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, 29 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/conffig.m b/sourcecodes/bnt-master/netlab3.3/conffig.m
new file mode 100644
index 00000000..23d774e0
--- /dev/null
+++ b/sourcecodes/bnt-master/netlab3.3/conffig.m
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+function fh=conffig(y, t)
+%CONFFIG Display a confusion matrix.
+%
+%	Description
+%	CONFFIG(Y, T) displays the confusion matrix  and classification
+%	performance for the predictions mat{y} compared with the targets T.
+%	The data is assumed to be in a 1-of-N encoding, unless there is just
+%	one column, when it is assumed to be a 2 class problem with a 0-1
+%	encoding.  Each row of Y and T corresponds to a single example.
+%
+%	In the confusion matrix, the rows represent the true classes and the
+%	columns the predicted classes.
+%
+%	FH = CONFFIG(Y, T) also returns the figure handle FH which  can be
+%	used, for instance, to delete the figure when it is no longer needed.
+%
+%	See also
+%	CONFMAT, DEMTRAIN
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+[C, rate] = confmat(y, t);
+
+fh = figure('Name', 'Confusion matrix', ...
+  'NumberTitle', 'off');
+
+plotmat(C, 'k', 'k', 14);
+title(['Classification rate: ' num2str(rate(1)) '%'], 'FontSize', 14);