# Path: mdpproblog/fluent.py
# class Fluent(object):
#     """
#     Factory class for building fluent terms. A fluent term is a
#     problog.logic.Term with a problog.logic.Constant as last argument
#     representing its timestep.
#     """
# 
#     @classmethod
#     def create_fluent(cls, term, timestep):
#         """"
#         Return a new fluent made from `term` with given `timestep`.
# 
#         :param term: any problog term
#         :type term: problog.logic.Term
#         :param timestep: timestep numeric value
#         :type timestep: int
#         :rtype: problog.logic.Term
#         """
#         args = term.args + (Constant(timestep),)
#         return term.with_args(*args)
# 
# 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 mdpproblog.engine as eng
from mdpproblog.fluent import Fluent, StateSpace, ActionSpace
            value = self.__reward(state, action)
            self.__reward_cache[cache] = value
        return value

    def __reward(self, state, action):
        """
        Return the immediate reward value of the transition
        induced by applying `action` to the given `state`.

        :param state: state vector representation of current state fluents
        :type state: list of 0/1 according to state fluents order
        :param action: action vector representation
        :type action: one-hot vector encoding of action as a list of 0/1
        :rtype: float
        """
        evidence = state.copy()
        evidence.update(action)
        total = 0
        for term, prob in self._engine.evaluate(self.__reward_queries, evidence):
            total += prob * self.__utilities[term].value
        return total

    def reward_model(self):
        """
        Return the reward model of all valid transitions.

        :rtype: dict of ((state,action), float)
        """
        rewards = {}
        states  = StateSpace(self.current_state_fluents())

# Path: mdpproblog/fluent.py
# class Fluent(object):
#     """
#     Factory class for building fluent terms. A fluent term is a
#     problog.logic.Term with a problog.logic.Constant as last argument
#     representing its timestep.
#     """
# 
#     @classmethod
#     def create_fluent(cls, term, timestep):
#         """"
#         Return a new fluent made from `term` with given `timestep`.
# 
#         :param term: any problog term
#         :type term: problog.logic.Term
#         :param timestep: timestep numeric value
#         :type timestep: int
#         :rtype: problog.logic.Term
#         """
#         args = term.args + (Constant(timestep),)
#         return term.with_args(*args)
# 
# 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/mdp.py
import mdpproblog.engine as eng
from mdpproblog.fluent import Fluent, StateSpace, ActionSpace

            value = self.__reward(state, action)
            self.__reward_cache[cache] = value
        return value

    def __reward(self, state, action):
        """
        Return the immediate reward value of the transition
        induced by applying `action` to the given `state`.

        :param state: state vector representation of current state fluents
        :type state: list of 0/1 according to state fluents order
        :param action: action vector representation
        :type action: one-hot vector encoding of action as a list of 0/1
        :rtype: float
        """
        evidence = state.copy()
        evidence.update(action)
        total = 0
        for term, prob in self._engine.evaluate(self.__reward_queries, evidence):
            total += prob * self.__utilities[term].value
        return total

    def reward_model(self):
        """
        Return the reward model of all valid transitions.

        :rtype: dict of ((state,action), float)
        """
        rewards = {}
        states  = StateSpace(self.current_state_fluents())

        actions = ActionSpace(self.actions())