TheAlgorithms Python

The conversions module provides algorithms for transforming data between different formats, numerical bases, or units. Common use cases include:

• Numerical Base Conversion: Converting numbers between bases (e.g., binary to decimal).
• Data Type Conversion: Converting between data types (e.g., integer to string).
• Unit Conversion: Converting between physical units (e.g., feet to meters).

The dynamic_programming directory contains implementations of various optimization and combinatorial problems. Key algorithms available include:

• String/Sequence Matching: edit_distance.py, longest_common_subsequence.py, longest_common_substring.py, regex_match.py, wildcard_matching.py, word_break.py.
• Optimization/Resource Allocation: knapsack.py, rod_cutting.py, minimum_coin_change.py, matrix_chain_multiplication.py.
• Pathfinding/Grid: min_distance_up_bottom.py, minimum_cost_path.py, trapped_water.py.
• Mathematical/Sequence: fibonacci.py, catalan_numbers.py, factorial.py, tribonacci.py.
• Subsequence/Subset: longest_increasing_subsequence.py, subset_generation.py, sum_of_subset.py.

The matrix/ directory provides implementations for various matrix-based operations and problems, such as:

• Core Operations: Matrix Multiplication (Recursive), Matrix Inversion, Matrix Class, and Matrix Operations.
• Search & Traversal: Binary Search in Matrix, Searching in Sorted Matrix, Spiral Print, and Counting Islands.
• Mathematical Rules: Cramer's Rule (2x2) and Sherman-Morrison formula.
• Problem Solving: Pascal's Triangle, Rotating a Matrix, Validating Sudoku Boards, and finding the Largest Square Area in a Matrix.

The graphs directory is a comprehensive collection of graph theory and traversal algorithms:

• Traversals: breadth_first_search.py, depth_first_search.py, bidirectional_search.py.
• Shortest Path: dijkstra.py, bellman_ford.py, a_star.py, johnson.py.
• Spanning Trees: kruskal.py, prims.py, boruvka.py.
• Connectivity & Cycles: check_cycle.py, connected_components.py, scc_kosaraju.py, tarjans_scc.py.
• Topological Sort: kahns_algorithm_topo.py, g_topological_sort.py.
• Flow & Matching: dinic.py, edmonds_karp_multiple_source_and_sink.py, gale_shapley_bigraph.py.
• Data Structures: graph_adjacency_list.py, graph_adjacency_matrix.py.

The repository contains a comprehensive collection of data structure implementations. You can find implementations for:

• Arrays: Including Equilibrium Index In Array, Kth Largest Element, Prefix Sum, and Sparse Table.
• Binary Trees: Including Avl Tree, Binary Search Tree, Red Black Tree, Segment Tree, and Treap.
• Hashing: Including Bloom Filter, Hash Map, and Hash Table.
• Heaps: Including Binomial Heap, Max Heap, Min Heap, and Skew Heap.
• Linked Lists: Including Circular Linked List, Doubly Linked List, and Singly Linked List.
• Queues & Stacks: Including Circular Queue, Priority Queue, Stack, and Deque.
• Specialized Structures: Such as Kd Tree, Suffix Tree, Trie, and Radix Tree.
• Disjoint Sets: Including Disjoint Set implementations.

The Algorithms - Python is an educational repository containing various algorithm implementations in Python.

Important Note: These implementations are intended for learning purposes only. They may be less efficient than the optimized implementations found in the Python standard library. Use them at your discretion for educational study rather than production environments.

To begin using or contributing to the project:

1. Review the Contribution Guidelines to understand the project's standards and processes.
2. Use the Directory to navigate the various algorithm implementations available in the repository.

The financial directory contains algorithms for common financial calculations:

• Interest & Value: interest.py, present_value.py.
• Moving Averages: exponential_moving_average.py, simple_moving_average.py.
• Payments & Depreciation: equated_monthly_installments.py, straight_line_depreciation.py, time_and_half_pay.py.
• Taxation: price_plus_tax.py.

Boolean algebra is a mathematical system used to perform arithmetic with bit values: True (1) or False (0). The core operations available in this implementation are:

• and: Logical conjunction
• or: Logical disjunction
• not: Logical negation

The digital_image_processing module provides various algorithms for manipulating and analyzing images. Key capabilities include:

• Basic Adjustments: Change Brightness, Change Contrast, Convert To Negative, and Sepia.
• Filters: Bilateral Filter, Gaussian Filter, Median Filter, Sobel Filter, and Laplacian Filter.
• Edge Detection: Canny edge detection.
• Morphological Operations: Dilation Operation and Erosion Operation.
• Histogram Operations: Histogram Stretch for equalization.
• Geometric Transformations: Resize and Rotation.
• Dithering: Burkes dithering algorithm.