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谷歌笔记本#xff08;可选#xff09;
准备数据#xff1a;从文本文件中解析数据
编写算法#xff1a;编写kNN算法
分析数据#xff1a;使用Matplotlib创建散点图
准备数据#xff1a;归一化数值
测试算法#xff1a;作为完整程序验证分类器
使用算法…目录
谷歌笔记本可选
准备数据从文本文件中解析数据
编写算法编写kNN算法
分析数据使用Matplotlib创建散点图
准备数据归一化数值
测试算法作为完整程序验证分类器
使用算法构建完整可用系统 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 0for line in arrayOfLines:line line.strip()listFromLine line.split(\t)returnMat[index, :] listFromLine[0:3]classLabelVector.append(int(listFromLine[-1]))index 1return returnMat, classLabelVector
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)
datingDataMat
array([[4.0920000e04, 8.3269760e00, 9.5395200e-01], [1.4488000e04, 7.1534690e00, 1.6739040e00], [2.6052000e04, 1.4418710e00, 8.0512400e-01], ..., [2.6575000e04, 1.0650102e01, 8.6662700e-01], [4.8111000e04, 9.1345280e00, 7.2804500e-01], [4.3757000e04, 7.8826010e00, 1.3324460e00]])
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 operatordef classify0(inX, dataSet, labels, k):dataSetSize dataSet.shape[0]diffMat tile(inX, (dataSetSize, 1)) - dataSetsqDiffMat diffMat ** 2sqDistances sqDiffMat.sum(axis1)distances sqDistances**0.5sortedDistIndicies distances.argsort()classCount {}for i in range(k):voteIlabel labels[sortedDistIndicies[i]]classCount[voteIlabel] classCount.get(voteIlabel, 0) 1sortedClassCount sorted(classCount.items(), keyoperator.itemgetter(1), reverseTrue)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 - minValsnormDataSet zeros(shape(dataSet))m dataSet.shape[0]normDataSet dataSet - tile(minVals, (m,1))normDataSet normDataSet/tile(ranges, (m,1))return normDataSet, ranges, minVals
normMat, ranges, minVals autoNorm(datingDataMat)
normMat
array([[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]])
ranges
array([9.1273000e04, 2.0919349e01, 1.6943610e00])
minVals
array([0. , 0. , 0.001156]) 测试算法作为完整程序验证分类器 def datingClassTest():hoRatio 0.1datingDataMat, 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 0for 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 1print(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 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: 2, the real answer is: 2
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: 3, the real answer is: 2
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: 3, the real answer is: 3
the classifierResult came back with: 2, the real answer is: 2
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: 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: 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: 2, the real answer is: 2
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: 2, the real answer is: 2
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: 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: 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: 2, the real answer is: 2
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: 2, the real answer is: 2
the classifierResult came back with: 2, the real answer is: 2
the classifierResult came back with: 2, the real answer is: 2
the classifierResult came back with: 2, the real answer is: 2
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: 2, the real answer is: 2
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: 2, the real answer is: 2
the classifierResult came back with: 2, the real answer is: 2
the classifierResult came back with: 2, the real answer is: 2
the classifierResult came back with: 2, the real answer is: 2
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: 3, the real answer is: 3
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: 3, the real answer is: 3
the classifierResult came back with: 2, the real answer is: 2
the classifierResult came back with: 2, the real answer is: 2
the classifierResult came back with: 3, 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: 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: 2, the real answer is: 2
the classifierResult came back with: 3, the real answer is: 3
the classifierResult came back with: 3, 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: 2, the real answer is: 2
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: 3, the real answer is: 3
the classifierResult came back with: 2, the real answer is: 3
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: 3, the real answer is: 3
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: 3, the real answer is: 1
the total error rate is: 0.050000 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
