"""
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()
- 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="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
model = OneClassSVM(kernel='linear')
model.fit(training)