95 lines
3.1 KiB
Python
95 lines
3.1 KiB
Python
from functools import partial
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import numpy as np
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from numpy.typing import NDArray
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from algorithms.neat.genome.utils import flatten_connections, set_operation_analysis
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def distance(nodes1: NDArray, connections1: NDArray, nodes2: NDArray, connections2: NDArray) -> NDArray:
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"""
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Calculate the distance between two genomes.
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nodes are a 2-d array with shape (N, 5), its columns are [key, bias, response, act, agg]
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connections are a 3-d array with shape (2, N, N), axis 0 means [weights, enable]
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"""
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node_distance = gene_distance(nodes1, nodes2, 'node')
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# refactor connections
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keys1, keys2 = nodes1[:, 0], nodes2[:, 0]
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cons1 = flatten_connections(keys1, connections1)
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cons2 = flatten_connections(keys2, connections2)
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connection_distance = gene_distance(cons1, cons2, 'connection')
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return node_distance + connection_distance
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def gene_distance(ar1, ar2, gene_type, compatibility_coe=0.5, disjoint_coe=1.):
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if gene_type == 'node':
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keys1, keys2 = ar1[:, :1], ar2[:, :1]
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else: # connection
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keys1, keys2 = ar1[:, :2], ar2[:, :2]
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n_sorted_indices, n_intersect_mask, n_union_mask = set_operation_analysis(keys1, keys2)
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nodes = np.concatenate((ar1, ar2), axis=0)
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sorted_nodes = nodes[n_sorted_indices]
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if gene_type == 'node':
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node_exist_mask = np.any(~np.isnan(sorted_nodes[:, 1:]), axis=1)
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else:
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node_exist_mask = np.any(~np.isnan(sorted_nodes[:, 2:]), axis=1)
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n_intersect_mask = n_intersect_mask & node_exist_mask
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n_union_mask = n_union_mask & node_exist_mask
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fr_sorted_nodes, sr_sorted_nodes = sorted_nodes[:-1], sorted_nodes[1:]
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non_homologous_cnt = np.sum(n_union_mask) - np.sum(n_intersect_mask)
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if gene_type == 'node':
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node_distance = batch_homologous_node_distance(fr_sorted_nodes, sr_sorted_nodes)
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else: # connection
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node_distance = batch_homologous_connection_distance(fr_sorted_nodes, sr_sorted_nodes)
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node_distance = np.where(np.isnan(node_distance), 0, node_distance)
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homologous_distance = np.sum(node_distance * n_intersect_mask[:-1])
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gene_cnt1 = np.sum(np.all(~np.isnan(ar1), axis=1))
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gene_cnt2 = np.sum(np.all(~np.isnan(ar2), axis=1))
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max_cnt = np.maximum(gene_cnt1, gene_cnt2)
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val = non_homologous_cnt * disjoint_coe + homologous_distance * compatibility_coe
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return np.where(max_cnt == 0, 0, val / max_cnt) # consider the case that both genome has no gene
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def batch_homologous_node_distance(b_n1, b_n2):
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res = []
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for n1, n2 in zip(b_n1, b_n2):
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d = homologous_node_distance(n1, n2)
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res.append(d)
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return np.stack(res, axis=0)
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def batch_homologous_connection_distance(b_c1, b_c2):
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res = []
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for c1, c2 in zip(b_c1, b_c2):
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d = homologous_connection_distance(c1, c2)
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res.append(d)
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return np.stack(res, axis=0)
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def homologous_node_distance(n1, n2):
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d = 0
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d += np.abs(n1[1] - n2[1]) # bias
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d += np.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]
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return d
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def homologous_connection_distance(c1, c2):
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d = 0
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d += np.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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