249 lines
11 KiB
Python
249 lines
11 KiB
Python
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import time
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import random
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import csv
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import os
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import sys
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import matplotlib.pyplot as plt
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sys.setrecursionlimit(20000)
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BASE_PATH = r"E:\репозиторий\2026-rff_mp\krasnovia\lab1"
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DOCS_PATH = os.path.join(BASE_PATH, "docs")
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DATA_PATH = os.path.join(DOCS_PATH, "data")
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for p in [DOCS_PATH, DATA_PATH]:
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if not os.path.exists(p):
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os.makedirs(p)
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def ll_insert(head, name, phone):
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return {'name': name, 'phone': phone, 'next': head}
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def ll_find(head, name):
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curr = head
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while curr:
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if curr['name'] == name: return curr['phone']
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curr = curr['next']
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return None
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def ll_delete(head, name):
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if not head: return None
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if head['name'] == name: return head['next']
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curr = head
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while curr['next']:
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if curr['next']['name'] == name:
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curr['next'] = curr['next']['next']
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return head
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curr = curr['next']
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return head
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def ll_list_all(head):
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res = []
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curr = head
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while curr:
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res.append((curr['name'], curr['phone']))
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curr = curr['next']
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return sorted(res)
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def ht_insert(buckets, name, phone):
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idx = hash(name) % len(buckets)
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buckets[idx] = ll_insert(buckets[idx], name, phone)
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def ht_find(buckets, name):
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idx = hash(name) % len(buckets)
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return ll_find(buckets[idx], name)
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def ht_delete(buckets, name):
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idx = hash(name) % len(buckets)
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buckets[idx] = ll_delete(buckets[idx], name)
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def ht_list_all(buckets):
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all_recs = []
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for b in buckets:
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curr = b
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while curr:
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all_recs.append((curr['name'], curr['phone']))
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curr = curr['next']
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return sorted(all_recs)
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def bst_insert(root, name, phone):
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if not root:
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return {'name': name, 'phone': phone, 'left': None, 'right': None}
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if name < root['name']:
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root['left'] = bst_insert(root['left'], name, phone)
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elif name > root['name']:
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root['right'] = bst_insert(root['right'], name, phone)
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else:
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root['phone'] = phone
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return root
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def bst_find(root, name):
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if not root: return None
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if root['name'] == name: return root['phone']
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if name < root['name']: return bst_find(root['left'], name)
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return bst_find(root['right'], name)
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def bst_delete(root, name):
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if not root: return None
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if name < root['name']:
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root['left'] = bst_delete(root['left'], name)
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elif name > root['name']:
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root['right'] = bst_delete(root['right'], name)
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else:
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if not root['left']: return root['right']
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if not root['right']: return root['left']
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temp = root['right']
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while temp['left']: temp = temp['left']
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root['name'], root['phone'] = temp['name'], temp['phone']
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root['right'] = bst_delete(root['right'], temp['name'])
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return root
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def bst_list_all(root):
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res = []
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def _inorder(node):
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if node:
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_inorder(node['left'])
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res.append((node['name'], node['phone']))
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_inorder(node['right'])
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_inorder(root)
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return res
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all_results_csv = []
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summary_for_report = []
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def run_experiment(struct_type, mode, data):
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print(f"Processing: {struct_type} ({mode})")
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ins_times, find_times, del_times = [], [], []
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for i in range(5):
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container = [None]*1000 if struct_type == "HashTable" else None
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start = time.perf_counter()
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for n, p in data:
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if struct_type == "LinkedList": container = ll_insert(container, n, p)
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elif struct_type == "HashTable": ht_insert(container, n, p)
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elif struct_type == "BST": container = bst_insert(container, n, p)
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ins_times.append(time.perf_counter() - start)
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search_list = [d[0] for d in random.sample(data, 100)] + [f"None_{j}" for j in range(10)]
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start = time.perf_counter()
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for s_name in search_list:
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if struct_type == "LinkedList": ll_find(container, s_name)
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elif struct_type == "HashTable": ht_find(container, s_name)
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elif struct_type == "BST": bst_find(container, s_name)
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find_times.append(time.perf_counter() - start)
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del_list = [d[0] for d in random.sample(data, 50)]
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start = time.perf_counter()
