""" from abraxas4.abraxasFrame import AbraxasFrame xgbAbra = False dtAbra = True if xgbAbra: xgbAbra = AbraxasFrame(numIrSensors=10, numFrSensors=2, windowWidth=100, windowShift=10, numCoeffs=5, numFreqs=1, enaStatFeats=True, wavelet='haar', waveletLvl1=False, featNormMethod='stand', trainFraction=0.66, classSortTT=True, randomSortTT=False, lineThresholdAfterNorm=10, enaRawFeats=False, corrPeaks=2) xgbAbra.selectSensorSubset(selectedSensors=[False, True, True], sensorType='bno') xgbAbra.addDataFiles(fileSourceName="igor.txt", fileSourcePath="../", startTime=3550, stopTime=3800,
from scipy.spatial.distance import euclidean from abraxasOne.helperFunctions import writeMatrixToCsvFile from abraxasThree.classifierClass import AbraxasClassifier from fastdtw import fastdtw from abraxasOne.gaussFilter import gaussFilter from tscAlgs.triang import triang from tscAlgs.dtwImp02 import dtwImp02 from abraxas4.abraxasFrame import AbraxasFrame b = AbraxasFrame(numIrSensors=10, numFrSensors=2, windowWidth=250, windowShift=50, 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=[0, 1, 2, 3, 5, 7, 9], sensorType='ir') b.addDataFiles(fileSourceName="igor2.txt", fileSourcePath="../",
""" cb, 07.08.2018 - user identification (with non walking data) using decision tree alg """ from abraxas4.abraxasFrame import AbraxasFrame 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, enaRawFeats=False) dtAbra.loadTeTrDump(dumpName='dtAbra.pkl') from sklearn.tree import DecisionTreeClassifier clf = DecisionTreeClassifier(criterion="entropy", max_depth=20, min_samples_leaf=1, min_samples_split=3, max_features=None,
""" cb, 07.08.2018 - user identification (with non walking data) using xgboost """ from abraxas4.abraxasFrame import AbraxasFrame xgbAbra = AbraxasFrame(numIrSensors=10, numFrSensors=2, windowWidth=100, windowShift=25, numCoeffs=5, numFreqs=1, enaStatFeats=True, wavelet='haar', waveletLvl1=False, featNormMethod='stand', trainFraction=0.66, classSortTT=True, randomSortTT=False, lineThresholdAfterNorm=10, enaRawFeats=False, corrPeaks=2) xgbAbra.loadTeTrDump(dumpName='xgbAbra.pkl') #from xgboost import XGBClassifier import xgboost clf = xgboost.XGBClassifier(max_depth=3, learning_rate=0.4) xgbAbra.trainClassifier(classifier=clf, supervised=True) xgbAbra.testClassifier()
from abraxas4.abraxasFrame import AbraxasFrame import numpy as np oc = AbraxasFrame(numIrSensors=10, numFrSensors=2, windowWidth=100, windowShift=10, numFreqs=1, numCoeffs=3, enaStatFeats=True, featNormMethod='stand', trainFraction=2 / 3, waveletLvl1=False, randomSortTT=False, classSortTT=True, corrPeaks=0, enaRawFeats=False, statStages=3) oc.setWindowFunction(functionName='tukey', alpha=0.1) oc.selectSensorSubset(selectedSensors=[False, True, True], sensorType='bno') # oc.selectSensorSubset(selectedSensors=[], sensorType='fr') # oc.selectSensorSubset(selectedSensors=[0], sensorType='ir') oc.addDataFiles(fileSourceName="chrisOut2.txt", fileSourcePath="../", startTime=1500, stopTime=5000, label=0) oc.addDataFiles(fileSourceName="chrisOut2.txt",
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
from abraxas4.abraxasFrame import AbraxasFrame import numpy as np oc = AbraxasFrame(numIrSensors=10, numFrSensors=2, windowWidth=2**8, windowShift=2**5, numFreqs=0, numCoeffs=0, enaStatFeats=True, featNormMethod='stand', trainFraction=2 / 3, waveletLvl1=False, randomSortTT=False, classSortTT=True, corrPeaks=0, enaRawFeats=False, statStages=4) oc.setWindowFunction(functionName='tukey', alpha=0.9) oc.selectSensorSubset(selectedSensors=[False, True, True], sensorType='bno') # oc.selectSensorSubset(selectedSensors=[], sensorType='fr') # oc.selectSensorSubset(selectedSensors=[0], sensorType='ir') oc.addDataFiles(fileSourceName="igor.txt", fileSourcePath="../", startTime=100, stopTime=1500, label=0, className="walking")
