KNN学习笔记

不说太多，上代码

from numpy import *
import operator

def createDataSet():
   group = array([[1.0,1.1],[1.0,1.0],[0.0],[0,0.1]])#这个是训练集
   labels = ['A','A','B','B']
   return group,labels

def classify0(inX,dataSet,labels,k):
	dataSetSize = dataSet.shape[0]
	diffMat = tile(inX, (dataSetSize,1)) - dataSet
	sqDiffMat = diffMat**2
	sqDistances =sqDiffMat.sum(axis=1)
	distances = sqDistances**0.5
	sortedDistIndices = distances.argsort()
	classCount={}
	for i in range(k):
		voteIlabel = labels[sortedDistIndices[i]]
		classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1
	sortedClassCount = sorted(classCount.iteritems(),key =operator.itemgetter(1),reverse= True)
	return sortedClassCount[0][0]


def file2matrix(filename):
	fr = open(filename)
	arrayOLines = fr.readlines()
	numberOfLines = len(arrayOLines)
	returnMat = zeros((numberOfLines,3))
	classLabelVector = []
	index = 0
	for line in arrayOLines:
		line = line.strip()
		listFromLine = line.split('\t')
		returnMat[index,:] = listFromLine[0:3]
		classLabelVector.append(int(listFromLine[-1]))
		index +=1
	return returnMat,classLabelVector
	
def autoNorm(dataSet):
    minVals = dataSet.min(0)
    maxVals = dataSet.max(0)
    ranges = maxVals - minVals
    normDataSet = zeros(shape(dataSet))
    m = dataSet.shape[0]
        normDataSet = dataSet - tile(minVals,(m,1))
    normDataSet = normDataSet/tile(ranges, (m,1))
    return normDataSet, ranges , minVals

def datingClassTest():
    hoRatio = 0.10
        datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')
    normMat, ranges,minVals = autoNorm(datingDataMat)
    m = normMat.shape[0]
    numTestVecs = int(m*hoRatio)
    errorCount = 0.0
    for i in range(numTestVecs):
	classifierResult = classify0(normMat[i,:],normMat[numTestVecs:m,:],datingLabels[numTestVecs:m],3)
	print "the classifier came back weith :%d, the real answer is :%d"%(classifierResult,datingLabels[i])
	if (classifierResult != datingLabels[i]):
		errorCount += 1.0
		
    print  "the total error rate is :%f"% (errorCount/float(numTestVecs))