Document visualization workflow for lab 5 report

lab5/main.py

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+"""Entry point for running the evolution strategy on the benchmark function."""
+
+from __future__ import annotations
+
+import numpy as np
+
+from es import EvolutionStrategyConfig, run_evolution_strategy
+from functions import axis_parallel_hyperellipsoid, default_bounds
+
+
+def run_for_dimension(
+    dimension: int,
+    *,
+    results_dir: str,
+    max_generations: int = 200,
+    seed: int | None = 17,
+    save_generations: list[int] | None = None,
+    log: bool = False,
+):
+    x_min, x_max = default_bounds(dimension)
+    search_range = x_max - x_min
+    initial_sigma = np.full(dimension, 0.15 * search_range[0], dtype=np.float64)
+
+    config = EvolutionStrategyConfig(
+        fitness_func=axis_parallel_hyperellipsoid,
+        dimension=dimension,
+        x_min=x_min,
+        x_max=x_max,
+        mu=20,
+        lambda_=80,
+        mutation_probability=0.7,
+        initial_sigma=initial_sigma,
+        max_generations=max_generations,
+        selection="comma",
+        recombination="intermediate",
+        parents_per_offspring=2,
+        success_rule_window=5,
+        success_rule_target=0.2,
+        sigma_increase=1.22,
+        sigma_decrease=0.82,
+        sigma_scale_min=1e-3,
+        sigma_scale_max=50.0,
+        sigma_min=1e-5,
+        sigma_max=2.0,
+        best_value_threshold=1e-6,
+        max_stagnation_generations=40,
+        save_generations=save_generations,
+        results_dir=results_dir,
+        log_every_generation=log,
+        seed=seed,
+    )
+
+    result = run_evolution_strategy(config)
+
+    print("=" * 80)
+    print(f"Результаты для размерности n={dimension}")
+    print(f"Лучшее решение: {result.best_generation.best.x}")
+    print(f"Лучшее значение функции: {result.best_generation.best.fitness:.8f}")
+    print(f"Количество поколений: {result.generations_count}")
+    print(f"Время выполнения: {result.time_ms:.2f} мс")
+    print("=" * 80)
+
+    return result
+
+
+def main() -> None:
+    # Для n=2 дополнительно сохраняем графики поколений
+    run_for_dimension(
+        2,
+        results_dir="lab5_results_2d",
+        save_generations=[1, 2, 3, 5, 8, 10, 15, 20, 25, 30, 40, 50, 75, 100, 150, 200],
+        log=True,
+    )
+
+    # Для n=3 графики не строим, но выводим статистику
+    run_for_dimension(
+        3,
+        results_dir="lab5_results_3d",
+        save_generations=None,
+        log=False,
+    )
+
+
+if __name__ == "__main__":
+    main()