def update_from_select(self):
phase_space = self.phase_space
creator = self.Creator
selector = self.Selector
version = self.version
phase_space.time = phase_space.time + 1
if phase_space.max_changed or phase_space.time > 2000:
phase_space.time = 0
num_particles = phase_space.num_particles
old_graph = phase_space.DNA_graph
old_center = old_graph.center
condition = old_graph.condition
typos = old_graph.typos
node_max = phase_space.node_max_particles
center = phase_space.node2key(node_max)
selector.update(old_graph, new_center=center)
actions = selector.get_predicted_actions()
x = old_graph.x_dim
y = old_graph.y_dim
space = DNA_Graph(center, 1, (x, y), condition, actions, version,
creator)
phase_space.DNA_graph = space
phase_space.objects = space.objects
phase_space.support = []
phase_space.create_particles(num_particles + 1)
phase_space.stream.signals_off()
phase_space.attach_balls()
phase_space.max_changed = False
phase_space.node_max_particles = None
self.space = space
self.phase_space = phase_space
self.objects = phase_space.objects
self.support = phase_space.support
self.Graph = phase_space.DNA_graph
def update_space_default(self):
phase_space = self.phase_space
if phase_space.max_changed:
num_particles = phase_space.num_particles
old_graph = phase_space.DNA_graph
condition = old_graph.condition
typos = old_graph.typos
node_max = phase_space.node_max_particles
center = phase_space.node2key(node_max)
x = old_graph.x_dim
y = old_graph.y_dim
space = DNA_Graph(center, self.dx, (x, y), condition, typos,
'inclusion')
#Phase_space = DNA_Phase_space(space)
phase_space.DNA_graph = space
phase_space.objects = space.objects
phase_space.support = []
phase_space.create_particles(num_particles + 1)
phase_space.attach_balls()
phase_space.max_changed = False
phase_space.node_max_particles = None
self.space = space
self.phase_space = phase_space
self.objects = phase_space.objects
self.support = phase_space.support
self.Graph = phase_space.DNA_graph
def generateSpace(x, y):
max_layers = 10
max_filters = 60
max_dense = 100
def condition_b(z):
return max_filter_dense(
max_filter(max_layer(z, max_layers), max_filters), max_dense)
center = ((-1, 1, 3, 32, 32), (0, 3, 5, 3, 3), (0, 5, 6, 3, 3),
(0, 6, 7, 3, 3, 2), (0, 7, 8, 16, 16), (1, 8, 10), (2, ),
(3, -1, 0), (3, 0, 1), (3, 1, 2), (3, 2, 3), (3, 3, 4), (3, 4,
5))
version = 'h'
mutations = ((4, 0, 0, 0), (1, 0, 0, 0), (0, 0, 1))
#mutations=((4,0,0,0),(1,0,0,0),(0,1,0,0),(0,1,0,0),(0,0,1))
sel = Selector_creator(condition=condition_b,
directions=version,
mutations=mutations,
num_actions=5)
print('The selector is')
print(sel)
sel.update(center)
actions = sel.get_predicted_actions()
creator = Creator_nm
space = DNA_Graph(center,
1, (x, y),
condition_b,
actions,
version,
creator=creator,
num_morphisms=5,
selector=sel)
return space
def __generate_new_space(self, firstTime=False):
oldSpace = self.__space
newCenter = self.__best_network.dna
if firstTime == True:
self.__selector.update(newCenter)
else:
self.__selector.update(oldSpace, newCenter)
predicted_actions = self.__selector.get_predicted_actions()
self.__actions = []
self.__actions = predicted_actions
stop = False
while stop == False:
newSpace = DNA_Graph(center=newCenter,
size=oldSpace.size,
dim=(oldSpace.x_dim, oldSpace.y_dim),
condition=oldSpace.condition,
typos=predicted_actions,
type_add_layer=oldSpace.version,
creator=Creator_s)
nodeCenter = self.__get_center_node(newSpace)
if len(nodeCenter.kids) >= 0:
