/usr/include/shogun/machine/LinearMachine.h is in libshogun-dev 1.1.0-4ubuntu2.
This file is owned by root:root, with mode 0o644.
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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.
*
* 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/features/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();
virtual ~CLinearMachine();
/** get w
*
* @param dst_w store w in this argument
* @param dst_dims dimension of w
*/
inline void get_w(float64_t*& dst_w, int32_t& dst_dims)
{
ASSERT(w && w_dim>0);
dst_w=w;
dst_dims=w_dim;
}
/** get w
*
* @return weight vector
*/
inline SGVector<float64_t> get_w()
{
return SGVector<float64_t>(w, w_dim, false);
}
/** set w
*
* @param src_w new w
*/
inline void set_w(SGVector<float64_t> src_w)
{
SG_FREE(w);
w=src_w.vector;
w_dim=src_w.vlen;
}
/** set bias
*
* @param b new bias
*/
inline void set_bias(float64_t b)
{
bias=b;
}
/** get bias
*
* @return bias
*/
inline float64_t get_bias()
{
return bias;
}
/** load from file
*
* @param srcfile file to load from
* @return if loading was successful
*/
virtual bool load(FILE* srcfile);
/** save to file
*
* @param dstfile file to save to
* @return if saving was successful
*/
virtual bool save(FILE* dstfile);
/** set features
*
* @param feat features to set
*/
virtual inline void set_features(CDotFeatures* feat)
{
SG_UNREF(features);
SG_REF(feat);
features=feat;
}
/** apply linear machine to all examples
*
* @return resulting labels
*/
virtual CLabels* apply();
/** apply linear machine to data
*
* @param data (test)data to be classified
* @return classified labels
*/
virtual CLabels* apply(CFeatures* data);
/// get output for example "vec_idx"
virtual float64_t apply(int32_t vec_idx)
{
return features->dense_dot(vec_idx, w, w_dim) + bias;
}
/** get features
*
* @return features
*/
virtual CDotFeatures* get_features() { SG_REF(features); return 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:
/** 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() {}
protected:
/** dimension of w */
int32_t w_dim;
/** w */
float64_t* w;
/** bias */
float64_t bias;
/** features */
CDotFeatures* features;
};
}
#endif
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