《機器學習實戰》第二章,KNN演算法在jupyter中實驗
阿新 • • 發佈:2019-01-02
1、首先在jupyter中New一個Untitle.ipynb,然後將它重新命名為kNN.py,接著在kNN.py中輸入一下程式碼(課本程式碼):
注:以下程式碼中,存在我自己的測試資料檔案的路徑,你們要改為自己測試資料檔案的路徑
from numpy import * import operator 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 sqdistance = sqdiffMat.sum(axis=1) #print(sqdistance) distance = sqdistance**0.5 sortedDistIndex = distance.argsort() classCount = {} for i in range(k): voteIlabel = labels[sortedDistIndex[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 = 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)) return normDataSet, ranges, minVals def datingClassTest(): hoRatio = 0.1 datingDataMat,datingLabels = file2matrix('F:\Softwares\Python\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))) def classifyPerson(): resultList = ["not at all","in small does","in large does"] percentTats = float(input("percentage of time spent playing video games?")) ffMiles = float(input("frequent flier miles earned per year?")) iceCream = float(input("liters of ice cream consumes per year?")) datingDataMat,datingLabels = file2matrix('F:\Softwares\Python\datingTestSet2.txt') normMat,ranges,minVals = autoNorm(datingDataMat) inArr = array([ffMiles,percentTats,iceCream]) classifierResult = classify0(((inArr-minVals)/ranges),datingDataMat,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 from os import listdir def handwritingClassTest(): hwLabels = [] trainingFileList = listdir('F:/Softwares/Python/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('F:/Softwares/Python/trainingDigits/%s' % fileNameStr) testFileList = listdir('F:/Softwares/Python/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('F:/Softwares/Python/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、將測試資料檔案放在和kNN.py同一個目錄下。或者像我一樣,直接在程式碼中修改成自己的測試檔案路徑
注:還有幾處,自己注意修改
3、現在可以開始測試資料,在jupyter中再建立一個檔案,命名為testKNN.ipynb