ncalab.autostepper
==================

.. py:module:: ncalab.autostepper


Classes
-------

.. autoapisummary::

   ncalab.autostepper.AutoStepper


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

.. py:class:: AutoStepper(min_steps: int = 10, max_steps: int = 100, plateau: int = 5, verbose: bool = False, threshold: float = 0.01)

   Helps selecting number of timesteps based on NCA activity.

   :param min_steps: Minimum number of timesteps to always execute, defaults to 10.
   :type min_steps: int, optional
   :param max_steps: Terminate after maximum number of steps, defaults to 100.
   :type max_steps: int, optional
   :param plateau: Number of steps that is considered a plateau, defaults to 5.
   :type plateau: int
   :param verbose: Whether to log interruption to stdout, defaults to False.
   :type verbose: bool
   :param threshold: Score threshold, defaults to 1e-2.
   :type threshold: float


   .. py:attribute:: min_steps
      :value: 10



   .. py:attribute:: max_steps
      :value: 100



   .. py:attribute:: plateau
      :value: 5



   .. py:attribute:: verbose
      :value: False



   .. py:attribute:: threshold
      :value: 0.01



   .. py:attribute:: cooldown
      :value: 0



   .. py:attribute:: hidden_i
      :type:  torch.Tensor | None
      :value: None



   .. py:attribute:: hidden_i_1
      :type:  torch.Tensor | None
      :value: None



   .. py:method:: _score() -> torch.Tensor

      Calculates activity score.

      Method check() uses this score to determine if the NCA is inactive.

      :return: Activity score estimate.
      :rtype: torch.Tensor



   .. py:method:: _check(step: int) -> bool

      Checks whether to interrupt inference after the current step.

      :param step: Current NCA inference step.
      :type step: int

      :return: Whether to interrupt inference after the current step.
      :rtype: bool



   .. py:method:: run(nca: ncalab.models.BasicNCAModel, x)


   .. py:method:: __call__(*args: Any, **kwargs: Any) -> Any