#
# This file is part of TransportMaps.
#
# TransportMaps is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# TransportMaps is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with TransportMaps. If not, see <http://www.gnu.org/licenses/>.
#
# Transport Maps Library
# Copyright (C) 2015-2018 Massachusetts Institute of Technology
# Uncertainty Quantification group
# Department of Aeronautics and Astronautics
#
# Authors: Transport Map Team
# Website: transportmaps.mit.edu
# Support: transportmaps.mit.edu/qa/
#
from ..Misc import \
required_kwargs, \
counted
from .MapBase import Map
__all__ = [
'InverseMap'
]
[docs]class InverseMap(Map):
r""" Defines the map :math:`S := T^{\dagger}`
"""
@required_kwargs('base_map')
def __init__(self, **kwargs):
r"""
Kwargs:
base_map (:class:`Map`): map :math:`T`
"""
base_map = kwargs['base_map']
if not isinstance(base_map, Map):
raise ValueError(
"The provided base_map is not a Map"
)
self.base_map = base_map
kwargs['dim_in'] = base_map.dim_out
kwargs['dim_out'] = base_map.dim_in
super(InverseMap, self).__init__(**kwargs)
@property
[docs] def base_map(self):
try:
return self._base_map
except AttributeError:
# Backward compatibility v < 3.0
return self.tm
@base_map.setter
def base_map(self, base_map):
self._base_map = base_map
[docs] def get_ncalls_tree(self, indent=""):
out = super(InverseMap, self).get_ncalls_tree(indent)
out += self.base_map.get_ncalls_tree(indent + " ")
return out
[docs] def get_nevals_tree(self, indent=""):
out = super(InverseMap, self).get_nevals_tree(indent)
out += self.base_map.get_nevals_tree(indent + " ")
return out
[docs] def get_teval_tree(self, indent=""):
out = super(InverseMap, self).get_teval_tree(indent)
out += self.base_map.get_teval_tree(indent + " ")
return out
[docs] def update_ncalls_tree(self, obj):
super(InverseMap, self).update_ncalls_tree(obj)
self.base_map.update_ncalls_tree( obj.tm )
[docs] def update_nevals_tree(self, obj):
super(InverseMap, self).update_nevals_tree(obj)
self.base_map.update_nevals_tree( obj.tm )
[docs] def update_teval_tree(self, obj):
super(InverseMap, self).update_teval_tree(obj)
self.base_map.update_teval_tree( obj.tm )
[docs] def reset_counters(self):
super(InverseMap, self).reset_counters()
self.base_map.reset_counters()
@counted
[docs] def evaluate(self, x, precomp=None, idxs_slice=slice(None), *args, **kwargs):
r""" Evaluate the map :math:`T^{-1}` at the points :math:`{\bf x} \in \mathbb{R}^{m \times d}`.
Args:
x (:class:`ndarray<numpy.ndarray>` [:math:`m,d`]): evaluation points
precomp (:class:`dict<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:
(:class:`ndarray<numpy.ndarray>` [:math:`m,d`]) -- transformed points
Raises:
ValueError: if :math:`d` does not match the dimension of the transport map.
"""
return self.base_map.inverse(x)
@counted
[docs] def grad_x(self, x, *args, **kwargs):
return self.base_map.grad_x_inverse(x, *args, **kwargs)
@counted
[docs] def tuple_grad_x(self, x, *args, **kwargs):
return self.base_map.tuple_grad_x_inverse(x, *args, **kwargs)
@counted
[docs] def hess_x(self, x, *args, **kwargs):
return self.base_map.hess_x_inverse(x, *args, **kwargs)
@counted
[docs] def action_hess_x(self, x, *args, **kwargs):
return self.base_map.action_hess_x_inverse(x, *args, **kwargs)
@counted
[docs] def inverse(self, x, *args, **kwargs):
r""" Evaluates :math:`T`
Args:
x (:class:`ndarray<numpy.ndarray>` [:math:`m,d`]): evaluation points
Returns:
(:class:`ndarray<numpy.ndarray>` [:math:`m,d`]) --
:math:`T({\bf x})` for every evaluation point
"""
return self.base_map.evaluate(x)
@counted
[docs] def grad_x_inverse(self, x, *args, **kwargs):
r""" Evaluates :math:`\nabla_{\bf x}T`
Args:
x (:class:`ndarray<numpy.ndarray>` [:math:`m,d`]): evaluation points
precomp (:class:`dict<dict>`): dictionary of precomputed values
Returns:
(:class:`ndarray<numpy.ndarray>` [:math:`m,d,d`]) --
gradient matrices for every evaluation point.
Raises:
ValueError: if :math:`d` does not match the dimension of the transport map.
"""
return self.base_map.grad_x(x)
@counted
[docs] def tuple_grad_x_inverse(self, x, *args, **kwargs):
return self.base_map.tuple_grad_x(x)
@counted
[docs] def hess_x_inverse(self, x, *args, **kwargs):
r""" Evaluates :math:`\nabla^2_{\bf x}T`
Args:
x (:class:`ndarray<numpy.ndarray>` [:math:`m,d`]): evaluation points
precomp (:class:`dict<dict>`): dictionary of precomputed values
Returns:
(:class:`ndarray<numpy.ndarray>` [:math:`m,d,d,d`]) --
Hessian matrices for every evaluation point and every dimension.
Raises:
ValueError: if :math:`d` does not match the dimension of the transport map.
"""
return self.base_map.hess_x(x)
@counted
[docs] def action_hess_x_inverse(self, x, *args, **kwargs):
return self.base_map.action_hess_x(x)