数据归一化详解编程语言

import numpy as np 
from sklearn import datasets 
from sklearn.model_selection import train_test_split 
from sklearn.preprocessing import StandardScaler 
 
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) 
 
standardScaler = StandardScaler() 
standardScaler.fit(X_train) 
 
standardScaler.mean_ #均值 
standardScaler.scale_ #方差 
 
X_train = standardScaler.transform(X_train) #归一化处理 
X_test_standerd = standardScaler.transform(X_test) #测试数据集归一化 
 
 
from sklearn.neighbors import KNeighborsClassifier 
KNN_classifier = KNeighborsClassifier(n_neighbors=3) 
KNN_classifier.fit(X_train,y_train) 
KNN_classifier.score(X_test_standerd,y_test)

class StandardScaler(object): 
 
    def __init__(self): 
        self.mean_ = None 
        self.scale_ = None 
     
    def fit(self,X): 
        self.mean_ = np.array([np.mean(X[:,i]) for i in range(X.shape[1])]) 
        self.scale_ = np.array([np.std(X[:,i]) for i in range(X.shape[1])]) 
 
        return self 
 
    def transform(self,X): 
        resX = np.empty(shape=X.shape,dtype=float) 
        for col in range(X.shape[1]): 
            resX[:,col] = (X[:,col] - self.mean_[col]) / self.scale_[col] 
        return resX