MLP.cpp

/*
GRT MIT License
Copyright (c) <2012> <Nicholas Gillian, Media Lab, MIT>

Permission is hereby granted, free of charge, to any person obtaining a copy of this software 
and associated documentation files (the "Software"), to deal in the Software without restriction, 
including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, 
and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, 
subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial 
portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT 
LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 
IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, 
WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE 
SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
*/

#include "MLP.h"

GRT_BEGIN_NAMESPACE

const Float MLP_NEURON_MIN_TARGET = -1.0;
const Float MLP_NEURON_MAX_TARGET = 1.0;
 
//Register the MLP module with the Regressifier base class
RegisterRegressifierModule< MLP > MLP::registerModule("MLP");

MLP::MLP(){
 inputLayerActivationFunction = Neuron::LINEAR;
 hiddenLayerActivationFunction = Neuron::LINEAR;
 outputLayerActivationFunction = Neuron::LINEAR;
 minNumEpochs = 10;
 numRandomTrainingIterations = 10;
 validationSetSize = 20; //20% of the training data will be set aside for the validation set
 trainingMode = ONLINE_GRADIENT_DESCENT;
 momentum = 0.5;
 gamma = 2.0;
 trainingError = 0;
 nullRejectionCoeff = 0.9;
 nullRejectionThreshold = 0;
 useValidationSet = true;
 randomiseTrainingOrder = false;
 useScaling = true;
 trained = false;
 initialized = false;
 classificationModeActive = false;
 useNullRejection = true;
 clear();
 classType = "MLP";
 regressifierType = classType;
 debugLog.setProceedingText("[DEBUG MLP]");
 errorLog.setProceedingText("[ERROR MLP]");
 trainingLog.setProceedingText("[TRAINING MLP]");
 warningLog.setProceedingText("[WARNING MLP]");
}
 
MLP::MLP(const MLP &rhs){
 classType = "MLP";
 regressifierType = classType;
 debugLog.setProceedingText("[DEBUG MLP]");
 errorLog.setProceedingText("[ERROR MLP]");
 trainingLog.setProceedingText("[TRAINING MLP]");
 warningLog.setProceedingText("[WARNING MLP]");
 
 *this = rhs;
}

MLP::~MLP(){
 clear();
}
 
MLP& MLP::operator=(const MLP &rhs){
 if( this != &rhs ){
 //MLP variables
 this->numInputNeurons = rhs.numInputNeurons;
 this->numHiddenNeurons = rhs.numHiddenNeurons;
 this->numOutputNeurons = rhs.numOutputNeurons;
 this->inputLayerActivationFunction = rhs.inputLayerActivationFunction;
 this->hiddenLayerActivationFunction = rhs.hiddenLayerActivationFunction;
 this->outputLayerActivationFunction = rhs.outputLayerActivationFunction;
 this->numRandomTrainingIterations = rhs.numRandomTrainingIterations;
 this->trainingMode = rhs.trainingMode;
 this->momentum = rhs.momentum;
 this->trainingError = rhs.trainingError;
 this->gamma = rhs.gamma;
 this->initialized = rhs.initialized;
 this->inputLayer = rhs.inputLayer;
 this->hiddenLayer = rhs.hiddenLayer;
 this->outputLayer = rhs.outputLayer;
 this->inputVectorRanges = rhs.inputVectorRanges;
 this->targetVectorRanges = rhs.targetVectorRanges;
 this->trainingErrorLog = rhs.trainingErrorLog;
 
 this->classificationModeActive = rhs.classificationModeActive;
 this->useNullRejection = rhs.useNullRejection;
 this->predictedClassLabel = rhs.predictedClassLabel;
 this->nullRejectionCoeff = rhs.nullRejectionCoeff;
 this->nullRejectionThreshold = rhs.nullRejectionThreshold;
 this->maxLikelihood = rhs.maxLikelihood;
 this->classLikelihoods = rhs.classLikelihoods;
 
 //Copy the base variables
 copyBaseVariables( (Regressifier*)&rhs );
 }
 return *this;
}
 
bool MLP::deepCopyFrom(const Regressifier *regressifier){
 
 if( regressifier == NULL ){
 errorLog << "deepCopyFrom(const Regressifier *regressifier) - regressifier is NULL!" << std::endl;
 return false;
 }
 
 if( this->getRegressifierType() != regressifier->getRegressifierType() ){
 errorLog << "deepCopyFrom(const Regressifier *regressifier) - regressifier is not the correct type!" << std::endl;
 return false;
 }
 
 *this = *dynamic_cast<const MLP*>(regressifier);
 
 return true;
}
 
//Classifier interface
bool MLP::train_(ClassificationData &trainingData){
 
 if( !initialized ){
 errorLog << "train_(ClassificationData trainingData) - The MLP has not been initialized!" << std::endl;
 return false;
 }
 
 if( trainingData.getNumDimensions() != numInputNeurons ){
 errorLog << "train_(ClassificationData trainingData) - The number of input dimensions in the training data (" << trainingData.getNumDimensions() << ") does not match that of the MLP (" << numInputNeurons << ")" << std::endl;
 return false;
 }
 if( trainingData.getNumClasses() != numOutputNeurons ){
 errorLog << "train_(ClassificationData trainingData) - The number of classes in the training data (" << trainingData.getNumClasses() << ") does not match that of the MLP (" << numOutputNeurons << ")" << std::endl;
 return false;
 }
 
 //Reformat the LabelledClassificationData as LabelledRegressionData
 RegressionData regressionData = trainingData.reformatAsRegressionData();
 
 //Flag that the MLP is being used for classification, not regression
 classificationModeActive = true;
 
 return trainModel(regressionData);
}
 
bool MLP::train_(RegressionData &trainingData){
 
 //Flag that the MLP is being used for regression, not classification
 classificationModeActive = false;

 return trainModel(trainingData);
}
 
//Classifier interface
bool MLP::predict_(VectorFloat &inputVector){
 
 if( !trained ){
 errorLog << "predict_(VectorFloat &inputVector) - Model not trained!" << std::endl;
 return false;
 }
 
 if( inputVector.size() != numInputNeurons ){
 errorLog << "predict_(VectorFloat &inputVector) - The sie of the input Vector (" << int(inputVector.size()) << ") does not match that of the number of input dimensions (" << numInputNeurons << ") " << std::endl;
 return false;
 }
 
 //Set the mapped data as the classLikelihoods
 regressionData = feedforward( inputVector );
 
 if( classificationModeActive ){
 
 //Estimate the class likelihoods 
 const UINT K = (UINT)regressionData.size();
 classLikelihoods = regressionData;
 
 //Make sure all the values are greater than zero, we do this by finding the min value and adding this onto all the values
 Float minValue = Util::getMin( classLikelihoods );
 for(UINT i=0; i<K; i++){
 classLikelihoods[i] += minValue;
 }
 
 //Normalize the likelihoods so they sum to 1
 Float sum = Util::sum(classLikelihoods);
 if( sum > 0 ){
 for(UINT i=0; i<K; i++){
 classLikelihoods[i] /= sum;
 }
 }
 
 //Find the best value
 Float bestValue = classLikelihoods[0];
 UINT bestIndex = 0;
 for(UINT i=1; i<K; i++){
 if( classLikelihoods[i] > bestValue ){
 bestValue = classLikelihoods[i];
 bestIndex = i;
 }
 }
 
 //Set the maximum likelihood and predicted class label
 maxLikelihood = bestValue;
 predictedClassLabel = bestIndex+1;
 
 if( useNullRejection ){
 if( maxLikelihood < nullRejectionCoeff ){
 predictedClassLabel = 0;
 }
 }
 }
 
 return true;
}
 
bool MLP::init(const UINT numInputNeurons, const UINT numHiddenNeurons, const UINT numOutputNeurons){
 return init(numInputNeurons, numHiddenNeurons, numOutputNeurons, inputLayerActivationFunction, hiddenLayerActivationFunction, outputLayerActivationFunction );
}
 
bool MLP::init(const UINT numInputNeurons,
 const UINT numHiddenNeurons,
 const UINT numOutputNeurons,
 const UINT inputLayerActivationFunction,
 const UINT hiddenLayerActivationFunction,
 const UINT outputLayerActivationFunction){
 
