knn.rar_K._k-d_k-d树

%------------------------------------------------------------- % K-Nearest Neighbours demo % Original Author: David Ross % Modified by: Debajyoti Ray %------------------------------------------------------------- %% Parameters for training and testing data X_MAX = 50; Y_MAX = 50; NUM_TRAIN = 130; RANDOM_LABELS = 0; %------------------------------------------------------------- % Generate the test points, on a regular grid. %------------------------------------------------------------- [test_x, test_y] = meshgrid(1:X_MAX, 1:Y_MAX); data_test = [test_x(:) test_y(:)]'; scatter(data_test(1,:), data_test(2,:), 'k.'); %------------------------------------------------------------- % Generate random training points. %------------------------------------------------------------- data_train = zeros(2,NUM_TRAIN); data_train(1,:) = rand(1,NUM_TRAIN)*X_MAX; data_train(2,:) = rand(1,NUM_TRAIN)*Y_MAX; if RANDOM_LABELS label_train = (rand(1,NUM_TRAIN) >= 0.5); else % label the training points according to the following quadratic function label_train = data_train(2,:) >= ((data_train(1,:) - X_MAX/2).^2 /4 + 3); % add label noise label_train = xor(label_train, (rand(size(label_train)) >= 0.8)); end plot_train(data_train, label_train); %------------------------------------------------------------- % Run kNN for various values of k, plotting the results %------------------------------------------------------------- set(gcf,'doublebuffer', 'on'); for k = [1 3 5 7 9] label_test = knn(k, data_train, label_train, data_test); disp('press any key to continue'); pause; clf; title(sprintf('%d Nearest Neighbours', k)); plot_test(data_test, label_test); plot_train(data_train, label_train); hold on; contour(reshape(label_test,[X_MAX Y_MAX])); hold off; end