329470 146.4546250 A +146.4546250 147.0931740 Gb +147.0931740 147.6968930 G +147.6968930 148.0451920 Ab +148.0451920 150.7619270 A +150.7619270 153.3625620 B +153.3625620 154.6512690 C +154.6512690 155.9980270 E +155.9980270 158.6567120 A +158.6567120 159.7132190 D:min +159.7132190 161.3153960 G/5 +161.3153960 162.3138540 D:min +162.3138540 163.9276410 G/5 +163.9276410 166.5863260 A +166.5863260 167.9446930 B +167.9446930 168.9199310 C +168.9199310 170.5801580 G/3 +170.5801580 173.1691830 A +173.1691830 174.1792510 C +174.1792510 175.8278680 G/3 +175.8278680 178.4285030 A +178.4285030 179.3921310 C +179.3921310 181.0639680 G/3 +181.0639680 183.7226530 A +183.7226530 184.6978910 C +184.6978910 186.3697270 G/3 +186.3697270 188.9471420 A +188.9471420 189.9223800 C +189.9223800 191.5709970 G/3 +191.5709970 194.1600220 A +194.1600220 195.1236500 C +195.1236500 196.7142170 G/3 +196.7142170 199.3148520 A +199.3148520 200.3133100 C +200.3133100 201.8806570 G/3 +201.8806570 204.4232420 A +204.4232420 205.3404300 C +205.3404300 206.9774370 G/3 +206.9774370 209.4735820 A +209.4735820 210.3791600 C +210.3791600 211.9465070 G/3 +211.9465070 214.5123120 A +214.5123120 215.3946710 C +215.3946710 216.9620180 G/3 +216.9620180 219.5394330 A +219.5394330 220.3753510 C +220.3753510 221.9543080 G/3 +221.9543080 224.4736730 A +224.4736730 225.3212010 C +225.3212010 226.9233780 G/3 +226.9233780 229.3730830 A +229.3730830 230.2786620 C +230.2786620 231.8460090 G/3 +231.8460090 234.3073240 A +234.3073240 235.2129020 C +235.2129020 236.7802490 G/3 +236.7802490 242.3180036 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/11final/10_-_Sun_King.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,39 @@ +0.0000000 0.1914761 N +0.1914761 1.3647390 E +1.3647390 7.7850560 E:maj6 +7.7850560 14.2750340 E:maj6 +14.2750340 20.2890020 F#:min7 +20.2890020 20.7301810 B:maj6 +20.7301810 27.1621080 E:maj6 +27.1621080 32.7348970 F#:min7 +32.7348970 33.5243760 B:maj6 +33.5243760 39.8750340 E:maj6 +39.8750340 45.7612920 F#:min7 +45.7612920 46.2837410 B:maj6 +46.2837410 52.6692290 E:maj6 +52.6692290 58.8921760 G:9(*3,11) +58.8921760 62.0965300 C +62.0965300 65.2428340 C:maj7 +65.2428340 68.4123580 G +68.4123580 71.6051020 A +71.6051020 74.7630150 C +74.7630150 77.9673690 C:maj7 +77.9673690 81.1717230 G +81.1717230 84.3064170 A +84.3064170 87.4991600 F +87.4991600 90.6802940 D:7 +90.6802940 93.9078680 F +93.9078680 97.1238320 D:7 +97.1238320 100.3630150 C +100.3630150 103.5441490 E:min7 +103.5441490 106.7252830 C:7 +106.7252830 109.9064170 F +109.9064170 115.4908160 F#:min7 +115.4908160 116.2570740 B:maj6 +116.2570740 122.7354420 E:maj6 +122.7354420 128.2501810 F#:min7 +128.2501810 129.0048290 B:maj6 +129.0048290 135.5296370 E:maj6 +135.5296370 141.0559860 F#:min7 +141.0559860 141.8106340 B:maj6 +141.8106340 146.2954868 E:maj6
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/11final/11_-_Mean_Mr_Mustard.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,33 @@ +0.0000000 1.7710880 E:maj6 +1.7710880 11.6743990 E +11.6743990 15.3199310 B +15.3199310 15.5869610 B +15.5869610 15.8656000 C +15.8656000 16.1674600 C# +16.1674600 20.0335820 D +20.0335820 20.3354420 D +20.3354420 20.6256910 C# +20.6256910 20.9275510 C +20.9275510 26.0127210 B +26.0127210 26.9066890 E +26.9066890 28.3695460 C +28.3695460 30.8076410 B +30.8076410 31.9918590 E +31.9918590 33.1760770 C +33.1760770 35.5677320 B +35.5677320 45.1227430 E +45.1227430 48.7218360 B +48.7218360 48.9772560 B +48.9772560 49.2558950 C +49.2558950 49.5461450 C# +49.5461450 53.3542170 D +53.3542170 53.6560770 D +53.6560770 53.9463260 C# +53.9463260 54.2249650 C +54.2249650 59.2172560 B +59.2172560 60.0996140 E +60.0996140 61.0051920 C +61.0051920 62.8511790 B +62.8511790 63.6870970 E +63.6870970 64.5926750 C +64.5926750 66.2124395 B
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/11final/12_-_Polythene_Pam.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,70 @@ +0.0000000 1.0148234 D/2 +1.0148234 1.7014280 A/5 +1.7014280 3.3268250 E +3.3268250 4.0814730 D/2 +4.0814730 4.7780720 A/5 +4.7780720 6.2873690 E +6.2873690 7.0071880 D +7.0071880 7.5644670 A +7.5644670 9.1550340 E +9.1550340 9.8748520 D +9.8748520 10.3973010 A +10.3973010 11.9762580 E +11.9762580 13.4158950 G +13.4158950 14.8439220 B +14.8439220 15.5753510 C +15.5753510 16.1210200 D +16.1210200 17.7231970 E +17.7231970 18.4546250 C +18.4546250 19.0002940 D +19.0002940 20.5908610 E +20.5908610 21.3455100 D/2 +21.3455100 22.0653280 A/5 +22.0653280 23.5397950 E +23.5397950 24.2596140 D/2 +24.2596140 25.0258730 A/5 +25.0258730 26.5351700 E +26.5351700 27.2317680 D +27.2317680 27.7890470 A +27.7890470 29.4260540 E +29.4260540 30.1342630 D +30.1342630 30.6799310 A +30.6799310 32.2588880 E +32.2588880 33.6753060 G +33.6753060 35.0917230 B +35.0917230 35.8231510 C +35.8231510 36.3688200 D +36.3688200 37.9942170 E +37.9942170 38.7140360 C +38.7140360 39.2597050 D +39.2597050 40.8734920 E +40.8734920 41.6165300 D/2 +41.6165300 42.3479590 A/5 +42.3479590 43.8688660 E +43.8688660 44.6931740 D/2 +44.6931740 45.4478230 A/5 +45.4478230 46.9919500 E +46.9919500 47.6769380 D +47.6769380 48.2226070 A +48.2226070 49.8480040 E +49.8480040 50.5562130 D +50.5562130 51.0902720 A +51.0902720 52.6808390 E +52.6808390 53.4006570 D +53.4006570 53.9463260 A +53.9463260 55.5601130 E +55.5601130 56.2915410 D +56.2915410 56.8488200 A +56.8488200 58.4742170 E +58.4742170 59.2056460 D +59.2056460 59.7513150 A +59.7513150 61.3534920 E +61.3534920 62.0733100 D +62.0733100 62.6073690 A +62.6073690 64.2327660 E +64.2327660 64.9758040 D +64.9758040 65.5098630 A +65.5098630 67.1236500 E +67.1236500 67.8666890 D +67.8666890 68.4239680 A +68.4239680 72.7226273 E
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/11final/13_-_She_Came_In_Through_The_Bathroom_Window.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,59 @@ +0.0000000 1.6466946 E +1.6466946 3.0365750 E/b7 +3.0365750 4.4413830 E/6 +4.4413830 5.9158500 E/5 +5.9158500 8.8996140 A +8.8996140 11.8601580 D +11.8601580 14.8671420 A +14.8671420 17.7348070 D +17.7348070 20.6721310 A +20.6721310 23.6210650 D +23.6210650 26.5700000 D +26.5700000 29.5305440 A +29.5305440 30.2723733 D:min +30.2723733 31.0382777 D:min/2 +31.0382777 31.7850346 D:min/b3 +31.7850346 32.5126438 D:min/4 +32.5126438 35.4516320 A +35.4516320 36.1506899 D:min +36.1506899 36.9292047 D:min/b7 +36.9292047 37.6824988 D:min/6 +37.6824988 38.3541260 D:min/5 +38.3541260 41.3611110 G +41.3611110 42.4756680 C +42.4756680 43.5670060 C/7 +43.5670060 44.2636050 C/6 +44.2636050 47.2705890 G +47.2705890 48.4199770 C +48.4199770 51.6591600 A +51.6591600 54.6661450 D +54.6661450 57.6847390 A +57.6847390 60.6917230 D +60.6917230 63.6290470 A +63.6290470 66.6128110 D +66.6128110 69.5849650 D +69.5849650 72.4990700 A +72.4990700 75.5176640 D +75.5176640 78.4782080 A +78.4782080 81.4503620 D +81.4503620 84.3876870 A +84.3876870 87.3714510 D +87.3714510 90.3319950 D +90.3319950 93.2925390 A +93.2925390 94.0909718 D:min +94.0909718 94.8008863 D:min/2 +94.8008863 95.5244530 D:min/b3 +95.5244530 96.2879130 D:min/4 +96.2879130 99.3297270 A +99.3297270 100.0843760 D:min +100.0843760 100.8506340 D:min/b7 +100.8506340 101.5240130 D:min/6 +101.5240130 102.2554420 D:min/5 +102.2554420 105.2624260 G +105.2624260 106.3769840 C +106.3769840 107.4799310 C/7 +107.4799310 108.2461900 C/6 +108.2461900 111.2415640 G +111.2415640 112.3793420 C +112.3793420 116.7812807 A +116.7812807 117.7147315 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/11final/14_-_Golden_Slumbers.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,32 @@ +0.0000000 0.3829522 N +0.3829522 6.3378593 A:min7 +6.3378593 9.3307930 A:min7 +9.3307930 15.1590020 D:min +15.1590020 21.1613600 G:7 +21.1613600 22.6358270 C +22.6358270 24.0290240 E +24.0290240 25.5731510 A:min +25.5731510 28.5685260 D:min9 +28.5685260 31.6219500 G:7 +31.6219500 34.5244440 C +34.5244440 37.4617680 C +37.4617680 40.3526530 F:maj7 +40.3526530 43.2087070 C +43.2087070 46.0647610 C +46.0647610 48.8859860 F:maj7 +48.8859860 50.3256230 C +50.3256230 51.8000900 E/5 +51.8000900 53.2977770 A:min +53.2977770 56.2118820 D:min9 +56.2118820 59.2304760 G:7 +59.2304760 62.2258500 C +62.2258500 65.2676640 A:min7 +65.2676640 68.2282080 A:min7 +68.2282080 74.1492970 D:min +74.1492970 80.0471650 G:7 +80.0471650 81.5564620 C +81.5564620 82.9496590 E +82.9496590 84.3660770 A:min +84.3660770 87.2685710 D:min9 +87.2685710 90.1826750 G:7 +90.1826750 91.5661566 C
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/11final/15_-_Carry_That_Weight.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,44 @@ +0.0000000 1.6201580 C +1.6201580 4.4762130 C +4.4762130 10.2347610 G +10.2347610 13.0559860 C +13.0559860 15.8656000 C +15.8656000 18.7680950 G +18.7680950 21.6357590 G +21.6357590 23.1102260 C +23.1102260 24.4918140 C:maj7/7 +24.4918140 27.3710880 A:min +27.3710880 28.4275960 D:min9 +28.4275960 30.2619720 D:min +30.2619720 33.0599770 G:7 +33.0599770 34.0584350 C:sus4 +34.0584350 35.8928110 C +35.8928110 38.7140360 F +38.7140360 39.7705440 B:dim +39.7705440 41.5236500 E:7 +41.5236500 44.4145350 A:min +44.4145350 47.3750790 A:min7 +47.3750790 48.3387070 D:min9 +48.3387070 50.2195230 D:min +50.2195230 53.0059180 G:7 +53.0059180 54.1320860 C:sus4 +54.1320860 55.8271420 C +55.8271420 58.7064170 F +58.7064170 59.7048750 B:dim +59.7048750 61.5160310 E:7 +61.5160310 64.4765750 A:min +64.4765750 65.2167637 G +65.2167637 66.9017535 C/5 +66.9017535 67.2978000 G +67.2978000 70.0994046 C +70.0994046 75.7614730 G +75.7614730 78.5246480 C +78.5246480 81.3574820 C +81.3574820 84.1670970 G +84.1670970 86.9999310 G +86.9999310 88.1260990 C +88.1260990 89.8559860 G/3 +89.8559860 92.6075510 A +92.6075510 93.6872780 C +93.6872780 95.4171650 G/3 +95.4171650 96.8677650 A
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/11final/16_-_The_End.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,58 @@ +0.0000000 0.2617910 A +0.2617910 1.5969380 D +1.5969380 2.1077770 B +2.1077770 3.8841040 E +3.8841040 5.8113600 A +5.8113600 7.7153960 D#:dim/b5 +7.7153960 11.2448290 A +11.2448290 11.7440580 A +11.7440580 13.1836960 D +13.1836960 13.7061450 B +13.7061450 15.3779810 E +15.3779810 17.3052380 A +17.3052380 19.1860540 D#:dim/b5 +19.1860540 19.5688586 A +19.5688586 34.7529143 N +34.7529143 36.7287300 A:7 +36.7287300 38.6908160 D:7 +38.6908160 40.6529020 A:7 +40.6529020 42.6265980 D:7 +42.6265980 44.5886840 A:7 +44.5886840 46.5623800 D:7 +46.5623800 48.4780270 A:7 +48.4780270 50.3820630 D:7 +50.3820630 52.2977090 A:7 +52.2977090 54.2597950 D:7 +54.2597950 56.2218820 A:7 +56.2218820 58.1955780 D:7 +58.1955780 60.1112240 A:7 +60.1112240 62.0733100 D:7 +62.0733100 63.9889560 A:7 +63.9889560 65.9162130 D:7 +65.9162130 67.8434690 A:7 +67.8434690 69.7242850 D:7 +69.7242850 71.6863710 A:7 +71.6863710 73.5323580 D:7 +73.5323580 75.5060540 A:7 +75.5060540 77.3752600 D:7 +77.3752600 79.2909070 A:7 +79.2909070 81.2646030 D:7 +81.2646030 83.3079590 A:7 +83.3079590 85.1423350 D:7 +85.1423350 87.0812010 A:7 +87.0812010 88.9968480 D:7 +88.9968480 92.8629700 A +92.8629700 96.8568020 A +96.8568020 100.7925850 G/2 +100.7925850 101.5472330 F/3 +101.5472330 102.2554420 C:maj6/5 +102.2554420 103.0217000 D:min7/b3 +103.0217000 103.8227890 A:min/5 +103.8227890 105.4481850 D:min +105.4481850 107.2129020 G +107.2129020 110.7423350 C +110.7423350 114.1092290 D/b7 +114.1092290 115.8623350 Eb/6 +115.8623350 117.6154420 F/5 +117.6154420 123.4063542 C +123.4063542 139.7775617 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/11final/17_-_Her_Majesty.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,30 @@ +0.0000000 0.3255094 N +0.3255094 2.0729470 D +2.0729470 2.6070060 D +2.6070060 3.1758950 D/7 +3.1758950 3.7563940 D/6 +3.7563940 4.3485030 D/5 +4.3485030 4.9754420 E:9 +4.9754420 5.5327210 A +5.5327210 6.7749880 D +6.7749880 7.3903170 D +7.3903170 7.9824260 D/7 +7.9824260 8.5861450 D/6 +8.5861450 9.1550340 D/5 +9.1550340 10.3740810 E:9 +10.3740810 11.5815190 A +11.5815190 12.7657360 B:min +12.7657360 13.9499540 B:sus2 +13.9499540 15.1341720 D:7 +15.1341720 16.3416090 G +16.3416090 17.5258270 E:dim/b3 +17.5258270 18.0947160 D +18.0947160 18.7100450 B +18.7100450 19.3137640 E:7 +19.3137640 19.9174820 A +19.9174820 20.4631510 D +20.4631510 21.0088200 B +21.0088200 21.6357590 E:7 +21.6357590 22.2046480 A +22.2046480 22.5750336 D +22.5750336 23.1253417 A:(1)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/11final/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,18 @@ +/01_-_Come_Together.lab/1.3/Tue Oct 17 21:58:53 2006// +/02_-_Something.lab/1.2/Tue Oct 17 21:58:53 2006// +/03_-_Maxwell's_Silver_Hammer.lab/1.1/Tue Jul 18 12:05:57 2006// +/04_-_Oh!_Darling.lab/1.2/Wed Aug 2 00:44:14 2006// +/05_-_Octopus's_Garden.lab/1.3/Tue Oct 17 21:58:53 2006// +/06_-_I_Want_You.lab/1.2/Wed Aug 2 00:44:14 2006// +/07_-_Here_Comes_The_Sun.lab/1.2/Wed Aug 2 00:44:14 2006// +/08_-_Because.lab/1.1/Tue Jul 18 12:05:57 2006// +/09_-_You_Never_Give_Me_Your_Money.lab/1.1/Tue Jul 18 12:05:57 2006// +/10_-_Sun_King.lab/1.1/Tue Jul 18 12:05:57 2006// +/11_-_Mean_Mr_Mustard.lab/1.1/Tue Jul 18 12:05:57 2006// +/12_-_Polythene_Pam.lab/1.1/Tue Jul 18 12:05:57 2006// +/13_-_She_Came_In_Trough_The_Bathroom_Window.lab/1.2/Tue Oct 17 21:58:53 2006// +/14_-_Golden_Slumbers.lab/1.2/Wed Aug 2 12:21:42 2006// +/15_-_Carry_That_Weight.lab/1.1/Tue Jul 18 12:05:57 2006// +/16_-_The_End.lab/1.3/Tue Oct 17 21:58:53 2006// +/17_-_Her_Majesty.lab/1.1/Tue Jul 18 12:05:57 2006// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/11final/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +chrish/transcriptions/11final
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/11final/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:matthiasm@dspmac1.elec.qmul.ac.uk:/cvsroot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/12final/01_-_Two_of_Us.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,97 @@ +0.0000000 10.4545957 N +10.4545957 14.6287750 G:maj(*1)/5 +14.6287750 18.8261450 G +18.8261450 24.2596140 G +24.2596140 25.3509520 C +25.3509520 26.4306800 G/3 +26.4306800 27.4768221 A:min7 +27.4768221 28.5901360 C/5 +28.5901360 33.9655550 G +33.9655550 35.0568930 C +35.0568930 36.1598410 G/3 +36.1598410 37.2395690 A:min7 +37.2395690 38.2612470 C/5 +38.2612470 39.8866430 G +39.8866430 40.9547610 D +40.9547610 41.5352600 C +41.5352600 43.0909970 G +43.0909970 44.1591150 D +44.1591150 44.7280040 C +44.7280040 46.3185710 G +46.3185710 47.4099090 C +47.4099090 49.6880511 G +49.6880511 53.9811560 G:maj(*1)/5 +53.9811560 58.3348970 G +58.3348970 63.7335370 G +63.7335370 64.8480950 C +64.8480950 65.9278230 G/3 +65.9278230 67.0307700 A:min7 +67.0307700 68.0756680 C/5 +68.0756680 73.5671880 G +73.5671880 74.6701360 C +74.6701360 75.7382530 G/3 +75.7382530 76.8644210 A:min7 +76.8644210 77.9209290 C/5 +77.9209290 79.5231060 G +79.5231060 80.6492740 D +80.6492740 81.2065530 C +81.2065530 82.8551700 G +82.8551700 83.9116780 D +83.9116780 84.5037860 C +84.5037860 86.0711330 G +86.0711330 87.1740810 C +87.1740810 89.8980346 G +89.8980346 91.4929930 N +91.4929930 93.6872780 Bb +93.6872780 95.9047840 D:min +95.9047840 98.0758500 G:min +98.0758500 102.4528110 A:min +102.4528110 104.5774370 D +104.5774370 110.1850560 G +110.1850560 111.2647840 C +111.2647840 112.3445120 G/3 +112.3445120 113.4126300 A:min7 +113.4126300 114.5039680 C/5 +114.5039680 119.9490470 G +119.9490470 121.0868250 C +121.0868250 122.1433330 G/3 +122.1433330 123.2811110 A:min7 +123.2811110 124.3724480 C/5 +124.3724480 126.0094550 G +126.0094550 127.1356230 D +127.1356230 127.6696820 C +127.6696820 129.3299090 G +129.3299090 130.4560770 D +130.4560770 131.0133560 C +131.0133560 132.6503620 G +132.6503620 133.7765300 C +133.7765300 136.5224678 G +136.5224678 138.1767120 N +138.1767120 140.4058270 Bb +140.4058270 142.6581630 D:min +142.6581630 144.8640580 G:min +144.8640580 149.2874600 A:min +149.2874600 151.4236960 D +151.4236960 156.9384350 G +156.9384350 158.0646030 C +158.0646030 159.1211110 G/3 +159.1211110 160.2463581 A:min7 +160.2463581 161.3618360 C/5 +161.3618360 166.9462350 G +166.9462350 168.0724030 C +168.0724030 169.1289110 G/3 +169.1289110 170.3015190 A:min7 +170.3015190 171.4276870 C/5 +171.4276870 173.0763030 G +173.0763030 174.1792510 D +174.1792510 174.7597500 C +174.7597500 176.4083670 G +176.4083670 177.5461450 D +177.5461450 178.1150340 C +178.1150340 179.7752600 G +179.7752600 180.9594780 C +180.9594780 184.1425769 G +184.1425769 185.3944890 N +185.3944890 189.7946710 G:maj(*1)/5 +189.7946710 211.2364467 G +211.2364467 216.5977779 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/12final/02_-_Dig_a_Pony.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,87 @@ +0.0000000 1.2305890 N +1.2305890 3.6802940 N +3.6802940 5.0502720 G +5.0502720 9.2308530 N +9.2308530 10.6114510 G +10.6114510 12.0046480 D +12.0046480 14.8490920 A +14.8490920 16.2422900 G +16.2422900 17.6703170 D +17.6703170 20.5031510 A +20.5031510 26.5635600 A +26.5635600 29.6053740 F#:min +29.6053740 31.0450110 B:min +31.0450110 34.2377550 G +34.2377550 35.7586620 B:min +35.7586620 37.3376190 G +37.3376190 40.5071420 E +40.5071420 46.8113600 A +46.8113600 50.0041040 F#:min +50.0041040 51.3973010 B:min +51.3973010 54.6132650 G +54.6132650 56.1341720 B:min +56.1341720 57.7131290 G +57.7131290 60.7317230 E +60.7317230 62.2642400 G +62.2642400 63.8199770 D +63.8199770 66.7108610 A +66.7108610 68.1156680 G +68.1156680 69.6249650 D +69.6249650 74.6965360 A +74.6965360 76.2857880 N +76.2857880 82.5236500 A +82.5236500 85.6002940 F#:min +85.6002940 87.1212010 B:min +87.1212010 90.2907250 G +90.2907250 91.8232420 B:min +91.8232420 93.4138090 G +93.4138090 96.5136730 E +96.5136730 102.8295010 A +102.8295010 105.9409750 F#:min +105.9409750 107.4372464 B:min +107.4372464 110.6081850 G +110.6081850 112.1755320 B:min +112.1755320 113.7196590 G +113.7196590 116.7498630 E +116.7498630 118.2939900 G +118.2939900 119.8729470 D +119.8729470 122.7638320 A +122.7638320 124.1918590 G +124.1918590 125.6779360 D +125.6779360 132.2143530 A +132.2143530 138.2863710 A +138.2863710 141.3746250 F#:min +141.3746250 142.8955320 B:min +142.8955320 145.9721760 G +145.9721760 147.4698630 B:min +147.4698630 148.9684151 G +148.9684151 151.9745350 E +151.9745350 158.1278230 A +158.1278230 161.2625170 F#:min +161.2625170 162.7834240 B:min +162.7834240 165.8832870 G +165.8832870 167.4041950 B:min +167.4041950 168.9947610 G +168.9947610 172.0714050 E +172.0714050 178.3756230 A +178.3756230 181.6612470 F#:min +181.6612470 183.1660487 B:min +183.1660487 186.3865070 G +186.3865070 187.9306340 B:min +187.9306340 189.5996461 G +189.5996461 192.6210650 E +192.6210650 194.1768020 G +194.1768020 195.7441490 D +195.7441490 198.7163030 A +198.7163030 200.1559410 G +200.1559410 201.6420180 D +201.6420180 206.7001680 A +206.7001680 208.1900450 N +208.1900450 209.6412920 G +209.6412920 211.0577090 D +211.0577090 213.9718140 A +213.9718140 215.4230610 G +215.4230610 216.8278680 D +216.8278680 219.7419720 A +219.7419720 225.8669270 A +225.8669270 234.8088610 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/12final/03_-_Across_the_Universe.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,65 @@ +0.0000000 0.4038050 N +0.4038050 1.9817680 Db +1.9817680 3.5607250 Db:maj7 +3.5607250 6.7882990 F:min +6.7882990 10.0507020 Ab:maj6 +10.0507020 11.6528790 Db +11.6528790 13.1970060 Bb:min +13.1970060 16.3549200 F:min +16.3549200 19.5012240 Eb:min7 +19.5012240 21.9257200 Ab +21.9257200 23.4718360 Ab:7 +23.4718360 25.0624030 Db +25.0624030 26.6761900 Bb:min +26.6761900 29.8341040 F:min +29.8341040 31.4478910 Eb:min7 +31.4478910 34.6174140 Gb:min +34.6174140 41.0377320 Db +41.0377320 44.3117460 Ab +44.3117460 50.5513980 Ab +50.5513980 53.7158270 Gb +53.7158270 56.8621310 Db +56.8621310 63.2445598 Ab +63.2445598 66.4039156 Gb +66.4039156 69.4821760 Db +69.4821760 71.0263030 Db +71.0263030 72.5588200 Bb:min +72.5588200 75.7051240 F:min +75.7051240 78.8978680 Eb:min7 +78.8978680 80.4216720 Ab +80.4216720 81.9861220 Ab:7 +81.9861220 83.5650790 Db +83.5650790 85.1208160 Bb:min +85.1208160 88.2787300 F:min +88.2787300 91.4366430 Eb:min7 +91.4366430 94.5909040 Ab +94.5909040 95.4072560 Ab:7 +95.4072560 101.7579130 Db +101.7579130 104.9738770 Ab +104.9738770 111.3013150 Ab +111.3013150 114.4243990 Gb +114.4243990 117.5242630 Db +117.5242630 123.7704300 Ab +123.7704300 126.9167340 Gb +126.9167340 129.9817680 Db +129.9817680 131.5491150 Db +131.5491150 133.0932420 Bb:min +133.0932420 136.1350560 F:min +136.1350560 137.7256230 Eb:min7 +137.7256230 140.7906570 Gb:min +140.7906570 142.3115640 Db +142.3115640 143.9253510 Bb:min +143.9253510 146.9903850 F:min +146.9903850 150.0786390 Eb:min7 +150.0786390 151.6124900 Ab +151.6124900 153.2017230 Ab:7 +153.2017230 159.5291600 Db +159.5291600 162.6870740 Ab +162.6870740 168.8751920 Ab +168.8751920 171.9634460 Gb +171.9634460 175.0981400 Db +175.0981400 181.2862580 Ab +181.2862580 184.4906120 Gb +184.4906120 187.4511560 Db +187.4511560 222.7841610 Db +222.7841610 228.5922790 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/12final/04_-_I_Me_Mine.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,81 @@ +0.0000000 0.4020998 N +0.4020998 1.4227890 A:min +1.4227890 2.5721760 A:min/b3 +2.5721760 4.6387520 D +4.6387520 5.6720400 G +5.6720400 6.7169380 E:7 +6.7169380 7.7734460 A:min +7.7734460 8.8415640 A:min/b3 +8.8415640 10.9081400 D +10.9081400 11.8833780 G +11.8833780 12.9514960 E:7 +12.9514960 14.9484120 A:min +14.9484120 15.9817000 A:min +15.9817000 17.0382080 A:min/b3 +17.0382080 19.0467340 D +19.0467340 20.0451920 G +20.0451920 21.0320400 E:7 +21.0320400 23.1218360 A:min +23.1218360 25.1419720 D:min +25.1419720 27.1156680 D:min/b3 +27.1156680 28.1257360 E +28.1257360 29.1474140 F#:hdim7/b7 +29.1474140 31.1095010 E:7 +31.1095010 32.1079590 A:min +32.1079590 33.1528570 G#:aug +33.1528570 34.1513150 C/5 +34.1513150 35.1613830 F#:hdim7 +35.1613830 39.0042850 F:maj7 +39.0042850 46.3998410 A:min7 +46.3998410 50.1266430 D:min7 +50.1266430 53.7953960 A:min7 +53.7953960 57.5105890 E +57.5105890 58.6019270 A:min +58.6019270 59.6236050 A:min/b3 +59.6236050 61.6785710 D +61.6785710 62.6073690 G +62.6073690 63.5942170 E:7 +63.5942170 65.5446930 A:min +65.5446930 66.5199310 A:min +66.5199310 67.4719500 A:min/b3 +67.4719500 69.5036960 D +69.5036960 70.4208840 G +70.4208840 71.4077320 E:7 +71.4077320 73.3465980 A:min +73.3465980 75.2854640 D:min +75.2854640 77.2359410 D:min/b3 +77.2359410 78.1879590 E +78.1879590 79.1748070 F#:hdim7/b7 +79.1748070 81.0904530 E:7 +81.0904530 82.0773010 A:min +82.0773010 83.1221990 G#:aug +83.1221990 84.1554870 C/5 +84.1554870 85.1191150 F#:hdim7 +85.1191150 89.0200680 F:maj7 +89.0200680 96.4156230 A:min7 +96.4156230 100.1192060 D:min7 +100.1192060 103.7415190 A:min7 +103.7415190 107.5031510 E +107.5031510 108.5712690 A:min +108.5712690 109.6277770 A:min/b3 +109.6277770 111.6595230 D +111.6595230 112.5883210 G +112.5883210 113.6216090 E:7 +113.6216090 115.5140360 A:min +115.5140360 116.5124940 A:min +116.5124940 117.4877320 A:min/b3 +117.4877320 119.4382080 D +119.4382080 120.4018360 G +120.4018360 121.4119040 E:7 +121.4119040 123.3507700 A:min +123.3507700 125.2315870 D:min +125.2315870 127.2401130 D:min/b3 +127.2401130 128.1573010 E +128.1573010 129.1557590 F#:hdim7/b7 +129.1557590 131.0481850 E:7 +131.0481850 132.0118140 A:min +132.0118140 133.0915410 G#:aug +133.0915410 134.0667800 C/5 +134.0667800 134.9723580 F#:hdim7 +134.9723580 144.2581028 F:maj7 +144.2581028 145.6558784 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/12final/05_-_Dig_It.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,42 @@ +0.0000000 2.5721760 N +2.5721760 3.7796140 F:7 +3.7796140 4.9173920 Bb +4.9173920 6.0900000 C +6.0900000 7.2393870 Bb +7.2393870 8.4003850 F:7 +8.4003850 9.5381630 Bb +9.5381630 10.6411110 C +10.6411110 11.8485480 Bb +11.8485480 13.0095460 F:7 +13.0095460 14.2053740 Bb +14.2053740 15.3547610 C +15.3547610 16.5041490 Bb +16.5041490 17.6419270 F:7 +17.6419270 18.7797050 Bb +18.7797050 19.9058730 C +19.9058730 21.0784800 Bb +21.0784800 22.2278680 F:7 +22.2278680 23.4120860 Bb +23.4120860 24.5498630 C +24.5498630 25.7573010 Bb +25.7573010 26.9299090 F:7 +26.9299090 28.0909070 Bb +28.0909070 29.2054640 C +29.2054640 30.3780720 Bb +30.3780720 31.5042400 F:7 +31.5042400 32.6187980 Bb +32.6187980 33.7449650 C +33.7449650 34.9291830 Bb +34.9291830 36.0437410 F:7 +36.0437410 37.1699090 Bb +37.1699090 38.2960770 C +38.2960770 39.4338540 Bb +39.4338540 40.5716320 F:7 +40.5716320 41.7210200 Bb +41.7210200 42.8704080 C +42.8704080 43.9617460 Bb +43.9617460 45.1227430 F:7 +45.1227430 46.3301810 Bb +46.3301810 47.4215190 C +47.4215190 48.7689657 Bb +48.7689657 49.9944128 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/12final/06_-_Let_It_Be.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,155 @@ +0.0000000 0.1751573 N +0.1751573 1.8523580 C +1.8523580 3.4545350 G +3.4545350 4.7200220 A:min +4.7200220 5.1263710 A:min/b7 +5.1263710 5.9506800 F:maj7 +5.9506800 6.7749880 F:maj6 +6.7749880 8.4236050 C +8.4236050 10.0141720 G +10.0141720 11.6511790 F +11.6511790 13.3926750 C +13.3926750 15.0180720 C +15.0180720 16.5273690 G +16.5273690 17.7348070 A:min +17.7348070 18.1295460 A:min/b7 +18.1295460 18.9190240 F:maj7 +18.9190240 19.6620630 F:maj6 +19.6620630 21.2642400 C +21.2642400 22.7967570 G +22.7967570 24.3989340 F +24.3989340 26.0359410 C +26.0359410 27.5916780 C +27.5916780 29.2170740 G +29.2170740 30.7612010 A:min +30.7612010 31.5622900 F:maj7 +31.5622900 32.2937180 F:maj6 +32.2937180 33.9307250 C +33.9307250 35.5909520 G +35.5909520 37.1699090 F +37.1699090 37.9826070 C +37.9826070 38.7836960 C/7 +38.7836960 40.4903620 A:min +40.4903620 42.1157590 E:min/b3 +42.1157590 43.7527660 F +43.7527660 45.4246030 C +45.4246030 47.0848290 C +47.0848290 48.7218360 G +48.7218360 50.3124030 F +50.3124030 52.0074600 C +52.0074600 53.6328570 C +53.6328570 55.2234240 G +55.2234240 56.8256000 A:min +56.8256000 57.6034690 F:maj7 +57.6034690 58.4393870 F:maj6 +58.4393870 60.0647840 C +60.0647840 61.7714510 G +61.7714510 63.3736280 F +63.3736280 65.0106340 C +65.0106340 66.6592510 C +66.6592510 68.2962580 G +68.2962580 69.9797050 A:min +69.9797050 70.8504530 F:maj7 +70.8504530 71.6283210 F:maj6 +71.6283210 73.3349880 C +73.3349880 75.0300450 G +75.0300450 76.7483210 F +76.7483210 78.3853280 C +78.3853280 80.2080950 A:min +80.2080950 81.8915410 E:min/b3 +81.8915410 83.5285480 F +83.5285480 85.2003850 C +85.2003850 86.9186620 C +86.9186620 88.5440580 G +88.5440580 90.2158950 F +90.2158950 91.8180720 C +91.8180720 93.5131290 A:min +93.5131290 95.1849650 E:min/b3 +95.1849650 96.8335820 F +96.8335820 98.5402490 C +98.5402490 100.2004760 C +100.2004760 101.8490920 G +101.8490920 103.5209290 F +103.5209290 105.1927660 C +105.1927660 106.9342630 F +106.9342630 108.6641490 C +108.6641490 109.4768480 G +109.4768480 110.3824260 F +110.3824260 112.1587520 C +112.1587520 113.9699090 F +113.9699090 115.6881850 C +115.6881850 116.4892740 G +116.4892740 117.3600220 F +117.3600220 119.1479590 C +119.1479590 120.8314050 C +120.8314050 122.5845120 G +122.5845120 124.2911790 A:min +124.2911790 126.0791150 F +126.0791150 127.7393420 C +127.7393420 129.4460090 G +129.4460090 131.1410650 F +131.1410650 132.9173920 C +132.9173920 134.6588880 C +134.6588880 136.3655550 G +136.3655550 138.1186620 A:min +138.1186620 139.8485480 F +139.8485480 141.5319950 C +141.5319950 143.2154420 G +143.2154420 144.9453280 F +144.9453280 146.6519950 C +146.6519950 148.4399310 A:min +148.4399310 150.2162580 E:min/b3 +150.2162580 151.9809750 F +151.9809750 153.7921310 C +153.7921310 155.5452380 C +155.5452380 157.3331740 G +157.3331740 159.0862810 F +159.0862810 160.8742170 C +160.8742170 162.6505440 C +162.6505440 164.3804300 G +164.3804300 166.1335370 A:min +166.1335370 166.9850938 F:maj7 +166.9850938 167.8982530 F:maj6 +167.8982530 169.5933100 C +169.5933100 171.3115870 G +171.3115870 173.0414730 F +173.0414730 174.7945800 C +174.7945800 176.5825170 C +176.5825170 178.2659630 G +178.2659630 180.0422900 A:min +180.0422900 180.9130380 F:maj7 +180.9130380 181.7257360 F:maj6 +181.7257360 183.4672330 C +183.4672330 185.2203400 G +185.2203400 186.9618360 F +186.9618360 188.6452830 C +188.6452830 190.3635600 A:min +190.3635600 192.1514960 E:min/b3 +192.1514960 193.8697730 F +193.8697730 195.6809290 C +195.6809290 197.4108160 C +197.4108160 199.0826530 G +199.0826530 200.8473690 F +200.8473690 202.5540360 C +202.5540360 204.2723120 A:min +204.2723120 206.0486390 E:min/b3 +206.0486390 207.7901360 F +207.7901360 209.5316320 C +209.5316320 211.2731290 C +211.2731290 213.0146250 G +213.0146250 214.7445120 F +214.7445120 216.4976190 C +216.4976190 218.1810650 A:min +218.1810650 219.9690020 E:min/b3 +219.9690020 221.7337180 F +221.7337180 223.4752150 C +223.4752150 225.1934920 C +225.1934920 226.9698180 G +226.9698180 228.7113150 F +228.7113150 230.5340810 C +230.5340810 232.3800680 F +232.3800680 234.2260540 C +234.2260540 235.1548520 G +235.1548520 236.3390700 F +236.3390700 241.0072816 C +241.0072816 243.2822359 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/12final/07_-_Maggie_Mae.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,14 @@ +0.0000000 0.3446570 N +0.3446570 3.1178450 G +3.1178450 5.7997500 C +5.7997500 11.3377090 G +11.3377090 14.1589340 D +14.1589340 17.0033780 G +17.0033780 19.8478230 C +19.8478230 21.2642400 D/5 +21.2642400 22.6806570 D +22.6806570 25.5831510 G +25.5831510 28.4972560 C +28.4972560 34.1977550 G +34.1977550 38.0845987 D +38.0845987 40.6012595 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/12final/08_-_I've_Got_A_Feeling.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,150 @@ +0.0000000 0.4212474 N +0.4212474 1.9220180 A +1.9220180 3.3384350 D/5 +3.3384350 4.8245120 A +4.8245120 6.2060990 D/5 +6.2060990 7.7037860 A +7.7037860 9.2246930 D/5 +9.2246930 10.6875510 A +10.6875510 12.1620180 D/5 +12.1620180 13.5900450 A +13.5900450 15.0645120 D/5 +15.0645120 16.5273690 A +16.5273690 17.9437860 D/5 +17.9437860 19.4414730 A +19.4414730 20.9043310 D/5 +20.9043310 22.3671880 A +22.3671880 23.8764850 D/5 +23.8764850 25.3509520 A +25.3509520 26.8138090 D/5 +26.8138090 28.2650560 A +28.2650560 29.6582530 D/5 +29.6582530 32.6071880 A:7 +32.6071880 33.3270060 E +33.3270060 34.0584350 G +34.0584350 35.5329020 D +35.5329020 37.0305890 A +37.0305890 38.4470060 D +38.4470060 39.9214730 A +39.9214730 41.3146710 D +41.3146710 42.8471880 A +42.8471880 44.2984350 D/5 +44.2984350 45.7032420 A +45.7032420 47.2357590 D/5 +47.2357590 48.6289560 A +48.6289560 50.0802040 D/5 +50.0802040 51.5430610 A +51.5430610 52.9594780 D/5 +52.9594780 54.4455550 A +54.4455550 55.8503620 D/5 +55.8503620 57.2551700 A +57.2551700 58.7528570 D/5 +58.7528570 60.1808840 A +60.1808840 61.5856910 D/5 +61.5856910 63.0021080 A +63.0021080 64.3836960 D/5 +64.3836960 67.2978000 A:7 +67.2978000 67.9943990 E +67.9943990 68.7258270 G +68.7258270 70.2119040 D +70.2119040 71.6283210 A +71.6283210 73.0679590 D +73.0679590 74.4959860 A +74.4959860 75.9356230 D +75.9356230 78.8497270 E +78.8497270 81.7406120 G +81.7406120 82.4604300 D +82.4604300 83.1802490 D/3 +83.1802490 83.9116780 G +83.9116780 84.6198860 G#:dim7 +84.6198860 90.5177550 A +90.5177550 92.0270520 A +92.0270520 93.4318590 D/5 +93.4318590 94.8831060 A +94.8831060 96.3459630 D/5 +96.3459630 97.7856000 A +97.7856000 99.2252380 D/5 +99.2252380 100.6764850 A +100.6764850 102.0812920 D/5 +102.0812920 103.5209290 A +103.5209290 104.9953960 D/5 +104.9953960 106.4466430 A +106.4466430 107.8978910 D/5 +107.8978910 109.3375280 A +109.3375280 110.7771650 D/5 +110.7771650 112.2051920 A +112.2051920 113.5635600 D/5 +113.5635600 116.5241040 A:7 +116.5241040 117.2555320 E +117.2555320 117.9521310 G +117.9521310 119.4382080 D +119.4382080 120.8894550 A +120.8894550 122.2826530 D +122.2826530 123.7455100 A +123.7455100 125.1619270 D +125.1619270 126.6247840 A +126.6247840 128.0644210 D +128.0644210 129.5504980 A +129.5504980 130.9785260 D +130.9785260 132.4413830 A +132.4413830 133.8461900 D +133.8461900 135.2858270 A +135.2858270 136.6790240 D +136.6790240 138.1534920 A +138.1534920 139.5699090 D +139.5699090 141.0327660 A +141.0327660 142.4027430 D +142.4027430 143.8656000 A +143.8656000 145.2936280 D +145.2936280 146.7216550 A +146.7216550 148.2193420 D +148.2193420 149.6473690 A +149.6473690 151.0986160 D +151.0986160 152.5846930 A +152.5846930 154.0243310 D +154.0243310 155.4407480 A:7 +155.4407480 156.1605660 A:dim7 +156.1605660 156.8339450 B:hdim7/b7 +156.8339450 158.2851920 A +158.2851920 158.9934010 B:hdim7/b7 +158.9934010 159.7132190 A:dim7 +159.7132190 161.1876870 A:7 +161.1876870 162.5924940 D/5 +162.5924940 164.0089110 A +164.0089110 165.4717680 D/5 +165.4717680 166.9230150 A +166.9230150 168.4090920 D/5 +168.4090920 169.8719500 A +169.8719500 171.3464170 D/5 +171.3464170 172.7628340 A +172.7628340 174.2241143 D +174.2241143 175.6653280 A +175.6653280 177.1049650 D +177.1049650 178.5562130 A +178.5562130 180.0074600 D/5 +180.0074600 181.4238770 A +181.4238770 182.8751240 D/5 +182.8751240 184.3263710 A +184.3263710 185.7776190 D +185.7776190 187.1940360 A +187.1940360 188.6104530 D +188.6104530 190.0500900 A +190.0500900 190.7582990 A:dim7 +190.7582990 191.4432870 B:hdim7/b7 +191.4432870 192.9061450 A:7 +192.9061450 193.6027430 A:dim7 +193.6027430 194.3690020 B:hdim7/b7 +194.3690020 195.7505890 A:7 +195.7505890 196.4355780 B:hdim7/b7 +196.4355780 197.1321760 A:dim7 +197.1321760 198.5834240 A:7 +198.5834240 199.2335820 A:dim7 +199.2335820 199.9766210 B:hdim7/b7 +199.9766210 201.3582080 A:7 +201.3582080 202.0431970 B:hdim7/b7 +202.0431970 202.7630150 A:dim7 +202.7630150 204.1562130 A:7 +204.1562130 204.8528110 A:dim7 +204.8528110 205.5494100 B:hdim7/b7 +205.5494100 208.8091711 A:7 +208.8091711 217.9276030 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/12final/09_-_One_After_909.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,57 @@ +0.000000 0.433805 N +0.433805 1.421269 B:(b3,5) +1.421269 9.107074 B +9.107074 19.602494 B +19.602494 22.168299 B +22.168299 24.838594 E +24.838594 26.162131 B +26.162131 27.450839 F# +27.450839 30.028253 B +30.028253 40.546893 B +40.546893 43.252018 B +43.252018 45.875873 E +45.875873 47.211020 B +47.211020 48.488117 F# +48.488117 51.111972 B +51.111972 53.747437 E +53.747437 56.394512 B +56.394512 59.041587 C# +59.041587 61.688662 F# +61.688662 64.370566 E +64.370566 66.994421 B +66.994421 69.560226 C# +69.560226 70.906984 F# +70.906984 72.218911 F#:aug +72.218911 82.749160 B +82.749160 85.431065 B +85.431065 87.985260 E +87.985260 89.285578 B +89.285578 90.597505 F# +90.597505 93.128480 B +93.128480 106.247755 B +106.247755 108.883219 E +108.883219 110.218367 B +110.218367 111.530294 F# +111.530294 114.130929 B +114.130929 116.778004 E +116.778004 119.436689 B +119.436689 122.118594 C# +122.118594 124.754058 F# +124.754058 127.389523 E +127.389523 130.106258 B +130.106258 132.706893 C# +132.706893 135.330748 F#:aug +135.330748 146.058367 B +146.058367 148.635782 B +148.635782 151.259637 E +151.259637 152.583174 B +152.583174 153.929931 F# +153.929931 155.218639 B +155.218639 156.565396 F# +156.565396 157.900544 B +157.900544 159.154421 F# +159.154421 161.101410 B +161.101410 161.580907 F#:aug +161.580907 164.969228 B +164.969228 169.468916 E +169.468916 175.519561 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/12final/10_-_The_Long_and_Winding_Road.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,97 @@ +0.0000000 0.3829522 N +0.3829522 3.7384350 C:min +3.7384350 4.5163030 Ab +4.5163030 7.6045570 Ab:maj(2)/2 +7.6045570 9.4273240 Eb +9.4273240 11.1456000 Eb:7/b7 +11.1456000 14.6750340 Ab +14.6750340 16.3700900 Ab +16.3700900 18.1464170 G:min +18.1464170 21.6990700 C:min +21.6990700 23.4289560 F:min7 +23.4289560 25.2517230 Bb +25.2517230 28.9553060 Eb:7 +28.9553060 30.7316320 Ab +30.7316320 32.4847390 G:min +32.4847390 36.0606120 C:min +36.0606120 37.8485480 F:min +37.8485480 39.7641950 Bb +39.7641950 43.3632870 Eb +43.3632870 46.4712524 C:min +46.4712524 47.2990700 Ab +47.2990700 50.4221540 Ab:maj(2)/2 +50.4221540 52.3145800 Eb +52.3145800 54.2186160 Eb:7/b7 +54.2186160 57.7248290 Ab +57.7248290 59.5011560 Ab +59.5011560 61.3819720 G:min +61.3819720 64.9926750 C:min +64.9926750 66.8270520 F:min7 +66.8270520 68.6382080 Bb +68.6382080 72.2140810 Eb:7 +72.2140810 74.0368480 Ab +74.0368480 75.8828340 G:min +75.8828340 79.5399770 C:min +79.5399770 81.3975730 F:min +81.3975730 83.2667800 Bb +83.2667800 86.9239220 Eb +86.9239220 88.7350790 Eb/5 +88.7350790 90.5810650 Ab +90.5810650 92.3225620 Eb/3 +92.3225620 93.1817000 F:min +93.1817000 94.1104980 Bb +94.1104980 95.9100450 Eb/5 +95.9100450 97.7095910 Ab +97.7095910 99.4975280 Eb/3 +99.4975280 100.3798860 F:min +100.3798860 101.2506340 Bb +101.2506340 104.4885143 C:min +104.4885143 105.3489560 Ab +105.3489560 108.4139900 Ab:maj(2)/2 +108.4139900 110.2715870 Eb +110.2715870 112.0363030 Eb:7 +112.0363030 115.6121760 Ab +115.6121760 117.4465530 Ab +117.4465530 119.2228790 G:min +119.2228790 122.8451920 C:min +122.8451920 124.7027890 F:min7 +124.7027890 126.5719950 Bb +126.5719950 130.3104080 Eb:7 +130.3104080 132.1563940 Ab +132.1563940 133.9675510 G:min +133.9675510 137.6943530 C:min +137.6943530 139.4939000 F:min +139.4939000 141.3747160 Bb +141.3747160 145.0550790 Eb +145.0550790 146.8778450 Eb/5 +146.8778450 148.6309520 Ab +148.6309520 150.4769380 Eb/3 +150.4769380 151.3592970 F:min +151.3592970 152.2532650 Bb +152.2532650 154.0063710 Eb/5 +154.0063710 155.8291380 Ab +155.8291380 157.6170740 Eb/3 +157.6170740 158.4878230 F:min +158.4878230 159.3585710 Bb +159.3585710 162.5513150 C:min +162.5513150 163.3988430 Ab +163.3988430 166.5103170 Ab:maj(2)/2 +166.5103170 168.3330830 Eb +168.3330830 170.2371200 Eb:7/b7 +170.2371200 173.8594330 Ab +173.8594330 175.6821990 Ab +175.6821990 177.5397950 G:min +177.5397950 181.2201580 C:min +181.2201580 183.0197050 F:min7 +183.0197050 184.9121310 Bb +184.9121310 188.4299540 Eb:7 +188.4299540 190.3688200 Ab +190.3688200 192.0638770 G:min +192.0638770 195.8022900 C:min +195.8022900 197.6714960 F:min +197.6714960 199.4942630 Bb +199.4942630 202.7682760 Eb +202.7682760 203.5925850 Ab/5 +203.5925850 206.9362580 Ab:maj(2)/2 +206.9362580 215.8701686 Eb +215.8701686 217.8615202 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/12final/11_-_For_You_Blue.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,62 @@ +0.0000000 3.3699796 N +3.3699796 4.3850340 A +4.3850340 6.1845800 D +6.1845800 7.9492970 G:7/3 +7.9492970 9.7604530 E:7 +9.7604530 13.4408160 A:7 +13.4408160 15.2519720 D +15.2519720 17.1754072 G:7 +17.1754072 20.8247610 D +20.8247610 24.5747840 G:7 +24.5747840 28.3131970 D +28.3131970 30.1707930 A +30.1707930 32.0748290 G:7 +32.0748290 33.9788660 D +33.9788660 35.8132420 A +35.8132420 37.7056680 D +37.7056680 39.5400450 G:7 +39.5400450 43.2668480 D +43.2668480 47.0400900 G:7 +47.0400900 50.7668930 D +50.7668930 52.6128790 A +52.6128790 54.4704760 G:7 +54.4704760 56.7152244 D +56.7152244 58.2553280 A +58.2553280 60.1129250 D +60.1129250 62.0053510 G:7 +62.0053510 65.6973240 D +65.6973240 69.4241260 G:7 +69.4241260 73.1160990 D +73.1160990 75.0777835 A +75.0777835 76.8661220 G:7 +76.8661220 78.7121080 D +78.7121080 80.5813150 A +80.5813150 82.4621310 D +82.4621310 84.4409654 G:7 +84.4409654 88.0813600 D +88.0813600 91.8197730 G:7 +91.8197730 95.5581850 D +95.5581850 99.2965980 A +99.2965980 101.1376823 D +101.1376823 102.9885710 A +102.9885710 104.8906141 D +104.8906141 106.6921540 G:7 +106.6921540 110.4537860 D +110.4537860 114.1689790 G:7 +114.1689790 117.9073920 D +117.9073920 119.7533780 A +119.7533780 121.5993650 G:7 +121.5993650 123.4829446 D +123.4829446 125.3029470 A +125.3029470 127.1837640 D +127.1837640 129.0994100 G:7 +129.0994100 132.7913830 D +132.7913830 136.5990583 G:7 +136.5990583 140.3136948 D +140.3136948 142.1374140 A +142.1374140 144.0530610 G:7 +144.0530610 145.0048596 D +145.0048596 145.9430925 G +145.9430925 146.7664398 A:aug +146.7664398 149.4471054 D +149.4471054 152.6639041 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/12final/12_-_Get_Back.lab Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,84 @@ +0.0000000 0.4020998 N +0.4020998 1.9147611 A:(1) +1.9147611 11.4119763 N +11.4119763 13.3267374 A:7 +13.3267374 17.2711453 N +17.2711453 20.6985677 G:(1,5) +20.6985677 27.4755780 A +27.4755780 27.9631970 G/2 +27.9631970 28.4508160 D/5 +28.4508160 32.3517680 A +32.3517680 34.2906340 D +34.2906340 36.2295010 A +36.2295010 40.0956230 A +40.0956230 42.0112690 D +42.0112690 43.9269160 A +43.9269160 47.8046480 A:7 +47.8046480 49.7551240 D +49.7551240 50.7651920 A +50.7651920 51.2063710 G/2 +51.2063710 51.6939900 D/5 +51.6939900 55.5485030 A +55.5485030 57.4757590 D +57.4757590 59.3681850 A +59.3681850 63.3504080 A +63.3504080 65.2660540 D +65.2660540 66.2761220 A +66.2761220 66.7637410 G +66.7637410 67.2397500 D +67.2397500 71.0942630 A +71.0942630 73.0215190 D +73.0215190 74.0083670 A +74.0083670 74.4843760 G/2 +74.4843760 74.9952150 D/5 +74.9952150 78.8845570 A:min7 +78.8845570 80.8002040 D +80.8002040 81.7638320 A +81.7638320 82.2978910 G/2 +82.2978910 82.7622900 D/5 +82.7622900 88.4279590 A:7 +88.4279590 92.6191600 D +92.6191600 96.4968930 A +96.4968930 98.4125390 D +98.4125390 99.4458270 A +99.4458270 99.9450560 G/2 +99.9450560 100.4326750 D/5 +100.4326750 104.2871880 A +104.2871880 106.2608840 D +106.2608840 107.2593420 A +107.2593420 107.7469610 G/2 +107.7469610 108.2461900 D/5 +108.2461900 112.1935820 A +112.1935820 114.1788880 D +114.1788880 116.1177550 A +116.1177550 120.0535370 A +120.0535370 122.0040130 D +122.0040130 122.9676410 A +122.9676410 123.9428790 G/2 +123.9428790 127.8438320 A:7 +127.8438320 129.8059180 D +129.8059180 130.7927660 A +130.7927660 131.2455550 G/2 +131.2455550 131.7563940 D/5 +131.7563940 135.6225170 A:7 +135.6225170 137.5497730 D +137.5497730 139.4770290 A +139.4770290 143.3083210 A +143.3083210 145.2587980 D +145.2587980 146.2456460 A +146.2456460 146.7332650 G/2 +146.7332650 147.2441040 D/5 +147.2441040 151.1102260 A +151.1102260 153.0607020 D +153.0607020 154.0243310 A +154.0243310 154.5235600 G/2 +154.5235600 155.0227890 D/5 +155.0227890 158.8540810 A:7 +158.8540810 160.7813370 D +160.7813370 161.4895460 A +161.4895460 162.1745350 G/2 +162.1745350 162.6969840 D/5 +162.6969840 166.4237860 A:7 +166.4237860 168.1926168 D +168.1926168 174.2815571 D:7 +174.2815571 186.9189805 N
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/12final/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +/01_-_Two_of_Us.lab/1.1/Sun Jul 23 18:26:12 2006// +/02_-_Dig_a_Pony.lab/1.3/Tue Oct 17 21:59:32 2006// +/03_-_Across_the_Universe.lab/1.3/Tue Oct 17 21:59:32 2006// +/04_-_I_Me_Mine.lab/1.1/Sun Jul 23 18:26:12 2006// +/05_-_Dig_It.lab/1.1/Sun Jul 23 18:26:12 2006// +/06_-_Let_It_Be.lab/1.1/Sun Jul 23 18:26:12 2006// +/07_-_Maggie_Mae.lab/1.1/Sun Jul 23 18:26:12 2006// +/08_-_I've_Got_A_Feeling.lab/1.1/Sun Jul 23 18:26:12 2006// +/09_-_One_After_909.lab/1.2/Wed Aug 2 12:22:35 2006// +/10_-_The_Long_and_Winding_Road.lab/1.2/Wed Aug 2 12:22:35 2006// +/11_-_For_You_Blue.lab/1.2/Tue Oct 17 21:59:32 2006// +/12_-_Get_Back.lab/1.2/Wed Aug 2 12:22:35 2006// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/12final/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +chrish/transcriptions/12final
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/12final/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:matthiasm@dspmac1.elec.qmul.ac.uk:/cvsroot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +A D/01final//// +A D/02final//// +A D/03final//// +A D/04final//// +A D/05final//// +A D/06final//// +A D/07final//// +A D/08final//// +A D/09final//// +A D/10CD1final//// +A D/10CD2final//// +A D/11final//// +A D/12final////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +chrish/transcriptions
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/transcriptions/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:matthiasm@dspmac1.elec.qmul.ac.uk:/cvsroot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/01tuning/01_-_I_Saw_Her_Standing_There.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/01tuning/02_-_Misery.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +434 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/01tuning/03_-_Anna_(Go_To_Him).tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +430 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/01tuning/04_-_Chains.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +430 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/01tuning/05_-_Boys.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +430 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/01tuning/06_-_Ask_Me_Why.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/01tuning/07_-_Please_Please_Me.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +436 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/01tuning/08_-_Love_Me_Do.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/01tuning/09_-_P._S._I_Love_You.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/01tuning/10_-_Baby_It's_You.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/01tuning/11_-_Do_You_Want_To_Know_A_Secret.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +431 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/01tuning/12_-_A_Taste_Of_Honey.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +435 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/01tuning/13_-_There's_A_Place.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/01tuning/14_-_Twist_And_Shout.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/01tuning/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +/01_-_I_Saw_Her_Standing_There.tun/1.1/Tue Aug 15 13:12:44 2006// +/02_-_Misery.tun/1.1/Tue Aug 15 13:12:44 2006// +/03_-_Anna_(Go_To_Him).tun/1.1/Tue Aug 15 13:12:44 2006// +/04_-_Chains.tun/1.1/Tue Aug 15 13:12:44 2006// +/05_-_Boys.tun/1.1/Tue Aug 15 13:12:44 2006// +/06_-_Ask_Me_Why.tun/1.1/Tue Aug 15 13:12:44 2006// +/07_-_Please_Please_Me.tun/1.1/Tue Aug 15 13:12:44 2006// +/08_-_Love_Me_Do.tun/1.1/Tue Aug 15 13:12:44 2006// +/09_-_P._S._I_Love_You.tun/1.1/Tue Aug 15 13:12:44 2006// +/10_-_Baby_It's_You.tun/1.1/Tue Aug 15 13:12:44 2006// +/11_-_Do_You_Want_To_Know_A_Secret.tun/1.1/Tue Aug 15 13:12:44 2006// +/12_-_A_Taste_Of_Honey.tun/1.1/Tue Aug 15 13:12:44 2006// +/13_-_There's_A_Place.tun/1.1/Tue Aug 15 13:12:44 2006// +/14_-_Twist_And_Shout.tun/1.1/Tue Aug 15 13:12:44 2006// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/01tuning/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +chrish/tuning/01tuning
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/01tuning/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:matthiasm@dspmac1.elec.qmul.ac.uk:/cvsroot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/02tuning/01_-_It_Won't_Be_Long.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/02tuning/02_-_All_I've_Got_To_Do.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/02tuning/03_-_All_My_Loving.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/02tuning/04_-_Don't_Bother_Me.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/02tuning/05_-_Little_Child.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/02tuning/06_-_Till_There_Was_You.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/02tuning/07_-_Please_Mister_Postman.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/02tuning/08_-_Roll_Over_Beethoven.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +437 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/02tuning/09_-_Hold_Me_Tight.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/02tuning/10_-_You_Really_Got_A_Hold_On_Me.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/02tuning/11_-_I_Wanna_Be_Your_Man.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +439 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/02tuning/12_-_Devil_In_Her_Heart.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/02tuning/13_-_Not_A_Second_Time.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/02tuning/14_-_Money.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/02tuning/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +/01_-_It_Won't_Be_Long.tun/1.1/Tue Aug 15 13:13:47 2006// +/02_-_All_I've_Got_To_Do.tun/1.1/Tue Aug 15 13:13:47 2006// +/03_-_All_My_Loving.tun/1.1/Tue Aug 15 13:13:47 2006// +/04_-_Don't_Bother_Me.tun/1.1/Tue Aug 15 13:13:47 2006// +/05_-_Little_Child.tun/1.1/Tue Aug 15 13:13:47 2006// +/06_-_Till_There_Was_You.tun/1.1/Tue Aug 15 13:13:47 2006// +/07_-_Please_Mister_Postman.tun/1.1/Tue Aug 15 13:13:47 2006// +/08_-_Roll_Over_Beethoven.tun/1.1/Tue Aug 15 13:13:47 2006// +/09_-_Hold_Me_Tight.tun/1.1/Tue Aug 15 13:13:47 2006// +/10_-_You_Really_Got_A_Hold_On_Me.tun/1.1/Tue Aug 15 13:13:47 2006// +/11_-_I_Wanna_Be_Your_Man.tun/1.1/Tue Aug 15 13:13:47 2006// +/12_-_Devil_In_Her_Heart.tun/1.1/Tue Aug 15 13:13:47 2006// +/13_-_Not_A_Second_Time.tun/1.1/Tue Aug 15 13:13:47 2006// +/14_-_Money.tun/1.1/Tue Aug 15 13:13:47 2006// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/02tuning/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +chrish/tuning/02tuning
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/02tuning/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:matthiasm@dspmac1.elec.qmul.ac.uk:/cvsroot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/03tuning/01_-_A_Hard_Day's_Night.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/03tuning/02_-_I_Should_Have_Known_Better.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/03tuning/03_-_If_I_Fell.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/03tuning/04_-_I'm_Happy_Just_To_Dance_With_You.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +442 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/03tuning/05_-_And_I_Love_Her.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/03tuning/06_-_Tell_Me_Why.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/03tuning/07_-_Can't_Buy_Me_Love.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +442 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/03tuning/08_-_Any_Time_At_All.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/03tuning/09_-_I'll_Cry_Instead.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/03tuning/10_-_Things_We_Said_Today.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/03tuning/11_-_When_I_Get_Home.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +442 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/03tuning/12_-_You_Can't_Do_That.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +442 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/03tuning/13_-_I'll_Be_Back.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/03tuning/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,14 @@ +/01_-_A_Hard_Day's_Night.tun/1.1/Tue Aug 15 13:14:26 2006// +/02_-_I_Should_Have_Known_Better.tun/1.1/Tue Aug 15 13:14:26 2006// +/03_-_If_I_Fell.tun/1.1/Tue Aug 15 13:14:26 2006// +/04_-_I'm_Happy_Just_To_Dance_With_You.tun/1.1/Tue Aug 15 13:14:26 2006// +/05_-_And_I_Love_Her.tun/1.1/Tue Aug 15 13:14:26 2006// +/06_-_Tell_Me_Why.tun/1.1/Tue Aug 15 13:14:26 2006// +/07_-_Can't_Buy_Me_Love.tun/1.1/Tue Aug 15 13:14:26 2006// +/08_-_Any_Time_At_All.tun/1.1/Tue Aug 15 13:14:26 2006// +/09_-_I'll_Cry_Instead.tun/1.1/Tue Aug 15 13:14:26 2006// +/10_-_Things_We_Said_Today.tun/1.1/Tue Aug 15 13:14:26 2006// +/11_-_When_I_Get_Home.tun/1.1/Tue Aug 15 13:14:26 2006// +/12_-_You_Can't_Do_That.tun/1.1/Tue Aug 15 13:14:26 2006// +/13_-_I'll_Be_Back.tun/1.1/Tue Aug 15 13:14:26 2006// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/03tuning/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +chrish/tuning/03tuning
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/03tuning/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:matthiasm@dspmac1.elec.qmul.ac.uk:/cvsroot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/04tuning/01_-_No_Reply.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/04tuning/02_-_I'm_a_Loser.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/04tuning/03_-_Baby's_In_Black.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/04tuning/04_-_Rock_and_Roll_Music.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/04tuning/05_-_I'll_Follow_the_Sun.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/04tuning/06_-_Mr._Moonlight.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +442 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/04tuning/07_-_Kansas_City-_Hey,_Hey,_Hey,_Hey.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/04tuning/08_-_Eight_Days_a_Week.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/04tuning/09_-_Words_of_Love.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +441 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/04tuning/10_-_Honey_Don't.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +442 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/04tuning/11_-_Every_Little_Thing.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/04tuning/12_-_I_Don't_Want_to_Spoil_the_Party.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +442 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/04tuning/13_-_What_You're_Doing.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/04tuning/14_-_Everybody's_Trying_to_Be_My_Baby.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +442 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/04tuning/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +/01_-_No_Reply.tun/1.1/Tue Aug 15 13:15:08 2006// +/02_-_I'm_a_Loser.tun/1.1/Tue Aug 15 13:15:08 2006// +/03_-_Baby's_In_Black.tun/1.1/Tue Aug 15 13:15:08 2006// +/04_-_Rock_and_Roll_Music.tun/1.1/Tue Aug 15 13:15:08 2006// +/05_-_I'll_Follow_the_Sun.tun/1.1/Tue Aug 15 13:15:08 2006// +/06_-_Mr._Moonlight.tun/1.1/Tue Aug 15 13:15:08 2006// +/07_-_Kansas_City-_Hey,_Hey,_Hey,_Hey.tun/1.1/Tue Aug 15 13:15:08 2006// +/08_-_Eight_Days_a_Week.tun/1.1/Tue Aug 15 13:15:08 2006// +/09_-_Words_of_Love.tun/1.1/Tue Aug 15 13:15:08 2006// +/10_-_Honey_Don't.tun/1.1/Tue Aug 15 13:15:08 2006// +/11_-_Every_Little_Thing.tun/1.1/Tue Aug 15 13:15:08 2006// +/12_-_I_Don't_Want_to_Spoil_the_Party.tun/1.1/Tue Aug 15 13:15:08 2006// +/13_-_What_You're_Doing.tun/1.1/Tue Aug 15 13:15:08 2006// +/14_-_Everybody's_Trying_to_Be_My_Baby.tun/1.1/Tue Aug 15 13:15:08 2006// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/04tuning/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +chrish/tuning/04tuning
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/04tuning/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:matthiasm@dspmac1.elec.qmul.ac.uk:/cvsroot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/05tuning/01_-_Help!.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/05tuning/02_-_The_Night_Before.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/05tuning/03_-_You've_Got_To_Hide_Your_Love_Away.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +434 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/05tuning/04_-_I_Need_You.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +428 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/05tuning/05_-_Another_Girl.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +431 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/05tuning/06_-_You're_Going_to_Lose_That_Girl.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +434 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/05tuning/07_-_Ticket_To_Ride.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +430 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/05tuning/08_-_Act_Naturally.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +441 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/05tuning/09_-_It's_Only_Love.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +435 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/05tuning/10_-_You_Like_Me_Too_Much.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +435 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/05tuning/11_-_Tell_Me_What_You_See.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +435 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/05tuning/12_-_I've_Just_Seen_a_Face.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +435 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/05tuning/13_-_Yesterday.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +436 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/05tuning/14_-_Dizzy_Miss_Lizzie.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +436 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/05tuning/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +/01_-_Help!.tun/1.1/Tue Aug 15 13:16:00 2006// +/02_-_The_Night_Before.tun/1.1/Tue Aug 15 13:16:00 2006// +/03_-_You've_Got_To_Hide_Your_Love_Away.tun/1.1/Tue Aug 15 13:16:00 2006// +/04_-_I_Need_You.tun/1.1/Tue Aug 15 13:16:00 2006// +/05_-_Another_Girl.tun/1.1/Tue Aug 15 13:16:00 2006// +/06_-_You're_Going_to_Lose_That_Girl.tun/1.1/Tue Aug 15 13:16:00 2006// +/07_-_Ticket_To_Ride.tun/1.1/Tue Aug 15 13:16:00 2006// +/08_-_Act_Naturally.tun/1.1/Tue Aug 15 13:16:00 2006// +/09_-_It's_Only_Love.tun/1.1/Tue Aug 15 13:16:00 2006// +/10_-_You_Like_Me_Too_Much.tun/1.1/Tue Aug 15 13:16:00 2006// +/11_-_Tell_Me_What_You_See.tun/1.1/Tue Aug 15 13:16:00 2006// +/12_-_I've_Just_Seen_a_Face.tun/1.1/Tue Aug 15 13:16:00 2006// +/13_-_Yesterday.tun/1.1/Tue Aug 15 13:16:00 2006// +/14_-_Dizzy_Miss_Lizzie.tun/1.1/Tue Aug 15 13:16:00 2006// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/05tuning/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +chrish/tuning/05tuning
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/05tuning/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:matthiasm@dspmac1.elec.qmul.ac.uk:/cvsroot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/06tuning/01_-_Drive_My_Car.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/06tuning/02_-_Norwegian_Wood_(This_Bird_Has_Flown).tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/06tuning/03_-_You_Won't_See_Me.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/06tuning/04_-_Nowhere_Man.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +433 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/06tuning/05_-_Think_For_Yourself.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +436 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/06tuning/06_-_The_Word.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/06tuning/07_-_Michelle.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/06tuning/08_-_What_Goes_On.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +439 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/06tuning/09_-_Girl.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +437 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/06tuning/10_-_I'm_Looking_Through_You.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/06tuning/11_-_In_My_Life.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +442 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/06tuning/12_-_Wait.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +439 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/06tuning/13_-_If_I_Needed_Someone.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +441 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/06tuning/14_-_Run_For_Your_Life.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +433 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/06tuning/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +/01_-_Drive_My_Car.tun/1.1/Tue Aug 15 13:16:32 2006// +/02_-_Norwegian_Wood_(This_Bird_Has_Flown).tun/1.1/Tue Aug 15 13:16:32 2006// +/03_-_You_Won't_See_Me.tun/1.1/Tue Aug 15 13:16:32 2006// +/04_-_Nowhere_Man.tun/1.1/Tue Aug 15 13:16:32 2006// +/05_-_Think_For_Yourself.tun/1.1/Tue Aug 15 13:16:32 2006// +/06_-_The_Word.tun/1.1/Tue Aug 15 13:16:32 2006// +/07_-_Michelle.tun/1.1/Tue Aug 15 13:16:32 2006// +/08_-_What_Goes_On.tun/1.1/Tue Aug 15 13:16:32 2006// +/09_-_Girl.tun/1.1/Tue Aug 15 13:16:32 2006// +/10_-_I'm_Looking_Through_You.tun/1.1/Tue Aug 15 13:16:32 2006// +/11_-_In_My_Life.tun/1.1/Tue Aug 15 13:16:32 2006// +/12_-_Wait.tun/1.1/Tue Aug 15 13:16:32 2006// +/13_-_If_I_Needed_Someone.tun/1.1/Tue Aug 15 13:16:32 2006// +/14_-_Run_For_Your_Life.tun/1.1/Tue Aug 15 13:16:32 2006// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/06tuning/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +chrish/tuning/06tuning
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/06tuning/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:matthiasm@dspmac1.elec.qmul.ac.uk:/cvsroot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/07tuning/01_-_Taxman.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +441 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/07tuning/02_-_Eleanor_Rigby.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/07tuning/03_-_I'm_Only_Sleeping.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +443 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/07tuning/04_-_Love_You_To.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/07tuning/05_-_Here,_There_And_Everywhere.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/07tuning/06_-_Yellow_Submarine.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/07tuning/07_-_She_Said_She_Said.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +443 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/07tuning/08_-_Good_Day_Sunshine.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/07tuning/09_-_And_Your_Bird_Can_Sing.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/07tuning/10_-_For_No_One.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +443 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/07tuning/11_-_Doctor_Robert.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/07tuning/12_-_I_Want_To_Tell_You.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/07tuning/13_-_Got_To_Get_You_Into_My_Life.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/07tuning/14_-_Tomorrow_Never_Knows.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +439 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/07tuning/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +/01_-_Taxman.tun/1.1/Tue Aug 15 13:17:14 2006// +/02_-_Eleanor_Rigby.tun/1.1/Tue Aug 15 13:17:14 2006// +/03_-_I'm_Only_Sleeping.tun/1.1/Tue Aug 15 13:17:14 2006// +/04_-_Love_You_To.tun/1.1/Tue Aug 15 13:17:14 2006// +/05_-_Here,_There_And_Everywhere.tun/1.1/Tue Aug 15 13:17:14 2006// +/06_-_Yellow_Submarine.tun/1.1/Tue Aug 15 13:17:14 2006// +/07_-_She_Said_She_Said.tun/1.1/Tue Aug 15 13:17:14 2006// +/08_-_Good_Day_Sunshine.tun/1.1/Tue Aug 15 13:17:14 2006// +/09_-_And_Your_Bird_Can_Sing.tun/1.1/Tue Aug 15 13:17:14 2006// +/10_-_For_No_One.tun/1.1/Tue Aug 15 13:17:14 2006// +/11_-_Doctor_Robert.tun/1.1/Tue Aug 15 13:17:14 2006// +/12_-_I_Want_To_Tell_You.tun/1.1/Tue Aug 15 13:17:14 2006// +/13_-_Got_To_Get_You_Into_My_Life.tun/1.1/Tue Aug 15 13:17:14 2006// +/14_-_Tomorrow_Never_Knows.tun/1.1/Tue Aug 15 13:17:14 2006// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/07tuning/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +chrish/tuning/07tuning
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/07tuning/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:matthiasm@dspmac1.elec.qmul.ac.uk:/cvsroot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/08tuning/01_-_Sgt._Pepper's_Lonely_Hearts_Club_Band.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/08tuning/02_-_With_A_Little_Help_From_My_Friends.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/08tuning/03_-_Lucy_In_The_Sky_With_Diamonds.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/08tuning/04_-_Getting_Better.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/08tuning/05_-_Fixing_A_Hole.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/08tuning/06_-_She's_Leaving_Home.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +436 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/08tuning/07_-_Being_For_The_Benefit_Of_Mr._Kite!.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +436 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/08tuning/08_-_Within_You_Without_You.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/08tuning/09_-_When_I'm_Sixty-Four.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +436 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/08tuning/10_-_Lovely_Rita.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +425 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/08tuning/11_-_Good_Morning_Good_Morning.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +436 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/08tuning/12_-_Sgt._Pepper's_Lonely_Hearts_Club_Band_(Reprise).tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/08tuning/13_-_A_Day_In_The_Life.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +436 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/08tuning/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,14 @@ +/01_-_Sgt._Pepper's_Lonely_Hearts_Club_Band.tun/1.1/Tue Aug 15 13:17:53 2006// +/02_-_With_A_Little_Help_From_My_Friends.tun/1.1/Tue Aug 15 13:17:53 2006// +/03_-_Lucy_In_The_Sky_With_Diamonds.tun/1.1/Tue Aug 15 13:17:53 2006// +/04_-_Getting_Better.tun/1.1/Tue Aug 15 13:17:53 2006// +/05_-_Fixing_A_Hole.tun/1.1/Tue Aug 15 13:17:53 2006// +/06_-_She's_Leaving_Home.tun/1.1/Tue Aug 15 13:17:53 2006// +/07_-_Being_For_The_Benefit_Of_Mr._Kite!.tun/1.1/Tue Aug 15 13:17:53 2006// +/08_-_Within_You_Without_You.tun/1.1/Tue Aug 15 13:17:53 2006// +/09_-_When_I'm_Sixty-Four.tun/1.1/Tue Aug 15 13:17:53 2006// +/10_-_Lovely_Rita.tun/1.1/Tue Aug 15 13:17:53 2006// +/11_-_Good_Morning_Good_Morning.tun/1.1/Tue Aug 15 13:17:53 2006// +/12_-_Sgt._Pepper's_Lonely_Hearts_Club_Band_(Reprise).tun/1.1/Tue Aug 15 13:17:53 2006// +/13_-_A_Day_In_The_Life.tun/1.1/Tue Aug 15 13:17:53 2006// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/08tuning/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +chrish/tuning/08tuning
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/08tuning/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:matthiasm@dspmac1.elec.qmul.ac.uk:/cvsroot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/09tuning/01_-_Magical_Mystery_Tour.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/09tuning/02_-_The_Fool_On_The_Hill.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/09tuning/03_-_Flying.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +436 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/09tuning/04_-_Blue_Jay_Way.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/09tuning/05_-_Your_Mother_Should_Know.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +439 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/09tuning/06_-_I_Am_The_Walrus.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/09tuning/07_-_Hello_Goodbye.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/09tuning/08_-_Strawberry_Fields_Forever.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +431 +435 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/09tuning/09_-_Penny_Lane.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +436 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/09tuning/10_-_Baby_You're_A_Rich_Man.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +436 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/09tuning/11_-_All_You_Need_Is_Love.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +428 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/09tuning/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +/01_-_Magical_Mystery_Tour.tun/1.1/Tue Aug 15 13:18:16 2006// +/02_-_The_Fool_On_The_Hill.tun/1.1/Tue Aug 15 13:18:16 2006// +/03_-_Flying.tun/1.1/Tue Aug 15 13:18:16 2006// +/04_-_Blue_Jay_Way.tun/1.1/Tue Aug 15 13:18:16 2006// +/05_-_Your_Mother_Should_Know.tun/1.1/Tue Aug 15 13:18:16 2006// +/06_-_I_Am_The_Walrus.tun/1.1/Tue Aug 15 13:18:16 2006// +/07_-_Hello_Goodbye.tun/1.1/Tue Aug 15 13:18:16 2006// +/08_-_Strawberry_Fields_Forever.tun/1.1/Tue Aug 15 13:18:16 2006// +/09_-_Penny_Lane.tun/1.1/Tue Aug 15 13:18:16 2006// +/10_-_Baby_You're_A_Rich_Man.tun/1.1/Tue Aug 15 13:18:16 2006// +/11_-_All_You_Need_Is_Love.tun/1.1/Tue Aug 15 13:18:16 2006// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/09tuning/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +chrish/tuning/09tuning
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/09tuning/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:matthiasm@dspmac1.elec.qmul.ac.uk:/cvsroot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD1tuning/CD1_-_01_-_Back_in_the_USSR.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD1tuning/CD1_-_02_-_Dear_Prudence.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +436 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD1tuning/CD1_-_03_-_Glass_Onion.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +435 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD1tuning/CD1_-_04_-_Ob-La-Di,_Ob-La-Da.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +443 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD1tuning/CD1_-_05_-_Wild_Honey_Pie.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +426 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD1tuning/CD1_-_06_-The_Continuing_Story_of_Bungalow_Bill.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD1tuning/CD1_-_07_-_While_My_Guitar_Gently_Weeps.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD1tuning/CD1_-_08_-_Happiness_is_a_Warm_Gun.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD1tuning/CD1_-_09_-_Martha_My_Dear.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD1tuning/CD1_-_10_-_I'm_So_Tired.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +439 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD1tuning/CD1_-_11_-_Black_Bird.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +437 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD1tuning/CD1_-_12_-_Piggies.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +435 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD1tuning/CD1_-_13_-_Rocky_Raccoon.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +434 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD1tuning/CD1_-_14_-_Don't_Pass_Me_By.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +436 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD1tuning/CD1_-_15_-_Why_Don't_We_Do_It_In_The_Road.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +441 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD1tuning/CD1_-_16_-_I_Will.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD1tuning/CD1_-_17_-_Julia.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD1tuning/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,18 @@ +/CD1_-_01_-_Back_in_the_USSR.tun/1.1/Tue Aug 15 13:19:18 2006// +/CD1_-_02_-_Dear_Prudence.tun/1.1/Tue Aug 15 13:19:18 2006// +/CD1_-_03_-_Glass_Onion.tun/1.1/Tue Aug 15 13:19:18 2006// +/CD1_-_04_-_Ob-La-Di,_Ob-La-Da.tun/1.1/Tue Aug 15 13:19:18 2006// +/CD1_-_05_-_Wild_Honey_Pie.tun/1.1/Tue Aug 15 13:19:18 2006// +/CD1_-_06_-The_Continuing_Story_of_Bungalow_Bill.tun/1.1/Tue Aug 15 13:19:18 2006// +/CD1_-_07_-_While_My_Guitar_Gently_Weeps.tun/1.1/Tue Aug 15 13:19:18 2006// +/CD1_-_08_-_Happiness_is_a_Warm_Gun.tun/1.1/Tue Aug 15 13:19:18 2006// +/CD1_-_09_-_Martha_My_Dear.tun/1.1/Tue Aug 15 13:19:18 2006// +/CD1_-_10_-_I'm_So_Tired.tun/1.1/Tue Aug 15 13:19:18 2006// +/CD1_-_11_-_Black_Bird.tun/1.1/Tue Aug 15 13:19:18 2006// +/CD1_-_12_-_Piggies.tun/1.1/Tue Aug 15 13:19:18 2006// +/CD1_-_13_-_Rocky_Raccoon.tun/1.1/Tue Aug 15 13:19:18 2006// +/CD1_-_14_-_Don't_Pass_Me_By.tun/1.1/Tue Aug 15 13:19:18 2006// +/CD1_-_15_-_Why_Don't_We_Do_It_In_The_Road.tun/1.1/Tue Aug 15 13:19:18 2006// +/CD1_-_16_-_I_Will.tun/1.1/Tue Aug 15 13:19:18 2006// +/CD1_-_17_-_Julia.tun/1.1/Tue Aug 15 13:19:18 2006// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD1tuning/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +chrish/tuning/10CD1tuning
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD1tuning/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:matthiasm@dspmac1.elec.qmul.ac.uk:/cvsroot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD2tuning/CD2_-_01_-_Birthday.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +443 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD2tuning/CD2_-_02_-_Yer_Blues.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD2tuning/CD2_-_03_-_Mother_Nature's_Son.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +433 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD2tuning/CD2_-_04_-_Everybody's_Got_Something_To_Hide_Except_Me_and_M.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +439 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD2tuning/CD2_-_05_-_Sexy_Sadie.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +439 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD2tuning/CD2_-_06_-_Helter_Skelter.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD2tuning/CD2_-_07_-_Long_Long_Long.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +437 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD2tuning/CD2_-_08_-_Revolution_1.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +436 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD2tuning/CD2_-_09_-_Honey_Pie.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +439 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD2tuning/CD2_-_10_-_Savoy_Truffle.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD2tuning/CD2_-_11_-_Cry_Baby_Cry.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD2tuning/CD2_-_12_-_Revolution_9.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD2tuning/CD2_-_13_-_Good_Night.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD2tuning/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,14 @@ +/CD2_-_01_-_Birthday.tun/1.1/Tue Aug 15 13:20:03 2006// +/CD2_-_02_-_Yer_Blues.tun/1.1/Tue Aug 15 13:20:03 2006// +/CD2_-_03_-_Mother_Nature's_Son.tun/1.1/Tue Aug 15 13:20:03 2006// +/CD2_-_04_-_Everybody's_Got_Something_To_Hide_Except_Me_and_M.tun/1.1/Tue Aug 15 13:20:03 2006// +/CD2_-_05_-_Sexy_Sadie.tun/1.1/Tue Aug 15 13:20:03 2006// +/CD2_-_06_-_Helter_Skelter.tun/1.1/Tue Aug 15 13:20:03 2006// +/CD2_-_07_-_Long_Long_Long.tun/1.1/Tue Aug 15 13:20:03 2006// +/CD2_-_08_-_Revolution_1.tun/1.1/Tue Aug 15 13:20:03 2006// +/CD2_-_09_-_Honey_Pie.tun/1.1/Tue Aug 15 13:20:03 2006// +/CD2_-_10_-_Savoy_Truffle.tun/1.1/Tue Aug 15 13:20:03 2006// +/CD2_-_11_-_Cry_Baby_Cry.tun/1.1/Tue Aug 15 13:20:03 2006// +/CD2_-_12_-_Revolution_9.tun/1.1/Tue Aug 15 13:20:03 2006// +/CD2_-_13_-_Good_Night.tun/1.1/Tue Aug 15 13:20:03 2006// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD2tuning/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +chrish/tuning/10CD2tuning
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/10CD2tuning/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:matthiasm@dspmac1.elec.qmul.ac.uk:/cvsroot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/11tuning/01_-_Come_Together.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/11tuning/02_-_Something.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/11tuning/03_-_Maxwell's_Silver_Hammer.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +446 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/11tuning/04_-_Oh!_Darling.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +435 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/11tuning/05_-_Octopus's_Garden.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +439 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/11tuning/06_-_I_Want_You.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/11tuning/07_-_Here_Comes_The_Sun.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +446 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/11tuning/08_-_Because.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/11tuning/09_-_You_Never_Give_Me_Your_Money.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/11tuning/10_-_Sun_King.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/11tuning/11_-_Mean_Mr_Mustard.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/11tuning/12_-_Polythene_Pam.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/11tuning/13_-_She_Came_In_Through_The_Bathroom_Window.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/11tuning/14_-_Golden_Slumbers.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +442 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/11tuning/15_-_Carry_That_Weight.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +442 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/11tuning/16_-_The_End.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +442 +451 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/11tuning/17_-_Her_Majesty.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +443 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/11tuning/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,18 @@ +/01_-_Come_Together.tun/1.1/Tue Aug 15 13:20:30 2006// +/02_-_Something.tun/1.1/Tue Aug 15 13:20:30 2006// +/03_-_Maxwell's_Silver_Hammer.tun/1.1/Tue Aug 15 13:20:30 2006// +/04_-_Oh!_Darling.tun/1.1/Tue Aug 15 13:20:30 2006// +/05_-_Octopus's_Garden.tun/1.1/Tue Aug 15 13:20:30 2006// +/06_-_I_Want_You.tun/1.1/Tue Aug 15 13:20:30 2006// +/07_-_Here_Comes_The_Sun.tun/1.1/Tue Aug 15 13:20:30 2006// +/08_-_Because.tun/1.1/Tue Aug 15 13:20:30 2006// +/09_-_You_Never_Give_Me_Your_Money.tun/1.1/Tue Aug 15 13:20:30 2006// +/10_-_Sun_King.tun/1.1/Tue Aug 15 13:20:30 2006// +/11_-_Mean_Mr_Mustard.tun/1.1/Tue Aug 15 13:20:30 2006// +/12_-_Polythene_Pam.tun/1.1/Tue Aug 15 13:20:30 2006// +/13_-_She_Came_In_Trough_The_Bathroom_Window.tun/1.1/Tue Aug 15 13:20:30 2006// +/14_-_Golden_Slumbers.tun/1.1/Tue Aug 15 13:20:30 2006// +/15_-_Carry_That_Weight.tun/1.1/Tue Aug 15 13:20:30 2006// +/16_-_The_End.tun/1.1/Tue Aug 15 13:20:30 2006// +/17_-_Her_Majesty.tun/1.1/Tue Aug 15 13:20:30 2006// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/11tuning/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +chrish/tuning/11tuning
