TransportMaps.Maps.Functionals.ParametricMonotoneFunctionalBase
¶
Module Contents¶
Classes¶
Abstract class for the prametric functional \(f \approx f_{\bf a} = \sum_{{\bf i} \in \mathcal{I}} {\bf a}_{\bf i} \Phi_{\bf i}\) assumed to be monotonic in \(x_d\) |
|
Abstract class for the prametric functional \(f \approx f_{\bf a} = \sum_{{\bf i} \in \mathcal{I}} {\bf a}_{\bf i} \Phi_{\bf i}\) assumed to be monotonic in \(x_d\) |
- class TransportMaps.Maps.Functionals.ParametricMonotoneFunctionalBase.ParametricMonotoneFunctional(dim)[source]¶
Bases:
TransportMaps.Maps.Functionals.ParametricFunctionalBase.ParametricFunctional
,TransportMaps.Maps.Functionals.MonotoneFunctionalBase.MonotoneFunctional
Abstract class for the prametric functional \(f \approx f_{\bf a} = \sum_{{\bf i} \in \mathcal{I}} {\bf a}_{\bf i} \Phi_{\bf i}\) assumed to be monotonic in \(x_d\)
- precomp_minimize_kl_divergence_component(x, params, precomp_type='uni')[source]¶
Precompute necessary structures for the speed up of
minimize_kl_divergence_component()
- Parameters:
- Returns:
- (
tuple
(None,:class:dict<dict>)) – dictionary of necessary strucutres. The first argument is needed for consistency with
- (
- allocate_cache_minimize_kl_divergence_component(x)[source]¶
Allocate cache space for the KL-divergence minimization
- Parameters:
x (
ndarray
[\(m,d\)]) – evaluation points
- class TransportMaps.Maps.Functionals.ParametricMonotoneFunctionalBase.MonotonicFunctionApproximation(*args, **kwars)[source]¶
Bases:
ParametricMonotoneFunctional
Abstract class for the prametric functional \(f \approx f_{\bf a} = \sum_{{\bf i} \in \mathcal{I}} {\bf a}_{\bf i} \Phi_{\bf i}\) assumed to be monotonic in \(x_d\)