# !!! A MORE GENERAL AND EFFICIENT GP PACKAGE EXISTS NOW AT AugmentedGaussianProcesses.jl (INCLUDING BSVM) !!!

# BayesianSVM.jl

This repository contains the Julia package for the Bayesian SVM algorithm described in the paper *"Bayesian Nonlinear Support Vector Machines for Big Data"* by Florian Wenzel, Théo Galy-Fajou, Matthäus Deutsch and Marius Kloft

## Requirements

The BayesianSVM only works for version of Julia > 0.6.
Other necessary packages will automatically be added in the installation.
It is also possible to run the package from Python, to do so please check Pyjulia.
If you prefer to use R you have the possibility to use RJulia
All these is a bit technical due to the fact that Julia is still a young package

## Installation

To install the last version of the package in Julia run

```
Pkg.clone("git://github.com/theogf/BayesianSVM.jl.git")
```

## Running the Algorithm

Here are the basic steps for using the algorithm :

```
using BayesianSVM
Model = BSVM(X_training,y_training)
Model.Train()
y_predic = sign.(Model.Predict(X_test))
y_uncertaintypredic = Model.PredictProb(X_test)
```

Where X_training should be a matrix of size NSamples x NFeatures, and y_training should be a vector of 1 and -1

You can find a more complete description in the Wiki