Normalization.cpp
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1//===========================================================================
2/*!
3 *
4 *
5 * \brief Data Normalization
6 *
7 * This file is part of the tutorial "Normalization of Input Data".
8 * By itself, it does not do anything particularly useful.
9 *
10 * \author T. Glasmachers
11 * \date 2014
12 *
13 *
14 * \par Copyright 1995-2017 Shark Development Team
15 *
16 * <BR><HR>
17 * This file is part of Shark.
18 * <https://shark-ml.github.io/Shark/>
19 *
20 * Shark is free software: you can redistribute it and/or modify
21 * it under the terms of the GNU Lesser General Public License as published
22 * by the Free Software Foundation, either version 3 of the License, or
23 * (at your option) any later version.
24 *
25 * Shark is distributed in the hope that it will be useful,
26 * but WITHOUT ANY WARRANTY; without even the implied warranty of
27 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
28 * GNU Lesser General Public License for more details.
29 *
30 * You should have received a copy of the GNU Lesser General Public License
31 * along with Shark. If not, see <http://www.gnu.org/licenses/>.
32 *
33 */
34//===========================================================================
35
36#include <shark/Data/Csv.h>
37
38#include <shark/Models/Normalizer.h>
39#include <shark/Algorithms/Trainers/NormalizeComponentsUnitVariance.h>
40using namespace shark;
41
42#include <shark/Models/LinearModel.h>
43#include <shark/Algorithms/Trainers/NormalizeComponentsWhitening.h>
44
45int main()
46{
47 // data container
48 UnlabeledData<RealVector> data;
49
50 // create and train data normalizer
51 bool removeMean = true;
52 Normalizer<RealVector> normalizer;
53 NormalizeComponentsUnitVariance<RealVector> normalizingTrainer(removeMean);
54 normalizingTrainer.train(normalizer, data);
55
56 // transform data
57 UnlabeledData<RealVector> normalizedData = transform(data, normalizer);
58
59 // create and train data normalizer
60 LinearModel<RealVector> whitener;
61 NormalizeComponentsWhitening whiteningTrainer;
62 whiteningTrainer.train(whitener, data);
63
64 // transform data
65 UnlabeledData<RealVector> whitenedData = transform(data, whitener);
66}