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
Attributes
The decoder.
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