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https://github.com/gsi-upm/sitc
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51 lines
1.9 KiB
Python
51 lines
1.9 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib.colors import ListedColormap
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from sklearn import neighbors, datasets
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from sklearn.neighbors import KNeighborsClassifier
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# Taken from http://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html
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def plot_classification_iris():
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"""
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Plot knn classification of the iris dataset
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"""
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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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n_neighbors = 15
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# Create color maps
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cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
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cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])
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for weights in ['uniform', 'distance']:
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# we create an instance of Neighbours Classifier and fit the data.
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clf = KNeighborsClassifier(n_neighbors, weights=weights)
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clf.fit(X, y)
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# Plot the decision boundary. For that, we will assign 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),
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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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plt.figure()
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plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
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# Plot also the training points
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plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
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plt.xlim(xx.min(), xx.max())
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plt.ylim(yy.min(), yy.max())
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plt.title("3-Class classification (k = %i, weights = '%s')"
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% (n_neighbors, weights))
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plt.show() |