def solve_instance(self):
prg = Control()
prg.load("puzzle15.lp")
prg.load(self.new_filename)
ret, parts, step = SolveResult.unsatisfiable, [], 1
parts.append(("base", []))
while ret == SolveResult.unsatisfiable:
parts.append(("step", [step]))
parts.append(("check", [step]))
prg.ground(parts)
prg.release_external(Function("query", [step - 1]))
prg.assign_external(Function("query", [step]), True)
#f = lambda m: stdout.write(str(m)+'\n')
print("step:" + str(step) + " Solving...")
ret, parts, step = prg.solve(on_model=self.on_model), [], step + 1
if ret.__repr__() == 'UNSAT':
ret = SolveResult.unsatisfiable
else:
ret = SolveResult.satisfiable
class Solver:
def __init__(self):
self.k = 0
self.prg = Control()
self.prg.load("client.lp")
self.prg.ground([("pigeon", []), ("sleep", [Number(self.k)])])
self.prg.assign_external(Function("sleep", [Number(self.k)]), True)
self.ret = None
self.models = []
def on_model(self, model):
self.models.append(str(model))
def start(self, on_finish):
if self.ret is not None and not self.ret.unknown():
self.k = self.k + 1
self.prg.ground([("sleep", [self.k])])
self.prg.release_external(Function("sleep", [Number(self.k - 1)]))
self.prg.assign_external(Function("sleep", [Number(self.k)]), True)
self.future = self.prg.solve(on_model=self.on_model,
on_finish=on_finish,
async_=True)
def stop(self):
self.future.cancel()
def finish(self):
ret = self.future.get()
return ret
def set_more_pigeon(self, more):
self.prg.assign_external(Function("p"), more)
def main(self, ctl: Control, files: Sequence[str]):
'''
Parse LP^MLN program and convert to ASP with weak constraints.
'''
observer = Observer()
ctl.register_observer(observer)
ctl.add("base", [], THEORY)
ctl.add("base", [], self.evidence_file)
if self.two_solve_calls:
ctl.add("base", [], '#external ext_helper.')
# TODO: Make sure the ext_helper atom is not contained in the program.
if not files:
files = ["-"]
self._convert(ctl, files)
ctl.ground([("base", [])])
if self.query != []:
self._ground_queries(ctl.symbolic_atoms)
bound_hr = 2**63 - 1
if self.two_solve_calls:
# First solve call
# Soft rules are deactivated
# TODO: Suppress output of first solve call, add flag
# TODO: Activate this per flag
ctl.assign_external(Function("ext_helper"), False)
with ctl.solve(yield_=True) as h:
for m in h:
bound_hr = m.cost[0]
# TODO: Don't show ext_helper
# ctl.release_external(Function("ext_helper"))
ctl.assign_external(Function("ext_helper"), True)
if self.display_all_probs:
ctl.configuration.solve.opt_mode = f'enum, {bound_hr}, {(2**63)-1}'
ctl.configuration.solve.models = 0
model_costs = []
with ctl.solve(yield_=True) as handle:
for model in handle:
if self.display_all_probs or self.query != []:
model_costs.append(model.cost)
if self.query != []:
self._check_model_for_query(model)
if model_costs != [] and (self.display_all_probs or self.query != []):
if 0 not in observer.priorities:
# TODO: Should this be error or warning?
print(
'No soft weights in program. Cannot calculate probabilites'
)
