comparison toolboxes/FullBNT-1.0.7/bnt/examples/static/fgraph/fg2.m @ 0:e9a9cd732c1e tip

first hg version after svn

+% make a factor graph corresponding to an  HMM, where we absorb the evidence up front,
+% and then eliminate the observed nodes.
+% Compare this with not absorbing the evidence.
+
+seed = 1;
+rand('state', seed);
+randn('state', seed);
+
+T = 3;
+Q = 3;
+O = 2;
+cts_obs = 0;
+param_tying = 1;
+bnet = mk_hmm_bnet(T, Q, O, cts_obs, param_tying);
+N = 2*T;
+onodes = bnet.observed;
+hnodes = mysetdiff(1:N, onodes);
+
+data = sample_bnet(bnet);
+
+init_factor = bnet.CPD{1};
+obs_factor = bnet.CPD{3};
+edge_factor = bnet.CPD{2}; % trans matrix
+
+nfactors = T;
+nvars = T; % hidden only
+G = zeros(nvars, nfactors);
+G(1,1) = 1;
+for t=1:T-1
+  G(t:t+1, t+1)=1;
+end
+
+node_sizes = Q*ones(1,T);
+
+% We tie params as follows:
+% the first hidden node use init_factor (number 1)
+% all hidden nodes on the backbone use edge_factor (number 2)
+% all observed nodes use the same factor, namely obs_factor
+
+small_fg = mk_fgraph_given_ev(G, node_sizes, {init_factor, edge_factor}, {obs_factor}, data(onodes), ...
+			 'equiv_class', [1 2*ones(1,T-1)], 'ev_equiv_class', ones(1,T));
+
+small_bnet = fgraph_to_bnet(small_fg);
+
+% don't pre-process evidence
+big_fg = bnet_to_fgraph(bnet);
+big_bnet = fgraph_to_bnet(big_fg);
+
+
+
+engine = {};
+engine{1} = jtree_inf_engine(bnet);
+engine{2} = belprop_fg_inf_engine(small_fg, 'max_iter', 2*T);
+engine{3} = jtree_inf_engine(small_bnet);
+engine{4} = belprop_fg_inf_engine(big_fg, 'max_iter', 3*T);
+engine{5} = jtree_inf_engine(big_bnet);
+nengines = length(engine);
+
+
+% on BN, use the original evidence
+evidence = cell(1, 2*T);
+evidence(onodes) = data(onodes);
+tic; [engine{1}, ll(1)] = enter_evidence(engine{1}, evidence); toc
+
+
+% on small_fg, we have already included the evidence
+evidence = cell(1,T);
+tic; [engine{2}, ll(2)] = enter_evidence(engine{2}, evidence); toc
+
+
+% on small_bnet, we must add evidence to the dummy nodes 
+V = small_fg.nvars;
+dummy = V+1:V+small_fg.nfactors;
+N = max(dummy);
+evidence = cell(1, N);
+evidence(dummy) = {1};
+tic; [engine{3}, ll(3)] = enter_evidence(engine{3}, evidence); toc
+
+
+% on big_fg, use the original evidence
+evidence = cell(1, 2*T);
+evidence(onodes) = data(onodes);
+tic; [engine{4}, ll(4)] = enter_evidence(engine{4}, evidence); toc
+
+
+% on big_bnet, we must add evidence to the dummy nodes
+V = big_fg.nvars;
+assert(V == 2*T);
+dummy = V+1:V+big_fg.nfactors;
+N = max(dummy);
+evidence = cell(1, N);
+evidence(onodes) = data(onodes);
+evidence(dummy) = {1};
+tic; [engine{5}, ll(5)] = enter_evidence(engine{5}, evidence); toc
+
+
+marg = zeros(T, nengines, Q); % marg(t,e,:)
+for t=1:T
+  for e=1:nengines
+    m = marginal_nodes(engine{e}, t);
+    marg(t,e,:) = m.T;
+  end
+end
+marg(:,:,1)