/usr/include/shogun/regression/LinearRidgeRegression.h is in libshogun-dev 3.2.0-7.5.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | /*
* 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 _LINEARRIDGEREGRESSION_H__
#define _LINEARRIDGEREGRESSION_H__
#include <shogun/lib/config.h>
#ifdef HAVE_LAPACK
#include <shogun/regression/Regression.h>
#include <shogun/machine/LinearMachine.h>
#include <shogun/features/DenseFeatures.h>
namespace shogun
{
/** @brief Class LinearRidgeRegression implements Ridge Regression - a regularized least square
* method for classification and regression.
*
* RR is closely related to Fishers Linear Discriminant (cf. LDA).
*
* 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 + \tau||{\bf w}||^2\right)
* \f]
*
* which boils down to solving a linear system
*
* \f[
* {\bf w} = \left(\tau {\bf I}+ \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]
*
* The expressed solution is a linear method with bias 0 (cf. CLinearMachine).
*/
class CLinearRidgeRegression : public CLinearMachine
{
public:
/** problem type */
MACHINE_PROBLEM_TYPE(PT_REGRESSION);
/** default constructor */
CLinearRidgeRegression();
/** constructor
*
* @param tau regularization constant tau
* @param data training data
* @param lab labels
*/
CLinearRidgeRegression(float64_t tau, CDenseFeatures<float64_t>* data, CLabels* lab);
virtual ~CLinearRidgeRegression() {}
/** set regularization constant
*
* @param tau new tau
*/
inline void set_tau(float64_t tau) { m_tau = tau; };
/** load regression from file
*
* @param srcfile file to load from
* @return if loading was successful
*/
virtual bool load(FILE* srcfile);
/** save regression to file
*
* @param dstfile file to save to
* @return if saving was successful
*/
virtual bool save(FILE* dstfile);
/** get classifier type
*
* @return classifier type LinearRidgeRegression
*/
virtual EMachineType get_classifier_type()
{
return CT_LINEARRIDGEREGRESSION;
}
/** @return object name */
virtual const char* get_name() const { return "LinearRidgeRegression"; }
protected:
/** train regression
*
* @param data training data (parameter can be avoided if distance or
* kernel-based regressors are used and distance/kernels are
* initialized with train data)
*
* @return whether training was successful
*/
virtual bool train_machine(CFeatures* data=NULL);
private:
void init();
protected:
/** regularization parameter tau */
float64_t m_tau;
};
}
#endif // HAVE_LAPACK
#endif // _LINEARRIDGEREGRESSION_H__
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