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Diffstat (limited to 'src/plugins/lipi-toolkit/3rdparty/lipi-toolkit/src/reco/shaperec/neuralnet/NeuralNetShapeRecognizer.h')
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diff --git a/src/plugins/lipi-toolkit/3rdparty/lipi-toolkit/src/reco/shaperec/neuralnet/NeuralNetShapeRecognizer.h b/src/plugins/lipi-toolkit/3rdparty/lipi-toolkit/src/reco/shaperec/neuralnet/NeuralNetShapeRecognizer.h deleted file mode 100644 index 6e32002a..00000000 --- a/src/plugins/lipi-toolkit/3rdparty/lipi-toolkit/src/reco/shaperec/neuralnet/NeuralNetShapeRecognizer.h +++ /dev/null @@ -1,1111 +0,0 @@ -#ifndef __NEURALNETSHAPERECOGNIZER_H -#define __NEURALNETSHAPERECOGNIZER_H - -/** Include files */ -#include "LTKInc.h" -#include "LTKTypes.h" -#include "LTKMacros.h" -#include "LTKShapeRecognizer.h" -#include "LTKShapeRecoUtil.h" -#include "LTKShapeSample.h" -#include "LTKCheckSumGenerate.h" - -/** Forward declaration of classes */ -class LTKTraceGroup; -class LTKPreprocessorInterface; -class LTKShapeSample; -class LTKShapeFeatureExtractor; -class LTKShapeFeature; - -#define SUPPORTED_MIN_VERSION "3.0.0" - -typedef int (*FN_PTR_CREATELTKLIPIPREPROCESSOR)(const LTKControlInfo& , LTKPreprocessorInterface** ); -typedef int (*FN_PTR_DELETELTKLIPIPREPROCESSOR)(LTKPreprocessorInterface* ); - - -/** - * @ingroup NeuralNetShapeRecognizer.h - * @brief The Header file for the NeuralNetShapeRecognizer - * @class NeuralNetShapeRecognizer - *<p> Fully connected feedforward multilayer perceptron - * backpropogation algorithm used for its training. - *</p> - */ - -class NeuralNetShapeRecognizer: public LTKShapeRecognizer -{ - - /** @name private data members */ - //@{ - private: - - FN_PTR_DELETELTKLIPIPREPROCESSOR m_deleteLTKLipiPreProcessor; - //Function pointer for deleteLTKLipiPreProcessor - - // preproc lib handle - void *m_libHandler; - - // feature extractor lib handle - void *m_libHandlerFE; - - unsigned short m_numShapes; - /**< @brief Number of shapes - * <p> - * It Defines the number of shapes to be recognized - * - * DEFAULT: 0 - * - * Note: If the project is dynamic, then this numShapes is set to 0 - * If the project is not dynamic, then the numShapes is read from project configuration file - * </p> - */ - - string m_featureExtractorName; - /**< @brief The Feature Extractor - * <p> - * - * DEFAULT:PointFloat - * - * </p> - */ - - bool m_projectTypeDynamic; - /**< @brief Project Dynamic - * <p> - * if projectTypeDynamic = true, then the project is dynamic i.e., the numShapes can take any number of value(s) - * = false, then the project is not dynamic i.e., the numShapes take value from project.cfg file - * - * DEFAULT: false - * </p> - */ - - LTKPreprocessorInterface *m_ptrPreproc; - /**< @brief Pointer to preprocessor instance - * <p> - * Instance which is used to call the preprocessing methods before recognition - * - * DEFAULT: NULL - * </p> - */ - - string m_neuralnetCfgFilePath; - /**< @brief Full path of NeuralNet configuration file - * <p> - * Assigned value in the NeuralNetShapeRecognizer::initialize function - * </p> - */ - - string m_neuralnetMDTFilePath; - /**< @brief Full path of Model data file - * <p> - * Assigned value in the NeuralNetShapeRecognizer::initialize function - * </p> - */ - - stringStringMap m_headerInfo; - /**< @brief Header Information - * <p> - * </p> - */ - - LTKShapeRecoUtil m_shapeRecUtil; - /**< @brief Pointer to LTKShapeRecoUtil class - * <p> - * Instance used to call Shape Recognizer Utility functions - * - * DEFAULT: NULL - */ - - string m_lipiRootPath; - /**< @brief Path of the Lipi Root - * <p> - * DEFAULT: LipiEngine::getLipiPath() - * </p> - */ - - string m_lipiLibPath; - /**< @brief Path of the Lipi Libraries - * <p> - * DEFAULT: LipiEngine::getLipiPath() - * </p> - */ - - LTKShapeFeatureExtractor *m_ptrFeatureExtractor; - /**< @brief Pointer to LTKShapeFeatureExtractor class - * <p> - * DEFAULT: NULL - * </p> - */ - - string m_preProcSeqn; - /**< @brief Preprocessor Sequence - * <p> - * This string holds the sequence of preprocessor methods - * </p> - */ - - vector<stringStringPair> m_preprocSequence; - /**< @brief Preprocessor Sequence vector - * <p> - * This vector