MLBase.cpp

#define GRT_DLL_EXPORTS
#include "MLBase.h"

GRT_BEGIN_NAMESPACE

MLBase::MLBase(void){
 trained = false;
 useScaling = false;
 baseType = BASE_TYPE_NOT_SET;
 inputType = DATA_TYPE_UNKNOWN;
 outputType = DATA_TYPE_UNKNOWN;
 numInputDimensions = 0;
 numOutputDimensions = 0;
 minNumEpochs = 0;
 maxNumEpochs = 100;
 validationSetSize = 20;
 validationSetAccuracy = 0;
 minChange = 1.0e-5;
 learningRate = 0.1;
 useValidationSet = false;
 randomiseTrainingOrder = true;
 rootMeanSquaredTrainingError = 0;
 totalSquaredTrainingError = 0;
}

MLBase::~MLBase(void){
 clear();
}

bool MLBase::copyMLBaseVariables(const MLBase *mlBase){
 
 if( mlBase == NULL ){
 errorLog << "copyMLBaseVariables(MLBase *mlBase) - mlBase pointer is NULL!" << std::endl;
 return false;
 }
 
 if( !copyGRTBaseVariables( mlBase ) ){
 errorLog << "copyMLBaseVariables(MLBase *mlBase) - Failed to copy GRT Base variables!" << std::endl;
 return false;
 }
 
 this->trained = mlBase->trained;
 this->useScaling = mlBase->useScaling;
 this->baseType = mlBase->baseType;
 this->inputType = mlBase->inputType;
 this->outputType = mlBase->outputType;
 this->numInputDimensions = mlBase->numInputDimensions;
 this->numOutputDimensions = mlBase->numOutputDimensions;
 this->minNumEpochs = mlBase->minNumEpochs;
 this->maxNumEpochs = mlBase->maxNumEpochs;
 this->validationSetSize = mlBase->validationSetSize;
 this->validationSetAccuracy = mlBase->validationSetAccuracy;
 this->validationSetPrecision = mlBase->validationSetPrecision;
 this->validationSetRecall = mlBase->validationSetRecall;
 this->minChange = mlBase->minChange;
 this->learningRate = mlBase->learningRate;
 this->rootMeanSquaredTrainingError = mlBase->rootMeanSquaredTrainingError;
 this->totalSquaredTrainingError = mlBase->totalSquaredTrainingError;
 this->useValidationSet = mlBase->useValidationSet;
 this->randomiseTrainingOrder = mlBase->randomiseTrainingOrder;
 this->numTrainingIterationsToConverge = mlBase->numTrainingIterationsToConverge;
 this->trainingResults = mlBase->trainingResults;
 this->trainingResultsObserverManager = mlBase->trainingResultsObserverManager;
 this->testResultsObserverManager = mlBase->testResultsObserverManager;
 
 return true;
}

bool MLBase::train(ClassificationData trainingData){ return train_( trainingData ); }

bool MLBase::train_(ClassificationData &trainingData){ return false; }

bool MLBase::train(RegressionData trainingData){ return train_( trainingData ); }

bool MLBase::train_(RegressionData &trainingData){ return false; }

bool MLBase::train(TimeSeriesClassificationData trainingData){ return train_( trainingData ); }

bool MLBase::train_(TimeSeriesClassificationData &trainingData){ return false; }

bool MLBase::train(ClassificationDataStream trainingData){ return train_( trainingData ); }

bool MLBase::train_(ClassificationDataStream &trainingData){ return false; }

bool MLBase::train(UnlabelledData trainingData){ return train_( trainingData ); }

