TransportMaps.Maps.PermutationTransportMapBase
¶
Module Contents¶
Classes¶
Map \(T({\bf x}) = [x_{p(0)}, \ldots, x_{p(d)}]^T\) |
- class TransportMaps.Maps.PermutationTransportMapBase.PermutationTransportMap(p)[source]¶
Bases:
TransportMaps.Maps.TransportMapBase.TransportMap
Map \(T({\bf x}) = [x_{p(0)}, \ldots, x_{p(d)}]^T\)
- Parameters:
p (list) – permutation list \(p\)
- evaluate(x, *args, **kwargs)[source]¶
[Abstract] Evaluate the map \(T\) at the points \({\bf x} \in \mathbb{R}^{m \times d_x}\).
- Parameters:
- Returns:
(
ndarray
[\(m,d_y\)]) – transformed points- Raises:
NotImplementedError – to be implemented in sub-classes
- grad_x(x, *args, **kwargs)[source]¶
[Abstract] Evaluate the gradient \(\nabla_{\bf x}T\) at the points \({\bf x} \in \mathbb{R}^{m \times d_x}\).
- Parameters:
- Returns:
(
ndarray
[\(m,d_y,d_x\)]) – transformed points- Raises:
NotImplementedError – to be implemented in sub-classes
- hess_x(x, *args, **kwargs)[source]¶
[Abstract] Evaluate the Hessian \(\nabla^2_{\bf x}T\) at the points \({\bf x} \in \mathbb{R}^{m \times d_x}\).
- Parameters:
- Returns:
(
ndarray
[\(m,d_y,d_x,d_x\)]) – transformed points- Raises:
NotImplementedError – to be implemented in sub-classes
- inverse(x, *args, **kwargs)[source]¶
[Abstract] Compute: \(T^{-1}({\bf x})\)
- Parameters:
- Returns:
(
ndarray
[\(m,d\)]) – \(T^{-1}({\bf x})\) for every evaluation point
- grad_x_inverse(x, *args, **kwargs)[source]¶
[Abstract] Compute \(\nabla_{\bf x} T^{-1}({\bf x})\).
- Parameters:
- Returns:
(
ndarray
[\(m,d,d\)]) – gradient matrices for every evaluation point.- Raises:
NotImplementedError – to be implemented in subclasses
- hess_x_inverse(x, *args, **kwargs)[source]¶
[Abstract] Compute \(\nabla_{\bf x}^2 T^{-1}({\bf x})\).
- Parameters:
- Returns:
(
ndarray
[\(m,d,d\)]) – Hessian tensors for every evaluation point.- Raises:
NotImplementedError – to be implemented in subclasses
- log_det_grad_x(x, *args, **kwargs)[source]¶
[Abstract] Compute: \(\log \det \nabla_{\bf x} T({\bf x}, {\bf a})\).
- Parameters:
- Returns:
(
ndarray
[\(m\)]) – \(\log \det \nabla_{\bf x} T({\bf x}, {\bf a})\) at every evaluation point
- grad_x_log_det_grad_x(x, *args, **kwargs)[source]¶
[Abstract] Compute: \(\nabla_{\bf x} \log \det \nabla_{\bf x} T({\bf x}, {\bf a})\)
- Parameters:
- Returns:
(
ndarray
[\(m,d\)]) – \(\nabla_{\bf x} \log \det \nabla_{\bf x} T({\bf x}, {\bf a})\) at every evaluation point
See also
- hess_x_log_det_grad_x(x, *args, **kwargs)[source]¶
[Abstract] Compute: \(\nabla^2_{\bf x} \log \det \nabla_{\bf x} T({\bf x}, {\bf a})\)
- Parameters:
- Returns:
(
ndarray
[\(m,d,d\)]) – \(\nabla^2_{\bf x} \log \det \nabla_{\bf x} T({\bf x}, {\bf a})\) at every evaluation point
See also
- det_grad_x(x, *args, **kwargs)[source]¶
[Abstract] Compute: \(\det \nabla_{\bf x} T({\bf x}, {\bf a})\).
- Parameters:
- Returns:
(
ndarray
[\(m\)]) – \(\det \nabla_{\bf x} T({\bf x}, {\bf a})\) at every evaluation point
- log_det_grad_x_inverse(x, *args, **kwargs)[source]¶
[Abstract] Compute: \(\log \det \nabla_{\bf x} T^{-1}({\bf x}, {\bf a})\).
- Parameters:
- Returns:
(
ndarray
[\(m\)]) – \(\log \det \nabla_{\bf x} T^{-1}({\bf x}, {\bf a})\) at every evaluation point
- grad_x_log_det_grad_x_inverse(x, *args, **kwargs)[source]¶
[Abstract] Compute: \(\nabla_{\bf x} \log \det \nabla_{\bf x} T^{-1}({\bf x}, {\bf a})\)
- Parameters:
- Returns:
(
ndarray
[\(m,d\)]) – \(\nabla_{\bf x} \log \det \nabla_{\bf x} T^{-1}({\bf x}, {\bf a})\) at every evaluation point
See also
- hess_x_log_det_grad_x_inverse(x, *args, **kwargs)[source]¶
[Abstract] Compute: \(\nabla^2_{\bf x} \log \det \nabla_{\bf x} T^{-1}({\bf x}, {\bf a})\)
- Parameters:
- Returns:
(
ndarray
[\(m,d,d\)]) – \(\nabla^2_{\bf x} \log \det \nabla_{\bf x} T^{-1}({\bf x}, {\bf a})\) at every evaluation point
See also