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Enhance Kalman filter return values with predictions #435

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Enhance Kalman filter return values with predictions #435

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8 changes: 5 additions & 3 deletions dynamax/linear_gaussian_ssm/inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -118,6 +118,8 @@ class PosteriorGSSMFiltered(NamedTuple):
:param marginal_loglik: marginal log likelihood, $p(y_{1:T} \mid u_{1:T})$
:param filtered_means: array of filtered means $\mathbb{E}[z_t \mid y_{1:t}, u_{1:t}]$
:param filtered_covariances: array of filtered covariances $\mathrm{Cov}[z_t \mid y_{1:t}, u_{1:t}]$
:param predicted_means: array of predicted means $\mathbb{E}[z_t \mid y_{1:t-1}, u_{1:t-1}]$
:param predicted_covariances: array of predicted covariances $\mathrm{Cov}[z_t \mid y_{1:t-1}, u_{1:t-1}]$

"""
marginal_loglik: Union[Scalar, Float[Array, " ntime"]]
Expand Down Expand Up @@ -504,12 +506,12 @@ def _step(carry, t):
# Predict the next state
pred_mean, pred_cov = _predict(filtered_mean, filtered_cov, F, B, b, Q, u)

return (ll, pred_mean, pred_cov), (filtered_mean, filtered_cov)
return (ll, pred_mean, pred_cov), (filtered_mean, filtered_cov, carry[1], carry[2])

# Run the Kalman filter
carry = (0.0, params.initial.mean, params.initial.cov)
(ll, _, _), (filtered_means, filtered_covs) = lax.scan(_step, carry, jnp.arange(num_timesteps))
return PosteriorGSSMFiltered(marginal_loglik=ll, filtered_means=filtered_means, filtered_covariances=filtered_covs)
(ll, _, _), (filtered_means, filtered_covs, predicted_means, predicted_covs) = lax.scan(_step, carry, jnp.arange(num_timesteps))
return PosteriorGSSMFiltered(marginal_loglik=ll, filtered_means=filtered_means, filtered_covariances=filtered_covs, predicted_means=predicted_means, predicted_covariances=predicted_covs)


@preprocess_args
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