print("kids: ", len(nodeCenter.kids))
stop = True
self.__space = None
self.__space = newSpace
def update_from_select_nm(self):
phase_space = self.phase_space
creator = self.Creator
selector = self.Selector
version = self.version
phase_space.time = phase_space.time + 1
list_particles = [
phase_space.node2particles(node) for node in phase_space.objects
]
print("particles: ", list_particles)
node_max = node_max_particles(phase_space)
p_m = Funct.node2num_particles(node_max)
node_c = phase_space.key2node(phase_space.DNA_graph.center)
p_c = Funct.node2num_particles(node_c)
print(f'The value of p_m is : {p_m} and pc is : {p_c} ')
if (p_m > p_c * 2):
phase_space.time = 0
num_particles = phase_space.num_particles
old_graph = phase_space.DNA_graph
old_center = old_graph.center
condition = old_graph.condition
typos = old_graph.typos
#node_max = phase_space.node_max_particles
node_max = node_max_particles(phase_space)
print("node_max particles:", Funct.node2num_particles(node_max))
center = phase_space.node2key(node_max)
status = phase_space.status
Alai = status.Alai
stream = phase_space.stream
delta = stream.key2len_hist(center)
Alai.update(delta)
stream.signals_off()
stream.key2signal_on(center)
stream.clear()
selector.update(center)
actions = selector.get_predicted_actions()
x = old_graph.x_dim
y = old_graph.y_dim
space = DNA_Graph(center,
1, (x, y),
condition,
actions,
version,
creator,
selector=selector,
num_morphisms=5)
phase_space.DNA_graph = space
phase_space.objects = space.objects
phase_space.support = []
phase_space.create_particles(num_particles + 1)
phase_space.attach_balls()
phase_space.max_changed = False
phase_space.node_max_particles = None
self.space = space
self.phase_space = phase_space
self.objects = phase_space.objects
self.support = phase_space.support
self.Graph = phase_space.DNA_graph
def create_objects(status):
status.Data_gen = GeneratorFromImage.GeneratorFromImage(status.Comp,
status.S,
cuda=status.cuda)
status.Data_gen.dataConv2d()
dataGen = status.Data_gen
x = dataGen.size[1]
y = dataGen.size[2]
max_layers = status.max_layer
max_filters = status.max_filter
def condition(DNA):
return max_filter(max_layer(DNA, max_layers), max_filters)
version = status.typos_version
"""
center=((-1,1,3,x,y),
(0,3, 4, x, y),
(1, 4, 2), (2,),(3,-1,0),(3,0,1),
(3,1,2))"""
center = ((-1, 1, 3, x, y), (0, 3, 15, 3, 3), (0, 18, 15, 3, 3),
(0, 33, 15, x, y), (1, 15, 2), (2, ), (3, -1, 0), (3, 0, 1),
(3, -1, 1), (3, 1, 2), (3, 0, 2), (3, -1, 2), (3, 2, 3), (3, 3,
4))
selector = status.Selector_creator(condition=condition, directions=version)
status.Selector = selector
creator = status.Creator
selector.update(center)
actions = selector.get_predicted_actions()
space = DNA_Graph(center, 1, (x, y), condition, actions, version, creator)
if status.Alai:
stream = TorchStream(status.Data_gen,
status.log_size,
min_size=status.min_log_size,
Alai=status.Alai)
else:
stream = TorchStream(status.Data_gen,
status.log_size,
min_size=status.min_log_size)
Phase_space = DNA_Phase_space(space, stream=stream)
Dynamics = Dynamic_DNA(space,
Phase_space,
status.dx,
Creator=creator,
Selector=selector,
update_velocity=velocity_updater,
update_space=space_updater,
version=version,
mutation_coefficient=status.mutation_coefficient,
clear_period=status.clear_period)
Phase_space.create_particles(status.n)
Phase_space.beta = status.beta
Phase_space.alpha = status.alpha
Phase_space.influence = status.influence
status.Dynamics = Dynamics
status.objects = Dynamics.objects
