机器学习实战笔记一：K-近邻算法在约会网站上的应用

#kNN分类器

def classify0(inX, dataSet, labels, k):

    dataSetSize = dataSet.shape[0]  #得到数据总量

    diffMat = tile(inX,(dataSetSize,1)) - dataSet #将输入数据扩充成与数据集同样大小的矩阵并作差

    sqDiffMat = diffMat**2

    sqDistances = sqDiffMat.sum(axis=1) #axis = 1 参数是维度参数等于1在此处表示将一个矩阵的每一行向量相加

    distances = sqDistances** 0.5

    sortedDistancesIndicies = distances .argsort() #将列表值进行对比返回一个按照数值升序的下标值

    classCount={}

    for i in range(k):

        voteIlabel = labels[sortedDistancesIndicies[i]]

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

        #dict.get("key") 返回value  dict.get("key",default= None)如果能找到就返回对应的value找不到返回默认值

    sortedClassCount = sorted(classCount.items(),key = operator.itemgetter(1),reverse=True)

    #sorted 返回一个list  operator.itemgetter(x,y)表示根据x+1维度的第y+1维度

    return sortedClassCount[0][0]

# Author：Thomas Wang

from numpy import *

def file2matrix(filename):

    with open(filename,'r') as fr:

        arrayOLines = fr.readlines()

        numberOfLines = len(arrayOLines)#存储文件数据数目

        returnMat = zeros((numberOfLines,3))#准备接收数据的numpy数组

        classLabelVector = []#准备接收标签向量的数组python列表

        index = 0

        for line in arrayOLines:

            line = line.strip() #截去每行的回车

            listFromLine = line.split('\t')#以‘\t’为分隔符将字符串截取成字符串数组

            returnMat[index,:] = listFromLine[0:3]  #[index,:] = [index][:] 将字符串数组赋值给numpy数组自动转换为浮点型

            classLabelVector.append(int(listFromLine[-1]))

            index +=1

        return returnMat,classLabelVector

datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')

print(datingDataMat)

print(datingLabels[0:20])

[[4.0920000e+04 8.3269760e+00 9.5395200e-01]

 [1.4488000e+04 7.1534690e+00 1.6739040e+00]

 [2.6052000e+04 1.4418710e+00 8.0512400e-01]

 ...

 [2.6575000e+04 1.0650102e+01 8.6662700e-01]

 [4.8111000e+04 9.1345280e+00 7.2804500e-01]

 [4.3757000e+04 7.8826010e+00 1.3324460e+00]]

[3, 2, 1, 1, 1, 1, 3, 3, 1, 3, 1, 1, 2, 1, 1, 1, 1, 1, 2, 3]

import matplotlib

import  matplotlib.pyplot as plt

fig = plt.figure()

ax = fig.add_subplot()

ax.scatter(datingDataMat[:,],datingDataMat[:,],15.0*array(datingLabels),15.0*array(datingLabels))

plt.show()

def autoNorm(dataSet):

    minVals = dataSet.min() #Return the minimum along a given axis.

    maxVals = dataSet.max() #Return the maximum along a given axis.

    ranges = maxVals - minVals #取到特征值最大最小值之间的范围

    normDataSet = zeros(shape(dataSet))

    m = dataSet.shape[] #取到数据量

    normDataSet = dataSet - tile(minVals,(m,))

    normDataSet = normDataSet/tile(ranges,(m,))

    return normDataSet,ranges,minVals

#完整程序验证分类器
def datingClassTest():
    hoRatio  = 0.1 #测试数据比例
    datingDataMat, datingLabels = file2matrix('datingTestSet2.txt') #文本文件转换成可以处理的矩阵
    normDataSet, ranges, minVals = autoNorm(datingDataMat) #将数据进行归一化
    m = normDataSet.shape[0] #获取数据总量
    numTestVecs = int(m * hoRatio) #获取测试数据数量
    errorCount = 0.0 #错误数据个数计数器
    for i in range(numTestVecs):
        #对每个测试数据利用KNN算法进行分类
        classifierResult = classify0(normDataSet[i,:],normDataSet[numTestVecs:m,:],datingLabels[numTestVecs:m],4)
        #打印预测结果与实际结果
        print("分类器结果：%s,真实结果：%s"%(classifierResult,datingLabels[i]))
        #计算总错误个数并计算错误率
        if (classifierResult != datingLabels[i]): errorCount+=1.0
    print("the total error rate is: %f"%(errorCount/float(numTestVecs)))
datingClassTest()

分类器结果：,真实结果：

分类器结果：,真实结果：

分类器结果：,真实结果：

......

分类器结果：,真实结果：

分类器结果：,真实结果：

分类器结果：,真实结果：

the total error rate is: 0.040000

#完整可用系统

def classifyPerson():

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

    percetTats = float(input("玩视频游戏所耗时间百分比："))

    ffMiles = float(input("每年获取的飞行常客里程数："))

    iceCream = float(input("每年消耗冰淇淋的公升数："))

    datingDataMat,datingLabels = file2matrix("datingTestSet2.txt")

    normMat,ranges,minVals = autoNorm(datingDataMat)

    inArr = array([ffMiles,percetTats,iceCream])

    classifierResult = classify0((inArr-minVals)/ranges,normMat,datingLabels,4)

    print("You probably like this person:",resultList[classifierResult-1])