###《Machine Learning in Action》 - KNN

时间：2020-12-06 11:01:57

初学Python；理解机器学习。

算法是需要实现的，纸上得来终觉浅。

// @author:       gr

// @date:         2015-01-16

// @email:        forgerui@gmail.com

一、简单的KNN

from numpy import *

import operator

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 classify0(inX, dataSet, labels, k):

    # 求输入向量与各个样例的距离

    dataSetSize = dataSet.shape[0]

    diffMat = tile(inX, (dataSetSize, 1)) - dataSet

    sqDiffMat = diffMat ** 2

    sqDistances = sqDiffMat.sum(axis = 1)

    distances = sqDistances ** 0.5

    # 按距离递增排序

    sortedDistIndicies = distances.argsort()

    classCount = {}

    # 对前k个样例的标签进行计数

    for i in range(k):

        voteIlabel = labels[sortedDistIndicies[i]]

        classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1

    # 按照计数对标签进行递减排序

    sortedClassCount = sorted(classCount.iteritems(),

            key = operator.itemgetter(1), reverse=True)

    # 返回最多计数的标签，即为该输入向量的预测标签

    return sortedClassCount[0][0]

二、KNN用于约会网站配对效果

def file2matrix(filename):

    # 读取文件

    fr = open(filename)

    arrayOLines = fr.readlines()

    numberOfLines = len(arrayOLines)

    returnMat = zeros((numberOfLines, 3))

    classLabelVector = []

    index = 0

    for line in arrayOLines:

        # 去除换行符

        line = line.strip()

        # 按Tab键分割列

        listFromLine = line.split('\t')

        returnMat[index, :] = listFromLine[0:3]

        # 存储标签

        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))

    # 数据的行数

    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]

    # 选取测试集数量

    numTestVecs = int(m * hoRatio)

    errorCount = 0.0

    for i in range(numTestVecs):

        classifierResult = classify0(normMat[i, :], normMat[numTestVecs:m, :], \

                datingLabels[numTestVecs:m], 7)

        print "the classifirer came back with: %d, the real answer is: %d"\

                % (classifierResult, datingLabels[i])

        # 记录错误数

        if (classifierResult != datingLabels[i]) : errorCount += 1.0

    print "numTestVecs: %f" % float(numTestVecs)

    print "the total error rate is: %f" % (errorCount/float(numTestVecs))

def classifyPerson():

    # 针对一个人判断

    resultList = ['not at all', 'in small doses', 'in large doses']

    percentTats = float(raw_input(\

            "percentage of time spent playing video games?"))

    ffMiles = float(raw_input("frequent flier miles earned per year?"))

    iceCream = float(raw_input("liters of ice cream consumed per year?"))

    datingDataMat, datingLabels = file2matrix('datingTestSet2.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: ", \

            resultList[classifierResult - 1]

三、手写识别系统

def img2vector(filename):

    # 32*32的图片转成一个向量

    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

def handwritingClassTest():

    hwLabels = []

    trainingFileList = listdir('trainingDigits')

    m = len(trainingFileList)

    trainingMat = zeros((m, 1024))

    # 把训练的文件图片转换成一个m*1024矩阵

    for i in range(m):

        fileNameStr = trainingFileList[i]

        fileStr = fileNameStr.split('.')[0]

        classNumStr = int(fileStr.split('_')[0])

        hwLabels.append(classNumStr)

        trainingMat[i, :] = img2vector('trainingDigits/%s' % fileNameStr)

    testFileList = listdir('testDigits')

    errorCount = 0.0

    # 在测试集上测试

    mTest = len(testFileList)

    for i in range(mTest):

        fileNameStr = testFileList[i]

        fileStr = fileNameStr.split('.')[0]

        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 "\n the total number of errors is: %d" % errorCount

    print "\n the total error rate is: %f" % (errorCount/float(mTest))

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