/usr/include/shogun/regression/LeastSquaresRegression.h is in libshogun-dev 3.2.0-7.3build4.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Copyright (C) 2012 Soeren Sonnenburg
*/
#ifndef _LEASTSQUARESREGRESSION_H__
#define _LEASTSQUARESREGRESSION_H__
#include <shogun/lib/config.h>
#include <shogun/regression/Regression.h>
#include <shogun/regression/LinearRidgeRegression.h>
#ifdef HAVE_LAPACK
#include <shogun/machine/LinearMachine.h>
namespace shogun
{
/** @brief class to perform Least Squares Regression
*
* Internally it is solved via minimizing the following system
*
* \f[
* \frac{1}{2}\left(\sum_{i=1}^N(y_i-{\bf w}\cdot {\bf x}_i)^2\right)
* \f]
*
* which boils down to solving the linear system
*
* \f[
* {\bf w} = \left(\sum_{i=1}^N{\bf x}_i{\bf x}_i^T\right)^{-1}\left(\sum_{i=1}^N y_i{\bf x}_i\right)
* \f]
* where x are the training examples and y the vector of labels.
*
* The expressed solution is a linear method with bias 0 (cf. CLinearMachine).
*/
class CLeastSquaresRegression : public CLinearRidgeRegression
{
public:
/** problem type */
MACHINE_PROBLEM_TYPE(PT_REGRESSION);
/** default constructor */
CLeastSquaresRegression();
/** constructor
*
* @param data training data
* @param lab labels
*/
CLeastSquaresRegression(CDenseFeatures<float64_t>* data, CLabels* lab);
virtual ~CLeastSquaresRegression() {}
/** get classifier type
*
* @return classifier type LeastSquaresRegression
*/
virtual EMachineType get_classifier_type()
{
return CT_LEASTSQUARESREGRESSION;
}
/** @return object name */
virtual const char* get_name() const { return "LeastSquaresRegression"; }
private:
void init();
};
}
#endif // HAVE_LAPACK
#endif // _LEASTSQUARESREGRESSION_H__
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