:orphan:

:mod:`torchfilter.data._single_step_dataset`
============================================

.. py:module:: torchfilter.data._single_step_dataset

.. autoapi-nested-parse::

   Private module; avoid importing from directly.



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

Classes
~~~~~~~

.. autoapisummary::

   torchfilter.data._single_step_dataset.SingleStepDataset



.. py:class:: SingleStepDataset(trajectories: List[types.TrajectoryNumpy])

   Bases: :class:`torch.utils.data.Dataset`

   .. autoapi-inheritance-diagram:: torchfilter.data._single_step_dataset.SingleStepDataset
      :parts: 1

   A dataset interface that returns single-step training examples:
   ``(previous_state, state, observation, control)``

   By default, extracts these examples from a list of trajectories.

   :param trajectories: List of trajectories.
   :type trajectories: List[torchfilter.types.TrajectoryNumpy]

   .. method:: __getitem__(self, index: int) -> Tuple[types.StatesNumpy, types.StatesNumpy, types.ObservationsNumpy, types.ControlsNumpy]

      Get a single-step prediction sample from our dataset.

      :param index: Subsequence number in our dataset.
      :type index: int

      :returns: *tuple* -- ``(previous_state, state, observation, control)`` tuple that
                contains data for a single subsequence. Each tuple member should be either a
                numpy array or dict of numpy arrays with shape ``(subsequence_length, ...)``.


   .. method:: __len__(self) -> int

      Total number of subsequences in the dataset.

      :returns: *int* -- Length of dataset.