SHOGUN
v2.0.0
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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) 1999-2009 Soeren Sonnenburg 00008 * Copyright (C) 1999-2009 Fraunhofer Institute FIRST and Max-Planck-Society 00009 */ 00010 00011 #ifndef _ONLINELINEARCLASSIFIER_H__ 00012 #define _ONLINELINEARCLASSIFIER_H__ 00013 00014 #include <shogun/lib/common.h> 00015 #include <shogun/labels/Labels.h> 00016 #include <shogun/labels/RegressionLabels.h> 00017 #include <shogun/features/streaming/StreamingDotFeatures.h> 00018 #include <shogun/machine/Machine.h> 00019 00020 #include <stdio.h> 00021 00022 namespace shogun 00023 { 00050 class COnlineLinearMachine : public CMachine 00051 { 00052 public: 00054 COnlineLinearMachine(); 00055 virtual ~COnlineLinearMachine(); 00056 00062 virtual inline void get_w(float32_t*& dst_w, int32_t& dst_dims) 00063 { 00064 ASSERT(w && w_dim>0); 00065 dst_w=w; 00066 dst_dims=w_dim; 00067 } 00068 00075 virtual void get_w(float64_t*& dst_w, int32_t& dst_dims) 00076 { 00077 ASSERT(w && w_dim>0); 00078 dst_w=SG_MALLOC(float64_t, w_dim); 00079 for (int32_t i=0; i<w_dim; i++) 00080 dst_w[i]=w[i]; 00081 dst_dims=w_dim; 00082 } 00083 00088 virtual inline SGVector<float32_t> get_w() 00089 { 00090 return SGVector<float32_t>(w, w_dim); 00091 } 00092 00098 virtual inline void set_w(float32_t* src_w, int32_t src_w_dim) 00099 { 00100 SG_FREE(w); 00101 w=SG_MALLOC(float32_t, src_w_dim); 00102 memcpy(w, src_w, size_t(src_w_dim)*sizeof(float32_t)); 00103 w_dim=src_w_dim; 00104 } 00105 00112 virtual void set_w(float64_t* src_w, int32_t src_w_dim) 00113 { 00114 SG_FREE(w); 00115 w=SG_MALLOC(float32_t, src_w_dim); 00116 for (int32_t i=0; i<src_w_dim; i++) 00117 w[i] = src_w[i]; 00118 w_dim=src_w_dim; 00119 } 00120 00125 virtual inline void set_bias(float32_t b) 00126 { 00127 bias=b; 00128 } 00129 00134 virtual inline float32_t get_bias() 00135 { 00136 return bias; 00137 } 00138 00143 virtual inline void set_features(CStreamingDotFeatures* feat) 00144 { 00145 if (features) 00146 SG_UNREF(features); 00147 SG_REF(feat); 00148 features=feat; 00149 } 00150 00157 virtual CRegressionLabels* apply_regression(CFeatures* data=NULL); 00158 00165 virtual CBinaryLabels* apply_binary(CFeatures* data=NULL); 00166 00168 virtual float64_t apply_one(int32_t vec_idx) 00169 { 00170 SG_NOTIMPLEMENTED; 00171 return CMath::INFTY; 00172 } 00173 00182 virtual float32_t apply_one(float32_t* vec, int32_t len); 00183 00189 virtual float32_t apply_to_current_example(); 00190 00195 virtual CStreamingDotFeatures* get_features() { SG_REF(features); return features; } 00196 00202 virtual const char* get_name() const { return "OnlineLinearMachine"; } 00203 00207 virtual void start_train() { } 00208 00212 virtual void stop_train() { } 00213 00223 virtual void train_example(CStreamingDotFeatures *feature, float64_t label) { SG_NOTIMPLEMENTED; } 00224 00225 protected: 00234 virtual bool train_machine(CFeatures* data=NULL); 00235 00241 SGVector<float64_t> apply_get_outputs(CFeatures* data); 00242 00244 virtual bool train_require_labels() const { return false; } 00245 00246 protected: 00248 int32_t w_dim; 00250 float32_t* w; 00252 float32_t bias; 00254 CStreamingDotFeatures* features; 00255 }; 00256 } 00257 #endif