Descenso por gradiente estocastico

stochasticgradientdescent.py

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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)