 //Clear any previous models
 clear();

 //Initialize the random seed
 random.setSeed( (UINT)time(NULL) );
 
 if( numInputNeurons == 0 || numHiddenNeurons == 0 || numOutputNeurons == 0 ){
 if( numInputNeurons == 0 ){ errorLog << "init(...) - The number of input neurons is zero!" << std::endl; }
 if( numHiddenNeurons == 0 ){ errorLog << "init(...) - The number of hidden neurons is zero!" << std::endl; }
 if( numOutputNeurons == 0 ){ errorLog << "init(...) - The number of output neurons is zero!" << std::endl; }
 return false;
 }
 
 //Validate the activation functions
 if( !validateActivationFunction(inputLayerActivationFunction) || !validateActivationFunction(hiddenLayerActivationFunction) || !validateActivationFunction(outputLayerActivationFunction) ){
 errorLog << "init(...) - One Of The Activation Functions Failed The Validation Check" << std::endl;
 return false;
 }

 //Set the size of the MLP
 this->numInputNeurons = numInputNeurons;
 this->numHiddenNeurons = numHiddenNeurons;
 this->numOutputNeurons = numOutputNeurons;
 
 //Set the regression IO
 this->numInputDimensions = numInputNeurons;
 this->numOutputDimensions = numOutputNeurons;
 
 //Set the validation layers
 this->inputLayerActivationFunction = inputLayerActivationFunction;
 this->hiddenLayerActivationFunction = hiddenLayerActivationFunction;
 this->outputLayerActivationFunction = outputLayerActivationFunction;
 
 //Setup the neurons for each of the layers
 inputLayer.resize(numInputNeurons);
 hiddenLayer.resize(numHiddenNeurons);
 outputLayer.resize(numOutputNeurons);
 
 //Init the neuron memory for each of the layers
 for(UINT i=0; i<numInputNeurons; i++){
 inputLayer[i].init(1,inputLayerActivationFunction); //The input size for each input neuron will always be 1
 inputLayer[i].weights[0] = 1.0; //The weights for the input layer should always be 1
 inputLayer[i].bias = 0.0; //The bias for the input layer should always be 0
 inputLayer[i].gamma = gamma;
 }
 
 for(UINT i=0; i<numHiddenNeurons; i++){
 //The number of inputs to a neuron in the output layer will always match the number of input neurons
 hiddenLayer[i].init(numInputNeurons,hiddenLayerActivationFunction);
 hiddenLayer[i].gamma = gamma;
 }
 
 for(UINT i=0; i<numOutputNeurons; i++){
 //The number of inputs to a neuron in the output layer will always match the number of hidden neurons
 outputLayer[i].init(numHiddenNeurons,outputLayerActivationFunction);
 outputLayer[i].gamma = gamma;
 }
 
 initialized = true;
 
 return true;
}

bool MLP::clear(){
 
 //Clear the base class
 Regressifier::clear();
 
 numInputNeurons = 0;
 numHiddenNeurons = 0;
 numOutputNeurons = 0;
 inputLayer.clear();
 hiddenLayer.clear();
 outputLayer.clear();
 initialized = false;
 
 return true;
}
 
bool MLP::print() const{
 printNetwork();
 return true;
}

bool MLP::trainModel(RegressionData &trainingData){
 
 trained = false;
 
 if( !initialized ){
 errorLog << "train(RegressionData trainingData) - The MLP has not be initialized!" << std::endl;
 return false;
 }
 
 if( trainingData.getNumSamples() == 0 ){
 errorLog << "train(RegressionData trainingData) - The training data is empty!" << std::endl;
 return false;
 }
 
 //Create a validation dataset, if needed
 RegressionData validationData;
 if( useValidationSet ){
 validationData = trainingData.partition( 100 - validationSetSize );
 }

 const UINT N = trainingData.getNumInputDimensions();
 const UINT T = trainingData.getNumTargetDimensions();

 if( N != numInputNeurons ){
 errorLog << "train(LabelledRegressionData trainingData) - The number of input dimensions in the training data (" << N << ") does not match that of the MLP (" << numInputNeurons << ")" << std::endl;
 return false;
 }
 if( T != numOutputNeurons ){
 errorLog << "train(LabelledRegressionData trainingData) - The number of target dimensions in the training data (" << T << ") does not match that of the MLP (" << numOutputNeurons << ")" << std::endl;
 return false;
 }
 
 //Set the Regressifier input and output dimensions
 numInputDimensions = numInputNeurons;
 numOutputDimensions = numOutputNeurons;

 //Scale the training and validation data, if needed
 if( useScaling ){
 //Find the ranges for the input data
 inputVectorRanges = trainingData.getInputRanges();
 
 //Find the ranges for the target data
 targetVectorRanges = trainingData.getTargetRanges();

 //Now scale the training data and the validation data if required
 trainingData.scale(inputVectorRanges,targetVectorRanges,MLP_NEURON_MIN_TARGET,MLP_NEURON_MAX_TARGET);
 
 if( useValidationSet ){
 validationData.scale(inputVectorRanges,targetVectorRanges,MLP_NEURON_MIN_TARGET,MLP_NEURON_MAX_TARGET);
 }
 }
 //If scaling is enabled then the training and validation data will be scaled - so turn it off so the we do not need to scale the data again
 //The actual scaling state will be reset at the end of traiing
 bool tempScalingState = useScaling;
 useScaling = false;
 
 //Setup the memory
 trainingErrorLog.clear();
 inputNeuronsOuput.resize(numInputNeurons);
 hiddenNeuronsOutput.resize(numHiddenNeurons);
 outputNeuronsOutput.resize(numOutputNeurons);
 deltaO.resize(numOutputNeurons);
 deltaH.resize(numHiddenNeurons);
 
 //Call the main training function
 switch( trainingMode ){
 case ONLINE_GRADIENT_DESCENT:
 if( classificationModeActive ){
 trained = trainOnlineGradientDescentClassification( trainingData, validationData );
 }else{
 trained = trainOnlineGradientDescentRegression( trainingData, validationData );
 }
 break;
 default:
 useScaling = tempScalingState;
 errorLog << "train(RegressionData trainingData) - Uknown training mode!" << std::endl;
 return false;
 break;
 }
 
 //Reset the scaling state so the prediction data will be scaled if needed
 useScaling = tempScalingState;

 return true;
}
 
bool MLP::trainOnlineGradientDescentClassification(const RegressionData &trainingData,const RegressionData &validationData){
 
 const UINT M = trainingData.getNumSamples();
 const UINT T = trainingData.getNumTargetDimensions();
 const UINT numTestingExamples = useValidationSet ? validationData.getNumSamples() : M;
 
 //Setup the training loop
 MLP bestNetwork;
 totalSquaredTrainingError = 0;
 rootMeanSquaredTrainingError = 0;
 trainingError = 0;
 bool keepTraining = true;
 UINT epoch = 0;
 UINT bestIter = 0;
 UINT bestIndex = 0;
 UINT classLabel = 0;
 Float alpha = learningRate;
 Float beta = momentum;
 Float error = 0;
 Float lastError = 0;
 Float accuracy = 0;
 Float trainingSetAccuracy = 0;
 Float trainingSetTotalSquaredError = 0;
 Float bestError = grt_numeric_limits< Float >::max();
 Float bestTSError = grt_numeric_limits< Float >::max();
 Float bestRMSError = grt_numeric_limits< Float >::max();
 Float bestAccuracy = 0;
 Float delta = 0;
 Float backPropError = 0;
 Float bestValue = 0;
 VectorFloat y;
 Vector< UINT > indexList(M);
 Vector< VectorFloat > tempTrainingErrorLog;
 TrainingResult result;
 trainingResults.reserve(M);
 
 //Reset the indexList, this is used to randomize the order of the training examples, if needed
 for(UINT i=0; i<M; i++) indexList[i] = i;
 
 for(UINT iter=0; iter<numRandomTrainingIterations; iter++){
 
 epoch = 0;
 keepTraining = true;
 tempTrainingErrorLog.clear();
 
