Introducing Online Inference with Hidden Markov Models (HMMs) #22
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…optional stroke color
…than just numpy arrays
glopesdev
approved these changes
Sep 5, 2024
Co-authored-by: glopesdev <glopesdev@users.noreply.github.com>
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Summary
This PR introduces a new package to the Bonsai.ML collection designed to perform online inference using Hidden Markov Models (HMMs).
Key Features
1. Online state inference
Users can infer the probabilities that the model is in a discrete latent state given new observations of data online.
2. Learning model parameters
Users can perform online learning of model parameters (initial state distribution, transition matrix, and emission probabilities) using mini batches of data without interrupting ongoing state inference.
3. Flexible model specification
Users can initialize the HMM using different model specifications, such as specifying different observation models (gaussian, exponential, etc.).
Implementation Details
Under the hood, the package uses the Bonsai - Python Scripting package to interface with the ssm package. The current implementation of the package exposes a high-level API that allows initializing HMMs, loading/saving model parameters, online learning, and inference, which is suitable for both novice and experienced Bonsai users.