SSH-Master-Thesis/ethan_data_processing_scripts/Vector.py

try:
    import cPickle as pickle
except ImportError:
    import pickle
import sample
import typing
from typing import List


class FeatureVector:
    def __init__(self):
        self.features = []
        self.activefeatures = []
        # list of key, value tuples represnting values for features.
        self.sampleInfo = {}
        self.classification = None

    # set which features are active using a binary list
    # ex: For 2nd, 4th and 5th features to be active, pass [0,1,0,1,1]
    def setFeatures(self, binlist):
        activefeatures = []
        if len(binlist) != len(self.features):
            print("Feature choice list must equal length of feature list")
        for b in range(len(binlist)):
            if binlist[b] == 1:
                activefeatures.append(self.features[b])
        self.activefeatures = activefeatures

    def __repr__(self):
        return str(self.features)


# use to make a sample into a vector
def SampleToFeatureVector(s: sample):
    print(s.__activities)
    v = FeatureVector()
    for a in s.__activities.keys():
        for d in s.__activities[a].keys():
            # a + d looks like: high.total_packets
            v.sampleInfo.update({a + "." + d: s.__activities[a][d]})
        v.features.extend(a["high"].values())
        v.features.extend(a["mid"].values())
        v.features.extend(a["low"].values())
    v.sampleInfo.update(("total_time", s.total_time))
    v.sampleInfo.update(("average_iat", s.average_iat))
    v.sampleInfo.update(("dead_time", s.dead_time))
    v.features.append(s.total_time)
    v.features.append(s.average_iat)
    v.features.append(s.dead_time)
    return v


def writePickledData(filename):
    v = FeatureVector()
    v.features = [1, 2, 3, 4]
    v.activefeatures = [1, 2, 3, 4]
    vs = [v,v]
    with open(filename, 'wb') as file:
        pickle.dump(vs, file)


def readPickledData(filename):
    with open(filename, 'rb') as file:
        x = pickle.load(file)
        # print(x)
    return x


def main():
    fv = FeatureVector()
    # writePickledData("test.bin")
    # readPickledData("test.txt")
    s = sample.Sample([{"delta": 0, "time": '09/19/18 13:55:26', }], open("./results.txt"))
    s.dead_time = 30
    s.average_iat = 3
    s.total_time = 30
    fv = SampleToFeatureVector(s)


if __name__ == '__main__':
    main()