- 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()
label=1)
oc.addDataFiles(fileSourceName="novcc.txt",
fileSourcePath="../",
startTime=0,
stopTime=10000,
label=1)
oc.addDataFiles(fileSourceName="nowalk.txt",
fileSourcePath="../",
startTime=0,
stopTime=10000,
label=1)
oc.addDataFiles(fileSourceName="nowalk2.txt",
fileSourcePath="../",
startTime=0,
stopTime=10000,
label=1)
oc.addDataFiles(fileSourceName="nowalk3.txt",
fileSourcePath="../",
startTime=0,
stopTime=10000,
label=1)
oc.readDataSet(equalLength=False, checkData=False)
oc.initFeatNormalization()
from sklearn.naive_bayes import GaussianNB
model = GaussianNB()
oc.trainClassifier(classifier=model)
oc.testClassifier()
"""
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,
max_leaf_nodes=None)
dtAbra.trainClassifier(classifier=clf)
dtAbra.testClassifier()
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()