Detection of Manipulated Pricing in Smart Energy CPS Scheduling
TO RUN:
- make classifies each element in TestingData.txt as 0 (Normal) or 1 (Abnormal) with associated confidence percentage, and the number of Abnormal elements and where they appear, all to stdout.
- make run same as above
- make print same as above but additionally prints results, in appropriate format, to TestingResults.txt.
- make accuracy performs an accuracy test as described in section \ref{knn+}, prints the number of correct classifications out of 10000 (TrainingData.txt) and the average confidence of correct identifications.
Requires:
- make
- git
- LPSolve
- python3
- pip
- numpy
- math
- collections
- sys
- os