bool MLBase::train_(UnlabelledData &trainingData){ return false; }

bool MLBase::train(MatrixFloat data){ return train_( data ); }

bool MLBase::train_(MatrixFloat &data){ return false; }

bool MLBase::predict(VectorFloat inputVector){ return predict_( inputVector ); }

bool MLBase::predict_(VectorFloat &inputVector){ return false; }

bool MLBase::predict(MatrixFloat inputMatrix){ return predict_( inputMatrix ); }

bool MLBase::predict_(MatrixFloat &inputMatrix){ return false; }

bool MLBase::map(VectorFloat inputVector){ return map_( inputVector ); }

bool MLBase::map_(VectorFloat &inputVector){ return false; }

bool MLBase::reset(){ return true; }

bool MLBase::clear(){
 trained = false;
 numInputDimensions = 0;
 numOutputDimensions = 0;
 numTrainingIterationsToConverge = 0;
 rootMeanSquaredTrainingError = 0;
 totalSquaredTrainingError = 0;
 trainingResults.clear();
 validationSetPrecision.clear();
 validationSetRecall.clear();
 validationSetAccuracy = 0;
 return true;
}

bool MLBase::print() const { std::cout << getModelAsString(); return true; }

bool MLBase::save(const std::string filename) const {
 
 if( !trained ) return false;
 
 std::fstream file;
 file.open(filename.c_str(), std::ios::out);
 
 if( !save( file ) ){
 return false;
 }
 
 file.close();
 
 return true;
}

bool MLBase::save(std::fstream &file) const {
 return false; //The base class returns false, as this should be overwritten by the inheriting class
}

bool MLBase::saveModelToFile(std::string filename) const { return save( filename ); }

bool MLBase::saveModelToFile(std::fstream &file) const { return save( file ); }

bool MLBase::load(const std::string filename){
 
 std::fstream file;
 file.open(filename.c_str(), std::ios::in);
 
 if( !load( file ) ){
 return false;
 }
 
 //Close the file
 file.close();
 
 return true;
}

bool MLBase::load(std::fstream &file) {
 return false; //The base class returns false, as this should be overwritten by the inheriting class
}

bool MLBase::loadModelFromFile(std::string filename){ return load( filename ); }

bool MLBase::loadModelFromFile(std::fstream &file){ return load( file ); }

bool MLBase::getModel(std::ostream &stream) const { return true; }

std::string MLBase::getModelAsString() const{
 std::stringstream stream;
 if( getModel( stream ) ){
 return stream.str();
 }
 return "";
}

DataType MLBase::getInputType() const {
 return inputType;
}

DataType MLBase::getOutputType() const {
 return outputType;
}

UINT MLBase::getBaseType() const{ return baseType; }

UINT MLBase::getNumInputFeatures() const{ return getNumInputDimensions(); }

UINT MLBase::getNumInputDimensions() const{ return numInputDimensions; }

UINT MLBase::getNumOutputDimensions() const{ return numOutputDimensions; }

UINT MLBase::getNumTrainingIterationsToConverge() const{
 if( trained ){
 return numTrainingIterationsToConverge;
 }
 return 0;
}

UINT MLBase::getMinNumEpochs() const{
 return minNumEpochs;
}

UINT MLBase::getMaxNumEpochs() const{
 return maxNumEpochs;
}

UINT MLBase::getValidationSetSize() const{
 return validationSetSize;
}

Float MLBase::getLearningRate() const{
 return learningRate;
}

Float MLBase::getRootMeanSquaredTrainingError() const{
 return rootMeanSquaredTrainingError;
}

Float MLBase::getTotalSquaredTrainingError() const{
 return totalSquaredTrainingError;
}

Float MLBase::getValidationSetAccuracy() const {
 return validationSetAccuracy;
}

VectorFloat MLBase::getValidationSetPrecision() const {
 return validationSetPrecision;
}

VectorFloat MLBase::getValidationSetRecall() const {
 return validationSetRecall;
}

bool MLBase::getTrained() const{ return trained; }

bool MLBase::getModelTrained() const{ return getTrained(); }

bool MLBase::getScalingEnabled() const{ return useScaling; }

bool MLBase::getIsBaseTypeClassifier() const{ return baseType==CLASSIFIER; }

bool MLBase::getIsBaseTypeRegressifier() const{ return baseType==REGRESSIFIER; }

bool MLBase::getIsBaseTypeClusterer() const{ return baseType==CLUSTERER; }

bool MLBase::enableScaling(bool useScaling){ this->useScaling = useScaling; return true; }

