ee1a2a8271
1. Add origin_node and origin_conn. 2. Change the behavior of crossover and mutation. Now, TensorNEAT will use all fix_attrs(include historical marker if it has one) as identifier for gene in crossover and distance calculation. 3. Other slightly change. 4. Add two related examples: xor_origin and hopper_origin 5. Add related test file.
60 lines
1.8 KiB
Python
60 lines
1.8 KiB
Python
import jax, jax.numpy as jnp
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from .default import DefaultConn
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class OriginConn(DefaultConn):
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"""
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Implementation of connections in origin NEAT Paper.
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Details at https://github.com/EMI-Group/tensorneat/issues/11.
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"""
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# add historical_marker into fixed_attrs
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fixed_attrs = ["input_index", "output_index", "historical_marker"]
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custom_attrs = ["weight"]
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def __init__(
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self,
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*args,
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**kwargs,
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):
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super().__init__(*args, **kwargs)
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def crossover(self, state, randkey, attrs1, attrs2):
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# random pick one of attrs, without attrs exchange
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return jnp.where(
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# origin code, generate multiple random numbers, without attrs exchange
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# jax.random.normal(randkey, attrs1.shape) > 0,
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jax.random.normal(randkey)
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> 0, # generate one random number, without attrs exchange
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attrs1,
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attrs2,
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)
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def get_historical_marker(self, state, gene_array):
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return gene_array[2]
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def repr(self, state, conn, precision=2, idx_width=3, func_width=8):
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in_idx, out_idx, historical_marker, weight = conn
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in_idx = int(in_idx)
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out_idx = int(out_idx)
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historical_marker = int(historical_marker)
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weight = round(float(weight), precision)
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return "{}(in: {:<{idx_width}}, out: {:<{idx_width}}, historical_marker: {:<{idx_width}}, weight: {:<{float_width}})".format(
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self.__class__.__name__,
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in_idx,
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out_idx,
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historical_marker,
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weight,
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idx_width=idx_width,
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float_width=precision + 3,
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)
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def to_dict(self, state, conn):
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return {
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"in": int(conn[0]),
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"out": int(conn[1]),
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"historical_marker": int(conn[2]),
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"weight": jnp.float32(conn[3]),
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} |