## Code description

Code for different approaches for improving performance of baseline HD computing approach. Tested on a use case of epileptic seizure detection.
Code compares several different approaches:
- standard single-pass single-centroid HD learning
- iterative (multi-pass) HD learning
- multi-centroid HD learning
- multi-centroid and multi-pass learning combined
- onlineHD (weighted) learning
- random forest

Analysis is done using publicly available CHB-MIT database.

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Python files description
script_prepareDataset.py
- loads raw CHB-MIT files in .edf format and transforms them to prepared dataset
- it can be done using differnt factor (in this case 10) that defines how much more non-seizure data we want to keep for each seizure episode
- outputs are .csv files where in each file is one seizure episode and 'factor' times more non seizure data

script_MultiClassItterativeOnline_forPaper.py
- main script that first calculates features for all files
- then performs personalized training with leave-one-out cross-validation of each subject using all mentione approaches one afer another
- plots and compares performances

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

PerformanceMetricsLib.py
- Library with functions to measure performance on episode and duration level (for epilepsy application)

paramtersSetup.py
- script where all important parameters are defined and grouped into several cathegories