class TVAEModel

class deeptime.decomposition.deep.TVAEModel(encoder, decoder, device=None, dtype=<class 'numpy.float32'>)

Model produced by the time-lagged variational autoencoder (TVAE). When transforming data, the encoded mean and log-variance are reparametrized and yielded.

See also

TAEModel

Attributes

decoder	The decoder.
encoder	The encoder.

Methods

copy()	Makes a deep copy of this model.
get_params([deep])	Get the parameters.
set_params(**params)	Set the parameters of this estimator.
transform(data, **kwargs)	Transforms a trajectory (or a list of trajectories) by passing them through the encoder network.

__call__(*args, **kwargs)

Call self as a function.

copy() → Model

Makes a deep copy of this model.

Returns:

A new copy of this model.

Return type:

copy

get_params(deep=False)

Get the parameters.

Returns:

params – Parameter names mapped to their values.

Return type:

mapping of string to any

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**params (dict) – Estimator parameters.

Returns:

self – Estimator instance.

Return type:

object

transform(data, **kwargs)

Transforms a trajectory (or a list of trajectories) by passing them through the encoder network.

Parameters:

• data (array_like or list of array_like) – The trajectory data.

• **kwargs – Ignored.

Returns:

latent_code – The trajectory / trajectories encoded to the latent representation.

Return type:

ndarray or list of ndarray

property decoder

The decoder.

Type:

torch.nn.Module

property encoder

The encoder.

Type:

torch.nn.Module