KNN约会预测和手写字识别python代码

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'''

Created on Sep 16, 2010 kNN: k Nearest Neighbors

Input: inX: vector to compare to existing dataset (1xN) dataSet: size m data set of known vectors (NxM) labels: data set labels (1xM vector)

k: number of neighbors to use for comparison (should be an odd number) Output: the most popular class label @author: pbharrin '''

from numpy import * import operator from os import listdir

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 = 0.50 #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

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))

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