约会网站预测函数

#coding:utf-8
#约会网站
from numpy import *
import matplotlib
import matplotlib.pyplot as plt
import operator
#创建数据
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
#k近邻算法
def classify0(inX , dataSet , labels ,k):
dataSetSize = dataSet.shape[0] #shape = (4,2) , size = 4
diffMat = tile(inX , (dataSetSize  , 1)) - dataSet #将inX整形成为dataSet的大小
sqDiffMat = diffMat **2 #矩阵平方
sqDistances = sqDiffMat.sum(axis = 1) #水平相加
distances = sqDistances **0.5 #取根号得距离
sortedDistIndicies = distances.argsort() #排序
classCount = {}
for i in range(k):
voteIlabel = labels[sortedDistIndicies[i]] #得到距离第i近的点的label
classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1 #D.get(k[,d]) -> D[k] if k in D, else d. d defaults to None.
#itemgetter(item, ...) --> itemgetter object
#Return a callable object that fetches the given item(s) from its operand.
#After f = itemgetter(2), the call f(r) returns r[2].
sortedClassCount = sorted(classCount.items() , key = operator.itemgetter(1) , reverse=True)
return sortedClassCount[0][0]

def file2matrix(filename):
fr = open(filename)
arrayOLines = fr.readlines()
numberOLines = len(arrayOLines) #得到文件行数
returnMat = zeros((numberOLines,3)) #创建返回的Numpy矩阵,保存特征
classLabelVector = [] #保存最后一列元素
index = 0
for line in arrayOLines:
#解析文件数据到列表
line = line.strip() #截取掉所有回车字符
listFromLine = line.split('\t') #用\t将整行数据分割成一个元素列表
returnMat[index,:] = listFromLine[0:3]
classLabelVector.append(int(listFromLine[-1]))
index += 1
return returnMat,classLabelVector

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))
return normDataSet , ranges , minVals

def datingClassTest():
hoRatio = 0.10
datingDataMat , datingLabels = file2matrix('datingTestSet2.txt')
normMat , ranges , minVals = autoNorm(datingDataMat)
m = normMat.shape[0]
numTestVecs = int(m*hoRatio) #测试样本数
errorCount = 0.0
for i in range(numTestVecs):
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
print("the total error rate is : %f" % (errorCount / float(numTestVecs)))

def classifyPerson():
resultList = ['not at all','in small doses','in large doses']
percentTats = float(input("percentage of time spent playing video games?"))
ffMile = float(input("frequent fliter 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([ffMile , percentTats , iceCream])
classifierResult = classify0((inArr - minVals)/ranges , normMat , datingLabels ,3)
print("You will probably like this this person :" , resultList[classifierResult - 1])

classifyPerson()