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].