pattern recognition and machine learning github

=filenamestr.split (".") [0]TenClasnumstr=int (Filestr.split ("_") [0])#gets the actual value of the sample into the label array One hwlabels.append (CLASNUMSTR) ATraningmat[i,:]=img2vector ("trainingdigits/{}". Format (FILENAMESTR))#to convert a sample into a 1*1024 line into a training sample sequence - -Testfilelist=listdir ("testdigits")#Test Sample Catalog theError=0 -mtest=Len (testfilelist) - forIinchRange (mtest): -Filenamestr=Testfilelist[i] +Filestr=filenamestr.split (".") [0] -C

[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

KNNAlgorithmIt is an excellent entry-level material for machine learning. The book explains as follows: "There is a sample data set, also known as a training sample set, and each data in the sample set has tags, that is, we know the correspondence between each piece of data in the sample set and its category. After entering new data without tags, compare each feature of the new data with the features corres

-Sortedclasscount = sorted (Classcount.items (), Key=operator.itemgetter (1), reverse=True) - returnSortedclasscount[0][0] - + - defimg2vector (filename): +f =open (filename) AReturnvect = Zeros ((1,1024)) at forIinchRange (32): -line =F.readline () - forJinchRange (32): -RETURNVECT[0,I*32+J] =Int (line[j]) - returnReturnvect - in - defhandwritingclasstest (): toFileList = Os.listdir ('trainingdigits') +m =Len (fileList) -Traingmat = Zeros ((M, 1024)) theHwlabels = []

sklearn.neighbors import Kneig Hborsclassifier import time if __name__ = = "__main__": train_num = 20000 Test_num = 30000 data = Pd.read_csv (' Train.csv ') Train_data = data.values[0:train_num,1:] Train_label = data.values[0:train_num,0] Test_data = data . values[train_num:test_num,1:] Test_label = data.values[train_num:test_num,0] t = time.time () Pca=PCA (n_compo nents = 0.8) train_x = Pca.fit_transform (train_data) test_x = Pca.transform (test_data) neighbors = KNeighborsC Lassifier (n_neig

)5Trainingmat = Zeros ((M, 1024))6 forIinchRange (m):7Filenamestr =Trainingfilelist[i]8Filestr = Filenamestr.split ('.') [0]9classnumstr = Int (Filestr.split ('_') [0])Ten hwlabels.append (CLASSNUMSTR) OneTrainingmat[i,:] = Img2vector ('trainingdigits/%s'%filenamestr) ATestfilelist = Listdir ('testdigits') -Errorcount = 0.0 -Mtest =Len (testfilelist) the forIinchRange (mtest): -Filenamestr =Testfilelist[i] -Filestr = Filenamestr.split ('.') [0] -classnumstr = Int (Filestr.split ('_') [

1 Preparing data: Converting an image to a test vectorThere are two kinds of data sets, the training data set and the test data set, respectively, there are 2000, 900.We will convert a 32*32 binary image matrix to a vector of 1 x 1024 so that the classifier used in the first two sections can process the digital image information.Code: return returnVectEffect:Test algorithmCode:Def handwritingtest ():Hwlabels = []Trainingfilelist = Os.listdir (' training