remove create_func....
This commit is contained in:
wls2002
36
examples/func_fit/xor.py
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36
examples/func_fit/xor.py
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from config import *
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from pipeline import Pipeline
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from algorithm import NEAT
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from algorithm.neat.gene import NormalGene, NormalGeneConfig
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from problem.func_fit import XOR, FuncFitConfig
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if __name__ == '__main__':
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config = Config(
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basic=BasicConfig(
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seed=42,
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fitness_target=-1e-2,
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pop_size=10000
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),
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neat=NeatConfig(
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max_nodes=50,
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max_conns=100,
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max_species=30,
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conn_add=0.8,
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conn_delete=0,
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node_add=0.4,
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node_delete=0,
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inputs=2,
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outputs=1
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),
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gene=NormalGeneConfig(),
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problem=FuncFitConfig(
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error_method='rmse'
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)
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)
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algorithm = NEAT(config, NormalGene)
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pipeline = Pipeline(config, algorithm, XOR)
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state = pipeline.setup()
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pipeline.pre_compile(state)
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state, best = pipeline.auto_run(state)
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pipeline.show(state, best)
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40
examples/func_fit/xor_hyperneat.py
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40
examples/func_fit/xor_hyperneat.py
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from config import *
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from pipeline import Pipeline
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from algorithm.neat import NormalGene, NormalGeneConfig
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from algorithm.hyperneat import HyperNEAT, NormalSubstrate, NormalSubstrateConfig
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from problem.func_fit import XOR3d, FuncFitConfig
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if __name__ == '__main__':
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config = Config(
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basic=BasicConfig(
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seed=42,
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fitness_target=0,
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pop_size=1000
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),
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neat=NeatConfig(
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max_nodes=50,
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max_conns=100,
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max_species=30,
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inputs=4,
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outputs=1
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),
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hyperneat=HyperNeatConfig(
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inputs=3,
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outputs=1
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),
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substrate=NormalSubstrateConfig(
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input_coors=((-1, -1), (-0.5, -1), (0.5, -1), (1, -1)),
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),
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gene=NormalGeneConfig(
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activation_default='tanh',
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activation_options=('tanh', ),
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),
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problem=FuncFitConfig()
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)
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algorithm = HyperNEAT(config, NormalGene, NormalSubstrate)
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pipeline = Pipeline(config, algorithm, XOR3d)
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state = pipeline.setup()
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state, best = pipeline.auto_run(state)
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pipeline.show(state, best)
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40
examples/func_fit/xor_recurrent.py
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40
examples/func_fit/xor_recurrent.py
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from config import *
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from pipeline import Pipeline
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from algorithm import NEAT
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from algorithm.neat.gene import RecurrentGene, RecurrentGeneConfig
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from problem.func_fit import XOR3d, FuncFitConfig
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if __name__ == '__main__':
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config = Config(
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basic=BasicConfig(
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seed=42,
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fitness_target=-1e-2,
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generation_limit=300,
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pop_size=1000
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),
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neat=NeatConfig(
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network_type="recurrent",
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max_nodes=50,
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max_conns=100,
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max_species=30,
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conn_add=0.5,
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conn_delete=0.5,
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node_add=0.4,
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node_delete=0.4,
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inputs=3,
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outputs=1
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),
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gene=RecurrentGeneConfig(
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activate_times=10
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),
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problem=FuncFitConfig(
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error_method='rmse'
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)
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)
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algorithm = NEAT(config, RecurrentGene)
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pipeline = Pipeline(config, algorithm, XOR3d)
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state = pipeline.setup()
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state, best = pipeline.auto_run(state)
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pipeline.show(state, best)
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84
examples/gymnax/cartpole.py
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84
examples/gymnax/cartpole.py
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import jax.numpy as jnp
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from config import *
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from pipeline import Pipeline
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from algorithm import NEAT
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from algorithm.neat.gene import NormalGene, NormalGeneConfig
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from problem.rl_env import GymNaxConfig, GymNaxEnv
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def example_conf1():
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return Config(
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basic=BasicConfig(
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seed=42,
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fitness_target=500,
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pop_size=10000
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),
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neat=NeatConfig(
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inputs=4,
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outputs=1,
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),
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gene=NormalGeneConfig(
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activation_default='sigmoid',
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activation_options=('sigmoid',),
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),
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problem=GymNaxConfig(
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env_name='CartPole-v1',
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output_transform=lambda out: jnp.where(out[0] > 0.5, 1, 0) # the action of cartpole is {0, 1}
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)
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)
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def example_conf2():
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return Config(
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basic=BasicConfig(
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seed=42,
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fitness_target=500,
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pop_size=10000
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),
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neat=NeatConfig(
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inputs=4,
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outputs=1,
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),
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gene=NormalGeneConfig(
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activation_default='tanh',
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activation_options=('tanh',),
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),
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problem=GymNaxConfig(
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env_name='CartPole-v1',
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output_transform=lambda out: jnp.where(out[0] > 0, 1, 0) # the action of cartpole is {0, 1}
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)
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)
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def example_conf3():
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return Config(
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basic=BasicConfig(
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seed=42,
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fitness_target=500,
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pop_size=10000
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),
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neat=NeatConfig(