# TODO: What about case where there are other priorities than 0/1?
# elif not self.two_solve_calls and any(
# x > 1 for x in observer.priorities):
# print(observer.priorities)
# print('testasd')
else:
probs = ProbabilityModule(
model_costs, observer.priorities,
[self.translate_hard_rules, self.two_solve_calls])
if self.display_all_probs:
probs.print_probs()
if self.query != []:
probs.get_query_probability(self.query)
class Solver:
def __init__(self, horizon=0):
self.__horizon = horizon
self.__prg = Control(['-t4'])
self.__future = None
self.__solution = None
self.__assign = []
self.__prg.load("board.lp")
self.__prg.load("robots.lp")
parts = [ ("base", [])
, ("check", [Number(0)])
, ("state", [Number(0)])
]
for t in range(1, self.__horizon+1):
parts.extend([ ("trans", [Number(t)])
, ("check", [Number(t)])
, ("state", [Number(t)])
])
self.__prg.ground(parts)
self.__prg.assign_external(Function("horizon", [Number(self.__horizon)]), True)
def __next(self):
assert(self.__horizon < 30)
self.__prg.assign_external(Function("horizon", [Number(self.__horizon)]), False)
self.__horizon += 1
self.__prg.ground([ ("trans", [Number(self.__horizon)])
, ("check", [Number(self.__horizon)])
, ("state", [Number(self.__horizon)])
])
self.__prg.assign_external(Function("horizon", [Number(self.__horizon)]), True)
def start(self, board):
self.__assign = []
for robot, (x, y) in board.pos.items():
self.__assign.append(Function("pos", [Function(robot), Number(x+1), Number(y+1), Number(0)]))
self.__assign.append(Function("target",
[ Function(board.current_target[0])
, Number(board.current_target[2] + 1)
, Number(board.current_target[3] + 1)
]))
for x in self.__assign:
self.__prg.assign_external(x, True)
self.__solution = None
self.__future = self.__prg.solve(on_model=self.__on_model, async_=True)
def busy(self):
if self.__future is None:
return False
if self.__future.wait(0):
if self.__solution is None:
self.__next()
self.__future = self.__prg.solve(on_model=self.__on_model, async_=True)
return True
else:
self.__future = None
return False
return True
def stop(self):
if self.__future is not None:
self.__future.cancel()
self.__future.wait()
self.__future = None
self.get()
def get(self):
solution = self.__solution
self.__solution = None
for x in self.__assign:
self.__prg.assign_external(x, False)
self.__assign = []
return solution
def __on_model(self, m):
self.__solution = []
for atom in m.symbols(atoms=True):
if atom.name == "move" and len(atom.arguments) == 4:
c, x, y, t = [(n.number if n.type == SymbolType.Number else str(n)) for n in atom.arguments]
self.__solution.append((c, x, y, t))
self.__solution.sort(key=lambda x: x[3])
p = None
i = 0
for x in self.__solution:
if p is not None and \
p[0] == x[0] and \
p[1] == x[1] and \
p[2] == x[2]:
break
p = x
i += 1
del self.__solution[i:]
class SolveThread(Thread):
STATE_SOLVE = 1
STATE_IDLE = 2
STATE_EXIT = 3
def __init__(self, connection):
Thread.__init__(self)
self.k = 0
self.prg = Control()
self.prg.load("client.lp")
self.prg.ground([("pigeon", []), ("sleep", [Number(self.k)])])
self.prg.assign_external(Function("sleep", [Number(self.k)]), True)
self.state = SolveThread.STATE_IDLE
self.input = Connection()
self.output = connection
def on_model(self, model):
self.output.send("answer: " + str(model)),
def on_finish(self, ret):
self.output.send("finish: " + str(ret) +
(" (INTERRUPTED)" if ret.interrupted else ""))
def handle_message(self, msg):
if msg == "interrupt":
self.state = SolveThread.STATE_IDLE
elif msg == "exit":
self.state = SolveThread.STATE_EXIT
elif msg == "less_pigeon_please":
self.prg.assign_external(Function("p"), False)
self.state = SolveThread.STATE_IDLE
elif msg == "more_pigeon_please":
self.prg.assign_external(Function("p"), True)
self.state = SolveThread.STATE_IDLE
elif msg == "solve":
self.state = SolveThread.STATE_SOLVE
else:
raise (RuntimeError("unexpected message: " + msg))
def run(self):
while True:
if self.state == SolveThread.STATE_SOLVE:
f = self.prg.solve(on_model=self.on_model,
on_finish=self.on_finish,
async_=True)
msg = self.input.receive()
if self.state == SolveThread.STATE_SOLVE:
f.cancel()
ret = f.get()
else:
ret = None
self.handle_message(msg)
if self.state == SolveThread.STATE_EXIT:
return
elif ret is not None and not ret.unknown:
self.k = self.k + 1
self.prg.ground([("sleep", [Number(self.k)])])
self.prg.release_external(
Function("sleep", [Number(self.k - 1)]))
self.prg.assign_external(Function("sleep", [Number(self.k)]),
True)
class VizloControl(Control):
def add_to_painter(self, model: Union[Model, PythonModel,
Collection[clingo.Symbol]]):
"""
will register model with the internal painter. On all consecutive calls to paint(), this model will be painted.
:param model: the model to add to the painter.
