《机器学习实战》之kNN-笔记1
2 python k近邻算法
便于阅读，先贴完整代码，有个大致印象。后面再分布讲解
# -*- coding: utf-8 -*-
"""
Created on Sat Sep 17 15:31:01 2016
@author: 打江南走过一阵
"""
from numpy import *
import operator
from os import listdir
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):
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.iteritems(), key=operator.itemgetter(1), reverse=True)
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,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)) #element wise divide
return normDataSet, ranges, minVals
def datingClassTest():
hoRatio = 0.10 #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 errorCount
#输入某人的信息，便得出对对方喜欢程度的预测值
#python中raw_input允许用户输入文本行命令并返回用户所输入的命令
def classifyPerson():
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]
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
def 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))2.1算法简单说明
这个算法主要工作是测量不同特征值之间的距离，有个这个距离，就可以进行分类了。
简称kNN。
已知：训练集，以及每个训练集的标签。
接下来：和训练集中的数据对比，计算最相似的k个距离。选择相似数据中最多的那个分类。作为新数据的分类。
fromnumpy import *#引入科学计算包
import operator #经典python函数库。运算符模块。
#创建数据集
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
#算法核心
#inX：用于分类的输入向量。即将对其进行分类。
#dataSet：训练样本集
#labels:标签向量
def classfy0(inX,dataSet,labels,k):
#距离计算
dataSetSize =dataSet.shape[0]#得到数组的行数。即知道有几个训练数据。.shape[0]读取第一维度长度
diffMat =tile(inX,(dataSetSize,1))-dataSet#tile:numpy中的函数。tile将原来的一个数组，扩充成了4个一样的数组。diffMat得到了目标与训练数值之间的差值。
sqDiffMat =diffMat**2#各个元素分别平方
sqDistances =sqDiffMat.sum(axis=1)# sum函数中加入参数。sum（a，axis=0）或者是.sum(axis=1)axis=0 就是普通的相加；加入axis=1以后就是将一个矩阵的每一行向量相加
distances =sqDistances**0.5#开方，得到距离。
sortedDistIndicies=distances.argsort()#升序排列
#选择距离最小的k个点。
classCount={}
for i in range(k):
voteIlabel=labels[sortedDistIndicies[i]]
classCount[voteIlabel]=classCount.get(voteIlabel,0)+1
#排序
sortedClassCount=sorted(classCount.iteritems(),key=operator.itemgetter(1),reverse=True)
return sortedClassCount[0][0]函数再解释：
.shape用于计算array各维度的长度，在python中都是从0开始的。
tile函数是numpy包中的，用于重复array，比如上面代码中的tile(inX,(dataSetSize,1))，表示重复inX，其行重复dataSetSize次，而列不重复
.sum是numpy中用于计算一个array内部行列求和，axis=1表示按列求和，即把每一行的元素加起来
.argsort是numpy中对array进行排序的函数，排序是升序
classCount = {} 其中{}表示生成的是字典，在字典这个类中，有方法get，对classCount元素赋值，其实是个计数器
sorted是内置函数，可以help(sorted)查看用法operator模块下的itemgetter函数，顾名思义就是提取第X个元素的意思
2.2示例：改进约会网站配对效果
2.2.1准备数据：从文本文件共解析数据
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,classLabelVector>>>reload(kNN)
>>>datingDataMat,datingLabels = kNN.file2matrix('datingTestSet2.txt')#此处为datingTestSet2.txt ，书中datingTestSet.txt错误！！
2.2.2分析数据：使用Matplotlib创建散点图
import matplotlib
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(datingDataMat[:,1],datingDataMat[:,2])
plt.show()
##不知什么错误，plt.show后没有显示图像，在导入下面两句后，图像显示。
import matplotlib as mpl
mpl.use('Agg')
import matplotlib as mpl
mpl.use('Agg')
图2-3 没有样本类别标签的约会数据散点图
利用颜色及尺寸标识数据点的属性类别。代码如下：
>>>ax.scatter(datingDataMat[:,1],datingDataMat[:,2],15.0*array(datingLabels),15.0*array(datingLabels))
图2-4 带样本类别标签的约会数据散点图
图2-3和图2-4中散点图采用datingDataMat矩阵中的第二列、第三列数据。虽然图2-4能够比较容易区分数据点从属类别，但依然很难根据这张图得出结论性信息。
针对上述不足，图2-5采用datingDataMat矩阵中的第一列、第二列数据，可以清晰的标识三个不同的样本分类区域。
ax.scatter(datingDataMat[:,0],datingDataMat[:,1],15.0*array(datingLabels),15.0*array(datingLabels))
图2-5 带样本类别标签的约会数据散点图
2.2.3 准备数据：归一化
为防止距离计算中，大数吃小数现象，在距离计算之前需要将数据归一化。
方法是：newValue = (oldValue - min) / (max - min)
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
重新加载kNN.py，函数归一化后的执行结果如下：
2.2.4 测试算法：作为完整程序验证分类器
定义一个计数器变量，每次分类器错误的分类数据，计数器就加1，程序执行完成后计数器的结果除以数据点总数即是错误率。
def datingClassTest():
hoRatio = 0.10 #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 errorCount重新加载kNN，输入
>>>kNN.datingClassTest()
结果显示错误率是5%
2.2.5 使用算法：构建完整可用系统
#输入某人的信息，便得出对对方喜欢程度的预测值
#python中raw_input允许用户输入文本行命令并返回用户所输入的命令
def classifyPerson():
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] 
现在分类器已经构建好了，用户可以自己输入数据，得到对他的喜好程度。
2.3 示例：手写识别系统
#将图像转换为测试向量
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
#将 from os import listdir 写在文件起始部分
#测试代码
def 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))
>>>kNN.handwritingClassTest()
执行结果如下：
