import numpy as np
import matplotlib.pyplot as plt
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
b = AbraxasClassifier(numIrSensors=10,
numFrSensors=2,
windowWidth=150,
windowShift=150,
numFreqs=0,
numCoeffs=0,
enaStatFeats=False,
featNormMethod='none',
kernel=0,
trainFraction=2 / 3,
waveletLvl1=False,
randomSortTT=False,
classSortTT=True)
b.setWindowFunction(functionName='tukey', alpha=0.15)
# b.plotWindowFunction()
b.selectSensorSubset(selectedSensors=[False, False, False], sensorType='bno')
b.selectSensorSubset(selectedSensors=[], sensorType='fr')
b.selectSensorSubset(selectedSensors=[0, 1, 2, 4, 6, 8], sensorType='ir')
b.addDataFiles(fileSourceName="igor.txt",
fileSourcePath="../",
startTime=100,
sys.path.append('../')
from abraxasThree.classifierClass import AbraxasClassifier
# user identification:
import numpy as np
import random
# user identification:
a = AbraxasClassifier(numIrSensors=10,
numFrSensors=2,
windowWidth=100,
windowShift=25,
numFreqs=3,
numCoeffs=10,
enaStatFeats=False,
featNormMethod='stand',
kernel='rbf',
trainFraction=2 / 3,
waveletLvl1=True,
randomSortTT=False,
classSortTT=True,
corrPeaks=2,
enaRawFeats=True)
a.setWindowFunction(functionName='tukey', alpha=0.9)
# a.plotWindowFunction()
a.selectSensorSubset(selectedSensors=[False, True, True], sensorType='bno')
a.selectSensorSubset(selectedSensors=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
sensorType='ir')
import numpy as np
import matplotlib.pyplot as plt
from abraxasThree.classifierClass import AbraxasClassifier
from misc.kernelDensityEstimator import kernelDensityEstimator
a = AbraxasClassifier(numIrSensors=10,
numFrSensors=2,
windowWidth=100,
windowShift=100,
numFreqs=0,
numCoeffs=0,
enaStatFeats=True,
featNormMethod='stand',
trainFraction=2 / 3,
waveletLvl1=False,
randomSortTT=False,
classSortTT=True)
a.selectSensorSubset(selectedSensors=[False, False, False], sensorType='bno')
# a.selectSensorSubset(selectedSensors=[0, 2, 4, 6, 8], sensorType='ir')
# a.selectSensorSubset(selectedSensors=[], sensorType='fr')
#a.addDataFiles(fileSourceName="igor.txt", fileSourcePath="../", startTime=3550, stopTime=3800, label=0,
# className="not walking")
# a.addDataFiles(fileSourceName="igor2.txt", fileSourcePath="../", startTime=300, stopTime=500, label=0)
# a.addDataFiles(fileSourceName="ankita.txt", fileSourcePath="../", startTime=0, stopTime=150, label=0)
# a.addDataFiles(fileSourceName="markusSchnell.txt", fileSourcePath="../", startTime=4100, stopTime=4300, label=0)
# a.addDataFiles(fileSourceName="stefan.txt", fileSourcePath="../", startTime=7600, stopTime=8600, label=0)
# a.addDataFiles(fileSourceName="stefan.txt", fileSourcePath="../", startTime=0, stopTime=300, label=0)
# a.addDataFiles(fileSourceName="ben.txt", fileSourcePath="../", startTime=0, stopTime=1000, label=0)
# a.addDataFiles(fileSourceName="ben.txt", fileSourcePath="../", startTime=7100, stopTime=8000, label=0)
def quitApp():
stopClassifier()
stopRecording()
master.quit()
if __name__ == '__main__':
a = AbraxasClassifier(numIrSensors=10,
numFrSensors=2,
windowWidth=100,
windowShift=10,
numFreqs=15,
numCoeffs=15,
enaStatFeats=True,
featNormMethod='stand',
kernel='rbf',
trainFraction=1,
waveletLvl1=True,
randomSortTT=False,
classSortTT=True)
from tkinter import *
master = Tk()
Label(master, text="File Name:").grid(row=0)
e1 = Entry(master)
e1.grid(row=0, column=1)
import numpy as np
import matplotlib.pyplot as plt
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
b = AbraxasClassifier(numIrSensors=10,
numFrSensors=2,
windowWidth=250,
windowShift=250,
numFreqs=0,
numCoeffs=0,
enaStatFeats=False,
featNormMethod='none',
kernel=0,
trainFraction=2 / 3,
waveletLvl1=False,
randomSortTT=False,
classSortTT=True)
b.setWindowFunction(functionName='tukey', alpha=0.5)
# b.plotWindowFunction()
b.selectSensorSubset(selectedSensors=[False, False, False], sensorType='bno')
b.selectSensorSubset(selectedSensors=[], sensorType='fr')
b.selectSensorSubset(selectedSensors=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
sensorType='ir')
# b.addDataFiles(fileSourceName="igor.txt", fileSourcePath="../", startTime=100, stopTime=1500, label=0,
from abraxasThree.classifierClass import AbraxasClassifier
a = AbraxasClassifier(numIrSensors=1,
numFrSensors=0,
windowWidth=1500,
windowShift=10,
numFreqs=0,
numCoeffs=0,
enaStatFeats=False,
featNormMethod='stand',
trainFraction=2 / 3,
waveletLvl1=False,
randomSortTT=False,
classSortTT=True)
a.setFileSink(fileSinkName="test.txt", fileSinkPath="../")
a.setWindowFunction(functionName='rect', alpha=0)
a.setupSerialInterface(port="/dev/ttyACM0", baudRate=57600)
a.startReceiveData()
a.startPlotStreamData(sensorNr=[5, 7, 8, 10])
import numpy as np
from abraxasThree.classifierClass import AbraxasClassifier
from sklearn import svm
from xgboost import XGBClassifier
import matplotlib.pyplot as plt
from sklearn.feature_selection import RFE
a = AbraxasClassifier(numIrSensors=10,
numFrSensors=2,
windowWidth=100,
windowShift=50,
numFreqs=3,
numCoeffs=10,
enaStatFeats=False,
featNormMethod='stand',
trainFraction=2 / 3,
waveletLvl1=False,
randomSortTT=False,
classSortTT=True,
enaRawFeats=False,
corrPeaks=2)
a.setWindowFunction(functionName='tukey', alpha=0.9)
# a.plotWindowFunction()
a.selectSensorSubset(selectedSensors=[False, True, True], sensorType='bno')
# a.selectSensorSubset(selectedSensors=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], sensorType='ir')
# a.selectSensorSubset(selectedSensors=[0, 1], sensorType='fr')
a.addDataFiles(fileSourceName="igor.txt",
fileSourcePath="../",