def _trainPath(self,path,className,subset,disp,verbose):
        count = 0
        files = []
        for ext in IMAGE_FORMATS:
            files.extend(glob.glob( os.path.join(path, ext)))
        if(subset is not 0):
            nfiles = min(subset,len(files))
        else:
            nfiles = len(files)
        badFeat = False
        for i in range(nfiles):
            infile = files[i]
            if verbose:
                print "Opening file: " + infile
            img = Image(infile)
            featureVector = []
            for extractor in self.mFeatureExtractors:
                feats = extractor.extract(img)
                if( feats is not None ):
                    featureVector.extend(feats)
                else:
                    badFeat = True

            if(badFeat):
                badFeat = False
                continue

            featureVector.extend([className])
            self.mDataSetRaw.append(featureVector)
            text = 'Training: ' + className
            self._WriteText(disp,img,text,Color.WHITE)
            count = count + 1
            del img
        return count
dataset/ETHPy150Open sightmachine/SimpleCV/SimpleCV/MachineLearning/NaiveBayesClassifier.py/NaiveBayesClassifier._trainPath/operator `>` -> `is not`