i think i've done this shit RMSE: 0.64 !!!!

lab4/main.py

@@ -7,7 +7,6 @@ from numpy.typing import NDArray
 from gp import Chromosome
 from gp.crossovers import crossover_subtree
 from gp.fitness import (
-    HuberFitness,
     MAEFitness,
     MSEFitness,
     NRMSEFitness,
@@ -26,12 +25,13 @@ from gp.population import ramped_initialization
 from gp.primitive import Const, Var
 from gp.selection import roulette_selection, tournament_selection
 
-NUM_VARS = 9
+NUM_VARS = 8
 TEST_POINTS = 10000
-MAX_DEPTH = 15
+MAX_DEPTH = 10
 MAX_GENERATIONS = 200
-np.random.seed(17)
-random.seed(17)
+SEED = 17
+np.random.seed(SEED)
+random.seed(SEED)
 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)
@@ -40,25 +40,6 @@ operations = [SQUARE, SIN, COS, EXP, ADD, SUB, MUL, DIV, POW]
 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:
@@ -72,23 +53,16 @@ def target_function(x: NDArray[np.float64]) -> NDArray[np.float64]:
     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 = HuberFitness(target_function, lambda: X, delta=0.5)
 # fitness_function = PenalizedFitness(
-#     target_function, lambda: X, base_fitness=fitness, lambda_=0.003
+#     target_function, lambda: X, base_fitness=fitness, lambda_=0.1
 # )
+# fitness_function = NRMSEFitness(target_function, lambda: X)
 fitness_function = RMSEFitness(target_function, lambda: X)
+
 # fitness_function = PenalizedFitness(
-#     target_function, lambda: X, base_fitness=fitness, lambda_=0.003
+#     target_function, lambda: X, base_fitness=fitness_function, lambda_=0.0001
 # )
 
 
@@ -98,14 +72,6 @@ def adaptive_mutation(
     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()
 
     if r < 0.4:
@@ -118,57 +84,25 @@ def adaptive_mutation(
     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)
+init_population = ramped_initialization(
+    20, [i for i in range(MAX_DEPTH - 9, MAX_DEPTH + 1)], terminals, operations
+)
 
+print("Population size:", len(init_population))
 
 config = GARunConfig(
     fitness_func=fitness_function,
-    crossover_fn=lambda p1, p2: crossover_subtree(p1, p2, max_depth=8),
+    crossover_fn=lambda p1, p2: crossover_subtree(p1, p2, max_depth=MAX_DEPTH),
     mutation_fn=lambda chrom, gen_num: adaptive_mutation(
         chrom, gen_num, MAX_GENERATIONS, MAX_DEPTH
     ),
     # selection_fn=roulette_selection,
     selection_fn=lambda p, f: tournament_selection(p, f, k=3),
-    init_population=ramped_initialization(
-        10, [4, 5, 6, 6, 7, 7, 8, 9, 10, 11], terminals, operations
-    ),
-    seed=17,
-    pc=0.9,
-    pm=0.3,
-    elitism=10,
+    init_population=init_population,
+    seed=SEED,
+    pc=0.85,
+    pm=0.15,
+    elitism=15,
     max_generations=MAX_GENERATIONS,
     log_every_generation=True,
 )
@@ -182,6 +116,7 @@ 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")
+print("Population size:", len(init_population))
 
 mse_fitness = MSEFitness(target_function, lambda: X)
 print(f"MSE: {mse_fitness(result.best_generation.best):.6f}")
@@ -189,7 +124,5 @@ 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}")