""" cb, 07.08.2018 - user identification (with non walking data) using decision tree alg """ from abraxas4.abraxasFrame import AbraxasFrame gNbAbra = AbraxasFrame(numIrSensors=10, numFrSensors=2, windowWidth=50, windowShift=50, numFreqs=1, numCoeffs=1, enaStatFeats=True, featNormMethod='minmax', trainFraction=2/3, waveletLvl1=False, randomSortTT=False, classSortTT=True, corrPeaks=0, enaRawFeats=False) gNbAbra.setWindowFunction(functionName='tukey', alpha=0.1) gNbAbra.selectSensorSubset(selectedSensors=[False, False, False], sensorType='bno') gNbAbra.selectSensorSubset(selectedSensors=[0, 1, 2], sensorType='ir') gNbAbra.selectSensorSubset(selectedSensors=[], sensorType='fr') ''' gNbAbra.addDataFiles(fileSourceName="igor.txt", fileSourcePath="../", startTime=3550, stopTime=3800, label=0, className="not walking") gNbAbra.addDataFiles(fileSourceName="igor2.txt", fileSourcePath="../", startTime=300, stopTime=500, label=0) gNbAbra.addDataFiles(fileSourceName="ankita.txt", fileSourcePath="../", startTime=0, stopTime=150, label=0) gNbAbra.addDataFiles(fileSourceName="markusSchnell.txt", fileSourcePath="../", startTime=4100, stopTime=4300, label=0) gNbAbra.addDataFiles(fileSourceName="stefan.txt", fileSourcePath="../", startTime=7600, stopTime=8600, label=0) gNbAbra.addDataFiles(fileSourceName="stefan.txt", fileSourcePath="../", startTime=0, stopTime=300, label=0) gNbAbra.addDataFiles(fileSourceName="ben.txt", fileSourcePath="../", startTime=0, stopTime=1000, label=0) gNbAbra.addDataFiles(fileSourceName="ben.txt", fileSourcePath="../", startTime=7100, stopTime=8000, label=0) gNbAbra.addDataFiles(fileSourceName="chris1.txt", fileSourcePath="../", startTime=5200, stopTime=6000, label=0) gNbAbra.addDataFiles(fileSourceName="novcc.txt", fileSourcePath="../", startTime=0, stopTime=10000, label=0) gNbAbra.addDataFiles(fileSourceName="nowalk.txt", fileSourcePath="../", startTime=0, stopTime=10000, label=0)
from abraxas4.abraxasFrame import AbraxasFrame import numpy as np import random import xgboost from sklearn.model_selection import StratifiedKFold from sklearn.model_selection import cross_val_score from sklearn.model_selection import train_test_split #abra = AbraxasFrame(numIrSensors=10, numFrSensors=2, windowWidth=100, windowShift=10, numCoeffs=5, numFreqs=1, # enaStatFeats=True, wavelet='haar', waveletLvl1=False, featNormMethod='stand', trainFraction=0.66, # classSortTT=True, randomSortTT=False, lineThresholdAfterNorm=10, enaRawFeats=False, corrPeaks=2) abra = AbraxasFrame(numIrSensors=10, numFrSensors=2, windowWidth=100, windowShift=10, numFreqs=1, numCoeffs=5, enaStatFeats=True, featNormMethod='stand', trainFraction=2/3, waveletLvl1=False, randomSortTT=False, classSortTT=True, corrPeaks=1, enaRawFeats=False) abra.setWindowFunction(functionName='tukey', alpha=0.3) abra.selectSensorSubset(selectedSensors=[False, False, False], sensorType='bno') abra.addDataFiles(fileSourceName="igor.txt", fileSourcePath="../", startTime=3550, stopTime=3800, label=0, className="not walking") abra.addDataFiles(fileSourceName="igor2.txt", fileSourcePath="../", startTime=300, stopTime=500, label=0) abra.addDataFiles(fileSourceName="ankita.txt", fileSourcePath="../", startTime=0, stopTime=150, label=0) abra.addDataFiles(fileSourceName="markusSchnell.txt", fileSourcePath="../", startTime=4100, stopTime=4300, label=0) abra.addDataFiles(fileSourceName="stefan.txt", fileSourcePath="../", startTime=7600, stopTime=8600, label=0) abra.addDataFiles(fileSourceName="stefan.txt", fileSourcePath="../", startTime=0, stopTime=300, label=0) abra.addDataFiles(fileSourceName="ben.txt", fileSourcePath="../", startTime=0, stopTime=1000, label=0) abra.addDataFiles(fileSourceName="ben.txt", fileSourcePath="../", startTime=7100, stopTime=8000, label=0) abra.addDataFiles(fileSourceName="chris1.txt", fileSourcePath="../", startTime=5200, stopTime=6000, label=0) abra.addDataFiles(fileSourceName="novcc.txt", fileSourcePath="../", startTime=0, stopTime=10000, label=0) abra.addDataFiles(fileSourceName="nowalk.txt", fileSourcePath="../", startTime=0, stopTime=10000, label=0)