/usr/include/shogun/machine/OnlineLinearMachine.h is in libshogun-dev 1.1.0-4ubuntu2.
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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 _ONLINELINEARCLASSIFIER_H__
#define _ONLINELINEARCLASSIFIER_H__
#include <shogun/lib/common.h>
#include <shogun/features/Labels.h>
#include <shogun/features/StreamingDotFeatures.h>
#include <shogun/machine/Machine.h>
#include <stdio.h>
namespace shogun
{
/** @brief Class OnlineLinearMachine is a generic interface for linear
* machines like classifiers which work through online algorithms.
*
* 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 arbitrary feature
* type, e.g. dense and sparse and even string based features. This is
* implemented by using CStreamingDotFeatures 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]
*
* */
class COnlineLinearMachine : public CMachine
{
public:
/** default constructor */
COnlineLinearMachine();
virtual ~COnlineLinearMachine();
/** get w
*
* @param dst_w store w in this argument
* @param dst_dims dimension of w
*/
virtual inline void get_w(float32_t*& dst_w, int32_t& dst_dims)
{
ASSERT(w && w_dim>0);
dst_w=w;
dst_dims=w_dim;
}
/**
* Get w as a _new_ float64_t array
*
* @param dst_w store w in this argument
* @param dst_dims dimension of w
*/
virtual void get_w(float64_t*& dst_w, int32_t& dst_dims)
{
ASSERT(w && w_dim>0);
dst_w=SG_MALLOC(float64_t, w_dim);
for (int32_t i=0; i<w_dim; i++)
dst_w[i]=w[i];
dst_dims=w_dim;
}
/** get w
*
* @return weight vector
*/
virtual inline SGVector<float32_t> get_w()
{
return SGVector<float32_t>(w, w_dim);
}
/** set w
*
* @param src_w new w
* @param src_w_dim dimension of new w
*/
virtual inline void set_w(float32_t* src_w, int32_t src_w_dim)
{
SG_FREE(w);
w=SG_MALLOC(float32_t, src_w_dim);
memcpy(w, src_w, size_t(src_w_dim)*sizeof(float32_t));
w_dim=src_w_dim;
}
/**
* Set weight vector from a float64_t vector
*
* @param src_w new w
* @param src_w_dim dimension of new w
*/
virtual void set_w(float64_t* src_w, int32_t src_w_dim)
{
SG_FREE(w);
w=SG_MALLOC(float32_t, src_w_dim);
for (int32_t i=0; i<src_w_dim; i++)
w[i] = src_w[i];
w_dim=src_w_dim;
}
/** set bias
*
* @param b new bias
*/
virtual inline void set_bias(float32_t b)
{
bias=b;
}
/** get bias
*
* @return bias
*/
virtual inline float32_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(CStreamingDotFeatures* feat)
{
if (features)
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)
{
SG_NOTIMPLEMENTED;
return CMath::INFTY;
}
/**
* apply linear machine to one vector
*
* @param vec feature vector
* @param len length of vector
*
* @return classified label
*/
virtual float32_t apply(float32_t* vec, int32_t len);
/**
* apply linear machine to vector currently being processed
*
* @return classified label
*/
virtual float32_t apply_to_current_example();
/** get features
*
* @return features
*/
virtual CStreamingDotFeatures* 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 "OnlineLinearMachine"; }
protected:
/** dimension of w */
int32_t w_dim;
/** w */
float32_t* w;
/** bias */
float32_t bias;
/** features */
CStreamingDotFeatures* features;
};
}
#endif
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