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LinearMachine.h
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LinearMachine.h
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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/config.h>
#include <shogun/lib/common.h>
#include <shogun/machine/Machine.h>
#include <shogun/lib/SGVector.h>
namespace shogun
{
class CBinaryLabels;
class CDotFeatures;
class CFeatures;
class CRegressionLabels;
/** @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();
/** Constructor
*
* @param compute_bias new m_compute_bias
* Determines if bias_compution is considered or not
*/
CLinearMachine(bool compute_bias);
/** destructor */
virtual ~CLinearMachine();
/** copy constructor */
CLinearMachine(CLinearMachine* machine);
/** Train machine
*
* @return whether training was successful
*/
virtual bool train(CFeatures* data=NULL);
/** 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 m_compute_bias
*
* Determines if bias compution is considered or not
* @param compute_bias new m_compute_bias
*/
virtual void set_compute_bias(bool compute_bias);
/** Get compute bias
*
* @return compute_bias
*/
virtual bool get_compute_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();
/** Computes the added bias. The bias is computed
* as the mean error between the predictions and
* the true labels.
*/
void compute_bias(CFeatures* data);
private:
void init();
protected:
/** w */
SGVector<float64_t> w;
/** bias */
float64_t bias;
/** features */
CDotFeatures* features;
/** If true, bias is computed in train method */
bool m_compute_bias;
};
}
#endif