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, 80 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/static/mfa1.m b/sourcecodes/bnt-master/BNT/examples/static/mfa1.m
new file mode 100644
index 00000000..17eb8667
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/mfa1.m
@@ -0,0 +1,80 @@
+% Factor analysis
+% Z -> X,  Z in R^k, X in R^D, k << D (high dimensional observations explained by small source)
+% Z ~ N(0,I),   X|Z ~ N(L z, Psi), where Psi is diagonal.
+%
+% Mixtures of FA
+% Now X|Z,W=i ~ N(mu(i) + L(i) Z, Psi(i))
+%
+% We compare to Zoubin Ghahramani's code.
+
+randn('state', 0);
+max_iter = 3;
+M = 2;
+k = 3;
+D = 5;
+
+n = 5;
+X1 = randn(n, D);
+X2 = randn(n, D) + 2; % move the mean to (2,2,2...)
+X = [X1; X2];
+N = size(X, 1);
+
+% initialise as in mfa
+tiny=exp(-700);
+mX = mean(X);
+cX=cov(X);
+scale=det(cX)^(1/D);
+randn('state',0); % must reset seed here so initial params are identical to mfa
+L0=randn(D*M,k)*sqrt(scale/k);
+W0 = permute(reshape(L0, [D M k]), [1 3 2]); % use D,K,M 
+Psi0=diag(cX)+tiny;
+Pi0=ones(M,1)/M;
+Mu0=randn(M,D)*sqrtm(cX)+ones(M,1)*mX;
+
+[Lh1, Ph1, Mu1, Pi1, LL1] = mfa(X,M,k,max_iter);
+Lh1 = permute(reshape(Lh1, [D M k]), [1 3 2]); % use D,K,M 
+
+
+ns = [M k D];
+dag = zeros(3);
+dag(1,3) = 1;
+dag(2,3) = 1;
+dnodes = 1;
+onodes = 3;
+
+bnet = mk_bnet(dag, ns, 'discrete', dnodes, 'observed', onodes);
+bnet.CPD{1} = tabular_CPD(bnet, 1, Pi0);
+
+%bnet.CPD{2} = gaussian_CPD(bnet, 2, zeros(k, 1), eye(k), [], 'diag', 'untied', 'clamp_mean',  'clamp_cov');
+
+bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', zeros(k, 1), 'cov', eye(k), 'cov_type', 'diag', ...
+			   'cov_prior_weight', 0, 'clamp_mean', 1, 'clamp_cov', 1);
+
+%bnet.CPD{3} = gaussian_CPD(bnet, 3, Mu0', repmat(diag(Psi0), [1 1 M]), W0, 'diag', 'tied');
+
+bnet.CPD{3} = gaussian_CPD(bnet, 3, 'mean', Mu0', 'cov', repmat(diag(Psi0), [1 1 M]), ...
+			   'weights', W0, 'cov_type', 'diag', 'cov_prior_weight', 0, 'tied_cov', 1);
+
+engine = jtree_inf_engine(bnet);
+evidence = cell(3, N);
+evidence(3,:) = num2cell(X', 1);
+
+[bnet2, LL2, engine2] = learn_params_em(engine, evidence, max_iter);
+
+s = struct(bnet2.CPD{1});
+Pi2 = s.CPT(:);
+s = struct(bnet2.CPD{3});
+Mu2 = s.mean;
+W2 = s.weights;
+Sigma2 = s.cov;
+
+
+% Compare to Zoubin's code
+assert(approxeq(LL1,LL2));
+for i=1:M
+  assert(approxeq(W2(:,:,i), Lh1(:,:,i)));
+  assert(approxeq(Sigma2(:,:,i), diag(Ph1)));
+  assert(approxeq(Mu2(:,i), Mu1(i,:)));
+  assert(approxeq(Pi2(:), Pi1(:)));
+end
+