# 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
#
# 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 Callable

from numpy import nonzero
from numpy.typing import NDArray
from scipy.spatial.distance import cdist

[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_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