def _evaluateSequence(self, f, seq, verbose = False):
        totalError = 0.
        ponderation = 0.
        for input, target in seq:
            res = f(input)
            e = 0.5 * sum((target-res).flatten()**2)
            totalError += e
            ponderation += len(target)
            if verbose:
                print((    'out:    ', fListToString( list( res ) )))
                print((    'correct:', fListToString( target )))
                print((    'error: % .8f' % e))
        return totalError, ponderation
Return the ponderated MSE over one sequence.
dataset/ETHPy150Open pybrain/pybrain/pybrain/datasets/supervised.py/SupervisedDataSet._evaluateSequence