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/11tuning/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:matthiasm@dspmac1.elec.qmul.ac.uk:/cvsroot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/12tuning/01_-_Two_of_Us.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +439 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/12tuning/02_-_Dig_a_Pony.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +442 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/12tuning/03_-_Across_the_Universe.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +446 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/12tuning/04_-_I_Me_Mine.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +439 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/12tuning/05_-_Dig_It.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/12tuning/06_-_Let_It_Be.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +440 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/12tuning/07_-_Maggie_Mae.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +438 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/12tuning/08_-_I've_Got_A_Feeling.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +442 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/12tuning/09_-_One_After_909.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +441 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/12tuning/10_-_The_Long_and_Winding_Road.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +439 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/12tuning/11_-_For_You_Blue.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +439 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/12tuning/12_-_Get_Back.tun Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +441 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/12tuning/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +/01_-_Two_of_Us.tun/1.1/Tue Aug 15 13:21:39 2006// +/02_-_Dig_a_Pony.tun/1.1/Tue Aug 15 13:21:39 2006// +/03_-_Across_the_Universe.tun/1.1/Tue Aug 15 13:21:39 2006// +/04_-_I_Me_Mine.tun/1.1/Tue Aug 15 13:21:39 2006// +/05_-_Dig_It.tun/1.1/Tue Aug 15 13:21:39 2006// +/06_-_Let_It_Be.tun/1.1/Tue Aug 15 13:21:39 2006// +/07_-_Maggie_Mae.tun/1.1/Tue Aug 15 13:21:39 2006// +/08_-_I've_Got_A_Feeling.tun/1.1/Tue Aug 15 13:21:39 2006// +/09_-_One_After_909.tun/1.1/Tue Aug 15 13:21:39 2006// +/10_-_The_Long_and_Winding_Road.tun/1.1/Tue Aug 15 13:21:39 2006// +/11_-_For_You_Blue.tun/1.1/Tue Aug 15 13:21:39 2006// +/12_-_Get_Back.tun/1.1/Tue Aug 15 13:21:39 2006// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/12tuning/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +chrish/tuning/12tuning
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/12tuning/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:matthiasm@dspmac1.elec.qmul.ac.uk:/cvsroot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/CVS/Entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/CVS/Entries.Log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,13 @@ +A D/01tuning//// +A D/02tuning//// +A D/03tuning//// +A D/04tuning//// +A D/05tuning//// +A D/06tuning//// +A D/07tuning//// +A D/08tuning//// +A D/09tuning//// +A D/10CD1tuning//// +A D/10CD2tuning//// +A D/11tuning//// +A D/12tuning////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/CVS/Repository Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +chrish/tuning
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/tuning/CVS/Root Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,1 @@ +:ext:matthiasm@dspmac1.elec.qmul.ac.uk:/cvsroot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chordtools/types.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,114 @@ +% +%TYPES for C4DM Chord Toolkit +% +% Help file on types, structures and enumerations used in chord symbol +% toolbox +% +% +% *** STRING TYPES *** +% +%NATURAL +% +% Character A|B|C|D|E|F|G +% +% +%MODIFIER +% +% Character b|# +% +% +%NOTE +% +% Character string containing one natural and optional modifiers. +% Modifiers may only occur after the natural. +% +% note = <natural> | <note> <modifier> +% +% +%INTERVAL +% +% Character string containing digits 1|2|3|4|5|6|7|8|9|10|11|12|13 +% +% +%DEGREE +% +% Character string containing one interval and optional modifiers. +% Modifiers may only occur before the interval. +% +% degree = <interval> | <modifier> <degree> +% +% +% *** OTHER TYPES *** +% +%ACCIDENTALS +% +% signed integer number of sharps or flats. If positive then denotes +% sharps, if negative then denotes flats. +% +% +%SEMITONE +% +% Integer value signifying a number of semitone intervals. +% +% +%PITCHCLASS +% +% Integer value between 0 and 11 enumerating pitch classes with C as +% reference pitch class 0. +% +% +%CHORDTYPE +% +% Enumerates shorthand chordtypes +% +% 0 maj +% 1 min +% 2 dim +% 3 aug +% 4 maj7 +% 5 min7 +% 6 7 +% 7 dim7 +% 8 hdim7 +% 9 minmaj7 +% 10 maj6 +% 11 min6 +% 12 9 +% 13 maj9 +% 14 min9 +% 15 sus4 +% 16 sus2 +% +%QUALITY +% +% Enumerates chord quality +% +% 0 Major +% 1 Minor +% 2 Diminished +% 3 Augmented +% 4 Suspended +% +% +% +% +% Author: Christopher Harte, August 2005 +% +% Copyright: Centre for Digital Music, Queen Mary University of London 2005 +% +% This file is part of the C4DM Chord Toolkit. +% +% The C4DM Chord Toolkit is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as published +% by the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% The C4DM Chord Toolkit is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with the C4DM Toolkit; if not, write to the Free Software +% Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA +%
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chroma/chroma_main.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,20 @@ +function [chroma, t] = chroma_main(filename, param) + +switch param.dsp.extractiontype + case 'ieee2008' + [chroma, t] = chroma_ieee2008(filename, param); + case 'MIREX2009' + [chroma, t] = chroma_MIREX2009(filename, param); + case 'MIREX2009precalculated' + [chroma, t] = chroma_MIREX2009precalculated(param); + case 'lsqnonneg' + [chroma, t] = chroma_lsqnonneg(filename,param); + case 'lsqnonnegfast' + [chroma, t] = chroma_lsqnonneg_fast(filename,param); + case 'vampprecalculated' + [chroma, t] = chroma_vampprecalculated(param); + case 'ISMIR2010' + [chroma, t] = chroma_lsqnonneg_ISMIR2010(filename, param); + otherwise + fprintf(1, 'chroma type %s not recognised', param.dsp.extractiontype) +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chroma/chroma_vampprecalculated.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +function [chroma, t] = chroma_vampprecalculated(param) +timeandchroma = dlmread(param.metafilename); + +t = timeandchroma(:,1); +chroma.bass = timeandchroma(:,[5:13 2:4])'; +chroma.treble = timeandchroma(:,[17:25 14:16])'; +chroma.wide = chroma.treble + 0.8 * chroma.bass;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chroma/fftbin2frequency.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +function frequency = fftbin2frequency(iBin,fs,blocksize) +frequency = (iBin-1) * fs / blocksize; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chroma/kldiv.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,2 @@ +function d = kldiv(p,q) +d = sum(p .* log2(p./q)); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chroma/midinote2frequency.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,3 @@ +function frequency = midinote2frequency(midinote) + +frequency = 440 * 2.^((midinote-69)/12); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chroma/newdict.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,165 @@ +s = chunkspectrogram(filename,4096,4096*7/8,4096,inf); +a = abs(s); +T = size(s,2); +%% +zeroind = find(sum(a)==0 | sum(a)==-inf); +a(:,zeroind) = randn(size(a,1),length(zeroind)).^2; + +wind_size = 6 * 3 + 1; +pad_size = (wind_size-1)/2; +wind = qnormalise(hamming(wind_size),1,1); + +atransf = wm' * qnormalise(a,2,1); + +nFbin = size(wm,2); + + + + +% CHANGED: this is an equivalent formulation in terms of phase. +fftr=fft(sum(atransf(3:end-2,:),2)); +th = -angle(fftr((nFbin - 4)/3+1)) - 2*pi/3; +th(th < -pi) = th(th < -pi) + 2*pi; +disp(440*2.^(th/2/pi/12)) + +for iFrame = 1:T + atransf(:,iFrame) = interp1(1:nFbin, atransf(:,iFrame), (1:nFbin) + th / (2 * pi) * 3,'spline',0); +end + + +% +nFbin = size(atransf,1); +T = size(atransf,2); + +% calculate running mean +m = conv2(atransf,wind(:),'valid'); +m = [repmat(m(1,:),pad_size,1); m ; repmat(m(end,:),pad_size,1)]; + +% calculate running standard deviation +stdev = sqrt(conv2((atransf-m).^2,wind(:),'valid')); +stdev = [repmat(stdev(1,:),pad_size,1); stdev ; repmat(stdev(end,:),pad_size,1)]; + +% loamp = logical(atransf < 1.5 * m); +% atransf(loamp) = atransf(loamp)/5; +atransf = (atransf - m)./stdev; +atransf(atransf<0) = 0; + +% atransf = qnormalise(atransf,inf,2); +% atransf(isnan(atransf)) = 0; + +%% generate dictionary +nSBin = 3; +fs = 11025; +blocksize = 4096; +minMidi = 20; +maxMidi = 105; +overs = 40; +midiFrequencies = midinote2frequency(minMidi:1/nSBin:maxMidi); + + + +dx = fs/blocksize/overs; +x = dx:dx:fs/2; +nX = length(x); + +nMidi = length(midiFrequencies); +% nFFT = length(fftFrequencies); + +midiWarp = semitonedistance(midiFrequencies,nSBin); +% fftWarp = ones(size(x)) * fs / blocksize; + +B = zeros(256, 7*12); + +for iMidi = 1:7*12 + f0 = midiFrequencies(3*iMidi+1); + f = f0; + factor = 1; + help = (log(f0) - log(midiFrequencies(1)))/log(midiFrequencies(end)); + factorred(iMidi) = 0.9 * (1 - help) + 0.9 * help; + fprintf(1,'%3.2f %%\r',iMidi/nMidi*100*3) + midiVector = cospuls_warped(midiFrequencies,f,midiWarp); + while f < 11025/2; + f = f + f0; + factor = factor * factorred(iMidi); + midiVector = midiVector + factor * cospuls_warped(midiFrequencies,f,midiWarp); + end + B(:,iMidi) = midiVector; +end +% %% + +%% +B = qnormalise(B,1,1); +% C = B(:,12:6*36+11)'; +C=B; +% atransf(atransf<0) = atransf(atransf<0)/5; +% mys = mldivide(C,(atransf)); + +atransf = sparse(atransf); + +for t = 1:T + fprintf('%0.1f %%\n',t/T*100) + mys(:,t) = lsqnonneg(C,atransf(:,t)); +end + +% mys = qnormalise(mys,1,1); +% mys(mys<0)=0; +imagesc(mys.*(mys>0)) +axis xy +%% + +n = size(mys,1); + +bt_spacing = 1/4.5; + +basswindow = cospuls(1:n,n * bt_spacing +0.5,n * 2 * bt_spacing); +% treblewindow = cospuls(1:n,2 * n * bt_spacing+0.5,n * 2 * bt_spacing) + cospuls(1:n,3*n * bt_spacing+0.5,n* 2 * bt_spacing); +treblewindow = cospuls(1:n,n * 0.5 + 0.5,n * 2 * 0.5); +% treblewindow = basswindow(12:6*36+11)/2 + treblewindow(12:6*36+11); +% basswindow = basswindow(12:6*36+11); + +%% +thirtysix2twelve = zeros(12,36); +for iSemitone = 0:11 + thirtysix2twelve(iSemitone+1,iSemitone*3+(1:3)) = 1; +end +if n == 256 + toPC = repmat(eye(36),7,1); + toPC = [toPC(end-1:end,:); toPC; toPC(1:2,:)]; + bass_chromagram = mys' * diag(basswindow) * toPC * thirtysix2twelve'; + treble_chromagram = mys' * diag(treblewindow) * toPC * thirtysix2twelve'; +else + toPC = repmat(eye(12),7,1); + bass_chromagram = mys' * diag(basswindow) * toPC; + treble_chromagram = mys' * diag(treblewindow) * toPC; +end +% chromagram = mys' * repmat(eye(36),6,1); +thr_l = 0.0; +thr_h = 3; +treble_chromagram(treble_chromagram<thr)=thr_l; +bass_chromagram(bass_chromagram<thr)=thr_l; + +treble_chromagram = circshift(treble_chromagram,[0 -3]); +bass_chromagram = circshift(bass_chromagram,[0, -3]); + +% treble_chromagram = treble_chromagram + bass_chromagram/4; + +% bass_chromagram = medfilt1(bass_chromagram,10); +% treble_chromagram = medfilt1(treble_chromagram,10); + +%% +some = 100 * 0; +% subplot(211) +imagesc(qnormalise([(bass_chromagram');(treble_chromagram')],2,1)) +hold on +ax = axis; +plot(ax(1:2),[12.5 12.5],'color',[0.7 0.7 0.7]) +axis xy +hold off +% xlim([0 800] + some) +% subplot(212) +% imagesc() +% xlim([0 800] + some) +% axis xy +%% +dlmwrite('/Users/matthiasmauch/temptreble.csv',treble_chromagram, 'delimiter','\t'); +dlmwrite('/Users/matthiasmauch/tempbass.csv',bass_chromagram, 'delimiter','\t');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chroma/pitch2octave.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function strout = pitch2octave(pitch) +octave = floor((pitch-60)/12); +if octave < 0 + sig = '-'; +else + sig = ''''; +end +strout = ''; +for iOct = 1:abs(octave) + strout = [strout,sig]; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chroma/semitonedistance.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,7 @@ +function df = semitonedistance(f,varargin) +if nargin == 1 + df = log(2)/12*f; +else + bins_per_semitone = varargin{1}; + df = log(2)/(12*bins_per_semitone)*f; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_chroma/sparsePrint.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,48 @@ +sizeMat = size(cosw); + +fid = fopen('/Users/matthiasmauch/Documents/_workspace/2010/2010-VampDevel-code/plugin-tutorial/coskernel-20-105-3-11025-4096-80.cpp','w'); + +fprintf(fid, 'const int lengthOfNoteIndex = %0.0f;\n\n', sum(cosw(:)>0)); + +fprintf(fid, 'const int nNote = %0.0f;\n\n', sizeMat(2)); + +fprintf(fid,'const int noteIndex[] = {') + +for iCol = 1:sizeMat(2) + for iRow = 1:sizeMat(1) + if cosw(iRow,iCol) > 0 + fprintf(fid, '%0.0f,',iCol-1); + end + end + fprintf(fid,'\n'); +end +fprintf(fid,'};\n\n');transform + +% --------------------------- +fprintf(fid,'const int fftBin[] = {') + +for iCol = 1:sizeMat(2) + for iRow = 1:sizeMat(1) + if cosw(iRow,iCol) > 0 + fprintf(fid, '%0.0f,',iRow-1); + end + end + fprintf(fid,'\n'); +end +fprintf(fid,'};\n\n'); + +% --------------------------- +fprintf(fid,'const double transfValue[] = {') + +for iCol = 1:sizeMat(2) + for iRow = 1:sizeMat(1) + if cosw(iRow,iCol) > 0 + fprintf(fid, '%0.5f,',cosw(iRow,iCol)); + end + end + fprintf(fid,'\n'); +end +fprintf(fid,'};\n\n'); + + +fclose(fid); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_dbn/add_chords.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,118 @@ +function param = add_chords(param) + + +patterns = []; +lilytypenames = {}; +bassn = []; +hasN = false; +for iChord = 1:length(param.dbn.chordclassset) + switch param.dbn.chordclassset{iChord} + case '' + patterns = [patterns; ... + 1 0 0 0 1 0 0 1 0 0 0 0]; % major + lilytypenames = [lilytypenames, ' ']; + bassn = [bassn, 1]; + case '/3' + patterns = [patterns; ... + 1 0 0 0 1 0 0 1 0 0 0 0]; % major/3 + lilytypenames = [lilytypenames, ' ']; + bassn = [bassn, 5]; + case '/5' + patterns = [patterns; ... + 1 0 0 0 1 0 0 1 0 0 0 0]; % major/5 + lilytypenames = [lilytypenames, ' ']; + bassn = [bassn, 8]; + case ':maj6' + patterns = [patterns; ... + 1 0 0 0 1 0 0 1 0 1 0 0]; % major 6 + lilytypenames = [lilytypenames, ':6']; + bassn = [bassn, 1]; + case ':maj7' + patterns = [patterns; ... + 1 0 0 0 1 0 0 1 0 0 0 1]; % major 7 + lilytypenames = [lilytypenames, ':maj7']; + bassn = [bassn, 1]; + case ':min' + patterns = [patterns; ... + 1 0 0 1 0 0 0 1 0 0 0 0]; % minor + lilytypenames = [lilytypenames, ':m']; + bassn = [bassn, 1]; + case ':min7' + patterns = [patterns; ... + 1 0 0 1 0 0 0 1 0 0 1 0]; % minor + lilytypenames = [lilytypenames, ':m7']; + bassn = [bassn, 1]; + case ':7' + patterns = [patterns; ... + 1 0 0 0 1 0 0 1 0 0 1 0]; % dominant + lilytypenames = [lilytypenames, ':7']; + bassn = [bassn, 1]; + case ':dim' + patterns = [patterns; ... + 1 0 0 1 0 0 1 0 0 0 0 0]; % diminished + lilytypenames = [lilytypenames, ':dim']; + bassn = [bassn, 1]; + case ':aug' + patterns = [patterns; ... + 1 0 0 0 1 0 0 0 1 0 0 0]; % augmented + lilytypenames = [lilytypenames, ':aug']; + bassn = [bassn, 1]; + case 'N' + hasN = true; + hasN0 = false; + case 'N0'; + hasN0 = true; + hasN = false; + end +end +nRow = size(patterns,1); + +sz = 12; + +bass_notes = zeros(1,sz*nRow+1); +root_notes = bass_notes; +chordnames = cell(1,sz*nRow+1); +lilychordnames = chordnames; +mu = zeros(sz,sz*nRow+1); +cova = zeros(sz,sz,sz*nRow+1); +count = 1; + +for iRow = 1:nRow + for iSemitone = 1:sz + mu(:,count) = circshift(patterns(iRow,:)',iSemitone-1); + cova(:,:,count) = eye(sz) * param.dbn.chromavar; + root_notes(count) = iSemitone; + chordnames{count} = param.dbn.chordclassset{iRow}; + lilychordnames{count} = lilytypenames{iRow}; + if bassn(iRow) ~= 13 + bass_notes(count) = mod(bassn(iRow)+iSemitone-2,12)+1; + else + bass_notes(count) = 13; + end + count = count + 1; + end +end +indexN = count; +if hasN + mu(:,indexN) = ones(12,1); % "no chord" + lilychordnames{count} = ' '; + cova(:,:,indexN) = mean(cova,3); + bass_notes(indexN) = 13; + root_notes(indexN) = 13; +end +if hasN0 + mu(:,indexN) = zeros(12,1); % "no chord" + cova(:,:,indexN) = mean(cova,3); + bass_notes(indexN) = 13; + root_notes(indexN) = 13; +end + +%% +param.dbn.nChord = size(mu,2); +param.dbn.chordpcs = mu; +param.dbn.cova = cova; +param.dbn.lilytypechordnames = lilychordnames; +param.dbn.chordnames = chordnames; +param.dbn.chordrootnotes = root_notes; +param.dbn.chordbassnotes = bass_notes; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_dbn/add_keys.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,36 @@ +function param = add_keys(param) +names = {'A','Bb','B','C','C#','D','Eb','E','F','F#','G','G#','Am','Bbm','Bm','Cm','C#m','Dm','Ebm','Em','Fm','F#m','Gm','G#m'}; +switch param.key.set + case 'major' + param.key.names = names(1:12); + profile = [1 0 1 0 1 1 0 1 0 1 0 1]; + case 'majorminor' + param.key.names = names; + profile = [1 0 1 0 1 1 0 1 0 1 0 1; + 1 0 1 1 0 1 0 1 1 0 0 1]; +end + +nMode = size(profile,1); + +param.key.profiles = zeros(12 * nMode, 12); +param.key.n = nMode * 12; + +for iMode = 1:nMode + for iSemitone = 1:12 + param.key.profiles(12 * (iMode-1) + iSemitone,:) = ... + circshift(profile(iMode,:),[0,iSemitone-1]); + end +end + + +param.chord.givenkey = zeros([param.key.n,param.chord.n]); + +for iKey = 1:param.key.n + for iChord = 1:param.chord.n + chord_prefit = ... + (1-param.key.profiles(iKey,:)) * param.chord.mu(:,iChord); + param.chord.givenkey(iKey,iChord) = 1 / (chord_prefit + param.key.c); + end +end +param.chord.givenkey = qnormalise(param.chord.givenkey,1,2); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_dbn/dbn_ISMIR2010.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,236 @@ +function bnet = dbn_thesis_mbk24bx(param) + +node_sizes = [param.dbn.nMetpos param.dbn.nKey param.dbn.nChord 13 13 12]; +discrete = 1:4; +observed = 5:6; +%% topology: metric position + +nodenames = {'metpos', 'key', 'chord', 'bass' , 'basschroma','treblechroma'}; + +%% topology: dependencies +% I first create full dependency matrices, and then eliminate the +% inapproapriate rows and columns + +intra = zeros(6); +inter = zeros(6); + +intra(1,3) = 1; % metpos to chord +intra(2,3) = 1; % key to chord +intra(3,4) = 1; % chord to bass +intra(3,6) = 1; % chord to treble chroma +intra(4,5) = 1; % bass to bass chroma + +inter(1,1) = 1; % metpos to metpos +inter(2,2) = 1; % key to key +inter(3,3) = 1; % chord to chord +inter(3,4) = 1; % chord to bass + +bnet = mk_dbn(intra, inter, ... + node_sizes, 'discrete', discrete, 'observed', observed, ... + 'names', nodenames); + +%------------------------------------------------------- +% Flat initialisation for hidden nodes in first slice +%------------------------------------------------------- +fprintf(1,' - First Slice\n'); +iNode = 1; +for nodename = nodenames(discrete) + fprintf(1,' %s \n', nodename{1}); + bnet.CPD{iNode} = tabular_CPD(bnet, iNode, 'CPT', 'unif'); + iNode = iNode + 1; +end + +%------------------------------------------------------- +% Observed nodes +%------------------------------------------------------- +bnet.CPD{bnet.names('basschroma')} = ... + gaussian_CPD(bnet,bnet.names('basschroma'), ... + 'mean', eye(13), 'cov', repmat(eye(13) * sqrt(param.dbn.basschromavar),[1,1,13])); +chordpcs = param.dbn.chordpcs; +chordpcs(:,end) = chordpcs(:,end) - 0.5; +cova = param.dbn.cova; +cova(:,:,end) = cova(:,:,end); +bnet.CPD{bnet.names('treblechroma')} = ... + gaussian_CPD(bnet,bnet.names('treblechroma'), ... + 'mean', chordpcs, 'cov', cova); + +%------------------------------------------------------- +% second slice -- more elaborate stuff ... +%------------------------------------------------------- + +fprintf(1,' - Second Slice\n'); +nodename = 'metpos'; +nodenumber = bnet.names(nodename) + bnet.nnodes_per_slice; +fprintf(1,' (%d) %s (slice 2) \n', nodenumber, nodename); +% columns are: +% (1) old metpos, +% (2) this metpos, +% (3) conditional probability. + +helper_table = get_field(tabular_CPD(bnet,nodenumber,'CPT','unif'),'cpt'); +helper_table = helper_table * 0; +for oldmetpos = 1:param.dbn.nMetpos + for thismetpos = 1:param.dbn.nMetpos + if oldmetpos <= 4 + if mod(oldmetpos + 1,4) == ... + mod(thismetpos,4) + helper_table(oldmetpos,thismetpos) = param.dbn.beattrans; + else + helper_table(oldmetpos,thismetpos) = (1-param.dbn.beattrans)/3; + end + else + helper_table(oldmetpos,thismetpos) = 1/param.dbn.nMetpos; + end + end +end + +bnet.CPD{nodenumber} = tabular_CPD(bnet,nodenumber,helper_table); + +%------------------------------------------------------- + +nodename = 'key'; +nodenumber = bnet.names(nodename) + bnet.nnodes_per_slice; +fprintf(1,' (%d) %s (slice 2) \n', nodenumber, nodename); +% columns are +% (1) old key +% (2) this key +% (3) probability. + +helper_table = get_field(tabular_CPD(bnet,nodenumber,'CPT','unif'),'cpt'); + +for iOld = 1:param.dbn.nKey + for iThis = 1:param.dbn.nKey + if iOld == iThis + helper_table(iOld,iThis) = 1-param.dbn.keychange; + else + helper_table(iOld,iThis) = param.dbn.keychange/(param.dbn.nKey-1); + end + end +end + +bnet.CPD{nodenumber} = tabular_CPD(bnet,nodenumber,helper_table); + +%------------------------------------------------------- +nodename = 'chord'; +nodenumber = bnet.names(nodename) + bnet.nnodes_per_slice; +fprintf(1,' (%d) %s (slice 2) \n', nodenumber, nodename); +% columns are: +% (1) old chord, +% (2) new metpos, +% (3) new harmonic meter, +% () new key, +% (5) this chord, +% (6) probability. + +helper_table = get_field(tabular_CPD(bnet,nodenumber,'CPT','unif'),'cpt'); +[cgivenk, param] = chordgivenkey(param); + +for iKey = 1:param.dbn.nKey + for iThis = 1:param.dbn.nChord + for iBeat = 1:param.dbn.nMetpos + for iOld = 1:param.dbn.nChord + if iOld == iThis + helper_table(iOld,iBeat,iKey,iThis) = ... + param.dbn.chordchange(iBeat) * cgivenk(iKey, iThis); + else + helper_table(iOld,iBeat,iKey,iThis) = ... + (1-param.dbn.chordchange(iBeat)) / (param.dbn.nChord - 1) * cgivenk(iKey, iThis); + end + end + end + end +end +bnet.CPD{nodenumber} = tabular_CPD(bnet,nodenumber,helper_table); + +%------------------------------------------------------- +nodename = 'bass'; +% (1) old chord +% (2) new chord +% (3) this bass +nodenumber = bnet.names(nodename) + bnet.nnodes_per_slice; +fprintf(1,' (%d) %s (slice 2) \n', nodenumber, nodename); +helper_table = get_field(tabular_CPD(bnet,nodenumber,'CPT','unif'),'cpt'); +for iOldchord = 1:param.dbn.nChord + for iNewchord = 1:param.dbn.nChord + if iOldchord ~= iNewchord % first beat of new chord + for iThisbass = 1:param.dbn.nBass + if iThisbass == param.dbn.chordbassnotes(iNewchord) % correct bass note + helper_table(iOldchord, iNewchord, iThisbass) = param.dbn.nominalbass; + else + helper_table(iOldchord, iNewchord, iThisbass) = (1 - param.dbn.nominalbass) / (param.dbn.nBass-1); + end + end + else + for iThisbass = 1:param.dbn.nBass + if ~param.dbn.basshaschordnotes + if iThisbass == param.dbn.chordbassnotes(iNewchord) % correct bass note + helper_table(iOldchord, iNewchord, iThisbass) = param.dbn.nominalbass_inner; + else + helper_table(iOldchord, iNewchord, iThisbass) = (1 - param.dbn.nominalbass_inner) / (param.dbn.nBass-1); + end + else + chordnbass = [param.dbn.chordpcs(:,iNewchord); 0]; + chordnbass(param.dbn.chordbassnotes(iNewchord)) = chordnbass(param.dbn.chordbassnotes(iNewchord))+1; + if ismember(iThisbass, find(chordnbass)) % chord bass note + helper_table(iOldchord, iNewchord, iThisbass) = param.dbn.nominalbass_inner * chordnbass(iThisbass) / sum(chordnbass); + else + helper_table(iOldchord, iNewchord, iThisbass) = (1 - param.dbn.nominalbass_inner) / (param.dbn.nBass-sum(chordnbass)-1); + end + end + end + end + end +end + +bnet.CPD{nodenumber} = tabular_CPD(bnet,nodenumber,helper_table); + + + +function [cgivenk, param] = chordgivenkey(param) +switch param.dbn.keyset + case 'major' + profile = [1 0 1 0 1 1 0 1 0 1 0 1]; + case 'majorminor' + profile = [1 0 1 0 1 1 0 1 0 1 0 1; + 1 0 1 1 0 1 0 1 1 1 1 1]; +end + +nMode = size(profile,1); + +profiles = zeros(12 * nMode, 12); +param.dbn.nKey = nMode * 12; + +for iMode = 1:nMode + for iSemitone = 1:12 + profiles(12 * (iMode-1) + iSemitone,:) = ... + circshift(profile(iMode,:),[0,iSemitone-1]); + end +end + +cgivenk = zeros([param.dbn.nKey,param.dbn.nChord]); + +% for iKey = 1:param.dbn.nKey +% for iChord = 1:param.dbn.nChord +% chord_prefit = ... +% (1-profiles(iKey,:)) * param.dbn.chordpcs(:,iChord); +% cgivenk(iKey,iChord) = 1 / (chord_prefit + param.dbn.key_c); +% end +% end + + +for iKey = 1:param.dbn.nKey + for iChord = 1:param.dbn.nChord + chord_prefit = ... + (1-profiles(iKey,:)) * param.dbn.chordpcs(:,iChord); + if chord_prefit == 0 && mod(iKey-iChord,12) == 0 % is in key and key and chord share root + chord_chord_prefit = 2; + else + chord_chord_prefit = 1; + end + cgivenk(iKey,iChord) = (chord_chord_prefit / exp(chord_prefit + 0.0997)) * 0.0363 + 0.0667; + end +end + +cgivenk = qnormalise(cgivenk,1,2); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_dbn/inference.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,89 @@ +function song = inference(song, bnet, param) + + +if param.dsp.separatenormalising % means that also bass and treble chroma are separate + evidence = cell(bnet.nnodes_per_slice,song.nBeat); + if param.dbn.nBass > 0 + basschromanode = bnet.names('basschroma'); + evidence(basschromanode,:) = ... + num2cell(song.segchroma.bass(:,1:param.dbn.nBass)',1); + end + + treblechromanode = bnet.names('treblechroma'); + evidence(treblechromanode,:) = ... + num2cell(song.segchroma.treble',1); + + if param.dbn.inferencebypart + fprintf(2,' '); + song = integratesmallparts(song); + song.mpe = cell(size(evidence)); + nIntegratedParts = numel(song.integratedparts); + if param.dbn.infermpe + for iIP = 1:nIntegratedParts + startind = song.integratedparts(iIP).indices; % only one index for integrated parts + endind = song.integratedparts(iIP).indices + song.integratedparts(iIP).n - 1; + leftoverlap = min(startind - 1, param.seg.inferenceoverlap); + rightoverlap = min(song.nBeat - endind, param.seg.inferenceoverlap); + currevidence = evidence(:,startind - leftoverlap:endind + rightoverlap); + engine = smoother_engine(jtree_2TBN_inf_engine(bnet)); + fprintf(1,'\nFinding maximum probability path (part %1.0f of %1.0f, beats %1.0f to %1.0f)... \n', iIP, nIntegratedParts, startind, endind) + fprintf(2,'\b\b\b\b%3.0f%%',iIP/nIntegratedParts*100); + currmpe = find_mpeMM(engine, currevidence); + song.mpe(:,startind:endind) = currmpe(:,leftoverlap + 1:size(currmpe,2)-rightoverlap); + end + else + error('inference by part + mpe not yet supported'); + end + else + + if param.dbn.infermpe + engine = smoother_engine(jtree_2TBN_inf_engine(bnet)); + fprintf(1,'\n__ Finding maximum probability path... _________\n') + song.mpe = find_mpeMM(engine, evidence); + else + engine = filter_engine(jtree_2TBN_inf_engine(bnet)); + fprintf(1,'\n__ Inference... _________\n') + tic + for t = 1:song.nBeat + t + param.engine = enter_evidence(engine,[evidence{:,t}],t); + end + toc + song.inferred = cell(bnet.nnodes_per_slice, song.nBeat); + for iBeat = 1:song.nBeat + for iNode = 1:bnet.nnodes_per_slice-2 + temp = marginal_nodes(engine,iNode,iBeat); + song.inferred{iNode, iBeat} = temp.T; + end + end + end + end +else + chromanode = bnet.names('chroma'); + + evidence = cell(bnet.nnodes_per_slice,song.nBeat); + + evidence(chromanode,:) = ... + num2cell([song.segchroma.bass, song.segchroma.treble]',1); + + if param.dbn.inferencebypart + else + engine = smoother_engine(jtree_2TBN_inf_engine(bnet)); + if param.dbn.infermpe + fprintf(1,'\n__ Finding maximum probability path... _________\n') + song.mpe = find_mpe(engine, evidence); + else + fprintf(1,'\n__ Inference... _________\n') + tic + param.engine = enter_evidence(engine,evidence); + toc + song.inferred = cell(bnet.nnodes_per_slice, song.nBeat); + for iBeat = 1:beat.n + for iNode = 1:bnet.nnodes_per_slice-1 + temp = marginal_nodes(engine,iNode,iBeat); + song.inferred{iNode, iBeat} = temp.T; + end + end + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_dbn/make_dbn.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +function bnet = make_dbn(param) +% generates the right dbn +switch param.dbn.name + case 'ieee2008mbk' + bnet = ieee2008mbk(param); + otherwise + bnet = []; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_logfiles/03_-_Across_the_Universe.wav.log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,342 @@ +CSVFeatureWriter::setParameters +stdout -> +NOTE: Using default sample rate of 44100 for default transform +NOTE: Loaded and initialised plugin for transform "vamp:matthiasm:nnls_chroma:bothchroma" with plugin step size 2048 and block size 16384 (adapter step and block size 16384) +NOTE: PluginInputDomainAdapter timestamp adjustment is 0.000000000R +Extracting features for: "/Users/matthias/Music/Other/The Beatles/12_-_Let_It_Be/03_-_Across_the_Universe.wav" + Retrieving audio data... Done +Opened 2-channel file or URL "/Users/matthias/Music/Other/The Beatles/12_-_Let_It_Be/03_-_Across_the_Universe.wav" +timestamp 0.000000000R: very low magnitude, setting magnitude to all zeros +timestamp 0.046439909R: very low magnitude, setting magnitude to all zeros +timestamp 0.092879818R: very low magnitude, setting magnitude to all zeros +timestamp 0.139319727R: very low magnitude, setting magnitude to all zeros +timestamp 0.185759637R: very low magnitude, setting magnitude to all zeros +timestamp 0.232199546R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 1% Extracting and writing features... 2% Extracting and writing features... 3% Extracting and writing features... 4% Extracting and writing features... 5% Extracting and writing features... 6% Extracting and writing features... 7% Extracting and writing features... 8% Extracting and writing features... 9% Extracting and writing features... 10% Extracting and writing features... 11% 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Extracting and writing features... 63% Extracting and writing features... 64% Extracting and writing features... 65% Extracting and writing features... 66% Extracting and writing features... 67% Extracting and writing features... 68% Extracting and writing features... 69% Extracting and writing features... 70% Extracting and writing features... 71% Extracting and writing features... 72% Extracting and writing features... 73% Extracting and writing features... 74% Extracting and writing features... 75% Extracting and writing features... 76% Extracting and writing features... 77% Extracting and writing features... 78% Extracting and writing features... 79% Extracting and writing features... 80% Extracting and writing features... 81% Extracting and writing features... 82% Extracting and writing features... 83% Extracting and writing features... 84% Extracting and writing features... 85% Extracting and writing features... 86% Extracting and writing features... 87% Extracting and writing features... 88% Extracting and writing features... 89% Extracting and writing features... 90% Extracting and writing features... 91% Extracting and writing features... 92% Extracting and writing features... 93% Extracting and writing features... 94% Extracting and writing features... 95%timestamp 218.824852607R: very low magnitude, setting magnitude to all zeros +timestamp 218.871292517R: very low magnitude, setting magnitude to all zeros +timestamp 218.917732426R: very low magnitude, setting magnitude to all zeros +timestamp 218.964172335R: very low magnitude, setting magnitude to all zeros +timestamp 219.010612244R: very low magnitude, setting magnitude to all zeros +timestamp 219.057052154R: very low magnitude, setting magnitude to all zeros +timestamp 219.103492063R: very low magnitude, setting magnitude to all zeros +timestamp 219.149931972R: very low magnitude, setting magnitude to all zeros +timestamp 219.196371882R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 96%timestamp 219.242811791R: very low magnitude, setting magnitude to all zeros +timestamp 219.289251700R: very low magnitude, setting magnitude to all zeros +timestamp 219.521451247R: very low magnitude, setting magnitude to all zeros +timestamp 219.567891156R: very low magnitude, setting magnitude to all zeros +timestamp 219.614331065R: very low magnitude, setting magnitude to all zeros +timestamp 219.660770975R: very low magnitude, setting magnitude to all zeros +timestamp 219.707210884R: very low magnitude, setting magnitude to all zeros +timestamp 219.753650793R: very low magnitude, setting magnitude to all zeros +timestamp 219.800090702R: very low magnitude, setting magnitude to all zeros +timestamp 219.846530612R: very low magnitude, setting magnitude to all zeros +timestamp 219.892970521R: very low magnitude, setting magnitude to all zeros +timestamp 219.939410430R: very low magnitude, setting magnitude to all zeros +timestamp 219.985850340R: very low magnitude, setting magnitude to all zeros +timestamp 220.032290249R: very low magnitude, setting magnitude to all zeros +timestamp 220.078730158R: very low magnitude, setting magnitude to all zeros +timestamp 220.125170068R: very low magnitude, setting magnitude to all zeros +timestamp 220.171609977R: very low magnitude, setting magnitude to all zeros +timestamp 220.218049886R: very low magnitude, setting magnitude to all zeros +timestamp 220.264489795R: very low magnitude, setting magnitude to all zeros +timestamp 220.310929705R: very low magnitude, setting magnitude to all zeros +timestamp 220.357369614R: very low magnitude, setting magnitude to all zeros +timestamp 220.403809523R: very low magnitude, setting magnitude to all zeros +timestamp 220.450249433R: very low magnitude, setting magnitude to all zeros +timestamp 220.496689342R: very low magnitude, setting magnitude to all zeros +timestamp 220.543129251R: very low magnitude, setting magnitude to all zeros +timestamp 220.589569160R: very low magnitude, setting magnitude to all zeros +timestamp 220.636009070R: very low magnitude, setting magnitude to all zeros +timestamp 220.682448979R: very low magnitude, setting magnitude to all zeros +timestamp 220.728888888R: very low magnitude, setting magnitude to all zeros +timestamp 220.775328798R: very low magnitude, setting magnitude to all zeros +timestamp 220.821768707R: very low magnitude, setting magnitude to all zeros +timestamp 220.868208616R: very low magnitude, setting magnitude to all zeros +timestamp 220.914648526R: very low magnitude, setting magnitude to all zeros +timestamp 220.961088435R: very low magnitude, setting magnitude to all zeros +timestamp 221.007528344R: very low magnitude, setting magnitude to all zeros +timestamp 221.053968253R: very low magnitude, setting magnitude to all zeros +timestamp 221.100408163R: very low magnitude, setting magnitude to all zeros +timestamp 221.146848072R: very low magnitude, setting magnitude to all zeros +timestamp 221.193287981R: very low magnitude, setting magnitude to all zeros +timestamp 221.239727891R: very low magnitude, setting magnitude to all zeros +timestamp 221.286167800R: very low magnitude, setting magnitude to all zeros +timestamp 221.332607709R: very low magnitude, setting magnitude to all zeros +timestamp 221.379047619R: very low magnitude, setting magnitude to all zeros +timestamp 221.425487528R: very low magnitude, setting magnitude to all zeros +timestamp 221.471927437R: very low magnitude, setting magnitude to all zeros +timestamp 221.518367346R: very low magnitude, setting magnitude to all zeros +timestamp 221.564807256R: very low magnitude, setting magnitude to all zeros +timestamp 221.611247165R: very low magnitude, setting magnitude to all zeros +timestamp 221.657687074R: very low magnitude, setting magnitude to all zeros +timestamp 221.704126984R: very low magnitude, setting magnitude to all zeros +timestamp 221.750566893R: very low magnitude, setting magnitude to all zeros +timestamp 221.797006802R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 97%timestamp 221.843446712R: very low magnitude, setting magnitude to all zeros +timestamp 221.889886621R: very low magnitude, setting magnitude to all zeros +timestamp 221.936326530R: very low magnitude, setting magnitude to all zeros +timestamp 221.982766439R: very low magnitude, setting magnitude to all zeros +timestamp 222.029206349R: very low magnitude, setting magnitude to all zeros +timestamp 222.075646258R: very low magnitude, setting magnitude to all zeros +timestamp 222.122086167R: very low magnitude, setting magnitude to all zeros +timestamp 222.168526077R: very low magnitude, setting magnitude to all zeros +timestamp 222.214965986R: very low magnitude, setting magnitude to all zeros +timestamp 222.261405895R: very low magnitude, setting magnitude to all zeros +timestamp 222.307845804R: very low magnitude, setting magnitude to all zeros +timestamp 222.354285714R: very low magnitude, setting magnitude to all zeros +timestamp 222.400725623R: very low magnitude, setting magnitude to all zeros +timestamp 222.447165532R: very low magnitude, setting magnitude to all zeros +timestamp 222.493605442R: very low magnitude, setting magnitude to all zeros +timestamp 222.540045351R: very low magnitude, setting magnitude to all zeros +timestamp 222.586485260R: very low magnitude, setting magnitude to all zeros +timestamp 222.632925170R: very low magnitude, setting magnitude to all zeros +timestamp 222.679365079R: very low magnitude, setting magnitude to all zeros +timestamp 222.725804988R: very low magnitude, setting magnitude to all zeros +timestamp 222.772244897R: very low magnitude, setting magnitude to all zeros +timestamp 222.818684807R: very low magnitude, setting magnitude to all zeros +timestamp 222.865124716R: very low magnitude, setting magnitude to all zeros +timestamp 222.911564625R: very low magnitude, setting magnitude to all zeros +timestamp 222.958004535R: very low magnitude, setting magnitude to all zeros +timestamp 223.004444444R: very low magnitude, setting magnitude to all zeros +timestamp 223.050884353R: very low magnitude, setting magnitude to all zeros +timestamp 223.097324263R: very low magnitude, setting magnitude to all zeros +timestamp 223.143764172R: very low magnitude, setting magnitude to all zeros +timestamp 223.190204081R: very low magnitude, setting magnitude to all zeros +timestamp 223.236643990R: very low magnitude, setting magnitude to all zeros +timestamp 223.283083900R: very low magnitude, setting magnitude to all zeros +timestamp 223.329523809R: very low magnitude, setting magnitude to all zeros +timestamp 223.375963718R: very low magnitude, setting magnitude to all zeros +timestamp 223.422403628R: very low magnitude, setting magnitude to all zeros +timestamp 223.468843537R: very low magnitude, setting magnitude to all zeros +timestamp 223.515283446R: very low magnitude, setting magnitude to all zeros +timestamp 223.561723356R: very low magnitude, setting magnitude to all zeros +timestamp 223.608163265R: very low magnitude, setting magnitude to all zeros +timestamp 223.654603174R: very low magnitude, setting magnitude to all zeros +timestamp 223.701043083R: very low magnitude, setting magnitude to all zeros +timestamp 223.747482993R: very low magnitude, setting magnitude to all zeros +timestamp 223.793922902R: very low magnitude, setting magnitude to all zeros +timestamp 223.840362811R: very low magnitude, setting magnitude to all zeros +timestamp 223.886802721R: very low magnitude, setting magnitude to all zeros +timestamp 223.933242630R: very low magnitude, setting magnitude to all zeros +timestamp 223.979682539R: very low magnitude, setting magnitude to all zeros +timestamp 224.026122448R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 98%timestamp 224.072562358R: very low magnitude, setting magnitude to all zeros +timestamp 224.119002267R: very low magnitude, setting magnitude to all zeros +timestamp 224.165442176R: very low magnitude, setting magnitude to all zeros +timestamp 224.211882086R: very low magnitude, setting magnitude to all zeros +timestamp 224.258321995R: very low magnitude, setting magnitude to all zeros +timestamp 224.304761904R: very low magnitude, setting magnitude to all zeros +timestamp 224.351201814R: very low magnitude, setting magnitude to all zeros +timestamp 224.397641723R: very low magnitude, setting magnitude to all zeros +timestamp 224.444081632R: very low magnitude, setting magnitude to all zeros +timestamp 224.490521541R: very low magnitude, setting magnitude to all zeros +timestamp 224.536961451R: very low magnitude, setting magnitude to all zeros +timestamp 224.583401360R: very low magnitude, setting magnitude to all zeros +timestamp 224.629841269R: very low magnitude, setting magnitude to all zeros +timestamp 224.676281179R: very low magnitude, setting magnitude to all zeros +timestamp 224.722721088R: very low magnitude, setting magnitude to all zeros +timestamp 224.769160997R: very low magnitude, setting magnitude to all zeros +timestamp 224.815600907R: very low magnitude, setting magnitude to all zeros +timestamp 224.862040816R: very low magnitude, setting magnitude to all zeros +timestamp 224.908480725R: very low magnitude, setting magnitude to all zeros +timestamp 224.954920634R: very low magnitude, setting magnitude to all zeros +timestamp 225.001360544R: very low magnitude, setting magnitude to all zeros +timestamp 225.047800453R: very low magnitude, setting magnitude to all zeros +timestamp 225.094240362R: very low magnitude, setting magnitude to all zeros +timestamp 225.140680272R: very low magnitude, setting magnitude to all zeros +timestamp 225.187120181R: very low magnitude, setting magnitude to all zeros +timestamp 225.233560090R: very low magnitude, setting magnitude to all zeros +timestamp 225.280000000R: very low magnitude, setting magnitude to all zeros +timestamp 225.326439909R: very low magnitude, setting magnitude to all zeros +timestamp 225.372879818R: very low magnitude, setting magnitude to all zeros +timestamp 225.419319727R: very low magnitude, setting magnitude to all zeros +timestamp 225.465759637R: very low magnitude, setting magnitude to all zeros +timestamp 225.512199546R: very low magnitude, setting magnitude to all zeros +timestamp 225.558639455R: very low magnitude, setting magnitude to all zeros +timestamp 225.605079365R: very low magnitude, setting magnitude to all zeros +timestamp 225.651519274R: very low magnitude, setting magnitude to all zeros +timestamp 225.697959183R: very low magnitude, setting magnitude to all zeros +timestamp 225.744399092R: very low magnitude, setting magnitude to all zeros +timestamp 225.790839002R: very low magnitude, setting magnitude to all zeros +timestamp 225.837278911R: very low magnitude, setting magnitude to all zeros +timestamp 225.883718820R: very low magnitude, setting magnitude to all zeros +timestamp 225.930158730R: very low magnitude, setting magnitude to all zeros +timestamp 225.976598639R: very low magnitude, setting magnitude to all zeros +timestamp 226.023038548R: very low magnitude, setting magnitude to all zeros +timestamp 226.069478458R: very low magnitude, setting magnitude to all zeros +timestamp 226.115918367R: very low magnitude, setting magnitude to all zeros +timestamp 226.162358276R: very low magnitude, setting magnitude to all zeros +timestamp 226.208798185R: very low magnitude, setting magnitude to all zeros +timestamp 226.255238095R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 99%timestamp 226.301678004R: very low magnitude, setting magnitude to all zeros +timestamp 226.348117913R: very low magnitude, setting magnitude to all zeros +timestamp 226.394557823R: very low magnitude, setting magnitude to all zeros +timestamp 226.440997732R: very low magnitude, setting magnitude to all zeros +timestamp 226.487437641R: very low magnitude, setting magnitude to all zeros +timestamp 226.533877551R: very low magnitude, setting magnitude to all zeros +timestamp 226.580317460R: very low magnitude, setting magnitude to all zeros +timestamp 226.626757369R: very low magnitude, setting magnitude to all zeros +timestamp 226.673197278R: very low magnitude, setting magnitude to all zeros +timestamp 226.719637188R: very low magnitude, setting magnitude to all zeros +timestamp 226.766077097R: very low magnitude, setting magnitude to all zeros +timestamp 226.812517006R: very low magnitude, setting magnitude to all zeros +timestamp 226.858956916R: very low magnitude, setting magnitude to all zeros +timestamp 226.905396825R: very low magnitude, setting magnitude to all zeros +timestamp 226.951836734R: very low magnitude, setting magnitude to all zeros +timestamp 226.998276643R: very low magnitude, setting magnitude to all zeros +timestamp 227.044716553R: very low magnitude, setting magnitude to all zeros +timestamp 227.091156462R: very low magnitude, setting magnitude to all zeros +timestamp 227.137596371R: very low magnitude, setting magnitude to all zeros +timestamp 227.184036281R: very low magnitude, setting magnitude to all zeros +timestamp 227.230476190R: very low magnitude, setting magnitude to all zeros +timestamp 227.276916099R: very low magnitude, setting magnitude to all zeros +timestamp 227.323356009R: very low magnitude, setting magnitude to all zeros +timestamp 227.369795918R: very low magnitude, setting magnitude to all zeros +timestamp 227.416235827R: very low magnitude, setting magnitude to all zeros +timestamp 227.462675736R: very low magnitude, setting magnitude to all zeros +timestamp 227.509115646R: very low magnitude, setting magnitude to all zeros +timestamp 227.555555555R: very low magnitude, setting magnitude to all zeros +timestamp 227.601995464R: very low magnitude, setting magnitude to all zeros +timestamp 227.648435374R: very low magnitude, setting magnitude to all zeros +timestamp 227.694875283R: very low magnitude, setting magnitude to all zeros +timestamp 227.741315192R: very low magnitude, setting magnitude to all zeros +timestamp 227.787755102R: very low magnitude, setting magnitude to all zeros +timestamp 227.834195011R: very low magnitude, setting magnitude to all zeros +timestamp 227.880634920R: very low magnitude, setting magnitude to all zeros +timestamp 227.927074829R: very low magnitude, setting magnitude to all zeros +timestamp 227.973514739R: very low magnitude, setting magnitude to all zeros +timestamp 228.019954648R: very low magnitude, setting magnitude to all zeros +timestamp 228.066394557R: very low magnitude, setting magnitude to all zeros +timestamp 228.112834467R: very low magnitude, setting magnitude to all zeros +timestamp 228.159274376R: very low magnitude, setting magnitude to all zeros +timestamp 228.205714285R: very low magnitude, setting magnitude to all zeros +timestamp 228.252154195R: very low magnitude, setting magnitude to all zeros +timestamp 228.298594104R: very low magnitude, setting magnitude to all zeros +timestamp 228.345034013R: very low magnitude, setting magnitude to all zeros +timestamp 228.391473922R: very low magnitude, setting magnitude to all zeros +timestamp 228.437913832R: very low magnitude, setting magnitude to all zeros +timestamp 228.484353741R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 100%timestamp 228.530793650R: very low magnitude, setting magnitude to all zeros +[NNLS Chroma Plugin] Tuning Log-Frequency Spectrogram ... done. +[NNLS Chroma Plugin] Performing NNLS and mapping to chroma ... done. +[NNLS Chroma Plugin] Chord Estimation ... done. + Extracting and writing features... Done + + < M A T L A B > + Copyright 1984-2007 The MathWorks, Inc. + Version 7.5.0.338 (R2007b) + August 9, 2007 + + + To get started, type one of these: helpwin, helpdesk, or demo. + For product information, visit www.mathworks.com. + + - First Slice + metpos + key + chord + bass + - Second Slice + (7) metpos (slice 2) + (8) key (slice 2) + (9) chord (slice 2) + (10) bass (slice 2) +onset detection function +beats + + Length: 28 Length: 32 Length: 36 Length: 40 Length: 44 Length: 48 Length: 52 Length: 56 Length: 60 Length: 64 Length: 68 Length: 72 Length: 76 Length: 80 Length: 84 Length: 88 Length: 92 Length: 96 Length: 100 Length: 104 Length: 108 Length: 112 Length: 116 Length: 120 Length: 124 Length: 128 +Finding maximum probability path (part 1 of 10, beats 1 to 17)... +forward step +backward step + +Finding maximum probability path (part 2 of 10, beats 18 to 51)... +forward step +backward step + +Finding maximum probability path (part 3 of 10, beats 52 to 88)... +forward step +backward step + +Finding maximum probability path (part 4 of 10, beats 89 to 199)... +forward step +backward step + +Finding maximum probability path (part 5 of 10, beats 200 to 227)... +forward step +backward step + +Finding maximum probability path (part 6 of 10, beats 228 to 242)... +forward step +backward step + +Finding maximum probability path (part 7 of 10, beats 243 to 342)... +forward step +backward step + +Finding maximum probability path (part 8 of 10, beats 343 to 390)... +forward step +backward step + +Finding maximum probability path (part 9 of 10, beats 391 to 490)... +forward step +backward step + +Finding maximum probability path (part 10 of 10, beats 491 to 584)... +forward step +backward step +0.0 0.023 N +0.023 0.372 N +0.372 2.821 Db +2.821 6.850 F:min +6.850 10.136 Ab +10.136 13.305 Db/5 +13.305 16.068 F:min +16.068 18.831 Eb:min7 +18.831 23.557 Ab +23.557 26.749 Db/5 +26.749 29.501 F:min +29.501 31.509 Eb:min7 +31.509 34.702 Gb:min +34.702 41.146 Db/5 +41.146 47.113 Ab +47.113 50.283 Ab:7 +50.283 53.418 Gb +53.418 56.959 Db/5 +56.959 59.710 Ab/5 +59.710 63.251 Ab:7 +63.251 66.014 Gb +66.014 73.085 Db/5 +73.085 75.418 F:min +75.418 78.565 Eb:min7 +78.565 82.071 Ab +82.071 85.217 Db/5 +85.217 87.980 F:min +87.980 91.127 Eb:min7 +91.127 95.469 Ab +95.469 101.854 Db/5 +101.854 107.381 Ab +107.381 111.351 Ab:7 +111.351 114.080 Gb +114.080 117.609 Db/5 +117.609 123.820 Ab +123.820 126.932 Gb +126.932 133.909 Db/5 +133.909 136.208 F:min +136.208 137.764 Eb:min7 +137.764 140.864 Gb:min +140.864 143.964 Db/5 +143.964 146.692 F:min +146.692 150.210 Eb:min7 +150.210 153.298 Ab:7 +153.298 159.602 Db/5 +159.602 165.082 Ab +165.082 168.983 Ab:7 +168.983 171.700 Gb +171.700 175.195 Db/5 +175.195 181.383 Ab +181.383 184.483 Gb +184.483 218.778 Db/5 +218.778 228.560 N0.0 0.023 N +0.023 20.410 Ab +20.410 228.560 Db \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_logfiles/06_-_Let_It_Be.wav.log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,269 @@ +CSVFeatureWriter::setParameters +stdout -> +NOTE: Using default sample rate of 44100 for default transform +NOTE: Loaded and initialised plugin for transform "vamp:matthiasm:nnls_chroma:bothchroma" with plugin step size 2048 and block size 16384 (adapter step and block size 16384) +NOTE: PluginInputDomainAdapter timestamp adjustment is 0.000000000R +Extracting features for: "/Users/matthias/Music/Other/The Beatles/12_-_Let_It_Be/06_-_Let_It_Be.wav" + Retrieving audio data... Done +Opened 2-channel file or URL "/Users/matthias/Music/Other/The Beatles/12_-_Let_It_Be/06_-_Let_It_Be.wav" +timestamp 0.000000000R: very low magnitude, setting magnitude to all zeros +timestamp 0.046439909R: very low magnitude, setting magnitude to all zeros +timestamp 0.092879818R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 1% Extracting and writing features... 2% Extracting and writing features... 3% Extracting and writing features... 4% Extracting and writing features... 5% Extracting and writing features... 6% Extracting and writing features... 7% Extracting and writing features... 8% Extracting and writing features... 9% Extracting and writing features... 10% Extracting and writing features... 11% Extracting and writing features... 12% Extracting and writing features... 13% Extracting and writing features... 14% Extracting and writing features... 15% Extracting and writing features... 16% Extracting and writing features... 17% Extracting and writing features... 18% Extracting and writing features... 19% Extracting and writing features... 20% Extracting and writing features... 21% Extracting and writing features... 22% Extracting and writing features... 23% Extracting and writing features... 24% Extracting and writing features... 25% Extracting and writing features... 26% Extracting and writing features... 27% Extracting and writing features... 28% Extracting and writing features... 29% Extracting and writing features... 30% Extracting and writing features... 31% Extracting and writing features... 32% Extracting and writing features... 33% Extracting and writing features... 34% Extracting and writing features... 35% Extracting and writing features... 36% Extracting and writing features... 37% Extracting and writing features... 38% Extracting and writing features... 39% Extracting and writing features... 40% Extracting and writing features... 41% Extracting and writing features... 42% Extracting and writing features... 43% Extracting and writing features... 44% Extracting and writing features... 45% Extracting and writing features... 46% Extracting and writing features... 47% Extracting and writing features... 48% Extracting and writing features... 49% Extracting and writing features... 50% Extracting and writing features... 51% Extracting and writing features... 52% Extracting and writing features... 53% Extracting and writing features... 54% Extracting and writing features... 55% Extracting and writing features... 56% Extracting and writing features... 57% Extracting and writing features... 58% Extracting and writing features... 59% Extracting and writing features... 60% Extracting and writing features... 61% Extracting and writing features... 62% Extracting and writing features... 63% Extracting and writing features... 64% Extracting and writing features... 65% Extracting and writing features... 66% Extracting and writing features... 67% Extracting and writing features... 68% Extracting and writing features... 69% Extracting and writing features... 70% Extracting and writing features... 71% Extracting and writing features... 72% Extracting and writing features... 73% Extracting and writing features... 74% Extracting and writing features... 75% Extracting and writing features... 76% Extracting and writing features... 77% Extracting and writing features... 78% Extracting and writing features... 79% Extracting and writing features... 80% Extracting and writing features... 81% Extracting and writing features... 82% Extracting and writing features... 83% Extracting and writing features... 84% Extracting and writing features... 85% Extracting and writing features... 86% Extracting and writing features... 87% Extracting and writing features... 88% Extracting and writing features... 89% Extracting and writing features... 90% Extracting and writing features... 91% Extracting and writing features... 92% Extracting and writing features... 93% Extracting and writing features... 94% Extracting and writing features... 95% Extracting and writing features... 96% Extracting and writing features... 97% Extracting and writing features... 98% Extracting and writing features... 99%timestamp 240.837369614R: very low magnitude, setting magnitude to all zeros +timestamp 240.883809523R: very low magnitude, setting magnitude to all zeros +timestamp 240.930249433R: very low magnitude, setting magnitude to all zeros +timestamp 240.976689342R: very low magnitude, setting magnitude to all zeros +timestamp 241.023129251R: very low magnitude, setting magnitude to all zeros +timestamp 241.069569160R: very low magnitude, setting magnitude to all zeros +timestamp 241.116009070R: very low magnitude, setting magnitude to all zeros +timestamp 241.162448979R: very low magnitude, setting magnitude to all zeros +timestamp 241.208888888R: very low magnitude, setting magnitude to all zeros +timestamp 241.255328798R: very low magnitude, setting magnitude to all zeros +timestamp 241.301768707R: very low magnitude, setting magnitude to all zeros +timestamp 241.348208616R: very low magnitude, setting magnitude to all zeros +timestamp 241.394648526R: very low magnitude, setting magnitude to all zeros +timestamp 241.441088435R: very low magnitude, setting magnitude to all zeros +timestamp 241.487528344R: very low magnitude, setting magnitude to all zeros +timestamp 241.533968253R: very low magnitude, setting magnitude to all zeros +timestamp 241.580408163R: very low magnitude, setting magnitude to all zeros +timestamp 241.626848072R: very low magnitude, setting magnitude to all zeros +timestamp 241.673287981R: very low magnitude, setting magnitude to all zeros +timestamp 241.719727891R: very low magnitude, setting magnitude to all zeros +timestamp 241.766167800R: very low magnitude, setting magnitude to all zeros +timestamp 241.812607709R: very low magnitude, setting magnitude to all zeros +timestamp 241.859047619R: very low magnitude, setting magnitude to all zeros +timestamp 241.905487528R: very low magnitude, setting magnitude to all zeros +timestamp 241.951927437R: very low magnitude, setting magnitude to all zeros +timestamp 241.998367346R: very low magnitude, setting magnitude to all zeros +timestamp 242.044807256R: very low magnitude, setting magnitude to all zeros +timestamp 242.091247165R: very low magnitude, setting magnitude to all zeros +timestamp 242.137687074R: very low magnitude, setting magnitude to all zeros +timestamp 242.184126984R: very low magnitude, setting magnitude to all zeros +timestamp 242.230566893R: very low magnitude, setting magnitude to all zeros +timestamp 242.277006802R: very low magnitude, setting magnitude to all zeros +timestamp 242.323446712R: very low magnitude, setting magnitude to all zeros +timestamp 242.369886621R: very low magnitude, setting magnitude to all zeros +timestamp 242.416326530R: very low magnitude, setting magnitude to all zeros +timestamp 242.462766439R: very low magnitude, setting magnitude to all zeros +timestamp 242.509206349R: very low magnitude, setting magnitude to all zeros +timestamp 242.555646258R: very low magnitude, setting magnitude to all zeros +timestamp 242.602086167R: very low magnitude, setting magnitude to all zeros +timestamp 242.648526077R: very low magnitude, setting magnitude to all zeros +timestamp 242.694965986R: very low magnitude, setting magnitude to all zeros +timestamp 242.741405895R: very low magnitude, setting magnitude to all zeros +timestamp 242.787845804R: very low magnitude, setting magnitude to all zeros +timestamp 242.834285714R: very low magnitude, setting magnitude to all zeros +timestamp 242.880725623R: very low magnitude, setting magnitude to all zeros +timestamp 242.927165532R: very low magnitude, setting magnitude to all zeros +timestamp 242.973605442R: very low magnitude, setting magnitude to all zeros +timestamp 243.020045351R: very low magnitude, setting magnitude to all zeros +[NNLS Chroma Plugin] Tuning Log-Frequency Spectrogram ... done. +[NNLS Chroma Plugin] Performing NNLS and mapping to chroma ... done. +[NNLS Chroma Plugin] Chord Estimation ... done. + Extracting and writing features... Done + + + < M A T L A B > + Copyright 1984-2007 The MathWorks, Inc. + Version 7.5.0.338 (R2007b) + August 9, 2007 + + + To get started, type one of these: helpwin, helpdesk, or demo. + For product information, visit www.mathworks.com. + + - First Slice + metpos + key + chord + bass + - Second Slice + (7) metpos (slice 2) + (8) key (slice 2) + (9) chord (slice 2) + (10) bass (slice 2) +onset detection function +beats + + Length: 28 Length: 32 Length: 36 Length: 40 Length: 44 Length: 48 Length: 52 Length: 56 Length: 60 Length: 64 Length: 68 Length: 72 Length: 76 Length: 80 Length: 84 Length: 88 Length: 92 Length: 96 Length: 100 Length: 104 Length: 108 Length: 112 Length: 116 Length: 120 Length: 124 Length: 128 +Finding maximum probability path (part 1 of 10, beats 1 to 34)... +forward step +backward step + +Finding maximum probability path (part 2 of 10, beats 35 to 128)... +forward step +backward step + +Finding maximum probability path (part 3 of 10, beats 129 to 220)... +forward step +backward step + +Finding maximum probability path (part 4 of 10, beats 221 to 276)... +forward step +backward step + +Finding maximum probability path (part 5 of 10, beats 277 to 288)... +forward step +backward step + +Finding maximum probability path (part 6 of 10, beats 289 to 384)... +forward step +backward step + +Finding maximum probability path (part 7 of 10, beats 385 to 476)... +forward step +backward step + +Finding maximum probability path (part 8 of 10, beats 477 to 508)... +forward step +backward step + +Finding maximum probability path (part 9 of 10, beats 509 to 564)... +forward step +backward step + +Finding maximum probability path (part 10 of 10, beats 565 to 577)... +forward step +backward step +0.0 0.197 N +0.197 1.869 C +1.869 3.506 G +3.506 5.166 A:min +5.166 5.979 F:maj7 +5.979 6.815 F:maj6 +6.815 8.452 C +8.452 10.077 G +10.077 11.738 F +11.738 14.559 C +14.559 16.370 G +16.370 18.158 A:min +18.158 19.725 F +19.725 21.293 C +21.293 22.848 G +22.848 23.650 F +23.650 27.516 C +27.516 29.013 G +29.013 30.569 A:min +30.569 32.171 F:maj6 +32.171 33.866 C +33.866 35.585 G +35.585 36.397 F +36.397 36.804 C/3 +36.804 37.198 D:min7 +37.198 38.893 C +38.893 40.565 A:min +40.565 42.249 C/5 +42.249 44.234 F +44.234 47.821 C +47.821 49.551 G +49.551 50.376 F +50.376 53.685 C +53.685 55.275 G +55.275 56.877 A:min +56.877 58.526 F +58.526 60.116 C +60.116 61.788 G +61.788 62.613 F +62.613 66.757 C +66.757 68.394 G +68.394 70.055 A:min +70.055 71.761 F:maj6 +71.761 73.433 C +73.433 75.117 G +75.117 75.952 F +75.952 76.382 C/3 +76.382 76.812 D:min7 +76.812 78.518 C +78.518 80.260 A:min +80.260 81.932 C/5 +81.932 83.592 F +83.592 86.924 C +86.924 88.584 G +88.584 89.420 F +89.420 91.893 C +91.893 93.588 A:min +93.588 95.248 C/5 +95.248 96.897 F +96.897 100.229 C +100.229 101.889 G +101.889 103.584 F +103.584 105.268 C +105.268 106.127 F +106.127 107.822 C/3 +107.822 108.263 Bb +108.263 108.704 C:maj6 +108.704 109.598 G +109.598 110.481 F +110.481 115.705 C +115.705 116.599 G +116.599 117.481 F +117.481 120.918 C +120.918 122.648 G +122.648 124.366 A:min +124.366 126.084 F +126.084 127.803 C +127.803 129.486 G +129.486 130.357 F +130.357 134.676 C +134.676 136.429 G +136.429 138.159 A:min +138.159 139.865 F:maj6 +139.865 141.572 C +141.572 143.290 G +143.290 144.161 F +144.161 144.579 C/3 +144.579 145.009 D:min7 +145.009 146.762 C +146.762 148.538 A:min +148.538 150.314 C/5 +150.314 152.091 F +152.091 155.620 C +155.620 157.385 G +157.385 158.279 F +158.279 162.702 C +162.702 164.455 G +164.455 166.197 A:min +166.197 167.938 F +167.938 169.668 C +169.668 171.386 G +171.386 172.257 F +172.257 176.646 C +176.646 178.387 G +178.387 180.106 A:min +180.106 181.835 F:maj6 +181.835 183.565 C +183.565 185.284 G +185.284 186.154 F +186.154 186.572 C/3 +186.572 187.002 D:min7 +187.002 188.720 C +188.720 190.450 A:min +190.450 192.192 C/5 +192.192 193.945 F +193.945 197.451 C +197.451 199.181 G +199.181 200.052 F +200.052 202.617 C +202.617 204.359 A:min +204.359 206.112 C/5 +206.112 207.853 F:maj7 +207.853 211.360 C +211.360 213.078 G +213.078 213.960 F +213.960 216.514 C/3 +216.514 218.256 A:min +218.256 220.021 C/5 +220.021 221.797 F +221.797 225.245 C +225.245 227.033 G +227.033 228.798 F +228.798 230.597 C +230.597 231.503 F +231.503 233.326 C/3 +233.326 233.767 Bb +233.767 234.220 C:maj6 +234.220 234.986 G +234.986 235.683 F +235.683 240.965 C +240.965 243.307 N0.0 0.197 N +0.197 243.307 C \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_logfiles/Bangles.EternalFlame.wav.log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,362 @@ +CSVFeatureWriter::setParameters +stdout -> +NOTE: Using default sample rate of 44100 for default transform +NOTE: Loaded and initialised plugin for transform "vamp:matthiasm:nnls_chroma:bothchroma" with plugin step size 2048 and block size 16384 (adapter step and block size 16384) +NOTE: PluginInputDomainAdapter timestamp adjustment is 0.000000000R +Extracting features for: "/Users/matthias/Music/MUSICDB44k/Bangles.EternalFlame.wav" + Retrieving audio data... Done +Opened 2-channel file or URL "/Users/matthias/Music/MUSICDB44k/Bangles.EternalFlame.wav" +timestamp 0.000000000R: very low magnitude, setting magnitude to all zeros +timestamp 0.046439909R: very low magnitude, setting magnitude to all zeros +timestamp 0.092879818R: very low magnitude, setting magnitude to all zeros +timestamp 0.139319727R: very low magnitude, setting magnitude to all zeros +timestamp 0.185759637R: very low magnitude, setting magnitude to all zeros +timestamp 0.232199546R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 1% Extracting and writing features... 2% Extracting and writing features... 3% Extracting and writing features... 4% Extracting and writing features... 5% Extracting and writing features... 6% Extracting and writing features... 7% Extracting and writing features... 8% Extracting and writing features... 9% Extracting and writing features... 10% Extracting and writing features... 11% Extracting and writing features... 12% Extracting and writing features... 13% Extracting and writing features... 14% Extracting and writing features... 15% Extracting and writing features... 16% Extracting and writing features... 17% Extracting and writing features... 18% Extracting and writing features... 19% Extracting and writing features... 20% Extracting and writing features... 21% Extracting and writing features... 22% Extracting and writing features... 23% Extracting and writing features... 24% Extracting and writing features... 25% Extracting and writing features... 26% Extracting and writing features... 27% Extracting and writing features... 28% Extracting and writing features... 29% Extracting and writing features... 30% Extracting and writing features... 31% Extracting and writing features... 32% Extracting and writing features... 33% Extracting and writing features... 34% Extracting and writing features... 35% Extracting and writing features... 36% Extracting and writing features... 37% Extracting and writing features... 38% Extracting and writing features... 39% Extracting and writing features... 40% Extracting and writing features... 41% Extracting and writing features... 42% Extracting and writing features... 43% Extracting and writing features... 44% Extracting and writing features... 45% Extracting and writing features... 46% Extracting and writing features... 47% Extracting and writing features... 48% Extracting and writing features... 49% Extracting and writing features... 50% Extracting and writing features... 51% Extracting and writing features... 52% Extracting and writing features... 53% Extracting and writing features... 54% Extracting and writing features... 55% Extracting and writing features... 56% Extracting and writing features... 57% Extracting and writing features... 58% Extracting and writing features... 59% Extracting and writing features... 60% Extracting and writing features... 61% Extracting and writing features... 62% Extracting and writing features... 63% Extracting and writing features... 64% Extracting and writing features... 65% Extracting and writing features... 66% Extracting and writing features... 67% Extracting and writing features... 68% Extracting and writing features... 69% Extracting and writing features... 70% Extracting and writing features... 71% Extracting and writing features... 72% Extracting and writing features... 73% Extracting and writing features... 74% Extracting and writing features... 75% Extracting and writing features... 76% Extracting and writing features... 77% Extracting and writing features... 78% Extracting and writing features... 79% Extracting and writing features... 80% Extracting and writing features... 81% Extracting and writing features... 82% Extracting and writing features... 83% Extracting and writing features... 84% Extracting and writing features... 85% Extracting and writing features... 86% Extracting and writing features... 87% Extracting and writing features... 88% Extracting and writing features... 89% Extracting and writing features... 90% Extracting and writing features... 91% Extracting and writing features... 92% Extracting and writing features... 93% Extracting and writing features... 94% Extracting and writing features... 95%timestamp 226.673197278R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 96%timestamp 228.902312925R: very low magnitude, setting magnitude to all zeros +timestamp 229.552471655R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 97%timestamp 230.481269841R: very low magnitude, setting magnitude to all zeros +timestamp 230.527709750R: very low magnitude, setting magnitude to all zeros +timestamp 230.759909297R: very low magnitude, setting magnitude to all zeros +timestamp 230.806349206R: very low magnitude, setting magnitude to all zeros +timestamp 230.852789115R: very low magnitude, setting magnitude to all zeros +timestamp 230.899229024R: very low magnitude, setting magnitude to all zeros +timestamp 230.945668934R: very low magnitude, setting magnitude to all zeros +timestamp 230.992108843R: very low magnitude, setting magnitude to all zeros +timestamp 231.038548752R: very low magnitude, setting magnitude to all zeros +timestamp 231.084988662R: very low magnitude, setting magnitude to all zeros +timestamp 231.131428571R: very low magnitude, setting magnitude to all zeros +timestamp 231.177868480R: very low magnitude, setting magnitude to all zeros +timestamp 231.224308390R: very low magnitude, setting magnitude to all zeros +timestamp 231.270748299R: very low magnitude, setting magnitude to all zeros +timestamp 231.317188208R: very low magnitude, setting magnitude to all zeros +timestamp 231.363628117R: very low magnitude, setting magnitude to all zeros +timestamp 231.410068027R: very low magnitude, setting magnitude to all zeros +timestamp 231.735147392R: very low magnitude, setting magnitude to all zeros +timestamp 231.781587301R: very low magnitude, setting magnitude to all zeros +timestamp 231.828027210R: very low magnitude, setting magnitude to all zeros +timestamp 231.874467120R: very low magnitude, setting magnitude to all zeros +timestamp 231.920907029R: very low magnitude, setting magnitude to all zeros +timestamp 231.967346938R: very low magnitude, setting magnitude to all zeros +timestamp 232.013786848R: very low magnitude, setting magnitude to all zeros +timestamp 232.060226757R: very low magnitude, setting magnitude to all zeros +timestamp 232.106666666R: very low magnitude, setting magnitude to all zeros +timestamp 232.153106575R: very low magnitude, setting magnitude to all zeros +timestamp 232.199546485R: very low magnitude, setting magnitude to all zeros +timestamp 232.245986394R: very low magnitude, setting magnitude to all zeros +timestamp 232.292426303R: very low magnitude, setting magnitude to all zeros +timestamp 232.338866213R: very low magnitude, setting magnitude to all zeros +timestamp 232.385306122R: very low magnitude, setting magnitude to all zeros +timestamp 232.431746031R: very low magnitude, setting magnitude to all zeros +timestamp 232.478185941R: very low magnitude, setting magnitude to all zeros +timestamp 232.524625850R: very low magnitude, setting magnitude to all zeros +timestamp 232.571065759R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 98%timestamp 232.617505668R: very low magnitude, setting magnitude to all zeros +timestamp 232.663945578R: very low magnitude, setting magnitude to all zeros +timestamp 232.710385487R: very low magnitude, setting magnitude to all zeros +timestamp 232.756825396R: very low magnitude, setting magnitude to all zeros +timestamp 232.803265306R: very low magnitude, setting magnitude to all zeros +timestamp 232.849705215R: very low magnitude, setting magnitude to all zeros +timestamp 232.896145124R: very low magnitude, setting magnitude to all zeros +timestamp 232.942585034R: very low magnitude, setting magnitude to all zeros +timestamp 232.989024943R: very low magnitude, setting magnitude to all zeros +timestamp 233.035464852R: very low magnitude, setting magnitude to all zeros +timestamp 233.081904761R: very low magnitude, setting magnitude to all zeros +timestamp 233.128344671R: very low magnitude, setting magnitude to all zeros +timestamp 233.174784580R: very low magnitude, setting magnitude to all zeros +timestamp 233.221224489R: very low magnitude, setting magnitude to all zeros +timestamp 233.267664399R: very low magnitude, setting magnitude to all zeros +timestamp 233.314104308R: very low magnitude, setting magnitude to all zeros +timestamp 233.360544217R: very low magnitude, setting magnitude to all zeros +timestamp 233.406984126R: very low magnitude, setting magnitude to all zeros +timestamp 233.453424036R: very low magnitude, setting magnitude to all zeros +timestamp 233.499863945R: very low magnitude, setting magnitude to all zeros +timestamp 233.546303854R: very low magnitude, setting magnitude to all zeros +timestamp 233.592743764R: very low magnitude, setting magnitude to all zeros +timestamp 233.639183673R: very low magnitude, setting magnitude to all zeros +timestamp 233.685623582R: very low magnitude, setting magnitude to all zeros +timestamp 233.732063492R: very low magnitude, setting magnitude to all zeros +timestamp 233.778503401R: very low magnitude, setting magnitude to all zeros +timestamp 233.824943310R: very low magnitude, setting magnitude to all zeros +timestamp 233.871383219R: very low magnitude, setting magnitude to all zeros +timestamp 233.917823129R: very low magnitude, setting magnitude to all zeros +timestamp 233.964263038R: very low magnitude, setting magnitude to all zeros +timestamp 234.010702947R: very low magnitude, setting magnitude to all zeros +timestamp 234.057142857R: very low magnitude, setting magnitude to all zeros +timestamp 234.103582766R: very low magnitude, setting magnitude to all zeros +timestamp 234.150022675R: very low magnitude, setting magnitude to all zeros +timestamp 234.196462585R: very low magnitude, setting magnitude to all zeros +timestamp 234.242902494R: very low magnitude, setting magnitude to all zeros +timestamp 234.289342403R: very low magnitude, setting magnitude to all zeros +timestamp 234.335782312R: very low magnitude, setting magnitude to all zeros +timestamp 234.382222222R: very low magnitude, setting magnitude to all zeros +timestamp 234.428662131R: very low magnitude, setting magnitude to all zeros +timestamp 234.475102040R: very low magnitude, setting magnitude to all zeros +timestamp 234.521541950R: very low magnitude, setting magnitude to all zeros +timestamp 234.567981859R: very low magnitude, setting magnitude to all zeros +timestamp 234.614421768R: very low magnitude, setting magnitude to all zeros +timestamp 234.660861678R: very low magnitude, setting magnitude to all zeros +timestamp 234.707301587R: very low magnitude, setting magnitude to all zeros +timestamp 234.753741496R: very low magnitude, setting magnitude to all zeros +timestamp 234.800181405R: very low magnitude, setting magnitude to all zeros +timestamp 234.846621315R: very low magnitude, setting magnitude to all zeros +timestamp 234.893061224R: very low magnitude, setting magnitude to all zeros +timestamp 234.939501133R: very low magnitude, setting magnitude to all zeros +timestamp 234.985941043R: very low magnitude, setting magnitude to all zeros +timestamp 235.032380952R: very low magnitude, setting magnitude to all zeros +timestamp 235.078820861R: very low magnitude, setting magnitude to all zeros +timestamp 235.125260770R: very low magnitude, setting magnitude to all zeros +timestamp 235.171700680R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 99%timestamp 235.218140589R: very low magnitude, setting magnitude to all zeros +timestamp 235.264580498R: very low magnitude, setting magnitude to all zeros +timestamp 235.311020408R: very low magnitude, setting magnitude to all zeros +timestamp 235.357460317R: very low magnitude, setting magnitude to all zeros +timestamp 235.403900226R: very low magnitude, setting magnitude to all zeros +timestamp 235.450340136R: very low magnitude, setting magnitude to all zeros +timestamp 235.496780045R: very low magnitude, setting magnitude to all zeros +timestamp 235.543219954R: very low magnitude, setting magnitude to all zeros +timestamp 235.589659863R: very low magnitude, setting magnitude to all zeros +timestamp 235.636099773R: very low magnitude, setting magnitude to all zeros +timestamp 235.682539682R: very low magnitude, setting magnitude to all zeros +timestamp 235.728979591R: very low magnitude, setting magnitude to all zeros +timestamp 235.775419501R: very low magnitude, setting magnitude to all zeros +timestamp 235.821859410R: very low magnitude, setting magnitude to all zeros +timestamp 235.868299319R: very low magnitude, setting magnitude to all zeros +timestamp 235.914739229R: very low magnitude, setting magnitude to all zeros +timestamp 235.961179138R: very low magnitude, setting magnitude to all zeros +timestamp 236.007619047R: very low magnitude, setting magnitude to all zeros +timestamp 236.054058956R: very low magnitude, setting magnitude to all zeros +timestamp 236.100498866R: very low magnitude, setting magnitude to all zeros +timestamp 236.146938775R: very low magnitude, setting magnitude to all zeros +timestamp 236.193378684R: very low magnitude, setting magnitude to all zeros +timestamp 236.239818594R: very low magnitude, setting magnitude to all zeros +timestamp 236.286258503R: very low magnitude, setting magnitude to all zeros +timestamp 236.332698412R: very low magnitude, setting magnitude to all zeros +timestamp 236.379138321R: very low magnitude, setting magnitude to all zeros +timestamp 236.425578231R: very low magnitude, setting magnitude to all zeros +timestamp 236.472018140R: very low magnitude, setting magnitude to all zeros +timestamp 236.518458049R: very low magnitude, setting magnitude to all zeros +timestamp 236.564897959R: very low magnitude, setting magnitude to all zeros +timestamp 236.611337868R: very low magnitude, setting magnitude to all zeros +timestamp 236.657777777R: very low magnitude, setting magnitude to all zeros +timestamp 236.704217687R: very low magnitude, setting magnitude to all zeros +timestamp 236.750657596R: very low magnitude, setting magnitude to all zeros +timestamp 236.797097505R: very low magnitude, setting magnitude to all zeros +timestamp 236.843537414R: very low magnitude, setting magnitude to all zeros +timestamp 236.889977324R: very low magnitude, setting magnitude to all zeros +timestamp 236.936417233R: very low magnitude, setting magnitude to all zeros +timestamp 236.982857142R: very low magnitude, setting magnitude to all zeros +timestamp 237.029297052R: very low magnitude, setting magnitude to all zeros +timestamp 237.075736961R: very low magnitude, setting magnitude to all zeros +timestamp 237.122176870R: very low magnitude, setting magnitude to all zeros +timestamp 237.168616780R: very low magnitude, setting magnitude to all zeros +timestamp 237.215056689R: very low magnitude, setting magnitude to all zeros +timestamp 237.261496598R: very low magnitude, setting magnitude to all zeros +timestamp 237.307936507R: very low magnitude, setting magnitude to all zeros +timestamp 237.354376417R: very low magnitude, setting magnitude to all zeros +timestamp 237.400816326R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 100%timestamp 237.447256235R: very low magnitude, setting magnitude to all zeros +[NNLS Chroma Plugin] Tuning Log-Frequency Spectrogram ... done. +[NNLS Chroma Plugin] Performing NNLS and mapping to chroma ... done. +[NNLS Chroma Plugin] Chord Estimation ... done. + Extracting and writing features... Done + + < M A T L A B > + Copyright 1984-2007 The MathWorks, Inc. + Version 7.5.0.338 (R2007b) + August 9, 2007 + + + To get started, type one of these: helpwin, helpdesk, or demo. + For product information, visit www.mathworks.com. + + - First Slice + metpos + key + chord + bass + - Second Slice + (7) metpos (slice 2) + (8) key (slice 2) + (9) chord (slice 2) + (10) bass (slice 2) +onset detection function +beats + + Length: 28 Length: 32 Length: 36 Length: 40 Length: 44 Length: 48 Length: 52 Length: 56 Length: 60 Length: 64 Length: 68 Length: 72 Length: 76 Length: 80 Length: 84 Length: 88 Length: 92 Length: 96 Length: 100 Length: 104 Length: 108 Length: 112 Length: 116 Length: 120 Length: 124 Length: 128 +Finding maximum probability path (part 1 of 11, beats 1 to 17)... +forward step +backward step + +Finding maximum probability path (part 2 of 11, beats 18 to 89)... +forward step +backward step + +Finding maximum probability path (part 3 of 11, beats 90 to 161)... +forward step +backward step + +Finding maximum probability path (part 4 of 11, beats 162 to 217)... +forward step +backward step + +Finding maximum probability path (part 5 of 11, beats 218 to 257)... +forward step +backward step + +Finding maximum probability path (part 6 of 11, beats 258 to 313)... +forward step +backward step + +Finding maximum probability path (part 7 of 11, beats 314 to 385)... +forward step +backward step + +Finding maximum probability path (part 8 of 11, beats 386 to 457)... +forward step +backward step + +Finding maximum probability path (part 9 of 11, beats 458 to 529)... +forward step +backward step + +Finding maximum probability path (part 10 of 11, beats 530 to 601)... +forward step +backward step + +Finding maximum probability path (part 11 of 11, beats 602 to 626)... +forward step +backward step +0.0 0.023 N +0.023 0.348 N +0.348 1.869 G +1.869 3.379 C/5 +3.379 7.941 G +7.941 9.462 E:min7 +9.462 10.983 C +10.983 12.492 D +12.492 15.523 G +15.523 17.043 C +17.043 18.564 D +18.564 20.085 E:min +20.085 21.606 B +21.606 23.139 E:min +23.139 24.648 A +24.648 26.157 D +26.157 27.678 B:min +27.678 33.750 A:min7 +33.750 35.260 G +35.260 36.769 E:min7 +36.769 38.301 C +38.301 39.822 D +39.822 42.864 G +42.864 44.373 C +44.373 45.906 D +45.906 47.427 E:min +47.427 48.924 B +48.924 50.434 E:min +50.434 51.966 A +51.966 53.487 D +53.487 54.996 B:min +54.996 61.068 A:min7 +61.068 62.624 D +62.624 64.134 D:min7 +64.134 65.260 G/5 +65.260 67.152 D +67.152 68.661 F +68.661 70.182 G +70.182 70.937 C +70.937 71.703 G/3 +71.703 72.469 A:min7 +72.469 73.224 C/5 +73.224 74.745 D +74.745 76.266 B:min +76.266 77.392 F/5 +77.392 79.296 C +79.296 82.303 D +82.303 83.847 E:min +83.847 85.368 B +85.368 86.877 E:min +86.877 88.398 A +88.398 89.908 D +89.908 91.429 B:min7 +91.429 94.470 A:min7 +94.470 95.225 D/5 +95.225 97.512 A:min +97.512 99.021 D +99.021 100.542 D:min7 +100.542 101.680 G/5 +101.680 103.573 D +103.573 105.105 F +105.105 106.638 G +106.638 107.381 C +107.381 108.124 G/3 +108.124 108.890 A:min7 +108.890 109.656 C/5 +109.656 111.177 D +111.177 112.721 B:min +112.721 113.836 F/5 +113.836 115.728 C +115.728 118.758 D +118.758 120.279 G +120.279 121.789 E:min7 +121.789 123.310 C +123.310 124.830 D +124.830 127.872 G +127.872 129.393 C +129.393 130.914 D +130.914 132.447 E:min +132.447 133.933 B +133.933 135.477 E:min +135.477 136.986 A +136.986 138.495 D +138.495 140.028 B:min +140.028 146.077 A:min7 +146.077 147.598 G +147.598 149.130 E:min7 +149.130 150.651 C +150.651 152.160 D +152.160 155.191 G +155.191 156.723 C +156.723 158.244 D +158.244 159.753 E:min +159.753 161.263 B +161.263 162.783 E:min +162.783 164.293 A +164.293 165.814 D +165.814 167.358 B:min +167.358 173.430 A:min7 +173.430 174.928 G +174.928 176.448 E:min7 +176.448 177.969 C +177.969 179.490 D +179.490 182.509 G +182.509 184.041 C +184.041 185.562 D +185.562 187.095 E:min +187.095 188.604 B +188.604 190.102 E:min +190.102 191.634 A +191.634 193.132 D +193.132 194.676 B:min +194.676 200.737 A:min7 +200.737 202.257 G +202.257 203.790 E:min7 +203.790 205.299 C +205.299 206.809 D +206.809 209.850 G +209.850 211.383 C +211.383 212.892 D +212.892 214.413 E:min +214.413 215.922 B +215.922 217.443 E:min +217.443 218.964 A +218.964 220.473 D +220.473 222.006 B:min +222.006 228.078 A:min7 +228.078 229.587 G +229.587 230.725 E:min7 +230.725 237.533 N0.0 0.023 N +0.023 82.303 G +82.303 97.512 D +97.512 237.533 G \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_logfiles/DuranDuran.OrdinaryWorld.wav.log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,438 @@ +CSVFeatureWriter::setParameters +stdout -> +NOTE: Using default sample rate of 44100 for default transform +NOTE: Loaded and initialised plugin for transform "vamp:matthiasm:nnls_chroma:bothchroma" with plugin step size 2048 and block size 16384 (adapter step and block size 16384) +NOTE: PluginInputDomainAdapter timestamp adjustment is 0.000000000R +Extracting features for: "/Users/matthias/Music/MUSICDB44k/DuranDuran.OrdinaryWorld.wav" + Retrieving audio data... Done +Opened 2-channel file or URL "/Users/matthias/Music/MUSICDB44k/DuranDuran.OrdinaryWorld.wav" +timestamp 0.000000000R: very low magnitude, setting magnitude to all zeros +timestamp 0.046439909R: very low magnitude, setting magnitude to all zeros +timestamp 0.092879818R: very low magnitude, setting magnitude to all zeros +timestamp 0.139319727R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 1% Extracting and writing features... 