dsaedge

dsaedge: Data Structures and Algorithms in Python

A comprehensive collection of various data structures and algorithms implemented in Python.

Installation

You can install this package using pip:

pip install dsaedge

Usage

Here are some examples of how to use the implemented data structures and algorithms:

Advanced Data Structures

# Disjoint Set Union (DSU)
from dsaedge.advanced_data_structures.disjoint_set_union import DSU

dsu = DSU()
dsu.make_set(1)
dsu.make_set(2)
dsu.make_set(3)
dsu.union(1, 2)
print(f"DSU - Find(1): {dsu.find(1)}")
print(f"DSU - Find(2): {dsu.find(2)}")
print(f"DSU - Are 1 and 3 in the same set? {dsu.find(1) == dsu.find(3)}")

# Fenwick Tree (Binary Indexed Tree)
from dsaedge.advanced_data_structures.fenwick_tree import FenwickTree

ft = FenwickTree(10)
ft.update(0, 5)  # Add 5 to index 0
ft.update(4, 3)  # Add 3 to index 4
print(f"Fenwick Tree - Sum up to index 0: {ft.query(0)}")
print(f"Fenwick Tree - Sum up to index 4: {ft.query(4)}")
print(f"Fenwick Tree - Sum in range [0, 4]: {ft.range_query(0, 4)}")

# Segment Tree
from dsaedge.advanced_data_structures.segment_tree import SegmentTree

arr_seg = [1, 3, 5, 7, 9, 11]
st = SegmentTree(arr_seg)
print(f"Segment Tree - Sum of range [1, 4]: {st.query(1, 4)}")
st.update(2, 10)  # Update index 2 to 10
print(f"Segment Tree - Sum of range [1, 4] after update: {st.query(1, 4)}")

# Trie (Prefix Tree)
from dsaedge.advanced_data_structures.trie import Trie

trie = Trie()
trie.insert("apple")
trie.insert("apricot")
print(f"Trie - Search 'apple': {trie.search('apple')}")
print(f"Trie - Search 'app': {trie.search('app')}")
print(f"Trie - Starts with 'app': {trie.starts_with('app')}")
print(f"Trie - Starts with 'ban': {trie.starts_with('ban')}")

Algorithmic Paradigms

# Knuth-Morris-Pratt (KMP) string searching
from dsaedge.algorithmic_paradigms.kmp_search import kmp_search

text = "ABABDABACDABABCABAB"
pattern = "ABABCABAB"
occurrences = kmp_search(text, pattern)
print(f"KMP Search - Pattern found at indices: {occurrences}")

# 0/1 Knapsack Problem
from dsaedge.algorithmic_paradigms.knapsack_problem import knapsack_01

weights = [1, 2, 3, 8, 7]
values = [20, 5, 10, 40, 15]
capacity = 10
max_value = knapsack_01(weights, values, capacity)
print(f"Knapsack Problem - Maximum value: {max_value}")

# Longest Common Subsequence (LCS)
from dsaedge.algorithmic_paradigms.longest_common_subsequence import longest_common_subsequence, reconstruct_lcs

s1 = "AGGTAB"
s2 = "GXTXAYB"
lcs_length = longest_common_subsequence(s1, s2)
lcs_string = reconstruct_lcs(s1, s2)
print(f"LCS - Length: {lcs_length}, Subsequence: {lcs_string}")

# N-Queens Problem
from dsaedge.algorithmic_paradigms.n_queens import solve_n_queens

n_queens_solutions = solve_n_queens(4)
print(f"N-Queens Problem - Solutions for N=4: {len(n_queens_solutions)}")
for sol in n_queens_solutions:
    for row in sol:
        print(row)
    print()

# Sudoku Solver
from dsaedge.algorithmic_paradigms.sudoku_solver import solve_sudoku, print_board

sudoku_board = [
    [5,3,0,0,7,0,0,0,0],
    [6,0,0,1,9,5,0,0,0],
    [0,9,8,0,0,0,0,6,0],
    [8,0,0,0,6,0,0,0,3],
    [4,0,0,8,0,3,0,0,1],
    [7,0,0,0,2,0,0,0,6],
    [0,6,0,0,0,0,2,8,0],
    [0,0,0,4,1,9,0,0,5],
    [0,0,0,0,8,0,0,7,9]
]
print("Sudoku Solver - Original Board:")
print_board(sudoku_board)
if solve_sudoku(sudoku_board):
    print("
Sudoku Solver - Solved Board:")
    print_board(sudoku_board)
else:
    print("
Sudoku Solver - No solution exists.")

