InitialReal

HW_1_PNS_EE379K-385V_Neural_Eng/confusion.m

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