 //Randomise the start values of the neurons
 init(numInputNeurons,numHiddenNeurons,numOutputNeurons,inputLayerActivationFunction,hiddenLayerActivationFunction,outputLayerActivationFunction);
 
 if( randomiseTrainingOrder ){
 for(UINT i=0; i<M; i++){
 SWAP(indexList[ i ], indexList[ random.getRandomNumberInt(0, M) ]);
 }
 }
 
 while( keepTraining ){
 
 //Perform one training epoch
 accuracy = 0;
 totalSquaredTrainingError = 0;
 
 for(UINT i=0; i<M; i++){
 //Get the i'th training and target Vectors
 const VectorFloat &trainingExample = trainingData[ indexList[i] ].getInputVector();
 const VectorFloat &targetVector = trainingData[ indexList[i] ].getTargetVector();
 
 //Perform the back propagation
 backPropError = back_prop(trainingExample,targetVector,alpha,beta);
 
 //debugLog << "i: " << i << " backPropError: " << backPropError << std::endl;
 
 if( isNAN(backPropError) ){
 keepTraining = false;
 errorLog << "train(RegressionData trainingData) - NaN found!" << std::endl;
 return false;
 }
 
 //Compute the error for the i'th example
 if( classificationModeActive ){
 y = feedforward( trainingExample );
 
 //Get the class label
 bestValue = targetVector[0];
 bestIndex = 0;
 for(UINT i=1; i<targetVector.size(); i++){
 if( targetVector[i] > bestValue ){
 bestValue = targetVector[i];
 bestIndex = i;
 }
 }
 classLabel = bestIndex + 1;
 
 //Get the predicted class label
 bestValue = y[0];
 bestIndex = 0;
 for(UINT i=1; i<numOutputNeurons; i++){
 if( y[i] > bestValue ){
 bestValue = y[i];
 bestIndex = i;
 }
 }
 predictedClassLabel = bestIndex+1;
 
 if( classLabel == predictedClassLabel ){
 accuracy++;
 }
 
 }else{
 totalSquaredTrainingError += backPropError; //The backPropError is already squared
 }
 }
 
 if( checkForNAN() ){
 keepTraining = false;
 errorLog << "train(RegressionData trainingData) - NaN found!" << std::endl;
 break;
 }
 
 //Compute the error over all the training/validation examples
 if( useValidationSet ){
 trainingSetAccuracy = accuracy;
 trainingSetTotalSquaredError = totalSquaredTrainingError;
 accuracy = 0;
 totalSquaredTrainingError = 0;
 
 //Iterate over the validation samples
 UINT numValidationSamples = validationData.getNumSamples();
 for(UINT i=0; i<numValidationSamples; i++){
 const VectorFloat &inputVector = validationData[i].getInputVector();
 const VectorFloat &targetVector = validationData[i].getTargetVector();
 
 y = feedforward( inputVector );
 
 if( classificationModeActive ){
 //Get the class label
 bestValue = targetVector[0];
 bestIndex = 0;
 for(UINT i=1; i<numInputNeurons; i++){
 if( targetVector[i] > bestValue ){
 bestValue = targetVector[i];
 bestIndex = i;
 }
 }
 classLabel = bestIndex + 1;
 
 //Get the predicted class label
 bestValue = y[0];
 bestIndex = 0;
 for(UINT i=1; i<numOutputNeurons; i++){
 if( y[i] > bestValue ){
 bestValue = y[i];
 bestIndex = i;
 }
 }
 predictedClassLabel = bestIndex+1;
 
 if( classLabel == predictedClassLabel ){
 accuracy++;
 }
 
 }else{
 //Update the total squared error
 for(UINT j=0; j<T; j++){
 totalSquaredTrainingError += SQR( targetVector[j]-y[j] );
 }
 }
 }
 
 accuracy = (accuracy/Float(numValidationSamples))*Float(numValidationSamples);
 rootMeanSquaredTrainingError = sqrt( totalSquaredTrainingError / Float(numValidationSamples) );
 
 }else{//We are not using a validation set
 accuracy = (accuracy/Float(M))*Float(M);
 rootMeanSquaredTrainingError = sqrt( totalSquaredTrainingError / Float(M) );
 }
 
 //Store the errors
 VectorFloat temp(2);
 temp[0] = 100.0 - trainingSetAccuracy;
 temp[1] = 100.0 - accuracy;
 tempTrainingErrorLog.push_back( temp );
 
 error = 100.0 - accuracy;
 
 //Store the training results
 result.setClassificationResult(iter,accuracy,this);
 trainingResults.push_back( result );

 delta = fabs( error - lastError );
 
 trainingLog << "Random Training Iteration: " << iter+1 << " Epoch: " << epoch << " Error: " << error << " Delta: " << delta << std::endl;
 
 //Check to see if we should stop training
 if( ++epoch >= maxNumEpochs ){
 keepTraining = false;
 }
 if( delta <= minChange && epoch >= minNumEpochs ){
 keepTraining = false;
 }
 
 //Update the last error
 lastError = error;
 
 //Notify any observers of the new training result
 trainingResultsObserverManager.notifyObservers( result );
 
 }//End of While( keepTraining )
 
 if( lastError < bestError ){
 bestIter = iter;
 bestError = lastError;
 bestTSError = totalSquaredTrainingError;
 bestRMSError = rootMeanSquaredTrainingError;
 bestAccuracy = accuracy;
 bestNetwork = *this;
 trainingErrorLog = tempTrainingErrorLog;
 }
 
 }//End of For( numRandomTrainingIterations )
 
 trainingLog << "Best Accuracy: " << bestAccuracy << " in Random Training Iteration: " << bestIter+1 << std::endl;
 
 //Check to make sure the best network has not got any NaNs in it
 if( checkForNAN() ){
 errorLog << "train(LabelledRegressionData trainingData) - NAN Found!" << std::endl;
 return false;
 }
 
 //Set the MLP model to the model that best during training
 *this = bestNetwork;
 trainingError = bestAccuracy;
 
 //Compute the rejection threshold
 if( useNullRejection ){

 Float averageValue = 0;
 VectorFloat classificationPredictions, inputVector, targetVector;
 
 for(UINT i=0; i<numTestingExamples; i++){
 inputVector = useValidationSet ? validationData[i].getInputVector() : trainingData[i].getInputVector();
 targetVector = useValidationSet ? validationData[i].getTargetVector() : trainingData[i].getTargetVector();
 
 //Make the prediction
 y = feedforward( inputVector );
 
 //Get the class label
 bestValue = targetVector[0];
 bestIndex = 0;
 for(UINT i=1; i<targetVector.size(); i++){
 if( targetVector[i] > bestValue ){
 bestValue = targetVector[i];
 bestIndex = i;
 }
 }
 classLabel = bestIndex + 1;
 
 //Get the predicted class label
 bestValue = y[0];
 bestIndex = 0;
 for(UINT i=1; i<y.size(); i++){
 if( y[i] > bestValue ){
 bestValue = y[i];
 bestIndex = i;
 }
 }
 predictedClassLabel = bestIndex+1;
 
 //Only add the max value if the prediction is correct
 if( classLabel == predictedClassLabel ){
 classificationPredictions.push_back( bestValue );
 averageValue += bestValue;
 }
 }
 
 averageValue /= Float(classificationPredictions.size());
 Float stdDev = 0;
 for(UINT i=0; i<classificationPredictions.size(); i++){
 stdDev += SQR(classificationPredictions[i]-averageValue);
 }
 stdDev = sqrt( stdDev / Float(classificationPredictions.size()-1) );
 
 nullRejectionThreshold = averageValue-(stdDev*nullRejectionCoeff);
 }
 
 //Return true to flag that the model was trained OK
 return true;
}
 
bool MLP::trainOnlineGradientDescentRegression(const RegressionData &trainingData,const RegressionData &validationData){
 
 const UINT M = trainingData.getNumSamples();
 const UINT T = trainingData.getNumTargetDimensions();
 const UINT numValidationSamples = useValidationSet ? validationData.getNumSamples() : M;
 
