def __call__(self, R):
        def smoothup(x):
            """ produces a mapping from [0,1] to [0,1], with a specific gini coefficient. """
            return power(x, 2 / self.gini - 1)
        ranks = rankedFitness(R)
        res = zeros(len(R))
        for i in range(len(ranks)):
            res[i] = ranks[i] * self.linearComponent + smoothup(ranks[i] / float(1 - len(R))) * (1 - self.linearComponent)
        res /= max(res)
        return res
dataset/ETHPy150Open pybrain/pybrain/pybrain/tools/rankingfunctions.py/SmoothGiniRanking.__call__/swapped operands of `-`