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, 52 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/ppca.m b/sourcecodes/bnt-master/netlab3.3/ppca.m
new file mode 100644
index 00000000..fd125b02
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+++ b/sourcecodes/bnt-master/netlab3.3/ppca.m
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+function [var, U, lambda] = ppca(x, ppca_dim)
+%PPCA	Probabilistic Principal Components Analysis
+%
+%	Description
+%	 [VAR, U, LAMBDA] = PPCA(X, PPCA_DIM) computes the principal
+%	component subspace U of dimension PPCA_DIM using a centred covariance
+%	matrix X. The variable VAR contains the off-subspace variance (which
+%	is assumed to be spherical), while the vector LAMBDA contains the
+%	variances of each of the principal components.  This is computed
+%	using the eigenvalue and eigenvector  decomposition of X.
+%
+%	See also
+%	EIGDEC, PCA
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+
+if ppca_dim ~= round(ppca_dim) | ppca_dim < 1 | ppca_dim > size(x, 2)
+   error('Number of PCs must be integer, >0, < dim');
+end
+
+[ndata, data_dim] = size(x);
+% Assumes that x is centred and responsibility weighted
+% covariance matrix
+[l Utemp] = eigdec(x, data_dim);
+% Zero any negative eigenvalues (caused by rounding)
+l(l<0) = 0;
+% Now compute the sigma squared values for all possible values
+% of q
+s2_temp = cumsum(l(end:-1:1))./[1:data_dim]';
+% If necessary, reduce the value of q so that var is at least
+% eps * largest eigenvalue
+q_temp = min([ppca_dim; data_dim-min(find(s2_temp/l(1) > eps))]);
+if q_temp ~= ppca_dim
+  wstringpart = 'Covariance matrix ill-conditioned: extracted';
+  wstring = sprintf('%s %d/%d PCs', ...
+      wstringpart, q_temp, ppca_dim);
+  warning(wstring);
+end
+if q_temp == 0
+  % All the latent dimensions have disappeared, so we are
+  % just left with the noise model
+  var = l(1)/data_dim;
+  lambda = var*ones(1, ppca_dim);
+else
+  var = mean(l(q_temp+1:end));
+end  
+U = Utemp(:, 1:q_temp);
+lambda(1:q_temp) = l(1:q_temp);
+
+