[Python] 实施kNN算法

Posted 2020-07-20 风马一族

一、在PyCharm 5.0.4(编写python程序的IDE) 编写kNN.py文件的代码

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1、 kNN.py  运算符模块

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1 from numpy import *
2 import operator
3 
4 #运算符模块   创建数据集和标签
5 def createDataSet():
6     group = array([[1.0, 1.1], [1.0, 1.0], [0, 0], [0, 0.1]])
7     labels = [\'A\', \'A\', \'B\', \'B\']
8     return group, labels

1)、打开命令行

　　先进入kNN.py的所在文件夹，在对kNN.py进行程序处理，效果如下图所示

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2、 kNN.py  k-近邻算法

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 1 from numpy import *
 2 import operator
 3 
 4 #运算符模块
 5 def createDataSet():
 6     group = array([[1.0, 1.1], [1.0, 1.0], [0, 0], [0, 0.1]])
 7     labels = [\'A\', \'A\', \'B\', \'B\']
 8     return group, labels
 9 
10 #k-近邻算法  此模块需要使用运算符模块的group/labels
11 def classify0(inX, dataSet, labels, k):
12     dataSetSize = dataSet.shape[0]
13     diffMat = tile(inX, (dataSetSize, 1)) - dataSet
14     sqDiffMat = diffMat ** 2
15     sqDistances = sqDiffMat.sum(axis=1)
16     distances = sqDistances ** 0.5
17     sortedDistIndicies = distances.argsort()
18     classCount = {}
19     for i in range(k):
20         voteIlabel = labels[sortedDistIndicies[i]]
21         classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1
22 
23     sortedClassCount = sorted(classCount.iteritems(),
24                               key=operator.itemgetter(1),
25                               reverse=True)
26     return sortedClassCount[0][0]

2） 在1）的基础上，进行2）的内容，可以修改参数
　　

　　

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3、 kNN.py  准备数据：从文本文件中解析数据

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 1 # 将文本记录转换为NumPy的解析程序
 2 def file2matrix(filename):
 3     fr = open(filename,\'r\')
 4     numberOfLines = len(fr.readlines())  # get the number of lines in the file
 5     returnMat = zeros((numberOfLines, 3))  # prepare matrix to return
 6     classLabelVector = []  # prepare labels return
 7     fr = open(filename)
 8     index = 0
 9     for line in fr.readlines():
10         line = line.strip()
11         listFromLine = line.split(\'\\t\')
12         returnMat[index, :] = listFromLine[0:3]
13         classLabelVector.append(int(listFromLine[-1]))
14         index += 1
15     return returnMat, classLabelVector

 datingTestSet2.txt文件可以从（博客园）文件下载

 

 

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4、 kNN.py  使用Matplotlib创建散点图

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datingTestSet2.txt 文件的数据通过matplotlib，图形化的表现出来

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5、 kNN.py  使用Matplotlib创建散点图　　表示不同属性的点，使用不同颜色进行表示

　　　　警告：import os

　　　　　　　from numpy import *

　　　　　　　这两句必须加上，否则会报如下，错误提示信息：

　　　　　　　　Traceback (most recent call last):
  　　　　　　　　　　File "<stdin>", line 1, in <module>
　　　　　　　　　　  NameError: name \'array\' is not defined

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让点出现颜色划分的关键代码是：

ax.scatter(datingDataMat[:,1],datingDataMat[:,2],15.0*array(datingLabels),15.0*array(datingLabels))

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6、 kNN.py　　归一化特征值

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 1 # 归一化特征值
 2 def autoNorm(dataSet):
 3     minVals = dataSet.min(0)
 4     maxVals = dataSet.max(0)
 5     ranges = maxVals - minVals
 6     normDataSet = zeros(shape(dataSet))
 7     m = dataSet.shape[0]
 8     normDataSet = dataSet - tile(minVals, (m, 1))
 9     normDataSet = normDataSet / tile(ranges, (m, 1))
10     return normDataSet, ranges, minVals