2% Extracting and writing features... 3% Extracting and writing features... 4% Extracting and writing features... 5% Extracting and writing features... 6% Extracting and writing features... 7% Extracting and writing features... 8% Extracting and writing features... 9% Extracting and writing features... 10% Extracting and writing features... 11% Extracting and writing features... 12% Extracting and writing features... 13% Extracting and writing features... 14% Extracting and writing features... 15% Extracting and writing features... 16% Extracting and writing features... 17% Extracting and writing features... 18% Extracting and writing features... 19% Extracting and writing features... 20% Extracting and writing features... 21% Extracting and writing features... 22% Extracting and writing features... 23% Extracting and writing features... 24% Extracting and writing features... 25% Extracting and writing features... 26% Extracting and writing features... 27% Extracting and writing features... 28% Extracting and writing features... 29% Extracting and writing features... 30% Extracting and writing features... 31% Extracting and writing features... 32% Extracting and writing features... 33% Extracting and writing features... 34% Extracting and writing features... 35% Extracting and writing features... 36% Extracting and writing features... 37% Extracting and writing features... 38% Extracting and writing features... 39% Extracting and writing features... 40% Extracting and writing features... 41% Extracting and writing features... 42% Extracting and writing features... 43% Extracting and writing features... 44% Extracting and writing features... 45% Extracting and writing features... 46% Extracting and writing features... 47% Extracting and writing features... 48% Extracting and writing features... 49% Extracting and writing features... 50% Extracting and writing features... 51% Extracting and writing features... 52% Extracting and writing features... 53% Extracting and writing features... 54% Extracting and writing features... 55% Extracting and writing features... 56% Extracting and writing features... 57% Extracting and writing features... 58% Extracting and writing features... 59% Extracting and writing features... 60% Extracting and writing features... 61% Extracting and writing features... 62% Extracting and writing features... 63% Extracting and writing features... 64% Extracting and writing features... 65% Extracting and writing features... 66% Extracting and writing features... 67% Extracting and writing features... 68% Extracting and writing features... 69% Extracting and writing features... 70% Extracting and writing features... 71% Extracting and writing features... 72% Extracting and writing features... 73% Extracting and writing features... 74% Extracting and writing features... 75% Extracting and writing features... 76% Extracting and writing features... 77% Extracting and writing features... 78% Extracting and writing features... 79% Extracting and writing features... 80% Extracting and writing features... 81% Extracting and writing features... 82% Extracting and writing features... 83% Extracting and writing features... 84% Extracting and writing features... 85% Extracting and writing features... 86% Extracting and writing features... 87% Extracting and writing features... 88% Extracting and writing features... 89% Extracting and writing features... 90% Extracting and writing features... 91% Extracting and writing features... 92% Extracting and writing features... 93% Extracting and writing features... 94% Extracting and writing features... 95% Extracting and writing features... 96% Extracting and writing features... 97%timestamp 333.067029478R: very low magnitude, setting magnitude to all zeros +timestamp 333.113469387R: very low magnitude, setting magnitude to all zeros +timestamp 333.159909297R: very low magnitude, setting magnitude to all zeros +timestamp 333.206349206R: very low magnitude, setting magnitude to all zeros +timestamp 333.252789115R: very low magnitude, setting magnitude to all zeros +timestamp 333.299229024R: very low magnitude, setting magnitude to all zeros +timestamp 333.345668934R: very low magnitude, setting magnitude to all zeros +timestamp 333.392108843R: very low magnitude, setting magnitude to all zeros +timestamp 333.438548752R: very low magnitude, setting magnitude to all zeros +timestamp 333.484988662R: very low magnitude, setting magnitude to all zeros +timestamp 333.531428571R: very low magnitude, setting magnitude to all zeros +timestamp 333.577868480R: very low magnitude, setting magnitude to all zeros +timestamp 333.624308390R: very low magnitude, setting magnitude to all zeros +timestamp 333.670748299R: very low magnitude, setting magnitude to all zeros +timestamp 333.717188208R: very low magnitude, setting magnitude to all zeros +timestamp 333.763628117R: very low magnitude, setting magnitude to all zeros +timestamp 333.810068027R: very low magnitude, setting magnitude to all zeros +timestamp 333.856507936R: very low magnitude, setting magnitude to all zeros +timestamp 333.902947845R: very low magnitude, setting magnitude to all zeros +timestamp 333.949387755R: very low magnitude, setting magnitude to all zeros +timestamp 333.995827664R: very low magnitude, setting magnitude to all zeros +timestamp 334.042267573R: very low magnitude, setting magnitude to all zeros +timestamp 334.088707482R: very low magnitude, setting magnitude to all zeros +timestamp 334.135147392R: very low magnitude, setting magnitude to all zeros +timestamp 334.181587301R: very low magnitude, setting magnitude to all zeros +timestamp 334.228027210R: very low magnitude, setting magnitude to all zeros +timestamp 334.274467120R: very low magnitude, setting magnitude to all zeros +timestamp 334.320907029R: very low magnitude, setting magnitude to all zeros +timestamp 334.367346938R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 98%timestamp 334.413786848R: very low magnitude, setting magnitude to all zeros +timestamp 334.460226757R: very low magnitude, setting magnitude to all zeros +timestamp 334.506666666R: very low magnitude, setting magnitude to all zeros +timestamp 334.553106575R: very low magnitude, setting magnitude to all zeros +timestamp 334.599546485R: very low magnitude, setting magnitude to all zeros +timestamp 334.645986394R: very low magnitude, setting magnitude to all zeros +timestamp 334.692426303R: very low magnitude, setting magnitude to all zeros +timestamp 334.738866213R: very low magnitude, setting magnitude to all zeros +timestamp 334.785306122R: very low magnitude, setting magnitude to all zeros +timestamp 334.831746031R: very low magnitude, setting magnitude to all zeros +timestamp 334.878185941R: very low magnitude, setting magnitude to all zeros +timestamp 334.924625850R: very low magnitude, setting magnitude to all zeros +timestamp 334.971065759R: very low magnitude, setting magnitude to all zeros +timestamp 335.017505668R: very low magnitude, setting magnitude to all zeros +timestamp 335.063945578R: very low magnitude, setting magnitude to all zeros +timestamp 335.110385487R: very low magnitude, setting magnitude to all zeros +timestamp 335.156825396R: very low magnitude, setting magnitude to all zeros +timestamp 335.203265306R: very low magnitude, setting magnitude to all zeros +timestamp 335.249705215R: very low magnitude, setting magnitude to all zeros +timestamp 335.296145124R: very low magnitude, setting magnitude to all zeros +timestamp 335.342585034R: very low magnitude, setting magnitude to all zeros +timestamp 335.389024943R: very low magnitude, setting magnitude to all zeros +timestamp 335.435464852R: very low magnitude, setting magnitude to all zeros +timestamp 335.481904761R: very low magnitude, setting magnitude to all zeros +timestamp 335.528344671R: very low magnitude, setting magnitude to all zeros +timestamp 335.574784580R: very low magnitude, setting magnitude to all zeros +timestamp 335.621224489R: very low magnitude, setting magnitude to all zeros +timestamp 335.667664399R: very low magnitude, setting magnitude to all zeros +timestamp 335.714104308R: very low magnitude, setting magnitude to all zeros +timestamp 335.760544217R: very low magnitude, setting magnitude to all zeros +timestamp 335.806984126R: very low magnitude, setting magnitude to all zeros +timestamp 335.853424036R: very low magnitude, setting magnitude to all zeros +timestamp 335.899863945R: very low magnitude, setting magnitude to all zeros +timestamp 335.946303854R: very low magnitude, setting magnitude to all zeros +timestamp 335.992743764R: very low magnitude, setting magnitude to all zeros +timestamp 336.039183673R: very low magnitude, setting magnitude to all zeros +timestamp 336.085623582R: very low magnitude, setting magnitude to all zeros +timestamp 336.132063492R: very low magnitude, setting magnitude to all zeros +timestamp 336.178503401R: very low magnitude, setting magnitude to all zeros +timestamp 336.224943310R: very low magnitude, setting magnitude to all zeros +timestamp 336.271383219R: very low magnitude, setting magnitude to all zeros +timestamp 336.317823129R: very low magnitude, setting magnitude to all zeros +timestamp 336.364263038R: very low magnitude, setting magnitude to all zeros +timestamp 336.410702947R: very low magnitude, setting magnitude to all zeros +timestamp 336.457142857R: very low magnitude, setting magnitude to all zeros +timestamp 336.503582766R: very low magnitude, setting magnitude to all zeros +timestamp 336.550022675R: very low magnitude, setting magnitude to all zeros +timestamp 336.596462585R: very low magnitude, setting magnitude to all zeros +timestamp 336.642902494R: very low magnitude, setting magnitude to all zeros +timestamp 336.689342403R: very low magnitude, setting magnitude to all zeros +timestamp 336.735782312R: very low magnitude, setting magnitude to all zeros +timestamp 336.782222222R: very low magnitude, setting magnitude to all zeros +timestamp 336.828662131R: very low magnitude, setting magnitude to all zeros +timestamp 336.875102040R: very low magnitude, setting magnitude to all zeros +timestamp 336.921541950R: very low magnitude, setting magnitude to all zeros +timestamp 336.967981859R: very low magnitude, setting magnitude to all zeros +timestamp 337.014421768R: very low magnitude, setting magnitude to all zeros +timestamp 337.060861678R: very low magnitude, setting magnitude to all zeros +timestamp 337.107301587R: very low magnitude, setting magnitude to all zeros +timestamp 337.153741496R: very low magnitude, setting magnitude to all zeros +timestamp 337.200181405R: very low magnitude, setting magnitude to all zeros +timestamp 337.246621315R: very low magnitude, setting magnitude to all zeros +timestamp 337.293061224R: very low magnitude, setting magnitude to all zeros +timestamp 337.339501133R: very low magnitude, setting magnitude to all zeros +timestamp 337.385941043R: very low magnitude, setting magnitude to all zeros +timestamp 337.432380952R: very low magnitude, setting magnitude to all zeros +timestamp 337.478820861R: very low magnitude, setting magnitude to all zeros +timestamp 337.525260770R: very low magnitude, setting magnitude to all zeros +timestamp 337.571700680R: very low magnitude, setting magnitude to all zeros +timestamp 337.618140589R: very low magnitude, setting magnitude to all zeros +timestamp 337.664580498R: very low magnitude, setting magnitude to all zeros +timestamp 337.711020408R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 99%timestamp 337.757460317R: very low magnitude, setting magnitude to all zeros +timestamp 337.803900226R: very low magnitude, setting magnitude to all zeros +timestamp 337.850340136R: very low magnitude, setting magnitude to all zeros +timestamp 337.896780045R: very low magnitude, setting magnitude to all zeros +timestamp 337.943219954R: very low magnitude, setting magnitude to all zeros +timestamp 337.989659863R: very low magnitude, setting magnitude to all zeros +timestamp 338.036099773R: very low magnitude, setting magnitude to all zeros +timestamp 338.082539682R: very low magnitude, setting magnitude to all zeros +timestamp 338.128979591R: very low magnitude, setting magnitude to all zeros +timestamp 338.175419501R: very low magnitude, setting magnitude to all zeros +timestamp 338.221859410R: very low magnitude, setting magnitude to all zeros +timestamp 338.268299319R: very low magnitude, setting magnitude to all zeros +timestamp 338.314739229R: very low magnitude, setting magnitude to all zeros +timestamp 338.361179138R: very low magnitude, setting magnitude to all zeros +timestamp 338.407619047R: very low magnitude, setting magnitude to all zeros +timestamp 338.454058956R: very low magnitude, setting magnitude to all zeros +timestamp 338.500498866R: very low magnitude, setting magnitude to all zeros +timestamp 338.546938775R: very low magnitude, setting magnitude to all zeros +timestamp 338.593378684R: very low magnitude, setting magnitude to all zeros +timestamp 338.639818594R: very low magnitude, setting magnitude to all zeros +timestamp 338.686258503R: very low magnitude, setting magnitude to all zeros +timestamp 338.732698412R: very low magnitude, setting magnitude to all zeros +timestamp 338.779138321R: very low magnitude, setting magnitude to all zeros +timestamp 338.825578231R: very low magnitude, setting magnitude to all zeros +timestamp 338.872018140R: very low magnitude, setting magnitude to all zeros +timestamp 338.918458049R: very low magnitude, setting magnitude to all zeros +timestamp 338.964897959R: very low magnitude, setting magnitude to all zeros +timestamp 339.011337868R: very low magnitude, setting magnitude to all zeros +timestamp 339.057777777R: very low magnitude, setting magnitude to all zeros +timestamp 339.104217687R: very low magnitude, setting magnitude to all zeros +timestamp 339.150657596R: very low magnitude, setting magnitude to all zeros +timestamp 339.197097505R: very low magnitude, setting magnitude to all zeros +timestamp 339.243537414R: very low magnitude, setting magnitude to all zeros +timestamp 339.289977324R: very low magnitude, setting magnitude to all zeros +timestamp 339.336417233R: very low magnitude, setting magnitude to all zeros +timestamp 339.382857142R: very low magnitude, setting magnitude to all zeros +timestamp 339.429297052R: very low magnitude, setting magnitude to all zeros +timestamp 339.475736961R: very low magnitude, setting magnitude to all zeros +timestamp 339.522176870R: very low magnitude, setting magnitude to all zeros +timestamp 339.568616780R: very low magnitude, setting magnitude to all zeros +timestamp 339.615056689R: very low magnitude, setting magnitude to all zeros +timestamp 339.661496598R: very low magnitude, setting magnitude to all zeros +timestamp 339.707936507R: very low magnitude, setting magnitude to all zeros +timestamp 339.754376417R: very low magnitude, setting magnitude to all zeros +timestamp 339.800816326R: very low magnitude, setting magnitude to all zeros +timestamp 339.847256235R: very low magnitude, setting magnitude to all zeros +timestamp 339.893696145R: very low magnitude, setting magnitude to all zeros +timestamp 339.940136054R: very low magnitude, setting magnitude to all zeros +timestamp 339.986575963R: very low magnitude, setting magnitude to all zeros +timestamp 340.033015873R: very low magnitude, setting magnitude to all zeros +timestamp 340.079455782R: very low magnitude, setting magnitude to all zeros +timestamp 340.125895691R: very low magnitude, setting magnitude to all zeros +timestamp 340.172335600R: very low magnitude, setting magnitude to all zeros +timestamp 340.218775510R: very low magnitude, setting magnitude to all zeros +timestamp 340.265215419R: very low magnitude, setting magnitude to all zeros +timestamp 340.311655328R: very low magnitude, setting magnitude to all zeros +timestamp 340.358095238R: very low magnitude, setting magnitude to all zeros +timestamp 340.404535147R: very low magnitude, setting magnitude to all zeros +timestamp 340.450975056R: very low magnitude, setting magnitude to all zeros +timestamp 340.497414965R: very low magnitude, setting magnitude to all zeros +timestamp 340.543854875R: very low magnitude, setting magnitude to all zeros +timestamp 340.590294784R: very low magnitude, setting magnitude to all zeros +timestamp 340.636734693R: very low magnitude, setting magnitude to all zeros +timestamp 340.683174603R: very low magnitude, setting magnitude to all zeros +timestamp 340.729614512R: very low magnitude, setting magnitude to all zeros +timestamp 340.776054421R: very low magnitude, setting magnitude to all zeros +timestamp 340.822494331R: very low magnitude, setting magnitude to all zeros +timestamp 340.868934240R: very low magnitude, setting magnitude to all zeros +timestamp 340.915374149R: very low magnitude, setting magnitude to all zeros +timestamp 340.961814058R: very low magnitude, setting magnitude to all zeros +timestamp 341.008253968R: very low magnitude, setting magnitude to all zeros +timestamp 341.054693877R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 100%timestamp 341.101133786R: very low magnitude, setting magnitude to all zeros +[NNLS Chroma Plugin] Tuning Log-Frequency Spectrogram ... done. +[NNLS Chroma Plugin] Performing NNLS and mapping to chroma ... done. +[NNLS Chroma Plugin] Chord Estimation ... done. + Extracting and writing features... Done + + < M A T L A B > + Copyright 1984-2007 The MathWorks, Inc. + Version 7.5.0.338 (R2007b) + August 9, 2007 + + + To get started, type one of these: helpwin, helpdesk, or demo. + For product information, visit www.mathworks.com. + + - First Slice + metpos + key + chord + bass + - Second Slice + (7) metpos (slice 2) + (8) key (slice 2) + (9) chord (slice 2) + (10) bass (slice 2) +onset detection function +beats + + Length: 28 Length: 32 Length: 36 Length: 40 Length: 44 Length: 48 Length: 52 Length: 56 Length: 60 Length: 64 Length: 68 Length: 72 Length: 76 Length: 80 Length: 84 Length: 88 Length: 92 Length: 96 Length: 100 Length: 104 Length: 108 Length: 112 Length: 116 Length: 120 Length: 124 Length: 128 +Finding maximum probability path (part 1 of 28, beats 1 to 29)... +forward step +backward step + +Finding maximum probability path (part 2 of 28, beats 30 to 57)... +forward step +backward step + +Finding maximum probability path (part 3 of 28, beats 58 to 70)... +forward step +backward step + +Finding maximum probability path (part 4 of 28, beats 71 to 102)... +forward step +backward step + +Finding maximum probability path (part 5 of 28, beats 103 to 134)... +forward step +backward step + +Finding maximum probability path (part 6 of 28, beats 135 to 157)... +forward step +backward step + +Finding maximum probability path (part 7 of 28, beats 158 to 189)... +forward step +backward step + +Finding maximum probability path (part 8 of 28, beats 190 to 217)... +forward step +backward step + +Finding maximum probability path (part 9 of 28, beats 218 to 246)... +forward step +backward step + +Finding maximum probability path (part 10 of 28, beats 247 to 278)... +forward step +backward step + +Finding maximum probability path (part 11 of 28, beats 279 to 310)... +forward step +backward step + +Finding maximum probability path (part 12 of 28, beats 311 to 334)... +forward step +backward step + +Finding maximum probability path (part 13 of 28, beats 335 to 365)... +forward step +backward step + +Finding maximum probability path (part 14 of 28, beats 366 to 398)... +forward step +backward step + +Finding maximum probability path (part 15 of 28, beats 399 to 430)... +forward step +backward step + +Finding maximum probability path (part 16 of 28, beats 431 to 458)... +forward step +backward step + +Finding maximum probability path (part 17 of 28, beats 459 to 470)... +forward step +backward step + +Finding maximum probability path (part 18 of 28, beats 471 to 502)... +forward step +backward step + +Finding maximum probability path (part 19 of 28, beats 503 to 523)... +forward step +backward step + +Finding maximum probability path (part 20 of 28, beats 524 to 558)... +forward step +backward step + +Finding maximum probability path (part 21 of 28, beats 559 to 590)... +forward step +backward step + +Finding maximum probability path (part 22 of 28, beats 591 to 621)... +forward step +backward step + +Finding maximum probability path (part 23 of 28, beats 622 to 654)... +forward step +backward step + +Finding maximum probability path (part 24 of 28, beats 655 to 686)... +forward step +backward step + +Finding maximum probability path (part 25 of 28, beats 687 to 718)... +forward step +backward step + +Finding maximum probability path (part 26 of 28, beats 719 to 751)... +forward step +backward step + +Finding maximum probability path (part 27 of 28, beats 752 to 779)... +forward step +backward step + +Finding maximum probability path (part 28 of 28, beats 780 to 796)... +forward step +backward step +0.0 0.163 N +0.163 1.265 N +1.265 4.679 B +4.679 8.115 F#:min7 +8.115 11.552 A +11.552 14.977 E +14.977 18.402 B +18.402 22.709 F#:min7 +22.709 25.263 A +25.263 28.688 Bbb:min +28.688 32.113 Db:min +32.113 33.820 Fb:maj6 +33.820 35.109 Gb:min +35.109 38.963 Db:min +38.963 40.681 Fb +40.681 41.970 Cb/5 +41.970 45.825 Db:min +45.825 47.531 Fb:maj6 +47.531 48.820 Gb:min +48.820 52.674 Db:min +52.674 54.393 Fb +54.393 55.670 Cb/5 +55.670 59.536 Db:min +59.536 62.949 Ab:min +62.949 66.386 Eb:7 +66.386 69.799 Fb +69.799 73.236 B +73.236 77.520 F#:min7 +77.520 80.086 A/3 +80.086 83.499 E +83.499 86.947 B +86.947 91.231 F#:min7 +91.231 93.797 A +93.797 97.210 Gb:dim +97.210 107.497 Db:min +107.497 109.215 Fb:maj6 +109.215 110.504 Gb:min +110.504 114.347 Db:min +114.347 116.065 Fb +116.065 117.342 Cb/5 +117.342 121.197 Db:min +121.197 122.926 Fb:maj6 +122.926 124.204 Gb:min +124.204 128.046 Db:min +128.046 129.765 Fb +129.765 131.042 Cb/5 +131.042 134.920 Db:min +134.920 138.344 Ab:min +138.344 141.781 Eb:7 +141.781 145.194 Fb +145.194 148.631 B +148.631 156.340 F#:min7 +156.340 158.906 E +158.906 162.331 B +162.331 166.615 F#:min7 +166.615 169.181 A/3 +169.181 172.617 E +172.617 176.042 B +176.042 180.315 F#:min7 +180.315 182.892 A +182.892 186.317 E +186.317 189.742 B +189.742 194.026 F#:min7 +194.026 196.603 A +196.603 200.028 Bbb:min +200.028 203.465 Db:min +203.465 205.172 Fb:maj6 +205.172 206.449 Gb:min +206.449 210.303 Db:min +210.303 212.033 Fb +212.033 213.299 Cb/5 +213.299 218.442 Db:min +218.442 219.301 Fb:min +219.301 220.148 Gb +220.148 224.015 Db:min +224.015 226.580 E +226.580 230.017 B +230.017 237.726 F#:min7 +237.726 241.139 E +241.139 244.576 B +244.576 251.426 F#:min7 +251.426 254.839 E +254.839 258.276 B +258.276 262.560 F#:min7 +262.560 265.125 A +265.125 268.562 E +268.562 271.987 B +271.987 276.271 F#:min7 +276.271 278.837 A +278.837 282.680 E +282.680 286.116 B +286.116 292.978 F#:min7 +292.978 296.403 E +296.403 299.828 B +299.828 306.678 F#:min7 +306.678 310.114 E +310.114 313.539 B +313.539 320.389 F#:min7 +320.389 324.243 E +324.243 327.680 B +327.680 331.952 F#:min7 +331.952 333.671 A +333.671 341.293 N0.0 0.163 N +0.163 24.404 E +24.404 57.377 Db:minor +57.377 67.245 Ab:minor +67.245 92.938 E +92.938 132.760 Db:minor +132.760 143.047 Ab:minor +143.047 196.162 E +196.162 224.015 Db:minor +224.015 333.671 E +333.671 341.293 A \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_logfiles/EltonJohn.IslandGirl.wav.log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,228 @@ +CSVFeatureWriter::setParameters +stdout -> +NOTE: Using default sample rate of 44100 for default transform +NOTE: Loaded and initialised plugin for transform "vamp:matthiasm:nnls_chroma:bothchroma" with plugin step size 2048 and block size 16384 (adapter step and block size 16384) +NOTE: PluginInputDomainAdapter timestamp adjustment is 0.000000000R +Extracting features for: "/Users/matthias/Music/MUSICDB44k/EltonJohn.IslandGirl.wav" + Retrieving audio data... Done +Opened 2-channel file or URL "/Users/matthias/Music/MUSICDB44k/EltonJohn.IslandGirl.wav" +timestamp 0.000000000R: very low magnitude, setting magnitude to all zeros +timestamp 0.046439909R: very low magnitude, setting magnitude to all zeros +timestamp 0.092879818R: very low magnitude, setting magnitude to all zeros +timestamp 0.139319727R: very low magnitude, setting magnitude to all zeros +timestamp 0.185759637R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 1% Extracting and writing features... 2% Extracting and writing features... 3% Extracting and writing features... 4% Extracting and writing features... 5% Extracting and writing features... 6% Extracting and writing features... 7% Extracting and writing features... 8% Extracting and writing features... 9% Extracting and writing features... 10% Extracting and writing features... 11% Extracting and writing features... 12% Extracting and writing features... 13% Extracting and writing features... 14% Extracting and writing features... 15% Extracting and writing features... 16% Extracting and writing features... 17% Extracting and writing features... 18% Extracting and writing features... 19% Extracting and writing features... 20% Extracting and writing features... 21% Extracting and writing features... 22% Extracting and writing features... 23% Extracting and writing features... 24% Extracting and writing features... 25% Extracting and writing features... 26% Extracting and writing features... 27% Extracting and writing features... 28% Extracting and writing features... 29% Extracting and writing features... 30% Extracting and writing features... 31% Extracting and writing features... 32% Extracting and writing features... 33% Extracting and writing features... 34% Extracting and writing features... 35% Extracting and writing features... 36% Extracting and writing features... 37% Extracting and writing features... 38% Extracting and writing features... 39% Extracting and writing features... 40% Extracting and writing features... 41% Extracting and writing features... 42% Extracting and writing features... 43% Extracting and writing features... 44% Extracting and writing features... 45% Extracting and writing features... 46% Extracting and writing features... 47% Extracting and writing features... 48% Extracting and writing features... 49% Extracting and writing features... 50% Extracting and writing features... 51% Extracting and writing features... 52% Extracting and writing features... 53% Extracting and writing features... 54% Extracting and writing features... 55% Extracting and writing features... 56% Extracting and writing features... 57% Extracting and writing features... 58% Extracting and writing features... 59% Extracting and writing features... 60% Extracting and writing features... 61% Extracting and writing features... 62% Extracting and writing features... 63% Extracting and writing features... 64% Extracting and writing features... 65% Extracting and writing features... 66% Extracting and writing features... 67% Extracting and writing features... 68% Extracting and writing features... 69% Extracting and writing features... 70% Extracting and writing features... 71% Extracting and writing features... 72% Extracting and writing features... 73% Extracting and writing features... 74% Extracting and writing features... 75% Extracting and writing features... 76% Extracting and writing features... 77% Extracting and writing features... 78% Extracting and writing features... 79% Extracting and writing features... 80% Extracting and writing features... 81% Extracting and writing features... 82% Extracting and writing features... 83% Extracting and writing features... 84% Extracting and writing features... 85% Extracting and writing features... 86% Extracting and writing features... 87% Extracting and writing features... 88% Extracting and writing features... 89% Extracting and writing features... 90% Extracting and writing features... 91% Extracting and writing features... 92% Extracting and writing features... 93% Extracting and writing features... 94% Extracting and writing features... 95% Extracting and writing features... 96% Extracting and writing features... 97% Extracting and writing features... 98%timestamp 223.190204081R: very low magnitude, setting magnitude to all zeros +timestamp 223.236643990R: very low magnitude, setting magnitude to all zeros +timestamp 223.283083900R: very low magnitude, setting magnitude to all zeros +timestamp 223.329523809R: very low magnitude, setting magnitude to all zeros +timestamp 223.375963718R: very low magnitude, setting magnitude to all zeros +timestamp 223.422403628R: very low magnitude, setting magnitude to all zeros +timestamp 223.468843537R: very low magnitude, setting magnitude to all zeros +timestamp 223.515283446R: very low magnitude, setting magnitude to all zeros +timestamp 223.561723356R: very low magnitude, setting magnitude to all zeros +timestamp 223.608163265R: very low magnitude, setting magnitude to all zeros +timestamp 223.654603174R: very low magnitude, setting magnitude to all zeros +timestamp 223.701043083R: very low magnitude, setting magnitude to all zeros +timestamp 223.747482993R: very low magnitude, setting magnitude to all zeros +timestamp 223.793922902R: very low magnitude, setting magnitude to all zeros +timestamp 223.840362811R: very low magnitude, setting magnitude to all zeros +timestamp 223.886802721R: very low magnitude, setting magnitude to all zeros +timestamp 223.933242630R: very low magnitude, setting magnitude to all zeros +timestamp 223.979682539R: very low magnitude, setting magnitude to all zeros +timestamp 224.026122448R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 99%timestamp 224.072562358R: very low magnitude, setting magnitude to all zeros +timestamp 224.119002267R: very low magnitude, setting magnitude to all zeros +timestamp 224.165442176R: very low magnitude, setting magnitude to all zeros +timestamp 224.211882086R: very low magnitude, setting magnitude to all zeros +timestamp 224.258321995R: very low magnitude, setting magnitude to all zeros +timestamp 224.304761904R: very low magnitude, setting magnitude to all zeros +timestamp 224.351201814R: very low magnitude, setting magnitude to all zeros +timestamp 224.397641723R: very low magnitude, setting magnitude to all zeros +timestamp 224.444081632R: very low magnitude, setting magnitude to all zeros +timestamp 224.490521541R: very low magnitude, setting magnitude to all zeros +timestamp 224.536961451R: very low magnitude, setting magnitude to all zeros +timestamp 224.583401360R: very low magnitude, setting magnitude to all zeros +timestamp 224.629841269R: very low magnitude, setting magnitude to all zeros +timestamp 224.676281179R: very low magnitude, setting magnitude to all zeros +timestamp 224.722721088R: very low magnitude, setting magnitude to all zeros +timestamp 224.769160997R: very low magnitude, setting magnitude to all zeros +timestamp 224.815600907R: very low magnitude, setting magnitude to all zeros +timestamp 224.862040816R: very low magnitude, setting magnitude to all zeros +timestamp 224.908480725R: very low magnitude, setting magnitude to all zeros +timestamp 224.954920634R: very low magnitude, setting magnitude to all zeros +timestamp 225.001360544R: very low magnitude, setting magnitude to all zeros +timestamp 225.047800453R: very low magnitude, setting magnitude to all zeros +timestamp 225.094240362R: very low magnitude, setting magnitude to all zeros +timestamp 225.140680272R: very low magnitude, setting magnitude to all zeros +timestamp 225.187120181R: very low magnitude, setting magnitude to all zeros +timestamp 225.233560090R: very low magnitude, setting magnitude to all zeros +timestamp 225.280000000R: very low magnitude, setting magnitude to all zeros +timestamp 225.326439909R: very low magnitude, setting magnitude to all zeros +timestamp 225.372879818R: very low magnitude, setting magnitude to all zeros +timestamp 225.419319727R: very low magnitude, setting magnitude to all zeros +timestamp 225.465759637R: very low magnitude, setting magnitude to all zeros +timestamp 225.512199546R: very low magnitude, setting magnitude to all zeros +timestamp 225.558639455R: very low magnitude, setting magnitude to all zeros +timestamp 225.605079365R: very low magnitude, setting magnitude to all zeros +timestamp 225.651519274R: very low magnitude, setting magnitude to all zeros +timestamp 225.697959183R: very low magnitude, setting magnitude to all zeros +timestamp 225.744399092R: very low magnitude, setting magnitude to all zeros +timestamp 225.790839002R: very low magnitude, setting magnitude to all zeros +timestamp 225.837278911R: very low magnitude, setting magnitude to all zeros +timestamp 225.883718820R: very low magnitude, setting magnitude to all zeros +timestamp 225.930158730R: very low magnitude, setting magnitude to all zeros +[NNLS Chroma Plugin] Tuning Log-Frequency Spectrogram ... done. +[NNLS Chroma Plugin] Performing NNLS and mapping to chroma ... done. +[NNLS Chroma Plugin] Chord Estimation ... done. + Extracting and writing features... Done + + + < M A T L A B > + Copyright 1984-2007 The MathWorks, Inc. + Version 7.5.0.338 (R2007b) + August 9, 2007 + + + To get started, type one of these: helpwin, helpdesk, or demo. + For product information, visit www.mathworks.com. + + - First Slice + metpos + key + chord + bass + - Second Slice + (7) metpos (slice 2) + (8) key (slice 2) + (9) chord (slice 2) + (10) bass (slice 2) +onset detection function +beats + + Length: 28 Length: 32 Length: 36 Length: 40 Length: 44 Length: 48 Length: 52 Length: 56 Length: 60 Length: 64 Length: 68 Length: 72 Length: 76 Length: 80 Length: 84 Length: 88 Length: 92 Length: 96 Length: 100 Length: 104 Length: 108 Length: 112 Length: 116 Length: 120 Length: 124 Length: 128 +Finding maximum probability path (part 1 of 7, beats 1 to 29)... +forward step +backward step + +Finding maximum probability path (part 2 of 7, beats 30 to 157)... +forward step +backward step + +Finding maximum probability path (part 3 of 7, beats 158 to 189)... +forward step +backward step + +Finding maximum probability path (part 4 of 7, beats 190 to 317)... +forward step +backward step + +Finding maximum probability path (part 5 of 7, beats 318 to 349)... +forward step +backward step + +Finding maximum probability path (part 6 of 7, beats 350 to 477)... +forward step +backward step + +Finding maximum probability path (part 7 of 7, beats 478 to 526)... +forward step +backward step +0.0 0.012 N +0.012 0.964 N +0.964 10.461 D:min +10.461 11.331 N +11.331 14.756 Bb/5 +14.756 16.475 C/5 +16.475 18.193 F/3 +18.193 21.629 Bb +21.629 24.207 D:min +24.207 25.066 F/5 +25.066 28.502 Bb/5 +28.502 30.221 F +30.221 31.927 C/5 +31.927 34.516 Ab:maj7 +34.516 35.376 Eb +35.376 38.847 Bb +38.847 41.436 F +41.436 42.307 Eb +42.307 45.755 Bb:maj6 +45.755 49.215 F +49.215 50.956 Bb +50.956 52.686 F/3 +52.686 54.416 F:min +54.416 56.134 Eb +56.134 59.594 Bb +59.594 61.324 Eb +61.324 63.054 C/3 +63.054 64.807 Bb/5 +64.807 65.666 Eb/3 +65.666 66.548 F/3 +66.548 70.194 Bb +70.194 79.702 D:min +79.702 80.562 C:maj6 +80.562 83.998 Bb/5 +83.998 85.716 C/5 +85.716 87.435 F/3 +87.435 90.883 Bb +90.883 93.425 D:min +93.425 94.285 F/5 +94.285 97.710 Bb/5 +97.710 99.439 F +99.439 101.158 C/5 +101.158 103.735 Ab:maj7 +103.735 104.606 Eb +104.606 108.089 Bb +108.089 110.678 F +110.678 111.549 Eb +111.549 115.008 Bb:maj6 +115.008 118.480 F +118.480 120.221 Bb +120.221 121.963 F/3 +121.963 123.693 F:min +123.693 125.423 Eb +125.423 128.859 Bb +128.859 130.577 Eb +130.577 132.296 C/3 +132.296 134.026 Bb/5 +134.026 134.885 Eb/3 +134.885 135.755 F/3 +135.755 139.343 Bb +139.343 148.840 D:min +148.840 149.699 C:maj6 +149.699 153.147 Bb/5 +153.147 154.865 C/5 +154.865 156.572 F/3 +156.572 159.985 Bb +159.985 162.575 D:min +162.575 163.422 F/5 +163.422 166.847 Bb/5 +166.847 168.577 F +168.577 170.295 C/5 +170.295 172.896 Ab:maj7 +172.896 173.767 Eb +173.767 177.215 Bb +177.215 179.815 F +179.815 180.674 Eb +180.674 184.123 Bb:maj6 +184.123 187.548 F +187.548 189.277 Bb +189.277 190.996 F/3 +190.996 192.702 F:min +192.702 194.421 Eb +194.421 197.857 Bb +197.857 199.552 Eb +199.552 201.271 C/3 +201.271 202.989 Bb/5 +202.989 203.848 Eb/3 +203.848 204.696 F/3 +204.696 206.414 Bb +206.414 207.261 Eb/3 +207.261 208.109 F/3 +208.109 209.816 Bb/5 +209.816 210.686 Eb/3 +210.686 211.603 F/3 +211.603 221.890 F:maj6 +221.890 226.240 N0.0 0.012 N +0.012 12.190 D:minor +12.190 205.555 Bb +205.555 226.240 F \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_logfiles/GloriaEstefanAndMiamiSoundMachine.AnythingForYou.wav.log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,497 @@ +CSVFeatureWriter::setParameters +stdout -> +NOTE: Using default sample rate of 44100 for default transform +NOTE: Loaded and initialised plugin for transform "vamp:matthiasm:nnls_chroma:bothchroma" with plugin step size 2048 and block size 16384 (adapter step and block size 16384) +NOTE: PluginInputDomainAdapter timestamp adjustment is 0.000000000R +Extracting features for: "/Users/matthias/Music/MUSICDB44k/GloriaEstefanAndMiamiSoundMachine.AnythingForYou.wav" + Retrieving audio data... Done +Opened 2-channel file or URL "/Users/matthias/Music/MUSICDB44k/GloriaEstefanAndMiamiSoundMachine.AnythingForYou.wav" +timestamp 0.000000000R: very low magnitude, setting magnitude to all zeros +timestamp 0.046439909R: very low magnitude, setting magnitude to all zeros +timestamp 0.092879818R: very low magnitude, setting magnitude to all zeros +timestamp 0.139319727R: very low magnitude, setting magnitude to all zeros +timestamp 0.185759637R: very low magnitude, setting magnitude to all zeros +timestamp 0.232199546R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 1% Extracting and writing features... 2% Extracting and writing features... 3% Extracting and writing features... 4% Extracting and writing features... 5% Extracting and writing features... 6% Extracting and writing features... 7% Extracting and writing features... 8% Extracting and writing features... 9% Extracting and writing features... 10% Extracting and writing features... 11% Extracting and writing features... 12% Extracting and writing features... 13% Extracting and writing features... 14% Extracting and writing features... 15% Extracting and writing features... 16% Extracting and writing features... 17% Extracting and writing features... 18% Extracting and writing features... 19% Extracting and writing features... 20% Extracting and writing features... 21% Extracting and writing features... 22% Extracting and writing features... 23% Extracting and writing features... 24% Extracting and writing features... 25% Extracting and writing features... 26% Extracting and writing features... 27% Extracting and writing features... 28% Extracting and writing features... 29% Extracting and writing features... 30% Extracting and writing features... 31% Extracting and writing features... 32% Extracting and writing features... 33% Extracting and writing features... 34% Extracting and writing features... 35% Extracting and writing features... 36% Extracting and writing features... 37% Extracting and writing features... 38% Extracting and writing features... 39% Extracting and writing features... 40% Extracting and writing features... 41% Extracting and writing features... 42% Extracting and writing features... 43% Extracting and writing features... 44% Extracting and writing features... 45% Extracting and writing features... 46% Extracting and writing features... 47% Extracting and writing features... 48% Extracting and writing features... 49% Extracting and writing features... 50% Extracting and writing features... 51% Extracting and writing features... 52% Extracting and writing features... 53% Extracting and writing features... 54% Extracting and writing features... 55% Extracting and writing features... 56% Extracting and writing features... 57% Extracting and writing features... 58% Extracting and writing features... 59% Extracting and writing features... 60% Extracting and writing features... 61% Extracting and writing features... 62% Extracting and writing features... 63% Extracting and writing features... 64% Extracting and writing features... 65% Extracting and writing features... 66% Extracting and writing features... 67% Extracting and writing features... 68% Extracting and writing features... 69% Extracting and writing features... 70% Extracting and writing features... 71% Extracting and writing features... 72% Extracting and writing features... 73% Extracting and writing features... 74% Extracting and writing features... 75% Extracting and writing features... 76% Extracting and writing features... 77% Extracting and writing features... 78% Extracting and writing features... 79% Extracting and writing features... 80% Extracting and writing features... 81% Extracting and writing features... 82% Extracting and writing features... 83% Extracting and writing features... 84% Extracting and writing features... 85% Extracting and writing features... 86% Extracting and writing features... 87% Extracting and writing features... 88% Extracting and writing features... 89% Extracting and writing features... 90% Extracting and writing features... 91% Extracting and writing features... 92% Extracting and writing features... 93% Extracting and writing features... 94%timestamp 231.224308390R: very low magnitude, setting magnitude to all zeros +timestamp 231.270748299R: very low magnitude, setting magnitude to all zeros +timestamp 231.317188208R: very low magnitude, setting magnitude to all zeros +timestamp 231.363628117R: very low magnitude, setting magnitude to all zeros +timestamp 231.410068027R: very low magnitude, setting magnitude to all zeros +timestamp 231.456507936R: very low magnitude, setting magnitude to all zeros +timestamp 231.502947845R: very low magnitude, setting magnitude to all zeros +timestamp 231.549387755R: very low magnitude, setting magnitude to all zeros +timestamp 231.595827664R: very low magnitude, setting magnitude to all zeros +timestamp 231.642267573R: very low magnitude, setting magnitude to all zeros +timestamp 231.688707482R: very low magnitude, setting magnitude to all zeros +timestamp 231.735147392R: very low magnitude, setting magnitude to all zeros +timestamp 231.781587301R: very low magnitude, setting magnitude to all zeros +timestamp 232.013786848R: very low magnitude, setting magnitude to all zeros +timestamp 232.060226757R: very low magnitude, setting magnitude to all zeros +timestamp 232.106666666R: very low magnitude, setting magnitude to all zeros +timestamp 232.153106575R: very low magnitude, setting magnitude to all zeros +timestamp 232.199546485R: very low magnitude, setting magnitude to all zeros +timestamp 232.245986394R: very low magnitude, setting magnitude to all zeros +timestamp 232.292426303R: very low magnitude, setting magnitude to all zeros +timestamp 232.338866213R: very low magnitude, setting magnitude to all zeros +timestamp 232.385306122R: very low magnitude, setting magnitude to all zeros +timestamp 232.431746031R: very low magnitude, setting magnitude to all zeros +timestamp 232.478185941R: very low magnitude, setting magnitude to all zeros +timestamp 232.524625850R: very low magnitude, setting magnitude to all zeros +timestamp 232.571065759R: very low magnitude, setting magnitude to all zeros +timestamp 232.617505668R: very low magnitude, setting magnitude to all zeros +timestamp 232.663945578R: very low magnitude, setting magnitude to all zeros +timestamp 232.710385487R: very low magnitude, setting magnitude to all zeros +timestamp 232.756825396R: very low magnitude, setting magnitude to all zeros +timestamp 232.803265306R: very low magnitude, setting magnitude to all zeros +timestamp 232.849705215R: very low magnitude, setting magnitude to all zeros +timestamp 232.896145124R: very low magnitude, setting magnitude to all zeros +timestamp 232.942585034R: very low magnitude, setting magnitude to all zeros +timestamp 233.128344671R: very low magnitude, setting magnitude to all zeros +timestamp 233.174784580R: very low magnitude, setting magnitude to all zeros +timestamp 233.221224489R: very low magnitude, setting magnitude to all zeros +timestamp 233.267664399R: very low magnitude, setting magnitude to all zeros +timestamp 233.406984126R: very low magnitude, setting magnitude to all zeros +timestamp 233.453424036R: very low magnitude, setting magnitude to all zeros +timestamp 233.499863945R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 95%timestamp 233.732063492R: very low magnitude, setting magnitude to all zeros +timestamp 233.778503401R: very low magnitude, setting magnitude to all zeros +timestamp 233.824943310R: very low magnitude, setting magnitude to all zeros +timestamp 233.871383219R: very low magnitude, setting magnitude to all zeros +timestamp 233.917823129R: very low magnitude, setting magnitude to all zeros +timestamp 233.964263038R: very low magnitude, setting magnitude to all zeros +timestamp 234.010702947R: very low magnitude, setting magnitude to all zeros +timestamp 234.057142857R: very low magnitude, setting magnitude to all zeros +timestamp 234.103582766R: very low magnitude, setting magnitude to all zeros +timestamp 234.150022675R: very low magnitude, setting magnitude to all zeros +timestamp 234.196462585R: very low magnitude, setting magnitude to all zeros +timestamp 234.242902494R: very low magnitude, setting magnitude to all zeros +timestamp 234.289342403R: very low magnitude, setting magnitude to all zeros +timestamp 234.335782312R: very low magnitude, setting magnitude to all zeros +timestamp 234.382222222R: very low magnitude, setting magnitude to all zeros +timestamp 234.428662131R: very low magnitude, setting magnitude to all zeros +timestamp 234.475102040R: very low magnitude, setting magnitude to all zeros +timestamp 234.521541950R: very low magnitude, setting magnitude to all zeros +timestamp 234.567981859R: very low magnitude, setting magnitude to all zeros +timestamp 234.614421768R: very low magnitude, setting magnitude to all zeros +timestamp 234.660861678R: very low magnitude, setting magnitude to all zeros +timestamp 234.707301587R: very low magnitude, setting magnitude to all zeros +timestamp 234.753741496R: very low magnitude, setting magnitude to all zeros +timestamp 234.800181405R: very low magnitude, setting magnitude to all zeros +timestamp 234.846621315R: very low magnitude, setting magnitude to all zeros +timestamp 234.893061224R: very low magnitude, setting magnitude to all zeros +timestamp 234.939501133R: very low magnitude, setting magnitude to all zeros +timestamp 234.985941043R: very low magnitude, setting magnitude to all zeros +timestamp 235.032380952R: very low magnitude, setting magnitude to all zeros +timestamp 235.078820861R: very low magnitude, setting magnitude to all zeros +timestamp 235.125260770R: very low magnitude, setting magnitude to all zeros +timestamp 235.171700680R: very low magnitude, setting magnitude to all zeros +timestamp 235.218140589R: very low magnitude, setting magnitude to all zeros +timestamp 235.264580498R: very low magnitude, setting magnitude to all zeros +timestamp 235.311020408R: very low magnitude, setting magnitude to all zeros +timestamp 235.357460317R: very low magnitude, setting magnitude to all zeros +timestamp 235.403900226R: very low magnitude, setting magnitude to all zeros +timestamp 235.450340136R: very low magnitude, setting magnitude to all zeros +timestamp 235.496780045R: very low magnitude, setting magnitude to all zeros +timestamp 235.543219954R: very low magnitude, setting magnitude to all zeros +timestamp 235.589659863R: very low magnitude, setting magnitude to all zeros +timestamp 235.636099773R: very low magnitude, setting magnitude to all zeros +timestamp 235.682539682R: very low magnitude, setting magnitude to all zeros +timestamp 235.728979591R: very low magnitude, setting magnitude to all zeros +timestamp 235.775419501R: very low magnitude, setting magnitude to all zeros +timestamp 235.821859410R: very low magnitude, setting magnitude to all zeros +timestamp 235.868299319R: very low magnitude, setting magnitude to all zeros +timestamp 235.914739229R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 96%timestamp 235.961179138R: very low magnitude, setting magnitude to all zeros +timestamp 236.007619047R: very low magnitude, setting magnitude to all zeros +timestamp 236.054058956R: very low magnitude, setting magnitude to all zeros +timestamp 236.100498866R: very low magnitude, setting magnitude to all zeros +timestamp 236.146938775R: very low magnitude, setting magnitude to all zeros +timestamp 236.193378684R: very low magnitude, setting magnitude to all zeros +timestamp 236.239818594R: very low magnitude, setting magnitude to all zeros +timestamp 236.286258503R: very low magnitude, setting magnitude to all zeros +timestamp 236.332698412R: very low magnitude, setting magnitude to all zeros +timestamp 236.379138321R: very low magnitude, setting magnitude to all zeros +timestamp 236.425578231R: very low magnitude, setting magnitude to all zeros +timestamp 236.472018140R: very low magnitude, setting magnitude to all zeros +timestamp 236.518458049R: very low magnitude, setting magnitude to all zeros +timestamp 236.564897959R: very low magnitude, setting magnitude to all zeros +timestamp 236.611337868R: very low magnitude, setting magnitude to all zeros +timestamp 236.657777777R: very low magnitude, setting magnitude to all zeros +timestamp 236.704217687R: very low magnitude, setting magnitude to all zeros +timestamp 236.750657596R: very low magnitude, setting magnitude to all zeros +timestamp 236.797097505R: very low magnitude, setting magnitude to all zeros +timestamp 236.843537414R: very low magnitude, setting magnitude to all zeros +timestamp 236.889977324R: very low magnitude, setting magnitude to all zeros +timestamp 236.936417233R: very low magnitude, setting magnitude to all zeros +timestamp 236.982857142R: very low magnitude, setting magnitude to all zeros +timestamp 237.029297052R: very low magnitude, setting magnitude to all zeros +timestamp 237.075736961R: very low magnitude, setting magnitude to all zeros +timestamp 237.122176870R: very low magnitude, setting magnitude to all zeros +timestamp 237.168616780R: very low magnitude, setting magnitude to all zeros +timestamp 237.215056689R: very low magnitude, setting magnitude to all zeros +timestamp 237.261496598R: very low magnitude, setting magnitude to all zeros +timestamp 237.307936507R: very low magnitude, setting magnitude to all zeros +timestamp 237.354376417R: very low magnitude, setting magnitude to all zeros +timestamp 237.400816326R: very low magnitude, setting magnitude to all zeros +timestamp 237.447256235R: very low magnitude, setting magnitude to all zeros +timestamp 237.493696145R: very low magnitude, setting magnitude to all zeros +timestamp 237.540136054R: very low magnitude, setting magnitude to all zeros +timestamp 237.586575963R: very low magnitude, setting magnitude to all zeros +timestamp 237.633015873R: very low magnitude, setting magnitude to all zeros +timestamp 237.679455782R: very low magnitude, setting magnitude to all zeros +timestamp 237.725895691R: very low magnitude, setting magnitude to all zeros +timestamp 237.772335600R: very low magnitude, setting magnitude to all zeros +timestamp 237.818775510R: very low magnitude, setting magnitude to all zeros +timestamp 237.865215419R: very low magnitude, setting magnitude to all zeros +timestamp 237.911655328R: very low magnitude, setting magnitude to all zeros +timestamp 237.958095238R: very low magnitude, setting magnitude to all zeros +timestamp 238.004535147R: very low magnitude, setting magnitude to all zeros +timestamp 238.050975056R: very low magnitude, setting magnitude to all zeros +timestamp 238.097414965R: very low magnitude, setting magnitude to all zeros +timestamp 238.143854875R: very low magnitude, setting magnitude to all zeros +timestamp 238.190294784R: very low magnitude, setting magnitude to all zeros +timestamp 238.236734693R: very low magnitude, setting magnitude to all zeros +timestamp 238.283174603R: very low magnitude, setting magnitude to all zeros +timestamp 238.329614512R: very low magnitude, setting magnitude to all zeros +timestamp 238.376054421R: very low magnitude, setting magnitude to all zeros +timestamp 238.422494331R: very low magnitude, setting magnitude to all zeros +timestamp 238.468934240R: very low magnitude, setting magnitude to all zeros +timestamp 238.515374149R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 97%timestamp 238.561814058R: very low magnitude, setting magnitude to all zeros +timestamp 238.608253968R: very low magnitude, setting magnitude to all zeros +timestamp 238.654693877R: very low magnitude, setting magnitude to all zeros +timestamp 238.701133786R: very low magnitude, setting magnitude to all zeros +timestamp 238.747573696R: very low magnitude, setting magnitude to all zeros +timestamp 238.794013605R: very low magnitude, setting magnitude to all zeros +timestamp 238.840453514R: very low magnitude, setting magnitude to all zeros +timestamp 238.886893424R: very low magnitude, setting magnitude to all zeros +timestamp 238.933333333R: very low magnitude, setting magnitude to all zeros +timestamp 238.979773242R: very low magnitude, setting magnitude to all zeros +timestamp 239.026213151R: very low magnitude, setting magnitude to all zeros +timestamp 239.072653061R: very low magnitude, setting magnitude to all zeros +timestamp 239.119092970R: very low magnitude, setting magnitude to all zeros +timestamp 239.165532879R: very low magnitude, setting magnitude to all zeros +timestamp 239.211972789R: very low magnitude, setting magnitude to all zeros +timestamp 239.258412698R: very low magnitude, setting magnitude to all zeros +timestamp 239.304852607R: very low magnitude, setting magnitude to all zeros +timestamp 239.351292517R: very low magnitude, setting magnitude to all zeros +timestamp 239.397732426R: very low magnitude, setting magnitude to all zeros +timestamp 239.444172335R: very low magnitude, setting magnitude to all zeros +timestamp 239.490612244R: very low magnitude, setting magnitude to all zeros +timestamp 239.537052154R: very low magnitude, setting magnitude to all zeros +timestamp 239.583492063R: very low magnitude, setting magnitude to all zeros +timestamp 239.629931972R: very low magnitude, setting magnitude to all zeros +timestamp 239.676371882R: very low magnitude, setting magnitude to all zeros +timestamp 239.722811791R: very low magnitude, setting magnitude to all zeros +timestamp 239.769251700R: very low magnitude, setting magnitude to all zeros +timestamp 239.815691609R: very low magnitude, setting magnitude to all zeros +timestamp 239.862131519R: very low magnitude, setting magnitude to all zeros +timestamp 239.908571428R: very low magnitude, setting magnitude to all zeros +timestamp 239.955011337R: very low magnitude, setting magnitude to all zeros +timestamp 240.001451247R: very low magnitude, setting magnitude to all zeros +timestamp 240.047891156R: very low magnitude, setting magnitude to all zeros +timestamp 240.094331065R: very low magnitude, setting magnitude to all zeros +timestamp 240.140770975R: very low magnitude, setting magnitude to all zeros +timestamp 240.187210884R: very low magnitude, setting magnitude to all zeros +timestamp 240.233650793R: very low magnitude, setting magnitude to all zeros +timestamp 240.280090702R: very low magnitude, setting magnitude to all zeros +timestamp 240.326530612R: very low magnitude, setting magnitude to all zeros +timestamp 240.372970521R: very low magnitude, setting magnitude to all zeros +timestamp 240.419410430R: very low magnitude, setting magnitude to all zeros +timestamp 240.465850340R: very low magnitude, setting magnitude to all zeros +timestamp 240.512290249R: very low magnitude, setting magnitude to all zeros +timestamp 240.558730158R: very low magnitude, setting magnitude to all zeros +timestamp 240.605170068R: very low magnitude, setting magnitude to all zeros +timestamp 240.651609977R: very low magnitude, setting magnitude to all zeros +timestamp 240.698049886R: very low magnitude, setting magnitude to all zeros +timestamp 240.744489795R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 98%timestamp 240.790929705R: very low magnitude, setting magnitude to all zeros +timestamp 240.837369614R: very low magnitude, setting magnitude to all zeros +timestamp 240.883809523R: very low magnitude, setting magnitude to all zeros +timestamp 240.930249433R: very low magnitude, setting magnitude to all zeros +timestamp 240.976689342R: very low magnitude, setting magnitude to all zeros +timestamp 241.023129251R: very low magnitude, setting magnitude to all zeros +timestamp 241.069569160R: very low magnitude, setting magnitude to all zeros +timestamp 241.116009070R: very low magnitude, setting magnitude to all zeros +timestamp 241.162448979R: very low magnitude, setting magnitude to all zeros +timestamp 241.208888888R: very low magnitude, setting magnitude to all zeros +timestamp 241.255328798R: very low magnitude, setting magnitude to all zeros +timestamp 241.301768707R: very low magnitude, setting magnitude to all zeros +timestamp 241.348208616R: very low magnitude, setting magnitude to all zeros +timestamp 241.394648526R: very low magnitude, setting magnitude to all zeros +timestamp 241.441088435R: very low magnitude, setting magnitude to all zeros +timestamp 241.487528344R: very low magnitude, setting magnitude to all zeros +timestamp 241.533968253R: very low magnitude, setting magnitude to all zeros +timestamp 241.580408163R: very low magnitude, setting magnitude to all zeros +timestamp 241.626848072R: very low magnitude, setting magnitude to all zeros +timestamp 241.673287981R: very low magnitude, setting magnitude to all zeros +timestamp 241.719727891R: very low magnitude, setting magnitude to all zeros +timestamp 241.766167800R: very low magnitude, setting magnitude to all zeros +timestamp 241.812607709R: very low magnitude, setting magnitude to all zeros +timestamp 241.859047619R: very low magnitude, setting magnitude to all zeros +timestamp 241.905487528R: very low magnitude, setting magnitude to all zeros +timestamp 241.951927437R: very low magnitude, setting magnitude to all zeros +timestamp 241.998367346R: very low magnitude, setting magnitude to all zeros +timestamp 242.044807256R: very low magnitude, setting magnitude to all zeros +timestamp 242.091247165R: very low magnitude, setting magnitude to all zeros +timestamp 242.137687074R: very low magnitude, setting magnitude to all zeros +timestamp 242.184126984R: very low magnitude, setting magnitude to all zeros +timestamp 242.230566893R: very low magnitude, setting magnitude to all zeros +timestamp 242.277006802R: very low magnitude, setting magnitude to all zeros +timestamp 242.323446712R: very low magnitude, setting magnitude to all zeros +timestamp 242.369886621R: very low magnitude, setting magnitude to all zeros +timestamp 242.416326530R: very low magnitude, setting magnitude to all zeros +timestamp 242.462766439R: very low magnitude, setting magnitude to all zeros +timestamp 242.509206349R: very low magnitude, setting magnitude to all zeros +timestamp 242.555646258R: very low magnitude, setting magnitude to all zeros +timestamp 242.602086167R: very low magnitude, setting magnitude to all zeros +timestamp 242.648526077R: very low magnitude, setting magnitude to all zeros +timestamp 242.694965986R: very low magnitude, setting magnitude to all zeros +timestamp 242.741405895R: very low magnitude, setting magnitude to all zeros +timestamp 242.787845804R: very low magnitude, setting magnitude to all zeros +timestamp 242.834285714R: very low magnitude, setting magnitude to all zeros +timestamp 242.880725623R: very low magnitude, setting magnitude to all zeros +timestamp 242.927165532R: very low magnitude, setting magnitude to all zeros +timestamp 242.973605442R: very low magnitude, setting magnitude to all zeros +timestamp 243.020045351R: very low magnitude, setting magnitude to all zeros +timestamp 243.066485260R: very low magnitude, setting magnitude to all zeros +timestamp 243.112925170R: very low magnitude, setting magnitude to all zeros +timestamp 243.159365079R: very low magnitude, setting magnitude to all zeros +timestamp 243.205804988R: very low magnitude, setting magnitude to all zeros +timestamp 243.252244897R: very low magnitude, setting magnitude to all zeros +timestamp 243.298684807R: very low magnitude, setting magnitude to all zeros +timestamp 243.345124716R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 99%timestamp 243.391564625R: very low magnitude, setting magnitude to all zeros +timestamp 243.438004535R: very low magnitude, setting magnitude to all zeros +timestamp 243.484444444R: very low magnitude, setting magnitude to all zeros +timestamp 243.530884353R: very low magnitude, setting magnitude to all zeros +timestamp 243.577324263R: very low magnitude, setting magnitude to all zeros +timestamp 243.623764172R: very low magnitude, setting magnitude to all zeros +timestamp 243.670204081R: very low magnitude, setting magnitude to all zeros +timestamp 243.716643990R: very low magnitude, setting magnitude to all zeros +timestamp 243.763083900R: very low magnitude, setting magnitude to all zeros +timestamp 243.809523809R: very low magnitude, setting magnitude to all zeros +timestamp 243.855963718R: very low magnitude, setting magnitude to all zeros +timestamp 243.902403628R: very low magnitude, setting magnitude to all zeros +timestamp 243.948843537R: very low magnitude, setting magnitude to all zeros +timestamp 243.995283446R: very low magnitude, setting magnitude to all zeros +timestamp 244.041723356R: very low magnitude, setting magnitude to all zeros +timestamp 244.088163265R: very low magnitude, setting magnitude to all zeros +timestamp 244.134603174R: very low magnitude, setting magnitude to all zeros +timestamp 244.181043083R: very low magnitude, setting magnitude to all zeros +timestamp 244.227482993R: very low magnitude, setting magnitude to all zeros +timestamp 244.273922902R: very low magnitude, setting magnitude to all zeros +timestamp 244.320362811R: very low magnitude, setting magnitude to all zeros +timestamp 244.366802721R: very low magnitude, setting magnitude to all zeros +timestamp 244.413242630R: very low magnitude, setting magnitude to all zeros +timestamp 244.459682539R: very low magnitude, setting magnitude to all zeros +timestamp 244.506122448R: very low magnitude, setting magnitude to all zeros +timestamp 244.552562358R: very low magnitude, setting magnitude to all zeros +timestamp 244.599002267R: very low magnitude, setting magnitude to all zeros +timestamp 244.645442176R: very low magnitude, setting magnitude to all zeros +timestamp 244.691882086R: very low magnitude, setting magnitude to all zeros +timestamp 244.738321995R: very low magnitude, setting magnitude to all zeros +timestamp 244.784761904R: very low magnitude, setting magnitude to all zeros +timestamp 244.831201814R: very low magnitude, setting magnitude to all zeros +timestamp 244.877641723R: very low magnitude, setting magnitude to all zeros +timestamp 244.924081632R: very low magnitude, setting magnitude to all zeros +timestamp 244.970521541R: very low magnitude, setting magnitude to all zeros +timestamp 245.016961451R: very low magnitude, setting magnitude to all zeros +timestamp 245.063401360R: very low magnitude, setting magnitude to all zeros +timestamp 245.109841269R: very low magnitude, setting magnitude to all zeros +timestamp 245.156281179R: very low magnitude, setting magnitude to all zeros +timestamp 245.202721088R: very low magnitude, setting magnitude to all zeros +timestamp 245.249160997R: very low magnitude, setting magnitude to all zeros +timestamp 245.295600907R: very low magnitude, setting magnitude to all zeros +timestamp 245.342040816R: very low magnitude, setting magnitude to all zeros +timestamp 245.388480725R: very low magnitude, setting magnitude to all zeros +timestamp 245.434920634R: very low magnitude, setting magnitude to all zeros +timestamp 245.481360544R: very low magnitude, setting magnitude to all zeros +timestamp 245.527800453R: very low magnitude, setting magnitude to all zeros +timestamp 245.574240362R: very low magnitude, setting magnitude to all zeros +timestamp 245.620680272R: very low magnitude, setting magnitude to all zeros +[NNLS Chroma Plugin] Tuning Log-Frequency Spectrogram ... done. +[NNLS Chroma Plugin] Performing NNLS and mapping to chroma ... done. +[NNLS Chroma Plugin] Chord Estimation ... done. + Extracting and writing features... Done + + + < M A T L A B > + Copyright 1984-2007 The MathWorks, Inc. + Version 7.5.0.338 (R2007b) + August 9, 2007 + + + To get started, type one of these: helpwin, helpdesk, or demo. + For product information, visit www.mathworks.com. + + - First Slice + metpos + key + chord + bass + - Second Slice + (7) metpos (slice 2) + (8) key (slice 2) + (9) chord (slice 2) + (10) bass (slice 2) +onset detection function +beats + + Length: 28 Length: 32 Length: 36 Length: 40 Length: 44 Length: 48 Length: 52 Length: 56 Length: 60 Length: 64 Length: 68 Length: 72 Length: 76 Length: 80 Length: 84 Length: 88 Length: 92 Length: 96 Length: 100 Length: 104 Length: 108 Length: 112 Length: 116 Length: 120 Length: 124 Length: 128 +Finding maximum probability path (part 1 of 17, beats 1 to 57)... +forward step +backward step + +Finding maximum probability path (part 2 of 17, beats 58 to 91)... +forward step +backward step + +Finding maximum probability path (part 3 of 17, beats 92 to 126)... +forward step +backward step + +Finding maximum probability path (part 4 of 17, beats 127 to 154)... +forward step +backward step + +Finding maximum probability path (part 5 of 17, beats 155 to 199)... +forward step +backward step + +Finding maximum probability path (part 6 of 17, beats 200 to 227)... +forward step +backward step + +Finding maximum probability path (part 7 of 17, beats 228 to 262)... +forward step +backward step + +Finding maximum probability path (part 8 of 17, beats 263 to 302)... +forward step +backward step + +Finding maximum probability path (part 9 of 17, beats 303 to 330)... +forward step +backward step + +Finding maximum probability path (part 10 of 17, beats 331 to 366)... +forward step +backward step + +Finding maximum probability path (part 11 of 17, beats 367 to 398)... +forward step +backward step + +Finding maximum probability path (part 12 of 17, beats 399 to 430)... +forward step +backward step + +Finding maximum probability path (part 13 of 17, beats 431 to 462)... +forward step +backward step + +Finding maximum probability path (part 14 of 17, beats 463 to 494)... +forward step +backward step + +Finding maximum probability path (part 15 of 17, beats 494 to 526)... +forward step +backward step + +Finding maximum probability path (part 16 of 17, beats 527 to 558)... +forward step +backward step + +Finding maximum probability path (part 17 of 17, beats 559 to 592)... +forward step +backward step +0.0 0.012 N +0.012 0.325 N +0.325 6.142 F#/5 +6.142 6.920 G#:min +6.920 12.016 F#/5 +12.016 13.259 G#:min +13.259 18.692 F#/5 +18.692 20.376 G#:min +20.376 21.618 B +21.618 22.860 F#/3 +22.860 23.696 G#:min7 +23.696 27.040 C# +27.040 31.335 F# +31.335 33.866 C# +33.866 36.223 F# +36.223 36.989 D#:min7 +36.989 39.253 C#:min +39.253 43.921 F# +43.921 46.951 F#/5 +46.951 50.585 D#:min7 +50.585 53.777 C# +53.777 57.144 Gb +57.144 60.442 Db +60.442 62.113 Gb +62.113 63.843 D#:min7 +63.843 65.457 C#:min +65.457 67.129 F# +67.129 70.484 B +70.484 73.828 F#/5 +73.828 75.453 D#:min7 +75.453 77.125 G#:7 +77.125 78.785 F#/5 +78.785 84.230 C# +84.230 87.574 Gb +87.574 90.918 Db +90.918 93.414 Gb +93.414 93.832 B/3 +93.832 95.504 E:maj6 +95.504 97.187 G# +97.187 107.207 F#/5 +107.207 108.867 C#:min +108.867 113.894 F# +113.894 117.214 F#/5 +117.214 120.570 D#:min7 +120.570 127.245 C# +127.245 130.577 Gb +130.577 133.909 Db +133.909 135.581 Gb +135.581 137.300 D#:min7 +137.300 138.913 C#:min +138.913 140.597 F# +140.597 143.929 B +143.929 147.296 F#/5 +147.296 148.956 D#:min7 +148.956 150.628 G#:7 +150.628 152.276 C#:min +152.276 157.292 F# +157.292 160.636 F#/5 +160.636 163.979 D#:min7 +163.979 165.640 C#:min +165.640 170.643 F# +170.643 173.999 F#/5 +173.999 177.331 D#:min7 +177.331 179.003 C#:min +179.003 184.018 F# +184.018 187.362 F#/5 +187.362 190.694 D#:min7 +190.694 192.366 C#:min +192.366 197.370 F# +197.370 200.713 F#/5 +200.713 203.627 D#:min7 +203.627 205.311 C#:min +205.311 210.315 F# +210.315 213.658 F#/5 +213.658 217.408 D#:min7 +217.408 219.080 C#:min +219.080 224.096 F# +224.096 227.439 F#/5 +227.439 230.772 D#:min7 +230.772 231.189 C#:min +231.189 245.893 N0.0 0.012 N +0.012 50.956 F# +50.956 62.113 Db +62.113 80.875 F# +80.875 92.578 Db +92.578 123.902 F# +123.902 135.581 Db +135.581 230.772 F# +230.772 245.893 B \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_logfiles/Martika.ToySoldiers.wav.log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,353 @@ +CSVFeatureWriter::setParameters +stdout -> +NOTE: Using default sample rate of 44100 for default transform +NOTE: Loaded and initialised plugin for transform "vamp:matthiasm:nnls_chroma:bothchroma" with plugin step size 2048 and block size 16384 (adapter step and block size 16384) +NOTE: PluginInputDomainAdapter timestamp adjustment is 0.000000000R +Extracting features for: "/Users/matthias/Music/MUSICDB44k/Martika.ToySoldiers.wav" + Retrieving audio data... Done +Opened 2-channel file or URL "/Users/matthias/Music/MUSICDB44k/Martika.ToySoldiers.wav" +timestamp 0.000000000R: very low magnitude, setting magnitude to all zeros +timestamp 0.046439909R: very low magnitude, setting magnitude to all zeros +timestamp 0.092879818R: very low magnitude, setting magnitude to all zeros +timestamp 0.139319727R: very low magnitude, setting magnitude to all zeros +timestamp 0.185759637R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 1% Extracting and writing features... 2% Extracting and writing features... 3% Extracting and writing features... 4% Extracting and writing features... 5% Extracting and writing features... 6% Extracting and writing features... 7% Extracting and writing features... 8% Extracting and writing features... 9% Extracting and writing features... 10% Extracting and writing features... 11% Extracting and writing features... 12% Extracting and writing features... 13% Extracting and writing features... 14% Extracting and writing features... 15% Extracting and writing features... 