Source code for gemseo.utils.derivatives.gradient_approximator_factory

# Copyright 2021 IRT Saint Exupéry,
# 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
# 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.
"""Factory for classes derived from :class:`GradientApproximator`."""
from __future__ import annotations

from typing import Any
from typing import Callable

from numpy import ndarray

from gemseo.algos.design_space import DesignSpace
from gemseo.core.base_factory import BaseFactory
from gemseo.utils.derivatives.approximation_modes import ApproximationMode
from gemseo.utils.derivatives.gradient_approximator import GradientApproximator

[docs]class GradientApproximatorFactory(BaseFactory): """A factory to create gradient approximators. In addition to the names of the classes, the factory can be queried with an :class:`ApproximationMode`. """ _CLASS = GradientApproximator _MODULE_NAMES = ("gemseo.utils.derivatives",) def __init__(self) -> None: # noqa:D107 super().__init__() for class_info in tuple(self._names_to_class_info.values()): approximation_mode = class_info.class_._APPROXIMATION_MODE self._names_to_class_info[approximation_mode] = class_info
[docs] def create( self, name: str | ApproximationMode, f_pointer: Callable, step: float | complex | ndarray | None = None, design_space: DesignSpace | None = None, normalize: bool = True, parallel: bool = False, **parallel_args: Any, ) -> GradientApproximator: """Create a gradient approximator. Args: name: The name of the class or the approximation mode. f_pointer: The pointer to the function to derive. step: The default differentiation step. design_space: The design space containing the upper bounds of the input variables. If ``None``, consider that the input variables are unbounded. normalize: Whether to normalize the function. parallel: Whether to differentiate the function in parallel. **parallel_args: The parallel execution options, see :mod:`gemseo.core.parallel_execution`. Returns: The gradient approximator. """ return super().create( name, f_pointer=f_pointer, step=step, design_space=design_space, normalize=normalize, parallel=parallel, **parallel_args, )
@property def gradient_approximators(self) -> list[str]: """The gradient approximators.""" return self.class_names