timesead.models.generative.omni_anomaly
=======================================

.. py:module:: timesead.models.generative.omni_anomaly


Classes
-------

.. autoapisummary::

   timesead.models.generative.omni_anomaly.KalmanFilter
   timesead.models.generative.omni_anomaly.OmniAnomaly
   timesead.models.generative.omni_anomaly.OmniAnomalyLoss
   timesead.models.generative.omni_anomaly.OmniAnomalyDetector


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

.. py:class:: KalmanFilter(state_dim: int, observation_dim: int)

   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:: state_dim


   .. py:attribute:: observation_dim


   .. py:attribute:: F


   .. py:attribute:: state_cov


   .. py:attribute:: H


   .. py:attribute:: obs_cov


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


.. py:class:: OmniAnomaly(input_dim: int, latent_dim: int = 3, rnn_hidden_dims: Sequence[int] = (500, ), dense_hidden_dims: Sequence[int] = (500, 500), nf_layers: int = 20)

   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

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


   .. py:attribute:: latent_dim
      :value: 3



   .. py:attribute:: prior


   .. py:attribute:: enc_rnn


   .. py:attribute:: encoder_vae


   .. py:attribute:: latent_nf


   .. py:attribute:: decoder_rnn


   .. py:attribute:: decoder_vae


   .. py:method:: forward(inputs: Tuple[torch.Tensor], num_samples: int = 1) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.nn.Module, torch.Tensor, torch.Tensor]


.. py:class:: OmniAnomalyLoss

   Bases: :py:obj:`timesead.optim.loss.Loss`


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


.. py:class:: OmniAnomalyDetector(model: OmniAnomaly, num_mc_samples: int = 1024)

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


   
   We decided not to include the reconstruction step from the paper here, since we don't have missing data.

   :param model:
   :param num_mc_samples:


   .. py:attribute:: model


   .. py:attribute:: num_mc_samples
      :value: 1024



   .. 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