###《Machine Learning in Action》 - KNN
2015-01-21 13:36
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初学
Python;理解机器学习。
算法是需要实现的,纸上得来终觉浅。
// @author: gr // @date: 2015-01-16 // @email: forgerui@gmail.com
一、简单的KNN
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): # 求输入向量与各个样例的距离 dataSetSize = dataSet.shape[0] diffMat = tile(inX, (dataSetSize, 1)) - dataSet sqDiffMat = diffMat ** 2 sqDistances = sqDiffMat.sum(axis = 1) distances = sqDistances ** 0.5 # 按距离递增排序 sortedDistIndicies = distances.argsort() classCount = {} # 对前k个样例的标签进行计数 for i in range(k): voteIlabel = labels[sortedDistIndicies[i]] classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1 # 按照计数对标签进行递减排序 sortedClassCount = sorted(classCount.iteritems(), key = operator.itemgetter(1), reverse=True) # 返回最多计数的标签,即为该输入向量的预测标签 return sortedClassCount[0][0]
二、KNN用于约会网站配对效果
def file2matrix(filename): # 读取文件 fr = open(filename) arrayOLines = fr.readlines() numberOfLines = len(arrayOLines) returnMat = zeros((numberOfLines, 3)) classLabelVector = [] index = 0 for line in arrayOLines: # 去除换行符 line = line.strip() # 按Tab键分割列 listFromLine = line.split('\t') returnMat[index, :] = listFromLine[0:3] # 存储标签 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)) # 数据的行数 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('datingTestSet2.txt') normMat, ranges, minVals = autoNorm(datingDataMat) m = normMat.shape[0] # 选取测试集数量 numTestVecs = int(m * hoRatio) errorCount = 0.0 for i in range(numTestVecs): classifierResult = classify0(normMat[i, :], normMat[numTestVecs:m, :], \ datingLabels[numTestVecs:m], 7) print "the classifirer came back with: %d, the real answer is: %d"\ % (classifierResult, datingLabels[i]) # 记录错误数 if (classifierResult != datingLabels[i]) : errorCount += 1.0 print "numTestVecs: %f" % float(numTestVecs) print "the total error rate is: %f" % (errorCount/float(numTestVecs)) def classifyPerson(): # 针对一个人判断 resultList = ['not at all', 'in small doses', 'in large doses'] percentTats = float(raw_input(\ "percentage of time spent playing video games?")) ffMiles = float(raw_input("frequent flier miles earned per year?")) iceCream = float(raw_input("liters of ice cream consumed per year?")) datingDataMat, datingLabels = file2matrix('datingTestSet2.txt') normMat, ranges, minVals = autoNorm(datingDataMat) inArr = array([ffMiles, percentTats, iceCream]) classifierResult = classify0((inArr-\ minVals)/ranges, normMat, datingLabels, 3) print "You will probably like this person: ", \ resultList[classifierResult - 1]
三、手写识别系统
def img2vector(filename): # 32*32的图片转成一个向量 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)) # 把训练的文件图片转换成一个m*1024矩阵 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 # 在测试集上测试 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 "\n the total number of errors is: %d" % errorCount print "\n the total error rate is: %f" % (errorCount/float(mTest))
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