import matplotlib

import matplotlib.pyplot as plt

fig = plt.figure()

ax = fig.add_subplot(111)

datingDataMat,datingLabels = kNN.file2matrix(‘f:\\datingTestSet.txt’)

ax.scatter(datingDataMat[:,1],datingDataMat[:,2],

15.0*numpy.array(datingLabels),15.0*numpy.array(datingLabels))

plt.xlabel(‘Percentage of Time Spent Playing Video Games’)

plt.ylabel(‘Liters of Ice Cream Consumed Per Week’)

plt.show()

3D图:

import numpy

import kNN

import matplotlib

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()

ax = fig.add_subplot(111,projection=‘3d’)

datingDataMat,datingLabels = kNN.file2matrix(‘f:\\datingTestSet.txt’)

ax.scatter(datingDataMat[:,0],datingDataMat[:,1],datingDataMat[:,2],

15.0*numpy.array(datingLabels),15.0*numpy.array(datingLabels),15.0*numpy.array(datingLabels))

ax.set_xlabel(‘mei nian huo qu de fei xing chang ke li cheng shu’)

ax.set_ylabel(‘wan you xi shi jian bi li’)

ax.set_zlabel(‘mei zhou xiao hao de bing qi li shuliang’)

plt.show()

多图:

import numpy

import kNN

import matplotlib

import matplotlib.pyplot as plt

fig = plt.figure()

ax1 = fig.add_subplot(311)

datingDataMat,datingLabels = kNN.file2matrix(‘f:\\datingTestSet.txt’)

ax1.scatter(datingDataMat[:,0],datingDataMat[:,1],

15.0*numpy.array(datingLabels),15.0*numpy.array(datingLabels))

ax1.set_xlabel(‘fly’)

ax2 = fig.add_subplot(312)

ax2.scatter(datingDataMat[:,0],datingDataMat[:,2],

15.0*numpy.array(datingLabels),15.0*numpy.array(datingLabels))

ax2 = fig.add_subplot(313)

ax2.scatter(datingDataMat[:,1],datingDataMat[:,2],

15.0*numpy.array(datingLabels),15.0*numpy.array(datingLabels))

plt.show()

不熟悉的函数:

add_subplot:用于指定图像的位置,例如111,指图像分成一行一列,在第一幅图上画

scatter:画散点图,必须输入的有x,y坐标,可选项有颜色形状等

zero:创建0矩阵

归一化:

处理不同取值范围的特征值时,通常需要将数值未硬化,如果将取值范围处理为0到1或者-1到1之间,下面公式可以将任意取值范围的特征值转化为0到1的区间内

newValue = (oldValue-min)/(max-min)

min,max分别是数据集中特征值最大值和最小值,程序如下

def autoNum(dataSet):

#获取每一列的最小值

minVals = dataSet.min(0)

#获取每一列的最大值

maxVals = dataSet.max(0)

#最大值和最小值的差

ranges = maxVals - minVals

#将每一行归一化

normDataSet = numpy.zeros(numpy.shape(dataSet))

m = dataSet.shape[0]

normDataSet = dataSet - numpy.tile(minVals,(m,1))

normDataSet = normDataSet/numpy.tile(ranges,(m,1))

return normDataSet,ranges,minVals

容易搞错的是min(0)返回的是每一列的最小值,而不是第0列的最小值,min()返回的是所有值的最小值,min(1)返回的是每一行的最小值

测试程序:

def datingClassTest():

‘’’

用于测试分类器

‘’’

hoRatio = 0.10

datingDataMating,datingLabels = file2matrix(‘f:\\datingTestSet.txt’)

normMat,ranges,minVals = autoNum(datingDataMating)

m = normMat.shape[0]

numTestVecs = int(m*hoRatio)

errorCount = 0.0

for i in range(numTestVecs):

classifierResult = classify0(normMat[i,:],normMat[numTestVecs:m,:],

datingLabels[numTestVecs:m],3)

print(‘the classifier came back with: %d,the real answer is:%d’%

(classifierResult,datingLabels[i]))

if(classifierResult != datingLabels[i]):errorCount += 1.0

print(‘the total error rate is:%f’%(errorCount/float(numTestVecs)))

测试结果:

