pattern recognition and machine learning pdf

=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

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 = []

[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

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 ('_') [

of biological specimens, and are the main developers of the Python Computer Vision Library Mahotas. He started developing open source software in 1998, began Python development in 2004, and contributed code to multiple Python open source libraries. In addition, Luis has a PhD from the world's leading Carnegie Mellon University in Machine learning and has published numerous scientific papers.Translator Prof

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

: Network Disk DownloadContent Profile ...This book is intended for all readers interested in the practice and competition of machine learning and data mining, starting from scratch, based on the Python programming language, and gradually leading the reader to familiarize themselves with the most popular machine learning