152 lines
4.6 KiB
Python
152 lines
4.6 KiB
Python
from functools import partial
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from typing import Tuple
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import jax
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from jax import jit, vmap, Array
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from jax import numpy as jnp
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# from .utils import flatten_connections, unflatten_connections
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from algorithms.neat.genome.utils import flatten_connections, unflatten_connections
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@vmap
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def batch_crossover(randkeys: Array, batch_nodes1: Array, batch_connections1: Array, batch_nodes2: Array,
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batch_connections2: Array) -> Tuple[Array, Array]:
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"""
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crossover a batch of genomes
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:param randkeys: batches of random keys
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:param batch_nodes1:
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:param batch_connections1:
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:param batch_nodes2:
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:param batch_connections2:
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:return:
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"""
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return crossover(randkeys, batch_nodes1, batch_connections1, batch_nodes2, batch_connections2)
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@jit
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def crossover(randkey: Array, nodes1: Array, connections1: Array, nodes2: Array, connections2: Array) \
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-> Tuple[Array, Array]:
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"""
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use genome1 and genome2 to generate a new genome
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notice that genome1 should have higher fitness than genome2 (genome1 is winner!)
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:param randkey:
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:param nodes1:
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:param connections1:
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:param nodes2:
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:param connections2:
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:return:
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"""
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randkey_1, randkey_2 = jax.random.split(randkey)
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# crossover nodes
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keys1, keys2 = nodes1[:, 0], nodes2[:, 0]
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nodes2 = align_array(keys1, keys2, nodes2, 'node')
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new_nodes = jnp.where(jnp.isnan(nodes1) | jnp.isnan(nodes2), nodes1, crossover_gene(randkey_1, nodes1, nodes2))
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# crossover connections
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cons1 = flatten_connections(keys1, connections1)
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cons2 = flatten_connections(keys2, connections2)
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con_keys1, con_keys2 = cons1[:, :2], cons2[:, :2]
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cons2 = align_array(con_keys1, con_keys2, cons2, 'connection')
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new_cons = jnp.where(jnp.isnan(cons1) | jnp.isnan(cons2), cons1, crossover_gene(randkey_2, cons1, cons2))
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new_cons = unflatten_connections(len(keys1), new_cons)
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return new_nodes, new_cons
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@partial(jit, static_argnames=['gene_type'])
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def align_array(seq1: Array, seq2: Array, ar2: Array, gene_type: str) -> Array:
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"""
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make ar2 align with ar1.
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:param seq1:
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:param seq2:
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:param ar2:
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:param gene_type:
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:return:
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align means to intersect part of ar2 will be at the same position as ar1,
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non-intersect part of ar2 will be set to Nan
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"""
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seq1, seq2 = seq1[:, jnp.newaxis], seq2[jnp.newaxis, :]
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mask = (seq1 == seq2) & (~jnp.isnan(seq1))
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if gene_type == 'connection':
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mask = jnp.all(mask, axis=2)
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intersect_mask = mask.any(axis=1)
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idx = jnp.arange(0, len(seq1))
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idx_fixed = jnp.dot(mask, idx)
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refactor_ar2 = jnp.where(intersect_mask[:, jnp.newaxis], ar2[idx_fixed], jnp.nan)
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return refactor_ar2
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@jit
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def crossover_gene(rand_key: Array, g1: Array, g2: Array) -> Array:
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"""
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crossover two genes
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:param rand_key:
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:param g1:
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:param g2:
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:return:
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only gene with the same key will be crossover, thus don't need to consider change key
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"""
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r = jax.random.uniform(rand_key, shape=g1.shape)
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return jnp.where(r > 0.5, g1, g2)
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if __name__ == '__main__':
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import numpy as np
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randkey = jax.random.PRNGKey(40)
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nodes1 = np.array([
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[4, 1, 1, 1, 1],
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[6, 2, 2, 2, 2],
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[1, 3, 3, 3, 3],
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[5, 4, 4, 4, 4],
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[np.nan, np.nan, np.nan, np.nan, np.nan]
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])
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nodes2 = np.array([
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[4, 1.5, 1.5, 1.5, 1.5],
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[7, 3.5, 3.5, 3.5, 3.5],
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[5, 4.5, 4.5, 4.5, 4.5],
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[np.nan, np.nan, np.nan, np.nan, np.nan],
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[np.nan, np.nan, np.nan, np.nan, np.nan],
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])
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weights1 = np.array([
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[
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[1, 2, 3, 4., np.nan],
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[5, np.nan, 7, 8, np.nan],
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[9, 10, 11, 12, np.nan],
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[13, 14, 15, 16, np.nan],
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[np.nan, np.nan, np.nan, np.nan, np.nan]
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],
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[
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[0, 1, 0, 1, np.nan],
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[0, np.nan, 0, 1, np.nan],
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[0, 1, 0, 1, np.nan],
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[0, 1, 0, 1, np.nan],
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[np.nan, np.nan, np.nan, np.nan, np.nan]
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]
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])
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weights2 = np.array([
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[
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[1.5, 2.5, 3.5, np.nan, np.nan],
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[3.5, 4.5, 5.5, np.nan, np.nan],
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[6.5, 7.5, 8.5, np.nan, np.nan],
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[np.nan, np.nan, np.nan, np.nan, np.nan],
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[np.nan, np.nan, np.nan, np.nan, np.nan]
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],
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[
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[1, 0, 1, np.nan, np.nan],
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[1, 0, 1, np.nan, np.nan],
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[1, 0, 1, np.nan, np.nan],
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[np.nan, np.nan, np.nan, np.nan, np.nan],
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[np.nan, np.nan, np.nan, np.nan, np.nan]
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]
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])
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res = crossover(randkey, nodes1, weights1, nodes2, weights2)
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print(*res, sep='\n')
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