python-study/src/binary-tree/examples.py at main · smaruf/python-study

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"""

examples.py

Comprehensive examples and use cases for binary trees and balanced trees.

This file demonstrates practical applications and comparisons.

"""

from binary_tree import BinaryTree, TreeNode

from balanced_tree import AVLTree, RedBlackTree

def example_basic_operations():

"""Demonstrate basic binary tree operations."""

print("=" * 70)

print("Example 1: Basic Binary Tree Operations")

print("=" * 70)

tree = BinaryTree()

# Insert values

values = [50, 30, 70, 20, 40, 60, 80, 10, 25, 35, 65]

print(f"\n1. Inserting values: {values}")

for val in values:

tree.insert(val)

# Display tree

print("\nTree Structure:")

tree.visualize()

# Search operations

print("\n2. Search Operations:")

search_vals = [25, 35, 100]

for val in search_vals:

result = tree.search(val)

print(f" Search for {val}: {'Found ✓' if result else 'Not found ✗'}")

# Tree properties

print("\n3. Tree Properties:")

print(f" Height: {tree.height()}")

print(f" Size: {tree.size()} nodes")

print(f" Min value: {tree.min_value()}")

print(f" Max value: {tree.max_value()}")

print(f" Is balanced: {tree.is_balanced()}")

# Traversals

print("\n4. Tree Traversals:")

print(f" Inorder: {tree.inorder()}")

print(f" Preorder: {tree.preorder()}")

print(f" Postorder: {tree.postorder()}")

print(f" Level-order: {tree.level_order()}")

print()

def example_balanced_vs_unbalanced():

"""Compare balanced and unbalanced trees."""

print("=" * 70)

print("Example 2: Balanced vs Unbalanced Trees")

print("=" * 70)

# Create a balanced tree

balanced = BinaryTree()

balanced_vals = [50, 25, 75, 12, 37, 62, 87, 6, 18, 31, 43]

for val in balanced_vals:

balanced.insert(val)

# Create an unbalanced tree (right-skewed)

unbalanced = BinaryTree()

unbalanced_vals = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110]

for val in unbalanced_vals:

unbalanced.insert(val)

print("\n1. Balanced Tree:")

print(f" Values: {balanced_vals}")

balanced.visualize()

print(f" Height: {balanced.height()}")

print(f" Is balanced: {balanced.is_balanced()}")

print("\n2. Unbalanced Tree (Right-skewed):")

print(f" Values: {unbalanced_vals}")

unbalanced.visualize()

print(f" Height: {unbalanced.height()}")

print(f" Is balanced: {unbalanced.is_balanced()}")

# Performance implications

print("\n3. Performance Impact:")

print(" ┌─────────────────┬──────────┬────────────┐")

print(" │ Metric │ Balanced │ Unbalanced │")

print(" ├─────────────────┼──────────┼────────────┤")

print(f" │ Height │ {balanced.height():8} │ {unbalanced.height():10} │")

print(f" │ Size │ {balanced.size():8} │ {unbalanced.size():10} │")

print(f" │ Is Balanced │ {'Yes':8} │ {'No':10} │")

print(" └─────────────────┴──────────┴────────────┘")

print("\n In the worst case (unbalanced tree):")

print(f" - Search might need to check all {unbalanced.size()} nodes")

print(f" - Time complexity degrades from O(log n) to O(n)")

print()

def example_avl_tree_rotations():

"""Demonstrate AVL tree rotations."""

print("=" * 70)

print("Example 3: AVL Tree Rotations")

print("=" * 70)

# Left-Left case (Right Rotation)

print("\n1. Left-Left Case (triggers Right Rotation):")

print(" Inserting: 30, 20, 10")

avl1 = AVLTree()

for val in [30, 20, 10]:

avl1.insert(val)

avl1.visualize()

print(f" Result: Balanced tree with height {avl1.height()}")

# Right-Right case (Left Rotation)

print("\n2. Right-Right Case (triggers Left Rotation):")

print(" Inserting: 10, 20, 30")

avl2 = AVLTree()

for val in [10, 20, 30]:

avl2.insert(val)

avl2.visualize()

print(f" Result: Balanced tree with height {avl2.height()}")

