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
