#
# 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, cached, get_sub_cache
from .ParametricTransportMapBase import ParametricTransportMap
from .IdentityEmbeddedTransportMapBase import IdentityEmbeddedTransportMap
__all__ = [
'IdentityEmbeddedParametricTransportMap'
]
[docs]class IdentityEmbeddedParametricTransportMap(
IdentityEmbeddedTransportMap,
ParametricTransportMap
):
@required_kwargs('tm', 'idxs', 'dim')
def __init__(self, **kwargs):
if not isinstance(kwargs['tm'], ParametricTransportMap):
raise AttributeError("tm must be a ParametricTransportMap")
super(IdentityEmbeddedParametricTransportMap, self).__init__(**kwargs)
@property
[docs] def n_coeffs(self):
return self.tm.n_coeffs
@property
[docs] def coeffs(self):
return self.tm.coeffs
@coeffs.setter
def coeffs(self, coeffs):
self.tm.coeffs = coeffs
[docs] def get_identity_coeffs(self):
return self.tm.get_identity_coeffs()
@cached([('tm', None)],False)
@counted
[docs] def grad_a(self, x, precomp=None, idxs_slice=slice(None), cache=None):
if x.shape[1] != self.dim_in:
raise ValueError("dimension mismatch")
tm_cache = get_sub_cache(cache, ('tm', None))
return self.tm.grad_a(
x[:,self.idxs], precomp=precomp, idxs_slice=idxs_slice, cache=tm_cache)
@cached([('tm', None)],False)
@counted
[docs] def tuple_grad_a(self, x, precomp=None, idxs_slice=slice(None), cache=None):
if x.shape[1] != self.dim_in:
raise ValueError("dimension mismatch")
tm_cache = get_sub_cache(cache, ('tm', None))
return self.tm.tuple_grad_a(
x[:,self.idxs], precomp=precomp, idxs_slice=idxs_slice, cache=tm_cache)
@cached([('tm', None)],False)
@counted
[docs] def hess_a(self, x, precomp=None, idxs_slice=slice(None), cache=None):
if x.shape[1] != self.dim_in:
raise ValueError("dimension mismatch")
tm_cache = get_sub_cache(cache, ('tm', None))
return self.tm.hess_a(
x[:,self.idxs], precomp=precomp, idxs_slice=idxs_slice, cache=tm_cache)
@cached([('tm', None)],False)
@counted
[docs] def action_hess_a(self, x, da, precomp=None, idxs_slice=slice(None), cache=None):
if x.shape[1] != self.dim_in:
raise ValueError("dimension mismatch")
tm_cache = get_sub_cache(cache, ('tm', None))
return self.tm.action_hess_a(
x[:,self.idxs], da, precomp=precomp, idxs_slice=idxs_slice, cache=tm_cache)
@cached([('tm', None)],False)
@counted
[docs] def grad_a_log_det_grad_x(self, x, precomp=None, idxs_slice=slice(None), cache=None):
if x.shape[1] != self.dim_in:
raise ValueError("dimension mismatch")
tm_cache = get_sub_cache(cache, ('tm', None))
return self.tm.grad_a_log_det_grad_x(
x[:,self.idxs], precomp=precomp, idxs_slice=idxs_slice, cache=tm_cache)
@cached([('tm', None)],False)
@counted
[docs] def hess_a_log_det_grad_x(self, x, precomp=None, idxs_slice=slice(None), cache=None):
if x.shape[1] != self.dim_in:
raise ValueError("dimension mismatch")
tm_cache = get_sub_cache(cache, ('tm', None))
return self.tm.hess_a_log_det_grad_x(
x[:,self.idxs], precomp=precomp, idxs_slice=idxs_slice, cache=tm_cache)
@cached([('tm', None)],False)
@counted
[docs] def action_hess_a_log_det_grad_x(self, x, da, precomp=None, idxs_slice=slice(None), cache=None):
if x.shape[1] != self.dim_in:
raise ValueError("dimension mismatch")
tm_cache = get_sub_cache(cache, ('tm', None))
return self.tm.action_hess_a_log_det_grad_x(
x[:,self.idxs], da, precomp=precomp, idxs_slice=idxs_slice, cache=tm_cache)
[docs] def precomp_minimize_kl_divergence(self, *args, **kwargs):
self.tm.precomp_minimize_kl_divergence(*args, **kwargs)
[docs] def allocate_cache_minimize_kl_divergence(self, *args, **kwargs):
return self.tm.allocate_cache_minimize_kl_divergence(*args, **kwargs)
[docs] def reset_cache_minimize_kl_divergence(self, *args, **kwargs):
self.tm.reset_cache_minimize_kl_divergence(*args, **kwargs)
[docs] def get_default_init_values_minimize_kl_divergence(self):
return self.tm.get_default_init_values_minimize_kl_divergence()