source: trunk/yat/statistics/tScore.cc @ 676

Last change on this file since 676 was 676, checked in by Jari Häkkinen, 16 years ago

Fixed #83. This message applies to this revision and
revision:675. Removed all references to c++_tools. Changed yat
internal #includes from <yat/...> to "yat/...". Moved #ifndef
_header_/#define _header_ to the top of the header files as suggested
by the coding style.

  • Property svn:eol-style set to native
  • Property svn:keywords set to Author Date Id Revision
File size: 3.3 KB
Line 
1// $Id: tScore.cc 676 2006-10-10 12:38:21Z jari $
2
3/*
4  Copyright (C) The authors contributing to this file.
5
6  This file is part of the yat library, http://lev.thep.lu.se/trac/yat
7
8  The yat library is free software; you can redistribute it and/or
9  modify it under the terms of the GNU General Public License as
10  published by the Free Software Foundation; either version 2 of the
11  License, or (at your option) any later version.
12
13  The yat library is distributed in the hope that it will be useful,
14  but WITHOUT ANY WARRANTY; without even the implied warranty of
15  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
16  General Public License for more details.
17
18  You should have received a copy of the GNU General Public License
19  along with this program; if not, write to the Free Software
20  Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA
21  02111-1307, USA.
22*/
23
24#include "yat/statistics/tScore.h"
25#include "yat/statistics/Averager.h"
26#include "yat/statistics/AveragerWeighted.h"
27#include "yat/classifier/DataLookupWeighted1D.h"
28#include "yat/classifier/Target.h"
29
30#include <cassert>
31#include <cmath>
32
33
34namespace theplu {
35namespace statistics { 
36
37  tScore::tScore(bool b) 
38    : Score(b),  t_(0)
39  {
40  }
41
42  double tScore::score(const classifier::Target& target, 
43                       const utility::vector& value)
44  {
45    weighted_=false;
46    statistics::Averager positive;
47    statistics::Averager negative;
48    for(size_t i=0; i<target.size(); i++){
49      if (target.binary(i))
50        positive.add(value(i));
51      else
52        negative.add(value(i));
53    }
54    double diff = positive.mean() - negative.mean();
55    dof_=positive.n()+negative.n()-2;
56    double s2=(positive.sum_xx_centered()+negative.sum_xx_centered())/dof_;
57
58    t_=diff/sqrt(s2/positive.n()+s2/negative.n());
59    if (t_<0 && absolute_)
60      t_=-t_;
61     
62    return t_;
63  }
64
65
66  double tScore::score(const classifier::Target& target, 
67                       const classifier::DataLookupWeighted1D& value)
68  {
69    weighted_=true;
70
71    statistics::AveragerWeighted positive;
72    statistics::AveragerWeighted negative;
73    for(size_t i=0; i<target.size(); i++){
74      if (target.binary(i))
75        positive.add(value.data(i),value.weight(i));
76      else
77        negative.add(value.data(i),value.weight(i));
78    }
79    double diff = positive.mean() - negative.mean();
80    dof_=positive.n()+negative.n()-2;
81    double s2=(positive.sum_xx_centered()+negative.sum_xx_centered())/dof_;
82    t_=diff/sqrt(s2/positive.n()+s2/(negative.n()));
83    if (t_<0 && absolute_)
84      t_=-t_;
85
86    if(positive.sum_w()==0 || negative.sum_w()==0)
87      t_=0;
88    return t_;
89  }
90
91
92  double tScore::score(const classifier::Target& target, 
93                       const utility::vector& value,
94                       const utility::vector& weight)
95  {
96    weighted_=true;
97
98    statistics::AveragerWeighted positive;
99    statistics::AveragerWeighted negative;
100    for(size_t i=0; i<target.size(); i++){
101      if (target.binary(i))
102        positive.add(value(i),weight(i));
103      else
104        negative.add(value(i),weight(i));
105    }
106    double diff = positive.mean() - negative.mean();
107    dof_=positive.n()+negative.n()-2;
108    double s2=(positive.sum_xx_centered()+negative.sum_xx_centered())/dof_;
109    t_=diff/sqrt(s2/positive.n()+s2/(negative.n()));
110    if (t_<0 && absolute_)
111      t_=-t_;
112
113    if(positive.sum_w()==0 || negative.sum_w()==0)
114      t_=0;
115    return t_;
116  }
117
118  double tScore::p_value(void) const
119  {
120    double p = gsl_cdf_tdist_Q(t_, dof_);
121    return (dof_ > 0 && !weighted_) ? p : 1;
122  }
123
124
125
126}} // of namespace statistics and namespace theplu
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