55 lines
1.5 KiB
Matlab
55 lines
1.5 KiB
Matlab
function [ accuracies, F1s, corrs, ccc, rms, classes ] = evaluate_au_prediction_results( labels, ground_truth )
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%EVALUATE_CLASSIFICATION_RESULTS Summary of this function goes here
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% Detailed explanation goes here
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classes = sort(unique(ground_truth));
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accuracies = zeros(numel(classes),1);
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F1s = zeros(numel(classes),1);
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corrs = corr(labels, ground_truth);
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rms = sqrt(mean((labels-ground_truth).^2));
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std_g = std(ground_truth);
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std_p = std(labels);
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ccc = 2 * corrs * std_g * std_p / (std_g^2 + std_p^2 + (mean(labels) - mean(ground_truth))^2);
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% the label is taken to belong to a class it is closest to
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label_dists = zeros(numel(labels), numel(classes));
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for i=1:numel(classes)
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label_dists(:,i) = abs(labels - classes(i));
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end
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[~, labels] = min(label_dists');
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labels = labels';
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for i=1:numel(classes)
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labels(labels==i) = classes(i);
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end
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for i=1:numel(classes)
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pos_samples = ground_truth == classes(i);
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neg_samples = ground_truth ~= classes(i);
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pos_labels = labels == classes(i);
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neg_labels = labels ~= classes(i);
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TPs = sum(pos_samples & pos_labels);
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TNs = sum(neg_samples & neg_labels);
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FPs = sum(pos_labels & neg_samples);
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FNs = sum(neg_labels & pos_samples);
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accuracies(i) = (TPs + TNs) / numel(pos_samples);
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F1s(i) = 2 * TPs / (2*TPs + FNs + FPs);
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end
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end
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