54 lines
No EOL
2.3 KiB
Matlab
54 lines
No EOL
2.3 KiB
Matlab
% implements KNN, returns the test error for the k-nearest neighbors
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% algorithms when using a specified number of neighbors (k) for
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% classification using a majority rules with tie-breaking.
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function [test_err] = KNN(k, training_data, test_data, training_labels, test_labels)
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n = length(test_data(:,1)); % get number of rows in test data
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preds = zeros(length(test_labels),1); % predict labels for each test point
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% compute pairwise euclidean distance between the test data and the training data
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pairwise_distance = pdist2(training_data, test_data);
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unique_classes = unique(training_labels);
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% for each data point (row) in the test data
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for t = 1:n
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% TODO: compute k-nearest neighbors for data point
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distances = pairwise_distance(:,t);
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[~, smallest_indexes] = sort(distances, 'ascend');
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smallest_k_indexes = smallest_indexes(1:k);
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distances_by_class = zeros(max(unique_classes), 2);
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for i = 1:length(unique_classes)
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class = unique_classes(i);
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this_class_distances = distances(training_labels == class,:);
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distances_by_class(i,1) = class;
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distances_by_class(i,2) = mean(this_class_distances);
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end
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distances_by_class_table = array2table(distances_by_class);
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% TODO: classify test point using majority rule. Include tie-breaking
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% using whichever class is closer by distance. Fill in preds with the
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% predicted label.
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smallest_k_labels = training_labels(smallest_k_indexes);
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labels_by_count = tabulate(smallest_k_labels);
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labels_by_count_sorted = sortrows(labels_by_count, 2);
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most_frequent_label = labels_by_count_sorted(1,:);
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most_frequent_label_count = most_frequent_label(2);
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labels_that_have_most_frequent_count = labels_by_count_sorted(labels_by_count_sorted(:,2) == most_frequent_label_count,1);
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if length(labels_that_have_most_frequent_count) > 1
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common_indexes = find(ismember(distances_by_class, labels_that_have_most_frequent_count));
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common_distances = distances_by_class(common_indexes,:);
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sorted_distances = sortrows(common_distances,2);
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preds(t) = sorted_distances(1,1);
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else
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winning_label = mode(smallest_k_labels);
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preds(t) = winning_label;
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end
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end
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test_err = sum(preds ~= test_labels)/n; % error rate
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end % Function end |