def infer_pos_label(neg_label=None):
    """Try to guess a positive classification label from a negative label
    Basis for an NLP function to find the "opposite" of a string
    yes->no, true->false, etc)
    >>> [infer_pos_label(x) for x in ('1', '-1', '0', '2', 1, 0, 'F', 'False', False, True)]
    ['0', '1', '1', '3', 0, 1, 'T', 'True', True, False]
    """
    # A class label should be a None, bool, int, or str
    if neg_label is None:
        return True
    typ = type(neg_label)
    # If class label isn't a bool or None then make it an int or str
    try:
        neg_label = int(float(neg_label))
        if neg_label in (0, 1):
            return typ(int(not neg_label))
        if neg_label < 0:
            return typ(-neg_label)
        return typ(neg_label + 1)
    except (ValueError, TypeError):
        neg_label = stringify(neg_label).strip()
    for xform, label_dict in zip((lambda x: x, lambda x: x, str.lower, str.lower,),
                                 (POS_LABELS, POS_LABELS_INVERSE, POS_LABELS_LOWER, POS_LABELS_LOWER_INVERSE)):
        try:
            return typ(label_dict[xform(neg_label)])
        except KeyError:
            pass
    # neg_label = neg.lower()
    # for labels in (POS_LABELS_LOWER, POS_LABELS_LOWER_INVERSE):
    #     try:
    #         return typ(labels[neg_label])
    #     except KeyError:
    #         pass
    # if not neg_label:
    #     return True
    # neg = neg[0]
    # try:
    #     return POS_LABELS_LOWER_FIRST[neg]
    # except KeyError:
    #     pass
    return 'P'