def __init__(self, nInputs=None, nHiddenLayers=None, bias=True, new=True, fileName=""):
self.params = {}
self.config = ConfigProvider().getNNTrainConfig()
if new:
self.params["nInputs"] = nInputs
self.params["nHiddenLayers"] = nHiddenLayers
self.params["bias"] = bias
self.nn = NeuralNetwork().createNew(nInputs, nHiddenLayers, N_OUTPUT, bias)
else:
self.nn = NeuralNetwork().load(fileName)
def test_saveAndLoad(self):
ds = self.createORData()
self.nn.train(ds)
self.nn.save(name)
nn2 = NeuralNetwork()
nn2.load(name)
self.assertNotEqual(self.nn, nn2)
self.assertTrue(sameEntries(self.nn.net.params, nn2.net.params))
for inpt, _ in ds:
self.assertEqual(self.nn.activate(inpt), nn2.activate(inpt))
self.removeFile()
class NetworkUtil(object):
'''
Class to train and test a neural network
'''
def __init__(self, nInputs=None, nHiddenLayers=None, bias=True, new=True, fileName=""):
self.params = {}
self.config = ConfigProvider().getNNTrainConfig()
if new:
self.params["nInputs"] = nInputs
self.params["nHiddenLayers"] = nHiddenLayers
self.params["bias"] = bias
self.nn = NeuralNetwork().createNew(nInputs, nHiddenLayers, N_OUTPUT, bias)
else:
self.nn = NeuralNetwork().load(fileName)
def train(self, trainData, convergence=False):
start = time.time()
print "start Training at " + str(datetime.fromtimestamp(start))
if convergence:
self.nn.trainConvergence(trainData, **self.config)
else:
self.nn.train(trainData, **self.config)
print "Training Done in %.2fs" % (time.time() - start)
def test(self, testData=None):
self.nn.test(testData)
print "Testing Done"
def activate(self, testData):
total = (len(testData) / 2) / 100.0
matrix = np.array([[0, 0, 0], [0, 0, 0]])
resArr = []
for inpt, target in testData:
res = self.nn.activate(inpt)
clazz = self.nn._clazz(res)
resArr.append([res, clazz, target])
matrix[target[0]][clazz] += 1
matrix[0, 2] = matrix[0, 0] / total
matrix[1, 2] = matrix[1, 1] / total
return matrix, np.array(resArr)
def save(self, name):
self.nn.save(name)
def __str__(self):
self.params.update(self.config)
return str(self.nn) + "\n" + str(self.params)
def _initNeuralNetwork(self, networkFile):
nnCreate = self.config.getNNInitConfig()
self.nn = NeuralNetwork()
if networkFile == None:
self.nn.createNew(**nnCreate)
else:
self.nn.load(networkFile)
def _initNeuralNetwork(self, networkFile):
nn_conf = self.config.getNeuralNetworkConfig()
self.nn = NeuralNetwork()
if networkFile == None:
self.nn.createNew(nn_conf["nInputs"], nn_conf["nHiddenLayers"], nn_conf["nOutput"], nn_conf["bias"])
else:
self.nn.load(networkFile)
def setUp(self):
self.nn = NeuralNetwork()
self.config = {
"learningrate": 0.01,
"momentum": 0.1,
"maxEpochs": 500,
"continueEpochs": 15,
"validationProportion": 0.25
}
self.nn.createNew(2, 4, 1, bias=True)
def setUp(self):
self.nn = NeuralNetwork()
self.config = ConfigProvider().getNeuralNetworkConfig()
self.nn.createNew(2, 4, 1, bias=True)
class TestNeuralNetwork(unittest.TestCase):
def setUp(self):
self.nn = NeuralNetwork()
self.config = ConfigProvider().getNeuralNetworkConfig()
self.nn.createNew(2, 4, 1, bias=True)
def _createData(self, nInput, nTarget, values):
ds = SupervisedDataSet(nInput, nTarget)
for inp, target in values:
ds.addSample(inp, target)
return ds
def createORData(self):
values = [((0, 0), (0,)), ((0, 1), (1,)), ((1, 0), (1,)), ((1, 1), (1,))]
return self._createData(2, 1, values)
def createXORData(self):
values = [((0, 0), (0,)), ((0, 1), (1,)), ((1, 0), (1,)), ((1, 1), (0,))]
return self._createData(2, 1, values)
def createANDData(self):
values = [((0, 0), (0,)), ((0, 1), (0,)), ((1, 0), (0,)), ((1, 1), (1,))]
return self._createData(2, 1, values)
def removeFile(self):
try:
os.remove(self.nn.path + name + ".nn")
except OSError as e:
print e.message
def test_xor(self):
ds = self.createXORData()
self.nn.train(ds, self.config["maxEpochs"], self.config["learningrate"], self.config["momentum"])
#TODO may fail with delta 0.2
for inpt, target in ds:
self.assertAlmostEqual(self.nn.activate(inpt), target, delta=0.2)
def test_and(self):
ds = self.createANDData()
self.nn.train(ds, self.config["maxEpochs"], self.config["learningrate"], self.config["momentum"])
#TODO may fail with delta 0.2
for inpt, target in ds:
self.assertAlmostEqual(self.nn.activate(inpt), target, delta=0.2)
def test_saveAndLoad(self):
ds = self.createORData()
self.nn.train(ds)
self.nn.save(name)
nn2 = NeuralNetwork()
nn2.load(name)
self.assertNotEqual(self.nn, nn2)
self.assertTrue(sameEntries(self.nn.net.params, nn2.net.params))
for inpt, _ in ds:
self.assertEqual(self.nn.activate(inpt), nn2.activate(inpt))
self.removeFile()
def test_test(self):
ds = self.createORData()
self.nn.train(ds)
self.nn.test(ds, False)
def test_testBeforeTrain(self):
with self.assertRaises(ValueError):
self.nn.test(None, False)
class PoSDBoS(object):
