机器学习实战第二章KNN算法(3)

归一化特征值

knn.py

def autoNorm(dataSet):
minVals = dataSet.min(0)    #找出样本集中的最小值
maxVals = dataSet.max(0)    #找出样本集中的最大值
ranges = maxVals - minVals  #最大最小值之间的差值
normDataSet = zeros(shape(dataSet)) #创建与样本集一样大小的零矩阵
m = dataSet.shape[0]
normDataSet = dataSet - tile(minVals, (m,1))    #样本集中的元素与最小值的差值
normDataSet = normDataSet/tile(ranges, (m,1))   #数据相除，归一化
return normDataSet, ranges, minVals

测试算法

knn.py

def datingClassTest():
hoRatio = 0.10  ＃ #选取多少数据测试分类器
datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')    #从datingTestSet2.txt中获取数据
normMat, ranges, minVals = autoNorm(datingDataMat)    #归一化数据
m = normMat.shape[0]
numTestVecs = int(m*hoRatio)    #设置测试个数
errorCount = 0.0    #记录错误数量
for i in range(numTestVecs):        #分类算法
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
#计算错误率
print "the total error rate is: %f" % (errorCount/float(numTestVecs))
print errorCount

预测函数

knn.py

def classifyPerson():
resultList = ['not at all', 'in small doses', 'in large doses']
percentTats = float(input("percentage of time spent playing video games ?"))
#raw_inout函数允许用户输入文本行命令并返回用户所输人的命令
ffMiles = float(input("frequent filer miles earned per year?"))
iceCream = float(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("You will probably like this person: ", resultList[classifierResult - 1])

错误解决