wolffd@0: function sample = sample_bnet(bnet, varargin)
wolffd@0: % SAMPLE_BNET Generate a random sample from a Bayes net.
wolffd@0: % SAMPLE = SAMPLE_BNET(BNET, ...)
wolffd@0: %
wolffd@0: % sample{i} contains the value of the i'th node.
wolffd@0: % i.e., the result is an Nx1 cell array.
wolffd@0: % Nodes are sampled in the order given by bnet.order.
wolffd@0: %
wolffd@0: % Optional arguments:
wolffd@0: %
wolffd@0: % evidence - initial evidence; if evidence{i} is non-empty, node i won't be sampled.
wolffd@0: 
wolffd@0: % set defauly params
wolffd@0: n = length(bnet.dag);
wolffd@0: sample = cell(n,1);
wolffd@0: 
wolffd@0: % get optional params
wolffd@0: args = varargin;
wolffd@0: nargs = length(args);
wolffd@0: for i=1:2:nargs
wolffd@0:   switch args{i},
wolffd@0:    case 'evidence',    sample = args{i+1}(:);
wolffd@0:    otherwise, error(['unrecognized argument ' args{i}])
wolffd@0:   end
wolffd@0: end
wolffd@0: 
wolffd@0: for j=bnet.order(:)'
wolffd@0:   if isempty(sample{j})
wolffd@0:     %ps = parents(bnet.dag, j);
wolffd@0:     ps = bnet.parents{j};
wolffd@0:     e = bnet.equiv_class(j);
wolffd@0:     sample{j} = sample_node(bnet.CPD{e}, sample(ps));
wolffd@0:   end
wolffd@0: end