scikit-learn中自带的均值方差归一化函数

import numpy as np
from sklearn import datasets
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

iris = datasets.load_iris()
x = iris.data
y = iris.target

x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=666)

standard = StandardScaler()
standard.fit(x_train)
x_train = standard.transform(x_train)

x_test_standard = standard.transform(x_test)

knn = KNeighborsClassifier(n_neighbors=3,n_jobs=-1)

knn.fit(x_train,y_train)


score = knn.score(x_test_standard,y_test)

print(score)