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20 lines
561 B
20 lines
561 B
import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.preprocessing import PolynomialFeatures
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from sklearn.linear_model import LinearRegression
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###############################
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#Datos originales
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###############################
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m = 100
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X = 6 * np.random.rand(m, 1) - 3
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y = 0.5 * X**2 + X + 2 + np.random.randn(m, 1)
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plt.plot(X,y,".")
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poly_features = PolynomialFeatures(degree=2, include_bias=False)
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X_poly = poly_features.fit_transform(X)
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lin_reg = LinearRegression()
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lin_reg.fit(X_poly, y)
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yout=lin_reg.predict(X_poly)
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plt.plot(X,yout,"*")
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plt.show()
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