def test_shortest_path(self):
try:
handler = DesktopHandler(DLV2DesktopService(self.getPath()))
ASPMapper.get_instance().register_class(Edge)
ASPMapper.get_instance().register_class(Path)
inputProgram = ASPInputProgram()
self.source = 0 # source node
self.destination = 7 # destination node
rules = "source(" + str(self.source) + "). destination(" + str(
self.destination) + ")."
rules += "path(X,Y,W) | notPath(X,Y,W) :- source(X), edge(X,Y,W)."
rules += "path(X,Y,W) | notPath(X,Y,W) :- path(_,X,_), edge(X,Y,W), not to(X)."
rules += "visited(X) :- path(_,X,_)."
rules += ":- destination(X), not visited(X)."
rules += ":~ path(X,Y,W). [W@1 ,X,Y]"
inputProgram.add_program(rules)
inputProgram.add_objects_input(self.getEdges())
handler.add_program(inputProgram)
answerSets = handler.start_sync()
self.assertIsNotNone(answerSets)
self.assertTrue(isinstance(answerSets, Output),
"Error, result object is not Output")
self.assertTrue(answerSets.get_errors() == "",
"Found error:\n" + str(answerSets.get_errors()))
self.assertTrue(len(answerSets.get_optimal_answer_sets()) != 0)
answerSet = answerSets.get_optimal_answer_sets()[0]
path = [] # edges in the shortest path (unsorted)
sum_ = 0 # total weight of the path
for obj in answerSet.get_atoms():
if isinstance(obj, Path):
path.append(obj)
sum_ += int(obj.get_weight())
sortedPath = [] # edges in the shortest path (sorted)
sortedPath.append(self.source)
self.join(self.source, path, sortedPath) # sorts the edges
self.show(sortedPath, sum_) # shows the path
except Exception as e:
print(str(e))
class AI: def __init__(self) -> None: # Instantiate the Handler. self.handler = DesktopHandler(DLV2DesktopService(os.path.join(dirname, "dlv2.exe"))) # Register input facts to provide to DLV2. ASPMapper.get_instance().register_class(MatrixCellPredicate) ASPMapper.get_instance().register_class(ShapePredicate) ASPMapper.get_instance().register_class(InCellPredicate) def instantiateProgram(self) -> None: # Instantiate the ASP program. self.inputProgram = ASPInputProgram() # Define the program's rules. logicProgram = open(os.path.join(dirname, "newShapeRules.lp"), "r") self.rules = logicProgram.read() logicProgram.close() # Add rules to the program self._addRules() def getOptimalPlace(self, matrix: list, shapes: list): self.instantiateProgram() matrixPredicates = [] for i in range(len(matrix)): for j in range(len(matrix)): if (matrix[i][j]): matrixPredicates.append(MatrixCellPredicate(i, j)) shapePredicate = ShapePredicate(0, shapes[0]) #DEBUG #self.printArray(matrixPredicates) #self.printArray(shapePredicate) # Add predicates to the program self.inputProgram.add_objects_input(matrixPredicates) self.inputProgram.add_object_input(shapePredicate) # Add program to the handler self.handler.add_program(self.inputProgram) print(self.inputProgram) # Spawn DLV synchronously and get the output output = self.handler.start_sync() optimalPlacement = [] optimalAnswerSets = output.get_optimal_answer_sets() #print(len(optimalAnswerSets)) for answerSet in output.get_optimal_answer_sets(): #print(answerSet.get_weights()) for atom in answerSet.get_atoms(): # Filter out inCellPredicates. The answer set contains facts, outCellPredicates etc. We are only interested in inCellPredicates. if isinstance(atom, InCellPredicate): optimalPlacement.append((atom.get_index(), atom.get_coordX(), atom.get_coordY())) self.inputProgram.clear_all() self.handler.remove_all() return optimalPlacement def getOptimalPlacement(self, matrix: list, shapes: list): """ Returns the optimal placement for a single `ShapeAggregate`, given the input matrix status. Returns `None` if the AS is empty (no solution). """ # Instantiate program. self.instantiateProgram() matrixPredicates = [] for i in range(len(matrix)): for j in range(len(matrix)): if (matrix[i][j]): matrixPredicates.append(MatrixCellPredicate(i, j)) shapePredicate = [] for x in range(len(shapes)): shapePredicate.append(ShapePredicate(x, shapes[x][0])) #self.printArray(matrixPredicates) #self.printArray(shapePredicate) # Add predicates to the program self.inputProgram.add_objects_input(matrixPredicates) self.inputProgram.add_objects_input(shapePredicate) # Add program to the handler self.handler.add_program(self.inputProgram) print(self.inputProgram) # Spawn DLV synchronously and get the output output = self.handler.start_sync() optimalPlacement = [] optimalAnswerSets = output.get_optimal_answer_sets() for answerSet in output.get_optimal_answer_sets(): #print(answerSet.get_weights()) for atom in answerSet.get_atoms(): # Filter out inCellPredicates. The answer set contains facts, outCellPredicates etc. We are only interested in inCellPredicates. if isinstance(atom, InCellPredicate): optimalPlacement.append((atom.get_index(), atom.get_coordX(), atom.get_coordY())) self.inputProgram.clear_all() self.handler.remove_all() return optimalPlacement def getOptimalPlacements(self, matrix, shapeAggregates): pass def _addRules(self) -> None: self.rules = re.sub(r'\s?\n\t+', '', self.rules) self.inputProgram.add_program(self.rules) def _reset(self) -> None: # Remove the program from the handler self.handler.remove_all() # Remove rules and facts from the program self.inputProgram.clear_all() # Add rules back self._addRules() def printArray(self, arr: list) -> None: for el in arr: print(el)