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finish all refactoring

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This commit is contained in:
wls2002
2024-02-21 15:41:08 +08:00
parent aac41a089d
commit 6970e6a6d5
44 changed files with 856 additions and 825 deletions
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43
examples/func_fit/xor.py
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@@ -1,32 +1,31 @@
from config import *
from pipeline import Pipeline
from algorithm import NEAT
from algorithm.neat.gene import NormalGene, NormalGeneConfig
from problem.func_fit import XOR, FuncFitConfig
from algorithm.neat import *
from problem.func_fit import XOR3d
if __name__ == '__main__':
# running config
config = Config(
basic=BasicConfig(
seed=42,
fitness_target=-1e-2,
pop_size=10000
pipeline = Pipeline(
algorithm=NEAT(
species=DefaultSpecies(
genome=DefaultGenome(
num_inputs=3,
num_outputs=1,
max_nodes=50,
max_conns=100,
),
pop_size=10000,
species_size=10,
compatibility_threshold=3.5,
),
),
neat=NeatConfig(
inputs=2,
outputs=1
),
gene=NormalGeneConfig(),
problem=FuncFitConfig(
error_method='rmse'
)
problem=XOR3d(),
generation_limit=10000,
fitness_target=-1e-8
)
# define algorithm: NEAT with NormalGene
algorithm = NEAT(config, NormalGene)
# full pipeline
pipeline = Pipeline(config, algorithm, XOR)
# initialize state
state = pipeline.setup()
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)
# show result

51
examples/func_fit/xor3d_hyperneat.py Normal file
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@@ -0,0 +1,51 @@
from pipeline import Pipeline
from algorithm.neat import *
from algorithm.hyperneat import *
from utils import Act
from problem.func_fit import XOR3d
if __name__ == '__main__':
pipeline = Pipeline(
algorithm=HyperNEAT(
substrate=FullSubstrate(
input_coors=[(-1, -1), (0.333, -1), (-0.333, -1), (1, -1)],
hidden_coors=[
(-1, -0.5), (0.333, -0.5), (-0.333, -0.5), (1, -0.5),
(-1, 0), (0.333, 0), (-0.333, 0), (1, 0),
(-1, 0.5), (0.333, 0.5), (-0.333, 0.5), (1, 0.5),
],
output_coors=[(0, 1), ],
),
neat=NEAT(
species=DefaultSpecies(
genome=DefaultGenome(
num_inputs=4, # [-1, -1, -1, 0]
num_outputs=1,
max_nodes=50,
max_conns=100,
node_gene=DefaultNodeGene(
activation_default=Act.tanh,
activation_options=(Act.tanh,),
),
),
pop_size=10000,
species_size=10,
compatibility_threshold=3.5,
),
),
activation=Act.sigmoid,
activate_time=10,
),
problem=XOR3d(),
generation_limit=300,
fitness_target=-1e-6
)
# initialize state
state = pipeline.setup()
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)
# show result
pipeline.show(state, best)

41
examples/func_fit/xor_hyperneat.py
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@@ -1,41 +0,0 @@
from config import *
from pipeline import Pipeline
from algorithm.neat import NormalGene, NormalGeneConfig
from algorithm.hyperneat import HyperNEAT, NormalSubstrate, NormalSubstrateConfig
from problem.func_fit import XOR3d, FuncFitConfig
from utils import Act
if __name__ == '__main__':
config = Config(
basic=BasicConfig(
seed=42,
fitness_target=0,
pop_size=1000
),
neat=NeatConfig(
max_nodes=50,
max_conns=100,
max_species=30,
inputs=4,
outputs=1
),
hyperneat=HyperNeatConfig(
inputs=3,
outputs=1
),
substrate=NormalSubstrateConfig(
input_coors=((-1, -1), (-0.5, -1), (0.5, -1), (1, -1)),
),
gene=NormalGeneConfig(
activation_default=Act.tanh,
activation_options=(Act.tanh, ),
),
problem=FuncFitConfig()
)
algorithm = HyperNEAT(config, NormalGene, NormalSubstrate)
pipeline = Pipeline(config, algorithm, XOR3d)
state = pipeline.setup()
state, best = pipeline.auto_run(state)
pipeline.show(state, best)

62
examples/func_fit/xor_recurrent.py
View File

@@ -1,41 +1,41 @@
from config import *
from pipeline import Pipeline
from algorithm import NEAT
from algorithm.neat.gene import RecurrentGene, RecurrentGeneConfig
from problem.func_fit import XOR3d, FuncFitConfig
from algorithm.neat import *
from problem.func_fit import XOR3d
from utils.activation import ACT_ALL
from utils.aggregation import AGG_ALL
if __name__ == '__main__':
config = Config(
basic=BasicConfig(
seed=42,
fitness_target=-1e-2,
generation_limit=300,
pop_size=1000
pipeline = Pipeline(
seed=0,
algorithm=NEAT(
species=DefaultSpecies(
genome=RecurrentGenome(
num_inputs=3,
num_outputs=1,
max_nodes=50,
max_conns=100,
activate_time=5,
node_gene=DefaultNodeGene(
activation_options=ACT_ALL,
# aggregation_options=AGG_ALL,
activation_replace_rate=0.2
),
),
pop_size=10000,
species_size=10,
compatibility_threshold=3.5,
),
),
neat=NeatConfig(
network_type="recurrent",
max_nodes=50,
max_conns=100,
max_species=30,
conn_add=0.5,
conn_delete=0.5,
node_add=0.4,
node_delete=0.4,
inputs=3,
outputs=1
),
gene=RecurrentGeneConfig(
activate_times=10
),
problem=FuncFitConfig(
error_method='rmse'
)
problem=XOR3d(),
generation_limit=10000,
fitness_target=-1e-8
)
algorithm = NEAT(config, RecurrentGene)
pipeline = Pipeline(config, algorithm, XOR3d)
# initialize state
state = pipeline.setup()
pipeline.pre_compile(state)
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)
# show result
pipeline.show(state, best)