def experimental_evaluation_data_bin_tree(repetitions): chart_data = [] for i in range(repetitions): current_node_count = 100 + i * 10000 current_tree = BinaryTree() for _ in range(current_node_count): num = random.randint(0, 160) current_tree.insert_node(current_tree.root, num) start_time = time.time() for _ in range(100 + i * 10000): current_tree.find(random.randint(0, 160)) duration = time.time() - start_time chart_data.append(dict(size=current_node_count, time=duration)) return chart_data
from Tree import BinaryTree from BPlusTree import BPlusTree import plotly.express as px import pandas import time import random tree = BinaryTree() for _ in range(16): num = random.randint(0, 150) tree.insert_node(tree.root, num) # tree.insert_node(tree.root, 5) # tree.insert_node(tree.root, 2) # tree.insert_node(tree.root, 7) # tree.insert_node(tree.root, 10) # tree.print() # tree.find(11) def experimental_evaluation_data_bin_tree(repetitions): chart_data = [] for i in range(repetitions): current_node_count = 100 + i * 10000 current_tree = BinaryTree() for _ in range(current_node_count): num = random.randint(0, 160) current_tree.insert_node(current_tree.root, num) start_time = time.time()