49 lines
1.3 KiB
Python
49 lines
1.3 KiB
Python
import jax, jax.numpy as jnp
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from tensorneat.common import State, StatefulBaseClass, hash_array
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class BaseGene(StatefulBaseClass):
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"Base class for node genes or connection genes."
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fixed_attrs = []
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custom_attrs = []
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def __init__(self):
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pass
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def new_identity_attrs(self, state):
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# the attrs which do identity transformation, used in mutate add node
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raise NotImplementedError
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def new_random_attrs(self, state, randkey):
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# random attributes of the gene. used in initialization.
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raise NotImplementedError
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def mutate(self, state, randkey, attrs):
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raise NotImplementedError
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def crossover(self, state, randkey, attrs1, attrs2):
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return jnp.where(
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jax.random.normal(randkey, attrs1.shape) > 0,
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attrs1,
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attrs2,
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)
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def distance(self, state, attrs1, attrs2):
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raise NotImplementedError
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def forward(self, state, attrs, inputs):
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raise NotImplementedError
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def update_by_batch(self, state, attrs, batch_inputs):
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raise NotImplementedError
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@property
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def length(self):
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return len(self.fixed_attrs) + len(self.custom_attrs)
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def repr(self, state, gene, precision=2):
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raise NotImplementedError
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def hash(self, gene):
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return hash_array(gene)
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