def generate_situation(self, **kwargs):
if 'randomness' not in kwargs:
randomness = 0
else:
randomness = kwargs['randomness']
if 'type_descriptors' not in kwargs:
type_descriptors = []
else:
type_descriptors = kwargs['type_descriptors']
if 'spatial_relation_descriptors' not in kwargs:
spatial_relation_descriptors = []
else:
spatial_relation_descriptors = kwargs[
'spatial_relation_descriptors']
entity_nodes = []
for descriptor in spatial_relation_descriptors:
for i in range(descriptor.weight):
for first_descriptor, second_descriptor in subsets_of_len_two(
descriptor.type_descriptors):
for first_node in self._generate_block_entity_nodes_from_description(
first_descriptor):
entity_nodes.append(first_node)
for second_node in self._generate_block_entity_nodes_from_description(
second_descriptor):
entity_nodes.append(second_node)
list_link = self.atomspace.add_link(
types.ListLink, [first_node, second_node])
predicate_node = self.atomspace.add_node(
types.PredicateNode, descriptor.relation,
_default_tv)
self.atomspace.add_link(
types.EvaluationLink,
[predicate_node, list_link], _default_tv)
for descriptor in type_descriptors:
entity_nodes += self._generate_block_entity_nodes_from_description(
descriptor)
number_of_entities = len(entity_nodes)
for i in range(int(round(randomness * number_of_entities))):
# select two random entities
index_of_first_entity = randrange(number_of_entities)
while True:
index_of_second_entity = randrange(number_of_entities)
if index_of_first_entity != index_of_second_entity:
break
list_link = self.atomspace.add_link(types.ListLink, [
entity_nodes[index_of_first_entity],
entity_nodes[index_of_second_entity]
])
predicate_node = self.spatial_relations[randrange(
len(self.spatial_relations))]
self.atomspace.add_link(types.EvaluationLink,
[predicate_node, list_link], _default_tv)
def triangulate(self):
junction_tree = JunctionTree()
vertices = list(self)
eliminated = set()
while len(vertices) > 0:
vertices.sort() # based on number of neighbours
node = vertices.pop()
for neighbour1, neighbour2 in subsets_of_len_two(node.neighbours):
if neighbour1 in eliminated or neighbour2 in eliminated:
continue
link = neighbour1.add_neighbour(neighbour2)
clique = Clique(neighbour1, node, neighbour2, junction_tree)
# clique = Clique(neighbour1)
clique.connect_link(link)
eliminated.add(self.pop(node))
for node in eliminated:
node.revert()
self.add(node)
return junction_tree
def generate_situation(self, **kwargs):
if 'randomness' not in kwargs:
randomness = 0
else:
randomness = kwargs['randomness']
if 'type_descriptors' not in kwargs:
type_descriptors = []
else:
type_descriptors = kwargs['type_descriptors']
if 'spatial_relation_descriptors' not in kwargs:
spatial_relation_descriptors = []
else:
spatial_relation_descriptors = kwargs['spatial_relation_descriptors']
entity_nodes = []
for descriptor in spatial_relation_descriptors:
for i in range(descriptor.weight):
for first_descriptor, second_descriptor in subsets_of_len_two(descriptor.type_descriptors):
for first_node in self._generate_block_entity_nodes_from_description(first_descriptor):
entity_nodes.append(first_node)
for second_node in self._generate_block_entity_nodes_from_description(second_descriptor):
entity_nodes.append(second_node)
list_link = self.atomspace.add_link(types.ListLink, [first_node, second_node])
predicate_node = self.atomspace.add_node(types.PredicateNode, descriptor.relation,
_default_tv)
self.atomspace.add_link(types.EvaluationLink, [predicate_node, list_link], _default_tv)
for descriptor in type_descriptors:
entity_nodes += self._generate_block_entity_nodes_from_description(descriptor)
number_of_entities = len(entity_nodes)
for i in range(int(round(randomness * number_of_entities))):
# select two random entities
index_of_first_entity = randrange(number_of_entities)
while True:
index_of_second_entity = randrange(number_of_entities)
if index_of_first_entity != index_of_second_entity:
break
list_link = self.atomspace.add_link(types.ListLink,
[entity_nodes[index_of_first_entity],
entity_nodes[index_of_second_entity]])
predicate_node = self.spatial_relations[randrange(len(self.spatial_relations))]
self.atomspace.add_link(types.EvaluationLink, [predicate_node, list_link], _default_tv)
def triangulate(self):
junction_tree = JunctionTree()
vertices = list(self)
eliminated = set()
while len(vertices) > 0:
vertices.sort() # based on number of neighbours
node = vertices.pop()
for neighbour1, neighbour2 in subsets_of_len_two(node.neighbours):
if neighbour1 in eliminated or neighbour2 in eliminated:
continue
link = neighbour1.add_neighbour(neighbour2)
clique = Clique(neighbour1, node, neighbour2, junction_tree)
# clique = Clique(neighbour1)
clique.connect_link(link)
eliminated.add(self.pop(node))
for node in eliminated:
node.revert()
self.add(node)
return junction_tree
def moralise(self):
for node in self:
for parent1, parent2 in subsets_of_len_two(node.parents):
parent1.add_neighbour(parent2)
def moralise(self):
for node in self:
for parent1, parent2 in subsets_of_len_two(node.parents):
parent1.add_neighbour(parent2)