16% Extracting and writing features... 17% Extracting and writing features... 18% Extracting and writing features... 19% Extracting and writing features... 20% Extracting and writing features... 21% Extracting and writing features... 22% Extracting and writing features... 23% Extracting and writing features... 24% Extracting and writing features... 25% Extracting and writing features... 26% Extracting and writing features... 27% Extracting and writing features... 28% Extracting and writing features... 29% Extracting and writing features... 30% Extracting and writing features... 31% Extracting and writing features... 32% Extracting and writing features... 33% Extracting and writing features... 34% Extracting and writing features... 35% Extracting and writing features... 36% Extracting and writing features... 37% Extracting and writing features... 38% Extracting and writing features... 39% Extracting and writing features... 40% Extracting and writing features... 41% Extracting and writing features... 42% Extracting and writing features... 43% Extracting and writing features... 44% Extracting and writing features... 45% Extracting and writing features... 46% Extracting and writing features... 47% Extracting and writing features... 48% Extracting and writing features... 49% Extracting and writing features... 50% Extracting and writing features... 51% Extracting and writing features... 52% Extracting and writing features... 53% Extracting and writing features... 54% Extracting and writing features... 55% Extracting and writing features... 56% Extracting and writing features... 57% Extracting and writing features... 58% Extracting and writing features... 59% Extracting and writing features... 60% Extracting and writing features... 61% Extracting and writing features... 62% Extracting and writing features... 63% Extracting and writing features... 64% Extracting and writing features... 65% Extracting and writing features... 66% Extracting and writing features... 67% Extracting and writing features... 68% Extracting and writing features... 69% Extracting and writing features... 70% Extracting and writing features... 71% Extracting and writing features... 72% Extracting and writing features... 73% Extracting and writing features... 74% Extracting and writing features... 75% Extracting and writing features... 76% Extracting and writing features... 77% Extracting and writing features... 78% Extracting and writing features... 79% Extracting and writing features... 80% Extracting and writing features... 81% Extracting and writing features... 82% Extracting and writing features... 83% Extracting and writing features... 84% Extracting and writing features... 85% Extracting and writing features... 86% Extracting and writing features... 87% Extracting and writing features... 88% Extracting and writing features... 89% Extracting and writing features... 90% Extracting and writing features... 91% Extracting and writing features... 92% Extracting and writing features... 93% Extracting and writing features... 94% Extracting and writing features... 95% Extracting and writing features... 96% Extracting and writing features... 97%timestamp 273.113106575R: very low magnitude, setting magnitude to all zeros +timestamp 273.159546485R: very low magnitude, setting magnitude to all zeros +timestamp 273.623945578R: very low magnitude, setting magnitude to all zeros +timestamp 273.670385487R: very low magnitude, setting magnitude to all zeros +timestamp 273.716825396R: very low magnitude, setting magnitude to all zeros +timestamp 273.763265306R: very low magnitude, setting magnitude to all zeros +timestamp 273.809705215R: very low magnitude, setting magnitude to all zeros +timestamp 273.856145124R: very low magnitude, setting magnitude to all zeros +timestamp 273.902585034R: very low magnitude, setting magnitude to all zeros +timestamp 273.949024943R: 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274.552743764R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 98%timestamp 274.599183673R: very low magnitude, setting magnitude to all zeros +timestamp 274.645623582R: very low magnitude, setting magnitude to all zeros +timestamp 274.692063492R: very low magnitude, setting magnitude to all zeros +timestamp 274.738503401R: very low magnitude, setting magnitude to all zeros +timestamp 274.784943310R: very low magnitude, setting magnitude to all zeros +timestamp 274.831383219R: very low magnitude, setting magnitude to all zeros +timestamp 274.877823129R: very low magnitude, setting magnitude to all zeros +timestamp 274.924263038R: very low magnitude, setting magnitude to all zeros +timestamp 274.970702947R: very low magnitude, setting magnitude to all zeros +timestamp 275.017142857R: very low magnitude, setting magnitude to all zeros +timestamp 275.063582766R: very low magnitude, setting magnitude to all zeros +timestamp 275.110022675R: very low 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275.713741496R: very low magnitude, setting magnitude to all zeros +timestamp 275.760181405R: very low magnitude, setting magnitude to all zeros +timestamp 275.806621315R: very low magnitude, setting magnitude to all zeros +timestamp 275.853061224R: very low magnitude, setting magnitude to all zeros +timestamp 275.899501133R: very low magnitude, setting magnitude to all zeros +timestamp 275.945941043R: very low magnitude, setting magnitude to all zeros +timestamp 275.992380952R: very low magnitude, setting magnitude to all zeros +timestamp 276.038820861R: very low magnitude, setting magnitude to all zeros +timestamp 276.085260770R: very low magnitude, setting magnitude to all zeros +timestamp 276.131700680R: very low magnitude, setting magnitude to all zeros +timestamp 276.178140589R: very low magnitude, setting magnitude to all zeros +timestamp 276.224580498R: very low magnitude, setting magnitude to all zeros +timestamp 276.271020408R: very low magnitude, setting magnitude to all 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low magnitude, setting magnitude to all zeros +timestamp 279.289614512R: very low magnitude, setting magnitude to all zeros +timestamp 279.336054421R: very low magnitude, setting magnitude to all zeros +timestamp 279.382494331R: very low magnitude, setting magnitude to all zeros +timestamp 279.428934240R: very low magnitude, setting magnitude to all zeros +timestamp 279.475374149R: very low magnitude, setting magnitude to all zeros +timestamp 279.521814058R: very low magnitude, setting magnitude to all zeros +timestamp 279.568253968R: very low magnitude, setting magnitude to all zeros +timestamp 279.614693877R: very low magnitude, setting magnitude to all zeros +timestamp 279.661133786R: very low magnitude, setting magnitude to all zeros +timestamp 279.707573696R: very low magnitude, setting magnitude to all zeros +timestamp 279.754013605R: very low magnitude, setting magnitude to all zeros +timestamp 279.800453514R: very low magnitude, setting magnitude to all zeros +timestamp 279.846893424R: very low magnitude, setting magnitude to all zeros +timestamp 279.893333333R: very low magnitude, setting magnitude to all zeros +timestamp 279.939773242R: very low magnitude, setting magnitude to all zeros +timestamp 279.986213151R: very low magnitude, setting magnitude to all zeros +timestamp 280.032653061R: very low magnitude, setting magnitude to all zeros +timestamp 280.079092970R: very low magnitude, setting magnitude to all zeros +timestamp 280.125532879R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 100%timestamp 280.171972789R: very low magnitude, setting magnitude to all zeros +[NNLS Chroma Plugin] Tuning Log-Frequency Spectrogram ... done. +[NNLS Chroma Plugin] Performing NNLS and mapping to chroma ... done. +[NNLS Chroma Plugin] Chord Estimation ... done. + Extracting and writing features... Done + + < M A T L A B > + Copyright 1984-2007 The MathWorks, Inc. + Version 7.5.0.338 (R2007b) + August 9, 2007 + + + To get started, type one of these: helpwin, helpdesk, or demo. + For product information, visit www.mathworks.com. + + - First Slice + metpos + key + chord + bass + - Second Slice + (7) metpos (slice 2) + (8) key (slice 2) + (9) chord (slice 2) + (10) bass (slice 2) +onset detection function +beats + + Length: 28 Length: 32 Length: 36 Length: 40 Length: 44 Length: 48 Length: 52 Length: 56 Length: 60 Length: 64 Length: 68 Length: 72 Length: 76 Length: 80 Length: 84 Length: 88 Length: 92 Length: 96 Length: 100 Length: 104 Length: 108 Length: 112 Length: 116 Length: 120 Length: 124 Length: 128 +Finding maximum probability path (part 1 of 8, beats 1 to 9)... +forward step +backward step + +Finding maximum probability path (part 2 of 8, beats 10 to 73)... +forward step +backward step + +Finding maximum probability path (part 3 of 8, beats 74 to 185)... +forward step +backward step + +Finding maximum probability path (part 4 of 8, beats 186 to 249)... +forward step +backward step + +Finding maximum probability path (part 5 of 8, beats 250 to 361)... +forward step +backward step + +Finding maximum probability path (part 6 of 8, beats 362 to 393)... +forward step +backward step + +Finding maximum probability path (part 7 of 8, beats 394 to 505)... +forward step +backward step + +Finding maximum probability path (part 8 of 8, beats 506 to 606)... +forward step +backward step +0.0 0.023 N +0.023 4.063 Db:min +4.063 6.850 A +6.850 7.755 B +7.755 10.542 A +10.542 11.447 B +11.447 14.234 C#:min +14.234 15.139 B:maj6 +15.139 18.820 C#:min +18.820 21.606 A +21.606 22.523 B +22.523 25.298 A +25.298 26.215 C#:min +26.215 28.990 B +28.990 29.907 A +29.907 33.611 B +33.611 35.457 C#:min +35.457 37.291 B +37.291 40.066 A +40.066 40.995 B +40.995 42.841 C#:min +42.841 44.664 B +44.664 45.592 F#:min7 +45.592 46.521 G#:min +46.521 52.059 A +52.059 53.905 E +53.905 55.751 B +55.751 57.597 C#:min +57.597 59.443 A#:dim +59.443 61.289 A +61.289 63.123 B +63.123 65.898 C#:min +65.898 66.839 B/3 +66.839 68.673 E +68.673 70.519 B +70.519 72.377 C#:min +72.377 74.211 A#:dim +74.211 76.057 A +76.057 77.903 B +77.903 80.666 C#:min +80.666 81.583 B/3 +81.583 83.429 E +83.429 85.287 B/5 +85.287 88.062 A +88.062 88.979 B +88.979 91.754 A +91.754 92.671 B +92.671 95.446 C#:min +95.446 96.363 B:maj6 +96.363 100.055 C#:min +100.055 102.830 A +102.830 103.747 B +103.747 106.533 A +106.533 107.439 C#:min +107.439 110.202 B +110.202 111.119 A +111.119 114.823 B +114.823 116.657 C#:min +116.657 118.491 B +118.491 121.278 A +121.278 122.207 B +122.207 124.053 C#:min +124.053 125.887 B +125.887 126.816 F#:min7 +126.816 127.745 G#:min +127.745 133.271 A +133.271 135.129 E +135.129 136.963 B +136.963 138.820 C#:min +138.820 140.655 A#:dim +140.655 142.501 A +142.501 144.347 B +144.347 147.110 C#:min +147.110 148.039 B/3 +148.039 149.873 E +149.873 151.731 B +151.731 153.600 C#:min +153.600 155.434 A#:dim +155.434 157.269 A +157.269 159.115 B +159.115 161.866 C#:min +161.866 162.807 B/3 +162.807 164.641 E +164.641 168.333 B +168.333 169.262 C#:min7 +169.262 170.179 B:maj6 +170.179 172.037 E +172.037 173.906 F#:min +173.906 181.255 G#:min +181.255 183.101 C#:min +183.101 184.947 B +184.947 187.722 A +187.722 188.639 B +188.639 190.485 C#:min +190.485 192.331 B +192.331 193.248 F#:min7 +193.248 194.177 G#:min +194.177 199.715 A +199.715 201.572 E +201.572 203.418 B +203.418 205.264 C#:min +205.264 207.110 A#:dim +207.110 208.945 A +208.945 210.791 B +210.791 213.554 C#:min +213.554 214.483 B/3 +214.483 216.329 E +216.329 218.186 B +218.186 220.032 C#:min +220.032 221.867 A#:dim +221.867 223.713 A +223.713 225.547 B +225.547 228.310 C#:min +228.310 229.251 B/3 +229.251 231.097 E +231.097 232.943 B/5 +232.943 234.789 Db:min +234.789 236.623 Bb:dim +236.623 238.481 Bbb +238.481 240.327 Cb +240.327 243.090 Db:min +243.090 244.019 Cb/3 +244.019 245.853 Fb +245.853 247.710 Cb +247.710 249.556 Db:min +249.556 251.437 Bb:dim +251.437 253.411 Bbb +253.411 255.338 Cb +255.338 263.628 Db:min +263.628 273.821 Db:min7 +273.821 280.200 N0.0 0.023 N +0.023 4.063 Db:minor +4.063 26.215 A +26.215 85.287 B +85.287 106.533 A +106.533 232.943 B +232.943 280.200 Db:minor \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_logfiles/OtisRedding.TheDockOfTheBay.wav.log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,270 @@ +CSVFeatureWriter::setParameters +stdout -> +NOTE: Using default sample rate of 44100 for default transform +NOTE: Loaded and initialised plugin for transform "vamp:matthiasm:nnls_chroma:bothchroma" with plugin step size 2048 and block size 16384 (adapter step and block size 16384) +NOTE: PluginInputDomainAdapter timestamp adjustment is 0.000000000R +Extracting features for: "/Users/matthias/Music/MUSICDB44k/OtisRedding.TheDockOfTheBay.wav" + Retrieving audio data... Done +Opened 2-channel file or URL "/Users/matthias/Music/MUSICDB44k/OtisRedding.TheDockOfTheBay.wav" +timestamp 0.000000000R: very low magnitude, setting magnitude to all zeros +timestamp 0.046439909R: very low magnitude, setting magnitude to all zeros +timestamp 0.092879818R: very low magnitude, setting magnitude to all zeros +timestamp 0.139319727R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 1% Extracting and writing features... 2% Extracting and writing features... 3% Extracting and writing features... 4% Extracting and writing features... 5% Extracting and writing features... 6% Extracting and writing features... 7% Extracting and writing features... 8% Extracting and writing features... 9% Extracting and writing features... 10% Extracting and writing features... 11% Extracting and writing features... 12% Extracting and writing features... 13% Extracting and writing features... 14% Extracting and writing features... 15% Extracting and writing features... 16% Extracting and writing features... 17% Extracting and writing features... 18% Extracting and writing features... 19% Extracting and writing features... 20% Extracting and writing features... 21% Extracting and writing features... 22% Extracting and writing features... 23% Extracting and writing features... 24% Extracting and writing features... 25% Extracting and writing features... 26% Extracting and writing features... 27% Extracting and writing features... 28% Extracting and writing features... 29% Extracting and writing features... 30% Extracting and writing features... 31% Extracting and writing features... 32% Extracting and writing features... 33% Extracting and writing features... 34% Extracting and writing features... 35% Extracting and writing features... 36% Extracting and writing features... 37% Extracting and writing features... 38% Extracting and writing features... 39% Extracting and writing features... 40% Extracting and writing features... 41% Extracting and writing features... 42% Extracting and writing features... 43% Extracting and writing features... 44% Extracting and writing features... 45% Extracting and writing features... 46% Extracting and writing features... 47% Extracting and writing features... 48% Extracting and writing features... 49% Extracting and writing features... 50% Extracting and writing features... 51% Extracting and writing features... 52% Extracting and writing features... 53% Extracting and writing features... 54% Extracting and writing features... 55% Extracting and writing features... 56% Extracting and writing features... 57% Extracting and writing features... 58% Extracting and writing features... 59% Extracting and writing features... 60% Extracting and writing features... 61% Extracting and writing features... 62% Extracting and writing features... 63% Extracting and writing features... 64% Extracting and writing features... 65% Extracting and writing features... 66% Extracting and writing features... 67% Extracting and writing features... 68% Extracting and writing features... 69% Extracting and writing features... 70% Extracting and writing features... 71% Extracting and writing features... 72% Extracting and writing features... 73% Extracting and writing features... 74% Extracting and writing features... 75% Extracting and writing features... 76% Extracting and writing features... 77% Extracting and writing features... 78% Extracting and writing features... 79% Extracting and writing features... 80% Extracting and writing features... 81% Extracting and writing features... 82% Extracting and writing features... 83% Extracting and writing features... 84% Extracting and writing features... 85% Extracting and writing features... 86% Extracting and writing features... 87% Extracting and writing features... 88% Extracting and writing features... 89% Extracting and writing features... 90% Extracting and writing features... 91% Extracting and writing features... 92% Extracting and writing features... 93% Extracting and writing features... 94% Extracting and writing features... 95%timestamp 155.062857142R: very low magnitude, setting magnitude to all zeros +timestamp 155.109297052R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 96%timestamp 155.434376417R: very low magnitude, setting magnitude to all zeros +timestamp 155.480816326R: very low magnitude, setting magnitude to all zeros +timestamp 155.527256235R: very low magnitude, setting magnitude to all zeros +timestamp 155.573696145R: very low magnitude, setting magnitude to all zeros +timestamp 155.620136054R: very low magnitude, setting magnitude to all zeros +timestamp 155.666575963R: very low magnitude, setting magnitude to all zeros +timestamp 155.945215419R: very low magnitude, setting magnitude to all zeros +timestamp 155.991655328R: very low magnitude, setting magnitude to all zeros +timestamp 156.038095238R: very low magnitude, setting magnitude to all zeros +timestamp 156.084535147R: very low magnitude, setting magnitude to all zeros +timestamp 156.130975056R: very low magnitude, setting magnitude to all zeros +timestamp 156.177414965R: very low magnitude, setting magnitude to all zeros +timestamp 156.223854875R: very low magnitude, setting magnitude to all zeros +timestamp 156.270294784R: very low magnitude, setting magnitude to all zeros +timestamp 156.316734693R: very low magnitude, setting magnitude to all zeros +timestamp 156.363174603R: very low magnitude, setting magnitude to all zeros +timestamp 156.409614512R: very low magnitude, setting magnitude to all zeros +timestamp 156.456054421R: very low magnitude, setting magnitude to all zeros +timestamp 156.502494331R: very low magnitude, setting magnitude to all zeros +timestamp 156.548934240R: very low magnitude, setting magnitude to all zeros +timestamp 156.595374149R: very low magnitude, setting magnitude to all zeros +timestamp 156.641814058R: very low magnitude, setting magnitude to all zeros +timestamp 156.688253968R: very low magnitude, setting magnitude to all zeros +timestamp 156.734693877R: very low magnitude, setting magnitude to all zeros +timestamp 156.781133786R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 97%timestamp 156.827573696R: very low magnitude, setting magnitude to all zeros +timestamp 156.874013605R: very low magnitude, setting magnitude to all zeros +timestamp 156.920453514R: very low magnitude, setting magnitude to all zeros +timestamp 156.966893424R: very low magnitude, setting magnitude to all zeros +timestamp 157.013333333R: very low magnitude, setting magnitude to all zeros +timestamp 157.059773242R: very low magnitude, setting magnitude to all zeros +timestamp 157.106213151R: very low magnitude, setting magnitude to all zeros +timestamp 157.152653061R: very low magnitude, setting magnitude to all zeros +timestamp 157.199092970R: very low magnitude, setting magnitude to all zeros +timestamp 157.245532879R: very low magnitude, setting magnitude to all zeros +timestamp 157.291972789R: very low magnitude, setting magnitude to all zeros +timestamp 157.338412698R: very low magnitude, setting magnitude to all zeros +timestamp 157.384852607R: very low magnitude, setting magnitude to all zeros +timestamp 157.431292517R: very low magnitude, setting magnitude to all zeros +timestamp 157.477732426R: very low magnitude, setting magnitude to all zeros +timestamp 157.524172335R: very low magnitude, setting magnitude to all zeros +timestamp 157.570612244R: very low magnitude, setting magnitude to all zeros +timestamp 157.617052154R: very low magnitude, setting magnitude to all zeros +timestamp 157.663492063R: very low magnitude, setting magnitude to all zeros +timestamp 157.709931972R: very low magnitude, setting magnitude to all zeros +timestamp 157.756371882R: very low magnitude, setting magnitude to all zeros +timestamp 157.802811791R: very low magnitude, setting magnitude to all zeros +timestamp 157.849251700R: very low magnitude, setting magnitude to all zeros +timestamp 157.895691609R: very low magnitude, setting magnitude to all zeros +timestamp 157.942131519R: very low magnitude, setting magnitude to all zeros +timestamp 157.988571428R: very low magnitude, setting magnitude to all zeros +timestamp 158.035011337R: very low magnitude, setting magnitude to all zeros +timestamp 158.081451247R: very low magnitude, setting magnitude to all zeros +timestamp 158.127891156R: very low magnitude, setting magnitude to all zeros +timestamp 158.174331065R: very low magnitude, setting magnitude to all zeros +timestamp 158.220770975R: very low magnitude, setting magnitude to all zeros +timestamp 158.267210884R: very low magnitude, setting magnitude to all zeros +timestamp 158.313650793R: very low magnitude, setting magnitude to all zeros +timestamp 158.360090702R: very low magnitude, setting magnitude to all zeros +timestamp 158.406530612R: very low magnitude, setting magnitude to all zeros +timestamp 158.452970521R: very low magnitude, setting magnitude to all zeros +timestamp 158.499410430R: very low magnitude, setting magnitude to all zeros +timestamp 158.545850340R: very low magnitude, setting magnitude to all zeros +timestamp 158.592290249R: very low magnitude, setting magnitude to all zeros +timestamp 158.638730158R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 98%timestamp 158.685170068R: very low magnitude, setting magnitude to all zeros +timestamp 158.731609977R: very low magnitude, setting magnitude to all zeros +timestamp 158.778049886R: very low magnitude, setting magnitude to all zeros +timestamp 158.824489795R: very low magnitude, setting magnitude to all zeros +timestamp 158.870929705R: very low magnitude, setting magnitude to all zeros +timestamp 158.917369614R: very low magnitude, setting magnitude to all zeros +timestamp 158.963809523R: very low magnitude, setting magnitude to all zeros +timestamp 159.010249433R: very low magnitude, setting magnitude to all zeros +timestamp 159.056689342R: very low magnitude, setting magnitude to all zeros +timestamp 159.103129251R: very low magnitude, setting magnitude to all zeros +timestamp 159.149569160R: very low magnitude, setting magnitude to all zeros +timestamp 159.196009070R: very low magnitude, setting magnitude to all zeros +timestamp 159.242448979R: very low magnitude, setting magnitude to all zeros +timestamp 159.288888888R: very low magnitude, setting magnitude to all zeros +timestamp 159.335328798R: very low magnitude, setting magnitude to all zeros +timestamp 159.381768707R: very low magnitude, setting magnitude to all zeros +timestamp 159.428208616R: very low magnitude, setting magnitude to all zeros +timestamp 159.474648526R: very low magnitude, setting magnitude to all zeros +timestamp 159.521088435R: very low magnitude, setting magnitude to all zeros +timestamp 159.567528344R: very low magnitude, setting magnitude to all zeros +timestamp 159.613968253R: very low magnitude, setting magnitude to all zeros +timestamp 159.660408163R: very low magnitude, setting magnitude to all zeros +timestamp 159.706848072R: very low magnitude, setting magnitude to all zeros +timestamp 159.753287981R: very low magnitude, setting magnitude to all zeros +timestamp 159.799727891R: very low magnitude, setting magnitude to all zeros +timestamp 159.846167800R: very low magnitude, setting magnitude to all zeros +timestamp 159.892607709R: very low magnitude, setting magnitude to all zeros +timestamp 159.939047619R: very low magnitude, setting magnitude to all zeros +timestamp 159.985487528R: very low magnitude, setting magnitude to all zeros +timestamp 160.031927437R: very low magnitude, setting magnitude to all zeros +timestamp 160.078367346R: very low magnitude, setting magnitude to all zeros +timestamp 160.124807256R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 99%timestamp 160.171247165R: very low magnitude, setting magnitude to all zeros +timestamp 160.217687074R: very low magnitude, setting magnitude to all zeros +timestamp 160.264126984R: very low magnitude, setting magnitude to all zeros +timestamp 160.310566893R: very low magnitude, setting magnitude to all zeros +timestamp 160.357006802R: very low magnitude, setting magnitude to all zeros +timestamp 160.403446712R: very low magnitude, setting magnitude to all zeros +timestamp 160.449886621R: very low magnitude, setting magnitude to all zeros +timestamp 160.496326530R: very low magnitude, setting magnitude to all zeros +timestamp 160.542766439R: very low magnitude, setting magnitude to all zeros +timestamp 160.589206349R: very low magnitude, setting magnitude to all zeros +timestamp 160.635646258R: very low magnitude, setting magnitude to all zeros +timestamp 160.682086167R: very low magnitude, setting magnitude to all zeros +timestamp 160.728526077R: very low magnitude, setting magnitude to all zeros +timestamp 160.774965986R: very low magnitude, setting magnitude to all zeros +timestamp 160.821405895R: very low magnitude, setting magnitude to all zeros +timestamp 160.867845804R: very low magnitude, setting magnitude to all zeros +timestamp 160.914285714R: very low magnitude, setting magnitude to all zeros +timestamp 160.960725623R: very low magnitude, setting magnitude to all zeros +timestamp 161.007165532R: very low magnitude, setting magnitude to all zeros +timestamp 161.053605442R: very low magnitude, setting magnitude to all zeros +timestamp 161.100045351R: very low magnitude, setting magnitude to all zeros +timestamp 161.146485260R: very low magnitude, setting magnitude to all zeros +timestamp 161.192925170R: very low magnitude, setting magnitude to all zeros +timestamp 161.239365079R: very low magnitude, setting magnitude to all zeros +timestamp 161.285804988R: very low magnitude, setting magnitude to all zeros +timestamp 161.332244897R: very low magnitude, setting magnitude to all zeros +timestamp 161.378684807R: very low magnitude, setting magnitude to all zeros +timestamp 161.425124716R: very low magnitude, setting magnitude to all zeros +timestamp 161.471564625R: very low magnitude, setting magnitude to all zeros +timestamp 161.518004535R: very low magnitude, setting magnitude to all zeros +timestamp 161.564444444R: very low magnitude, setting magnitude to all zeros +timestamp 161.610884353R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 100%timestamp 161.657324263R: very low magnitude, setting magnitude to all zeros +[NNLS Chroma Plugin] Tuning Log-Frequency Spectrogram ... done. +[NNLS Chroma Plugin] Performing NNLS and mapping to chroma ... done. +[NNLS Chroma Plugin] Chord Estimation ... done. + Extracting and writing features... Done + + < M A T L A B > + Copyright 1984-2007 The MathWorks, Inc. + Version 7.5.0.338 (R2007b) + August 9, 2007 + + + To get started, type one of these: helpwin, helpdesk, or demo. + For product information, visit www.mathworks.com. + + - First Slice + metpos + key + chord + bass + - Second Slice + (7) metpos (slice 2) + (8) key (slice 2) + (9) chord (slice 2) + (10) bass (slice 2) +onset detection function +beats + + Length: 28 Length: 32 Length: 36 Length: 40 Length: 44 Length: 48 Length: 52 Length: 56 Length: 60 Length: 64 Length: 68 Length: 72 Length: 76 Length: 80 Length: 84 Length: 88 Length: 92 Length: 96 Length: 100 Length: 104 Length: 108 Length: 112 Length: 116 Length: 120 Length: 124 Length: 128 +Finding maximum probability path (part 1 of 6, beats 1 to 34)... +forward step +backward step + +Finding maximum probability path (part 2 of 6, beats 35 to 162)... +forward step +backward step + +Finding maximum probability path (part 3 of 6, beats 163 to 290)... +forward step +backward step + +Finding maximum probability path (part 4 of 6, beats 291 to 354)... +forward step +backward step + +Finding maximum probability path (part 5 of 6, beats 355 to 482)... +forward step +backward step + +Finding maximum probability path (part 6 of 6, beats 483 to 559)... +forward step +backward step +0.0 0.023 N +0.023 11.529 G +11.529 14.141 B +14.141 15.581 C +15.581 16.149 N +16.149 18.762 A +18.762 20.770 G +20.770 23.359 B +23.359 24.811 C +24.811 25.391 N +25.391 27.992 A +27.992 30.023 G +30.023 32.311 E +32.311 34.633 G +34.633 37.222 E +37.222 39.253 G +39.253 41.575 A +41.575 43.862 G/5 +43.862 46.475 E +46.475 48.506 G +48.506 51.142 B +51.142 52.605 C +52.605 53.197 N +53.197 55.821 A +55.821 57.876 G +57.876 60.500 B +60.500 61.962 C +61.962 62.566 N +62.566 65.202 A +65.202 67.257 G +67.257 69.613 E +69.613 71.912 G +71.912 74.513 E +74.513 76.556 G +76.556 78.890 A +78.890 81.200 G/5 +81.200 83.824 E +83.824 85.008 G +85.008 85.867 D +85.867 88.155 C +88.155 88.433 F#:dim +88.433 89.594 G +89.594 90.465 D +90.465 93.077 C +93.077 94.250 G +94.250 95.411 D +95.411 96.583 C +96.583 97.744 G +97.744 98.348 G:7 +98.348 99.532 F:maj7 +99.532 100.113 F/5 +100.113 102.412 D +102.412 104.455 G +104.455 107.067 B +107.067 108.518 C +108.518 109.099 N +109.099 111.700 A +111.700 113.697 G +113.697 116.320 B +116.320 117.772 C +117.772 118.340 N +118.340 120.953 A +120.953 122.984 G +122.984 125.295 E +125.295 127.594 G +127.594 130.229 E +130.229 132.261 G +132.261 134.594 A +134.594 136.916 G/5 +136.916 139.517 E +139.517 146.460 G +146.460 148.793 E +148.793 155.400 G +155.400 161.733 N0.0 0.023 N +0.023 161.733 G \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_logfiles/RollingStonesRipThisJoint.wav.log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,166 @@ +CSVFeatureWriter::setParameters +stdout -> +NOTE: Using default sample rate of 44100 for default transform +NOTE: Loaded and initialised plugin for transform "vamp:matthiasm:nnls_chroma:bothchroma" with plugin step size 2048 and block size 16384 (adapter step and block size 16384) +NOTE: PluginInputDomainAdapter timestamp adjustment is 0.000000000R +Extracting features for: "/Users/matthiasm/data/RollingStonesRipThisJoint.wav" + Retrieving audio data... Done +Opened 2-channel file or URL "/Users/matthiasm/data/RollingStonesRipThisJoint.wav" +timestamp 0.000000000R: very low magnitude, setting magnitude to all zeros +timestamp 0.046439909R: very low magnitude, setting magnitude to all zeros +timestamp 0.092879818R: very low magnitude, setting magnitude to all zeros +timestamp 0.139319727R: very low magnitude, setting magnitude to all zeros +timestamp 0.185759637R: very low magnitude, setting magnitude to all zeros +timestamp 0.232199546R: very low magnitude, setting magnitude to all zeros +timestamp 0.278639455R: very low magnitude, setting magnitude to all zeros +timestamp 0.325079365R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 1% Extracting and writing features... 2% Extracting and writing features... 3% Extracting and writing features... 4% Extracting and writing features... 5% Extracting and writing features... 6% Extracting and writing features... 7% Extracting and writing features... 8% Extracting and writing features... 9% Extracting and writing features... 10% Extracting and writing features... 11% Extracting and writing features... 12% Extracting and writing features... 13% Extracting and writing features... 14% Extracting and writing features... 15% Extracting and writing features... 16% Extracting and writing features... 17% Extracting and writing features... 18% Extracting and writing features... 19% Extracting and writing features... 20% Extracting and writing features... 21% Extracting and writing features... 22% Extracting and writing features... 23% Extracting and writing features... 24% Extracting and writing features... 25% Extracting and writing features... 26% Extracting and writing features... 27% Extracting and writing features... 28% Extracting and writing features... 29% Extracting and writing features... 30% Extracting and writing features... 31% Extracting and writing features... 32% Extracting and writing features... 33% Extracting and writing features... 34% Extracting and writing features... 35% Extracting and writing features... 36% Extracting and writing features... 37% Extracting and writing features... 38% Extracting and writing features... 39% Extracting and writing features... 40% Extracting and writing features... 41% Extracting and writing features... 42% Extracting and writing features... 43% Extracting and writing features... 44% Extracting and writing features... 45% Extracting and writing features... 46% Extracting and writing features... 47% Extracting and writing features... 48% Extracting and writing features... 49% Extracting and writing features... 50% Extracting and writing features... 51% Extracting and writing features... 52% Extracting and writing features... 53% Extracting and writing features... 54% Extracting and writing features... 55% Extracting and writing features... 56% Extracting and writing features... 57% Extracting and writing features... 58% Extracting and writing features... 59% Extracting and writing features... 60% Extracting and writing features... 61% Extracting and writing features... 62% Extracting and writing features... 63% Extracting and writing features... 64% Extracting and writing features... 65% Extracting and writing features... 66% Extracting and writing features... 67% Extracting and writing features... 68% Extracting and writing features... 69% Extracting and writing features... 70% Extracting and writing features... 71% Extracting and writing features... 72% Extracting and writing features... 73% Extracting and writing features... 74% Extracting and writing features... 75% Extracting and writing features... 76% Extracting and writing features... 77% Extracting and writing features... 78% Extracting and writing features... 79% Extracting and writing features... 80% Extracting and writing features... 81% Extracting and writing features... 82% Extracting and writing features... 83% Extracting and writing features... 84% Extracting and writing features... 85% Extracting and writing features... 86% Extracting and writing features... 87% Extracting and writing features... 88% Extracting and writing features... 89% Extracting and writing features... 90% Extracting and writing features... 91% Extracting and writing features... 92% Extracting and writing features... 93% Extracting and writing features... 94% Extracting and writing features... 95% Extracting and writing features... 96% Extracting and writing features... 97% Extracting and writing features... 98%timestamp 140.852244897R: very low magnitude, setting magnitude to all zeros +timestamp 140.898684807R: very low magnitude, setting magnitude to all zeros +timestamp 140.945124716R: very low magnitude, setting magnitude to all zeros +timestamp 140.991564625R: very low magnitude, setting magnitude to all zeros +timestamp 141.038004535R: very low magnitude, setting magnitude to all zeros +timestamp 141.084444444R: very low magnitude, setting magnitude to all zeros +timestamp 141.130884353R: very low magnitude, setting magnitude to all zeros +timestamp 141.177324263R: very low magnitude, setting magnitude to all zeros +timestamp 141.223764172R: very low magnitude, setting magnitude to all zeros +timestamp 141.270204081R: very low magnitude, setting magnitude to all zeros +timestamp 141.316643990R: very low magnitude, setting magnitude to all zeros +timestamp 141.363083900R: very low magnitude, setting magnitude to all zeros +timestamp 141.409523809R: very low magnitude, setting magnitude to all zeros +timestamp 141.455963718R: very low magnitude, setting magnitude to all zeros +timestamp 141.502403628R: very low magnitude, setting magnitude to all zeros +timestamp 141.548843537R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 99%timestamp 141.595283446R: very low magnitude, setting magnitude to all zeros +timestamp 141.641723356R: very low magnitude, setting magnitude to all zeros +timestamp 141.688163265R: very low magnitude, setting magnitude to all zeros +timestamp 141.734603174R: very low magnitude, setting magnitude to all zeros +timestamp 141.781043083R: very low magnitude, setting magnitude to all zeros +timestamp 141.827482993R: very low magnitude, setting magnitude to all zeros +timestamp 141.873922902R: very low magnitude, setting magnitude to all zeros +timestamp 141.920362811R: very low magnitude, setting magnitude to all zeros +timestamp 141.966802721R: very low magnitude, setting magnitude to all zeros +timestamp 142.013242630R: very low magnitude, setting magnitude to all zeros +timestamp 142.059682539R: very low magnitude, setting magnitude to all zeros +timestamp 142.106122448R: very low magnitude, setting magnitude to all zeros +timestamp 142.152562358R: very low magnitude, setting magnitude to all zeros +timestamp 142.199002267R: very low magnitude, setting magnitude to all zeros +timestamp 142.245442176R: very low magnitude, setting magnitude to all zeros +timestamp 142.291882086R: very low magnitude, setting magnitude to all zeros +timestamp 142.338321995R: very low magnitude, setting magnitude to all zeros +timestamp 142.384761904R: very low magnitude, setting magnitude to all zeros +timestamp 142.431201814R: very low magnitude, setting magnitude to all zeros +timestamp 142.477641723R: very low magnitude, setting magnitude to all zeros +timestamp 142.524081632R: very low magnitude, setting magnitude to all zeros +timestamp 142.570521541R: very low magnitude, setting magnitude to all zeros +timestamp 142.616961451R: very low magnitude, setting magnitude to all zeros +timestamp 142.663401360R: very low magnitude, setting magnitude to all zeros +timestamp 142.709841269R: very low magnitude, setting magnitude to all zeros +[NNLS Chroma Plugin] Tuning Log-Frequency Spectrogram ... done. +[NNLS Chroma Plugin] Performing NNLS and mapping to chroma ... done. +[NNLS Chroma Plugin] Chord Estimation ... done. + Extracting and writing features... Done + + + < M A T L A B (R) > + Copyright 1984-2011 The MathWorks, Inc. + R2011b (7.13.0.564) 64-bit (maci64) + August 13, 2011 + + +To get started, type one of these: helpwin, helpdesk, or demo. +For product information, visit www.mathworks.com. + + - First Slice + metpos + key + chord + bass + - Second Slice + (7) metpos (slice 2) + (8) key (slice 2) + (9) chord (slice 2) + (10) bass (slice 2) +onset detection function +beats + + Length: 28 Length: 32 Length: 36 Length: 40 Length: 44 Length: 48 Length: 52 Length: 56 Length: 60 Length: 64 Length: 68 Length: 72 Length: 76 Length: 80 Length: 84 Length: 88 Length: 92 Length: 96 Length: 100 Length: 104 Length: 108 Length: 112 Length: 116 Length: 120 Length: 124 Length: 128 +Finding maximum probability path (part 1 of 11, beats 1 to 45)... +forward step +backward step + +Finding maximum probability path (part 2 of 11, beats 46 to 115)... +forward step +backward step + +Finding maximum probability path (part 3 of 11, beats 116 to 143)... +forward step +backward step + +Finding maximum probability path (part 4 of 11, beats 144 to 155)... +forward step +backward step + +Finding maximum probability path (part 5 of 11, beats 156 to 185)... +forward step +backward step + +Finding maximum probability path (part 6 of 11, beats 186 to 221)... +forward step +backward step + +Finding maximum probability path (part 7 of 11, beats 222 to 285)... +forward step +backward step + +Finding maximum probability path (part 8 of 11, beats 286 to 331)... +forward step +backward step + +Finding maximum probability path (part 9 of 11, beats 332 to 361)... +forward step +backward step + +Finding maximum probability path (part 10 of 11, beats 362 to 397)... +forward step +backward step + +Finding maximum probability path (part 11 of 11, beats 398 to 469)... +forward step +backward step +0.0 0.151 N +0.151 0.464 N +0.464 12.655 D +12.655 13.897 A +13.897 14.826 A:min7 +14.826 15.755 D/5 +15.755 16.370 G +16.370 17.589 D +17.589 19.725 A:min +19.725 42.109 D +42.109 44.246 A:min +44.246 54.358 D +54.358 56.750 A:min +56.750 66.456 D +66.456 67.686 A:min +67.686 68.603 A:min7 +68.603 69.532 D/5 +69.532 70.136 G +70.136 71.332 D +71.332 73.468 A:min +73.468 90.895 D +90.895 93.019 A +93.019 95.759 D +95.759 98.197 A:min +98.197 107.950 D +107.950 110.039 A:min +110.039 110.353 D/5 +110.353 120.070 D +120.070 120.999 A +120.999 121.313 A:min7 +121.313 123.147 D/5 +123.147 123.751 G +123.751 124.970 D +124.970 127.118 A:min +127.118 140.690 D +140.690 142.960 N0.0 0.151 N +0.151 142.960 D \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_logfiles/U2.WithOrWithoutYou.wav.log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,612 @@ +CSVFeatureWriter::setParameters +stdout -> +NOTE: Using default sample rate of 44100 for default transform +NOTE: Loaded and initialised plugin for transform "vamp:matthiasm:nnls_chroma:bothchroma" with plugin step size 2048 and block size 16384 (adapter step and block size 16384) +NOTE: PluginInputDomainAdapter timestamp adjustment is 0.000000000R +Extracting features for: "/Users/matthias/Music/MUSICDB44k/U2.WithOrWithoutYou.wav" + Retrieving audio data... Done +Opened 2-channel file or URL "/Users/matthias/Music/MUSICDB44k/U2.WithOrWithoutYou.wav" +timestamp 0.000000000R: very low magnitude, setting magnitude to all zeros +timestamp 0.046439909R: very low magnitude, setting magnitude to all zeros +timestamp 0.092879818R: very low magnitude, setting magnitude to all zeros +timestamp 0.139319727R: very low magnitude, setting magnitude to all zeros +timestamp 0.185759637R: very low magnitude, setting magnitude to all zeros +timestamp 0.232199546R: very low magnitude, setting magnitude to all zeros +timestamp 0.278639455R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 1% Extracting and writing features... 2% Extracting and writing features... 3% Extracting and writing features... 4% Extracting and writing features... 5% Extracting and writing features... 6% Extracting and writing features... 7% Extracting and writing features... 8% Extracting and writing features... 9% Extracting and writing features... 10% Extracting and writing features... 11% Extracting and writing features... 12% Extracting and writing features... 13% Extracting and writing features... 14% Extracting and writing features... 15% Extracting and writing features... 16% Extracting and writing features... 17% Extracting and writing features... 18% Extracting and writing features... 19% Extracting and writing features... 20% Extracting and writing features... 21% Extracting and writing features... 22% Extracting and writing features... 23% Extracting and writing features... 24% Extracting and writing features... 25% Extracting and writing features... 26% Extracting and writing features... 27% Extracting and writing features... 28% Extracting and writing features... 29% Extracting and writing features... 30% Extracting and writing features... 31% Extracting and writing features... 32% Extracting and writing features... 33% Extracting and writing features... 34% Extracting and writing features... 35% Extracting and writing features... 36% Extracting and writing features... 37% Extracting and writing features... 38% Extracting and writing features... 39% Extracting and writing features... 40% Extracting and writing features... 41% Extracting and writing features... 42% Extracting and writing features... 43% Extracting and writing features... 44% Extracting and writing features... 45% Extracting and writing features... 46% Extracting and writing features... 47% Extracting and writing features... 48% Extracting and writing features... 49% Extracting and writing features... 50% Extracting and writing features... 51% Extracting and writing features... 52% Extracting and writing features... 53% Extracting and writing features... 54% Extracting and writing features... 55% Extracting and writing features... 56% Extracting and writing features... 57% Extracting and writing features... 58% Extracting and writing features... 59% Extracting and writing features... 60% Extracting and writing features... 61% Extracting and writing features... 62% Extracting and writing features... 63% Extracting and writing features... 64% Extracting and writing features... 65% Extracting and writing features... 66% Extracting and writing features... 67% Extracting and writing features... 68% Extracting and writing features... 69% Extracting and writing features... 70% Extracting and writing features... 71% Extracting and writing features... 72% Extracting and writing features... 73% Extracting and writing features... 74% Extracting and writing features... 75% Extracting and writing features... 76% Extracting and writing features... 77% Extracting and writing features... 78% Extracting and writing features... 79% Extracting and writing features... 80% Extracting and writing features... 81% Extracting and writing features... 82% Extracting and writing features... 83% Extracting and writing features... 84% Extracting and writing features... 85% Extracting and writing features... 86% Extracting and writing features... 87% Extracting and writing features... 88% Extracting and writing features... 89% Extracting and writing features... 90% Extracting and writing features... 91% Extracting and writing features... 92% Extracting and writing features... 93% Extracting and writing features... 94% Extracting and writing features... 95% Extracting and writing features... 96% Extracting and writing features... 97%timestamp 292.060589569R: very low magnitude, setting magnitude to all zeros +timestamp 292.107029478R: very low magnitude, setting magnitude to all zeros +timestamp 292.292789115R: very low magnitude, setting magnitude to all zeros +timestamp 292.339229024R: very low magnitude, setting magnitude to all zeros +timestamp 292.385668934R: very low magnitude, setting magnitude to all zeros +timestamp 292.432108843R: very low magnitude, setting magnitude to all zeros +timestamp 292.478548752R: very low magnitude, setting magnitude to all zeros +timestamp 292.524988662R: very low magnitude, setting magnitude to all zeros +timestamp 293.407346938R: very low magnitude, setting magnitude to all zeros +timestamp 293.453786848R: very low magnitude, setting magnitude to all zeros +timestamp 293.500226757R: very low magnitude, setting magnitude to all zeros +timestamp 293.546666666R: very low magnitude, setting magnitude to all zeros +timestamp 293.593106575R: very low magnitude, setting magnitude to all zeros +timestamp 293.639546485R: very low magnitude, setting magnitude to all zeros +timestamp 293.685986394R: very low magnitude, setting magnitude to all zeros +timestamp 293.732426303R: very low magnitude, setting magnitude to all zeros +timestamp 293.778866213R: very low magnitude, setting magnitude to all zeros +timestamp 293.825306122R: very low magnitude, setting magnitude to all zeros +timestamp 293.871746031R: very low magnitude, setting magnitude to all zeros +timestamp 293.918185941R: very low magnitude, setting magnitude to all zeros +timestamp 293.964625850R: very low magnitude, setting magnitude to all zeros +timestamp 294.011065759R: very low magnitude, setting magnitude to all zeros +timestamp 294.057505668R: very low magnitude, setting magnitude to all zeros +timestamp 294.103945578R: very low magnitude, setting magnitude to all zeros +timestamp 294.150385487R: very low magnitude, setting magnitude to all zeros +timestamp 294.196825396R: very low magnitude, setting magnitude to all zeros +timestamp 294.243265306R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 98%timestamp 294.289705215R: very low magnitude, setting magnitude to all zeros +timestamp 294.336145124R: very low magnitude, setting magnitude to all zeros +timestamp 294.382585034R: very low magnitude, setting magnitude to all zeros +timestamp 294.429024943R: very low magnitude, setting magnitude to all zeros +timestamp 294.475464852R: very low magnitude, setting magnitude to all zeros +timestamp 294.521904761R: very low 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magnitude to all zeros +timestamp 299.862494331R: very low magnitude, setting magnitude to all zeros +timestamp 299.908934240R: very low magnitude, setting magnitude to all zeros +timestamp 299.955374149R: very low magnitude, setting magnitude to all zeros +timestamp 300.001814058R: very low magnitude, setting magnitude to all zeros +timestamp 300.048253968R: very low magnitude, setting magnitude to all zeros +timestamp 300.094693877R: very low magnitude, setting magnitude to all zeros +timestamp 300.141133786R: very low magnitude, setting magnitude to all zeros +timestamp 300.187573696R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 100%timestamp 300.234013605R: very low magnitude, setting magnitude to all zeros +[NNLS Chroma Plugin] Tuning Log-Frequency Spectrogram ... done. +[NNLS Chroma Plugin] Performing NNLS and mapping to chroma ... done. +[NNLS Chroma Plugin] Chord Estimation ... done. + Extracting and writing features... Done +ep + +Finding maximum probability path (part 6 of 26, beats 174 to 205)... +forward step +backward step + + < M A T L A B > + Copyright 1984-2007 The MathWorks, Inc. + Version 7.5.0.338 (R2007b) + August 9, 2007 + + + To get started, type one of these: helpwin, helpdesk, or demo. + For product information, visit www.mathworks.com. + + - First Slice + metpos + key + chord + bass + - Second Slice + (7) metpos (slice 2) + (8) key (slice 2) + (9) chord (slice 2) + (10) bass (slice 2) + +Finding maximum probability path (part 7 of 26, beats 206 to 237)... +forward step +onset detection function +beats +backward step + +Finding maximum probability path (part 8 of 26, beats 238 to 268)... +forward step + + Length: 28backward step + Length: 32 +Finding maximum probability path (part 9 of 26, beats 269 to 301)... +forward step + Length: 36 Length: 40 Length: 44backward step + Length: 48 Length: 52 +Finding maximum probability path (part 10 of 26, beats 302 to 333)... +forward step + Length: 56 Length: 60 Length: 64backward step + Length: 68 Length: 72 +Finding maximum probability path (part 11 of 26, beats 334 to 365)... +forward step + Length: 76 Length: 80 Length: 84backward step + Length: 88 Length: 92 +Finding maximum probability path (part 12 of 26, beats 366 to 396)... +forward step + Length: 96 Length: 100 Length: 104backward step + Length: 108 Length: 112 +Finding maximum probability path (part 13 of 26, beats 397 to 429)... +forward step + Length: 116 Length: 120backward step + Length: 124 Length: 128 +Finding maximum probability path (part 14 of 26, beats 430 to 461)... +forward step + +Finding maximum probability path (part 1 of 26, beats 1 to 35)... +forward step +backward step +backward step + +Finding maximum probability path (part 15 of 26, beats 462 to 493)... +forward step + +Finding maximum probability path (part 2 of 26, beats 36 to 67)... +forward step +backward step +backward step + +Finding maximum probability path (part 16 of 26, beats 494 to 524)... +forward step + +Finding maximum probability path (part 3 of 26, beats 68 to 99)... +forward step +backward step +backward step + +Finding maximum probability path (part 17 of 26, beats 525 to 557)... +forward step + +Finding maximum probability path (part 4 of 26, beats 100 to 132)... +forward step +backward step +backward step + +Finding maximum probability path (part 18 of 26, beats 558 to 585)... +forward step + +Finding maximum probability path (part 5 of 26, beats 133 to 164)... +forward step +backward step + +Finding maximum probability path (part 19 of 26, beats 586 to 654)... +forward step +backward step + +Finding maximum probability path (part 6 of 26, beats 165 to 193)... +forward step +backward step +backward step + +Finding maximum probability path (part 7 of 26, beats 194 to 227)... +forward step + +Finding maximum probability path (part 20 of 26, beats 655 to 682)... +forward step +backward step +backward step + +Finding maximum probability path (part 8 of 26, beats 228 to 255)... +forward step + +Finding maximum probability path (part 21 of 26, beats 683 to 718)... +forward step +backward step +backward step + +Finding maximum probability path (part 9 of 26, beats 256 to 291)... +forward step + +Finding maximum probability path (part 22 of 26, beats 719 to 746)... +forward step +backward step +backward step + +Finding maximum probability path (part 10 of 26, beats 292 to 323)... +forward step + +Finding maximum probability path (part 23 of 26, beats 747 to 766)... +forward step +backward step +backward step + +Finding maximum probability path (part 11 of 26, beats 324 to 355)... +forward step + +Finding maximum probability path (part 24 of 26, beats 767 to 798)... +forward step +backward step +backward step + +Finding maximum probability path (part 25 of 26, beats 799 to 826)... +forward step + +Finding maximum probability path (part 12 of 26, beats 356 to 388)... +forward step +backward step +backward step + +Finding maximum probability path (part 26 of 26, beats 827 to 847)... +forward step + +Finding maximum probability path (part 13 of 26, beats 389 to 419)... +forward step +backward step +0.0 0.197 N +0.197 0.383 N +0.383 11.355 D +11.355 13.549 D/5 +13.549 15.743 B:min7 +15.743 17.392 G +17.392 20.143 D +20.143 22.326 D/5 +22.326 24.520 B:min7 +24.520 26.169 G +26.169 28.909 D +28.909 31.103 D/5 +31.103 33.309 B:min7 +33.309 34.958 G +34.958 37.674 D +37.674 39.880 D/5 +39.880 42.063 B:min7 +42.063 43.700 G +43.700 46.463 D +46.463 48.634 D/5 +48.634 50.828 B:min7 +50.828 52.477 G +52.477 55.240 D +55.240 57.423 D/5 +57.423 59.617 B:min7 +59.617 61.254 G +61.254 63.994 D +63.994 66.177 D/5 +66.177 68.383 B:min7 +68.383 70.020 G +70.020 72.783 D +72.783 74.687 D/5 +74.687 76.870 B:min7 +76.870 78.785 G +78.785 81.514 D +81.514 83.720 D/5 +83.720 85.914 B:min7 +85.914 87.562 G +87.562 90.314 D +90.314 92.497 D/5 +92.497 94.691 B:min7 +94.691 96.340 G +96.340 99.091 D +99.091 101.262 D/5 +101.262 103.468 B:min7 +103.468 105.105 G +105.105 107.833 D +107.833 109.749 D/5 +109.749 111.955 B:min7 +111.955 113.569 G +113.569 116.309 D +116.309 116.843 G/5 +116.843 118.782 D/5 +118.782 120.953 B:min7 +120.953 122.601 G +122.601 125.353 D +125.353 126.456 A:7 +126.456 127.536 D/5 +127.536 129.741 B:min7 +129.741 131.390 G +131.390 134.118 D +134.118 136.313 D/5 +136.313 138.507 B:min7 +138.507 140.156 G +140.156 142.884 D +142.884 144.823 D/5 +144.823 147.006 B:min7 +147.006 148.654 G +148.654 151.394 D +151.394 151.940 D:maj6 +151.940 153.855 D/5 +153.855 156.050 B:min7 +156.050 157.698 G +157.698 160.438 D +160.438 162.644 D/5 +162.644 164.629 B:min +164.629 167.021 G +167.021 170.295 D +170.295 171.386 A:7 +171.386 173.581 B:min +173.581 175.775 G +175.775 180.164 D +180.164 182.370 B:min +182.370 184.552 G:maj7 +184.552 188.929 D +188.929 191.135 B:min7 +191.135 193.318 G +193.318 196.627 D/5 +196.627 201.004 B:min7 +201.004 204.289 G +204.289 215.261 D +215.261 217.466 B:min7 +217.466 219.103 G +219.103 231.724 D +231.724 236.112 B:min7 +236.112 239.398 G +239.398 250.392 D +250.392 252.575 B:min +252.575 254.769 G +254.769 258.067 D/5 +258.067 262.455 B:min7 +262.455 265.752 G +265.752 271.221 D +271.221 272.335 G +272.335 275.632 D/5 +275.632 279.998 B:min7 +279.998 283.295 G +283.295 292.084 D +292.084 300.373 N0.0 0.197 N +0.197 300.373 Dbackward step + +Finding maximum probability path (part 14 of 26, beats 420 to 452)... +forward step +backward step + +Finding maximum probability path (part 15 of 26, beats 453 to 483)... +forward step +backward step + +Finding maximum probability path (part 16 of 26, beats 484 to 515)... +forward step +backward step + +Finding maximum probability path (part 17 of 26, beats 516 to 548)... +forward step +backward step + +Finding maximum probability path (part 18 of 26, beats 549 to 579)... +forward step +backward step + +Finding maximum probability path (part 19 of 26, beats 580 to 608)... +forward step +backward step + +Finding maximum probability path (part 20 of 26, beats 609 to 640)... +forward step +backward step + +Finding maximum probability path (part 21 of 26, beats 641 to 672)... +forward step +backward step + +Finding maximum probability path (part 22 of 26, beats 673 to 705)... +forward step +backward step + +Finding maximum probability path (part 23 of 26, beats 706 to 740)... +forward step +backward step + +Finding maximum probability path (part 24 of 26, beats 741 to 768)... +forward step +backward step + +Finding maximum probability path (part 25 of 26, beats 769 to 800)... +forward step +backward step + +Finding maximum probability path (part 26 of 26, beats 801 to 847)... +forward step +backward step +0.0 0.197 N +0.197 0.383 N +0.383 11.355 D +11.355 13.549 D/5 +13.549 15.743 B:min7 +15.743 17.937 G +17.937 20.143 D +20.143 22.326 D/5 +22.326 24.520 B:min7 +24.520 26.169 G +26.169 28.909 D +28.909 31.103 D/5 +31.103 33.309 B:min7 +33.309 34.958 G +34.958 37.953 D +37.953 40.147 D/5 +40.147 42.330 B:min7 +42.330 43.979 G +43.979 46.730 D +46.730 48.913 D/5 +48.913 51.096 B:min7 +51.096 52.744 G +52.744 54.683 D +54.683 56.877 D/5 +56.877 59.060 B:min7 +59.060 61.800 G +61.800 63.994 D +63.994 66.177 D/5 +66.177 68.383 B:min7 +68.383 70.565 G +70.565 72.783 D +72.783 74.954 D/5 +74.954 77.148 B:min +77.148 79.343 G:maj7 +79.343 81.514 D +81.514 83.720 D/5 +83.720 85.914 B:min7 +85.914 88.120 G +88.120 90.314 D +90.314 92.497 D/5 +92.497 94.691 B:min7 +94.691 96.340 G +96.340 99.091 D +99.091 101.262 D/5 +101.262 103.468 B:min7 +103.468 105.639 G +105.639 108.112 D +108.112 110.306 D/5 +110.306 112.477 B:min7 +112.477 114.126 G +114.126 116.576 D +116.576 118.782 D/5 +118.782 120.953 B:min7 +120.953 122.601 G +122.601 125.620 D +125.620 127.814 D/5 +127.814 130.009 B:min7 +130.009 131.669 G +131.669 134.118 D +134.118 136.313 D/5 +136.313 138.507 B:min7 +138.507 140.156 G +140.156 142.884 D +142.884 145.101 D/5 +145.101 147.284 B:min7 +147.284 149.478 G +149.478 151.940 D +151.940 154.134 D/5 +154.134 156.317 B:min7 +156.317 157.977 G +157.977 160.438 D +160.438 162.644 D/5 +162.644 165.489 B:min7 +165.489 168.647 G +168.647 169.192 D/5 +169.192 171.386 D +171.386 173.581 B:min7 +173.581 175.775 G +175.775 180.164 D +180.164 182.370 B:min7 +182.370 184.552 G +184.552 188.929 D +188.929 191.135 B:min7 +191.135 193.318 G +193.318 197.718 D +197.718 199.912 B:min7 +199.912 202.095 G +202.095 206.483 D +206.483 208.689 B:min7 +208.689 210.872 G +210.872 215.261 D +215.261 217.466 B:min7 +217.466 219.661 G +219.661 225.686 D +225.686 230.075 D/5 +230.075 234.475 B:min7 +234.475 237.749 G +237.749 244.889 D +244.889 249.313 D/5 +249.313 253.678 B:min7 +253.678 256.975 G +256.975 259.170 D/5 +259.170 261.364 B:min7 +261.364 263.558 G +263.558 267.935 D +267.935 270.129 B:min7 +270.129 272.335 G +272.335 276.724 D +276.724 278.906 B:min +278.906 281.089 G +281.089 283.295 D +283.295 285.489 A:7 +285.489 287.672 G/3 +287.672 292.084 D +292.084 300.373 N0.0 0.197 N +0.197 300.373 D \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_logfiles/friends.wav.log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,56 @@ +CSVFeatureWriter::setParameters +stdout -> +NOTE: Using default sample rate of 44100 for default transform +NOTE: Loaded and initialised plugin for transform "vamp:matthiasm:nnls_chroma:bothchroma" with plugin step size 2048 and block size 16384 (adapter step and block size 16384) +NOTE: PluginInputDomainAdapter timestamp adjustment is 0.000000000R +Extracting features for: "/Users/matthias/Workspace/2010/Thesis-code/wav/friends.wav" + Retrieving audio data... Done +Opened 2-channel file or URL "/Users/matthias/Workspace/2010/Thesis-code/wav/friends.wav" + Extracting and writing features... 1% Extracting and writing features... 3% Extracting and writing features... 5% Extracting and writing features... 7% Extracting and writing features... 8% Extracting and writing features... 10% Extracting and writing features... 12% Extracting and writing features... 14% Extracting and writing features... 15% Extracting and writing features... 17% Extracting and writing features... 19% Extracting and writing features... 21% Extracting and writing features... 22% Extracting and writing features... 24% Extracting and writing features... 26% Extracting and writing features... 28% Extracting and writing features... 30% Extracting and writing features... 31% Extracting and writing features... 33% Extracting and writing features... 35% Extracting and writing features... 37% Extracting and writing features... 38% Extracting and writing features... 40% Extracting and writing features... 42% Extracting and writing features... 44% Extracting and writing features... 45% Extracting and writing features... 47% Extracting and writing features... 49% Extracting and writing features... 51% Extracting and writing features... 52% Extracting and writing features... 54% Extracting and writing features... 56% Extracting and writing features... 58% Extracting and writing features... 59% Extracting and writing features... 61% Extracting and writing features... 63% Extracting and writing features... 65% Extracting and writing features... 66% Extracting and writing features... 68% Extracting and writing features... 70% Extracting and writing features... 72% Extracting and writing features... 73% Extracting and writing features... 75% Extracting and writing features... 77% Extracting and writing features... 79% Extracting and writing features... 81% Extracting and writing features... 82% Extracting and writing features... 84% Extracting and writing features... 86% Extracting and writing features... 88% Extracting and writing features... 89% Extracting and writing features... 91% Extracting and writing features... 93% Extracting and writing features... 95% Extracting and writing features... 96% Extracting and writing features... 98%[NNLS Chroma Plugin] Tuning Log-Frequency Spectrogram ... done. +[NNLS Chroma Plugin] Performing NNLS and mapping to chroma ... done. +[NNLS Chroma Plugin] Chord Estimation ... done. + Extracting and writing features... Done + + + < M A T L A B > + Copyright 1984-2007 The MathWorks, Inc. + Version 7.5.0.338 (R2007b) + August 9, 2007 + + + To get started, type one of these: helpwin, helpdesk, or demo. + For product information, visit www.mathworks.com. + + - First Slice + metpos + key + chord + bass + - Second Slice + (7) metpos (slice 2) + (8) key (slice 2) + (9) chord (slice 2) + (10) bass (slice 2) +onset detection function +beats + + Length: 28 Length: 32 Length: 36 Length: 40 Length: 44 Length: 48 Length: 52 Length: 56 Length: 60 Length: 64 Length: 68 Length: 72 Length: 76 Length: 80 Length: 84 Length: 88 Length: 92 Length: 96 Length: 100 Length: 104 Length: 108 Length: 112 Length: 116 Length: 120 Length: 124 Length: 128 +Finding maximum probability path (part 1 of 1, beats 1 to 53)... +forward step +backward step +0.0 0.023 N +0.023 0.418 A:min +0.418 3.622 D +3.622 5.224 C +5.224 6.815 C#:min +6.815 8.429 G +8.429 10.031 E:min7 +10.031 11.622 D +11.622 13.224 D#:min +13.224 14.814 E:min +14.814 16.417 Eb +16.417 18.019 G/5 +18.019 19.621 A/3 +19.621 20.863 C +20.863 21.119 N0.0 0.023 N +0.023 21.119 D \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_logfiles/show.wav.log Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,348 @@ +CSVFeatureWriter::setParameters +stdout -> +NOTE: Using default sample rate of 44100 for default transform +NOTE: Loaded and initialised plugin for transform "vamp:matthiasm:nnls_chroma:bothchroma" with plugin step size 2048 and block size 16384 (adapter step and block size 16384) +NOTE: PluginInputDomainAdapter timestamp adjustment is 0.000000000R +Extracting features for: "testdata/show.wav" + Retrieving audio data... Done +Opened 1-channel file or URL "testdata/show.wav" +timestamp 0.000000000R: very low magnitude, setting magnitude to all zeros +timestamp 0.046439909R: very low magnitude, setting magnitude to all zeros +timestamp 0.092879818R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 1% Extracting and writing features... 2% Extracting and writing features... 3% Extracting and writing features... 4% Extracting and writing features... 5% Extracting and writing features... 6% Extracting and writing features... 7% Extracting and writing features... 8% Extracting and writing features... 9% Extracting and writing features... 10% Extracting and writing features... 11% Extracting and writing features... 12% Extracting and writing features... 13% Extracting and writing features... 14% Extracting and writing features... 15% Extracting and writing features... 16% Extracting and writing features... 17% Extracting and writing features... 18% Extracting and writing features... 19% Extracting and writing features... 20% Extracting and writing features... 21% Extracting and writing features... 22% Extracting and writing features... 23% Extracting and writing features... 24% Extracting and writing features... 25% Extracting and writing features... 26% Extracting and writing features... 27% Extracting and writing features... 28% Extracting and writing features... 29% Extracting and writing features... 30% Extracting and writing features... 31% Extracting and writing features... 32% Extracting and writing features... 33% Extracting and writing features... 34% Extracting and writing features... 35% Extracting and writing features... 36% Extracting and writing features... 37% Extracting and writing features... 38% Extracting and writing features... 39% Extracting and writing features... 40% Extracting and writing features... 41% Extracting and writing features... 42% Extracting and writing features... 43% Extracting and writing features... 44% Extracting and writing features... 45% Extracting and writing features... 46% Extracting and writing features... 47% Extracting and writing features... 48% Extracting and writing features... 49% Extracting and writing features... 50% Extracting and writing features... 51% Extracting and writing features... 52% Extracting and writing features... 53% Extracting and writing features... 54% Extracting and writing features... 55% Extracting and writing features... 56% Extracting and writing features... 57% Extracting and writing features... 58% Extracting and writing features... 59% Extracting and writing features... 60% Extracting and writing features... 61% Extracting and writing features... 62% Extracting and writing features... 63% Extracting and writing features... 64% Extracting and writing features... 65% Extracting and writing features... 66% Extracting and writing features... 67% Extracting and writing features... 68% Extracting and writing features... 69% Extracting and writing features... 70% Extracting and writing features... 71% Extracting and writing features... 72% Extracting and writing features... 73% Extracting and writing features... 74% Extracting and writing features... 75% Extracting and writing features... 76% Extracting and writing features... 77% Extracting and writing features... 78% Extracting and writing features... 79% Extracting and writing features... 80% Extracting and writing features... 81% Extracting and writing features... 82% Extracting and writing features... 83% Extracting and writing features... 84% Extracting and writing features... 85% Extracting and writing features... 86% Extracting and writing features... 87% Extracting and writing features... 88% Extracting and writing features... 89% Extracting and writing features... 90% Extracting and writing features... 91% Extracting and writing features... 92% Extracting and writing features... 93%timestamp 246.688798185R: very low magnitude, setting magnitude to all zeros +timestamp 247.431836734R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 94%timestamp 248.081995464R: very low magnitude, setting magnitude to all zeros +timestamp 248.128435374R: very low magnitude, setting magnitude to all zeros +timestamp 248.174875283R: very low magnitude, setting magnitude to all zeros +timestamp 248.732154195R: very low magnitude, setting magnitude to all zeros +timestamp 248.778594104R: very low magnitude, setting magnitude to all zeros +timestamp 248.825034013R: very low magnitude, setting magnitude to all zeros +timestamp 248.871473922R: very low magnitude, setting magnitude to all zeros +timestamp 248.917913832R: very low magnitude, setting magnitude to all zeros +timestamp 248.964353741R: very low magnitude, setting magnitude to all zeros +timestamp 249.010793650R: very low magnitude, setting magnitude to all zeros +timestamp 249.057233560R: very low magnitude, setting magnitude to all zeros +timestamp 249.103673469R: very low magnitude, setting magnitude to all zeros +timestamp 249.150113378R: very low magnitude, setting magnitude to all zeros +timestamp 249.196553287R: very low magnitude, setting magnitude to all zeros +timestamp 249.242993197R: very low magnitude, setting magnitude to all zeros +timestamp 249.289433106R: very low magnitude, setting magnitude to all zeros +timestamp 249.335873015R: very low magnitude, setting magnitude to all zeros +timestamp 249.382312925R: very low magnitude, setting magnitude to all zeros +timestamp 249.428752834R: very low magnitude, setting magnitude to all zeros +timestamp 249.475192743R: very low magnitude, setting magnitude to all zeros +timestamp 249.521632653R: very low magnitude, setting magnitude to all zeros +timestamp 249.568072562R: very low magnitude, setting magnitude to all zeros +timestamp 249.614512471R: very low magnitude, setting magnitude to all zeros +timestamp 249.846712018R: very low magnitude, setting magnitude to all zeros +timestamp 249.893151927R: very low magnitude, setting magnitude to all zeros +timestamp 249.939591836R: very low magnitude, setting magnitude to all zeros +timestamp 249.986031746R: very low magnitude, setting magnitude to all zeros +timestamp 250.032471655R: very low magnitude, setting magnitude to all zeros +timestamp 250.078911564R: very low magnitude, setting magnitude to all zeros +timestamp 250.125351473R: very low magnitude, setting magnitude to all zeros +timestamp 250.171791383R: very low magnitude, setting magnitude to all zeros +timestamp 250.218231292R: very low magnitude, setting magnitude to all zeros +timestamp 250.264671201R: very low magnitude, setting magnitude to all zeros +timestamp 250.311111111R: very low magnitude, setting magnitude to all zeros +timestamp 250.357551020R: very low magnitude, setting magnitude to all zeros +timestamp 250.403990929R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 95%timestamp 250.450430839R: very low magnitude, setting magnitude to all zeros +timestamp 250.496870748R: very low magnitude, setting magnitude to all zeros +timestamp 250.543310657R: very low magnitude, setting magnitude to all zeros +timestamp 250.589750566R: very low magnitude, setting magnitude to all zeros +timestamp 250.636190476R: very low magnitude, setting magnitude to all zeros +timestamp 250.682630385R: very low magnitude, setting magnitude to all zeros +timestamp 250.729070294R: very low magnitude, setting magnitude to all zeros +timestamp 250.775510204R: very low magnitude, setting magnitude to all zeros +timestamp 250.821950113R: very low magnitude, setting magnitude to all zeros +timestamp 250.868390022R: very low magnitude, setting magnitude to all zeros +timestamp 250.914829931R: very low magnitude, setting magnitude to all zeros +timestamp 250.961269841R: very low magnitude, setting magnitude to all zeros +timestamp 251.007709750R: very low magnitude, setting magnitude to all zeros +timestamp 251.054149659R: very low magnitude, setting magnitude to all zeros +timestamp 251.100589569R: very low magnitude, setting magnitude to all zeros +timestamp 251.147029478R: very low magnitude, setting magnitude to all zeros +timestamp 251.193469387R: very low magnitude, setting magnitude to all zeros +timestamp 251.239909297R: very low magnitude, setting magnitude to all zeros +timestamp 251.286349206R: very low magnitude, setting magnitude to all zeros +timestamp 251.332789115R: very low magnitude, setting magnitude to all zeros +timestamp 251.379229024R: very low magnitude, setting magnitude to all zeros +timestamp 251.425668934R: very low magnitude, setting magnitude to all zeros +timestamp 251.472108843R: very low magnitude, setting magnitude to all zeros +timestamp 251.518548752R: very low magnitude, setting magnitude to all zeros +timestamp 251.564988662R: very low magnitude, setting magnitude to all zeros +timestamp 251.611428571R: very low magnitude, setting magnitude to all zeros +timestamp 251.657868480R: very low magnitude, setting magnitude to all zeros +timestamp 251.704308390R: very low magnitude, setting magnitude to all zeros +timestamp 251.750748299R: very low magnitude, setting magnitude to all zeros +timestamp 251.797188208R: very low magnitude, setting magnitude to all zeros +timestamp 251.843628117R: very low magnitude, setting magnitude to all zeros +timestamp 251.890068027R: very low magnitude, setting magnitude to all zeros +timestamp 251.936507936R: very low magnitude, setting magnitude to all zeros +timestamp 251.982947845R: very low magnitude, setting magnitude to all zeros +timestamp 252.029387755R: very low magnitude, setting magnitude to all zeros +timestamp 252.075827664R: very low magnitude, setting magnitude to all zeros +timestamp 252.122267573R: very low magnitude, setting magnitude to all zeros +timestamp 252.168707482R: very low magnitude, setting magnitude to all zeros +timestamp 252.215147392R: very low magnitude, setting magnitude to all zeros +timestamp 252.261587301R: very low magnitude, setting magnitude to all zeros +timestamp 252.308027210R: very low magnitude, setting magnitude to all zeros +timestamp 252.354467120R: very low magnitude, setting magnitude to all zeros +timestamp 252.400907029R: very low magnitude, setting magnitude to all zeros +timestamp 252.447346938R: very low magnitude, setting magnitude to all zeros +timestamp 252.493786848R: very low magnitude, setting magnitude to all zeros +timestamp 252.540226757R: very low magnitude, setting magnitude to all zeros +timestamp 252.586666666R: very low magnitude, setting magnitude to all zeros +timestamp 252.633106575R: very low magnitude, setting magnitude to all zeros +timestamp 252.679546485R: very low magnitude, setting magnitude to all zeros +timestamp 252.725986394R: very low magnitude, setting magnitude to all zeros +timestamp 252.772426303R: very low magnitude, setting magnitude to all zeros +timestamp 252.818866213R: very low magnitude, setting magnitude to all zeros +timestamp 252.865306122R: very low magnitude, setting magnitude to all zeros +timestamp 252.911746031R: very low magnitude, setting magnitude to all zeros +timestamp 252.958185941R: very low magnitude, setting magnitude to all zeros +timestamp 253.004625850R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 96%timestamp 253.051065759R: very low magnitude, setting magnitude to all zeros +timestamp 253.097505668R: very low magnitude, setting magnitude to all zeros +timestamp 253.143945578R: very low magnitude, setting magnitude to all zeros +timestamp 253.190385487R: very low magnitude, setting magnitude to all zeros +timestamp 253.236825396R: very low magnitude, setting magnitude to all zeros +timestamp 253.283265306R: very low magnitude, setting magnitude to all zeros +timestamp 253.329705215R: very low magnitude, setting magnitude to all zeros +timestamp 253.376145124R: very low magnitude, setting magnitude to all zeros +timestamp 253.422585034R: very low magnitude, setting magnitude to all zeros +timestamp 253.469024943R: very low magnitude, setting magnitude to all zeros +timestamp 253.515464852R: very low magnitude, setting magnitude to all zeros +timestamp 253.561904761R: very low magnitude, setting magnitude to all zeros +timestamp 253.608344671R: very low magnitude, setting magnitude to all zeros +timestamp 253.654784580R: very low magnitude, setting magnitude to all zeros +timestamp 253.701224489R: very low magnitude, setting magnitude to all zeros +timestamp 253.747664399R: very low magnitude, setting magnitude to all zeros +timestamp 253.794104308R: very low magnitude, setting magnitude to all zeros +timestamp 253.840544217R: very low magnitude, setting magnitude to all zeros +timestamp 253.886984126R: very low magnitude, setting magnitude to all zeros +timestamp 253.933424036R: very low magnitude, setting magnitude to all zeros +timestamp 253.979863945R: very low magnitude, setting magnitude to all zeros +timestamp 254.026303854R: very low magnitude, setting magnitude to all zeros +timestamp 254.072743764R: very low magnitude, setting magnitude to all zeros +timestamp 254.119183673R: very low magnitude, setting magnitude to all zeros +timestamp 254.165623582R: very low magnitude, setting magnitude to all zeros +timestamp 254.212063492R: very low magnitude, setting magnitude to all zeros +timestamp 254.258503401R: very low magnitude, setting magnitude to all zeros +timestamp 254.304943310R: very low magnitude, setting magnitude to all zeros +timestamp 254.351383219R: very low magnitude, setting magnitude to all zeros +timestamp 254.397823129R: very low magnitude, setting magnitude to all zeros +timestamp 254.444263038R: very low magnitude, setting magnitude to all zeros +timestamp 254.490702947R: very low magnitude, setting magnitude to all zeros +timestamp 254.537142857R: very low magnitude, setting magnitude to all zeros +timestamp 254.583582766R: very low magnitude, setting magnitude to all zeros +timestamp 254.630022675R: very low magnitude, setting magnitude to all zeros +timestamp 254.676462585R: very low magnitude, setting magnitude to all zeros +timestamp 254.722902494R: very low magnitude, setting magnitude to all zeros +timestamp 254.769342403R: very low magnitude, setting magnitude to all zeros +timestamp 254.815782312R: very low magnitude, setting magnitude to all zeros +timestamp 254.862222222R: very low magnitude, setting magnitude to all zeros +timestamp 254.908662131R: very low magnitude, setting magnitude to all zeros +timestamp 254.955102040R: very low magnitude, setting magnitude to all zeros +timestamp 255.001541950R: very low magnitude, setting magnitude to all zeros +timestamp 255.047981859R: very low magnitude, setting magnitude to all zeros +timestamp 255.094421768R: very low magnitude, setting magnitude to all zeros +timestamp 255.140861678R: very low magnitude, setting magnitude to all zeros +timestamp 255.187301587R: very low magnitude, setting magnitude to all zeros +timestamp 255.233741496R: very low magnitude, setting magnitude to all zeros +timestamp 255.280181405R: very low magnitude, setting magnitude to all zeros +timestamp 255.326621315R: very low magnitude, setting magnitude to all zeros +timestamp 255.373061224R: very low magnitude, setting magnitude to all zeros +timestamp 255.419501133R: very low magnitude, setting magnitude to all zeros +timestamp 255.465941043R: very low magnitude, setting magnitude to all zeros +timestamp 255.512380952R: very low magnitude, setting magnitude to all zeros +timestamp 255.558820861R: very low magnitude, setting magnitude to all zeros +timestamp 255.605260770R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 97%timestamp 255.651700680R: very low magnitude, setting magnitude to all zeros +timestamp 255.698140589R: very low magnitude, setting magnitude to all zeros +timestamp 255.744580498R: very low magnitude, setting magnitude to all zeros +timestamp 255.791020408R: very low magnitude, setting magnitude to all zeros +timestamp 255.837460317R: very low magnitude, setting magnitude to all zeros +timestamp 255.883900226R: very low magnitude, setting magnitude to all zeros +timestamp 255.930340136R: very low magnitude, setting magnitude to all zeros +timestamp 255.976780045R: very low magnitude, setting magnitude to all zeros +timestamp 256.023219954R: very low magnitude, setting magnitude to all zeros +timestamp 256.069659863R: very low magnitude, setting magnitude to all zeros +timestamp 256.116099773R: very low magnitude, setting magnitude to all zeros +timestamp 256.162539682R: very low magnitude, setting magnitude to all zeros +timestamp 256.208979591R: very low magnitude, setting magnitude to all zeros +timestamp 256.255419501R: very low magnitude, setting magnitude to all zeros +timestamp 256.301859410R: very low magnitude, setting magnitude to all zeros +timestamp 256.348299319R: very low magnitude, setting magnitude to all zeros +timestamp 256.394739229R: very low magnitude, setting magnitude to all zeros +timestamp 256.441179138R: very low magnitude, setting magnitude to all zeros +timestamp 256.487619047R: very low magnitude, setting magnitude to all zeros +timestamp 256.534058956R: very low magnitude, setting magnitude to all zeros +timestamp 256.580498866R: very low magnitude, setting magnitude to all zeros +timestamp 256.626938775R: very low magnitude, setting magnitude to all zeros +timestamp 256.673378684R: very low magnitude, setting magnitude to all zeros +timestamp 256.719818594R: very low magnitude, setting magnitude to all zeros +timestamp 256.766258503R: very low magnitude, setting magnitude to all zeros +timestamp 256.812698412R: very low magnitude, setting magnitude to all zeros +timestamp 256.859138321R: very low magnitude, setting magnitude to all zeros +timestamp 256.905578231R: very low magnitude, setting 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258.113015873R: very low magnitude, setting magnitude to all zeros +timestamp 258.159455782R: very low magnitude, setting magnitude to all zeros +timestamp 258.205895691R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 98%timestamp 258.252335600R: very low magnitude, setting magnitude to all zeros +timestamp 258.298775510R: very low magnitude, setting magnitude to all zeros +timestamp 258.345215419R: very low magnitude, setting magnitude to all zeros +timestamp 258.391655328R: very low magnitude, setting magnitude to all zeros +timestamp 258.438095238R: very low magnitude, setting magnitude to all zeros +timestamp 258.484535147R: very low magnitude, setting magnitude to all zeros +timestamp 258.530975056R: very low magnitude, setting magnitude to all zeros +timestamp 258.577414965R: very low magnitude, setting magnitude to all zeros +timestamp 258.623854875R: very low magnitude, setting magnitude to all zeros +timestamp 258.670294784R: very low 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zeros +timestamp 259.877732426R: very low magnitude, setting magnitude to all zeros +timestamp 259.924172335R: very low magnitude, setting magnitude to all zeros +timestamp 259.970612244R: very low magnitude, setting magnitude to all zeros +timestamp 260.017052154R: very low magnitude, setting magnitude to all zeros +timestamp 260.063492063R: very low magnitude, setting magnitude to all zeros +timestamp 260.109931972R: very low magnitude, setting magnitude to all zeros +timestamp 260.156371882R: very low magnitude, setting magnitude to all zeros +timestamp 260.202811791R: very low magnitude, setting magnitude to all zeros +timestamp 260.249251700R: very low magnitude, setting magnitude to all zeros +timestamp 260.295691609R: very low magnitude, setting magnitude to all zeros +timestamp 260.342131519R: very low magnitude, setting magnitude to all zeros +timestamp 260.388571428R: very low magnitude, setting magnitude to all zeros +timestamp 260.435011337R: very low magnitude, setting 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low magnitude, setting magnitude to all zeros +timestamp 262.849886621R: very low magnitude, setting magnitude to all zeros +timestamp 262.896326530R: very low magnitude, setting magnitude to all zeros +timestamp 262.942766439R: very low magnitude, setting magnitude to all zeros +timestamp 262.989206349R: very low magnitude, setting magnitude to all zeros +timestamp 263.035646258R: very low magnitude, setting magnitude to all zeros +timestamp 263.082086167R: very low magnitude, setting magnitude to all zeros +timestamp 263.128526077R: very low magnitude, setting magnitude to all zeros +timestamp 263.174965986R: very low magnitude, setting magnitude to all zeros +timestamp 263.221405895R: very low magnitude, setting magnitude to all zeros +timestamp 263.267845804R: very low magnitude, setting magnitude to all zeros +timestamp 263.314285714R: very low magnitude, setting magnitude to all zeros +timestamp 263.360725623R: very low magnitude, setting magnitude to all zeros +timestamp 263.407165532R: very low magnitude, setting magnitude to all zeros + Extracting and writing features... 100%timestamp 263.453605442R: very low magnitude, setting magnitude to all zeros +[NNLS Chroma Plugin] Tuning Log-Frequency Spectrogram ... done. +[NNLS Chroma Plugin] Performing NNLS and mapping to chroma ... done. +[NNLS Chroma Plugin] Chord Estimation ... done. + Extracting and writing features... Done + + < M A T L A B (R) > + Copyright 1984-2011 The MathWorks, Inc. + R2011b (7.13.0.564) 64-bit (maci64) + August 13, 2011 + + +To get started, type one of these: helpwin, helpdesk, or demo. +For product information, visit www.mathworks.com. + +onset detection function +beats + + Length: 28 Length: 32 Length: 36 Length: 40 Length: 44 Length: 48 Length: 52 Length: 56 Length: 60 Length: 64 Length: 68 Length: 72 Length: 76 Length: 80 Length: 84 Length: 88 Length: 92 Length: 96 Length: 100 Length: 104 Length: 108 Length: 112 Length: 116 Length: 120 Length: 124 Length: 128 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/.svn/all-wcprops Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,107 @@ +K 25 +svn:wc:ra_dav:version-url +V 61 +/svnroot/!svn/ver/7216/matlabtoolboxes/misc/featureextraction +END +chunkspectrogram_ISMIR2010.m +K 25 +svn:wc:ra_dav:version-url +V 90 +/svnroot/!svn/ver/7216/matlabtoolboxes/misc/featureextraction/chunkspectrogram_ISMIR2010.m +END +basschroma13.m +K 25 +svn:wc:ra_dav:version-url +V 76 +/svnroot/!svn/ver/1421/matlabtoolboxes/misc/featureextraction/basschroma13.m +END +readaudiochunk.m +K 25 +svn:wc:ra_dav:version-url +V 78 +/svnroot/!svn/ver/1403/matlabtoolboxes/misc/featureextraction/readaudiochunk.m +END +shadedplot.m +K 25 +svn:wc:ra_dav:version-url +V 74 +/svnroot/!svn/ver/1403/matlabtoolboxes/misc/featureextraction/shadedplot.m +END +mmcq2hpcp.m +K 25 +svn:wc:ra_dav:version-url +V 73 +/svnroot/!svn/ver/1403/matlabtoolboxes/misc/featureextraction/mmcq2hpcp.m +END +synchronisechroma.m~ +K 25 +svn:wc:ra_dav:version-url +V 82 +/svnroot/!svn/ver/5903/matlabtoolboxes/misc/featureextraction/synchronisechroma.m~ +END +myframefft.m~ +K 25 +svn:wc:ra_dav:version-url +V 75 +/svnroot/!svn/ver/5883/matlabtoolboxes/misc/featureextraction/myframefft.m~ +END +synchronisechroma.m +K 25 +svn:wc:ra_dav:version-url +V 81 +/svnroot/!svn/ver/7216/matlabtoolboxes/misc/featureextraction/synchronisechroma.m +END +myframefft.m +K 25 +svn:wc:ra_dav:version-url +V 74 +/svnroot/!svn/ver/5883/matlabtoolboxes/misc/featureextraction/myframefft.m +END +chromamatthias.m +K 25 +svn:wc:ra_dav:version-url +V 78 +/svnroot/!svn/ver/1403/matlabtoolboxes/misc/featureextraction/chromamatthias.m +END +beatchroma.m +K 25 +svn:wc:ra_dav:version-url +V 74 +/svnroot/!svn/ver/1421/matlabtoolboxes/misc/featureextraction/beatchroma.m +END +hpcp2chroma.m +K 25 +svn:wc:ra_dav:version-url +V 75 +/svnroot/!svn/ver/1403/matlabtoolboxes/misc/featureextraction/hpcp2chroma.m +END +bigframefft.m +K 25 +svn:wc:ra_dav:version-url +V 75 +/svnroot/!svn/ver/1403/matlabtoolboxes/misc/featureextraction/bigframefft.m +END +mychroma.m~ +K 25 +svn:wc:ra_dav:version-url +V 73 +/svnroot/!svn/ver/5883/matlabtoolboxes/misc/featureextraction/mychroma.m~ +END +chunkspectrogram.m +K 25 +svn:wc:ra_dav:version-url +V 80 +/svnroot/!svn/ver/7216/matlabtoolboxes/misc/featureextraction/chunkspectrogram.m +END +mychroma.m +K 25 +svn:wc:ra_dav:version-url +V 72 +/svnroot/!svn/ver/1456/matlabtoolboxes/misc/featureextraction/mychroma.m +END +chromagenerator.m +K 25 +svn:wc:ra_dav:version-url +V 79 +/svnroot/!svn/ver/5903/matlabtoolboxes/misc/featureextraction/chromagenerator.m +END
