confMatrix

 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 Computer Vision Matlab Toolbox      Version 2.12
 Copyright 2014 Piotr Dollar.  [pdollar-at-gmail.com]
 Licensed under the Simplified BSD License [see external/bsd.txt]