Calculation of tp,tn,fp,fn for multi classes

Output=[1,1,1,-1,1,2,9,2,2,2,3,3,3,3,3,4,4,4,4,4,5,5,5,2,5,6,4,14,3,4]
Labels=[1,1,1,1,1,2,2,2,2,2,3,3,3,3,3,4,4,4,4,4,5,5,5,5,5,6,6,6,6,6]

from these values I have to calculate TP,TN,FP,FN..

[Guillaume]

TP,TN,FP,FN might mean something to you and to people in your field of work. But to others (and me) they're just meaningless abbreviations.

Give more details or a link, and add a tag specifying which domains this apply to.

[asmi]

TP= true positive,TN=True negative ,FP=false positive and FN=false negative .I have calculated this related to Face Recognition code.

[asmi]

I have calculated this parameter from 6 classes.(1,1,1,1,1) means it is one image like wise.Firstly I have to train the training data form feedforward neural network and getting that trained net I put the testing data and got these above output array.then match the labels and output matrix to calculate the tp,tn,fp,fn

Answer 1

[Greg Heath]

The standard approach for c classes is to use a target matrix of size [ c N ]that only contains columns of the matrix eye(c). The correspondence between the true class indices 1,2,...c and the target is

 N      = length(truclassindices)
 target = ind2vec(truclassindices)

The assigned classes and corresponding errors are obtained from the net output via

 output          = net(input);
 assignedclasses = vec2ind(output);
 errors          = assignedclasses~=truclassindices;
 Nerr            = sum(errors)
 PctErr          = 100*Nerr/N
 [cm order]      = confusionmat(target,output)

Hope this helps.

Thank you for formally accepting my answer

Greg

Answer 2

[Star Strider]

I don’t understand your output. In theory, your classifier should assign one of the labels for each input (1-6), but your output contains classes such as -1, 9, and 14. That fails.

Anyway, when you get that problem sorted (and you must before you can go any further), see the documentation for confusionmat.