


Generates a confusion matrix according to true and predicted data labels.
CM(i,j) denotes the number of elements of class i that were given label
j. In other words, each row i contains the predictions for elements whos
actual class was i. If IDXpred is perfect, then CM is a diagonal matrix
with CM(i,i) equal to the number of instances of class i.
To normalize CM to [0,1], divide each row by sum of that row:
CMnorm = CM ./ repmat( sum(CM,2), [1 size(CM,2)] );
USAGE
CM = confMatrix( IDXtrue, IDXpred, ntypes )
INPUTS
IDXtrue - [nx1] array of true labels [int values in 1-ntypes]
IDXpred - [nx1] array of predicted labels [int values in 1-ntypes]
ntypes - maximum number of types (should be > max(IDX))
OUTPUTS
CM - ntypes x ntypes confusion array with integer values
EXAMPLE
IDXtrue = [ones(1,25) ones(1,25)*2];
IDXpred = [ones(1,10) randint2(1,30,[1 2]) ones(1,10)*2];
CM = confMatrix( IDXtrue, IDXpred, 2 )
confMatrixShow( CM, {'class-A','class-B'}, {'FontSize',20} )
See also CONFMATRIXSHOW
Piotr's Image&Video Toolbox Version 2.12
Copyright 2008 Piotr Dollar. [pdollar-at-caltech.edu]
Please email me if you find bugs, or have suggestions or questions!
Licensed under the Lesser GPL [see external/lgpl.txt]