Source code for gemseo.core.mdofunctions.func_operations

# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
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# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
# Lesser General Public License for more details.
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"""
Functional operations
*********************
"""
from __future__ import annotations

from numpy import delete
from numpy import insert
from numpy import ndarray

from gemseo.core.mdofunctions.mdo_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: MDOFunction, restriction_indices: ndarray, restriction_values: ndarray, ) -> None: """ Args: orig_function: The original function to restrict. restriction_indices: The indices array of the input vector to fix. restriction_values: The values of the input vector at the indices, 'restriction_indices' are set to 'restriction_values'. Raises: ValueError: If the shape of the restriction values is not consistent with the shape of the restriction indices. """ if not restriction_indices.shape == restriction_values.shape: raise ValueError("Inconsistent shapes for restriction values and indices.") self.restriction_values = restriction_values self._restriction_indices = restriction_indices self._orig_function = orig_function name = str(orig_function.name) + "_restr" super().__init__( self.__restricted_function, name, jac=self.__restricted_jac, f_type=orig_function.f_type, expr=orig_function.expr, args=orig_function.args, dim=orig_function.dim, outvars=orig_function.outvars, ) def __restricted_function(self, x_vect: ndarray) -> MDOFunction: """Wrap the provided function in order to be given to the optimizer. Args: x_vect: The design variables values. Returns: The evaluation of the function at x_vect. """ x_full = insert(x_vect, self._restriction_indices, self.restriction_values) return self._orig_function(x_full) def __restricted_jac(self, x_vect: ndarray) -> MDOFunction.jac: """Wrap the provided Jacobian in order to be given to the optimizer. Args: x_vect: The design variables values. Returns: The evaluation of the Jacobian at x_vect. """ x_full = insert(x_vect, self._restriction_indices, self.restriction_values) jac = self._orig_function.jac(x_full) jac = delete(jac, self._restriction_indices, axis=0) return jac
[docs]class LinearComposition(MDOFunction): """Compose a function with a linear operator defined by a matrix. Compute orig_f(Mat.dot(x)). """ def __init__( self, orig_function: MDOFunction, interp_operator: ndarray, ): """Constructor. Args: orig_function: The original function to be restricted. interp_operator: The operator matrix, the output of the function will be f(interp_operator.dot(x)). """ self._orig_function = orig_function self._interp_operator = interp_operator self._orig_function = orig_function super().__init__( self._restricted_function, str(orig_function.name) + "_comp", jac=self._restricted_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, ) def _restricted_function(self, x_vect: ndarray) -> MDOFunction: """Wrap the provided function in order to be given to the optimizer. Args: x_vect: The design variable values. Returns: The evaluation of the function at x_vect. """ x_full = self._interp_operator.dot(x_vect) return self._orig_function(x_full) def _restricted_jac(self, x_vect: ndarray) -> MDOFunction.jac: """Wrap the provided Jacobian in order to be given to the optimizer. Args: x_vect: The design variable values. Returns: The evaluation of the function at x_vect. """ x_full = self._interp_operator.dot(x_vect) jac = self._orig_function.jac(x_full) return self._interp_operator.T.dot(jac)