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, 73 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/reveal1.m b/sourcecodes/bnt-master/BNT/examples/dynamic/reveal1.m
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+% Make a DBN with the following inter-connectivity matrix
+%    1
+%   /  \
+%  2   3
+%   \ /
+%    4 
+%    |
+%    5
+% where all arcs point down. In addition, there are persistence arcs from each node to itself.
+% There are no intra-slice connections.
+% Nodes have noisy-or CPDs.
+% Node 1 turns on spontaneously due to its leaky source.
+% This effect trickles down to the other nodes in the order shown.
+% All the other nodes inhibit their leaks.
+% None of the nodes inhibit the connection from themselves, so that once they are on, they remain
+% on (persistence).
+%
+% This model was used in the experiments reported in
+% - "Learning the structure of DBNs", Friedman, Murphy and Russell, UAI 1998.
+% where the structure was learned even in the presence of missing data.
+% In that paper, we used the structural EM algorithm.
+% Here, we assume full observability and tabular CPDs for the learner, so we can use a much
+% simpler learning algorithm.
+
+ss = 5;
+
+inter = eye(ss);
+inter(1,[2 3]) = 1;
+inter(2,4)=1;
+inter(3,4)=1;
+inter(4,5)=1;
+
+intra = zeros(ss);
+ns = 2*ones(1,ss);
+
+bnet = mk_dbn(intra, inter, ns);
+
+% All nodes start out off
+for i=1:ss
+  bnet.CPD{i} = tabular_CPD(bnet, i, [1.0 0.0]');
+end
+
+% The following params correspond to Fig 4a in the UAI 98 paper
+% The first arg is the leak inhibition prob.
+% The vector contains the inhib probs from the parents in the previous slice;
+% the last element is self, which is never inhibited.
+bnet.CPD{1+ss} = noisyor_CPD(bnet, 1+ss, 0.8, 0);
+bnet.CPD{2+ss} = noisyor_CPD(bnet, 2+ss, 1, [0.9 0]);
+bnet.CPD{3+ss} = noisyor_CPD(bnet, 3+ss, 1, [0.8 0]);
+bnet.CPD{4+ss} = noisyor_CPD(bnet, 4+ss, 1, [0.7 0.6 0]);
+bnet.CPD{5+ss} = noisyor_CPD(bnet, 5+ss, 1, [0.5 0]);
+
+
+% Generate some training data
+
+nseqs = 20;
+seqs = cell(1,nseqs);
+T = 30;
+for i=1:nseqs
+  seqs{i} = sample_dbn(bnet, T);
+end
+
+max_fan_in = 3; % let's cheat a little here
+
+% computing num. incorrect edges as a fn of the size of the training set
+%sz = [5 10 15 20];    
+sz = [5 10];    
+h = zeros(1, length(sz));
+for i=1:length(sz)
+  inter2 = learn_struct_dbn_reveal(seqs(1:sz(i)), ns, max_fan_in);
+  h(i) = sum(abs(inter(:)-inter2(:))); % hamming distance
+end
+h