Sample usage of Nearest Centroid classification. It will plot the decision boundaries for each class.
Out:
1 2 | None 0.8133333333330.2 0.82 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | print(__doc__)import numpy as npimport matplotlib.pyplot as pltfrom matplotlib.colors import ListedColormapfrom sklearn import datasetsfrom sklearn.neighbors import NearestCentroidn_neighbors = 15# import some data to play withiris = datasets.load_iris()X = iris.data[:, :2] # we only take the first two features. We could # avoid this ugly slicing by using a two-dim datasety = iris.targeth = .02 # step size in the mesh# Create color mapscmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])for shrinkage in [None, .2]: # we create an instance of Neighbours Classifier and fit the data. clf = NearestCentroid(shrink_threshold=shrinkage) clf.fit(X, y) y_pred = clf.predict(X) print(shrinkage, np.mean(y == y_pred)) # 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) plt.title("3-Class classification (shrink_threshold=%r)" % shrinkage) plt.axis('tight')plt.show() |
Total running time of the script: (0 minutes 0.177 seconds)
Download Python source code:
plot_nearest_centroid.py
Download IPython notebook:
plot_nearest_centroid.ipynb
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