Image Component Library (ICL)
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00001 /******************************************************************** 00002 ** Image Component Library (ICL) ** 00003 ** ** 00004 ** Copyright (C) 2006-2013 CITEC, University of Bielefeld ** 00005 ** Neuroinformatics Group ** 00006 ** Website: www.iclcv.org and ** 00007 ** http://opensource.cit-ec.de/projects/icl ** 00008 ** ** 00009 ** File : ICLMath/src/ICLMath/LLM.h ** 00010 ** Module : ICLMath ** 00011 ** Authors: Christof Elbrechter ** 00012 ** ** 00013 ** ** 00014 ** GNU LESSER GENERAL PUBLIC LICENSE ** 00015 ** This file may be used under the terms of the GNU Lesser General ** 00016 ** Public License version 3.0 as published by the ** 00017 ** ** 00018 ** Free Software Foundation and appearing in the file LICENSE.LGPL ** 00019 ** included in the packaging of this file. Please review the ** 00020 ** following information to ensure the license requirements will ** 00021 ** be met: http://www.gnu.org/licenses/lgpl-3.0.txt ** 00022 ** ** 00023 ** The development of this software was supported by the ** 00024 ** Excellence Cluster EXC 277 Cognitive Interaction Technology. ** 00025 ** The Excellence Cluster EXC 277 is a grant of the Deutsche ** 00026 ** Forschungsgemeinschaft (DFG) in the context of the German ** 00027 ** Excellence Initiative. ** 00028 ** ** 00029 ********************************************************************/ 00030 00031 #pragma once 00032 00033 #include <ICLUtils/CompatMacros.h> 00034 #include <ICLUtils/Range.h> 00035 #include <ICLUtils/Configurable.h> 00036 #include <vector> 00037 00038 namespace icl{ 00039 namespace math{ 00041 00107 class ICLMath_API LLM : public utils::Configurable{ 00108 public: 00110 struct ICLMath_API Kernel{ 00112 Kernel(); 00114 Kernel(unsigned int inputDim, unsigned int outputDim); 00116 Kernel(const Kernel &k); 00118 ~Kernel(); 00120 Kernel &operator=(const Kernel &k); 00121 00123 float *w_in; 00124 00126 float *w_out; 00127 00129 float *A; 00130 00132 float *dw_in; 00133 00135 float *var; 00136 00138 unsigned int inputDim; 00139 00141 unsigned int outputDim; 00142 00144 void show(unsigned int idx=0) const; 00145 00147 00155 void set(const float *w_in, const float *w_out, const float *A); 00156 }; 00157 00158 00159 static const int TRAIN_CENTERS = 1; 00160 static const int TRAIN_SIGMAS = 2; 00161 static const int TRAIN_OUTPUTS = 4; 00162 static const int TRAIN_MATRICES = 8; 00163 static const int TRAIN_ALL = TRAIN_CENTERS | TRAIN_SIGMAS | TRAIN_OUTPUTS | TRAIN_MATRICES; 00165 private: 00166 void init_private(unsigned int inputDim,unsigned int outputDim); 00167 00168 public: 00169 00171 LLM(unsigned int inputDim, unsigned int outputDim); 00172 00173 LLM(unsigned int inputDim, unsigned int outputDim, unsigned int numCenters, 00174 const std::vector<utils::Range<icl32f> > &ranges, 00175 const std::vector<float> &var=std::vector<float>(1,1)); 00176 00178 00190 void init(unsigned int numCenters, const std::vector<utils::Range<icl32f> > &ranges, 00191 const std::vector<float> &var=std::vector<float>(1,1)); 00192 00194 00197 void init(const std::vector<float*> ¢ers, 00198 const std::vector<float> &var=std::vector<float>(1,1)); 00199 00201 00203 const float *apply(const float *x); 00204 00206 00208 void train(const float *x,const float *y, int trainflags = TRAIN_ALL); 00209 00211 void trainCenters(const float *x); 00212 00214 void trainSigmas(const float *x); 00215 00217 void trainOutputs(const float *x,const float *y); 00218 00220 void trainMatrices(const float *x,const float *y); 00221 00222 private: 00224 00226 const float *updateGs(const float *x); 00227 00228 public: 00230 const float *getErrorVec(const float *x, const float *y); 00231 00232 00234 void setEpsilonIn(float val) { setPropertyValue("epsilon In",val); } 00235 00237 void setEpsilonOut(float val) { setPropertyValue("epsilon Out",val); } 00238 00240 void setEpsilonA(float val) { setPropertyValue("epsilon A",val); } 00241 00243 00244 void setEpsilonSigma(float val) { setPropertyValue("epsilon Sigma",val); } 00245 00247 void showKernels() const; 00248 00250 unsigned int numKernels() const { return m_kernels.size(); } 00251 00253 const Kernel &operator[](unsigned int i) const { return m_kernels[i]; } 00254 00256 00260 Kernel &operator[](unsigned int i) { return m_kernels[i]; } 00261 00263 bool isSoftMaxUsed() const { return const_cast<Configurable*>(static_cast<const Configurable*>(this))->getPropertyValue("soft max enabled").as<bool>(); } 00264 00266 void setSoftMaxEnabled(bool enabled) { setPropertyValue("soft max enabled",enabled); } 00267 00268 private: 00269 00271 void trainCentersIntern(const float *x,const float *g); 00272 00274 void trainSigmasIntern(const float *x,const float *g); 00275 00277 void trainOutputsIntern(const float *x,const float *y,const float *g, const float *dy, bool useDeltaWin); 00278 00280 void trainMatricesIntern(const float *x,const float *y,const float *g, const float *dy); 00281 00283 const float *applyIntern(const float *x,const float *g); 00284 00286 const float *getErrorVecIntern(const float *y, const float *ynet); 00287 00289 unsigned int m_inputDim; 00290 00292 unsigned int m_outputDim; 00293 00294 #if 0 00295 00296 float m_epsilonIn; 00297 00299 float m_epsilonOut; 00300 00302 float m_epsilonA; 00303 00305 float m_epsilonSigma; 00306 #endif 00307 00308 std::vector<Kernel> m_kernels; 00309 00311 std::vector<float> m_outBuf; 00312 00314 std::vector<float> m_gBuf; 00315 00317 std::vector<float> m_errorBuf; 00318 00319 #if 0 00320 00321 bool m_bUseSoftMax; 00322 #endif 00323 }; 00324 00325 } // namespace math 00326 }