This repository contains an implementation of Gaussian Process Switching Linear Dynamical Systems (gpSLDS), described in the paper here (insert link).
gpslds/ Source code for gpSLDS model implementation.
em.py Implements variational EM and contains the main gpSLDS fitting function.
initialization.py Functions for initializing model parameters.
kernels.py GP kernel functions, including our smoothly switching linear kernel.
likelihoods.py Gaussian and Poisson observation models.
quadrature.py Quadrature object for approximating kernel expectations.
simulate_data.py Helper functions for sampling from the model.
transition.py Defines GP object for model fitting.
utils.py Variety of helper functions.
data/ Code and data files for main synthetic data example.
fit_plds.py Script for fitting Poisson LDS to initialize Poisson Process observation model parameters.
generate_synthetic_data.py Script for generating synthetic data.
synthetic_data.pkl Pickle file containing synthetic data.
synthetic_plds_emissions.pkl Pickle file containing initial observation model parameters for synthetic data.
synthetic_data_demo.ipynb Demo notebook fitting gpSLDS to synthetic data.
To use the gpSLDS on your own data, you will need to ensure that you have:
- A JAX array
ys_binnedof shape(n_trials, n_timesteps, n_output_dims). To process data in effectively continuous-time,n_timestepsshould represent the number of time bins at a small discretization step relative to the data sampling rate. We assume that data has been zero-padded in the case of varying length trials. - A JAX array
t_maskof shape(n_trials, n_timesteps). This is 1 for observed timesteps and 0 for unobserved timesteps. - A JAX array
trial_maskof shape(n_trials, n_timesteps). This is 1 for timesteps in an observed trial and 0 for a zero-padded timestep. - (Optional) A JAX array
inputsof shape(n_trials, n_timesteps, n_input_dims)consisting of external stimuli.
For an example, please see synthetic_data_demo.ipynb which demonstrates data formatting and model fitting on a synthetic example.
The current recommended way to run this code is by using a NVIDIA A100 GPU. The fastest way to get this running is by using Google colab with an A100 GPU runtime, which is demonstrated in synthetic_data_demo.ipynb.
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