K近邻分类算法实现 in Python

K近邻(KNN):分类算法

* Machine Learning的Python库很多,比如mlpy更多packages),这里实现只是为了掌握方法

* MATLAB 中的调用,见《MATLAB分类器大全(svm,knn,随机森林等)

* KNN算法复杂度高(可用KD树优化,C中可以用libkdtree或者ANN

* k越小越容易过拟合,但是k很大会降分类精度(设想极限情况:k=1和k=N(样本数))



本文不介绍理论了,注释见代码。

KNN.py

from numpy import *
import operator

class KNN:
    def createDataset(self):
        group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])
        labels = [‘A‘,‘A‘,‘B‘,‘B‘]
        return group,labels

    def KnnClassify(self,testX,trainX,labels,K):
        [N,M]=trainX.shape
    
    #calculate the distance between testX and other training samples
        difference = tile(testX,(N,1)) - trainX # tile for array and repeat for matrix in Python, == repmat in Matlab
        difference = difference ** 2 # take pow(difference,2)
        distance = difference.sum(1) # take the sum of difference from all dimensions
        distance = distance ** 0.5
        sortdiffidx = distance.argsort()
    
    # find the k nearest neighbours
        vote = {} #create the dictionary
        for i in range(K):
            ith_label = labels[sortdiffidx[i]];
            vote[ith_label] = vote.get(ith_label,0)+1 #get(ith_label,0) : if dictionary ‘vote‘ exist key ‘ith_label‘, return vote[ith_label]; else return 0
        sortedvote = sorted(vote.iteritems(),key = lambda x:x[1], reverse = True)
        # ‘key = lambda x: x[1]‘ can be substituted by operator.itemgetter(1)
        return sortedvote[0][0]

k = KNN() #create KNN object
group,labels = k.createDataset()
cls = k.KnnClassify([0,0],group,labels,3)
print cls


-------------------
运行:

1. 在Python Shell 中可以运行KNN.py

>>>import os

>>>os.chdir("/Users/mba/Documents/Study/Machine_Learning/Python/KNN")

>>>execfile("KNN.py")

输出B

(B表示类别)


2. 或者terminal中直接运行

$ python KNN.py


3. 也可以不在KNN.py中写输出,而选择在Shell中获得结果,i.e.,

>>>import KNN

>>> KNN.k.KnnClassify([0,0],KNN.group,KNN.labels,3)




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