[1] Законченная программа, таблица, графики

BorisovMI/lab_1/docs/data/structures.py

@@ -5,7 +5,7 @@ import os
 import matplotlib.pyplot as plt
 import numpy as np
 import sys
-
+sys.setrecursionlimit(20000) 
 # Связный список
 
 def ll_insert(head, name, phone):
@@ -177,7 +177,7 @@ def bst_list_all(root):
     inorder_traversal(root)
     return records
 
- # Генерация тесотовых данных
+ # Генерация тестовых данных
 
 def generate_records(count=10000):
     
@@ -228,7 +228,6 @@ def measure_insertion(structure_name, records):
     
     return times, filled_structure
 
-
 def measure_search(structure_name, structure, search_names):
     
     times = []
@@ -249,7 +248,6 @@ def measure_search(structure_name, structure, search_names):
     
     return times
 
-
 def measure_deletion(structure_name, original_structure, delete_names):
     
     times = []
@@ -287,3 +285,193 @@ def measure_deletion(structure_name, original_structure, delete_names):
         times.append(end - start)
     
     return times
+
+# Основная часть
+
+def run_experiment():
+    
+    current_dir = os.path.dirname(__name__)
+    docs_dir = os.path.dirname(current_dir)
+    csv_file = os.path.join(docs_dir, "experiment_results.csv")
+    
+    print("=" * 70)
+    print("ЭКСПЕРИМЕНТАЛЬНОЕ СРАВНЕНИЕ СТРУКТУР ДАННЫХ")
+    print("Телефонный справочник - 10000 записей")
+    print("=" * 70)
+    print(f"\nРезультаты будут сохранены в: {csv_file}")
+    
+    print("\n1. Генерация тестовых данных...")
+    shuffled_records, sorted_records = generate_records(10000)
+    print(f"   Сгенерировано 10000 записей")
+    
+    existing_names = [shuffled_records[i][0] for i in random.sample(range(10000), 100)]
+    nonexisting_names = [f"NotExist_{i}" for i in range(10)]
+    search_names = existing_names + nonexisting_names
+    delete_names = [shuffled_records[i][0] for i in random.sample(range(10000), 50)]
+    
+    results = [["Структура", "Режим", "Операция", 
+                "Замер1(с)", "Замер2(с)", "Замер3(с)", "Замер4(с)", "Замер5(с)", 
+                "Среднее(с)"]]
+    
+    for mode_name, records in [("случайный", shuffled_records), 
+                                ("отсортированный", sorted_records)]:
+        
+        print(f"\n2. Тестирование режима: {mode_name}")
+        print("-" * 50)
+        
+        for struct_name in ["linked_list", "hash_table", "bst"]:
+            print(f"\n   {struct_name.upper()}:")
+            
+            print("      Вставка 10000 записей...")
+            insert_times, filled_struct = measure_insertion(struct_name, records)
+            avg_insert = sum(insert_times) / 5
+            print(f"      Время: {avg_insert:.4f} сек (среднее)")
+            
+            print("      Поиск 110 записей...")
+            search_times = measure_search(struct_name, filled_struct, search_names)
+            avg_search = sum(search_times) / 5
+            print(f"      Время: {avg_search:.4f} сек (среднее)")
+            
+            print("      Удаление 50 записей...")
+            delete_times = measure_deletion(struct_name, filled_struct, delete_names)
+            avg_delete = sum(delete_times) / 5
+            print(f"      Время: {avg_delete:.4f} сек (среднее)")
+            
+            results.append([struct_name, mode_name, "вставка"] + insert_times + [avg_insert])
+            results.append([struct_name, mode_name, "поиск"] + search_times + [avg_search])
+            results.append([struct_name, mode_name, "удаление"] + delete_times + [avg_delete])
+    
+    print("\n3. Сохранение результатов...")
+    with open(csv_file, "w", newline="", encoding="utf-8") as f:
+        writer = csv.writer(f)
+        writer.writerows(results)
+    print(f"   Результаты сохранены в: {csv_file}")
+    
+    print("\n" + "=" * 70)
+    print("СВОДНАЯ ТАБЛИЦА РЕЗУЛЬТАТОВ")
+    print("=" * 70)
+    print(f"{'Структура':<15} {'Режим':<12} {'Операция':<10} {'Среднее время (сек)':<20}")
+    print("-" * 70)
+    
+    for row in results[1:]:
+        struct, mode, op, t1, t2, t3, t4, t5, avg = row
+        print(f"{struct:<15} {mode:<12} {op:<10} {avg:<20.6f}")
