機械学習—python+sklearn実現KNN&KDツリーアルゴリズム

from sklearn import datasets#          
from sklearn.neighbors import KNeighborsClassifier#   sklearn.neighbors   KNN 
import numpy as np
from sklearn.neighbors import KDTree#  KD  

np.random.seed(0)#       ，                 ，                               
iris = datasets.load_iris()#          ，iris            ，       ，             
iris_x = iris.data#     150*4    ，  150   ，    4             、 
iris_y = iris.target#  150     ，       
indices = np.random.permutation(len(iris_x)) # permutation         (150),    0-149    ，         ，                  ，            
iris_x_train = iris_x[indices[:-10]]#     140          
iris_y_train = iris_y[indices[:-10]]#      140                
iris_x_test = iris_x[indices[-10:]]#    10          
iris_y_test = iris_y[indices[-10:]]#      10                 

knn = KNeighborsClassifier()#     knn     
knn.fit(iris_x_train, iris_y_train)#           ，        ：           
iris_y_predict = knn.predict(iris_x_test)#           ，        ：     
probility = knn.predict_proba(iris_x_test)#               
neighborpoint = knn.kneighbors(iris_x_test[0].reshape(1,-1),n_neighbors=1,return_distance=True)#                  5  ，                
score = knn.score(iris_x_test, iris_y_test, sample_weight=None)#           ，      

tree = KDTree(iris_x_train)
# ind：   3      
# dist：     3   
X = iris_x_train[0].reshape(1,-1)
dist, ind = tree.query(X, k=3)

print ('ind:',ind)
print ('dist:',dist)



# np.random.seed(0)
# X = np.array([(2, 3), (5, 4), (9, 6), (4, 7), (8, 1), (7, 2)])
# tree = KDTree(X, leaf_size=2)