pass inputs into the LDS model
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Description
Hello,
I have a very basic question: how to pass N X T X D inputs ("X") into the LDS model (N trials, T time steps and D dimensional inputs)?
In the linear_gaussian_ssm model.py file, the inputs is Optional[Float[Array, "ntime input_dim"]], so there's no dimension for trials (N)?
I tried to do things as in the Kalman filter/ smoother example. But the problem is that I also need to include d latent trajectoreis into the model (i.e. the state dimension should be D + d, if I encode the covariates into the emission matrix).
Not sure how to do it correctly...
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Research direction
Start with the linear_gaussian_ssm model.py file and the Kalman filter/smoother example, checking how inputs are shaped and whether trial batching is supported. Trace how the model handles latent trajectories and define the expected N × T × D interface; done means the supported behavior is implemented or clearly documented with a corresponding example or test.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100