Table of Contents

Bonsai - Machine Learning

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

Core Package

  • Bonsai.ML Provides common tools and functionalities across all Bonsai.ML packages.

Available Packages

Bonsai.ML.LinearDynamicalSystems

This package facilitates inference of linear dynamical systems. 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.HiddenMarkovModels

This package provides tools for building and using Hidden Markov Models (HMMs). It interfaces with the ssm package using the Bonsai - Python Scripting library.

  • Bonsai.ML.HiddenMarkovModels.Observations Provides an interface for using different types of observation models.

  • Bonsai.ML.HiddenMarkovModels.Transitions Provides an interface for using different types of transition models.

Bonsai.ML.Visualizers

Offers a set of tools for dynamic graphing and plotting to visualize data.

  • Bonsai.ML.Visualizers.LinearDynamicalSystems Visualizers specific to the LinearDynamicalSystems package.

  • Bonsai.ML.Visualizers.HiddenMarkovModels Visualizers specific to 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].

Edit this page