of string holds the sequence of preprocessor methods - * </p> - */ - - vector<LTKShapeRecoResult> m_vecRecoResult; - /**< @brief Store Recognize results - * <p> - * This vector hold shape ID and its confidance value - * </p> - */ - - int m_neuralnetRandomNumberSeed; - /**< @brief Store seed value for random number generator - * - * <p> - * This integer variable hold the seed value of random number generator for initialization of network weights - * - * DEFAULT : NEURALNET_DEF_RANDOM_NUMBER_SEED - * </p> - */ - - float m_neuralnetNormalizationFactor; - /**< @brief Store the normalisation factor to normalise the input feature component value between (-1 to +1) - * It is used during genaration of training / testing feature vector - * <p> - * - * DEFAULT : NEURALNET_DEF_NORMALIZE_FACTOR - * </p> - */ - - float m_neuralnetLearningRate; - /**< @brief Learning rate of backpropagation algorithm - * - * <p> - * - * DEFAULT : NEURALNET_DEF_LEARNING_RATE - * </p> - */ - - float m_neuralnetMomemtumRate; - /**< @brief Momentum rate of backpropagation algorithm - * - * <p> - * - * DEFAULT : NEURALNET_DEF_MOMEMTUM_RATE - * </p> - */ - - double m_neuralnetTotalError; - /**< @brief Store the threshold value of the system error - * (i.e total error of network response during an epoch) - * - * <p> - * Used for training convergence criterion - * - * DEFAULT : NEURALNET_DEF_TOTAL_ERROR - * </p> - */ - - double m_neuralnetIndividualError; - /**< @brief Store the threshold value of the network response error per - * inidividual sample - * - * <p> - * Used for training convergence criterion - * - * DEFAULT : NEURALNET_DEF_INDIVIDUAL_ERROR - * </p> - */ - - int m_neuralnetNumHiddenLayers; - /**< @brief Number of hidden layers of the neural net - * - * <p> - * - * DEFAULT : NEURALNET_DEF_HIDDEN_LAYERS_SIZE - * </p> - */ - - int m_neuralnetMaximumIteration; - /**< @brief Number of iteration - * - * <p> - * Used for upper bound of traning iteration - * - * DEFAULT : NEURALNET_DEF_MAX_ITR - * </p> - */ - - bool m_isCreateTrainingSequence; - /**< @brief If true generate input sequence of feature vectors for training - * - * <p> - * - * DEFAULT : true - * </p> - */ - - double2DVector m_connectionWeightVec; - /**< @brief Store network connection weight - * <p> - * - * </p> - */ - - double2DVector m_delW; - /**< @brief Store error derivatives with respect to network weights - * <p> - * - * </p> - */ - - double2DVector m_previousDelW; - /**< @brief Store values of m_delW during last but one iteration - * <p> - * - * </p> - */ - - double2DVector m_outputLayerContentVec; - /**< @brief Store values of output layer node - * <p> - * - * </p> - */ - - double2DVector m_targetOutputVec; - /**< @brief Store values of target output vactor components for each training sample - * <p> - * - * </p> - */ - - intVector m_layerOutputUnitVec; - /**< @brief Stores numbere of nodes at different layers of the network - * <p> - * - * </p> - */ - - bool m_isNeuralnetWeightReestimate; - /**< @brief If true the neural net is initialised using a previously trained network information - * this information is obtained from neuralnet.mdt file - * <p> - * - * </p> - */ - - float m_rejectThreshold; - /**< @brief Threshold on the confidence to declare a test sample rejected - * <p> - * DEFAULT : 0.01 - * </p> - */ - - intVector m_sampleCountVec; - /**< @brief Number of training samples present in each category - * <p> - * - * </p> - */ - - LTKCaptureDevice m_captureDevice; - - LTKOSUtil* m_OSUtilPtr; - /**< @brief Pointer to LTKOSUtil interface - * <p> - * - * </p> - */ - - vector<LTKShapeSample> m_trainSet; - /**< @brief Store ShapeSampleFeatures for all training samples - * Used during neuralnet training - * <p> - * - * DEFAULT : 0.01 - * </p> - */ - - string m_MDTFileOpenMode; - /**< @brief Store the neuralnet.mdt file open mode (Ascii or Binary) - * <p> - * Used during open neuralnet.mdt file - * - * DEFAULT : Ascii - * </p> - */ - - //@} - - string m_currentVersion; - - public: - - /** @name Constructors and Destructor */ - //@{ - - NeuralNetShapeRecognizer(const LTKControlInfo& controlInfo); - - /** - * Destructor - */ - ~NeuralNetShapeRecognizer(); - - //@} - - /** - * This method initializes the