FINISH

создана функция, проводящая замеры, функция сохраняющая замеры

novikovsd/hashtab.py

@@ -217,4 +217,90 @@ def measure_delete(obj, struct_name, names_to_delete):
         for name in names_to_delete:
             obj = bst_delete(obj, name)
     elapsed = time.perf_counter() - start
-    return elapsed, obj
+    return elapsed, obj
+
+def run_experiment(n=10000, repeats=5):
+    records_sorted, records_shuffled = generate_test_data(n)
+    existing_names = [name for name, _ in records_sorted[:100]]
+    nonexisting_names = [f"None_{i}" for i in range(10)]
+    all_names = [name for name, _ in records_sorted]
+
+    structures = ["LinkedList", "HashTable", "BST"]
+    modes = [("shuffled", records_shuffled), ("sorted", records_sorted)]
+
+    results = []  
+    for struct_name in structures:
+        for mode_name, records in modes:
+            for rep in range(repeats):
+                insert_time, obj = measure_insert(struct_name, records)
+                results.append([struct_name, mode_name, "insert", rep+1, insert_time])
+
+                find_time = measure_find(obj, struct_name, existing_names, nonexisting_names)
+                results.append([struct_name, mode_name, "find", rep+1, find_time])
+
+                random.seed(rep)
+                to_delete = random.sample(all_names, 50)
+                delete_time, obj = measure_delete(obj, struct_name, to_delete)
+                results.append([struct_name, mode_name, "delete", rep+1, delete_time])
+
+    return results
+
+def save_results_to_csv(results, filename="docs/data/results.csv"):
+    os.makedirs(os.path.dirname(filename), exist_ok=True)
+    with open(filename, 'w', newline='', encoding='utf-8') as f:
+        writer = csv.writer(f)
+        writer.writerow(["Structure", "Mode", "Operation", "Repeat", "Time_sec"])
+        writer.writerows(results)
+    print(f"Результаты сохранены в {filename}")
+
+
+def aggregate_results(results):
+    from collections import defaultdict
+    agg = defaultdict(list)
+    for row in results:
+        struct, mode, op, rep, t = row
+        agg[(struct, mode, op)].append(t)
+    means = {k: sum(v)/len(v) for k, v in agg.items()}
+    return means
+
+
+def plot_results(means, output_dir="docs"):
+    try:
+        import matplotlib.pyplot as plt
+        import numpy as np
+    except ImportError:
+        print("Matplotlib не установлен. Графики не построены.")
+        return
+
+    operations = ["insert", "find", "delete"]
+    structures = ["LinkedList", "HashTable", "BST"]
+    modes = ["shuffled", "sorted"]
+
+    fig, axes = plt.subplots(1, 3, figsize=(15, 5))
+    for idx, op in enumerate(operations):
+        ax = axes[idx]
+        x = np.arange(len(structures))
+        width = 0.35
+        shuffled_means = [means.get((struct, "shuffled", op), 0) for struct in structures]
+        sorted_means = [means.get((struct, "sorted", op), 0) for struct in structures]
+        ax.bar(x - width/2, shuffled_means, width, label='случайный порядок', color='skyblue')
+        ax.bar(x + width/2, sorted_means, width, label='отсортированный порядок', color='salmon')
+        ax.set_xticks(x)
+        ax.set_xticklabels(structures, rotation=15)
+        ax.set_ylabel('Время (сек)')
+        ax.set_title(f'{op.upper()}')
+        ax.legend()
+        ax.grid(axis='y', linestyle='--', alpha=0.7)
+
+    plt.tight_layout()
+    plt.savefig(os.path.join(output_dir, "comparison.png"), dpi=150)
+    plt.show()
+
+if __name__ == "__main__":
+    results = run_experiment(n=10000, repeats=5)
+    save_results_to_csv(results)
+    means = aggregate_results(results)
+    print("\nСреднее время по операциям (сек):")
+    for (struct, mode, op), t in sorted(means.items()):
+        print(f"{struct:12} {mode:8} {op:6} : {t:.6f}")
+    plot_results(means)