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: