def min_conflicts(
constraint_problem: ConstraintProblem,
max_steps: int,
tabu_size: int = -1,
with_history: bool = False) -> Optional[Deque[Tuple[Variable, Any]]]:
__tabu_queue.clear()
read_only_variables = constraint_problem.get_assigned_variables()
if tabu_size == -1:
tabu_size = 0
assert tabu_size + len(read_only_variables) < len(constraint_problem.get_variables()), \
"tabu_size + len(read_only_variables) is equal or bigger than constraint_problem's variables amount."
if tabu_size == 0:
tabu_size = -1
actions_history = None
if with_history:
actions_history = deque()
rand_assignmt_history = constraint_problem.assign_variables_with_random_values(
read_only_variables, actions_history)
if with_history:
actions_history.extend(rand_assignmt_history)
best_min_conflicts = len(constraint_problem.get_unsatisfied_constraints())
best_min_conflicts_assignment = constraint_problem.get_current_assignment()
for i in range(max_steps):
if constraint_problem.is_completely_consistently_assigned():
return actions_history
conflicted_variable = __get_random_conflicted_variable(
constraint_problem, read_only_variables, tabu_size)
conflicted_variable.unassign()
if with_history:
actions_history.append((conflicted_variable, None))
min_conflicts_value = __get_min_conflicts_value(
constraint_problem, conflicted_variable)
conflicted_variable.assign(min_conflicts_value)
if with_history:
actions_history.append((conflicted_variable, min_conflicts_value))
if len(__tabu_queue) == tabu_size:
__tabu_queue.popleft()
if __tabu_queue:
__tabu_queue.append(conflicted_variable)
curr_conflicts_count = len(
constraint_problem.get_unsatisfied_constraints())
if curr_conflicts_count < best_min_conflicts:
best_min_conflicts = curr_conflicts_count
best_min_conflicts_assignment = constraint_problem.get_current_assignment(
)
constraint_problem.unassign_all_variables()
constraint_problem.assign_variables_from_assignment(
best_min_conflicts_assignment)
return actions_history
def __get_best_reduction_variable_value(
constraint_problem: ConstraintProblem,
constraints_weights: Dict[Constraint, int],
read_only_variables: FrozenSet[Variable]) -> Tuple[int, Variable, Any]:
pairs_to_weight_reduction = dict()
weight = __calculate_weight(constraint_problem, constraints_weights)
original_assignment = constraint_problem.get_current_assignment()
constraint_problem.unassign_all_variables()
for variable in constraint_problem.get_variables() - read_only_variables:
for value in variable.domain:
variable.assign(value)
curr_weight = __calculate_weight(constraint_problem,
constraints_weights)
pairs_to_weight_reduction[(variable, value)] = weight - curr_weight
variable.unassign()
constraint_problem.unassign_all_variables()
constraint_problem.assign_variables_from_assignment(original_assignment)
max_variable, max_value = max(pairs_to_weight_reduction,
key=pairs_to_weight_reduction.get)
return pairs_to_weight_reduction[(max_variable,
max_value)], max_variable, max_value
def constraints_weighting(constraint_problem: ConstraintProblem, max_tries: int, with_history: bool = False) \
-> Optional[Deque[Tuple[Variable, Any]]]:
actions_history = None
if with_history:
actions_history = deque()
constraints_weights = {
constraint: 1
for constraint in constraint_problem.get_constraints()
}
read_only_variables = constraint_problem.get_assigned_variables()
for i in range(max_tries):
constraint_problem.assign_variables_with_random_values(
read_only_variables)
last_reduction = float("inf")
while 0 < last_reduction:
if constraint_problem.is_completely_consistently_assigned():
return actions_history
reduction, variable, value = __get_best_reduction_variable_value(
constraint_problem, constraints_weights, read_only_variables)
variable.unassign()
if with_history:
actions_history.append((variable, None))
variable.assign(value)
if with_history:
actions_history.append((variable, value))
last_reduction = reduction
for unsatisfied_constraint in constraint_problem.get_unsatisfied_constraints(
):
constraints_weights[unsatisfied_constraint] += 1
if i != max_tries - 1:
constraint_problem.unassign_all_variables(read_only_variables)
return actions_history
def generate_start_state_randomly(
constraint_problem: ConstraintProblem) -> None:
constraint_problem.unassign_all_variables()
for var in constraint_problem.get_variables():
var.assign(choice(var.domain))