def __call__(self, R):
        ranks = rankedFitness(R)
        res = zeros(len(R))
        transitionpoint = 4 * len(ranks) / 5
        for i in range(len(ranks)):
            if ranks[i] < transitionpoint:
                res[i] = ranks[i]
            else:
                res[i] = ranks[i] + (ranks[i] - transitionpoint) * self.bilinearFactor
        res /= max(res)
        return res
dataset/ETHPy150Open pybrain/pybrain/pybrain/tools/rankingfunctions.py/BilinearRanking.__call__/original