Source code for gemseo.algos.doe.lib_scalable

# -*- coding: utf-8 -*-
# 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.

# Contributors:
#    INITIAL AUTHORS - initial API and implementation and/or initial
#                           documentation
#        :author: Matthias De Lozzo
Build a diagonal DOE for scalable model construction
from __future__ import absolute_import, division, print_function, unicode_literals

from builtins import range, super

from future import standard_library
from numpy import array

from gemseo.algos.doe.doe_lib import DOELibrary


from gemseo import LOGGER

[docs]class DiagonalDOE(DOELibrary): """Class used for creation of a diagonal DOE.""" ALGO_LIST = ["DiagonalDOE"] ALGO_DESC = {} ALGO_DESC["DiagonalDOE"] = "Diagonal design of experiments" def __init__(self): """ Constructor, initializes the DOE samples """ super(DiagonalDOE, self).__init__() for algo in self.ALGO_LIST: description = DiagonalDOE.ALGO_DESC[algo] self.lib_dict[algo] = { DOELibrary.LIB: self.__class__.__name__, DOELibrary.INTERNAL_NAME: algo, DOELibrary.DESCRIPTION: description, } def _get_options( self, eval_jac=False, n_processes=1, wait_time_between_samples=0.0, n_samples=1, reverse=None, **kwargs ): # pylint: disable=W0221 """Sets the options :param eval_jac: evaluate jacobian :type eval_jac: bool :param n_processes: number of processes :type n_processes: int :param wait_time_between_samples: waiting time between two samples :type wait_time_between_samples: float :param n_samples: number of samples :type n_samples: int :param reverse: list of dimensions or variables to sample from their upper bounds to their lower bounds. Default: None. :type reverse: list(str) :param kwargs: additional arguments """ wtbs = wait_time_between_samples return self._process_options( eval_jac=eval_jac, n_processes=n_processes, wait_time_between_samples=wtbs, n_samples=n_samples, reverse=reverse, **kwargs ) def _generate_samples(self, **options): """ Generates the list of x samples :param options: the options dict for the algorithm, see associated JSON file """ reverse = options.get("reverse", []) names = self.problem.design_space.variables_names sizes = self.problem.design_space.variables_sizes name_by_index = {} start = 0 for name in names: for index in range(start, start + sizes[name]): name_by_index[index] = name start += sizes[name] samples = [] for index in range(self.problem.dimension): if str(index) in reverse or name_by_index[index] in reverse: samples.append( [ point / (options[self.N_SAMPLES] - 1.0) for point in range(options[self.N_SAMPLES] - 1, -1, -1) ] ) else: samples.append( [ point / (options[self.N_SAMPLES] - 1.0) for point in range(0, options[self.N_SAMPLES]) ] ) samples = array(samples).T return samples