# -*- coding: utf-8 -*-
# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
#
# This program 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 this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
"""
Functional operations
*********************
"""
from __future__ import division, unicode_literals
from numpy import delete, insert
from gemseo.core.function import MDOFunction
[docs]class RestrictedFunction(MDOFunction):
"""Restrict an MDOFunction to a subset of its input vector Fixes the rest of the
indices."""
def __init__(self, orig_function, restriction_indices, restriction_values):
"""Constructor.
:param orig_function: the original function to restrict
:param restriction_indices: indices array of the input vector to fix
:param restriction_values: the values of the input vector at indices
'restriction_indices' are set to restriction_values
"""
if not restriction_indices.shape == restriction_values.shape:
raise ValueError(
"Inconsistent shapes for restriction" + "values and indices "
)
self.restriction_values = restriction_values
func, jac = self._restrict(orig_function, restriction_indices)
self._orig_function = orig_function
name = str(orig_function.name) + "_restr"
super(RestrictedFunction, self).__init__(
func,
name,
jac=jac,
f_type=orig_function.f_type,
expr=orig_function.expr,
args=orig_function.args,
dim=orig_function.dim,
outvars=orig_function.outvars,
)
def _restrict(self, orig_function, restriction_indices):
"""Restricts the function, builds the pointer to f and jac.
@param orig_function: original MDOFunction pointer
@param restriction_indices: indices array for restriction
"""
def restricted_function(x_vect):
"""Wrapped provided function in order to give to optimizer.
:param x_vect: design variable
:returns: evaluation of function at x_vect
"""
x_full = insert(x_vect, restriction_indices, self.restriction_values)
return orig_function(x_full)
def restricted_jac(x_vect):
"""Wrapped provided jacobian in order to give to optimizer.
:param x_vect: design variable
:returns: evaluation of jacobian at x_vect
"""
x_full = insert(x_vect, restriction_indices, self.restriction_values)
jac = orig_function.jac(x_full)
jac = delete(jac, restriction_indices, axis=0)
return jac
return restricted_function, restricted_jac
[docs]class LinerarComposition(MDOFunction):
"""Composes a function with a linear operator defined by a matrix computes
orig_f(Mat.dot(x))"""
def __init__(self, orig_function, interp_operator):
"""Constructor.
:param orig_function: the original function to restrict
:param interp_operator: operator matrix, the output of the
function will be f(interp_operator.dot(x))
"""
func, jac = self._restrict(orig_function, interp_operator)
self._orig_function = orig_function
super(LinerarComposition, self).__init__(
func,
str(orig_function.name) + "_comp",
jac=jac,
f_type=orig_function.f_type,
expr="Mat*" + str(orig_function.expr),
args=orig_function.args,
dim=orig_function.dim,
outvars=orig_function.outvars,
)
@staticmethod
def _restrict(orig_function, interp_operator):
"""Generates the function restriction.
@param orig_function : the original function to restrict
@param interp_operator: operator matrix, the output of the
function will be f(interp_operator.dot(x))
"""
def restricted_function(x_vect):
"""Wrapped provided function in order to give to optimizer.
:param x_vect: design variable
:returns: evaluation of function at x_vect
"""
x_full = interp_operator.dot(x_vect)
return orig_function(x_full)
def restricted_jac(x_vect):
"""Wrapped provided jacobian in order to give to optimizer.
:param x_vect: design variable
:returns: evaluation of jacobian at x_vect
"""
x_full = interp_operator.dot(x_vect)
jac = orig_function.jac(x_full)
return interp_operator.T.dot(jac)
return restricted_function, restricted_jac
