# 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
r"""Combination of the expectation and standard deviation of the regression model.

Statistics:

.. math::

E_sigma[x] = E[x] + \kappa \times sigma[x]

Estimator:

.. math::

\widehat{E_sigma}[x] = \widehat{E}[x] + \kappa \times \widehat{sigma}[x]
"""
from __future__ import annotations

from typing import Callable

from numpy.typing import NDArray

[docs]class MeanSigma(MLDataAcquisitionCriterion):
"""Combination of the expectation and standard deviation of the regression model.

This criterion is scaled by the output range.
"""

kappa: float
"""The factor associated with the standard deviation to increase the mean value."""

def __init__(
self, algo_distribution: MLRegressorDistribution, kappa: float
) -> None:
"""# noqa: D205 D212 D415
Args:
kappa: A factor associated with the standard deviation
to increase or decrease the mean value.
"""
self.kappa = kappa
super().__init__(algo_distribution)

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.
"""
mean = self.algo_distribution.compute_mean(input_data)
sigma = self.algo_distribution.compute_standard_deviation(input_data)
return (mean + self.kappa * sigma) / self._scaling_factor

return func