Python3 k-邻近算法（KNN）

 # -*- coding: utf-8 -*-
 """
 Created on Fri Dec 29 13:13:44 2017

 @author: markli
 """
 import numpy as np;
 #两点之间的距离采用欧式几何距离
 '''
 采用欧式距离进行K最小临近分类
 x 未知分类点 m*1 向量
 y n个测试样本点 m*n 维向量
 '''
 def ComputeDistance(x,y):
     m = len(x); #获取维度数量
     #print(m);
     tempeye = -np.eye(m);
     tempone = np.ones((1,m));
     C = np.vstack((tempone,tempeye));#中间过渡矩阵 m+1 * m 按列合并，列数不变扩张行
     translateMatrix = np.hstack((x,y)); #按行合并，行数不变，扩张列
     tempresult = np.dot(translateMatrix,C);
     result = np.multiply(tempresult,tempresult);
     #result = [d**2 for d in np.array(tempresult)];
     result = np.sum(result,axis=0)
     distance = [pow(d,1/m) for d in np.array(result)];
     return distance;

 '''
 k 选取点的个数
 distance 带预测点与每个样本点的距离
 labels 每个样本点的类别标记
 return 返回距离最近的k的样本点的类别标记
 '''
 def KNN(k,distance,labels):
     dis_label = [];
     for i in range(len(labels)):
         tup = (distance[i],labels[i]);
         dis_label.append(tup);
     dis_label = sorted(dis_label,lambda x:x[0]);
     Kmin = [];
     for i in range(k-1):
         label = dis_label[i][1];
         if label not in Kmin:
             Kmin.append(label);
     return Kmin;

 #sklearn 中的KNN
 # -*- coding: utf-8 -*-
 """
 Created on Sat Dec 30 09:36:18 2017

 @author: markli
 """
 from sklearn import neighbors;
 from sklearn import datasets;
 import numpy as np;
 import matplotlib.pyplot as plt;

 KNN = neighbors.KNeighborsClassifier(n_neighbors=5,weights='distance');
 iris = datasets.load_iris();

 #print(iris);

 KNN.fit(iris.data,iris.target);
 x = [0.2,0.4,0.3,0.5];
 y = KNN.predict(np.array(x).reshape((1,4)));
 print(iris.target_names[y]);

 #k = neighbors.NearestNeighbors();
 #A = k.kneighbors_graph(iris.data,n_neighbors=5,mode='distance');