def train(self):
        """ Train the network for one epoch """
        self.module.resetDerivatives()
        errors = 0
        ponderation = 0
        for seq in self.ds._provideSequences():
            e, p = self._calcDerivs(seq)
            errors += e
            ponderation += p
        if self.verbose:
            print(("epoch {epoch:6d}  total error {error:12.5g}   avg weight  {weight:12.5g}".format(
                epoch=self.epoch,
                error=errors / ponderation,
                weight=sqrt((self.module.params ** 2).mean()))))
        self.module._setParameters(self.descent(self.module.derivs - self.weightdecay * self.module.params))
        self.epoch += 1
        self.totalepochs += 1
        return ponderation / errors
dataset/ETHPy150Open pybrain/pybrain/pybrain/supervised/trainers/rprop.py/RPropMinusTrainer.train/swapped operands of `/`