[Machine learning]KNN algorithm Python Implementation (example: digital recognition)

# The premise of using any machine learning algorithm is to choose features.

From numpy import *import operatorfrom os import listdirdef classify0 (InX, DataSet, labels, k):    datasetsize = dataset.shape[0]    Diffmat = Tile (InX, (datasetsize,1))-dataset    Sqdiffmat = diffmat**2  &N Bsp Sqdistances = Sqdiffmat.sum (Axis=1)     distances = sqdistances**0.5    Sorteddistindicies = Distances.argsort ()         classcount={}              for I I N Range (k):        Voteilabel = labels[sorteddistindicies[i]]        CLASSCOUNT[V Oteilabel] = Classcount.get (voteilabel,0) + 1    Sortedclasscount = sorted (Classcount.iteritems (), key= Operator.itemgetter (1), reverse=true)     return sortedclasscount[0][0]def CreateDataSet ():    Group = Array ([[[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])     labels = [' A ', ' a ', ' B ', ' B ']    return group, Labelsdef file2matrix (filename):    FR =Open (filename)     NumberOfLines = Len (Fr.readlines ())         #get the number of lines in th  E file    Returnmat = zeros ((numberoflines,3))         #prepare matrix to return     Classlabelvector = []                       #prepare labels return       FR = open (filename)     index = 0    for line in Fr.readlines ():  &nbsp ;     line = Line.strip ()         Listfromline = line.split (' t ')         RE Turnmat[index,:] = listfromline[0:3]        classlabelvector.append (int (listfromline[-1))         Index + = 1    return returnmat,classlabelvector    def autonorm (dataSet): & nbsp    Minvals = dataset.min (0)     maxvals = Dataset.max (0)     ranges = maxvals-minvals    Normdataset = Zeros (shApe (DataSet))     m = dataset.shape[0]    Normdataset = Dataset-tile (Minvals, (m,1))     NOR Mdataset = Normdataset/tile (ranges, (m,1))   #element wise divide    return normdataset, ranges, MINVALS&NB Sp  def datingclasstest ():    HoRatio = 0.50       #hold out 10%    Datingdatamat,dati Nglabels = File2matrix (' datingTestSet2.txt ')       #load data setfrom file    Normmat, ranges, MI Nvals = Autonorm (datingdatamat)     m = normmat.shape[0]    numtestvecs = Int (m*horatio)     E Rrorcount = 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 (classifierresu Lt! = Datinglabels[i]): ErrOrcount + = 1.0    print "The total error rate is:%f"% (Errorcount/float (numtestvecs))     Print error count    def img2vector (filename):    Returnvect = zeros ((1,1024))     FR = open ( FileName)     For I in range (+):        LINESTR = Fr.readline ()         F or J in range (+):            RETURNVECT[0,32*I+J] = Int (linestr[j])     RETURN RET Urnvectdef handwritingclasstest ():    hwlabels = []    trainingfilelist = Listdir (' trainingDigits ')           #load the training set    m = Len (trainingfilelist)     Trainingmat = Zeros ((m,1024))     for I in range (m):        FILENAMESTR = trainingfilelist[i]  &nbsp ;     FILESTR = Filenamestr.split ('. ') [0]     #take off .txt        CLASSNUMSTR = Int (Filestr.split ('_') [0])        Hwlabels.append (CLASSNUMSTR)         trainingmat[i,:] = Img2vector (' trainingdigits/%s '% filenamestr)     testfilelist = Listdir (' testdigits ')         #iterate t Hrough the test set    Errorcount = 0.0    mtest = Len (testfilelist)     for I in range (mtest ):        FILENAMESTR = testfilelist[i]        FILESTR = Filenamestr.split ('. ') [0]     #take off .txt        CLASSNUMSTR = Int (Filestr.split ('_') [0])       & nbsp Vectorundertest = Img2vector (' testdigits/%s '% filenamestr)         Classifierresult = Classify0 ( Vectorundertest, Trainingmat, Hwlabels, 3)         print "The classifier came back with:%d, the real a Nswer is:%d "% (Classifierresult, classnumstr)         if (classifierresult! = classnumstr): Errorcoun T + = 1.0    print "\nthe total nUmber of Errors is:%d "% errorcount    print" \nthe total error rate is:%f "% (Errorcount/float (mtest)) 

[Machine learning]KNN algorithm Python Implementation (example: digital recognition)