Source code for gemseo_mlearning.adaptive.criteria.distances.criterion_min

# 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 - API and implementation and/or documentation
#        :author: Matthias De Lozzo
#    OTHER AUTHORS   - MACROSCOPIC CHANGES
"""Minimum distance between a point and the learning dataset."""
from __future__ import annotations

from numpy import ndarray
from numpy import nonzero
from scipy.spatial.distance import cdist

from gemseo_mlearning.adaptive.criterion import MLDataAcquisitionCriterion


class MinimumDistance(MLDataAcquisitionCriterion):
    """Minimum distance to the learning dataset.

    This infill criterion computes the minimum distance between a new point and the
    point of the learning dataset, scaled by the maximum distance between two learning
    points.
    """

    def _get_func(self):
        def func(input_data: ndarray) -> float:
            """Evaluation function.

            Args:
                input_data: The model input data.

            Returns:
                The acquisition criterion value.
            """
            train = self.algo_distribution.learning_set
            train = train.get_data_by_group(train.INPUT_GROUP)
            distance = cdist(input_data.reshape((1, -1)), train).min()
            dist_train = cdist(train, train)
            d_max = dist_train[nonzero(dist_train)].min() / 2.0
            distance /= d_max
            return distance

        return func