BeautifulAlgorithms.jlConcise and beautiful algorithms written in Julia
BeautifulAlgorithms.jl
Concise algorithms written in Julia and formatted with Carbon.
Algorithms for machine learning, optimization, reinforcement learning, online planning, decision making under uncertainty, and sorting. All implementations are working and self-contained; refer to the test cases.
Note, these are primarily for academic purposes and are not designed for real-world usage. There are many other Julia packages that implement more sound versons of these algorithms.
] add http://github.com/mossr/BeautifulAlgorithms.jl- Gradient descent
- Stochastic gradient descent
- Two-layer neural network
- Multi-layer neural network
- Loss functions
- Distance functions
- Nearest neighbor
- K-nearest neighbors
- K-means clustering
- The EM algorithm
- Linear regression
- Radial basis regression
- Cross-entropy method
- Finite difference methods
- Simulated annealing
- Twiddle
- Newton's method
- Gaussian process
- Thompson sampling
- Particle filter
- Value iteration
- Branch and bound
- Monte Carlo tree search
- Huffman coding
- Hailstone sequence (Collatz conjecture)
- Bubble sort
- Merge sort
- Insertion sort
- Bogo sort
- Quine
Note: Algorithms are modified from their original sources.
Gradient descent
Percy Liang and Dorsa Sadigh, Artificial Intelligence: Principles and Techniques, Stanford University, 2019.
Stochastic gradient descent
Percy Liang and Dorsa Sadigh, Artificial Intelligence: Principles and Techniques, Stanford University, 2019.
Two-layer neural network
Two-layer neural network (one-liner)
Multi-layer neural network
Loss functions
Distance functions
Nearest neighbor
K-nearest neighbors
K-means clustering
Percy Liang and Dorsa Sadigh, Artificial Intelligence: Principles and Techniques, Stanford University, 2019.
The EM algorithm
Andrew Ng, Mixtures of Gaussians and the EM algorithm, Stanford University, 2020.1
Linear regression
Mykel J. Kochenderfer and Tim A. Wheeler, Algorithms for Optimization, MIT Press, 2019.
Radial basis regression
Mykel J. Kochenderfer and Tim A. Wheeler, Algorithms for Optimization, MIT Press, 2019.
Cross-entropy method
Mykel J. Kochenderfer and Tim A. Wheeler, Algorithms for Optimization, MIT Press, 2019.
Finite difference methods
Mykel J. Kochenderfer and Tim A. Wheeler, Algorithms for Optimization, MIT Press, 2019.
Simulated annealing
Mykel J. Kochenderfer and Tim A. Wheeler, Algorithms for Optimization, MIT Press, 2019.
Twiddle
Sebatian Thurn, Artificial Intelligence for Robotics, Udacity, 2012.
Newton's method
John Wallis, A Treatise of Algebra both Historical and Practical, 1685.
Gaussian process
Mykel J. Kochenderfer and Tim A. Wheeler, Algorithms for Optimization, MIT Press, 2019.
Gaussian process kernels
Thompson sampling
Daniel J. Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, and Zheng Wen, A Tutorial on Thompson Sampling, arXiv:1707.02038, 2020.
Particle filter
Mykel J. Kochenderfer, Tim A. Wheeler, and Kyle H. Wray, Algorithms for Decision Making, Preprint.
Value iteration
Mykel J. Kochenderfer, Tim A. Wheeler, and Kyle H. Wray, Algorithms for Decision Making, Preprint.
Branch and bound
Mykel J. Kochenderfer, Tim A. Wheeler, and Kyle H. Wray, Algorithms for Decision Making, Preprint.
Monte Carlo tree search
Mykel J. Kochenderfer, Tim A. Wheeler, and Kyle H. Wray, Algorithms for Decision Making, Preprint.
Huffman coding
David A. Huffman, A Method for the Construction of Minimum-Redundancy Codes, IEEE, 1952.
Hailstone sequence (Collatz conjecture)
Bubble sort
Karey Shi, Design and Analysis of Algorithms, Stanford University, 2020.
Merge sort
Karey Shi, Design and Analysis of Algorithms, Stanford University, 2020.
Insertion sort
Karey Shi, Design and Analysis of Algorithms, Stanford University, 2020.
Bogo sort
Bogo sort (one-liner)
Quine
Nathan Daly, Julia Discord, 2019.2
Written by Robert Moss.