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()
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()
windowedData, windowLabels = abra.windowSplitSourceDataTT()
index = np.linspace(0, len(windowedData) - 1, len(windowedData), dtype=int)
random.shuffle(index)
trainingData = []
trainingLabels = []
testData = []
testLabels = []
for i in range(int(len(windowedData))):
if i/len(windowedData) < 0.8:
trainingData.append(windowedData[index[i]])
trainingLabels.append(windowLabels[index[i]])
else:
testData.append(windowedData[index[i]])
testLabels.append(windowLabels[index[i]])
trainingData = abra.initFeatNormalization(inputData=trainingData)
for i in range(len(testData)):
testData[i] = abra.featureNormalization(features=abra.extractFeatures(data=testData[i]), initDone=True)
#model = xgboost.XGBClassifier(max_depth=3, learning_rate=0.3)
from sklearn.neural_network import MLPClassifier
model = MLPClassifier()
#from sklearn.neighbors import KNeighborsClassifier
#model = KNeighborsClassifier(n_neighbors=4, metric='euclidean')
kfold = StratifiedKFold(n_splits=3)
results = cross_val_score(model, np.array(trainingData), np.array(trainingLabels), cv=kfold)
print("Accuracy: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))