dicee.models.quaternion
=======================

.. py:module:: dicee.models.quaternion


Classes
-------

.. autoapisummary::

   dicee.models.quaternion.QMult
   dicee.models.quaternion.ConvQ
   dicee.models.quaternion.AConvQ


Functions
---------

.. autoapisummary::

   dicee.models.quaternion.quaternion_mul_with_unit_norm


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

.. py:function:: quaternion_mul_with_unit_norm(*, Q_1, Q_2)

.. py:class:: QMult(args)

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


   Base class for all neural network modules.

   Your models should also subclass this class.

   Modules can also contain other Modules, allowing them to be nested in
   a tree structure. You can assign the submodules as regular attributes::

       import torch.nn as nn
       import torch.nn.functional as F


       class Model(nn.Module):
           def __init__(self) -> None:
               super().__init__()
               self.conv1 = nn.Conv2d(1, 20, 5)
               self.conv2 = nn.Conv2d(20, 20, 5)

           def forward(self, x):
               x = F.relu(self.conv1(x))
               return F.relu(self.conv2(x))

   Submodules assigned in this way will be registered, and will also have their
   parameters converted when you call :meth:`to`, etc.

   .. note::
       As per the example above, an ``__init__()`` call to the parent class
       must be made before assignment on the child.

   :ivar training: Boolean represents whether this module is in training or
                   evaluation mode.
   :vartype training: bool


   .. py:attribute:: name
      :value: 'QMult'



   .. py:attribute:: explicit
      :value: True



   .. py:method:: quaternion_multiplication_followed_by_inner_product(h, r, t)

      :param h: shape: (`*batch_dims`, dim)
          The head representations.
      :param r: shape: (`*batch_dims`, dim)
          The head representations.
      :param t: shape: (`*batch_dims`, dim)
          The tail representations.
      :return:
          Triple scores.



   .. py:method:: quaternion_normalizer(x: torch.FloatTensor) -> torch.FloatTensor
      :staticmethod:


      Normalize the length of relation vectors, if the forward constraint has not been applied yet.

      Absolute value of a quaternion

      .. math::

          |a + bi + cj + dk| = \sqrt{a^2 + b^2 + c^2 + d^2}

      L2 norm of quaternion vector:

      .. math::
          \|x\|^2 = \sum_{i=1}^d |x_i|^2
                   = \sum_{i=1}^d (x_i.re^2 + x_i.im_1^2 + x_i.im_2^2 + x_i.im_3^2)
      :param x:
          The vector.

      :return:
          The normalized vector.



   .. py:method:: score(head_ent_emb: torch.FloatTensor, rel_ent_emb: torch.FloatTensor, tail_ent_emb: torch.FloatTensor)


   .. py:method:: k_vs_all_score(bpe_head_ent_emb, bpe_rel_ent_emb, E)

      :param bpe_head_ent_emb:
      :param bpe_rel_ent_emb:
      :param E:



   .. py:method:: forward_k_vs_all(x)

      :param x:



   .. py:method:: forward_k_vs_sample(x, target_entity_idx)

      Completed.
      Given a head entity and a relation (h,r), we compute scores for all possible triples,i.e.,
      [score(h,r,x)|x \in Entities] => [0.0,0.1,...,0.8], shape=> (1, |Entities|)
      Given a batch of head entities and relations => shape (size of batch,| Entities|)



.. py:class:: ConvQ(args)

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


   Convolutional Quaternion Knowledge Graph Embeddings




   .. py:attribute:: name
      :value: 'ConvQ'



   .. py:attribute:: entity_embeddings


   .. py:attribute:: relation_embeddings


   .. py:attribute:: conv2d


   .. py:attribute:: fc_num_input


   .. py:attribute:: fc1


   .. py:attribute:: bn_conv1


   .. py:attribute:: bn_conv2


   .. py:attribute:: feature_map_dropout


   .. py:method:: residual_convolution(Q_1, Q_2)


   .. py:method:: forward_triples(indexed_triple: torch.Tensor) -> torch.Tensor

      :param x:



   .. py:method:: forward_k_vs_all(x: torch.Tensor)

      Given a head entity and a relation (h,r), we compute scores for all entities.
      [score(h,r,x)|x \in Entities] => [0.0,0.1,...,0.8], shape=> (1, |Entities|)
      Given a batch of head entities and relations => shape (size of batch,| Entities|)



.. py:class:: AConvQ(args)

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


   Additive Convolutional Quaternion Knowledge Graph Embeddings


   .. py:attribute:: name
      :value: 'AConvQ'



   .. py:attribute:: entity_embeddings


   .. py:attribute:: relation_embeddings


   .. py:attribute:: conv2d


   .. py:attribute:: fc_num_input


   .. py:attribute:: fc1


   .. py:attribute:: bn_conv1


   .. py:attribute:: bn_conv2


   .. py:attribute:: feature_map_dropout


   .. py:method:: residual_convolution(Q_1, Q_2)


   .. py:method:: forward_triples(indexed_triple: torch.Tensor) -> torch.Tensor

      :param x:



   .. py:method:: forward_k_vs_all(x: torch.Tensor)

      Given a head entity and a relation (h,r), we compute scores for all entities.
      [score(h,r,x)|x \in Entities] => [0.0,0.1,...,0.8], shape=> (1, |Entities|)
      Given a batch of head entities and relations => shape (size of batch,| Entities|)