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