the classifier came back with: 3,the real answer is:3

the classifier came back with: 2,the real answer is:2

the classifier came back with: 1,the real answer is:1

the classifier came back with: 1,the real answer is:1

the classifier came back with: 1,the real answer is:1

the classifier came back with: 1,the real answer is:1

the classifier came back with: 3,the real answer is:3

the classifier came back with: 3,the real answer is:3

the classifier came back with: 1,the real answer is:1

the classifier came back with: 3,the real answer is:3

the classifier came back with: 1,the real answer is:1

the classifier came back with: 1,the real answer is:1

the classifier came back with: 2,the real answer is:2

the classifier came back with: 1,the real answer is:1

the classifier came back with: 1,the real answer is:1

the classifier came back with: 1,the real answer is:1

the classifier came back with: 1,the real answer is:1

the classifier came back with: 1,the real answer is:1

the classifier came back with: 2,the real answer is:2

the classifier came back with: 3,the real answer is:3

the classifier came back with: 2,the real answer is:2

the classifier came back with: 1,the real answer is:1

the classifier came back with: 3,the real answer is:2

the classifier came back with: 3,the real answer is:3

the classifier came back with: 2,the real answer is:2

the classifier came back with: 3,the real answer is:3

the classifier came back with: 2,the real answer is:2

the classifier came back with: 3,the real answer is:3

the classifier came back with: 2,the real answer is:2

the classifier came back with: 1,the real answer is:1

the classifier came back with: 3,the real answer is:3

the classifier came back with: 1,the real answer is:1

the classifier came back with: 3,the real answer is:3

the classifier came back with: 1,the real answer is:1

the classifier came back with: 2,the real answer is:2

the classifier came back with: 1,the real answer is:1

the classifier came back with: 1,the real answer is:1

the classifier came back with: 2,the real answer is:2

the classifier came back with: 3,the real answer is:3

the classifier came back with: 3,the real answer is:3

the classifier came back with: 1,the real answer is:1

the classifier came back with: 2,the real answer is:2

the classifier came back with: 3,the real answer is:3

the classifier came back with: 3,the real answer is:3

the classifier came back with: 3,the real answer is:3

the classifier came back with: 1,the real answer is:1

the classifier came back with: 1,the real answer is:1

the classifier came back with: 1,the real answer is:1

the classifier came back with: 1,the real answer is:1

the classifier came back with: 2,the real answer is:2

the classifier came back with: 2,the real answer is:2

the classifier came back with: 1,the real answer is:1

the classifier came back with: 3,the real answer is:3

the classifier came back with: 2,the real answer is:2

the classifier came back with: 2,the real answer is:2

the classifier came back with: 2,the real answer is:2

the classifier came back with: 2,the real answer is:2

the classifier came back with: 3,the real answer is:3

the classifier came back with: 1,the real answer is:1

the classifier came back with: 2,the real answer is:2

the classifier came back with: 1,the real answer is:1

the classifier came back with: 2,the real answer is:2

the classifier came back with: 2,the real answer is:2

the classifier came back with: 2,the real answer is:2

the classifier came back with: 2,the real answer is:2

the classifier came back with: 2,the real answer is:2

the classifier came back with: 3,the real answer is:3

the classifier came back with: 2,the real answer is:2

the classifier came back with: 3,the real answer is:3

the classifier came back with: 1,the real answer is:1

the classifier came back with: 2,the real answer is:2

the classifier came back with: 3,the real answer is:3

the classifier came back with: 2,the real answer is:2

the classifier came back with: 2,the real answer is:2

the classifier came back with: 3,the real answer is:1

the classifier came back with: 3,the real answer is:3

the classifier came back with: 1,the real answer is:1

the classifier came back with: 1,the real answer is:1

the classifier came back with: 3,the real answer is:3

the classifier came back with: 3,the real answer is:3

the classifier came back with: 1,the real answer is:1

the classifier came back with: 2,the real answer is:2

the classifier came back with: 3,the real answer is:3

the classifier came back with: 3,the real answer is:1

the classifier came back with: 3,the real answer is:3

the classifier came back with: 1,the real answer is:1

the classifier came back with: 2,the real answer is:2

the classifier came back with: 2,the real answer is:2

the classifier came back with: 1,the real answer is:1

the classifier came back with: 1,the real answer is:1

the classifier came back with: 3,the real answer is:3

the classifier came back with: 2,the real answer is:3

the classifier came back with: 1,the real answer is:1

the classifier came back with: 2,the real answer is:2

the classifier came back with: 1,the real answer is:1

the classifier came back with: 3,the real answer is:3

the classifier came back with: 3,the real answer is:3

the classifier came back with: 2,the real answer is:2

the classifier came back with: 1,the real answer is:1

the classifier came back with: 3,the real answer is:1

the total error rate is:0.050000

完整的程序(python3可运行):

import numpy

import operator

def createDataSet():

‘’’

返回一个训练集和标签向量

‘’’

#训练集

group = numpy.array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])

#标签向量

labels = [‘A’,‘A’,‘B’,‘B’]

return group,labels

def classify0(inX,dataSet,labels,k):

‘’’

用于实现k_近邻算法,接收输入一个向量,一个训练集,一个标签向量,一个K值

判断向量所属的类别

‘’’

#读取矩阵第一维度的长度

dataSetSize = dataSet.shape[0]

#输入向量与训练集差值的数组

diffMat = numpy.tile(inX,(dataSetSize,1)) - dataSet

#计算各点与训练集的距离

sqDiffMat = diffMat**2

sqDistances = sqDiffMat.sum(axis=1)

distance = sqDistances**0.5

#将距离数组的下标按照距离大小排序

sortedDistIndicies = distance.argsort()

classCount = {}

#在k的范围内,分别计算两类的数目

for i in range(k):

voteIlabel = labels[sortedDistIndicies[i]]

classCount[voteIlabel] = classCount.get(voteIlabel,0)+1

#以k以内类别数目排序

sortedClassCount = sorted(classCount.items(),key=operator.itemgetter(1),

reverse = True)

#返回数目最多的类(即输入向量应该属于的类)

return sortedClassCount[0][0]

def file2matrix(filename):

‘’’

用于解析训练集文件

‘’’

ValueOfClassLabel = {}

Value = [1,2,3]

def getValueOfClassLabel(ClassLabel):

val = 1;

if not ClassLabel in ValueOfClassLabel.keys():

ValueOfClassLabel[ClassLabel] = Value.pop()

return ValueOfClassLabel[ClassLabel]

file = open(filename)

arrayOLines = file.readlines()

#文件的行数

numberOfLines = len(arrayOLines)

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