31 #ifndef GRT_PRE_PROCESSING_HEADER
32 #define GRT_PRE_PROCESSING_HEADER
198 std::string preProcessingType;
202 static StringPreProcessingMap *getMap() {
204 return stringPreProcessingMap;
208 static StringPreProcessingMap *stringPreProcessingMap;
209 static UINT numPreProcessingInstances;
213 template<
typename T >
PreProcessing *newPreProcessingModuleInstance() {
return new T; }
215 template<
typename T >
219 getMap()->insert( std::pair< std::string,
PreProcessing*(*)() >(newPreProcessingModuleName, &newPreProcessingModuleInstance< T > ) );
225 #endif // GRT_POST_PROCESSING_HEADER
virtual ~PreProcessing(void)
UINT getNumInputDimensions() const
UINT getNumOutputDimensions() const
virtual bool deepCopyFrom(const PreProcessing *rhs)
virtual bool saveModelToFile(std::fstream &file) const
bool savePreProcessingSettingsToFile(std::fstream &file) const
bool loadPreProcessingSettingsFromFile(std::fstream &file)
PreProcessing * createNewInstance() const
static PreProcessing * createInstanceFromString(std::string const &preProcessingType)
virtual bool loadModelFromFile(std::string filename)
This is the main base class that all GRT machine learning algorithms should inherit from...
std::string getPreProcessingType() const
virtual bool saveModelToFile(std::string filename) const
VectorFloat getProcessedData() const
virtual bool loadModelFromFile(std::fstream &file)
bool copyBaseVariables(const PreProcessing *preProcessingModule)
virtual bool process(const VectorFloat &inputVector)
std::map< std::string, PreProcessing *(*)() > StringPreProcessingMap
bool getInitialized() const