Implementing Stochastic Gradient Descent Python

Question

I've been trying to implement stochastic gradient descent as part of a recommendation system following these equations:

I have:

for step in range(max_iter):
        e = 0
        for x in range(len(R)):
            for i in range(len(R[x])):
                if R[x][i] > 0:
                    exi = 2 * (R[x][i] - np.dot(Q[:,i], P[x,:]))
                    qi, px = Q[:,i], P[x,:]

                    qi += _mu_2 * (exi * px - (2 * _lambda_1 * qi))
                    px += _mu_1 * (exi * qi - (2 * _lambda_2 * px))

                    Q[:,i], P[x,:] = qi, px

The output I expect isn't quite right but I can't really put a finger on it. Please help me to identify the problem I have in my code.

Much appreciate your support

Answer 1

更新qi和px时 ，应交换mu1和mu2 。