Daniel@0: function [engine, loglik] = enter_evidence(engine, evidence, varargin)
Daniel@0: % ENTER_EVIDENCE Add the specified evidence to the network (jtree)
Daniel@0: % [engine, loglik] = enter_evidence(engine, evidence, ...)
Daniel@0: %
Daniel@0: % evidence{i} = [] if X(i) is hidden, and otherwise contains its observed value (scalar or column vector).
Daniel@0: %
Daniel@0: % The following optional arguments can be specified in the form of name/value pairs:
Daniel@0: % [default value in brackets]
Daniel@0: %
Daniel@0: % soft    - a cell array of soft/virtual evidence;
Daniel@0: %           soft{i} is a prob. distrib. over i's values, or [] [ cell(1,N) ]
Daniel@0: %
Daniel@0: % e.g., engine = enter_evidence(engine, ev, 'soft', soft_ev)
Daniel@0: 
Daniel@0: bnet = bnet_from_engine(engine);
Daniel@0: ns = bnet.node_sizes(:);
Daniel@0: N = length(bnet.dag);
Daniel@0: 
Daniel@0: engine.evidence = evidence; % store this for marginal_nodes with add_ev option
Daniel@0: engine.maximize = 0;
Daniel@0: 
Daniel@0: % set default params
Daniel@0: exclude = [];
Daniel@0: soft_evidence = cell(1,N);
Daniel@0: 
Daniel@0: % parse optional params
Daniel@0: args = varargin;
Daniel@0: nargs = length(args);
Daniel@0: for i=1:2:nargs
Daniel@0:   switch args{i},
Daniel@0:    case 'soft',    soft_evidence = args{i+1}; 
Daniel@0:    case 'maximize', engine.maximize = args{i+1};
Daniel@0:    otherwise,  
Daniel@0:     error(['invalid argument name ' args{i}]);       
Daniel@0:   end
Daniel@0: end
Daniel@0: 
Daniel@0: onodes = find(~isemptycell(evidence));
Daniel@0: hnodes = find(isemptycell(evidence));
Daniel@0: pot_type = determine_pot_type(bnet, onodes);
Daniel@0:  if strcmp(pot_type, 'cg')
Daniel@0:   check_for_cd_arcs(onodes, bnet.cnodes, bnet.dag);
Daniel@0: end
Daniel@0: 
Daniel@0: if is_mnet(bnet)
Daniel@0:   pot = engine.user_pot;
Daniel@0:   clqs = engine.nums_ass_to_user_clqs;
Daniel@0: else
Daniel@0:   % Evaluate CPDs with evidence, and convert to potentials  
Daniel@0:   pot = cell(1, N);
Daniel@0:   for n=1:N
Daniel@0:     fam = family(bnet.dag, n);
Daniel@0:     e = bnet.equiv_class(n);
Daniel@0:     if isempty(bnet.CPD{e})
Daniel@0:       error(['must define CPD ' num2str(e)])
Daniel@0:     else
Daniel@0:       pot{n} = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), evidence);
Daniel@0:     end
Daniel@0:   end
Daniel@0:   clqs = engine.clq_ass_to_node(1:N);
Daniel@0: end
Daniel@0: 
Daniel@0: % soft evidence
Daniel@0: soft_nodes = find(~isemptycell(soft_evidence));
Daniel@0: S = length(soft_nodes);
Daniel@0: if S > 0
Daniel@0:   assert(pot_type == 'd');
Daniel@0:   assert(mysubset(soft_nodes, bnet.dnodes));
Daniel@0: end
Daniel@0: for i=1:S
Daniel@0:   n = soft_nodes(i);
Daniel@0:   pot{end+1} = dpot(n, ns(n), soft_evidence{n});
Daniel@0: end
Daniel@0: clqs = [clqs engine.clq_ass_to_node(soft_nodes)]; 
Daniel@0: 
Daniel@0: 
Daniel@0: [clpot, seppot] = init_pot(engine, clqs, pot, pot_type, onodes);
Daniel@0: [clpot, seppot] = collect_evidence(engine, clpot, seppot);
Daniel@0: [clpot, seppot] = distribute_evidence(engine, clpot, seppot);
Daniel@0: 
Daniel@0: C = length(clpot);
Daniel@0: ll = zeros(1, C);
Daniel@0: for i=1:C
Daniel@0:   [clpot{i}, ll(i)] = normalize_pot(clpot{i});
Daniel@0: end
Daniel@0: loglik = ll(1); % we can extract the likelihood from any clique
Daniel@0: 
Daniel@0: engine.clpot = clpot;