timesead.models.layers.autocorrelation
======================================

.. py:module:: timesead.models.layers.autocorrelation


Classes
-------

.. autoapisummary::

   timesead.models.layers.autocorrelation.AutoCorrelation
   timesead.models.layers.autocorrelation.AutoCorrelationLayer


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

.. py:class:: AutoCorrelation(factor=1, scale=None, attention_dropout=0.1, output_attention=False)

   Bases: :py:obj:`torch.nn.Module`


   AutoCorrelation Mechanism with the following two phases:
   (1) period-based dependencies discovery
   (2) time delay aggregation
   This block can replace the self-attention family mechanism seamlessly.

   Initialize internal Module state, shared by both nn.Module and ScriptModule.


   .. py:attribute:: factor
      :value: 1



   .. py:attribute:: scale
      :value: None



   .. py:attribute:: output_attention
      :value: False



   .. py:attribute:: dropout


   .. py:method:: time_delay_agg_training(values, corr)

      SpeedUp version of Autocorrelation (a batch-normalization style design)
      This is for the training phase.



   .. py:method:: time_delay_agg_inference(values, corr)

      SpeedUp version of Autocorrelation (a batch-normalization style design)
      This is for the inference phase.



   .. py:method:: time_delay_agg_full(values, corr)

      Standard version of Autocorrelation



   .. py:method:: forward(queries, keys, values, attn_mask)


.. py:class:: AutoCorrelationLayer(correlation, d_model, n_heads, d_keys=None, d_values=None)

   Bases: :py:obj:`torch.nn.Module`


   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

   Initialize internal Module state, shared by both nn.Module and ScriptModule.


   .. py:attribute:: inner_correlation


   .. py:attribute:: query_projection


   .. py:attribute:: key_projection


   .. py:attribute:: value_projection


   .. py:attribute:: out_projection


   .. py:attribute:: n_heads


   .. py:method:: forward(queries, keys, values, attn_mask)