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+## 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. 
+
+----------------------------------
+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 
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