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, 56 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/confmat.m b/sourcecodes/bnt-master/netlab3.3/confmat.m
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+function [C,rate]=confmat(Y,T)
+%CONFMAT Compute a confusion matrix.
+%
+%	Description
+%	[C, RATE] = CONFMAT(Y, T) computes the confusion matrix C and
+%	classification performance RATE 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.  The vector RATE has two entries: the
+%	percentage of correct classifications and the total number of correct
+%	classifications.
+%
+%	See also
+%	CONFFIG, DEMTRAIN
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+[n c]=size(Y);
+[n2 c2]=size(T);
+
+if n~=n2 | c~=c2
+  error('Outputs and targets are different sizes')
+end
+
+if c > 1
+  % Find the winning class assuming 1-of-N encoding
+  [maximum Yclass] = max(Y', [], 1);
+
+  TL=[1:c]*T';
+else
+  % Assume two classes with 0-1 encoding
+  c = 2;
+  class2 = find(T > 0.5);
+  TL = ones(n, 1);
+  TL(class2) = 2;
+  class2 = find(Y > 0.5);
+  Yclass = ones(n, 1);
+  Yclass(class2) = 2;
+end
+
+% Compute 
+correct = (Yclass==TL);
+total=sum(sum(correct));
+rate=[total*100/n total];
+
+C=zeros(c,c);
+for i=1:c
+  for j=1:c
+    C(i,j) = sum((Yclass==j).*(TL==i));
+  end
+end