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, 81 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/static/mog1.m b/sourcecodes/bnt-master/BNT/examples/static/mog1.m
new file mode 100644
index 00000000..442f067b
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/mog1.m
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+% Fit a mixture of Gaussians using netlab and BNT
+
+rand('state', 0);
+randn('state', 0);
+
+% Q -> Y
+ncenters = 2; dim = 2;
+cov_type = 'full';
+
+% Generate the data from a mixture of 2 Gaussians
+%mu = randn(dim, ncenters);
+mu = zeros(dim, ncenters);
+mu(:,1) = [-1 -1]';
+mu(:,1) = [1 1]';
+Sigma = repmat(0.1*eye(dim),[1 1 ncenters]);
+ndat1 = 8; ndat2 = 8;
+%ndat1 = 2; ndat2 = 2;
+ndata = ndat1+ndat2;
+x1 = gsamp(mu(:,1), Sigma(:,:,1), ndat1);
+x2 = gsamp(mu(:,2), Sigma(:,:,2), ndat2);
+data = [x1; x2];
+%plot(x1(:,1),x1(:,2),'ro', x2(:,1),x2(:,2),'bx')
+
+% Fit using netlab
+max_iter = 3;
+mix = gmm(dim, ncenters, cov_type);
+options = foptions;
+options(1) = 1; % verbose
+options(14) = max_iter;
+
+% extract initial params
+%mix = gmminit(mix, x, options); % Initialize with K-means
+mu0 = mix.centres';
+pi0 = mix.priors(:);
+Sigma0 = mix.covars; % repmat(eye(dim), [1 1 ncenters]);
+
+[mix, options] = gmmem(mix, data, options);
+
+% Final params
+ll1 = options(8);
+mu1 = mix.centres';
+pi1 = mix.priors(:);
+Sigma1 = mix.covars;
+
+
+
+
+% BNT
+
+dag = zeros(2);
+dag(1,2) = 1;
+node_sizes = [ncenters dim];
+discrete_nodes = 1;
+onodes = 2;
+
+bnet = mk_bnet(dag, node_sizes, 'discrete', discrete_nodes, 'observed', onodes);
+bnet.CPD{1} = tabular_CPD(bnet, 1, pi0);
+bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', mu0, 'cov', Sigma0, 'cov_type', cov_type, ...
+			   'cov_prior_weight', 0);
+
+engine = jtree_inf_engine(bnet);
+
+evidence = cell(2, ndata);
+evidence(2,:) = num2cell(data', 1);
+
+[bnet2, LL] = learn_params_em(engine, evidence, max_iter);
+
+ll2 = LL(end);
+s1 = struct(bnet2.CPD{1});
+pi2 = s1.CPT(:);
+
+s2 = struct(bnet2.CPD{2});
+mu2 = s2.mean;
+Sigma2 = s2.cov;
+
+% assert(approxeq(ll1, ll2)); % gmmem returns the value after the final M step, GMT before
+assert(approxeq(mu1, mu2));
+assert(approxeq(Sigma1, Sigma2))
+assert(approxeq(pi1, pi2))
+
+