 

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7、 kNN.py 　　分类器针对约会网站的测试代码

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 1 # 分类器针对约会网站的测试代码
 2 def datingClassTest():
 3     hoRatio = 0.10
 4     datingDataMat, datingLabels = file2matrix(\'datingTestSet2.txt\')
 5     normMat, ranges, minVals = autoNorm(datingDataMat)
 6     m = normMat.shape[0]
 7     numTestVecs = int(m * hoRatio)
 8     errorCount = 0.0
 9     for i in range(numTestVecs):
10         classifierResult = classify0(normMat[i, :], normMat[numTestVecs:m, :], datingLabels[numTestVecs:m], 3)
11         print "the classifier came back with: %d, the real answer is : %d" % (classifierResult, datingLabels[i])
12 
13         if (classifierResult != datingLabels[i]):
14             errorCount += 1.0
15 
16     print  "the total error rate is : %f " % (errorCount / float(numTestVecs))

 

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8、 kNN.py 　　使用算法：构建完整可用系统

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 1 #! /usr/bin/env python
 2 # -*- coding: gbk -*-        用来解决中文乱码的注解
 3 
 4 from numpy import *
 5 import operator
 6 
 7 # 运算符模块
 8 def createDataSet():
 9     group = array([[1.0, 1.1], [1.0, 1.0], [0, 0], [0, 0.1]])
10     labels = [\'A\', \'A\', \'B\', \'B\']
11     return group, labels
12 
13 
14 # k-近邻算法
15 def classify0(inX, dataSet, labels, k):
16     dataSetSize = dataSet.shape[0]
17     diffMat = tile(inX, (dataSetSize, 1)) - dataSet
18     sqDiffMat = diffMat ** 2
19     sqDistances = sqDiffMat.sum(axis=1)
20     distances = sqDistances ** 0.5
21     sortedDistIndicies = distances.argsort()
22     classCount = {}
23     for i in range(k):
24         voteIlabel = labels[sortedDistIndicies[i]]
25         classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1
26 
27     sortedClassCount = sorted(classCount.iteritems(),
28                               key=operator.itemgetter(1),
29                               reverse=True)
30     return sortedClassCount[0][0]
31 
32 
33 # 将文本记录转换为NumPy的解析程序
34 def file2matrix(filename):
35     fr = open(filename, \'r\')
36     numberOfLines = len(fr.readlines())  # get the number of lines in the file
37     returnMat = zeros((numberOfLines, 3))  # prepare matrix to return
38     classLabelVector = []  # prepare labels return
39     fr = open(filename)
40     index = 0
41     for line in fr.readlines():
42         line = line.strip()
43         listFromLine = line.split(\'\\t\')
44         returnMat[index, :] = listFromLine[0:3]
45         classLabelVector.append(int(listFromLine[-1]))
46         index += 1
47     return returnMat, classLabelVector
48 
49 
50 # 归一化特征值
51 def autoNorm(dataSet):
52     minVals = dataSet.min(0)
53     maxVals = dataSet.max(0)
54     ranges = maxVals - minVals
55     normDataSet = zeros(shape(dataSet))
56     m = dataSet.shape[0]
57     normDataSet = dataSet - tile(minVals, (m, 1))
58     normDataSet = normDataSet / tile(ranges, (m, 1))
59     return normDataSet, ranges, minVals
60 
61 
62 # 分类器针对约会网站的测试代码
63 def datingClassTest():
64     hoRatio = 0.10
65     datingDataMat, datingLabels = file2matrix(\'datingTestSet2.txt\')
66     normMat, ranges, minVals = autoNorm(datingDataMat)
67     m = normMat.shape[0]
68     numTestVecs = int(m * hoRatio)
69     errorCount = 0.0
70     for i in range(numTestVecs):
71         classifierResult = classify0(normMat[i, :], normMat[numTestVecs:m, :], datingLabels[numTestVecs:m], 3)
72         print "分类器的回复是：%d，真正的答案是：%d" % (classifierResult, datingLabels[i])
73 
74         if (classifierResult != datingLabels[i]):
75             errorCount += 1.0
76 
77     print "总误差率  : %f " % (errorCount / float(numTestVecs))
78 
79 
80 # 约会网预测函数
81 def classifyPerson():
82     resultList = [\'完全没有兴趣\', \'有一点吧\', \'特别感兴趣\']
83     percentTats = float(raw_input("玩电子游戏的时间百分比?"))
84     ffMiles = float(raw_input("每年的飞行里程数是多少?"))
85     iceCream = float(raw_input("每年的冰淇淋消费量是多少?"))
86     datingDataMat, datingLabels = file2matrix(\'datingTestSet2.txt\')
87     norMat, ranges, minVals = autoNorm(datingDataMat)
88     inArr = array([ffMiles, percentTats, iceCream])
89     classifierResult = classify0((inArr - minVals) / ranges, norMat, datingLabels, 3)
90     print "你可能会喜欢这个人 :", resultList[classifierResult - 1]

