【用Python玩Machine Learning】KNN * 代碼 * 二

繼續之前的寫。

三、對單個樣本進行分類。

'''
function: classify the input sample by voting from its K nearest neighbor
input:
1. the input feature vector
2. the feature matrix
3. the label list
4. the value of k
return: the result label
'''
def ClassifySampleByKNN(featureVectorIn, featureMatrix, labelList, kValue):

    # calculate the distance between feature input vector and the feature matrix
    disValArray = CalcEucDistance(featureVectorIn,featureMatrix)

    # sort and return the index
    theIndexListOfSortedDist = disValArray.argsort()

    # consider the first k index, vote for the label
    labelAndCount = {}
    for i in range(kValue):
        theLabelIndex = theIndexListOfSortedDist[i]
        theLabel = labelList[theLabelIndex]
        labelAndCount[theLabel] = labelAndCount.get(theLabel,0) + 1
    sortedLabelAndCount = sorted(labelAndCount.iteritems(), key=lambda x:x[1], reverse=True)

    return sortedLabelAndCount[0][0]

基本思路就是，首先計算輸入樣本和訓練樣本集合的歐氏距離，然後根據距離進行排序，選擇距離最小的k個樣本，用這些樣本對應的標籤進行投票，票數最多的標籤就是輸入樣本所對應的標籤。

比較有特色的寫法是這一句：

# sort and return the index
    theIndexListOfSortedDist = disValArray.argsort()

disValArray是numpy的一維數組，存儲的僅僅是歐式距離的值。argsort直接對這些值進行排序，並且把排序結果所對應的原索引返回回來。很方便。另外一句是sorted函數的調用，按照value來對字典進行排序，用到了函數式編程的lambda表達式。這個用operator也能達到同樣的目的。

四、對測試樣本文件進行分類，並統計錯誤率

'''
function: classify the samples in test file by KNN algorithm
input:
1. the name of training sample file
2. the name of testing sample file
3. the K value for KNN
4. the name of log file
'''
def ClassifySampleFileByKNN(sampleFileNameForTrain, sampleFileNameForTest, kValue, logFileName):

    logFile = open(logFileName,'w')

    # load the feature matrix and normailize them
    feaMatTrain, labelListTrain = LoadFeatureMatrixAndLabels(sampleFileNameForTrain)
    norFeaMatTrain = AutoNormalizeFeatureMatrix(feaMatTrain)
    feaMatTest, labelListTest = LoadFeatureMatrixAndLabels(sampleFileNameForTest)
    norFeaMatTest = AutoNormalizeFeatureMatrix(feaMatTest)

    # classify the test sample and write the result into log
    errorNumber = 0.0
    testSampleNum = norFeaMatTest.shape[0]
    for i in range(testSampleNum):
        label = ClassifySampleByKNN(norFeaMatTest[i,:],norFeaMatTrain,labelListTrain,kValue)
        if label == labelListTest[i]:
            logFile.write("%d:right\n"%i)
        else:
            logFile.write("%d:wrong\n"%i)
            errorNumber += 1
    errorRate = errorNumber / testSampleNum
    logFile.write("the error rate: %f" %errorRate)

    logFile.close()

    return

代碼挺多，不過邏輯上就很簡單了。沒什麼好說的。另外，不知道python中的命名是什麼習慣？我發現如果完全把變量名字展開，太長了——我的macbook pro顯示起來太難看。這裏就沿用c/c++的變量簡寫命名方式了。

五、入口調用函數

類似c/c++的main函數。只要運行kNN.py這個腳本，就會先執行這一段代碼：

if __name__ == '__main__':

    print "You are running KNN.py"

    ClassifySampleFileByKNN('datingSetOne.txt','datingSetTwo.txt',3,'log.txt')

kNN中的k值我選擇的是3。

未完，待續。

如有轉載，請註明出處：http://blog.csdn.net/xceman1997/article/details/44994215