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/.svn/entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,606 @@ +10 + +dir +7329 +https://parrot.dcs.qmul.ac.uk/svnroot/matlabtoolboxes/misc/featureextraction +https://parrot.dcs.qmul.ac.uk/svnroot + + + +2010-03-18T15:36:32.961649Z +7216 +matthiasm + + + + + + + + + + + + + + +214cd2a9-030a-0410-b9a1-da4e402363b6 + +chunkspectrogram_ISMIR2010.m +file + + + + +2010-04-07T10:16:15.000000Z +89293138f75a03b0346ce694a4e4705d +2010-03-18T15:36:32.961649Z +7216 +matthiasm +has-props + + + + + + + + + + + + + + + + + + + + +1055 + +readaudiochunk.m +file + + + + +2010-04-07T10:16:15.000000Z +6fbf766050031cebb16eceb746d89d11 +2009-02-20T16:30:45.085047Z +1403 +matthiasm + + + + + + + + + + + + + + + + + + + + + +473 + +basschroma13.m +file + + + + +2010-04-07T10:16:15.000000Z +d96b8625968bc9eeabf3c0520ed6ebc0 +2009-03-06T16:14:34.602437Z +1421 +matthiasm + + + + + + + + + + + + + + + + + + + + + +157 + +shadedplot.m +file + + + + +2010-04-07T10:16:15.000000Z +ddfcfef326e89837da17cdd32b8499ac +2009-02-20T16:30:45.085047Z +1403 +matthiasm + + + + + + + + + + + + + + + + + + + + + +2124 + +mmcq2hpcp.m +file + + + + +2010-04-07T10:16:15.000000Z +0c1d93851b2bc5bbeb925d1a9878c8ba +2009-02-20T16:30:45.085047Z +1403 +matthiasm + + + + + + + + + + + + + + + + + + + + + +1125 + +synchronisechroma.m~ +file + + + + +2010-04-07T10:16:15.000000Z +c91b2ff658248b0b784ae66cc294db46 +2009-05-07T16:17:08.483462Z +5903 +matthiasm + + + + + + + + + + + + + + + + + + + + + +463 + +synchronisechroma.m +file + + + + +2010-04-07T10:16:15.000000Z +875e110c61fef598065a8b7f81107d98 +2010-03-18T15:36:32.961649Z +7216 +matthiasm + + + + + + + + + + + + + + + + + + + + + +765 + +myframefft.m~ +file + + + + +2010-04-07T10:16:15.000000Z +86c231607c073aa1be71667c09c4c9e4 +2009-05-01T16:56:49.073671Z +5883 +matthiasm + + + + + + + + + + + + + + + + + + + + + +1287 + +myframefft.m +file + + + + +2010-04-07T10:16:15.000000Z +b06c6bfc85fbe7ff537c442920821b92 +2009-05-01T16:56:49.073671Z +5883 +matthiasm + + + + + + + + + + + + + + + + + + + + + +1461 + +chromamatthias.m +file + + + + +2010-04-07T10:16:15.000000Z +eaedef2354cf063123300bef2bd80c59 +2009-02-20T16:30:45.085047Z +1403 +matthiasm + + + + + + + + + + + + + + + + + + + + + +5228 + +beatchroma.m +file + + + + +2010-04-07T10:16:15.000000Z +9d77c0c014c5c2654cd614b3f666a845 +2009-03-06T16:14:34.602437Z +1421 +matthiasm + + + + + + + + + + + + + + + + + + + + + +705 + +hpcp2chroma.m +file + + + + +2010-04-07T10:16:15.000000Z +107d11dff0c8dce973b7efdc3870599a +2009-02-20T16:30:45.085047Z +1403 +matthiasm + + + + + + + + + + + + + + + + + + + + + +1558 + +bigframefft.m +file + + + + +2010-04-07T10:16:15.000000Z +05546bac5869d08d5110e3e91b0eeae4 +2009-02-20T16:30:45.085047Z +1403 +matthiasm + + + + + + + + + + + + + + + + + + + + + +1013 + +mychroma.m~ +file + + + + +2010-04-07T10:16:15.000000Z +0c5cebc9155999ddd1b0927803de3f3f +2009-05-01T16:56:49.073671Z +5883 +matthiasm + + + + + + + + + + + + + + + + + + + + + +5666 + +mychroma.m +file + + + + +2010-04-07T10:16:15.000000Z +5207db104c1e8caee4529631dfb964b0 +2009-04-06T10:13:34.612045Z +1456 +matthiasm + + + + + + + + + + + + + + + + + + + + + +5676 + +chunkspectrogram.m +file + + + + +2010-04-07T10:16:15.000000Z +25be0d0e89228e9a6ec2a30d75a34181 +2010-03-18T15:36:32.961649Z +7216 +matthiasm + + + + + + + + + + + + + + + + + + + + + +1928 + +chromagenerator.m +file + + + + +2010-04-07T10:16:15.000000Z +b8c5b05ddcf0da7f9ac7bada17c55ccf +2009-05-07T16:17:08.483462Z +5903 +matthiasm + + + + + + + + + + + + + + + + + + + + + +7718 +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/.svn/prop-base/chunkspectrogram_ISMIR2010.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +K 14 +svn:executable +V 1 +* +END
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/.svn/text-base/basschroma13.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function bc13 = basschroma13(bc) +bc(isnan(sum(bc,2)),:) = 1; +bc = qnormalise(bc,1,2); +bc13 = [bc,(1./12./max(bc,[],2)).^2]; +bc13 = qnormalise(bc13, inf, 2);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/.svn/text-base/beatchroma.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,25 @@ +function bc = beatchroma(chromag, beats, frame_t) + +% assigning beats to frames +nBeat = length(beats); +nFrame = size(chromag, 2); +beatassign = zeros(1,nFrame); +iBeat = 1; + +for iFrame = 1:nFrame + if frame_t(iFrame) > beats(iBeat) && frame_t(iFrame) < beats(end) + iBeat = iBeat + 1; + end + beatassign(iFrame) = iBeat; +end +% averaging over frames belonging to one beat +fprintf(1,'__ preprocessing chromagram... ___________________________________________\n'); +bc = zeros(nBeat,12); +chromag(:,isnan(sum(chromag))) = 1; + +for iBeat = 1:nBeat + bc(iBeat,:) = median(chromag(:,beatassign == iBeat),2); + if ~any(isnan(bc(iBeat,:))) + bc(iBeat,:) = bc(iBeat,:)/max(bc(iBeat,:)); + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/.svn/text-base/bigframefft.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,61 @@ + +% segment audio data into overlapping frames and take fft of each frame + +function [fftframes,info] = bigframefft(audiodata, info) + +fprintf(1,'framefft\n'); + +info.undertest = [info.undertest '\nfft']; + +% set window length +win = info.windowlength; + + +% Pad audio with zeros so that first analysis frame corresponds with first +% audio sample + +pad = zeros((win/2),1); + +%audiodata = [pad; audiodata; pad]; + +% find size of input +ilength = length(audiodata); + + +%pad audio data +audiodata = [pad; audiodata; pad]; + +% set hop size +hop = fix(win.*info.overlap); + +info.hopsize = hop; + +% set number of windows +num_win = ceil((ilength+1)/hop); + +info.numberframes = num_win; + +start=1; + +fftframes = zeros(win,num_win); + +check = ceil(num_win/10); + + +% do fft analysis for each window: +for i = 1 : num_win; + + segment = audiodata(start:(start + win - 1)); + + fftframes(:,i) = fft(segment, win)'; + + start = start + hop; + + if mod(i,check) == 0 + fprintf(1,'.'); + end + +end; +fprintf(1,'\n'); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/.svn/text-base/chromagenerator.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,212 @@ +function [chroma, t, salience, tuning, URIstring] = chromagenerator(filename, kammerton, nBin) + +%% make URI string +URIstring = strcat('chromagenerator-', num2str(kammerton), '-',num2str(nBin)); + +%% parameters +% set the bins per semitone (i.e. binsperoctave/12) +% kammerton = 440; +% nBin = 7; +midbin = ceil(nBin/2); + +% minimum and maximum MIDI bin, should be n octaves apart. +midimin = 21; % 69 has 440 Hz +% midimin = 45; % 69 has 440 Hz +midimax = 92; +% midimax = 68; +noterange = midimax - midimin + 1; + +% midi note values including the fractions "between" actual MIDI notes +midibins = midimin-(midbin-1)/nBin:(1/nBin):midimax+(midbin-1)/nBin; +% the same in frequencies +fbins = kammerton * 2.^((midibins-69)/12); +nFbin = length(fbins); + +% the sampling frequency everything is (down)sampled to +used_fs = 11025; + +% intuitive setting of how long the frame is (quite short here!) +fracofsecond = 4; + +hopspersecond = 20; + +% calculate a convenient FFT length +nFFT = 2^nextpow2(used_fs/fracofsecond); +% hopsize in samples +hopsize = used_fs/hopspersecond/nFFT; + +% "instrument" means a particular spectral shape +used_instruments = 1:2; +nInstrument = length(used_instruments); + +%% generate dictionary +% (complex) tones for all fundamental frequencies are generated. then their +% amplitude spectrum is calculated and normalised according to the L2 norm. + +% preliminary t for dictionary generation +t = (1:nFFT)/used_fs; +% initialise note dictionary +A = zeros(nFbin,nFFT/2,nInstrument); + +% loop over all instruments +for iInstrument = used_instruments + % loop over all fundamental frequencies + for iFbin = 1:nFbin; + f0 = fbins(iFbin); + switch iInstrument + case 1 + wave = sum([sin(t*2*pi*f0*1); ... + 0.9^1 * sin(t*2*pi*f0*2); ... + 0.9^2 * sin(t*2*pi*f0*3); ... + 0.9^3 * sin(t*2*pi*f0*4); ... + ...0.9^4 * sin(t*2*pi*f0*5); ... + ]); + case 2 + wave = sin(t*2*pi*f0*1); + end + % window wave and fft + wave = hamming(nFFT)' .* wave; + fullfft = fft(wave); + % calculate amplitude spectrum from fft + A(iFbin,:,iInstrument) = abs(fullfft(1:round(nFFT/2))); + end + % normalise (L2 norm) + A(:,:,iInstrument) = qnormalise(A(:,:,iInstrument),2,2); +end + +%% get the fft frames from a wave file +%this is probably ending up not being used in a vamp plugin +% fprintf(1,'%s\n',filename) +% [audiosize,fs] = wavread(filename,'size'); +% chunk_sec = 20; +% nChunk = ceil(audiosize(1)/fs/chunk_sec); +% songframes = []; +% start = 1; +% % extract fft from chunks (for less memory consumption) +% for iChunk = 1:nChunk +% samplemin = (iChunk-1) * chunk_sec * fs + 1; +% samplemax = min(audiosize(1), (iChunk * chunk_sec + 1) * fs); +% audiodata0 = wavread(filename,[samplemin samplemax]); +% audiodata = resample(mean(audiodata0,2),used_fs,fs,20); +% songframes0 = myframefft(audiodata,nFFT,hopsize,'hamming'); +% if size(songframes0,2)>=start +% songframes = [songframes(:,1:end-start) abs(songframes0(1:round(nFFT/2),start:end))]; +% end +% start = round(hopspersecond/2)+1; +% end +% clear songframes0 +% clear audiodata +% clear audiodata0 +% t = (0:T-1)./hopspersecond; + +%% +[songframes,f,t]=chunkspectrogram(filename,4096,4096*7/8,4096,inf); +hopspersecond = 1/(t(2)-t(1)); +songframes = abs(songframes); +%% +T = size(songframes,2); + +fprintf(1, '%d different notes, %d frequency bins, %d time frames\n', noterange,nFFT/2, T) + +notespectrum0 = zeros(nFbin, T, nInstrument); +songframes = qnormalise(songframes,2,1); +songframes(isnan(songframes)) = 0; + +%% calculate cosine distance +for iInstrument = 1:nInstrument + for iTone = 1:nFbin + notespectrum0(iTone,:,iInstrument) = A(iTone,:,iInstrument) * songframes; + end +end +%% get only the bins with "more than 1 st. deviation" +notespectrum = zeros(size(notespectrum0)); +wind_size = 6 * nBin; +wind = hamming(wind_size)/sum(hamming(wind_size)); +stdthresh = 1; +for iInstrument = used_instruments + % calculate running mean + m = conv2(notespectrum0(:,:,iInstrument),wind(:),'same'); + % calculate running standard deviation + stdev = sqrt(conv2((notespectrum0(:,:,iInstrument)-m).^2,wind(:),'same')); + % take only those tones that are above 1 standard deviation from the + % mean + notespectrum(:,:,iInstrument) = (notespectrum0(:,:,iInstrument)-m)./stdev; + temp = notespectrum(:,:,iInstrument); + temp(temp<stdthresh) = 0; + notespectrum(:,:,iInstrument) = temp; +end +% notes that are one were not "good enough" (not greater than 1 st. +% deviation reater than the mean) and are hence set to 0. +notespectrum(notespectrum<=stdthresh) = 0; +% we require that the notes be on in *both* "instruments". hence the final +% note spectrum note_s is their product, which works a bit like a minimum +note_s = notespectrum(:,:,1) .* notespectrum(:,:,2); + +note_s(1:nBin*6,:) = 0; +note_s(isnan(note_s)) = 0; +%% tuning +% I wrap the note spectrum to one semitone, kind of generating the note +% spectrum modulo one semitone. one gets a matrix of size nBin x T. from +% that matrix, the tuning can be calculated by extracting the angle of the +% sum of all columns, the sum of all columns being a nBin-dimensional +% vector. +th = tuner(note_s(36*nBin+(1:12*nBin),:), nBin) +% th = tuner(notespectrum0(36*nBin+(1:12*nBin),:,2), nBin) +tuning = kammerton*2^((th/2/pi)/12) +% tune the spectrum by interpolating the bins such that now the middle bin +% of every semitone corresponds to the right frequency in equal +% temperament +tuned_s = zeros(size(note_s)); +for iFrame = 1:T + tuned_s(:,iFrame) = interp1(1:nFbin, note_s(:,iFrame), (1:nFbin) + th / (2 * pi) * nBin,[],0); +end + +%% reduce to 1 bin per semitone +% just sum the nBin bins that correspond to one semitone. +wrapped_tuned_s = reshape(tuned_s,[nBin,noterange*T]); +% if you want to emphasize the middle bin, use a fancy window, otherwise +% just use the rectangular window +wei = rectwin(nBin)'; +salience.reduced = reshape(wei * wrapped_tuned_s,noterange,T); +% median filter in time direction, if you like +salience.reduced = medfilt1(salience.reduced,floor(hopspersecond/5)-1,[],2); + +%% make chromagram +% actually the edge of the note spectrum is a bit noisy (due to the +% convolution three parts above. that's why I start some bins from the +% lower edge (i.e. midimin_used). maybe one could do this properly +% sometime. +midimin_used = midimin+6; +% uebergang is where bass and treble chroma overlap equally +uebergang = 50; + +% some simple linear functions to generate the treble and bass profiles +leftfunc = zeros(2,noterange); +rightfunc = zeros(2,noterange); +leftfunc(1,:) = 2/12*((midimin:midimax)-midimin_used); +leftfunc(2,:) = 1/12*((midimin:midimax)-uebergang) + 0.5; +rightfunc(1,:) = -1/12*((midimin:midimax)-uebergang) + 0.5; +rightfunc(2,:) = -(.5)/12*((midimin:midimax)-midimax); + +% initialise stuff +salience.bass = zeros(size(salience.reduced)); +salience.treble = zeros(size(salience.reduced)); +profiles = zeros(noterange,3); + +% bass and treble profiles +for i = 1:2 + profiles(:,i) = max(0,min(1,min(leftfunc(i,:),rightfunc(i,:))))'; +end +% wide profile (i.e. both bass and treble) +profiles(:,3) = min(1,sum(profiles(:,1:2),2)); + +% calculate specific saliences (windowed in f0 direction) +salience.bass = salience.reduced .* profiles(:,ones(1,T)); +salience.treble = salience.reduced .* profiles(:,2 * ones(1,T)); +salience.wide = salience.reduced .* profiles(:,3 * ones(1,T)); + +% calculate chromagrams +chroma.bass = squeeze(max(reshape(salience.bass,[12,6,T]),[],2)); +chroma.treble = squeeze(max(reshape(salience.treble,[12,6,T]),[],2)); +chroma.wide = squeeze(max(reshape(salience.wide,[12,6,T]),[],2)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/.svn/text-base/chromamatthias.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,138 @@ +function [results, info] = chromamatthias(filename, prod, maxx) + +%--------------------------------------------------------------- +% Part of the matthiasmISMIR08 project +% Written by Christopher Harte, modified by Matthias Mauch +% @ Queen Mary, University of London +% 2008 +%--------------------------------------------------------------- + + +% Set up info structure (initialise variables to 0 if we don't have values +% now... + +info = {}; +%% +info.filename = filename; +% info.filename = '/Volumes/Music/Music/Beatles/12_-_Let_It_Be/wav/06_-_Let_It_Be.wav'; +% info.filename = '/Volumes/Music/Music/Beatles/unrevised/01wave/01_-_I_Saw_Her_Standing_There.wav'; +info.undertest = ''; % which algorithms are run + +% Signal Parameters: +info.fs = 44100; % Sampling freq of audio file +info.downsample = 4; % downsampling factor +info.fsdownsampled = ... + info.fs / info.downsample; % fs after downsampling +info.preprocessing = 'downsample'; % What kind of preprocessing? +info.numberframes = 0; % number of frames calculated +info.windowlength = 0; % window length in samples +info.overlap = ... + 1/8; % window overlap factor +info.overl = ... + 1 - info.overlap; +info.hopsize = 0; % window overlap in samples +info.framespersecond = 0; % effective frames per second + +% Constant Q parameters: +info.binsperoctave = 36; % bins per octave for const q +info.fmax = 1760; % top frequency for const q +info.fmin = 110; % bottom frequency for const q +info.numcqbins = 0; % number of bins in a Constant Q vector +info.sparkernelfile = ''; % mat file containing sparse spectral kernels matrix +info.sparsethreshold = 0.0054; % sparsekernel threshold +info.maxx = maxx; +% Get constant Q parameters - need to do this first to set window length +info = getcqparameters(info); + +% Prepare chunking +info.chunkoverlap = 2 * 1/info.overlap; % frames +info.audiofilesize = wavread(info.filename,'size'); +info.chunksize = 500; % frames +info.samplechunksize = (info.chunksize - 1) * ... + info.hopsize * info.downsample; % chunk length in original samples +info.samplechunkhopsize = (info.chunksize - 1 - info.chunkoverlap) * ... + info.hopsize * info.downsample; +info.framespersecond = info.fsdownsampled/info.hopsize; +%% +nChunk = ceil(info.audiofilesize / info.samplechunkhopsize); +results = {}; +iChunk = 0; +for iChunk = 1:nChunk +% + info.startsample = (iChunk - 1) * info.samplechunkhopsize + 1; + info.endsample = min(... + info.startsample + info.samplechunksize -1, ... + info.audiofilesize(1)); +% + [audiodata, info] = readaudiochunk(info); + + % Perform downsampling here if required + + [audiodata, info] = preprocessing(audiodata,info); + + % Perform FFT + [fftframes,info] = bigframefft(audiodata,info); + + % Harmonics adjustment + if prod + [fftframes] = prodfft(fftframes, 3); + end + + + % Perform Constant Q + [constqframes, info] = constq(fftframes,info); + + % Smooth constqframes for transient removal (time direction) + constqframes = medfilt1(constqframes,round(info.framespersecond/2),[],2); + + % 'Normalise' Constant Q + % constqframes = constqframes ./ repmat(sum(abs(constqframes),1),size(constqframes,1),1); + + % fftframes = 10 * log10(abs(fftframes).^2); + + % Calculate HPCP + [hpcpframes,info] = hpcp(constqframes,info); + + % Peakpick HPCP + % [peakpickframes,info] = framepeakpick(hpcpframes,info); + + + % Calculate Tuning + % [tuningframes,info] = tuning(peakpickframes,info); + + % Calculate slightly adjusted chromagram + [chromaframes, info] = tunebyshift(hpcpframes, info); + % figure, + % imagesc(info.t,[],(chromaframes')), axis xy + + % put results in output structure + if iChunk == 1 +% results.fftframes = fftframes; +% results.constqframes = constqframes; +% results.hpcpframes = hpcpframes; + results.chromaframes = chromaframes; + else +% results.fftframes = ... +% [results.fftframes(:,1:end-info.frameoverlap),... +% results.fftframes(:,end-info.frameoverlap+1:end) ... +% + fftframes(:,1:info.frameoverlap),... +% fftframes(:,info.frameoverlap+1:end)]; +% results.constqframes = ... +% [results.constqframes(:,1:end-info.frameoverlap),... +% results.constqframes(:,end-info.frameoverlap+1:end) ... +% + constqframes(:,1:info.frameoverlap),... +% constqframes(:,info.frameoverlap+1:end)]; +% results.hpcpframes = ... +% [results.hpcpframes(1:end-info.frameoverlap,:);... +% results.hpcpframes(end-info.frameoverlap+1:end,:) ... +% + hpcpframes(1:info.frameoverlap,:);... +% hpcpframes(info.frameoverlap+1:end,:)]; + x = round(info.chunkoverlap/2); +% results.chromaframes = chromaframes; + results.chromaframes = ... + [results.chromaframes(1:end-info.chunkoverlap+x,:);... + chromaframes(2+x:end,:)]; + end +end +info.numberframes = size(results.chromaframes,1); +info.t = ((0:info.numberframes-1) * info.hopsize) / info.fsdownsampled; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/.svn/text-base/chunkspectrogram.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,70 @@ +function [s,f,t] = chunkspectrogram(filename,d_windo,d_nOverlap,d_nFFT, maxf) +%% +% d_ are the downsampled things +% o_ are the original things + + +d_fs = 11025; + +[temp, o_fs] = wavread(filename,'size'); + +if mod(o_fs/d_fs,1) ~= 0 + error('The sample frequency of the wave file has to be an integer multiple of %1.0f.',d_fs); +end + +o_nSample = temp(1); +d_nSample = floor(o_nSample*d_fs/o_fs); + +o_nOverlap = d_nOverlap * o_fs/d_fs; + + +d_nSamplePerChunk = d_windo+200*(d_windo-d_nOverlap); + +if d_nSample < d_nSamplePerChunk + d_nSamplePerChunk = d_nSample; +end + +d_endsample = d_nSamplePerChunk:d_nSamplePerChunk:d_nSample; + +o_startsample = [1, d_endsample * o_fs / d_fs + 1]; +o_endsample = d_endsample * o_fs / d_fs; +o_endsample = min(o_endsample + o_fs / d_fs * d_nOverlap, o_nSample); % extend so that chunks overlap + +% in most cases the file length is not exactly a multiple of +if o_endsample(end) < o_nSample + o_endsample = [o_endsample o_nSample]; +end + +%% + +nChunk = length(o_startsample); + +for iChunk = 1:nChunk + fprintf(1,'_'); +end +leftpad = zeros(d_windo/2,1); +fprintf(1,'\n.') +x = resample(mean(wavread(filename,[o_startsample(1) o_endsample(1)]),2),d_fs,o_fs); +s = spectrogram([leftpad; x], d_windo,d_nOverlap,d_nFFT, d_fs); +f = (0:d_nFFT/2-1)/d_nFFT*d_fs; +if maxf < max(f) + maxbin = find(f>maxf,1)-1; +else + maxbin = length(f); +end +s = s(1:maxbin,:); +f = f(1:maxbin); +for iChunk = 2:nChunk-1 + fprintf(1,'.') + temps = spectrogram(resample(mean(wavread(filename,[o_startsample(iChunk) o_endsample(iChunk)]),2),d_fs,o_fs), ... + d_windo,d_nOverlap,d_nFFT, d_fs); + s = [s(1:maxbin,:),temps(1:maxbin,:)]; +end +x = resample(mean(wavread(filename,[o_startsample(nChunk) o_nSample]),2),d_fs,o_fs); +if length(x)>d_windo + temps = spectrogram(x, ... + d_windo,d_nOverlap,d_nFFT, d_fs); + s = [s(1:maxbin,:),temps(1:maxbin,:)]; +end +fprintf(1,'.\n') +t = (0:size(s,2)-1) * (d_windo-d_nOverlap)/d_fs;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/.svn/text-base/chunkspectrogram_ISMIR2010.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,45 @@ +function [s,f,t] = chunkspectrogram_ISMIR2010(filename,d_windo,d_nOverlap,d_nFFT, maxf) +%% +% d_ are the downsampled things +% o_ are the original things + + +d_fs = 11025; + +[filesize, o_fs] = wavread(filename,'size'); + + + +if mod(o_fs/d_fs,1) ~= 0 + error('The sample frequency of the wave file has to be an integer multiple of %1.0f.',d_fs); +end +downsample = o_fs/d_fs; + +o_windo = d_windo * downsample; +o_nOverlap = d_nOverlap * downsample; +o_hopSize = o_windo-o_nOverlap; +nFrame = floor((filesize(1) - o_windo) / o_hopSize); + +s = zeros(d_nFFT/2, nFrame); +f = (0:d_nFFT/2-1)/d_fs; +t = zeros(nFrame,1); + +wind = hamming(d_windo); +wind = wind/sum(wind); + +for iLine = 1:round(nFrame/200) + fprintf(1,'_'); +end +fprintf(1,'\n'); + +for iFrame = 1:nFrame + if mod(iFrame,200) == 0 + fprintf(1,'.'); + end + ind = [1,o_windo] + (iFrame-1) * (o_hopSize); + wave = sum(wavread(filename, ind),2) / 2; + temp = fft(resample(wave,d_fs,o_fs) .* wind); + s(:, iFrame) = temp(1:d_nFFT/2); + t(iFrame) = (ind(1) + ind(2)) / 2 / o_fs; +end +fprintf(1,'\n');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/.svn/text-base/hpcp2chroma.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,53 @@ +function [chromaframes, info] = hpcp2chroma(hpcpframes, info) + +% calculate the best shift +% (0 - none, 1 - one third of semitone down, +% 2 - two thirds of semitone down) + +bps = info.binsperoctave/12; + +indices = (0:11) * bps; + + +%%%%% NANs! $%%%%%%%%%%%% +% nanrows = isnan(sum(hpcpframes,2)); +% binweights = sum(hpcpframes(~nanrows,:)); +% condensedweights = zeros(bps,12); +% for iBin = 1:bps +% condensedweights(iBin,1:12) = sum(binweights(indices+iBin)); +% end +% % [maximum, maxind] = max(... +% % [sum(binweights(indices+1)),... +% % sum(binweights(indices+2)),... +% % sum(binweights(indices+3))]); +% [maximum, maxind] = max(sum(condensedweights,2)); +% shiftposs = [1 0 -1]; +% shift = shiftposs(maxind); +% if bps ==3 +% hpcpframes = circshift(hpcpframes',shift)'; +% info.tuning6th = shift; +% end +maxind = 1; + +% fprintf(1,['(max. energy at ' num2str(maxind) ', shift by ' num2str(shift) ')\n\n']) +if (~info.maxx) + if bps == 3 + chromaframes = ... + 0 * hpcpframes(:,indices+1) + ... + 1 * hpcpframes(:,indices+2) + ... + 0 * hpcpframes(:,indices+3); + elseif bps == 2 + chromaframes = ... + (1-shift)* hpcpframes(:,indices+1) + ... + shift* hpcpframes(:,indices+2); + elseif bps == 1 + chromaframes = hpcpframes; + end +else + chromaframes = zeros(size(hpcpframes,1),12); + for iChroma = 1:12 + chromaframes(:,iChroma) = max(hpcpframes(:,indices(iChroma)+(1:3)),[],2); + end +end + +chromaframes = chromaframes ./ repmat(sum(chromaframes,2),1,12); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/.svn/text-base/mmcq2hpcp.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,48 @@ + + + + +% CQ2HPCP Convert Constant Q spectrum to a HPCP +% +% CQ2HPCP(cqframes, info) +% +% cqframes = vector or matrix constant q spectrum / spectra +% info = info structure containing parameters +% + +function [hpcpframes] = mmcq2hpcp(cqframes, info) + +%get cq parameters from info structure +num_win = info.numberframes; + +cqbins = size(cqframes,1); + +bpo = info.binsperoctave; + +% calculate the number of octaves we have in the spectrum +num_oct = floor(cqbins/bpo)-1; + +hpcpframes(1:num_win, 1:bpo) = 0; + + + +% for each window calculate chromagram +for i = 1 : num_win + % add each octave of the spectrum into the chromagram + for oct = 0:num_oct; + ind = oct*bpo; + + hpcpframes(i, 1:bpo) = hpcpframes(i, 1:bpo) + abs(cqframes(ind+1:ind+bpo,i))'; + + end; + + % normalise +% hpcpframes(i,1:bpo) = hpcpframes(i,1:bpo)/max(hpcpframes(i,1:bpo)); + meanc = mean(hpcpframes(i,1:bpo)); + if meanc > 0.0000001 + hpcpframes(i,1:bpo) = hpcpframes(i,1:bpo)/mean(hpcpframes(i,1:bpo)); %scale to be a distribution! + else + hpcpframes(i,1:bpo) = (bpo)^(-1); + end +end; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/.svn/text-base/mychroma.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,170 @@ +function [chromagram,t,salience] = ... + mychroma(filename, nBins, fmin, used_fs, fracofsecond, calcu) + +if isempty(nBins) + nBins = 3; +end +midbin = ceil(nBins/2); +if isempty(fmin) + fmin = 55 * 2^(-7/12-(midbin-1)/12/nBins); +end + +if isempty(used_fs) + used_fs=round(44100/4); +end + +hopspersecond = 20; +nFFT = 2^nextpow2(used_fs/fracofsecond); +hopsize = used_fs/hopspersecond/nFFT; +noterange = 61; +used_instruments = 1:2; +nInstrument = length(used_instruments); + +%% load or generate dictionary +dirc = what('./data'); +dicfilename = ['mydict-' num2str(nBins) '.mat']; +if ~calcu && any(strcmp(dirc.mat, dicfilename)) + load(['data/' dicfilename]) +else + % dictionary parameters + midbin = nBins/2 + .5; + %hopspersecond = 10; + secs = .8; + t = linspace(0,secs,secs*used_fs)'; + + % make note dictionary + A = zeros(noterange*nBins,nFFT/2,nInstrument); + for iInstrument = used_instruments + for iMidiNote = 1:noterange + for iBin = 1:nBins + f0 = fmin * 2^((iMidiNote-1)/12 + (iBin-1)/12/nBins); + %wave = zeros(size(t)); + switch iInstrument + case 1 + wave = sum([sin(t*2*pi*f0*1) ... + 0.6^1 * sin(t*2*pi*f0*2) ... + 0.6^2 * sin(t*2*pi*f0*3) ... + 0.6^3 * sin(t*2*pi*f0*4)],2); + case 2 + wave = sin(t*2*pi*f0*1); + case 3 + wave = sum([... + 0.6^1 * sin(t*2*pi*f0*1/2) ... + 0.6^2 * sin(t*2*pi*f0*1/3) ... + 0.6^3 * sin(t*2*pi*f0*1/4) ... + 0.6^4 * sin(t*2*pi*f0*1/5) ... + 0.6^5 * sin(t*2*pi*f0*1/6) ... + 0.6^6 * sin(t*2*pi*f0*1/7) ... + 0.6^7 * sin(t*2*pi*f0*1/8) ... + 0.6^8 * sin(t*2*pi*f0*1/9)],2); + end + fftframes0 = myframefft(wave,nFFT,hopsize,'hamming'); + A(nBins*(iMidiNote-1)+iBin,:,iInstrument) = mean(abs(fftframes0(1:round(nFFT/2),:)),2); + end + end + end + save(['data/' dicfilename], 'A', 'nFFT', 'hopsize'); +end + +%% get the fft frames from a wave file +fprintf(1,'%s\n',filename) +[audiosize,fs] = wavread(filename,'size'); +chunk_sec = 20; +nChunk = ceil(audiosize(1)/fs/chunk_sec); +songframes = []; +s = []; +start = 1; + +for iChunk = 1:nChunk + samplemin = (iChunk-1) * chunk_sec * fs + 1; + samplemax = min(audiosize(1), (iChunk * chunk_sec + 1) * fs); + + audiodata0 = wavread(filename,[samplemin samplemax]); + + audiodata = resample(mean(audiodata0,2),used_fs,fs,20); + + songframes0 = myframefft(audiodata,nFFT,hopsize,'hamming'); + + if size(songframes0,2)>=start + songframes = [songframes(:,1:end-start) abs(songframes0(1:round(nFFT/2),start:end))]; + end + start = round(hopspersecond/2)+1; +end +clear songframes0 +clear audiodata +clear audiodata0 + +T = size(songframes,2); +t = (0:T-1)./hopspersecond; +fprintf(1, '%d different notes, %d frequency bins, %d time frames\n', noterange,nFFT/2, T) + +simpleS = zeros(noterange*nBins,T, nInstrument+1); +songframes(isnan(songframes)) = 0; + +for iInstrument = 1:nInstrument + At = A(:,:,iInstrument); + for kFrame = 1:T + simpleS(:,kFrame,iInstrument) = At * songframes(:,kFrame); + end +end +simpleS(:,:,2) = max(0,conv2(simpleS(:,:,2), ... + repmat([-1 -1 4 -1 -1]',1,1),'same')); + +%% add the instruments (i.e. the different partial decay bla) +%--------------------------------------------------------- +note_s = (simpleS(:,:,1) .* simpleS(:,:,2)); +clear simpleS + +%% tuning + +wrapped_s = zeros(nBins,T); +for iBin = 1:nBins + wrapped_s(iBin,:) = sum(note_s(iBin:nBins:end,:)); +end +on_circle = exp(i * ((1:nBins)-midbin)/nBins * 2 * pi); +complex_tuning = on_circle * wrapped_s; +smooth_complex_tuning = conv2(complex_tuning,hamming(20*hopspersecond)','same'); +tooflat = angle(mean(smooth_complex_tuning)) / (2 * pi) * nBins; +tooflat = repmat(tooflat,T,1); +tuned_s = note_s; +for iFrame = 1:T + tuned_s(:,iFrame) = interp1(1:nBins*noterange, note_s(:,iFrame), (1:nBins*noterange) + 1 * tooflat(iFrame),[],0); +end + +%% reduce to 1 bin per semitone +wrapped_tuned_s = reshape(tuned_s,[nBins,noterange*T]); +wei = rectwin(nBins)'; +reduced = reshape(wei * wrapped_tuned_s,noterange,T); + +salience.full = s; +salience.reduced = reduced; + +%% make chromagram + +reduced = medfilt1(reduced',9)'; +chromagen = [zeros(12,1) repmat(eye(12),1,5)]; + + +treble_profile = ([0 zeros(1,24) linspace(0,1,6) linspace(1,1,6) ones(1,12) linspace(1,0,6) zeros(1,6)]); +bass_profile = [0 ones(1,18) linspace(1,0,12) ,zeros(1,noterange-31)]; +wide_profile = max(treble_profile, bass_profile); + +treble_salience = reduced .* repmat(treble_profile(:),1,T); +bass_salience = reduced .* repmat(bass_profile(:),1,T); +wide_salience = reduced .* repmat(wide_profile(:),1,T); + +bass_chromag = circshift(chromagen * bass_salience,6); +wide_chromag = circshift(chromagen * wide_salience,6); + +[peakindex,peakindex]=sort(treble_salience,'descend'); +for iFrame = 1:T + treble_salience(peakindex(1,iFrame),iFrame) = treble_salience(peakindex(2,iFrame),iFrame); +end +treble_chromag = circshift(chromagen * treble_salience,6); + +chromagram.treble = treble_chromag; +chromagram.bass = bass_chromag; +chromagram.wide = wide_chromag; + +% csvwrite('chr.csv',[ones(25,1) [maxn(circshift(bass_chromag,4));ones(1,size(treble_chromag,2)); maxn(circshift(treble_chromag,4))]]'); +% csvwrite('tuned_s.csv',[maxn(reduced)]');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/.svn/text-base/mychroma.m~.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,170 @@ +function [chromagram,t,salience] = ... + mychroma(filename, nBins, fmin, used_fs, fracofsecond, calcu) + +if isempty(nBins) + nBins = 3; +end +midbin = ceil(nBins/2); +if isempty(fmin) + fmin = 55 * 2^(-7/12-(midbin-1)/12/nBins); +end + +if isempty(used_fs) + used_fs=round(44100/4); +end + +hopspersecond = 20; +nFFT = 2^nextpow2(used_fs/fracofsecond); +hopsize = used_fs/hopspersecond/nFFT; +noterange = 61; +used_instruments = 1:2; +nInstrument = length(used_instruments); + +%% load or generate dictionary +dirc = what('./data'); +dicfilename = ['mydict-' num2str(nBins) '.mat']; +if ~calcu && any(strcmp(dirc.mat, dicfilename)) + load(['data/' dicfilename]) +else + % dictionary parameters + midbin = nBins/2 + .5; + %hopspersecond = 10; + secs = .8; + t = linspace(0,secs,secs*used_fs)'; + + % make note dictionary + A = zeros(noterange*nBins,nFFT/2,nInstrument); + for iInstrument = used_instruments + for iMidiNote = 1:noterange + for iBin = 1:nBins + f0 = fmin * 2^((iMidiNote-1)/12 + (iBin-1)/12/nBins); + %wave = zeros(size(t)); + switch iInstrument + case 1 + wave = sum([sin(t*2*pi*f0*1) ... + 0.6^1 * sin(t*2*pi*f0*2) ... + 0.6^2 * sin(t*2*pi*f0*3) ... + 0.6^3 * sin(t*2*pi*f0*4)],2); + case 2 + wave = sin(t*2*pi*f0*1); + case 3 + wave = sum([... + 0.6^1 * sin(t*2*pi*f0*1/2) ... + 0.6^2 * sin(t*2*pi*f0*1/3) ... + 0.6^3 * sin(t*2*pi*f0*1/4) ... + 0.6^4 * sin(t*2*pi*f0*1/5) ... + 0.6^5 * sin(t*2*pi*f0*1/6) ... + 0.6^6 * sin(t*2*pi*f0*1/7) ... + 0.6^7 * sin(t*2*pi*f0*1/8) ... + 0.6^8 * sin(t*2*pi*f0*1/9)],2); + end + fftframes0 = myframefft(wave,nFFT,hopsize,'hamming'); + A(nBins*(iMidiNote-1)+iBin,:,iInstrument) = mean(abs(fftframes0(1:round(nFFT/2),:)),2); + end + end + end + save(dicfilename, 'A', 'nFFT', 'hopsize'); +end + +%% get the fft frames from a wave file +fprintf(1,'%s\n',filename) +[audiosize,fs] = wavread(filename,'size'); +chunk_sec = 20; +nChunk = ceil(audiosize(1)/fs/chunk_sec); +songframes = []; +s = []; +start = 1; + +for iChunk = 1:nChunk + samplemin = (iChunk-1) * chunk_sec * fs + 1; + samplemax = min(audiosize(1), (iChunk * chunk_sec + 1) * fs); + + audiodata0 = wavread(filename,[samplemin samplemax]); + + audiodata = resample(mean(audiodata0,2),used_fs,fs,20); + + songframes0 = myframefft(audiodata,nFFT,hopsize,'hamming'); + + if size(songframes0,2)>=start + songframes = [songframes(:,1:end-start) abs(songframes0(1:round(nFFT/2),start:end))]; + end + start = round(hopspersecond/2)+1; +end +clear songframes0 +clear audiodata +clear audiodata0 + +T = size(songframes,2); +t = (0:T-1)./hopspersecond; +fprintf(1, '%d different notes, %d frequency bins, %d time frames\n', noterange,nFFT/2, T) + +simpleS = zeros(noterange*nBins,T, nInstrument+1); +songframes(isnan(songframes)) = 0; + +for iInstrument = 1:nInstrument + At = A(:,:,iInstrument); + for kFrame = 1:T + simpleS(:,kFrame,iInstrument) = At * songframes(:,kFrame); + end +end +simpleS(:,:,2) = max(0,conv2(simpleS(:,:,2), ... + repmat([-1 -1 4 -1 -1]',1,1),'same')); + +%% add the instruments (i.e. the different partial decay bla) +%--------------------------------------------------------- +note_s = (simpleS(:,:,1) .* simpleS(:,:,2)); +clear simpleS + +%% tuning + +wrapped_s = zeros(nBins,T); +for iBin = 1:nBins + wrapped_s(iBin,:) = sum(note_s(iBin:nBins:end,:)); +end +on_circle = exp(i * ((1:nBins)-midbin)/nBins * 2 * pi); +complex_tuning = on_circle * wrapped_s; +smooth_complex_tuning = conv2(complex_tuning,hamming(20*hopspersecond)','same'); +tooflat = angle(mean(smooth_complex_tuning)) / (2 * pi) * nBins; +tooflat = repmat(tooflat,T,1); +tuned_s = note_s; +for iFrame = 1:T + tuned_s(:,iFrame) = interp1(1:nBins*noterange, note_s(:,iFrame), (1:nBins*noterange) + 1 * tooflat(iFrame),[],0); +end + +%% reduce to 1 bin per semitone +wrapped_tuned_s = reshape(tuned_s,[nBins,noterange*T]); +wei = rectwin(nBins)'; +reduced = reshape(wei * wrapped_tuned_s,noterange,T); + +salience.full = s; +salience.reduced = reduced; + +%% make chromagram + +reduced = medfilt1(reduced',9)'; +chromagen = [zeros(12,1) repmat(eye(12),1,5)]; + + +treble_profile = ([0 zeros(1,24) linspace(0,1,6) linspace(1,1,6) ones(1,12) linspace(1,0,6) zeros(1,6)]); +bass_profile = [0 ones(1,18) linspace(1,0,12) ,zeros(1,noterange-31)]; +wide_profile = max(treble_profile, bass_profile); + +treble_salience = reduced .* repmat(treble_profile(:),1,T); +bass_salience = reduced .* repmat(bass_profile(:),1,T); +wide_salience = reduced .* repmat(wide_profile(:),1,T); + +bass_chromag = circshift(chromagen * bass_salience,6); +wide_chromag = circshift(chromagen * wide_salience,6); + +[peakindex,peakindex]=sort(treble_salience,'descend'); +for iFrame = 1:T + treble_salience(peakindex(1,iFrame),iFrame) = treble_salience(peakindex(2,iFrame),iFrame); +end +treble_chromag = circshift(chromagen * treble_salience,6); + +chromagram.treble = treble_chromag; +chromagram.bass = bass_chromag; +chromagram.wide = wide_chromag; + +% csvwrite('chr.csv',[ones(25,1) [maxn(circshift(bass_chromag,4));ones(1,size(treble_chromag,2)); maxn(circshift(treble_chromag,4))]]'); +% csvwrite('tuned_s.csv',[maxn(reduced)]');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/.svn/text-base/myframefft.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,76 @@ + +% segment audio data into overlapping frames and take fft of each frame + +function fftframes = myframefft(audiodata, winlength, overlap, windowkind, varargin) + +if nargin > 4 + nPad = varargin{1}; + zeropadding = true; +else + zeropadding = false; +end + +fprintf(1,''); + +win = winlength; + +% Pad audio with zeros so that first analysis frame corresponds with first +% audio sample + +pad = zeros((win/2),1); + +%audiodata = [pad; audiodata; pad]; + +% find size of input +ilength = length(audiodata); + + +%pad audio data +audiodata = [pad; audiodata; pad]; + +% set hop size +hop = fix(win.*overlap); + +% set number of windows +num_win = ceil((ilength+1)/win/overlap); + +start=1; +if ~zeropadding + fftframes = zeros(win,num_win); +else + fftframes = zeros(win*(nPad+1),num_win); +end + +check = ceil(num_win/10); + +switch windowkind + case 'hamming' + window = hamming(win); + case 'rect' + window = rectwin(win); + case 'kaiser' + window = kaiser(win); +end + + +% do fft analysis for each window: +for i = 1 : num_win; + start = floor(win * overlap * (i-1) + 1); + if ~zeropadding + segment = window .* audiodata(start:(start + win - 1)); + fftframes(:,i) = fft(segment, win)'; + else + segment = window .* audiodata(start:(start + win - 1)); + fftframes(:,i) = fft(segment, win * (nPad+1))'; + end + +% start = start + hop + + if mod(i,check) == 0 + fprintf(1,'.'); + end + +end; +fprintf(1,'\n'); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/.svn/text-base/myframefft.m~.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,68 @@ + +% segment audio data into overlapping frames and take fft of each frame + +function fftframes = myframefft(audiodata, winlength, overlap, windowkind, varargin) + +if nargin > 4 + nPad = varargin{1}; + zeropadding = true; +end + +fprintf(1,''); + +win = winlength; + +% Pad audio with zeros so that first analysis frame corresponds with first +% audio sample + +pad = zeros((win/2),1); + +%audiodata = [pad; audiodata; pad]; + +% find size of input +ilength = length(audiodata); + + +%pad audio data +audiodata = [pad; audiodata; pad]; + +% set hop size +hop = fix(win.*overlap); + +% set number of windows +num_win = ceil((ilength+1)/win/overlap); + +start=1; + +fftframes = zeros(win,num_win); + +check = ceil(num_win/10); + +if windowkind == 'hamming' + window = hamming(win); +else + window = rectwin(win); +end + + +% do fft analysis for each window: +for i = 1 : num_win; + start = floor(win * overlap * (i-1) + 1); + if ~zeropadding + segment = window .* audiodata(start:(start + win - 1)); + fftframes(:,i) = fft(segment, win)'; + else + segment = [window .* audiodata(start:(start + win - 1)) zeros(nPad*win)]; + fftframes(:,i) = fft(segment, win)'; + end + +% start = start + hop + + if mod(i,check) == 0 + fprintf(1,'.'); + end + +end; +fprintf(1,'\n'); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/.svn/text-base/readaudiochunk.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,30 @@ + +% Read in an audio file + + +function [audiodata, info] = readaudiochunk(info) + +fprintf(1,'reading audio chunk\n'); + +% read in wav file + +[rawdata, info.fs] = wavread(info.filename,[info.startsample info.endsample]); + +info.fsdownsampled = info.fs; + +% take average of stereo signals to give one mono audio vector + +meandata = mean(rawdata,2); + +% normalise audio +if max(meandata) > 0.00001 + audiodata = meandata/max(meandata); +else + audiodata = meandata; +end + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/.svn/text-base/shadedplot.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,65 @@ +function [ha hb hc] = shadedplot(x, y1, y2, varargin) + +% SHADEDPLOT draws two lines on a plot and shades the area between those +% lines. +% +% SHADEDPLOT(x, y1, y2) +% All of the arguments are vectors of the same length, and each y-vector is +% horizontal (i.e. size(y1) = [1 N]). Vector x contains the x-axis values, +% and y1:y2 contain the y-axis values. +% +% Plot y1 and y2 vs x, then shade the area between those two +% lines. Highlight the edges of that band with lines. +% +% SHADEDPLOT(x, y1, y2, areacolor, linecolor) +% The arguments areacolor and linecolor allow the user to set the color +% of the shaded area and the boundary lines. These arguments must be +% either text values (see the help for the PLOT function) or a +% 3-element vector with the color values in RGB (see the help for +% COLORMAP). +% +% [HA HB HC = SHADEDPLOT(x, y1, y2) returns three handles to the calling +% function. HA is a vector of handles to areaseries objects (HA(2) is the +% shaded area), HB is the handle to the first line (x vs y1), and HC is +% the handle to the second line (x vs y2). +% +% Example: +% +% x1 = [1 2 3 4 5 6]; +% y1 = x1; +% y2 = x1+1; +% x3 = [1.5 2 2.5 3 3.5 4]; +% y3 = 2*x3; +% y4 = 4*ones(size(x3)); +% ha = shadedplot(x1, y1, y2, [1 0.7 0.7], 'r'); %first area is red +% hold on +% hb = shadedplot(x3, y3, y4, [0.7 0.7 1]); %second area is blue +% hold off + +% plot the shaded area +y = [y1; (y2-y1)]'; +ha = area(x, y); +set(ha(1), 'FaceColor', 'none') % this makes the bottom area invisible +set(ha, 'LineStyle', 'none') + +% plot the line edges +hold on +hb = plot(x, y1, 'LineWidth', 1); +hc = plot(x, y2, 'LineWidth', 1); +hold off + +% set the line and area colors if they are specified +switch length(varargin) + case 0 + case 1 + set(ha(2), 'FaceColor', varargin{1}) + case 2 + set(ha(2), 'FaceColor', varargin{1}) + set(hb, 'Color', varargin{2}) + set(hc, 'Color', varargin{2}) + otherwise +end + +% put the grid on top of the colored area +set(gca, 'Layer', 'top') +grid on \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/.svn/text-base/synchronisechroma.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,26 @@ +function bc = synchronisechroma(chromag, beats, frame_t) + +% assigning beats to frames +nBeat = size(beats,1); +nFrame = size(chromag, 2); + +bc = ones(nBeat,size(chromag,1)); +chromag(:,isnan(sum(chromag))) = 1; + +if size(beats,2) < 2 + for iBeat = 1:nBeat-1 + currind = find(frame_t >= beats(iBeat) & frame_t < beats(iBeat+1)); + bc(iBeat,:) = median(chromag(:,currind),2); + if ~any(isnan(bc(iBeat,:))) + bc(iBeat,:) = bc(iBeat,:)/max(bc(iBeat,:)); + end + end +else + for iBeat = 1:nBeat + currind = (frame_t >= beats(iBeat,1) & frame_t < beats(iBeat,2)); + bc(iBeat,:) = median(chromag(:,currind),2); + if ~any(isnan(bc(iBeat,:))) + bc(iBeat,:) = bc(iBeat,:)/max(bc(iBeat,:)); + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/.svn/text-base/synchronisechroma.m~.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,19 @@ +function bc = synchronisechroma(chromag, beats, frame_t) + +% assigning beats to frames +nBeat = length(beats); +nFrame = size(chromag, 2); +beatassign = zeros(nFrame,1); +iBeat = 2; +minFrame = find(frame_t > beats(1),1); + +bc = ones(nBeat,12); +chromag(:,isnan(sum(chromag))) = 1; + +for iBeat = 1:nBeat-1 + find() + bc(iBeat,:) = median(chromag(:,beatassign == iBeat),2); + if ~any(isnan(bc(iBeat,:))) + bc(iBeat,:) = bc(iBeat,:)/max(bc(iBeat,:)); + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/basschroma13.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function bc13 = basschroma13(bc) +bc(isnan(sum(bc,2)),:) = 1; +bc = qnormalise(bc,1,2); +bc13 = [bc,(1./12./max(bc,[],2)).^2]; +bc13 = qnormalise(bc13, inf, 2);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/beatchroma.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,25 @@ +function bc = beatchroma(chromag, beats, frame_t) + +% assigning beats to frames +nBeat = length(beats); +nFrame = size(chromag, 2); +beatassign = zeros(1,nFrame); +iBeat = 1; + +for iFrame = 1:nFrame + if frame_t(iFrame) > beats(iBeat) && frame_t(iFrame) < beats(end) + iBeat = iBeat + 1; + end + beatassign(iFrame) = iBeat; +end +% averaging over frames belonging to one beat +fprintf(1,'__ preprocessing chromagram... ___________________________________________\n'); +bc = zeros(nBeat,12); +chromag(:,isnan(sum(chromag))) = 1; + +for iBeat = 1:nBeat + bc(iBeat,:) = median(chromag(:,beatassign == iBeat),2); + if ~any(isnan(bc(iBeat,:))) + bc(iBeat,:) = bc(iBeat,:)/max(bc(iBeat,:)); + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/bigframefft.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,61 @@ + +% segment audio data into overlapping frames and take fft of each frame + +function [fftframes,info] = bigframefft(audiodata, info) + +fprintf(1,'framefft\n'); + +info.undertest = [info.undertest '\nfft']; + +% set window length +win = info.windowlength; + + +% Pad audio with zeros so that first analysis frame corresponds with first +% audio sample + +pad = zeros((win/2),1); + +%audiodata = [pad; audiodata; pad]; + +% find size of input +ilength = length(audiodata); + + +%pad audio data +audiodata = [pad; audiodata; pad]; + +% set hop size +hop = fix(win.*info.overlap); + +info.hopsize = hop; + +% set number of windows +num_win = ceil((ilength+1)/hop); + +info.numberframes = num_win; + +start=1; + +fftframes = zeros(win,num_win); + +check = ceil(num_win/10); + + +% do fft analysis for each window: +for i = 1 : num_win; + + segment = audiodata(start:(start + win - 1)); + + fftframes(:,i) = fft(segment, win)'; + + start = start + hop; + + if mod(i,check) == 0 + fprintf(1,'.'); + end + +end; +fprintf(1,'\n'); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/chromagenerator.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,212 @@ +function [chroma, t, salience, tuning, URIstring] = chromagenerator(filename, kammerton, nBin) + +%% make URI string +URIstring = strcat('chromagenerator-', num2str(kammerton), '-',num2str(nBin)); + +%% parameters +% set the bins per semitone (i.e. binsperoctave/12) +% kammerton = 440; +% nBin = 7; +midbin = ceil(nBin/2); + +% minimum and maximum MIDI bin, should be n octaves apart. +midimin = 21; % 69 has 440 Hz +% midimin = 45; % 69 has 440 Hz +midimax = 92; +% midimax = 68; +noterange = midimax - midimin + 1; + +% midi note values including the fractions "between" actual MIDI notes +midibins = midimin-(midbin-1)/nBin:(1/nBin):midimax+(midbin-1)/nBin; +% the same in frequencies +fbins = kammerton * 2.^((midibins-69)/12); +nFbin = length(fbins); + +% the sampling frequency everything is (down)sampled to +used_fs = 11025; + +% intuitive setting of how long the frame is (quite short here!) +fracofsecond = 4; + +hopspersecond = 20; + +% calculate a convenient FFT length +nFFT = 2^nextpow2(used_fs/fracofsecond); +% hopsize in samples +hopsize = used_fs/hopspersecond/nFFT; + +% "instrument" means a particular spectral shape +used_instruments = 1:2; +nInstrument = length(used_instruments); + +%% generate dictionary +% (complex) tones for all fundamental frequencies are generated. then their +% amplitude spectrum is calculated and normalised according to the L2 norm. + +% preliminary t for dictionary generation +t = (1:nFFT)/used_fs; +% initialise note dictionary +A = zeros(nFbin,nFFT/2,nInstrument); + +% loop over all instruments +for iInstrument = used_instruments + % loop over all fundamental frequencies + for iFbin = 1:nFbin; + f0 = fbins(iFbin); + switch iInstrument + case 1 + wave = sum([sin(t*2*pi*f0*1); ... + 0.9^1 * sin(t*2*pi*f0*2); ... + 0.9^2 * sin(t*2*pi*f0*3); ... + 0.9^3 * sin(t*2*pi*f0*4); ... + ...0.9^4 * sin(t*2*pi*f0*5); ... + ]); + case 2 + wave = sin(t*2*pi*f0*1); + end + % window wave and fft + wave = hamming(nFFT)' .* wave; + fullfft = fft(wave); + % calculate amplitude spectrum from fft + A(iFbin,:,iInstrument) = abs(fullfft(1:round(nFFT/2))); + end + % normalise (L2 norm) + A(:,:,iInstrument) = qnormalise(A(:,:,iInstrument),2,2); +end + +%% get the fft frames from a wave file +%this is probably ending up not being used in a vamp plugin +% fprintf(1,'%s\n',filename) +% [audiosize,fs] = wavread(filename,'size'); +% chunk_sec = 20; +% nChunk = ceil(audiosize(1)/fs/chunk_sec); +% songframes = []; +% start = 1; +% % extract fft from chunks (for less memory consumption) +% for iChunk = 1:nChunk +% samplemin = (iChunk-1) * chunk_sec * fs + 1; +% samplemax = min(audiosize(1), (iChunk * chunk_sec + 1) * fs); +% audiodata0 = wavread(filename,[samplemin samplemax]); +% audiodata = resample(mean(audiodata0,2),used_fs,fs,20); +% songframes0 = myframefft(audiodata,nFFT,hopsize,'hamming'); +% if size(songframes0,2)>=start +% songframes = [songframes(:,1:end-start) abs(songframes0(1:round(nFFT/2),start:end))]; +% end +% start = round(hopspersecond/2)+1; +% end +% clear songframes0 +% clear audiodata +% clear audiodata0 +% t = (0:T-1)./hopspersecond; + +%% +[songframes,f,t]=chunkspectrogram(filename,4096,4096*7/8,4096,inf); +hopspersecond = 1/(t(2)-t(1)); +songframes = abs(songframes); +%% +T = size(songframes,2); + +fprintf(1, '%d different notes, %d frequency bins, %d time frames\n', noterange,nFFT/2, T) + +notespectrum0 = zeros(nFbin, T, nInstrument); +songframes = qnormalise(songframes,2,1); +songframes(isnan(songframes)) = 0; + +%% calculate cosine distance +for iInstrument = 1:nInstrument + for iTone = 1:nFbin + notespectrum0(iTone,:,iInstrument) = A(iTone,:,iInstrument) * songframes; + end +end +%% get only the bins with "more than 1 st. deviation" +notespectrum = zeros(size(notespectrum0)); +wind_size = 6 * nBin; +wind = hamming(wind_size)/sum(hamming(wind_size)); +stdthresh = 1; +for iInstrument = used_instruments + % calculate running mean + m = conv2(notespectrum0(:,:,iInstrument),wind(:),'same'); + % calculate running standard deviation + stdev = sqrt(conv2((notespectrum0(:,:,iInstrument)-m).^2,wind(:),'same')); + % take only those tones that are above 1 standard deviation from the + % mean + notespectrum(:,:,iInstrument) = (notespectrum0(:,:,iInstrument)-m)./stdev; + temp = notespectrum(:,:,iInstrument); + temp(temp<stdthresh) = 0; + notespectrum(:,:,iInstrument) = temp; +end +% notes that are one were not "good enough" (not greater than 1 st. +% deviation reater than the mean) and are hence set to 0. +notespectrum(notespectrum<=stdthresh) = 0; +% we require that the notes be on in *both* "instruments". hence the final +% note spectrum note_s is their product, which works a bit like a minimum +note_s = notespectrum(:,:,1) .* notespectrum(:,:,2); + +note_s(1:nBin*6,:) = 0; +note_s(isnan(note_s)) = 0; +%% tuning +% I wrap the note spectrum to one semitone, kind of generating the note +% spectrum modulo one semitone. one gets a matrix of size nBin x T. from +% that matrix, the tuning can be calculated by extracting the angle of the +% sum of all columns, the sum of all columns being a nBin-dimensional +% vector. +th = tuner(note_s(36*nBin+(1:12*nBin),:), nBin) +% th = tuner(notespectrum0(36*nBin+(1:12*nBin),:,2), nBin) +tuning = kammerton*2^((th/2/pi)/12) +% tune the spectrum by interpolating the bins such that now the middle bin +% of every semitone corresponds to the right frequency in equal +% temperament +tuned_s = zeros(size(note_s)); +for iFrame = 1:T + tuned_s(:,iFrame) = interp1(1:nFbin, note_s(:,iFrame), (1:nFbin) + th / (2 * pi) * nBin,[],0); +end + +%% reduce to 1 bin per semitone +% just sum the nBin bins that correspond to one semitone. +wrapped_tuned_s = reshape(tuned_s,[nBin,noterange*T]); +% if you want to emphasize the middle bin, use a fancy window, otherwise +% just use the rectangular window +wei = rectwin(nBin)'; +salience.reduced = reshape(wei * wrapped_tuned_s,noterange,T); +% median filter in time direction, if you like +salience.reduced = medfilt1(salience.reduced,floor(hopspersecond/5)-1,[],2); + +%% make chromagram +% actually the edge of the note spectrum is a bit noisy (due to the +% convolution three parts above. that's why I start some bins from the +% lower edge (i.e. midimin_used). maybe one could do this properly +% sometime. +midimin_used = midimin+6; +% uebergang is where bass and treble chroma overlap equally +uebergang = 50; + +% some simple linear functions to generate the treble and bass profiles +leftfunc = zeros(2,noterange); +rightfunc = zeros(2,noterange); +leftfunc(1,:) = 2/12*((midimin:midimax)-midimin_used); +leftfunc(2,:) = 1/12*((midimin:midimax)-uebergang) + 0.5; +rightfunc(1,:) = -1/12*((midimin:midimax)-uebergang) + 0.5; +rightfunc(2,:) = -(.5)/12*((midimin:midimax)-midimax); + +% initialise stuff +salience.bass = zeros(size(salience.reduced)); +salience.treble = zeros(size(salience.reduced)); +profiles = zeros(noterange,3); + +% bass and treble profiles +for i = 1:2 + profiles(:,i) = max(0,min(1,min(leftfunc(i,:),rightfunc(i,:))))'; +end +% wide profile (i.e. both bass and treble) +profiles(:,3) = min(1,sum(profiles(:,1:2),2)); + +% calculate specific saliences (windowed in f0 direction) +salience.bass = salience.reduced .* profiles(:,ones(1,T)); +salience.treble = salience.reduced .* profiles(:,2 * ones(1,T)); +salience.wide = salience.reduced .* profiles(:,3 * ones(1,T)); + +% calculate chromagrams +chroma.bass = squeeze(max(reshape(salience.bass,[12,6,T]),[],2)); +chroma.treble = squeeze(max(reshape(salience.treble,[12,6,T]),[],2)); +chroma.wide = squeeze(max(reshape(salience.wide,[12,6,T]),[],2)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/chromamatthias.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,138 @@ +function [results, info] = chromamatthias(filename, prod, maxx) + +%--------------------------------------------------------------- +% Part of the matthiasmISMIR08 project +% Written by Christopher Harte, modified by Matthias Mauch +% @ Queen Mary, University of London +% 2008 +%--------------------------------------------------------------- + + +% Set up info structure (initialise variables to 0 if we don't have values +% now... + +info = {}; +%% +info.filename = filename; +% info.filename = '/Volumes/Music/Music/Beatles/12_-_Let_It_Be/wav/06_-_Let_It_Be.wav'; +% info.filename = '/Volumes/Music/Music/Beatles/unrevised/01wave/01_-_I_Saw_Her_Standing_There.wav'; +info.undertest = ''; % which algorithms are run + +% Signal Parameters: +info.fs = 44100; % Sampling freq of audio file +info.downsample = 4; % downsampling factor +info.fsdownsampled = ... + info.fs / info.downsample; % fs after downsampling +info.preprocessing = 'downsample'; % What kind of preprocessing? +info.numberframes = 0; % number of frames calculated +info.windowlength = 0; % window length in samples +info.overlap = ... + 1/8; % window overlap factor +info.overl = ... + 1 - info.overlap; +info.hopsize = 0; % window overlap in samples +info.framespersecond = 0; % effective frames per second + +% Constant Q parameters: +info.binsperoctave = 36; % bins per octave for const q +info.fmax = 1760; % top frequency for const q +info.fmin = 110; % bottom frequency for const q +info.numcqbins = 0; % number of bins in a Constant Q vector +info.sparkernelfile = ''; % mat file containing sparse spectral kernels matrix +info.sparsethreshold = 0.0054; % sparsekernel threshold +info.maxx = maxx; +% Get constant Q parameters - need to do this first to set window length +info = getcqparameters(info); + +% Prepare chunking +info.chunkoverlap = 2 * 1/info.overlap; % frames +info.audiofilesize = wavread(info.filename,'size'); +info.chunksize = 500; % frames +info.samplechunksize = (info.chunksize - 1) * ... + info.hopsize * info.downsample; % chunk length in original samples +info.samplechunkhopsize = (info.chunksize - 1 - info.chunkoverlap) * ... + info.hopsize * info.downsample; +info.framespersecond = info.fsdownsampled/info.hopsize; +%% +nChunk = ceil(info.audiofilesize / info.samplechunkhopsize); +results = {}; +iChunk = 0; +for iChunk = 1:nChunk +% + info.startsample = (iChunk - 1) * info.samplechunkhopsize + 1; + info.endsample = min(... + info.startsample + info.samplechunksize -1, ... + info.audiofilesize(1)); +% + [audiodata, info] = readaudiochunk(info); + + % Perform downsampling here if required + + [audiodata, info] = preprocessing(audiodata,info); + + % Perform FFT + [fftframes,info] = bigframefft(audiodata,info); + + % Harmonics adjustment + if prod + [fftframes] = prodfft(fftframes, 3); + end + + + % Perform Constant Q + [constqframes, info] = constq(fftframes,info); + + % Smooth constqframes for transient removal (time direction) + constqframes = medfilt1(constqframes,round(info.framespersecond/2),[],2); + + % 'Normalise' Constant Q + % constqframes = constqframes ./ repmat(sum(abs(constqframes),1),size(constqframes,1),1); + + % fftframes = 10 * log10(abs(fftframes).^2); + + % Calculate HPCP + [hpcpframes,info] = hpcp(constqframes,info); + + % Peakpick HPCP + % [peakpickframes,info] = framepeakpick(hpcpframes,info); + + + % Calculate Tuning + % [tuningframes,info] = tuning(peakpickframes,info); + + % Calculate slightly adjusted chromagram + [chromaframes, info] = tunebyshift(hpcpframes, info); + % figure, + % imagesc(info.t,[],(chromaframes')), axis xy + + % put results in output structure + if iChunk == 1 +% results.fftframes = fftframes; +% results.constqframes = constqframes; +% results.hpcpframes = hpcpframes; + results.chromaframes = chromaframes; + else +% results.fftframes = ... +% [results.fftframes(:,1:end-info.frameoverlap),... +% results.fftframes(:,end-info.frameoverlap+1:end) ... +% + fftframes(:,1:info.frameoverlap),... +% fftframes(:,info.frameoverlap+1:end)]; +% results.constqframes = ... +% [results.constqframes(:,1:end-info.frameoverlap),... +% results.constqframes(:,end-info.frameoverlap+1:end) ... +% + constqframes(:,1:info.frameoverlap),... +% constqframes(:,info.frameoverlap+1:end)]; +% results.hpcpframes = ... +% [results.hpcpframes(1:end-info.frameoverlap,:);... +% results.hpcpframes(end-info.frameoverlap+1:end,:) ... +% + hpcpframes(1:info.frameoverlap,:);... +% hpcpframes(info.frameoverlap+1:end,:)]; + x = round(info.chunkoverlap/2); +% results.chromaframes = chromaframes; + results.chromaframes = ... + [results.chromaframes(1:end-info.chunkoverlap+x,:);... + chromaframes(2+x:end,:)]; + end +end +info.numberframes = size(results.chromaframes,1); +info.t = ((0:info.numberframes-1) * info.hopsize) / info.fsdownsampled; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/chunkspectrogram.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,70 @@ +function [s,f,t] = chunkspectrogram(filename,d_windo,d_nOverlap,d_nFFT, maxf) +%% +% d_ are the downsampled things +% o_ are the original things + + +d_fs = 11025; + +[temp, o_fs] = wavread(filename,'size'); + +if mod(o_fs/d_fs,1) ~= 0 + error('The sample frequency of the wave file has to be an integer multiple of %1.0f.',d_fs); +end + +o_nSample = temp(1); +d_nSample = floor(o_nSample*d_fs/o_fs); + +o_nOverlap = d_nOverlap * o_fs/d_fs; + + +d_nSamplePerChunk = d_windo+200*(d_windo-d_nOverlap); + +if d_nSample < d_nSamplePerChunk + d_nSamplePerChunk = d_nSample; +end + +d_endsample = d_nSamplePerChunk:d_nSamplePerChunk:d_nSample; + +o_startsample = [1, d_endsample * o_fs / d_fs + 1]; +o_endsample = d_endsample * o_fs / d_fs; +o_endsample = min(o_endsample + o_fs / d_fs * d_nOverlap, o_nSample); % extend so that chunks overlap + +% in most cases the file length is not exactly a multiple of +if o_endsample(end) < o_nSample + o_endsample = [o_endsample o_nSample]; +end + +%% + +nChunk = length(o_startsample); + +for iChunk = 1:nChunk + fprintf(1,'_'); +end +leftpad = zeros(d_windo/2,1); +fprintf(1,'\n.') +x = resample(mean(wavread(filename,[o_startsample(1) o_endsample(1)]),2),d_fs,o_fs); +s = spectrogram([leftpad; x], d_windo,d_nOverlap,d_nFFT, d_fs); +f = (0:d_nFFT/2-1)/d_nFFT*d_fs; +if maxf < max(f) + maxbin = find(f>maxf,1)-1; +else + maxbin = length(f); +end +s = s(1:maxbin,:); +f = f(1:maxbin); +for iChunk = 2:nChunk-1 + fprintf(1,'.') + temps = spectrogram(resample(mean(wavread(filename,[o_startsample(iChunk) o_endsample(iChunk)]),2),d_fs,o_fs), ... + d_windo,d_nOverlap,d_nFFT, d_fs); + s = [s(1:maxbin,:),temps(1:maxbin,:)]; +end +x = resample(mean(wavread(filename,[o_startsample(nChunk) o_nSample]),2),d_fs,o_fs); +if length(x)>d_windo + temps = spectrogram(x, ... + d_windo,d_nOverlap,d_nFFT, d_fs); + s = [s(1:maxbin,:),temps(1:maxbin,:)]; +end +fprintf(1,'.\n') +t = (0:size(s,2)-1) * (d_windo-d_nOverlap)/d_fs;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/chunkspectrogram_ISMIR2010.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,45 @@ +function [s,f,t] = chunkspectrogram_ISMIR2010(filename,d_windo,d_nOverlap,d_nFFT, maxf) +%% +% d_ are the downsampled things +% o_ are the original things + + +d_fs = 11025; + +[filesize, o_fs] = wavread(filename,'size'); + + + +if mod(o_fs/d_fs,1) ~= 0 + error('The sample frequency of the wave file has to be an integer multiple of %1.0f.',d_fs); +end +downsample = o_fs/d_fs; + +o_windo = d_windo * downsample; +o_nOverlap = d_nOverlap * downsample; +o_hopSize = o_windo-o_nOverlap; +nFrame = floor((filesize(1) - o_windo) / o_hopSize); + +s = zeros(d_nFFT/2, nFrame); +f = (0:d_nFFT/2-1)/d_fs; +t = zeros(nFrame,1); + +wind = hamming(d_windo); +wind = wind/sum(wind); + +for iLine = 1:round(nFrame/200) + fprintf(1,'_'); +end +fprintf(1,'\n'); + +for iFrame = 1:nFrame + if mod(iFrame,200) == 0 + fprintf(1,'.'); + end + ind = [1,o_windo] + (iFrame-1) * (o_hopSize); + wave = sum(wavread(filename, ind),2) / 2; + temp = fft(resample(wave,d_fs,o_fs) .* wind); + s(:, iFrame) = temp(1:d_nFFT/2); + t(iFrame) = (ind(1) + ind(2)) / 2 / o_fs; +end +fprintf(1,'\n');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/hpcp2chroma.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,53 @@ +function [chromaframes, info] = hpcp2chroma(hpcpframes, info) + +% calculate the best shift +% (0 - none, 1 - one third of semitone down, +% 2 - two thirds of semitone down) + +bps = info.binsperoctave/12; + +indices = (0:11) * bps; + + +%%%%% NANs! $%%%%%%%%%%%% +% nanrows = isnan(sum(hpcpframes,2)); +% binweights = sum(hpcpframes(~nanrows,:)); +% condensedweights = zeros(bps,12); +% for iBin = 1:bps +% condensedweights(iBin,1:12) = sum(binweights(indices+iBin)); +% end +% % [maximum, maxind] = max(... +% % [sum(binweights(indices+1)),... +% % sum(binweights(indices+2)),... +% % sum(binweights(indices+3))]); +% [maximum, maxind] = max(sum(condensedweights,2)); +% shiftposs = [1 0 -1]; +% shift = shiftposs(maxind); +% if bps ==3 +% hpcpframes = circshift(hpcpframes',shift)'; +% info.tuning6th = shift; +% end +maxind = 1; + +% fprintf(1,['(max. energy at ' num2str(maxind) ', shift by ' num2str(shift) ')\n\n']) +if (~info.maxx) + if bps == 3 + chromaframes = ... + 0 * hpcpframes(:,indices+1) + ... + 1 * hpcpframes(:,indices+2) + ... + 0 * hpcpframes(:,indices+3); + elseif bps == 2 + chromaframes = ... + (1-shift)* hpcpframes(:,indices+1) + ... + shift* hpcpframes(:,indices+2); + elseif bps == 1 + chromaframes = hpcpframes; + end +else + chromaframes = zeros(size(hpcpframes,1),12); + for iChroma = 1:12 + chromaframes(:,iChroma) = max(hpcpframes(:,indices(iChroma)+(1:3)),[],2); + end +end + +chromaframes = chromaframes ./ repmat(sum(chromaframes,2),1,12); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/mmcq2hpcp.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,48 @@ + + + + +% CQ2HPCP Convert Constant Q spectrum to a HPCP +% +% CQ2HPCP(cqframes, info) +% +% cqframes = vector or matrix constant q spectrum / spectra +% info = info structure containing parameters +% + +function [hpcpframes] = mmcq2hpcp(cqframes, info) + +%get cq parameters from info structure +num_win = info.numberframes; + +cqbins = size(cqframes,1); + +bpo = info.binsperoctave; + +% calculate the number of octaves we have in the spectrum +num_oct = floor(cqbins/bpo)-1; + +hpcpframes(1:num_win, 1:bpo) = 0; + + + +% for each window calculate chromagram +for i = 1 : num_win + % add each octave of the spectrum into the chromagram + for oct = 0:num_oct; + ind = oct*bpo; + + hpcpframes(i, 1:bpo) = hpcpframes(i, 1:bpo) + abs(cqframes(ind+1:ind+bpo,i))'; + + end; + + % normalise +% hpcpframes(i,1:bpo) = hpcpframes(i,1:bpo)/max(hpcpframes(i,1:bpo)); + meanc = mean(hpcpframes(i,1:bpo)); + if meanc > 0.0000001 + hpcpframes(i,1:bpo) = hpcpframes(i,1:bpo)/mean(hpcpframes(i,1:bpo)); %scale to be a distribution! + else + hpcpframes(i,1:bpo) = (bpo)^(-1); + end +end; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/mychroma.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,170 @@ +function [chromagram,t,salience] = ... + mychroma(filename, nBins, fmin, used_fs, fracofsecond, calcu) + +if isempty(nBins) + nBins = 3; +end +midbin = ceil(nBins/2); +if isempty(fmin) + fmin = 55 * 2^(-7/12-(midbin-1)/12/nBins); +end + +if isempty(used_fs) + used_fs=round(44100/4); +end + +hopspersecond = 20; +nFFT = 2^nextpow2(used_fs/fracofsecond); +hopsize = used_fs/hopspersecond/nFFT; +noterange = 61; +used_instruments = 1:2; +nInstrument = length(used_instruments); + +%% load or generate dictionary +dirc = what('./data'); +dicfilename = ['mydict-' num2str(nBins) '.mat']; +if ~calcu && any(strcmp(dirc.mat, dicfilename)) + load(['data/' dicfilename]) +else + % dictionary parameters + midbin = nBins/2 + .5; + %hopspersecond = 10; + secs = .8; + t = linspace(0,secs,secs*used_fs)'; + + % make note dictionary + A = zeros(noterange*nBins,nFFT/2,nInstrument); + for iInstrument = used_instruments + for iMidiNote = 1:noterange + for iBin = 1:nBins + f0 = fmin * 2^((iMidiNote-1)/12 + (iBin-1)/12/nBins); + %wave = zeros(size(t)); + switch iInstrument + case 1 + wave = sum([sin(t*2*pi*f0*1) ... + 0.6^1 * sin(t*2*pi*f0*2) ... + 0.6^2 * sin(t*2*pi*f0*3) ... + 0.6^3 * sin(t*2*pi*f0*4)],2); + case 2 + wave = sin(t*2*pi*f0*1); + case 3 + wave = sum([... + 0.6^1 * sin(t*2*pi*f0*1/2) ... + 0.6^2 * sin(t*2*pi*f0*1/3) ... + 0.6^3 * sin(t*2*pi*f0*1/4) ... + 0.6^4 * sin(t*2*pi*f0*1/5) ... + 0.6^5 * sin(t*2*pi*f0*1/6) ... + 0.6^6 * sin(t*2*pi*f0*1/7) ... + 0.6^7 * sin(t*2*pi*f0*1/8) ... + 0.6^8 * sin(t*2*pi*f0*1/9)],2); + end + fftframes0 = myframefft(wave,nFFT,hopsize,'hamming'); + A(nBins*(iMidiNote-1)+iBin,:,iInstrument) = mean(abs(fftframes0(1:round(nFFT/2),:)),2); + end + end + end + save(['data/' dicfilename], 'A', 'nFFT', 'hopsize'); +end + +%% get the fft frames from a wave file +fprintf(1,'%s\n',filename) +[audiosize,fs] = wavread(filename,'size'); +chunk_sec = 20; +nChunk = ceil(audiosize(1)/fs/chunk_sec); +songframes = []; +s = []; +start = 1; + +for iChunk = 1:nChunk + samplemin = (iChunk-1) * chunk_sec * fs + 1; + samplemax = min(audiosize(1), (iChunk * chunk_sec + 1) * fs); + + audiodata0 = wavread(filename,[samplemin samplemax]); + + audiodata = resample(mean(audiodata0,2),used_fs,fs,20); + + songframes0 = myframefft(audiodata,nFFT,hopsize,'hamming'); + + if size(songframes0,2)>=start + songframes = [songframes(:,1:end-start) abs(songframes0(1:round(nFFT/2),start:end))]; + end + start = round(hopspersecond/2)+1; +end +clear songframes0 +clear audiodata +clear audiodata0 + +T = size(songframes,2); +t = (0:T-1)./hopspersecond; +fprintf(1, '%d different notes, %d frequency bins, %d time frames\n', noterange,nFFT/2, T) + +simpleS = zeros(noterange*nBins,T, nInstrument+1); +songframes(isnan(songframes)) = 0; + +for iInstrument = 1:nInstrument + At = A(:,:,iInstrument); + for kFrame = 1:T + simpleS(:,kFrame,iInstrument) = At * songframes(:,kFrame); + end +end +simpleS(:,:,2) = max(0,conv2(simpleS(:,:,2), ... + repmat([-1 -1 4 -1 -1]',1,1),'same')); + +%% add the instruments (i.e. the different partial decay bla) +%--------------------------------------------------------- +note_s = (simpleS(:,:,1) .* simpleS(:,:,2)); +clear simpleS + +%% tuning + +wrapped_s = zeros(nBins,T); +for iBin = 1:nBins + wrapped_s(iBin,:) = sum(note_s(iBin:nBins:end,:)); +end +on_circle = exp(i * ((1:nBins)-midbin)/nBins * 2 * pi); +complex_tuning = on_circle * wrapped_s; +smooth_complex_tuning = conv2(complex_tuning,hamming(20*hopspersecond)','same'); +tooflat = angle(mean(smooth_complex_tuning)) / (2 * pi) * nBins; +tooflat = repmat(tooflat,T,1); +tuned_s = note_s; +for iFrame = 1:T + tuned_s(:,iFrame) = interp1(1:nBins*noterange, note_s(:,iFrame), (1:nBins*noterange) + 1 * tooflat(iFrame),[],0); +end + +%% reduce to 1 bin per semitone +wrapped_tuned_s = reshape(tuned_s,[nBins,noterange*T]); +wei = rectwin(nBins)'; +reduced = reshape(wei * wrapped_tuned_s,noterange,T); + +salience.full = s; +salience.reduced = reduced; + +%% make chromagram + +reduced = medfilt1(reduced',9)'; +chromagen = [zeros(12,1) repmat(eye(12),1,5)]; + + +treble_profile = ([0 zeros(1,24) linspace(0,1,6) linspace(1,1,6) ones(1,12) linspace(1,0,6) zeros(1,6)]); +bass_profile = [0 ones(1,18) linspace(1,0,12) ,zeros(1,noterange-31)]; +wide_profile = max(treble_profile, bass_profile); + +treble_salience = reduced .