41 lines
1.2 KiB
Python
41 lines
1.2 KiB
Python
# import package
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn import linear_model, datasets
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%matplotlib inline
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# load data : we only use target==0 and target==1 (2 types classify) and feature 0 and feature 2 ()
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iris = datasets.load_iris()
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X = iris.data[iris.target!=2][:, [0,2]]
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Y = iris.target[iris.target!=2]
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h = .02 # step size in the mesh
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logreg = linear_model.LogisticRegression(C=1e5)
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logreg.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() - .5, X[:, 0].max() + .5
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y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
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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 = logreg.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(1, figsize=(4, 3))
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plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)
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plt.xlabel('Sepal length')
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plt.ylabel('Sepal width')
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# Plot also the training points
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plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors='k', cmap=plt.cm.Paired)
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plt.xlabel('Sepal length')
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plt.ylabel('Sepal width')
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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.xticks(())
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plt.yticks(())
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