# Netlab

### Ian Nabney (view profile)

• 1 file
• 3.06667

06 Nov 2002 (Updated )

Pattern analysis toolbox.

[C,rate]=confmat(Y,T)
```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.
%
%	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
```