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#ifndef _theplu_yat_regression_polynomial_ |
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#define _theplu_yat_regression_polynomial_ |
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|
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// $Id$ |
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|
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/* |
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Copyright (C) 2005, 2006 Jari Häkkinen, Peter Johansson |
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Copyright (C) 2007 Peter Johansson |
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Copyright (C) 2008 Jari Häkkinen, Peter Johansson |
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|
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This file is part of the yat library, http://dev.thep.lu.se/yat |
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|
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The yat library is free software; you can redistribute it and/or |
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modify it under the terms of the GNU General Public License as |
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published by the Free Software Foundation; either version 3 of the |
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License, or (at your option) any later version. |
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|
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The yat library is distributed in the hope that it will be useful, |
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but WITHOUT ANY WARRANTY; without even the implied warranty of |
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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General Public License for more details. |
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|
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You should have received a copy of the GNU General Public License |
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along with yat. If not, see <http://www.gnu.org/licenses/>. |
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*/ |
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|
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#include "OneDimensional.h" |
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#include "MultiDimensional.h" |
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|
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namespace theplu { |
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namespace yat { |
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namespace utility { |
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class VectorBase; |
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} |
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namespace regression { |
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/** |
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@brief Polynomial regression |
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|
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Data are modeled as \f$ y = \alpha + \beta x + \gamma x^2 + |
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... + \delta x_i^{\textrm{power}} + \epsilon_i \f$ |
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*/ |
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class Polynomial : public OneDimensional |
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{ |
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public: |
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|
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/// |
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/// @param power degree of polynomial, e.g. 1 for a linear model |
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/// |
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explicit Polynomial(size_t power); |
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|
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/// |
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/// @brief Destructor |
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/// |
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~Polynomial(void); |
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|
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/// |
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/// @brief covariance of parameters |
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/// |
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const utility::Matrix& covariance(void) const; |
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|
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/// |
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/// Fit the model by minimizing the mean squared deviation between |
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/// model and data. |
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/// |
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void fit(const utility::VectorBase& x, const utility::VectorBase& y); |
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|
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/// |
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/// @return parameters of the model |
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/// |
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/// @see MultiDimensional |
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/// |
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const utility::Vector& fit_parameters(void) const; |
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/// |
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/// @return value in @a x of model |
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/// |
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double predict(const double x) const; |
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/** |
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\f$ \frac{\sum \epsilon_i^2}{N-\textrm{DF}} \f$ |
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where DF is number of parameters in model. |
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|
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@return variance of residuals |
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*/ |
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double s2(void) const; |
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/// |
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/// @return squared error of model value in @a x |
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/// |
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double standard_error2(const double x) const; |
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|
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private: |
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MultiDimensional md_; |
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size_t power_; |
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|
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}; |
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|
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}}} // of namespaces regression, yat, and theplu |
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|
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#endif |