def run(self):
count = 0
previous = {} # Used to keep track of previous predictions
self.parent.sendPacket.value = True
q = multiQueue()
last = bciComms.discardTill(self.parent.pipeReceive, self.parent.endPacketRegex, self.parent.headerRegex)
last = int(last)
collecting = True
while True:
count += 1
last = bciComms.collectData(self.parent.pipeReceive, self.period, self.parent.headerRegex,
self.parent.endPacketRegex, q, last)
data = q.get()
# Takes the raw data and decomposes it into a dict of the corresponding terms
processed = bciComms.rawToDict(data)
classWith = self.parent.trainingDataFormater.formatFrequencies(processed["Raw FFT"])
prediction = None
accuracy = None
try:
prediction, accuracy = self.classifier.predict(classWith)
except Exception:
continue
# Changed what predict returns
accuracy = accuracy[prediction]
if count * self.period < self.timeout and collecting:
if not previous.has_key(prediction):
previous[prediction] = 0.0
previous[prediction] += accuracy
elif collecting:
likely = sorted(previous, key=previous.get, reverse=True)[0]
if previous[likely] < count * self.threshold:
self.coms.txt.emit("None")
else:
self.coms.txt.emit(likely)
previous = {}
count = 0
collecting = False
elif count * self.period < self.timeout and not collecting:
self.coms.txt.emit("Robot Moving")
elif not collecting:
collecting = True
count = 0
self.coms.txt.emit("")
def run(self):
self.parent.sendPacket.value = True
q = multiQueue()
last = bciComms.discardTill(self.parent.pipeReceive, self.parent.endPacketRegex, self.parent.headerRegex)
last = int(last)
count = 0
priors = self.classifier.priors
while True:
newQuestion = self.runner.new
last = bciComms.collectData(self.parent.pipeReceive, self.period, self.parent.headerRegex,
self.parent.endPacketRegex, q, last)
data = q.get()
# A new question has not arrived
if not newQuestion:
continue
count += 1
# Want to give the user a second to read the text and ignore the brain signals
if count * self.period < 1.0: continue
# Takes the raw data and decomposes it into a dict of the corresponding terms
processed = bciComms.rawToDict(data)
classWith = self.parent.trainingDataFormater.formatFrequencies(processed["Raw FFT"])
logging.info(processed["Raw FFT"])
probs = None
try:
_, probs = self.classifier.predict(classWith)
except Exception:
continue
# Updating priors
for k,v in probs.iteritems():
priors[k] *= v
# Renormalizing
s = sum(priors.values())
priors = {k: priors[k] / s for k in priors}
print count
# Emit prediction if time has exceeded 6 seconds or hit threshold
highest = max(priors, key=lambda k: priors[k])
if count * self.period >= 6.0 or priors[highest] >= self.threshold:
self.coms.txt.emit(highest)
self.runner.new = False
count = 0
priors = self.classifier.priors
def trainingScreen(self):
""" Displays a training screen that displays arrows to different frequency lights
and records the data within each of these periods. Saves this data in a JSON file """
window = drawWidget(self)
# Creates a random ordering of the trials
channels = [str(i) for i in self.settings["channels"].values()
if not i == "None"]
trials = self.settings["numTrials"]
time = self.settings["trialLength"]
bciData = {}
for t in range(trials):
print (t+1), "/", trials
random.shuffle(channels)
bciData[t] = {}
for c in channels:
# Setup
param = self.settings["freqMap"][c]
q = multiQueue()
window.drawCrossTimed(0.4, 3, True)
# Starting storing packets sent through the pipe when a
# batch arriving from BCI2000 completes. This is signalled
# by the last Signal(X,Y) received.
self.sendPacket.value = True
lastStamp = bciComms.discardTill(self.pipeReceive, self.endPacketRegex, self.headerRegex)
lastStamp = int(lastStamp)
collectProc = Process(target=bciComms.collectData,
args=(self.pipeReceive, time, self.headerRegex, self.endPacketRegex, q, lastStamp))
collectProc.start()
window.drawArrowTimed(param["x"], param["y"], 200, param["theta"], time, True)
bciData[t][c] = q.get()
self.writeFile(bciData[t][c], t, c)
self.sendPacket.value = False
# Write the training data to a file, and get the formated training data
fName, _ = QtGui.QFileDialog.getSaveFileName(self, "Save the test data", "", "JSON (*.json)", "JSON (*.json)")
# User didn't pick a filename, ask them again and if repeated return
if fName == "":
self.showMessage("You didn't select a file! Try again otherwise data is lost")
fName, _ = QtGui.QFileDialog.getSaveFileName(self, "Save the test data", "", "JSON (*.json)", "JSON (*.json)")
if fName == "":
window.close()
return
formatedJson = None
# List of frequencies collected on
freqList = [int(x.split(" ")[0]) for x in channels]
freqList.sort()
with open(fName, "w") as f:
formatedJson = bciComms.processTrainData(bciData, freqList, f)
# Saves the training data to the instance field, trains the classifier and transitions to run screen
self.trainingDataFormater = FormatJson(formatedJson, loadDict=True)
if self.trainClassifier():
self.transitionToRun()
window.close()