Note
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Sample usage of Nearest Neighbors classification. It will plot the decision boundaries for each class.
print(__doc__) import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap from sklearn import neighbors, datasets n_neighbors = 15 # import some data to play with iris = datasets.load_iris() # we only take the first two features. We could avoid this ugly # slicing by using a two-dim dataset X = iris.data[:, :2] y = iris.target h = .02 # step size in the mesh # Create color maps cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF']) cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF']) for weights in ['uniform', 'distance']: # we create an instance of Neighbours Classifier and fit the data. clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights) clf.fit(X, y) # Plot the decision boundary. For that, we will assign a color to each # point in the mesh [x_min, x_max]x[y_min, y_max]. x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) # Put the result into a color plot Z = Z.reshape(xx.shape) plt.figure() plt.pcolormesh(xx, yy, Z, cmap=cmap_light) # Plot also the training points plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, edgecolor='k', s=20) plt.xlim(xx.min(), xx.max()) plt.ylim(yy.min(), yy.max()) plt.title("3-Class classification (k = %i, weights = '%s')" % (n_neighbors, weights)) plt.show()
Total running time of the script: ( 0 minutes 0.452 seconds)
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http://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html