44 lines
1.2 KiB
Python
44 lines
1.2 KiB
Python
"""
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=================
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Nearest Neighbors
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=================
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Sample usage of Nearest Neighbors classification.
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It will plot the decision boundaries for each class.
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"""
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print __doc__
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import numpy as np
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import pylab as pl
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from sklearn import neighbors, datasets
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# import some data to play with
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iris = datasets.load_iris()
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X = iris.data[:, :2] # we only take the first two features. We could
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# avoid this ugly slicing by using a two-dim dataset
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Y = iris.target
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h = .02 # step size in the mesh
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# we create an instance of Neighbours Classifier and fit the data.
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clf = neighbors.NeighborsClassifier()
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clf.fit(X, Y)
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# Plot the decision boundary. For that, we will asign a color to each
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# point in the mesh [x_min, m_max]x[y_min, y_max].
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x_min, x_max = X[:,0].min()-1, X[:,0].max() + 1
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y_min, y_max = X[:,1].min()-1, X[:,1].max() + 1
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xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
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Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
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# Put the result into a color plot
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Z = Z.reshape(xx.shape)
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pl.set_cmap(pl.cm.Paired)
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pl.pcolormesh(xx, yy, Z)
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# Plot also the training points
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pl.scatter(X[:,0], X[:,1], c=Y)
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pl.title('3-Class classification using Nearest Neighbors')
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pl.axis('tight')
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pl.show()
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