MLKit 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.
This project is under active development and is not ready for use in commercial or personal projects.
Add pod 'MachineLearningKit to your PodFile and then run pod install.
- Download the repository.
cd [Project_Path_Goes_Here]/Examplepod install
The 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.
To 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).
To contribute to an already existant algorithm within the framework, please create an issue and state any changes or additions you have made.
- Simple Linear Regression (1 Feature)
- Polynomial Regression
- Lasso Regression
- Ridge Regression
- Flappy Bird Example Project Explanation
- Genetic Algorithms
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.
- KMeans Implementation
- KMeans Clustering Documentation
- Neural Network Documentation
- Logistic Regression
- Decision Trees
- Convolutional Neural Network
- Recurrent Neural Network
- Artificial Neural Network using Metal
- Game Playing AI (MiniMax, Alpha-Beta Pruning)
- Self Organizing Maps
- Matrix and Vector Operations (uses Upsurge framework)
- Simple Linear Regression (Allows for 1 feature set)
- Polynomial Regression (Allows for multiple features)
- Ridge Regression
- Multi-Layer Feed Forward Neural Network
- K-Means Clustering
- Genetic Algorithms
- Allows for splitting your data into training, validation, and test sets.
- K-Fold Cross Validation & Ability to test various L2 penalties for Ridge Regression
- Single Layer Perceptron, Multi-Layer Perceptron, & Adaline ANN Architectures
TBD
MIT License
Copyright (c) 2017 Guled Ahmed
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.