多项式

import  numpy as np
import  matplotlib.pyplot as  plt

x = np.random.uniform(-3,3, size=100)
X  = x.reshape(-1,1)
y =0.5 * x**2 + x + np.random.normal(0,1,size=100)

#plt.scatter(x,y)
#plt.show()

#from sklearn.linear_model import LinearRegression
#lin_reg  = LinearRegression()
#lin_reg.fit(X,y)
#y_predict  = lin_reg.predict(X)

#plt.scatter(x,y)
#plt.plot(x,y_predict,color='r')
#plt.show()

##d多项式

#X2 = np.hstack([X,X**2])


#lin_reg2  = LinearRegression()
#lin_reg2.fit(X2,y)
#y_predict2  = lin_reg2.predict(X2)


#plt.scatter(x,y)
#plt.plot(np.sort(x),y_predict2[np.argsort(x)],color='r')
#plt.show()

#print(lin_reg2.coef_)
#print(lin_reg2.intercept_)

from sklearn.preprocessing import  PolynomialFeatures

poly = PolynomialFeatures(degree=2)
poly.fix(X)