wolffd@0: function bnet = mk_dbn(intra, inter, node_sizes, varargin)
wolffd@0: % MK_DBN Make a Dynamic Bayesian Network.
wolffd@0: %
wolffd@0: % BNET = MK_DBN(INTRA, INTER, NODE_SIZES, ...) makes a DBN with arcs
wolffd@0: % from i in slice t to j in slice t iff intra(i,j) = 1, and 
wolffd@0: % from i in slice t to j in slice t+1 iff inter(i,j) = 1,
wolffd@0: % for i,j in {1, 2, ..., n}, where n = num. nodes per slice, and t >= 1.
wolffd@0: % node_sizes(i) is the number of values node i can take on.
wolffd@0: % The nodes are assumed to be in topological order. Use TOPOLOGICAL_SORT if necessary.
wolffd@0: % See also mk_bnet.
wolffd@0: %
wolffd@0: % Optional arguments [default in brackets]
wolffd@0: % 'discrete' - list of discrete nodes [1:n]
wolffd@0: % 'observed' - the list of nodes which will definitely be observed in every slice of every case [ [] ]
wolffd@0: % 'eclass1' - equiv class for slice 1 [1:n]
wolffd@0: % 'eclass2' - equiv class for slice 2 [tie nodes with equivalent parents to slice 1]
wolffd@0: %    equiv_class1(i) = j means node i in slice 1 gets its parameters from bnet.CPD{j},
wolffd@0: %    i.e., nodes i and j have tied parameters.
wolffd@0: % 'intra1' - topology of first slice, if different from others
wolffd@0: % 'names' - a cell array of strings to be associated with nodes 1:n [{}]
wolffd@0: %    This creates an associative array, so you write e.g.
wolffd@0: %     'evidence(bnet.names{'bar'}) = 42' instead of  'evidence(2} = 42' 
wolffd@0: %     assuming names = { 'foo', 'bar', ...}.
wolffd@0: %    
wolffd@0: % For backwards compatibility with BNT2, arguments can also be specified as follows
wolffd@0: %   bnet = mk_dbn(intra, inter, node_sizes, dnodes, eclass1, eclass2, intra1)
wolffd@0: %
wolffd@0: % After calling this function, you must specify the parameters (conditional probability
wolffd@0: % distributions) using bnet.CPD{i} = gaussian_CPD(...) or tabular_CPD(...) etc.
wolffd@0: 
wolffd@0: 
wolffd@0: n = length(intra);
wolffd@0: ss = n;
wolffd@0: bnet.nnodes_per_slice = ss;
wolffd@0: bnet.intra = intra;
wolffd@0: bnet.inter = inter;
wolffd@0: bnet.intra1 = intra;
wolffd@0: dag = zeros(2*n);
wolffd@0: dag(1:n,1:n) = bnet.intra1;
wolffd@0: dag(1:n,(1:n)+n) = bnet.inter;
wolffd@0: dag((1:n)+n,(1:n)+n) = bnet.intra;
wolffd@0: bnet.dag = dag;
wolffd@0: bnet.names = {};
wolffd@0: 
wolffd@0: directed = 1;
wolffd@0: if ~acyclic(dag,directed)
wolffd@0:   error('graph must be acyclic')
wolffd@0: end
wolffd@0: 
wolffd@0: 
wolffd@0: bnet.eclass1 = 1:n;
wolffd@0: %bnet.eclass2 = (1:n)+n;
wolffd@0: bnet.eclass2 = bnet.eclass1;
wolffd@0: for i=1:ss
wolffd@0:   if isequal(parents(dag, i+ss), parents(dag, i)+ss)
wolffd@0:     %fprintf('%d has isomorphic parents, eclass %d\n', i, bnet.eclass2(i))
wolffd@0:   else
