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, 65 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlabKPM/gmmem_multi_restart.m b/sourcecodes/bnt-master/netlabKPM/gmmem_multi_restart.m
new file mode 100644
index 00000000..cc24b6a7
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+++ b/sourcecodes/bnt-master/netlabKPM/gmmem_multi_restart.m
@@ -0,0 +1,65 @@
+function [means, covs, weights, ll] = gmmem_multi_restart(K, data, varargin)
+% GMMEM_MULTI_RESTART Multiple restart wrapper for gmmem_kpm
+% function [means, covs, weights, ll] = gmmem_multi_restart(K, data, varargin)
+%
+% Input:
+% K = number of mixture components
+% data(i,:) is the i'th example (feature vector)
+%
+% Output:
+% The parameters for the k'th mixture component, k=1:K, are
+%   means(k,:), covs(:,:,k) and weights(k)
+%
+% [ ... ] = gmmem_multi_restart(..., 'param1',val1, 'param2',val2, ...)
+% allows you to specify optional parameter name/value pairs.
+% Parameters are below [default value in brackets]
+%
+% 'nrestarts' - number of EM restarts [2]
+% 'cov_type' - 'full', 'diag' or 'spherical' ['full']
+% 'init_cov' - the initial covariance matrix [0.1*cov(data) for each k]
+% 'init_means' - [] means sample from randn(); otherwise, use
+%               init_means(k,:,r) for the k'th comp. on the r'th restart [ [] ]
+% 'restartfn'  - this function, if non-empty,  will be called before/after every restart
+%               (e.g., to display the parameters as they evolve) [ [] ]
+%               The fn is called as fn(mix{r}, data, restart_num, niter, outerfnargs)
+%               where niter is the number of iterations performed (0 initially)
+% 'restartfnargs' - additional arguments to be passed to restartfn [ {} ]
+%
+% Optional arguments for gmmem_kpm are passed through.
+%
+% Written by Kevin P Murphy, 30 Dec 2002
+
+[ndata nfeatures] = size(data);
+
+%Cinit = repmat(0.1*diag(diag(cov(data))), [1 1 K]);
+Cinit = repmat(0.1*cov(data), [1 1 K]);
+
+[nrestarts, init_cov, init_means, cov_type, ...
+ restartfn, restartfnargs, unused_args] = ...
+    process_options(varargin, ...
+	'nrestarts', 2, 'init_cov',  Cinit,  'init_means', [], ...
+	'cov_type', 'full', 'restartfn', [], 'restartfnargs', {});
+
+mix = cell(1, nrestarts);
+cost = inf*ones(1,nrestarts);
+
+for r=1:nrestarts
+  mix{r} = gmm(nfeatures, K, cov_type); % random centers
+  if ~isempty(init_means), mix{r}.centres = init_means(:,:,r); end
+  mix{r}.covars = init_cov;
+  if ~isempty(restartfn)
+    feval(restartfn, mix{r}, data, r, 0, restartfnargs{:});
+  end
+  [mix{r}, niter, ll] = gmmem_kpm(mix{r}, data, unused_args{:}); 
+  cost(r) = -ll; %-sum(log(gmmprob(mix{r}, data)));
+  if ~isempty(restartfn)
+    feval(restartfn, mix{r}, data, r, niter, restartfnargs{:});
+  end
+end
+
+[nll, bestr] = min(cost);
+fprintf('best r = %d\n', bestr);
+ll = -nll;
+means = mix{bestr}.centres;
+covs = mix{bestr}.covars;
+weights = mix{bestr}.priors;