I want to compare perfomance of my own implementation to publicly availably ones
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 3.1. 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 project that uses the AIToolbox library to do image recognition.
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 to reference the package from your code.
I have started a manual 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.
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
This framework is made available with the Apache license.
See the contribution document for information on contributing to this framework