def step_gradient(c_current, m_current, points, learning_rate):
    # Gradient descent
    c_gradient = 0
    m_gradient = 0
    N = float(len(points))
    # Iterate
    for i in range(0, len(points)):
        x = points[i, 0]
        y = points[i, 1]
        c_gradient += -(2 / N) * (y - ((m_current * x) + c_current))
        m_gradient += -(2 / N) * x * (y - ((m_current * x) + c_current))
    # Update m and c
    new_c = c_current - (learning_rate * c_gradient)
    new_m = m_current - (learning_rate * m_gradient)
    return [new_c, new_m]