def test_is_satisfiable_bigger_problem(self):
actors_400 = [[1,2] for i in range(200)]
actors_400.extend([[-1,-2,-3] for i in range(200)])
result = do_solve(
variables=actors_400,
reference_value=0,
csp_solver_config=csp_solver_config
)
assert result['satisfiable_bool'] == True
assert result['solution_list'] == [-1, -1, -1, -1, -3, -1, -1, -3, -1,
-3, -1, -1, -3, -1, -1, -3, -1, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 1, 2, 2,
1, 2, 1, 2, 2, 1, 2, 2, -3, -1, -1, -3, -1, -1, -1, -1, -3, -1, -3, -1,
-3, -1, -1, -3, -1, -3, -1, -1, -1, -3, -1, -1, -3, -1, -1, -3, -3, -1,
-3, -3, 2, 1, 2, 1, 1, 2, 1, 2, 2, 2, 2, -1, -1, 2, 2, 2, 2, -1, 2, 2,
-1, -1, 1, 2, 2, 2, 2, 1, -1, -1, -1, -1, -3, -1, -1, -1, -1, 2, 2, 2,
1, 1, 2, 2, 2, 2, 1, -1, -3, -3, -1, -1, -1, -1, -1, -3, -1, -1, -3,
-1, -1, -3, -1, 2, 2, 1, 2, 1, 2, 2, 1, 2, 2, 2, 1, 2, 1, 1, 2, -3, -1,
-1, -1, -1, -1, -1, -3, -3, -1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 1, 1, -3,
-1, -1, -3, -1, -3, -3, -1, -3, -1, -1, -1, -1, -3, 1, 2, 2, 2, 2, 2,
2, 1, 2, 1, -1, -3, -3, -1, -1, -1, -1, -3, -1, -1, 1, 2, 2, 2, 2, 1,
1, 2, -1, -1, -1, -3, -1, -3, 2, 1, -3, -1, 1, 2, 2, 2, 2, 1, 2, 2, 1,
2, -3, -3, -1, -1, -1, -1, 1, 2, 2, 2, 1, 2, 2, 1, 2, 1, 2, 2, 2, 1,
1, 2, 2, 2, 1, 2, 2, 1, 2, 1, 1, 2, 2, 2, 2, 1, -1, -1, 2, -3, 2, 1,
1, 2, -1, -3, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 1, -1, -1, -3, -1, -1,
-1, -1, -3, -1, -3, -3, -1, -3, -1, -1, -1, 2, 2, 1, 2, 2, 1, 2, 2, 1,
2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, -1, -3, -3, -1, -1, -1, -1, -3,
-3, -1, -3, -3, -1, -1, -1, -3, -1, -1, -3, -1, -1, -1, -3, -3, -1,
-3, -1, -1, -1, -1, -1, -3, -1, -1, -3, 1, -3, -1, -1, -1, -1, 2, -3,
2, 2, 1, 2, 2, 1, 2, 2, -1, 1, -1, -1, -1, -3, -1, -3, -3, -1, 1, 2,
2, 1, 2, 2, 1, 2, 2, 1, -1, -3], result['solution_list']
def set_value(self, new_value):
set_value = self.validate(new_value)
all_actor_ranges = list(
self._parallel_apply_for_actors_n_wait(get_actor_vr))
csp_result = csp_solver.do_solve(
variables=all_actor_ranges,
reference_value=set_value,
csp_solver_config=self._csp_solver_config)
if ('satisfiable_bool' in csp_result
and csp_result['satisfiable_bool'] == True):
# Using a subprocess will pickle the used objects
# --> the "set" values are lost.
# RemoteActor manipulates outside of
# context actors --> ok.
if self.actors_cls in [ControllerActor, RemoteActor]:
iterable = [
(self._actors[index], assigned_value)
for index, assigned_value \
in enumerate(csp_result['solution_list'])
]
_query_results = self._parallel_apply_for_actors_n_wait(
set_actor_value, iterable)
else:
for index, assigned_value in enumerate(
csp_result['solution_list']):
set_actor_value((self._actors[index], assigned_value))
return set_value
else:
raise NotSolvable('{0} is not an satisfiable'.format(set_value))
def test_is_satisfiable2(self):
result = do_solve(
variables=[[1,2], [1,2]],
reference_value=4,
csp_solver_config=csp_solver_config
)
assert result['satisfiable_bool'] == True
assert result['solution_list'] == [2, 2]
def test_is_satisfiable1(self):
result = do_solve(
variables=[[1,2]],
reference_value=1,
csp_solver_config=csp_solver_config
)
assert ('satisfiable_bool' in result and
result['satisfiable_bool'] == True), result
assert result['solution_list'] == [1], result
def test_is_satisfiable3(self):
result = do_solve(
variables=[
[-1, 0, 1], [-1,-2,-3], [-1, 0, 1], [-1,-2,-3]
],
reference_value=0,
csp_solver_config=csp_solver_config
)
assert result['satisfiable_bool'] == True
assert result['solution_list'] == [1, -1, 1, -1]
def test_not_satisfiable(self):
result = do_solve(
variables=[[1,2], [1,2]],
reference_value=5,
csp_solver_config=csp_solver_config
)
assert result['satisfiable_bool'] == False, \
result['satisfiable_bool']