wolffd@0:     bnet.eclass2(i) = max(bnet.eclass2) + 1;
wolffd@0:     %fprintf('%d has non isomorphic parents, eclass %d\n', i, bnet.eclass2(i))
wolffd@0:   end
wolffd@0: end
wolffd@0: 
wolffd@0: dnodes = 1:n;
wolffd@0: bnet.observed = [];
wolffd@0: 
wolffd@0: if nargin >= 4
wolffd@0:   args = varargin;
wolffd@0:   nargs = length(args);
wolffd@0:   if ~isstr(args{1})
wolffd@0:     if nargs >= 1, dnodes = args{1}; end
wolffd@0:     if nargs >= 2, bnet.eclass1 = args{2}; end
wolffd@0:     if nargs >= 3, bnet.eclass2 = args{3}; end
wolffd@0:     if nargs >= 4, bnet.intra1 = args{4}; end
wolffd@0:   else
wolffd@0:     for i=1:2:nargs
wolffd@0:       switch args{i},
wolffd@0:        case 'discrete', dnodes = args{i+1}; 
wolffd@0:        case 'observed', bnet.observed = args{i+1}; 
wolffd@0:        case 'eclass1',  bnet.eclass1 = args{i+1}; 
wolffd@0:        case 'eclass2',  bnet.eclass2 = args{i+1}; 
wolffd@0:        case 'intra1',  bnet.intra1 = args{i+1}; 
wolffd@0:        %case 'ar_hmm',  bnet.ar_hmm = args{i+1};  % should check topology
wolffd@0:        case 'names',  bnet.names = assocarray(args{i+1}, num2cell(1:n)); 
wolffd@0:        otherwise,  
wolffd@0: 	error(['invalid argument name ' args{i}]);       
wolffd@0:       end
wolffd@0:     end
wolffd@0:   end
wolffd@0: end
wolffd@0: 
wolffd@0: 
wolffd@0: bnet.observed = sort(bnet.observed); % for comparing sets
wolffd@0: ns = node_sizes;
wolffd@0: bnet.node_sizes_slice = ns(:)';
wolffd@0: bnet.node_sizes = [ns(:) ns(:)];
wolffd@0: 
wolffd@0: cnodes = mysetdiff(1:n, dnodes);
wolffd@0: bnet.dnodes_slice = dnodes;
wolffd@0: bnet.cnodes_slice = cnodes;
wolffd@0: bnet.dnodes = [dnodes dnodes+n];
wolffd@0: bnet.cnodes = [cnodes cnodes+n];
wolffd@0: 
wolffd@0: bnet.equiv_class = [bnet.eclass1(:) bnet.eclass2(:)];
wolffd@0: bnet.CPD = cell(1,max(bnet.equiv_class(:)));
wolffd@0: eclass = bnet.equiv_class(:);
wolffd@0: E = max(eclass);
wolffd@0: bnet.rep_of_eclass = zeros(1,E);
wolffd@0: for e=1:E
wolffd@0:   mems = find(eclass==e);
wolffd@0:   bnet.rep_of_eclass(e) = mems(1);
wolffd@0: end
wolffd@0: 
wolffd@0: ss = n;
wolffd@0: onodes = bnet.observed;
wolffd@0: hnodes = mysetdiff(1:ss, onodes);
wolffd@0: bnet.hidden_bitv = zeros(1,2*ss);
wolffd@0: bnet.hidden_bitv(hnodes) = 1;
wolffd@0: bnet.hidden_bitv(hnodes+ss) = 1;
wolffd@0: 
wolffd@0: bnet.parents = cell(1, 2*ss);
wolffd@0: for i=1:ss
wolffd@0:   bnet.parents{i} = parents(bnet.dag, i);
wolffd@0:   bnet.parents{i+ss} = parents(bnet.dag, i+ss);
wolffd@0: end
wolffd@0: 
wolffd@0: bnet.auto_regressive = zeros(1,ss);
wolffd@0: % ar(i)=1 means (observed) node i depends on i in the  previous slice
wolffd@0: for o=bnet.observed(:)'
wolffd@0:   if any(bnet.parents{o+ss} <= ss)
wolffd@0:     bnet.auto_regressive(o) = 1;
wolffd@0:   end
wolffd@0: end
wolffd@0: