Source code for gemseo.mlearning.transform.scaler.min_max_scaler
# -*- coding: utf-8 -*-
# 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 - initial API and implementation and/or initial
# documentation
# :author: Matthias De Lozzo, Syver Doving Agdestein
# OTHER AUTHORS - MACROSCOPIC CHANGES
"""
Min-max data scaler
===================
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 absolute_import, division, unicode_literals
from future import standard_library
from gemseo.mlearning.transform.scaler.scaler import Scaler
standard_library.install_aliases()
[docs]class MinMaxScaler(Scaler):
""" Min-max scaler. """
def __init__(self, name="MinMaxScaler", offset=0.0, coefficient=1.0):
"""Constructor.
:param str name: name of the scaler. Default: 'MinMaxScaler'.
:param float offset: offset of the linear transformation.
Default: 0.
:param float coefficient: coefficient of the linear transformation.
Default: 1.
"""
super(MinMaxScaler, self).__init__(name, offset, coefficient)
[docs] def fit(self, data):
"""Fit offset and coefficient terms from a data array. The min and the
max are computed along the first axis of the data.
:param array data: data to be fitted.
"""
super(MinMaxScaler, self).fit(data)
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)