gflownet.envs.alaninedipeptide
==============================

.. py:module:: gflownet.envs.alaninedipeptide


Attributes
----------

.. autoapisummary::

   gflownet.envs.alaninedipeptide.path_to_data


Classes
-------

.. autoapisummary::

   gflownet.envs.alaninedipeptide.AlanineDipeptide


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

.. py:class:: AlanineDipeptide(path_to_dataset, url_to_dataset, **kwargs)

   Bases: :py:obj:`gflownet.envs.ctorus.ContinuousTorus`


   Initializes a ContinuousCube environent.

   :param ndim: Dimensionality of the torus
   :type ndim: int
   :param length_traj: Fixed length of the trajectory.
   :type length_traj: int
   :param n_comp: Number of components in the mixture of von Mises distributions used to
                  sample angle increments.
   :type n_comp: int
   :param policy_encoding_dim_per_angle: Dimensionality of the policy encodings of the angles.
   :type policy_encoding_dim_per_angle: int
   :param vonmises_min_concentration: Minimum value allowed for the concentration parameter of the von Mises
                                      distributions.
   :type vonmises_min_concentration: float
   :param fixed_distr_params: Dictionary of parameters of the von Mises distribution that defines the
                              fixed distribution of the environment. It must contain two keys with float
                              values: ``vonmises_mean`` and ``vonmises_concentration``.
   :type fixed_distr_params: dict
   :param random_distr_params: Dictionary of parameters of the von Mises distribution that defines the
                               random distribution of the environment. It must contain two keys with float
                               values: ``vonmises_mean`` and ``vonmises_concentration``.
   :type random_distr_params: dict


   .. py:attribute:: atom_positions_dataset


   .. py:attribute:: conformer


   .. py:method:: sync_conformer_with_state(state = None)


   .. py:method:: states2proxy(states)

      Prepares a batch of states in "environment format" for the proxy: each state is
      a vector of length n_dim where each value is an angle in radians. The n_actions
      item is removed. This transformation is obtained from the parent states2proxy.

      Important: this method returns a numpy array, unlike in most other
      environments.

      :param states: A batch of states in environment format, either as a list of states or as a
                     single tensor.
      :type states: list or tensor

      :returns: *A numpy array containing all the states in the batch.*



.. py:data:: path_to_data