 //Setup the training loop
 bool keepTraining = true;
 UINT epoch = 0;
 Float alpha = learningRate;
 Float beta = momentum;
 UINT bestIter = 0;
 MLP bestNetwork;
 totalSquaredTrainingError = 0;
 rootMeanSquaredTrainingError = 0;
 trainingError = 0;
 Float error = 0;
 Float lastError = 0;
 Float trainingSetTotalSquaredError = 0;
 Float bestError = grt_numeric_limits< Float >::max();
 Float bestTSError = grt_numeric_limits< Float >::max();
 Float bestRMSError = grt_numeric_limits< Float >::max();
 Float delta = 0;
 Vector< UINT > indexList(M);
 Vector< VectorFloat > tempTrainingErrorLog;
 TrainingResult result;
 trainingResults.reserve(M);
 
 //Reset the indexList, this is used to randomize the order of the training examples, if needed
 for(UINT i=0; i<M; i++) indexList[i] = i;
 
 for(UINT iter=0; iter<numRandomTrainingIterations; iter++){
 
 epoch = 0;
 keepTraining = true;
 tempTrainingErrorLog.clear();
 
 //Randomise the start values of the neurons
 init(numInputNeurons,numHiddenNeurons,numOutputNeurons,inputLayerActivationFunction,hiddenLayerActivationFunction,outputLayerActivationFunction);
 
 if( randomiseTrainingOrder ){
 for(UINT i=0; i<M; i++){
 SWAP(indexList[ i ], indexList[ random.getRandomNumberInt(0, M) ]);
 }
 }
 
 while( keepTraining ){
 
 //Perform one training epoch
 totalSquaredTrainingError = 0;
 
 for(UINT i=0; i<M; i++){
 //Get the i'th training and target Vectors
 const VectorFloat &trainingExample = trainingData[ indexList[i] ].getInputVector();
 const VectorFloat &targetVector = trainingData[ indexList[i] ].getTargetVector();
 
 //Perform the back propagation
 Float backPropError = back_prop(trainingExample,targetVector,alpha,beta);
 
 //debugLog << "i: " << i << " backPropError: " << backPropError << std::endl;
 
 if( isNAN(backPropError) ){
 keepTraining = false;
 errorLog << "train(RegressionData trainingData) - NaN found!" << std::endl;
 }
 
 //Compute the error for the i'th example
 totalSquaredTrainingError += backPropError; //The backPropError is already squared
 }
 
 if( checkForNAN() ){
 keepTraining = false;
 errorLog << "train(RegressionData trainingData) - NaN found!" << std::endl;
 break;
 }
 
 //Compute the error over all the training/validation examples
 if( useValidationSet ){
 trainingSetTotalSquaredError = totalSquaredTrainingError;
 totalSquaredTrainingError = 0;
 
 //Iterate over the validation samples
 for(UINT i=0; i<numValidationSamples; i++){
 const VectorFloat &trainingExample = validationData[i].getInputVector();
 const VectorFloat &targetVector = validationData[i].getTargetVector();
 
 VectorFloat y = feedforward(trainingExample);
 
 //Update the total squared error
 for(UINT j=0; j<T; j++){
 totalSquaredTrainingError += SQR( targetVector[j]-y[j] );
 }
 
 }
 
 rootMeanSquaredTrainingError = sqrt( totalSquaredTrainingError / Float(numValidationSamples) );
 
 }else{//We are not using a validation set
 rootMeanSquaredTrainingError = sqrt( totalSquaredTrainingError / Float(M) );
 }
 
 //Store the errors
 VectorFloat temp(2);
 temp[0] = trainingSetTotalSquaredError;
 temp[1] = rootMeanSquaredTrainingError;
 tempTrainingErrorLog.push_back( temp );
 
 error = rootMeanSquaredTrainingError;
 
 //Store the training results
 result.setRegressionResult(iter,totalSquaredTrainingError,rootMeanSquaredTrainingError,this);
 trainingResults.push_back( result );
 
 delta = fabs( error - lastError );
 
 trainingLog << "Random Training Iteration: " << iter+1 << " Epoch: " << epoch << " Error: " << error << " Delta: " << delta << std::endl;
 
 //Check to see if we should stop training
 if( ++epoch >= maxNumEpochs ){
 keepTraining = false;
 }
 if( delta <= minChange && epoch >= minNumEpochs ){
 keepTraining = false;
 }
 
 //Update the last error
 lastError = error;
 
 //Notify any observers of the new training result
 trainingResultsObserverManager.notifyObservers( result );
 
 }//End of While( keepTraining )
 
 //Check to see if this is the best model so far
 if( lastError < bestError ){
 bestIter = iter;
 bestError = lastError;
 bestTSError = totalSquaredTrainingError;
 bestRMSError = rootMeanSquaredTrainingError;
 bestNetwork = *this;
 trainingErrorLog = tempTrainingErrorLog;
 }
 
 }//End of For( numRandomTrainingIterations )
 
 trainingLog << "Best RMSError: " << bestRMSError << " in Random Training Iteration: " << bestIter+1 << std::endl;
 
 //Check to make sure the best network has not got any NaNs in it
 if( checkForNAN() ){
 errorLog << "train(RegressionData trainingData) - NAN Found!" << std::endl;
 return false;
 }
 
 //Set the MLP model to the model that best during training
 *this = bestNetwork;
 trainingError = bestRMSError;
 
 //Return true to flag that the model was trained OK
 return true;
}
 
Float MLP::back_prop(const VectorFloat &trainingExample,const VectorFloat &targetVector,const Float alpha,const Float beta){
 
 Float update = 0;
 Float sum = 0;
 Float omBeta = 1.0 - beta;
 
 //Forward propagation
 feedforward(trainingExample,inputNeuronsOuput,hiddenNeuronsOutput,outputNeuronsOutput);
 
 //Compute the error of the output layer: the derivative of the function times the error of the output
 for(UINT i=0; i<numOutputNeurons; i++){
 deltaO[i] = outputLayer[i].getDerivative( outputNeuronsOutput[i] ) * (targetVector[i]-outputNeuronsOutput[i]);
 }
 
 //Compute the error of the hidden layer
 for(UINT i=0; i<numHiddenNeurons; i++){
 sum = 0;
 for(UINT j=0; j<numOutputNeurons; j++){
 sum += outputLayer[j].weights[i] * deltaO[j];
 }
 deltaH[i] = hiddenLayer[i].getDerivative( hiddenNeuronsOutput[i] ) * sum;
 }
 
 //Update the hidden weights: old hidden weights + (learningRate * inputToTheHiddenNeuron * deltaHidden )
 for(UINT i=0; i<numHiddenNeurons; i++){
 for(UINT j=0; j<numInputNeurons; j++){
 //Compute the update
 //update = alpha * ((beta * hiddenLayer[i].previousUpdate[j]) + (omBeta * inputNeuronsOuput[j])) * deltaH[i];
 update = alpha * inputNeuronsOuput[j] * deltaH[i];

 //Update the weights
 hiddenLayer[i].weights[j] += update;

 //Store the previous update
 hiddenLayer[i].previousUpdate[j] = update; 
 }
 }
 
 //Update the output weights
 for(UINT i=0; i<numOutputNeurons; i++){
 for(UINT j=0; j<numHiddenNeurons; j++){
 //Compute the update
 //update = alpha * ((beta * outputLayer[i].previousUpdate[j]) + (omBeta * hiddenNeuronsOutput[j])) * deltaO[i];
 update = alpha * hiddenNeuronsOutput[j] * deltaO[i];

 //Update the weights
 outputLayer[i].weights[j] += update;

 //Store the update
 outputLayer[i].previousUpdate[j] = update;
 }
 }
 
 //Update the hidden bias
 for(UINT i=0; i<numHiddenNeurons; i++){
 //Compute the update
 //update = alpha * ((beta * hiddenLayer[i].previousBiasUpdate) + (omBeta * deltaH[i]));
 update = alpha * deltaH[i];

 //Update the bias
 hiddenLayer[i].bias += update;

