机器学习实战（二）个人笔记

 1 # -*- coding:utf-8 -*-
 2 from numpy import *
 3 import operator
 4 import math
 5 import matplotlib
 6 import matplotlib.pyplot as plt
 7 import numpy as np
 8 import random
 9 import collections
10 
11 def classify0(inX,dataSet,labels,k):
12      dataSetSize = dataSet.shape[0]
13      diffMat = tile(inX,(dataSetSize,1)) - dataSet
14      sqDiffMat = diffMat**2
15      print sqDiffMat
16      sqDistances = sqDiffMat.sum(axis = 1)
17      distances = sqDistances.argsort()
18     # sortedDistIndicies = distances.argsort()
19      classCount=collections.OrderedDict()
20      for i in range(k):
21          voteIlabel = labels[distances[i]]
22          classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1
23      print classCount
24      sortedClassCount = sorted(classCount.iteritems(),key = operator.itemgetter(1),reverse = True)
25      print sortedClassCount
26      return sortedClassCount[0][0]
27 
28 def file2matrix(filename):
29     fr = open(filename)
30     arrayOLines = fr.readlines()
31     numberOfLines = len(arrayOLines)
32     returnMat = zeros((numberOfLines,3))
33     classLabelVector = []
34     index = 0
35     for line in arrayOLines:
36         line = line.strip()
37         listFromLine = line.split(‘ ‘)
38         returnMat[index,:] = listFromLine[0:3]
39         classLabelVector.append(int(listFromLine[0]))
40         index+=1
41     return returnMat,classLabelVector
42 
43 def autoNorm(dataSet):
44     minVals = dataSet.min(0)
45     maxVals = dataSet.max(0)
46     ranges = maxVals - minVals
47     normDataSet = zeros(shape(dataSet))
48     m = dataSet.shape[0]
49     normDataSet = dataSet - tile(minVals,(m,1))
50     normDataSet = normDataSet/tile(ranges,(m,1))
51     return normDataSet,ranges,minVals
52 
53 datingdataMat,datingLabels = file2matrix(‘/Users/tiemuer/PycharmProjects/kNN/data.txt‘)
54 fig = plt.figure()
55 ax = fig.add_subplot(111)
56 ax.scatter(datingdataMat[:,0],datingdataMat[:,1],15.0*array(datingLabels),15.0*array(datingLabels))
57 plt.show()
58 
59 def datingClassTest():
60     hoRatio = 0.10
61     datingdataMat,datingLabels = file2matrix(‘/Users/tiemuer/PycharmProjects/kNN/data.txt‘)
62     normMat,ranges,minVals = autoNorm(datingdataMat)
63     m = normMat.shape[0]
64     numTestVecs = int(m*hoRatio)
65     errorCount = 0.0
66     n = []
67     for i in range(numTestVecs):
68        n.append(int(random.randint(0,m)))
69     for i in n:
70         classifierResult = classify0(normMat[i,:],normMat[:m,:],datingLabels[:m],3)
71         print "the classifier cameback with %d,the real answer is:%d"%(classifierResult,datingLabels[i])
72         if(classifierResult!=datingLabels[i]):errorCount+=1.0
73     print "the total error rate is %f"%(errorCount/float(numTestVecs))
74 
75 
76 datingClassTest()