{"id":13702741,"url":"https://github.com/Somnibyte/MLKit","last_synced_at":"2025-05-05T06:32:28.661Z","repository":{"id":62447440,"uuid":"62202086","full_name":"Somnibyte/MLKit","owner":"Somnibyte","description":"A simple machine learning framework written in Swift 🤖","archived":false,"fork":false,"pushed_at":"2018-08-28T21:23:35.000Z","size":3440,"stargazers_count":152,"open_issues_count":4,"forks_count":14,"subscribers_count":12,"default_branch":"master","last_synced_at":"2025-04-21T09:53:35.380Z","etag":null,"topics":["artificial-intelligence","backpropagation","feedforward-neural-network","genetic-algorithm","kmeans","kmeans-clustering","lasso-regression","linear-regression","machine-learning","machine-learning-algorithms","machine-learning-library","mlkit","neural-network","polynomial-regression","regression","ridge-regression","swift"],"latest_commit_sha":null,"homepage":"","language":"Swift","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Somnibyte.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2016-06-29T06:38:45.000Z","updated_at":"2024-02-17T02:39:37.000Z","dependencies_parsed_at":"2022-11-01T23:17:28.019Z","dependency_job_id":null,"html_url":"https://github.com/Somnibyte/MLKit","commit_stats":null,"previous_names":[],"tags_count":8,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Somnibyte%2FMLKit","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Somnibyte%2FMLKit/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Somnibyte%2FMLKit/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Somnibyte%2FMLKit/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Somnibyte","download_url":"https://codeload.github.com/Somnibyte/MLKit/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252451948,"owners_count":21750008,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["artificial-intelligence","backpropagation","feedforward-neural-network","genetic-algorithm","kmeans","kmeans-clustering","lasso-regression","linear-regression","machine-learning","machine-learning-algorithms","machine-learning-library","mlkit","neural-network","polynomial-regression","regression","ridge-regression","swift"],"created_at":"2024-08-02T21:00:41.787Z","updated_at":"2025-05-05T06:32:25.682Z","avatar_url":"https://github.com/Somnibyte.png","language":"Swift","funding_links":[],"categories":["Libs","Swift"],"sub_categories":["AI","General-Purpose Machine Learning"],"readme":"![](https://github.com/Somnibyte/MLKit/blob/master/MLKitSmallerLogo.png)\n\n# MLKit (a.k.a Machine Learning Kit) 🤖\nMLKit is a simple machine learning framework written in Swift. Currently MLKit features machine learning algorithms that deal with the topic of regression, but the framework will expand over time with topics such as classification, clustering, recommender systems, and deep learning. The vision and goal of this framework is to provide developers with a toolkit to create products that can learn from data. MLKit is a side project of mine in order to make it easier for developers to implement machine learning algorithms on the go, and to familiarlize myself with machine learning concepts.\n\n*This project is under active development and is not ready for use in commercial or personal projects.*\n\n[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/josephmisiti/awesome-machine-learning)\n[![Version](https://img.shields.io/cocoapods/v/MLKit.svg?style=flat)](https://cocoapods.org/pods/MachineLearningKit)\n[![License](https://img.shields.io/cocoapods/l/MLKit.svg?style=flat)](https://cocoapods.org/pods/MachineLearningKit)\n[![Platform](https://img.shields.io/cocoapods/p/MLKit.svg?style=flat)](https://cocoapods.org/pods/MachineLearningKit)\n\n[MachineLearningKit Reference](http://cocoadocs.org/docsets/MachineLearningKit/0.1.8/)\n\n## Requirements\n\n## Installation\n\n#### Cocoapods\n\nAdd `pod 'MachineLearningKit` to your PodFile and then run `pod install`. \n\n#### Without Cocoapods\n1. Download the repository.