k-Nearest Neighbors(KNN)算法—程序和總結篇

下面是python3.4代碼，我修改過。是根據《machine learning in action》中第二章的算法改變的。

from numpy import *
import operator
from os import listdir

def file2matrix(filename):
	fr = open(filename)
	numberOfLines = len(fr.readlines())
	returnMat = zeros((numberOfLines, 3))
	classLabelVector = []
	fr = open(filename)
	index = 0
	for line in fr.readlines():
		line = line.strip()
		listFromLine = line.split('\t')
		returnMat[index,:] = listFromLine[0:3]
		classLabelVector.append(str(listFromLine[3]))
		index += 1
	return returnMat, classLabelVector
#測試案例
def classifyPerson():
	resultList = ['not at all', 'in small doses', 'in large doses']
	percentTats = float(input("percentage of time spent playing video games?"))
	ffMiles = float(input("frequent filer miles earned per year?"))
	iceCream = float(input("liters of ice cream consumed per year?"))
	datingDataMat, datingLabels = file2matrix('D:\machinelearninginaction\Ch02\datingTestSet.txt')
	normMat, ranges, minVals = autoNorm(datingDataMat)
	inArr = array([ffMiles, percentTats, iceCream])
	classifierResult = classify0((inArr-minVals)/ranges, normMat, datingLabels, 3)
	print ("You will probably like this person:  numbers of results:", resultList[2])
#簡單的knn算法	
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
    sortedDistIndicies = distances.argsort()     
    classCount={}          
    for i in range(k):
        voteIlabel = labels[sortedDistIndicies[i]]
        classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1
    sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)
    return sortedClassCount[0][0]

def createDataSet():
    group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])
    labels = ['A','A','B','B']
    return group, labels

def file2matrix(filename):
    fr = open(filename)
    numberOfLines = len(fr.readlines())         #get the number of lines in the file
    returnMat = zeros((numberOfLines,3))        #prepare matrix to return
    classLabelVector = []                       #prepare labels return   
    fr = open(filename)
    index = 0
    for line in fr.readlines():
        line = line.strip()
        listFromLine = line.split('\t')
        returnMat[index,:] = listFromLine[0:3]
        classLabelVector.append(str(listFromLine[3]))
        index += 1
    return returnMat,classLabelVector
#正規化    
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))   #element wise divide
    return normDataSet, ranges, minVals
#測試案例   
def datingClassTest():
    hoRatio = 0.50      #hold out 10%
    datingDataMat,datingLabels = file2matrix('D:\machinelearninginaction\Ch02\datingTestSet2.txt')       #load data setfrom file
    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 with: %d, the real answer is: %s" % (classifierResult, datingLabels[i]))
        if (classifierResult != datingLabels[i]): errorCount += 1.0
    print ("the total error rate is: %f" % (errorCount/float(numTestVecs)))
    print (errorCount)
    
def img2vector(filename):
    returnVect = zeros((1,1024))
    fr = open(filename)
    for i in range(32):
        lineStr = fr.readline()
        for j in range(32):
            returnVect[0,32*i+j] = int(lineStr[j])
    return returnVect
#測試案例
def handwritingClassTest():
    hwLabels = []
    trainingFileList = listdir('D:\\machinelearninginaction\\Ch02\\trainingDigits')           #load the training set
    m = len(trainingFileList)
    trainingMat = zeros((m,1024))
    for i in range(m):
        fileNameStr = trainingFileList[i]
        fileStr = fileNameStr.split('.')[0]     #take off .txt
        classNumStr = int(fileStr.split('_')[0])
        hwLabels.append(classNumStr)
        trainingMat[i,:] = img2vector('D:\machinelearninginaction\Ch02\\trainingDigits\%s' % fileNameStr)
    testFileList = listdir('D:\\machinelearninginaction\\Ch02\\testDigits')        #iterate through the test set
    errorCount = 0.0
    mTest = len(testFileList)
    for i in range(mTest):
        fileNameStr = testFileList[i]
        fileStr = fileNameStr.split('.')[0]     #take off .txt
        classNumStr = int(fileStr.split('_')[0])
        vectorUnderTest = img2vector('D:\machinelearninginaction\Ch02\\testDigits\%s' % fileNameStr)
        classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)
        print ("the classifier came back with: %d, the real answer is: %d" % (classifierResult, classNumStr))
        if (classifierResult != classNumStr): errorCount += 1.0
    print ("\nthe total number of errors is: %d" % errorCount)
    print ("\nthe total error rate is: %f" % (errorCount/float(mTest)))

在python3.4控制是臺輸入下面的代碼進行測試：

>>>kNN.classify0([0,0], group, labels, 3)

>>> reload(kNN)
>>> datingDataMat,datingLabels = kNN.file2matrix('datingTestSet.txt')

>>> kNN.datingClassTest()

>>> kNN.classifyPerson()

>>> testVector = kNN.img2vector('testDigits/0_13.txt')

>>> kNN.handwritingClassTest()

下面的圖片是我實現程序例子的部分圖片：

總結：kNN是一個簡單和有效的數據分類的算法。它是基於實例的機器學習算法，只是需要手邊有數據進行學習。它需要遍歷整個數據集，對於大量的數據，需要將待預測的一條數據同整個數據集中的每一條數據都要進行距離計算，這是有些棘手的（耗時），而且佔用存儲資源。

kNN的第一個缺點就是，我們對數據的底層結構（符合正態是還是伯努利分佈）沒有清晰的看法；而且，也不知道均值，和在某一個分類中的案例看起來應該有什麼樣的特點。