 //Store the update
 hiddenLayer[i].previousBiasUpdate = update;
 }
 
 //Update the output bias
 for(UINT i=0; i<numOutputNeurons; i++){
 //Compute the update
 //update = alpha * (beta * outputLayer[i].previousBiasUpdate) + (omBeta * deltaO[i]);
 update = alpha * deltaO[i];

 //Update the bias
 outputLayer[i].bias += update;
 
 //Store the update
 outputLayer[i].previousBiasUpdate = update;
 }
 
 //Compute the squared error between the output of the network and the target Vector
 Float error = 0;
 for(UINT i=0; i<numOutputNeurons; i++){
 error += SQR( targetVector[i] - outputNeuronsOutput[i] );
 }

 return error;
}

VectorFloat MLP::feedforward(VectorFloat trainingExample){
 
 if( inputNeuronsOuput.size() != numInputNeurons ) inputNeuronsOuput.resize(numInputNeurons,0);
 if( hiddenNeuronsOutput.size() != numHiddenNeurons ) hiddenNeuronsOutput.resize(numHiddenNeurons,0);
 if( outputNeuronsOutput.size() != numOutputNeurons ) outputNeuronsOutput.resize(numOutputNeurons,0);

 //Scale the input Vector if required
 if( useScaling ){
 for(UINT i=0; i<numInputNeurons; i++){
 trainingExample[i] = scale(trainingExample[i],inputVectorRanges[i].minValue,inputVectorRanges[i].maxValue,MLP_NEURON_MIN_TARGET,MLP_NEURON_MAX_TARGET);
 }
 }
 
 //Input layer
 VectorFloat input(1);
 for(UINT i=0; i<numInputNeurons; i++){
 input[0] = trainingExample[i];
 inputNeuronsOuput[i] = inputLayer[i].fire( input );
 }
 
 //Hidden Layer
 for(UINT i=0; i<numHiddenNeurons; i++){
 hiddenNeuronsOutput[i] = hiddenLayer[i].fire( inputNeuronsOuput );
 }
 
 //Output Layer
 for(UINT i=0; i<numOutputNeurons; i++){
 outputNeuronsOutput[i] = outputLayer[i].fire( hiddenNeuronsOutput );
 }

 //Scale the output Vector if required
 if( useScaling ){
 for(UINT i=0; i<numOutputNeurons; i++){
 outputNeuronsOutput[i] = scale(outputNeuronsOutput[i],MLP_NEURON_MIN_TARGET,MLP_NEURON_MAX_TARGET,targetVectorRanges[i].minValue,targetVectorRanges[i].maxValue);
 }
 }
 
 return outputNeuronsOutput;
 
}

void MLP::feedforward(const VectorFloat &data,VectorFloat &inputNeuronsOuput,VectorFloat &hiddenNeuronsOutput,VectorFloat &outputNeuronsOutput){
 
 if( inputNeuronsOuput.size() != numInputNeurons ) inputNeuronsOuput.resize(numInputNeurons,0);
 if( hiddenNeuronsOutput.size() != numHiddenNeurons ) hiddenNeuronsOutput.resize(numHiddenNeurons,0);
 if( outputNeuronsOutput.size() != numOutputNeurons ) outputNeuronsOutput.resize(numOutputNeurons,0);
 
 //Input layer
 VectorFloat input(1);
 for(UINT i=0; i<numInputNeurons; i++){
 input[0] = data[i];
 inputNeuronsOuput[i] = inputLayer[i].fire( input );
 }
 
 //Hidden Layer
 for(UINT i=0; i<numHiddenNeurons; i++){
 hiddenNeuronsOutput[i] = hiddenLayer[i].fire( inputNeuronsOuput );
 }
 
 //Output Layer
 for(UINT i=0; i<numOutputNeurons; i++){
 outputNeuronsOutput[i] = outputLayer[i].fire( hiddenNeuronsOutput );
 }
 
}

void MLP::printNetwork() const{
 std::cout<<"***************** MLP *****************\n";
 std::cout<<"NumInputNeurons: "<<numInputNeurons<< std::endl;
 std::cout<<"NumHiddenNeurons: "<<numHiddenNeurons<< std::endl;
 std::cout<<"NumOutputNeurons: "<<numOutputNeurons<< std::endl;
 
 std::cout<<"InputWeights:\n";
 for(UINT i=0; i<numInputNeurons; i++){
 std::cout<<"Neuron: "<<i<<" Bias: " << inputLayer[i].bias << " Weights: ";
 for(UINT j=0; j<inputLayer[i].weights.getSize(); j++){
 std::cout << inputLayer[i].weights[j] << "\t";
 } std::cout << std::endl;
 }
 
 std::cout<<"HiddenWeights:\n";
 for(UINT i=0; i<numHiddenNeurons; i++){
 std::cout<<"Neuron: "<<i<<" Bias: " << hiddenLayer[i].bias << " Weights: ";
 for(UINT j=0; j<hiddenLayer[i].weights.getSize(); j++){
 std::cout << hiddenLayer[i].weights[j] << "\t";
 } std::cout << std::endl;
 }
 
 std::cout<<"OutputWeights:\n";
 for(UINT i=0; i<numOutputNeurons; i++){
 std::cout<<"Neuron: "<<i<<" Bias: " << outputLayer[i].bias << " Weights: ";
 for(UINT j=0; j<outputLayer[i].weights.getSize(); j++){
 std::cout << outputLayer[i].weights[j] << "\t";
 } std::cout << std::endl;
 }
 
}

bool MLP::checkForNAN() const{
 
 UINT N = 0;
 for(UINT i=0; i<numInputNeurons; i++){
 if( isNAN(inputLayer[i].bias) ) return true;
 N = inputLayer[i].weights.getSize();
 for(UINT j=0; j<N; j++){
 if( isNAN(inputLayer[i].weights[j]) ) return true;
 }
 }
 
 for(UINT i=0; i<numHiddenNeurons; i++){
 if( isNAN(hiddenLayer[i].bias) ) return true;
 N = hiddenLayer[i].weights.getSize();
 for(UINT j=0; j<N; j++){
 if( isNAN(hiddenLayer[i].weights[j]) ) return true;
 }
 }
 
 for(UINT i=0; i<numOutputNeurons; i++){
 if( isNAN(outputLayer[i].bias) ) return true;
 N = outputLayer[i].weights.getSize();
 for(UINT j=0; j<N; j++){
 if( isNAN(outputLayer[i].weights[j]) ) return true;
 }
 }
 
 return false;
}

bool inline MLP::isNAN(const Float v) const{
 if( v != v ) return true;
 return false;
}

bool MLP::saveModelToFile( std::fstream &file ) const{

 if( !file.is_open() ){
 errorLog << "saveModelToFile(fstream &file) - File is not open!" << std::endl;
 return false;
 }

 file << "GRT_MLP_FILE_V2.0\n";
 
 //Write the regressifier settings to the file
 if( !Regressifier::saveBaseSettingsToFile(file) ){
 errorLog <<"saveModelToFile(fstream &file) - Failed to save Regressifier base settings to file!" << std::endl;
 return false;
 }

 file << "NumInputNeurons: "<<numInputNeurons<< std::endl;
 file << "NumHiddenNeurons: "<<numHiddenNeurons<< std::endl;
 file << "NumOutputNeurons: "<<numOutputNeurons<< std::endl;
 file << "InputLayerActivationFunction: " <<activationFunctionToString(inputLayerActivationFunction)<< std::endl;
 file << "HiddenLayerActivationFunction: " <<activationFunctionToString(hiddenLayerActivationFunction)<< std::endl;
 file << "OutputLayerActivationFunction: " <<activationFunctionToString(outputLayerActivationFunction)<< std::endl;
 file << "NumRandomTrainingIterations: " << numRandomTrainingIterations << std::endl;
 file << "Momentum: " << momentum << std::endl;
 file << "Gamma: " << gamma << std::endl;
 file << "ClassificationMode: " << classificationModeActive << std::endl;
 file << "UseNullRejection: " << useNullRejection << std::endl;
 file << "RejectionThreshold: " << nullRejectionThreshold << std::endl;
 
