tony/tensorneat-mend
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change fitness from list to array

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optimize the code of reproduction.
This commit is contained in:
wls2002
2023-05-11 08:15:06 +08:00
parent 299ff1f8f1
commit acc9eab64a
1 changed files with 15 additions and 22 deletions
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37
algorithms/neat/pipeline.py
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@@ -53,9 +53,9 @@ class Pipeline:
self.species_controller.update_species_fitnesses(fitnesses) self.species_controller.update_species_fitnesses(fitnesses)
crossover_pair = self.species_controller.reproduce(self.generation) winner_part, loser_part, elite_mask = self.species_controller.reproduce(fitnesses, self.generation)
self.update_next_generation(crossover_pair) self.update_next_generation(winner_part, loser_part, elite_mask)
self.species_controller.speciate(self.pop_nodes, self.pop_connections, self.generation, self.species_controller.speciate(self.pop_nodes, self.pop_connections, self.generation,
self.o2o_distance, self.o2m_distance) self.o2o_distance, self.o2m_distance)
@@ -82,36 +82,29 @@ class Pipeline:
print("Generation limit reached!") print("Generation limit reached!")
return self.best_genome return self.best_genome
def update_next_generation(self, crossover_pair: List[Union[int, Tuple[int, int]]]) -> None: def update_next_generation(self, winner_part, loser_part, elite_mask) -> None:
""" """
create the next generation create next generation
:param crossover_pair: created from self.reproduce() :param winner_part:
:param loser_part:
:param elite_mask:
:return:
""" """
assert self.pop_nodes.shape[0] == self.pop_size assert self.pop_nodes.shape[0] == self.pop_size
k1, k2, self.randkey = jax.random.split(self.randkey, 3) k1, k2, self.randkey = jax.random.split(self.randkey, 3)
# crossover
# prepare elitism mask and crossover pair
elitism_mask = np.full(self.pop_size, False)
for i, pair in enumerate(crossover_pair):
if not isinstance(pair, tuple): # elitism
elitism_mask[i] = True
crossover_pair[i] = (pair, pair)
crossover_pair = np.array(crossover_pair)
crossover_rand_keys = jax.random.split(k1, self.pop_size) crossover_rand_keys = jax.random.split(k1, self.pop_size)
mutate_rand_keys = jax.random.split(k2, self.pop_size) mutate_rand_keys = jax.random.split(k2, self.pop_size)
# batch crossover # batch crossover
wpn = self.pop_nodes[crossover_pair[:, 0]] # winner pop nodes wpn = self.pop_nodes[winner_part] # winner pop nodes
wpc = self.pop_connections[crossover_pair[:, 0]] # winner pop connections wpc = self.pop_connections[winner_part] # winner pop connections
lpn = self.pop_nodes[crossover_pair[:, 1]] # loser pop nodes lpn = self.pop_nodes[loser_part] # loser pop nodes
lpc = self.pop_connections[crossover_pair[:, 1]] # loser pop connections lpc = self.pop_connections[loser_part] # loser pop connections
npn, npc = self.crossover_func(crossover_rand_keys, wpn, wpc, lpn, npn, npc = self.crossover_func(crossover_rand_keys, wpn, wpc, lpn,
lpc) # new pop nodes, new pop connections lpc) # new pop nodes, new pop connections
# mutate # mutate
new_node_keys = np.arange(self.generation * self.pop_size, self.generation * self.pop_size + self.pop_size) new_node_keys = np.arange(self.generation * self.pop_size, self.generation * self.pop_size + self.pop_size)
@@ -120,8 +113,8 @@ class Pipeline:
# elitism don't mutate # elitism don't mutate
npn, npc, m_npn, m_npc = jax.device_get([npn, npc, m_npn, m_npc]) npn, npc, m_npn, m_npc = jax.device_get([npn, npc, m_npn, m_npc])
self.pop_nodes = np.where(elitism_mask[:, None, None], npn, m_npn) self.pop_nodes = np.where(elite_mask[:, None, None], npn, m_npn)
self.pop_connections = np.where(elitism_mask[:, None, None, None], npc, m_npc) self.pop_connections = np.where(elite_mask[:, None, None, None], npc, m_npc)
def expand(self): def expand(self):
""" """