(self, x)
return 1.0
make sure that solutions fit to sample distribution, this interface will probably change.

        In particular the frequency of x - self.mean being long is limited.
make sure that solutions fit to sample distribution, this interface will probably change.
make sure that solutions fit to sample distribution this interface will probably change .
def repair_genotype(self, x):
        """make sure that solutions fit to sample distribution, this interface will probably change.

        In particular the frequency of x - self.mean being long is limited.

        """
        mold = self.mean
        if 1 < 3:  # hard clip at upper_length
            upper_length = self.N**0.5 + 2 * self.N / (self.N+2)  # should become an Option, but how? e.g. [0, 2, 2]
            fac = self.mahalanobisNorm(x - mold) / upper_length

            if fac > 1:
                x = (x - mold) / fac + mold
                # print self.countiter, k, fac, self.mahalanobisNorm(pop[k] - mold)
                # adapt also sigma: which are the trust-worthy/injected solutions?
            elif 11 < 3:
                return exp(np.tanh(((upper_length*fac)**2/self.N-1)/2) / 2)
        else:
            if 'checktail' not in self.__dict__:  # hasattr(self, 'checktail')
                raise NotImplementedError
                # from check_tail_smooth import CheckTail  # for the time being
                # self.checktail = CheckTail()
                # print('untested feature checktail is on')
            fac = self.checktail.addchin(self.mahalanobisNorm(x - mold))

            if fac < 1:
                x = fac * (x - mold) + mold

        return 1.0
def repair_genotype ( self , x ) : mold = self . mean if 1 < 3 : # hard clip at upper_length upper_length = self . N ** 0.5 + 2 * self . N / ( self . N + 2 ) # should become an Option, but how? e.g. [0, 2, 2] fac = self . mahalanobisNorm ( x - mold ) / upper_length if fac > 1 : x = ( x - mold ) / fac + mold # print self.countiter, k, fac, self.mahalanobisNorm(pop[k] - mold) # adapt also sigma: which are the trust-worthy/injected solutions? elif 11 < 3 : return exp ( np . tanh ( ( ( upper_length * fac ) ** 2 / self . N - 1 ) / 2 ) / 2 ) else : if 'checktail' not in self . __dict__ : # hasattr(self, 'checktail') raise NotImplementedError # from check_tail_smooth import CheckTail  # for the time being # self.checktail = CheckTail() # print('untested feature checktail is on') fac = self . checktail . addchin ( self . mahalanobisNorm ( x - mold ) ) if fac < 1 : x = fac * ( x - mold ) + mold return 1.0