OpenNN
Open-source neural networks library
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scaling.h
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1// OpenNN: Open Neural Networks Library
2// www.opennn.net
3//
4// S C A L I N G H E A D E R
5//
6// Artificial Intelligence Techniques, SL
7// artelnics@artelnics.com
8
9#pragma once
10
11#include "statistics.h"
12
13namespace opennn
14{
20
23
25 void scale_minimum_maximum(MatrixMap, Index, const Descriptives&, float = -1.0f, float = 1.0f);
26
29
31 void unscale_minimum_maximum(MatrixMap, Index, const Descriptives&, float = -1.0f, float = 1.0f);
32
35
38
41
44}
45
46// OpenNN: Open Neural Networks Library.
47// Copyright(C) 2005-2026 Artificial Intelligence Techniques, SL.
48// Licensed under the GNU Lesser General Public License v2.1 or later.
Definition adaptive_moment_estimation.h:14
void unscale_logarithmic(MatrixMap, Index)
Inverse of scale_logarithmic() for the given column.
void scale_standard_deviation(MatrixMap, Index, const Descriptives &)
Divides a column of the matrix by its standard deviation in place.
void unscale_minimum_maximum(MatrixMap, Index, const Descriptives &, float=-1.0f, float=1.0f)
Inverse of scale_minimum_maximum(): reconstructs original values for the given column.
void unscale_mean_standard_deviation(MatrixMap, Index, const Descriptives &)
Inverse of scale_mean_standard_deviation() for the given column.
void scale_mean_standard_deviation(MatrixMap, Index, const Descriptives &)
Standardises a column of the matrix in place using its descriptives' mean and standard deviation.
void scale_logarithmic(MatrixMap, Index)
Applies an element-wise logarithm to the given column.
void unscale_image_minimum_maximum(MatrixMap, Index)
Maps a column back from [-1, 1] to the [0, 255] image-pixel range.
void unscale_standard_deviation(MatrixMap, Index, const Descriptives &)
Inverse of scale_standard_deviation() for the given column.
void scale_minimum_maximum(MatrixMap, Index, const Descriptives &, float=-1.0f, float=1.0f)
Rescales a column to the [min_range, max_range] interval using its descriptives.
Map< MatrixR, Layout|AlignedMax > MatrixMap
Definition pch.h:186
Summary statistics (minimum, maximum, mean, standard deviation) for one variable.
Definition statistics.h:18