 if( trained ){
 file << "InputLayer: \n";
 for(UINT i=0; i<numInputNeurons; i++){
 file << "InputNeuron: " << i+1 << std::endl;
 file << "NumInputs: " << inputLayer[i].numInputs << std::endl;
 file << "Bias: " << inputLayer[i].bias << std::endl;
 file << "Gamma: " << inputLayer[i].gamma << std::endl;
 file << "Weights: " << std::endl;
 for(UINT j=0; j<inputLayer[i].numInputs; j++){
 file << inputLayer[i].weights[j] << "\t";
 }
 file << std::endl;
 }
 file << "\n";

 file << "HiddenLayer: \n";
 for(UINT i=0; i<numHiddenNeurons; i++){
 file << "HiddenNeuron: " << i+1 << std::endl;
 file << "NumInputs: " << hiddenLayer[i].numInputs << std::endl;
 file << "Bias: " << hiddenLayer[i].bias << std::endl;
 file << "Gamma: " << hiddenLayer[i].gamma << std::endl;
 file << "Weights: " << std::endl;
 for(UINT j=0; j<hiddenLayer[i].numInputs; j++){
 file << hiddenLayer[i].weights[j] << "\t";
 }
 file << std::endl;
 }
 file << "\n";

 file << "OutputLayer: \n";
 for(UINT i=0; i<numOutputNeurons; i++){
 file << "OutputNeuron: " << i+1 << std::endl;
 file << "NumInputs: " << outputLayer[i].numInputs << std::endl;
 file << "Bias: " << outputLayer[i].bias << std::endl;
 file << "Gamma: " << outputLayer[i].gamma << std::endl;
 file << "Weights: " << std::endl;
 for(UINT j=0; j<outputLayer[i].numInputs; j++){
 file << outputLayer[i].weights[j] << "\t";
 }
 file << std::endl;
 }
 }

 return true;
}
 
bool MLP::loadModelFromFile( std::fstream &file ){

 std::string activationFunction;

 //Clear any previous models
 clear();

 if( !file.is_open() ){
 errorLog << "loadModelFromFile(fstream &file) - File is not open!" << std::endl;
 return false;
 }

 std::string word;
 
 file >> word;
 
 //See if we should load a legacy file
 if( word == "GRT_MLP_FILE_V1.0" ){
 return loadLegacyModelFromFile( file );
 }
 
 //Check to make sure this is a file with the MLP File Format
 if( word != "GRT_MLP_FILE_V2.0" ){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find file header!" << std::endl;
 return false;
 }
 
 //Load the base settings from the file
 if( !Regressifier::loadBaseSettingsFromFile( file ) ){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to load regressifier base settings from file!" << std::endl;
 return false;
 }

 file >> word;
 if(word != "NumInputNeurons:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find NumInputNeurons!" << std::endl;
 return false;
 }
 file >> numInputNeurons;
 numInputDimensions = numInputNeurons;

 file >> word;
 if(word != "NumHiddenNeurons:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find NumHiddenNeurons!" << std::endl;
 return false;
 }
 file >> numHiddenNeurons;

 file >> word;
 if(word != "NumOutputNeurons:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find NumOutputNeurons!" << std::endl;
 return false;
 }
 file >> numOutputNeurons;

 file >> word;
 if(word != "InputLayerActivationFunction:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find InputLayerActivationFunction!" << std::endl;
 return false;
 }
 file >> activationFunction;
 inputLayerActivationFunction = activationFunctionFromString(activationFunction);

 file >> word;
 if(word != "HiddenLayerActivationFunction:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find HiddenLayerActivationFunction!" << std::endl;
 return false;
 }
 file >> activationFunction;
 hiddenLayerActivationFunction = activationFunctionFromString(activationFunction);

 file >> word;
 if(word != "OutputLayerActivationFunction:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find OutputLayerActivationFunction!" << std::endl;
 return false;
 }
 file >> activationFunction;
 outputLayerActivationFunction = activationFunctionFromString(activationFunction);

 file >> word;
 if(word != "NumRandomTrainingIterations:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find NumRandomTrainingIterations!" << std::endl;
 return false;
 }
 file >> numRandomTrainingIterations;

 file >> word;
 if(word != "Momentum:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find Momentum!" << std::endl;
 return false;
 }
 file >> momentum;

 file >> word;
 if(word != "Gamma:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find Gamma!" << std::endl;
 return false;
 }
 file >> gamma;
 
 file >> word;
 if(word != "ClassificationMode:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find ClassificationMode!" << std::endl;
 return false;
 }
 file >> classificationModeActive;
 
 file >> word;
 if(word != "UseNullRejection:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find UseNullRejection!" << std::endl;
 return false;
 }
 file >> useNullRejection;
 
 file >> word;
 if(word != "RejectionThreshold:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find RejectionThreshold!" << std::endl;
 return false;
 }
 file >> nullRejectionThreshold;
 
 if( trained ) initialized = true;
 else init(numInputNeurons,numHiddenNeurons,numOutputNeurons);
 
 if( trained ){
 
 //Resize the layers
 inputLayer.resize( numInputNeurons );
 hiddenLayer.resize( numHiddenNeurons );
 outputLayer.resize( numOutputNeurons );
 
 //Load the neuron data
 file >> word;
 if(word != "InputLayer:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find InputLayer!" << std::endl;
 return false;
 }
 
 for(UINT i=0; i<numInputNeurons; i++){
 UINT tempNeuronID = 0;
 
 file >> word;
 if(word != "InputNeuron:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find InputNeuron!" << std::endl;
 return false;
 }
 file >> tempNeuronID;
 
 if( tempNeuronID != i+1 ){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - InputNeuron ID does not match!" << std::endl;
 return false;
 }
 
 file >> word;
 if(word != "NumInputs:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find NumInputs!" << std::endl;
 return false;
 }
 file >> inputLayer[i].numInputs;
 
 //Resize the buffers
 inputLayer[i].weights.resize( inputLayer[i].numInputs );
 
 file >> word;
 if(word != "Bias:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find Bias!" << std::endl;
 return false;
 }
 file >> inputLayer[i].bias;
 
 file >> word;
 if(word != "Gamma:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find Gamma!" << std::endl;
 return false;
 }
 file >> inputLayer[i].gamma;
 
 file >> word;
 if(word != "Weights:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find Weights!" << std::endl;
 return false;
 }
 
 for(UINT j=0; j<inputLayer[i].numInputs; j++){
 file >> inputLayer[i].weights[j];
 }
 }
 
 //Load the Hidden Layer
 file >> word;
 if(word != "HiddenLayer:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find HiddenLayer!" << std::endl;
 return false;
 }
 
 for(UINT i=0; i<numHiddenNeurons; i++){
 UINT tempNeuronID = 0;
 
 file >> word;
 if(word != "HiddenNeuron:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find HiddenNeuron!" << std::endl;
 return false;
 }
 file >> tempNeuronID;
 
 if( tempNeuronID != i+1 ){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find HiddenNeuron ID does not match!" << std::endl;
 return false;
 }
 
 file >> word;
 if(word != "NumInputs:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find NumInputs!" << std::endl;
 return false;
 }
 file >> hiddenLayer[i].numInputs;
 
 //Resize the buffers
 hiddenLayer[i].weights.resize( hiddenLayer[i].numInputs );
 
 file >> word;
 if(word != "Bias:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find Bias!" << std::endl;
 return false;
 }
 file >> hiddenLayer[i].bias;
 
 file >> word;
 if(word != "Gamma:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find Gamma!" << std::endl;
 return false;
 }
 file >> hiddenLayer[i].gamma;
 
 file >> word;
 if(word != "Weights:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find Weights!" << std::endl;
 return false;
 }
 
 for(unsigned int j=0; j<hiddenLayer[i].numInputs; j++){
 file >> hiddenLayer[i].weights[j];
 }
 }
 
 //Load the Output Layer
 file >> word;
 if(word != "OutputLayer:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find OutputLayer!" << std::endl;
 return false;
 }
 
 for(UINT i=0; i<numOutputNeurons; i++){
 UINT tempNeuronID = 0;
 
 file >> word;
 if(word != "OutputNeuron:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find OutputNeuron!" << std::endl;
 return false;
 }
 file >> tempNeuronID;
 
 if( tempNeuronID != i+1 ){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find OuputNeuron ID does not match!!" << std::endl;
 return false;
 }
 
 file >> word;
 if(word != "NumInputs:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find NumInputs!" << std::endl;
 return false;
 }
 file >> outputLayer[i].numInputs;
 
