def batchDiscover(self, td_errors, all_phi_s, s):
        # need states here instead?
        addedFeature = False
        # PLACEHOLDER for norm of function
        norm = max(abs(td_errors))  # Norm of function
        for j in xrange(self.maxBatchDiscovery):
            self.features.append(self.getFunctionApproximation(s, td_errors))
            if self.batchThreshold > norm:
                self.addNewWeight()
                addedFeature = True
                self.features_num += 1
                self.logger.debug(
                    'Added feature. \t %d total feats' %
                    self.features_num)
            else:
                break
        return addedFeature
dataset/ETHPy150Open rlpy/rlpy/rlpy/Representations/BEBF.py/BEBF.batchDiscover/swapped operands of `>`