* repmat(treble_profile(:),1,T); +bass_salience = reduced .* repmat(bass_profile(:),1,T); +wide_salience = reduced .* repmat(wide_profile(:),1,T); + +bass_chromag = circshift(chromagen * bass_salience,6); +wide_chromag = circshift(chromagen * wide_salience,6); + +[peakindex,peakindex]=sort(treble_salience,'descend'); +for iFrame = 1:T + treble_salience(peakindex(1,iFrame),iFrame) = treble_salience(peakindex(2,iFrame),iFrame); +end +treble_chromag = circshift(chromagen * treble_salience,6); + +chromagram.treble = treble_chromag; +chromagram.bass = bass_chromag; +chromagram.wide = wide_chromag; + +% csvwrite('chr.csv',[ones(25,1) [maxn(circshift(bass_chromag,4));ones(1,size(treble_chromag,2)); maxn(circshift(treble_chromag,4))]]'); +% csvwrite('tuned_s.csv',[maxn(reduced)]');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/mychroma.m~ Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,170 @@ +function [chromagram,t,salience] = ... + mychroma(filename, nBins, fmin, used_fs, fracofsecond, calcu) + +if isempty(nBins) + nBins = 3; +end +midbin = ceil(nBins/2); +if isempty(fmin) + fmin = 55 * 2^(-7/12-(midbin-1)/12/nBins); +end + +if isempty(used_fs) + used_fs=round(44100/4); +end + +hopspersecond = 20; +nFFT = 2^nextpow2(used_fs/fracofsecond); +hopsize = used_fs/hopspersecond/nFFT; +noterange = 61; +used_instruments = 1:2; +nInstrument = length(used_instruments); + +%% load or generate dictionary +dirc = what('./data'); +dicfilename = ['mydict-' num2str(nBins) '.mat']; +if ~calcu && any(strcmp(dirc.mat, dicfilename)) + load(['data/' dicfilename]) +else + % dictionary parameters + midbin = nBins/2 + .5; + %hopspersecond = 10; + secs = .8; + t = linspace(0,secs,secs*used_fs)'; + + % make note dictionary + A = zeros(noterange*nBins,nFFT/2,nInstrument); + for iInstrument = used_instruments + for iMidiNote = 1:noterange + for iBin = 1:nBins + f0 = fmin * 2^((iMidiNote-1)/12 + (iBin-1)/12/nBins); + %wave = zeros(size(t)); + switch iInstrument + case 1 + wave = sum([sin(t*2*pi*f0*1) ... + 0.6^1 * sin(t*2*pi*f0*2) ... + 0.6^2 * sin(t*2*pi*f0*3) ... + 0.6^3 * sin(t*2*pi*f0*4)],2); + case 2 + wave = sin(t*2*pi*f0*1); + case 3 + wave = sum([... + 0.6^1 * sin(t*2*pi*f0*1/2) ... + 0.6^2 * sin(t*2*pi*f0*1/3) ... + 0.6^3 * sin(t*2*pi*f0*1/4) ... + 0.6^4 * sin(t*2*pi*f0*1/5) ... + 0.6^5 * sin(t*2*pi*f0*1/6) ... + 0.6^6 * sin(t*2*pi*f0*1/7) ... + 0.6^7 * sin(t*2*pi*f0*1/8) ... + 0.6^8 * sin(t*2*pi*f0*1/9)],2); + end + fftframes0 = myframefft(wave,nFFT,hopsize,'hamming'); + A(nBins*(iMidiNote-1)+iBin,:,iInstrument) = mean(abs(fftframes0(1:round(nFFT/2),:)),2); + end + end + end + save(dicfilename, 'A', 'nFFT', 'hopsize'); +end + +%% get the fft frames from a wave file +fprintf(1,'%s\n',filename) +[audiosize,fs] = wavread(filename,'size'); +chunk_sec = 20; +nChunk = ceil(audiosize(1)/fs/chunk_sec); +songframes = []; +s = []; +start = 1; + +for iChunk = 1:nChunk + samplemin = (iChunk-1) * chunk_sec * fs + 1; + samplemax = min(audiosize(1), (iChunk * chunk_sec + 1) * fs); + + audiodata0 = wavread(filename,[samplemin samplemax]); + + audiodata = resample(mean(audiodata0,2),used_fs,fs,20); + + songframes0 = myframefft(audiodata,nFFT,hopsize,'hamming'); + + if size(songframes0,2)>=start + songframes = [songframes(:,1:end-start) abs(songframes0(1:round(nFFT/2),start:end))]; + end + start = round(hopspersecond/2)+1; +end +clear songframes0 +clear audiodata +clear audiodata0 + +T = size(songframes,2); +t = (0:T-1)./hopspersecond; +fprintf(1, '%d different notes, %d frequency bins, %d time frames\n', noterange,nFFT/2, T) + +simpleS = zeros(noterange*nBins,T, nInstrument+1); +songframes(isnan(songframes)) = 0; + +for iInstrument = 1:nInstrument + At = A(:,:,iInstrument); + for kFrame = 1:T + simpleS(:,kFrame,iInstrument) = At * songframes(:,kFrame); + end +end +simpleS(:,:,2) = max(0,conv2(simpleS(:,:,2), ... + repmat([-1 -1 4 -1 -1]',1,1),'same')); + +%% add the instruments (i.e. the different partial decay bla) +%--------------------------------------------------------- +note_s = (simpleS(:,:,1) .* simpleS(:,:,2)); +clear simpleS + +%% tuning + +wrapped_s = zeros(nBins,T); +for iBin = 1:nBins + wrapped_s(iBin,:) = sum(note_s(iBin:nBins:end,:)); +end +on_circle = exp(i * ((1:nBins)-midbin)/nBins * 2 * pi); +complex_tuning = on_circle * wrapped_s; +smooth_complex_tuning = conv2(complex_tuning,hamming(20*hopspersecond)','same'); +tooflat = angle(mean(smooth_complex_tuning)) / (2 * pi) * nBins; +tooflat = repmat(tooflat,T,1); +tuned_s = note_s; +for iFrame = 1:T + tuned_s(:,iFrame) = interp1(1:nBins*noterange, note_s(:,iFrame), (1:nBins*noterange) + 1 * tooflat(iFrame),[],0); +end + +%% reduce to 1 bin per semitone +wrapped_tuned_s = reshape(tuned_s,[nBins,noterange*T]); +wei = rectwin(nBins)'; +reduced = reshape(wei * wrapped_tuned_s,noterange,T); + +salience.full = s; +salience.reduced = reduced; + +%% make chromagram + +reduced = medfilt1(reduced',9)'; +chromagen = [zeros(12,1) repmat(eye(12),1,5)]; + + +treble_profile = ([0 zeros(1,24) linspace(0,1,6) linspace(1,1,6) ones(1,12) linspace(1,0,6) zeros(1,6)]); +bass_profile = [0 ones(1,18) linspace(1,0,12) ,zeros(1,noterange-31)]; +wide_profile = max(treble_profile, bass_profile); + +treble_salience = reduced .* repmat(treble_profile(:),1,T); +bass_salience = reduced .* repmat(bass_profile(:),1,T); +wide_salience = reduced .* repmat(wide_profile(:),1,T); + +bass_chromag = circshift(chromagen * bass_salience,6); +wide_chromag = circshift(chromagen * wide_salience,6); + +[peakindex,peakindex]=sort(treble_salience,'descend'); +for iFrame = 1:T + treble_salience(peakindex(1,iFrame),iFrame) = treble_salience(peakindex(2,iFrame),iFrame); +end +treble_chromag = circshift(chromagen * treble_salience,6); + +chromagram.treble = treble_chromag; +chromagram.bass = bass_chromag; +chromagram.wide = wide_chromag; + +% csvwrite('chr.csv',[ones(25,1) [maxn(circshift(bass_chromag,4));ones(1,size(treble_chromag,2)); maxn(circshift(treble_chromag,4))]]'); +% csvwrite('tuned_s.csv',[maxn(reduced)]');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/myframefft.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,76 @@ + +% segment audio data into overlapping frames and take fft of each frame + +function fftframes = myframefft(audiodata, winlength, overlap, windowkind, varargin) + +if nargin > 4 + nPad = varargin{1}; + zeropadding = true; +else + zeropadding = false; +end + +fprintf(1,''); + +win = winlength; + +% Pad audio with zeros so that first analysis frame corresponds with first +% audio sample + +pad = zeros((win/2),1); + +%audiodata = [pad; audiodata; pad]; + +% find size of input +ilength = length(audiodata); + + +%pad audio data +audiodata = [pad; audiodata; pad]; + +% set hop size +hop = fix(win.*overlap); + +% set number of windows +num_win = ceil((ilength+1)/win/overlap); + +start=1; +if ~zeropadding + fftframes = zeros(win,num_win); +else + fftframes = zeros(win*(nPad+1),num_win); +end + +check = ceil(num_win/10); + +switch windowkind + case 'hamming' + window = hamming(win); + case 'rect' + window = rectwin(win); + case 'kaiser' + window = kaiser(win); +end + + +% do fft analysis for each window: +for i = 1 : num_win; + start = floor(win * overlap * (i-1) + 1); + if ~zeropadding + segment = window .* audiodata(start:(start + win - 1)); + fftframes(:,i) = fft(segment, win)'; + else + segment = window .* audiodata(start:(start + win - 1)); + fftframes(:,i) = fft(segment, win * (nPad+1))'; + end + +% start = start + hop + + if mod(i,check) == 0 + fprintf(1,'.'); + end + +end; +fprintf(1,'\n'); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/myframefft.m~ Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,68 @@ + +% segment audio data into overlapping frames and take fft of each frame + +function fftframes = myframefft(audiodata, winlength, overlap, windowkind, varargin) + +if nargin > 4 + nPad = varargin{1}; + zeropadding = true; +end + +fprintf(1,''); + +win = winlength; + +% Pad audio with zeros so that first analysis frame corresponds with first +% audio sample + +pad = zeros((win/2),1); + +%audiodata = [pad; audiodata; pad]; + +% find size of input +ilength = length(audiodata); + + +%pad audio data +audiodata = [pad; audiodata; pad]; + +% set hop size +hop = fix(win.*overlap); + +% set number of windows +num_win = ceil((ilength+1)/win/overlap); + +start=1; + +fftframes = zeros(win,num_win); + +check = ceil(num_win/10); + +if windowkind == 'hamming' + window = hamming(win); +else + window = rectwin(win); +end + + +% do fft analysis for each window: +for i = 1 : num_win; + start = floor(win * overlap * (i-1) + 1); + if ~zeropadding + segment = window .* audiodata(start:(start + win - 1)); + fftframes(:,i) = fft(segment, win)'; + else + segment = [window .* audiodata(start:(start + win - 1)) zeros(nPad*win)]; + fftframes(:,i) = fft(segment, win)'; + end + +% start = start + hop + + if mod(i,check) == 0 + fprintf(1,'.'); + end + +end; +fprintf(1,'\n'); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/readaudiochunk.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,30 @@ + +% Read in an audio file + + +function [audiodata, info] = readaudiochunk(info) + +fprintf(1,'reading audio chunk\n'); + +% read in wav file + +[rawdata, info.fs] = wavread(info.filename,[info.startsample info.endsample]); + +info.fsdownsampled = info.fs; + +% take average of stereo signals to give one mono audio vector + +meandata = mean(rawdata,2); + +% normalise audio +if max(meandata) > 0.00001 + audiodata = meandata/max(meandata); +else + audiodata = meandata; +end + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/shadedplot.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,65 @@ +function [ha hb hc] = shadedplot(x, y1, y2, varargin) + +% SHADEDPLOT draws two lines on a plot and shades the area between those +% lines. +% +% SHADEDPLOT(x, y1, y2) +% All of the arguments are vectors of the same length, and each y-vector is +% horizontal (i.e. size(y1) = [1 N]). Vector x contains the x-axis values, +% and y1:y2 contain the y-axis values. +% +% Plot y1 and y2 vs x, then shade the area between those two +% lines. Highlight the edges of that band with lines. +% +% SHADEDPLOT(x, y1, y2, areacolor, linecolor) +% The arguments areacolor and linecolor allow the user to set the color +% of the shaded area and the boundary lines. These arguments must be +% either text values (see the help for the PLOT function) or a +% 3-element vector with the color values in RGB (see the help for +% COLORMAP). +% +% [HA HB HC = SHADEDPLOT(x, y1, y2) returns three handles to the calling +% function. HA is a vector of handles to areaseries objects (HA(2) is the +% shaded area), HB is the handle to the first line (x vs y1), and HC is +% the handle to the second line (x vs y2). +% +% Example: +% +% x1 = [1 2 3 4 5 6]; +% y1 = x1; +% y2 = x1+1; +% x3 = [1.5 2 2.5 3 3.5 4]; +% y3 = 2*x3; +% y4 = 4*ones(size(x3)); +% ha = shadedplot(x1, y1, y2, [1 0.7 0.7], 'r'); %first area is red +% hold on +% hb = shadedplot(x3, y3, y4, [0.7 0.7 1]); %second area is blue +% hold off + +% plot the shaded area +y = [y1; (y2-y1)]'; +ha = area(x, y); +set(ha(1), 'FaceColor', 'none') % this makes the bottom area invisible +set(ha, 'LineStyle', 'none') + +% plot the line edges +hold on +hb = plot(x, y1, 'LineWidth', 1); +hc = plot(x, y2, 'LineWidth', 1); +hold off + +% set the line and area colors if they are specified +switch length(varargin) + case 0 + case 1 + set(ha(2), 'FaceColor', varargin{1}) + case 2 + set(ha(2), 'FaceColor', varargin{1}) + set(hb, 'Color', varargin{2}) + set(hc, 'Color', varargin{2}) + otherwise +end + +% put the grid on top of the colored area +set(gca, 'Layer', 'top') +grid on \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/synchronisechroma.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,26 @@ +function bc = synchronisechroma(chromag, beats, frame_t) + +% assigning beats to frames +nBeat = size(beats,1); +nFrame = size(chromag, 2); + +bc = ones(nBeat,size(chromag,1)); +chromag(:,isnan(sum(chromag))) = 1; + +if size(beats,2) < 2 + for iBeat = 1:nBeat-1 + currind = find(frame_t >= beats(iBeat) & frame_t < beats(iBeat+1)); + bc(iBeat,:) = median(chromag(:,currind),2); + if ~any(isnan(bc(iBeat,:))) + bc(iBeat,:) = bc(iBeat,:)/max(bc(iBeat,:)); + end + end +else + for iBeat = 1:nBeat + currind = (frame_t >= beats(iBeat,1) & frame_t < beats(iBeat,2)); + bc(iBeat,:) = median(chromag(:,currind),2); + if ~any(isnan(bc(iBeat,:))) + bc(iBeat,:) = bc(iBeat,:)/max(bc(iBeat,:)); + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/featureextraction/synchronisechroma.m~ Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,19 @@ +function bc = synchronisechroma(chromag, beats, frame_t) + +% assigning beats to frames +nBeat = length(beats); +nFrame = size(chromag, 2); +beatassign = zeros(nFrame,1); +iBeat = 2; +minFrame = find(frame_t > beats(1),1); + +bc = ones(nBeat,12); +chromag(:,isnan(sum(chromag))) = 1; + +for iBeat = 1:nBeat-1 + find() + bc(iBeat,:) = median(chromag(:,beatassign == iBeat),2); + if ~any(isnan(bc(iBeat,:))) + bc(iBeat,:) = bc(iBeat,:)/max(bc(iBeat,:)); + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/figures/.svn/all-wcprops Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,47 @@ +K 25 +svn:wc:ra_dav:version-url +V 51 +/svnroot/!svn/ver/1421/matlabtoolboxes/misc/figures +END +imxy.m +K 25 +svn:wc:ra_dav:version-url +V 58 +/svnroot/!svn/ver/1403/matlabtoolboxes/misc/figures/imxy.m +END +exportfig.m +K 25 +svn:wc:ra_dav:version-url +V 63 +/svnroot/!svn/ver/1403/matlabtoolboxes/misc/figures/exportfig.m +END +uimage.m +K 25 +svn:wc:ra_dav:version-url +V 60 +/svnroot/!svn/ver/1421/matlabtoolboxes/misc/figures/uimage.m +END +rotateticklabel.m +K 25 +svn:wc:ra_dav:version-url +V 69 +/svnroot/!svn/ver/1403/matlabtoolboxes/misc/figures/rotateticklabel.m +END +uimagesc.m +K 25 +svn:wc:ra_dav:version-url +V 62 +/svnroot/!svn/ver/1421/matlabtoolboxes/misc/figures/uimagesc.m +END +addmax.m +K 25 +svn:wc:ra_dav:version-url +V 60 +/svnroot/!svn/ver/1403/matlabtoolboxes/misc/figures/addmax.m +END +printme.m +K 25 +svn:wc:ra_dav:version-url +V 61 +/svnroot/!svn/ver/1403/matlabtoolboxes/misc/figures/printme.m +END
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/figures/.svn/entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,266 @@ +10 + +dir +7329 +https://parrot.dcs.qmul.ac.uk/svnroot/matlabtoolboxes/misc/figures +https://parrot.dcs.qmul.ac.uk/svnroot + + + +2009-03-06T16:14:34.602437Z +1421 +matthiasm + + + + + + + + + + + + + + +214cd2a9-030a-0410-b9a1-da4e402363b6 + +imxy.m +file + + + + +2010-04-07T10:16:15.000000Z +593215c20d55052d29e75d09f527e7df +2009-02-20T16:30:45.085047Z +1403 +matthiasm + + + + + + + + + + + + + + + + + + + + + +133 + +exportfig.m +file + + + + +2010-04-07T10:16:15.000000Z +5258cb74eb732a511a7f81782faa774a +2009-02-20T16:30:45.085047Z +1403 +matthiasm + + + + + + + + + + + + + + + + + + + + + +31654 + +uimage.m +file + + + + +2010-04-07T10:16:15.000000Z +8cf09f5e178e103d6bfc0ffe54be1d78 +2009-03-06T16:14:34.602437Z +1421 +matthiasm +has-props + + + + + + + + + + + + + + + + + + + + +3873 + +rotateticklabel.m +file + + + + +2010-04-07T10:16:15.000000Z +a8a3bf8dee99631c41da4a3304a2a47a +2009-02-20T16:30:45.085047Z +1403 +matthiasm + + + + + + + + + + + + + + + + + + + + + +1720 + +uimagesc.m +file + + + + +2010-04-07T10:16:15.000000Z +6ad496d4a4fdfb67e36eece540164b8b +2009-03-06T16:14:34.602437Z +1421 +matthiasm +has-props + + + + + + + + + + + + + + + + + + + + +1413 + +addmax.m +file + + + + +2010-04-07T10:16:15.000000Z +fa25c8d98cc68c5c881042ca565434be +2009-02-20T16:30:45.085047Z +1403 +matthiasm + + + + + + + + + + + + + + + + + + + + + +345 + +printme.m +file + + + + +2010-04-07T10:16:15.000000Z +b1d815d085882132c06bf84c83463e42 +2009-02-20T16:30:45.085047Z +1403 +matthiasm + + + + + + + + + + + + + + + + + + + + + +222 +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/figures/.svn/prop-base/uimage.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +K 14 +svn:executable +V 0 + +END
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/figures/.svn/prop-base/uimagesc.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +K 14 +svn:executable +V 0 + +END
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/figures/.svn/text-base/addmax.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,19 @@ +function addmax(varargin) +if length(varargin)>0 && strcmp(varargin,'rows') + dimension = 2; +else + dimension = 1; +end + +chil=get(gca,'children'); +z = get(chil(1),'CData'); +x = get(chil(1),'XData'); +[maxind, maxind] = max(z,[],dimension); + +if length(x) == 2 + x = x(1):x(2); +end + +hold on +m = plot(x, maxind, '.k','Erasemode','xor') +hold off
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/figures/.svn/text-base/exportfig.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,991 @@ +function varargout = exportfig(varargin) +%EXPORTFIG Export a figure. +% EXPORTFIG(H, FILENAME) writes the figure H to FILENAME. H is +% a figure handle and FILENAME is a string that specifies the +% name of the output file. +% +% EXPORTFIG(H, FILENAME, OPTIONS) writes the figure H to FILENAME +% with options initially specified by the structure OPTIONS. The +% field names of OPTIONS must be legal parameters listed below +% and the field values must be legal values for the corresponding +% parameter. Default options can be set in releases prior to R12 +% by storing the OPTIONS structure in the root object's appdata +% with the command +% setappdata(0,'exportfigdefaults', OPTIONS) +% and for releases after R12 by setting the preference with the +% command +% setpref('exportfig', 'defaults', OPTIONS) +% +% EXPORTFIG(...,PARAM1,VAL1,PARAM2,VAL2,...) specifies +% parameters that control various characteristics of the output +% file. Any parameter value can be the string 'auto' which means +% the parameter uses the default factory behavior, overriding +% any other default for the parameter. +% +% Format Paramter: +% 'Format' a string +% specifies the output format. Defaults to 'eps'. For a +% list of export formats type 'help print'. +% 'Preview' one of the strings 'none', 'tiff' +% specifies a preview for EPS files. Defaults to 'none'. +% +% Size Parameters: +% 'Width' a positive scalar +% specifies the width in the figure's PaperUnits +% 'Height' a positive scalar +% specifies the height in the figure's PaperUnits +% 'Bounds' one of the strings 'tight', 'loose' +% specifies a tight or loose bounding box. Defaults to 'tight'. +% 'Reference' an axes handle or a string +% specifies that the width and height parameters +% are relative to the given axes. If a string is +% specified then it must evaluate to an axes handle. +% +% Specifying only one dimension sets the other dimension +% so that the exported aspect ratio is the same as the +% figure's or reference axes' current aspect ratio. +% If neither dimension is specified the size defaults to +% the width and height from the figure's or reference +% axes' size. Tight bounding boxes are only computed for +% 2-D views and in that case the computed bounds enclose all +% text objects. +% +% Rendering Parameters: +% 'Color' one of the strings 'bw', 'gray', 'cmyk' +% 'bw' specifies that lines and text are exported in +% black and all other objects in grayscale +% 'gray' specifies that all objects are exported in grayscale +% 'rgb' specifies that all objects are exported in color +% using the RGB color space +% 'cmyk' specifies that all objects are exported in color +% using the CMYK color space +% 'Renderer' one of 'painters', 'zbuffer', 'opengl' +% specifies the renderer to use +% 'Resolution' a positive scalar +% specifies the resolution in dots-per-inch. +% 'LockAxes' one of 0 or 1 +% specifies that all axes limits and ticks should be fixed +% while exporting. +% +% The default color setting is 'bw'. +% +% Font Parameters: +% 'FontMode' one of the strings 'scaled', 'fixed' +% 'FontSize' a positive scalar +% in 'scaled' mode multiplies with the font size of each +% text object to obtain the exported font size +% in 'fixed' mode specifies the font size of all text +% objects in points +% 'DefaultFixedFontSize' a positive scalar +% in 'fixed' mode specified the default font size in +% points +% 'FontSizeMin' a positive scalar +% specifies the minimum font size allowed after scaling +% 'FontSizeMax' a positive scalar +% specifies the maximum font size allowed after scaling +% 'FontEncoding' one of the strings 'latin1', 'adobe' +% specifies the character encoding of the font +% 'SeparateText' one of 0 or 1 +% specifies that the text objects are stored in separate +% file as EPS with the base filename having '_t' appended. +% +% If FontMode is 'scaled' but FontSize is not specified then a +% scaling factor is computed from the ratio of the size of the +% exported figure to the size of the actual figure. +% +% The default 'FontMode' setting is 'scaled'. +% +% Line Width Parameters: +% 'LineMode' one of the strings 'scaled', 'fixed' +% 'LineWidth' a positive scalar +% 'DefaultFixedLineWidth' a positive scalar +% 'LineWidthMin' a positive scalar +% specifies the minimum line width allowed after scaling +% 'LineWidthMax' a positive scalar +% specifies the maximum line width allowed after scaling +% The semantics of 'Line' parameters are exactly the +% same as the corresponding 'Font' parameters, except that +% they apply to line widths instead of font sizes. +% +% Style Map Parameter: +% 'LineStyleMap' one of [], 'bw', or a function name or handle +% specifies how to map line colors to styles. An empty +% style map means styles are not changed. The style map +% 'bw' is a built-in mapping that maps lines with the same +% color to the same style and otherwise cycles through the +% available styles. A user-specified map is a function +% that takes as input a cell array of line objects and +% outputs a cell array of line style strings. The default +% map is []. +% +% Examples: +% exportfig(gcf,'fig1.eps','height',3); +% Exports the current figure to the file named 'fig1.eps' with +% a height of 3 inches (assuming the figure's PaperUnits is +% inches) and an aspect ratio the same as the figure's aspect +% ratio on screen. +% +% opts = struct('FontMode','fixed','FontSize',10,'height',3); +% exportfig(gcf, 'fig2.eps', opts, 'height', 5); +% Exports the current figure to 'fig2.eps' with all +% text in 10 point fonts and with height 5 inches. +% +% See also PREVIEWFIG, APPLYTOFIG, RESTOREFIG, PRINT. + +% Copyright 2000 Ben Hinkle +% Email bug reports and comments to bhinkle@mathworks.com + +if (nargin < 2) + error('Too few input arguments'); +end + +% exportfig(H, filename, [options,] ...) +H = varargin{1}; +if ~LocalIsHG(H,'figure') + error('First argument must be a handle to a figure.'); +end +filename = varargin{2}; +if ~ischar(filename) + error('Second argument must be a string.'); +end +paramPairs = {varargin{3:end}}; +if nargin > 2 + if isstruct(paramPairs{1}) + pcell = LocalToCell(paramPairs{1}); + paramPairs = {pcell{:}, paramPairs{2:end}}; + end +end +verstr = version; +majorver = str2num(verstr(1)); +defaults = []; +if majorver > 5 + if ispref('exportfig','defaults') + defaults = getpref('exportfig','defaults'); + end +elseif exist('getappdata') + defaults = getappdata(0,'exportfigdefaults'); +end +if ~isempty(defaults) + dcell = LocalToCell(defaults); + paramPairs = {dcell{:}, paramPairs{:}}; +end + +% Do some validity checking on param-value pairs +if (rem(length(paramPairs),2) ~= 0) + error(['Invalid input syntax. Optional parameters and values' ... + ' must be in pairs.']); +end + +auto.format = 'eps'; +auto.preview = 'none'; +auto.width = -1; +auto.height = -1; +auto.color = 'bw'; +auto.defaultfontsize=10; +auto.fontsize = -1; +auto.fontmode='scaled'; +auto.fontmin = 8; +auto.fontmax = 60; +auto.defaultlinewidth = 1.0; +auto.linewidth = -1; +auto.linemode=[]; +auto.linemin = 0.5; +auto.linemax = 100; +auto.fontencoding = 'latin1'; +auto.renderer = []; +auto.resolution = []; +auto.stylemap = []; +auto.applystyle = 0; +auto.refobj = -1; +auto.bounds = 'tight'; +explicitbounds = 0; +auto.lockaxes = 1; +auto.separatetext = 0; +opts = auto; + +% Process param-value pairs +args = {}; +for k = 1:2:length(paramPairs) + param = lower(paramPairs{k}); + if ~ischar(param) + error('Optional parameter names must be strings'); + end + value = paramPairs{k+1}; + + switch (param) + case 'format' + opts.format = LocalCheckAuto(lower(value),auto.format); + if strcmp(opts.format,'preview') + error(['Format ''preview'' no longer supported. Use PREVIEWFIG' ... + ' instead.']); + end + case 'preview' + opts.preview = LocalCheckAuto(lower(value),auto.preview); + if ~strcmp(opts.preview,{'none','tiff'}) + error('Preview must be ''none'' or ''tiff''.'); + end + case 'width' + opts.width = LocalToNum(value, auto.width); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.width) + error('Width must be a numeric scalar > 0'); + end + end + case 'height' + opts.height = LocalToNum(value, auto.height); + if ~ischar(value) | ~strcmp(value,'auto') + if(~LocalIsPositiveScalar(opts.height)) + error('Height must be a numeric scalar > 0'); + end + end + case 'color' + opts.color = LocalCheckAuto(lower(value),auto.color); + if ~strcmp(opts.color,{'bw','gray','rgb','cmyk'}) + error('Color must be ''bw'', ''gray'',''rgb'' or ''cmyk''.'); + end + case 'fontmode' + opts.fontmode = LocalCheckAuto(lower(value),auto.fontmode); + if ~strcmp(opts.fontmode,{'scaled','fixed'}) + error('FontMode must be ''scaled'' or ''fixed''.'); + end + case 'fontsize' + opts.fontsize = LocalToNum(value,auto.fontsize); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.fontsize) + error('FontSize must be a numeric scalar > 0'); + end + end + case 'defaultfixedfontsize' + opts.defaultfontsize = LocalToNum(value,auto.defaultfontsize); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.defaultfontsize) + error('DefaultFixedFontSize must be a numeric scalar > 0'); + end + end + case 'fontsizemin' + opts.fontmin = LocalToNum(value,auto.fontmin); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.fontmin) + error('FontSizeMin must be a numeric scalar > 0'); + end + end + case 'fontsizemax' + opts.fontmax = LocalToNum(value,auto.fontmax); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.fontmax) + error('FontSizeMax must be a numeric scalar > 0'); + end + end + case 'fontencoding' + opts.fontencoding = LocalCheckAuto(lower(value),auto.fontencoding); + if ~strcmp(opts.fontencoding,{'latin1','adobe'}) + error('FontEncoding must be ''latin1'' or ''adobe''.'); + end + case 'linemode' + opts.linemode = LocalCheckAuto(lower(value),auto.linemode); + if ~strcmp(opts.linemode,{'scaled','fixed'}) + error('LineMode must be ''scaled'' or ''fixed''.'); + end + case 'linewidth' + opts.linewidth = LocalToNum(value,auto.linewidth); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.linewidth) + error('LineWidth must be a numeric scalar > 0'); + end + end + case 'defaultfixedlinewidth' + opts.defaultlinewidth = LocalToNum(value,auto.defaultlinewidth); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.defaultlinewidth) + error(['DefaultFixedLineWidth must be a numeric scalar >' ... + ' 0']); + end + end + case 'linewidthmin' + opts.linemin = LocalToNum(value,auto.linemin); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.linemin) + error('LineWidthMin must be a numeric scalar > 0'); + end + end + case 'linewidthmax' + opts.linemax = LocalToNum(value,auto.linemax); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.linemax) + error('LineWidthMax must be a numeric scalar > 0'); + end + end + case 'linestylemap' + opts.stylemap = LocalCheckAuto(value,auto.stylemap); + case 'renderer' + opts.renderer = LocalCheckAuto(lower(value),auto.renderer); + if ~ischar(value) | ~strcmp(value,'auto') + if ~strcmp(opts.renderer,{'painters','zbuffer','opengl'}) + error(['Renderer must be ''painters'', ''zbuffer'' or' ... + ' ''opengl''.']); + end + end + case 'resolution' + opts.resolution = LocalToNum(value,auto.resolution); + if ~ischar(value) | ~strcmp(value,'auto') + if ~(isnumeric(value) & (prod(size(value)) == 1) & (value >= 0)); + error('Resolution must be a numeric scalar >= 0'); + end + end + case 'applystyle' % means to apply the options and not export + opts.applystyle = 1; + case 'reference' + if ischar(value) + if strcmp(value,'auto') + opts.refobj = auto.refobj; + else + opts.refobj = eval(value); + end + else + opts.refobj = value; + end + if ~LocalIsHG(opts.refobj,'axes') + error('Reference object must evaluate to an axes handle.'); + end + case 'bounds' + opts.bounds = LocalCheckAuto(lower(value),auto.bounds); + explicitbounds = 1; + if ~strcmp(opts.bounds,{'tight','loose'}) + error('Bounds must be ''tight'' or ''loose''.'); + end + case 'lockaxes' + opts.lockaxes = LocalToNum(value,auto.lockaxes); + case 'separatetext' + opts.separatetext = LocalToNum(value,auto.separatetext); + otherwise + error(['Unrecognized option ' param '.']); + end +end + +% make sure figure is up-to-date +drawnow; + +allLines = findall(H, 'type', 'line'); +allText = findall(H, 'type', 'text'); +allAxes = findall(H, 'type', 'axes'); +allImages = findall(H, 'type', 'image'); +allLights = findall(H, 'type', 'light'); +allPatch = findall(H, 'type', 'patch'); +allSurf = findall(H, 'type', 'surface'); +allRect = findall(H, 'type', 'rectangle'); +allFont = [allText; allAxes]; +allColor = [allLines; allText; allAxes; allLights]; +allMarker = [allLines; allPatch; allSurf]; +allEdge = [allPatch; allSurf]; +allCData = [allImages; allPatch; allSurf]; + +old.objs = {}; +old.prop = {}; +old.values = {}; + +% Process format +if strncmp(opts.format,'eps',3) & ~strcmp(opts.preview,'none') + args = {args{:}, ['-' opts.preview]}; +end + +hadError = 0; +oldwarn = warning; +try + + % lock axes limits, ticks and labels if requested + if opts.lockaxes + old = LocalManualAxesMode(old, allAxes, 'TickMode'); + old = LocalManualAxesMode(old, allAxes, 'TickLabelMode'); + old = LocalManualAxesMode(old, allAxes, 'LimMode'); + end + + % Process size parameters + figurePaperUnits = get(H, 'PaperUnits'); + oldFigureUnits = get(H, 'Units'); + oldFigPos = get(H,'Position'); + set(H, 'Units', figurePaperUnits); + figPos = get(H,'Position'); + refsize = figPos(3:4); + if opts.refobj ~= -1 + oldUnits = get(opts.refobj, 'Units'); + set(opts.refobj, 'Units', figurePaperUnits); + r = get(opts.refobj, 'Position'); + refsize = r(3:4); + set(opts.refobj, 'Units', oldUnits); + end + aspectRatio = refsize(1)/refsize(2); + if (opts.width == -1) & (opts.height == -1) + opts.width = refsize(1); + opts.height = refsize(2); + elseif (opts.width == -1) + opts.width = opts.height * aspectRatio; + elseif (opts.height == -1) + opts.height = opts.width / aspectRatio; + end + wscale = opts.width/refsize(1); + hscale = opts.height/refsize(2); + sizescale = min(wscale,hscale); + old = LocalPushOldData(old,H,'PaperPositionMode', ... + get(H,'PaperPositionMode')); + set(H, 'PaperPositionMode', 'auto'); + newPos = [figPos(1) figPos(2)+figPos(4)*(1-hscale) ... + wscale*figPos(3) hscale*figPos(4)]; + set(H, 'Position', newPos); + set(H, 'Units', oldFigureUnits); + + % process line-style map + if ~isempty(opts.stylemap) & ~isempty(allLines) + oldlstyle = LocalGetAsCell(allLines,'LineStyle'); + old = LocalPushOldData(old, allLines, {'LineStyle'}, ... + oldlstyle); + newlstyle = oldlstyle; + if ischar(opts.stylemap) & strcmpi(opts.stylemap,'bw') + newlstyle = LocalMapColorToStyle(allLines); + else + try + newlstyle = feval(opts.stylemap,allLines); + catch + warning(['Skipping stylemap. ' lasterr]); + end + end + set(allLines,{'LineStyle'},newlstyle); + end + + % Process rendering parameters + switch (opts.color) + case {'bw', 'gray'} + if ~strcmp(opts.color,'bw') & strncmp(opts.format,'eps',3) + opts.format = [opts.format 'c']; + end + args = {args{:}, ['-d' opts.format]}; + + %compute and set gray colormap + oldcmap = get(H,'Colormap'); + newgrays = 0.30*oldcmap(:,1) + 0.59*oldcmap(:,2) + 0.11*oldcmap(:,3); + newcmap = [newgrays newgrays newgrays]; + old = LocalPushOldData(old, H, 'Colormap', oldcmap); + set(H, 'Colormap', newcmap); + + %compute and set ColorSpec and CData properties + old = LocalUpdateColors(allColor, 'color', old); + old = LocalUpdateColors(allAxes, 'xcolor', old); + old = LocalUpdateColors(allAxes, 'ycolor', old); + old = LocalUpdateColors(allAxes, 'zcolor', old); + old = LocalUpdateColors(allMarker, 'MarkerEdgeColor', old); + old = LocalUpdateColors(allMarker, 'MarkerFaceColor', old); + old = LocalUpdateColors(allEdge, 'EdgeColor', old); + old = LocalUpdateColors(allEdge, 'FaceColor', old); + old = LocalUpdateColors(allCData, 'CData', old); + + case {'rgb','cmyk'} + if strncmp(opts.format,'eps',3) + opts.format = [opts.format 'c']; + args = {args{:}, ['-d' opts.format]}; + if strcmp(opts.color,'cmyk') + args = {args{:}, '-cmyk'}; + end + else + args = {args{:}, ['-d' opts.format]}; + end + otherwise + error('Invalid Color parameter'); + end + if (~isempty(opts.renderer)) + args = {args{:}, ['-' opts.renderer]}; + end + if (~isempty(opts.resolution)) | ~strncmp(opts.format,'eps',3) + if isempty(opts.resolution) + opts.resolution = 0; + end + args = {args{:}, ['-r' int2str(opts.resolution)]}; + end + + % Process font parameters + if ~isempty(opts.fontmode) + oldfonts = LocalGetAsCell(allFont,'FontSize'); + oldfontunits = LocalGetAsCell(allFont,'FontUnits'); + set(allFont,'FontUnits','points'); + switch (opts.fontmode) + case 'fixed' + if (opts.fontsize == -1) + set(allFont,'FontSize',opts.defaultfontsize); + else + set(allFont,'FontSize',opts.fontsize); + end + case 'scaled' + if (opts.fontsize == -1) + scale = sizescale; + else + scale = opts.fontsize; + end + newfonts = LocalScale(oldfonts,scale,opts.fontmin,opts.fontmax); + set(allFont,{'FontSize'},newfonts); + otherwise + error('Invalid FontMode parameter'); + end + old = LocalPushOldData(old, allFont, {'FontSize'}, oldfonts); + old = LocalPushOldData(old, allFont, {'FontUnits'}, oldfontunits); + end + if strcmp(opts.fontencoding,'adobe') & strncmp(opts.format,'eps',3) + args = {args{:}, '-adobecset'}; + end + + % Process line parameters + if ~isempty(opts.linemode) + oldlines = LocalGetAsCell(allMarker,'LineWidth'); + old = LocalPushOldData(old, allMarker, {'LineWidth'}, oldlines); + switch (opts.linemode) + case 'fixed' + if (opts.linewidth == -1) + set(allMarker,'LineWidth',opts.defaultlinewidth); + else + set(allMarker,'LineWidth',opts.linewidth); + end + case 'scaled' + if (opts.linewidth == -1) + scale = sizescale; + else + scale = opts.linewidth; + end + newlines = LocalScale(oldlines, scale, opts.linemin, opts.linemax); + set(allMarker,{'LineWidth'},newlines); + end + end + + % adjust figure bounds to surround axes + if strcmp(opts.bounds,'tight') + if (~strncmp(opts.format,'eps',3) & LocalHas3DPlot(allAxes)) | ... + (strncmp(opts.format,'eps',3) & opts.separatetext) + if (explicitbounds == 1) + warning(['Cannot compute ''tight'' bounds. Using ''loose''' ... + ' bounds.']); + end + opts.bounds = 'loose'; + end + end + warning('off'); + if ~isempty(allAxes) + if strncmp(opts.format,'eps',3) + if strcmp(opts.bounds,'loose') + args = {args{:}, '-loose'}; + end + old = LocalPushOldData(old,H,'Position', oldFigPos); + elseif strcmp(opts.bounds,'tight') + oldaunits = LocalGetAsCell(allAxes,'Units'); + oldapos = LocalGetAsCell(allAxes,'Position'); + oldtunits = LocalGetAsCell(allText,'units'); + oldtpos = LocalGetAsCell(allText,'Position'); + set(allAxes,'units','points'); + apos = LocalGetAsCell(allAxes,'Position'); + oldunits = get(H,'Units'); + set(H,'units','points'); + origfr = get(H,'position'); + fr = []; + for k=1:length(allAxes) + if ~strcmpi(get(allAxes(k),'Tag'),'legend') + axesR = apos{k}; + r = LocalAxesTightBoundingBox(axesR, allAxes(k)); + r(1:2) = r(1:2) + axesR(1:2); + fr = LocalUnionRect(fr,r); + end + end + if isempty(fr) + fr = [0 0 origfr(3:4)]; + end + for k=1:length(allAxes) + ax = allAxes(k); + r = apos{k}; + r(1:2) = r(1:2) - fr(1:2); + set(ax,'Position',r); + end + old = LocalPushOldData(old, allAxes, {'Position'}, oldapos); + old = LocalPushOldData(old, allText, {'Position'}, oldtpos); + old = LocalPushOldData(old, allText, {'Units'}, oldtunits); + old = LocalPushOldData(old, allAxes, {'Units'}, oldaunits); + old = LocalPushOldData(old, H, 'Position', oldFigPos); + old = LocalPushOldData(old, H, 'Units', oldFigureUnits); + r = [origfr(1) origfr(2)+origfr(4)-fr(4) fr(3:4)]; + set(H,'Position',r); + else + args = {args{:}, '-loose'}; + old = LocalPushOldData(old,H,'Position', oldFigPos); + end + end + + % Process text in a separate file if needed + if opts.separatetext & ~opts.applystyle + % First hide all text and export + oldtvis = LocalGetAsCell(allText,'visible'); + set(allText,'visible','off'); + oldax = LocalGetAsCell(allAxes,'XTickLabel',1); + olday = LocalGetAsCell(allAxes,'YTickLabel',1); + oldaz = LocalGetAsCell(allAxes,'ZTickLabel',1); + null = cell(length(oldax),1); + [null{:}] = deal([]); + set(allAxes,{'XTickLabel'},null); + set(allAxes,{'YTickLabel'},null); + set(allAxes,{'ZTickLabel'},null); + print(H, filename, args{:}); + set(allText,{'Visible'},oldtvis); + set(allAxes,{'XTickLabel'},oldax); + set(allAxes,{'YTickLabel'},olday); + set(allAxes,{'ZTickLabel'},oldaz); + % Now hide all non-text and export as eps in painters + [path, name, ext] = fileparts(filename); + tfile = fullfile(path,[name '_t.eps']); + tfile2 = fullfile(path,[name '_t2.eps']); + foundRenderer = 0; + for k=1:length(args) + if strncmp('-d',args{k},2) + args{k} = '-deps'; + elseif strncmp('-zbuffer',args{k},8) | ... + strncmp('-opengl', args{k},6) + args{k} = '-painters'; + foundRenderer = 1; + end + end + if ~foundRenderer + args = {args{:}, '-painters'}; + end + allNonText = [allLines; allLights; allPatch; ... + allImages; allSurf; allRect]; + oldvis = LocalGetAsCell(allNonText,'visible'); + oldc = LocalGetAsCell(allAxes,'color'); + oldaxg = LocalGetAsCell(allAxes,'XGrid'); + oldayg = LocalGetAsCell(allAxes,'YGrid'); + oldazg = LocalGetAsCell(allAxes,'ZGrid'); + [null{:}] = deal('off'); + set(allAxes,{'XGrid'},null); + set(allAxes,{'YGrid'},null); + set(allAxes,{'ZGrid'},null); + set(allNonText,'Visible','off'); + set(allAxes,'Color','none'); + print(H, tfile2, args{:}); + set(allNonText,{'Visible'},oldvis); + set(allAxes,{'Color'},oldc); + set(allAxes,{'XGrid'},oldaxg); + set(allAxes,{'YGrid'},oldayg); + set(allAxes,{'ZGrid'},oldazg); + %hack up the postscript file + fid1 = fopen(tfile,'w'); + fid2 = fopen(tfile2,'r'); + line = fgetl(fid2); + while ischar(line) + if strncmp(line,'%%Title',7) + fprintf(fid1,'%s\n',['%%Title: ', tfile]); + elseif (length(line) < 3) + fprintf(fid1,'%s\n',line); + elseif ~strcmp(line(end-2:end),' PR') & ... + ~strcmp(line(end-1:end),' L') + fprintf(fid1,'%s\n',line); + end + line = fgetl(fid2); + end + fclose(fid1); + fclose(fid2); + delete(tfile2); + + elseif ~opts.applystyle + drawnow; + print(H, filename, args{:}); + end + warning(oldwarn); + +catch + warning(oldwarn); + hadError = 1; +end + +% Restore figure settings +if opts.applystyle + varargout{1} = old; +else + for n=1:length(old.objs) + if ~iscell(old.values{n}) & iscell(old.prop{n}) + old.values{n} = {old.values{n}}; + end + set(old.objs{n}, old.prop{n}, old.values{n}); + end +end + +if hadError + error(deblank(lasterr)); +end + +% +% Local Functions +% + +function outData = LocalPushOldData(inData, objs, prop, values) +outData.objs = {objs, inData.objs{:}}; +outData.prop = {prop, inData.prop{:}}; +outData.values = {values, inData.values{:}}; + +function cellArray = LocalGetAsCell(fig,prop,allowemptycell); +cellArray = get(fig,prop); +if nargin < 3 + allowemptycell = 0; +end +if ~iscell(cellArray) & (allowemptycell | ~isempty(cellArray)) + cellArray = {cellArray}; +end + +function newArray = LocalScale(inArray, scale, minv, maxv) +n = length(inArray); +newArray = cell(n,1); +for k=1:n + newArray{k} = min(maxv,max(minv,scale*inArray{k}(1))); +end + +function gray = LocalMapToGray1(color) +gray = color; +if ischar(color) + switch color(1) + case 'y' + color = [1 1 0]; + case 'm' + color = [1 0 1]; + case 'c' + color = [0 1 1]; + case 'r' + color = [1 0 0]; + case 'g' + color = [0 1 0]; + case 'b' + color = [0 0 1]; + case 'w' + color = [1 1 1]; + case 'k' + color = [0 0 0]; + end +end +if ~ischar(color) + gray = 0.30*color(1) + 0.59*color(2) + 0.11*color(3); +end + +function newArray = LocalMapToGray(inArray); +n = length(inArray); +newArray = cell(n,1); +for k=1:n + color = inArray{k}; + if ~isempty(color) + color = LocalMapToGray1(color); + end + if isempty(color) | ischar(color) + newArray{k} = color; + else + newArray{k} = [color color color]; + end +end + +function newArray = LocalMapColorToStyle(inArray); +inArray = LocalGetAsCell(inArray,'Color'); +n = length(inArray); +newArray = cell(n,1); +styles = {'-','--',':','-.'}; +uniques = []; +nstyles = length(styles); +for k=1:n + gray = LocalMapToGray1(inArray{k}); + if isempty(gray) | ischar(gray) | gray < .05 + newArray{k} = '-'; + else + if ~isempty(uniques) & any(gray == uniques) + ind = find(gray==uniques); + else + uniques = [uniques gray]; + ind = length(uniques); + end + newArray{k} = styles{mod(ind-1,nstyles)+1}; + end +end + +function newArray = LocalMapCData(inArray); +n = length(inArray); +newArray = cell(n,1); +for k=1:n + color = inArray{k}; + if (ndims(color) == 3) & isa(color,'double') + gray = 0.30*color(:,:,1) + 0.59*color(:,:,2) + 0.11*color(:,:,3); + color(:,:,1) = gray; + color(:,:,2) = gray; + color(:,:,3) = gray; + end + newArray{k} = color; +end + +function outData = LocalUpdateColors(inArray, prop, inData) +value = LocalGetAsCell(inArray,prop); +outData.objs = {inData.objs{:}, inArray}; +outData.prop = {inData.prop{:}, {prop}}; +outData.values = {inData.values{:}, value}; +if (~isempty(value)) + if strcmp(prop,'CData') + value = LocalMapCData(value); + else + value = LocalMapToGray(value); + end + set(inArray,{prop},value); +end + +function bool = LocalIsPositiveScalar(value) +bool = isnumeric(value) & ... + prod(size(value)) == 1 & ... + value > 0; + +function value = LocalToNum(value,auto) +if ischar(value) + if strcmp(value,'auto') + value = auto; + else + value = str2num(value); + end +end + +%convert a struct to {field1,val1,field2,val2,...} +function c = LocalToCell(s) +f = fieldnames(s); +v = struct2cell(s); +opts = cell(2,length(f)); +opts(1,:) = f; +opts(2,:) = v; +c = {opts{:}}; + +function c = LocalIsHG(obj,hgtype) +c = 0; +if (length(obj) == 1) & ishandle(obj) + c = strcmp(get(obj,'type'),hgtype); +end + +function c = LocalHas3DPlot(a) +zticks = LocalGetAsCell(a,'ZTickLabel'); +c = 0; +for k=1:length(zticks) + if ~isempty(zticks{k}) + c = 1; + return; + end +end + +function r = LocalUnionRect(r1,r2) +if isempty(r1) + r = r2; +elseif isempty(r2) + r = r1; +elseif max(r2(3:4)) > 0 + left = min(r1(1),r2(1)); + bot = min(r1(2),r2(2)); + right = max(r1(1)+r1(3),r2(1)+r2(3)); + top = max(r1(2)+r1(4),r2(2)+r2(4)); + r = [left bot right-left top-bot]; +else + r = r1; +end + +function c = LocalLabelsMatchTicks(labs,ticks) +c = 0; +try + t1 = num2str(ticks(1)); + n = length(ticks); + tend = num2str(ticks(n)); + c = strncmp(labs(1),t1,length(labs(1))) & ... + strncmp(labs(n),tend,length(labs(n))); +end + +function r = LocalAxesTightBoundingBox(axesR, a) +r = []; +atext = findall(a,'type','text','visible','on'); +if ~isempty(atext) + set(atext,'units','points'); + res=LocalGetAsCell(atext,'extent'); + for n=1:length(atext) + r = LocalUnionRect(r,res{n}); + end +end +if strcmp(get(a,'visible'),'on') + r = LocalUnionRect(r,[0 0 axesR(3:4)]); + oldunits = get(a,'fontunits'); + set(a,'fontunits','points'); + label = text(0,0,'','parent',a,... + 'units','points',... + 'fontsize',get(a,'fontsize'),... + 'fontname',get(a,'fontname'),... + 'fontweight',get(a,'fontweight'),... + 'fontangle',get(a,'fontangle'),... + 'visible','off'); + fs = get(a,'fontsize'); + + % handle y axis tick labels + ry = [0 -fs/2 0 axesR(4)+fs]; + ylabs = get(a,'yticklabels'); + yticks = get(a,'ytick'); + maxw = 0; + if ~isempty(ylabs) + for n=1:size(ylabs,1) + set(label,'string',ylabs(n,:)); + ext = get(label,'extent'); + maxw = max(maxw,ext(3)); + end + if ~LocalLabelsMatchTicks(ylabs,yticks) & ... + strcmp(get(a,'xaxislocation'),'bottom') + ry(4) = ry(4) + 1.5*ext(4); + end + if strcmp(get(a,'yaxislocation'),'left') + ry(1) = -(maxw+5); + else + ry(1) = axesR(3); + end + ry(3) = maxw+5; + r = LocalUnionRect(r,ry); + end + + % handle x axis tick labels + rx = [0 0 0 fs+5]; + xlabs = get(a,'xticklabels'); + xticks = get(a,'xtick'); + if ~isempty(xlabs) + if strcmp(get(a,'xaxislocation'),'bottom') + rx(2) = -(fs+5); + if ~LocalLabelsMatchTicks(xlabs,xticks); + rx(4) = rx(4) + 2*fs; + rx(2) = rx(2) - 2*fs; + end + else + rx(2) = axesR(4); + % exponent is still below axes + if ~LocalLabelsMatchTicks(xlabs,xticks); + rx(4) = rx(4) + axesR(4) + 2*fs; + rx(2) = -2*fs; + end + end + set(label,'string',xlabs(1,:)); + ext1 = get(label,'extent'); + rx(1) = -ext1(3)/2; + set(label,'string',xlabs(size(xlabs,1),:)); + ext2 = get(label,'extent'); + rx(3) = axesR(3) + (ext2(3) + ext1(3))/2; + r = LocalUnionRect(r,rx); + end + set(a,'fontunits',oldunits); + delete(label); +end + +function c = LocalManualAxesMode(old, allAxes, base) +xs = ['X' base]; +ys = ['Y' base]; +zs = ['Z' base]; +oldXMode = LocalGetAsCell(allAxes,xs); +oldYMode = LocalGetAsCell(allAxes,ys); +oldZMode = LocalGetAsCell(allAxes,zs); +old = LocalPushOldData(old, allAxes, {xs}, oldXMode); +old = LocalPushOldData(old, allAxes, {ys}, oldYMode); +old = LocalPushOldData(old, allAxes, {zs}, oldZMode); +set(allAxes,xs,'manual'); +set(allAxes,ys,'manual'); +set(allAxes,zs,'manual'); +c = old; + +function val = LocalCheckAuto(val, auto) +if ischar(val) & strcmp(val,'auto') + val = auto; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/figures/.svn/text-base/imxy.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +function imxy(mat) +imagesc(mat) +axis xy +set(gcf,'Color',[0 0 .001]) +set(gca,'XColor',[1 1 1] *.5,'YColor',[1 1 1] *.5) +colormap(bone) \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/figures/.svn/text-base/printme.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +function printme(width) + +set(gcf, 'PaperUnits', 'centimeters'); +papersize = get(gcf, 'PaperSize'); +paperratio = papersize(2) / papersize(1); +set(gcf, 'PaperSize', [1 paperratio] * width); +set(gcf, 'Renderer', 'painters') +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/figures/.svn/text-base/rotateticklabel.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,58 @@ +function th=rotateticklabel(h,rot,demo) +%ROTATETICKLABEL rotates tick labels +% TH=ROTATETICKLABEL(H,ROT) is the calling form where H is a handle to +% the axis that contains the XTickLabels that are to be rotated. ROT is +% an optional parameter that specifies the angle of rotation. The default +% angle is 90. TH is a handle to the text objects created. For long +% strings such as those produced by datetick, you may have to adjust the +% position of the axes so the labels don't get cut off. +% +% Of course, GCA can be substituted for H if desired. +% +% TH=ROTATETICKLABEL([],[],'demo') shows a demo figure. +% +% Known deficiencies: if tick labels are raised to a power, the power +% will be lost after rotation. +% +% See also datetick. + +% Written Oct 14, 2005 by Andy Bliss +% Copyright 2005 by Andy Bliss + +%DEMO: +if nargin==3 + x=[now-.7 now-.3 now]; + y=[20 35 15]; + figure + plot(x,y,'.-') + datetick('x',0,'keepticks') + h=gca; + set(h,'position',[0.13 0.35 0.775 0.55]) + rot=90; +end + +%set the default rotation if user doesn't specify +if nargin==1 + rot=90; +end +%make sure the rotation is in the range 0:360 (brute force method) +while rot>360 + rot=rot-360; +end +while rot<0 + rot=rot+360; +end +%get current tick labels +a=get(h,'XTickLabel'); +%erase current tick labels from figure +set(h,'XTickLabel',[]); +%get tick label positions +b=get(h,'XTick'); +c=get(h,'YTick'); +%make new tick labels +if rot<180 + th=text(b,repmat(c(1)-.1*(c(2)-c(1)),length(b),1),a,'HorizontalAlignment','right','rotation',rot); +else + th=text(b,repmat(c(1)-.1*(c(2)-c(1)),length(b),1),a,'HorizontalAlignment','left','rotation',rot); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/figures/.svn/text-base/uimage.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,132 @@ +function h = uimage(varargin) +%UIMAGE Display image with uneven axis. +% UIMAGE(X,Y,C) displays matrix C as an image, using the vectors X and +% Y to specify the X and Y coordinates. X and Y may be unevenly spaced +% vectors, but must be increasing. The size of C must be LENGTH(Y)* +% LENGTH(X). (Most probably you'll want to display C' instead of C). +% +% Contrary to Matlab's original IMAGE function, here the vectors X and Y +% do not need to be linearly spaced. Whereas IMAGE linearly interpolates +% the X-axis between X(1) and X(end), ignoring all other values (idem +% for Y), UIMAGE allows for X and/or Y to be unevenly spaced vectors, by +% locally stretching the matrix C (ie, by duplicating some elements of C) +% for larger X and/or Y intervals. +% +% The syntax for UIMAGE(X,Y,C,...) is the same as IMAGE(X,Y,C,...) +% (all the remaining arguments, eg 'PropertyName'-PropertyValue pairs, +% are passed to IMAGE). See IMAGE for details. +% +% Use UIMAGESC to scale the data using the full colormap. The syntax for +% UIMAGESC(X,Y,C,...) is the same as IMAGESC(X,Y,C,...). +% +% Typical uses: +% - Plotting a spatio-temporal diagram (T,X), with unevenly spaced +% time intervals for T (eg, when some values are missing, or when +% using a non-constant sampling rate). +% - Plotting a set of power spectra with frequency in log-scale. +% +% h = UIMAGE(X,Y,C,...) returns a handle to the image. +% +% Example: +% c = randn(50,20); % Random 50x20 matrix +% x = logspace(1,3,50); % log-spaced X-axis, between 10 and 1000 +% y = linspace(3,8,20); % lin-spaced Y-axis, between 3 and 8 +% uimagesc(x,y,c'); % displays the matrix +% +% F. Moisy +% Revision: 1.03, Date: 2006/06/14. +% +% See also IMAGE, IMAGESC, UIMAGESC. + + +% History: +% 2006/06/12: v1.00, first version. +% 2006/06/14: v1.03, minor bug fixed; works in ML6. + +error(nargchk(3,inf,nargin)); + +% maximum number of matrix elements to interpolate the uneven axis +% (typically between 500 and 5000): +nmax = 2000; + +x = varargin{1}; +y = varargin{2}; +c = varargin{3}; + +if any(diff(x)<=0) || any(diff(y)<=0) + error('The X and Y axis should be increasing.'); +end + +dx = min(diff(x)); % smallest interval for X +dy = min(diff(y)); % smallest interval for Y + +% test if X and Y are linearly spaced (to within 10^-12): +evenx = all(abs(diff(x)/dx-1)<1e-12); % true if X is linearly spaced +eveny = all(abs(diff(y)/dy-1)<1e-12); % true if Y is linearly spaced + + +if evenx && eveny % X and Y both evenly spaced + + xe = x; + ye = y; + ce = c; + +elseif evenx && ~eveny % X even and Y uneven + + nx = length(x); + xe = x; + + ny = ceil(1 + (y(end) - y(1))/dy); % number of points for Y + ny = min(ny, nmax); + ye = linspace(y(1), y(end), ny); + + ce = zeros(ny,nx); + + for j=1:ny + indj = find(y<=ye(j)); + ce(j,1:nx) = c(indj(end), 1:nx); + end; + +elseif ~evenx && eveny % X uneven and Y even + + nx = ceil(1 + (x(end) - x(1))/dx); % number of points for X + nx = min(nx, nmax); + xe = linspace(x(1), x(end), nx); + + ny = length(y); + ye = y; + + ce = zeros(ny,nx); + + for i=1:nx + indi = find(x<=xe(i)); + ce(1:ny,i) = c(1:ny, indi(end)); + end; + +elseif ~evenx && ~eveny % X and Y both uneven + + nx = ceil(1 + (x(end) - x(1))/dx); % number of points for X + nx = min(nx, nmax); + xe = linspace(x(1), x(end), nx); + + ny = ceil(1 + (y(end) - y(1))/dy); % number of points for Y + ny = min(ny, nmax); + ye = linspace(y(1), y(end), ny); + + ce = zeros(ny,nx); + + for i=1:nx + for j=1:ny + indi = find(x<=xe(i)); + indj = find(y<=ye(j)); + ce(j,i) = c(indi(end), indj(end)); + end; + end; + +end + +hh = image(xe, ye, ce, varargin{4:end}); + +if nargout>0 + h = hh; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/figures/.svn/text-base/uimagesc.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,57 @@ +function h = uimagesc(varargin) +%UIMAGESC Display scaled image with uneven axis. +% UIMAGESC(...) is the same as UIMAGE(...) except the data is scaled +% to use the full colormap. See UIMAGE for details. +% +% Note: UIMAGESC is based on Matlab's original IMAGESC, Revision 5.11.4.5. +% UIMAGESC simply calls UIMAGE with a scaled colormap. +% +% F. Moisy - adapted from TMW +% Revision: 1.01, Date: 2006/06/13. +% +% See also IMAGE, IMAGESC, UIMAGE. + +% History: +% 2006/06/12: v1.00, first version. + +clim = []; +switch (nargin), + case 0, + hh = uimage('CDataMapping','scaled'); + case 1, + hh = uimage(varargin{1},'CDataMapping','scaled'); + case 3, + hh = uimage(varargin{:},'CDataMapping','scaled'); + otherwise, + + % Determine if last input is clim + if isequal(size(varargin{end}),[1 2]) + str = false(length(varargin),1); + for n=1:length(varargin) + str(n) = ischar(varargin{n}); + end + str = find(str); + if isempty(str) || (rem(length(varargin)-min(str),2)==0), + clim = varargin{end}; + varargin(end) = []; % Remove last cell + else + clim = []; + end + else + clim = []; + end + hh = uimage(varargin{:},'CDataMapping','scaled'); +end + +% Get the parent Axes of the image +cax = ancestor(hh,'axes'); + +if ~isempty(clim), + set(cax,'CLim',clim) +elseif ~ishold(cax), + set(cax,'CLimMode','auto') +end + +if nargout > 0 + h = hh; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/figures/addmax.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,19 @@ +function addmax(varargin) +if length(varargin)>0 && strcmp(varargin,'rows') + dimension = 2; +else + dimension = 1; +end + +chil=get(gca,'children'); +z = get(chil(1),'CData'); +x = get(chil(1),'XData'); +[maxind, maxind] = max(z,[],dimension); + +if length(x) == 2 + x = x(1):x(2); +end + +hold on +m = plot(x, maxind, '.k','Erasemode','xor') +hold off
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/figures/exportfig.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,991 @@ +function varargout = exportfig(varargin) +%EXPORTFIG Export a figure. +% EXPORTFIG(H, FILENAME) writes the figure H to FILENAME. H is +% a figure handle and FILENAME is a string that specifies the +% name of the output file. +% +% EXPORTFIG(H, FILENAME, OPTIONS) writes the figure H to FILENAME +% with options initially specified by the structure OPTIONS. The +% field names of OPTIONS must be legal parameters listed below +% and the field values must be legal values for the corresponding +% parameter. Default options can be set in releases prior to R12 +% by storing the OPTIONS structure in the root object's appdata +% with the command +% setappdata(0,'exportfigdefaults', OPTIONS) +% and for releases after R12 by setting the preference with the +% command +% setpref('exportfig', 'defaults', OPTIONS) +% +% EXPORTFIG(...,PARAM1,VAL1,PARAM2,VAL2,...) specifies +% parameters that control various characteristics of the output +% file. Any parameter value can be the string 'auto' which means +% the parameter uses the default factory behavior, overriding +% any other default for the parameter. +% +% Format Paramter: +% 'Format' a string +% specifies the output format. Defaults to 'eps'. For a +% list of export formats type 'help print'. +% 'Preview' one of the strings 'none', 'tiff' +% specifies a preview for EPS files. Defaults to 'none'. +% +% Size Parameters: +% 'Width' a positive scalar +% specifies the width in the figure's PaperUnits +% 'Height' a positive scalar +% specifies the height in the figure's PaperUnits +% 'Bounds' one of the strings 'tight', 'loose' +% specifies a tight or loose bounding box. Defaults to 'tight'. +% 'Reference' an axes handle or a string +% specifies that the width and height parameters +% are relative to the given axes. If a string is +% specified then it must evaluate to an axes handle. +% +% Specifying only one dimension sets the other dimension +% so that the exported aspect ratio is the same as the +% figure's or reference axes' current aspect ratio. +% If neither dimension is specified the size defaults to +% the width and height from the figure's or reference +% axes' size. Tight bounding boxes are only computed for +% 2-D views and in that case the computed bounds enclose all +% text objects. +% +% Rendering Parameters: +% 'Color' one of the strings 'bw', 'gray', 'cmyk' +% 'bw' specifies that lines and text are exported in +% black and all other objects in grayscale +% 'gray' specifies that all objects are exported in grayscale +% 'rgb' specifies that all objects are exported in color +% using the RGB color space +% 'cmyk' specifies that all objects are exported in color +% using the CMYK color space +% 'Renderer' one of 'painters', 'zbuffer', 'opengl' +% specifies the renderer to use +% 'Resolution' a positive scalar +% specifies the resolution in dots-per-inch. +% 'LockAxes' one of 0 or 1 +% specifies that all axes limits and ticks should be fixed +% while exporting. +% +% The default color setting is 'bw'. +% +% Font Parameters: +% 'FontMode' one of the strings 'scaled', 'fixed' +% 'FontSize' a positive scalar +% in 'scaled' mode multiplies with the font size of each +% text object to obtain the exported font size +% in 'fixed' mode specifies the font size of all text +% objects in points +% 'DefaultFixedFontSize' a positive scalar +% in 'fixed' mode specified the default font size in +% points +% 'FontSizeMin' a positive scalar +% specifies the minimum font size allowed after scaling +% 'FontSizeMax' a positive scalar +% specifies the maximum font size allowed after scaling +% 'FontEncoding' one of the strings 'latin1', 'adobe' +% specifies the character encoding of the font +% 'SeparateText' one of 0 or 1 +% specifies that the text objects are stored in separate +% file as EPS with the base filename having '_t' appended. +% +% If FontMode is 'scaled' but FontSize is not specified then a +% scaling factor is computed from the ratio of the size of the +% exported figure to the size of the actual figure. +% +% The default 'FontMode' setting is 'scaled'. +% +% Line Width Parameters: +% 'LineMode' one of the strings 'scaled', 'fixed' +% 'LineWidth' a positive scalar +% 'DefaultFixedLineWidth' a positive scalar +% 'LineWidthMin' a positive scalar +% specifies the minimum line width allowed after scaling +% 'LineWidthMax' a positive scalar +% specifies the maximum line width allowed after scaling +% The semantics of 'Line' parameters are exactly the +% same as the corresponding 'Font' parameters, except that +% they apply to line widths instead of font sizes. +% +% Style Map Parameter: +% 'LineStyleMap' one of [], 'bw', or a function name or handle +% specifies how to map line colors to styles. An empty +% style map means styles are not changed. The style map +% 'bw' is a built-in mapping that maps lines with the same +% color to the same style and otherwise cycles through the +% available styles. A user-specified map is a function +% that takes as input a cell array of line objects and +% outputs a cell array of line style strings. The default +% map is []. +% +% Examples: +% exportfig(gcf,'fig1.eps','height',3); +% Exports the current figure to the file named 'fig1.eps' with +% a height of 3 inches (assuming the figure's PaperUnits is +% inches) and an aspect ratio the same as the figure's aspect +% ratio on screen. +% +% opts = struct('FontMode','fixed','FontSize',10,'height',3); +% exportfig(gcf, 'fig2.eps', opts, 'height', 5); +% Exports the current figure to 'fig2.eps' with all +% text in 10 point fonts and with height 5 inches. +% +% See also PREVIEWFIG, APPLYTOFIG, RESTOREFIG, PRINT. + +% Copyright 2000 Ben Hinkle +% Email bug reports and comments to bhinkle@mathworks.com + +if (nargin < 2) + error('Too few input arguments'); +end + +% exportfig(H, filename, [options,] ...) +H = varargin{1}; +if ~LocalIsHG(H,'figure') + error('First argument must be a handle to a figure.'); +end +filename = varargin{2}; +if ~ischar(filename) + error('Second argument must be a string.'); +end +paramPairs = {varargin{3:end}}; +if nargin > 2 + if isstruct(paramPairs{1}) + pcell = LocalToCell(paramPairs{1}); + paramPairs = {pcell{:}, paramPairs{2:end}}; + end +end +verstr = version; +majorver = str2num(verstr(1)); +defaults = []; +if majorver > 5 + if ispref('exportfig','defaults') + defaults = getpref('exportfig','defaults'); + end +elseif exist('getappdata') + defaults = getappdata(0,'exportfigdefaults'); +end +if ~isempty(defaults) + dcell = LocalToCell(defaults); + paramPairs = {dcell{:}, paramPairs{:}}; +end + +% Do some validity checking on param-value pairs +if (rem(length(paramPairs),2) ~= 0) + error(['Invalid input syntax. Optional parameters and values' ... + ' must be in pairs.']); +end + +auto.format = 'eps'; +auto.preview = 'none'; +auto.width = -1; +auto.height = -1; +auto.color = 'bw'; +auto.defaultfontsize=10; +auto.fontsize = -1; +auto.fontmode='scaled'; +auto.fontmin = 8; +auto.fontmax = 60; +auto.defaultlinewidth = 1.0; +auto.linewidth = -1; +auto.linemode=[]; +auto.linemin = 0.5; +auto.linemax = 100; +auto.fontencoding = 'latin1'; +auto.renderer = []; +auto.resolution = []; +auto.stylemap = []; +auto.applystyle = 0; +auto.refobj = -1; +auto.bounds = 'tight'; +explicitbounds = 0; +auto.lockaxes = 1; +auto.separatetext = 0; +opts = auto; + +% Process param-value pairs +args = {}; +for k = 1:2:length(paramPairs) + param = lower(paramPairs{k}); + if ~ischar(param) + error('Optional parameter names must be strings'); + end + value = paramPairs{k+1}; + + switch (param) + case 'format' + opts.format = LocalCheckAuto(lower(value),auto.format); + if strcmp(opts.format,'preview') + error(['Format ''preview'' no longer supported. Use PREVIEWFIG' ... + ' instead.']); + end + case 'preview' + opts.preview = LocalCheckAuto(lower(value),auto.preview); + if ~strcmp(opts.preview,{'none','tiff'}) + error('Preview must be ''none'' or ''tiff''.'); + end + case 'width' + opts.width = LocalToNum(value, auto.width); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.width) + error('Width must be a numeric scalar > 0'); + end + end + case 'height' + opts.height = LocalToNum(value, auto.height); + if ~ischar(value) | ~strcmp(value,'auto') + if(~LocalIsPositiveScalar(opts.height)) + error('Height must be a numeric scalar > 0'); + end + end + case 'color' + opts.color = LocalCheckAuto(lower(value),auto.color); + if ~strcmp(opts.color,{'bw','gray','rgb','cmyk'}) + error('Color must be ''bw'', ''gray'',''rgb'' or ''cmyk''.'); + end + case 'fontmode' + opts.fontmode = LocalCheckAuto(lower(value),auto.fontmode); + if ~strcmp(opts.fontmode,{'scaled','fixed'}) + error('FontMode must be ''scaled'' or ''fixed''.'); + end + case 'fontsize' + opts.fontsize = LocalToNum(value,auto.fontsize); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.fontsize) + error('FontSize must be a numeric scalar > 0'); + end + end + case 'defaultfixedfontsize' + opts.defaultfontsize = LocalToNum(value,auto.defaultfontsize); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.defaultfontsize) + error('DefaultFixedFontSize must be a numeric scalar > 0'); + end + end + case 'fontsizemin' + opts.fontmin = LocalToNum(value,auto.fontmin); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.fontmin) + error('FontSizeMin must be a numeric scalar > 0'); + end + end + case 'fontsizemax' + opts.fontmax = LocalToNum(value,auto.fontmax); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.fontmax) + error('FontSizeMax must be a numeric scalar > 0'); + end + end + case 'fontencoding' + opts.fontencoding = LocalCheckAuto(lower(value),auto.fontencoding); + if ~strcmp(opts.fontencoding,{'latin1','adobe'}) + error('FontEncoding must be ''latin1'' or ''adobe''.'); + end + case 'linemode' + opts.linemode = LocalCheckAuto(lower(value),auto.linemode); + if ~strcmp(opts.linemode,{'scaled','fixed'}) + error('LineMode must be ''scaled'' or ''fixed''.'); + end + case 'linewidth' + opts.linewidth = LocalToNum(value,auto.linewidth); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.linewidth) + error('LineWidth must be a numeric scalar > 0'); + end + end + case 'defaultfixedlinewidth' + opts.defaultlinewidth = LocalToNum(value,auto.defaultlinewidth); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.defaultlinewidth) + error(['DefaultFixedLineWidth must be a numeric scalar >' ... + ' 0']); + end + end + case 'linewidthmin' + opts.linemin = LocalToNum(value,auto.linemin); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.linemin) + error('LineWidthMin must be a numeric scalar > 0'); + end + end + case 'linewidthmax' + opts.linemax = LocalToNum(value,auto.linemax); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.linemax) + error('LineWidthMax must be a numeric scalar > 0'); + end + end + case 'linestylemap' + opts.stylemap = LocalCheckAuto(value,auto.stylemap); + case 'renderer' + opts.renderer = LocalCheckAuto(lower(value),auto.renderer); + if ~ischar(value) | ~strcmp(value,'auto') + if ~strcmp(opts.renderer,{'painters','zbuffer','opengl'}) + error(['Renderer must be ''painters'', ''zbuffer'' or' ... + ' ''opengl''.']); + end + end + case 'resolution' + opts.resolution = LocalToNum(value,auto.resolution); + if ~ischar(value) | ~strcmp(value,'auto') + if ~(isnumeric(value) & (prod(size(value)) == 1) & (value >= 0)); + error('Resolution must be a numeric scalar >= 0'); + end + end + case 'applystyle' % means to apply the options and not export + opts.applystyle = 1; + case 'reference' + if ischar(value) + if strcmp(value,'auto') + opts.refobj = auto.refobj; + else + opts.refobj = eval(value); + end + else + opts.refobj = value; + end + if ~LocalIsHG(opts.refobj,'axes') + error('Reference object must evaluate to an axes handle.'); + end + case 'bounds' + opts.bounds = LocalCheckAuto(lower(value),auto.bounds); + explicitbounds = 1; + if ~strcmp(opts.bounds,{'tight','loose'}) + error('Bounds must be ''tight'' or ''loose''.'); + end + case 'lockaxes' + opts.lockaxes = LocalToNum(value,auto.lockaxes); + case 'separatetext' + opts.separatetext = LocalToNum(value,auto.separatetext); + otherwise + error(['Unrecognized option ' param '.']); + end +end + +% make sure figure is up-to-date +drawnow; + +allLines = findall(H, 'type', 'line'); +allText = findall(H, 'type', 'text'); +allAxes = findall(H, 'type', 'axes'); +allImages = findall(H, 'type', 'image'); +allLights = findall(H, 'type', 'light'); +allPatch = findall(H, 'type', 'patch'); +allSurf = findall(H, 'type', 'surface'); +allRect = findall(H, 'type', 'rectangle'); +allFont = [allText; allAxes]; +allColor = [allLines; allText; allAxes; allLights]; +allMarker = [allLines; allPatch; allSurf]; +allEdge = [allPatch; allSurf]; +allCData = [allImages; allPatch; allSurf]; + +old.objs = {}; +old.prop = {}; +old.values = {}; + +% Process format +if strncmp(opts.format,'eps',3) & ~strcmp(opts.preview,'none') + args = {args{:}, ['-' opts.preview]}; +end + +hadError = 0; +oldwarn = warning; +try + + % lock axes limits, ticks and labels if requested + if opts.lockaxes + old = LocalManualAxesMode(old, allAxes, 'TickMode'); + old = LocalManualAxesMode(old, allAxes, 'TickLabelMode'); + old = LocalManualAxesMode(old, allAxes, 'LimMode'); + end + + % Process size parameters + figurePaperUnits = get(H, 'PaperUnits'); + oldFigureUnits = get(H, 'Units'); + oldFigPos = get(H,'Position'); + set(H, 'Units', figurePaperUnits); + figPos = get(H,'Position'); + refsize = figPos(3:4); + if opts.refobj ~= -1 + oldUnits = get(opts.refobj, 'Units'); + set(opts.refobj, 'Units', figurePaperUnits); + r = get(opts.refobj, 'Position'); + refsize = r(3:4); + set(opts.refobj, 'Units', oldUnits); + end + aspectRatio = refsize(1)/refsize(2); + if (opts.width == -1) & (opts.height == -1) + opts.width = refsize(1); + opts.height = refsize(2); + elseif (opts.width == -1) + opts.width = opts.height * aspectRatio; + elseif (opts.height == -1) + opts.height = opts.width / aspectRatio; + end + wscale = opts.width/refsize(1); + hscale = opts.height/refsize(2); + sizescale = min(wscale,hscale); + old = LocalPushOldData(old,H,'PaperPositionMode', ... + get(H,'PaperPositionMode')); + set(H, 'PaperPositionMode', 'auto'); + newPos = [figPos(1) figPos(2)+figPos(4)*(1-hscale) ... + wscale*figPos(3) hscale*figPos(4)]; + set(H, 'Position', newPos); + set(H, 'Units', oldFigureUnits); + + % process line-style map + if ~isempty(opts.stylemap) & ~isempty(allLines) + oldlstyle = LocalGetAsCell(allLines,'LineStyle'); + old = LocalPushOldData(old, allLines, {'LineStyle'}, ... + oldlstyle); + newlstyle = oldlstyle; + if ischar(opts.stylemap) & strcmpi(opts.stylemap,'bw') + newlstyle = LocalMapColorToStyle(allLines); + else + try + newlstyle = feval(opts.stylemap,allLines); + catch + warning(['Skipping stylemap. ' lasterr]); + end + end + set(allLines,{'LineStyle'},newlstyle); + end + + % Process rendering parameters + switch (opts.color) + case {'bw', 'gray'} + if ~strcmp(opts.color,'bw') & strncmp(opts.format,'eps',3) + opts.format = [opts.format 'c']; + end + args = {args{:}, ['-d' opts.format]}; + + %compute and set gray colormap + oldcmap = get(H,'Colormap'); + newgrays = 0.30*oldcmap(:,1) + 0.59*oldcmap(:,2) + 0.11*oldcmap(:,3); + newcmap = [newgrays newgrays newgrays]; + old = LocalPushOldData(old, H, 'Colormap', oldcmap); + set(H, 'Colormap', newcmap); + + %compute and set ColorSpec and CData properties + old = LocalUpdateColors(allColor, 'color', old); + old = LocalUpdateColors(allAxes, 'xcolor', old); + old = LocalUpdateColors(allAxes, 'ycolor', old); + old = LocalUpdateColors(allAxes, 'zcolor', old); + old = LocalUpdateColors(allMarker, 'MarkerEdgeColor', old); + old = LocalUpdateColors(allMarker, 'MarkerFaceColor', old); + old = LocalUpdateColors(allEdge, 'EdgeColor', old); + old = LocalUpdateColors(allEdge, 'FaceColor', old); + old = LocalUpdateColors(allCData, 'CData', old); + + case {'rgb','cmyk'} + if strncmp(opts.format,'eps',3) + opts.format = [opts.format 'c']; + args = {args{:}, ['-d' opts.format]}; + if strcmp(opts.color,'cmyk') + args = {args{:}, '-cmyk'}; + end + else + args = {args{:}, ['-d' opts.format]}; + end + otherwise + error('Invalid Color parameter'); + end + if (~isempty(opts.renderer)) + args = {args{:}, ['-' opts.renderer]}; + end + if (~isempty(opts.resolution)) | ~strncmp(opts.format,'eps',3) + if isempty(opts.resolution) + opts.resolution = 0; + end + args = {args{:}, ['-r' int2str(opts.resolution)]}; + end + + % Process font parameters + if ~isempty(opts.fontmode) + oldfonts = LocalGetAsCell(allFont,'FontSize'); + oldfontunits = LocalGetAsCell(allFont,'FontUnits'); + set(allFont,'FontUnits','points'); + switch (opts.fontmode) + case 'fixed' + if (opts.fontsize == -1) + set(allFont,'FontSize',opts.defaultfontsize); + else + set(allFont,'FontSize',opts.fontsize); + end + case 'scaled' + if (opts.fontsize == -1) + scale = sizescale; + else + scale = opts.fontsize; + end + newfonts = LocalScale(oldfonts,scale,opts.fontmin,opts.fontmax); + set(allFont,{'FontSize'},newfonts); + otherwise + error('Invalid FontMode parameter'); + end + old = LocalPushOldData(old, allFont, {'FontSize'}, oldfonts); + old = LocalPushOldData(old, allFont, {'FontUnits'}, oldfontunits); + end + if strcmp(opts.fontencoding,'adobe') & strncmp(opts.format,'eps',3) + args = {args{:}, '-adobecset'}; + end + + % Process line parameters + if ~isempty(opts.linemode) + oldlines = LocalGetAsCell(allMarker,'LineWidth'); + old = LocalPushOldData(old, allMarker, {'LineWidth'}, oldlines); + switch (opts.linemode) + case 'fixed' + if (opts.linewidth == -1) + set(allMarker,'LineWidth',opts.defaultlinewidth); + else + set(allMarker,'LineWidth',opts.linewidth); + end + case 'scaled' + if (opts.linewidth == -1) + scale = sizescale; + else + scale = opts.linewidth; + end + newlines = LocalScale(oldlines, scale, opts.linemin, opts.linemax); + set(allMarker,{'LineWidth'},newlines); + end + end + + % adjust figure bounds to surround axes + if strcmp(opts.bounds,'tight') + if (~strncmp(opts.format,'eps',3) & LocalHas3DPlot(allAxes)) | ... + (strncmp(opts.format,'eps',3) & opts.separatetext) + if (explicitbounds == 1) + warning(['Cannot compute ''tight'' bounds. Using ''loose''' ... + ' bounds.']); + end + opts.bounds = 'loose'; + end + end + warning('off'); + if ~isempty(allAxes) + if strncmp(opts.format,'eps',3) + if strcmp(opts.bounds,'loose') + args = {args{:}, '-loose'}; + end + old = LocalPushOldData(old,H,'Position', oldFigPos); + elseif strcmp(opts.bounds,'tight') + oldaunits = LocalGetAsCell(allAxes,'Units'); + oldapos = LocalGetAsCell(allAxes,'Position'); + oldtunits = LocalGetAsCell(allText,'units'); + oldtpos = LocalGetAsCell(allText,'Position'); + set(allAxes,'units','points'); + apos = LocalGetAsCell(allAxes,'Position'); + oldunits = get(H,'Units'); + set(H,'units','points'); + origfr = get(H,'position'); + fr = []; + for k=1:length(allAxes) + if ~strcmpi(get(allAxes(k),'Tag'),'legend') + axesR = apos{k}; + r = LocalAxesTightBoundingBox(axesR, allAxes(k)); + r(1:2) = r(1:2) + axesR(1:2); + fr = LocalUnionRect(fr,r); + end + end + if isempty(fr) + fr = [0 0 origfr(3:4)]; + end + for k=1:length(allAxes) + ax = allAxes(k); + r = apos{k}; + r(1:2) = r(1:2) - fr(1:2); + set(ax,'Position',r); + end + old = LocalPushOldData(old, allAxes, {'Position'}, oldapos); + old = LocalPushOldData(old, allText, {'Position'}, oldtpos); + old = LocalPushOldData(old, allText, {'Units'}, oldtunits); + old = LocalPushOldData(old, allAxes, {'Units'}, oldaunits); + old = LocalPushOldData(old, H, 'Position', oldFigPos); + old = LocalPushOldData(old, H, 'Units', oldFigureUnits); + r = [origfr(1) origfr(2)+origfr(4)-fr(4) fr(3:4)]; + set(H,'Position',r); + else + args = {args{:}, '-loose'}; + old = LocalPushOldData(old,H,'Position', oldFigPos); + end + end + + % Process text in a separate file if needed + if opts.separatetext & ~opts.applystyle + % First hide all text and export + oldtvis = LocalGetAsCell(allText,'visible'); + set(allText,'visible','off'); + oldax = LocalGetAsCell(allAxes,'XTickLabel',1); + olday = LocalGetAsCell(allAxes,'YTickLabel',1); + oldaz = LocalGetAsCell(allAxes,'ZTickLabel',1); + null = cell(length(oldax),1); + [null{:}] = deal([]); + set(allAxes,{'XTickLabel'},null); + set(allAxes,{'YTickLabel'},null); + set(allAxes,{'ZTickLabel'},null); + print(H, filename, args{:}); + set(allText,{'Visible'},oldtvis); + set(allAxes,{'XTickLabel'},oldax); + set(allAxes,{'YTickLabel'},olday); + set(allAxes,{'ZTickLabel'},oldaz); + % Now hide all non-text and export as eps in painters + [path, name, ext] = fileparts(filename); + tfile = fullfile(path,[name '_t.eps']); + tfile2 = fullfile(path,[name '_t2.eps']); + foundRenderer = 0; + for k=1:length(args) + if strncmp('-d',args{k},2) + args{k} = '-deps'; + elseif strncmp('-zbuffer',args{k},8) | ... + strncmp('-opengl', args{k},6) + args{k} = '-painters'; + foundRenderer = 1; + end + end + if ~foundRenderer + args = {args{:}, '-painters'}; + end + allNonText = [allLines; allLights; allPatch; ... + allImages; allSurf; allRect]; + oldvis = LocalGetAsCell(allNonText,'visible'); + oldc = LocalGetAsCell(allAxes,'color'); + oldaxg = LocalGetAsCell(allAxes,'XGrid'); + oldayg = LocalGetAsCell(allAxes,'YGrid'); + oldazg = LocalGetAsCell(allAxes,'ZGrid'); + [null{:}] = deal('off'); + set(allAxes,{'XGrid'},null); + set(allAxes,{'YGrid'},null); + set(allAxes,{'ZGrid'},null); + set(allNonText,'Visible','off'); + set(allAxes,'Color','none'); + print(H, tfile2, args{:}); + set(allNonText,{'Visible'},oldvis); + set(allAxes,{'Color'},oldc); + set(allAxes,{'XGrid'},oldaxg); + set(allAxes,{'YGrid'},oldayg); + set(allAxes,{'ZGrid'},oldazg); + %hack up the postscript file + fid1 = fopen(tfile,'w'); + fid2 = fopen(tfile2,'r'); + line = fgetl(fid2); + while ischar(line) + if strncmp(line,'%%Title',7) + fprintf(fid1,'%s\n',['%%Title: ', tfile]); + elseif (length(line) < 3) + fprintf(fid1,'%s\n',line); + elseif ~strcmp(line(end-2:end),' PR') & ... + ~strcmp(line(end-1:end),' L') + fprintf(fid1,'%s\n',line); + end + line = fgetl(fid2); + end + fclose(fid1); + fclose(fid2); + delete(tfile2); + + elseif ~opts.applystyle + drawnow; + print(H, filename, args{:}); + end + warning(oldwarn); + +catch + warning(oldwarn); + hadError = 1; +end + +% Restore figure settings +if opts.applystyle + varargout{1} = old; +else + for n=1:length(old.objs) + if ~iscell(old.values{n}) & iscell(old.prop{n}) + old.values{n} = {old.values{n}}; + end + set(old.objs{n}, old.prop{n}, old.values{n}); + end +end + +if hadError + error(deblank(lasterr)); +end + +% +% Local Functions +% + +function outData = LocalPushOldData(inData, objs, prop, values) +outData.objs = {objs, inData.objs{:}}; +outData.prop = {prop, inData.prop{:}}; +outData.values = {values, inData.values{:}}; + +function cellArray = LocalGetAsCell(fig,prop,allowemptycell); +cellArray = get(fig,prop); +if nargin < 3 + allowemptycell = 0; +end +if ~iscell(cellArray) & (allowemptycell | ~isempty(cellArray)) + cellArray = {cellArray}; +end + +function newArray = LocalScale(inArray, scale, minv, maxv) +n = length(inArray); +newArray = cell(n,1); +for k=1:n + newArray{k} = min(maxv,max(minv,scale*inArray{k}(1))); +end + +function gray = LocalMapToGray1(color) +gray = color; +if ischar(color) + switch color(1) + case 'y' + color = [1 1 0]; + case 'm' + color = [1 0 1]; + case 'c' + color = [0 1 1]; + case 'r' + color = [1 0 0]; + case 'g' + color = [0 1 0]; + case 'b' + color = [0 0 1]; + case 'w' + color = [1 1 1]; + case 'k' + color = [0 0 0]; + end +end +if ~ischar(color) + gray = 0.30*color(1) + 0.59*color(2) + 0.11*color(3); +end + +function newArray = LocalMapToGray(inArray); +n = length(inArray); +newArray = cell(n,1); +for k=1:n + color = inArray{k}; + if ~isempty(color) + color = LocalMapToGray1(color); + end + if isempty(color) | ischar(color) + newArray{k} = color; + else + newArray{k} = [color color color]; + end +end + +function newArray = LocalMapColorToStyle(inArray); +inArray = LocalGetAsCell(inArray,'Color'); +n = length(inArray); +newArray = cell(n,1); +styles = {'-','--',':','-.'