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- import numpy as np
- import matplotlib.pyplot as plt
- ###############################
- #Datos originales
- ###############################
- X = 2 * np.random.rand(100, 1)
- y = 4 + 3 * X + np.random.randn(100,1)
-
- plt.plot(X,y,".")
- ###############################
- X_b = np.c_[np.ones((100,1)), X] #Se agrega x0=1 para cada instancia
- m=100
- n_epochs = 50
- t0, t1 = 5, 50 # learning schedule hyperparameters
-
- def learning_schedule(t):
- return t0 / (t + t1)
-
- theta = np.random.randn(2,1) # random initialization
-
- for epoch in range(n_epochs):
- for i in range(m):
- random_index = np.random.randint(m)
- xi = X_b[random_index:random_index+1]
- yi = y[random_index:random_index+1]
- gradients = 2 * xi.T.dot(xi.dot(theta) - yi)
- eta = learning_schedule(epoch * m + i)
- theta = theta - eta * gradients
-
- print(theta)
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