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 diagnol matrix with CM(i,i) equal to the number of instances of class i. To normalize CM to fall between [0,1], divide each row by the sum of that row: CMnorm = CM ./ repmat( sum(CM,2), [1 size(CM,2)] ); INPUTS IDXtrue - nx1 array of true labels [int values between 1 and ntypes] IDXpred - nx1 array of predicted labels [int values between 1 and ntypes] ntypes - maximum number of types (should be > max(IDX)) OUTPUTS CM - ntypes x ntypes confusion array with integer values DATESTAMP 29-Sep-2005 2:00pm See also CONFMATRIX_SHOW