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matlab
2 years ago
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%Fai preprocessing ecc... % Estrai le features usando AlexNext features = activations(alexnet, images, 'fc7', 'OutputAs', 'rows'); % Splitti sempre i dati in training e test sets [train_ind, test_ind] = holdout(numel(images), 0.7); train_features = features(train_ind, :); train_labels = labels(train_ind); test_features = features(test_ind, :); test_labels = labels(test_ind); % Alleni il classificatore classifier = fitcecoc(train_features, train_labels); % Classifichi il test set predictions = predict(classifier, test_features); % Calcolo confusion Matrix confusion = confusionmat(test_labels, predictions); % Visualizzi la matrice di confusione plotconfusion(test_labels, predictions); % Calcolo le performance metrics per ogni classificatore report = classification_report(test_labels, predictions);
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