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()
