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, 41 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/sample_from_map.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/sample_from_map.m
new file mode 100644
index 00000000..816b741e
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/sample_from_map.m
@@ -0,0 +1,41 @@
+if 0
+% Generate some sample paths
+
+bnet = mk_map_hhmm('p', 1);
+% assign numbers to the nodes in topological order
+U = 1; A = 2; C = 3; F = 4; O = 5;
+
+
+seed = 0;
+rand('state', seed);
+randn('state', seed);
+
+% control policy = sweep right then left
+T = 10;
+ss = 5;
+ev = cell(ss, T);
+ev(U,:) = num2cell([R*ones(1,5) L*ones(1,5)]);
+
+% fix initial conditions to be in left most state
+ev{A,1} = 1; 
+ev{C,1} = 1; 
+evidence = sample_dbn(bnet, 'length', T, 'evidence', ev)
+
+
+% Now do same but with noisy actuators
+
+bnet = mk_map_hhmm('p', 0.8);
+evidence = sample_dbn(bnet, 'length', T, 'evidence', ev)
+
+end
+
+% Now do same but with 4 observations per slice
+
+bnet = mk_map_hhmm('p', 0.8, 'obs_model', 'four');
+ss = bnet.nnodes_per_slice;
+
+ev = cell(ss, T);
+ev(U,:) = num2cell([R*ones(1,5) L*ones(1,5)]);
+ev{A,1} = 1; 
+ev{C,1} = 1; 
+evidence = sample_dbn(bnet, 'length', T, 'evidence', ev)