\n2. `cd [Project_Path_Goes_Here]/Example` \n3. `pod install`\n\n----------------------------------------------\n\n## Contributing \nThe mission of this project is to give developers the ability to incorporate Machine Learning algorithms into their projects with ease and to enable the creation of advanced projects using the Swift programing language. With this being said, I encourage all developers interested in making Machine Learning accessible to the anyone who works with iOS apps and TVOS apps to contribute to this project. \n\nTo contribute an algorithm **not** currently available within the framework, please create an issue and state what algorithm you have implemented. Make sure that there are unit tests involved where applicable. Also, provide a brief overview of how to use your algorithm. You are also welcome to impelment algorithms within the **Roadmap** section (below). \n\nTo contribute to an already existant algorithm within the framework, please create an issue and state any changes or additions you have made.\n\n----------------------------------------------\n\n## Wiki \n\n- [x] [Simple Linear Regression (1 Feature)](https://github.com/Somnibyte/MLKit/wiki/Simple-Linear-Regression-Tutorial)\n- [x] [Polynomial Regression](https://github.com/Somnibyte/MLKit/wiki/Polynomial-Regression-Tutorial)\n- [x] [Lasso Regression](https://github.com/Somnibyte/MLKit/wiki/Lasso-Regression-Tutorial)\n- [x] [Ridge Regression](https://github.com/Somnibyte/MLKit/wiki/Ridge-Regression-Tutorial)\n- [x] [Flappy Bird Example Project Explanation](https://github.com/Somnibyte/MLKit/wiki/Genetic-Algorithm-(Flappy-Bird-Game-Explanation)) \n- [x] [Genetic Algorithms](https://github.com/Somnibyte/MLKit/wiki/Genetic-Algorithm)\n\n## Example Project \n![](https://github.com/Somnibyte/MLKit/blob/master/flappybirdai.gif)\n\n⚠️️  The Flappy Bird Example Project is located in the `Example` folder. When you run the example you will see the fitness and the decisions that each Flappy Bird is making. The example project has comments to help with understanding how it was made. \n\n\n----------------------------------------------\n\n### Roadmap:\n\n- [ ] KMeans++ Implementation \n- [ ] KMeans Clustering Documentation\n- [ ] Neural Network Documentation \n- [ ] Logistic Regression\n- [ ] Decision Trees \n\n### Future Releases:\n- [ ] Convolutional Neural Network \n- [ ] Recurrent Neural Network \n- [ ] Artificial Neural Network using Metal\n- [ ] Game Playing AI (MiniMax, Alpha-Beta Pruning)\n- [ ] Self Organizing Maps\n\n----------------------------------------------\n## Features (So Far)\n\n- [x] Matrix and Vector Operations (uses [Upsurge framework](https://github.com/aleph7/Upsurge))\n- [x] Simple Linear Regression (Allows for 1 feature set)\n- [x] Polynomial Regression (Allows for multiple features)\n- [x] Ridge Regression\n- [x] Multi-Layer Feed Forward Neural Network \n- [x] K-Means Clustering \n- [x] Genetic Algorithms \n- [x] Allows for splitting your data into training, validation, and test sets.\n- [x] K-Fold Cross Validation \u0026 Ability to test various L2 penalties for Ridge Regression\n- [x] Single Layer Perceptron, Multi-Layer Perceptron, \u0026 Adaline ANN Architectures \n\n----------------------------------------------\n\n## Frameworks that MLKit uses\n\n- 🙌 [Upsurge](https://github.com/aleph7/Upsurge) (Matrix and Vector Operations)\n- 🙌 [CSVReader](https://github.com/peterentwistle/SwiftCSVReader) (CSV Reading) (Used in Unit Testing)\n\n----------------------------------------------\n\n## Development Schedule\n\n### Week of August 28th\nTBD \n\n# License\nMIT License\n\nCopyright (c) 2017 Guled Ahmed\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FSomnibyte%2FMLKit","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FSomnibyte%2FMLKit","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FSomnibyte%2FMLKit/lists"}