目录
谷歌笔记本（可选）
from google.colab import drive
drive.mount("/content/drive")Mounted at /content/drive
准备数据：从文本文件中解析数据
def file2matrix(filename):
fr = open(filename)
arrayOfLines = fr.readlines()
numberOfLines = len(arrayOfLines)
returnMat = zeros((numberOfLines, 3))
classLabelVector = []
index = 0
for line in arrayOfLines:
line = line.strip()
listFromLine = line.split('\t')
returnMat[index, :] = listFromLine[0:3]
classLabelVector.append(int(listFromLine[-1]))
index += 1
return returnMat, classLabelVectordatingDataMat, datingLabels = file2matrix('/content/drive/MyDrive/MachineLearning/机器学习/k-近邻算法/使用k-近邻算法改进约会网站的配对效果/datingTestSet2.txt')
datingDataMatarray([[4.0920000e+04, 8.3269760e+00, 9.5395200e-01], [1.4488000e+04, 7.1534690e+00, 1.6739040e+00], [2.6052000e+04, 1.4418710e+00, 8.0512400e-01], ..., [2.6575000e+04, 1.0650102e+01, 8.6662700e-01], [4.8111000e+04, 9.1345280e+00, 7.2804500e-01], [4.3757000e+04, 7.8826010e+00, 1.3324460e+00]])
datingLabels[:10][3, 2, 1, 1, 1, 1, 3, 3, 1, 3]
编写算法：编写kNN算法
from numpy import *
import operator
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.items(), key=operator.itemgetter(1), reverse=True)
return sortedClassCount[0][0]分析数据：使用Matplotlib创建散点图
import matplotlib
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(datingDataMat[:, 1], datingDataMat[:, 2])
plt.show()
import matplotlib
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(datingDataMat[:, 1], datingDataMat[:, 2],
15.0*array(datingLabels), 15.0*array(datingLabels))
plt.show()import matplotlib
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(datingDataMat[:, 0], datingDataMat[:, 1],
15.0*array(datingLabels), 15.0*array(datingLabels))
plt.show()准备数据：归一化数值
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, minValsnormMat, ranges, minVals = autoNorm(datingDataMat)
normMatarray([[0.44832535, 0.39805139, 0.56233353],
[0.15873259, 0.34195467, 0.98724416],
[0.28542943, 0.06892523, 0.47449629],
...,
[0.29115949, 0.50910294, 0.51079493],
[0.52711097, 0.43665451, 0.4290048 ],
[0.47940793, 0.3768091 , 0.78571804]]) rangesarray([9.1273000e+04, 2.0919349e+01, 1.6943610e+00])
minValsarray([0. , 0. , 0.001156])
测试算法：作为完整程序验证分类器
def datingClassTest():
hoRatio = 0.1
datingDataMat, datingLabels = file2matrix('/content/drive/MyDrive/MachineLearning/机器学习/k-近邻算法/使用k-近邻算法改进约会网站的配对效果/datingTestSet2.txt')
normMat, ranges, minVals = autoNorm(datingDataMat)
m = normMat.shape[0]
numTestVecs = int(m*hoRatio)
errorCount = 0
for i in range(numTestVecs):
classifierResult = classify0(normMat[i,:], normMat[numTestVecs:m,:],
datingLabels[numTestVecs:m],3)
print("the classifierResult 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)))datingClassTest()the classifierResult came back with: 3, the real answer is: 3 the classifierResult came back with: 2, the real answer is: 2 the classifierResult came back with: 1, the real answer is: 1 the classifierResult came back with: 1, the real answer is: 1 the classifierResult came back with: 1, the real answer is: 1 the classifierResult came back with: 1, the real answer is: 1 the classifierResult came back with: 3, the real answer is: 3 the classifierResult came back with: 3, the real answer is: 3 the classifierResult came back with: 1, the real answer is: 1 the classifierResult came back with: 3, the real answer is: 3 the classifierResult came back with: 1, the real answer is: 1 the classifierResult came back with: 1, the real answer is: 1 the classifierResult came back with: 2, the real answer is: 2 the classifierResult came back with: 1, the real answer is: 1 the classifierResult came back with: 1, the 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使用算法：构建完整可用系统
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('/content/drive/MyDrive/MachineLearning/机器学习/k-近邻算法/使用k-近邻算法改进约会网站的配对效果/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])classifyPerson()percentage of time spent playing video games?10 frequent flier miles earned per year?10000 liters of ice cream consumed per year?0.5 You will probably like this person: in small doses