TheAlgorithms/C-Sharp

Features

• Algorithmic Reference Implementations - Provides a comprehensive collection of modular C# implementations of standard sorting, searching, and graph algorithms for educational study.
• Algorithm Reference Libraries - Provides a comprehensive collection of standard algorithms and data structures implemented in C# for educational reference.
• Machine Learning Frameworks - Implements fundamental machine learning frameworks including regression, classification, and recommendation systems.
• Machine Learning Implementations - Provides code-based reference implementations of core machine learning algorithms including regression and classification.
• Computational Mathematics Tools - Provides tools for linear algebra, numerical analysis, and number theory to solve quantitative problems.
• Data Compression Algorithms - Implements various encoding and compression algorithms to reduce the storage footprint of data.
• Graph Theory - Implements standard graph theory algorithms for modeling and solving problems with nodes and edges.
• Cryptographic Ciphers - Provides educational implementations of symmetric keys, block ciphers, and classical encryption algorithms.
• Machine Learning Fundamentals - Provides foundational implementations of regression and classification to demonstrate core machine learning concepts.
• Scientific & Mathematical Computing - Provides a broad suite of scientific and mathematical computing tools covering linear algebra and numerical analysis.
• C# Mathematical Libraries - Implements linear algebra, number theory, and numerical analysis specifically using the C# language.
• Linear Algebra Routines - Implements fundamental linear algebra operations including Gauss-Jordan elimination and pseudoinverse computations.
• Linear System Solvers - Provides direct linear solvers and optimization techniques including Gaussian elimination and Pareto methods.
• C# Cryptography Implementations - Provides reference code for classical and symmetric encryption algorithms written in C#.
• Cryptographic Operations - Provides standard cryptographic primitives for encryption, decryption, and authenticity verification.
• Activation Functions - Provides implementations of non-linear activation functions like Sigmoid, Tanh, ReLU, and SoftMax for neural networks.
• K-Nearest Neighbor Classifiers - Implements K-Nearest Neighbors for classifying data points based on the similarity of training examples.
• Logistic Regression Models - Implements logistic regression models for binary outcome prediction using sigmoid functions.
• Linear Regression Implementations - Provides educational from-scratch implementations of linear regression models for predictive modeling.
• Collection Search Utilities - Provides linear, binary, and heuristic search algorithms for locating elements within data structures.
• Graph Coloring Algorithms - Implements graph coloring algorithms to assign labels to vertices such that no two adjacent vertices share the same color.
• Graph Traversal - Implements breadth-first and depth-first search strategies to explore reachable vertices in a network.
• Topological Sorting - Produces a linear ordering of vertices based on directed dependencies to determine execution sequence.
• Differential Equation Solvers - Implements numerical solvers for ordinary differential equations using Euler and Runge Kutta methods.
• Shannon-Fano Coding - Implements Shannon-Fano coding to reduce data size using variable-length prefixes based on character probabilities.
• Greatest Common Divisor Algorithms - Implements the Euclidean algorithm to find the greatest common divisor and linear combination coefficients.
• Huffman Coding Implementations - Provides Huffman coding implementations for lossless data compression using frequency-based binary codes.
• Job Sequence Optimizations - Implements strategies for determining the most efficient order and timing of tasks to minimize cost or completion time.
• Mathematical Sequences - Computes terms for various mathematical sequences, including prime, binomial, and recursive series.
• Minimum Spanning Tree Algorithms - Implements greedy algorithms to identify the minimum spanning tree in weighted graphs.
• Shortest Path Algorithms - Calculates the most efficient path that visits a set of locations and returns to the origin.
• Pathfinding Algorithms - Calculates valid sequences of moves for pieces on a board to reach targets or visit all squares.
• Integer Property Verifiers - Provides tools to check if integers are prime, perfect, narcissistic, or amicable.
• Chinese Remainder Theorem Solvers - Solves systems of multiple remainder conditions using the Chinese Remainder Theorem.
• Modular Inverse Calculators - Determines the modular multiplicative inverse for a given integer modulo m.
• Prime Factorization Algorithms - Implements randomized algorithms for decomposing composite numbers into their prime factors.
• Prime Generation Algorithms - Implements prime number determination using mathematical tests and sieving methods.
• Numerical Computing - Implements fundamental numerical computing operations including factorials and binomial coefficients.
• Constraint Solvers - Implements algorithms for finding configurations that satisfy a predefined set of constraints, such as board-game piece placement.
• Transform Coding - The project rearranges characters into runs of similar values to improve the efficiency of subsequent compression steps.
• Symmetric Cipher Implementations - Implements symmetric block cipher algorithms, including Blowfish and Feistel networks, to protect data confidentiality.
• Classical Ciphers - Implements historical encryption algorithms such as Caesar, Vigenere, Hill, and Autokey ciphers.
• Knapsack Problem Solving - Provides solvers for the knapsack problem to determine the most valuable item combination within a weight limit.
• Optimization Problem Solvers - Provides algorithmic solutions for complex optimization problems using dynamic programming and greedy strategies.
• Strongly Connected Component Algorithms - Implements algorithms to detect strongly connected components, articulation points, and bridges in networks.

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