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 ## Code description
 
 Code necassary for testing different feature approaches used with HD computing for epileptioc seizure detection. 
 It compares approaches in three aspects: 
 1) Prediction performance 
 2) Memory needed to store all the HD vectors
 3) Computational complexity (time needed for execution)
 
-# PYTHON files description
+----------------------------------
+Python files description
 
 Script_runAllApproaches.py 
 Script that based on parameters set runs training and testing of specific HD features model
 - it is possible to choose between two different databases and which patients to use
 - it can perform personalized or generalized training
     - personalized is for each subject performing leave one seizure out and training on the rest of seizures
     - generalized requires to define CViterations_testSubj variable - which determines CV iterations and which subjects are in test set (all others are in training)
 - outputs prediction labels for each input file with saved original predictions, and predictions after two sets of labels postprocessing (smoothing) and also true labels
     - by analysing and plotting this labes many things can be analized
 - plots performances in dependence of performance measures and amount of postprocessing
 
 Script_compareApproaches.py
 Script that:
 - meausres number of seizure per subjects for databases
 - compares different approaches in terms of performance and plots comparison 
 - meausures time for calculation for different approaches
 - plots time and memory requirements for different approaches
 
 HdfunctionsLib.py
 - library with different functions on HD vectors, uses torch library
 
 VariousFunctionsLib.py 
 - library including various functions for HD project but not necessarily related to HD vectors