KNN最近鄰演算法numpy版本——深度學習
阿新 • • 發佈:2018-11-07
#!python from numpy import * import operator from os import listdir def createDataSet(): group = array([[1.0,1.1],[1.0,1.0],[0.1,0.1],[0.2,0.1]]) 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.items(),key=\ operator.itemgetter(1),reverse=True) return sortedClassCount[0][0]; def file2matrix(filename): fr = open(filename) arrayOLines = fr.readlines() numberOfLines = len(arrayOLines) returnMat = zeros((numberOfLines,3)) classLabelVector = [] index=0 for line in arrayOLines: line = line.strip() listFromLine = lie.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)) return normDataSet,ranges,minVals def datingClassTest(): hoRatio = 0.10 datingDataMat,datingLabels = file2matrix('datingTestSet.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[numTestVec:m,:],\ datingLabels[numTestVecs:m],3) print("the classifier came backwith: %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))) 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 cameback with:%d,the real answer is:%d"\ %(classifierResult,classNumStr)) if(classifierResult != classNumStr): errorCount += 1.0 print ("\n the total number of errors is:%d" %errorCount) print("\nthe total error rate is:%f" %(errorCount/float(mTest)))