From 8070dc963753142bb86c4ed698d91fd623ed28e7 Mon Sep 17 00:00:00 2001
From: ziejd2
Date: Thu, 28 Sep 2017 15:04:40 -0500
Subject: 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
---
 sourcecodes/bnt-master/KPMstats/eigdec.m | 59 ++++++++++++++++++++++++++++++++
 1 file changed, 59 insertions(+)
 create mode 100644 sourcecodes/bnt-master/KPMstats/eigdec.m

(limited to 'sourcecodes/bnt-master/KPMstats/eigdec.m')

diff --git a/sourcecodes/bnt-master/KPMstats/eigdec.m b/sourcecodes/bnt-master/KPMstats/eigdec.m
new file mode 100644
index 00000000..321cee4d
--- /dev/null
+++ b/sourcecodes/bnt-master/KPMstats/eigdec.m
@@ -0,0 +1,59 @@
+function [evals, evec] = eigdec(x, N)
+%EIGDEC	Sorted eigendecomposition
+%
+%	Description
+%	 EVALS = EIGDEC(X, N computes the largest N eigenvalues of the
+%	matrix X in descending order.  [EVALS, EVEC] = EIGDEC(X, N) also
+%	computes the corresponding eigenvectors.
+%
+%	See also
+%	PCA, PPCA
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+if nargout == 1
+   evals_only = logical(1);
+else
+   evals_only = logical(0);
+end
+
+if N ~= round(N) | N < 1 | N > size(x, 2)
+   error('Number of PCs must be integer, >0, < dim');
+end
+
+% Find the eigenvalues of the data covariance matrix
+if evals_only
+   % Use eig function as always more efficient than eigs here
+   temp_evals = eig(x);
+else
+   % Use eig function unless fraction of eigenvalues required is tiny
+   if (N/size(x, 2)) > 0.04
+     fprintf('netlab pca: using eig\n');
+      [temp_evec, temp_evals] = eig(x);
+   else
+      options.disp = 0;
+      fprintf('netlab pca: using eigs\n');
+      [temp_evec, temp_evals] = eigs(x, N, 'LM', options);
+   end
+   temp_evals = diag(temp_evals);
+end
+
+% Eigenvalues nearly always returned in descending order, but just
+% to make sure.....
+[evals perm] = sort(-temp_evals);
+evals = -evals(1:N);
+%evec=temp_evec(:,1:N);
+if ~evals_only
+  if evals == temp_evals(1:N)
+    % Originals were in order
+    evec = temp_evec(:, 1:N);
+    return
+  else
+    fprintf('netlab pca: sorting evec\n');
+    % Need to reorder the eigenvectors
+    for i=1:N
+      evec(:,i) = temp_evec(:,perm(i));
+    end
+  end
+end
-- 
cgit 1.4.1