NeuralNet shape recognizer - * <p> - * Semantics - * - Set the project name in NeuralNetShapeRecognizer::headerInfo with the parameter passed <br> - * m_headerInfo[PROJNAME] = strProjectName; - * - * - Initialize NeuralNetShapeRecognizer::m_neuralnetCfgFilePath <br> - * m_neuralnetCfgFilePath = NeuralNetShapeRecognizer::m_lipiRootPath + LTKMacros::PROJECTS_PATH_STRING + - * strProjectName + LTKMacros::PROFILE_PATH_STRING + strProfileName + - * LTKInc::SEPARATOR + LTKInc::NEURALNET + LTKInc::CONFIGFILEEXT; - * - * - Initializes NeuralNetShapeRecognizer::m_neuralnetMDTFilePath <br> - * m_neuralnetMDTFilePath = NeuralNetShapeRecognizer::m_lipiRootPath + LTKMacros::PROJECTS_PATH_STRING + - * strProjectName + LTKMacros::PROFILE_PATH_STRING + strProfileName + - * LTKInc::SEPARATOR + LTKInc::NEURALNET + LTKInc::DATFILEEXT; - * - * - Initializes NeuralNetShapeRecognizer::m_projectTypeDynamic with the value returned from LTKShapeRecoUtil::isProjectDynamic - * - * - Initialize the preprocessor using LTKShapeRecoUtil::initializePreprocessor and assign - * default values for - * -# Normalised size - * -# Threshold size - * -# Aspect ratio - * -# Dot threshold - * - * - Initialize the instance variables of neuralnet recognizer with the values given in classifier config file - * - * </p> - * @param strProjectName : string : Holds the name of the Project - * @param strProfileName : string : Holds the name of the Profile - * - * @return int : LTKInc::SUCCESS if initialization done successfully - * errorValues if initialization has some errors - * - * @exception LTKErrorList::ECONFIG_FILE_OPEN Could not open project.cfg - * @exception LTKErrorList::EINVALID_NUM_OF_SHAPES Negative value for number of shapes - * @exception LTKErrorList::ELOAD_PREPROC_DLL Could not load preprocessor DLL - * @exception LTKErrorList::EDLL_FUNC_ADDRESS_CREATE Could not map createPreprocInst - * @exception LTKErrorList::EDLL_FUNC_ADDRESS_DELETE Could not map destroyPreprocInst - */ - - /** - * This method calls the train method of the NeuralNet classifier - * - */ - int train(const string& trainingInputFilePath, - const string& mdtHeaderFilePath, - const string &comment,const string &dataset, - const string &trainFileType=INK_FILE) ; - - /** - * This method loads the Training Data of the NeuralNet classifier - * @param - * @return LTKInc::SUCCESS : if the model data was loaded successfully - * @exception - */ - int loadModelData(); - - /** - * This method unloads all the training data - * @param none - * @return LTKInc::SUCCESS : if the model data was unloaded successfully - * @exception none - */ - int unloadModelData(); - - /** - * This method sets the device context for the recognition - * - * @param deviceInfo parameter to be set - * @return - * @exception - */ - int setDeviceContext(const LTKCaptureDevice& deviceInfo); - - /** - * Populates a vector of LTKShapeRecoResult consisting of top classes with their confidences - * - * Semantics - * - * - Validate the input arguments - * - Extract the features from traceGroup - * - Compute the confidences of the classes, call computeConfidence() - * - Check if the first element of resultVector has confidence less than m_rejectThreshold, if so, - empty the resultVector (reject the sample), log and return. - * - If the confThreshold value was specified by the user (not equal to -1), - delete the entries from resultVector with confidence values less than confThreshold - * - If the numChoices value was specified by the user (not equal to -1), - update the resultVector with top numChoices entries, delete other values - * - * @param traceGroup The co-ordinates of the shape which is to be recognized - * @param screenContext Contains information about the input sample whether it is written inside a preprinted box - * or unconstrained continuous writing - * @param subSetOfClasses A subset of the entire class space which is to be used for - * recognizing the input shape. - * @param confThreshold Classes with confidence below this threshold are not returned, - * valid range of confThreshold: (0,1) - * @param numOfChoices Number of top choices to be returned in the result structure - * @param resultVector The result of recognition - * - * @return