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, 86 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/static/learn1.m b/sourcecodes/bnt-master/BNT/examples/static/learn1.m
new file mode 100644
index 00000000..d2b7522b
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+++ b/sourcecodes/bnt-master/BNT/examples/static/learn1.m
@@ -0,0 +1,86 @@
+% Lawn sprinker example from Russell and Norvig p454
+% See www.cs.berkeley.edu/~murphyk/Bayes/usage.html for details.
+
+N = 4; 
+dag = zeros(N,N);
+C = 1; S = 2; R = 3; W = 4;
+dag(C,[R S]) = 1;
+dag(R,W) = 1;
+dag(S,W)=1;
+
+false = 1; true = 2;
+ns = 2*ones(1,N); % binary nodes
+
+bnet = mk_bnet(dag, ns);
+bnet.CPD{C} = tabular_CPD(bnet, C, [0.5 0.5]);
+bnet.CPD{R} = tabular_CPD(bnet, R, [0.8 0.2 0.2 0.8]);
+bnet.CPD{S} = tabular_CPD(bnet, S, [0.5 0.9 0.5 0.1]);
+bnet.CPD{W} = tabular_CPD(bnet, W, [1 0.1 0.1 0.01 0 0.9 0.9 0.99]);
+
+CPT = cell(1,N);
+for i=1:N
+  s=struct(bnet.CPD{i});  % violate object privacy
+  CPT{i}=s.CPT;
+end
+
+% Generate training data
+nsamples = 50;
+samples = cell(N, nsamples);
+for i=1:nsamples
+  samples(:,i) = sample_bnet(bnet);
+end
+data = cell2num(samples);
+
+% Make a tabula rasa
+bnet2 = mk_bnet(dag, ns);
+seed = 0;
+rand('state', seed);
+bnet2.CPD{C} = tabular_CPD(bnet2, C, 'clamped', 1, 'CPT', [0.5 0.5], ...
+			   'prior_type', 'dirichlet', 'dirichlet_weight', 0);
+bnet2.CPD{R} = tabular_CPD(bnet2, R, 'prior_type', 'dirichlet', 'dirichlet_weight', 0);
+bnet2.CPD{S} = tabular_CPD(bnet2, S, 'prior_type', 'dirichlet', 'dirichlet_weight', 0);
+bnet2.CPD{W} = tabular_CPD(bnet2, W, 'prior_type', 'dirichlet', 'dirichlet_weight', 0);
+
+
+% Find MLEs from fully observed data
+bnet4 = learn_params(bnet2, samples);
+
+% Bayesian updating with 0 prior is equivalent to ML estimation
+bnet5 = bayes_update_params(bnet2, samples);
+
+CPT4 = cell(1,N);
+for i=1:N
+  s=struct(bnet4.CPD{i});  % violate object privacy
+  CPT4{i}=s.CPT;
+end
+
+CPT5 = cell(1,N);
+for i=1:N
+  s=struct(bnet5.CPD{i});  % violate object privacy
+  CPT5{i}=s.CPT;
+  assert(approxeq(CPT5{i}, CPT4{i}))
+end
+
+
+if 1
+% Find MLEs from partially observed data
+
+% hide 50% of the nodes
+samplesH = samples;
+hide = rand(N, nsamples) > 0.5;
+[I,J]=find(hide);
+for k=1:length(I)
+  samplesH{I(k), J(k)} = [];
+end
+
+engine = jtree_inf_engine(bnet2);
+max_iter = 5;
+[bnet6, LL] = learn_params_em(engine, samplesH, max_iter);
+
+CPT6 = cell(1,N);
+for i=1:N
+  s=struct(bnet6.CPD{i});  % violate object privacy
+  CPT6{i}=s.CPT;
+end
+
+end