Graphs

# Graph Representation (Adjacency List) and Algorithms (BFS, DFS, Dijkstra, Prim)
from dsaedge.graphs.graph_representation import Graph

g = Graph()
g.add_edge('A', 'B', 1)
g.add_edge('A', 'C', 4)
g.add_edge('B', 'C', 2)
g.add_edge('B', 'D', 5)
g.add_edge('C', 'D', 1)

print(f"Graph - BFS from A: {g.bfs('A')}")
print(f"Graph - DFS from A: {g.dfs('A')}")

distances_dijkstra, _ = g.dijkstra('A')
print(f"Graph - Dijkstra distances from A: {distances_dijkstra}")

mst_cost, mst_edges = g.prims_algorithm('A')
print(f"Graph - Prim's MST cost: {mst_cost}, Edges: {mst_edges}")

# Kruskal's Algorithm
from dsaedge.graphs.kruskal_algorithm import kruskal_algorithm

vertices_kruskal = ['A', 'B', 'C', 'D', 'E']
edges_kruskal = [
    (1, 'A', 'B'), (4, 'A', 'C'), (2, 'B', 'C'),
    (5, 'B', 'D'), (1, 'C', 'D'), (3, 'D', 'E')
]
mst_cost_kruskal, mst_edges_kruskal = kruskal_algorithm(vertices_kruskal, edges_kruskal)
print(f"Kruskal's Algorithm - MST cost: {mst_cost_kruskal}, Edges: {mst_edges_kruskal}")

# Topological Sort
from dsaedge.graphs.topological_sort import Graph as TopologicalGraph, topological_sort

g_ts = TopologicalGraph(6)
g_ts.add_edge(5, 2)
g_ts.add_edge(5, 0)
g_ts.add_edge(4, 0)
g_ts.add_edge(4, 1)
g_ts.add_edge(2, 3)
g_ts.add_edge(3, 1)

top_order = topological_sort(g_ts)
print(f"Topological Sort - Order: {top_order}")

Hash Tables

# Hash Table
from dsaedge.hash_tables.hash_table import HashTable

ht = HashTable()
ht.set("name", "Alice")
ht.set("age", 30)
ht["city"] = "New York" # Using dictionary-style assignment

print(f"Hash Table - Name: {ht.get('name')}")
print(f"Hash Table - City: {ht['city']}")

try:
    print(f"Hash Table - Occupation: {ht.get('occupation')}")
except KeyError as e:
    print(f"Hash Table - Error: {e}")

del ht["age"] # Using dictionary-style deletion
print(f"Hash Table - After deleting age: {ht}")

Heaps

# Min-Heap
from dsaedge.heaps.min_heap import MinHeap

min_heap = MinHeap()
min_heap.insert(3)
min_heap.insert(1)
min_heap.insert(4)
min_heap.insert(1)
min_heap.insert(5)

print(f"Min-Heap - Min element: {min_heap.get_min()}")
print(f"Min-Heap - Extracted min: {min_heap.extract_min()}")
print(f"Min-Heap - New min element: {min_heap.get_min()}")
print(f"Min-Heap - Is empty: {min_heap.is_empty()}")
print(f"Min-Heap - Size: {min_heap.size()}")

Linked Lists

# Singly Linked List
from dsaedge.linked_lists.singly_linked_list import SinglyLinkedList

sll = SinglyLinkedList()
sll.append(10)
sll.prepend(5)
sll.insert_at_position(7, 1)
print(f"Singly Linked List: {sll}")
sll.delete(7)
print(f"Singly Linked List after deleting 7: {sll}")
print(f"Singly Linked List - Length: {len(sll)}")

# Doubly Linked List
from dsaedge.linked_lists.doubly_linked_list import DoublyLinkedList

dll = DoublyLinkedList()
dll.append(1)
dll.prepend(0)
dll.insert_at_position(2, 2)
print(f"Doubly Linked List: {dll}")
dll.delete(1)
print(f"Doubly Linked List after deleting 1: {dll}")