def create_objects(status):
status.Data_gen = GeneratorFromImage.GeneratorFromImage(status.Comp,
status.S,
cuda=False)
status.Data_gen.dataConv2d()
dataGen = status.Data_gen
x = dataGen.size[1]
y = dataGen.size[2]
def condition(DNA):
output = True
if DNA:
for num_layer in range(0, len(DNA) - 3):
layer = DNA[num_layer]
x_l = layer[3]
y_l = layer[4]
output = output and (x_l < x) and (y_l < y)
if output:
return max_filter(max_layer(DNA, 3), 40)
typos = []
print('The value of typos is')
print(status.typos)
#for element in status.typos:
# if type(element) == list:
# typos.append(tuple(element))
# else:
# typos.append(element)
#status.typos=tuple(typos)
#Number of filters
#center=((0, 3, 2, x, y), (1, 2, 2), (2,))
#space=DNA_Graph(center,status.dx,(x,y),condition,(0,(1,1,0,0)))
#Dimension of kernel
center = ((0, 3, 5, 3, 3), (0, 8, 8, 3, 3), (0, 11, 5, x, y), (1, 5, 2),
(2, ))
#center=((0, 3, 2, x, y), (1, 2, 2), (2,))
version = 'inclusion'
space = space = DNA_Graph(center, status.dx, (x, y), condition,
(0, (1, 0, 0, 0), (0, 0, 1, 1), (0, 1, 0, 0)),
version)
#space=space=DNA_Graph(center,1,(x,y),condition,(0,(1,0,0,0)),version)
Phase_space = DNA_Phase_space(space)
Dynamics = Dynamic_DNA(space, Phase_space, status.dx)
Phase_space.create_particles(status.n)
Phase_space.beta = status.beta
Phase_space.alpha = status.alpha
Phase_space.influence = status.influence
status.Dynamics = Dynamics
status.objects = Dynamics.objects
def update_from_select_Alai(self):
phase_space = self.phase_space
creator = self.Creator
selector = self.Selector
version = self.version
phase_space.time = phase_space.time + 1
node_max = node_max_particles(phase_space)
node_c = phase_space.key2node(phase_space.DNA_graph.center)
p_c = Funct.node2num_particles(node_c)
p_m = Funct.node2num_particles(node_max)
print(f'The value of p_m is : {p_m} and p_c is : {p_c} ')
if (p_m > p_c * 2) or (phase_space.time > 4000):
phase_space.time = 0
num_particles = phase_space.num_particles
old_graph = phase_space.DNA_graph
old_center = old_graph.center
condition = old_graph.condition
typos = old_graph.typos
node_max = phase_space.node_max_particles
center = phase_space.node2key(node_max)
selector.update(old_graph, new_center=center)
actions = selector.get_predicted_actions()
x = old_graph.x_dim
y = old_graph.y_dim
space = DNA_Graph(center, 1, (x, y), condition, actions, version,
creator)
phase_space.DNA_graph = space
phase_space.objects = space.objects
phase_space.support = []
phase_space.create_particles(num_particles + 1)
phase_space.stream.signals_off()
phase_space.attach_balls()
phase_space.max_changed = False
phase_space.node_max_particles = None
self.space = space
self.phase_space = phase_space
self.objects = phase_space.objects
self.support = phase_space.support
self.Graph = phase_space.DNA_graph
elif False:
#elif phase_space.time>self.clear_period:
phase_space.time = 0
node2remove = phase_space2node2remove(phase_space)
node_c = phase_space.key2node(phase_space.DNA_graph.center)
if node2remove and not (node2remove == node_c):
remove_node(phase_space, node2remove)
new_DNA = add_node(phase_space, selector)
def DNA_Creator_s(x, y, dna, version):
def condition(DNA):
return DNA_conditions.dict2condition(DNA, list_conditions)
selector = None
selector = LSTMSelector(condition=condition,
directions=version,
num_actions=num_actions,
mutations=((1, 0, 0, 0), (1, 0, 0, 0),
(0, 1, 0, 0), (0, 1, 0, 0),
(4, 0, 0, 0), (0, 0, 1), (0, 0, -1),
(0, 0, 1, 1), (0, 0, -1, -1), (0, 0,
