1、k-邻近算法

1、首先要搭建环境

这里需要numpy、scipy、matplotlib三个库，下载地址如下，双击安装即可：

（需要注意python的版本号和32/64位）

numpy：http://sourceforge.net/projects/numpy/files/NumPy/1.7.0/
matplotlab：http://sourceforge.net/projects/matplotlib/files/matplotlib/matplotlib-1.1.1/
scipy：http://sourceforge.net/projects/scipy/files/scipy/0.12.0/

然后来个最简单的例子：

import pylab as pllistOfInt = []for c in range(10):    listOfInt.append(c*2)print listOfIntpl.plot(listOfInt)pl.show()

这个程序相当于绘制y=2x的图像

结果如下：

2、k-邻近算法

kNN算法非常简单，不再详述它的思路。这段代码来自《机器学习实战》一书，感觉数据集挺理想化的，分类出来效果还是不错的。

代码中只对classify0做了详细注释，其他部分不太重要，我们可以忽略。

测试代码：

# -*- coding: utf-8 -*- from numpy import *import operatorimport matplotlibimport matplotlib.pyplot as pltfrom os import listdirdef createDatabase():    groups=array([[1.0,1,1],[1.0,1.0],[0,0],[0,0.1]])    labels=['A','A','B','B']    return groups,labelsdef classify0(inX, dataSet, labels, k):    dataSetSize = dataSet.shape[0]    #shape[0]就是读取矩阵第一维度的长度    diffMat = tile(inX, (dataSetSize,1)) - dataSet  #tile：构造dataSetSize*1个copy    sqDiffMat = diffMat**2     #各个项平方    sqDistances = sqDiffMat.sum(axis=1)#sum(axis=0),普通相加;axis=1,将一个矩阵的每一行向量相加    distances = sqDistances**0.5     #开根    sortedDistIndicies = distances.argsort()   #argsort:返回排序的位次,参考补充1    classCount={}              for i in range(k):       #这是一段计数过程        voteIlabel = labels[sortedDistIndicies[i]]        classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1    #sorted参考python下相关日志    sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True)    #iteritor是迭代器的意思，一次返回一个数据项，直到没有为止    #operator模块提供的itemgetter函数用于获取对象的哪些维的数据，参数为一些序号（即需要获取的数据在对象中的序号）    return sortedClassCount[0][0]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(int(listFromLine[-1]))        index += 1    return returnMat,classLabelVectordef 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, minValsdef datingClassTest():    hoRatio = 0.50      #hold out 10%    datingDataMat,datingLabels = file2matrix('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: %d" % (classifierResult, datingLabels[i])        if (classifierResult != datingLabels[i]): errorCount += 1.0    print "the total error rate is: %f" % (errorCount/float(numTestVecs))    print errorCountdef 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 returnVectdef handwritingClassTest():    hwLabels = []    trainingFileList = listdir('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('trainingDigits/%s' % fileNameStr)    testFileList = listdir('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('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))if __name__ == "__main__":    #约会测试    datingClassTest()    #手写测试    handwritingClassTest()    

测试结果：

约会：the total error rate is: 0.064000

手写数字：the total error rate is: 0.011628

感觉挺准的，有点离谱了

我猜想数据集是线性可分的（或者近似线性可分），才会得到这么好的结果。

如图，分的还是挺开的。