bool MLBase::setMaxNumEpochs(const UINT maxNumEpochs){
 if( maxNumEpochs == 0 ){
 warningLog << "setMaxNumEpochs(const UINT maxNumEpochs) - The maxNumEpochs must be greater than 0!" << std::endl;
 return false;
 }
 this->maxNumEpochs = maxNumEpochs;
 return true;
}

bool MLBase::setMinNumEpochs(const UINT minNumEpochs){
 this->minNumEpochs = minNumEpochs;
 return true;
}

bool MLBase::setMinChange(const Float minChange){
 if( minChange < 0 ){
 warningLog << "setMinChange(const Float minChange) - The minChange must be greater than or equal to 0!" << std::endl;
 return false;
 }
 this->minChange = minChange;
 return true;
}

bool MLBase::setLearningRate(const Float learningRate){
 if( learningRate > 0 ){
 this->learningRate = learningRate;
 return true;
 }
 return false;
}

bool MLBase::setValidationSetSize(const UINT validationSetSize){
 
 if( validationSetSize > 0 && validationSetSize < 100 ){
 this->validationSetSize = validationSetSize;
 return true;
 }
 
 warningLog << "setValidationSetSize(const UINT validationSetSize) - The validation size must be in the range [1 99]!" << std::endl;
 
 return false;
}

bool MLBase::setUseValidationSet(const bool useValidationSet){
 this->useValidationSet = useValidationSet;
 return true;
}

bool MLBase::setRandomiseTrainingOrder(const bool randomiseTrainingOrder){
 this->randomiseTrainingOrder = randomiseTrainingOrder;
 return true;
}

bool MLBase::setTrainingLoggingEnabled(const bool loggingEnabled){
 this->trainingLog.setEnableInstanceLogging( loggingEnabled );
 return true;
}

bool MLBase::registerTrainingResultsObserver( Observer< TrainingResult > &observer ){
 return trainingResultsObserverManager.registerObserver( observer );
}

bool MLBase::registerTestResultsObserver( Observer< TestInstanceResult > &observer ){
 return testResultsObserverManager.registerObserver( observer );
}

bool MLBase::removeTrainingResultsObserver( const Observer< TrainingResult > &observer ){
 return trainingResultsObserverManager.removeObserver( observer );
}

bool MLBase::removeTestResultsObserver( const Observer< TestInstanceResult > &observer ){
 return testResultsObserverManager.removeObserver( observer );
}

bool MLBase::removeAllTrainingObservers(){
 return trainingResultsObserverManager.removeAllObservers();
}

bool MLBase::removeAllTestObservers(){
 return testResultsObserverManager.removeAllObservers();
}

bool MLBase::notifyTrainingResultsObservers( const TrainingResult &data ){
 return trainingResultsObserverManager.notifyObservers( data );
}

bool MLBase::notifyTestResultsObservers( const TestInstanceResult &data ){
 return testResultsObserverManager.notifyObservers( data );
}

MLBase* MLBase::getMLBasePointer(){
 return this;
}

const MLBase* MLBase::getMLBasePointer() const{
 return this;
}

Vector< TrainingResult > MLBase::getTrainingResults() const{
 return trainingResults;
}

bool MLBase::saveBaseSettingsToFile( std::fstream &file ) const{
 
 if( !file.is_open() ){
 errorLog << "saveBaseSettingsToFile(fstream &file) - The file is not open!" << std::endl;
 return false;
 }
 
 file << "Trained: " << trained << std::endl;
 file << "UseScaling: " << useScaling << std::endl;
 file << "NumInputDimensions: " << numInputDimensions << std::endl;
 file << "NumOutputDimensions: " << numOutputDimensions << std::endl;
 file << "NumTrainingIterationsToConverge: " << numTrainingIterationsToConverge << std::endl;
 file << "MinNumEpochs: " << minNumEpochs << std::endl;
 file << "MaxNumEpochs: " << maxNumEpochs << std::endl;
 file << "ValidationSetSize: " << validationSetSize << std::endl;
 file << "LearningRate: " << learningRate << std::endl;
 file << "MinChange: " << minChange << std::endl;
 file << "UseValidationSet: " << useValidationSet << std::endl;
 file << "RandomiseTrainingOrder: " << randomiseTrainingOrder << std::endl;
 
 return true;
}

bool MLBase::loadBaseSettingsFromFile( std::fstream &file ){
 
 //Clear any previous setup
 clear();
 
 if( !file.is_open() ){
 errorLog << "loadBaseSettingsFromFile(fstream &file) - The file is not open!" << std::endl;
 return false;
 }
 
 std::string word;
 
