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):
dataSetSize = dataSet.shape[0]
diffMat = tile(inX, (dataSetSize, 1)) - dataSet
sqDiffMat = diffMat ** 2
sqDistances = sqDiffMat.sum(axis = 1)
distance = sqDistances ** 0.5
sortedDistIndicies = distance.argsort()
#argsort()函数,是numpy库中的函数，返回的是数组值从小到大的索引值
classCount = {}
for i in range(k):
voteIlabel = labels[sortedDistIndicies[i]]
classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1
#字典get()方法返回指定键的值，如果键不在字典中，返回一个指定值，默认为None。
sortedClassCount = sorted(classCount.items(),
key = operator.itemgetter(1), reverse = True)
return sortedClassCount[0][0]
'''
sorted 语法：
sorted(iterable[, cmp[, key[, reverse]]])
参数说明：
iterable -- 可迭代对象。
cmp -- 比较的函数，这个具有两个参数，参数的值都是从可迭代对象中取出，
此函数必须遵守的规则为，大于则返回1，小于则返回-1，等于则返回0。
key -- 主要是用来进行比较的元素，只有一个参数，
具体的函数的参数就是取自于可迭代对象中，指定可迭代对象中的一个元素来进行排序。
reverse -- 排序规则，reverse = True 降序 ， reverse = False 升序（默认）。
'''
'''
operator.itemgetter函数
operator模块提供的itemgetter函数用于获取对象的哪些维的数据，参数为一些序号。看下面的例子
a = [1,2,3]
>>> b=operator.itemgetter(1) //定义函数b，获取对象的第1个域的值
>>> b(a)
2
>>> b=operator.itemgetter(1,0) //定义函数b，获取对象的第1个域和第0个的值
>>> b(a)
(2, 1)
要注意，operator.itemgetter函数获取的不是值，而是定义了一个函数，通过该函数作用到对象上才能获取值。
sorted函数用来排序，sorted(iterable[, cmp[, key[, reverse]]])
其中key的参数为一个函数或者lambda函数。所以itemgetter可以用来当key的参数
a = [('john', 'A', 15), ('jane', 'B', 12), ('dave', 'B', 10)]
根据第二个域和第三个域进行排序
sorted(students, key=operator.itemgetter(1,2))
'''
def file2matrix(filename):
fr = open(filename)
arrayOLines = fr.readlines()
numberOfLines = len(arrayOLines)
returnMat = zeros((numberOfLines, 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)
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 = 0.1
datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')
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[numTestVecs:m, :],
datingLabels[numTestVecs:m], 3)
print("the classfier came back with: %d, the real answer is: %d"
% (classifierResult, datingLabels[i]))
if classifierResult != datingLabels[i]:
errorCount += 1
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?"))
ffMiles = float(input("frequent flier 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])
