def getRabeData(sensors, length, shift=256):
from abraxas4.abraxasFrame import AbraxasFrame
b = AbraxasFrame(numIrSensors=10, numFrSensors=2, windowWidth=length, windowShift=shift, numFreqs=0, numCoeffs=0,
enaStatFeats=False, featNormMethod='none', trainFraction=2/3, waveletLvl1=False,
randomSortTT=False, classSortTT=True)
b.setWindowFunction(functionName='rect', alpha=0.25)
# b.plotWindowFunction()
b.selectSensorSubset(selectedSensors=[False, False, False], sensorType='bno')
b.selectSensorSubset(selectedSensors=[], sensorType='fr')
b.selectSensorSubset(selectedSensors=sensors, sensorType='ir')
#b.selectSensorSubset(selectedSensors=[2], sensorType='ir')
b.addDataFiles(fileSourceName="igor2.txt", fileSourcePath="../", startTime=600, stopTime=6000, label=0)
b.addDataFiles(fileSourceName="ankita_pos2_lrRl.txt", fileSourcePath="../", startTime=150, stopTime=2500, label=1)
b.addDataFiles(fileSourceName="markus.txt", fileSourcePath="../", startTime=500, stopTime=3300, label=2)
dataSet = b.readDataSet(checkData=False, equalLength=True)
# dataSet := Array with shape dataSet[i][j, k], where i refers to the i-th file loaded, k indicates the sensor and
# j is the "time"-index.
wData, wLabels = b.windowSplitSourceDataTT(inputData=dataSet, inputLabels=np.array([0, 1, 2]))
wLabels = np.array(wLabels)
print("Number of windows, Igor: ", str(np.size(wLabels[wLabels == 0])))
print("Number of windows, Ankita: ", str(np.size(wLabels[wLabels == 1])))
print("Number of windows, Markus: ", str(np.size(wLabels[wLabels == 2])))
igor = []
ankita = []
markus = []
#for i in range(len(wLabels)):
# wData[i] = wData[i]*(1+0*np.random.random([250, 1]))
for i in range(len(wLabels)):
if wLabels[i]==0:
igor.append(wData[i]*1)
if wLabels[i]==1:
ankita.append(wData[i]*1)
if wLabels[i]==2:
markus.append(wData[i]*1)
return igor, ankita, markus
stopTime=6000,
label=0)
b.addDataFiles(fileSourceName="ankita_pos2_lrRl.txt",
fileSourcePath="../",
startTime=150,
stopTime=2500,
label=1)
b.addDataFiles(fileSourceName="markus.txt",
fileSourcePath="../",
startTime=500,
stopTime=3300,
label=2)
dataSet = b.readDataSet(checkData=False, equalLength=True)
# dataSet := Array with shape dataSet[i][j, k], where i refers to the i-th file loaded, k indicates the sensor and
# j is the "time"-index.
wData, wLabels = b.windowSplitSourceDataTT(inputData=dataSet,
inputLabels=np.array([0, 1, 2]))
wLabels = np.array(wLabels)
print("Number of windows, Igor: ", str(np.size(wLabels[wLabels == 0])))
print("Number of windows, Ankita: ", str(np.size(wLabels[wLabels == 1])))
print("Number of windows, Markus: ", str(np.size(wLabels[wLabels == 2])))
igor = []
ankita = []
markus = []
label=5,
className="markus")
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,
fileSourcePath="../",
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
className="chris")
gNbAbra.addDataFiles(fileSourceName="chris_pos2.txt", fileSourcePath="../", startTime=100, stopTime=1700, label=2)
gNbAbra.addDataFiles(fileSourceName="chris1.txt", fileSourcePath="../", startTime=500, stopTime=5000, label=2)
gNbAbra.addDataFiles(fileSourceName="chris2.txt", fileSourcePath="../", startTime=1000, stopTime=8600, label=2)
gNbAbra.addDataFiles(fileSourceName="chrisOut.txt", fileSourcePath="../", startTime=1000, stopTime=9000, label=2)
gNbAbra.addDataFiles(fileSourceName="chrisOut2.txt", fileSourcePath="../", startTime=1000, stopTime=4000, label=2)
gNbAbra.addDataFiles(fileSourceName="chrisOut2.txt", fileSourcePath="../", startTime=4250, stopTime=5250, label=2)
gNbAbra.addDataFiles(fileSourceName="chrisOut2.txt", fileSourcePath="../", startTime=6000, stopTime=14000, label=2)
gNbAbra.addDataFiles(fileSourceName="chrisOut2.txt", fileSourcePath="../", startTime=14000, stopTime=22000, label=2)
gNbAbra.addDataFiles(fileSourceName="chris_c.txt", fileSourcePath="../", startTime=100, stopTime=1600, label=3,
className="crooked")
gNbAbra.addDataFiles(fileSourceName="ben.txt", fileSourcePath="../", startTime=2000, stopTime=6000, label=4,
className="ben")
gNbAbra.addDataFiles(fileSourceName="markus.txt", fileSourcePath="../", startTime=500, stopTime=3300, label=5,
className="markus")
gNbAbra.addDataFiles(fileSourceName="igor.txt", fileSourcePath="../", startTime=100, stopTime=2900, label=6,
className="igor")
gNbAbra.addDataFiles(fileSourceName="igor2.txt", fileSourcePath="../", startTime=600, stopTime=6000, label=6)
gNbAbra.readDataSet(equalLength=False, checkData=True)
gNbAbra.initFeatNormalization(dumpName="throwAway")
from sklearn.naive_bayes import GaussianNB
clf = GaussianNB()
gNbAbra.trainClassifier(classifier=clf)
gNbAbra.testClassifier()
abra.addDataFiles(fileSourceName="chrisOut2.txt", fileSourcePath="../", startTime=14000, stopTime=22000, label=2)
abra.addDataFiles(fileSourceName="chris_c.txt", fileSourcePath="../", startTime=100, stopTime=1600, label=3,
className="crooked")
abra.addDataFiles(fileSourceName="ben.txt", fileSourcePath="../", startTime=2000, stopTime=6000, label=4,
className="ben")
abra.addDataFiles(fileSourceName="markus.txt", fileSourcePath="../", startTime=500, stopTime=3300, label=5,
className="markus")
abra.addDataFiles(fileSourceName="igor.txt", fileSourcePath="../", startTime=100, stopTime=2900, label=6,
className="igor")
abra.addDataFiles(fileSourceName="igor2.txt", fileSourcePath="../", startTime=600, stopTime=6000, label=6)
abra.readDataSet(checkData=False, equalLength=True)
windowedData, windowLabels = abra.windowSplitSourceDataTT()
index = np.linspace(0, len(windowedData) - 1, len(windowedData), dtype=int)
random.shuffle(index)
trainingData = []
trainingLabels = []
testData = []
testLabels = []
for i in range(int(len(windowedData))):
if i/len(windowedData) < 0.8:
trainingData.append(windowedData[index[i]])
trainingLabels.append(windowLabels[index[i]])
else: