# 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 typing import TYPE_CHECKING
from typing import Callable
from numpy import nonzero
from scipy.spatial.distance import cdist
from gemseo_mlearning.adaptive.criterion import MLDataAcquisitionCriterion
if TYPE_CHECKING:
from numpy.typing import NDArray
[docs]
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) -> Callable[[NDArray[float]], float]:
def func(input_data: NDArray[float]) -> float:
"""Evaluation function.
Args:
input_data: The model input data.
Returns:
The acquisition criterion value.
"""
train = self.algo_distribution.learning_set
train = train.get_view(group_names=train.INPUT_GROUP).to_numpy()
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