# 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.
"""Discipline computing a linear combination of its inputs."""
from __future__ import annotations
from typing import TYPE_CHECKING
from numpy import zeros
from scipy.sparse import eye
from gemseo.core.discipline import Discipline
if TYPE_CHECKING:
from collections.abc import Iterable
from gemseo.typing import StrKeyMapping
[docs]
class LinearCombination(Discipline):
r"""Discipline computing a linear combination of its inputs.
The user can specify the coefficients related to the variables
as well as the offset.
E.g.,
a discipline
computing the output :math:`y`
from :math:`d` inputs :math:`x_1,\ldots,x_d`
with the function
:math:`f(x_1,\ldots,x_d)=a_0+\sum_{i=1}^d a_i x_i`.
When the offset :math:`a_0` is equal to 0
and the coefficients :math:`a_1,\ldots,a_d` are equal to 1,
the discipline simply sums the inputs.
Notes:
By default,
the :class:`.LinearCombination` simply sums the inputs.
Examples:
>>> discipline = LinearCombination(["alpha", "beta", "gamma"], "delta",
input_coefficients={"alpha": 1.,"beta": 2.,"gamma": 3.})
>>> input_data = {"alpha": array([1.0]), "beta": array([1.0]),
"gamma": array([1.0])}
>>> discipline.execute(input_data)
>>> delta = discipline.io.data["delta"] # delta = array([6.])
"""
__offset: float
r"""The offset :math:`a_0` in :math:`a_0+\sum_{i=1}^d a_i x_i`."""
__coefficients: dict[str, float]
r"""The coefficients :math:`a_1,\ldots,a_d` in :math:`a_0+\sum_{i=1}^d a_i x_i`."""
__output_name: str
"""The name of the output."""
def __init__(
self,
input_names: Iterable[str],
output_name: str,
input_coefficients: dict[str, float] | None = None,
offset: float = 0.0,
input_size: int | None = None,
) -> None:
"""
Args:
input_names: The names of input variables.
output_name: The name of the output variable.
input_coefficients: The coefficients related to the input variables.
If ``None``, use 1 for all the input variables.
offset: The output value when all the input variables are equal to zero.
input_size: The size of the inputs.
If ``None``, the default inputs are initialized with size 1 arrays.
""" # noqa: D205, D212, D415
super().__init__()
self.input_grammar.update_from_names(input_names)
self.output_grammar.update_from_names([output_name])
default_size = 1 if input_size is None else input_size
self.default_input_data.update({
input_name: zeros(default_size) for input_name in input_names
})
self.__coefficients = dict.fromkeys(input_names, 1.0)
if input_coefficients:
self.__coefficients.update(input_coefficients)
self.__offset = offset
self.__output_name = output_name
def _run(self, input_data: StrKeyMapping) -> StrKeyMapping | None:
output_data = {}
output_data[self.__output_name] = self.__offset
for input_name, input_value in input_data.items():
output_data[self.__output_name] += (
self.__coefficients[input_name] * input_value
)
return output_data
def _compute_jacobian(
self,
input_names: Iterable[str] = (),
output_names: Iterable[str] = (),
) -> None:
self._init_jacobian(
input_names, output_names, init_type=self.InitJacobianType.SPARSE
)
identity = eye(self.io.data[self.__output_name].size, format="csr")
jac = self.jac[self.__output_name]
for input_name in self.io.input_grammar.names:
jac[input_name] = self.__coefficients[input_name] * identity