:return:
"""
self.painter.append(PythonModel(model))
def __init__(self,
arguments: List[str] = [],
logger=None,
message_limit: int = 20,
print_entire_models=False,
atom_draw_maximum=15):
self.control = Control(arguments, logger, message_limit)
self.painter: List[PythonModel] = list()
self.program: ASTProgram = list()
self.raw_program: str = ""
self.transformer = JustTheRulesTransformer()
self._print_changes_only = not print_entire_models
self._atom_draw_maximum = atom_draw_maximum
def _set_print_only_changes(self, value: bool) -> None:
self._print_changes_only = value
def ground(self,
parts: List[Tuple[str, List[Symbol]]],
context: Any = None) -> None:
self.control.ground(parts, context)
def solve(self,
assumptions: List[Union[Tuple[Symbol, bool], int]] = [],
on_model=None,
on_statistics=None,
on_finish=None,
yield_: bool = False,
async_: bool = False) -> Union[SolveHandle, SolveResult]:
return self.control.solve(assumptions, on_model, on_statistics,
on_finish, yield_, async_)
def load(self, path):
prg = ""
with open(path) as f:
for line in f:
prg += line
self.program += prg
self.control.load(path)
def add(self, name: str, parameters: List[str], program: str) -> None:
self.raw_program += program
self.control.add(name, parameters, program)
def find_nodes_corresponding_to_stable_models(self, g, stable_models):
correspoding_nodes = set()
for model in stable_models:
for node in g.nodes():
log(f"{node} {type(node.model)} == {model} {type(model)} -> {set(node.model) == model}"
)
if set(node.model) == model and len(
g.edges(node)) == 0: # This is a leaf
log(f"{node} <-> {model}")
correspoding_nodes.add(node)
break
return correspoding_nodes
def prune_graph_leading_to_models(self, graph: nx.DiGraph,
models_as_nodes):
before = len(graph)
relevant_nodes = set()
for model in models_as_nodes:
for relevant_node in nx.all_simple_paths(graph, INITIAL_EMPTY_SET,
model):
relevant_nodes.update(relevant_node)
all_nodes = set(graph.nodes())
irrelevant_nodes = all_nodes - relevant_nodes
graph.remove_nodes_from(irrelevant_nodes)
after = len(graph)
log(f"Removed {before - after} of {before} nodes ({(before - after) / before})"
)
def _make_graph(self, _sort=True):
"""
Ties together transformation and solving. Transforms the already added program parts and creates a solving tree.
:param _sort: Whether the program should be sorted automatically. Setting this to false will likely result into
wrong results!
:return:
:raises ValueError:
"""
if not len(self.raw_program):
raise ValueError("Can't paint an empty program.")
else:
t = JustTheRulesTransformer()
program = t.transform(self.raw_program, _sort)
if len(self.painter):
universe = get_ground_universe(program)
global_assumptions = make_global_assumptions(
universe, self.painter)
solve_runner = SolveRunner(program, t.rule2signatures)
g = solve_runner.make_graph(global_assumptions)
else:
solve_runner = SolveRunner(program,
symbols_in_heads_map=t.rule2signatures)
g = solve_runner.make_graph()
return g
def paint(self,
atom_draw_maximum: int = 20,
show_entire_model: bool = False,
sort_program: bool = True,
**kwargs):
"""
Will create a graph visualization of the solving process. If models have been added using add_to_painter,
only the solving paths that lead to these models will be drawn.
:param atom_draw_maximum: int
The maximum amount of atoms that will be printed for each partial model. (default=20)
:param show_entire_model: bool
If false, only the atoms that have been added at a solving step will be printed (up to atom_draw_maximum).
If true, all atoms will always be printed (up to atom_draw_maximum). (default=False)
:param sort_program:
If true, the rules of a program will be sorted and grouped by their dependencies.
Each set of rules will contain all rules in which each atom in its heads is contained in a head.
:param kwargs:
kwargs will be forwarded to the visualisation module. See graph.draw()
:return:
"""
if type(atom_draw_maximum) != int:
raise ValueError(
f"Argument atom_draw_maximum should be an integer (received {atom_draw_maximum})."
)
g = self._make_graph(sort_program)
display = NetworkxDisplay(g, atom_draw_maximum, not show_entire_model)
img = display.draw(**kwargs)
return img
def _add_and_ground(self, prg):
"""Short cut for complex add and ground calls, should only be used for debugging purposes."""
self.add("base", [], prg)
self.ground([("base", [])])
##################
# Just pass-through stuff
##################
@property
def configuration(self) -> Configuration:
return self.control.configuration
@property
def is_conflicting(self) -> bool:
return self.control.is_conflicting
@property
def statistics(self) -> dict:
return self.control.statistics
@property
def symbolic_atoms(self) -> SymbolicAtoms:
return self.control.symbolic_atoms
@property
def theory_atoms(self) -> TheoryAtomIter:
return self.control.theory_atoms
@property
def use_enumeration_assumption(self) -> bool:
return self.control.use_enumeration_assumption
def assign_external(self, external: Union[Symbol, int],
truth: Optional[bool], **kwargs) -> None:
self.control.assign_external(external, truth, **kwargs)
def backend(self) -> Backend:
return self.control.backend()
def builder(self) -> ProgramBuilder:
return self.control.builder()
def cleanup(self) -> None:
self.control.cleanup()
def get_const(self, name: str) -> Optional[Symbol]:
return self.control.get_const(name)
def interrupt(self):
self.control.interrupt()
def register_observer(self, observer, replace=False):
self.register_observer(observer, replace)
def release_external(self, symbol: Union[Symbol, int]) -> None:
self.control.release_external(symbol)