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KNN=K-Nearest Neighbour
原理:我们取前K个相似的数据(排序过的)中概率最大的种类,作为预测的种类。通常,K不会大于20。
下边是一个简单的实例,具体的含义在注释中:
import numpy as np import operator import os def createDataSet(): group = np.array([[1.0, 1.1],[1.0, 1.0],[0, 0],[0, 0.1]]) labels = [‘A‘, ‘A‘, ‘B‘, ‘B‘] return group, labels def classify(inX, dataSet, labels, k): dataSetSize = dataSet.shape[0]#lines num; samples num diffMat = np.tile(inX, (dataSetSize,1)) - dataSet#dataSize*(1*inX) sqDiffMat = diffMat**2 sqDistances = sqDiffMat.sum(axis=1)#add as the first dim distances = sqDistances**0.5 #return indicies array from min to max #this is an array sortedDistanceIndices = distances.argsort() #classCount={} classCount=dict() #define a dictionary for i in range(k): voteIlabel = labels[sortedDistanceIndices[i]] classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1#get(key,default=none) #return a list like [(‘C‘,4),(‘B‘,3),(‘A‘,2)], not a dict #itemgetter(0) is the 1st element #default: from min to max sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True) return sortedClassCount[0][0]
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原文地址:http://www.cnblogs.com/AmitX-moten/p/4176598.html