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kNN

kNN_约会网站匹配效果改进

【准备数据】数据处理函数

import numpy as np
import os
def file2matrix(filename):fr = open(filename)arrayOLines = fr.readlines()numberOfLines = len(arrayOLines)returnMat = np.zeros((numberOfLines,3))classLabelVector = []index = 0for line in arrayOLines:line = line.strip()listFromLine = line.split('\t')returnMat[index,:] = listFromLine[0:3]classLabelVector.append(label2int(listFromLine[-1]))index += 1return returnMat,classLabelVectordef label2int(labelName):if labelName == 'didntLike':return 0elif labelName == 'smallDoses':return 1elif labelName == 'largeDoses':return 2datingDataMat,datingLabels = file2matrix('datingTestSet.txt')

【分析数据】绘制数据散点图

import matplotlib
import matplotlib.pyplot as pltdef arrColor(labels):arrColor = []for i in datingLabels:if i == 0:arrColor.append('r')elif i == 1:arrColor.append('y')elif i == 2:arrColor.append('g')  return arrColorfig = plt.figure(figsize=(8,20))
#plt.axis([-1,22,-0.1,1.8])
ax1 = fig.add_subplot(311)
ax1.scatter(datingDataMat[:,0],datingDataMat[:,1],c = arrColor(datingLabels))ax2 = fig.add_subplot(312)
ax2.scatter(datingDataMat[:,1],datingDataMat[:,2],c = arrColor(datingLabels))ax3 = fig.add_subplot(313)
ax3.scatter(datingDataMat[:,0],datingDataMat[:,2],c = arrColor(datingLabels))plt.show()

由数据两两对比的三幅散点图分布可知，取第一列和第二列为x，y轴绘制散点图（图一）时，三种类型的人基本分属于不同的区域。

注:用scatter绘制散点图时，当数据在列表中未分类时，无法按照颜色给出图例。
想要显示图例，需对数据进行分类，然后分别用不同的scatter绘制，则可有不同分类的图例。

import matplotlib.font_manager as fm
myfont = fm.FontProperties(fname='C:/Windows/Fonts/msyh.ttf')def showClassify(datingDataMat,datingLabels,x,y,x_name='',y_name=''):type1_x = []type1_y = []type2_x = []type2_y = []type3_x = []type3_y = []for i in range(len(datingLabels)):if datingLabels[i] == 0:type1_x.append(datingDataMat[i][x])type1_y.append(datingDataMat[i][y])if datingLabels[i] == 1:type2_x.append(datingDataMat[i][x])type2_y.append(datingDataMat[i][y])if datingLabels[i] == 2:type3_x.append(datingDataMat[i][x])type3_y.append(datingDataMat[i][y])fig = plt.figure()plt.xlabel(x_name,fontproperties=myfont)plt.ylabel(y_name,fontproperties=myfont)#plt.title("pythoner.com",fontproperties=myfont)ax = fig.add_subplot(111)type1 = ax.scatter(type1_x,type1_y,c = 'r')type2 = ax.scatter(type2_x,type2_y,c = 'y')type3 = ax.scatter(type3_x,type3_y,c = 'g') ax.legend((type1, type2, type3), (u'不喜欢', u'魅力一般', u'极具魅力'),loc=2,prop=myfont)plt.show()showClassify(datingDataMat,datingLabels,0,1,u'每年获取的飞行常客里程数',u'玩视频游戏所耗时间百分比')

【准备数据】归一化特征值函数

def autoNorm(dataSet):minValues = dataSet.min(0)maxValues = dataSet.max(0)ranges = maxValues - minValuesnormDataSet = np.zeros(np.shape(dataSet))m = dataSet.shape[0]normDataSet = dataSet - np.tile(minValues,(m,1))normDataSet = normDataSet / np.tile(ranges,(m,1))return normDataSet, ranges, minValuesnormMat, ranges, minValues = autoNorm(datingDataMat)

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]])

k-近邻算法

import operator
def classify0(inX, dataSet, labels, k):dataSetSize = dataSet.shape[0]diffMat = np.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]classify0([ 0.28542943,  0.06892523,  0.47449629],normMat,datingLabels,5)
0

【测试算法】测试错误率函数

def datingClassTest():hoRatio = 0.1datingDataMat,datingLabels = file2matrix('datingTestSet.txt')normMat, ranges, minValues = autoNorm(datingDataMat)m=normMat.shape[0]numTestVecs = int(m*hoRatio)errorCount = 0.0for i in range(numTestVecs):classifierResult = classify0(normMat[i,:],normMat[numTestVecs:,:],datingLabels[numTestVecs:],5)if(classifierResult != datingLabels[i]):errorCount += 1.0print("the total error rate is: %f" % (errorCount/float(numTestVecs)))datingClassTest()
the total error rate is: 0.040000

【使用算法】采集数据并输出预测结果

def classifyPerson():resultList = ['not at all','in small doses','in large doses']percentTats = float(input("percentage of thime 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('datingTestSet.txt')normMat, ranges, minValues = autoNorm(datingDataMat)inArr = np.array([ffMiles,percentTats,iceCream])classifierResult = classify0((inArr-minValues)/ranges,normMat,datingLabels,5)print("You will probably like this person:",resultList[classifierResult])classifyPerson()  
percentage of thime spent playing video games?8
frequent flier miles earned per year?40000
liters of ice cream consumed per year?0.95
You will probably like this person: in large doses

本文标签： KNN

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