# Path: mdpproblog/fluent.py
# class StateSpace(object):
#     """
#     Iterator class for looping over vector representations of
#     states in a factored MDP defined by `state_fluents`. Each state
#     is implemented by an OrderedDict of (problog.logic.Term, 0/1).
# 
#     :param state_fluents: predicates defining a state in a given timestep
#     :type state_fluents: list of problog.logic.Term
#     """
# 
#     def __init__(self, state_fluents):
#         self.__state_fluents = state_fluents
#         self.__state_space_size = 2**len(self.__state_fluents)
# 
#     def __len__(self):
#         """ Return the number of states of the state space. """
#         return self.__state_space_size
# 
#     def __iter__(self):
#         """ Return an iterator over the state space. """
#         self.__state_number = 0
#         self.__state = OrderedDict([ (fluent, 1) for fluent in self.__state_fluents ])
#         return self
# 
#     def __next__(self):
#         """ Return representation of next state in the sequence. """
#         if self.__state_number == self.__state_space_size:
#             raise StopIteration
# 
#         for fluent, value in self.__state.items():
#             if value == 1:
#                 self.__state[fluent] = 0
#             else:
#                 self.__state[fluent] = 1
#                 break
# 
#         self.__state_number += 1
#         return self.__state
# 
#     def __getitem__(self, index):
#         """
#         Return the state representation with given `index`.
# 
#         :param index: state index in state space
#         :type index: int
#         """
#         state = []
#         for fluent in self.__state_fluents:
#             value = index % 2
#             index //= 2
#             state.append((fluent, value))
#         return tuple(state)
# 
#     @classmethod
#     def state(cls, valuation):
#         """
#         Return the state representation of a `valuation` of fluents.
# 
#         :param valuation: mapping from fluent to boolean value
#         :type valuation: list of pairs (Fluent, bool)
#         :rtype: OrderedDict
#         """
#         return OrderedDict(valuation)
# 
#     @classmethod
#     def index(cls, state):
#         """
#         Return the `state` index in the state space.
# 
#         :param state: state representation
#         :type state: OrderedDict
#         :rtype: int
#         """
#         i = 0
#         index = 0
#         for _, value in state.items():
#             index += value * 2 ** i
#             i += 1
#         return index
# 
# class ActionSpace(object):
#     """
#     Iterator class for looping over vector representations of
#     `actions` in a factored MDP. Each action is implemented by
#     an OrderedDict of (problog.logic.Term, 0/1).
# 
#     :param actions: predicates listing possible actions
#     :type actions: list of problog.logic.Term
#     """
# 
#     def __init__(self, actions):
#         self.__actions = actions
#         self.__action_space_size = len(self.__actions)
# 
#     def __len__(self):
#         """ Return the number of actions of the action space. """
#         return self.__action_space_size
# 
#     def __iter__(self):
#         """ Return an iterator over the action space. """
#         self.__action_number = 0
#         self.__action = OrderedDict([ (action, 0) for action in self.__actions ])
#         self.__action[self.__actions[-1]] = 1
#         return self
# 
#     def __next__(self):
#         """ Return representation of next action in the sequence. """
#         if self.__action_number == self.__action_space_size:
#             raise StopIteration
# 
#         self.__action[self.__actions[self.__action_number - 1]] = 0
#         self.__action[self.__actions[self.__action_number]] = 1
# 
#         self.__action_number += 1
#         return self.__action
# 
#     def __getitem__(self, index):
#         """
#         Return the action representation with given `index`.
# 
#         :param index: action index in action space
#         :type index: int
#         """
#         return self.__actions[index]
# 
#     @classmethod
#     def index(cls, action):
#         """
#         Return action index in the action space.
# 
#         :param action: action representation
#         :type action: OrderedDict
#         :rtype: int
#         """
#         for index, fluent in enumerate(action):
#             if action[fluent] == 1:
#                 return index


import random
from mdpproblog.fluent import StateSpace, ActionSpace
            state  = self.__sample_next_state(state, action)
            total += discount * reward
            path.extend([action, state])
            discount *= gamma
        return total, path

    def __select_action(self, state):
        """
        Return the action prescribed by its policy for the given `state`.

        :param state: state represented as a valuation over fluents
        :type state: tuple of pairs (str, bool)
        :rtype: str
        """
        a = self._policy[state]
        for action in self.__actions:
            if action[a] == 1:
                return action

    def __collect_reward(self, state, action):
        """
        Return the reward for applying `action` to `state`.

