def second_selectivity(self, d):
    '\n        Return the selectivity of the second largest value in the distribution.\n        If there is one feature_bin only, the selectivity is 0, since there is no second\n        peack at all, and this value is also used to discriminate the validity\n        of second_max_value_bin()\n        Selectivity is computed in two ways depending on whether the variable is\n        a cyclic, as in selectivity()\n        '
    if (len(d._data) <= 1):
        return 0.0
    if (d.cyclic == True):
        return self._vector_second_selectivity(d)
    else:
        return self._relative_second_selectivity(d)
Return the selectivity of the second largest value in the distribution.
If there is one feature_bin only, the selectivity is 0, since there is no second
peack at all, and this value is also used to discriminate the validity
of second_max_value_bin()
Selectivity is computed in two ways depending on whether the variable is
a cyclic, as in selectivity()
def second_selectivity(self, d):
    '\n        Return the selectivity of the second largest value in the distribution.\n        If there is one feature_bin only, the selectivity is 0, since there is no second\n        peack at all, and this value is also used to discriminate the validity\n        of second_max_value_bin()\n        Selectivity is computed in two ways depending on whether the variable is\n        a cyclic, as in selectivity()\n        '
    if (len(d._data) <= 1):
        return 0.0
    if (d.cyclic == True):
        return self._vector_second_selectivity(d)
    else:
        return self._relative_second_selectivity(d)