def __call__(self, R):
        res = zeros(len(R))
        ranks = rankedFitness(R)
        cutoff = len(R) * (1. - self.topFraction)
        for i in range(len(R)):
            if cutoff >= ranks[i]:
                res[i] = 1.0
            else:
                res[i] = 0.0
        return res
dataset/ETHPy150Open pybrain/pybrain/pybrain/tools/rankingfunctions.py/TopSelection.__call__/swapped operands of `>=`