Gradient Descent

art2gd.py

#gradient descent
def gradientDescent(X,y,theta,iters,alpha):
    cost = np.zeros(iters)
    for i in range(iters):
        theta = theta - (alpha/len(X)) * np.sum(X * (X @ theta.T - y), axis=0)
        cost[i] = computeCost(X, y, theta)
    
    return theta,cost

#running the gd and cost function
g,cost = gradientDescent(X,y,theta,iters,alpha)
print(g)

finalCost = computeCost(X,y,g)
print(finalCost)