}; +uniques = []; +nstyles = length(styles); +for k=1:n + gray = LocalMapToGray1(inArray{k}); + if isempty(gray) | ischar(gray) | gray < .05 + newArray{k} = '-'; + else + if ~isempty(uniques) & any(gray == uniques) + ind = find(gray==uniques); + else + uniques = [uniques gray]; + ind = length(uniques); + end + newArray{k} = styles{mod(ind-1,nstyles)+1}; + end +end + +function newArray = LocalMapCData(inArray); +n = length(inArray); +newArray = cell(n,1); +for k=1:n + color = inArray{k}; + if (ndims(color) == 3) & isa(color,'double') + gray = 0.30*color(:,:,1) + 0.59*color(:,:,2) + 0.11*color(:,:,3); + color(:,:,1) = gray; + color(:,:,2) = gray; + color(:,:,3) = gray; + end + newArray{k} = color; +end + +function outData = LocalUpdateColors(inArray, prop, inData) +value = LocalGetAsCell(inArray,prop); +outData.objs = {inData.objs{:}, inArray}; +outData.prop = {inData.prop{:}, {prop}}; +outData.values = {inData.values{:}, value}; +if (~isempty(value)) + if strcmp(prop,'CData') + value = LocalMapCData(value); + else + value = LocalMapToGray(value); + end + set(inArray,{prop},value); +end + +function bool = LocalIsPositiveScalar(value) +bool = isnumeric(value) & ... + prod(size(value)) == 1 & ... + value > 0; + +function value = LocalToNum(value,auto) +if ischar(value) + if strcmp(value,'auto') + value = auto; + else + value = str2num(value); + end +end + +%convert a struct to {field1,val1,field2,val2,...} +function c = LocalToCell(s) +f = fieldnames(s); +v = struct2cell(s); +opts = cell(2,length(f)); +opts(1,:) = f; +opts(2,:) = v; +c = {opts{:}}; + +function c = LocalIsHG(obj,hgtype) +c = 0; +if (length(obj) == 1) & ishandle(obj) + c = strcmp(get(obj,'type'),hgtype); +end + +function c = LocalHas3DPlot(a) +zticks = LocalGetAsCell(a,'ZTickLabel'); +c = 0; +for k=1:length(zticks) + if ~isempty(zticks{k}) + c = 1; + return; + end +end + +function r = LocalUnionRect(r1,r2) +if isempty(r1) + r = r2; +elseif isempty(r2) + r = r1; +elseif max(r2(3:4)) > 0 + left = min(r1(1),r2(1)); + bot = min(r1(2),r2(2)); + right = max(r1(1)+r1(3),r2(1)+r2(3)); + top = max(r1(2)+r1(4),r2(2)+r2(4)); + r = [left bot right-left top-bot]; +else + r = r1; +end + +function c = LocalLabelsMatchTicks(labs,ticks) +c = 0; +try + t1 = num2str(ticks(1)); + n = length(ticks); + tend = num2str(ticks(n)); + c = strncmp(labs(1),t1,length(labs(1))) & ... + strncmp(labs(n),tend,length(labs(n))); +end + +function r = LocalAxesTightBoundingBox(axesR, a) +r = []; +atext = findall(a,'type','text','visible','on'); +if ~isempty(atext) + set(atext,'units','points'); + res=LocalGetAsCell(atext,'extent'); + for n=1:length(atext) + r = LocalUnionRect(r,res{n}); + end +end +if strcmp(get(a,'visible'),'on') + r = LocalUnionRect(r,[0 0 axesR(3:4)]); + oldunits = get(a,'fontunits'); + set(a,'fontunits','points'); + label = text(0,0,'','parent',a,... + 'units','points',... + 'fontsize',get(a,'fontsize'),... + 'fontname',get(a,'fontname'),... + 'fontweight',get(a,'fontweight'),... + 'fontangle',get(a,'fontangle'),... + 'visible','off'); + fs = get(a,'fontsize'); + + % handle y axis tick labels + ry = [0 -fs/2 0 axesR(4)+fs]; + ylabs = get(a,'yticklabels'); + yticks = get(a,'ytick'); + maxw = 0; + if ~isempty(ylabs) + for n=1:size(ylabs,1) + set(label,'string',ylabs(n,:)); + ext = get(label,'extent'); + maxw = max(maxw,ext(3)); + end + if ~LocalLabelsMatchTicks(ylabs,yticks) & ... + strcmp(get(a,'xaxislocation'),'bottom') + ry(4) = ry(4) + 1.5*ext(4); + end + if strcmp(get(a,'yaxislocation'),'left') + ry(1) = -(maxw+5); + else + ry(1) = axesR(3); + end + ry(3) = maxw+5; + r = LocalUnionRect(r,ry); + end + + % handle x axis tick labels + rx = [0 0 0 fs+5]; + xlabs = get(a,'xticklabels'); + xticks = get(a,'xtick'); + if ~isempty(xlabs) + if strcmp(get(a,'xaxislocation'),'bottom') + rx(2) = -(fs+5); + if ~LocalLabelsMatchTicks(xlabs,xticks); + rx(4) = rx(4) + 2*fs; + rx(2) = rx(2) - 2*fs; + end + else + rx(2) = axesR(4); + % exponent is still below axes + if ~LocalLabelsMatchTicks(xlabs,xticks); + rx(4) = rx(4) + axesR(4) + 2*fs; + rx(2) = -2*fs; + end + end + set(label,'string',xlabs(1,:)); + ext1 = get(label,'extent'); + rx(1) = -ext1(3)/2; + set(label,'string',xlabs(size(xlabs,1),:)); + ext2 = get(label,'extent'); + rx(3) = axesR(3) + (ext2(3) + ext1(3))/2; + r = LocalUnionRect(r,rx); + end + set(a,'fontunits',oldunits); + delete(label); +end + +function c = LocalManualAxesMode(old, allAxes, base) +xs = ['X' base]; +ys = ['Y' base]; +zs = ['Z' base]; +oldXMode = LocalGetAsCell(allAxes,xs); +oldYMode = LocalGetAsCell(allAxes,ys); +oldZMode = LocalGetAsCell(allAxes,zs); +old = LocalPushOldData(old, allAxes, {xs}, oldXMode); +old = LocalPushOldData(old, allAxes, {ys}, oldYMode); +old = LocalPushOldData(old, allAxes, {zs}, oldZMode); +set(allAxes,xs,'manual'); +set(allAxes,ys,'manual'); +set(allAxes,zs,'manual'); +c = old; + +function val = LocalCheckAuto(val, auto) +if ischar(val) & strcmp(val,'auto') + val = auto; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/figures/imxy.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +function imxy(mat) +imagesc(mat) +axis xy +set(gcf,'Color',[0 0 .001]) +set(gca,'XColor',[1 1 1] *.5,'YColor',[1 1 1] *.5) +colormap(bone) \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/figures/printme.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +function printme(width) + +set(gcf, 'PaperUnits', 'centimeters'); +papersize = get(gcf, 'PaperSize'); +paperratio = papersize(2) / papersize(1); +set(gcf, 'PaperSize', [1 paperratio] * width); +set(gcf, 'Renderer', 'painters') +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/figures/rotateticklabel.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,58 @@ +function th=rotateticklabel(h,rot,demo) +%ROTATETICKLABEL rotates tick labels +% TH=ROTATETICKLABEL(H,ROT) is the calling form where H is a handle to +% the axis that contains the XTickLabels that are to be rotated. ROT is +% an optional parameter that specifies the angle of rotation. The default +% angle is 90. TH is a handle to the text objects created. For long +% strings such as those produced by datetick, you may have to adjust the +% position of the axes so the labels don't get cut off. +% +% Of course, GCA can be substituted for H if desired. +% +% TH=ROTATETICKLABEL([],[],'demo') shows a demo figure. +% +% Known deficiencies: if tick labels are raised to a power, the power +% will be lost after rotation. +% +% See also datetick. + +% Written Oct 14, 2005 by Andy Bliss +% Copyright 2005 by Andy Bliss + +%DEMO: +if nargin==3 + x=[now-.7 now-.3 now]; + y=[20 35 15]; + figure + plot(x,y,'.-') + datetick('x',0,'keepticks') + h=gca; + set(h,'position',[0.13 0.35 0.775 0.55]) + rot=90; +end + +%set the default rotation if user doesn't specify +if nargin==1 + rot=90; +end +%make sure the rotation is in the range 0:360 (brute force method) +while rot>360 + rot=rot-360; +end +while rot<0 + rot=rot+360; +end +%get current tick labels +a=get(h,'XTickLabel'); +%erase current tick labels from figure +set(h,'XTickLabel',[]); +%get tick label positions +b=get(h,'XTick'); +c=get(h,'YTick'); +%make new tick labels +if rot<180 + th=text(b,repmat(c(1)-.1*(c(2)-c(1)),length(b),1),a,'HorizontalAlignment','right','rotation',rot); +else + th=text(b,repmat(c(1)-.1*(c(2)-c(1)),length(b),1),a,'HorizontalAlignment','left','rotation',rot); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/figures/uimage.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,132 @@ +function h = uimage(varargin) +%UIMAGE Display image with uneven axis. +% UIMAGE(X,Y,C) displays matrix C as an image, using the vectors X and +% Y to specify the X and Y coordinates. X and Y may be unevenly spaced +% vectors, but must be increasing. The size of C must be LENGTH(Y)* +% LENGTH(X). (Most probably you'll want to display C' instead of C). +% +% Contrary to Matlab's original IMAGE function, here the vectors X and Y +% do not need to be linearly spaced. Whereas IMAGE linearly interpolates +% the X-axis between X(1) and X(end), ignoring all other values (idem +% for Y), UIMAGE allows for X and/or Y to be unevenly spaced vectors, by +% locally stretching the matrix C (ie, by duplicating some elements of C) +% for larger X and/or Y intervals. +% +% The syntax for UIMAGE(X,Y,C,...) is the same as IMAGE(X,Y,C,...) +% (all the remaining arguments, eg 'PropertyName'-PropertyValue pairs, +% are passed to IMAGE). See IMAGE for details. +% +% Use UIMAGESC to scale the data using the full colormap. The syntax for +% UIMAGESC(X,Y,C,...) is the same as IMAGESC(X,Y,C,...). +% +% Typical uses: +% - Plotting a spatio-temporal diagram (T,X), with unevenly spaced +% time intervals for T (eg, when some values are missing, or when +% using a non-constant sampling rate). +% - Plotting a set of power spectra with frequency in log-scale. +% +% h = UIMAGE(X,Y,C,...) returns a handle to the image. +% +% Example: +% c = randn(50,20); % Random 50x20 matrix +% x = logspace(1,3,50); % log-spaced X-axis, between 10 and 1000 +% y = linspace(3,8,20); % lin-spaced Y-axis, between 3 and 8 +% uimagesc(x,y,c'); % displays the matrix +% +% F. Moisy +% Revision: 1.03, Date: 2006/06/14. +% +% See also IMAGE, IMAGESC, UIMAGESC. + + +% History: +% 2006/06/12: v1.00, first version. +% 2006/06/14: v1.03, minor bug fixed; works in ML6. + +error(nargchk(3,inf,nargin)); + +% maximum number of matrix elements to interpolate the uneven axis +% (typically between 500 and 5000): +nmax = 2000; + +x = varargin{1}; +y = varargin{2}; +c = varargin{3}; + +if any(diff(x)<=0) || any(diff(y)<=0) + error('The X and Y axis should be increasing.'); +end + +dx = min(diff(x)); % smallest interval for X +dy = min(diff(y)); % smallest interval for Y + +% test if X and Y are linearly spaced (to within 10^-12): +evenx = all(abs(diff(x)/dx-1)<1e-12); % true if X is linearly spaced +eveny = all(abs(diff(y)/dy-1)<1e-12); % true if Y is linearly spaced + + +if evenx && eveny % X and Y both evenly spaced + + xe = x; + ye = y; + ce = c; + +elseif evenx && ~eveny % X even and Y uneven + + nx = length(x); + xe = x; + + ny = ceil(1 + (y(end) - y(1))/dy); % number of points for Y + ny = min(ny, nmax); + ye = linspace(y(1), y(end), ny); + + ce = zeros(ny,nx); + + for j=1:ny + indj = find(y<=ye(j)); + ce(j,1:nx) = c(indj(end), 1:nx); + end; + +elseif ~evenx && eveny % X uneven and Y even + + nx = ceil(1 + (x(end) - x(1))/dx); % number of points for X + nx = min(nx, nmax); + xe = linspace(x(1), x(end), nx); + + ny = length(y); + ye = y; + + ce = zeros(ny,nx); + + for i=1:nx + indi = find(x<=xe(i)); + ce(1:ny,i) = c(1:ny, indi(end)); + end; + +elseif ~evenx && ~eveny % X and Y both uneven + + nx = ceil(1 + (x(end) - x(1))/dx); % number of points for X + nx = min(nx, nmax); + xe = linspace(x(1), x(end), nx); + + ny = ceil(1 + (y(end) - y(1))/dy); % number of points for Y + ny = min(ny, nmax); + ye = linspace(y(1), y(end), ny); + + ce = zeros(ny,nx); + + for i=1:nx + for j=1:ny + indi = find(x<=xe(i)); + indj = find(y<=ye(j)); + ce(j,i) = c(indi(end), indj(end)); + end; + end; + +end + +hh = image(xe, ye, ce, varargin{4:end}); + +if nargout>0 + h = hh; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/figures/uimagesc.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,57 @@ +function h = uimagesc(varargin) +%UIMAGESC Display scaled image with uneven axis. +% UIMAGESC(...) is the same as UIMAGE(...) except the data is scaled +% to use the full colormap. See UIMAGE for details. +% +% Note: UIMAGESC is based on Matlab's original IMAGESC, Revision 5.11.4.5. +% UIMAGESC simply calls UIMAGE with a scaled colormap. +% +% F. Moisy - adapted from TMW +% Revision: 1.01, Date: 2006/06/13. +% +% See also IMAGE, IMAGESC, UIMAGE. + +% History: +% 2006/06/12: v1.00, first version. + +clim = []; +switch (nargin), + case 0, + hh = uimage('CDataMapping','scaled'); + case 1, + hh = uimage(varargin{1},'CDataMapping','scaled'); + case 3, + hh = uimage(varargin{:},'CDataMapping','scaled'); + otherwise, + + % Determine if last input is clim + if isequal(size(varargin{end}),[1 2]) + str = false(length(varargin),1); + for n=1:length(varargin) + str(n) = ischar(varargin{n}); + end + str = find(str); + if isempty(str) || (rem(length(varargin)-min(str),2)==0), + clim = varargin{end}; + varargin(end) = []; % Remove last cell + else + clim = []; + end + else + clim = []; + end + hh = uimage(varargin{:},'CDataMapping','scaled'); +end + +% Get the parent Axes of the image +cax = ancestor(hh,'axes'); + +if ~isempty(clim), + set(cax,'CLim',clim) +elseif ~ishold(cax), + set(cax,'CLimMode','auto') +end + +if nargout > 0 + h = hh; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/general/.svn/all-wcprops Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,47 @@ +K 25 +svn:wc:ra_dav:version-url +V 51 +/svnroot/!svn/ver/5883/matlabtoolboxes/misc/general +END +dpcore.mexmac +K 25 +svn:wc:ra_dav:version-url +V 65 +/svnroot/!svn/ver/1425/matlabtoolboxes/misc/general/dpcore.mexmac +END +qnormalise.m +K 25 +svn:wc:ra_dav:version-url +V 64 +/svnroot/!svn/ver/1425/matlabtoolboxes/misc/general/qnormalise.m +END +filecollection.m +K 25 +svn:wc:ra_dav:version-url +V 68 +/svnroot/!svn/ver/5883/matlabtoolboxes/misc/general/filecollection.m +END +qnorm.m +K 25 +svn:wc:ra_dav:version-url +V 59 +/svnroot/!svn/ver/1425/matlabtoolboxes/misc/general/qnorm.m +END +dpfast.m +K 25 +svn:wc:ra_dav:version-url +V 60 +/svnroot/!svn/ver/1425/matlabtoolboxes/misc/general/dpfast.m +END +dpcore.c +K 25 +svn:wc:ra_dav:version-url +V 60 +/svnroot/!svn/ver/1425/matlabtoolboxes/misc/general/dpcore.c +END +dpcore.mexmaci +K 25 +svn:wc:ra_dav:version-url +V 66 +/svnroot/!svn/ver/1425/matlabtoolboxes/misc/general/dpcore.mexmaci +END
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/general/.svn/entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,266 @@ +10 + +dir +7329 +https://parrot.dcs.qmul.ac.uk/svnroot/matlabtoolboxes/misc/general +https://parrot.dcs.qmul.ac.uk/svnroot + + + +2009-05-01T16:56:49.073671Z +5883 +matthiasm + + + + + + + + + + + + + + +214cd2a9-030a-0410-b9a1-da4e402363b6 + +dpcore.mexmac +file + + + + +2010-04-07T10:16:15.000000Z +a5cf615458376a40e33d04f1f0b5019d +2009-03-06T16:34:10.036388Z +1425 +matthiasm +has-props + + + + + + + + + + + + + + + + + + + + +8772 + +qnormalise.m +file + + + + +2010-04-07T10:16:15.000000Z +47bd4f0da774f49513f2ca2f913a4903 +2009-03-06T16:34:10.036388Z +1425 +matthiasm + + + + + + + + + + + + + + + + + + + + + +464 + +filecollection.m +file + + + + +2010-04-07T10:16:15.000000Z +eb91069ed44d03bfb421e392b9afafcd +2009-05-01T16:56:49.073671Z +5883 +matthiasm + + + + + + + + + + + + + + + + + + + + + +24223 + +qnorm.m +file + + + + +2010-04-07T10:16:15.000000Z +02cb634d7cf6403e5ecfb93d6487fb3e +2009-03-06T16:34:10.036388Z +1425 +matthiasm + + + + + + + + + + + + + + + + + + + + + +237 + +dpfast.m +file + + + + +2010-04-07T10:16:15.000000Z +75130f5b5398df7017be3aa23c7e9715 +2009-03-06T16:34:10.036388Z +1425 +matthiasm + + + + + + + + + + + + + + + + + + + + + +2317 + +dpcore.c +file + + + + +2010-04-07T10:16:15.000000Z +c66b75d95a40cd9bc79422573d2800b7 +2009-03-06T16:34:10.036388Z +1425 +matthiasm + + + + + + + + + + + + + + + + + + + + + +3382 + +dpcore.mexmaci +file + + + + +2010-04-07T10:16:15.000000Z +872c19b5d99ae0a72bb8df1deca0fefd +2009-03-06T16:34:10.036388Z +1425 +matthiasm +has-props + + + + + + + + + + + + + + + + + + + + +12696 +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/general/.svn/prop-base/dpcore.mexmac.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +K 13 +svn:mime-type +V 24 +application/octet-stream +END
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/general/.svn/prop-base/dpcore.mexmaci.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,9 @@ +K 14 +svn:executable +V 1 +* +K 13 +svn:mime-type +V 24 +application/octet-stream +END
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/general/.svn/text-base/dpcore.c.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,133 @@ +/* + * dpcore.c + * Core of dynamic programming/DTW calculation + * 2003-04-02 dpwe@ee.columbia.edu + * $Header: /Users/dpwe/projects/dtw/RCS/dpcore.c,v 1.3 2006/01/18 20:05:51 dpwe Exp $ +% Copyright (c) 2003-05 Dan Ellis <dpwe@ee.columbia.edu> +% released under GPL - see file COPYRIGHT + */ + +#include <stdio.h> +#include <math.h> +#include <ctype.h> +#include "mex.h" + +/* #define DEBUG */ + +#define INF HUGE_VAL + +void +mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) +{ + int i,j; + long pvl, pvb[16]; + +#ifdef DEBUG + mexPrintf("dpcore: Got %d lhs args and %d rhs args.\n", + nlhs, nrhs); + for (i=0;i<nrhs;i++) { + mexPrintf("RHArg #%d is size %d x %d\n", + (long)i, mxGetM(prhs[i]), mxGetN(prhs[i])); + } + for (i=0;i<nlhs;i++) + if (plhs[i]) { + mexPrintf("LHArg #%d is size %d x %d\n", + (long)i, mxGetM(plhs[i]), mxGetN(plhs[i])); + } +#endif /* DEBUG */ + + if (nrhs < 1){ + mexPrintf("dpcore [D,P] = dpcore(S[,C]) dynamic programming core\n"); + mexPrintf(" Calculate the best cost to every point in score\n"); + mexPrintf(" cost matrix S; return it in D along with traceback\n"); + mexPrintf(" indices in P. Optional C defines allowable steps\n"); + mexPrintf(" and costs; default [1 1 1.0;1 0 1.0;0 1 1.0]\n"); + return; + } + + if (nlhs > 0){ + mxArray *DMatrix, *PMatrix; + int rows, cols, i, j, k, tb; + double *pM, *pD, *pP, *pC; + double d1, d2, d3, v; + double *costs; + int *steps; + int ncosts; + + rows = mxGetM(prhs[0]); + cols = mxGetN(prhs[0]); + pM = mxGetPr(prhs[0]); + + DMatrix = mxCreateDoubleMatrix(rows, cols, mxREAL); + pD = mxGetPr(DMatrix); + PMatrix = mxCreateDoubleMatrix(rows, cols, mxREAL); + pP = mxGetPr(PMatrix); + plhs[0] = DMatrix; + if (nlhs > 1) { + plhs[1] = PMatrix; + } + + /* setup costs */ + if (nrhs == 1) { + /* default C matrix */ + int ii; + + ncosts = 3; + costs = (double *)malloc(ncosts*sizeof(double)); + for (ii = 0; ii<ncosts; ++ii) costs[ii] = 1.0; + steps = (int *)malloc(ncosts*2*sizeof(int)); + steps[0] = 1; steps[1] = 1; + steps[2] = 1; steps[3] = 0; + steps[4] = 0; steps[5] = 1; + } else { + int ii, crows, ccols; + crows = mxGetM(prhs[1]); + ccols = mxGetN(prhs[1]); + pC = mxGetPr(prhs[1]); + /* mexPrintf("C has %d rows and %d cols\n", crows, ccols); */ + if (ccols != 3) { + mexPrintf("Cost matrix must have 3 cols (i step, j step, cost factor)\n"); + return; + } + ncosts = crows; + costs = (double *)malloc(ncosts*sizeof(double)); + steps = (int *)malloc(ncosts*2*sizeof(int)); + for (ii = 0; ii < ncosts; ++ii) { + steps[2*ii] = (int)(pC[ii]); + steps[2*ii+1] = (int)(pC[ii+crows]); + costs[ii] = pC[ii+2*crows]; + /* mexPrintf("step=%d,%d cost=%f\n", steps[2*ii],steps[2*ii+1],costs[ii]); */ + } + } + + + /* do dp */ + v = 0; + tb = 1; /* value to use for 0,0 */ + for (j = 0; j < cols; ++j) { + for (i = 0; i < rows; ++i) { + d1 = pM[i + j*rows]; + for (k = 0; k < ncosts; ++k) { + if ( i >= steps[2*k] && j >= steps[2*k+1] ) { + d2 = costs[k]*d1 + pD[(i-steps[2*k]) + (j-steps[2*k+1])*rows]; + if (d2 < v) { + v = d2; + tb = k+1; + } + } + } + + pD[i + j*rows] = v; + pP[i + j*rows] = (double)tb; + v = INF; + } + } + free((void *)costs); + free((void *)steps); + } + +#ifdef DEBUG + mexPrintf("dpcore: returning...\n"); +#endif /* DEBUG */ +} +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/general/.svn/text-base/dpfast.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,92 @@ +function [p,q,D,sc] = dpfast(M,C,T,G) +% [p,q,D,sc] = dpfast(M,C,T,G) +% Use dynamic programming to find a min-cost path through matrix M. +% Return state sequence in p,q; full min cost matrix as D and +% local costs along best path in sc. +% This version gives the same results as dp.m, but uses dpcore.mex +% to run ~200x faster. +% C is a step matrix, with rows (i step, j step, cost factor) +% Default is [1 1 1.0;0 1 1.0;1 0 1.0]; +% Another good one is [1 1 1;1 0 1;0 1 1;1 2 2;2 1 2] +% T selects traceback origin: 0 is to any edge; 1 is top right (default); +% T > 1 finds path to min of anti-diagonal T points away from top-right. +% Optional G defines length of 'gulleys' for T=0 mode; default 0.5 +% (i.e. accept path to only 50% of edge nearest top-right) +% 2003-04-04,2005-04-04 dpwe@ee.columbia.edu $Header: /Users/dpwe/projects/dtw/RCS/dpfast.m,v 1.6 2008/03/14 14:40:50 dpwe Exp $ + +% Copyright (c) 2003 Dan Ellis <dpwe@ee.columbia.edu> +% released under GPL - see file COPYRIGHT + +if nargin < 2 + % Default step / cost matrix + C = [1 1 1.0;0 1 1.0;1 0 1.0]; +end + +if nargin < 3 + % Default: path to top-right + T = 1; +end + +if nargin < 4 + % how big are gulleys? + G = 0.5; % half the extent +end + +if sum(isnan(M(:)))>0 + error('dpwe:dpfast:NAN','Error: Cost matrix includes NaNs'); +end + +if min(M(:)) < 0 + disp('Warning: cost matrix includes negative values; results may not be what you expect'); +end + +[r,c] = size(M); + +% Core cumulative cost calculation coded as mex +[D,phi] = dpcore(M,C); + +p = []; +q = []; + +%% Traceback from top left? +%i = r; +%j = c; + +if T == 0 + % Traceback from lowest cost "to edge" (gulleys) + TE = D(r,:); + RE = D(:,c); + % eliminate points not in gulleys + TE(1:round((1-G)*c)) = max(max(D)); + RE(1:round((1-G)*r)) = max(max(D)); + if (min(TE) < min(RE)) + i = r; + j = max(find(TE==min(TE))); + else + i = max(find(RE==min(RE))); + j = c; + end +else + % Traceback from min of antidiagonal + %stepback = floor(0.1*c); + stepback = T; + slice = diag(fliplr(D),-(r-stepback)); + [mm,ii] = min(slice); + i = r - stepback + ii; + j = c + 1 - ii; +end + +p=i; +q=j; + +sc = M(p,q); + +while i > 1 & j > 1 +% disp(['i=',num2str(i),' j=',num2str(j)]); + tb = phi(i,j); + i = i - C(tb,1); + j = j - C(tb,2); + p = [i,p]; + q = [j,q]; + sc = [M(i,j),sc]; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/general/.svn/text-base/filecollection.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,360 @@ +function allfilenames = filecollection(coll, ext, varargin) + +nArg = length(varargin); +% varargin +if nArg > 0 && ~isempty(varargin{1}) + data_dir = varargin{1}; + if data_dir(end) == '/', data_dir = data_dir(1:end-1); end +else + data_dir = '/Volumes/data/Content/BeatlesWav'; +end +if iscell(coll) + collec = coll; +else + collec = {coll}; +end + +allfilenames = []; +sla = '/'; +% data_dir = '/Volumes/data/Content/BeatlesWav'; +for iCollec = 1:length(collec) + switch collec{iCollec} + case 'omras2' + filenames = {... + ['/Users/matthiasmauch/Desktop/OMRAS2 demo/demo' ext]}; + case 'testletitbe' + filenames = {... + [data_dir sla '12_-_Let_It_Be/06_-_Let_It_Be' ext]}; + case 'teo' + filenames = {... + [data_dir sla 'nonBeatles/10 Teo Torriatte (Let Us Cling Together)' ext]}; + case 'testsawher' + filenames = {... + [data_dir sla '01_-_Please_Please_Me/01_-_I_Saw_Her_Standing_There' ext]}; + + case 'testlose' + filenames = {... + [data_dir sla '05_-_Help!/06_-_You''re_Going_to_Lose_That_Girl' ext]}; + + case 'testwords' + filenames = {... + [data_dir sla '04_-_Beatles_For_Sale/09_-_Words_Of_Love' ext]}; + case 'testyesterday' + filenames = {... + [data_dir sla '05_-_Help!/13_-_Yesterday' ext]}; + case 'testcometogether' + filenames = {... + [data_dir sla '11_-_Abbey_Road/01_-_Come_Together' ext]}; + case 'testfriends' + filenames = {... + [data_dir sla 'nonBeatles/15-Friends Will Be Friends' ext]}; + case 'testfriends2' + filenames = {... + ['/Volumes/Private/iTunes/iTunes Music/Queen/A Kind of Magic/05 Friends will be Friends' ext]}; + case 'carole' + filenames = {... + [data_dir sla 'Carole King/Tapestry/06 Way Over Yonder' ext],... + [data_dir sla 'Carole King/Tapestry/05 Beautiful' ext],... + [data_dir sla 'Carole King/Tapestry/04 Home Again' ext],... + [data_dir sla 'Carole King/Tapestry/03 It''s Too Late' ext],... + [data_dir sla 'Carole King/Tapestry/02 So Far Away' ext],... + [data_dir sla 'Carole King/Tapestry/01 I Feel The Earth Move' ext],... + [data_dir sla 'Carole King/Tapestry/11 Tapestry' ext],... + [data_dir sla 'Carole King/Tapestry/10 Smackwater Jack' ext],... + [data_dir sla 'Carole King/Tapestry/09 Will You Love Me Tomorrow_' ext],... + [data_dir sla 'Carole King/Tapestry/08 Where You Lead' ext],... + [data_dir sla 'Carole King/Tapestry/07 You''ve Got A Friend' ext],... + [data_dir sla 'Carole King/Tapestry/14 Smackwater Jack [Live]' ext],... + [data_dir sla 'Carole King/Tapestry/13 Out In The Cold' ext],... + [data_dir sla 'Carole King/Tapestry/12 Feel Like A Natural Woman' ext]}; + case 'u2' + filenames = {... + [data_dir sla 'U2/U218 Singles/02 Beautiful Day' ext]}; + case 'dinverno' + filenames = { ... + ['/Volumes/Private/CubaseProjects/dinverno/Audio/dinverno_autumnleaves1' ext],... + ['/Volumes/Private/CubaseProjects/dinverno/Audio/dinverno_autumnleaves2' ext],... + ['/Volumes/Private/CubaseProjects/dinverno/Audio/dinverno_shookup1' ext],... + ['/Volumes/Private/CubaseProjects/dinverno/Audio/dinverno_helpfrommyfriends1' ext],... + ['/Volumes/Private/CubaseProjects/dinverno/Audio/dinverno_autumnleaves3' ext]}; + case '01_-_Please_Please_Me' + filenames = {... + [data_dir sla '01_-_Please_Please_Me/01_-_I_Saw_Her_Standing_There' ext],... + [data_dir sla '01_-_Please_Please_Me/02_-_Misery' ext],... + [data_dir sla '01_-_Please_Please_Me/03_-_Anna_(Go_To_Him)' ext],... + [data_dir sla '01_-_Please_Please_Me/04_-_Chains' ext],... + [data_dir sla '01_-_Please_Please_Me/05_-_Boys' ext],... + [data_dir sla '01_-_Please_Please_Me/06_-_Ask_Me_Why' ext],... + [data_dir sla '01_-_Please_Please_Me/07_-_Please_Please_Me' ext],... + [data_dir sla '01_-_Please_Please_Me/08_-_Love_Me_Do' ext],... + [data_dir sla '01_-_Please_Please_Me/09_-_P._S._I_Love_You' ext],... + [data_dir sla '01_-_Please_Please_Me/10_-_Baby_It''s_You' ext],... + [data_dir sla '01_-_Please_Please_Me/11_-_Do_You_Want_To_Know_A_Secret' ext],... + ...[data_dir sla '01_-_Please_Please_Me/12_-_A_Taste_Of_Honey' ext],... + [data_dir sla '01_-_Please_Please_Me/13_-_There''s_A_Place' ext],... + [data_dir sla '01_-_Please_Please_Me/14_-_Twist_And_Shout' ext]}; + + case '02_-_With_the_Beatles' + filenames = {... + [data_dir sla '02_-_With_the_Beatles/01_-_It_Won''t_Be_Long' ext],... + [data_dir sla '02_-_With_the_Beatles/02_-_All_I''ve_Got_To_Do' ext],... + [data_dir sla '02_-_With_the_Beatles/03_-_All_My_Loving' ext],... + [data_dir sla '02_-_With_the_Beatles/04_-_Don''t_Bother_Me' ext],... + [data_dir sla '02_-_With_the_Beatles/05_-_Little_Child' ext],... + [data_dir sla '02_-_With_the_Beatles/06_-_Till_There_Was_You' ext],... + [data_dir sla '02_-_With_the_Beatles/07_-_Please_Mister_Postman' ext],... + [data_dir sla '02_-_With_the_Beatles/08_-_Roll_Over_Beethoven' ext],... + [data_dir sla '02_-_With_the_Beatles/09_-_Hold_Me_Tight' ext],... + ...[data_dir sla '02_-_With_the_Beatles/10_-_You_Really_Got_A_Hold_On_Me' ext],... + [data_dir sla '02_-_With_the_Beatles/11_-_I_Wanna_Be_Your_Man' ext],... + [data_dir sla '02_-_With_the_Beatles/12_-_Devil_In_Her_Heart' ext],... + [data_dir sla '02_-_With_the_Beatles/13_-_Not_A_Second_Time' ext],... + [data_dir sla '02_-_With_the_Beatles/14_-_Money' ext]}; + + case '03_-_A_Hard_Day''s_Night' + filenames = {... + [data_dir sla '03_-_A_Hard_Day''s_Night/01_-_A_Hard_Day''s_Night' ext],... + [data_dir sla '03_-_A_Hard_Day''s_Night/02_-_I_Should_Have_Known_Better' ext],... + [data_dir sla '03_-_A_Hard_Day''s_Night/03_-_If_I_Fell' ext],... + [data_dir sla '03_-_A_Hard_Day''s_Night/04_-_I''m_Happy_Just_To_Dance_With_You' ext],... + [data_dir sla '03_-_A_Hard_Day''s_Night/05_-_And_I_Love_Her' ext],... + [data_dir sla '03_-_A_Hard_Day''s_Night/06_-_Tell_Me_Why' ext],... + [data_dir sla '03_-_A_Hard_Day''s_Night/07_-_Can''t_Buy_Me_Love' ext],... + [data_dir sla '03_-_A_Hard_Day''s_Night/08_-_Any_Time_At_All' ext],... + [data_dir sla '03_-_A_Hard_Day''s_Night/09_-_I''ll_Cry_Instead' ext],... + [data_dir sla '03_-_A_Hard_Day''s_Night/10_-_Things_We_Said_Today' ext],... + [data_dir sla '03_-_A_Hard_Day''s_Night/11_-_When_I_Get_Home' ext],... + [data_dir sla '03_-_A_Hard_Day''s_Night/12_-_You_Can''t_Do_That' ext],... + [data_dir sla '03_-_A_Hard_Day''s_Night/13_-_I''ll_Be_Back' ext]}; + + + case '04_-_Beatles_for_Sale' + filenames = {... + [data_dir sla '04_-_Beatles_for_Sale/01_-_No_Reply' ext],... + [data_dir sla '04_-_Beatles_for_Sale/02_-_I''m_a_Loser' ext],... + ...[data_dir sla '04_-_Beatles_for_Sale/03_-_Baby''s_In_Black' ext],... + [data_dir sla '04_-_Beatles_for_Sale/04_-_Rock_and_Roll_Music' ext],... + [data_dir sla '04_-_Beatles_for_Sale/05_-_I''ll_Follow_the_Sun' ext],... + [data_dir sla '04_-_Beatles_for_Sale/06_-_Mr._Moonlight' ext],... + [data_dir sla '04_-_Beatles_for_Sale/07_-_Kansas_City-_Hey,_Hey,_Hey,_Hey' ext],... + [data_dir sla '04_-_Beatles_for_Sale/08_-_Eight_Days_a_Week' ext],... + [data_dir sla '04_-_Beatles_for_Sale/09_-_Words_of_Love' ext],... + [data_dir sla '04_-_Beatles_for_Sale/10_-_Honey_Don''t' ext],... + [data_dir sla '04_-_Beatles_for_Sale/11_-_Every_Little_Thing' ext],... + [data_dir sla '04_-_Beatles_for_Sale/12_-_I_Don''t_Want_to_Spoil_the_Party' ext],... + [data_dir sla '04_-_Beatles_for_Sale/13_-_What_You''re_Doing' ext],... + [data_dir sla '04_-_Beatles_for_Sale/14_-_Everybody''s_Trying_to_Be_My_Baby' ext]}; + + + case '05_-_Help!' + filenames = {... + [data_dir sla '05_-_Help!/01_-_Help!' ext],... + [data_dir sla '05_-_Help!/02_-_The_Night_Before' ext],... + ...[data_dir sla '05_-_Help!/03_-_You''ve_Got_To_Hide_Your_Love_Away' ext],... + [data_dir sla '05_-_Help!/04_-_I_Need_You' ext],... + [data_dir sla '05_-_Help!/05_-_Another_Girl' ext],... + [data_dir sla '05_-_Help!/06_-_You''re_Going_to_Lose_That_Girl' ext],... + [data_dir sla '05_-_Help!/07_-_Ticket_To_Ride' ext],... + [data_dir sla '05_-_Help!/08_-_Act_Naturally' ext],... + [data_dir sla '05_-_Help!/09_-_It''s_Only_Love' ext],... + [data_dir sla '05_-_Help!/10_-_You_Like_Me_Too_Much' ext],... + [data_dir sla '05_-_Help!/11_-_Tell_Me_What_You_See' ext],... + [data_dir sla '05_-_Help!/12_-_I''ve_Just_Seen_a_Face' ext],... + [data_dir sla '05_-_Help!/13_-_Yesterday' ext],... + [data_dir sla '05_-_Help!/14_-_Dizzy_Miss_Lizzy' ext]}; + + case '06_-_Rubber_Soul' + filenames = {... + [data_dir sla '06_-_Rubber_Soul/01_-_Drive_My_Car' ext],... + ... [data_dir sla '06_-_Rubber_Soul/02_-_Norwegian_Wood_(This_Bird_Has_Flown)' ext],... + [data_dir sla '06_-_Rubber_Soul/03_-_You_Won''t_See_Me' ext],... + [data_dir sla '06_-_Rubber_Soul/04_-_Nowhere_Man' ext],... + [data_dir sla '06_-_Rubber_Soul/05_-_Think_For_Yourself' ext],... + [data_dir sla '06_-_Rubber_Soul/06_-_The_Word' ext],... + [data_dir sla '06_-_Rubber_Soul/07_-_Michelle' ext],... + [data_dir sla '06_-_Rubber_Soul/08_-_What_Goes_On' ext],... + [data_dir sla '06_-_Rubber_Soul/09_-_Girl' ext],... + [data_dir sla '06_-_Rubber_Soul/10_-_I''m_Looking_Through_You' ext],... + [data_dir sla '06_-_Rubber_Soul/11_-_In_My_Life' ext],... + [data_dir sla '06_-_Rubber_Soul/12_-_Wait' ext],... + [data_dir sla '06_-_Rubber_Soul/13_-_If_I_Needed_Someone' ext],... + [data_dir sla '06_-_Rubber_Soul/14_-_Run_For_Your_Life' ext]}; + + case '07_-_Revolver' + filenames = {... + [data_dir sla '07_-_Revolver/01_-_Taxman' ext],... + [data_dir sla '07_-_Revolver/02_-_Eleanor_Rigby' ext],... + [data_dir sla '07_-_Revolver/03_-_I''m_Only_Sleeping' ext],... + ...[data_dir sla '07_-_Revolver/04_-_Love_You_To' ext],... + [data_dir sla '07_-_Revolver/05_-_Here,_There_And_Everywhere' ext],... + [data_dir sla '07_-_Revolver/06_-_Yellow_Submarine' ext],... + [data_dir sla '07_-_Revolver/07_-_She_Said_She_Said' ext],... + ...[data_dir sla '07_-_Revolver/08_-_Good_Day_Sunshine' ext],... + [data_dir sla '07_-_Revolver/09_-_And_Your_Bird_Can_Sing' ext],... + [data_dir sla '07_-_Revolver/10_-_For_No_One' ext],... + [data_dir sla '07_-_Revolver/11_-_Doctor_Robert' ext],... + [data_dir sla '07_-_Revolver/12_-_I_Want_To_Tell_You' ext],... + [data_dir sla '07_-_Revolver/13_-_Got_To_Get_You_Into_My_Life' ext],... + ...[data_dir sla '07_-_Revolver/14_-_Tomorrow_Never_Knows' ext]... + }; + + case '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band' + filenames = {... + [data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/01_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band' ext],... + [data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/02_-_With_A_Little_Help_From_My_Friends' ext],... + ...[data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/03_-_Lucy_In_The_Sky_With_Diamonds' ext],... + [data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/04_-_Getting_Better' ext],... + [data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/05_-_Fixing_A_Hole' ext],... + ...[data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/06_-_She''s_Leaving_Home' ext],... + [data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/07_-_Being_For_The_Benefit_Of_Mr._Kite!' ext],... + ...[data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/08_-_Within_You_Without_You' ext],... + [data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/09_-_When_I''m_Sixty-Four' ext],... + [data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/10_-_Lovely_Rita' ext],... + ...[data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/11_-_Good_Morning_Good_Morning' ext],... + [data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/12_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band_(Reprise)' ext],... + [data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/13_-_A_Day_In_The_Life' ext]}; + + case '09_-_Magical_Mystery_Tour' + filenames = {... + [data_dir sla '09_-_Magical_Mystery_Tour/01_-_Magical_Mystery_Tour' ext],... + [data_dir sla '09_-_Magical_Mystery_Tour/02_-_The_Fool_On_The_Hill' ext],... + [data_dir sla '09_-_Magical_Mystery_Tour/03_-_Flying' ext],... + ...[data_dir sla '09_-_Magical_Mystery_Tour/04_-_Blue_Jay_Way' ext],... + [data_dir sla '09_-_Magical_Mystery_Tour/05_-_Your_Mother_Should_Know' ext],... + [data_dir sla '09_-_Magical_Mystery_Tour/06_-_I_Am_The_Walrus' ext],... + [data_dir sla '09_-_Magical_Mystery_Tour/07_-_Hello_Goodbye' ext],... + [data_dir sla '09_-_Magical_Mystery_Tour/08_-_Strawberry_Fields_Forever' ext],... + [data_dir sla '09_-_Magical_Mystery_Tour/09_-_Penny_Lane' ext],... + [data_dir sla '09_-_Magical_Mystery_Tour/10_-_Baby_You''re_A_Rich_Man' ext],... + ...[data_dir sla '09_-_Magical_Mystery_Tour/11_-_All_You_Need_Is_Love' ext] ... + }; + + case '10CD1_-_The_Beatles' + filenames = {... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_01_-_Back_in_the_USSR' ext],... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_02_-_Dear_Prudence' ext],... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_03_-_Glass_Onion' ext],... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_04_-_Ob-La-Di,_Ob-La-Da' ext],... + ...[data_dir sla '10CD1_-_The_Beatles/CD1_-_05_-_Wild_Honey_Pie' ext],... + ...[data_dir sla '10CD1_-_The_Beatles/CD1_-_06_-_The_Continuing_Story_of_Bungalow_Bill' ext],... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_07_-_While_My_Guitar_Gently_Weeps' ext],... + ...[data_dir sla '10CD1_-_The_Beatles/CD1_-_08_-_Happiness_is_a_Warm_Gun' ext],... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_09_-_Martha_My_Dear' ext],... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_10_-_I''m_So_Tired' ext],... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_11_-_Black_Bird' ext],... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_12_-_Piggies' ext],... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_13_-_Rocky_Raccoon' ext],... + ...[data_dir sla '10CD1_-_The_Beatles/CD1_-_14_-_Don''t_Pass_Me_By' ext],... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_15_-_Why_Don''t_We_Do_It_In_The_Road' ext],... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_16_-_I_Will' ext],... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_17_-_Julia' ext]}; + + case '10CD2_-_The_Beatles' + filenames = {... + [data_dir sla '10CD2_-_The_Beatles/CD2_-_01_-_Birthday' ext],... + ...[data_dir sla '10CD2_-_The_Beatles/CD2_-_02_-_Yer_Blues' ext],... + [data_dir sla '10CD2_-_The_Beatles/CD2_-_03_-_Mother_Nature''s_Son' ext],... + [data_dir sla '10CD2_-_The_Beatles/CD2_-_04_-_Everybody''s_Got_Something_To_Hide_Except_Me_and_M' ext],... + [data_dir sla '10CD2_-_The_Beatles/CD2_-_05_-_Sexy_Sadie' ext],... + [data_dir sla '10CD2_-_The_Beatles/CD2_-_06_-_Helter_Skelter' ext],... + ...[data_dir sla '10CD2_-_The_Beatles/CD2_-_07_-_Long_Long_Long' ext],... + [data_dir sla '10CD2_-_The_Beatles/CD2_-_08_-_Revolution_1' ext],... + [data_dir sla '10CD2_-_The_Beatles/CD2_-_09_-_Honey_Pie' ext],... + [data_dir sla '10CD2_-_The_Beatles/CD2_-_10_-_Savoy_Truffle' ext],... + [data_dir sla '10CD2_-_The_Beatles/CD2_-_11_-_Cry_Baby_Cry' ext],... + ...[data_dir sla '10CD2_-_The_Beatles/CD2_-_12_-_Revolution_9' ext],... + [data_dir sla '10CD2_-_The_Beatles/CD2_-_13_-_Good_Night' ext]}; + + case '11_-_Abbey_Road' + filenames = {... + [data_dir sla '11_-_Abbey_Road/01_-_Come_Together' ext],... + [data_dir sla '11_-_Abbey_Road/02_-_Something' ext],... + [data_dir sla '11_-_Abbey_Road/03_-_Maxwell''s_Silver_Hammer' ext],... + ...[data_dir sla '11_-_Abbey_Road/04_-_Oh!_Darling' ext],... + [data_dir sla '11_-_Abbey_Road/05_-_Octopus''s_Garden' ext],... + ...[data_dir sla '11_-_Abbey_Road/06_-_I_Want_You' ext],... + [data_dir sla '11_-_Abbey_Road/07_-_Here_Comes_The_Sun' ext],... + [data_dir sla '11_-_Abbey_Road/08_-_Because' ext],... + [data_dir sla '11_-_Abbey_Road/09_-_You_Never_Give_Me_Your_Money' ext],... + [data_dir sla '11_-_Abbey_Road/10_-_Sun_King' ext],... + [data_dir sla '11_-_Abbey_Road/11_-_Mean_Mr_Mustard' ext],... + [data_dir sla '11_-_Abbey_Road/12_-_Polythene_Pam' ext],... + [data_dir sla '11_-_Abbey_Road/13_-_She_Came_In_Through_The_Bathroom_Window' ext],... + [data_dir sla '11_-_Abbey_Road/14_-_Golden_Slumbers' ext],... + [data_dir sla '11_-_Abbey_Road/15_-_Carry_That_Weight' ext],... + [data_dir sla '11_-_Abbey_Road/16_-_The_End' ext],... + [data_dir sla '11_-_Abbey_Road/17_-_Her_Majesty' ext]}; + + case '12_-_Let_It_Be' + filenames = {... + ...[data_dir sla '12_-_Let_It_Be/01_-_Two_of_Us' ext],... + ...[data_dir sla '12_-_Let_It_Be/02_-_Dig_a_Pony' ext],... + [data_dir sla '12_-_Let_It_Be/03_-_Across_the_Universe' ext],... + ... [data_dir sla '12_-_Let_It_Be/04_-_I_Me_Mine' ext],... + ... [data_dir sla '12_-_Let_It_Be/05_-_Dig_It' ext],... + [data_dir sla '12_-_Let_It_Be/06_-_Let_It_Be' ext],... + [data_dir sla '12_-_Let_It_Be/07_-_Maggie_Mae' ext],... + [data_dir sla '12_-_Let_It_Be/08_-_I''ve_Got_A_Feeling' ext],... + [data_dir sla '12_-_Let_It_Be/09_-_One_After_909' ext],... + [data_dir sla '12_-_Let_It_Be/10_-_The_Long_and_Winding_Road' ext],... + [data_dir sla '12_-_Let_It_Be/11_-_For_You_Blue' ext],... + [data_dir sla '12_-_Let_It_Be/12_-_Get_Back' ext]}; + case 'discarded' + filenames = {... + [data_dir sla '01_-_Please_Please_Me/12_-_A_Taste_Of_Honey' ext],... + [data_dir sla '02_-_With_the_Beatles/10_-_You_Really_Got_A_Hold_On_Me' ext],... + [data_dir sla '04_-_Beatles_for_Sale/03_-_Baby''s_In_Black' ext],... + [data_dir sla '05_-_Help!/03_-_You''ve_Got_To_Hide_Your_Love_Away' ext],... + [data_dir sla '06_-_Rubber_Soul/02_-_Norwegian_Wood_(This_Bird_Has_Flown)' ext],... + [data_dir sla '07_-_Revolver/04_-_Love_You_To' ext],... + [data_dir sla '07_-_Revolver/08_-_Good_Day_Sunshine' ext],... + [data_dir sla '07_-_Revolver/14_-_Tomorrow_Never_Knows' ext]... + [data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/03_-_Lucy_In_The_Sky_With_Diamonds' ext],... + [data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/06_-_She''s_Leaving_Home' ext],... + [data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/08_-_Within_You_Without_You' ext],... + [data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/11_-_Good_Morning_Good_Morning' ext],... + [data_dir sla '09_-_Magical_Mystery_Tour/04_-_Blue_Jay_Way' ext],... + [data_dir sla '09_-_Magical_Mystery_Tour/11_-_All_You_Need_Is_Love' ext] ... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_05_-_Wild_Honey_Pie' ext],... + [data_dir sla '10CD2_-_The_Beatles/CD2_-_02_-_Yer_Blues' ext],... + [data_dir sla '10CD2_-_The_Beatles/CD2_-_07_-_Long_Long_Long' ext],... + [data_dir sla '11_-_Abbey_Road/04_-_Oh!_Darling' ext],... + [data_dir sla '12_-_Let_It_Be/01_-_Two_of_Us' ext],... + [data_dir sla '12_-_Let_It_Be/02_-_Dig_a_Pony' ext],... + [data_dir sla '12_-_Let_It_Be/04_-_I_Me_Mine' ext],... + [data_dir sla '12_-_Let_It_Be/05_-_Dig_It' ext],... + [data_dir sla '11_-_Abbey_Road/06_-_I_Want_You' ext]... + }; + case 'notinMIREX' + filenames = {... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_06_-_The_Continuing_Story_of_Bungalow_Bill' ext],... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_14_-_Don''t_Pass_Me_By' ext],... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_08_-_Happiness_is_a_Warm_Gun' ext],... + }; + case 'revolution9' + filenames = {[data_dir sla '10CD2_-_The_Beatles/CD2_-_12_-_Revolution_9' ext]}; + case 'leftovers' + filenames = {... + [data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/06_-_She''s_Leaving_Home' ext],... + [data_dir sla '07_-_Revolver/04_-_Love_You_To' ext],... + }; + case 'semma' + filenames = {... + [data_dir sla 'SeMMA/audio/Britney_Spear_##_Baby_Hit_Me_One_More_Time', ext],... + [data_dir sla 'SeMMA/audio/Bjork_##_Its_Oh_So_Quiet', ext],... + [data_dir sla 'SeMMA/audio/Alanis_Morissette_##_Thank_U', ext],... + [data_dir sla 'SeMMA/audio/Alanis_Morissette_##_Head_Over_Feet', ext],... + [data_dir sla 'SeMMA/audio/A_Ha_##_Take_On_Me', ext],... + [data_dir sla 'SeMMA/audio/The_Clash_##_Should_I_Stay_Or_Should_I_Go', ext],... + [data_dir sla 'SeMMA/audio/Spice_Girls_##_Wannabe', ext],... + [data_dir sla 'SeMMA/audio/Seal_##_Crazy', ext],... + [data_dir sla 'SeMMA/audio/Radiohead_##_Creep', ext],... + [data_dir sla 'SeMMA/audio/Oasis_##_Wonderwall', ext],... + [data_dir sla 'SeMMA/audio/Nirvana_##_Smells_Like_Teen_Spirit', ext],... + [data_dir sla 'SeMMA/audio/Deus_##_Suds_and_Soda', ext],... + [data_dir sla 'SeMMA/audio/Cranberries_##_Zombie', ext],... + [data_dir sla 'SeMMA/audio/Chumbawamba_##_Tubthumping', ext],... + }; + end + allfilenames = [allfilenames,filenames]; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/general/.svn/text-base/qnorm.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +function s = qnorm(inmat, varargin) +dim = 1; +if nargin > 1 + q = varargin{1}; + if nargin > 2 + dim = varargin{2}; + end +else + q = 2; +end +if ~(q == Inf) + s = sum(inmat.^q,dim).^(1/q); +else + s = max(inmat,[],dim); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/general/.svn/text-base/qnormalise.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,17 @@ +function [outmat, scale] = qnormalise(inmat, varargin) +% qnormalise(inmat, q, dim) returns the field inmat normalised by the q +% norm over dimension dim +dim = 1; +if nargin > 1 + q = varargin{1}; + if nargin > 2 + dim = varargin{2}; + end +else + q = 2; +end +sizeInmat = size(inmat); +nDim = length(sizeInmat); +repeatpattern = [ones(1,dim-1) sizeInmat(dim) ones(1,nDim-dim)]; +scale = qnorm(inmat, q, dim); +outmat = inmat ./ repmat(scale, repeatpattern);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/general/dpcore.c Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,133 @@ +/* + * dpcore.c + * Core of dynamic programming/DTW calculation + * 2003-04-02 dpwe@ee.columbia.edu + * $Header: /Users/dpwe/projects/dtw/RCS/dpcore.c,v 1.3 2006/01/18 20:05:51 dpwe Exp $ +% Copyright (c) 2003-05 Dan Ellis <dpwe@ee.columbia.edu> +% released under GPL - see file COPYRIGHT + */ + +#include <stdio.h> +#include <math.h> +#include <ctype.h> +#include "mex.h" + +/* #define DEBUG */ + +#define INF HUGE_VAL + +void +mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) +{ + int i,j; + long pvl, pvb[16]; + +#ifdef DEBUG + mexPrintf("dpcore: Got %d lhs args and %d rhs args.\n", + nlhs, nrhs); + for (i=0;i<nrhs;i++) { + mexPrintf("RHArg #%d is size %d x %d\n", + (long)i, mxGetM(prhs[i]), mxGetN(prhs[i])); + } + for (i=0;i<nlhs;i++) + if (plhs[i]) { + mexPrintf("LHArg #%d is size %d x %d\n", + (long)i, mxGetM(plhs[i]), mxGetN(plhs[i])); + } +#endif /* DEBUG */ + + if (nrhs < 1){ + mexPrintf("dpcore [D,P] = dpcore(S[,C]) dynamic programming core\n"); + mexPrintf(" Calculate the best cost to every point in score\n"); + mexPrintf(" cost matrix S; return it in D along with traceback\n"); + mexPrintf(" indices in P. Optional C defines allowable steps\n"); + mexPrintf(" and costs; default [1 1 1.0;1 0 1.0;0 1 1.0]\n"); + return; + } + + if (nlhs > 0){ + mxArray *DMatrix, *PMatrix; + int rows, cols, i, j, k, tb; + double *pM, *pD, *pP, *pC; + double d1, d2, d3, v; + double *costs; + int *steps; + int ncosts; + + rows = mxGetM(prhs[0]); + cols = mxGetN(prhs[0]); + pM = mxGetPr(prhs[0]); + + DMatrix = mxCreateDoubleMatrix(rows, cols, mxREAL); + pD = mxGetPr(DMatrix); + PMatrix = mxCreateDoubleMatrix(rows, cols, mxREAL); + pP = mxGetPr(PMatrix); + plhs[0] = DMatrix; + if (nlhs > 1) { + plhs[1] = PMatrix; + } + + /* setup costs */ + if (nrhs == 1) { + /* default C matrix */ + int ii; + + ncosts = 3; + costs = (double *)malloc(ncosts*sizeof(double)); + for (ii = 0; ii<ncosts; ++ii) costs[ii] = 1.0; + steps = (int *)malloc(ncosts*2*sizeof(int)); + steps[0] = 1; steps[1] = 1; + steps[2] = 1; steps[3] = 0; + steps[4] = 0; steps[5] = 1; + } else { + int ii, crows, ccols; + crows = mxGetM(prhs[1]); + ccols = mxGetN(prhs[1]); + pC = mxGetPr(prhs[1]); + /* mexPrintf("C has %d rows and %d cols\n", crows, ccols); */ + if (ccols != 3) { + mexPrintf("Cost matrix must have 3 cols (i step, j step, cost factor)\n"); + return; + } + ncosts = crows; + costs = (double *)malloc(ncosts*sizeof(double)); + steps = (int *)malloc(ncosts*2*sizeof(int)); + for (ii = 0; ii < ncosts; ++ii) { + steps[2*ii] = (int)(pC[ii]); + steps[2*ii+1] = (int)(pC[ii+crows]); + costs[ii] = pC[ii+2*crows]; + /* mexPrintf("step=%d,%d cost=%f\n", steps[2*ii],steps[2*ii+1],costs[ii]); */ + } + } + + + /* do dp */ + v = 0; + tb = 1; /* value to use for 0,0 */ + for (j = 0; j < cols; ++j) { + for (i = 0; i < rows; ++i) { + d1 = pM[i + j*rows]; + for (k = 0; k < ncosts; ++k) { + if ( i >= steps[2*k] && j >= steps[2*k+1] ) { + d2 = costs[k]*d1 + pD[(i-steps[2*k]) + (j-steps[2*k+1])*rows]; + if (d2 < v) { + v = d2; + tb = k+1; + } + } + } + + pD[i + j*rows] = v; + pP[i + j*rows] = (double)tb; + v = INF; + } + } + free((void *)costs); + free((void *)steps); + } + +#ifdef DEBUG + mexPrintf("dpcore: returning...\n"); +#endif /* DEBUG */ +} +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/general/dpfast.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,92 @@ +function [p,q,D,sc] = dpfast(M,C,T,G) +% [p,q,D,sc] = dpfast(M,C,T,G) +% Use dynamic programming to find a min-cost path through matrix M. +% Return state sequence in p,q; full min cost matrix as D and +% local costs along best path in sc. +% This version gives the same results as dp.m, but uses dpcore.mex +% to run ~200x faster. +% C is a step matrix, with rows (i step, j step, cost factor) +% Default is [1 1 1.0;0 1 1.0;1 0 1.0]; +% Another good one is [1 1 1;1 0 1;0 1 1;1 2 2;2 1 2] +% T selects traceback origin: 0 is to any edge; 1 is top right (default); +% T > 1 finds path to min of anti-diagonal T points away from top-right. +% Optional G defines length of 'gulleys' for T=0 mode; default 0.5 +% (i.e. accept path to only 50% of edge nearest top-right) +% 2003-04-04,2005-04-04 dpwe@ee.columbia.edu $Header: /Users/dpwe/projects/dtw/RCS/dpfast.m,v 1.6 2008/03/14 14:40:50 dpwe Exp $ + +% Copyright (c) 2003 Dan Ellis <dpwe@ee.columbia.edu> +% released under GPL - see file COPYRIGHT + +if nargin < 2 + % Default step / cost matrix + C = [1 1 1.0;0 1 1.0;1 0 1.0]; +end + +if nargin < 3 + % Default: path to top-right + T = 1; +end + +if nargin < 4 + % how big are gulleys? + G = 0.5; % half the extent +end + +if sum(isnan(M(:)))>0 + error('dpwe:dpfast:NAN','Error: Cost matrix includes NaNs'); +end + +if min(M(:)) < 0 + disp('Warning: cost matrix includes negative values; results may not be what you expect'); +end + +[r,c] = size(M); + +% Core cumulative cost calculation coded as mex +[D,phi] = dpcore(M,C); + +p = []; +q = []; + +%% Traceback from top left? +%i = r; +%j = c; + +if T == 0 + % Traceback from lowest cost "to edge" (gulleys) + TE = D(r,:); + RE = D(:,c); + % eliminate points not in gulleys + TE(1:round((1-G)*c)) = max(max(D)); + RE(1:round((1-G)*r)) = max(max(D)); + if (min(TE) < min(RE)) + i = r; + j = max(find(TE==min(TE))); + else + i = max(find(RE==min(RE))); + j = c; + end +else + % Traceback from min of antidiagonal + %stepback = floor(0.1*c); + stepback = T; + slice = diag(fliplr(D),-(r-stepback)); + [mm,ii] = min(slice); + i = r - stepback + ii; + j = c + 1 - ii; +end + +p=i; +q=j; + +sc = M(p,q); + +while i > 1 & j > 1 +% disp(['i=',num2str(i),' j=',num2str(j)]); + tb = phi(i,j); + i = i - C(tb,1); + j = j - C(tb,2); + p = [i,p]; + q = [j,q]; + sc = [M(i,j),sc]; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/general/filecollection.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,360 @@ +function allfilenames = filecollection(coll, ext, varargin) + +nArg = length(varargin); +% varargin +if nArg > 0 && ~isempty(varargin{1}) + data_dir = varargin{1}; + if data_dir(end) == '/', data_dir = data_dir(1:end-1); end +else + data_dir = '/Volumes/data/Content/BeatlesWav'; +end +if iscell(coll) + collec = coll; +else + collec = {coll}; +end + +allfilenames = []; +sla = '/'; +% data_dir = '/Volumes/data/Content/BeatlesWav'; +for iCollec = 1:length(collec) + switch collec{iCollec} + case 'omras2' + filenames = {... + ['/Users/matthiasmauch/Desktop/OMRAS2 demo/demo' ext]}; + case 'testletitbe' + filenames = {... + [data_dir sla '12_-_Let_It_Be/06_-_Let_It_Be' ext]}; + case 'teo' + filenames = {... + [data_dir sla 'nonBeatles/10 Teo Torriatte (Let Us Cling Together)' ext]}; + case 'testsawher' + filenames = {... + [data_dir sla '01_-_Please_Please_Me/01_-_I_Saw_Her_Standing_There' ext]}; + + case 'testlose' + filenames = {... + [data_dir sla '05_-_Help!/06_-_You''re_Going_to_Lose_That_Girl' ext]}; + + case 'testwords' + filenames = {... + [data_dir sla '04_-_Beatles_For_Sale/09_-_Words_Of_Love' ext]}; + case 'testyesterday' + filenames = {... + [data_dir sla '05_-_Help!/13_-_Yesterday' ext]}; + case 'testcometogether' + filenames = {... + [data_dir sla '11_-_Abbey_Road/01_-_Come_Together' ext]}; + case 'testfriends' + filenames = {... + [data_dir sla 'nonBeatles/15-Friends Will Be Friends' ext]}; + case 'testfriends2' + filenames = {... + ['/Volumes/Private/iTunes/iTunes Music/Queen/A Kind of Magic/05 Friends will be Friends' ext]}; + case 'carole' + filenames = {... + [data_dir sla 'Carole King/Tapestry/06 Way Over Yonder' ext],... + [data_dir sla 'Carole King/Tapestry/05 Beautiful' ext],... + [data_dir sla 'Carole King/Tapestry/04 Home Again' ext],... + [data_dir sla 'Carole King/Tapestry/03 It''s Too Late' ext],... + [data_dir sla 'Carole King/Tapestry/02 So Far Away' ext],... + [data_dir sla 'Carole King/Tapestry/01 I Feel The Earth Move' ext],... + [data_dir sla 'Carole King/Tapestry/11 Tapestry' ext],... + [data_dir sla 'Carole King/Tapestry/10 Smackwater Jack' ext],... + [data_dir sla 'Carole King/Tapestry/09 Will You Love Me Tomorrow_' ext],... + [data_dir sla 'Carole King/Tapestry/08 Where You Lead' ext],... + [data_dir sla 'Carole King/Tapestry/07 You''ve Got A Friend' ext],... + [data_dir sla 'Carole King/Tapestry/14 Smackwater Jack [Live]' ext],... + [data_dir sla 'Carole King/Tapestry/13 Out In The Cold' ext],... + [data_dir sla 'Carole King/Tapestry/12 Feel Like A Natural Woman' ext]}; + case 'u2' + filenames = {... + [data_dir sla 'U2/U218 Singles/02 Beautiful Day' ext]}; + case 'dinverno' + filenames = { ... + ['/Volumes/Private/CubaseProjects/dinverno/Audio/dinverno_autumnleaves1' ext],... + ['/Volumes/Private/CubaseProjects/dinverno/Audio/dinverno_autumnleaves2' ext],... + ['/Volumes/Private/CubaseProjects/dinverno/Audio/dinverno_shookup1' ext],... + ['/Volumes/Private/CubaseProjects/dinverno/Audio/dinverno_helpfrommyfriends1' ext],... + ['/Volumes/Private/CubaseProjects/dinverno/Audio/dinverno_autumnleaves3' ext]}; + case '01_-_Please_Please_Me' + filenames = {... + [data_dir sla '01_-_Please_Please_Me/01_-_I_Saw_Her_Standing_There' ext],... + [data_dir sla '01_-_Please_Please_Me/02_-_Misery' ext],... + [data_dir sla '01_-_Please_Please_Me/03_-_Anna_(Go_To_Him)' ext],... + [data_dir sla '01_-_Please_Please_Me/04_-_Chains' ext],... + [data_dir sla '01_-_Please_Please_Me/05_-_Boys' ext],... + [data_dir sla '01_-_Please_Please_Me/06_-_Ask_Me_Why' ext],... + [data_dir sla '01_-_Please_Please_Me/07_-_Please_Please_Me' ext],... + [data_dir sla '01_-_Please_Please_Me/08_-_Love_Me_Do' ext],... + [data_dir sla '01_-_Please_Please_Me/09_-_P._S._I_Love_You' ext],... + [data_dir sla '01_-_Please_Please_Me/10_-_Baby_It''s_You' ext],... + [data_dir sla '01_-_Please_Please_Me/11_-_Do_You_Want_To_Know_A_Secret' ext],... + ...[data_dir sla '01_-_Please_Please_Me/12_-_A_Taste_Of_Honey' ext],... + [data_dir sla '01_-_Please_Please_Me/13_-_There''s_A_Place' ext],... + [data_dir sla '01_-_Please_Please_Me/14_-_Twist_And_Shout' ext]}; + + case '02_-_With_the_Beatles' + filenames = {... + [data_dir sla '02_-_With_the_Beatles/01_-_It_Won''t_Be_Long' ext],... + [data_dir sla '02_-_With_the_Beatles/02_-_All_I''ve_Got_To_Do' ext],... + [data_dir sla '02_-_With_the_Beatles/03_-_All_My_Loving' ext],... + [data_dir sla '02_-_With_the_Beatles/04_-_Don''t_Bother_Me' ext],... + [data_dir sla '02_-_With_the_Beatles/05_-_Little_Child' ext],... + [data_dir sla '02_-_With_the_Beatles/06_-_Till_There_Was_You' ext],... + [data_dir sla '02_-_With_the_Beatles/07_-_Please_Mister_Postman' ext],... + [data_dir sla '02_-_With_the_Beatles/08_-_Roll_Over_Beethoven' ext],... + [data_dir sla '02_-_With_the_Beatles/09_-_Hold_Me_Tight' ext],... + ...[data_dir sla '02_-_With_the_Beatles/10_-_You_Really_Got_A_Hold_On_Me' ext],... + [data_dir sla '02_-_With_the_Beatles/11_-_I_Wanna_Be_Your_Man' ext],... + [data_dir sla '02_-_With_the_Beatles/12_-_Devil_In_Her_Heart' ext],... + [data_dir sla '02_-_With_the_Beatles/13_-_Not_A_Second_Time' ext],... + [data_dir sla '02_-_With_the_Beatles/14_-_Money' ext]}; + + case '03_-_A_Hard_Day''s_Night' + filenames = {... + [data_dir sla '03_-_A_Hard_Day''s_Night/01_-_A_Hard_Day''s_Night' ext],... + [data_dir sla '03_-_A_Hard_Day''s_Night/02_-_I_Should_Have_Known_Better' ext],... + [data_dir sla '03_-_A_Hard_Day''s_Night/03_-_If_I_Fell' ext],... + [data_dir sla '03_-_A_Hard_Day''s_Night/04_-_I''m_Happy_Just_To_Dance_With_You' ext],... + [data_dir sla '03_-_A_Hard_Day''s_Night/05_-_And_I_Love_Her' ext],... + [data_dir sla '03_-_A_Hard_Day''s_Night/06_-_Tell_Me_Why' ext],... + [data_dir sla '03_-_A_Hard_Day''s_Night/07_-_Can''t_Buy_Me_Love' ext],... + [data_dir sla '03_-_A_Hard_Day''s_Night/08_-_Any_Time_At_All' ext],... + [data_dir sla '03_-_A_Hard_Day''s_Night/09_-_I''ll_Cry_Instead' ext],... + [data_dir sla '03_-_A_Hard_Day''s_Night/10_-_Things_We_Said_Today' ext],... + [data_dir sla '03_-_A_Hard_Day''s_Night/11_-_When_I_Get_Home' ext],... + [data_dir sla '03_-_A_Hard_Day''s_Night/12_-_You_Can''t_Do_That' ext],... + [data_dir sla '03_-_A_Hard_Day''s_Night/13_-_I''ll_Be_Back' ext]}; + + + case '04_-_Beatles_for_Sale' + filenames = {... + [data_dir sla '04_-_Beatles_for_Sale/01_-_No_Reply' ext],... + [data_dir sla '04_-_Beatles_for_Sale/02_-_I''m_a_Loser' ext],... + ...[data_dir sla '04_-_Beatles_for_Sale/03_-_Baby''s_In_Black' ext],... + [data_dir sla '04_-_Beatles_for_Sale/04_-_Rock_and_Roll_Music' ext],... + [data_dir sla '04_-_Beatles_for_Sale/05_-_I''ll_Follow_the_Sun' ext],... + [data_dir sla '04_-_Beatles_for_Sale/06_-_Mr._Moonlight' ext],... + [data_dir sla '04_-_Beatles_for_Sale/07_-_Kansas_City-_Hey,_Hey,_Hey,_Hey' ext],... + [data_dir sla '04_-_Beatles_for_Sale/08_-_Eight_Days_a_Week' ext],... + [data_dir sla '04_-_Beatles_for_Sale/09_-_Words_of_Love' ext],... + [data_dir sla '04_-_Beatles_for_Sale/10_-_Honey_Don''t' ext],... + [data_dir sla '04_-_Beatles_for_Sale/11_-_Every_Little_Thing' ext],... + [data_dir sla '04_-_Beatles_for_Sale/12_-_I_Don''t_Want_to_Spoil_the_Party' ext],... + [data_dir sla '04_-_Beatles_for_Sale/13_-_What_You''re_Doing' ext],... + [data_dir sla '04_-_Beatles_for_Sale/14_-_Everybody''s_Trying_to_Be_My_Baby' ext]}; + + + case '05_-_Help!' + filenames = {... + [data_dir sla '05_-_Help!/01_-_Help!' ext],... + [data_dir sla '05_-_Help!/02_-_The_Night_Before' ext],... + ...[data_dir sla '05_-_Help!/03_-_You''ve_Got_To_Hide_Your_Love_Away' ext],... + [data_dir sla '05_-_Help!/04_-_I_Need_You' ext],... + [data_dir sla '05_-_Help!/05_-_Another_Girl' ext],... + [data_dir sla '05_-_Help!/06_-_You''re_Going_to_Lose_That_Girl' ext],... + [data_dir sla '05_-_Help!/07_-_Ticket_To_Ride' ext],... + [data_dir sla '05_-_Help!/08_-_Act_Naturally' ext],... + [data_dir sla '05_-_Help!/09_-_It''s_Only_Love' ext],... + [data_dir sla '05_-_Help!/10_-_You_Like_Me_Too_Much' ext],... + [data_dir sla '05_-_Help!/11_-_Tell_Me_What_You_See' ext],... + [data_dir sla '05_-_Help!/12_-_I''ve_Just_Seen_a_Face' ext],... + [data_dir sla '05_-_Help!/13_-_Yesterday' ext],... + [data_dir sla '05_-_Help!/14_-_Dizzy_Miss_Lizzy' ext]}; + + case '06_-_Rubber_Soul' + filenames = {... + [data_dir sla '06_-_Rubber_Soul/01_-_Drive_My_Car' ext],... + ... [data_dir sla '06_-_Rubber_Soul/02_-_Norwegian_Wood_(This_Bird_Has_Flown)' ext],... + [data_dir sla '06_-_Rubber_Soul/03_-_You_Won''t_See_Me' ext],... + [data_dir sla '06_-_Rubber_Soul/04_-_Nowhere_Man' ext],... + [data_dir sla '06_-_Rubber_Soul/05_-_Think_For_Yourself' ext],... + [data_dir sla '06_-_Rubber_Soul/06_-_The_Word' ext],... + [data_dir sla '06_-_Rubber_Soul/07_-_Michelle' ext],... + [data_dir sla '06_-_Rubber_Soul/08_-_What_Goes_On' ext],... + [data_dir sla '06_-_Rubber_Soul/09_-_Girl' ext],... + [data_dir sla '06_-_Rubber_Soul/10_-_I''m_Looking_Through_You' ext],... + [data_dir sla '06_-_Rubber_Soul/11_-_In_My_Life' ext],... + [data_dir sla '06_-_Rubber_Soul/12_-_Wait' ext],... + [data_dir sla '06_-_Rubber_Soul/13_-_If_I_Needed_Someone' ext],... + [data_dir sla '06_-_Rubber_Soul/14_-_Run_For_Your_Life' ext]}; + + case '07_-_Revolver' + filenames = {... + [data_dir sla '07_-_Revolver/01_-_Taxman' ext],... + [data_dir sla '07_-_Revolver/02_-_Eleanor_Rigby' ext],... + [data_dir sla '07_-_Revolver/03_-_I''m_Only_Sleeping' ext],... + ...[data_dir sla '07_-_Revolver/04_-_Love_You_To' ext],... + [data_dir sla '07_-_Revolver/05_-_Here,_There_And_Everywhere' ext],... + [data_dir sla '07_-_Revolver/06_-_Yellow_Submarine' ext],... + [data_dir sla '07_-_Revolver/07_-_She_Said_She_Said' ext],... + ...[data_dir sla '07_-_Revolver/08_-_Good_Day_Sunshine' ext],... + [data_dir sla '07_-_Revolver/09_-_And_Your_Bird_Can_Sing' ext],... + [data_dir sla '07_-_Revolver/10_-_For_No_One' ext],... + [data_dir sla '07_-_Revolver/11_-_Doctor_Robert' ext],... + [data_dir sla '07_-_Revolver/12_-_I_Want_To_Tell_You' ext],... + [data_dir sla '07_-_Revolver/13_-_Got_To_Get_You_Into_My_Life' ext],... + ...[data_dir sla '07_-_Revolver/14_-_Tomorrow_Never_Knows' ext]... + }; + + case '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band' + filenames = {... + [data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/01_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band' ext],... + [data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/02_-_With_A_Little_Help_From_My_Friends' ext],... + ...[data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/03_-_Lucy_In_The_Sky_With_Diamonds' ext],... + [data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/04_-_Getting_Better' ext],... + [data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/05_-_Fixing_A_Hole' ext],... + ...[data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/06_-_She''s_Leaving_Home' ext],... + [data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/07_-_Being_For_The_Benefit_Of_Mr._Kite!' ext],... + ...[data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/08_-_Within_You_Without_You' ext],... + [data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/09_-_When_I''m_Sixty-Four' ext],... + [data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/10_-_Lovely_Rita' ext],... + ...[data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/11_-_Good_Morning_Good_Morning' ext],... + [data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/12_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band_(Reprise)' ext],... + [data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/13_-_A_Day_In_The_Life' ext]}; + + case '09_-_Magical_Mystery_Tour' + filenames = {... + [data_dir sla '09_-_Magical_Mystery_Tour/01_-_Magical_Mystery_Tour' ext],... + [data_dir sla '09_-_Magical_Mystery_Tour/02_-_The_Fool_On_The_Hill' ext],... + [data_dir sla '09_-_Magical_Mystery_Tour/03_-_Flying' ext],... + ...[data_dir sla '09_-_Magical_Mystery_Tour/04_-_Blue_Jay_Way' ext],... + [data_dir sla '09_-_Magical_Mystery_Tour/05_-_Your_Mother_Should_Know' ext],... + [data_dir sla '09_-_Magical_Mystery_Tour/06_-_I_Am_The_Walrus' ext],... + [data_dir sla '09_-_Magical_Mystery_Tour/07_-_Hello_Goodbye' ext],... + [data_dir sla '09_-_Magical_Mystery_Tour/08_-_Strawberry_Fields_Forever' ext],... + [data_dir sla '09_-_Magical_Mystery_Tour/09_-_Penny_Lane' ext],... + [data_dir sla '09_-_Magical_Mystery_Tour/10_-_Baby_You''re_A_Rich_Man' ext],... + ...[data_dir sla '09_-_Magical_Mystery_Tour/11_-_All_You_Need_Is_Love' ext] ... + }; + + case '10CD1_-_The_Beatles' + filenames = {... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_01_-_Back_in_the_USSR' ext],... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_02_-_Dear_Prudence' ext],... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_03_-_Glass_Onion' ext],... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_04_-_Ob-La-Di,_Ob-La-Da' ext],... + ...[data_dir sla '10CD1_-_The_Beatles/CD1_-_05_-_Wild_Honey_Pie' ext],... + ...[data_dir sla '10CD1_-_The_Beatles/CD1_-_06_-_The_Continuing_Story_of_Bungalow_Bill' ext],... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_07_-_While_My_Guitar_Gently_Weeps' ext],... + ...[data_dir sla '10CD1_-_The_Beatles/CD1_-_08_-_Happiness_is_a_Warm_Gun' ext],... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_09_-_Martha_My_Dear' ext],... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_10_-_I''m_So_Tired' ext],... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_11_-_Black_Bird' ext],... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_12_-_Piggies' ext],... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_13_-_Rocky_Raccoon' ext],... + ...[data_dir sla '10CD1_-_The_Beatles/CD1_-_14_-_Don''t_Pass_Me_By' ext],... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_15_-_Why_Don''t_We_Do_It_In_The_Road' ext],... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_16_-_I_Will' ext],... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_17_-_Julia' ext]}; + + case '10CD2_-_The_Beatles' + filenames = {... + [data_dir sla '10CD2_-_The_Beatles/CD2_-_01_-_Birthday' ext],... + ...[data_dir sla '10CD2_-_The_Beatles/CD2_-_02_-_Yer_Blues' ext],... + [data_dir sla '10CD2_-_The_Beatles/CD2_-_03_-_Mother_Nature''s_Son' ext],... + [data_dir sla '10CD2_-_The_Beatles/CD2_-_04_-_Everybody''s_Got_Something_To_Hide_Except_Me_and_M' ext],... + [data_dir sla '10CD2_-_The_Beatles/CD2_-_05_-_Sexy_Sadie' ext],... + [data_dir sla '10CD2_-_The_Beatles/CD2_-_06_-_Helter_Skelter' ext],... + ...[data_dir sla '10CD2_-_The_Beatles/CD2_-_07_-_Long_Long_Long' ext],... + [data_dir sla '10CD2_-_The_Beatles/CD2_-_08_-_Revolution_1' ext],... + [data_dir sla '10CD2_-_The_Beatles/CD2_-_09_-_Honey_Pie' ext],... + [data_dir sla '10CD2_-_The_Beatles/CD2_-_10_-_Savoy_Truffle' ext],... + [data_dir sla '10CD2_-_The_Beatles/CD2_-_11_-_Cry_Baby_Cry' ext],... + ...[data_dir sla '10CD2_-_The_Beatles/CD2_-_12_-_Revolution_9' ext],... + [data_dir sla '10CD2_-_The_Beatles/CD2_-_13_-_Good_Night' ext]}; + + case '11_-_Abbey_Road' + filenames = {... + [data_dir sla '11_-_Abbey_Road/01_-_Come_Together' ext],... + [data_dir sla '11_-_Abbey_Road/02_-_Something' ext],... + [data_dir sla '11_-_Abbey_Road/03_-_Maxwell''s_Silver_Hammer' ext],... + ...[data_dir sla '11_-_Abbey_Road/04_-_Oh!_Darling' ext],... + [data_dir sla '11_-_Abbey_Road/05_-_Octopus''s_Garden' ext],... + ...[data_dir sla '11_-_Abbey_Road/06_-_I_Want_You' ext],... + [data_dir sla '11_-_Abbey_Road/07_-_Here_Comes_The_Sun' ext],... + [data_dir sla '11_-_Abbey_Road/08_-_Because' ext],... + [data_dir sla '11_-_Abbey_Road/09_-_You_Never_Give_Me_Your_Money' ext],... + [data_dir sla '11_-_Abbey_Road/10_-_Sun_King' ext],... + [data_dir sla '11_-_Abbey_Road/11_-_Mean_Mr_Mustard' ext],... + [data_dir sla '11_-_Abbey_Road/12_-_Polythene_Pam' ext],... + [data_dir sla '11_-_Abbey_Road/13_-_She_Came_In_Through_The_Bathroom_Window' ext],... + [data_dir sla '11_-_Abbey_Road/14_-_Golden_Slumbers' ext],... + [data_dir sla '11_-_Abbey_Road/15_-_Carry_That_Weight' ext],... + [data_dir sla '11_-_Abbey_Road/16_-_The_End' ext],... + [data_dir sla '11_-_Abbey_Road/17_-_Her_Majesty' ext]}; + + case '12_-_Let_It_Be' + filenames = {... + ...[data_dir sla '12_-_Let_It_Be/01_-_Two_of_Us' ext],... + ...[data_dir sla '12_-_Let_It_Be/02_-_Dig_a_Pony' ext],... + [data_dir sla '12_-_Let_It_Be/03_-_Across_the_Universe' ext],... + ... [data_dir sla '12_-_Let_It_Be/04_-_I_Me_Mine' ext],... + ... [data_dir sla '12_-_Let_It_Be/05_-_Dig_It' ext],... + [data_dir sla '12_-_Let_It_Be/06_-_Let_It_Be' ext],... + [data_dir sla '12_-_Let_It_Be/07_-_Maggie_Mae' ext],... + [data_dir sla '12_-_Let_It_Be/08_-_I''ve_Got_A_Feeling' ext],... + [data_dir sla '12_-_Let_It_Be/09_-_One_After_909' ext],... + [data_dir sla '12_-_Let_It_Be/10_-_The_Long_and_Winding_Road' ext],... + [data_dir sla '12_-_Let_It_Be/11_-_For_You_Blue' ext],... + [data_dir sla '12_-_Let_It_Be/12_-_Get_Back' ext]}; + case 'discarded' + filenames = {... + [data_dir sla '01_-_Please_Please_Me/12_-_A_Taste_Of_Honey' ext],... + [data_dir sla '02_-_With_the_Beatles/10_-_You_Really_Got_A_Hold_On_Me' ext],... + [data_dir sla '04_-_Beatles_for_Sale/03_-_Baby''s_In_Black' ext],... + [data_dir sla '05_-_Help!/03_-_You''ve_Got_To_Hide_Your_Love_Away' ext],... + [data_dir sla '06_-_Rubber_Soul/02_-_Norwegian_Wood_(This_Bird_Has_Flown)' ext],... + [data_dir sla '07_-_Revolver/04_-_Love_You_To' ext],... + [data_dir sla '07_-_Revolver/08_-_Good_Day_Sunshine' ext],... + [data_dir sla '07_-_Revolver/14_-_Tomorrow_Never_Knows' ext]... + [data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/03_-_Lucy_In_The_Sky_With_Diamonds' ext],... + [data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/06_-_She''s_Leaving_Home' ext],... + [data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/08_-_Within_You_Without_You' ext],... + [data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/11_-_Good_Morning_Good_Morning' ext],... + [data_dir sla '09_-_Magical_Mystery_Tour/04_-_Blue_Jay_Way' ext],... + [data_dir sla '09_-_Magical_Mystery_Tour/11_-_All_You_Need_Is_Love' ext] ... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_05_-_Wild_Honey_Pie' ext],... + [data_dir sla '10CD2_-_The_Beatles/CD2_-_02_-_Yer_Blues' ext],... + [data_dir sla '10CD2_-_The_Beatles/CD2_-_07_-_Long_Long_Long' ext],... + [data_dir sla '11_-_Abbey_Road/04_-_Oh!_Darling' ext],... + [data_dir sla '12_-_Let_It_Be/01_-_Two_of_Us' ext],... + [data_dir sla '12_-_Let_It_Be/02_-_Dig_a_Pony' ext],... + [data_dir sla '12_-_Let_It_Be/04_-_I_Me_Mine' ext],... + [data_dir sla '12_-_Let_It_Be/05_-_Dig_It' ext],... + [data_dir sla '11_-_Abbey_Road/06_-_I_Want_You' ext]... + }; + case 'notinMIREX' + filenames = {... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_06_-_The_Continuing_Story_of_Bungalow_Bill' ext],... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_14_-_Don''t_Pass_Me_By' ext],... + [data_dir sla '10CD1_-_The_Beatles/CD1_-_08_-_Happiness_is_a_Warm_Gun' ext],... + }; + case 'revolution9' + filenames = {[data_dir sla '10CD2_-_The_Beatles/CD2_-_12_-_Revolution_9' ext]}; + case 'leftovers' + filenames = {... + [data_dir sla '08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band/06_-_She''s_Leaving_Home' ext],... + [data_dir sla '07_-_Revolver/04_-_Love_You_To' ext],... + }; + case 'semma' + filenames = {... + [data_dir sla 'SeMMA/audio/Britney_Spear_##_Baby_Hit_Me_One_More_Time', ext],... + [data_dir sla 'SeMMA/audio/Bjork_##_Its_Oh_So_Quiet', ext],... + [data_dir sla 'SeMMA/audio/Alanis_Morissette_##_Thank_U', ext],... + [data_dir sla 'SeMMA/audio/Alanis_Morissette_##_Head_Over_Feet', ext],... + [data_dir sla 'SeMMA/audio/A_Ha_##_Take_On_Me', ext],... + [data_dir sla 'SeMMA/audio/The_Clash_##_Should_I_Stay_Or_Should_I_Go', ext],... + [data_dir sla 'SeMMA/audio/Spice_Girls_##_Wannabe', ext],... + [data_dir sla 'SeMMA/audio/Seal_##_Crazy', ext],... + [data_dir sla 'SeMMA/audio/Radiohead_##_Creep', ext],... + [data_dir sla 'SeMMA/audio/Oasis_##_Wonderwall', ext],... + [data_dir sla 'SeMMA/audio/Nirvana_##_Smells_Like_Teen_Spirit', ext],... + [data_dir sla 'SeMMA/audio/Deus_##_Suds_and_Soda', ext],... + [data_dir sla 'SeMMA/audio/Cranberries_##_Zombie', ext],... + [data_dir sla 'SeMMA/audio/Chumbawamba_##_Tubthumping', ext],... + }; + end + allfilenames = [allfilenames,filenames]; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/general/qnorm.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,15 @@ +function s = qnorm(inmat, varargin) +dim = 1; +if nargin > 1 + q = varargin{1}; + if nargin > 2 + dim = varargin{2}; + end +else + q = 2; +end +if ~(q == Inf) + s = sum(inmat.^q,dim).^(1/q); +else + s = max(inmat,[],dim); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/general/qnormalise.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,17 @@ +function [outmat, scale] = qnormalise(inmat, varargin) +% qnormalise(inmat, q, dim) returns the field inmat normalised by the q +% norm over dimension dim +dim = 1; +if nargin > 1 + q = varargin{1}; + if nargin > 2 + dim = varargin{2}; + end +else + q = 2; +end +sizeInmat = size(inmat); +nDim = length(sizeInmat); +repeatpattern = [ones(1,dim-1) sizeInmat(dim) ones(1,nDim-dim)]; +scale = qnorm(inmat, q, dim); +outmat = inmat ./ repmat(scale, repeatpattern);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/kde.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,98 @@ +function [bandwidth,density,xmesh]=kde(data,n,MIN,MAX) +% Reliable and extremely fast kernel density estimator for 1 dimensional data; +% Gaussian kernel is assumed and the bandwidth is chosen automatically; +% +% INPUTS: +% data - a vector of data from which the density estimate is constructed; +% MIN, MAX - defines the interval [MIN,MAX] on which the density estimate is constructed; +% the default values of MIN and MAX are: +% MIN=min(data)-Range/100 and MAX=max(data)+Range/100, where Range=max(data)-min(data); +% n - the number of mesh points used in the uniform discretization of the +% interval [MIN, MAX]; n has to be a power of two; if n is not a power of two, then +% n is rounded up to the next power of two, i.e., n is set to n=2^ceil(log2(n)); +% the default value of n is n=2^12; +% OUTPUTS: +% bandwidth - the optimal bandwidth (Gaussian kernel assumed); +% density - column vector of length 'n' with the values of the density +% estimate at the grid points; +% xmesh - the grid over which the density estimate is computed; +% Reference: Botev, Z. I., +% "A Novel Nonparametric Density Estimator",Technical Report,The University of Queensland +% http://espace.library.uq.edu.au/view.php?pid=UQ:12535 +% +% Example: +% data=randn(1000,1); +% [bandwidth,density,xmesh]=kde(data,2^12,min(data)-1,max(data)+1); +% plot(xmesh,density) + +data=data(:); %make data a column vector +if nargin<2 % if n is not supplied switch to the default + n=2^12; +end +n=2^ceil(log2(n)); % round up n to the next power of 2; + +if nargin<4 %define the default interval [MIN,MAX] + minimum=min(data); maximum=max(data); + Range=maximum-minimum; + MIN=minimum-Range/10; MAX=maximum+Range/10; +end +% set up the grid over which the density estimate is computed; +R=MAX-MIN; dx=R/(n-1); xmesh=MIN+[0:dx:R]; N=length(data); +%bin the data uniformly using the grid define above; +initial_data=histc(data,xmesh)/N; +a=dct1d(initial_data); % discrete cosine transform of initial data +% now compute the optimal bandwidth^2 using the GCE method +t_star=gce(a,n,N); +% smooth the discrete cosine transform of initial data using t_star +a_t=a.*exp(-[0:n-1]'.^2*pi^2*t_star/2); +% now apply the inverse discrete cosine transform + if nargout>1 + density=idct1d(a_t)/R; + end +bandwidth=sqrt(t_star)*R; +end +function t_star=gce(a,n,N) +a=a(2:end)/2; +I=[1:n-1]'.^2; +a2=a.^2; +Var_a=zeros(n-1,1); +Var_a(1:n/2-1)=(1/2+1/2*a(2:2:n-1)-a2(1:n/2-1))/N; + +t_star=fzero(@mise,[0,1]); +NORM=2*pi^4*sum(I.^2.*a2.*exp(-I*pi^2*t_star)); +%NORM=.5*pi^4*sum([1:n-1]'.^4.*a_t(2:end).^2)/R^5; +t_star=[2*N*sqrt(pi)*NORM]^(-2/5); + function out=mise(t) + + out=sum((a2+Var_a).*(1-exp(-I*pi^2*t/2)).^2./I)+... + sqrt(t/pi)/N*(pi^2/2)-sum(Var_a./I); + end +end + +function data=dct1d(data) +% computes the discrete cosine transform of the column vector data +[nrows,ncols]= size(data); +% Compute weights to multiply DFT coefficients +weight = [1;2*(exp(-i*(1:nrows-1)*pi/(2*nrows))).']; +% Re-order the elements of the columns of x +data = [ data(1:2:end,:); data(end:-2:2,:) ]; +% Multiply FFT by weights: +data= real(weight.* fft(data)); +end +function out = idct1d(data) +% computes the inverse discrete cosine transform +[nrows,ncols]=size(data); +% Compute weights +weights = nrows*exp(i*(0:nrows-1)*pi/(2*nrows)).'; +% Compute x tilde using equation (5.93) in Jain +data = real(ifft(weights.*data)); +% Re-order elements of each column according to equations (5.93) and +% (5.94) in Jain +out = zeros(nrows,1); +out(1:2:nrows) = data(1:nrows/2); +out(2:2:nrows) = data(nrows:-1:nrows/2+1); +% Reference: +% A. K. Jain, "Fundamentals of Digital Image +% Processing", pp. 150-153. +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/probability/.svn/all-wcprops Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,23 @@ +K 25 +svn:wc:ra_dav:version-url +V 55 +/svnroot/!svn/ver/1425/matlabtoolboxes/misc/probability +END +KLDiv.m +K 25 +svn:wc:ra_dav:version-url +V 63 +/svnroot/!svn/ver/1425/matlabtoolboxes/misc/probability/KLDiv.m +END +JSDiv.m +K 25 +svn:wc:ra_dav:version-url +V 63 +/svnroot/!svn/ver/1425/matlabtoolboxes/misc/probability/JSDiv.m +END +kde.m +K 25 +svn:wc:ra_dav:version-url +V 61 +/svnroot/!svn/ver/1425/matlabtoolboxes/misc/probability/kde.m +END
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/probability/.svn/entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,130 @@ +10 + +dir +7329 +https://parrot.dcs.qmul.ac.uk/svnroot/matlabtoolboxes/misc/probability +https://parrot.dcs.qmul.ac.uk/svnroot + + + +2009-03-06T16:34:10.036388Z +1425 +matthiasm + + + + + + + + + + + + + + +214cd2a9-030a-0410-b9a1-da4e402363b6 + +KLDiv.m +file + + + + +2010-04-07T10:16:14.000000Z +283dfddcbfb2419c115d1fd071673485 +2009-03-06T16:34:10.036388Z +1425 +matthiasm + + + + + + + + + + + + + + + + + + + + + +881 + +JSDiv.m +file + + + + +2010-04-07T10:16:14.000000Z +e64eca575121a7d8b4fd79b6224adbd9 +2009-03-06T16:34:10.036388Z +1425 +matthiasm + + + + + + + + + + + + + + + + + + + + + +601 + +kde.m +file + + + + +2010-04-07T10:16:14.000000Z +2e692dc92798b5446318544dd2f9d684 +2009-03-06T16:34:10.036388Z +1425 +matthiasm +has-props + + + + + + + + + + + + + + + + + + + + +3883 +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/probability/.svn/prop-base/kde.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +K 14 +svn:executable +V 1 +* +END
