[Issue-Id: RICAPP-142] Updated the commnents in the programs and add
files for jjb
Updated README file and comments for the programs(ad_train.py, processing.py, ad_model.py)
Added __init__ in ad folder, setup.py and tox file for jjb
[Issue-Id: RICAPP-142] Implemented HDBScan for clustering and Random Forest for classification to detect the anomaly
Added and updated the below files.
main.py: Main program to predict the anomaly for the selected UEID.
Send the UEID and timestamp for the anomalous entries to the Traffic Steering (rmr with the message type as 30003)
ad_train.py: Train the machine learning algorithm and save the model using the input csv files and save the model.
ue_test.csv: Input csv file has 1000 samples and for each UEID has one or more than one entries for poor signal.
ue_data: List of UEID specific csv files to train the model
[Issue-Id: RICAPP-142] Anomaly detection xApp that integrates with the traffic steering use case
Signed-off-by: deepanshuk <deepanshu.k@hcl.com>
Change-Id: I13f890244cf5ce27b4d07e617a1f8b26adde4b9f
125 files changed