""" 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)