weight_update_MAE.py

def update_weights_MAE(m, b, X, Y, learning_rate):
    m_deriv = 0
    b_deriv = 0
    N = len(X)
    for i in range(N):
        # Calculate partial derivatives
        # -x(y - (mx + b)) / |mx + b|
        m_deriv += - X[i] * (Y[i] - (m*X[i] + b)) / abs(Y[i] - (m*X[i] + b))

        # -(y - (mx + b)) / |mx + b|
        b_deriv += -(Y[i] - (m*X[i] + b)) / abs(Y[i] - (m*X[i] + b))

    # We subtract because the derivatives point in direction of steepest ascent
    m -= (m_deriv / float(N)) * learning_rate
    b -= (b_deriv / float(N)) * learning_rate

    return m, b