def _backtracking(problem, assignment, domains, variable_chooser, values_sorter, inference=True):
'''
Internal recursive backtracking algorithm.
'''
from simpleai.search.arc import arc_consistency_3
if len(assignment) == len(problem.variables):
return assignment
pending = [v for v in problem.variables
if v not in assignment]
variable = variable_chooser(problem, pending, domains)
values = values_sorter(problem, assignment, variable, domains)
for value in values:
new_assignment = deepcopy(assignment)
new_assignment[variable] = value
if not _count_conflicts(problem, new_assignment): # TODO on aima also checks if using fc
new_domains = deepcopy(domains)
new_domains[variable] = [value]
if not inference or arc_consistency_3(new_domains, problem.constraints):
result = _backtracking(problem,
new_assignment,
new_domains,
variable_chooser,
values_sorter,
inference=inference)
if result:
return result
return None
def test_chained_revise_calls_remove_non_obvious_problems(self):
# if A, B, C must be all different, with domains [1, 1], [1, 2], [2, 2] you
# can't find a solution, but it requires several chained calls to
# revise:
# revise(A, B) -> ok! [1, 1] [1, 2] [2, 2]
# revise(A, C) -> ok! [1, 1] [1, 2] [2, 2]
# revise(B, C) -> fail, remove 2 from B [1, 1] [1] [2, 2]
# and re-revise A, B and C, B
# revise(A, B) -> fail, remove 1 from A [] [1] [2, 2]
# and re-revise ...
# here A has no more values, ac3 returns a failure
domains = {'A': [1, 1], 'B': [1, 2], 'C': [2, 2]}
different = lambda variables, values: len(set(values)) == len(variables
)
constraints = [(('A', 'B'), different), (('A', 'C'), different),
(('B', 'C'), different)]
result = arc_consistency_3(domains, constraints)
self.assertFalse(result)
def _backtracking(problem,
assignment,
domains,
variable_chooser,
values_sorter,
inference=True):
'''
Internal recursive backtracking algorithm.
'''
from simpleai.search.arc import arc_consistency_3
if len(assignment) == len(problem.variables):
return assignment
pending = [v for v in problem.variables if v not in assignment]
variable = variable_chooser(problem, pending, domains)
values = values_sorter(problem, assignment, variable, domains)
for value in values:
new_assignment = deepcopy(assignment)
new_assignment[variable] = value
if not _count_conflicts(
problem,
new_assignment): # TODO on aima also checks if using fc
new_domains = deepcopy(domains)
new_domains[variable] = [value]
if not inference or arc_consistency_3(new_domains,
problem.constraints):
result = _backtracking(problem,
new_assignment,
new_domains,
variable_chooser,
values_sorter,
inference=inference)
if result:
return result
return None
def test_chained_revise_calls_remove_non_obvious_problems(self):
# if A, B, C must be all different, with domains [1, 1], [1, 2], [2, 2] you
# can't find a solution, but it requires several chained calls to
# revise:
# revise(A, B) -> ok! [1, 1] [1, 2] [2, 2]
# revise(A, C) -> ok! [1, 1] [1, 2] [2, 2]
# revise(B, C) -> fail, remove 2 from B [1, 1] [1] [2, 2]
# and re-revise A, B and C, B
# revise(A, B) -> fail, remove 1 from A [] [1] [2, 2]
# and re-revise ...
# here A has no more values, ac3 returns a failure
domains = {'A': [1, 1],
'B': [1, 2],
'C': [2, 2]}
different = lambda variables, values: len(set(values)) == len(variables)
constraints = [(('A', 'B'), different),
(('A', 'C'), different),
(('B', 'C'), different)]
result = arc_consistency_3(domains, constraints)
self.assertFalse(result)
def ac3(self, domain_a, domain_b):
domains = {'A': domain_a, 'B': domain_b}
constraints = [(('A', 'B'), is_square)]
return arc_consistency_3(domains, constraints), domains
def test_ac3(self):
self.assertTrue(arc_consistency_3(self.domains, self.constraints))
self.assertEqual(self.domains, {'X': [1, 2, 3, 4], 'Y': [1, 4, 9, 16]})
def test_ac3(self):
self.assertTrue(arc_consistency_3(self.domains, self.constraints))
self.assertEqual(self.domains, {'X': [1, 2, 3, 4], 'Y': [1, 4, 9, 16]})
def ac3(self, domain_a, domain_b):
domains = {'A': domain_a, 'B': domain_b}
constraints = [(('A', 'B'), is_square)]
return arc_consistency_3(domains, constraints), domains
