K相邻算法

刚开始学习机器学习,先跟这《机器学习实战》学一些基本的算法

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该算法是用来判定一个点的分类,首先先找到离该点最近的k个点,然后找出这k个点的哪种分类出现次数最多,就把该点设为那个分类
距离公式选用欧式距离公式:

下面给出例子(来自《机器学习实战》)

1.约会对象喜欢程度的判定:

现需要一个约会对象喜欢程度的分类器

给定数据集,属性包含

每年获得的飞行常客里程数,玩视频游戏所耗时间百分比,每周消费的冰淇淋公升数,是否喜欢且喜欢程度(不喜欢,一般喜欢,特别喜欢),四个属性

分类器代码:

先获取整个数据集的约会对象个数t,然后把我们要分类的矩阵复制t次

然后减去原数据集,得到xA0-xB0和xA1-xB1,然后对矩阵每个元素平方,行内求和,开根号,得到所有点的距离

统计前k个数据类别出现次数,返回次数最多的类别

 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]

先从文件中分别读前三个属性和喜欢程度到矩阵

然后进行归一化,最后分类得到结果统计一下就好了

 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

2.数字识别

给定训练集包含32*32的01字符串,然后判定测试集的数字

对于把32*32每个数字都看作一个属性,然后直接算距离分类...

 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('digits/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('digits/trainingDigits/%s' % fileNameStr)
     testFileList = listdir('digits/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('digits/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))