102 lines
3.7 KiB
Python
102 lines
3.7 KiB
Python
from typing import Tuple
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import jax, jax.numpy as jnp
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from utils import Act, Agg, act, agg, mutate_int, mutate_float
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from . import BaseNodeGene
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class DefaultNodeGene(BaseNodeGene):
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"Default node gene, with the same behavior as in NEAT-python."
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custom_attrs = ['bias', 'response', 'aggregation', 'activation']
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def __init__(
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self,
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bias_init_mean: float = 0.0,
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bias_init_std: float = 1.0,
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bias_mutate_power: float = 0.5,
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bias_mutate_rate: float = 0.7,
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bias_replace_rate: float = 0.1,
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response_init_mean: float = 1.0,
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response_init_std: float = 0.0,
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response_mutate_power: float = 0.5,
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response_mutate_rate: float = 0.7,
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response_replace_rate: float = 0.1,
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activation_default: callable = Act.sigmoid,
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activation_options: Tuple = (Act.sigmoid,),
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activation_replace_rate: float = 0.1,
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aggregation_default: callable = Agg.sum,
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aggregation_options: Tuple = (Agg.sum,),
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aggregation_replace_rate: float = 0.1,
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):
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super().__init__()
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self.bias_init_mean = bias_init_mean
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self.bias_init_std = bias_init_std
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self.bias_mutate_power = bias_mutate_power
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self.bias_mutate_rate = bias_mutate_rate
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self.bias_replace_rate = bias_replace_rate
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self.response_init_mean = response_init_mean
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self.response_init_std = response_init_std
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self.response_mutate_power = response_mutate_power
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self.response_mutate_rate = response_mutate_rate
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self.response_replace_rate = response_replace_rate
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self.activation_default = activation_options.index(activation_default)
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self.activation_options = activation_options
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self.activation_indices = jnp.arange(len(activation_options))
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self.activation_replace_rate = activation_replace_rate
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self.aggregation_default = aggregation_options.index(aggregation_default)
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self.aggregation_options = aggregation_options
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self.aggregation_indices = jnp.arange(len(aggregation_options))
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self.aggregation_replace_rate = aggregation_replace_rate
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def new_custom_attrs(self):
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return jnp.array(
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[self.bias_init_mean, self.response_init_mean, self.activation_default, self.aggregation_default]
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)
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def mutate(self, key, node):
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k1, k2, k3, k4 = jax.random.split(key, num=4)
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index = node[0]
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bias = mutate_float(k1, node[1], self.bias_init_mean, self.bias_init_std,
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self.bias_mutate_power, self.bias_mutate_rate, self.bias_replace_rate)
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res = mutate_float(k2, node[2], self.response_init_mean, self.response_init_std,
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self.response_mutate_power, self.response_mutate_rate, self.response_replace_rate)
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act = mutate_int(k3, node[3], self.activation_indices, self.activation_replace_rate)
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agg = mutate_int(k4, node[4], self.aggregation_indices, self.aggregation_replace_rate)
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return jnp.array([index, bias, res, act, agg])
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def distance(self, node1, node2):
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return (
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jnp.abs(node1[1] - node2[1]) +
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jnp.abs(node1[2] - node2[2]) +
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(node1[3] != node2[3]) +
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(node1[4] != node2[4])
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)
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def forward(self, attrs, inputs, is_output_node=False):
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bias, res, act_idx, agg_idx = attrs
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z = agg(agg_idx, inputs, self.aggregation_options)
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z = bias + res * z
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# the last output node should not be activated
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z = jax.lax.cond(
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is_output_node,
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lambda: z,
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lambda: act(act_idx, z, self.activation_options)
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)
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return z
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