2)))
selector.update(dna)
actions = selector.get_predicted_actions()
space = DNA_Graph(dna, 1, (x, y), condition, actions, version, Creator_s)
return [space, selector]
def update_from_select_stochastic(self):
phase_space = self.phase_space
creator = self.Creator
selector = self.Selector
version = self.version
phase_space.time = phase_space.time + 1
p = randint(0, 300)
if phase_space.node_max_particles:
node_max = phase_space.node_max_particles
node_c = phase_space.key2node(phase_space.DNA_graph.center)
p_c = Funct.node2num_particles(node_c)
p_m = Funct.node2num_particles(node_max)
if (phase_space.max_changed
and p_m * 0.9 > p_c) or (phase_space.time > 4000):
phase_space.time = 0
num_particles = phase_space.num_particles
old_graph = phase_space.DNA_graph
old_center = old_graph.center
condition = old_graph.condition
typos = old_graph.typos
node_max = phase_space.node_max_particles
center = phase_space.node2key(node_max)
selector.update(old_graph, new_center=center)
actions = selector.get_predicted_actions()
x = old_graph.x_dim
y = old_graph.y_dim
space = DNA_Graph(center, 1, (x, y), condition, actions, version,
creator)
phase_space.DNA_graph = space
phase_space.objects = space.objects
phase_space.support = []
phase_space.create_particles(num_particles + 1)
phase_space.stream.signals_off()
phase_space.attach_balls()
phase_space.max_changed = False
phase_space.node_max_particles = None
self.space = space
self.phase_space = phase_space
self.objects = phase_space.objects
self.support = phase_space.support
self.Graph = phase_space.DNA_graph
def create_objects(status, loaded_network):
settings = status.settings
status.Alai = Alai(min=status.dt_min,
max=status.dt_Max,
max_time=status.restart_period)
if loaded_network == True:
status.Alai.time = 14077 - 14076
status.Alai.reset_count = 1
status.Data_gen = GeneratorFromCIFAR.GeneratorFromCIFAR(
status.Comp,
status.S,
cuda=status.cuda,
threads=status.threads,
dataAugmentation=settings.enable_augmentation,
transforms_mode=settings.transformations_compose)
status.Data_gen.dataConv2d()
dataGen = status.Data_gen
x = dataGen.size[1]
y = dataGen.size[2]
condition = status.condition
mutations = status.mutations
version = status.version
center = status.Center
num_actions = status.num_actions
selector = status.Selector_creator(condition=condition,
directions=version,
num_actions=num_actions,
mutations=mutations)
status.Selector = selector
creator = status.Creator
selector.update(center)
actions = selector.get_predicted_actions()
space = DNA_Graph(center,
1, (x, y),
condition,
actions,
version,
creator,
selector=status.Selector,
num_morphisms=5)
if status.Alai:
stream = TorchStream(status.Data_gen,
status.log_size,
min_size=status.min_log_size,
Alai=status.Alai,
status=status)
else:
stream = TorchStream(status.Data_gen,
status.log_size,
min_size=status.min_log_size,
status=status)
status.stream = stream
Phase_space = DNA_Phase_space(space, stream=stream, status=status)
Dynamics = Dynamic_DNA(
space,
Phase_space,
status.dx,
Creator=creator,
Selector=selector,
update_velocity=velocity_updater,
update_space=space_updater,
version=version,
mutation_coefficient=status.mutation_coefficient,
clear_period=status.clear_period,
update_force_field=status.update_force_field,
)
Phase_space.create_particles(status.n)
Phase_space.beta = status.beta
Phase_space.alpha = status.alpha
Phase_space.influence = status.influence
status.Dynamics = Dynamics
status.objects = Dynamics.objects