source: trunk/yat/regression/OneDimensional.h @ 728

Last change on this file since 728 was 728, checked in by Peter, 16 years ago

added virtual function s2 in OneDimensional?.

  • Property svn:eol-style set to native
  • Property svn:keywords set to Author Date Id Revision
File size: 3.8 KB
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1#ifndef _theplu_yat_regression_onedimensional_
2#define _theplu_yat_regression_onedimensional_
3
4// $Id: OneDimensional.h 728 2007-01-04 16:07:16Z peter $
5
6/*
7  Copyright (C) The authors contributing to this file.
8
9  This file is part of the yat library, http://lev.thep.lu.se/trac/yat
10
11  The yat library is free software; you can redistribute it and/or
12  modify it under the terms of the GNU General Public License as
13  published by the Free Software Foundation; either version 2 of the
14  License, or (at your option) any later version.
15
16  The yat library is distributed in the hope that it will be useful,
17  but WITHOUT ANY WARRANTY; without even the implied warranty of
18  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
19  General Public License for more details.
20
21  You should have received a copy of the GNU General Public License
22  along with this program; if not, write to the Free Software
23  Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA
24  02111-1307, USA.
25*/
26
27#include "yat/statistics/AveragerPair.h"
28
29#include <ostream>
30
31namespace theplu {
32namespace yat {
33namespace utility {
34  class vector;
35}
36namespace regression {
37
38  ///
39  /// Abstract Base Class for One Dimensional fitting.   
40  ///
41  /// @see OneDimensionalWeighted.
42  ///
43  class OneDimensional
44  {
45
46  public:
47    ///
48    /// @brief The default constructor
49    ///
50    OneDimensional(void);
51
52    ///
53    /// @brief The destructor
54    ///
55    virtual ~OneDimensional(void);
56 
57    /**
58       @brief Chi-squared
59       
60       Chi-squared is defined as the \f$ \frac
61       {\sum{(\hat{y_i}-y_i)^2}}{1} \f$
62    */
63    virtual double chisq(void) const=0;
64   
65    /**
66       This function computes the best-fit given a model (see specific
67       class for details) by minimizing \f$ \sum{(\hat{y_i}-y_i)^2}
68       \f$, where \f$ \hat{y} \f$ is the fitted value.
69    */
70    virtual void fit(const utility::vector& x, const utility::vector& y)=0; 
71   
72    ///
73    /// @return expected value in @a x accrding to the fitted model
74    ///
75    virtual double predict(const double x) const=0;
76   
77    /**
78       The prediction error is defined as the expected squared
79       deviation a new data point will have from value the model
80       provides: \f$ E(Y|x - \hat{y}(x))^2 \f$ and is typically
81       divided into two terms \f$ E(Y|x - E(Y|x))^2 \f$ and \f$
82       E(E(Y|x) - \hat{y}(x))^2 \f$, which is the conditional variance
83       given \f$ x \f$ and the squared standard error (see
84       standard_error2()) of the model estimation in \f$ x \f$,
85       respectively.
86       
87       @return expected squared prediction error for a new data point
88       in @a x
89    */
90    double prediction_error2(const double x) const; 
91
92    ///
93    /// @brief print output to ostream @a os
94    ///
95    /// Printing estimated model to @a os in the points defined by @a
96    /// min, @a max, and @a n. The values printed for each point is
97    /// the x-value, the estimated y-value, and the estimated standard
98    /// deviation of a new data poiunt will have from the y-value
99    /// given the x-value (see prediction_error()).
100    ///
101    /// @param os Ostream printout is sent to
102    /// @param n number of points printed
103    /// @param min smallest x-value for which the model is printed
104    /// @param max largest x-value for which the model is printed
105    ///
106    std::ostream& print(std::ostream& os,const double min, 
107                        double max, const u_int n) const;
108
109    /**
110       r-squared is defined as \f$ \frac{Var(Y|x)}{Var(Y)} \f$ or the
111       fraction of the variance explained by the regression model.
112    */
113    double r_squared(void) const;
114
115    /**
116       @return variance of residuals
117    */
118    virtual double s2(void) const=0;
119
120    /**
121       The standard error is defined as \f$ E(Y|x - \hat{y}(x))^2 \f$
122
123       @return expected squared error of model value in @a x
124    */
125    virtual double standard_error2(const double x) const=0;
126
127  protected:
128    ///
129    /// Variance of y
130    ///
131    double variance(void) const;
132
133    ///
134    /// Averager for pair of x and y
135    ///
136    statistics::AveragerPair ap_;
137
138  };
139
140}}} // of namespaces regression, yat, and theplu
141
142#endif
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