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optimize function reproduce for time usage

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This commit is contained in:
wls2002
2023-05-08 16:28:13 +08:00
parent dde338696f
commit 8653f49826
2 changed files with 5 additions and 8 deletions
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5
algorithms/neat/pipeline.py
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@@ -2,7 +2,6 @@ from typing import List, Union, Tuple, Callable
import time import time
import jax import jax
import jax.numpy as jnp
import numpy as np import numpy as np
from .species import SpeciesController from .species import SpeciesController
@@ -19,6 +18,7 @@ class Pipeline:
def __init__(self, config, seed=42): def __init__(self, config, seed=42):
self.generation_timestamp = time.time() self.generation_timestamp = time.time()
self.randkey = jax.random.PRNGKey(seed) self.randkey = jax.random.PRNGKey(seed)
np.random.seed(seed)
self.config = config self.config = config
self.N = config.basic.init_maximum_nodes self.N = config.basic.init_maximum_nodes
@@ -34,9 +34,6 @@ class Pipeline:
self.generation = 0 self.generation = 0
self.species_controller.init_speciate(self.pop_nodes, self.pop_connections) self.species_controller.init_speciate(self.pop_nodes, self.pop_connections)
# self.species_controller.speciate(self.pop_nodes, self.pop_connections,
# self.generation, self.o2o_distance, self.o2m_distance)
self.best_fitness = float('-inf') self.best_fitness = float('-inf')
def ask(self, batch: bool): def ask(self, batch: bool):

8
algorithms/neat/species.py
View File

@@ -239,7 +239,9 @@ class SpeciesController:
self.species = {} self.species = {}
# int -> idx in the pop_nodes, pop_connections of elitism # int -> idx in the pop_nodes, pop_connections of elitism
# (int, int) -> the father and mother idx to be crossover # (int, int) -> the father and mother idx to be crossover
crossover_pair: List[Union[int, Tuple[int, int]]] = [] crossover_pair: List[Union[int, Tuple[int, int]]] = []
for spawn, s in zip(spawn_amounts, remaining_species): for spawn, s in zip(spawn_amounts, remaining_species):
assert spawn >= self.genome_elitism assert spawn >= self.genome_elitism
@@ -264,10 +266,8 @@ class SpeciesController:
# only use good genomes to crossover # only use good genomes to crossover
sorted_members = sorted_members[:repro_cutoff] sorted_members = sorted_members[:repro_cutoff]
# Randomly choose parents and produce the number of offspring allotted to the species. list_idx1, list_idx2 = np.random.choice(len(sorted_members), size=(2, spawn), replace=True)
for _ in range(spawn): for c1, c2 in zip(list_idx1, list_idx2):
# 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] idx1, fitness1 = sorted_members[c1], sorted_fitnesses[c1]
idx2, fitness2 = sorted_members[c2], sorted_fitnesses[c2] idx2, fitness2 = sorted_members[c2], sorted_fitnesses[c2]
if fitness1 >= fitness2: if fitness1 >= fitness2: