# Path: lcs/TypedList.py
# class TypedList(collections.abc.MutableSequence, Generic[T]):
#     __slots__ = ['_items', 'oktypes']
# 
#     def __init__(self, *args, oktypes):
#         self._items: List[T] = list()
#         self.oktypes = oktypes
# 
#         for el in args:
#             check_types(self.oktypes, el)
# 
#         self._items.extend(list(args))
# 
#     def insert(self, index: int, o: T) -> None:
#         check_types(self.oktypes, o)
#         self._items.insert(index, o)
# 
#     def safe_remove(self, o: T) -> None:
#         try:
#             self.remove(o)
#         except ValueError:
#             pass
# 
#     def sort(self, *args, **kwargs) -> None:
#         self._items.sort(*args, **kwargs)
# 
#     def __iter__(self) -> Iterator[T]:
#         return super().__iter__()
# 
#     def __repr__(self):
#         return f"{len(self._items)} items"
# 
#     def __setitem__(self, i: int, o: T):
#         check_types(self.oktypes, o)
#         self._items[i] = o
# 
#     def __delitem__(self, i):
#         del self._items[i]
# 
#     def __getitem__(self, i) -> T:
#         return self._items[i]
# 
#     def __len__(self) -> int:
#         return len(self._items)
# 
#     def __hash__(self):
#         return hash((self.oktypes, self._items))
# 
#     def __eq__(self, o) -> bool:
#         return self.oktypes == o.oktypes \
#                and self._items == o._items
# 
# 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/agents/racs/Configuration.py
# class Configuration:
#     def __init__(self, **kwargs) -> None:
#         self.classifier_length: int = kwargs['classifier_length']
#         self.number_of_possible_actions: int = kwargs[
#             'number_of_possible_actions']
#         self.encoder = kwargs['encoder']
# 
#         self.beta: float = kwargs.get('beta', 0.05)
#         self.gamma: float = kwargs.get('gamma', 0.95)
#         self.theta_i: float = kwargs.get('theta_i', 0.1)
#         self.theta_r: float = kwargs.get('theta_r', 0.9)
#         self.epsilon: float = kwargs.get('epsilon', 0.5)
#         self.biased_exploration: float = kwargs.get('biased_exploration', 0.5)
# 
#         # Max range of uniform noise distribution that can alter
#         # the perception during covering U[0, cover_noise]
#         self.cover_noise: float = kwargs.get('cover_noise', 0.1)
#         self.u_max: int = kwargs.get('u_max', 100000)
#         self.do_subsumption: bool = kwargs.get('do_subsumption', True)
#         self.do_ga: bool = kwargs.get('do_ga', False)
#         self.mu: float = kwargs.get('mu', 0.3)
# 
#         # Max range of uniform noise distribution that can broaden the
#         # phenotype interval range.
#         self.mutation_noise: float = kwargs.get('mutation_noise', 0.1)
#         self.chi: float = kwargs.get('chi', 0.8)
#         self.theta_exp: int = kwargs.get('theta_exp', 20)
#         self.theta_ga: int = kwargs.get('theta_ga', 100)
#         self.theta_as: int = kwargs.get('theta_as', 20)
# 
#         self.metrics_trial_frequency: int = kwargs.get(
#             'metrics_trial_frequency', 5)
#         self.user_metrics_collector_fcn: Optional[Callable] = kwargs.get(
#             'user_metrics_collector_fcn', None)
#         self.model_checkpoint_freq: Optional[int] = kwargs.get(
#             'model_checkpoint_frequency', None)
# 
#         self.oktypes = (UBR,)
#         self.classifier_wildcard = UBR(*self.encoder.range)


import random
import lcs.agents.racs.components.alp as alp_racs
import lcs.strategies.anticipatory_learning_process as alp
import lcs.strategies.genetic_algorithms as ga
import lcs.strategies.reinforcement_learning as rl
from itertools import chain
from typing import Optional, List
from lcs import TypedList, Perception
from lcs.agents.racs import Configuration
from lcs.agents.racs.components.genetic_algorithm import mutate, crossover
from . import Classifier
from __future__ import annotations




class ClassifierList(TypedList):

    def __init__(self, *args) -> None:
        super().__init__(*args, oktypes=(Classifier,))