        :param state: state represented as a valuation over fluents
        :type state: tuple of pairs (str, bool)
        :param action: action represented as a valuation over fluents
        :type action: tuple of pairs (str, bool)
        :rtype: float
        """
        state = StateSpace.state(state)

# Path: mdpproblog/fluent.py
# class StateSpace(object):
#     """
#     Iterator class for looping over vector representations of
#     states in a factored MDP defined by `state_fluents`. Each state
#     is implemented by an OrderedDict of (problog.logic.Term, 0/1).
# 
#     :param state_fluents: predicates defining a state in a given timestep
#     :type state_fluents: list of problog.logic.Term
#     """
# 
#     def __init__(self, state_fluents):
#         self.__state_fluents = state_fluents
#         self.__state_space_size = 2**len(self.__state_fluents)
# 
#     def __len__(self):
#         """ Return the number of states of the state space. """
#         return self.__state_space_size
# 
#     def __iter__(self):
#         """ Return an iterator over the state space. """
#         self.__state_number = 0
#         self.__state = OrderedDict([ (fluent, 1) for fluent in self.__state_fluents ])
#         return self
# 
#     def __next__(self):
#         """ Return representation of next state in the sequence. """
#         if self.__state_number == self.__state_space_size:
#             raise StopIteration
# 
#         for fluent, value in self.__state.items():
#             if value == 1:
#                 self.__state[fluent] = 0
#             else:
#                 self.__state[fluent] = 1
#                 break
# 
#         self.__state_number += 1
#         return self.__state
# 
#     def __getitem__(self, index):
#         """
#         Return the state representation with given `index`.
# 
#         :param index: state index in state space
#         :type index: int
#         """
#         state = []
#         for fluent in self.__state_fluents:
#             value = index % 2
#             index //= 2
#             state.append((fluent, value))
#         return tuple(state)
# 
#     @classmethod
#     def state(cls, valuation):
#         """
#         Return the state representation of a `valuation` of fluents.
# 
#         :param valuation: mapping from fluent to boolean value
#         :type valuation: list of pairs (Fluent, bool)
#         :rtype: OrderedDict
#         """
#         return OrderedDict(valuation)
# 
#     @classmethod
#     def index(cls, state):
#         """
#         Return the `state` index in the state space.
# 
#         :param state: state representation
#         :type state: OrderedDict
#         :rtype: int
#         """
#         i = 0
#         index = 0
#         for _, value in state.items():
#             index += value * 2 ** i
#             i += 1
#         return index
# 
# class ActionSpace(object):
#     """
#     Iterator class for looping over vector representations of
#     `actions` in a factored MDP. Each action is implemented by
#     an OrderedDict of (problog.logic.Term, 0/1).
# 
#     :param actions: predicates listing possible actions
#     :type actions: list of problog.logic.Term
#     """
# 
#     def __init__(self, actions):
#         self.__actions = actions
#         self.__action_space_size = len(self.__actions)
# 
#     def __len__(self):
#         """ Return the number of actions of the action space. """
#         return self.__action_space_size
# 
#     def __iter__(self):
#         """ Return an iterator over the action space. """
#         self.__action_number = 0
#         self.__action = OrderedDict([ (action, 0) for action in self.__actions ])
#         self.__action[self.__actions[-1]] = 1
#         return self
# 
#     def __next__(self):
#         """ Return representation of next action in the sequence. """
#         if self.__action_number == self.__action_space_size:
#             raise StopIteration
# 
#         self.__action[self.__actions[self.__action_number - 1]] = 0
#         self.__action[self.__actions[self.__action_number]] = 1
# 
#         self.__action_number += 1
#         return self.__action
# 
#     def __getitem__(self, index):
#         """
#         Return the action representation with given `index`.
# 
#         :param index: action index in action space
#         :type index: int
#         """
#         return self.__actions[index]
# 
#     @classmethod
#     def index(cls, action):
#         """
#         Return action index in the action space.
# 
#         :param action: action representation
#         :type action: OrderedDict
#         :rtype: int
#         """
#         for index, fluent in enumerate(action):
#             if action[fluent] == 1:
#                 return index


# Path: mdpproblog/simulator.py
import random
from mdpproblog.fluent import StateSpace, ActionSpace

            state  = self.__sample_next_state(state, action)
            total += discount * reward
            path.extend([action, state])
            discount *= gamma
        return total, path

    def __select_action(self, state):
        """
        Return the action prescribed by its policy for the given `state`.

        :param state: state represented as a valuation over fluents
        :type state: tuple of pairs (str, bool)
        :rtype: str
        """
        a = self._policy[state]
        for action in self.__actions:
            if action[a] == 1:
                return action

    def __collect_reward(self, state, action):
        """
        Return the reward for applying `action` to `state`.

        :param state: state represented as a valuation over fluents
        :type state: tuple of pairs (str, bool)
        :param action: action represented as a valuation over fluents
        :type action: tuple of pairs (str, bool)
        :rtype: float
        """
        state = StateSpace.state(state)

        cache = (StateSpace.index(state), ActionSpace.index(action))