学习kNN算法的感受

本来预计的打算是一天一个十大挖掘算法，然而由于同时要兼顾数据结构面试的事情，所以很难办到，但至少在回家前要把数据挖掘十大算法看完，过个好年，在course上学习老吴的课程还是帮了我很大的忙，虽然浪费了时间，但是也无形中帮助我很多，所以说还是很值得的，今天就总结KNN算法的一部分，这部分老吴的课程中没有太多涉及到，所以我又重新关注了一下，下面是我的总结，希望能对大家有所帮组。

介绍环镜：python2.7 IDLE Pycharm5.0.3

操作系统：windows

第一步：因为没有numpy，所以要安装numpy，详情见另一篇安装numpy的博客，这里不再多说.

第二步：贴代码：

<span style="color:#ff0000;background-color: rgb(255, 255, 255);"><strong>from numpy import *
import operator
from os import listdir</strong></span><span style="background-color: rgb(255, 255, 0);">
</span>
<strong><span style="color:#ff0000;">def classify0(inX, dataSet, labels, k):</span></strong>
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]

<strong><span style="color:#ff0000;">def createDataSet():</span></strong>
group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])
labels = ['A','A','B','B']
return group, labels

<strong><span style="color:#ff0000;">def file2matrix(filename):</span></strong>
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

<strong><span style="color:#ff0000;">def autoNorm(dataSet):</span></strong>
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

<strong><span style="color:#ff0000;">def datingClassTest():</span></strong>
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 errorCount

<strong><span style="color:#ff0000;">def img2vector(filename):</span></strong>
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

<strong><span style="color:#ff0000;">def handwritingClassTest():</span></strong>
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))

第三步：通过命令行交互

（1）：先将上述代码保存为kNN.py

（2）：再在IDLE下的run菜单下run一下，将其生成python模块

（3）： import kNN（因为上一步已经生成knn模块）

（4）： kNN.classify0([0,0],group,labels,3) （讨论[0,0]点属于哪一个类）

注：其中【0,0】可以随意换

即【】内的坐标就是我们要判断的点的坐标：

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

'B'

>>> kNN.classify0([0,1],group,labels,3)

'B'

>>> kNN.classify0([0.6,0.6],group,labels,3)

'A'