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OnlineLibLinear.h
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00001 /*
00002  * This program is free software; you can redistribute it and/or modify
00003  * it under the terms of the GNU General Public License as published by
00004  * the Free Software Foundation; either version 3 of the License, or
00005  * (at your option) any later version.
00006  *
00007  * Written (W) 2007-2010 Soeren Sonnenburg
00008  * Written (W) 2011 Shashwat Lal Das
00009  * Modifications (W) 2013 Thoralf Klein
00010  * Copyright (c) 2007-2009 The LIBLINEAR Project.
00011  * Copyright (C) 2007-2010 Fraunhofer Institute FIRST and Max-Planck-Society
00012  */
00013 
00014 #ifndef _ONLINELIBLINEAR_H__
00015 #define _ONLINELIBLINEAR_H__
00016 
00017 #include <shogun/lib/config.h>
00018 
00019 #include <shogun/lib/SGVector.h>
00020 #include <shogun/lib/common.h>
00021 #include <shogun/base/Parameter.h>
00022 #include <shogun/machine/OnlineLinearMachine.h>
00023 
00024 namespace shogun
00025 {
00028 class COnlineLibLinear : public COnlineLinearMachine
00029 {
00030 public:
00031 
00033         MACHINE_PROBLEM_TYPE(PT_BINARY);
00034 
00036         COnlineLibLinear();
00037 
00043         COnlineLibLinear(float64_t C);
00044 
00051         COnlineLibLinear(float64_t C, CStreamingDotFeatures* traindat);
00052 
00057         COnlineLibLinear(COnlineLibLinear *mch);
00058 
00060         virtual ~COnlineLibLinear();
00061 
00068         virtual void set_C(float64_t c_neg, float64_t c_pos) { C1=c_neg; C2=c_pos; }
00069 
00075         virtual float64_t get_C1() { return C1; }
00076 
00082         float64_t get_C2() { return C2; }
00083 
00089         virtual void set_bias_enabled(bool enable_bias) { use_bias=enable_bias; }
00090 
00096         virtual bool get_bias_enabled() { return use_bias; }
00097 
00099         virtual const char* get_name() const { return "OnlineLibLinear"; }
00100 
00102         virtual void start_train();
00103 
00105         virtual void stop_train();
00106 
00116         virtual void train_example(CStreamingDotFeatures *feature, float64_t label);
00117 
00122         virtual void train_one(SGVector<float32_t> ex, float64_t label);
00123 
00128         virtual void train_one(SGSparseVector<float32_t> ex, float64_t label);
00129 
00130 private:
00132         void init();
00133 
00134 private:
00136         bool use_bias;
00138         float64_t C1;
00140         float64_t C2;
00141 
00142 private:
00143         //========================================
00144         // "local" variables used during training
00145 
00146         float64_t C, d, G;
00147         float64_t QD;
00148 
00149         // y and alpha for example being processed
00150         int32_t y_current;
00151         float64_t alpha_current;
00152 
00153         // Cost constants
00154         float64_t Cp;
00155         float64_t Cn;
00156 
00157         // PG: projected gradient, for shrinking and stopping
00158         float64_t PG;
00159         float64_t PGmax_old;
00160         float64_t PGmin_old;
00161         float64_t PGmax_new;
00162         float64_t PGmin_new;
00163 
00164         // Diag is probably unnecessary
00165         float64_t diag[3];
00166         float64_t upper_bound[3];
00167 
00168         // Objective value = v/2
00169         float64_t v;
00170         // Number of support vectors
00171         int32_t nSV;
00172 };
00173 }
00174 #endif // _ONLINELIBLINEAR_H__
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