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, 111 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/KPMstats/mixgauss_prob_test.m b/sourcecodes/bnt-master/KPMstats/mixgauss_prob_test.m
new file mode 100644
index 00000000..a93eaf97
--- /dev/null
+++ b/sourcecodes/bnt-master/KPMstats/mixgauss_prob_test.m
@@ -0,0 +1,111 @@
+function test_eval_pdf_cond_mixgauss()
+
+%Q = 10; M = 100; d = 20; T = 500;
+Q = 2; M = 3; d = 4; T = 5;
+
+mu = rand(d,Q,M);
+data = randn(d,T);
+%mixmat = mk_stochastic(rand(Q,M));
+mixmat = mk_stochastic(ones(Q,M));
+
+% tied scalar
+Sigma = 0.01;
+
+mu = rand(d,M,Q);
+weights = mixmat';
+N = M*ones(1,Q);
+tic; [B, B2, D] = parzen(data, mu, Sigma, N, weights); toc
+tic; [BC, B2C, DC] = parzenC(data, mu, Sigma, N); toc
+approxeq(B,BC)
+B2C = reshape(B2C,[M Q T]);
+approxeq(B2,B2C)
+DC = reshape(DC,[M Q T]);
+approxeq(D,DC)
+
+
+return
+
+tic; [B, B2] = eval_pdf_cond_mixgauss(data, mu, Sigma, mixmat); toc
+tic; C = eval_pdf_cond_parzen(data, mu, Sigma); toc
+approxeq(B,C)
+
+return;
+
+
+mu = reshape(mu, [d Q*M]);
+
+data = mk_unit_norm(data);
+mu = mk_unit_norm(mu);
+tic; D = 2 -2*(data'*mu); toc % avoid an expensive repmat
+tic; D2 = sqdist(data, mu); toc
+approxeq(D,D2)
+
+
+% D(t,m) = sq dist between data(:,t) and mu(:,m)
+mu = reshape(mu, [d Q*M]);
+D = dist2(data', mu');
+%denom = (2*pi)^(d/2)*sqrt(abs(det(C)));
+denom = (2*pi*Sigma)^(d/2); % sqrt(det(2*pi*Sigma))
+numer = exp(-0.5/Sigma  * D');
+B2 = numer / denom;
+B2 = reshape(B2, [Q M T]);
+
+tic; B = squeeze(sum(B2 .* repmat(mixmat, [1 1 T]), 2)); toc
+
+tic
+A = zeros(Q,T);
+for q=1:Q
+  A(q,:) = mixmat(q,:) * squeeze(B2(q,:,:)); % sum over m
+end
+toc
+assert(approxeq(A,B))
+
+tic
+A = zeros(Q,T);
+for t=1:T
+  A(:,t) = sum(mixmat .* B2(:,:,t), 2); % sum over m
+end
+toc
+assert(approxeq(A,B))
+
+    
+
+
+mu = reshape(mu, [d Q M]);
+B3 = zeros(Q,M,T);
+for j=1:Q
+  for k=1:M
+    B3(j,k,:) = gaussian_prob(data, mu(:,j,k), Sigma*eye(d));
+  end
+end
+assert(approxeq(B2, B3))
+
+logB4 = -(d/2)*log(2*pi*Sigma) - (1/(2*Sigma))*D; % det(sigma*I) = sigma^d
+B4 = reshape(exp(logB4), [Q M T]);
+assert(approxeq(B4, B3))
+
+
+
+  
+% tied cov matrix
+
+Sigma = rand_psd(d,d);
+mu = reshape(mu, [d Q*M]);
+D = sqdist(data, mu, inv(Sigma))';
+denom = sqrt(det(2*pi*Sigma));
+numer = exp(-0.5 * D);
+B2 = numer / denom;
+B2 = reshape(B2, [Q M T]);
+
+mu = reshape(mu, [d Q M]);
+B3 = zeros(Q,M,T);
+for j=1:Q
+  for k=1:M
+    B3(j,k,:) = gaussian_prob(data, mu(:,j,k), Sigma);
+  end
+end
+assert(approxeq(B2, B3))
+
+logB4 = -(d/2)*log(2*pi) - 0.5*logdet(Sigma) - 0.5*D;
+B4 = reshape(exp(logB4), [Q M T]);
+assert(approxeq(B4, B3))