74 lines
3.0 KiB
Python
74 lines
3.0 KiB
Python
from typing import Type
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from jax import Array, numpy as jnp, vmap
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from core import Gene
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def create_distance(gene_type: Type[Gene]):
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def node_distance(state, nodes1: Array, nodes2: Array):
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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, jnp.full((1, nodes.shape[1]), jnp.nan)], 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(gene_type.distance_node, in_axes=(None, 0, 0))(state, 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 * state.compatibility_disjoint + homologous_distance * state.compatibility_weight
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return jnp.where(max_cnt == 0, 0, val / max_cnt) # avoid zero division
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def connection_distance(state, cons1: Array, cons2: Array):
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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, jnp.full((1, cons.shape[1]), jnp.nan)], 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(gene_type.distance_conn, in_axes=(None, 0, 0))(state, 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 * state.compatibility_disjoint + homologous_distance * state.compatibility_weight
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return jnp.where(max_cnt == 0, 0, val / max_cnt)
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def distance(state, genome1, genome2):
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return node_distance(state, genome1.nodes, genome2.nodes) + connection_distance(state, genome1.conns, genome2.conns)
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return distance
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