# Circular Singly Linked List
from dsaedge.linked_lists.circular_singly_linked_list import CircularSinglyLinkedList

csll = CircularSinglyLinkedList()
csll.append(1)
csll.append(2)
csll.prepend(0)
print(f"Circular Singly Linked List: {csll}")
csll.delete(1)
print(f"Circular Singly Linked List after deleting 1: {csll}")

# Circular Doubly Linked List
from dsaedge.linked_lists.circular_doubly_linked_list import CircularDoublyLinkedList

cdll = CircularDoublyLinkedList()
cdll.append(10)
cdll.append(20)
cdll.prepend(5)
print(f"Circular Doubly Linked List: {cdll.display()}")

Queue

# Queue (implemented with Linked List)
from dsaedge.queue import Queue

q = Queue()
q.enqueue(10)
q.enqueue(20)
print(f"Queue: {q.display()}")
print(f"Peek: {q.peek()}")
q.dequeue()
print(f"Queue after dequeue: {q.display()}")

Stack

# Stack (implemented with Linked List)
from dsaedge.stack import Stack

s = Stack()
s.push(100)
s.push(200)
print(f"Stack: {s.display()}")
print(f"Peek: {s.peek()}")
s.pop()
print(f"Stack after pop: {s.display()}")

Searching

# Searching Algorithms
from dsaedge.searching.linear_search import Linear_Search
from dsaedge.searching.binary_search import Binary_Search, Binary_Search_Recursive

arr_search = [1, 5, 2, 8, 3, 9, 4]
target_linear = 8
print(f"Linear Search - Index of {target_linear}: {Linear_Search(arr_search, target_linear)}")

arr_sorted = [1, 2, 3, 4, 5, 8, 9]
target_binary = 4
print(f"Binary Search (Iterative) - Index of {target_binary}: {Binary_Search(arr_sorted, target_binary)}")
print(f"Binary Search (Recursive) - Index of {target_binary}: {Binary_Search_Recursive(arr_sorted, 0, len(arr_sorted) - 1, target_binary)}")

Sorting

# Sorting Algorithms
from dsaedge.sorting.bubble_sort import Bubble_Sort
from dsaedge.sorting.heap_sort import Heap_Sort
from dsaedge.sorting.insertion_sort import Insertion_Sort
from dsaedge.sorting.merge_sort import Merge_Sort
from dsaedge.sorting.quick_sort import Quick_Sort
from dsaedge.sorting.selection_sort import Selection_Sort

arr_sort = [64, 34, 25, 12, 22, 11, 90]

# These functions sort the list in-place and return it.
print(f"Original Array: {arr_sort}")
print(f"Bubble Sort: {Bubble_Sort(arr_sort[:])}")
print(f"Heap Sort: {Heap_Sort(arr_sort[:])}")
print(f"Insertion Sort: {Insertion_Sort(arr_sort[:])}")
print(f"Merge Sort: {Merge_Sort(arr_sort[:])}")
print(f"Quick Sort: {Quick_Sort(arr_sort[:])}")
print(f"Selection Sort: {Selection_Sort(arr_sort[:])}")

Trees

# Binary Search Tree (BST)
from dsaedge.trees.binary_search_tree import BinarySearchTree

bst = BinarySearchTree()
bst.insert(50)
bst.insert(30)
bst.insert(70)
bst.insert(20)
bst.insert(40)
bst.insert(60)
bst.insert(80)
print(f"BST - Search 40: {bst.search(40).data if bst.search(40) else None}")
bst.delete(30)
print(f"BST - In-order traversal after deleting 30: {bst.in_order_traversal()}")

# AVL Tree
from dsaedge.trees.avl_tree import AVLTree

avl = AVLTree()
avl.insert(10)
avl.insert(20)
avl.insert(30)
avl.insert(40)
avl.insert(50)
avl.insert(25)
print(f"AVL Tree - In-order traversal: {avl.in_order_traversal()}")
avl.delete(30)
print(f"AVL Tree - In-order traversal after deleting 30: {avl.in_order_traversal()}")

Implemented Data Structures and Algorithms

The dsaedge package is organized into several modules, each focusing on a specific category of data structures or algorithms.

