梯度下降返回NaN值以表示斜率和误差

问题描述

我是机器学习的新手，正在尝试实现梯度下降。我的代码看起来像这样，并已为所有参数生成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(0,1]
        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,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,points)))
    print("Running...")
    [b,m] = gradient_descent_runner(points,initial_b,num_iterations)
    print("After {0} iterations b = {1},m = {2},error = {3}".format(num_iterations,b,compute_error_for_line_given_points(b,points)))

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

附上我的数据集中的一个样本。有人可以帮我解决这个问题吗？谢谢！

解决方法

暂无找到可以解决该程序问题的有效方法，小编努力寻找整理中！

如果你已经找到好的解决方法，欢迎将解决方案带上本链接一起发送给小编。

小编邮箱:dio#foxmail.com (将#修改为@）