Source code for gemseo.mlearning.transform.scaler.min_max_scaler

# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
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# Lesser General Public License for more details.
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# Contributors:
#    INITIAL AUTHORS - initial API and implementation and/or initial
#                         documentation
#        :author: Matthias De Lozzo, Syver Doving Agdestein
#    OTHER AUTHORS   - MACROSCOPIC CHANGES
"""Scaling a variable with a geometrical linear transformation.

The :class:`.MinMaxScaler` class implements the MinMax scaling method
applying to some parameter :math:`z`:

.. math::

    \\bar{z} := \\text{offset} + \\text{coefficient}\\times z
    = \\frac{z-\\text{min}(z)}{(\\text{max}(z)-\\text{min}(z))},

where :math:`\\text{offset}=-\\text{min}(z)/(\\text{max}(z)-\\text{min}(z))`
and :math:`\\text{coefficient}=1/(\\text{max}(z)-\\text{min}(z))`.

In the MinMax scaling method,
the scaling operation linearly transforms the original variable :math:`z`
such that the minimum of the original data corresponds to 0 and the maximum to 1.
"""
from __future__ import annotations

from numpy import ndarray

from gemseo.mlearning.transform.scaler.scaler import Scaler
from gemseo.mlearning.transform.transformer import TransformerFitOptionType


[docs]class MinMaxScaler(Scaler): """Min-max scaler.""" def __init__( self, name: str = "MinMaxScaler", offset: float = 0.0, coefficient: float = 1.0, ) -> None: """ Args: name: A name for this transformer. offset: The offset of the linear transformation. coefficient: The coefficient of the linear transformation. """ super().__init__(name, offset, coefficient) def _fit( self, data: ndarray, *args: TransformerFitOptionType, ) -> None: l_b = data.min(0) u_b = data.max(0) self.offset = -l_b / (u_b - l_b) self.coefficient = 1 / (u_b - l_b)