agedi.diffusion.sdes.ve
=======================

.. py:module:: agedi.diffusion.sdes.ve


Classes
-------

.. autoapisummary::

   agedi.diffusion.sdes.ve.VE


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

.. py:class:: VE(sigma_min: float = 0.01, sigma_max: float = 1.0, **kwargs)

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


   Implements variance-exploding (VE) SDE.

   :param sigma_min: The minimum value of the sigma parameter.
   :type sigma_min: float
   :param sigma_max: The maximum value of the sigma parameter.
   :type sigma_max: float

   :rtype: VP


   .. py:attribute:: sigma_min
      :value: 0.01



   .. py:attribute:: sigma_max
      :value: 1.0



   .. py:attribute:: noise_schedule


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

      Return hyperparameters for this VE 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:: sigma(t: torch.Tensor) -> torch.Tensor

      VE sigma function

      Calculates the value of sigma at time t.

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

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