KNN Python 实现

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code:

#coding:utf-8
import numpy as np
from matplotlib import pyplot as plt
import random


def KNN(X_train,y_train,X_test,k):
    X_train=np.array(X_train)
    y_train = np.array(y_train)
    X_test = np.array(X_test)

    X_train_len=len(X_train)
    X_test_len=len(X_test)
    pre_lable=[]

    for test_len in range(X_test_len):
        dis=[]

        #caculate the distance
        for train_len in range(X_train_len):
            #print ('train_len',X_train[train_len,:])
            #print ('test_len', X_test[test_len,:])
            temp_dis = 0
            temp_dis+=sum(abs(X_train[train_len,:]-X_test[test_len,:]))

            dis.append(temp_dis**0.5)
            #print (temp_dis)

        dis=np.array(dis)
        sort_id=dis.argsort()
        #print ('sort_id',sort_id) 
        dic={}
        for i in range(k):
            vlable=y_train[sort_id[i]]
            #print ("vlable",vlable)
            dic[vlable]=dic.get(vlable,0)+1
            #print ('dic[vlable]',dic[vlable])
        max1 = 0
        #print ("dic",dic)
        for index, v in dic.items():
            if v > max1:
                max1 = v
                maxIndex = index
            #print ("maxIndex",maxIndex)
        pre_lable.append(maxIndex)
    x_show = X_test[:,0]
    y_show = X_test[:,1]
    color = '#FF0000'
    for i in range(len(pre_lable)):
        if pre_lable[i] == 1:
            color = '#00FF00'
        elif pre_lable[i] == 2:
            color = '#00FFCC'
        elif pre_lable[i] == 3:
            color = '#FF00CC'
        else:
            color = '#6622CC'
        plt.scatter(x_show[i],y_show[i],color=color)

    print (pre_lable)
    plt.ylabel('Y axis')
    plt.xlabel('X axis')
    plt.show()
if __name__=="__main__":

    X_train = [
               [45,  45, 1],
               [40,  50, 1],
               [60,  70, 1],
               [60,  20, 1],
               
               [-45, 45, 1],
               [-50, 50, 1],
               [-70, 20, 1],
               [-30, 70, 1],
               
               [-45, -45, 1],
               [-50, -50, 1],
               [-30, -60, 1],
               
               [50,  -20, 1],
               [10,  -70, 1],
               [30,  -50, 1]
              ]
        
    y_train = [1,1,1,1, 2,2,2,2 , 3,3,3, 4,4,4]
        
    X_test = []
    X_test = [[50,-20,1]]
            
    for i in range(500):
        temp = []
        temp.append(random.randint(-100,100))
        temp.append(random.randint(-100,100))
        temp.append(1)
        X_test.append(temp)
        
    KNN(X_train,y_train,X_test,1)
~                                  

显示结果如下：