weight_update_Hinge.py

def update_weights_Hinge(m1, m2, b, X1, X2, Y, learning_rate):
    m1_deriv = 0
    m2_deriv = 0
    b_deriv = 0
    N = len(X1)
    for i in range(N):
        # Calculate partial derivatives
        if Y[i]*(m1*X1[i] + m2*X2[i] + b) <= 1:
          m1_deriv += -X1[i] * Y[i]
          m2_deriv += -X2[i] * Y[i]
          b_deriv += -Y[i]
        # else derivatives are zero

    # We subtract because the derivatives point in direction of steepest ascent
    m1 -= (m1_deriv / float(N)) * learning_rate
    m2 -= (m2_deriv / float(N)) * learning_rate
    b -= (b_deriv / float(N)) * learning_rate

    return m1, m2, b