Daniel@0: function fg = mk_fgraph_given_ev(G, node_sizes, factors, ev_CPD, evidence, varargin)
Daniel@0: % MK_FGRAPH_GIVEN_EV Make a factor graph where each node has its own private evidence term
Daniel@0: % fg = mk_fgraph(G, node_sizes, factors, ev_CPD, evidence, ...)
Daniel@0: %
Daniel@0: % G, node_sizes and factors are as in mk_fgraph, but they refer to the hidden nodes.
Daniel@0: % ev_CPD{i} is a CPD for the i'th hidden node; this will be converted into a factor
Daniel@0: % for node i using evidence{i}.
Daniel@0: % We currently assume all hidden nodes are discrete, for simplicity.
Daniel@0: %
Daniel@0: % The list below gives optional arguments [default value in brackets].
Daniel@0: % 
Daniel@0: % equiv_class - equiv_class(i)=j  means factor node i gets its params from factors{j} [1:F]
Daniel@0: % ev_equiv_class - ev_equiv_class(i)=j  means evidence node i gets its params from ev_CPD{j} [1:N]
Daniel@0: 
Daniel@0: 
Daniel@0: N = length(node_sizes);
Daniel@0: nfactors = length(factors);
Daniel@0: 
Daniel@0: % default values for parameters
Daniel@0: eclass = 1:nfactors;
Daniel@0: ev_eclass = 1:N;
Daniel@0: 
Daniel@0: if nargin >= 6
Daniel@0:   args = varargin;
Daniel@0:   nargs = length(args);
Daniel@0:   for i=1:2:nargs
Daniel@0:     switch args{i},
Daniel@0:      case 'equiv_class', eclass = args{i+1}; 
Daniel@0:      case 'ev_equiv_class', ev_eclass = args{i+1}; 
Daniel@0:      otherwise,  
Daniel@0:       error(['invalid argument name ' args{i}]);       
Daniel@0:     end
Daniel@0:   end
Daniel@0: end
Daniel@0: 
Daniel@0: pot_type = 'd';
Daniel@0: for x=1:N
Daniel@0:   ev = cell(1,2); % cell 1 is the hidden parent, cell 2 is the observed child
Daniel@0:   ev(2) = evidence(x); 
Daniel@0:   dom = 1:2;
Daniel@0:   F = convert_to_pot(ev_CPD{ev_eclass(x)}, pot_type, dom(:), ev);
Daniel@0:   M = pot_to_marginal(F);
Daniel@0:   %factors{end+1} = tabular_CPD('self', 1, 'ps', [], 'sz', node_sizes(x), 'CPT', M.T);
Daniel@0:   factors{end+1} = mk_isolated_tabular_CPD(node_sizes(x), {'CPT', M.T});
Daniel@0: end
Daniel@0: 
Daniel@0: E = max(eclass);
Daniel@0: fg = mk_fgraph([G eye(N)], node_sizes, factors, 'equiv_class', [eclass E+1:E+N]);