Python sklearn implements linear regression and nonlinear regression

import pandas as pd
import numpy as np
import matplotlib
import random
from matplotlib import pyplot as plt
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression

x = np.array(range(30))
temp_y = 10 + 2*x + x**2 + x**3
y = temp_y + 1500*np.random.normal(size=30)  #添加噪声

x = x.reshape(30,1)
y = y.reshape(30,1)

#线性回归
clf1 = LinearRegression()  
clf1.fit(x,y)
y_l = clf1.predict(x)  #线性回归预测值

#非线性回归
ployfeat = PolynomialFeatures(degree=3)  #根据degree的值转换为相应的多项式（非线性回归）
x_p = ployfeat.fit_transform(x)
clf2 = LinearRegression()
clf2.fit(x_p,y)

 

font={"family":"FangSong",'size':12}
matplotlib.rc("font",**font)
plt.figure(figsize = (12,6))
plt.plot(x,y_l,label = "线性回归")
plt.scatter(x,y,label="real value")
plt.plot(x,np.matmul(x_p,clf.coef_.reshape(4,1)) + clf.intercept_,label="非线性回归")
plt.legend()
plt.show()

print("线性回归方程为: y = {} + {}x".format(clf1.intercept_[0],clf1.coef_[0,0]))
print("非线性回归曲线方程为 y = {}+{}x+{}x^2+{}x^3".format(clf2.intercept_[0],clf2.coef_[0,1],clf2.coef_[0,2],clf2.coef_[0,3]))

The linear regression equation is: y = -4556.410727239843 + 790.8913721234021x
The nonlinear regression curve equation is y = 466.91615911474946+-105.82078955667033x+12.7617011678153x^2+0.6880360959150948x^3