timesead.models.generative.gru_gmm_vae
======================================

.. py:module:: timesead.models.generative.gru_gmm_vae


Classes
-------

.. autoapisummary::

   timesead.models.generative.gru_gmm_vae.RNNVAECategoricalEncoder
   timesead.models.generative.gru_gmm_vae.GRUGMMVAE
   timesead.models.generative.gru_gmm_vae.GMMVAELoss
   timesead.models.generative.gru_gmm_vae.GMMVAEAnomalyDetector


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

.. py:class:: RNNVAECategoricalEncoder(input_dim: int, rnn_type: str = 'lstm', rnn_hidden_dims: List[int] = [60], categories: int = 10, bidirectional: bool = False, mode: str = 's2s')

   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:: rnn


   .. py:attribute:: linear


   .. py:method:: forward(x: torch.Tensor) -> torch.Tensor


.. py:class:: GRUGMMVAE(input_dim: int, gru_hidden_dims: List[int] = [60], latent_dim: int = 8, gmm_components: int = 2)

   Bases: :py:obj:`timesead.models.BaseModel`


   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

   Guo2018 (more or less)

   :param input_dim:
   :param gru_hidden_dims:
   :param latent_dim:


   .. py:attribute:: latent_dim
      :value: 8



   .. py:attribute:: gmm_components
      :value: 2



   .. py:attribute:: encoder_rnn


   .. py:attribute:: encoder_component


   .. py:attribute:: vae


   .. py:attribute:: prior_means


   .. py:attribute:: prior_std


   .. py:attribute:: softplus


   .. py:method:: get_prior(batch_size: int, seq_len: int) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor]]


   .. py:method:: forward(inputs: Tuple[torch.Tensor]) -> Tuple[torch.Tensor, Ellipsis]


.. py:class:: GMMVAELoss

   Bases: :py:obj:`timesead.models.common.VAELoss`


   .. py:method:: forward(predictions: Tuple[torch.Tensor, Ellipsis], targets: Tuple[torch.Tensor, Ellipsis], *args, **kwargs) -> torch.Tensor


.. py:class:: GMMVAEAnomalyDetector(model: GRUGMMVAE, num_mc_samples: int = 1)

   Bases: :py:obj:`timesead.models.common.AnomalyDetector`


   
   Use sampled log likelihood of data

   :param model:
   :param num_mc_samples:


   .. py:attribute:: model


   .. py:attribute:: num_mc_samples
      :value: 1



   .. py:method:: compute_online_anomaly_score(inputs: Tuple[torch.Tensor, Ellipsis]) -> torch.Tensor


   .. py:method:: compute_offline_anomaly_score(inputs: Tuple[torch.Tensor, Ellipsis]) -> torch.Tensor


   .. py:method:: fit(dataset: torch.utils.data.DataLoader) -> None


   .. py:method:: format_online_targets(targets: Tuple[torch.Tensor, Ellipsis]) -> torch.Tensor