Accord.NET Framework

The Accord.NET project provides machine learning, statistics, artificial intelligence, computer vision and image processing methods to .NET. It can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile.

After merging with the AForge.NET project, the framework now offers a unified API for learning/training machine learning models that is both easy to use and extensible. It is based on the following pattern:

• Choose a learning algorithm that provides a Learn(x, y) or Learn(x) method;
• Use the Learn(x, y) to create a machine learning model learned from the data;
• Use the model's Transform, Decide, Scores, Probabilities or LogLikelihoods methods.

For more information, please see the getting started guide, and check the classfication wiki. Please do not hesitate to edit the wiki if you would like!

Update (10/05/2020): Please see the current status section below before you start using this library in any new projects.

Installing

To install the framework in your application, please use NuGet. If you are on Visual Studio, right-click on the "References" item in your solution folder, and select "Manage NuGet Packages." Search for Accord.MachineLearning (or equivalently, Accord.Math, Accord.Statistics or Accord.Imaging depending on your initial goal) and select "Install."

If you would like to install the framework on Unity3D applications, download the "libsonly" compressed archive from the framework releases page. Navigate to the Releases/Mono folder, and copy the .dll files to the Plugins folder in your Unity project. Finally, find and add the System.ComponentModel.DataAnnotations.dll assembly that should be available from your system to the Plugin folders as well.

Sample applications

The framework comes with a wide range of sample applications to help get you started quickly. If you downloaded the framework sources or cloned the repository, open the Samples.sln solution file in the Samples folder.

Building

With Visual Studio 2015

Please download and install the following dependencies:

• T4 Toolbox for Visual Studio 2015
• Sandcastle Help File Builder (with VS2015 extension)
• NUnit 3 Test Adapter

Then navigate to the Sources directory, and open the Accord.NET.sln solution file. Note: the solution includes F# unit test projects that can be disabled/unloaded from the solution in case you do not have support for F# tools in your version of Visual Studio.

With Visual Studio 2017

Please download and install the following dependencies:

• T4 Toolbox for Visual Studio 2017
• Sandcastle Help File Builder (with VS2017 extension)
• NUnit 3 Test Adapter
• Visual C++ Redistributable for Visual Studio 2015 (both x64 and x86)

Then navigate to the Sources directory, and open the Accord.NET.sln solution file. Note: the solution includes F# unit test projects that can be disabled/unloaded from the solution in case you do not have support for F# tools in your version of Visual Studio.

With Mono in Linux

# Install Mono
sudo apt-get install mono-complete monodevelop monodevelop-nunit git autoconf make

# Clone the repository
git clone https://github.com/accord-net/framework.git

# Enter the directory
cd framework

# Build the framework solution using Mono
./autogen.sh
make build
make samples
make test

With Mono in OS X

# Install Mono
brew update
brew cask install mono-mdk pkg-config automake

# Clone the repository
git clone https://github.com/accord-net/framework.git

# Enter the directory
cd framework

# Set some environment variables with OSX-specific paths
export PKG_CONFIG_PATH=/Library/Frameworks/Mono.framework/Versions/Current/lib/pkgconfig/
export MONO=/Library/Frameworks/Mono.framework/Versions/Current/bin/mono
export XBUILD=/Library/Frameworks/Mono.framework/Versions/Current/bin/xbuild

# Build the framework solution using Mono
./autogen.sh
make build
make samples
make test

Contributing

If you would like to contribute, please do so by helping us update the project's Wiki pages. While you could also make a donation through PayPal , Flattr , or any of the cryptocurrencies shown below, as well as fill-in bug reports and contribute code in the form of pull requests, the most precious donation we could receive would be a bit of your time - please take some minutes to submit us more documentation examples to our Wiki pages 😉

Donate using cryptocurrencies:

• BTC: 1FC5gxLs2TsvuiHPP1tRLh5mPboQJQghvZ
• ETH: 0x36FDA635Ef5773d8B376037D7BAfF22FeB987d92
• LTC: LNjkZkMdSyncUvg5WnnhDNirdux4Q95gdt

Note: all donations are 100% invested towards improving the framework, including, but not limited to, the hiring of extra developers to work on issues currently present at the project's issue tracker. If you would like to donate resources towards the development of a particular issue, please let us know!

Join the chat at https://gitter.im/accord-net/framework - but to have issues and questions answered, post it as an issue.

Current status

Before you decide to use the framework for new projects, please see the following personal note below.

I am writing this note to give an official status for the project.

This project has certainly been the most important thing I have ever created, but I could not keep up with maintaining it as well as I wanted. This project allowed me to achieve the biggest dream I had, and that I never though I would have been able to achieve in my life, which was (some may laugh and possibly not understand) starting a life and career in Europe.

For about 10 years, I had worked on this project almost every day of my life.

But with the new life, came new steps to be climbed, I had new responsabilities and things to go. Then I started a PhD and had to focus on it so I could not keep up maintaining the library. I even tried to hire freelance developers to help, and it worked to some extent, but at some point I did not have the resources anymore. Eventually, I developed anxiety of even opening the issues page or checking my e-mails because I feel I might have left so many people behind. Next, a few months before my defense, Microsoft announced that they wanted to make ML.net, meaning that Accord.NET would eventually become obsolete as ML.net should become the de-facto ML library for .NET.

In addition, I've also published in, and attended, the most important machine learning conferences in the world, and in academia, no one has ever heard of the framework. People may even laugh or mistreat you if you mention you have developed something in C# for machine learning, as everyone (understandably) uses Python (I myself only use Python to do my work, and while I love C#/.NET, there is nothing that can compete with Python/Pytorch).

In the past months, I have been pondering about archiving the project. To avoid that, I am willing to make someone who would like, also an administrator of the project.

I am also willing to change the license of any file where I am the single author (you can check the copyright headers in each file) to MIT so people can reuse individual pieces of code more easily. Anyone who becomes administrator is welcome to slice the parts of the project that still make sense to exist (e.g., the FFmpeg wrappers, statistical distributions, statistical tests and the simple transforms like PCA) and even start new libraries (hopefully in .NET Core) providing only them if wanted.

Also, when I started this project back in 2007 (and when the original AForge library started, even way before that), there were almost no other libraries we could built upon, so we had to do start almost everything from scratch. This is not the case anymore. Any new libraries coming out of this project should definitely reuse existing libraries for basic tasks such as matrix operations and image processing.

Cesar De Souza
10-May-2020

Citing

Please cite this work as:

@misc{souza2014accord,
  title={The Accord.NET Framework},
  author={C{\'e}sar Souza and Andrew Kirillov and Marcos Diego Catalano and Accord.NET contributors},
  year={2014},
  doi={10.5281/zenodo.1029480},
  url={http://accord-framework.net}
}

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