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  * <http://shark-ml.org/>
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>
40 using namespace shark;
41 
42 #include <shark/Models/LinearModel.h>
43 #include <shark/Algorithms/Trainers/NormalizeComponentsWhitening.h>
44 
45 int 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 }