class Processor:
def __init__(self,files,chanNum):
self.signal = []
self.stimulusCode = []
self.phaseInSequence = []
self.targetLetters = []
self.firsttrain = 1
self.cl = Classifier()
self.sf = SpatialFilter(chanNum)
self.rate = 0
self.files = files
self.chanNum = chanNum
# Z cisla trenovacieho znaku urci jeho poziciu v speller matici
def getCharIndexes (self,num):
output = []
#row index
output.append((num/6)+6)
#col index
output.append(num % 6)
return output
# Pomocou opisneho pola phaseInSequence zisti na akych indexoch su prechody medzi cielovymi znakmi
def findCharEnds (self):
# najdi prechody medzi pismenami na ktore pouzivatel mysli (len pre dataset)
charEnds = []
for i in range (len(self.phaseInSequence)):
if self.phaseInSequence[i] == 3 and self.phaseInSequence[i-1] == 2:
tmp = (int)(i/12)
#tmp = i
charEnds.append(tmp)
return charEnds
# Transformuje list dat na numpy array
def prepairSignalArray (self,finalData):
converted = []
for i in range(len(finalData)):
converted.append(np.array(finalData[i]))
return np.array(converted)
# Vytvorenie znackovacieho pola pre IsiBinList - hovori ci dane vysvietenia zodpovedali cielovemu znaku
def prepairTargetArray (self,targIndexes, isiCount = 12):
output = []
for i in range(isiCount):
if i in targIndexes:
output.append(1)
else:
output.append(0)
return np.array(output)
# Metoda trenuje klasifikator a updatuje pouzivatelske rozhranie
def trainClassifier(self, trainLetter, progress, progLab, maxSets):
# nacitanie a predspracovanie signalu
signalLoader = SignalLoader(self.chanNum,self.files)
prpr = Preprocessor(self.chanNum,[])
signal,stimCode,phaseInSequence = signalLoader.loadSignal()
self.signal = prpr.preprocess(240,1E-1,30E0,self.sf,signal,stimCode,phaseInSequence,0)
self.stimulusCode = prpr.stimulusCode
self.phaseInSequence = prpr.phaseInSequence
self.targetLetters = sum(trainLetter,[])
# najdenie prechodov medzi znakmi
charEnds = self.findCharEnds()
# rozdelenie dat do epoch
em = EpochManager(self.signal,self.stimulusCode,self.phaseInSequence)
isiList = em.createEpochs()
# trening jednotlivych znakov
for i in range(len(charEnds)):
progress["value"] = i
progLab["text"] = ("Trénujem znak: {}/{}").format(i+1, len(charEnds))
print "Averaging character:",i,"\n"
hi = charEnds[i]
if i == 0:
lo = 0
else:
lo = charEnds[i-1]
rowColBinList = em.getAveragedEpochs(hi,lo,isiList,maxSets)
finalDataArray = rowColBinList
classMarks = self.prepairTargetArray(self.getCharIndexes(self.targetLetters[i]))
if self.firsttrain == 1:
self.cl.learn(finalDataArray,classMarks,0)
self.firsttrain = 0
else:
self.cl.learn(finalDataArray,classMarks)
# Metoda hada cielove znaky a updatuje pouzivatelske rozhranie
def guessChars(self,subset,files,targetLetter,testProgress,progTestLabel,guessView,guessLab,maxSets):
aktCharNum = 0
totalChars = len(sum(targetLetter,[]))
if self.chanNum != 64:
files.sort()
files = self.createTriplets(files)
for m in range(len(files)):
# nacitanie a predspracovanie signalu
signalLoader = SignalLoader(self.chanNum,files[m])
prpr = Preprocessor(self.chanNum,subset)
signal, stimCode, phaseInSequence = signalLoader.loadSignal()
self.signal = prpr.preprocess(240,1E-1,30E0,self.sf,signal,stimCode,phaseInSequence,1)
self.stimulusCode = prpr.stimulusCode
self.phaseInSequence = prpr.phaseInSequence
if (len(targetLetter) > m):
self.targetLetters = targetLetter[m]
else:
self.targetLetters = []
print "Processing file:",m,"\n"
# najdenie prechodov medzi znakmi
charEnds = self.findCharEnds()
# rozdelenie dat do epoch
em = EpochManager(self.signal,self.stimulusCode,self.phaseInSequence)
isiList = em.createEpochs()
hit = 0
# hadanie jednotlivych znakov
for i in range(len(charEnds)):
testProgress["value"] = aktCharNum
progTestLabel["text"] = ("Hádam znak: {}/{}").format(aktCharNum+1, totalChars)
aktCharNum +=1
hi = charEnds[i]
if i == 0:
lo = 0
else:
lo = charEnds[i-1]
rowColBinList = em.getAveragedEpochs(hi,lo,isiList,maxSets)
finalDataArray = self.prepairSignalArray(self.sf.grandAveragingFilter(rowColBinList,subset,1))
#pomocou klasifikatora
char = self.cl.predictTarget(finalDataArray,self.cl.reduce(self.sf,self,subset))
if len(self.targetLetters) > i:
if char == self.targetLetters[i]:
hit+=1
print "Succesfully guessed char:",char,"\n"
else:
print "Guessed char:",char,"\n"
if i == 0:
text = "(" + char + ","
elif i == len(charEnds) - 1:
text = char + ")"
else:
text = char + ","
guessView.configure(state='normal')
guessView.insert(INSERT, text)
guessView.configure(state='disabled')
self.rate += (hit)*100/float(totalChars)
print "\n Success rate= ",self.rate, "\n"
guessLab["text"]=("Presnosť: {}").format(self.rate)
return self.rate
# Pomocna funkcia pre spracovanie csv suborov epoc dat
def createTriplets(self, epocFiles):
triplets = []
for i in range(len(epocFiles)/3):
triplet = []
triplet.append(epocFiles[i])
triplet.append(epocFiles[i+len(epocFiles)/3])
triplet.append(epocFiles[i+2*len(epocFiles)/3])
triplets.append(triplet)
return triplets
