:orphan:

:mod:`torchfilter.base._virtual_sensor_model`
=============================================

.. py:module:: torchfilter.base._virtual_sensor_model

.. autoapi-nested-parse::

   Private module; avoid importing from directly.



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

Classes
~~~~~~~

.. autoapisummary::

   torchfilter.base._virtual_sensor_model.VirtualSensorModel



.. py:class:: VirtualSensorModel(state_dim: int)

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

   .. autoapi-inheritance-diagram:: torchfilter.base._virtual_sensor_model.VirtualSensorModel
      :parts: 1

   Virtual sensor base class for our differentiable Kalman filters.

   Maps each observation input to a predicted state and uncertainty, in the style of
   BackpropKF. This is often necessary for complex observation spaces like images or
   point clouds, where it's not possible to learn a standard state->observation
   measurement model.

   .. attribute:: state_dim
      

      Dimensionality of our state.

      :type: int


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

      Predicts states and uncertainties from observation inputs.

      Uncertainties should be lower-triangular Cholesky decompositions of covariance
      matrices.

      :param observations: Measurement inputs. Should be
                           either a dict of tensors or tensor of size ``(N, ...)``.
      :type observations: dict or torch.Tensor

      :returns: *Tuple[torch.Tensor, torch.Tensor]* -- Predicted states & uncertainties. States
                should have shape ``(N, state_dim)``\ , and uncertainties should be lower
                triangular with shape ``(N, state_dim, state_dim).``