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inputs=4,
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outputs=2,
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),
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gene=NormalGeneConfig(
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activation_default='tanh',
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activation_options=('tanh',),
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),
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problem=GymNaxConfig(
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env_name='CartPole-v1',
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output_transform=lambda out: jnp.argmax(out) # the action of cartpole is {0, 1}
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)
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)
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if __name__ == '__main__':
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# all config files above can solve cartpole
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conf = example_conf3()
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algorithm = NEAT(conf, NormalGene)
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pipeline = Pipeline(conf, algorithm, GymNaxEnv)
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state = pipeline.setup()
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state, best = pipeline.auto_run(state)
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pipeline.show(state, best)
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@@ -1,31 +0,0 @@
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from functools import partial
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import jax
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from utils import unflatten_conns, act, agg, Activation, Aggregation
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from algorithm.neat.gene import RecurrentGeneConfig
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config = RecurrentGeneConfig(
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activation_options=("tanh", "sigmoid"),
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activation_default="tanh",
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)
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class A:
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def __init__(self):
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self.act_funcs = [Activation.name2func[name] for name in config.activation_options]
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self.agg_funcs = [Aggregation.name2func[name] for name in config.aggregation_options]
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self.isTrue = False
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@partial(jax.jit, static_argnums=(0,))
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def step(self):
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i = jax.numpy.array([0, 1])
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z = jax.numpy.array([
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[1, 1],
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[2, 2]
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])
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print(self.act_funcs)
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return jax.vmap(act, in_axes=(0, 0, None))(i, z, self.act_funcs)
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AA = A()
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print(AA.step())
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@@ -1,40 +0,0 @@
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import jax
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import numpy as np
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from config import Config, BasicConfig, NeatConfig
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from pipeline import Pipeline
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from algorithm import NEAT
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from algorithm.neat.gene import NormalGene, NormalGeneConfig
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xor_inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)
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xor_outputs = np.array([[0], [1], [1], [0]], dtype=np.float32)
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def evaluate(forward_func):
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"""
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:param forward_func: (4: batch, 2: input size) -> (pop_size, 4: batch, 1: output size)
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:return:
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"""
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outs = forward_func(xor_inputs)
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outs = jax.device_get(outs)
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fitnesses = 4 - np.sum((outs - xor_outputs) ** 2, axis=(1, 2))
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return fitnesses
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if __name__ == '__main__':
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config = Config(
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basic=BasicConfig(
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fitness_target=3.9999999,
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pop_size=10000
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),
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neat=NeatConfig(
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maximum_nodes=50,
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maximum_conns=100,
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compatibility_threshold=4
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),
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gene=NormalGeneConfig()
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)
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algorithm = NEAT(config, NormalGene)
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pipeline = Pipeline(config, algorithm)
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pipeline.auto_run(evaluate)
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@@ -1,49 +0,0 @@
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import jax
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import numpy as np
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from config import Config, BasicConfig, NeatConfig
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from pipeline import Pipeline
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from algorithm import NEAT, HyperNEAT
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from algorithm.neat.gene import RecurrentGene, RecurrentGeneConfig
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from algorithm.hyperneat.substrate import NormalSubstrate, NormalSubstrateConfig
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xor_inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)
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xor_outputs = np.array([[0], [1], [1], [0]], dtype=np.float32)
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def evaluate(forward_func):
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"""
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:param forward_func: (4: batch, 2: input size) -> (pop_size, 4: batch, 1: output size)
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:return:
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"""
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outs = forward_func(xor_inputs)
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outs = jax.device_get(outs)
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fitnesses = 4 - np.sum((outs - xor_outputs) ** 2, axis=(1, 2))
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return fitnesses
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if __name__ == '__main__':
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config = Config(
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basic=BasicConfig(
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fitness_target=3.99999,
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pop_size=10000
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),
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neat=NeatConfig(
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network_type="recurrent",
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maximum_nodes=50,
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maximum_conns=100,
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inputs=4,
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outputs=1
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),
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gene=RecurrentGeneConfig(
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activation_default="tanh",
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activation_options=("tanh",),
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),
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substrate=NormalSubstrateConfig(),
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)
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neat = NEAT(config, RecurrentGene)
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hyperNEAT = HyperNEAT(config, neat, NormalSubstrate)
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pipeline = Pipeline(config, hyperNEAT)
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pipeline.auto_run(evaluate)
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@@ -1,42 +0,0 @@
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import jax
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import numpy as np
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from config import Config, BasicConfig, NeatConfig
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from pipeline import Pipeline
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from algorithm import NEAT
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from algorithm.neat.gene import RecurrentGene, RecurrentGeneConfig
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xor_inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)
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xor_outputs = np.array([[0], [1], [1], [0]], dtype=np.float32)
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def evaluate(forward_func):
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"""
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:param forward_func: (4: batch, 2: input size) -> (pop_size, 4: batch, 1: output size)
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:return:
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"""
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outs = forward_func(xor_inputs)
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outs = jax.device_get(outs)
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fitnesses = 4 - np.sum((outs - xor_outputs) ** 2, axis=(1, 2))
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return fitnesses
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if __name__ == '__main__':
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config = Config(
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basic=BasicConfig(
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fitness_target=3.99999,
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pop_size=10000
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),
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neat=NeatConfig(
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network_type="recurrent",
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maximum_nodes=50,
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maximum_conns=100
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),
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gene=RecurrentGeneConfig(
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activate_times=3
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)
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)
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algorithm = NEAT(config, RecurrentGene)
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pipeline = Pipeline(config, algorithm)
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pipeline.auto_run(evaluate)
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