source: trunk/yat/statistics/SNRScore.h

Last change on this file was 4207, checked in by Peter, 5 months ago

update copyright statements

  • Property svn:eol-style set to native
  • Property svn:keywords set to Id
  • Property svndigest:ignore set to 855
File size: 2.9 KB
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1#ifndef _theplu_yat_statistics_snr
2#define _theplu_yat_statistics_snr
3
4// $Id: SNRScore.h 4207 2022-08-26 04:36:28Z peter $
5
6/*
7  Copyright (C) 2006, 2007 Jari Häkkinen, Peter Johansson, Markus Ringnér
8  Copyright (C) 2008 Jari Häkkinen, Peter Johansson
9  Copyright (C) 2022 Peter Johansson
10
11  This file is part of the yat library, http://dev.thep.lu.se/yat
12
13  The yat library is free software; you can redistribute it and/or
14  modify it under the terms of the GNU General Public License as
15  published by the Free Software Foundation; either version 3 of the
16  License, or (at your option) any later version.
17
18  The yat library is distributed in the hope that it will be useful,
19  but WITHOUT ANY WARRANTY; without even the implied warranty of
20  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
21  General Public License for more details.
22
23  You should have received a copy of the GNU General Public License
24  along with yat. If not, see <http://www.gnu.org/licenses/>.
25*/
26
27#include "Score.h"
28
29#include <gsl/gsl_cdf.h>
30
31namespace theplu {
32  namespace yat {
33    namespace utility {
34      class VectorBase;
35    }
36    namespace classifier {
37      class DataLookWeighted1D;
38    }
39    namespace statistics {
40
41      /**
42         @brief Class for score based on signal-to-noise ratio (SNRScore).
43
44         Also
45         sometimes referred to as Golub score. The score is the ratio
46         between difference in mean and the sum of standard deviations
47         for two groups:  \f$ \frac{ m_x-m_y}{ s_x + s_y} \f$ where \f$
48         s \f$ is standard deviation.
49      */
50      class SNRScore : public Score
51      {
52
53      public:
54        ///
55        /// @brief Default Constructor.
56        ///
57        SNRScore(bool absolute=true);
58
59        ///
60        /// @brief The destructor.
61        ///
62        virtual ~SNRScore(void);
63
64        /**
65           SNRScore is defined as \f$ \frac{m_x-m_y}{s_x+s_y} \f$ where \f$ m
66           \f$ and \f$ s \f$ are mean and standard deviation,
67           respectively. @see Averager
68
69           @return SNRScore score. If absolute=true absolute value of SNRScore is
70           returned
71        */
72        double score(const classifier::Target& target,
73                     const utility::VectorBase& value) const;
74
75        /**
76           SNRScore is defined as \f$ \frac{m_x-m_y}{s_x+s_y} \f$ where \f$ m
77           \f$ and \f$ s \f$ are weighted versions of mean and standard
78           deviation, respectively. @see AveragerWeighted
79
80           @return SNRScore score. If absolute=true absolute value of SNRScore is
81           returned
82        */
83        double score(const classifier::Target& target,
84                     const classifier::DataLookupWeighted1D& value) const;
85
86        /**
87           SNRScore is defined as \f$ \frac{m_x-m_y}{s_x+s_y} \f$ where \f$ m
88           \f$ and \f$ s \f$ are weighted versions of mean and standard
89           deviation, respectively. @see AveragerWeighted
90
91           @return SNRScore score. If absolute=true absolute value of SNRScore is
92           returned
93        */
94        double score(const classifier::Target& target,
95                     const utility::VectorBase& value,
96                     const utility::VectorBase& weight) const;
97
98      };
99
100
101    }}} // of namespace statistics, yat, and theplu
102
103#endif
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