dicee.models.dualE
==================

.. py:module:: dicee.models.dualE


Classes
-------

.. autoapisummary::

   dicee.models.dualE.DualE


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

.. py:class:: DualE(args)

   Bases: :py:obj:`dicee.models.base_model.BaseKGE`


   Dual Quaternion Knowledge Graph Embeddings (https://ojs.aaai.org/index.php/AAAI/article/download/16850/16657)


   .. py:attribute:: name
      :value: 'DualE'



   .. py:attribute:: entity_embeddings


   .. py:attribute:: relation_embeddings


   .. py:attribute:: num_ent
      :value: None



   .. py:method:: kvsall_score(e_1_h, e_2_h, e_3_h, e_4_h, e_5_h, e_6_h, e_7_h, e_8_h, e_1_t, e_2_t, e_3_t, e_4_t, e_5_t, e_6_t, e_7_t, e_8_t, r_1, r_2, r_3, r_4, r_5, r_6, r_7, r_8) -> torch.tensor

      KvsAll scoring function

      Input
      ---------
      x: torch.LongTensor with (n, ) shape

      Output
      -------
      torch.FloatTensor with (n) shape



   .. py:method:: forward_triples(idx_triple: torch.tensor) -> torch.tensor

      Negative Sampling forward pass:

      Input
      ---------
      x: torch.LongTensor with (n, ) shape

      Output
      -------
      torch.FloatTensor with (n) shape



   .. py:method:: forward_k_vs_all(x)

      KvsAll forward pass

      Input
      ---------
      x: torch.LongTensor with (n, ) shape

      Output
      -------
      torch.FloatTensor with (n) shape




   .. py:method:: T(x: torch.tensor) -> torch.tensor

      Transpose function

      Input: Tensor with shape (nxm)
      Output: Tensor with shape (mxn)