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9、 kNN.py 　　准备数据：将图像转换为测试向量

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1 # 准备数据：将图像转换为测试向量
2 def img2vector(filename):
3     returnVect = zeros((1, 1024))
4     fr = open(filename)
5     for i in range(32):
6         lineStr = fr.readline()
7         for j in range(32):
8             returnVect[0, 32 * i + j] = int(lineStr[j])
9     return returnVect

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10、 kNN.py 　　测试算法：使用k-近邻算法识别手写数字

　　　　　　注意：本文需要使用  from os import listdir

　　　　　　数据digits.zip 存放在博客园的文件夹中，或者下载《机器学习实战》的源代码，里面有

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 1 #! /usr/bin/env python
 2 # -*- coding: gbk -*-
 3 
 4 from numpy import *
 5 import operator
 6 from os import listdir
 7 
 8 # 测试算法：使用K-近邻算法识别手写数字
 9 def handwritingClassTest():
10     hwLabels = []
11     trainingFileList = listdir(\'trainingDigits\')
12     m = len(trainingFileList)
13     trainingMat = zeros((m, 1024))
14     for i in range(m):
15         fileNameStr = trainingFileList[i]
16         fileStr = fileNameStr.split(\'.\')[0]
17         classNumStr = int(fileStr.split(\'_\')[0])
18         hwLabels.append(classNumStr)
19         trainingMat[i, :] = img2vector(\'trainingDigits/%s\' % fileNameStr)
20 
21     testFileList = listdir(\'testDigits\')
22     errorCount = 0.0
23     mTest = len(testFileList)
24     for i in range(mTest):
25         fileNameStr = testFileList[i]
26         fileStr = fileNameStr.split(\'.\')[0]
27         classNameStr = int(fileStr.split(\'_\')[0])
28         vectorUnderTest = img2vector(\'testDigits/%s\' % fileNameStr)
29         classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)
30         print "\\n分类器的回复是：%d，真正的答案是：%d" % (classifierResult, classNumStr)
31 
32         if (classifierResult != classNumStr):
33             errorCount += 1.0
34 
35         print "\\t 错误的总数是 : %d " % errorCount
36         print "\\t 总误差率是 : %f" % (errorCount / float(mTest))

 

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　　图片太长，其中截断了，读者可以自行测试看看效果

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总结：以上就是机器学习实战的第二章的代码内容，没想到，三月份开始学习的内容，等到六月份才开始能够成功实现，主要是Numpy的安装，太狗了!

　　其间，学习安装Numpy与.whl类型的文件，会使用基本的matplotlib。k-近邻算法的模样还没有认清楚。接下来，进行决策树，过一段时间就可以

　　认识k-近邻算法了吧