New Bonsai.ML.Lds.Torch package to extract latents from high-dimensional data using TorchSharp

[ncguilbeault]

Summary

This PR introduces a new package, Bonsai.ML.Lds.Torch, which implements linear dynamical systems (LDS) using TorchSharp for online filtering, smoothing, and parameter estimation in Bonsai. This package differs from the Bonsai.ML.LinearDynamicalSystems package in that it does not depend on Python, and the models parameters can be customised. A test was added to compare the output of the TorchSharp implementation with the output of the lds_python package following parameter estimation and filtering of neural recordings.

The PR in #77 should be merged first.

Refactoring the unit test to download the expected results instead of generating them with a Python script fixes #75

[glopesdev]

Leaving here a summary of what we discussed earlier about deprecating the Bonsai.ML.LinearDynamicalSystems package and adopting back-end suffixes for systems with different implementations. In this case the steps would be:

• Rename the package to Bonsai.ML.Lds.Torch
• Rename Bonsai.ML.LinearDynamicalSystems to Bonsai.ML.Lds.Python
• Deprecate the Bonsai.ML.LinearDynamicalSystems package

The one consideration we discussed was whether to "nest" namespaces, e.g. we could use the name Bonsai.ML.Torch.Lds to make it so that all types in Bonsai.ML.Torch would be automatically visible to ML packages using the Torch backend. In the end we decided to go with the suffix, and import functionality where needed, similar to what is done with most non-overlapping .NET namespaces.

[ncguilbeault]

Updating the namespace of the existing Bonsai.ML.LinearDynamicalSystems resulted in quite a few changes. Because of this, I decided to push these changes to a separate PR which we should merge first before this one. The new PR is here: #74

[glopesdev]

Looks good, left only a few minor comments to review.