Pythong 梯度下降线性回归计算不好

Question

我在 Coursera 上的 Andrew NG 课程之后开始学习机器学习。我正在尝试实施 gradient descent with linear regression 但我不确定我错过了什么。根据这个

我已尝试实施它，但出了点问题。这是代码。值得指出的是，这是我第一次接触python，没有学习基础。

import numpy as np
import matplotlib.pyplot as plt

plt.ion()

x = [1,2,3,4,5]
y = [1,2,3,4,5]

def Gradient_Descent(x, y, learning_rate, iterations):
  theta_1=np.random.randint(low=2, high=5); 
  theta_0=np.random.randint(low=2, high=5);
  m = x.shape[0]

def mean_error(a, b, factor):
  sum_mean = 0
  for i in range(m):
    sum_mean += (theta_0 + theta_1 * a[i]) - b[i]  # h(x) = (theta0 + theta1 * x) - y 
    if factor:
      sum_mean *= a[i]
  return sum_mean

def perform_cal(theta_0, theta_1, m):
  temp_0 = theta_0 - learning_rate * ((1 / m) * mean_error(x, y, False))
  temp_1 = theta_1 - learning_rate * ((1 / m) * mean_error(x, y, True))
  return temp_0 , temp_1

fig = plt.figure()
ax = fig.add_subplot(111)

for i in range(iterations):
    theta_0, theta_1 = perform_cal(theta_0, theta_1, m)
    ax.clear()
    ax.plot(x, y, linestyle='None', marker='o')
    ax.plot(x, theta_0 + theta_1*x)
    fig.canvas.draw()


x = np.array(x)
y = np.array(y)
Gradient_Descent(x,y, 0.1, 500)

input("Press enter to close program")

我做错了什么？

Answer 1

import numpy as np
import matplotlib.pyplot as plt

plt.ion()

x = [1,2,3,4,5]
y = [1,2,3,4,5]

def Gradient_Descent(x, y, learning_rate, iterations):
  theta_1=0
  theta_0=0
  m = x.shape[0]
  for i in range(iterations):
      theta_0, theta_1 = perform_cal(theta_0, theta_1, m, learning_rate)
      ax.clear()
      ax.plot(x, y, linestyle='None', marker='o')
      ax.plot(x, theta_0 + theta_1*x)
      fig.canvas.draw()

def mean_error(a, b, factor, m, theta_0, theta_1):
  sum_mean = 0
  for i in range(m):
    sum_mean += (theta_0 + theta_1 * a[i]) - b[i]  # h(x) = (theta0 + theta1 * x) - y 
    if factor:
      sum_mean *= a[i]
  print(sum_mean)
  return sum_mean

def perform_cal(theta_0, theta_1, m, learning_rate):
  temp_0 = theta_0 - learning_rate * ((1 / m) * mean_error(x, y, False, m, theta_0, theta_1))
  temp_1 = theta_1 - learning_rate * ((1 / m) * mean_error(x, y, True, m, theta_0, theta_1))
  return temp_0 , temp_1

fig = plt.figure()
ax = fig.add_subplot(111)




x = np.array(x)
y = np.array(y)
Gradient_Descent(x,y, 0.01, 100)

对您的代码进行了一些更改（主要是重新排列几行，并且没有更改您所做的任何事情，因此看起来不会令人困惑），现在可以正常工作了。我建议您先学习该语言的基础知识，因为大多数错误都非常基础，例如参数传递等。但是，值得称赞的是，您正在按照 Andrew Ng 的课程实施自己的尝试。