# source:trunk/yat/regression/Poisson.h@3615

Last change on this file since 3615 was 3615, checked in by Peter, 6 years ago

some trivial tests for regression::Poisson. refs #882

• Property svn:eol-style set to native
• Property svn:keywords set to Id
File size: 1.8 KB
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1#ifndef theplu_yat_regression_poisson
2#define theplu_yat_regression_poisson
3
4// $Id: Poisson.h 3615 2017-02-06 02:31:36Z peter$
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
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
25#include "Multivariate.h"
26
27#include "yat/utility/Vector.h"
28
29namespace theplu {
30namespace yat {
31namespace regression {
32
33  /**
34     Poisson regression models count data from a poisson distribution
35     \f$y \in Po(m(x)) \f$ in which the the expectation value, \f$m 36 \f$ is modeled as \f$log(m) = \Beta_0 + \Beta_1 x_1 + ... + 37 \Beta_p x_p \f$, or for a given model, \f$\Beta \f$, and input
38     vector \f$x \f$, the expectation value \f$E(Y | x, \Beta) = 39 \exp(\Beta'x) \f$
40
41     \since new in yat 0.15
42   */
43  class Poisson : public Multivariate
44  {
45  public:
46    /**
47       \brief fit model
48     */
49    void fit(const utility::Matrix& x, const utility::VectorBase& y);
50
51    /**
52       \return parameters \f$\Beta \f$
53     */
54    const utility::Vector& fit_parameters(void) const;
55
56    /**
57       Predicted value given \a x. The prediction is calculated as
58       \f$\exp{\beta_0 + \beta_1 x_1 +...+\beta_p x_p} \f$
59     */
60    double predict(const utility::VectorBase& x) const;
61  private:
62    utility::Vector beta_;
63  };
64
65}}}
66
67#endif
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