SUCCESS: resultVector populated successfully - * FAILURE: return ErrorCode - * @exception none - */ - int recognize(const LTKTraceGroup& traceGroup, - const LTKScreenContext& screenContext, - const vector<int>& subSetOfClasses, - float confThreshold, - int numChoices, - vector<LTKShapeRecoResult>& outResultVector); - - - /* Overloaded the above function to take vector<LTKShapeFeaturePtr> as - * input - */ - int recognize(const vector<LTKShapeFeaturePtr>& shapeFeatureVec, - const vector<int>& subSetOfClasses, - float confThreshold, - int numChoices, - vector<LTKShapeRecoResult>& resultVector); - - /** - * This method add Class - * - * Semantics - * - * - * @param sampleTraceGroup : LTKTraceGroup : Holds TraceGroup of sample to add - * @param shapeID : int : Holds shapeID of new Class - * - * @return SUCCESS:Shape Class added successfully - * FAILURE: return ErrorCode - * @exception none - */ - - int addClass(const LTKTraceGroup& sampleTraceGroup, int& shapeID); - - /** - * This method add Sample Class for adapt - * - * Semantics - * - * - * @param sampleTraceGroup : LTKTraceGroup : Holds TraceGroup of sample to Add - * @param shapeID : int : Holds shapeID of new Class - * - * @return SUCCESS:Shape Class added successfully - * FAILURE: return ErrorCode - * @exception none - */ - int addSample(const LTKTraceGroup& sampleTraceGroup, int shapeID); - - /** - * This method delete Class - * - * Semantics - * - * @param shapeID : int : Holds shapeID of Shape to be deleted - * - * @return SUCCESS: Shape Class deleted successfully - * FAILURE: return ErrorCode - * @exception none - */ - int deleteClass(int shapeID); - - /** - * This method converts features to TraceGroup - * - * Semantics - * - * - * @param shapeID : int : Holds shapeID - * @param numberOfTraceGroups : int : Maximum number of Trace Groups to populate - * @param outTraceGroups : vector<LTKTraceGroup> : TraceGroup - * - * @return SUCCESS: - * FAILURE: - * @exception none - */ - int getTraceGroups(int shapeID, int numberOfTraceGroups, vector<LTKTraceGroup> &outTraceGroups); - - - private: - /** - * This function is the method for generation of network architecture, - * creation of training samples and learning of network weights - * - * - * Semantics - * - * - Note the start time for computation of execution time - * - * - Call train method depending on the inFileType - * - NeuralNetShapeRecognizer::trainFromListFile() if inFileType = LTKMacros::INK_FILE - * - NeuralNetShapeRecognizer::trainFromFeatureFile() if inFileType = LTKMacros ::FEATURE_FILE - * - * - Call prepareNeuralNetTrainingSequence method depending on the m_isCreateTratningSequence - * - NeuralNetShapeRecognizer::prepareNeuralNetTrainingSequence() if m_isCreateTrainingSequence = true - * - * - Call NeuralNetShapeRecognizer::prepareNetworkArchitecture() - * - * - Update the headerInfo with algorithm version and name using NeuralNetShapeRecognizer::updateHeaderWithAlgoInfo() method - * - * - Calculate the checksum. - * - * - Call NeuralNetShapeRecognizer::writePrototypeSetToMDTFile() - * - * - Note the termination time for execution time computation - * - * - * @param inputFilePath :string : Path of trainListFile / featureFile - * @param strModelDataHeaderInfoFile : string : Holds the Header information of Model Data File - * @param inFileType : string : Possible values ink / featureFile - * - * @return LTKInc::SUCCESS : if the training done successfully - * @return errorCode : if it contains some error training fails - */ - int trainNetwork(const string& trainingInputFilePath, - const string& mdtHeaderFilePath, - const string& trainFileType); - - - /** - * This method maps between the module names and respective function names from the cfg file - * - * Semantics - * - * - Read the Preprocess Sequence from the neuralnet.cfg file - * - * - Split the sequence into tokens with delimiter LTKMacros::DELEMITER_SEQUENCE using LTKStringUtil::tokenizeString - * - * - Split each token with delimiter LTKMacrosDELEMITER_FUNC using LTKStringUtil::tokenizeString - * to get the Module name and Function name - * - * - Store the Module name and the Function name into a structure - * - * - * @param none - * @return LTKInc::SUCCESS : if