batch.py

# Batch Gradient Descent
import numpy as np
eta = 0.1 # learning rate
n_iterations = 1000
m = 100

X = 2 * np.random.rand(100, 1)
y = 4 + 3 * X + np.random.randn(100, 1)
X_b = np.c_[np.ones((100, 1)), X]  # add x0 = 1 to each instance
theta = np.random.randn(2,1) #random initilization
for iteration in range(n_iterations):
    gradients = 2/m * X_b.T.dot(X_b.dot(theta)-y)
    theta = theta - eta * gradients
print(theta)