(self, fit)
return np.argsort(array(fit, copy=False)[:2 * (reev + 1)])[:reev]
return the set of indices to be reevaluted for noise measurement,
        taking the ``lam_reeval`` best from the first ``2 * lam_reeval + 2``
        values.

        Given the first values are the earliest, this is a useful policy also
        with a time changing objective.
return the set of indices to be reevaluted for noise measurement,
        taking the ``lam_reeval`` best from the first ``2 * lam_reeval + 2``
        values.
return the set of indices to be reevaluted for noise measurement taking the lam_reeval best from the first 2 * lam_reeval + 2 values .
def indices(self, fit):
        """return the set of indices to be reevaluted for noise measurement,
        taking the ``lam_reeval`` best from the first ``2 * lam_reeval + 2``
        values.

        Given the first values are the earliest, this is a useful policy also
        with a time changing objective.

        """
        lam = self.lam_reeval if self.lam_reeval else 2 + len(fit) / 20
        reev = int(lam) + ((lam % 1) > np.random.rand())
        return np.argsort(array(fit, copy=False)[:2 * (reev + 1)])[:reev]
def indices ( self , fit ) : lam = self . lam_reeval if self . lam_reeval else 2 + len ( fit ) / 20 reev = int ( lam ) + ( ( lam % 1 ) > np . random . rand ( ) ) return np . argsort ( array ( fit , copy = False ) [ : 2 * ( reev + 1 ) ] ) [ : reev ]