python使用梯度下降算法实现一个多线性回归

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python使用梯度下降算法实现一个多线性回归，供大家参考，具体内容如下

图示：

import pandas as pd
import matplotlib.pylab as plt
import numpy as np
# Read data from csv
pga = pd.read_csv("D:\python3\data\Test.csv")
# Normalize the data 归一化值 (x - mean) / (std)
pga.AT = (pga.AT - pga.AT.mean()) / pga.AT.std()
pga.V = (pga.V - pga.V.mean()) / pga.V.std()
pga.AP = (pga.AP - pga.AP.mean()) / pga.AP.std()
pga.RH = (pga.RH - pga.RH.mean()) / pga.RH.std()
pga.PE = (pga.PE - pga.PE.mean()) / pga.PE.std()

def cost(theta0, theta1, theta2, theta3, theta4, x1, x2, x3, x4, y):

Initialize cost

J = 0

The number of observations

m = len(x1)