timesead.models.generative.madgan
=================================

.. py:module:: timesead.models.generative.madgan


Classes
-------

.. autoapisummary::

   timesead.models.generative.madgan.MADGAN
   timesead.models.generative.madgan.MADGANTrainer
   timesead.models.generative.madgan.MADGANAnomalyDetector


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

.. py:class:: MADGAN(input_dim: int, latent_dim: int = 15, generator_hidden_dims: List[int] = [100, 100, 100], discriminator_hidden_dims: List[int] = [100])

   Bases: :py:obj:`timesead.models.common.GAN`, :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: 15



   .. py:method:: grouped_parameters() -> Tuple[Iterator[torch.nn.Parameter], Ellipsis]


.. py:class:: MADGANTrainer(*args, disc_iterations: int = 1, gen_iterations: int = 3, **kwargs)

   Bases: :py:obj:`timesead.optim.trainer.Trainer`


   .. py:attribute:: disc_iterations
      :value: 1



   .. py:attribute:: gen_iterations
      :value: 3



   .. py:method:: validate_batch(network: MADGAN, val_metrics: Dict[str, Callable], b_inputs: Tuple[torch.Tensor, Ellipsis], b_targets: Tuple[torch.Tensor, Ellipsis], *args, **kwargs) -> Dict[str, float]


   .. py:method:: train_batch(network: MADGAN, losses: List[timesead.optim.loss.Loss], optimizers: List[torch.optim.Optimizer], epoch: int, num_epochs: int, b_inputs: Tuple[torch.Tensor, Ellipsis], b_targets: Tuple[torch.Tensor, Ellipsis]) -> List[float]


.. py:class:: MADGANAnomalyDetector(model: MADGAN, max_iter: int = 1000, lambder: float = 0.5, rec_error_tolerance: float = 0.1)

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


   .. py:attribute:: model


   .. py:attribute:: max_iter
      :value: 1000



   .. py:attribute:: lambder
      :value: 0.5



   .. py:attribute:: rec_error_tolerance
      :value: 0.1



   .. py:attribute:: rbf_kernel


   .. 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