gflownet.proxy.torus
====================

.. py:module:: gflownet.proxy.torus


Classes
-------

.. autoapisummary::

   gflownet.proxy.torus.Torus


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

.. py:class:: Torus(normalize, alpha=1.0, beta=1.0, **kwargs)

   Bases: :py:obj:`gflownet.proxy.base.Proxy`


   Base Proxy class for GFlowNet proxies.

   A proxy is the input to a reward function. Depending on the
   ``reward_function``, the reward may be directly the output of the proxy or a
   function of it.

   :param device: The device to be passed to torch tensors.
   :type device: str or torch.device
   :param float_precision: The floating point precision to be passed to torch tensors.
   :type float_precision: int or torch.dtype
   :param reward_function: The transformation applied to the proxy outputs to obtain a GFlowNet
                           reward. See :py:meth:`Proxy._get_reward_functions`.
   :type reward_function: str or Callable
   :param logreward_function: The transformation applied to the proxy outputs to obtain a GFlowNet
                              log reward. See :meth:`Proxy._get_reward_functions`. If None (default), the
                              log of the reward function is used. The Callable may be used to improve the
                              numerical stability of the transformation.
   :type logreward_function: Callable
   :param reward_function_kwargs: A dictionary of arguments to be passed to the reward function.
   :type reward_function_kwargs: dict
   :param reward_min: The minimum value allowed for rewards, 0.0 by default, which results in a
                      minimum log reward of :py:const:`LOGZERO`. Note that certain loss
                      functions, for example the Forward Looking loss may not work as desired if
                      the minimum reward is 0.0. It may be set to a small (positive) value close
                      to zero in order to prevent numerical stability issues.
   :type reward_min: float
   :param do_clip_rewards: Whether to clip the rewards according to the minimum value.
   :type do_clip_rewards: bool


   .. py:attribute:: normalize


   .. py:attribute:: alpha
      :value: 1.0



   .. py:attribute:: beta
      :value: 1.0



   .. py:method:: setup(env=None)


   .. py:property:: optimum

      Returns the optimum value of the proxy.

      Not implemented by default but may be implemented for synthetic proxies or when
      the optimum is known.

      The optimum is used, for example, to accelerate rejection sampling, to sample
      from the reward function.


   .. py:property:: norm


   .. py:method:: __call__(states)

      :param states: tensor

      :returns: list of scores

      technically an oracle, hence used variable name energies