function [TPR, FPR, AUC] = stimROC(C, T)
%build an ROC curve
%   C - class labels as an array of binary values (1 = true positive)
%   T - threshold used for classification

    %sort the thresholds in descending order and get the indices
    [~, I] = sort(T, 'descend');
    
    %sort the class labels in the same order as the thresholds
    Cs = C(I);
    
    %calculate the number of measurements
    M = size(C, 2);
    
    %calculate the number of positives
    P = nnz(C);

    %calculate the number of negatives
    N = M - P;
    
    %if all examples are positives or negatives, return a perfect score?
    if P == M
        error('ERROR: no positive observations');
    end
    if P == 0
        error('ERROR: no negative observations');
    end
    
    %allocate space for the ROC curve
    TPR = zeros(1, M);
    FPR = zeros(1, M);
    
    
    
    %calculate the number of inflection points
    ip = 0;
    for i = 2:M
        if Cs(i) ~= Cs(i-1)
            ip = ip + 1;
        end
    end
    
    %initialize the true and false positive rates to zero
    TP = 0;
    FP = 0;
    for i = 1:M
        if Cs(i) == 1
            TP = TP + 1;
        else
            FP = FP + 1;
        end
        
        TPR(i) = TP / P;
        FPR(i) = FP / N;
    end
    
    %calculate the area under the ROC curve
    AUC = 0;
    for i = 2:M
        w = FPR(i) - FPR(i-1);
        h = TPR(i);
        AUC = AUC + w * h;
    end