def __init__(self, networkFile=None, demo=False, demoFile=None):
'''Main class for drowsiness detection
:param string networkFile: file name of the saved neural network (path: "/../../data/<networkFile>.nn")
'''
self.demo = demo
self.running = True
self.config = ConfigProvider()
self._initPoSDBoS()
self._initNeuralNetwork(networkFile)
self._initFeatureExtractor(demoFile)
self.dm = DrowsinessMonitor()
self.fileUtil = EEGTableFileUtil()
def _initPoSDBoS(self):
posdbosConfig = self.config.getPoSDBoSConfig()
self.drowsyMinCount = posdbosConfig.get("drowsyMinCount")
self.awakeMinCount = posdbosConfig.get("awakeMinCount")
self.classified = [0, 0]
self.curClass = 0
self.classCount = 0
self.found = 0
def _initNeuralNetwork(self, networkFile):
nnCreate = self.config.getNNInitConfig()
self.nn = NeuralNetwork()
if networkFile == None:
self.nn.createNew(**nnCreate)
else:
self.nn.load(networkFile)
def _initFeatureExtractor(self, demoFile):
self.demoFile = demoFile
collector = self._initDataCollector(self.demoFile)
self.fe = FeatureExtractor(collector)
self.inputQueue = self.fe.extractQueue
def _initDataCollector(self, demoFile):
collectorConfig = self.config.getCollectorConfig()
if self.demo:
return DummyDataCollector(demoFile, **collectorConfig)
else:
return EEGDataCollector(None, **collectorConfig)
def close(self):
self.running = False
def run(self):
fet = threading.Thread(target=self.fe.start)
fet.start()
dmt = threading.Thread(target=self.dm.run)
dmt.start()
features = []
total = 0
start = time.time()
c = []
while self.running and dmt.is_alive():
try:
#awake = 0, drowsy = 1
data = self.inputQueue.get(timeout=1)
features.append(data)
clazz = self.nn.activate(data, True)
c.append([clazz, clazz])
self.setStatus(clazz)
total += 1
except Empty:
print "needed %sms for %d windows" % (time.time() - start, total)
pass
except KeyboardInterrupt:
self.close()
except Exception as e:
print e.message
self.close()
#self.writeFeature(c)
self.fe.close()
self.dm.close()
dmt.join()
def setStatus(self, clazz):
self.classified[clazz] += 1
if self.curClass == clazz:
self.classCount += 1
else:
self.curClass = clazz
self.classCount = 0
info = "class %d row (%s)" % (clazz, str(self.classCount))
if clazz == 1 and self.classCount >= self.drowsyMinCount:
self.dm.setStatus(clazz, info)
self.found += 1
elif clazz == 0 and self.classCount >= self.awakeMinCount:
self.dm.setStatus(clazz, info)
def writeFeature(self, data):
filePath = scriptPath + "/../data/" + "classes.csv"
#filePath = scriptPath + "/../data/" + "drowsy_full_.csv"
header = ["clazz", "clazz2"]
#start = 4
#end = start + len(data[0])/6
#for field in self.config.getCollectorConfig().get("fields"):
# header.extend([str(x) + "Hz" + field for x in range(start, end)])
self.fileUtil.writeFile(filePath, data, header)
class PoSDBoS(object):
def __init__(self, networkFile=None, demo=False, demoFile=None):
'''Main class for drowsiness detection
:param string networkFile: file name of the saved neural network (path: "/../../data/<networkFile>.nn")
'''
self.demo = demo
self.running = True
self.config = ConfigProvider()
self._initNeuralNetwork(networkFile)
self._initFeatureExtractor(demoFile)
self.dm = DrowsinessMonitor()
self.fileUtil = EEGTableFileUtil()
def _initNeuralNetwork(self, networkFile):
nn_conf = self.config.getNeuralNetworkConfig()
self.nn = NeuralNetwork()
if networkFile == None:
self.nn.createNew(nn_conf["nInputs"], nn_conf["nHiddenLayers"], nn_conf["nOutput"], nn_conf["bias"])
else:
self.nn.load(networkFile)
def _initFeatureExtractor(self, demoFile):
collector = self._initDataCollector(demoFile)
self.fe = FeatureExtractor(collector)
self.inputQueue = self.fe.extractQueue
def _initDataCollector(self, demoFile):
collectorConfig = self.config.getCollectorConfig()
if self.demo:
return DummyDataCollector(demoFile, **collectorConfig)
else:
return EEGDataCollector(None, **collectorConfig)
def close(self):
self.running = False
def run(self):
fet = threading.Thread(target=self.fe.start)
fet.start()
dmt = threading.Thread(target=self.dm.run)
dmt.start()
features = []
while self.running and dmt.is_alive():
try:
data = self.inputQueue.get(timeout=1)
features.append(data)
x = random.randint(1, 10)%2
y = random.randint(1, 10)%2
data = (x, y)
clazz = self.nn.activate(data)
info = "%d XOR %d is %d; queue: %d" % (x, y, clazz, self.inputQueue.qsize())
self.dm.setStatus(clazz, info)
#sleep(1)
except Empty:
pass
#if self.demo:
# self.close()
except KeyboardInterrupt:
self.close()
except Exception as e:
print e.message
self.close()
self.writeFeature(features)
self.fe.close()
self.dm.close()
dmt.join()
def writeFeature(self, data):
filePath = scriptPath + "/../data/" + "test.csv"
header = []
for field in ["F3", "F4", "F7", "F8"]:
for i in range(1, 5):
header.append("%s_%s" % (field ,str(i)))
self.fileUtil.writeFile(filePath, data, header)