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for d_name in del_list:
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if struct_type == "LinkedList": container = ll_delete(container, d_name)
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elif struct_type == "HashTable": ht_delete(container, d_name)
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elif struct_type == "BST": container = bst_delete(container, d_name)
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del_times.append(time.perf_counter() - start)
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all_results_csv.append([struct_type, mode, f"Run {i+1}", ins_times[-1], find_times[-1], del_times[-1]])
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avg_ins = sum(ins_times) / 5
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avg_find = sum(find_times) / 5
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avg_del = sum(del_times) / 5
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all_results_csv.append([struct_type, mode, "AVERAGE", avg_ins, avg_find, avg_del])
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summary_for_report.append({"name": struct_type, "mode": mode, "ins": avg_ins, "find": avg_find, "del": avg_del})
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N = 10000
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records_raw = [(f"User_{i:05d}", f"8-900-{random.randint(100, 999)}") for i in range(N)]
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records_shuffled = records_raw[:]
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random.shuffle(records_shuffled)
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records_sorted = sorted(records_raw)
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for m_name, d_set in [("случайный", records_shuffled), ("сортированный", records_sorted)]:
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for s_type in ["LinkedList", "HashTable", "BST"]:
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run_experiment(s_type, m_name, d_set)
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with open(os.path.join(DATA_PATH, "results.csv"), "w", newline="", encoding="utf-8") as f:
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writer = csv.writer(f)
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writer.writerow(["Структура", "Режим", "Итерация", "Вставка", "Поиск", "Удаление"])
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writer.writerows(all_results_csv)
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def create_plots():
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labels = ["insert", "find", "delete"]
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structs = ["LinkedList", "HashTable", "BST"]
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colors = ['#5dade2', '#e67e22', '#58d68d']
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fig1, axs = plt.subplots(1, 3, figsize=(18, 6))
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fig1.suptitle("Влияние порядка данных на время операций", fontsize=16, fontweight='bold')
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for i, s_name in enumerate(structs):
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rand_data = next(r for r in summary_for_report if r['name'] == s_name and r['mode'] == "случайный")
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sort_data = next(r for r in summary_for_report if r['name'] == s_name and r['mode'] == "сортированный")
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x = [0, 1, 2]
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width = 0.35
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axs[i].bar([p - width/2 for p in x], [rand_data['ins'], rand_data['find'], rand_data['del']], width, label='случайный', color=colors[0])
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axs[i].bar([p + width/2 for p in x], [sort_data['ins'], sort_data['find'], sort_data['del']], width, label='сортированный', color='#e74c3c', alpha=0.8)
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axs[i].set_title(s_name, fontweight='bold')
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axs[i].set_xticks(x)
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axs[i].set_xticklabels(labels)
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axs[i].set_ylabel("Время (с)")
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axs[i].legend()
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axs[i].grid(axis='y', linestyle='--', alpha=0.3)
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plt.tight_layout(rect=[0, 0.03, 1, 0.95])
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plt.savefig(os.path.join(DATA_PATH, "order_impact.png"))
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fig2, axs2 = plt.subplots(1, 3, figsize=(18, 6))
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fig2.suptitle(f"Сравнение структур данных (N={N})", fontsize=16, fontweight='bold')
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op_keys = ['ins', 'find', 'del']
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op_names = ['insert', 'find', 'delete']
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for i, op in enumerate(op_keys):
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plot_labels = []
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plot_values = []
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plot_colors = []
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for r in summary_for_report:
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plot_labels.append(f"{r['name']}\n({r['mode'][:4]})")
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plot_values.append(r[op])
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if r['name'] == "LinkedList": plot_colors.append(colors[0])
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elif r['name'] == "HashTable": plot_colors.append(colors[1])
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else: plot_colors.append(colors[2])
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bars = axs2[i].bar(plot_labels, plot_values, color=plot_colors)
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axs2[i].set_title(f"Операция: {op_names[i]}", fontweight='bold')
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axs2[i].set_ylabel("Время (с)")
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axs2[i].tick_params(axis='x', rotation=15)
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for bar in bars:
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height = bar.get_height()
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axs2[i].text(bar.get_x() + bar.get_width()/2., height, f'{height:.4f}', ha='center', va='bottom', fontsize=8)
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plt.tight_layout(rect=[0, 0.03, 1, 0.95])
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plt.savefig(os.path.join(DATA_PATH, "struct_comparison.png"))
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create_plots()
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with open(os.path.join(DOCS_PATH, "report.md"), "w", encoding="utf-8") as f:
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f.write("# Технический отчет: Сравнительный анализ структур данных\n\n")
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f.write("## 1. Вводные данные\n")
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f.write(f"Целью теста является оценка производительности LinkedList, HashTable и BST на массиве из {N} элементов. ")
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f.write("Анализировались сценарии со случайным распределением и предварительной сортировкой ключей.\n\n")
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f.write("## 2. Результаты измерений (AVG)\n")
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f.write("| Алгоритм | Входные данные | Вставка (с) | Поиск (с) | Удаление (с) |\n")
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f.write("| :--- | :--- | :--- | :--- | :--- |\n")
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for r in summary_for_report:
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f.write(f"| {r['name']} | {r['mode']} | {r['ins']:.6f} | {r['find']:.6f} | {r['del']:.6f} |\n")
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f.write("\n## 3. Визуальный анализ\n")
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f.write("### Сравнение по типам операций\n\n\n")
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f.write("### Влияние упорядоченности на производительность\n\n\n")
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f.write("## 4. Экспертные выводы\n")
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f.write("- **Эффект вырождения BST:** На отсортированных последовательностях BST демонстрирует критический рост времени выполнения (деградация до $O(N)$). ")
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f.write("Это связано с отсутствием балансировки, превращающим дерево в линейный список.\n")
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f.write("- **Инвариантность HashTable:** Хеш-таблица показывает наиболее стабильные результаты. Скорость доступа не коррелирует с порядком входных данных.\n")
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f.write("- **Линейная сложность LinkedList:** Связный список предсказуемо неэффективен при поиске, так как требует итерации по всей глубине структуры.\n")
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f.write("- **Итоговая оценка:** Для систем с высокой интенсивностью поиска и вставки оптимальным выбором является HashTable.")
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print("Готово.")
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