0cb2f9473d
use jax.lax.while_loop in graph algorithms and forward function; fix "enabled not care" bug in forward
120 lines
3.9 KiB
Python
120 lines
3.9 KiB
Python
"""
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Calculate the distance between two genomes.
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The calculation method is the same as the distance calculation in NEAT-python.
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See https://github.com/CodeReclaimers/neat-python/blob/master/neat/genome.py
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"""
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from typing import Dict
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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 EMPTY_NODE, EMPTY_CON
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@jit
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def distance(nodes1: Array, cons1: Array, nodes2: Array, cons2: Array, jit_config: Dict) -> Array:
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"""
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Calculate the distance between two genomes.
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args:
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nodes1: Array(N, 5)
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cons1: Array(C, 4)
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nodes2: Array(N, 5)
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cons2: Array(C, 4)
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returns:
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distance: Array(, )
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"""
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nd = node_distance(nodes1, nodes2, jit_config) # node distance
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cd = connection_distance(cons1, cons2, jit_config) # connection distance
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return nd + cd
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@jit
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def node_distance(nodes1: Array, nodes2: Array, jit_config: Dict):
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"""
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Calculate the distance between nodes of two genomes.
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"""
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# statistics nodes count of two genomes
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node_cnt1 = jnp.sum(~jnp.isnan(nodes1[:, 0]))
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node_cnt2 = jnp.sum(~jnp.isnan(nodes2[:, 0]))
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max_cnt = jnp.maximum(node_cnt1, node_cnt2)
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# align homologous nodes
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# this process is similar to np.intersect1d.
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nodes = jnp.concatenate((nodes1, nodes2), axis=0)
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keys = nodes[:, 0]
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sorted_indices = jnp.argsort(keys, axis=0)
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nodes = nodes[sorted_indices]
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nodes = jnp.concatenate([nodes, EMPTY_NODE], axis=0) # add a nan row to the end
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fr, sr = nodes[:-1], nodes[1:] # first row, second row
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# flag location of homologous nodes
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intersect_mask = (fr[:, 0] == sr[:, 0]) & ~jnp.isnan(nodes[:-1, 0])
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# calculate the count of non_homologous of two genomes
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non_homologous_cnt = node_cnt1 + node_cnt2 - 2 * jnp.sum(intersect_mask)
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# calculate the distance of homologous nodes
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hnd = vmap(homologous_node_distance)(fr, sr)
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hnd = jnp.where(jnp.isnan(hnd), 0, hnd)
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homologous_distance = jnp.sum(hnd * intersect_mask)
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val = non_homologous_cnt * jit_config['compatibility_disjoint'] + homologous_distance * jit_config[
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'compatibility_weight']
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return jnp.where(max_cnt == 0, 0, val / max_cnt) # avoid zero division
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@jit
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def connection_distance(cons1: Array, cons2: Array, jit_config: Dict):
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"""
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Calculate the distance between connections of two genomes.
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Similar process as node_distance.
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"""
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con_cnt1 = jnp.sum(~jnp.isnan(cons1[:, 0]))
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con_cnt2 = jnp.sum(~jnp.isnan(cons2[:, 0]))
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max_cnt = jnp.maximum(con_cnt1, con_cnt2)
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cons = jnp.concatenate((cons1, cons2), axis=0)
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keys = cons[:, :2]
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sorted_indices = jnp.lexsort(keys.T[::-1])
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cons = cons[sorted_indices]
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cons = jnp.concatenate([cons, EMPTY_CON], axis=0) # add a nan row to the end
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fr, sr = cons[:-1], cons[1:] # first row, second row
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# both genome has such connection
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intersect_mask = jnp.all(fr[:, :2] == sr[:, :2], axis=1) & ~jnp.isnan(fr[:, 0])
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non_homologous_cnt = con_cnt1 + con_cnt2 - 2 * jnp.sum(intersect_mask)
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hcd = vmap(homologous_connection_distance)(fr, sr)
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hcd = jnp.where(jnp.isnan(hcd), 0, hcd)
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homologous_distance = jnp.sum(hcd * intersect_mask)
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val = non_homologous_cnt * jit_config['compatibility_disjoint'] + homologous_distance * jit_config[
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'compatibility_weight']
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return jnp.where(max_cnt == 0, 0, val / max_cnt)
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@jit
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def homologous_node_distance(n1: Array, n2: Array):
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"""
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Calculate the distance between two homologous nodes.
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"""
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d = 0
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d += jnp.abs(n1[1] - n2[1]) # bias
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d += jnp.abs(n1[2] - n2[2]) # response
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d += n1[3] != n2[3] # activation
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d += n1[4] != n2[4] # aggregation
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return d
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@jit
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def homologous_connection_distance(c1: Array, c2: Array):
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"""
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Calculate the distance between two homologous connections.
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"""
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d = 0
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d += jnp.abs(c1[2] - c2[2]) # weight
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d += c1[3] != c2[3] # enable
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return d
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