import numpy as np
from sklearn.preprocessing import PolynomialFeatures  #这哥用于生成多项式
x=np.arange(6).reshape(3,2) #生成三行二列数组
reg = PolynomialFeatures(degree=3) #这个3看下面的解释
reg.fit_transform(x)

def multi_feature(x,n):
  c = np.empty((x.shape[0],0)) #np.empty((3,1))并不会生成一个3行1列的空数组,np.empty((3,0))才会生成3行1列空数组
  for i in range(n+1):
    for m in range(i,-1,-1):
      h=(x[:,0]**m) * (x[:,1]**(i-m))
      c=np.c_[c,h]
  return c

multi_feature(x,3)

print('n=4时，sklearn的输出是：')
reg = PolynomialFeatures(degree=4) 
print(reg.fit_transform(x))
print('\n')

#对比
print('n=4时，函数的输出是：')
print(multi_feature(x,4))