51 lines
1.3 KiB
Python
51 lines
1.3 KiB
Python
__author__ = 'm.bashari'
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import numpy as np
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from sklearn import datasets, linear_model
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import matplotlib.pyplot as plt
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def generate_data():
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np.random.seed(0)
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X, y = datasets.make_moons(200, noise=0.20)
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return X, y
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def visualize(X, y, clf):
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# plt.scatter(X[:, 0], X[:, 1], s=40, c=y, cmap=plt.cm.Spectral)
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# plt.show()
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plot_decision_boundary(lambda x: clf.predict(x), X, y)
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plt.title("Logistic Regression")
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def plot_decision_boundary(pred_func, X, y):
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# Set min and max values and give it some padding
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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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h = 0.01
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# Generate a grid of points with distance h between them
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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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# Predict the function value for the whole gid
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Z = pred_func(np.c_[xx.ravel(), yy.ravel()])
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Z = Z.reshape(xx.shape)
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# Plot the contour and training examples
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plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)
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plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral)
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plt.show()
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def classify(X, y):
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clf = linear_model.LogisticRegressionCV()
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clf.fit(X, y)
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return clf
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def main():
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X, y = generate_data()
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# visualize(X, y)
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clf = classify(X, y)
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visualize(X, y, clf)
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if __name__ == "__main__":
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main()
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