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38 lines
735 B
Python
38 lines
735 B
Python
import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.linear_model import LinearRegression
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def plot_linear_regression():
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a = 0.5
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b = 1.0
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# x from 0 to 10
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x = 30 * np.random.random(20)
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# y = a*x + b with noise
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y = a * x + b + np.random.normal(size=x.shape)
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# create a linear regression classifier
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clf = LinearRegression()
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clf.fit(x[:, None], y)
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# predict y from the data
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x_new = np.linspace(0, 30, 100)
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y_new = clf.predict(x_new[:, None])
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# plot the results
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ax = plt.axes()
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ax.scatter(x, y)
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ax.plot(x_new, y_new)
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ax.set_xlabel('x')
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ax.set_ylabel('y')
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ax.axis('tight')
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if __name__ == '__main__':
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plot_linear_regression()
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plt.show()
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