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    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