fitnesses
This commit is contained in:
215
lab4/main.py
215
lab4/main.py
@@ -1,36 +1,185 @@
|
||||
import random
|
||||
from math import log
|
||||
|
||||
import numpy as np
|
||||
from numpy.typing import NDArray
|
||||
|
||||
from gp import Chromosome, ops
|
||||
from gp.population import ramped_initialization
|
||||
from gp.primitive import Var
|
||||
|
||||
operations = ops.ALL
|
||||
terminals = [Var(f"x{i}") for i in range(1, 9)]
|
||||
|
||||
chrom = Chromosome.full_init(terminals, operations, max_depth=3)
|
||||
print("Depth:", chrom.root.get_subtree_depth())
|
||||
print("Formula:", chrom)
|
||||
print("Tree:\n", chrom.root.to_str_tree())
|
||||
|
||||
values = [
|
||||
np.array([1.0]),
|
||||
np.array([2.0]),
|
||||
np.array([3.0]),
|
||||
np.array([4.0]),
|
||||
np.array([5.0]),
|
||||
np.array([6.0]),
|
||||
np.array([7.0]),
|
||||
np.array([8.0]),
|
||||
]
|
||||
context = {var: value for var, value in zip(terminals, values)}
|
||||
print("Value for ", values, ":", chrom.root.eval(context))
|
||||
|
||||
population = ramped_initialization(
|
||||
5,
|
||||
[3, 4, 5, 6, 7, 8],
|
||||
terminals,
|
||||
operations,
|
||||
from gp import Chromosome
|
||||
from gp.crossovers import crossover_subtree
|
||||
from gp.fitness import (
|
||||
HuberFitness,
|
||||
MAEFitness,
|
||||
MSEFitness,
|
||||
NRMSEFitness,
|
||||
PenalizedFitness,
|
||||
RMSEFitness,
|
||||
)
|
||||
print("Population size:", len(population))
|
||||
print("Population:")
|
||||
[print(str(chrom)) for chrom in population]
|
||||
from gp.ga import GARunConfig, genetic_algorithm
|
||||
from gp.mutations import grow_mutation, shrink_mutation
|
||||
from gp.ops import ADD, COS, DIV, EXP, MUL, NEG, POW, SIN, SQUARE, SUB
|
||||
from gp.population import ramped_initialization
|
||||
from gp.primitive import Const, Var
|
||||
from gp.selection import roulette_selection
|
||||
|
||||
NUM_VARS = 9
|
||||
TEST_POINTS = 10000
|
||||
MAX_DEPTH = 13
|
||||
MAX_GENERATIONS = 500
|
||||
np.random.seed(17)
|
||||
random.seed(17)
|
||||
X = np.random.uniform(-5.536, 5.536, size=(TEST_POINTS, NUM_VARS))
|
||||
# axes = [np.linspace(-5.536, 5.536, TEST_POINTS) for _ in range(NUM_VARS)]
|
||||
# X = np.array(np.meshgrid(*axes)).T.reshape(-1, NUM_VARS)
|
||||
operations = [SQUARE, SIN, COS, EXP, ADD, SUB, MUL, DIV, POW]
|
||||
# operations = [SQUARE, ADD, SUB, MUL]
|
||||
terminals = [Var(f"x{i}") for i in range(1, NUM_VARS + 1)]
|
||||
|
||||
|
||||
# def target_function(x: NDArray[np.float64]) -> NDArray[np.float64]:
|
||||
# """
|
||||
# f(x) = x1 + x2 + sin(x1)
|
||||
# x имеет форму (n_samples, n_vars)
|
||||
# """
|
||||
# x1 = x[:, 0]
|
||||
# x2 = x[:, 1]
|
||||
# return x1 + x2 + np.sin(x1) + np.sin(x2) + np.exp(-x1) + np.cos(x2)
|
||||
|
||||
|
||||
# def target_function(x: NDArray[np.float64]) -> NDArray[np.float64]:
|
||||
# """
|
||||
# Простая тестовая функция: сумма косинусов всех переменных.
|
||||
# f(x) = sum_i cos(x_i)
|
||||
# x имеет форму (n_samples, n_vars)
|
||||
# """
|
||||
# return np.sum(x, axis=1)
|
||||
|
||||
|
||||
def target_function(x: NDArray[np.float64]) -> NDArray[np.float64]:
|
||||
"""
|
||||
Векторизованная версия функции:
|
||||
f(x) = sum_{i=1}^n sum_{j=1}^i x_j^2
|
||||
x имеет форму (n_samples, n_vars)
|
||||
"""
|
||||
# Префиксные суммы квадратов по оси переменных
|
||||
x_sq = x**2
|
||||
prefix_sums = np.cumsum(x_sq, axis=1)
|
||||
# Суммируем по i (ось 1)
|
||||
return np.sum(prefix_sums, axis=1)