 //Resize the buffers
 outputLayer[i].weights.resize( outputLayer[i].numInputs );
 
 file >> word;
 if(word != "Bias:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find Bias!" << std::endl;
 return false;
 }
 file >> outputLayer[i].bias;
 
 file >> word;
 if(word != "Gamma:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find Gamma!" << std::endl;
 return false;
 }
 file >> outputLayer[i].gamma;
 
 file >> word;
 if(word != "Weights:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find Weights!" << std::endl;
 return false;
 }
 
 for(UINT j=0; j<outputLayer[i].numInputs; j++){
 file >> outputLayer[i].weights[j];
 }
 }
 
 }

 return true;
}
 
UINT MLP::getNumClasses() const{
 if( classificationModeActive )
 return numOutputNeurons;
 return 0;
}
 
UINT MLP::getNumInputNeurons() const{
 return numInputNeurons;
}

UINT MLP::getNumHiddenNeurons() const{
 return numHiddenNeurons;
}
 
UINT MLP::getNumOutputNeurons() const{
 return numOutputNeurons;
}
 
UINT MLP::getInputLayerActivationFunction() const{
 return inputLayerActivationFunction;
}
 
UINT MLP::getHiddenLayerActivationFunction() const{
 return hiddenLayerActivationFunction;
}
 
UINT MLP::getOutputLayerActivationFunction() const{
 return outputLayerActivationFunction;
}
 
UINT MLP::getNumRandomTrainingIterations() const{
 return numRandomTrainingIterations;
}
 
Float MLP::getTrainingRate() const{
 return learningRate;
}
 
Float MLP::getMomentum() const{
 return momentum;
}
 
Float MLP::getGamma() const{
 return gamma;
}
 
Float MLP::getTrainingError() const{
 return trainingError;
}
 
bool MLP::getClassificationModeActive() const{
 return classificationModeActive;
}
 
bool MLP::getRegressionModeActive() const{
 return !classificationModeActive;
}
 
Vector< Neuron > MLP::getInputLayer() const{
 return inputLayer;
}
 
Vector< Neuron > MLP::getHiddenLayer() const{
 return hiddenLayer;
}
 
Vector< Neuron > MLP::getOutputLayer() const{
 return outputLayer;
}
 
Vector< VectorFloat > MLP::getTrainingLog() const{
 return trainingErrorLog;
}
 
bool MLP::getNullRejectionEnabled() const{
 return useNullRejection;
}

Float MLP::getNullRejectionCoeff() const{
 return nullRejectionCoeff;
}
 
Float MLP::getNullRejectionThreshold() const{
 return nullRejectionThreshold;
}
 
Float MLP::getMaximumLikelihood() const{
 if( trained ) return maxLikelihood;
 return DEFAULT_NULL_LIKELIHOOD_VALUE;
}

VectorFloat MLP::getClassLikelihoods() const{
 if( trained && classificationModeActive ) return classLikelihoods;
 return VectorFloat();
}
 
VectorFloat MLP::getClassDistances() const{
 //The class distances is simply the regression data
 if( trained && classificationModeActive ) return regressionData;
 return VectorFloat();
}

UINT MLP::getPredictedClassLabel() const{
 if( trained && classificationModeActive ) return predictedClassLabel;
 return 0;
}

std::string MLP::activationFunctionToString(const UINT activationFunction) const{
 std::string activationName;

 switch(activationFunction){
 case(Neuron::LINEAR):
 activationName = "LINEAR";
 break;
 case(Neuron::SIGMOID):
 activationName = "SIGMOID";
 break;
 case(Neuron::BIPOLAR_SIGMOID):
 activationName = "BIPOLAR_SIGMOID";
 break;
 default:
 activationName = "UNKNOWN";
 break;
 }

 return activationName;
}

UINT MLP::activationFunctionFromString(const std::string activationName) const{
 UINT activationFunction = 0;

 if(activationName == "LINEAR" ){
 activationFunction = 0;
 return activationFunction;
 }
 if(activationName == "SIGMOID" ){
 activationFunction = 1;
 return activationFunction;
 }
 if(activationName == "BIPOLAR_SIGMOID" ){
 activationFunction = 2;
 return activationFunction;
 }
 return activationFunction;
}

bool MLP::validateActivationFunction(const UINT actvationFunction) const{
 if( actvationFunction >= Neuron::LINEAR && actvationFunction < Neuron::NUMBER_OF_ACTIVATION_FUNCTIONS ) return true;
 return false;
}

bool MLP::setInputLayerActivationFunction(const UINT activationFunction){
 
 if( !validateActivationFunction(activationFunction) ){
 warningLog << "setInputLayerActivationFunction(const UINT activationFunction) - The activation function is not valid. It should be one of the Neuron ActivationFunctions enums." << std::endl;
 }
 
 this->inputLayerActivationFunction = activationFunction;
 
 if( initialized ){
 return init(numInputNeurons,numHiddenNeurons,numOutputNeurons);
 }
 
 return true;
}


bool MLP::setHiddenLayerActivationFunction(const UINT activationFunction){
 
 if( !validateActivationFunction(activationFunction) ){
 warningLog << "setHiddenLayerActivationFunction(const UINT activationFunction) - The activation function is not valid. It should be one of the Neuron ActivationFunctions enums." << std::endl;
 }
 
 this->hiddenLayerActivationFunction = activationFunction;
 
 if( initialized ){
 return init(numInputNeurons,numHiddenNeurons,numOutputNeurons);
 }
 
 return true;
}
 

bool MLP::setOutputLayerActivationFunction(const UINT activationFunction){
 
 if( !validateActivationFunction(activationFunction) ){
 warningLog << "setOutputLayerActivationFunction(const UINT activationFunction) - The activation function is not valid. It should be one of the Neuron ActivationFunctions enums." << std::endl;
 }
 
 this->outputLayerActivationFunction = activationFunction;
 
 if( initialized ){
 return init(numInputNeurons,numHiddenNeurons,numOutputNeurons);
 }
 
 return true;
}
 
bool MLP::setTrainingRate(const Float trainingRate){
 return setLearningRate( trainingRate );
}

bool MLP::setMomentum(const Float momentum){
 if( momentum >= 0 && momentum <= 1.0 ){
 this->momentum = momentum;
 return true;
 }
 return false;
}

bool MLP::setGamma(const Float gamma){
 
 if( gamma < 0 ){
 warningLog << "setGamma(const Float gamma) - Gamma must be greater than zero!" << std::endl;
 }
 
 this->gamma = gamma;
 
 if( initialized ){
 return init(numInputNeurons,numHiddenNeurons,numOutputNeurons);
 }
 
 return true;
}
 
bool MLP::setNumRandomTrainingIterations(const UINT numRandomTrainingIterations){
 if( numRandomTrainingIterations > 0 ){
 this->numRandomTrainingIterations = numRandomTrainingIterations;
 return true;
 }
 return false;
}
 
bool MLP::setNullRejection(const bool useNullRejection){
 this->useNullRejection = useNullRejection;
 return true;
}
 
bool MLP::setNullRejectionCoeff(const Float nullRejectionCoeff){
 if( nullRejectionCoeff > 0 ){
 this->nullRejectionCoeff = nullRejectionCoeff;
 return true;
 }
 return false;
}
 
bool MLP::loadLegacyModelFromFile( std::fstream &file ){
 
 std::string word;
 
 file >> word;
 if(word != "NumInputNeurons:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find NumInputNeurons!" << std::endl;
 return false;
 }
 file >> numInputNeurons;
 numInputDimensions = numInputNeurons;
 
 file >> word;
 if(word != "NumHiddenNeurons:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find NumHiddenNeurons!" << std::endl;
 return false;
 }
 file >> numHiddenNeurons;
 
