机器学习实战之KNN（交友网站网友预测）

一、数据集：datingTestData.txt，格式如下：



前三个为特征，分别是：

（1）每年获得的飞行常客里程数

（2）玩视频游戏所耗的时间百分比

（3）每周消费的冰淇淋公升数

标签：不喜欢、魅力一般、极具魅力。

二、使用python处理文本文件，转为矩阵形式。

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(listFromLine[-1])
index += 1
return returnMat,classLabelVector

三.因为是算每两个样本间的欧氏距离，易发现，上面数字差值最大的属性对最后结果影响太大，所以要先做归一化处理，将任意特征值的取值范围控制在相同的0-1之间。

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

四. 程序主要部分，KNN算法实现部分。

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]

五。整个过程实现部分

def classifyPerson():
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 classifierResult
print "you will like this person:",resultList[int(classifierResult) - 1]

六. 结果：

>>>classifyPerson()
percentage of time spent playing video games?10
frequent flier miles earned per year?10000
liters of ice cream consumed per year?0.5
2
you will like this person: in small doses