Merge pull request #1 from ziejd2/octave_php_separate

Octave php separate

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