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30 lines
799 B
30 lines
799 B
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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m=100
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n_epochs = 50
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t0, t1 = 5, 50 # learning schedule hyperparameters
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def learning_schedule(t):
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return t0 / (t + t1)
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theta = np.random.randn(2,1) # random initialization
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for epoch in range(n_epochs):
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for i in range(m):
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random_index = np.random.randint(m)
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xi = X_b[random_index:random_index+1]
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yi = y[random_index:random_index+1]
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gradients = 2 * xi.T.dot(xi.dot(theta) - yi)
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eta = learning_schedule(epoch * m + i)
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theta = theta - eta * gradients
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print(theta)
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