`torchfilter.base._kalman_filter_base`

Private module; avoid importing from directly.

Module Contents

Base class for a generic Kalman-style filter. Parameterizes beliefs with a mean

class torchfilter.base._kalman_filter_base.KalmanFilterBase(*, dynamics_model: DynamicsModel, measurement_model: KalmanFilterMeasurementModel, **unused_kwargs)[source]

Base class for a generic Kalman-style filter. Parameterizes beliefs with a mean and covariance.

Subclasses should override _predict_step() and _update_step().

dynamics_model

Forward model.

measurement_model

Measurement model.

forward(self, *, observations: types.ObservationsTorch, controls: types.ControlsTorch) → types.StatesTorch[source]

Kalman filter forward pass, single timestep.

Parameters:

observations (dict or torch.Tensor) – Observation inputs. Should be either a dict of tensors or tensor of shape (N, ...).
controls (dict or torch.Tensor) – Control inputs. Should be either a dict of tensors or tensor of shape (N, ...).

Returns:

torch.Tensor – Predicted state for each batch element. Shape should be (N, state_dim).

initialize_beliefs(self, *, mean: types.StatesTorch, covariance: types.CovarianceTorch) → None[source]

Set filter belief to a given mean and covariance.

Parameters:

mean (torch.Tensor) – Mean of belief. Shape should be (N, state_dim).
covariance (torch.Tensor) – Covariance of belief. Shape should be (N, state_dim, state_dim).