59 lines
2.0 KiB
Python
59 lines
2.0 KiB
Python
import numpy as np
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from .utils import flatten_connections, set_operation_analysis
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def distance(nodes1, connections1, nodes2, connections2):
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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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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 = homologous_node_distance(fr_sorted_nodes, sr_sorted_nodes)
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else: # connection
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node_distance = 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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val = non_homologous_cnt * disjoint_coe + homologous_distance * compatibility_coe
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return val / np.where(gene_cnt1 > gene_cnt2, gene_cnt1, gene_cnt2)
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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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