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| ''' | |
| Created on Jul 27, 2015 | |
| @author: mkulin | |
| This file creates the training dataset for Weka by adding the header naming row and modifying the class labels from numeric to nominal values | |
| ''' | |
| import csv | |
| pathToCSVfile='/home/mkulin/Documents/UGhent/Progress/January/TUTORIAL_paper/DATASETS/isolatedTestbedData/' | |
| inputFilename='TrainingSet.csv' | |
| outputFilename='TrainingSet_final.csv' | |
| inputcsvfile=pathToCSVfile+inputFilename | |
| outputcsvfile=pathToCSVfile+outputFilename | |
| dataset=csv.reader(open(inputcsvfile)) | |
| #Set the number of bins | |
| bins=500 #E.g. 500 (for cleaned dataset) as in the paper | |
| lines=[] | |
| firstrow=[] | |
| #Create the row for naming the features and labels | |
| for i in range(bins+1): | |
| firstrow.append('B'+str(i+1)) | |
| lines.append(firstrow) | |
| #Convert 4 numeric class labels for device type classification to nominal labels | |
| for instance in dataset: | |
| if instance[bins]=='1': | |
| instance[bins]='dell' | |
| elif instance[bins]=='2': | |
| instance[bins]='ipad' | |
| elif instance[bins]=='3': | |
| instance[bins]='iphone' | |
| elif instance[bins]=='4': | |
| instance[bins]='nokia' | |
| lines.append(instance) | |
| #print lines | |
| writer = csv.writer(open(outputcsvfile, 'w')) | |
| writer.writerows(lines) |