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https://github.com/mossr/BeautifulAlgorithms.jl

Concise and beautiful algorithms written in Julia
https://github.com/mossr/BeautifulAlgorithms.jl

algorithms decision-making-under-uncertainty julia machine-learning neural-network optimization quine regression reinforcement-learning sorting

Last synced: about 2 months ago
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Concise and beautiful algorithms written in Julia

Host: GitHub
URL: https://github.com/mossr/BeautifulAlgorithms.jl
Owner: mossr
Created: 2020-09-15T19:58:22.000Z (about 4 years ago)
Default Branch: master
Last Pushed: 2023-12-17T17:11:46.000Z (9 months ago)
Last Synced: 2024-06-01T16:14:35.895Z (4 months ago)
Topics: algorithms, decision-making-under-uncertainty, julia, machine-learning, neural-network, optimization, quine, regression, reinforcement-learning, sorting
Language: Julia
Homepage:
Size: 2.37 MB
Stars: 1,308
Watchers: 21
Forks: 89
Open Issues: 3
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

README

        # BeautifulAlgorithms.jl

[![Build Status](https://github.com/mossr/BeautifulAlgorithms.jl/workflows/CI/badge.svg)](https://github.com/mossr/BeautifulAlgorithms.jl/actions)

[![codecov](https://codecov.io/github/mossr/BeautifulAlgorithms.jl/coverage.svg?branch=master)](https://codecov.io/gh/mossr/BeautifulAlgorithms.jl)

Concise algorithms written in Julia and formatted with [Carbon](https://carbon.now.sh/).

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](./test/).

> _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._

```julia

] add http://github.com/mossr/BeautifulAlgorithms.jl

```

- [Gradient descent](#gradient-descent)

- [Stochastic gradient descent](#stochastic-gradient-descent)

- [Two-layer neural network](#two-layer-neural-network)

    - [Two-layer neural network (one-liner)](#two-layer-neural-network-one-liner)

- [Multi-layer neural network](#multi-layer-neural-network)

- [Loss functions](#loss-functions)

- [Distance functions](#distance-functions)

- [Nearest neighbor](#nearest-neighbor)

- [K-nearest neighbors](#k-nearest-neighbors)

- [K-means clustering](#k-means-clustering)

- [The EM algorithm](#the-em-algorithm)

- [Linear regression](#linear-regression)

    - [Linear regression (one-liner)](#linear-regression-one-liner)

- [Ridge regression](#ridge-regression)

- [Basis regression](#basis-regression)

- [Radial basis regression](#radial-basis-regression)

- [Logistic regression](#logistic-regression)

- [Cross-entropy method](#cross-entropy-method)

- [Finite difference methods](#finite-difference-methods)

- [Simulated annealing](#simulated-annealing)

- [Twiddle](#twiddle)

- [Newton's method](#newtons-method)

- [Gaussian process](#gaussian-process)

    - [Gaussian process kernels](#gaussian-process-kernels)

- [Thompson sampling](#thompson-sampling)

- [Particle filter](#particle-filter)

- [Value iteration](#value-iteration)

- [Branch and bound](#branch-and-bound)

- [Monte Carlo tree search](#monte-carlo-tree-search)

- [Huffman coding](#huffman-coding)

- [Hailstone sequence (Collatz conjecture)](#hailstone-sequence-collatz-conjecture)

- [Bubble sort](#bubble-sort)

- [Merge sort](#merge-sort)

- [Insertion sort](#insertion-sort)

- [Bogo sort](#bogo-sort)

    - [Bogo sort (one-liner)](#bogo-sort-one-liner)

- [Quine](#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. (Credit [@HenriDeh](https://github.com/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](https://github.com/mossr).