def __generate_structure(self):
# Grafo de objetos Node
graph = Graphs.Graph(True)
# Proceso para contar la cantidad de objetos Node que tendrá el grafo dependiendo de la cantidad y tipos de tuplas.
for i in range(len(self.dna)):
layer_tuple = self.dna[i]
# Todas las tuplas que no sean conexiones entre capas.
if layer_tuple[0] != 3:
self.__len_nodes += 1
# Se construye el arreglo de objetos Node.
for i in range(self.__len_nodes):
self.nodes.append(nd.Node())
# Se realiza la conexión entre los objetos Node adyacentes.
for i in range(self.__len_nodes):
if i < (self.__len_nodes - 1):
graph.add_node(i, self.nodes[i])
graph.add_node(i + 1, self.nodes[i + 1])
graph.add_edges(i, [i + 1])
self.nodes = list(graph.key2node.values())
# Método para asignar las capas (objetos de Pytorch) respectivas a cada objeto Node.
self.__assign_layers()
def create(self, center, size, g=None):
condition = self.condition
if size > 0:
if isinstance(center, tuple):
g = gr.Graph()
self.add_node(g, center)
center = g.key2node.get(center)
self.create(center, size, g)
else:
q = center.objects[0]
DNA_o = q.shape
node_o = center
for direction in self.directions:
if DNA_o:
for k in range(len(DNA_o) - 2):
# print('The value of k is')
# print(k)
# print('The value of DNA_o is:')
# print(DNA_o)
DNA_f = condition(direction(k, DNA_o))
if DNA_f:
self.add_node(g, DNA_f)
node_f = g.key2node.get(DNA_f)
g.add_edges(DNA_o, [DNA_f])
if self.type_add_layer:
label = self.directions_labels.get(
direction)
node = g.key2node.get(DNA_f)
p = self.node2plane(node)
if not (p.direction):
p.direction = (k, label)
if center.kids:
for kid in center.kids:
self.create(kid, size - 1, g)
return g
def create(self, center, size, g=None):
selector = self.Selector
condition = self.condition
num_actions = selector.num_actions
if size > 0:
if isinstance(center, tuple):
g = gr.Graph()
self.add_node(g, center)
center = g.key2node.get(center)
self.create(center, size, g)
else:
q = center.objects[0]
DNA_o = q.shape
node_o = center
for l in range(num_actions):
if DNA_o:
DNA_f = DNA_o
label = []
path = []
mutations_to_do = self.get_random_nm()
print("morphisms: ", mutations_to_do)
time.sleep(2)
for m in range(mutations_to_do):
selector.update(DNA_f)
stop = False
while stop == False:
selector.update_predicted_actions()
actions = selector.get_predicted_actions()
typo = actions[0]
direction = self.directions.get(typo[1])
num_layer = typo[0]
temp = condition(direction(num_layer, DNA_f))
if isinstance(temp, tuple) == True:
DNA_f = temp
stop = True
label.append(typo)
path.append(DNA_f)
#print(f'The label is {label}')
if DNA_f:
self.add_node(g, DNA_f)
node_f = g.key2node.get(DNA_f)
g.add_edges(DNA_o, [DNA_f])
if self.type_add_layer:
node = g.key2node.get(DNA_f)
p = self.node2plane(node)
p.direction = label
p.path = path
#print(f'The label is{label}')
#print(f'The label is{len(p.path)}')
if center.kids:
for kid in center.kids:
self.create(kid, size - 1, g)
return g
def add_edgest_test():
g=gr.Graph()
g.add_node(0,nd.Node())
g.add_node(1,nd.Node())
g.add_node(2,nd.Node())
g.add_edges(1,[0])
g.add_edges(2,[1])
print('The kids are')
print(len(g.key2node[0].kids))
print(len(g.key2node[1].kids))
print(len(g.key2node[2].kids))
def DNA2graph(DNA):
layers = DNA2layers(DNA)
synapses = DNA2synapses(DNA)
g = gr.Graph(True)
k = -1
for layer in layers:
node = nd.Node()
node.objects.append(layer)
g.add_node(k, node)
k = k + 1
for synapse in synapses:
g.add_edges(synapse[1], [synapse[2]])
return g
def create_objects(status):
status.Transfer = tran.TransferLocal(status, 'local2remote.txt',
'remote2local.txt')
status.Transfer.un_load()
status.Transfer.write()
#status.Data_gen=dgen.Data_gen()
#status.Data_gen.gen_data()
def add_node(g, i):
node = nd.Node()
q = qu.Quadrant(i)
p = tplane.tangent_plane()
node.objects.append(q)
q.objects.append(p)
g.add_node(i, node)
#status.objects.append(node)
#Initializes graph
g = gr.Graph()
add_node(g, 0)
k = 0
while k < status.dx:
add_node(g, k + 1)
g.add_edges(k, [k + 1])
k = k + 1
k = 0
status.objects = list(g.key2node.values())
node = status.objects[0]
p = node_plane(node)
#Initializes particles
while k < status.n:
par = particle()
par.position.append(node)
par.velocity.append(node)
#print(status.Data_gen.size)
# par.objects.append(nw.Network([status.Data_gen.size[0],
# status.Data_gen.size[1],2]))
p.particles.append(par)
p.num_particles += 1
k = k + 1
#Initializes conectivity radius
attach_balls(status, status.r)
def create_objects(status):
status.Data_gen=dgen.Data_gen()
status.Data_gen.S=status.S
status.Data_gen.Comp=status.Comp
status.Data_gen.gen_data()
def add_node(g,i):
node=nd.Node()
q=qu.Quadrant(i)
p=tplane.tangent_plane()
node.objects.append(q)
q.objects.append(p)
g.add_node(i,node)
#status.objects.append(node)
#Initializes graph
g=gr.Graph()
add_node(g,0)
k=0
while k<status.dx:
add_node(g,k+1)
g.add_edges(k,[k+1])
k=k+1
k=0
status.objects=list(g.key2node.values())
node=status.objects[0]
sf.safe_update(status.nets,0,
nw.Network([status.Data_gen.size[0],
status.Data_gen.size[1],2]))
p=node_plane(node)
#Initializes particles
while k<status.n:
par=particle()
par.position.append(node)
par.velocity.append(node)
#print(status.Data_gen.size)
par.objects.append(status.nets[0])
p.particles.append(par)
p.num_particles+=1
k=k+1
#Initializes conectivity radius
attach_balls(status,status.r)
def attach_balls(self):
for node in self.objects:
p = self.node2plane(node)
p.ball = gr.spanning_tree(node, n=self.radius)
p.distance = tr.tree_distances(p.ball)
def attach_balls(status, n_r):
for node in status.objects:
q = node.objects[0]
p = q.objects[0]
p.ball = gr.spanning_tree(node, n=n_r)
p.distance = tr.tree_distances(p.ball)
def __init__(self, log_creator: Charge_log):
self.Graph = gr.Graph()
self.log_creator = log_creator
