# Source code for gemseo.problems.dataset.rosenbrock

```
# 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.
# Contributors:
# INITIAL AUTHORS - initial API and implementation and/or initial
# documentation
# :author: Matthias De Lozzo
# OTHER AUTHORS - MACROSCOPIC CHANGES
"""
Rosenbrock dataset
==================
This :class:`.Dataset` contains 100 evaluations
of the well-known Rosenbrock function:
.. math::
f(x,y)=(1-x)^2+100(y-x^2)^2
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
<https://en.wikipedia.org/wiki/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)
```