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="../",