# Left-Right case

print("\n3. Left-Right Case (triggers Left-Right Rotation):")

print(" Inserting: 30, 10, 20")

avl3 = AVLTree()

for val in [30, 10, 20]:

avl3.insert(val)

avl3.visualize()

print(f" Result: Balanced tree with height {avl3.height()}")

# Right-Left case

print("\n4. Right-Left Case (triggers Right-Left Rotation):")

print(" Inserting: 10, 30, 20")

avl4 = AVLTree()

for val in [10, 30, 20]:

avl4.insert(val)

avl4.visualize()

print(f" Result: Balanced tree with height {avl4.height()}")

print()

def example_avl_maintains_balance():

"""Show how AVL tree maintains balance with many insertions."""

print("=" * 70)

print("Example 4: AVL Tree Automatically Maintains Balance")

print("=" * 70)

print("\nInserting sequential values: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10")

print("(This would create a completely unbalanced tree without AVL)")

avl = AVLTree()

for i in range(1, 11):

avl.insert(i)

print("\nResulting AVL Tree:")

avl.visualize()

print(f"\nTree Properties:")

print(f" Size: {avl.size()} nodes")

print(f" Height: {avl.height()}")

print(f" Is balanced: {avl.is_balanced()}")

print(f" Theoretical minimum height: 3 (for 10 nodes)")

print(f" Actual height: {avl.height()}")

# Compare with unbalanced

regular = BinaryTree()

for i in range(1, 11):

regular.insert(i)

print(f"\nComparison:")

print(f" Without AVL balancing, height would be: {regular.height()}")

print(f" With AVL balancing, height is: {avl.height()}")

print(f" Height reduction: {regular.height() - avl.height()} levels")

print()

def example_tree_traversal_use_cases():

"""Demonstrate practical use cases for different traversals."""

print("=" * 70)

print("Example 5: Tree Traversal Use Cases")

print("=" * 70)

tree = BinaryTree()

values = [50, 30, 70, 20, 40, 60, 80]

for val in values:

tree.insert(val)

print("\nTree Structure:")

tree.visualize()

print("\n1. Inorder Traversal (Left → Root → Right):")

print(f" Result: {tree.inorder()}")

print(" Use case: Get elements in sorted order")

print(" → Perfect for getting sorted list from BST")

print("\n2. Preorder Traversal (Root → Left → Right):")

print(f" Result: {tree.preorder()}")

print(" Use case: Create a copy of the tree, prefix expressions")

print(" → Root comes first, useful for serialization")

print("\n3. Postorder Traversal (Left → Right → Root):")

print(f" Result: {tree.postorder()}")

print(" Use case: Delete tree, postfix expressions, calculate sizes")

print(" → Children processed before parent")

print("\n4. Level-order Traversal (BFS):")

print(f" Result: {tree.level_order()}")

print(" Use case: Find shortest path, level-wise processing")

levels = tree.level_order_by_levels()

for i, level in enumerate(levels):

print(f" Level {i}: {level}")

print()

def example_balance_factor_analysis():

"""Analyze balance factors in different tree configurations."""

print("=" * 70)

print("Example 6: Balance Factor Analysis")

print("=" * 70)

def print_tree_with_balance_factors(tree, name):

print(f"\n{name}:")

tree.visualize()

def analyze_node(node, path="root"):

if node:

bf = tree.balance_factor(node)

status = "✓ OK" if abs(bf) <= 1 else "✗ UNBALANCED"

h_left = tree.height(node.left) if node.left else 0

h_right = tree.height(node.right) if node.right else 0

print(f" {path:20} → Node {node.val:3}: BF = {bf:2} (L:{h_left}, R:{h_right}) {status}")

if node.left:

analyze_node(node.left, f"{path}/left")

if node.right:

analyze_node(node.right, f"{path}/right")

if tree.root:

analyze_node(tree.root)

# Perfectly balanced

balanced = BinaryTree()

for val in [50, 25, 75, 12, 37, 62, 87]:

balanced.insert(val)

print_tree_with_balance_factors(balanced, "1. Perfectly Balanced Tree")

# Left-heavy but still balanced

left_heavy = BinaryTree()

for val in [50, 25, 75, 12, 37, 6]:

left_heavy.insert(val)

print_tree_with_balance_factors(left_heavy, "2. Left-Heavy but Balanced")

