Machine-Learning-Pattern-Recognition/Parzen window/myPlotRoc.m at master · niravnb/Machine-Learning-Pattern-Recognition · GitHub

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function [  ] = myPlotRoc( scores,actual )
% Calculates Target, Non Target Scores for all classes and Plots ROC Curve.

% Scores(i,j) dimension is 900 X 3
 TPR = zeros(1,101);
 FPR = zeros(1,101);
 FNR = zeros(1,101);
 accuracy = zeros(1,101);

 t1 = 1;
for t = 0:0.01:1

    TP = 0;
    FP = 0;
    TN = 0;
    FN = 0;

    for i = 1:size(scores,1)
        for j = 1:size(scores,2)

            if actual(i) == j

                if scores(i,j) >= t
                    TP = TP + 1;
                else
                    FN = FN + 1;
                end
            else

                 if scores(i,j) >= t
                    FP = FP + 1;
                else
                    TN = TN + 1;
                 end

            end

        end
    end

    TPR(t1) = TP/(TP + FN);
    FPR(t1) = FP/(FP + TN);
    FNR(t1) = FN/(FN + TP);
    accuracy(t1) = ((TP + TN)/(TP + FP + TN + FN))*100;
   t1 = t1+1;

end

disp(['Accuracy of the system from ROC is : ',num2str(max(accuracy)),' %']);
%figure;
plot(FPR,TPR,'LineWidth',3);
hold on

xlabel('False positive rate')
ylabel('True positive rate')


end