 file >> word;
 if(word != "NumOutputNeurons:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find NumOutputNeurons!" << std::endl;
 return false;
 }
 file >> numOutputNeurons;
 
 file >> word;
 if(word != "InputLayerActivationFunction:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find InputLayerActivationFunction!" << std::endl;
 return false;
 }
 file >> word;
 inputLayerActivationFunction = activationFunctionFromString(word);
 
 file >> word;
 if(word != "HiddenLayerActivationFunction:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find HiddenLayerActivationFunction!" << std::endl;
 return false;
 }
 file >> word;
 hiddenLayerActivationFunction = activationFunctionFromString(word);
 
 file >> word;
 if(word != "OutputLayerActivationFunction:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find OutputLayerActivationFunction!" << std::endl;
 return false;
 }
 file >> word;
 outputLayerActivationFunction = activationFunctionFromString(word);
 
 file >> word;
 if(word != "MinNumEpochs:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find MinNumEpochs!" << std::endl;
 return false;
 }
 file >> minNumEpochs;
 
 file >> word;
 if(word != "MaxNumEpochs:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find MaxNumEpochs!" << std::endl;
 return false;
 }
 file >> maxNumEpochs;
 
 file >> word;
 if(word != "NumRandomTrainingIterations:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find NumRandomTrainingIterations!" << std::endl;
 return false;
 }
 file >> numRandomTrainingIterations;
 
 file >> word;
 if(word != "ValidationSetSize:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find ValidationSetSize!" << std::endl;
 return false;
 }
 file >> validationSetSize;
 
 file >> word;
 if(word != "MinChange:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find MinChange!" << std::endl;
 return false;
 }
 file >> minChange;
 
 file >> word;
 if(word != "TrainingRate:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find TrainingRate!" << std::endl;
 return false;
 }
 file >> learningRate;
 
 file >> word;
 if(word != "Momentum:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find Momentum!" << std::endl;
 return false;
 }
 file >> momentum;
 
 file >> word;
 if(word != "Gamma:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find Gamma!" << std::endl;
 return false;
 }
 file >> gamma;
 
 file >> word;
 if(word != "UseValidationSet:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find UseValidationSet!" << std::endl;
 return false;
 }
 file >> useValidationSet;
 
 file >> word;
 if(word != "RandomiseTrainingOrder:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find RandomiseTrainingOrder!" << std::endl;
 return false;
 }
 file >> randomiseTrainingOrder;
 
 file >> word;
 if(word != "UseScaling:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find UseScaling!" << std::endl;
 return false;
 }
 file >> useScaling;
 
 file >> word;
 if(word != "ClassificationMode:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find ClassificationMode!" << std::endl;
 return false;
 }
 file >> classificationModeActive;
 
 file >> word;
 if(word != "UseNullRejection:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find UseNullRejection!" << std::endl;
 return false;
 }
 file >> useNullRejection;
 
 file >> word;
 if(word != "RejectionThreshold:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find RejectionThreshold!" << std::endl;
 return false;
 }
 file >> nullRejectionThreshold;
 
 //Resize the layers
 inputLayer.resize( numInputNeurons );
 hiddenLayer.resize( numHiddenNeurons );
 outputLayer.resize( numOutputNeurons );
 
 //Load the neuron data
 file >> word;
 if(word != "InputLayer:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find InputLayer!" << std::endl;
 return false;
 }
 
 for(UINT i=0; i<numInputNeurons; i++){
 UINT tempNeuronID = 0;
 
 file >> word;
 if(word != "InputNeuron:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find InputNeuron!" << std::endl;
 return false;
 }
 file >> tempNeuronID;
 
 if( tempNeuronID != i+1 ){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - InputNeuron ID does not match!" << std::endl;
 return false;
 }
 
 file >> word;
 if(word != "NumInputs:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find NumInputs!" << std::endl;
 return false;
 }
 file >> inputLayer[i].numInputs;
 
 //Resize the buffers
 inputLayer[i].weights.resize( inputLayer[i].numInputs );
 
 file >> word;
 if(word != "Bias:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find Bias!" << std::endl;
 return false;
 }
 file >> inputLayer[i].bias;
 
 file >> word;
 if(word != "Gamma:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find Gamma!" << std::endl;
 return false;
 }
 file >> inputLayer[i].gamma;
 
 file >> word;
 if(word != "Weights:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find Weights!" << std::endl;
 return false;
 }
 
 for(UINT j=0; j<inputLayer[i].numInputs; j++){
 file >> inputLayer[i].weights[j];
 }
 }
 
 //Load the Hidden Layer
 file >> word;
 if(word != "HiddenLayer:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find HiddenLayer!" << std::endl;
 return false;
 }
 
 for(UINT i=0; i<numHiddenNeurons; i++){
 UINT tempNeuronID = 0;
 
 file >> word;
 if(word != "HiddenNeuron:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find HiddenNeuron!" << std::endl;
 return false;
 }
 file >> tempNeuronID;
 
 if( tempNeuronID != i+1 ){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find HiddenNeuron ID does not match!" << std::endl;
 return false;
 }
 
 file >> word;
 if(word != "NumInputs:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find NumInputs!" << std::endl;
 return false;
 }
 file >> hiddenLayer[i].numInputs;
 
 //Resize the buffers
 hiddenLayer[i].weights.resize( hiddenLayer[i].numInputs );
 
 file >> word;
 if(word != "Bias:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find Bias!" << std::endl;
 return false;
 }
 file >> hiddenLayer[i].bias;
 
 file >> word;
 if(word != "Gamma:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find Gamma!" << std::endl;
 return false;
 }
 file >> hiddenLayer[i].gamma;
 
 file >> word;
 if(word != "Weights:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find Weights!" << std::endl;
 return false;
 }
 
 for(unsigned int j=0; j<hiddenLayer[i].numInputs; j++){
 file >> hiddenLayer[i].weights[j];
 }
 }
 
 //Load the Output Layer
 file >> word;
 if(word != "OutputLayer:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find OutputLayer!" << std::endl;
 return false;
 }
 
 for(UINT i=0; i<numOutputNeurons; i++){
 UINT tempNeuronID = 0;
 
 file >> word;
 if(word != "OutputNeuron:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find OutputNeuron!" << std::endl;
 return false;
 }
 file >> tempNeuronID;
 
 if( tempNeuronID != i+1 ){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find OuputNeuron ID does not match!!" << std::endl;
 return false;
 }
 
 file >> word;
 if(word != "NumInputs:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find NumInputs!" << std::endl;
 return false;
 }
 file >> outputLayer[i].numInputs;
 
 //Resize the buffers
 outputLayer[i].weights.resize( outputLayer[i].numInputs );
 
 file >> word;
 if(word != "Bias:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find Bias!" << std::endl;
 return false;
 }
 file >> outputLayer[i].bias;
 
 file >> word;
 if(word != "Gamma:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find Gamma!" << std::endl;
 return false;
 }
 file >> outputLayer[i].gamma;
 
 file >> word;
 if(word != "Weights:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find Weights!" << std::endl;
 return false;
 }
 
 for(UINT j=0; j<outputLayer[i].numInputs; j++){
 file >> outputLayer[i].weights[j];
 }
 }
 
 if( useScaling ){
 //Resize the ranges buffers
 inputVectorRanges.resize( numInputNeurons );
 targetVectorRanges.resize( numOutputNeurons );
 
 //Load the ranges
 file >> word;
 if(word != "InputVectorRanges:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find InputVectorRanges!" << std::endl;
 return false;
 }
 for(UINT j=0; j<inputVectorRanges.size(); j++){
 file >> inputVectorRanges[j].minValue;
 file >> inputVectorRanges[j].maxValue;
 }
 
 file >> word;
 if(word != "OutputVectorRanges:"){
 file.close();
 errorLog << "loadModelFromFile(fstream &file) - Failed to find OutputVectorRanges!" << std::endl;
 return false;
 }
 for(UINT j=0; j<targetVectorRanges.size(); j++){
 file >> targetVectorRanges[j].minValue;
 file >> targetVectorRanges[j].maxValue;
 }
 }
 
 initialized = true;
 trained = true;
 
 return true;
}
 
GRT_END_NAMESPACE