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, 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([[4.0920000e+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, 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([9.1273000e+04, 2.0919349e+01, 1.6943610e+00])
minVals
array([0. , 0. , 0.001156])
测试算法：作为完整程序验证分类器
def datingClassTest(): hoRatio = 0.1 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()
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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
