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, 47 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/viterbi1.m b/sourcecodes/bnt-master/BNT/examples/dynamic/viterbi1.m
new file mode 100644
index 00000000..6ad07d91
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/viterbi1.m
@@ -0,0 +1,47 @@
+% Compute Viterbi path discrete HMM by different methods
+
+intra = zeros(2);
+intra(1,2) = 1;
+inter = zeros(2);
+inter(1,1) = 1;
+n = 2;
+
+Q = 2; % num hidden states
+O = 2; % num observable symbols
+
+ns = [Q O];
+dnodes = 1:2;
+onodes = [2];
+eclass1 = [1 2];
+eclass2 = [3 2];
+bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'eclass1', eclass1, 'eclass2', eclass2, ...
+	      'observed', onodes);
+
+for seed=1:10
+rand('state', seed);
+prior = normalise(rand(Q,1));
+transmat = mk_stochastic(rand(Q,Q));
+obsmat = mk_stochastic(rand(Q,O));
+bnet.CPD{1} = tabular_CPD(bnet, 1, prior);
+bnet.CPD{2} = tabular_CPD(bnet, 2, obsmat);
+bnet.CPD{3} = tabular_CPD(bnet, 3, transmat);
+
+
+% Create a sequence
+T = 5; 
+ev = sample_dbn(bnet, T);
+evidence = cell(2,T);
+evidence(2,:) = ev(2,:); % extract observed component
+data = cell2num(ev(2,:));
+
+%obslik = mk_dhmm_obs_lik(data, obsmat);
+obslik = multinomial_prob(data, obsmat);
+path = viterbi_path(prior, transmat, obslik);
+
+engine = {};
+engine{end+1} = smoother_engine(jtree_2TBN_inf_engine(bnet));
+
+mpe = find_mpe(engine{1}, evidence);
+
+assert(isequal(cell2num(mpe(1,:)), path)) % extract values of hidden nodes
+end