comparison toolboxes/FullBNT-1.0.7/bnt/general/mk_dbn.m @ 0:e9a9cd732c1e tip

first hg version after svn

+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
+