source: trunk/yat/regression/Multivariate.h @ 3614

Last change on this file since 3614 was 3614, checked in by Peter, 6 years ago

refs #867 and #882. Interface for Negative Binomiual and Poisson regression

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1#ifndef theplu_yat_regression_multivariate
2#define theplu_yat_regression_multivariate
3
4// $Id: Multivariate.h 3614 2017-02-06 01:37:33Z peter $
5
6/*
7  Copyright (C) 2017 Peter Johansson
8
9  This file is part of the yat library, http://dev.thep.lu.se/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 3 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 yat. If not, see <http://www.gnu.org/licenses/>.
23*/
24
25namespace theplu {
26namespace yat {
27namespace utility {
28  class Matrix;
29  class Vector;
30  class VectorBase;
31}
32namespace regression {
33
34  /**
35     Interface class for multivariate regression classes.
36
37     \since new in yat 0.15
38   */
39  class Multivariate
40  {
41  public:
42    /**
43       \brief destructor
44     */
45    virtual ~Multivariate(void);
46
47    /**
48       Estimating model parameters based on \a X to fit output data \a
49       y. Each row in \a X corresponds to one data point, i.e., number
50       of rows in \a X must match size of \a y.
51     */
52    virtual void fit(const utility::Matrix& x, const utility::VectorBase& y)=0;
53
54    /**
55       \return parameters of the model
56     */
57    virtual const utility::Vector& fit_parameters(void) const=0;
58
59    /**
60       \brief predict value in \a x according to model
61     */
62    virtual double predict(const utility::VectorBase& x) const=0;
63  };
64
65}}}
66
67#endif
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