class TAEModel

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

Model produced by time-lagged autoencoders. Contains the encoder, decoder, and can transform data to the latent code.

Parameters:

• encoder (torch.nn.Module) – The encoder module.

• decoder (torch.nn.Module) – The decoder module.

• device (torch device or None, default=None) – The device to use.

• dtype (numpy datatype, default=np.float32) – The dtype that is used for transformation of data.

See also

TAE

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