67 lines
2.2 KiB
Python
67 lines
2.2 KiB
Python
import jax
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from jax import jit, Array, numpy as jnp
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def crossover(randkey, nodes1: Array, conns1: Array, nodes2: Array, conns2: 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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"""
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randkey_1, randkey_2, key= jax.random.split(randkey, 3)
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# crossover nodes
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keys1, keys2 = nodes1[:, 0], nodes2[:, 0]
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# make homologous genes align in nodes2 align with nodes1
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nodes2 = align_array(keys1, keys2, nodes2, False)
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# For not homologous genes, use the value of nodes1(winner)
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# For homologous genes, use the crossover result between nodes1 and nodes2
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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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con_keys1, con_keys2 = conns1[:, :2], conns2[:, :2]
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cons2 = align_array(con_keys1, con_keys2, conns2, True)
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new_cons = jnp.where(jnp.isnan(conns1) | jnp.isnan(cons2), conns1, crossover_gene(randkey_2, conns1, cons2))
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return new_nodes, new_cons
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def align_array(seq1: Array, seq2: Array, ar2: Array, is_conn: bool) -> Array:
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"""
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After I review this code, I found that it is the most difficult part of the code. Please never change it!
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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 is_conn:
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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 is_conn:
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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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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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