def classify(self, image):
        featureVector = []
        for extractor in self.mFeatureExtractors: #get the features
            feats = extractor.extract(image)
            if( feats is not None ):
                featureVector.extend(feats)
        featureVector.extend([self.mClassNames[0]])
        test = orange.ExampleTable(self.mOrangeDomain,[featureVector])
        c = self.mClassifier(test[0]) #classify
        return str(c) #return to class name
Ensure periodic jobs sets correctly ttl.
dataset/ETHPy150Open sightmachine/SimpleCV/SimpleCV/MachineLearning/NaiveBayesClassifier.py/NaiveBayesClassifier.classify--crossover with--dataset/ETHPy150Open ui/rq-scheduler/tests/test_scheduler.py/TestScheduler.test_periodic_jobs_sets_ttl