DM/ML学习实践(一)

  1 //knn.py
  2 from numpy import *
  3 import operator
  4 import os
  5 
  6 def createDataSet():
  7     group = array([[1.0, 1.1], [1.0, 1.0], [0, 0], [0, 0.1]])
  8     labels = [‘A‘, ‘A‘, ‘B‘, ‘B‘]
  9     return group, labels
 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     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     sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True)
 23     return sortedClassCount[0][0]
 24 
 25 def file2matrix(filename):
 26     fr = open(filename)
 27     numberOfLines = len(fr.readlines())         #get the number of lines in the file
 28     returnMat = zeros((numberOfLines,3))        #prepare matrix to return
 29     classLabelVector = []                       #prepare labels return
 30     fr = open(filename)
 31     index = 0
 32     for line in fr.readlines():
 33         line = line.strip()
 34         listFromLine = line.split(‘\t‘)
 35         returnMat[index,:] = listFromLine[0:3]
 36         classLabelVector.append(round(float(listFromLine[-1])))
 37         index += 1
 38     return returnMat,classLabelVector
 39 
 40 def autoNorm(dataSet):
 41     minVals = dataSet.min(0)
 42     maxVals = dataSet.max(0)
 43     ranges = maxVals-minVals
 44     normDataSet = zeros(shape(dataSet))
 45     m = dataSet.shape[0]
 46     normDataSet = dataSet-tile(minVals, (m, 1))
 47     normDataSet = normDataSet/tile(ranges, (m, 1))
 48     return normDataSet, ranges, minVals
 49 
 50 def datingClassTest():
 51     hoRatio = 0.1
 52     datingDataMat, datingLabels = file2matrix(‘C:\\Python27\\source.txt‘)
 53     normMat, ranges, minVals = autoNorm(datingDataMat)
 54     m = normMat.shape[0]
 55     numTestVecs = int(m*hoRatio)
 56     errorCount = 0
 57     for i in range(numTestVecs):
 58         classifierResult = classify0(normMat[i,:], normMat[numTestVecs:m, :], 59                                      datingLabels[numTestVecs:m], 3);
 60         print "result :%d, the real answer is:%d" % (classifierResult, datingLabels[i])
 61         if (classifierResult != datingLabels[i]): errorCount += 1.0
 62     print "the total error rate is : %f" % (errorCount/float(numTestVecs))
 63 
 64 def img2vector(filename):
 65     returnVect = zeros((1, 1024))
 66     fr = open(filename, ‘r‘)
 67     for i in range(32):
 68         lineStr = fr.readline()
 69         for j in range(32):
 70             returnVect[0, 32*i+j] = int(lineStr[j])
 71     return returnVect
 72 
 73 def handwritingClassTest():
 74     hwLabels = []
 75     trainingFileList = os.listdir(‘D:\\PDF_BOOK\\ML\\machinelearninginaction\\Ch02\\traing\\trainingDigits‘)
 76     m = len(trainingFileList)
 77     trainingMat = zeros((m, 1024))
 78     for i in range(m):
 79         fileNameStr = trainingFileList[i]
 80         fileStr = fileNameStr.split(‘.‘)[0]
 81         classNumStr = int(fileStr.split(‘_‘)[0])
 82         hwLabels.append(classNumStr)
 83         trainingMat[i,:] = img2vector(‘D:\\PDF_BOOK\\ML\\machinelearninginaction\\Ch02\\traing\\trainingDigits\\%s‘ % fileNameStr)
 84     testFileList = os.listdir(‘D:\\PDF_BOOK\\ML\\machinelearninginaction\\Ch02\\traing\\testDigits‘)
 85     errorCount = 0.0
 86     mTest = len(testFileList)
 87     for i in range(mTest):
 88         fileNameStr = testFileList[i]
 89         fileStr = fileNameStr.split(‘.‘)[0]
 90         classNumStr = int(fileStr.split(‘_‘)[0])
 91         vectorUnderTest = img2vector(‘D:\\PDF_BOOK\\ML\\machinelearninginaction\\Ch02\\traing\\testDigits\\%s‘ % fileNameStr)
 92         classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)
 93         print "the classifier came back with:%d, the real answer is:%d" % (classifierResult, classNumStr)
 94         if (classifierResult != classNumStr): errorCount += 1.0
 95     print "\nthe total number of errors is %d" % errorCount
 96     print "\nthe total error rate is %f" % (errorCount/float(mTest))
 97 
 98 def judgeClass(filename):
 99     hwLabels = []
100     trainingFileList = os.listdir(‘D:\\PDF_BOOK\\ML\\machinelearninginaction\\Ch02\\traing\\trainingDigits‘)
101     m = len(trainingFileList)
102     trainingMat = zeros((m, 1024))
103     for i in range(m):
104         fileNameStr = trainingFileList[i]
105         fileStr = fileNameStr.split(‘.‘)[0]
106         classNumStr = int(fileStr.split(‘_‘)[0])
107         hwLabels.append(classNumStr)
108         trainingMat[i, : ] = img2vector(‘D:\\PDF_BOOK\\ML\\machinelearninginaction\\Ch02\\traing\\trainingDigits\\%s‘ % fileNameStr)
109     testVector = img2vector(filename)
110     result = classify0(testVector, trainingMat, hwLabels, 3)
111     return result

 1 import Image, ImageDraw, ImageFont, ImageFilter
 2 import random
 3 import array
 4 from numpy import *
 5 import sys
 6 import knn
 7 
 8 s = ‘2‘
 9 
10 filename = ‘C:\\Users\\lg\\Desktop\\‘ + s + ‘.txt‘
11 im = Image.open(‘C:\\Users\\lg\\Desktop\\‘+s+‘.png‘)
12 f = open(filename, ‘w+‘)
13 
14 data = zeros(32*32)
15 for x in range(32):
16     for y in range(32):
17         val = 1
18         if im.getpixel((x, y)) == (255, 255, 255):
19             val = 0
20         data[32*y+x] = val
21 
22 for x in range(32):
23     for y in range(32):
24         f.write(str(int(data[32*x+y])))
25     if (x != 31): f.write(‘\n‘)
26 f.close()
27 print knn.judgeClass(filename)