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import numpy as np |
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import matplotlib.pyplot as plt |
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############################### |
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#Datos originales |
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############################### |
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X = 2 * np.random.rand(100, 1) |
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y = 4 + 3 * X + np.random.randn(100,1) |
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plt.plot(X,y,".") |
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############################### |
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X_b = np.c_[np.ones((100,1)), X] #Se agrega x0=1 para cada instancia |
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theta_best=np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y) |
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X_new = np.array([[0], [2]]) |
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X_new_b = np.c_[np.ones((2, 1)), X_new] #Se agrega x0=1 para cada instancia |
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y_predict = X_new_b.dot(theta_best) |
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plt.plot(X_new, y_predict, "r-") |
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plt.plot(X, y, "b.") |
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plt.axis([0, 2, 0, 15]) |
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plt.show() |