# is not a minisat result, sugar returns that!
assert result['solution_list'] == None, result['solution_list']
def test_minisat_is_deterministic():
for _ in range(20):
result = do_solve(
variables=[
[-1, 0, 1], [-1, 0, 1], [-1, 0, 1], [-1, 0, 1],
],
reference_value=0,
quiet=False,
csp_solver_config=csp_solver_config
)
# possible solutions:
#[0,0,0,0], [-1,-1,1,1]...
assert result['satisfiable_bool'] == True
assert result['solution_list'] == [0, -1, 0, 1], \
result['solution_list']
def get_value_range(self):
# the alternative algorithm (try every possible answer) is
# not an option here as there might be an infinite
# amount of possibilities
# --> calculate a few small problems by trying out every
# possibility is solvable and faster
range_min = 0
range_max = 0
all_actor_ranges = []
query_results = self._parallel_apply_for_actors_n_wait(
get_actor_vr
)
for actor_value_range in query_results:
all_actor_ranges.append(actor_value_range)
actor_min = min(actor_value_range)
range_min += actor_min
actor_max = max(actor_value_range)
range_max += actor_max
range_theo_max = range(range_min, range_max+1)
range_theo_max_length = len(range_theo_max)
if range_theo_max_length > 500:
self.log('warning: big interval to check: {0}'.format(
range_theo_max_length))
raise ValueError(
'please improve algorithm for large numbers')
own_value_range = set()
for possibly_a_value_range_value in range_theo_max:
csp_result = csp_solver.do_solve(
variables=all_actor_ranges,
reference_value=possibly_a_value_range_value,
csp_solver_config=self._csp_solver_config
)
if ('satisfiable_bool' in csp_result
and csp_result['satisfiable_bool'] == True):
own_value_range.add(possibly_a_value_range_value)
else:
self.log('not a solution: {0}'.format(
possibly_a_value_range_value))
return own_value_range
def set_value(self, new_value):
set_value = self.validate(new_value)
all_actor_ranges = list(self._parallel_apply_for_actors_n_wait(
get_actor_vr
))
csp_result = csp_solver.do_solve(
variables=all_actor_ranges,
reference_value=set_value,
csp_solver_config=self._csp_solver_config
)
if ('satisfiable_bool' in csp_result
and csp_result['satisfiable_bool'] == True):
# Using a subprocess will pickle the used objects
# --> the "set" values are lost.
# RemoteActor manipulates outside of
# context actors --> ok.
if self.actors_cls in [
ControllerActor, RemoteActor
]:
iterable = [
(self._actors[index], assigned_value)
for index, assigned_value \
in enumerate(csp_result['solution_list'])
]
_query_results = self._parallel_apply_for_actors_n_wait(
set_actor_value,
iterable
)
else:
for index, assigned_value in enumerate(
csp_result['solution_list']):
set_actor_value(
(self._actors[index], assigned_value)
)
return set_value
else:
raise NotSolvable(
'{0} is not an satisfiable'.format(set_value)
)
def get_value_range(self):
# the alternative algorithm (try every possible answer) is
# not an option here as there might be an infinite
# amount of possibilities
# --> calculate a few small problems by trying out every
# possibility is solvable and faster
range_min = 0
range_max = 0
all_actor_ranges = []
query_results = self._parallel_apply_for_actors_n_wait(get_actor_vr)
for actor_value_range in query_results:
all_actor_ranges.append(actor_value_range)
actor_min = min(actor_value_range)
range_min += actor_min
actor_max = max(actor_value_range)
range_max += actor_max
range_theo_max = range(range_min, range_max + 1)
range_theo_max_length = len(range_theo_max)
if range_theo_max_length > 500:
self.log('warning: big interval to check: {0}'.format(
range_theo_max_length))
raise ValueError('please improve algorithm for large numbers')
own_value_range = set()
for possibly_a_value_range_value in range_theo_max:
csp_result = csp_solver.do_solve(
variables=all_actor_ranges,
reference_value=possibly_a_value_range_value,
csp_solver_config=self._csp_solver_config)
if ('satisfiable_bool' in csp_result
and csp_result['satisfiable_bool'] == True):
own_value_range.add(possibly_a_value_range_value)
else:
self.log(
'not a solution: {0}'.format(possibly_a_value_range_value))
return own_value_range