# Path: lcs/TypedList.py
# class TypedList(collections.abc.MutableSequence, Generic[T]):
#     __slots__ = ['_items', 'oktypes']
# 
#     def __init__(self, *args, oktypes):
#         self._items: List[T] = list()
#         self.oktypes = oktypes
# 
#         for el in args:
#             check_types(self.oktypes, el)
# 
#         self._items.extend(list(args))
# 
#     def insert(self, index: int, o: T) -> None:
#         check_types(self.oktypes, o)
#         self._items.insert(index, o)
# 
#     def safe_remove(self, o: T) -> None:
#         try:
#             self.remove(o)
#         except ValueError:
#             pass
# 
#     def sort(self, *args, **kwargs) -> None:
#         self._items.sort(*args, **kwargs)
# 
#     def __iter__(self) -> Iterator[T]:
#         return super().__iter__()
# 
#     def __repr__(self):
#         return f"{len(self._items)} items"
# 
#     def __setitem__(self, i: int, o: T):
#         check_types(self.oktypes, o)
#         self._items[i] = o
# 
#     def __delitem__(self, i):
#         del self._items[i]
# 
#     def __getitem__(self, i) -> T:
#         return self._items[i]
# 
#     def __len__(self) -> int:
#         return len(self._items)
# 
#     def __hash__(self):
#         return hash((self.oktypes, self._items))
# 
#     def __eq__(self, o) -> bool:
#         return self.oktypes == o.oktypes \
#                and self._items == o._items
# 
# 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/agents/racs/Configuration.py
# class Configuration:
#     def __init__(self, **kwargs) -> None:
#         self.classifier_length: int = kwargs['classifier_length']
#         self.number_of_possible_actions: int = kwargs[
#             'number_of_possible_actions']
#         self.encoder = kwargs['encoder']
# 
#         self.beta: float = kwargs.get('beta', 0.05)
#         self.gamma: float = kwargs.get('gamma', 0.95)
#         self.theta_i: float = kwargs.get('theta_i', 0.1)
#         self.theta_r: float = kwargs.get('theta_r', 0.9)
#         self.epsilon: float = kwargs.get('epsilon', 0.5)
#         self.biased_exploration: float = kwargs.get('biased_exploration', 0.5)
# 
#         # Max range of uniform noise distribution that can alter
#         # the perception during covering U[0, cover_noise]
#         self.cover_noise: float = kwargs.get('cover_noise', 0.1)
#         self.u_max: int = kwargs.get('u_max', 100000)
#         self.do_subsumption: bool = kwargs.get('do_subsumption', True)
#         self.do_ga: bool = kwargs.get('do_ga', False)
#         self.mu: float = kwargs.get('mu', 0.3)
# 
#         # Max range of uniform noise distribution that can broaden the
#         # phenotype interval range.
#         self.mutation_noise: float = kwargs.get('mutation_noise', 0.1)
#         self.chi: float = kwargs.get('chi', 0.8)
#         self.theta_exp: int = kwargs.get('theta_exp', 20)
#         self.theta_ga: int = kwargs.get('theta_ga', 100)
#         self.theta_as: int = kwargs.get('theta_as', 20)
# 
#         self.metrics_trial_frequency: int = kwargs.get(
#             'metrics_trial_frequency', 5)
#         self.user_metrics_collector_fcn: Optional[Callable] = kwargs.get(
#             'user_metrics_collector_fcn', None)
#         self.model_checkpoint_freq: Optional[int] = kwargs.get(
#             'model_checkpoint_frequency', None)
# 
#         self.oktypes = (UBR,)
#         self.classifier_wildcard = UBR(*self.encoder.range)


# Path: lcs/agents/racs/ClassifierList.py
import random
import lcs.agents.racs.components.alp as alp_racs
import lcs.strategies.anticipatory_learning_process as alp
import lcs.strategies.genetic_algorithms as ga
import lcs.strategies.reinforcement_learning as rl
from itertools import chain
from typing import Optional, List
from lcs import TypedList, Perception
from lcs.agents.racs import Configuration
from lcs.agents.racs.components.genetic_algorithm import mutate, crossover
from . import Classifier

from __future__ import annotations




class ClassifierList(TypedList):

    def __init__(self, *args) -> None:
        super().__init__(*args, oktypes=(Classifier,))


    def form_match_set(self, situation: Perception) -> ClassifierList: