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view toolboxes/FullBNT-1.0.7/bnt/general/mk_higher_order_dbn.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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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