source: trunk/yat/regression/OneDimensional.h @ 1000

Last change on this file since 1000 was 1000, checked in by Jari Häkkinen, 15 years ago

trac moved to new location.

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1#ifndef _theplu_yat_regression_onedimensional_
2#define _theplu_yat_regression_onedimensional_
3
4// $Id: OneDimensional.h 1000 2007-12-23 20:09:15Z jari $
5
6/*
7  Copyright (C) 2004 Peter Johansson
8  Copyright (C) 2005, 2006 Jari Häkkinen, Peter Johansson
9  Copyright (C) 2007 Peter Johansson
10
11  This file is part of the yat library, http://trac.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 2 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 this program; if not, write to the Free Software
25  Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA
26  02111-1307, USA.
27*/
28
29#include "yat/statistics/AveragerPair.h"
30
31#include <ostream>
32
33namespace theplu {
34namespace yat {
35namespace utility {
36  class vector;
37}
38namespace regression {
39
40  ///
41  /// @brief Interface Class for One Dimensional fitting.   
42  ///
43  /// @see OneDimensionalWeighted.
44  ///
45  class OneDimensional
46  {
47
48  public:
49    ///
50    /// @brief The default constructor
51    ///
52    OneDimensional(void);
53
54    ///
55    /// @brief The destructor
56    ///
57    virtual ~OneDimensional(void);
58 
59    /**
60       @brief Chi-squared
61       
62       Chi-squared is defined as the \f$
63       \sum{(\hat{y_i}-y_i)^2} \f$
64    */
65    double chisq(void) const;
66   
67    /**
68       This function computes the best-fit given a model (see specific
69       class for details) by minimizing \f$ \sum{(\hat{y_i}-y_i)^2}
70       \f$, where \f$ \hat{y} \f$ is the fitted value.
71    */
72    virtual void fit(const utility::vector& x, const utility::vector& y)=0; 
73   
74    ///
75    /// @return expected value in @a x accrding to the fitted model
76    ///
77    virtual double predict(const double x) const=0;
78   
79    /**
80       The prediction error is defined as the expected squared
81       deviation a new data point will have from value the model
82       provides: \f$ E(Y|x - \hat{y}(x))^2 \f$ and is typically
83       divided into the conditional variance ( see s2() )
84       given \f$ x \f$ and the squared standard error ( see
85       standard_error2() ) of the model estimation in \f$ x \f$.
86       
87       @return expected squared prediction error for a new data point
88       in @a x
89    */
90    double prediction_error2(const double x) const; 
91
92    ///
93    /// @brief print output to ostream @a os
94    ///
95    /// Printing estimated model to @a os in the points defined by @a
96    /// min, @a max, and @a n. The values printed for each point is
97    /// the x-value, the estimated y-value, and the estimated standard
98    /// deviation of a new data poiunt will have from the y-value
99    /// given the x-value (see prediction_error()).
100    ///
101    /// @param os Ostream printout is sent to
102    /// @param n number of points printed
103    /// @param min smallest x-value for which the model is printed
104    /// @param max largest x-value for which the model is printed
105    ///
106    std::ostream& print(std::ostream& os,const double min, 
107                        double max, const u_int n) const;
108
109    /**
110       r2 is defined as \f$ 1 - \frac{Var(Y|x)}{Var(Y)} \f$ or the
111       fraction of the variance explained by the regression model.
112
113       @see s2()
114    */
115    double r2(void) const;
116
117    /**
118       \f$ E(Y|x - E(Y|x))^2 \f$
119
120       @return Conditional variance of Y
121    */
122    virtual double s2(void) const=0;
123
124    /**
125       The standard error is defined as \f$ E(Y|x - \hat{y}(x))^2 \f$
126
127       @return expected squared error of model value in @a x
128    */
129    virtual double standard_error2(const double x) const=0;
130
131  protected:
132    ///
133    /// Variance of y
134    ///
135    double variance(void) const;
136
137    ///
138    /// Averager for pair of x and y
139    ///
140    statistics::AveragerPair ap_;
141
142    ///
143    /// @see chisq()
144    ///
145    double chisq_;
146  };
147
148}}} // of namespaces regression, yat, and theplu
149
150#endif
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