TransportMaps.Maps.Functionals.ParametricMonotoneFunctionalBase

Module Contents

Classes

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 \(x_d\)

MonotonicFunctionApproximation

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 \(x_d\)

get_default_init_values_regression()[source]
get_default_init_values_minimize_kl_divergence_component()[source]
precomp_minimize_kl_divergence_component(x, params, precomp_type='uni')[source]

Precompute necessary structures for the speed up of minimize_kl_divergence_component()

Parameters:
  • x (ndarray [\(m,d\)]) – evaluation points

  • params (dict) – parameters to be updated

  • precomp_type (str) – whether to precompute univariate Vandermonde matrices ‘uni’ or multivariate Vandermonde matrices ‘multi’

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

reset_cache_minimize_kl_divergence_component(cache)[source]

Reset the objective part of the cache space for the KL-divergence minimization

Parameters:

params2 (dict) – dictionary of precomputed values

minimize_kl_divergence_component_callback(xk)[source]
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 \(x_d\)