【用Python玩Machine Learning】KNN * 代码 * 2

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

继续之前的写。

三、对单个样本进行分类。

'''
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，首先计算输入样本和训练样本集合的欧氏距离，然后根据距离进行排序，选择距离最小的k个样本，用这些样本对应的标签进行投票，票数最多的标签就是输入样本所对应的标签。

比较有特色的写法是这一句：

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

disValArray是numpy的一维数组，存储的仅仅是欧式距离的值。argsort直接对这些值进行排序，并且把排序结果所对应的原索引返回回来。很方便。另外一句是sorted函数的调用，按照value来对字典进行排序，用到了函数式编程的lambda表达式。这个用operator也能达到同样的目的。

四、对测试样本文件进行分类，并统计错误率

'''
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

代码挺多，不过逻辑上就很简单了。没什么好说的。另外，不知道python中的命名是什么习惯？我发现如果完全把变量名字展开，太长了——我的macbook pro显示起来太难看。这里就沿用c/c++的变量简写命名方式了。

五、入口调用函数

类似c/c++的main函数。只要运行kNN.py这个脚本，就会先执行这一段代码：

if __name__ == '__main__':

    print "You are running KNN.py"

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

kNN中的k值我选择的是3。

未完，待续。

如有转载，请注明出处：http://blog.csdn.net/xceman1997/article/details/44994215

【用Python玩Machine Learning】KNN * 代码 * 2

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