 //Load the trained state
 file >> word;
 if( word != "Trained:" ){
 errorLog << "loadBaseSettingsFromFile(fstream &file) - Failed to read Trained header!" << std::endl;
 return false;
 }
 file >> trained;
 
 //Load the scaling state
 file >> word;
 if( word != "UseScaling:" ){
 errorLog << "loadBaseSettingsFromFile(fstream &file) - Failed to read UseScaling header!" << std::endl;
 return false;
 }
 file >> useScaling;
 
 //Load the NumInputDimensions
 file >> word;
 if( word != "NumInputDimensions:" ){
 errorLog << "loadBaseSettingsFromFile(fstream &file) - Failed to read NumInputDimensions header!" << std::endl;
 return false;
 }
 file >> numInputDimensions;
 
 //Load the NumOutputDimensions
 file >> word;
 if( word != "NumOutputDimensions:" ){
 errorLog << "loadBaseSettingsFromFile(fstream &file) - Failed to read NumOutputDimensions header!" << std::endl;
 return false;
 }
 file >> numOutputDimensions;
 
 //Load the numTrainingIterationsToConverge
 file >> word;
 if( word != "NumTrainingIterationsToConverge:" ){
 errorLog << "loadBaseSettingsFromFile(fstream &file) - Failed to read NumTrainingIterationsToConverge header!" << std::endl;
 return false;
 }
 file >> numTrainingIterationsToConverge;
 
 //Load the MinNumEpochs
 file >> word;
 if( word != "MinNumEpochs:" ){
 errorLog << "loadBaseSettingsFromFile(fstream &file) - Failed to read MinNumEpochs header!" << std::endl;
 return false;
 }
 file >> minNumEpochs;
 
 //Load the maxNumEpochs
 file >> word;
 if( word != "MaxNumEpochs:" ){
 errorLog << "loadBaseSettingsFromFile(fstream &file) - Failed to read MaxNumEpochs header!" << std::endl;
 return false;
 }
 file >> maxNumEpochs;
 
 //Load the ValidationSetSize
 file >> word;
 if( word != "ValidationSetSize:" ){
 errorLog << "loadBaseSettingsFromFile(fstream &file) - Failed to read ValidationSetSize header!" << std::endl;
 return false;
 }
 file >> validationSetSize;
 
 //Load the LearningRate
 file >> word;
 if( word != "LearningRate:" ){
 errorLog << "loadBaseSettingsFromFile(fstream &file) - Failed to read LearningRate header!" << std::endl;
 return false;
 }
 file >> learningRate;
 
 //Load the MinChange
 file >> word;
 if( word != "MinChange:" ){
 errorLog << "loadBaseSettingsFromFile(fstream &file) - Failed to read MinChange header!" << std::endl;
 return false;
 }
 file >> minChange;
 
 //Load the UseValidationSet
 file >> word;
 if( word != "UseValidationSet:" ){
 errorLog << "loadBaseSettingsFromFile(fstream &file) - Failed to read UseValidationSet header!" << std::endl;
 return false;
 }
 file >> useValidationSet;
 
 //Load the RandomiseTrainingOrder
 file >> word;
 if( word != "RandomiseTrainingOrder:" ){
 errorLog << "loadBaseSettingsFromFile(fstream &file) - Failed to read RandomiseTrainingOrder header!" << std::endl;
 return false;
 }
 file >> randomiseTrainingOrder;
 
 return true;
}

GRT_END_NAMESPACE