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, 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 operatordef classify0(inX, dataSet, labels, k):dataSetSize = dataSet.shape[0]diffMat = tile(inX, (dataSetSize, 1)) - dataSetsqDiffMat = diffMat ** 2sqDistances = sqDiffMat.sum(axis=1)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(), 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 - minValsnormDataSet = 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 = 0.1datingDataMat, 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
之SpringCloud Consul)
