class KernelEDMDModel¶
- class deeptime.decomposition.KernelEDMDModel(data: ndarray, eigenvalues: ndarray, eigenvectors: ndarray, kernel: Kernel)¶
The kEDMD model containing eigenvalues and eigenfunctions evaluated in the instantaneous data.
- Parameters:
eigenvalues ((d,) ndarray) – The eigenvalues.
eigenvectors ((T, d) ndarray) – The eigenfunction evaluation.
kernel (Kernel) – The kernel that was used for estimation.
See also
Attributes
Transforms the current state \(x_t\) to \(f(x_t)\).
Same as
operator
.The operator \(K\) so that \(\mathbb{E}[g(x_{t+\tau})] = K^\top \mathbb{E}[f(x_t)]\) in transformed bases.
Inverse of the operator \(K\), i.e., \(K^{-1}\).
The dimension of data after propagation by \(K\).
Transforms the future state \(x_{t+\tau}\) to \(g(x_{t+\tau})\).
Methods
backward
(data[, propagate])Maps data backward in time.
copy
()Makes a deep copy of this model.
forward
(data[, propagate])Maps data forward in time.
get_params
([deep])Get the parameters.
set_params
(**params)Set the parameters of this estimator.
transform
(data[, propagate])Transforms data by applying the observables to it and then mapping them onto the modes of \(K\).
- __call__(*args, **kwargs)¶
Call self as a function.
- backward(data: ndarray, propagate=True)¶
Maps data backward in time.
- Parameters:
data ((T, n) ndarray) – Input data
propagate (bool, default=True) – Whether to apply the Koopman operator to the featurized data.
- Returns:
mapped_data – Mapped data.
- Return type:
(T, m) ndarray
- copy() Model ¶
Makes a deep copy of this model.
- Returns:
A new copy of this model.
- Return type:
copy
- forward(data: ndarray, propagate=True)¶
Maps data forward in time.
- Parameters:
data ((T, n) ndarray) – Input data
propagate (bool, default=True) – Whether to apply the Koopman operator to the featurized data.
- Returns:
mapped_data – Mapped data.
- Return type:
(T, m) ndarray
- 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: ndarray, propagate=True)¶
Transforms data by applying the observables to it and then mapping them onto the modes of \(K\).
- Parameters:
data ((T,n) ndarray) – Input data.
- Returns:
transformed_data – The transformed data.
- Return type:
(T,m) ndarray
- property instantaneous_obs: Callable[[ndarray], ndarray]¶
Transforms the current state \(x_t\) to \(f(x_t)\). Defaults to f(x) = x.
- property operator: ndarray¶
The operator \(K\) so that \(\mathbb{E}[g(x_{t+\tau})] = K^\top \mathbb{E}[f(x_t)]\) in transformed bases.
- property operator_inverse: ndarray¶
Inverse of the operator \(K\), i.e., \(K^{-1}\). Potentially pseudo-inverse instead of true inverse.
- property output_dimension¶
The dimension of data after propagation by \(K\).
- property timelagged_obs: Callable[[ndarray], ndarray]¶
Transforms the future state \(x_{t+\tau}\) to \(g(x_{t+\tau})\). Defaults to f(x) = x.