'''
kNN: K
Input: inX:
dataSet:
labels:
k:
Output: k
KNN
'''
from numpy import *
import operator
from os import listdir
def classify0(inX, dataSet, labels, k):
"""
，inx
"""
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 # 1
sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True) #
return sortedClassCount[0][0] #
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 file2matrix(filename):
"""
，
"""
fr = open(filename) #
numberOfLines = len(fr.readlines()) # ，
returnMat = zeros((numberOfLines,3)) # ， ，
classLabelVector = [] #
fr = open(filename)
index = 0
for line in fr.readlines(): #
line = line.strip() #
listFromLine = line.split('\t') # tab
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)) # minVals m ，1 ，
normDataSet = normDataSet/tile(ranges, (m,1)) # ， [0-1]
return normDataSet, ranges, minVals # ， ，
def datingClassTest():
"""
"""
hoRatio = 0.50 #hold out 10%
datingDataMat,datingLabels = file2matrix('datingTestSet2.txt') # ， 、
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
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') # ，
m = len(trainingFileList) #
trainingMat = zeros((m,1024)) #
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
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 "
the total number of errors is: %d" % errorCount
print "
the total error rate is: %f" % (errorCount/float(mTest))