Source code for gemseo.problems.dataset.rosenbrock

# Copyright 2021 IRT Saint Exupéry,
# This program is free software; you can redistribute it and/or
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# License version 3 as published by the Free Software Foundation.
# This program is distributed in the hope that it will be useful,
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# Lesser General Public License for more details.
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# 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
Rosenbrock dataset

This :class:`.Dataset` contains 100 evaluations
of the well-known Rosenbrock function:

.. math::


This function is known for its global minimum at point (1,1),
its banana valley and the difficulty to reach its minimum.

This :class:`.Dataset` is based on a full-factorial
design of experiments.

`More information about the Rosenbrock function
from __future__ import annotations

from numpy import hstack
from numpy import linspace
from numpy import meshgrid

from gemseo.core.dataset import Dataset

[docs]class RosenbrockDataset(Dataset): """Rosenbrock dataset parametrization.""" def __init__( self, name: str = "Rosenbrock", by_group: bool = True, n_samples: int = 100, categorize: bool = True, opt_naming: bool = True, ) -> None: """ Args: name: The name of the dataset. by_group: Whether to store the data by group. Otherwise, store them by variables. n_samples: The number of samples. categorize: Whether to distinguish between the different groups of variables. opt_naming: Whether to use an optimization naming. """ super().__init__(name, by_group) root_n_samples = int(n_samples**0.5) x_i = linspace(-2.0, 2.0, root_n_samples) x_i, y_i = meshgrid(x_i, x_i) x_i = x_i.reshape((-1, 1)) y_i = y_i.reshape((-1, 1)) z_i = 100 * (y_i - x_i**2) ** 2 + (1 - x_i) ** 2 data = hstack((x_i, y_i, z_i)) if categorize: if opt_naming: groups = {"x": Dataset.DESIGN_GROUP, "rosen": Dataset.FUNCTION_GROUP} else: groups = {"x": Dataset.INPUT_GROUP, "rosen": Dataset.OUTPUT_GROUP} else: groups = None self.set_from_array(data, ["x", "rosen"], {"x": 2, "rosen": 1}, groups=groups) self.set_metadata("root_n_samples", root_n_samples)