def ac3(constraint_problem: ConstraintProblem,
assigned_variable: Variable = None) -> bool:
if assigned_variable is not None: # usage of ac3 as part of Maintaining Arc Consistency (MAC) algorithm
unassigned_neighbors = constraint_problem.get_unassigned_neighbors(
assigned_variable)
arcs = {(unassigned_neighbor, assigned_variable)
for unassigned_neighbor in unassigned_neighbors}
else:
arcs = {(variable, neighbor)
for variable in constraint_problem.get_unassigned_variables()
for neighbor in constraint_problem.get_neighbors(variable)}
while arcs:
variable, neighbor = arcs.pop()
if __revise(constraint_problem, variable, neighbor):
if not constraint_problem.get_consistent_domain(variable):
return False
rest_of_neighbors = constraint_problem.get_neighbors(variable) - {
neighbor
}
if rest_of_neighbors:
for other_neighbor in rest_of_neighbors:
arcs.add((other_neighbor, variable))
for var in constraint_problem.get_variables():
if not var.domain or not constraint_problem.get_consistent_domain(var):
return False
return True
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 i_consistency(constraint_problem: ConstraintProblem, i: int) -> bool:
variables = constraint_problem.get_variables()
assert 0 < i <= len(variables), "for i = {0}: i <= 0 or (number of variables in constraint_problem) < i.".format(i)
i_minus_one_sized_subsets = combinations(variables, i - 1)
i_subsets_consistent_assignments = __initialize_i_consistency(variables, i_minus_one_sized_subsets)
reducing_domains = True
while reducing_domains:
reducing_domains = False
for subset, ith_variable in i_subsets_consistent_assignments:
if __revise_i(constraint_problem, subset, ith_variable, i_subsets_consistent_assignments):
reducing_domains = True
for var in constraint_problem.get_variables():
if not var.domain or not constraint_problem.get_consistent_domain(var):
return False
return True
def __calculate_weight(constraint_problem: ConstraintProblem,
constraints_weights: Dict[Constraint, int]) -> int:
weight = 0
for variable in constraint_problem.get_variables():
unsatisfied_constraints = filterfalse(
None,
constraint_problem.get_constraints_containing_variable(variable))
for unsatisfied_const in unsatisfied_constraints:
weight += constraints_weights[unsatisfied_const]
return weight
def naive_cycle_cutset(constraint_problem: ConstraintProblem, with_history: bool = False) \
-> Optional[Deque[Tuple[Variable, Any]]]:
actions_history = None
if with_history:
actions_history = deque()
variables = constraint_problem.get_variables()
read_only_variables = constraint_problem.get_assigned_variables()
constraints = list(constraint_problem.get_constraints())
constraints.sort(key=lambda constraint: len(constraint.variables), reverse=True)
constraint_graph = constraint_problem.get_constraint_graph_as_adjacency_list()
for i in range(1, len(constraints)):
cutset_constraints = constraints[:i]
cutset_variables = set()
for cutset_const in cutset_constraints:
cutset_variables.update(cutset_const.variables)
reduced_graph = {var: neighbors for var, neighbors in constraint_graph.items() if var not in cutset_variables}
for var in reduced_graph:
reduced_graph[var] -= cutset_variables
if __is_tree(reduced_graph):
consistent_assignments_list = __get_consistent_assignments(cutset_variables, cutset_constraints,
read_only_variables)
non_cutset_variables = variables - cutset_variables
non_cutset_vars_to_original_domains_map = {var: var.domain for var in non_cutset_variables}
for consist_assignment in consistent_assignments_list:
for var, value in zip(cutset_variables, consist_assignment):
if var not in read_only_variables:
var.assign(value)
if with_history:
actions_history.append((var, value))
for non_cutset_var in non_cutset_variables:
if non_cutset_var not in read_only_variables:
non_cutset_var.domain = list(constraint_problem.get_consistent_domain(non_cutset_var))
tree_csp_action_history = tree_csp_solver(constraint_problem, with_history)
if with_history:
actions_history.extend(tree_csp_action_history)
if constraint_problem.is_completely_consistently_assigned():
return actions_history
for var in variables:
if var not in read_only_variables:
var.unassign()
if with_history:
actions_history.append((var, None))
for var in non_cutset_vars_to_original_domains_map:
if var not in read_only_variables:
var.domain = non_cutset_vars_to_original_domains_map[var]
return actions_history
def ac4(constraint_problem: ConstraintProblem) -> bool:
support_counter = collections.Counter()
variable_value_pairs_supported_by = collections.defaultdict(set)
unsupported_variable_value_pairs = collections.deque()
__initialize_ac4(constraint_problem.get_constraints(), support_counter, variable_value_pairs_supported_by,
unsupported_variable_value_pairs)
while unsupported_variable_value_pairs:
second_variable, second_value = unsupported_variable_value_pairs.popleft()
for first_variable, first_value in variable_value_pairs_supported_by[(second_variable, second_value)]:
if first_value in first_variable.domain:
support_counter[(first_variable, first_value, second_variable)] -= 1
if support_counter[(first_variable, first_value, second_variable)] == 0:
first_variable.remove_from_domain(first_value)
unsupported_variable_value_pairs.append((first_variable, first_value))
for var in constraint_problem.get_variables():
if not var.domain or not constraint_problem.get_consistent_domain(var):
return False
return True
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 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))