本文整理汇总了Python中Classifier.Classifier.learn方法的典型用法代码示例。如果您正苦于以下问题：Python Classifier.learn方法的具体用法？Python Classifier.learn怎么用？Python Classifier.learn使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Classifier.Classifier的用法示例。
在下文中一共展示了Classifier.learn方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: from Classifier import Classifier [as 别名]
# 或者: from Classifier.Classifier import learn [as 别名]
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:
#.........这里部分代码省略.........