『机器学习实战』使用 k-近邻算法改进约会网站的配对效果

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):
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)

print 'sorted class count: ', sortedClassCount

return sortedClassCount[0][0]

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

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)
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 samall 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 "You will probably like this person: ", \
resultList[classifierResult - 1]

运行代码：
import kNN_L

group, labels = kNN_L.createDataSet()
print group
print labels

print kNN_L.classify0([0, 0], group, labels, 3)

datingDataMat, datingLabels = kNN_L.file2matrix('datingTestSet2.txt')
print datingDataMat
print datingLabels[0: 20]

import matplotlib
import matplotlib.pyplot as plt

fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(datingDataMat[:, 1], datingDataMat[:, 2])
plt.show()

normMat, ranges, minVals = kNN_L.autoNorm(datingDataMat)
print normMat

print ranges

print minVals

kNN_L.datingClassTest()

kNN_L.classifyPerson()