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/probability/.svn/text-base/JSDiv.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,23 @@ +function dist=JSDiv(P,Q) +% Jensen-Shannon divergence of two probability distributions +% dist = JSD(P,Q) Kullback-Leibler divergence of two discrete probability +% distributions +% P and Q are automatically normalised to have the sum of one on rows +% have the length of one at each +% P = n x nbins +% Q = 1 x nbins +% dist = n x 1 + + +if size(P,2)~=size(Q,2) + error('the number of columns in P and Q should be the same'); +end + +% normalizing the P and Q +Q = Q ./sum(Q); +Q = repmat(Q,[size(P,1) 1]); +P = P ./repmat(sum(P,2),[1 size(P,2)]); + +M = 0.5.*(P + Q); + +dist = 0.5.*KLDiv(P,M) + 0.5*KLDiv(Q,M); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/probability/.svn/text-base/KLDiv.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,35 @@ +function dist=KLDiv(P,Q) +% dist = KLDiv(P,Q) Kullback-Leibler divergence of two discrete probability +% distributions +% P and Q are automatically normalised to have the sum of one on rows +% have the length of one at each +% P = n x nbins +% Q = 1 x nbins or n x nbins(one to one) +% dist = n x 1 + + + +if size(P,2)~=size(Q,2) + error('the number of columns in P and Q should be the same'); +end + +if sum(~isfinite(P(:))) + sum(~isfinite(Q(:))) + error('the inputs contain non-finite values!') +end + +% normalizing the P and Q +if size(Q,1)==1 + Q = Q ./sum(Q); + P = P ./repmat(sum(P,2),[1 size(P,2)]); + dist = sum(P.*log(P./repmat(Q,[size(P,1) 1])),2); + +elseif size(Q,1)==size(P,1) + + Q = Q ./repmat(sum(Q,2),[1 size(Q,2)]); + P = P ./repmat(sum(P,2),[1 size(P,2)]); + dist = sum(P.*log(P./Q),2); +end + +% resolving the case when P(i)==0 +dist(isnan(dist))=0; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/probability/.svn/text-base/kde.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,98 @@ +function [bandwidth,density,xmesh]=kde(data,n,MIN,MAX) +% Reliable and extremely fast kernel density estimator for 1 dimensional data; +% Gaussian kernel is assumed and the bandwidth is chosen automatically; +% +% INPUTS: +% data - a vector of data from which the density estimate is constructed; +% MIN, MAX - defines the interval [MIN,MAX] on which the density estimate is constructed; +% the default values of MIN and MAX are: +% MIN=min(data)-Range/100 and MAX=max(data)+Range/100, where Range=max(data)-min(data); +% n - the number of mesh points used in the uniform discretization of the +% interval [MIN, MAX]; n has to be a power of two; if n is not a power of two, then +% n is rounded up to the next power of two, i.e., n is set to n=2^ceil(log2(n)); +% the default value of n is n=2^12; +% OUTPUTS: +% bandwidth - the optimal bandwidth (Gaussian kernel assumed); +% density - column vector of length 'n' with the values of the density +% estimate at the grid points; +% xmesh - the grid over which the density estimate is computed; +% Reference: Botev, Z. I., +% "A Novel Nonparametric Density Estimator",Technical Report,The University of Queensland +% http://espace.library.uq.edu.au/view.php?pid=UQ:12535 +% +% Example: +% data=randn(1000,1); +% [bandwidth,density,xmesh]=kde(data,2^12,min(data)-1,max(data)+1); +% plot(xmesh,density) + +data=data(:); %make data a column vector +if nargin<2 % if n is not supplied switch to the default + n=2^12; +end +n=2^ceil(log2(n)); % round up n to the next power of 2; + +if nargin<4 %define the default interval [MIN,MAX] + minimum=min(data); maximum=max(data); + Range=maximum-minimum; + MIN=minimum-Range/10; MAX=maximum+Range/10; +end +% set up the grid over which the density estimate is computed; +R=MAX-MIN; dx=R/(n-1); xmesh=MIN+[0:dx:R]; N=length(data); +%bin the data uniformly using the grid define above; +initial_data=histc(data,xmesh)/N; +a=dct1d(initial_data); % discrete cosine transform of initial data +% now compute the optimal bandwidth^2 using the GCE method +t_star=gce(a,n,N); +% smooth the discrete cosine transform of initial data using t_star +a_t=a.*exp(-[0:n-1]'.^2*pi^2*t_star/2); +% now apply the inverse discrete cosine transform + if nargout>1 + density=idct1d(a_t)/R; + end +bandwidth=sqrt(t_star)*R; +end +function t_star=gce(a,n,N) +a=a(2:end)/2; +I=[1:n-1]'.^2; +a2=a.^2; +Var_a=zeros(n-1,1); +Var_a(1:n/2-1)=(1/2+1/2*a(2:2:n-1)-a2(1:n/2-1))/N; + +t_star=fzero(@mise,[0,1]); +NORM=2*pi^4*sum(I.^2.*a2.*exp(-I*pi^2*t_star)); +%NORM=.5*pi^4*sum([1:n-1]'.^4.*a_t(2:end).^2)/R^5; +t_star=[2*N*sqrt(pi)*NORM]^(-2/5); + function out=mise(t) + + out=sum((a2+Var_a).*(1-exp(-I*pi^2*t/2)).^2./I)+... + sqrt(t/pi)/N*(pi^2/2)-sum(Var_a./I); + end +end + +function data=dct1d(data) +% computes the discrete cosine transform of the column vector data +[nrows,ncols]= size(data); +% Compute weights to multiply DFT coefficients +weight = [1;2*(exp(-i*(1:nrows-1)*pi/(2*nrows))).']; +% Re-order the elements of the columns of x +data = [ data(1:2:end,:); data(end:-2:2,:) ]; +% Multiply FFT by weights: +data= real(weight.* fft(data)); +end +function out = idct1d(data) +% computes the inverse discrete cosine transform +[nrows,ncols]=size(data); +% Compute weights +weights = nrows*exp(i*(0:nrows-1)*pi/(2*nrows)).'; +% Compute x tilde using equation (5.93) in Jain +data = real(ifft(weights.*data)); +% Re-order elements of each column according to equations (5.93) and +% (5.94) in Jain +out = zeros(nrows,1); +out(1:2:nrows) = data(1:nrows/2); +out(2:2:nrows) = data(nrows:-1:nrows/2+1); +% Reference: +% A. K. Jain, "Fundamentals of Digital Image +% Processing", pp. 150-153. +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/probability/JSDiv.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,23 @@ +function dist=JSDiv(P,Q) +% Jensen-Shannon divergence of two probability distributions +% dist = JSD(P,Q) Kullback-Leibler divergence of two discrete probability +% distributions +% P and Q are automatically normalised to have the sum of one on rows +% have the length of one at each +% P = n x nbins +% Q = 1 x nbins +% dist = n x 1 + + +if size(P,2)~=size(Q,2) + error('the number of columns in P and Q should be the same'); +end + +% normalizing the P and Q +Q = Q ./sum(Q); +Q = repmat(Q,[size(P,1) 1]); +P = P ./repmat(sum(P,2),[1 size(P,2)]); + +M = 0.5.*(P + Q); + +dist = 0.5.*KLDiv(P,M) + 0.5*KLDiv(Q,M); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/probability/KLDiv.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,35 @@ +function dist=KLDiv(P,Q) +% dist = KLDiv(P,Q) Kullback-Leibler divergence of two discrete probability +% distributions +% P and Q are automatically normalised to have the sum of one on rows +% have the length of one at each +% P = n x nbins +% Q = 1 x nbins or n x nbins(one to one) +% dist = n x 1 + + + +if size(P,2)~=size(Q,2) + error('the number of columns in P and Q should be the same'); +end + +if sum(~isfinite(P(:))) + sum(~isfinite(Q(:))) + error('the inputs contain non-finite values!') +end + +% normalizing the P and Q +if size(Q,1)==1 + Q = Q ./sum(Q); + P = P ./repmat(sum(P,2),[1 size(P,2)]); + dist = sum(P.*log(P./repmat(Q,[size(P,1) 1])),2); + +elseif size(Q,1)==size(P,1) + + Q = Q ./repmat(sum(Q,2),[1 size(Q,2)]); + P = P ./repmat(sum(P,2),[1 size(P,2)]); + dist = sum(P.*log(P./Q),2); +end + +% resolving the case when P(i)==0 +dist(isnan(dist))=0; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/probability/kde.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,98 @@ +function [bandwidth,density,xmesh]=kde(data,n,MIN,MAX) +% Reliable and extremely fast kernel density estimator for 1 dimensional data; +% Gaussian kernel is assumed and the bandwidth is chosen automatically; +% +% INPUTS: +% data - a vector of data from which the density estimate is constructed; +% MIN, MAX - defines the interval [MIN,MAX] on which the density estimate is constructed; +% the default values of MIN and MAX are: +% MIN=min(data)-Range/100 and MAX=max(data)+Range/100, where Range=max(data)-min(data); +% n - the number of mesh points used in the uniform discretization of the +% interval [MIN, MAX]; n has to be a power of two; if n is not a power of two, then +% n is rounded up to the next power of two, i.e., n is set to n=2^ceil(log2(n)); +% the default value of n is n=2^12; +% OUTPUTS: +% bandwidth - the optimal bandwidth (Gaussian kernel assumed); +% density - column vector of length 'n' with the values of the density +% estimate at the grid points; +% xmesh - the grid over which the density estimate is computed; +% Reference: Botev, Z. I., +% "A Novel Nonparametric Density Estimator",Technical Report,The University of Queensland +% http://espace.library.uq.edu.au/view.php?pid=UQ:12535 +% +% Example: +% data=randn(1000,1); +% [bandwidth,density,xmesh]=kde(data,2^12,min(data)-1,max(data)+1); +% plot(xmesh,density) + +data=data(:); %make data a column vector +if nargin<2 % if n is not supplied switch to the default + n=2^12; +end +n=2^ceil(log2(n)); % round up n to the next power of 2; + +if nargin<4 %define the default interval [MIN,MAX] + minimum=min(data); maximum=max(data); + Range=maximum-minimum; + MIN=minimum-Range/10; MAX=maximum+Range/10; +end +% set up the grid over which the density estimate is computed; +R=MAX-MIN; dx=R/(n-1); xmesh=MIN+[0:dx:R]; N=length(data); +%bin the data uniformly using the grid define above; +initial_data=histc(data,xmesh)/N; +a=dct1d(initial_data); % discrete cosine transform of initial data +% now compute the optimal bandwidth^2 using the GCE method +t_star=gce(a,n,N); +% smooth the discrete cosine transform of initial data using t_star +a_t=a.*exp(-[0:n-1]'.^2*pi^2*t_star/2); +% now apply the inverse discrete cosine transform + if nargout>1 + density=idct1d(a_t)/R; + end +bandwidth=sqrt(t_star)*R; +end +function t_star=gce(a,n,N) +a=a(2:end)/2; +I=[1:n-1]'.^2; +a2=a.^2; +Var_a=zeros(n-1,1); +Var_a(1:n/2-1)=(1/2+1/2*a(2:2:n-1)-a2(1:n/2-1))/N; + +t_star=fzero(@mise,[0,1]); +NORM=2*pi^4*sum(I.^2.*a2.*exp(-I*pi^2*t_star)); +%NORM=.5*pi^4*sum([1:n-1]'.^4.*a_t(2:end).^2)/R^5; +t_star=[2*N*sqrt(pi)*NORM]^(-2/5); + function out=mise(t) + + out=sum((a2+Var_a).*(1-exp(-I*pi^2*t/2)).^2./I)+... + sqrt(t/pi)/N*(pi^2/2)-sum(Var_a./I); + end +end + +function data=dct1d(data) +% computes the discrete cosine transform of the column vector data +[nrows,ncols]= size(data); +% Compute weights to multiply DFT coefficients +weight = [1;2*(exp(-i*(1:nrows-1)*pi/(2*nrows))).']; +% Re-order the elements of the columns of x +data = [ data(1:2:end,:); data(end:-2:2,:) ]; +% Multiply FFT by weights: +data= real(weight.* fft(data)); +end +function out = idct1d(data) +% computes the inverse discrete cosine transform +[nrows,ncols]=size(data); +% Compute weights +weights = nrows*exp(i*(0:nrows-1)*pi/(2*nrows)).'; +% Compute x tilde using equation (5.93) in Jain +data = real(ifft(weights.*data)); +% Re-order elements of each column according to equations (5.93) and +% (5.94) in Jain +out = zeros(nrows,1); +out(1:2:nrows) = data(1:nrows/2); +out(2:2:nrows) = data(nrows:-1:nrows/2+1); +% Reference: +% A. K. Jain, "Fundamentals of Digital Image +% Processing", pp. 150-153. +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/tools/.svn/all-wcprops Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,23 @@ +K 25 +svn:wc:ra_dav:version-url +V 49 +/svnroot/!svn/ver/1403/matlabtoolboxes/misc/tools +END +localmaxmin.m +K 25 +svn:wc:ra_dav:version-url +V 63 +/svnroot/!svn/ver/1403/matlabtoolboxes/misc/tools/localmaxmin.m +END +getFileDependencies.m +K 25 +svn:wc:ra_dav:version-url +V 71 +/svnroot/!svn/ver/1403/matlabtoolboxes/misc/tools/getFileDependencies.m +END +localmax_logical.m +K 25 +svn:wc:ra_dav:version-url +V 68 +/svnroot/!svn/ver/1403/matlabtoolboxes/misc/tools/localmax_logical.m +END
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/tools/.svn/entries Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,130 @@ +10 + +dir +7329 +https://parrot.dcs.qmul.ac.uk/svnroot/matlabtoolboxes/misc/tools +https://parrot.dcs.qmul.ac.uk/svnroot + + + +2009-02-20T16:30:45.085047Z +1403 +matthiasm + + + + + + + + + + + + + + +214cd2a9-030a-0410-b9a1-da4e402363b6 + +localmaxmin.m +file + + + + +2010-04-07T10:16:15.000000Z +ec04f41f5dc39d7c75360f48c3122936 +2009-02-20T16:30:45.085047Z +1403 +matthiasm + + + + + + + + + + + + + + + + + + + + + +3662 + +getFileDependencies.m +file + + + + +2010-04-07T10:16:15.000000Z +82970f27e19757664e2ed6c8128efb4d +2009-02-20T16:30:45.085047Z +1403 +matthiasm + + + + + + + + + + + + + + + + + + + + + +5564 + +localmax_logical.m +file + + + + +2010-04-07T10:16:15.000000Z +1164efb3815d15725ad3834ba7352af8 +2009-02-20T16:30:45.085047Z +1403 +matthiasm +has-props + + + + + + + + + + + + + + + + + + + + +203 +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/tools/.svn/prop-base/localmax_logical.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +K 14 +svn:executable +V 1 +* +END
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/tools/.svn/text-base/getFileDependencies.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,156 @@ +function list = getFileDependencies(file) +% searches the paths of the .m-Files the given file is using. This function +% depends on the depfun-Function of Matlab. Though it is not guaranteed, +% that all files will be found. E.g. files in evaluate-constructs won't be +% found!! +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% Parameters: +% - file: the file for which the dependencies should be searched. This +% could be only a string with the function-name, the filename +% or the path to a file. +% +% Returns: +% - list: a cell-Array with the paths to all files the given file uses +% as strings. list is -1 if an error occured while processing. +% See errormessage for details. +% +% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% author: Torsten Hopp +% last change: 03.07.2007 +% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +% checking the input-parameter +if(isempty(file)) + disp('ERROR. Input Parameter is empty') + list = -1; + return; +end +if(~ischar(file)) + disp('ERROR. Input Parameter is not char') + list = -1; + return; +end + + +% list: a cell-array for the result +list = {which(file)}; +% fileList: array of strings to search in the current iteration +fileList = list; + +% While new files where found in last iteration +while (~isempty(fileList)) + fileListSize = length(fileList); + newOnes = []; + newCounter = 1; + currentFileList = fileList; + fileList = []; + + % running trough the current file list + for k=1:fileListSize + % calling depfun. -toponly indicates non-recursive search. -quiet + % suppresses output to command line + templist = depfun(currentFileList{k},'-toponly','-quiet'); + + % the first file is already included + templist(1) = []; + + % calling the helper-function to delete files which contain + % <matlabroot> in the path from the list. + templist = deleteMatlabFiles(templist); + + listSize = length(list); + tempListSize = length(templist); + + % excluding first iteration with this if-clause + if(listSize > 1) + + % comparing lists --> Searching for files that are not in the + % list yet + for i=1:tempListSize + contained = 0; + j=1; + % first condition stops comparing if something was found. + while contained == 0 & j<=listSize + if((strcmp(templist{i},list{j})==1) ) + contained = 1; + end + j = j+1; + end + + % if file isn't in the list yet... + if(contained == 0) + + % add it to a cellArray for new filenames... + % if-clause excludes first run with empty + % newOnes-variable + if(~isempty(newOnes)) + + % if not first run: comparing the filename with the + % filenames that are in the newOnes-List to avoid + % double-enties + a = strfind(newOnes,templist{i}); + counter = 0; + for p=1:length(a) + if(~isempty(a{p})) + counter = counter +1; + end + end + % if not found in list: add it to the list! + if(counter==0) + newOnes{newCounter} = templist{i}; + newCounter = newCounter + 1; + end + else + % if list is empty: add it without checking! + newOnes{newCounter} = templist{i}; + newCounter = newCounter + 1; + end + end + end + + % if new filenames where found in this run... + if(~isempty(newOnes)) + % ... search the new found files in the next iteration + fileList = [fileList newOnes]; + % ... add the new found files to the list + list(length(list)+1:length(list)+length(newOnes)) = newOnes; + newOnes = []; + newCounter = 1; + end + else + % if first iteration: set the first-level dependecies as + % filelist for next iteration and add them to the list. + fileList = templist; + list = [list; templist]; + end + end +end + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% HELPER FUNCTIONS % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +function rList = deleteMatlabFiles(pList) +% deletes filenames with matlabroot in path from the given list. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Parameters: +% - pList: a cell-Array of filenames as string +% +% Returns: +% - rList: a cell-Array of filenames as string +% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +excludedDir = matlabroot; +indexes = strfind(pList,excludedDir); +keepIndexes = cellfun(@isempty,indexes); +rList = pList(keepIndexes); + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/tools/.svn/text-base/localmax_logical.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function lm = localmax_logical(mat,n) + +diffmat = [mat(1,:)*0; sign(diff(mat))]; % !!!! attention rethink the 0!!!!! +convolver = -[ones(n,1);-ones(n,1)]; +lm = conv2(diffmat,convolver,'same') == 2 * n; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/tools/.svn/text-base/localmaxmin.m.svn-base Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,124 @@ +function [x,n]=localmaxmin(y,xn) +%LOCALMAXMIN(Y) Local Maxima and Minima. +% X = LOCALMAXMIN(Y) or LOCALMAXMIN(Y,'max') for vector Y returns a logical +% vector X the same size as Y such that Y(X) contains the local maxima in Y. +% +% N = LOCALMAXMIN(Y,'min') for vector Y returns a logical vector N the same +% size as Y such that Y(N) contains the local minima in Y. +% +% [X,N] = LOCALMAXMIN(Y) for vector Y returns logical vectors X and N such +% that Y(X) contains the local maxima and Y(N) contains the local minima. +% +% When Y is a matrix, outputs are logical array(s) the same size as Y, and +% the minima and/or maxima are computed down the rows of each column in Y. +% +% See also MAX, MIN. + +% D.C. Hanselman, University of Maine, Orono, ME 04469 +% MasteringMatlab@yahoo.com +% Mastering MATLAB 7 +% 2006-03-08 + +mm=['min';'max']; +if nargin==1 + xn='max'; +elseif nargin~=2 + error('localmaxmin:NotEnoughInputArguments',... + 'One or Two Input Arguments Required.') +end +if ~ischar(xn) || isempty(strcmp(xn,mm)) + error('localmaxmin:UnknownArgument','Unknown Second Input Argument.') +end +if ~isnumeric(y) || ~isreal(y) + error('localmaxmin:IncorrectDataType','Y Must be Real Valued.') +end +ysiz=size(y); +isrow=ysiz(1)==1; +if isrow % if row, convert to column for now + y=y'; + ysiz(1)=ysiz(2); + ysiz(2)=1; +end +if ysiz(1)<4 + error('localmaxmin:InsufficientData','Y Must Have at Least 4 Elements.') +end +x=false(ysiz); + +sdiff=sign(diff(y)); +zdiff=sdiff==0; +idz=find(sum(zdiff)); % columns where zero differences appear + +if strcmp(xn,'min') || nargout==2 % get minima + x(2:end-1,:)=diff(sdiff)==2; + x(1,:)=sdiff(1,:)>0; + x(end,:)=sdiff(end,:)<0; + if any(zdiff) + ir=1:ysiz(1); + for k=idz % handle columns with zero differences + itmp=ir; + itmp(zdiff(:,k))=[]; + if all(sdiff(:,k)==0) % entire column is flat + x(:,k)=true; + else + tmp=diff(sign(diff(y(itmp,k))))==2; + x(itmp(tmp)+1,k)=true; + if sdiff(1,k)==0 + nr=find(sdiff(:,k)>0,1); + nf=find(sdiff(:,k)<0,1); + if ~(isempty(nr)||isempty(nf)) + x(1,k)=nr<nf; + end + end + if sdiff(end,k)==0 + nr=find(sdiff(:,k)>0,1,'last'); + nf=find(sdiff(:,k)<0,1,'last'); + if ~(isempty(nr)||isempty(nf)) + x(end,k)=nr<nf; + end + end + end + end + end + if isrow + x=x.'; + end + if nargout==2 + n=x; + end +end +if strcmp(xn,'max') || nargout==2 % get maxima + x=false(ysiz); + x(2:end-1,:)=diff(sdiff)==-2; + x(1,:)=sdiff(1,:)<0; + x(end,:)=sdiff(end,:)>0; + if any(zdiff) + ir=1:ysiz(1); + for k=idz % handle columns with zero differences + itmp=ir; + itmp(zdiff(:,k))=[]; + if all(sdiff(:,k)==0) % entire column is flat + x(:,k)=true; + else + tmp=diff(sign(diff(y(itmp,k))))==-2; + x(itmp(tmp)+1,k)=true; + if sdiff(1,k)==0 + nr=find(sdiff(:,k)>0,1); + nf=find(sdiff(:,k)<0,1); + if ~(isempty(nr)||isempty(nf)) + x(1,k)=nr>nf; + end + end + if sdiff(end,k)==0 + nr=find(sdiff(:,k)>0,1,'last'); + nf=find(sdiff(:,k)<0,1,'last'); + if ~(isempty(nr)||isempty(nf)) + x(end,k)=nr>nf; + end + end + end + end + end + if isrow + x=x'; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/tools/getFileDependencies.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,156 @@ +function list = getFileDependencies(file) +% searches the paths of the .m-Files the given file is using. This function +% depends on the depfun-Function of Matlab. Though it is not guaranteed, +% that all files will be found. E.g. files in evaluate-constructs won't be +% found!! +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% Parameters: +% - file: the file for which the dependencies should be searched. This +% could be only a string with the function-name, the filename +% or the path to a file. +% +% Returns: +% - list: a cell-Array with the paths to all files the given file uses +% as strings. list is -1 if an error occured while processing. +% See errormessage for details. +% +% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% author: Torsten Hopp +% last change: 03.07.2007 +% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +% checking the input-parameter +if(isempty(file)) + disp('ERROR. Input Parameter is empty') + list = -1; + return; +end +if(~ischar(file)) + disp('ERROR. Input Parameter is not char') + list = -1; + return; +end + + +% list: a cell-array for the result +list = {which(file)}; +% fileList: array of strings to search in the current iteration +fileList = list; + +% While new files where found in last iteration +while (~isempty(fileList)) + fileListSize = length(fileList); + newOnes = []; + newCounter = 1; + currentFileList = fileList; + fileList = []; + + % running trough the current file list + for k=1:fileListSize + % calling depfun. -toponly indicates non-recursive search. -quiet + % suppresses output to command line + templist = depfun(currentFileList{k},'-toponly','-quiet'); + + % the first file is already included + templist(1) = []; + + % calling the helper-function to delete files which contain + % <matlabroot> in the path from the list. + templist = deleteMatlabFiles(templist); + + listSize = length(list); + tempListSize = length(templist); + + % excluding first iteration with this if-clause + if(listSize > 1) + + % comparing lists --> Searching for files that are not in the + % list yet + for i=1:tempListSize + contained = 0; + j=1; + % first condition stops comparing if something was found. + while contained == 0 & j<=listSize + if((strcmp(templist{i},list{j})==1) ) + contained = 1; + end + j = j+1; + end + + % if file isn't in the list yet... + if(contained == 0) + + % add it to a cellArray for new filenames... + % if-clause excludes first run with empty + % newOnes-variable + if(~isempty(newOnes)) + + % if not first run: comparing the filename with the + % filenames that are in the newOnes-List to avoid + % double-enties + a = strfind(newOnes,templist{i}); + counter = 0; + for p=1:length(a) + if(~isempty(a{p})) + counter = counter +1; + end + end + % if not found in list: add it to the list! + if(counter==0) + newOnes{newCounter} = templist{i}; + newCounter = newCounter + 1; + end + else + % if list is empty: add it without checking! + newOnes{newCounter} = templist{i}; + newCounter = newCounter + 1; + end + end + end + + % if new filenames where found in this run... + if(~isempty(newOnes)) + % ... search the new found files in the next iteration + fileList = [fileList newOnes]; + % ... add the new found files to the list + list(length(list)+1:length(list)+length(newOnes)) = newOnes; + newOnes = []; + newCounter = 1; + end + else + % if first iteration: set the first-level dependecies as + % filelist for next iteration and add them to the list. + fileList = templist; + list = [list; templist]; + end + end +end + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% HELPER FUNCTIONS % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +function rList = deleteMatlabFiles(pList) +% deletes filenames with matlabroot in path from the given list. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Parameters: +% - pList: a cell-Array of filenames as string +% +% Returns: +% - rList: a cell-Array of filenames as string +% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +excludedDir = matlabroot; +indexes = strfind(pList,excludedDir); +keepIndexes = cellfun(@isempty,indexes); +rList = pList(keepIndexes); + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/tools/localmax_logical.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,5 @@ +function lm = localmax_logical(mat,n) + +diffmat = [mat(1,:)*0; sign(diff(mat))]; % !!!! attention rethink the 0!!!!! +convolver = -[ones(n,1);-ones(n,1)]; +lm = conv2(diffmat,convolver,'same') == 2 * n; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_misc/tools/localmaxmin.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,124 @@ +function [x,n]=localmaxmin(y,xn) +%LOCALMAXMIN(Y) Local Maxima and Minima. +% X = LOCALMAXMIN(Y) or LOCALMAXMIN(Y,'max') for vector Y returns a logical +% vector X the same size as Y such that Y(X) contains the local maxima in Y. +% +% N = LOCALMAXMIN(Y,'min') for vector Y returns a logical vector N the same +% size as Y such that Y(N) contains the local minima in Y. +% +% [X,N] = LOCALMAXMIN(Y) for vector Y returns logical vectors X and N such +% that Y(X) contains the local maxima and Y(N) contains the local minima. +% +% When Y is a matrix, outputs are logical array(s) the same size as Y, and +% the minima and/or maxima are computed down the rows of each column in Y. +% +% See also MAX, MIN. + +% D.C. Hanselman, University of Maine, Orono, ME 04469 +% MasteringMatlab@yahoo.com +% Mastering MATLAB 7 +% 2006-03-08 + +mm=['min';'max']; +if nargin==1 + xn='max'; +elseif nargin~=2 + error('localmaxmin:NotEnoughInputArguments',... + 'One or Two Input Arguments Required.') +end +if ~ischar(xn) || isempty(strcmp(xn,mm)) + error('localmaxmin:UnknownArgument','Unknown Second Input Argument.') +end +if ~isnumeric(y) || ~isreal(y) + error('localmaxmin:IncorrectDataType','Y Must be Real Valued.') +end +ysiz=size(y); +isrow=ysiz(1)==1; +if isrow % if row, convert to column for now + y=y'; + ysiz(1)=ysiz(2); + ysiz(2)=1; +end +if ysiz(1)<4 + error('localmaxmin:InsufficientData','Y Must Have at Least 4 Elements.') +end +x=false(ysiz); + +sdiff=sign(diff(y)); +zdiff=sdiff==0; +idz=find(sum(zdiff)); % columns where zero differences appear + +if strcmp(xn,'min') || nargout==2 % get minima + x(2:end-1,:)=diff(sdiff)==2; + x(1,:)=sdiff(1,:)>0; + x(end,:)=sdiff(end,:)<0; + if any(zdiff) + ir=1:ysiz(1); + for k=idz % handle columns with zero differences + itmp=ir; + itmp(zdiff(:,k))=[]; + if all(sdiff(:,k)==0) % entire column is flat + x(:,k)=true; + else + tmp=diff(sign(diff(y(itmp,k))))==2; + x(itmp(tmp)+1,k)=true; + if sdiff(1,k)==0 + nr=find(sdiff(:,k)>0,1); + nf=find(sdiff(:,k)<0,1); + if ~(isempty(nr)||isempty(nf)) + x(1,k)=nr<nf; + end + end + if sdiff(end,k)==0 + nr=find(sdiff(:,k)>0,1,'last'); + nf=find(sdiff(:,k)<0,1,'last'); + if ~(isempty(nr)||isempty(nf)) + x(end,k)=nr<nf; + end + end + end + end + end + if isrow + x=x.'; + end + if nargout==2 + n=x; + end +end +if strcmp(xn,'max') || nargout==2 % get maxima + x=false(ysiz); + x(2:end-1,:)=diff(sdiff)==-2; + x(1,:)=sdiff(1,:)<0; + x(end,:)=sdiff(end,:)>0; + if any(zdiff) + ir=1:ysiz(1); + for k=idz % handle columns with zero differences + itmp=ir; + itmp(zdiff(:,k))=[]; + if all(sdiff(:,k)==0) % entire column is flat + x(:,k)=true; + else + tmp=diff(sign(diff(y(itmp,k))))==-2; + x(itmp(tmp)+1,k)=true; + if sdiff(1,k)==0 + nr=find(sdiff(:,k)>0,1); + nf=find(sdiff(:,k)<0,1); + if ~(isempty(nr)||isempty(nf)) + x(1,k)=nr>nf; + end + end + if sdiff(end,k)==0 + nr=find(sdiff(:,k)>0,1,'last'); + nf=find(sdiff(:,k)<0,1,'last'); + if ~(isempty(nr)||isempty(nf)) + x(end,k)=nr>nf; + end + end + end + end + end + if isrow + x=x'; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_parameters/param_mirex.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,66 @@ +function param = param_mirex(chromafilename) +param = param_skeleton(); +param.beattype = 'auto'; +param.metafilename = chromafilename; + +param.dsp.extractiontype = 'vampprecalculated'; +param.dsp.resampleto = 11025; +param.dsp.blocksize = 4096; +param.dsp.hopsize = 512; +param.dsp.nSbin = 3; +param.dsp.nrmsmoothlen = 1; % no norm smoothing +param.dsp.separatenormalising = true; +param.dsp.mutesilent = true; +param.dsp.norm = 'max'; +param.dsp.nochord05 = true; + +param.newchroma.whitemethod = 'standardize'; +param.newchroma.paling = []; +param.newchroma.partials1 = 0.6; +param.newchroma.partials2 = 0.6; + +param.dbn.infermpe = true; +param.dbn.name = 'ISMIR_2010'; % name of the model +param.dbn.nMeter = 1; % number of different meters +param.dbn.nMetpos = 4; % number of metric positions (in the different meters) +param.dbn.nBass = 13; +param.dbn.keyset = 'majorminor'; +param.dbn.keynames = {'C','Db','D','Eb','E','F','F#','G','Ab','A','Bb','B',... + 'C:minor','Db:minor','D:minor','Eb:minor','E:minor','F:minor','F#:minor','G:minor','Ab:minor','A:minor','Bb:minor','B:minor'}; % set of key names +param.dbn.lilykeynames = {'c \major','des \major','d \major','es \major','e \major','f \major','fis \major','g \major','aes \major','a \major','bes \major','b \major',... + 'c \minor','des \minor','d \minor','es \minor','e \minor','f \minor','fis \minor','g \minor','aes \minor','a \minor','bes \minor','b \minor'}; % set of key names +param.dbn.key_c = 4.83; % strange variable c for expert function +param.dbn.nKey = 24; +param.dbn.chordclassset = {'','/3','/5',':maj6',':maj7',':min',':7',':min7',':dim',':aug','N'}; % set of chord classes +% param.dbn.nChord = 121; +param.dbn.chordpcs = []; % chord pitch class set (binary vector of length 12) +param.dbn.basshaschordnotes = true; +param.dbn.chromavar = 0.2; % variance of the chroma vector, applicable in manually set model +param.dbn.basschromavar = 0.1; % same as above for the bass +param = add_chords(param); % adding chord parameters +param.dbn.beattrans = 0.95; % transition probability to next beat (manually set) +param.dbn.keychange = 0.08; % key change probability (manually set) +param.dbn.nominalbass = 0.8; % probability of nominal bass pc sounding on first beat of chord +param.dbn.nominalbass_inner = 0.8; % as above, but on the rest of the beats of a chord +param.dbn.chordchange = 1 - [.5 .1 .4 .1]; % one minus prob of chord change at different beats +param.dbn.inferencebypart = true; + +param.seg.type = 'auto'; % segmentation with editor intervention, or no segmentation, etc. +param.seg.quantile = .1; +param.seg.editor = false; +param.seg.beat_pairs = false; +param.seg.medfilt_length = 5; +param.seg.barline_spacing = 1; +param.seg.thresh_beat = .85; +param.seg.minlength = 28; +param.seg.maxlength = 128; +param.seg.standardise = false; +param.seg.thresh_seg = .8; +param.seg.precalc = 0; +param.seg.newcheck = false; +% param.seg.inferencebypart = true; +if param.seg.newcheck + param.seg.segext = '.seg'; +else + param.seg.segext = '.segold'; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_parameters/param_segmentation.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,66 @@ +function param = param_segmentation(chromafilename) +param = param_skeleton(); +param.beattype = 'auto'; +param.metafilename = chromafilename; + +param.dsp.extractiontype = 'vampprecalculated'; +param.dsp.resampleto = 11025; +param.dsp.blocksize = 4096; +param.dsp.hopsize = 512; +param.dsp.nSbin = 3; +param.dsp.nrmsmoothlen = 1; % no norm smoothing +param.dsp.separatenormalising = true; +param.dsp.mutesilent = true; +param.dsp.norm = 'max'; +param.dsp.nochord05 = true; + +param.newchroma.whitemethod = 'standardize'; +param.newchroma.paling = []; +param.newchroma.partials1 = 0.6; +param.newchroma.partials2 = 0.6; + +param.dbn.infermpe = true; +param.dbn.name = 'ISMIR_2010'; % name of the model +param.dbn.nMeter = 1; % number of different meters +param.dbn.nMetpos = 4; % number of metric positions (in the different meters) +param.dbn.nBass = 13; +param.dbn.keyset = 'majorminor'; +param.dbn.keynames = {'C','Db','D','Eb','E','F','F#','G','Ab','A','Bb','B',... + 'C:minor','Db:minor','D:minor','Eb:minor','E:minor','F:minor','F#:minor','G:minor','Ab:minor','A:minor','Bb:minor','B:minor'}; % set of key names +param.dbn.lilykeynames = {'c \major','des \major','d \major','es \major','e \major','f \major','fis \major','g \major','aes \major','a \major','bes \major','b \major',... + 'c \minor','des \minor','d \minor','es \minor','e \minor','f \minor','fis \minor','g \minor','aes \minor','a \minor','bes \minor','b \minor'}; % set of key names +param.dbn.key_c = 4.83; % strange variable c for expert function +param.dbn.nKey = 24; +param.dbn.chordclassset = {'','/3','/5',':maj6',':maj7',':min',':7',':min7',':dim',':aug','N'}; % set of chord classes +% param.dbn.nChord = 121; +param.dbn.chordpcs = []; % chord pitch class set (binary vector of length 12) +param.dbn.basshaschordnotes = true; +param.dbn.chromavar = 0.2; % variance of the chroma vector, applicable in manually set model +param.dbn.basschromavar = 0.1; % same as above for the bass +param = add_chords(param); % adding chord parameters +param.dbn.beattrans = 0.95; % transition probability to next beat (manually set) +param.dbn.keychange = 0.08; % key change probability (manually set) +param.dbn.nominalbass = 0.8; % probability of nominal bass pc sounding on first beat of chord +param.dbn.nominalbass_inner = 0.8; % as above, but on the rest of the beats of a chord +param.dbn.chordchange = 1 - [.5 .1 .4 .1]; % one minus prob of chord change at different beats +param.dbn.inferencebypart = true; + +param.seg.type = 'auto'; % segmentation with editor intervention, or no segmentation, etc. +param.seg.quantile = .1; +param.seg.editor = false; +param.seg.beat_pairs = false; +param.seg.medfilt_length = 5; +param.seg.barline_spacing = 1; +param.seg.thresh_beat = .85; +param.seg.minlength = 28; +param.seg.maxlength = 128; +param.seg.standardise = false; +param.seg.thresh_seg = .8; +param.seg.precalc = 0; +param.seg.newcheck = false; +% param.seg.inferencebypart = true; +if param.seg.newcheck + param.seg.segext = '.seg'; +else + param.seg.segext = '.segold'; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_parameters/param_skeleton.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,65 @@ +function param = param_skeleton() +% Algorithm parameter skeleton for Matthias Mauch's thesis code +% Parameters concerning input, output and verbosity are not in here. +param.beattype = 'auto'; +param.dsp.extractiontype = ''; +param.dsp.resampleto = []; +param.dsp.blocksize = []; +param.dsp.hopsize = []; +param.dsp.fmin = []; +param.dsp.fmax = []; +param.dsp.nSbin = []; +param.dsp.noterange = []; +param.dsp.minMidinote = []; +param.dsp.maxMidinote = []; +param.dsp.medfiltlength = []; % length of a median filter in frames +param.dsp.nrmsmoothlen = []; +param.dsp.bassbin13 = true; % is there a thirteenth bin corresponding to "no bass" +param.dsp.norm = 'max'; +param.dsp.separatenormalising = true; +param.dsp.mutesilent = false; +param.dsp.manualbeats = false; +param.dsp.nochord05 = false; + +param.newchroma.partials1 = 0.6; +param.newchroma.partials2 = 0.6; +param.newchroma.standardise = true; +param.newchroma.whitemethod = 'standardize'; + +param.dbn.name = ''; % name of the model +param.dbn.nMeter = []; % number of different meters +param.dbn.nMetpos = []; % number of metric positions (in the different meters) +param.dbn.nBass = []; +param.dbn.keyset = {}; % set of key names +param.dbn.key_c = []; % strange variable c for expert function +param.dbn.nKey = []; +param.dbn.chordclassset = {}; % set of chord classes +param.dbn.nChord = []; +param.dbn.chordpcs = []; % chord pitch class set (binary vector of length 12) +param.dbn.chordbassnotes = []; +param.dbn.basshaschordnotes = false; +param.dbn.chromavar = []; % variance of the chroma vector, applicable in manually set model +param.dbn.basschromavar = []; % same as above for the bass +param.dbn.beattrans = []; % transition probability to next beat (manually set) +param.dbn.keychange = []; % key change probability (manually set) +param.dbn.nominalbass = []; % probability of nominal bass pc sounding on first beat of chord +param.dbn.nominalbass_inner = []; % as above, but on the rest of the beats of a chord +param.dbn.chordchange = []; % one minus prob of chord change at different beats +param.dbn.infermpe = true; +param.dbn.inferencebypart = false; + +param.seg.type = ''; % "standard" segmentation, with automatic "editor" intervention, "none", or "manual" segmentation, tc. +param.seg.beattype = ''; % what kind of beat tracking +param.seg.chroma = ''; % what kind of chroma (not just wide or treble, but also which techinque) +param.seg.quantile = []; % quantile as in ISMIR paper equation (3) +param.seg.editor = false; % if true, look for frequent sub-segments +param.seg.beat_pairs = false; % pairing up beats for decreased chance of random match +param.seg.medfilt_length = []; % length of median filter over similarity matrix +param.seg.barline_spacing = []; % +param.seg.thresh_beat = []; % beat similarity has to be at least this +param.seg.minlength = []; % min length of segment +param.seg.maxlength = []; % max length of segment +param.seg.dynamic = false; % use dynamic time warping +param.seg.standardise = false; % +param.seg.thresh_seg = []; % take segments over this threshold (in units above std) +param.seg.inferenceoverlap = 8;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_segmentation/integratesmallparts.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,40 @@ +function song = integratesmallparts(song) + +parts = song.parts; +nParts = numel(parts); + + + +%% break up parts (every part will have one instance) +iNewpart = 0; +partindices = []; +for iPart = 1:nParts + parts(iPart).nInd = length(parts(iPart).indices); + for iInstance = 1:parts(iPart).nInd + iNewpart = iNewpart + 1; + newparts(iNewpart) = parts(iPart); + newparts(iNewpart).indices = parts(iPart).indices(iInstance); + newparts(iNewpart).nInd = 1; + partindices = [partindices; parts(iPart).indices(iInstance)]; + end +end +nNewparts = numel(newparts); + +%% sort the parts in order of occurrence +[sortind, sortind] = sort(partindices); +for iNewpart = 1:nNewparts + sortednewparts(iNewpart) = newparts(sortind(iNewpart)); +end + +%% +eraselist = []; +for iNewpart = 2:nNewparts + if sortednewparts(iNewpart).n < 12 + eraselist = [eraselist; iNewpart]; + sortednewparts(iNewpart-1).n = ... + sortednewparts(iNewpart-1).n + sortednewparts(iNewpart).n; + end +end + +sortednewparts(eraselist) = []; +song.integratedparts = sortednewparts; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_segmentation/makeparts.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,44 @@ +function outparts = makeparts(beattimes,segtimes,seglevel) + +parts = struct('n',[], 'indices',[], 'letter', [],'level',[]); +nPart = 0; +nSeg = size(segtimes,1); + +for iSeg = 1:nSeg + newpart = true; +% tempbeat = struct([]); + segbeats = find(mean(beattimes,2) >= segtimes(iSeg,1) & mean(beattimes,2) < segtimes(iSeg,2)); + lenSegbeats = length(segbeats); + for iPart = 1:nPart + if seglevel(iSeg) == parts(iPart).level && lenSegbeats == parts(iPart).n + parts(iPart).indices = [parts(iPart).indices segbeats(1)]; +% parts(iPart).beat = [parts(iPart).beat tempbeat]; + newpart = false; + break; + else + newpart = true; + end + end + if newpart && ~isempty(segbeats) + nPart = nPart + 1; + parts(nPart).indices = segbeats(1); + parts(nPart).n = lenSegbeats; + parts(nPart).level = seglevel(iSeg); +% parts(nPart).beat = tempbeat; + end + +end +partnind = zeros(nPart,1); +partlen = zeros(nPart,1); +for iPart = 1:nPart + partnind(iPart) = length(parts(iPart).indices); + partlen(iPart) = parts(iPart).n; +end +[partind,partind] = sort(partnind.*partlen,'descend'); +letters = {'A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T','U'}; +outparts = parts; +for iPart = 1:nPart + kPart = partind(iPart); + parts(kPart).letter = letters{iPart}; + outparts(iPart) = parts(kPart); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_segmentation/maxfilt1.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,8 @@ +function outmat = maxfilt1(inmat, len) +outmat = inmat; +for i = find(sum(inmat)>0) + indi = max(1,i-len):min(size(inmat,2),i+len-1); + temp = inmat(indi,:); + outmat(i,:) = max(temp,[],1); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_segmentation/mergenulls.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,24 @@ +function parts = mergenulls(parts) + +for iPart = 1:length(parts) + newparts = []; + if parts(iPart).letter == '-' + parts(iPart).indices = sort(parts(iPart).indices); + newpartind = 0; + indices = [-1 parts(iPart).indices]; + for iInd = 2:length(indices) + if indices(iInd) - indices(iInd-1) > 1 + newpartind = newpartind + 1; + newparts(newpartind).letter = ['n' num2str(newpartind)]; + newparts(newpartind).n = 1; + newparts(newpartind).indices = indices(iInd); + newparts(newpartind).level = 0; + else + newparts(newpartind).n = newparts(newpartind).n + 1; + end + end + parts(iPart) = []; + parts = [parts newparts]; + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_segmentation/nullpart.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,11 @@ +function np = nullpart(parts, barlines) +nullindices = ones(size(barlines)); +for iPart = 1:length(parts) + for iIndex = 1:length(parts(iPart).indices) + nullindices(parts(iPart).indices(iIndex)+(0:parts(iPart).n-1)) = 0; + end +end +np.n = 1; +np.indices = find(nullindices); +np.letter = '-'; +np.level = 0; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_segmentation/parts2csv.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,31 @@ +function parts2csv(song, filename, varargin) +if nargin > 2 && strcmp(varargin{1}, 'integrated') + parts = song.integratedparts; +else + parts = song.parts; +end + +beattimes = song.beattimes; +% beattimes = [0; beattimes]; +indices = []; +levels = []; +letters = {}; + +for iPart = 1:length(parts) + for iInstance = 1:length(parts(iPart).indices) + indices = [indices parts(iPart).indices(iInstance)]; + levels = [levels parts(iPart).level]; + letters = [letters parts(iPart).letter]; + end +end + +[sorted, sortind] = sort(indices); + +f = fopen(filename,'w'); + +for iSeg = 1:length(indices) + fprintf(f,'%1.2f,%1.3f,"%s"\n',beattimes(sorted(iSeg)),levels(sortind(iSeg)),letters{sortind(iSeg)}); +end + +fclose(f); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_segmentation/parts2mirex.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,36 @@ +function parts2mirex(song, filename, varargin) +if nargin > 2 && strcmp(varargin{1}, 'integrated') + parts = song.integratedparts; +else + parts = song.parts; +end + +beattimes = song.beattimes; +% beattimes = [0; beattimes]; +startIndex = []; +letters = {}; + +for iPart = 1:length(parts) + for iInstance = 1:length(parts(iPart).indices) + startIndex = [startIndex parts(iPart).indices(iInstance)]; + letters = [letters parts(iPart).letter]; + end +end + +[sorted, sortind] = sort(startIndex); +endIndex = zeros(length(sorted),1); +for iIndex = 1:length(sorted)-1; + endIndex(iIndex) = sorted(iIndex+1); +end +endIndex(end) = length(beattimes); + +f = fopen(filename,'w'); + +fprintf(f,'0.000 %1.3f "%s"\n',beattimes(endIndex(1)),letters{sortind(1)}); +for iSeg = 2:length(sorted)-1 + fprintf(f,'%1.3f %1.3f "%s"\n',beattimes(sorted(iSeg)),beattimes(endIndex(iSeg)),letters{sortind(iSeg)}); +end +fprintf(f,'%1.3f %1.3f "%s"\n',beattimes(sorted(end)),song.t(end),letters{sortind(end)}); + +fclose(f); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_segmentation/segmentation_auto.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,180 @@ +function song = segmentation_auto(song, param, varargin) + + +%% calculating similarity matrix +% exp of minus the cosine distance + +if any(strcmp(fieldnames(song.syncchroma),'wide')) + v = song.syncchroma.wide; +else + v = song.syncchroma.treble + song.syncchroma.bass(:,1:12) * 0.8; +end +nBeat = song.nBeat; +flatPCP = std(v')<.000001; +v(flatPCP,1) = 1000; % arbitrary +simmat0 = squareform(pdist(v,'correlation')); +% simmat0(flatPCP,:) = 2; +% simmat0(:,flatPCP) = 2; +% simmat = qnormalise(exp(-simmat0),inf,1); +% simmat(flatPCP,:) = 0; +% simmat(:,flatPCP) = 0; + +simmat0(flatPCP,:) = 1; +simmat0(:,flatPCP) = 1; +simmat = 1-simmat0/2; +simmat(flatPCP,:) = 0; +simmat(:,flatPCP) = 0; + +%% +median_simmat = zeros(size(simmat)); +for i = 1:nBeat + temp1 = diag(medfilt1(diag(simmat,i-1),param.seg.medfilt_length,[],1),i-1); + median_simmat = median_simmat + temp1; +end +median_simmat = median_simmat + median_simmat' - diag(diag(median_simmat)); +median_simmat(isnan(median_simmat)) = 0; +%%median_simmat = (median_simmat - mean(median_simmat(:)))/std(median_simmat(:)); +%%%%%%%% ATTENTION!!!!!!!! using MEDIAN %%%%%%%%%%%%% +% median_simmat = (median_simmat - median(median_simmat(:)))/std(median_simmat(:)); +if param.seg.standardise + med_median_simmat = repmat(median(median_simmat),nBeat,1); + std_median_simmat = repmat(std(median_simmat),nBeat,1); + median_simmat = (median_simmat - med_median_simmat) ./ std_median_simmat; +end +%% get bar boundaries!!! + +potential_duplicates = triu(median_simmat > param.seg.thresh_beat); + +%% +partlengths = param.seg.minlength:4:param.seg.maxlength; +nPartlengths = length(partlengths); + +% initialise arrays +simArray = zeros(nBeat,nBeat,nPartlengths); +decisionArray2 = zeros(nBeat,nBeat,nPartlengths); + + + +fprintf(1,'\n'); +fprintf(2,' '); +for iLength = 1:nPartlengths + len = partlengths(iLength); + nUsedBeat = nBeat - len + 1; % number of potential rep beginnings: they can't overlap at the end of the song + fprintf(1,'\rLength: %1.0d',len) + fprintf(2,'\b\b\b\b%3.0f%%',iLength/nPartlengths*100); + for iBeat = 1:nUsedBeat % looping over all columns (arbitrarily chosen columns) + help2 = find(potential_duplicates(1:nUsedBeat,iBeat)); + for kBeat = help2' % check only potential duplicates + % measure how well two length len segments go together + simArray(iBeat,kBeat,iLength) = ... + quantile(diag(median_simmat(iBeat+(0:len-1),kBeat+(0:len-1))),param.seg.quantile); + end + end + simArray(1:nUsedBeat,1:nUsedBeat,iLength) = simArray(1:nUsedBeat,1:nUsedBeat,iLength) ... + + simArray(1:nUsedBeat,1:nUsedBeat,iLength)' ... + - eye(nUsedBeat) .* simArray(1:nUsedBeat,1:nUsedBeat,iLength); + + simArray(:,:,iLength) = conv2(simArray(:,:,iLength),[.01 .98 .01], 'same'); + % take only over-average bars that do not overlap + for iBeat = 1:nUsedBeat + temp = simArray(:,iBeat,iLength) > param.seg.thresh_seg; + decisionArray2(temp,iBeat,iLength) = ... + simArray(temp,iBeat,iLength); + end + decisionArray2(:,:,iLength) = decisionArray2(:,:,iLength) .* (decisionArray2(:,:,iLength) >= maxfilt1(decisionArray2(:,:,iLength),len-1)); + decisionArray2(:,:,iLength) = decisionArray2(:,:,iLength) .* decisionArray2(:,:,iLength)'; +% potential_duplicates = triu(simArray(:,:,iLength) > param.seg.thresh_seg * 0.7); + potential_duplicates = potential_duplicates .* (simArray(:,:,iLength) > param.seg.thresh_seg); +end +%% milk the data + +bestvals = []; +for iLength = 1:nPartlengths + currLogicSum = sum(decisionArray2(:,:,iLength)>0,2); + for iBeat = 1:nBeat + if currLogicSum(iBeat) > 1.0 + currSum = (mean(decisionArray2(decisionArray2(:,iBeat,iLength)>0,iBeat,iLength)))/2; + bestvals = [bestvals; (currLogicSum(iBeat)-1) * partlengths(iLength), currSum, iLength, iBeat, currLogicSum(iBeat)]; + end + end +end +%% +%% +% make a table of all valid sets of parts +partletters = {'A','B','C','D','E','F','G', 'H','I','J','K','L','M','N','O','P','Q','R','S'}; +valid_sets = ones(1,size(bestvals,1)); +parts = []; +if ~isempty(bestvals) + bestvals(:,2) = bestvals(:,2) / max(bestvals(:,2)*2); + bestvals(:,1) = bestvals(:,1) + bestvals(:,2); + bestvals(:,2) = []; + + for kSeg = 1:7 + currbestvals = bestvals(logical(valid_sets),:); + [m,i] = max(currbestvals(:,1)); + if isempty(m) + break + end + + bestLength = partlengths(currbestvals(i,2)); + bestIndices = decisionArray2(currbestvals(i,3),:,currbestvals(i,2)); + islands = conv2(1*(bestIndices > 0), [zeros(1,bestLength-1) ones(1,bestLength)], 'same'); + %% + if param.seg.newcheck + iBetter = i; + bestSubval = 0; + subm1 = find(currbestvals(:,4) > currbestvals(i,4)); % has to have more part instances + for kSub = 1:length(subm1) + iSub = subm1(kSub); + subIndices = decisionArray2(currbestvals(iSub,3),:,currbestvals(iSub,2)); + subLength = partlengths(currbestvals(iSub,2)); + subIslands = conv2(1*(subIndices > 0), [zeros(1,subLength-1) ones(1,subLength)], 'same'); + if sum(islands.*subIslands) == currbestvals(i,4) * subLength ... + && currbestvals(iSub,1) > bestSubval + iBetter = iSub; + bestSubval = currbestvals(iSub,1); + end + end + i = iBetter; + bestLength = partlengths(currbestvals(i,2)); + bestIndices = decisionArray2(currbestvals(i,3),:,currbestvals(i,2)); + islands = conv2(1*(bestIndices > 0), [zeros(1,bestLength-1) ones(1,bestLength)], 'same'); + end + %% + part.n = bestLength; + part.indices = find(bestIndices); + part.letter = partletters{kSeg}; + part.level = kSeg; + parts = [parts part]; + + for iSet = find(valid_sets) + currislands = conv2(2*(decisionArray2(bestvals(iSet,3),:,bestvals(iSet,2)) > 0), ... + [zeros(1,partlengths(bestvals(iSet,2))-1) ones(1,partlengths(bestvals(iSet,2)))], 'same'); + if any(islands .* currislands > 0) + valid_sets(iSet) = false; + else + % stem(islands+currislands*.5) + % aaa = 1; + end + end + end +else + part.n = nBeat; + part.indices = 1; + part.letter = 'A'; + part.level = 1; + parts = [parts part]; +end + +parts = [parts, nullpart(parts,1:nBeat)]; +if param.seg.editor + [pa, ta] = partarray(parts); + parts = editorssearch(pa, ta, parts); + parts = [parts, nullpart(parts,1:nBeat)]; +end +parts = mergenulls(parts); + +song.parts = parts; +% song.parts + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_segmentation/segmentation_main.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,6 @@ +function song = segmentation_main(song, param) +switch param.seg.type + case 'none' + song.segments = [1, song.nBeat]; + otherwise +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_song/song_beat.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,20 @@ +function song = song_beat(song, param) +switch param.beattype + case 'auto' + rhythm = getmeasures4(fullfile(song.wavpath, [song.filenametrunk, '.wav'])); + beattimes = rhythm.beats'; + + [sz, fs] = wavread(fullfile(song.wavpath, [song.filenametrunk, '.wav']),'size'); + songLength = sz(1)/fs; + + song.beattimes = [beattimes, [beattimes(2:end); songLength]]; + song.nBeat = size(song.beattimes,1); + case 'autoprecalculated' + load(param.metafilename); + song.beattimes = beattimes_auto; + song.nBeat = size(song.beattimes,1); + case 'manual' + load(param.metafilename); + song.beattimes = beattimes_manual; + song.nBeat = size(song.beattimes,1); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_song/song_chroma.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,3 @@ +function song = song_chroma(song, param) +% fprintf(1,'loading wave: %s\n',fullfile(song.wavpath, [song.filenametrunk, '.wav'])); +[song.chroma, song.t] = chroma_main(fullfile(song.wavpath, [song.filenametrunk, '.wav']), param); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_song/song_segchroma.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,31 @@ +function song = song_segchroma(song, param) +switch param.seg.type + case 'none' % no segmentation, segchroma equals syncchroma + song.segchroma = song.syncchroma; + otherwise + song = overlaychroma(song, param); +end + +function song = overlaychroma(song,param) +song.segchroma.treble = zeros(size(song.syncchroma.treble)); +song.segchroma.bass = zeros(size(song.syncchroma.bass)); +nPart = numel(song.parts); +for iPart = 1:nPart + song.parts(iPart).nInd = length(song.parts(iPart).indices); + for iOccur = 1:song.parts(iPart).nInd + iStartIndex = song.parts(iPart).indices(iOccur); + iEndIndex = iStartIndex + song.parts(iPart).n - 1; + for kOccur = 1:song.parts(iPart).nInd + kStartIndex = song.parts(iPart).indices(kOccur); + kEndIndex = kStartIndex + song.parts(iPart).n - 1; + song.segchroma.treble(iStartIndex:iEndIndex,:) = ... + song.segchroma.treble(iStartIndex:iEndIndex,:) + ... + song.syncchroma.treble(kStartIndex:kEndIndex,:); + song.segchroma.bass(iStartIndex:iEndIndex,:) = ... + song.segchroma.bass(iStartIndex:iEndIndex,:) + ... + song.syncchroma.bass(kStartIndex:kEndIndex,:); + end + end +end +song.segchroma.treble = qnormalise(song.segchroma.treble,inf,2); +song.segchroma.bass = qnormalise(song.segchroma.bass,inf,2); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_song/song_segment.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,12 @@ +function song = song_segment(song, param) +switch param.seg.type + case 'none' + + case 'auto' + song = segmentation_auto(song,param); + case 'autoprecalculated' + song = segmentation_autoprecalculated(song,param); + case 'manual' + song = segmentation_manual(song,param); + otherwise +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_song/song_skeleton.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,18 @@ +function song = song_skeleton(filename) + +[song.wavpath, song.filenametrunk] = fileparts(filename); +song.chordgroundtruthpath = ''; +song.beatgroundtruthpath = ''; +song.t = []; +song.beattimes = []; +song.nBeat = []; +song.chroma = []; +song.syncchroma = []; +song.segments = []; +song.beatchords = {}; +song.beatbass = {}; +song.beatmetpos = []; +song.beatkey = []; +song.chordgroundtruth.times = []; +song.chordgroundtruth.label = {}; +song.beatgroundtruth.times = []; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_song/song_syncchroma.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,86 @@ +function song = song_syncchroma(song, param) + +[song.syncchroma.treble, treblenrm] = syncchroma(song.chroma.treble, song.beattimes, song.t); +[song.syncchroma.bass, bassnrm] = syncchroma(song.chroma.bass, song.beattimes, song.t(:)); + +song.syncchroma.bass(isnan(sum(song.syncchroma.bass,2)),:) = 1; +song.syncchroma.treble(isnan(sum(song.syncchroma.treble,2)),:) = 1; + +song.syncchroma.bass(sum(song.syncchroma.bass,2)<.01,:) = 1; +song.syncchroma.treble(sum(song.syncchroma.treble,2)<.01,:) = 1; + +bcnrm = qnorm(song.syncchroma.bass,1,2); +if param.dsp.bassbin13 % if the model requires a thirteenth bass bin + bc = qnormalise(song.syncchroma.bass,1,2); + bc13 = (12 * max(bc,[],2)).^(-2) .* bcnrm; + bc13(isnan(bc13)) = 1; + song.syncchroma.bass = [song.syncchroma.bass, bc13]; +end + +switch param.dsp.norm + case 'max' + song.syncchroma.bass = qnormalise(song.syncchroma.bass,inf,2); + song.syncchroma.treble = qnormalise(song.syncchroma.treble,inf,2); + case 'L1' + song.syncchroma.bass = qnormalise(song.syncchroma.bass,1,2); + song.syncchroma.treble = qnormalise(song.syncchroma.treble,1,2); + case 'L2' + disp('L2 norm') + song.syncchroma.bass = qnormalise(song.syncchroma.bass,2,2); + song.syncchroma.treble = qnormalise(song.syncchroma.treble,2,2); + case 'standardize' + song.syncchroma.bass = standardize(song.syncchroma.bass); + song.syncchroma.treble = standardize(song.syncchroma.treble); + case 'meta' + song.syncchroma.bass = qnormalise(song.syncchroma.bass,inf,2); + disp('no standardization for treble chroma; max normalisation for bass') + otherwise + 'blub' +end + +% make all NaNs to 1 +song.syncchroma.bass(isnan(sum(song.syncchroma.bass,2)),:) = 1; +song.syncchroma.treble(isnan(sum(song.syncchroma.treble,2)),:) = 1; +% aah, what does this do??? +% song.syncchroma.bass(sum(song.syncchroma.bass,2) < 1,:) = 1; +% song.syncchroma.treble(sum(song.syncchroma.treble,2) < 1,:) = 1; + + + + +% below "wide" chroma processing, that's neglected ... +% try +% [song.syncchroma.wide, widenrm] = syncchroma(song.chroma.wide, song.beattimes, song.t); +% for iBeat = 1:song.nBeat +% song.syncchroma.wide(iBeat,:) = song.syncchroma.wide(iBeat,:) / widenrm(iBeat); +% end +% catch +% warning('could not find wide chroma'); +% end + + + +function [bc, nrm] = syncchroma(chromag,beats,frame_t) +% assigning beats to frames +nBeat = size(beats,1); +nFrame = length(frame_t); + +bc = ones(nBeat,size(chromag,1)); +chromag(:,isnan(sum(chromag))) = 1; + +if size(beats,2) < 2 + for iBeat = 1:nBeat-1 + currind = find(frame_t >= beats(iBeat) & frame_t < beats(iBeat+1)); + bc(iBeat,:) = median(chromag(:,currind),2); +% if ~any(isnan(bc(iBeat,:))) +% bc(iBeat,:) = bc(iBeat,:)/max(bc(iBeat,:)); +% end + end +else + for iBeat = 1:nBeat + currind = find(frame_t >= beats(iBeat,1) & frame_t < beats(iBeat,2)); + bc(iBeat,:) = median(chromag(:,currind),2)'; + end +end +nrm = qnorm(bc,inf,2); +% bc = qnormalise(bc,inf,2);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_song/song_syncchroma_special.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,95 @@ +function song = song_syncchroma_special(song, param) + +[song.syncchroma.treble, treblenrm, beats] = syncchroma(song.chroma.treble, song.beattimes, song.t); +[song.syncchroma.bass, bassnrm] = syncchroma(song.chroma.bass, song.beattimes, song.t(:)); + +song.syncchroma.bass(isnan(sum(song.syncchroma.bass,2)),:) = 1; +song.syncchroma.treble(isnan(sum(song.syncchroma.treble,2)),:) = 1; + +song.syncchroma.bass(sum(song.syncchroma.bass,2)<.01,:) = 1; +song.syncchroma.treble(sum(song.syncchroma.treble,2)<.01,:) = 1; + +bcnrm = qnorm(song.syncchroma.bass,1,2); +if param.dsp.bassbin13 % if the model requires a thirteenth bass bin + bc = qnormalise(song.syncchroma.bass,1,2); + bc13 = (12 * max(bc,[],2)).^(-2) .* bcnrm; + bc13(isnan(bc13)) = 1; + song.syncchroma.bass = [song.syncchroma.bass, bc13]; +end + +switch param.dsp.norm + case 'max' + song.syncchroma.bass = qnormalise(song.syncchroma.bass,inf,2); + song.syncchroma.treble = qnormalise(song.syncchroma.treble,inf,2); + case 'L1' + song.syncchroma.bass = qnormalise(song.syncchroma.bass,1,2); + song.syncchroma.treble = qnormalise(song.syncchroma.treble,1,2); + case 'L2' + song.syncchroma.bass = qnormalise(song.syncchroma.bass,2,2); + song.syncchroma.treble = qnormalise(song.syncchroma.treble,2,2); + case 'standardize' + song.syncchroma.bass = standardize(song.syncchroma.bass); + song.syncchroma.treble = standardize(song.syncchroma.treble); + case 'meta' + song.syncchroma.bass = qnormalise(song.syncchroma.bass,inf,2); + disp('no standardization for treble chroma; max normalisation for bass') + otherwise + 'blub' +end + +% make all NaNs to 1 +song.syncchroma.bass(isnan(sum(song.syncchroma.bass,2)),:) = 1; +song.syncchroma.treble(isnan(sum(song.syncchroma.treble,2)),:) = 1; +% aah, what does this do??? +% song.syncchroma.bass(sum(song.syncchroma.bass,2) < 1,:) = 1; +% song.syncchroma.treble(sum(song.syncchroma.treble,2) < 1,:) = 1; + + + + +% below "wide" chroma processing, that's neglected ... +try + [song.syncchroma.wide, widenrm] = syncchroma(song.chroma.wide, song.beattimes, song.t); + for iBeat = 1:song.nBeat + song.syncchroma.wide(iBeat,:) = song.syncchroma.wide(iBeat,:) / widenrm(iBeat); + end +catch + warning('could not find wide chroma'); +end + + + +function [bc, nrm, beats] = syncchroma(chromag,beats,frame_t) +% assigning beats to frames +nBeat = size(beats,1); +nFrame = length(frame_t); + +bc = ones(nBeat,size(chromag,1)); +chromag(:,isnan(sum(chromag))) = 1; +% first beat +currind = find(frame_t >= beats(1,1) & frame_t < beats(1,2)); +bc(1,:) = median(chromag(:,currind),2)'; +for iBeat = 2:nBeat +% prestarttimes = [beats(iBeat - 1,1), beats(iBeat,1), beats(iBeat,2)]; +% starttimes = (prestarttimes + beats(iBeat,1))/2; + starttimes = beats(iBeat,1) + [-.1 -.05 0 .05 .1]; + entro = 0; + for iST = 1:5 + currind1 = find(frame_t >= beats(iBeat - 1,1) & frame_t < starttimes(iST)); + currind2 = find(frame_t >= starttimes(iST) & frame_t < beats(iBeat,2)); + vec = [ mean(chromag(:,currind2),2)']; + vec = vec/sum(vec); + vec = vec(vec>0); + ent = -vec * log2(vec'); + if ent > entro + entro = ent; + beats(iBeat - 1, 2) = starttimes(iST); + beats(iBeat, 1) = starttimes(iST); + end + end + currind = find(frame_t >= beats(iBeat,1) & frame_t < beats(iBeat,2)); + bc(iBeat,:) = median(chromag(:,currind),2)'; +end + +nrm = qnorm(bc,inf,2); +% bc = qnormalise(bc,inf,2);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_writetools/chordroot_spelling.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,64 @@ +function rootspelling = chordroot_spelling(key, root, degrees) + +temp = strsplit(':', key); +keyroot = temp{1}; +if length(temp) > 1 + keymode = temp{2}; +else + keymode = 'major'; +end + + + +% major scale +scale{1} = [0 2 4 5 7 9 11]; +% harmonic minor scale +scale{2} = [0 2 3 5 7 8 11]; + +diatonic = {'C','D','E','F','G','A','B'}; +keymode_number = find(strcmp({'major','minor'},keymode)); + +if ischar(root) + rootdegree = find(strcmp(diatonic,root(1))); + rootpc = scale{1}(strcmp(diatonic,root(1))) + length(strfind(root,'#')) - length(strfind(root,'b')); +else + [rootdegree, rootdegree]= min(abs(scale{1}-root)); + rootpc = root; +end + + +deg = []; +pcs = []; +for ii = 1:length(degrees) + deg = [deg mod(rootdegree + str2num(degrees{ii}(end)) - 2,7)+1]; + pcs = [pcs mod(rootpc + degree2semitone(degrees{ii}),12)]; +end + +keyrootpc = scale{1}(strcmp(diatonic,keyroot(1))) + length(strfind(keyroot,'#')) - length(strfind(keyroot,'b')); +keydegree = find(strcmp(diatonic,keyroot(1))); + +scalesemitone = circshift(mod(scale{keymode_number} + keyrootpc,12),[0 keydegree-1]); + +fit = [0 0 0]; + +for deviation = -1:1 % test if the best degree is lower, just right or higher + currdeg = mod(deg+deviation-1,7) + 1; + chordaccidentals = mod(scalesemitone(currdeg) - pcs + 12 + 6,12) - 6; + fit(deviation+2) = sum(abs(chordaccidentals)); +end +[bestfitval, bestfit] = min(fit); +bestrootdegree = mod(rootdegree + bestfit - 2 - 1,7) + 1; +bestrootaccidentals = mod(rootpc - scale{1}(bestrootdegree)+6,12)-6; +switch bestrootaccidentals + case -2 + suff = 'bb'; + case -1 + suff = 'b'; + case 0 + suff = ''; + case 2 + suff = '##'; + case 1 + suff = '#'; +end +rootspelling = [diatonic{bestrootdegree}, suff]; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_writetools/keynroot2notetext.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,40 @@ +function outnote = keynroot2notetext(key,root,type) + +switch type + case 'normal' + if root ~= 13 + scale{1} = {'A','Bb','B','C','C#','D','D#','E','F','F#','G','G#'}; + scale{2} = {'Bb','B','C','Db','D','Eb','E','F','Gb','G','Ab','A'}; + scale{3} = {'B','C','C#','D','D#','E','F','F#','G','G#','A','A#'}; + scale{4} = {'C','C#','D','Eb','E','F','F#','G','Ab','A','Bb','B'}; + scale{5} = {'Db','D','Eb','E','F','Gb','G','Ab','A','Bb','B','C'}; + scale{6} = {'D','D#','E','F','F#','G','G#','A','Bb','B','C','C#'}; + scale{7} = {'Eb','E','F','Gb','G','Ab','A','Bb','B','C','Db','D'}; + scale{8} = {'E','F','F#','G','G#','A','Bb','B','C','C#','D','D#'}; + scale{9} = {'F','Gb','G','Ab','A','Bb','B','C','Db','D','Eb','E'}; + scale{10} = {'Gb','G','Ab','A','Bb','Cb','C','Db','D','Eb','E','F'}; + scale{11} = {'G','G#','A','Bb','B','C','C#','D','D#','E','F','F#'}; + scale{12} = {'Ab','A','Bb','B','C','Db','D','Eb','E','F','Gb','G'}; + outnote = scale{key}{mod(root - key,12)+1}; + else + outnote = ''; + end + case 'lily' + if root ~= 13 + scale{1} = {'a','bes','b','c','cis','d','dis','e','f','fis','g','gis'}; + scale{2} = {'bes','b','c','des','d','es','e','f','ges','g','as','a'}; + scale{3} = {'b','c','cis','d','dis','e','f','fis','g','gis','a','ais'}; + scale{4} = {'c','cis','d','es','e','f','fis','g','as','a','bes','b'}; + scale{5} = {'des','d','es','e','f','ges','g','as','a','bes','b','c'}; + scale{6} = {'d','dis','e','f','fis','g','gis','a','bes','b','c','cis'}; + scale{7} = {'es','e','f','ges','g','as','a','bes','b','c','des','d'}; + scale{8} = {'e','f','fis','g','gis','a','bes','b','c','cis','d','dis'}; + scale{9} = {'f','ges','g','as','a','bes','b','c','des','d','es','e'}; + scale{10} = {'ges','g','as','a','bes','ces','c','des','d','es','e','f'}; + scale{11} = {'g','gis','a','bes','b','c','cis','d','dis','e','f','fis'}; + scale{12} = {'as','a','bes','b','c','des','d','es','e','f','ges','g'}; + outnote = scale{key}{mod(root - key,12)+1}; + else + outnote = 'r'; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_writetools/spellnote.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,69 @@ +function outnote = spellnote(key,root,varargin) +if nargin > 2 + type = varargin{1}; +else + type = 'normal'; +end + +switch type + case 'normal' + if root ~= 13 + scale{1} = {'C','C#','D','Eb','E','F','F#','G','Ab','A','Bb','B'}; + scale{2} = {'Db','D','Eb','E','F','Gb','G','Ab','A','Bb','B','C'}; + scale{3} = {'D','D#','E','F','F#','G','G#','A','Bb','B','C','C#'}; + scale{4} = {'Eb','E','F','Gb','G','Ab','A','Bb','B','C','Db','D'}; + scale{5} = {'E','F','F#','G','G#','A','Bb','B','C','C#','D','D#'}; + scale{6} = {'F','Gb','G','Ab','A','Bb','B','C','Db','D','Eb','E'}; + scale{7} = {'Gb','G','Ab','A','Bb','Cb','C','Db','D','Eb','E','F'}; + scale{8} = {'G','G#','A','Bb','B','C','C#','D','D#','E','F','F#'}; + scale{9} = {'Ab','A','Bb','B','C','Db','D','Eb','E','F','Gb','G'}; + scale{10} = {'A','Bb','B','C','C#','D','D#','E','F','F#','G','G#'}; + scale{11} = {'Bb','B','C','Db','D','Eb','E','F','Gb','G','Ab','A'}; + scale{12} = {'B','C','C#','D','D#','E','F','F#','G','G#','A','A#'}; + scale{13} = {'Eb','E','F','Gb','G','Ab','A','Bb','B','C','Db','D'}; + scale{14} = {'E','F','F#','G','G#','A','Bb','B','C','C#','D','D#'}; + scale{15} = {'F','Gb','G','Ab','A','Bb','B','C','Db','D','Eb','E'}; + scale{16} = {'Gb','G','Ab','A','Bb','Cb','C','Db','D','Eb','E','F'}; + scale{17} = {'G','G#','A','Bb','B','C','C#','D','D#','E','F','F#'}; + scale{18} = {'Ab','A','Bb','B','C','Db','D','Eb','E','F','Gb','G'}; + scale{19} = {'A','Bb','B','C','C#','D','D#','E','F','F#','G','G#'}; + scale{20} = {'Bb','B','C','Db','D','Eb','E','F','Gb','G','Ab','A'}; + scale{21} = {'B','C','C#','D','D#','E','F','F#','G','G#','A','A#'}; + scale{22} = {'C','C#','D','Eb','E','F','F#','G','Ab','A','Bb','B'}; + scale{23} = {'Db','D','Eb','E','F','Gb','G','Ab','A','Bb','B','C'}; + scale{24} = {'D','D#','E','F','F#','G','G#','A','Bb','B','C','C#'}; + outnote = scale{key}{mod(root - key,12)+1}; + else + outnote = ''; + end + case 'lily' + if root ~= 13 + scale{1} = {'c','cis','d','es','e','f','fis','g','as','a','bes','b'}; + scale{2} = {'des','d','es','e','f','ges','g','as','a','bes','b','c'}; + scale{3} = {'d','dis','e','f','fis','g','gis','a','bes','b','c','cis'}; + scale{4} = {'es','e','f','ges','g','as','a','bes','b','c','des','d'}; + scale{5} = {'e','f','fis','g','gis','a','bes','b','c','cis','d','dis'}; + scale{6} = {'f','ges','g','as','a','bes','b','c','des','d','es','e'}; + scale{7} = {'ges','g','as','a','bes','ces','c','des','d','es','e','f'}; + scale{8} = {'g','gis','a','bes','b','c','cis','d','dis','e','f','fis'}; + scale{9} = {'as','a','bes','b','c','des','d','es','e','f','ges','g'}; + scale{10} = {'a','bes','b','c','cis','d','dis','e','f','fis','g','gis'}; + scale{11} = {'bes','b','c','des','d','es','e','f','ges','g','as','a'}; + scale{12} = {'b','c','cis','d','dis','e','f','fis','g','gis','a','ais'}; + scale{13} = {'es','e','f','ges','g','as','a','bes','b','c','des','d'}; + scale{14} = {'e','f','fis','g','gis','a','bes','b','c','cis','d','dis'}; + scale{15} = {'f','ges','g','as','a','bes','b','c','des','d','es','e'}; + scale{16} = {'ges','g','as','a','bes','ces','c','des','d','es','e','f'}; + scale{17} = {'g','gis','a','bes','b','c','cis','d','dis','e','f','fis'}; + scale{18} = {'as','a','bes','b','c','des','d','es','e','f','ges','g'}; + scale{19} = {'a','bes','b','c','cis','d','dis','e','f','fis','g','gis'}; + scale{20} = {'bes','b','c','des','d','es','e','f','ges','g','as','a'}; + scale{21} = {'b','c','cis','d','dis','e','f','fis','g','gis','a','ais'}; + scale{22} = {'c','cis','d','es','e','f','fis','g','as','a','bes','b'}; + scale{23} = {'des','d','es','e','f','ges','g','as','a','bes','b','c'}; + scale{24} = {'d','dis','e','f','fis','g','gis','a','bes','b','c','cis'}; + outnote = scale{key}{mod(root - key,12)+1}; + else + outnote = 'r'; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_writetools/write_song.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,174 @@ +function write_song(song, bnet, param, outfilename, filetype, verbose) + +prelabels = {'C','Db','D','Eb','E','F','Gb','G','Ab','A','Bb','B','N'}; +chordindex = bnet.names('chord'); +metposindex = bnet.names('metpos'); +if param.dbn.nKey > 0 + keyindex = bnet.names('key'); +end + +switch filetype + case 'chordlab' + chordsequence = [song.mpe{chordindex,:}]; + oldchord = 0; + oldlabel ='N'; + outfile = fopen(outfilename,'w'); + if verbose + fprintf(1, '0.0 '); + end + fprintf(outfile, '0.0 '); + for iChord= 1:length(chordsequence) + newchord = song.mpe{chordindex, iChord}; + if newchord ~= oldchord + if param.dbn.nKey > 0 + keylabel = param.dbn.keynames{song.mpe{keyindex,iChord}}; + else + keylabel = 'C'; + end + if ischar(param.dbn.chordnames{newchord}) + prelabel = ['C',param.dbn.chordnames{newchord}]; + [c.rootnote, c.shorthand, c.degreelist, c.bassdegree, success, error] =... + getchordinfo(prelabel); + c.string = addshort2list(c.shorthand, c.degreelist); + label = [chordroot_spelling(keylabel, param.dbn.chordrootnotes(newchord)-1 ,strsplit(',',c.string)),param.dbn.chordnames{newchord}]; + else + label = 'N'; + end + if verbose + fprintf(1,'%0.3f %s\n%0.3f ', song.beattimes(iChord,1), oldlabel,song.beattimes(iChord,1)); + end + fprintf(outfile,'%0.3f %s\n%0.3f ', song.beattimes(iChord,1), oldlabel,song.beattimes(iChord,1)); + oldlabel = label; + end + oldchord = newchord; + end + if verbose + fprintf(1,'%0.3f %s', song.beattimes(iChord,2), oldlabel); + end + fprintf(outfile,'%0.3f %s', song.beattimes(iChord,2), oldlabel); + fclose(outfile); + case 'keylab' + keysequence = [song.mpe{keyindex,:}]; + oldkey = 0; + oldlabel ='N'; + outfile = fopen(outfilename,'w'); + if verbose + fprintf(1, '0.0 '); + end + fprintf(outfile, '0.0 '); + for iKey = 1:length(keysequence) + newkey = song.mpe{keyindex, iKey}; + if newkey ~= oldkey + keylabel = param.dbn.keynames{song.mpe{keyindex,iKey}}; + if verbose + fprintf(1,'%0.3f %s\n%0.3f ', song.beattimes(iKey,1), oldlabel,song.beattimes(iKey,1)); + end + fprintf(outfile,'%0.3f %s\n%0.3f ', song.beattimes(iKey,1), oldlabel,song.beattimes(iKey,1)); + oldlabel = keylabel; + end + oldkey = newkey; + end + if verbose + fprintf(1,'%0.3f %s', song.beattimes(iKey,2), oldlabel); + end + fprintf(outfile,'%0.3f %s', song.beattimes(iKey,2), oldlabel); + fclose(outfile); + case 'lilypond' + bassindex = bnet.names('bass'); + lilychordnames = repmat(param.dbn.lilytypechordnames,[1,12]); + lilychordnames{numel(lilychordnames)+1} = ''; + + chordsequence = [song.mpe{chordindex,:}]; + + nBeat = length(chordsequence); + + keysequence = [song.mpe{keyindex,:}]; + + label = cell(nBeat,1); + keylabel = cell(nBeat,1); + + metpossequence = [song.mpe{metposindex,:}]; + oldchord = 0; + oldkey = 0; + oldbass = 0; + oldlabel =''; + oldkeylabel = ''; + oldbasslabel = ''; + + outfile = fopen(outfilename,'w'); + if verbose + fprintf(1, '0.0 '); + end + fprintf(outfile, '0.0 '); + chordcount = 0; + basscount = 0; + chordduration = 0; + bassduration = 0; + for iBeat= 1:length(chordsequence) + newchord = song.mpe{chordindex, iBeat}; + newkey = song.mpe{keyindex, iBeat}; + newbass = song.mpe{bassindex, iBeat}; + newmetpos = song.mpe{metposindex, iBeat}; + if param.dbn.nKey > 0 + prekeylabel = param.dbn.keynames{song.mpe{keyindex,iBeat}}; + else + prekeylabel = 'C'; + end + if newchord ~= oldchord || newmetpos == 1 + % get the chord name + chordcount = chordcount + 1; + if ischar(param.dbn.chordnames{newchord}) + prelabel = ['C',param.dbn.chordnames{newchord}]; + [c.rootnote, c.shorthand, c.degreelist, c.bassdegree, success, error] =... + getchordinfo(prelabel); + if isempty(c.bassdegree) + preroot = spellnote(newkey, param.dbn.chordrootnotes(newchord)); + label{iBeat} = [preroot,lilychordnames{newchord}]; + else + preroot = spellnote(newkey, param.dbn.chordrootnotes(newchord)); + temp = degree2note(c.bassdegree,preroot); + bass = ['/',temp{1}]; + label{iBeat} = [preroot ,lilychordnames{newchord},bass]; + end + else + label{iBeat} = 'nc'; + end + label{iBeat} = strrep(label{iBeat},'#','is'); + label{iBeat} = strrep(label{iBeat},'b','es'); + label{iBeat} = lower(label{iBeat}); + +% oldlabel = label{iBeat}; + chordduration = 1; + else + chordduration = chordduration + 1; + end + if oldkey ~= newkey + keylabel{iBeat} = param.dbn.lilykeynames{song.mpe{keyindex,iBeat}}; + keylabel{iBeat} = strrep(keylabel{iBeat},'#','is'); + keylabel{iBeat} = strrep(keylabel{iBeat},'b','es'); + keylabel{iBeat} = lower(keylabel{iBeat}); + oldkeylabel = keylabel{iBeat}; + end + if oldbass ~= newbass || newmetpos == 1 + basscount = basscount + 1; + if basscount > 1 + basslabelduration{basscount-1} = bassduration; + end + basslabel{basscount} = spellnote(newkey, param.dbn.chordbassnotes(newbass),'lily'); + bassduration = 1; + else + bassduration = bassduration + 1; + end + oldbass = newbass; + oldkey = newkey; + oldchord = newchord; + end + + % finish off the durations etc + basslabelduration{basscount} = bassduration; + + fclose(outfile); + for iLabel = 1:numel(basslabel) + fprintf(1,'%10s %4.0f\n', basslabel{iLabel}, basslabelduration{iLabel}) + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/_writetools/write_song2.m Fri Apr 11 15:54:25 2014 +0100 @@ -0,0 +1,226 @@ +function write_song2(song, bnet, param, outfilename, filetype, verbose) + +prelabels = {'C','Db','D','Eb','E','F','Gb','G','Ab','A','Bb','B','N'}; +chordindex = bnet.names('chord'); +metposindex = bnet.names('metpos'); +if param.dbn.nKey > 0 + keyindex = bnet.names('key'); +end + +switch filetype + case 'chordlab' + chordsequence = [song.mpe{chordindex,:}]; + oldchord = 0; + oldlabel ='N'; + outfile = fopen(outfilename,'w'); + if verbose + fprintf(1, '0.0 '); + end + fprintf(outfile, '0.0 '); + for iChord= 1:length(chordsequence) + newchord = song.mpe{chordindex, iChord}; + if newchord ~= oldchord + if param.dbn.nKey > 0 + keylabel = param.dbn.keynames{song.mpe{keyindex,iChord}}; + else + keylabel = 'C'; + end + if ischar(param.dbn.chordnames{newchord}) + prelabel = ['C',param.dbn.chordnames{newchord}]; + [c.rootnote, c.shorthand, c.degreelist, c.bassdegree, success, error] =... + getchordinfo(prelabel); + c.string = addshort2list(c.shorthand, c.degreelist); + label = [chordroot_spelling(keylabel, param.dbn.chordrootnotes(newchord)-1 ,strsplit(',',c.string)),param.dbn.chordnames{newchord}]; + else + label = 'N'; + end + if verbose + fprintf(1,'%0.3f %s\n%0.3f ', song.beattimes(iChord,1), oldlabel,song.beattimes(iChord,1)); + end + fprintf(outfile,'%0.3f %s\n%0.3f ', song.beattimes(iChord,1), oldlabel,song.beattimes(iChord,1)); + oldlabel = label; + end + oldchord = newchord; + end + if verbose + fprintf(1,'%0.3f %s', song.beattimes(iChord,2), oldlabel); + end + fprintf(outfile,'%0.3f %s', song.beattimes(iChord,2), oldlabel); + fclose(outfile); + case 'keylab' + keysequence = [song.mpe{keyindex,:}]; + oldkey = 0; + oldlabel ='N'; + outfile = fopen(outfilename,'w'); + if verbose + fprintf(1, '0.0 '); + end + fprintf(outfile, '0.0 '); + for iKey = 1:length(keysequence) + newkey = song.mpe{keyindex, iKey}; + if newkey ~= oldkey + keylabel = param.dbn.keynames{song.mpe{keyindex,iKey}}; + if verbose + fprintf(1,'%0.3f %s\n%0.3f ', song.beattimes(iKey,1), oldlabel,song.beattimes(iKey,1)); + end + fprintf(outfile,'%0.3f %s\n%0.3f ', song.beattimes(iKey,1), oldlabel,song.beattimes(iKey,1)); + oldlabel = keylabel; + end + oldkey = newkey; + end + if verbose + fprintf(1,'%0.3f %s', song.beattimes(iKey,2), oldlabel); + end + fprintf(outfile,'%0.3f %s', song.beattimes(iKey,2), oldlabel); + fclose(outfile); + case 'lilypond' + bassindex = bnet.names('bass'); + lilychordnames = repmat(param.dbn.lilytypechordnames,[1,12]); + lilychordnames{numel(lilychordnames)+1} = ''; + + chordsequence = [song.mpe{chordindex,:}]; + + nBeat = length(chordsequence); + + keysequence = [song.mpe{keyindex,:}]; + +% label = cell(nBeat,1); +% keylabel = cell(nBeat,1); + + metpossequence = [song.mpe{metposindex,:}]; + oldchord = 0; + oldkey = 0; + oldbass = 0; + + + + chordcount = 0; + basscount = 0; + chordduration = 1; + bassduration = 1; + + % time signature stuff + partial = find(metpossequence == 1,1)-1; + if partial > 0 + partial = ['\partial ' lilylength(partial)]; + else + partial = ''; + end + barlines = find(metpossequence == 1); + timesig = diff(barlines); % for barlines, not beats + ind = min(length(timesig),union(find(timesig~=4),find(timesig~=4)+1)); + non4timesig = barlines(ind); % in beats + timesig = timesig(ind); + + % chord and bass loop + + for iBeat= 1:length(chordsequence) + newchord = song.mpe{chordindex, iBeat}; + newkey = song.mpe{keyindex, iBeat}; + newbass = song.mpe{bassindex, iBeat}; + newmetpos = song.mpe{metposindex, iBeat}; + if param.dbn.nKey > 0 + prekeylabel = param.dbn.keynames{song.mpe{keyindex,iBeat}}; + else + prekeylabel = 'C'; + end + if newchord ~= oldchord || newmetpos == 1 || chordduration == 4 + % get the chord name + chordcount = chordcount + 1; + if iBeat > 1 + chordlabelduration(chordcount-1) = chordduration; + end + if ischar(param.dbn.chordnames{newchord}) + preroot = spellnote(newkey, param.dbn.chordrootnotes(newchord),'lily'); + root{chordcount} = preroot; + if isempty(strfind(param.dbn.chordnames{newchord},'/')) + label{chordcount} = lilychordnames{newchord}; + else + bass = spellnote(newkey, param.dbn.chordbassnotes(newchord),'lily'); + label{chordcount} = [strrep(lilychordnames{newchord},' ',''),'/',bass]; + end + else + root{chordcount} = 'r'; + label{chordcount} = ''; + end + chordbeat(chordcount) = iBeat; + +% oldlabel = label{iBeat}; + chordduration = 1; + else + chordduration = chordduration + 1; + end + if oldkey ~= newkey + keylabel{iBeat} = param.dbn.lilykeynames{song.mpe{keyindex,iBeat}}; +% keylabel{iBeat} = strrep(keylabel{iBeat},'#','is'); +% keylabel{iBeat} = strrep(keylabel{iBeat},'b','es'); +% keylabel{iBeat} = lower(keylabel{iBeat}); +% oldkeylabel = keylabel{iBeat}; + end + if oldbass ~= newbass || newmetpos == 1 || bassduration == 4 + basscount = basscount + 1; + if iBeat > 1 + basslabelduration(basscount-1) = bassduration; + end + basslabel{basscount} = spellnote(newkey, param.dbn.chordbassnotes(newbass),'lily'); + bassbeat(basscount) = iBeat; + bassduration = 1; + else + bassduration = bassduration + 1; + end + + oldbass = newbass; + oldkey = newkey; + oldchord = newchord; + end + + + % finish off the durations etc + basslabelduration(basscount) = bassduration; + chordlabelduration(chordcount) = chordduration; + + + chord_string = 'chordsymb = { \chordmode { '; + bass_string = 'bassnotes = { \override TextScript #''box-padding = #.7 \override TextScript #''font-size = #-3 \override TextScript #''thickness = #.03 \clef bass'; + for iBeat = 1:nBeat + chordind = find(chordbeat == iBeat); + bassind = find(bassbeat == iBeat); + timesigind = find(non4timesig == iBeat); + if ~(iBeat > numel(keylabel) || isempty(keylabel{iBeat})) + chord_string = [chord_string sprintf(' } \\key %s \\chordmode { ', keylabel{iBeat})]; + bass_string = [bass_string sprintf(' \\key %s', keylabel{iBeat})]; + end + if ~isempty(chordind) + if ~isempty(timesigind) + chord_string = [chord_string sprintf(' \\time %0.0f/4',timesig(timesigind))]; + end + chord_string = [chord_string sprintf(' %s%s%s',root{chordind}, lilylength(chordlabelduration(chordind)), label{chordind})]; + end + if ~isempty(bassind) + bass_string = [bass_string sprintf(' %s%s',basslabel{bassind},lilylength(basslabelduration(bassind)))]; + + end + + + end + fid = fopen(outfilename); + fprintf(fid,'\\header{title = "%s"}\n%s}}\n%s\n}\n<<\n\\new ChordNames {\n\\set chordChanges = ##t\n%s\n\\chordsymb\n\n}\n\\new Voice {\n\\chordsymb\n}\n\\new Voice {\n\\bassnotes\n}\n>>\n\\version "2.10.33"\n',... + song.filenametrunk, chord_string, bass_string, partial); + fclose(fid); + if verbose + fprintf(1,'\\header{title = "%s"}\n%s}}\n%s\n}\n<<\n\\new ChordNames {\n\\set chordChanges = ##t\n%s\n\\chordsymb\n\n}\n\\new Voice {\n\\chordsymb\n}\n\\new Voice {\n\\bassnotes\n}\n>>\n\\version "2.10.33"\n',... + song.filenametrunk, chord_string, bass_string, partial); + end +end + +function L = lilylength(beats) +switch beats + case 1 + L = '4'; + case 2 + L = '2'; + case 3 + L = '2.'; + case 4 + L = '1'; +end \ No newline at end of file