Bonsai.ML.Design 0.3.0

Prefix Reserved
.NET Framework 4.7.2 This package targets .NET Framework 4.7.2. The package is compatible with this framework or higher.
There is a newer version of this package available.
See the version list below for details. 
dotnet add package Bonsai.ML.Design --version 0.3.0                

                    
                

            
NuGet\Install-Package Bonsai.ML.Design -Version 0.3.0
This command is intended to be used within the Package Manager Console in Visual Studio, as it uses the NuGet module's version of Install-Package. 
<PackageReference Include="Bonsai.ML.Design" Version="0.3.0" />
For projects that support PackageReference, copy this XML node into the project file to reference the package. 
paket add Bonsai.ML.Design --version 0.3.0                

                    
                

            
#r "nuget: Bonsai.ML.Design, 0.3.0"
#r directive can be used in F# Interactive and Polyglot Notebooks. Copy this into the interactive tool or source code of the script to reference the package. 
// Install Bonsai.ML.Design as a Cake Addin
#addin nuget:?package=Bonsai.ML.Design&version=0.3.0

// Install Bonsai.ML.Design as a Cake Tool
#tool nuget:?package=Bonsai.ML.Design&version=0.3.0

Bonsai - Machine Learning

The Bonsai.ML project is a collection of packages designed to integrate machine learning algorithms with Bonsai. This document provides an overview of the available packages and their functionalities.

Core Packages

  • Bonsai.ML Provides common tools and functionality.

  • Bonsai.ML.Design Provides common tools and functionality for visualizers and editor features.

  • Bonsai.ML.Data Provides common tools and functionality for working with data.

  • Bonsai.ML.Python Provides common tools and functionality for C# packages to interface with Python.

Available Packages

Bonsai.ML.LinearDynamicalSystems

Facilitates inference using linear dynamical systems (LDS). It interfaces with the lds_python package using the Bonsai - Python Scripting library.

  • Bonsai.ML.LinearDynamicalSystems.Kinematics Supports the use of the Kalman Filter for inferring kinematic data.

  • Bonsai.ML.LinearDynamicalSystems.LinearRegression Utilizes the Kalman Filter to perform online Bayesian linear regression.

Bonsai.ML.LinearDynamicalSystems.Design

Visualizers and editor features for the LinearDynamicalSystems package.

Bonsai.ML.HiddenMarkovModels

Facilitates inference using Hidden Markov Models (HMMs). It interfaces with the ssm package using the Bonsai - Python Scripting library.

  • Bonsai.ML.HiddenMarkovModels.Observations Provides functionality for specifying different types of observations.

  • Bonsai.ML.HiddenMarkovModels.Transitions Provides functionality for specifying different types of transition models.

Bonsai.ML.HiddenMarkovModels.Design

Visualizers and editor features for the HiddenMarkovModels package.

[!NOTE] Bonsai.ML packages can be installed through Bonsai's integrated package manager and are generally ready for immediate use. However, some packages may require additional installation steps. Refer to the specific package section for detailed installation guides and documentation.

Acknowledgments

Development of the Bonsai.ML package is supported by the Biotechnology and Biological Sciences Research Council [grant number BB/W019132/1].

Product Compatible and additional computed target framework versions.
.NET Framework net472 is compatible.  net48 was computed.  net481 was computed. 
Compatible target framework(s)
Included target framework(s) (in package)
Learn more about Target Frameworks and .NET Standard.

NuGet packages (2)

Showing the top 2 NuGet packages that depend on Bonsai.ML.Design:

Package Downloads
Bonsai.ML.HiddenMarkovModels.Design

A package for visualizing data from the Bonsai.ML.HiddenMarkovModels package.
Bonsai.ML.LinearDynamicalSystems.Design

A package for visualizing data from the Bonsai.ML.LinearDynamicalSystems package.

GitHub repositories

This package is not used by any popular GitHub repositories.

Version Downloads Last updated
0.3.1 89 10/10/2024
0.3.0 94 10/3/2024