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%matplotlib inline import numpy as np import matplotlib.pyplot as plt from sklearn import neighbors # Generate sample data amplitude = 10 num_points = 100 X = amplitude * np.random.rand(num_points, 1) - 0.5 * amplitude # Compute target and add noise y = np.sinc(X).ravel() y += 0.2 * (0.5 - np.random.rand(y.size)) # Plot input data plt.figure() plt.scatter(X, y, s=40, c='k', facecolors='none') plt.title('Input data') # Create the 1D grid with 10 times the density of the input data x_values = np.linspace(-0.5*amplitude, 0.5*amplitude, 10*num_points)[:, np.newaxis] # Number of neighbors to consider n_neighbors = 8 # Define and train the regressor knn_regressor = neighbors.KNeighborsRegressor(n_neighbors, weights='distance') y_values = knn_regressor.fit(X, y).predict(x_values) plt.figure() plt.scatter(X, y, s=40, c='k', facecolors='none', label='input data') plt.plot(x_values, y_values, c='k', linestyle='--', label='predicted values') plt.xlim(X.min() - 1, X.max() + 1) plt.ylim(y.min() - 0.2, y.max() + 0.2) plt.axis('tight') plt.legend() plt.title('K Nearest Neighbors Regressor') plt.show() |
