:orphan:

:mod:`torchfilter.base._kalman_filter_measurement_model`
========================================================

.. py:module:: torchfilter.base._kalman_filter_measurement_model

.. autoapi-nested-parse::

   Private module; avoid importing from directly.



Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   torchfilter.base._kalman_filter_measurement_model.KalmanFilterMeasurementModel



.. py:class:: KalmanFilterMeasurementModel(*, state_dim, observation_dim)

   Bases: :class:`abc.ABC`, :class:`torch.nn.Module`

   .. autoapi-inheritance-diagram:: torchfilter.base._kalman_filter_measurement_model.KalmanFilterMeasurementModel
      :parts: 1

   Helper class that provides a standard way to create an ABC using
   inheritance.

   .. attribute:: state_dim
      

      State dimensionality.

      :type: int


   .. attribute:: observation_dim
      

      Observation dimensionality.

      :type: int


   .. method:: forward(self, *, states: types.StatesTorch) -> Tuple[types.ObservationsNoDictTorch, types.ScaleTrilTorch]
      :abstractmethod:

      Observation model forward pass, over batch size ``N``.

      :param states: States to pass to our observation model.
                     Shape should be ``(N, state_dim)``.
      :type states: torch.Tensor

      :returns: *Tuple[torch.Tensor, torch.Tensor]* -- tuple containing expected observations
                and cholesky decomposition of covariance.  Shape should be ``(N, M)``.


   .. method:: jacobian(self, *, states: types.StatesTorch) -> torch.Tensor

      Returns Jacobian of the measurement model.

      :param states: Current state, size ``(N, state_dim)``.
      :type states: torch.Tensor

      :returns: *torch.Tensor* -- Jacobian, size ``(N, observation_dim, state_dim)``