TransportMaps.Maps.InverseTransportMapBase

Module Contents

Classes

InverseTransportMap

Given the transport map \(T\), define \(S=T^{-1}\).
class TransportMaps.Maps.InverseTransportMapBase.InverseTransportMap(**kwargs)[source]

Bases: TransportMaps.Maps.InverseMapBase.InverseMap, TransportMaps.Maps.TransportMapBase.TransportMap

Given the transport map \(T\), define \(S=T^{-1}\).

log_det_grad_x(x, *args, **kwargs)[source]

[Abstract] Compute: \(\log \det \nabla_{\bf x} T({\bf x}, {\bf a})\).

Parameters:

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

  • precomp (dict) – dictionary of precomputed values

  • idxs_slice (slice) – if precomputed values are present, this parameter indicates at which of the points to evaluate. The number of indices represented by idxs_slice must match x.shape[0].

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:

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

  • precomp (dict) – dictionary of precomputed values

  • idxs_slice (slice) – if precomputed values are present, this parameter indicates at which of the points to evaluate. The number of indices represented by idxs_slice must match x.shape[0].

Returns:

(ndarray [\(m,d\)]) – \(\nabla_{\bf x} \log \det \nabla_{\bf x} T({\bf x}, {\bf a})\) at every evaluation point

See also

log_det_grad_x().

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:

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

  • precomp (dict) – dictionary of precomputed values

  • idxs_slice (slice) – if precomputed values are present, this parameter indicates at which of the points to evaluate. The number of indices represented by idxs_slice must match x.shape[0].

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

log_det_grad_x() and grad_x_log_det_grad_x().

log_det_grad_x_inverse(x, *args, **kwargs)[source]

[Abstract] Compute: \(\log \det \nabla_{\bf x} T^{-1}({\bf x}, {\bf a})\).

Parameters:

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

  • precomp (dict) – dictionary of precomputed values

  • idxs_slice (slice) – if precomputed values are present, this parameter indicates at which of the points to evaluate. The number of indices represented by idxs_slice must match x.shape[0].

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:

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

  • precomp (dict) – dictionary of precomputed values

  • idxs_slice (slice) – if precomputed values are present, this parameter indicates at which of the points to evaluate. The number of indices represented by idxs_slice must match x.shape[0].

Returns:

(ndarray [\(m,d\)]) – \(\nabla_{\bf x} \log \det \nabla_{\bf x} T^{-1}({\bf x}, {\bf a})\) at every evaluation point

See also

log_det_grad_x().

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:

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

  • precomp (dict) – dictionary of precomputed values

  • idxs_slice (slice) – if precomputed values are present, this parameter indicates at which of the points to evaluate. The number of indices represented by idxs_slice must match x.shape[0].

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

log_det_grad_x() and grad_x_log_det_grad_x().