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 versions of these algorithms.
] add http://github.com/mossr/BeautifulAlgorithms.jl
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. (Credit @HenriDeh for use of ones)

Linear regression (one-liner)

Ridge regression

Basis 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.

Logistic regression

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 Thrun, 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.
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