机器学习python kNN算法

from numpy import *
import operator
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

def classify0(inX,dataSet,labels,k):
#inX为输入向量，dataSet为训练向量，labels为训练的标签向量，k为最近邻居的数目
dataSetSize = dataSet.shape[0]
diffMat = tile(inX,(dataSetSize,1)) - dataSet  #将输入向量拓展为训练向量规模一样
pingfang = diffMat ** 2
d_pingfang = pingfang.sum(axis=1) #逐行操作
d = d_pingfang ** 0.5
sortedDistIndicies = d.argsort()
classCount = {}
for i in range(k):
voteIlabel = labels[sortedDistIndicies[i]]
classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1
sortedClassCount = sorted(classCount.items(),key=operator.itemgetter(1),reverse=True)
return sortedClassCount[0][0]

#将文本数据转化为Numpy的解析函数
def file2matrix(filename):
fr = open(filename)
arrayOLines = fr.readlines()
numbersOfLines = len(arrayOLines)
returnMat = zeros((numbersOfLines,3))
classLabelVector = []
index = 0
for line in arrayOLines:
line = line.strip()
listFromLine = line.split('\t')
returnMat[index,:] = listFromLine[0:3]
classLabelVector.append(int(listFromLine[-1]))
index += 1
return returnMat,classLabelVector

#归一化特征值
def autoNorm(dataSet):
minVals = dataSet.min(0) #按照列寻找特征值
maxVals = dataSet.max(0)
ranges = maxVals - minVals
m = dataSet.shape[0]
normDataSet = zeros(shape(dataSet))
normDataSet = dataSet - tile(minVals,(m,1))
normDataSet = normDataSet / tile(ranges,(m,1))
return normDataSet,ranges,minVals

def datingClassTest():
hoRatio = 0.1
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[:m-numTestVecs,:],datingLabels[:m-numTestVecs],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)