kNN algorithm

kNN

# classifier method
# dataSet: the training data set
# inx: the test sample
# labels: the corresponding labels about the data set
# k: the top k classes to be selected
def classify0(inX, dataSet, labels, k):
    sortedDistIndicies = euclideanMetric(dataSet, inX)
    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]


# euclidean metric
# dataSet: the training data set
# inX: the testing data sample
def euclideanMetric(dataSet, inX):
    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()
    return sortedDistIndicies


# create data set and related labels
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


# get matrix from the file data
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


# data normalization
# matrix operation
def autoNorm(dataSet):
    minVals = dataSet.min(0)
    maxVals = dataSet.max(0)
    ranges = maxVals - minVals
    m = dataSet.shape[0]
    normDataSet = dataSet - tile(minVals, (m, 1))
    normDataSet = normDataSet / tile(ranges, (m, 1))  # element wise divide
    return normDataSet, ranges, minVals


# dating class test method
# the same logic compared with handwriting digit method
# the differences between it is the [data source]
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


# convert a two dimensional matrix to a one dimensional vector
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


# handwriting digit test method
def handwritingClassTest():
    hwLabels, trainingMat = training()
    testFileList = listdir('testDigits')  # iterate through the test set
    errorCount = 0.0
    mTest = len(testFileList)
    for i in range(mTest):
        fileNameStr = testFileList[i]
        classNumStr = extractLabel(fileNameStr)
        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))


# get the training result
# trainingMat: the trainingMat
# hwLabels: the corresponding trainingMat labels
def training():
    hwLabels = []
    trainingFileList = listdir('trainingDigits')  # load the training set
    m = len(trainingFileList)
    trainingMat = zeros((m, 1024))
    for i in range(m):
        fileNameStr = trainingFileList[i]
        classNumStr = extractLabel(fileNameStr)
        hwLabels.append(classNumStr)
        trainingMat[i, :] = img2vector('trainingDigits/%s' % fileNameStr)
    return hwLabels, trainingMat


# extract label from the file name
def extractLabel(fileNameStr):
    fileStr = fileNameStr.split('.')[0]  # take off .txt
    classNumStr = int(fileStr.split('_')[0])
    return classNumStr

#kNN Algorithm

1. data normalization
2. euclidean metric