functions are successfully mapped, - * @return errorCodes : if contains any error - * @exception none - */ - int mapPreprocFunctions(); - - /** - * This method will assign default values to the members - * - * Semantics - * - * - Assign Default values to all the data members - * - * - * @param none - * - * @return none - */ - void assignDefaultValues(); - - /** Reads the NeuralNet.cfg and initializes the instance variable of the classifier with the user defined - * values. - * - * Semantics - * - * - Open the NeuralNet.cfg using LTKConfigFileReader - * - * - In case of file open failure (NeuralNet.cfg), default values of the classifier parameters are used - * - * - The valid values of the classifier parameters are cached into the class data members - * LTKConfigFileReader::getConfigValue is used to get the value for a key defined in the config file - * - * - Exception is thrown if the user has specified an invalid value for a parameter - * - * - * @param none - * @return SUCCESS : If the Config file is read successfully - * @return errorCode : If it contains some error - * @exception LTKErrorList::ECONFIG_FILE_RANGE The config file variable is not within the correct range - */ - int readClassifierConfig(); - - /** - * This method creates a custom feature extractor instance and stores it's address in - * - * Semantics - * - * - * - Intialize the NeuralNetShapeRecognizer::m_ptrFeatureExtractor with address of the feature extractor instance created - * using LTKShapeFeatureExtractorFactory::createFeatureExtractor - * - * @param none - * - * @return 0 on LTKInc::SUCCESS and 1 on LTKInc::FAILURE - * - * @exception none - */ - int initializeFeatureExtractorInstance(const LTKControlInfo& controlInfo); - - /** - * This method trains the classifier from the file containing train list whose path is passed as a paramater - * - * Semantics - * - * - Open the trainListFile for reading - * - * - Open the mdt file for writing - * - * - Write header information to the mdt file - * - NeuralNetShapeRecognizer::m_numShapes - * - NeuralNetShapeRecognizer::m_traceDimension - * - NeuralNetShapeRecognizer::m_flexibilityIndex - * - * - Get a valid line from the train list file - * - Skip commented lines - * - Skip lines where number_of_tokens != 2 - * - Throw error LTKErrorList::EINITSHAPE_NONZERO, if the first shape in the list file is not zero - * - Throw error LTKErrorList::EINVALID_ORDER_LISTFILE if the shapes are not in sequential order - * - * - For every valid line get the ShapeSample from the ink file using NeuralNetShapeRecognizer::getShapeSampleFromInkFile - * - Read ink from UNIPEN ink file - * - Skip if the trace group is empty - * - Pre process the trace group read from the ink file - * - Extract features - * - * - Push all the ShapeSamples corresponding to a shape into a vector of ShapeSample ShapeSamplesVec - * - * - * @param listFilePath : string : Holds the path for train list file - * - * @return none - * - * @exception LTKErrorList::EFILE_OPEN_ERROR : Error in Opening a file (may be mdt file or list file) - * @exception LTKErrorList::EINVALID_NUM_OF_SHAPES : Invalid value for number of shapes - * @exception LTKErrorList::EINVALID_ORDER_LISTFILE: Invalid order of shapeId in List file - * @exception LTKErrorList::EINITSHAPE_NONZERO : Initial shapeId must not be zero - */ - int trainFromListFile(const string& listFilePath); - - /** - * This method trains the classifier from the feature file whose path is passed as paramater - * - * Semantics - * - * - * @param featureFilePath : string : Holds the path of Feature file - * - * @return none - */ - int trainFromFeatureFile(const string& featureFilePath); - - /** - * This method is used to store the preprocess parameter for header of the .mdt file - * - * Semantics - * - * - * @param headerSequence : stringStringMap : Holds header value of .mdt file - * - * @return none - */ - int PreprocParametersForFeatureFile(stringStringMap& headerSequence); - - /** - * This method returns the ShapeSample by using the ink file path as input - * - * Semantics - * - * - Call the LTKShapeRecoUtil::readInkFromFile() method (Utility Method) to read the ink file - * By reading this file, an inTraceGroup is generated - * - * - Preprocess the inTraceGroup and get the preprocessed trace group - * LTKTraceGroup preprocessedTraceGroup - * - * - Extract features from the preprocessed trace group to get the ShapeSamples. - * - * - * @param path : string : The path for Ink file - * @param ShapeSample : ShapeSample : The ShapeSample generated after feature extraction - * - * @return SUCCESS : If the ShapeSample is generated successfully - * @return FAILURE : Empty traces group detected for current shape - * - * @exception LTKErrorList::EINKFILE_EMPTY : Ink file is empty - * @exception LTKErrorList::EINK_FILE_OPEN : Unable to open unipen ink file - * @exception LTKErrorList::EINKFILE_CORRUPTED : Incorrect or corrupted unipen ink file - * @exception LTKErrorList::EEMPTY_TRACE : Number of points in the trace is zero - * @exception LTKErrorList::EEMPTY_TRACE_GROUP : Number of traces in the trace group is zero - */ - int getShapeFeatureFromInkFile(const string& inkFilePath, - vector<LTKShapeFeaturePtr>& shapeFeatureVec); - - /** - * This method will Update the Header information for the MDT file - * - * Semantics - * - * - Copy the version number to a string - * - * - Update the version info and algoName to NeuralNetShapeRecognizer::m_headerInfo, which specifies the - * header information for MDT file - * - * - * @param none - * - * @return none - - * @exception none - */ - void updateHeaderWithAlgoInfo(); - - /** - * This method do preprocessing for input trace group - * - * Semantics - * - * @param inTraceGroup : LTKTraceGroup : Store unipen ink file - outPreprocessedTraceGroup : LTKTraceGroup : Store preprocess ink of input ink - * @return - * @exception - */ - int preprocess (const LTKTraceGroup& inTraceGroup, LTKTraceGroup& outPreprocessedTraceGroup); - - /** - * This method write training results to the mdt file - * - * Semantics - * - * - If mdt file open mode is ASCII - * - Write the number of Shape - * - Write the connection weight - * - Write the derivative of connection weight - * - * - If mdt file open mode is BINARY - * - Write the number of Shape - * - Write the connection weight - * - Write the connection weight vector size - * - Write the derivative of connection weight - * - * @param none - * @return - * @exception - */ - int writeNeuralNetDetailsToMDTFile(); - - /** - * This method will writes training results to the mdt file - * - * Semantics - * - * - If mdt file open mode is ASCII - * - Write the number of Shape - * - Write the connection weight - * - Write the derivative of connection weight - * - * - If mdt file open mode is BINARY - * - Write the number of Shape - * - Write the connection weight - * - Write the connection weight and derivative of connection weight vectors size - * - Write the derivative of connection weight - * - * - * @param resultVector : double2DVector : A vector of weight or weight derivative created as a result of training - * mdtFileHandle : ofstream : Specifies the outut stream - * isWeight : bool : If true then resultVector contain weight, if false then resultVector contain weight derivative - * - * @return none - * - * @exception none - */ - int appendNeuralNetDetailsToMDTFile(const double2DVector& resultVector, const bool isWeight, ofstream & mdtFileHandle); - - int deleteFeatureExtractorInstance(); - - /** - * This method computes the confidences for underlying classes of a test sample - * - * Semantics - * - * - Compute the confidence based on the values of m_outputLayerContentVec (output layer value of each node) - * - Populate the resultVector - * - Sort the resultVector - * - * @param none - * - * @return SUCCESS: resultVector populated - * FAILURE: return ErrorCode - * @exception none - */ - - int computeConfidence(); - - /** - * The comparison function object of STL's sort() method, overloaded for class LTKShapeRecoResult, used to sort the vector of LTKShapeRecoResult based on the member variable confidence - * - * Semantics - * - * - Compare the values of two objects - * - Return true or false - * - - * - * @param x : LTKShapeRecoResult : First object for comparison - * @param y : LTKShapeRecoResult : Second object for comparison - * - * @return true: If x.confidence > y.confidence - * false: If x.confidence <= y.confidence - * @exception none - */ - static bool sortResultByConfidence(const LTKShapeRecoResult& x, const LTKShapeRecoResult& y); - - /** This method is used to initialize the PreProcessor - * - * Semantics - * - * - Load the preprocessor DLL using LTKLoadDLL(). - * - * - Get the proc address for creating and deleting the preprocessor instance. - * - * - Create preprocessor instance. - * - * - Start the logging for the preprocessor module. - * - * @param preprocDLLPath : string : Holds the Path of the Preprocessor DLL, - * @param errorStatus : int : Holds SUCCESS or Error Values, if occurs - * @return preprocessor instance - * - * @exception ELOAD_PREPROC_DLL Could not load preprocessor DLL - * @exception EDLL_FUNC_ADDRESS_CREATE Could not map createPreprocInst - * @exception EDLL_FUNC_ADDRESS_DELETE Could not map destroyPreprocInst - */ - int initializePreprocessor(const LTKControlInfo& controlInfo, - LTKPreprocessorInterface** preprocInstance); - - - /** This method is used to delete the PreProcessor instance - * - * Semantics - * - * - Call deleteLTKPreprocInst from the preproc.dll. - * - * - Unload the preprocessor DLL. - * - * @param ptrPreprocInstance : Holds the pointer to the LTKPreprocessorInterface - * @return none - * @exception none - */ - - int deletePreprocessor(); - - /** This method is used to unload the preprocessor DLL. - * - * Semantics - * - * - If m_libHandler != NULL, unload the DLL - * LTKUnloadDLL(m_libHandler); - * m_libHandler = NULL; - * - * @param none - * @return none - * @exception none - */ - int unloadPreprocessorDLL(); - - /** This method is used to check the preprocess parameter are specified correcty in cfg file - * - * Semantics - * - * - * @param headerSequence : stringStringMap : - * @return - * @exception - */ - int validatePreprocParameters(stringStringMap& headerSequence); - - /** - * MLP is trained by backpropagation algorithm. Training samples (feature vector representation) from underlying - * classes are presented in an alternative sequence. Error for each such pattern is calculated at the output layer - * Error is propagated backward for top-to-bottom layer weight adjustments - * - * Semantics - * - * - Call feedForward method for bottom-to-top propagation of training feature - * - * - Calculate the error at output layer for each nuron - * - * - Calculate weight adjustment amount for each sample presented - * - * @param : outptr : double2DVector : Hold the conteint of each node after forward propagation of a sample - * errptr : double2DVector : Hold intermediate error values used for computation of error derivatives - * at hidden & output layer - * ep : doubleVector : Hold output layer absolute error for individual samples - * - * @return SUCCESS: successfully complete - * FAILURE: return ErrorCode - * - * @exception LTKErrorList::EEMPTY_VECTOR Vector which are used in this method is empty - */ - int adjustWeightByErrorBackpropagation(double2DVector& outptr,double2DVector& errptr,doubleVector& ep); - - /** This method is used to arrange training samples in an alternate sequence required for effective application of backpropagation algorithm - * - * Semantics - * - * - If the number of training samples for each class is unequal, then it repeats samples of those classes - * which have fewer samples in the training set - * - * - If m_trainSet is empty it returns error and stop training process. - * - * @param none - * @return SUCCESS: successfully complete - * FAILURE: return ErrorCode - * @exception - */ - int prepareNeuralNetTrainingSequence(); - - /** This method is used to prepare architecture of neural network - * - * Semantics - * - * - Call constractNeuralnetLayeredStructure method - * - For creating input, hidden, and output layer structure - * - * - Call initialiseNetwork method. - * - * - Call adjustWeightByErrorBackpropagation method. - * - For neuralnet training - * - * @param none - * @return SUCCESS: successfully complete - * FAILURE: return ErrorCode - * @exception none - */ - int prepareNetworkArchitecture(); - - /** This method is used to initialise neuralnet parameters - * - * Semantics - * - * - Check specified network architecture during reestimation of network weights - * - * - Generally the network weights are initialised by random numbers - * - * - During reestimation of network (weights, prevDelW) neuralnet parameters are initialised by previously - * train network - * - Call loadModelData method - * - * - * @param - * @return SUCCESS: successfully complete - * FAILURE: return ErrorCode - * - * @exception LTKErrorList::EINVALID_NETWORK_LAYER Network layer are not specified correctly - */ - int initialiseNetwork(double2DVector& outptr,double2DVector& errptr); - - /** - * Feedforward input training sample - * Bottom up calculation of response at each node of network for a specific input pattern - * - * Semantics - * - * - It checks whether necessary vectors are empty - * - * - Normalise the featurte vector so that each feature component should be in the range 0 to 1 - * - * - Calculate hidden and output layer node activation values using - * calculateSigmoid method - * - * - Call NeuralNetShapeRecognizer::calculateSigmoid() method for sigmoid units, the output varies - * continuously but not linearly as the input changes <br> - * Sigmoid units bear a greater resemblance to real neurones than do linear or threshold units <br> - * Also, sigmoidal transfer function being derivable, it is - * useful for calculation of the error derivatives in backpropagation algorithm - * - * @param : shapeFeature : vector<LTKShapeFeaturePtr> : Hold feature representation of input sample - outptr : double2DVector : Hold the output layer content - * currentIndex : const int : Hold index of current sample - * - * @return SUCCESS: successfully complete - * FAILURE: return ErrorCode - * - * @exception LTKErrorList::EEMPTY_VECTOR Vector which is used in this method is empty - * LTKErrorList::ENON_POSITIVE_NUM Normalised factor should be positive - */ - int feedForward(const vector<LTKShapeFeaturePtr>& shapeFeature,double2DVector& outptr,const int& currentIndex); - - /** This method is used to take dissection for terminating training process if network converge or maximum itaretion reach - * - * Semantics - * - * - Check if individual error set for each neuron are empty - * - * - Check if current error is negative - * - * - Check if training iteration is negative - * - * - If criteria for convergence or maximum training iteration is reached - * - terminate else training process continue - * - * @param ep : const doubleVector : Hold output layer absolute error for individual neuron - * currentError : const double : Hold the current error - * currentItr : const int : Itaretion number - * outConvergeStatus : int : Status of iterative learning after each iteration - * - * @return SUCCESS: successfully complete - * FAILURE: return ErrorCode - * - * @exception LTKErrorList::EEMPTY_VECTOR Empty individual error set - * @exception LTKErrorList::ENEGATIVE_NUM Current error can't be nagative - * @exception LTKErrorList::ENEGATIVE_NUM Itaretion can't be nagative - */ - int introspective(const doubleVector& ep, const double currentError,const int& currentItr, int& outConvergeStatus); - - /** This method is used to construct the layered structure of network. - * - * Semantics - * - * - It initialise input layer and output layer nodes - * - * @param none - * @return SUCCESS: successfully complete - * - * @exception LTKErrorList::EEMPTY_VECTOR If the feature set is empty - * LTKErrorList::EINVALID_NUM_OF_INPUT_NODE If input layer nodes are not correctly specified - * LTKErrorList::EINVALID_NUM_OF_OUTPUT_NODE If output layer nodes are not correctly specified - */ - int constractNeuralnetLayeredStructure(); - - /** This method is used to check if the network architecture parameters are specified correctly or not - * - * Semantics - * - * - Check network cofig parameters - * - * @param none - * @return SUCCESS: successfully complete - * FAILURE: return ErrorCode - * @exception - */ - int validateNeuralnetArchitectureParameters(stringStringMap& headerSequence); - - /** This method is used to calculate sigmoid value of input value to a node - * - * Semantics - * - * @param - * @return - * @exception - */ - double calculateSigmoid(double inNet); - - int getShapeSampleFromString(const string& inString, LTKShapeSample& outShapeSample); -}; - - -#endif |