+    
+    return results, docs_dir
+
+# Графики
+
+def create_graphs(results, docs_dir):
+    
+    print("\n4. Построение графиков...")
+    
+    data = {}
+    for row in results[1:]:
+        struct = row[0]
+        mode = row[1]
+        op = row[2]
+        avg = row[8]
+        
+        if struct not in data:
+            data[struct] = {}
+        if mode not in data[struct]:
+            data[struct][mode] = {}
+        data[struct][mode][op] = avg
+    
+    
+    struct_labels = {
+        'linked_list': 'LinkedList',
+        'hash_table': 'HashTable',
+        'bst': 'BST'
+    }
+    
+    
+    colors = {
+        'linked_list': '#3498db',  
+        'hash_table': '#2ecc71',   
+        'bst': '#e74c3c'            
+    }
+    
+    
+    fig, axes = plt.subplots(1, 3, figsize=(15, 6))
+    fig.suptitle('Сравнение производительности структур данных', fontsize=16, fontweight='bold')
+    
+    operations = ['вставка', 'поиск', 'удаление']
+    operation_titles = ['Вставка\n(10000 записей)', 'Поиск\n(110 запросов)', 'Удаление\n(50 записей)']
+    modes = ['случайный', 'отсортированный']
+    mode_labels = ['Случайный', 'Отсортированный']
+    
+    for idx, (op, op_title) in enumerate(zip(operations, operation_titles)):
+        ax = axes[idx]
+        
+        # Позиции для групп столбцов
+        x = np.arange(len(modes))  # [0, 1]
+        width = 0.25  # ширина одного столбца
+        multiplier = 0
+        
+        for struct in ['linked_list', 'hash_table', 'bst']:
+            values = [data[struct][mode][op] for mode in modes]
+            offset = width * multiplier
+            bars = ax.bar(x + offset, values, width, 
+                         label=struct_labels[struct], 
+                         color=colors[struct],
+                         edgecolor='black', linewidth=0.5)
+            
+            
+            for bar, val in zip(bars, values):
+                if val < 0.01:
+                    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + val*0.05, 
+                           f'{val:.5f}', ha='center', va='bottom', fontsize=8, rotation=0)
+                else:
+                    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + val*0.02, 
+                           f'{val:.4f}', ha='center', va='bottom', fontsize=8, rotation=0)
+            
+            multiplier += 1
+        
+        
+        ax.set_title(op_title, fontsize=12, fontweight='bold')
+        ax.set_ylabel('Время (секунды)', fontsize=10)
+        ax.set_xlabel('Режим данных', fontsize=10)
+        ax.set_xticks(x + width)
+        ax.set_xticklabels(mode_labels)
+        ax.legend(loc='upper left', fontsize=8)
+        ax.grid(True, alpha=0.3, axis='y')
+        
+        
+        all_values = [data[s][m][op] for s in ['linked_list', 'hash_table', 'bst'] for m in modes]
+        if max(all_values) / min(all_values) > 100:
+            ax.set_yscale('log')
+            ax.set_ylabel('Время (секунды) - логарифмическая шкала', fontsize=9)
+    
+    plt.tight_layout()
+    graph_path = os.path.join(docs_dir, "performance_graphs.png")
+    plt.savefig(graph_path, dpi=150, bbox_inches='tight')
+    plt.close()
+    print(f"   Графики сохранены в: {graph_path}")
+    
+    return graph_path
+
+
+
+if __name__ == "__main__":
+    
+    results, docs_dir = run_experiment()
+    
+    
+    try:
+        graph_file = create_graphs(results, docs_dir)
+        
+        print("\n" + "=" * 70)
+        print("ЭКСПЕРИМЕНТ ЗАВЕРШЕН УСПЕШНО!")
+        print("=" * 70)
+        print("\nСОЗДАННЫЕ ФАЙЛЫ:")
+        print(f"   Данные: {os.path.join(docs_dir, 'experiment_results.csv')}")
+        print(f"   Графики: {graph_file}")
+        
+    except Exception as e:
+        print(f"\nОшибка при построении графиков: {e}")
+        print("   Убедитесь, что установлен matplotlib: pip install matplotlib")
+        print("\n" + "=" * 70)
+        print("ЭКСПЕРИМЕНТ ЗАВЕРШЕН (без графиков)")
+        print("=" * 70)
+        print(f"\nCSV файл сохранен: {os.path.join(docs_dir, 'experiment_results.csv')}")

