本文通過海倫約會的例子來測試之前寫的KDTree的效果，並且探討了特徵是否進行歸一化對整個模型的表現的影響。
最後發現在機器學習中，特徵歸一化確實對模型能提供非常大的幫助。
 1 from KDTree import  KDTree # 參考實現KDtree的隨筆
 2 from sklearn import model_selection,preprocessing
 3 import  pandas as pd
 4 class KNN(object):
 5     def __init__(self,K=1,p=2):
 6         self.kdtree= KDTree()
 7
 
         self.K =K
 8         self.p=p
 9     def fit(self,x_data,y_data):
10         self.kdtree.build_tree(x_data,y_data)
11     def predict(self,pre_x,label):
12         if 'class' in label:
13             return self.kdtree.predict_classification(pre_x,K=self.K)
14         else :
15             return
 
 self.kdtree.predict_regression(pre_x,K=self.K)
16     def test_check(self,test_xx,test_y):
17         # only support classification problem
18         correct =0
19         for i,xi in enumerate(test_xx):
20             pre_y = self.kdtree.predict_classification(Xi=xi,K=self.K)
21             if pre_y == test_y[i]:
 
22                 correct+=1
23         return correct/len(test_y)
24 
25 
26 file_path = "datingTestSet.txt"
27 data = pd.read_csv(file_path, sep="\t",header=None)
28 XX = data.iloc[:,:-1].values
29 Y = data.iloc[:,-1].values
30 train_xx , test_xx, train_y,test_y = model_selection.train_test_split(XX,Y,test_size= 0.2,random_state=123,shuffle=True)
31 knn=KNN(K=5,p=2)
32 knn.fit(train_xx,train_y)
33 acc = knn.test_check(test_xx,test_y)
34 print("No Standard Scale Accuracy: ",acc)
35 # 考慮到資料中不同維度之間的數值相差過大，進行特徵縮放
36 scaler = preprocessing.StandardScaler()
37 # 計算均值和標準差只能用訓練集的資料
38 scaler.fit(train_xx)
39 stand_train_xx = scaler.transform(train_xx)
40 stand_test_xx = scaler.transform(test_xx)
41 new_knn = KNN(K=5,p=2)
42 new_knn.fit(stand_train_xx,train_y)
43 new_acc = new_knn.test_check(stand_test_xx,test_y)
44 print("Standard Scale Accuracy: ",new_acc)