36 lines
No EOL
1.2 KiB
Matlab
36 lines
No EOL
1.2 KiB
Matlab
% implements KNN_Error, returns nothing but prints out a table of test
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% errors for k-nearest neighbors using different values of k.
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function [] = KNN_Error(neigenvectors, ks, training_data, test_data, training_labels, test_labels)
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% perform PCA
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data = vertcat(training_data, test_data);
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coeff = pca(data);
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% project data using the number of eigenvectors defined by neigenvectors
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eigenvectors = coeff(:,1:neigenvectors);
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projected_data = data * eigenvectors;
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% split matrix back out
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training_rows = size(training_data, 1);
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projected_training_data = projected_data(1:training_rows,:);
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projected_test_data = projected_data(training_rows+1:end,:);
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% compute test error for kNN with differents k's. Fill in
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% test_errors with the results for each k in ks.
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test_errors = zeros(1,length(ks));
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for i = 1:length(ks)
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k = ks(i);
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test_errors(i) = KNN(k, projected_training_data, projected_test_data, training_labels, test_labels);
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end
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% print error table
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fprintf("-----------------------------\n");
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fprintf("| k | test error\n")
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fprintf("-----------------------------\n");
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for i = 1:length(ks)
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fprintf("| %d | %.8f \n", ks(i), test_errors(i));
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end
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fprintf("-----------------------------\n");
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end % Function end |