BorisovMI/lab_1/docs/experiment_results.csv

@@ -0,0 +1,19 @@
+Структура,Режим,Операция,Замер1(с),Замер2(с),Замер3(с),Замер4(с),Замер5(с),Среднее(с)
+linked_list,случайный,вставка,11.072574299992993,12.38099840003997,12.155311000009533,12.81002289999742,13.201067199988756,12.323994760005736
+linked_list,случайный,поиск,0.1209169999929145,0.13400630000978708,0.1302103999769315,0.12179599999217317,0.13267739996081218,0.12792141998652368
+linked_list,случайный,удаление,0.05278969998471439,0.05019940005149692,0.05289009999250993,0.0488043999648653,0.061356800026260316,0.05320808000396937
+hash_table,случайный,вставка,0.694432099990081,0.7192347000236623,0.7134853000170551,0.6891152000171132,0.6973775000078604,0.7027289600111544
+hash_table,случайный,поиск,0.006584499962627888,0.004749000014271587,0.004359900020062923,0.006477299961261451,0.006112299975939095,0.005656599986832589
+hash_table,случайный,удаление,0.004070399969350547,0.004140400036703795,0.003542299964465201,0.0032371999695897102,0.0030583000043407083,0.0036097199888899924
+bst,случайный,вставка,0.08137199998600408,0.08989329996984452,0.08159760001581162,0.07841239997651428,0.1001471999916248,0.08628449998795987
+bst,случайный,поиск,0.0006561999907717109,0.00043180002830922604,0.00042429997120052576,0.0004227000172249973,0.00043509999522939324,0.0004740200005471706
+bst,случайный,удаление,0.19723930000327528,0.166880399978254,0.147482400003355,0.18100009998306632,0.16498840000713244,0.1715181199950166
+linked_list,отсортированный,вставка,12.294058899977244,11.597062399960123,11.59195000003092,11.623520500026643,11.401553199975751,11.701628999994137
+linked_list,отсортированный,поиск,0.10066379996715114,0.0991929000010714,0.09696789999725297,0.09909789997618645,0.09873830003198236,0.09893215999472886
+linked_list,отсортированный,удаление,0.05669830000260845,0.048193000024184585,0.05076910002389923,0.04920080001465976,0.05905079998774454,0.05278240001061931
+hash_table,отсортированный,вставка,0.5518350999918766,0.5407364999991842,0.5291212999727577,0.5373308000271209,0.529426000022795,0.5376899400027468
+hash_table,отсортированный,поиск,0.005108800018206239,0.004848999960813671,0.004103399987798184,0.0038874000310897827,0.005848900007549673,0.00475950000109151
+hash_table,отсортированный,удаление,0.0037329999613575637,0.003185699984896928,0.0028516000020317733,0.0030229000258259475,0.0037879000301472843,0.003316220000851899
+bst,отсортированный,вставка,32.23637629998848,33.77348939998774,33.518200399994384,33.78866110002855,33.55791890004184,33.3749292200082
+bst,отсортированный,поиск,0.1479294000309892,0.1305704999831505,0.13548930000979453,0.1314462999580428,0.130273999995552,0.1351418999955058
+bst,отсортированный,удаление,0.1574716999894008,0.16461069998331368,0.15460990002611652,0.15385339997010306,0.15677610004786402,0.15746436000335962