|
||||
|
||||
|
||||
# def target_function(x: NDArray[np.float64]) -> NDArray[np.float64]:
|
||||
# """
|
||||
# Rastrigin function.
|
||||
# f(x) = 10 * n + sum(x_i^2 - 10 * cos(2πx_i))
|
||||
# x: shape (n_samples, n_vars)
|
||||
# """
|
||||
# n = x.shape[1]
|
||||
# return 10 * n + np.sum(x**2 - 10 * np.cos(2 * np.pi * x), axis=1)
|
||||
|
||||
# fitness_function = MSEFitness(target_function, lambda: X)
|
||||
# fitness = HuberFitness(target_function, lambda: X, delta=1.0)
|
||||
# fitness_function = PenalizedFitness(
|
||||
# target_function, lambda: X, base_fitness=fitness, lambda_=0.003
|
||||
# )
|
||||
fitness_function = HuberFitness(target_function, lambda: X)
|
||||
# fitness_function = PenalizedFitness(
|
||||
# target_function, lambda: X, base_fitness=fitness, lambda_=0.003
|
||||
# )
|
||||
|
||||
|
||||
def adaptive_mutation(
|
||||
chromosome: Chromosome,
|
||||
generation: int,
|
||||
max_generations: int,
|
||||
max_depth: int,
|
||||
) -> Chromosome:
|
||||
"""Адаптивная мутация.
|
||||
|
||||
Меняет вероятность типов мутации по ходу эволюции:
|
||||
- Ранняя фаза (<30%): 70% grow, 30% shrink
|
||||
- Средняя фаза (30–70%): 40% grow, 60% shrink
|
||||
- Поздняя фаза (>=70%): 20% grow, 80% shrink
|
||||
"""
|
||||
|
||||
r = random.random()
|
||||
|
||||
# 50% grow, 50% shrink
|
||||
if r < 0.5:
|
||||
return grow_mutation(chromosome, max_depth=max_depth)
|
||||
return shrink_mutation(chromosome)
|
||||
|
||||
|
||||
# def adaptive_mutation(
|
||||
# chromosome: Chromosome,
|
||||
# generation: int,
|
||||
# max_generations: int,
|
||||
# max_depth: int,
|
||||
# ) -> Chromosome:
|
||||
# """Адаптивная мутация.
|
||||
|
||||
# Меняет вероятность типов мутации по ходу эволюции:
|
||||
# - Ранняя фаза (<30%): 70% grow, 30% shrink
|
||||
# - Средняя фаза (30–70%): 40% grow, 60% shrink
|
||||
# - Поздняя фаза (>=70%): 20% grow, 80% shrink
|
||||
# """
|
||||
|
||||
# # Вычисляем прогресс в диапазоне [0, 1]
|
||||
# if max_generations <= 0:
|
||||
# progress = 0.0
|
||||
# else:
|
||||
# progress = min(1.0, max(0.0, generation / max_generations))
|
||||
|
||||
# r = random.random()
|
||||
|
||||
# # Определяем тип мутации
|
||||
# if progress < 0.3:
|
||||
# do_grow = r < 0.7
|
||||
# elif progress < 0.7:
|
||||
# do_grow = r < 0.4
|
||||
# else:
|
||||
# do_grow = r < 0.2
|
||||
|
||||
# # Выполняем выбранную мутацию
|
||||
# if do_grow:
|
||||
# return grow_mutation(chromosome, max_depth=max_depth)
|
||||
# return shrink_mutation(chromosome)
|
||||
|
||||
|
||||
config = GARunConfig(
|
||||
fitness_func=fitness_function,
|
||||
crossover_fn=lambda p1, p2: crossover_subtree(p1, p2, max_depth=8),
|
||||
mutation_fn=lambda chrom, gen_num: adaptive_mutation(
|
||||
chrom, gen_num, MAX_GENERATIONS, MAX_DEPTH
|
||||
),
|
||||
selection_fn=roulette_selection,
|
||||
init_population=ramped_initialization(
|
||||
15, [4, 5, 6, 6, 7, 7, 8, 9, 10, 11], terminals, operations
|
||||
),
|
||||
seed=17,
|
||||
pc=0.9,
|
||||
pm=0.3,
|
||||
elitism=30,
|
||||
max_generations=MAX_GENERATIONS,
|
||||
log_every_generation=True,
|
||||
)
|
||||
|
||||
result = genetic_algorithm(config)
|
||||
|
||||
|
||||
# Выводим результаты
|
||||
print(f"Лучшая особь: {result.best_generation.best}")
|
||||
print(result.best_generation.best.root.to_str_tree())
|
||||
print(f"Лучшее значение фитнеса: {result.best_generation.best_fitness:.6f}")
|
||||
print(f"Количество поколений: {result.generations_count}")
|
||||
print(f"Время выполнения: {result.time_ms:.2f} мс")
|
||||
|
||||
mse_fitness = MSEFitness(target_function, lambda: X)
|
||||
print(f"MSE: {mse_fitness(result.best_generation.best):.6f}")
|
||||
rmse_fitness = RMSEFitness(target_function, lambda: X)
|
||||
print(f"RMSE: {rmse_fitness(result.best_generation.best):.6f}")
|
||||
mae_fitness = MAEFitness(target_function, lambda: X)
|
||||
print(f"MAE: {mae_fitness(result.best_generation.best):.6f}")
|
||||
huber_fitness = HuberFitness(target_function, lambda: X, delta=1.0)
|
||||
print(f"Huber: {huber_fitness(result.best_generation.best):.6f}")
|
||||
nrmse_fitness = NRMSEFitness(target_function, lambda: X)
|
||||
print(f"NRMSE: {nrmse_fitness(result.best_generation.best):.6f}")
|
||||
|
||||
Reference in New Issue
Block a user