# Path: lcs/Perception.py
# class Perception(collections.abc.Sequence):
#     """
#     Represents current state of the environment at given time instance.
#     By default each environment attribute is represented as `str` type.
#     """
# 
#     __slots__ = ['_items', 'oktypes']
# 
#     def __init__(self, observation, oktypes=(str,)):
#         assert all(type(e) in oktypes for e in observation)
#         self._items = tuple(observation)
# 
#     @classmethod
#     def empty(cls):
#         return cls([], oktypes=(None,))
# 
#     def __hash__(self):
#         return hash(self._items)
# 
#     def __getitem__(self, i):
#         return self._items[i]
# 
#     def __len__(self) -> int:
#         return len(self._items)
# 
#     def __repr__(self):
#         return ' '.join(map(str, self))
# 
#     def __eq__(self, other):
#         return all(p0 == p1 for p0, p1 in zip(self, other))
# 
# Path: lcs/strategies/subsumption.py
# def find_subsumers(cl, population, theta_exp: int) -> List:
#     """
#     Looks for subsumers of `cl` inside `population`.
# 
#     Parameters
#     ----------
#     cl:
#         classifier
#     population:
#         population of classifiers
#     theta_exp: int
#         subsumption experience threshold
# 
#     Returns
#     -------
#     List
#         list of subsumers (classifiers) sorted by specificity (most general
#         are first)
#     """
#     subsumers = [sub for sub in population if does_subsume(sub, cl, theta_exp)]
#     return sorted(subsumers, key=lambda cl: cl.condition.specificity)


import random
import numpy as np
from typing import Callable, Dict
from lcs import Perception
from lcs.strategies.subsumption import find_subsumers
    ----------
    cl_del: Classifier marked for deletion
    cl: Examined classifier

    Returns
    -------
    bool
        True if `cl` is "worse" than `cl_del`. False otherwise
    """
    if cl.q - cl_del.q < -0.1:
        return True

    if abs(cl.q - cl_del.q) <= 0.1:
        if cl.is_marked() and not cl_del.is_marked():
            return True
        elif cl.is_marked or not cl_del.is_marked():
            if cl.tav > cl_del.tav:
                return True

    return False


def _find_old_classifier(population,
                         cl,
                         use_subsumption: bool,
                         theta_exp: int):

    old_cl = None

    if use_subsumption:

# Path: lcs/Perception.py
# class Perception(collections.abc.Sequence):
#     """
#     Represents current state of the environment at given time instance.
#     By default each environment attribute is represented as `str` type.
#     """
# 
#     __slots__ = ['_items', 'oktypes']
# 
#     def __init__(self, observation, oktypes=(str,)):
#         assert all(type(e) in oktypes for e in observation)
#         self._items = tuple(observation)
# 
#     @classmethod
#     def empty(cls):
#         return cls([], oktypes=(None,))
# 
#     def __hash__(self):
#         return hash(self._items)
# 
#     def __getitem__(self, i):
#         return self._items[i]
# 
#     def __len__(self) -> int:
#         return len(self._items)
# 
#     def __repr__(self):
#         return ' '.join(map(str, self))
# 
#     def __eq__(self, other):
#         return all(p0 == p1 for p0, p1 in zip(self, other))
# 
# Path: lcs/strategies/subsumption.py
# def find_subsumers(cl, population, theta_exp: int) -> List:
#     """
#     Looks for subsumers of `cl` inside `population`.
# 
#     Parameters
#     ----------
#     cl:
#         classifier
#     population:
#         population of classifiers
#     theta_exp: int
#         subsumption experience threshold
# 
#     Returns
#     -------
#     List
#         list of subsumers (classifiers) sorted by specificity (most general
#         are first)
#     """
#     subsumers = [sub for sub in population if does_subsume(sub, cl, theta_exp)]
#     return sorted(subsumers, key=lambda cl: cl.condition.specificity)


# Path: lcs/strategies/genetic_algorithms.py
import random
import numpy as np
from typing import Callable, Dict
from lcs import Perception
from lcs.strategies.subsumption import find_subsumers

    ----------
    cl_del: Classifier marked for deletion
    cl: Examined classifier

    Returns
    -------
    bool
        True if `cl` is "worse" than `cl_del`. False otherwise
    """
    if cl.q - cl_del.q < -0.1:
        return True

    if abs(cl.q - cl_del.q) <= 0.1:
        if cl.is_marked() and not cl_del.is_marked():
            return True
        elif cl.is_marked or not cl_del.is_marked():
            if cl.tav > cl_del.tav:
                return True

    return False


def _find_old_classifier(population,
                         cl,
                         use_subsumption: bool,
                         theta_exp: int):

    old_cl = None

    if use_subsumption:

        subsumers = find_subsumers(cl, population, theta_exp)