79 lines
1.7 KiB
Mathematica
79 lines
1.7 KiB
Mathematica
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function [ConfMatClass ConfMatAll Accuracy Error] = confusion(TrueLabels, FoundLabels)
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% function [ConfMatClass ConfMatAll Accuracy Error] =
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% eegc3_confusion_matrix(TrueLabels, FoundLabels)
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%
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% Function to recover a confusion matric of classification problem given
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% the groiund truth classes and the classes found by a classifier
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%
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% Inputs:
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%
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% TrueLabels: Ground truth data labels
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%
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% FoundLabels: Labels predicted by a classifier
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%
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% Outputs:
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%
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% ConfMatClass: Confusion Matrx ClassNum x ClassNum in percentages per
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% class
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%
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% ConfMatAll: Confusion Matrx ClassNum x ClassNum in overall percentages
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%
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% Accuracy: Total accuracy % (diagonal of Confusion Matrix)
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%
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% Error: Total Error % (100 - Accuracy)
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if(~isvector(TrueLabels) || ~isvector(FoundLabels))
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disp('[eegc3_confusion_matrix] Labels must be vectors');
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ConfMat = [];
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Accuracy = [];
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Error = [];
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return;
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end
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N1 = length(TrueLabels);
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N2 = length(FoundLabels);
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if(N1 ~= N2)
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disp('[confusion] True and predicted labels must be the same size');
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ConfMat = [];
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Accuracy = [];
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Error = [];
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return;
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else
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NSample = N1;
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end
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% Find number of classes
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Classes = sort(unique(TrueLabels));
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NClass = length(Classes);
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if(~isequal(Classes,[1:NClass]) && ~isequal(Classes,[1:NClass]'))
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disp('[confusion] Classes must be a vector [1:N] or [1:N]');
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end
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% Compute Confusion Matrix
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ConfMat = zeros(NClass);
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ConfMatAll = zeros(NClass);
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for i=1:NSample
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ConfMat(TrueLabels(i),FoundLabels(i)) = ...
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ConfMat(TrueLabels(i),FoundLabels(i)) + 1;
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end
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ConfMatAll = 100*ConfMat/sum(ConfMat(:));
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ConfMatClass = zeros(NClass);
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for i=1:NClass
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ConfMatClass(i,:) = 100*ConfMat(i,:)/sum(ConfMat(i,:));
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end
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Accuracy = trace(ConfMatAll);
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Error = 100 - Accuracy;
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