agedi.diffusion.sdes.vp
=======================

.. py:module:: agedi.diffusion.sdes.vp


Classes
-------

.. autoapisummary::

   agedi.diffusion.sdes.vp.VP


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

.. py:class:: VP(beta_min: float = 0.01, beta_max: float = 3, **kwargs)

   Bases: :py:obj:`agedi.diffusion.sdes.SDE`


   Implements variance-preserving (VP) SDE.

   :param beta_min: The minimum value of the beta parameter.
   :type beta_min: float
   :param beta_max: The maximum value of the beta parameter.
   :type beta_max: float

   :rtype: VP


   .. py:attribute:: beta_min
      :value: 0.01



   .. py:attribute:: beta_max
      :value: 3



   .. py:attribute:: noise_schedule


   .. py:method:: get_hparams() -> Dict

      Return hyperparameters for this VP SDE.



   .. py:method:: drift(x: torch.Tensor, t: torch.Tensor) -> torch.Tensor

      Implement VP drift term.

      Defines the drift term of the SDE: f(x, t).

      :param x: The positions of the atoms.
      :type x: torch.Tensor
      :param t: The time at which to calculate the drift term.
      :type t: torch.Tensor

      :returns: **drift** -- The drift term of the SDE.
      :rtype: torch.Tensor



   .. py:method:: diffusion(t: torch.Tensor) -> torch.Tensor

      Implement VP diffusion term.

      Defines the diffusion term of the SDE: g(t).

      :param t: The time at which to calculate the diffusion term.
      :type t: torch.Tensor

      :returns: **diffusion** -- The diffusion term of the SDE.
      :rtype: torch.Tensor



   .. py:method:: mean(t: torch.Tensor) -> torch.Tensor

      Implement VP mean term.

      Calculates the mean of transition kernel at time t: mu(t).

      :param t: The time at which to calculate the mean.
      :type t: torch.Tensor

      :returns: **mean** -- The mean of the diffusion process.
      :rtype: torch.Tensor



   .. py:method:: var(t: torch.Tensor) -> torch.Tensor

      Implement VP variance term.

      Calculates the variance of transition kernel at time t: sigma^2(t).

      :param t: The time at which to calculate the variance.
      :type t: torch.Tensor

      :returns: **var** -- The variance of the diffusion process.
      :rtype: torch.Tensor



   .. py:method:: beta(t: torch.Tensor) -> torch.Tensor

      VP Beta function

      Calculates the value of beta at time t.

      :param t: The time at which to calculate beta.
      :type t: torch.Tensor

      :returns: **beta** -- The value of beta at time t.
      :rtype: torch.Tensor



   .. py:method:: alpha(t: torch.Tensor) -> torch.Tensor

      VP Alpha function

      Calculates the value of alpha at time t with
      .. math::
      lpha(t) = int_{0}^{t} beta(s) ds.

      :param t: The time at which to calculate alpha.
      :type t: torch.Tensor

      :returns: **alpha** -- The value of alpha at time t.
      :rtype: torch.Tensor