Data Structures

• advanced_data_structures
  • disjoint_set_union.py: Disjoint Set Union (DSU) with path compression and union by size.
  • fenwick_tree.py: Fenwick Tree (Binary Indexed Tree) for prefix sums.
  • segment_tree.py: Segment Tree for range queries and point updates.
  • trie.py: Trie (Prefix Tree) for string searching.
• hash_tables
  • hash_table.py: Hash Table with chaining for collision resolution.
• heaps
  • min_heap.py: Min-Heap implementation.
• linked_lists
  • singly_linked_list.py: Standard Singly Linked List.
  • doubly_linked_list.py: Standard Doubly Linked List.
  • circular_singly_linked_list.py: Circular Singly Linked List.
  • circular_doubly_linked_list.py: Circular Doubly Linked List.
• queue
  • queue.py: Queue implementation using a linked list.
• stack
  • stack.py: Stack implementation using a linked list.
• trees
  • binary_tree.py: Generic Binary Tree with traversal methods.
  • binary_search_tree.py: Binary Search Tree (BST).
  • avl_tree.py: Self-balancing AVL Tree.
  • red_black_tree.py: Self-balancing Red-Black Tree.
  • splay_tree.py: Self-balancing Splay Tree.
  • tree_node.py: Generic tree node class.
  • And modules for tree-related operations, properties, traversals, utilities, exceptions, serialization, and visualization.

Algorithms

• algorithmic_paradigms
  • kmp_search.py: Knuth-Morris-Pratt (KMP) string searching.
  • knapsack_problem.py: 0/1 Knapsack problem (Dynamic Programming).
  • longest_common_subsequence.py: Longest Common Subsequence (LCS) (Dynamic Programming).
  • n_queens.py: N-Queens problem solver (Backtracking).
  • sudoku_solver.py: Sudoku solver (Backtracking).
• graphs
  • graph_representation.py: Basic graph representation with BFS, DFS, Dijkstra's, and Prim's.
  • bellman_ford.py: Bellman-Ford algorithm for shortest paths with negative weights.
  • dijkstra.py: Dijkstra's algorithm for single-source shortest paths.
  • floyd_warshall.py: Floyd-Warshall algorithm for all-pairs shortest paths.
  • johnson.py: Johnson's algorithm for all-pairs shortest paths in sparse graphs.
  • kruskal_algorithm.py: Kruskal's algorithm for Minimum Spanning Tree (MST).
  • prim.py: Prim's algorithm for Minimum Spanning Tree (MST).
  • topological_sort.py: Topological Sort for Directed Acyclic Graphs (DAG).
  • cycle_detection.py: Detects cycles in directed and undirected graphs.
  • articulation_points.py: Finds articulation points (cut vertices).
  • bridges.py: Finds bridges in a graph.
  • biconnected_components.py: Finds biconnected components.
  • kosaraju.py: Kosaraju's algorithm for Strongly Connected Components (SCCs).
  • tarjan.py: Tarjan's algorithm for Strongly Connected Components (SCCs).
  • network_flow.py: Edmonds-Karp algorithm for maximum flow.
  • graph_matching.py: Hopcroft-Karp algorithm for maximum bipartite matching.
  • graph_coloring.py: Greedy algorithm for vertex coloring.
  • graph_partitioning.py: Kernighan-Lin algorithm for graph partitioning.
  • graph_clustering.py: Girvan-Newman algorithm for community detection.
  • And modules for graph analysis, search, statistics, utilities, and more.
• searching
  • linear_search.py: Linear Search.
  • binary_search.py: Binary Search (iterative and recursive).
• sorting
  • bubble_sort.py: Bubble Sort.
  • selection_sort.py: Selection Sort.
  • insertion_sort.py: Insertion Sort.
  • merge_sort.py: Merge Sort.
  • quick_sort.py: Quick Sort.
  • heap_sort.py: Heap Sort.

Contributing

Contributions are welcome! Please feel free to open issues or submit pull requests.

License

This project is licensed under the MIT License - see the LICENSE file for details.