dicee.models.octonion
=====================

.. py:module:: dicee.models.octonion


Classes
-------

.. autoapisummary::

   dicee.models.octonion.OMult
   dicee.models.octonion.ConvO
   dicee.models.octonion.AConvO


Functions
---------

.. autoapisummary::

   dicee.models.octonion.octonion_mul
   dicee.models.octonion.octonion_mul_norm


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

.. py:function:: octonion_mul(*, O_1, O_2)

.. py:function:: octonion_mul_norm(*, O_1, O_2)

.. py:class:: OMult(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: 'OMult'



   .. py:method:: octonion_normalizer(emb_rel_e0, emb_rel_e1, emb_rel_e2, emb_rel_e3, emb_rel_e4, emb_rel_e5, emb_rel_e6, emb_rel_e7)
      :staticmethod:



   .. 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)


   .. py:method:: forward_k_vs_all(x)

      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:: ConvO(args: dict)

   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: 'ConvO'



   .. py:attribute:: conv2d


   .. py:attribute:: fc_num_input


   .. py:attribute:: fc1


   .. py:attribute:: bn_conv2d


   .. py:attribute:: norm_fc1


   .. py:attribute:: feature_map_dropout


   .. py:method:: octonion_normalizer(emb_rel_e0, emb_rel_e1, emb_rel_e2, emb_rel_e3, emb_rel_e4, emb_rel_e5, emb_rel_e6, emb_rel_e7)
      :staticmethod:



   .. py:method:: residual_convolution(O_1, O_2)


   .. py:method:: forward_triples(x: 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:: AConvO(args: dict)

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


   Additive Convolutional Octonion Knowledge Graph Embeddings


   .. py:attribute:: name
      :value: 'AConvO'



   .. py:attribute:: conv2d


   .. py:attribute:: fc_num_input


   .. py:attribute:: fc1


   .. py:attribute:: bn_conv2d


   .. py:attribute:: norm_fc1


   .. py:attribute:: feature_map_dropout


   .. py:method:: octonion_normalizer(emb_rel_e0, emb_rel_e1, emb_rel_e2, emb_rel_e3, emb_rel_e4, emb_rel_e5, emb_rel_e6, emb_rel_e7)
      :staticmethod:



   .. py:method:: residual_convolution(O_1, O_2)


   .. py:method:: forward_triples(x: 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|)