59 lines
1.6 KiB
Python
59 lines
1.6 KiB
Python
import jax.numpy as jnp
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from tensorneat.pipeline import Pipeline
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from tensorneat.algorithm.neat import NEAT
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from tensorneat.genome import DefaultGenome, BiasNode
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from tensorneat.problem.func_fit import CustomFuncFit
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from tensorneat.common import ACT, AGG
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# define a custom function fit problem
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def pagie_polynomial(inputs):
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x, y = inputs
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res = 1 / (1 + jnp.pow(x, -4)) + 1 / (1 + jnp.pow(y, -4))
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# important! returns an array, NOT a scalar
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return jnp.array([res])
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# define custom activate function and register it
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def square(x):
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return x ** 2
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ACT.add_func("square", square)
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if __name__ == "__main__":
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custom_problem = CustomFuncFit(
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func=pagie_polynomial,
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low_bounds=[-1, -1],
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upper_bounds=[1, 1],
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method="sample",
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num_samples=100,
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)
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pipeline = Pipeline(
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algorithm=NEAT(
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pop_size=10000,
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species_size=20,
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survival_threshold=0.01,
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genome=DefaultGenome(
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num_inputs=2,
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num_outputs=1,
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init_hidden_layers=(),
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node_gene=BiasNode(
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activation_options=[ACT.identity, ACT.inv, ACT.square],
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aggregation_options=[AGG.sum, AGG.product],
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),
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output_transform=ACT.identity,
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),
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),
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problem=custom_problem,
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generation_limit=50,
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fitness_target=-1e-4,
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seed=42,
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)
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# initialize state
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state = pipeline.setup()
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# run until terminate
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state, best = pipeline.auto_run(state)
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# show result
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pipeline.show(state, best)
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