117 lines
4.4 KiB
Python
117 lines
4.4 KiB
Python
import jax
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from jax import Array, numpy as jnp
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from . import BaseGene
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class NormalGene(BaseGene):
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node_attrs = ['bias', 'response', 'aggregation', 'activation']
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conn_attrs = ['weight']
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@staticmethod
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def setup(state, config):
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return state.update(
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bias_init_mean=config['bias_init_mean'],
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bias_init_std=config['bias_init_std'],
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bias_mutate_power=config['bias_mutate_power'],
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bias_mutate_rate=config['bias_mutate_rate'],
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bias_replace_rate=config['bias_replace_rate'],
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response_init_mean=config['response_init_mean'],
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response_init_std=config['response_init_std'],
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response_mutate_power=config['response_mutate_power'],
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response_mutate_rate=config['response_mutate_rate'],
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response_replace_rate=config['response_replace_rate'],
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activation_default=config['activation_default'],
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activation_options=config['activation_options'],
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activation_replace_rate=config['activation_replace_rate'],
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aggregation_default=config['aggregation_default'],
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aggregation_options=config['aggregation_options'],
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aggregation_replace_rate=config['aggregation_replace_rate'],
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weight_init_mean=config['weight_init_mean'],
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weight_init_std=config['weight_init_std'],
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weight_mutate_power=config['weight_mutate_power'],
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weight_mutate_rate=config['weight_mutate_rate'],
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weight_replace_rate=config['weight_replace_rate'],
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)
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@staticmethod
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def new_node_attrs(state):
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return jnp.array([state.bias_init_mean, state.response_init_mean,
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state.activation_default, state.aggregation_default])
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@staticmethod
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def new_conn_attrs(state):
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return jnp.array([state.weight_init_mean])
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@staticmethod
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def mutate_node(state, attrs: Array, key):
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k1, k2, k3, k4 = jax.random.split(key, num=4)
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bias = NormalGene._mutate_float(k1, attrs[0], state.bias_init_mean, state.bias_init_std,
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state.bias_mutate_power, state.bias_mutate_rate, state.bias_replace_rate)
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res = NormalGene._mutate_float(k2, attrs[1], state.response_init_mean, state.response_init_std,
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state.response_mutate_power, state.response_mutate_rate,
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state.response_replace_rate)
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act = NormalGene._mutate_int(k3, attrs[2], state.activation_options, state.activation_replace_rate)
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agg = NormalGene._mutate_int(k4, attrs[3], state.aggregation_options, state.aggregation_replace_rate)
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return jnp.array([bias, res, act, agg])
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@staticmethod
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def mutate_conn(state, attrs: Array, key):
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weight = NormalGene._mutate_float(key, attrs[0], state.weight_init_mean, state.weight_init_std,
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state.weight_mutate_power, state.weight_mutate_rate,
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state.weight_replace_rate)
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return jnp.array([weight])
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@staticmethod
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def distance_node(state, array1: Array, array2: Array):
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# bias + response + activation + aggregation
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return jnp.abs(array1[1] - array2[1]) + jnp.abs(array1[2] - array2[2]) + \
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(array1[3] != array2[3]) + (array1[4] != array2[4])
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@staticmethod
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def distance_conn(state, array1: Array, array2: Array):
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return (array1[2] != array2[2]) + jnp.abs(array1[3] - array2[3]) # enable + weight
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@staticmethod
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def forward(state, array: Array):
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return array
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@staticmethod
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def _mutate_float(key, val, init_mean, init_std, mutate_power, mutate_rate, replace_rate):
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k1, k2, k3 = jax.random.split(key, num=3)
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noise = jax.random.normal(k1, ()) * mutate_power
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replace = jax.random.normal(k2, ()) * init_std + init_mean
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r = jax.random.uniform(k3, ())
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val = jnp.where(
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r < mutate_rate,
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val + noise,
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jnp.where(
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(mutate_rate < r) & (r < mutate_rate + replace_rate),
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replace,
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val
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)
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)
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return val
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@staticmethod
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def _mutate_int(key, val, options, replace_rate):
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k1, k2 = jax.random.split(key, num=2)
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r = jax.random.uniform(k1, ())
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val = jnp.where(
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r < replace_rate,
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jax.random.choice(k2, options),
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val
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)
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return val
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