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https://github.com/KevinCoble/AIToolbox

A toolbox of AI modules written in Swift: Graphs/Trees, Support Vector Machines, Neural Networks, PCA, K-Means, Genetic Algorithms
https://github.com/KevinCoble/AIToolbox

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A toolbox of AI modules written in Swift: Graphs/Trees, Support Vector Machines, Neural Networks, PCA, K-Means, Genetic Algorithms

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# AIToolbox

A toolbox of AI modules written in Swift:  Graphs/Trees, Linear Regression, Support Vector Machines, Neural Networks, PCA, KMeans, Genetic Algorithms, MDP, Mixture of Gaussians, Logistic Regression



This framework uses the Accelerate library to speed up computations, except the Linux package versions.

Written for Swift 3.0.  Earlier versions are Swift 2.2 compatible



SVM ported from the public domain LIBSVM repository

See https://www.csie.ntu.edu.tw/~cjlin/libsvm/ for more information



The Metal Neural Network uses the Metal framework for a Neural Network using the GPU.  While it works in preliminary testing, more work could be done with this class



Use the XCTest files for examples on how to use the classes



Playgrounds for Linear Regression, SVM, and Neural Networks are available.  Now available in both macOS and iOS versions.



###New - Convolution Program

For the Deep Network classes, please look at the [Convolution](https://github.com/KevinCoble/Convolution) project that uses the AIToolbox library to do image recognition.



### New Swift Package - Mac and Linux compatible!

The package is a sub-set of the full framework.  Classes that require GCD or LAPACK have not been ported.  I am investigating LAPACK on Linux alternatives, and may someday figure out how to get libdispatch to compile on Ubuntu...

Use [this subdirectory](Package) to reference the package from your code.



##  Manual



I have started a [manual](Manual/AIToolbox.md) for the framework.  It is a work-in-progress, but adds some useful explanation to pieces of the framework.  All protocols, structures, and enumerations are well defined.  Class descriptions are there, but not class variables and methods.



## Classes/Algorithms supported:



    Graphs/Trees

        Depth-first search

        Breadth-first search

        Hill-climb search

        Beam Search

        Optimal Path search



    Alpha-Beta (game tree)



    Genetic Algorithms

        mutations

        mating

        integer/double alleles



    Constraint Propogation

        i.e. 3-color map problem



    Linear Regression

        arbitrary function in model

        regularization can be used

        convenience constructor for standard polygons

        Least-squares error



    Non-Linear Regression

        parameter-delta

        Gradient-Descent

        Gauss-Newton



    Logistic Regression

        Use any non-linear solution method

        Multi-class capability



    Neural Networks

        multiple layers, several non-linearity models

        on-line and batch training

        feed-forward or simple recurrent layers can be mixed in one network

        simple network training using GPU via Apple's Metal

        LSTM network layer implemented - needs more testing

        gradient check routines



    Support Vector Machine

        Classification

        Regression

        More-than-2 classes classification



    K-Means

        unlabelled data grouping



    Principal Component Analysis

        data dimension reduction



    Markov Decision Process

        value iteration

        policy iteration

        fitted value iteration for continuous state MDPs - uses any Regression class for fit

                (see my MDPRobot project on github for an example use)

        Monte-Carlo (every-visit, and first-visit)

        SARSA



    Gaussians

        Single variable

        Multivariate - with full covariance matrix or diagonal only



    Mixture Of Gaussians

        Learn density function of a mixture of gaussians from data

        EM algorithm to converge model with data



    Validation

        Use to select model or parameters of model

        Simple validation (percentage of data becomes test data)

        N-Fold validation



    Deep-Network

        Convolution layers

        Pooling layers

        Fully-connected NN layers

        multi-threaded



    Plotting

        NSView based MLView for displaying regression data, classification data, functions, and classifier areas!

        UIView based MLView for iOS applications, same as NSView based for macOS

![Regression Plot Image](PlotImage.png)

![Classification Plot Image](PlotImage2.png)



## License



This framework is made available with the [Apache license](LICENSE.md).



##  Contributions



See the [contribution document](CONTRIBUTIONS.md) for information on contributing to this framework