31 #ifndef GRT_FEATURE_EXTRACTION_HEADER
32 #define GRT_FEATURE_EXTRACTION_HEADER
91 virtual bool reset(){
return true; }
123 std::string getFeatureExtractionType()
const;
144 bool getInitialized()
const;
151 bool getFeatureDataReady()
const;
178 static FeatureExtraction* createInstanceFromString(
const std::string &featureExtractionType );
187 using MLBase::saveModelToFile;
188 using MLBase::loadModelFromFile;
203 bool saveFeatureExtractionSettingsToFile( std::fstream &file )
const;
210 bool loadFeatureExtractionSettingsFromFile( std::fstream &file );
212 std::string featureExtractionType;
214 bool featureDataReady;
218 static StringFeatureExtractionMap *getMap() {
219 if( !stringFeatureExtractionMap ){ stringFeatureExtractionMap =
new StringFeatureExtractionMap; }
220 return stringFeatureExtractionMap;
224 static StringFeatureExtractionMap *stringFeatureExtractionMap;
225 static UINT numFeatureExtractionInstances;
230 template<
typename T >
FeatureExtraction *newFeatureExtractionModuleInstance() {
return new T; }
232 template<
typename T >
236 getMap()->insert( std::pair< std::string,
FeatureExtraction*(*)()>(newFeatureExtractionModuleName, &newFeatureExtractionModuleInstance< T > ) );
242 #endif //GRT_FEATURE_EXTRACTION_HEADER
UINT getNumOutputDimensions() const
This is the main base class that all GRT machine learning algorithms should inherit from...
UINT getNumInputDimensions() const