1 files changed, 95 insertions, 0 deletions
diff --git a/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/enter_evidence.m b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/enter_evidence.m
new file mode 100644
index 00000000..8cda6012
--- /dev/null
+++ b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/enter_evidence.m
@@ -0,0 +1,95 @@
+function [engine, loglik] = enter_evidence(engine, evidence, varargin)
+% ENTER_EVIDENCE Add the specified evidence to the network (jtree)
+% [engine, loglik] = enter_evidence(engine, evidence, ...)
+%
+% evidence{i} = [] if X(i) is hidden, and otherwise contains its observed value (scalar or column vector).
+%
+% The following optional arguments can be specified in the form of name/value pairs:
+% [default value in brackets]
+%
+% soft    - a cell array of soft/virtual evidence;
+%           soft{i} is a prob. distrib. over i's values, or [] [ cell(1,N) ]
+%
+% e.g., engine = enter_evidence(engine, ev, 'soft', soft_ev)
+
+bnet = bnet_from_engine(engine);
+ns = bnet.node_sizes(:);
+N = length(bnet.dag);
+
+engine.evidence = evidence; % store this for marginal_nodes with add_ev option
+engine.maximize = 0;
+
+% set default params
+exclude = [];
+soft_evidence = cell(1,N);
+
+% parse optional params
+args = varargin;
+nargs = length(args);
+for i=1:2:nargs
+  switch args{i},
+   case 'soft',    soft_evidence = args{i+1}; 
+   case 'maximize', engine.maximize = args{i+1};
+   otherwise,  
+    error(['invalid argument name ' args{i}]);       
+  end
+end
+
+onodes = find(~isemptycell(evidence));
+hnodes = find(isemptycell(evidence));
+pot_type = determine_pot_type(bnet, onodes);
+ if strcmp(pot_type, 'cg')
+  check_for_cd_arcs(onodes, bnet.cnodes, bnet.dag);
+end
+
+if is_mnet(bnet)
+  pot = engine.user_pot;
+  clqs = engine.nums_ass_to_user_clqs;
+else
+  % Evaluate CPDs with evidence, and convert to potentials  
+  pot = cell(1, N);
+  for n=1:N
+    fam = family(bnet.dag, n);
+    e = bnet.equiv_class(n);
+    if isempty(bnet.CPD{e})
+      error(['must define CPD ' num2str(e)])
+    else
+      pot{n} = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), ...
+                              evidence);
+    end
+  end
+  clqs = engine.clq_ass_to_node(1:N);
+end
+
+
+% soft evidence
+soft_nodes = find(~isemptycell(soft_evidence));
+S = length(soft_nodes);
+if S > 0
+  assert(pot_type == 'd');
+  assert(mysubset(soft_nodes, bnet.dnodes));
+end
+for i=1:S
+    n = soft_nodes(i);
+    % Modif RD - 2006/12/22
+    % Why end+1 it doesn't work for me...
+    % replace with n and it is ok
+    pot{ n } = dpot( n, ns( n ), soft_evidence{ n } );
+end
+% Modif RD - 2006/12/22
+% But now we have to comment this line to ensure dimension matching
+%clqs = [clqs engine.clq_ass_to_node(soft_nodes)]; 
+
+
+[clpot, seppot] = init_pot(engine, clqs, pot, pot_type, onodes);
+[clpot, seppot] = collect_evidence(engine, clpot, seppot);
+[clpot, seppot] = distribute_evidence(engine, clpot, seppot);
+
+C = length(clpot);
+ll = zeros(1, C);
+for i=1:C
+  [clpot{i}, ll(i)] = normalize_pot(clpot{i});
+end
+loglik = ll(1); % we can extract the likelihood from any clique
+
+engine.clpot = clpot;