/usr/include/shogun/machine/LinearMachine.h is in libshogun-dev 3.2.0-7.5.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 | /*
* 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.
*
* Written (W) 1999-2009 Soeren Sonnenburg
* Copyright (C) 1999-2009 Fraunhofer Institute FIRST and Max-Planck-Society
*/
#ifndef _LINEARCLASSIFIER_H__
#define _LINEARCLASSIFIER_H__
#include <shogun/lib/common.h>
#include <shogun/labels/Labels.h>
#include <shogun/features/DotFeatures.h>
#include <shogun/machine/Machine.h>
#include <stdio.h>
namespace shogun
{
class CDotFeatures;
class CMachine;
class CLabels;
/** @brief Class LinearMachine is a generic interface for all kinds of linear
* machines like classifiers.
*
* A linear classifier computes
*
* \f[
* f({\bf x})= {\bf w} \cdot {\bf x} + b
* \f]
*
* where \f${\bf w}\f$ are the weights assigned to each feature in training
* and \f$b\f$ the bias.
*
* To implement a linear classifier all that is required is to define the
* train() function that delivers \f${\bf w}\f$ above.
*
* Note that this framework works with linear classifiers of arbitraty feature
* type, e.g. dense and sparse and even string based features. This is
* implemented by using CDotFeatures that may provide a mapping function
* \f$\Phi({\bf x})\mapsto {\cal R^D}\f$ encapsulating all the required
* operations (like the dot product). The decision function is thus
*
* \f[
* f({\bf x})= {\bf w} \cdot \Phi({\bf x}) + b.
* \f]
*
* The following linear classifiers are implemented
* \li Linear Descriminant Analysis (CLDA)
* \li Linear Programming Machines (CLPM, CLPBoost)
* \li Perceptron (CPerceptron)
* \li Linear SVMs (CSVMSGD, CLibLinear, CSVMOcas, CSVMLin, CSubgradientSVM)
*
* \sa CDotFeatures
*
* */
class CLinearMachine : public CMachine
{
public:
/** default constructor */
CLinearMachine();
/** destructor */
virtual ~CLinearMachine();
/** copy constructor */
CLinearMachine(CLinearMachine* machine);
/** get w
*
* @return weight vector
*/
virtual SGVector<float64_t> get_w() const;
/** set w
*
* @param src_w new w
*/
virtual void set_w(const SGVector<float64_t> src_w);
/** set bias
*
* @param b new bias
*/
virtual void set_bias(float64_t b);
/** get bias
*
* @return bias
*/
virtual float64_t get_bias();
/** set features
*
* @param feat features to set
*/
virtual void set_features(CDotFeatures* feat);
/** apply linear machine to data
* for binary classification problem
*
* @param data (test)data to be classified
* @return classified labels
*/
virtual CBinaryLabels* apply_binary(CFeatures* data=NULL);
/** apply linear machine to data
* for regression problem
*
* @param data (test)data to be classified
* @return classified labels
*/
virtual CRegressionLabels* apply_regression(CFeatures* data=NULL);
/** applies to one vector */
virtual float64_t apply_one(int32_t vec_idx);
/** get features
*
* @return features
*/
virtual CDotFeatures* get_features();
/** Returns the name of the SGSerializable instance. It MUST BE
* the CLASS NAME without the prefixed `C'.
*
* @return name of the SGSerializable
*/
virtual const char* get_name() const { return "LinearMachine"; }
protected:
/** apply get outputs
*
* @param data features to compute outputs
* @return outputs
*/
virtual SGVector<float64_t> apply_get_outputs(CFeatures* data);
/** Stores feature data of underlying model. Does nothing because
* Linear machines store the normal vector of the separating hyperplane
* and therefore the model anyway
*/
virtual void store_model_features();
private:
void init();
protected:
/** w */
SGVector<float64_t> w;
/** bias */
float64_t bias;
/** features */
CDotFeatures* features;
};
}
#endif
|