<s> import itertools <EOL> import os <EOL> import scipy <EOL> import struct <EOL> from pybrain . datasets import SupervisedDataSet <EOL> def labels ( filename ) : <EOL> fp = open ( filename ) <EOL> magicnumber , length = struct . unpack ( '<STR_LIT>' , fp . read ( <NUM_LIT:8> ) ) <EOL> assert magicnumber in ( <NUM_LIT> , <NUM_LIT> ) , ( "<STR_LIT>" % magicnumber ) <EOL> for _ in range ( length ) : <EOL> label , = struct . unpack ( '<STR_LIT:B>' , fp . read ( <NUM_LIT:1> ) ) <EOL> yield label <EOL> def images ( filename ) : <EOL> fp = open ( filename , '<STR_LIT:rb>' ) <EOL> chunk = fp . read ( <NUM_LIT:16> ) <EOL> magicnumber , length , numrows , numcols = struct . unpack ( '<STR_LIT>' , chunk ) <EOL> assert magicnumber in ( <NUM_LIT> , <NUM_LIT> ) , ( "<STR_LIT>" % magicnumber ) <EOL> imagesize = numrows * numcols <EOL> for _ in range ( length ) : <EOL> imagestring = fp . read ( imagesize ) <EOL> image = struct . unpack ( '<STR_LIT:B>' * imagesize , imagestring ) <EOL> yield scipy . array ( image ) <EOL> def flaggedArrayByIndex ( idx , length ) : <EOL> arr = scipy . zeros ( length ) <EOL> arr [ idx ] = <NUM_LIT:1.> <EOL> return arr <EOL> def makeMnistDataSets ( path ) : <EOL> """<STR_LIT>""" <EOL> test = SupervisedDataSet ( <NUM_LIT> * <NUM_LIT> , <NUM_LIT:10> ) <EOL> test_image_file = os . path . join ( path , '<STR_LIT>' ) <EOL> test_label_file = os . path . join ( path , '<STR_LIT>' ) <EOL> test_images = images ( test_image_file ) <EOL> test_labels = ( flaggedArrayByIndex ( l , <NUM_LIT:10> ) for l in labels ( test_label_file ) ) <EOL> for image , label in zip ( test_images , test_labels ) : <EOL> test . addSample ( image , label ) <EOL> train = SupervisedDataSet ( <NUM_LIT> * <NUM_LIT> , <NUM_LIT:10> ) <EOL> train_image_file = os . path . join ( path , '<STR_LIT>' ) <EOL> train_label_file = os . path . join ( path , '<STR_LIT>' ) <EOL> train_images = images ( train_image_file ) <EOL> train_labels = ( flaggedArrayByIndex ( l , <NUM_LIT:10> ) for l in labels ( train_label_file ) ) <EOL> for image , label in zip ( train_images , train_labels ) : <EOL> train . addSample ( image , label ) <EOL> return train , test </s>
