BNW using Octave instead of Matlab.

This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02

1 files changed, 83 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/static/fgraph/fg3.m b/sourcecodes/bnt-master/BNT/examples/static/fgraph/fg3.m
new file mode 100644
index 00000000..ec3f28f2
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/fgraph/fg3.m
@@ -0,0 +1,83 @@
+% make a factor graph corresponding to an  HMM with Gaussian outputs, where we absorb the
+% evidence up front 
+
+seed = 1;
+rand('state', seed);
+randn('state', seed);
+
+T = 3;
+Q = 3;
+O = 2;
+cts_obs = 1;
+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); % can't handle Gaussian node
+
+
+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);
+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
+
+
+
+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)