# Unbalanced

unbalanced = BinaryTree()

for val in [50, 40, 30, 20, 10]:

unbalanced.insert(val)

print_tree_with_balance_factors(unbalanced, "3. Unbalanced Tree")

print()

def example_avl_vs_regular_bst():

"""Compare AVL tree with regular BST for worst-case scenario."""

print("=" * 70)

print("Example 7: AVL Tree vs Regular BST (Worst Case)")

print("=" * 70)

# Insert sorted data (worst case for regular BST)

values = list(range(1, 16)) # 1 to 15

print(f"\nInserting sorted values: {values}")

print("This is the WORST CASE for regular BST (becomes linked list)")

# Regular BST

regular = BinaryTree()

for val in values:

regular.insert(val)

# AVL Tree

avl = AVLTree()

for val in values:

avl.insert(val)

print("\n1. Regular BST (Unbalanced):")

print(f" Height: {regular.height()}")

print(f" Is balanced: {regular.is_balanced()}")

print(f" Search complexity: O({regular.height()}) ≈ O(n)")

print("\n2. AVL Tree (Self-Balancing):")

avl.visualize()

print(f" Height: {avl.height()}")

print(f" Is balanced: {avl.is_balanced()}")

print(f" Search complexity: O({avl.height()}) ≈ O(log n)")

print("\n3. Performance Comparison:")

print(" ┌────────────────────┬─────────────┬───────────┐")

print(" │ Metric │ Regular BST │ AVL Tree │")

print(" ├────────────────────┼─────────────┼───────────┤")

print(f" │ Height │ {regular.height():11} │ {avl.height():9} │")

print(f" │ Balanced │ {'No':11} │ {'Yes':9} │")

print(f" │ Search worst case │ {'O(n)':11} │ {'O(log n)':9} │")

print(" └────────────────────┴─────────────┴───────────┘")

print(f"\n Height reduction: {regular.height() - avl.height()} levels")

print(f" This means AVL reduces operations by ~{100 * (1 - avl.height() / regular.height()):.0f}%!")

print()

def example_red_black_tree():

"""Demonstrate Red-Black tree properties."""

print("=" * 70)

print("Example 8: Red-Black Tree")

print("=" * 70)

rb_tree = RedBlackTree()

values = [10, 20, 30, 15, 25, 5, 1, 35, 40]

print(f"\nInserting values: {values}")

for val in values:

rb_tree.insert(val)

print("\nRed-Black Tree Structure:")

print("🔴 = RED node, ⚫ = BLACK node")

rb_tree.visualize()

print(f"\nInorder traversal (sorted): {rb_tree.inorder()}")

print("\nRed-Black Tree Properties:")

print(" 1. Every node is either RED or BLACK ✓")

print(" 2. The root is BLACK ✓")

print(" 3. All leaves (NIL) are BLACK ✓")

print(" 4. RED nodes have BLACK children ✓")

print(" 5. All paths have same number of BLACK nodes ✓")

print("\nKey Differences from AVL:")

print(" • AVL: Strictly balanced (balance factor ≤ 1)")

print(" • RB: Loosely balanced (red-black properties)")

print(" • AVL: Better for search-heavy workloads")

print(" • RB: Better for insert-heavy workloads")

print(" • RB: Used in: Java TreeMap, C++ std::map, Linux kernel")

print()

def run_all_examples():

"""Run all examples in sequence."""

examples = [

example_basic_operations,

example_balanced_vs_unbalanced,

example_avl_tree_rotations,

example_avl_maintains_balance,

example_tree_traversal_use_cases,

example_balance_factor_analysis,

example_avl_vs_regular_bst,

example_red_black_tree,

]

for i, example in enumerate(examples, 1):

try:

example()

if i < len(examples):

input("Press Enter to continue to next example...")

print("\n" * 2)

except KeyboardInterrupt:

print("\n\nExamples interrupted by user.")

break

except Exception as e:

print(f"\nError in example: {e}")

continue

print("=" * 70)

print("All Examples Completed!")

print("=" * 70)

if __name__ == "__main__":

print("\n")

print("╔" + "═" * 68 + "╗")

print("║" + " " * 15 + "BINARY TREE COMPREHENSIVE EXAMPLES" + " " * 19 + "║")

print("╚" + "═" * 68 + "╝")

print("\nThis script demonstrates various binary tree concepts and operations.")

print("Press Ctrl+C at any time to exit.\n")

run_all_examples()

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