xgbAbra.addDataFiles(fileSourceName="igor.txt",
fileSourcePath="../",
startTime=100,
stopTime=2900,
label=6,
className="igor")
xgbAbra.addDataFiles(fileSourceName="igor2.txt",
fileSourcePath="../",
startTime=600,
stopTime=6000,
label=6)
xgbAbra.readDataSet(checkData=False, equalLength=False)
xgbAbra.dumpTeTrData(dumpName='xgbAbra.pkl')
if dtAbra:
dtAbra = AbraxasFrame(numIrSensors=10,
numFrSensors=2,
windowWidth=100,
windowShift=25,
numFreqs=1,
numCoeffs=0,
enaStatFeats=True,
featNormMethod='stand',
trainFraction=2 / 3,
waveletLvl1=False,
randomSortTT=False,
classSortTT=True,
corrPeaks=0,
startTime=13600,
stopTime=13700,
label=1)
oc.addDataFiles(fileSourceName="chrisOut2.txt",
fileSourcePath="../",
startTime=14350,
stopTime=14550,
label=1)
oc.addDataFiles(fileSourceName="chrisOut2.txt",
fileSourcekPath="../",
startTime=20300,
stopTime=20400,
label=1)
oc.readDataSet(equalLength=False, checkData=False)
oc.dumpTeTrData(dumpName="anomaly.pkl")
TrainFeat, TrainLabel, TestFeat, TestLabel = oc.loadTeTrDump(
dumpName="anomaly.pkl")
data = np.concatenate([TestFeat, TrainFeat])
label = np.concatenate([TestLabel, TrainLabel])
normal = data[label == 0]
anomal = data[label == 1]
training = normal[0:int(2 / 3 * len(normal))]
test = normal[int(2 / 3 * len(normal))::]
from sklearn.svm import OneClassSVM