#
# 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/
#
import numpy as np
from ..Misc import \
cached, counted, get_sub_cache
from .ListStackedParametricTransportMapBase import ListStackedParametricTransportMap
from .TriangularListStackedTransportMapBase import TriangularListStackedTransportMap
__all__ = [
'TriangularListStackedParametricTransportMap'
]
[docs]class TriangularListStackedParametricTransportMap(
TriangularListStackedTransportMap, ListStackedParametricTransportMap):
@cached([('map_list',"n_maps")])
@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")
map_list_cache = get_sub_cache(cache, ('map_list',self.n_maps))
out = np.zeros((x.shape[0], self.n_coeffs))
for tm, avars, tm_cache in zip(self.map_list, self.active_vars, map_list_cache):
out[:,avars] = tm.grad_a_log_det_grad_x(
x[:,avars], idxs_slice=idxs_slice, cache=tm_cache)
return out
@cached([('map_list',"n_maps")])
@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")
map_list_cache = get_sub_cache(cache, ('map_list',self.n_maps))
out = np.zeros((x.shape[0], self.n_coeffs, self.n_coeffs))
for tm, avars, tm_cache in zip(self.map_list, self.active_vars, map_list_cache):
# 2d numpy advanced indexing
nvar = len(avars)
rr, cc = np.meshgrid(avars,avars)
rr = list( rr.flatten() )
cc = list( cc.flatten() )
idxs = (slice(None), rr, cc)
# Evaluate
out[idxs] = tm.hess_a_log_det_grad_x(
x[:,avars], idxs_slice=idxs_slice, cache=tm_cache)
return out
@cached([('map_list', "n_maps")])
@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")
map_list_cache = get_sub_cache(cache, ('map_list',self.n_maps))
out = np.zeros((x.shape[0], self.n_coeffs))
start = 0
for tm, avars, tm_cache in zip(self.map_list, self.active_vars, map_list_cache):
stop = start + tm.n_coeffs
# Evaluate
out[:,avars] = tm.hess_a_log_det_grad_x(
x[:,avars], da[start:stop], idxs_slice=idxs_slice, cache=tm_cache)
start = stop
return out