#
# 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
#
# Author: Transport Map Team
# Website: transportmaps.mit.edu
# Support: transportmaps.mit.edu/qa/
#
import logging
import pickle
import numpy as np
import numpy.linalg as npla
import scipy.optimize as sciopt
from ..Misc import \
required_kwargs, \
deprecate
from ..MPI import mpi_map, mpi_map_alloc_dmem, mpi_bcast_dmem
from .LinearSpanParametricTriangularComponentwiseMapBase import \
LinearSpanParametricTriangularComponentwiseMap, \
CommonBasisLinearSpanParametricTriangularComponentwiseMap
from .ParametricTriangularComponentwiseTransportMapBase import \
ParametricTriangularComponentwiseTransportMap
__all__ = [
'NonMonotoneParametricTriangularComponentwiseTransportMap',
'NonMonotoneLinearSpanParametricTriangularComponentwiseTransportMap',
'NonMonotoneCommonBasisLinearSpanParametricTriangularComponentwiseTransportMap',
# Deprecated
'LinearSpanTriangularTransportMap',
'CommonBasisLinearSpanTriangularTransportMap',
'MonotonicLinearSpanTriangularTransportMap',
'MonotonicCommonBasisLinearSpanTriangularTransportMap'
]
nax = np.newaxis
[docs]class NonMonotoneParametricTriangularComponentwiseTransportMap(
ParametricTriangularComponentwiseTransportMap
):
pass
[docs]class NonMonotoneLinearSpanParametricTriangularComponentwiseTransportMap(
LinearSpanParametricTriangularComponentwiseMap,
NonMonotoneParametricTriangularComponentwiseTransportMap
):
r""" :class:`LinearSpanParametricTriangularComponentwiseMap` which allows for kl-minimization by enforcing pointwise constraints
"""
@required_kwargs('active_vars', 'approx_list')
def __init__(self, **kwargs):
r"""
Kwargs:
active_vars (:class:`list<list>` [:math:`d`] of :class:`list<list>`): for
each dimension lists the active variables.
approx_list (:class:`list<list>` [:math:`d`] of :class:`LinearSpanTensorizedParametricFunctional<TransportMaps.Maps.Functionals.LinearSpanTensorizedParametricFunctional`):
list of parametric functionals for each dimension
full_basis_list (:class:`list` of :class:`list`): list of basis for each input
of each component for a full triangular map
(this is needed for some adaptivity algorithm)
"""
super(NonMonotoneLinearSpanParametricTriangularComponentwiseTransportMap,
self).__init__(**kwargs)
[docs] def get_default_init_values_minimize_kl_divergence(self):
return self.get_identity_coeffs()
[docs]class NonMonotoneCommonBasisLinearSpanParametricTriangularComponentwiseTransportMap(
CommonBasisLinearSpanParametricTriangularComponentwiseMap,
NonMonotoneParametricTriangularComponentwiseTransportMap
):
r""" :class:`CommonBasisLinearSpanParametricTriangularComponentwiseMap` which allows for kl-minimization by enforcing pointwise constraints
"""
@required_kwargs('active_vars', 'approx_list')
def __init__(self, **kwargs):
r"""
Kwargs:
active_vars (:class:`list<list>` [:math:`d`] of :class:`list<list>`): for
each dimension lists the active variables.
approx_list (:class:`list<list>` [:math:`d`] of :class:`LinearSpanTensorizedParametricFunctional<TransportMaps.Maps.Functionals.LinearSpanTensorizedParametricFunctional`):
list of parametric functionals for each dimension
full_basis_list (:class:`list` of :class:`list`): list of basis for each input
of each component for a full triangular map
(this is needed for some adaptivity algorithm)
"""
super(NonMonotoneCommonBasisLinearSpanParametricTriangularComponentwiseTransportMap,
self).__init__(**kwargs)
[docs] def get_default_init_values_minimize_kl_divergence(self):
return self.get_identity_coeffs()
##############
# DEPRECATED #
##############
[docs]class MonotonicLinearSpanTriangularTransportMap(
NonMonotoneLinearSpanParametricTriangularComponentwiseTransportMap
):
@deprecate(
'MonotonicLinearSpanTriangularTransportMap',
'3.0',
'Use Maps.NonMonotoneLinearSpanParametricTriangularComponentwiseTransportMap instead.'
)
def __init__(self, active_vars, approx_list):
super(MonotonicLinearSpanTriangularTransportMap, self).__init__(
active_vars=active_vars,
approx_list=approx_list
)
[docs]class MonotonicCommonBasisLinearSpanTriangularTransportMap(
NonMonotoneCommonBasisLinearSpanParametricTriangularComponentwiseTransportMap
):
@deprecate(
'MonotonicCommonBasisLinearSpanTriangularTransportMap',
'3.0',
'Use Maps.NonMonotoneCommonBasisLinearSpanParametricTriangularComponentwiseTransportMap instead.'
)
def __init__(self, active_vars, approx_list):
super(MonotonicCommonBasisLinearSpanTriangularTransportMap,
self).__init__(
active_vars=active_vars,
approx_list=approx_list
)
[docs]class LinearSpanTriangularTransportMap(
NonMonotoneLinearSpanParametricTriangularComponentwiseTransportMap
):
@deprecate(
'LinearSpanTriangularTransportMap',
'3.0',
'Use Maps.NonMonotoneLinearSpanParametricTriangularComponentwiseTransportMap instead.'
)
def __init__(self, active_vars, approx_list):
super(LinearSpanTriangularTransportMap, self).__init__(
active_vars=active_vars,
approx_list=approx_list
)
[docs]class CommonBasisLinearSpanTriangularTransportMap(
NonMonotoneCommonBasisLinearSpanParametricTriangularComponentwiseTransportMap
):
@deprecate(
'CommonBasisLinearSpanTriangularTransportMap',
'3.0',
'Use Maps.NonMonotoneCommonBasisLinearSpanParametricTriangularComponentwiseTransportMap instead.'
)
def __init__(self, active_vars, approx_list):
super(CommonBasisLinearSpanTriangularTransportMap, self).__init__(
active_vars=active_vars,
approx_list=approx_list
)