渐变下降返回斜率和误差的NaN值

Question

我是机器学习新手，正在尝试实现梯度下降。我编写的代码如下所示，并为所有参数生成了NaN值：

def compute_error_for_line_given_points(b,m,points):
    totalError = 0 #sum of square error formula
    for i in range (0, len(points)):
        x = points[i, 0]
        y = points[i, 1]
        totalError += (y-(m*x + b)) ** 2
    return totalError/ float(len(points))

def step_gradient(b_current, m_current, points, learning_rate):
    #gradient descent
    b_gradient = 0
    m_gradient = 0
    N = float(len(points))
    for i in range(0, len(points)):
        x = points[i, 0]
        y = points[i, 1]
        b_gradient += -(2/N) * (y - (m_current * x + b_current))
        m_gradient += -(2/N) * x * (y - (m_current * x + b_current))
    new_b = b_current - (learning_rate * b_gradient)
    new_m = m_current - (learning_rate * m_gradient) 
    return [new_b,new_m]

def gradient_descent_runner(points, starting_b, starting_m, learning_rate, num_iterations):
    b = starting_b
    m = starting_m
    for i in range(num_iterations):
        b,m = step_gradient(b, m, array(points), learning_rate)
    return [b,m]

def run():
    #Step 1: Collect the data
    points = genfromtxt("C:/Users/mishruti/Downloads/For Linear Regression.csv", delimiter = ",")
    #Step 2: Define our Hyperparameters
    learning_rate = 0.0000001 #how fast the data converge
    #y=mx+b (Slope formule)
    initial_b = 0 # initial y-intercept guess
    initial_m = 0 # initial slope guess
    num_iterations = 4
    print("Starting gradient descent at b = {0}, m = {1}, error = {2}".format(initial_b, initial_m, compute_error_for_line_given_points(initial_b, initial_m, points)))
    print("Running...")
    [b, m] = gradient_descent_runner(points, initial_b, initial_m, learning_rate, num_iterations)
    print("After {0} iterations b = {1}, m = {2}, error = {3}".format(num_iterations, b, m, compute_error_for_line_given_points(b, m, points)))

# main function
if __name__ == "__main__":
    run()

附上我的数据集样本。有人能帮我弄清楚吗？谢谢