还是不使用sklearn:
class KNN():
def __init__(self,X_train,Y_train,n_neighbors=3,p=2):
self.n=n_neighbors # 邻居个数默认为3
self.p=p #默认为欧式距离
self.X_train=X_train
self.Y_train=Y_train
def prediction(self,X):
knn_list=[]
#先取出训练集中n个点，knn_list存放测试集点与训练集点的距离及其对应标签
for i in range(self.n):
dist=np.linalg.norm(X-self.X_train[i],ord=self.p)
#linalg=linear（线性）+algebra（代数），norm则表示范数。
knn_list.append((dist,self.Y_train[i]))
#再取出训练集剩下的点，然后与n_neighbors个点比较大小，将距离大的点更新
#保证knn_list列表中的点是距离最小的点
for i in range(self.n,len(self.X_train)):
max_index=knn_list.index(max(knn_list,key=lambda x:x[0]))
dist=np.linalg.norm(X-self.X_train[i],ord=self.p)
if knn_list[max_index][0]>dist:
knn_list[max_index]=(dist,self.Y_train[i])
# 统计分类最多的点，确定预测数据的分类
# 应该是多数表决
knn=[k[-1] for k in knn_list ]
count1=Counter(knn)
max_count=sorted(count1.items(),key=lambda x:x[1])[-1][0]
return max_count
def score(self,X_test,Y_test):
right_counts=0
#self.n=10
for X,Y in zip(X_test,Y_test):
y_pre=self.prediction(X)
#返回数据分类，判断正确率
if y_pre==Y:
right_counts+=1
return right_counts/len(X_test)
sklearn还是更简单些：
clf_sk = KNeighborsClassifier() clf_sk.fit(X_train, y_train)
XD
完整代码：
from sklearn import datasets
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from collections import Counter
from sklearn.neighbors import KNeighborsClassifier
iris = datasets.load_iris()
X_train,X_test,Y_train,Y_test = train_test_split(iris.data,iris.target,test_size=0.2,shuffle=True)
#print(X_train[:50][:,0])
"""plt.scatter(X_train[:50][:,0],X_train[:50][:,1],label=0)
plt.scatter(X_train[50:100][:,0],X_train[50:100][:,1],label=1)
plt.xlabel('sepal length')
plt.ylabel('sepal width')
plt.legend()
plt.show()
"""
X_train = X_train[:][:,0:2]
Y_train = Y_train[:]
X_test = X_test[:20][:,0:2]
Y_test = Y_test[:20]
#print(np.shape(X_train))
#print(np.shape(Y_train))
class KNN():
def __init__(self,X_train,Y_train,n_neighbors=3,p=2):
self.n=n_neighbors # 邻居个数默认为3
self.p=p #默认为欧式距离
self.X_train=X_train
self.Y_train=Y_train
def prediction(self,X):
knn_list=[]
#先取出n个点，knn_list存放预测点与训练集点的距离及其对应标签
for i in range(self.n):
dist=np.linalg.norm(X-self.X_train[i],ord=self.p)
#linalg=linear（线性）+algebra（代数），norm则表示范数。
knn_list.append((dist,self.Y_train[i]))
#再取出训练集剩下的点，然后与n_neighbors个点比较大小，将距离大的点更新
#保证knn_list列表中的点是距离最小的点
for i in range(self.n,len(self.X_train)):
max_index=knn_list.index(max(knn_list,key=lambda x:x[0]))
dist=np.linalg.norm(X-self.X_train[i],ord=self.p)
if knn_list[max_index][0]>dist:
knn_list[max_index]=(dist,self.Y_train[i])
# 统计分类最多的点，确定预测数据的分类
knn=[k[-1] for k in knn_list ]
count1=Counter(knn)
max_count=sorted(count1.items(),key=lambda x:x[1])[-1][0]
return max_count
def score(self,X_test,Y_test):
right_counts=0
#self.n=10
for X,Y in zip(X_test,Y_test):
y_pre=self.prediction(X)
if y_pre==Y:
right_counts+=1
return right_counts/len(X_test)
Model = KNN(X_train,Y_train)
print(Model.score(X_test,Y_test))
#——————————————————————————————————————————————————
clf_sk = KNeighborsClassifier()
clf_sk.fit(X_train,Y_train)
print(clf_sk.score(X_test,Y_test))
代码参考来自：https://github.com/fengdu78/lihang-code
