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generally complete, but not work well. Debug

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This commit is contained in:
wls2002
2023-05-06 11:35:44 +08:00
parent 8f780b63d6
commit 73ac1bcfe0
8 changed files with 233 additions and 84 deletions
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74
algorithms/neat/species.py
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@@ -1,4 +1,4 @@
from typing import List, Tuple, Dict, Optional
from typing import List, Tuple, Dict, Union
from itertools import count
import jax
@@ -45,7 +45,6 @@ class SpeciesController:
self.species_idxer = count(0)
self.species: Dict[int, Species] = {} # species_id -> species
self.genome_to_species: Dict[int, int] = {}
self.o2m_distance_func = jax.vmap(distance, in_axes=(None, None, 0, 0)) # one to many
# self.o2o_distance_func = np_distance # one to one
@@ -79,36 +78,37 @@ class SpeciesController:
# Partition population into species based on genetic similarity.
# First, fast match the population to previous species
rid_list = [new_representatives[sid] for sid in previous_species_list]
res_pop_distance = [
jax.device_get(
[
if previous_species_list: # exist previous species
rid_list = [new_representatives[sid] for sid in previous_species_list]
res_pop_distance = [
jax.device_get(
self.o2m_distance_func(pop_nodes[rid], pop_connections[rid], pop_nodes, pop_connections)
for rid in rid_list
]
)
]
pop_res_distance = np.stack(res_pop_distance, axis=0).T
for i in range(pop_res_distance.shape[0]):
if not unspeciated[i]:
continue
min_idx = np.argmin(pop_res_distance[i])
min_val = pop_res_distance[i, min_idx]
if min_val <= self.compatibility_threshold:
species_id = previous_species_list[min_idx]
new_members[species_id].append(i)
unspeciated[i] = False
)
for rid in rid_list
]
pop_res_distance = np.stack(res_pop_distance, axis=0).T
for i in range(pop_res_distance.shape[0]):
if not unspeciated[i]:
continue
min_idx = np.argmin(pop_res_distance[i])
min_val = pop_res_distance[i, min_idx]
if min_val <= self.compatibility_threshold:
species_id = previous_species_list[min_idx]
new_members[species_id].append(i)
unspeciated[i] = False
# Second, slowly match the lonely population to new-created species.
# lonely genome is proved to be not compatible with any previous species, so they only need to be compared with
# the new representatives.
new_species_list = []
for i in range(pop_nodes.shape[0]):
if not unspeciated[i]:
continue
unspeciated[i] = False
if len(new_representatives) != 0:
rid = [new_representatives[sid] for sid in new_representatives] # the representatives of new species
# the representatives of new species
sid, rid = list(zip(*[(k, v) for k, v in new_representatives.items()]))
distances = [
self.o2o_distance_func(pop_nodes[i], pop_connections[i], pop_nodes[r], pop_connections[r])
for r in rid
@@ -117,18 +117,17 @@ class SpeciesController:
min_idx = np.argmin(distances)
min_val = distances[min_idx]
if min_val <= self.compatibility_threshold:
species_id = new_species_list[min_idx]
species_id = sid[min_idx]
new_members[species_id].append(i)
continue
continue
# create a new species
species_id = next(self.species_idxer)
new_species_list.append(species_id)
new_representatives[species_id] = i
new_members[species_id] = [i]
assert np.all(~unspeciated)
# Update species collection based on new speciation.
self.genome_to_species = {}
for sid, rid in new_representatives.items():
s = self.species.get(sid)
if s is None:
@@ -136,12 +135,7 @@ class SpeciesController:
self.species[sid] = s
members = new_members[sid]
for gid in members:
self.genome_to_species[gid] = sid
s.update((pop_nodes[rid], pop_connections[rid]), members)
for s in self.species.values():
print(s.members)
def update_species_fitnesses(self, fitnesses):
"""
@@ -189,11 +183,11 @@ class SpeciesController:
result.append((sid, s, is_stagnant))
return result
def reproduce(self, generation: int) -> List[Optional[int, Tuple[int, int]]]:
def reproduce(self, generation: int) -> List[Union[int, Tuple[int, int]]]:
"""
code modified from neat-python!
:param generation:
:return: next population indices.
:return: crossover_pair for next generation.
# int -> idx in the pop_nodes, pop_connections of elitism
# (int, int) -> the father and mother idx to be crossover
"""
@@ -235,7 +229,7 @@ class SpeciesController:
self.species = {}
# int -> idx in the pop_nodes, pop_connections of elitism
# (int, int) -> the father and mother idx to be crossover
new_population: List[Optional[int, Tuple[int, int]]] = []
crossover_pair: List[Union[int, Tuple[int, int]]] = []
for spawn, s in zip(spawn_amounts, remaining_species):
assert spawn >= self.genome_elitism
@@ -248,7 +242,7 @@ class SpeciesController:
sorted_members, sorted_fitnesses = sort_element_with_fitnesses(old_members, fitnesses)
if self.genome_elitism > 0:
for m in sorted_members[:self.genome_elitism]:
new_population.append(m)
crossover_pair.append(m)
spawn -= 1
if spawn <= 0:
@@ -262,16 +256,16 @@ class SpeciesController:
# Randomly choose parents and produce the number of offspring allotted to the species.
for _ in range(spawn):
assert len(sorted_members) >= 2
c1, c2 = np.random.choice(len(sorted_members), size=2, replace=False)
# allow to replace, for the case that the species only has one genome
c1, c2 = np.random.choice(len(sorted_members), size=2, replace=True)
idx1, fitness1 = sorted_members[c1], sorted_fitnesses[c1]
idx2, fitness2 = sorted_members[c2], sorted_fitnesses[c2]
if fitness1 >= fitness2:
new_population.append((idx1, idx2))
crossover_pair.append((idx1, idx2))
else:
new_population.append((idx2, idx1))
crossover_pair.append((idx2, idx1))
return new_population
return crossover_pair
def compute_spawn(adjusted_fitness, previous_sizes, pop_size, min_species_size):