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refactor genome.py use (C, 4) to replace (2, N, N) to represent connections

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faster, faster and faster!
This commit is contained in:
wls2002
2023-05-12 00:57:55 +08:00
parent e5fc1167d9
commit 47b1a1dbb2
16 changed files with 363 additions and 419 deletions
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45
algorithms/neat/pipeline.py
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@@ -8,6 +8,7 @@ from .species import SpeciesController
from .genome import expand, expand_single
from .function_factory import FunctionFactory
from .genome.genome import count
from .genome.debug.tools import check_array_valid
class Pipeline:
"""
@@ -23,6 +24,7 @@ class Pipeline:
self.config = config
self.N = config.basic.init_maximum_nodes
self.C = config.basic.init_maximum_connections
self.expand_coe = config.basic.expands_coe
self.pop_size = config.neat.population.pop_size
@@ -57,6 +59,8 @@ class Pipeline:
self.update_next_generation(winner_part, loser_part, elite_mask)
# pop_analysis(self.pop_nodes, self.pop_connections, self.input_idx, self.output_idx)
self.species_controller.speciate(self.pop_nodes, self.pop_connections, self.generation,
self.o2o_distance, self.o2m_distance)
@@ -105,16 +109,25 @@ class Pipeline:
npn, npc = self.crossover_func(crossover_rand_keys, wpn, wpc, lpn,
lpc) # new pop nodes, new pop connections
# for i in range(self.pop_size):
# n, c = np.array(npn[i]), np.array(npc[i])
# check_array_valid(n, c, self.input_idx, self.output_idx)
# mutate
new_node_keys = np.arange(self.generation * self.pop_size, self.generation * self.pop_size + self.pop_size)
m_npn, m_npc = self.mutate_func(mutate_rand_keys, npn, npc, new_node_keys) # mutate_new_pop_nodes
# for i in range(self.pop_size):
# n, c = np.array(m_npn[i]), np.array(m_npc[i])
# check_array_valid(n, c, self.input_idx, self.output_idx)
# elitism don't mutate
npn, npc, m_npn, m_npc = jax.device_get([npn, npc, m_npn, m_npc])
self.pop_nodes = np.where(elite_mask[:, None, None], npn, m_npn)
self.pop_connections = np.where(elite_mask[:, None, None, None], npc, m_npc)
self.pop_connections = np.where(elite_mask[:, None, None], npc, m_npc)
def expand(self):
"""
@@ -128,20 +141,38 @@ class Pipeline:
max_node_size = np.max(pop_node_sizes)
if max_node_size >= self.N:
self.N = int(self.N * self.expand_coe)
print(f"expand to {self.N}!")
self.pop_nodes, self.pop_connections = expand(self.pop_nodes, self.pop_connections, self.N)
print(f"node expand to {self.N}!")
self.pop_nodes, self.pop_connections = expand(self.pop_nodes, self.pop_connections, self.N, self.C)
# don't forget to expand representation genome in species
for s in self.species_controller.species.values():
s.representative = expand_single(*s.representative, self.N)
s.representative = expand_single(*s.representative, self.N, self.C)
# update functions
self.compile_functions(debug=True)
pop_con_keys = self.pop_connections[:, :, 0]
pop_node_sizes = np.sum(~np.isnan(pop_con_keys), axis=1)
max_con_size = np.max(pop_node_sizes)
if max_con_size >= self.C:
self.C = int(self.C * self.expand_coe)
print(f"connections expand to {self.C}!")
self.pop_nodes, self.pop_connections = expand(self.pop_nodes, self.pop_connections, self.N, self.C)
# don't forget to expand representation genome in species
for s in self.species_controller.species.values():
s.representative = expand_single(*s.representative, self.N, self.C)
# update functions
self.compile_functions(debug=True)
def compile_functions(self, debug=False):
self.mutate_func = self.function_factory.create_mutate(self.N)
self.crossover_func = self.function_factory.create_crossover(self.N)
self.o2o_distance, self.o2m_distance = self.function_factory.create_distance(self.N)
self.mutate_func = self.function_factory.create_mutate(self.N, self.C)
self.crossover_func = self.function_factory.create_crossover(self.N, self.C)
self.o2o_distance, self.o2m_distance = self.function_factory.create_distance(self.N, self.C)
def default_analysis(self, fitnesses):
max_f, min_f, mean_f, std_f = max(fitnesses), min(fitnesses), np.mean(fitnesses), np.std(fitnesses)