31 #ifndef GRT_FEATURE_EXTRACTION_HEADER
32 #define GRT_FEATURE_EXTRACTION_HEADER
82 virtual bool reset(){
return true; }
162 static FeatureExtraction* createInstanceFromString(
string const &featureExtractionType);
196 string featureExtractionType;
198 bool featureDataReady;
199 VectorDouble featureVector;
201 static StringFeatureExtractionMap *getMap() {
203 return stringFeatureExtractionMap;
207 static StringFeatureExtractionMap *stringFeatureExtractionMap;
208 static UINT numFeatureExtractionInstances;
213 template<
typename T > FeatureExtraction *newFeatureExtractionModuleInstance() {
return new T; }
215 template<
typename T >
219 getMap()->insert( std::pair<
string,
FeatureExtraction*(*)()>(newFeatureExtractionModuleName, &newFeatureExtractionModuleInstance< T > ) );
225 #endif //GRT_FEATURE_EXTRACTION_HEADER
virtual bool saveModelToFile(string filename) const
virtual bool loadModelFromFile(string filename)
This is the main base class that all GRT machine learning algorithms should inherit from...