SOMQuantizer.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.
*/

#define GRT_DLL_EXPORTS
#include "SOMQuantizer.h"

GRT_BEGIN_NAMESPACE

//Define the string that will be used to identify the object
std::string SOMQuantizer::id = "SOMQuantizer";
std::string SOMQuantizer::getId() { return SOMQuantizer::id; }

//Register your module with the FeatureExtraction base class
RegisterFeatureExtractionModule< SOMQuantizer > SOMQuantizer::registerModule( SOMQuantizer::getId() );

SOMQuantizer::SOMQuantizer(const UINT numClusters) : FeatureExtraction( SOMQuantizer::getId() )
{
 this->numClusters = numClusters;
}

SOMQuantizer::SOMQuantizer(const SOMQuantizer &rhs) : FeatureExtraction( SOMQuantizer::getId() )
{
 //Invoke the equals operator to copy the data from the rhs instance to this instance
 *this = rhs;
}

SOMQuantizer::~SOMQuantizer(){
 //Here you should add any specific code to cleanup your custom feature extraction module if needed
}

SOMQuantizer& SOMQuantizer::operator=(const SOMQuantizer &rhs){
 if(this!=&rhs){
 //Here you should copy any class variables from the rhs instance to this instance
 this->numClusters = rhs.numClusters;
 this->som = rhs.som;
 this->quantizationDistances = rhs.quantizationDistances;
 
 //Copy the base variables
 copyBaseVariables( (FeatureExtraction*)&rhs );
 }
 return *this;
}

bool SOMQuantizer::deepCopyFrom(const FeatureExtraction *featureExtraction){
 
 if( featureExtraction == NULL ) return false;
 
 if( this->getId() == featureExtraction->getId() ){
 
 //Cast the feature extraction pointer to a pointer to your custom feature extraction module
 //Then invoke the equals operator
 *this = *dynamic_cast<const SOMQuantizer*>(featureExtraction);
 
 return true;
 }
 
 errorLog << "deepCopyFrom(FeatureExtraction *featureExtraction) - FeatureExtraction Types Do Not Match!" << std::endl;
 
 return false;
}

bool SOMQuantizer::computeFeatures(const VectorFloat &inputVector){
 
 //Run the quantize algorithm
 quantize( inputVector );
 
 return true;
}

bool SOMQuantizer::reset(){
 
 //Reset the base class
 FeatureExtraction::reset();
 
 if( trained ){
 som.reset();
 std::fill(quantizationDistances.begin(),quantizationDistances.end(),0);
 }
 
 return true;
}

bool SOMQuantizer::clear(){
 
 //Clear the base class
 FeatureExtraction::clear();
 
 som.clear();
 quantizationDistances.clear();
 
 return true;
}

bool SOMQuantizer::save( std::fstream &file ) const{
 
 if( !file.is_open() ){
 errorLog << "save(fstream &file) - The file is not open!" << std::endl;
 return false;
 }
 
 //First, you should add a header (with no spaces) e.g.
 file << "SOM_QUANTIZER_FILE_V1.0" << std::endl;
 
 //Second, you should save the base feature extraction settings to the file
 if( !saveFeatureExtractionSettingsToFile( file ) ){
 errorLog << "saveFeatureExtractionSettingsToFile(fstream &file) - Failed to save base feature extraction settings to file!" << std::endl;
 return false;
 }
 
 file << "QuantizerTrained: " << trained << std::endl;
 file << "NumClusters: " << numClusters << std::endl;
 
 if( trained ){
 file << "SOM: \n";
 if( !som.save( file ) ){
 errorLog << "save(fstream &file) - Failed to save SelfOrganizingMap settings to file!" << std::endl;
 return false;
 }
 }
 
 return true;
}

bool SOMQuantizer::load( std::fstream &file ){
 
 //Clear any previous model
 clear();
 
 if( !file.is_open() ){
 errorLog << "load(fstream &file) - The file is not open!" << std::endl;
 return false;
 }
 
 std::string word;
 
 //First, you should read and validate the header
 file >> word;
 if( word != "SOM_QUANTIZER_FILE_V1.0" ){
 errorLog << "load(fstream &file) - Invalid file format!" << std::endl;
 return false;
 }
 
 //Second, you should load the base feature extraction settings to the file
 if( !loadFeatureExtractionSettingsFromFile( file ) ){
 errorLog << "loadFeatureExtractionSettingsFromFile(fstream &file) - Failed to load base feature extraction settings from file!" << std::endl;
 return false;
 }
 
 file >> word;
 if( word != "QuantizerTrained:" ){
 errorLog << "load(fstream &file) - Failed to load QuantizerTrained!" << std::endl;
 return false;
 }
 file >> trained;
 
 file >> word;
 if( word != "NumClusters:" ){
 errorLog << "load(fstream &file) - Failed to load NumClusters!" << std::endl;
 return false;
 }
 file >> numClusters;
 
 if( trained ){
 file >> word;
 if( word != "SOM:" ){
 errorLog << "load(fstream &file) - Failed to load SOM!" << std::endl;
 return false;
 }
 
 if( !som.load( file ) ){
 errorLog << "load(fstream &file) - Failed to load SelfOrganizingMap settings from file!" << std::endl;
 return false;
 }
 
 initialized = true;
 featureDataReady = false;
 quantizationDistances.resize(numClusters,0);
 }
 
 return true;
}

bool SOMQuantizer::train_(ClassificationData &trainingData){
 MatrixFloat data = trainingData.getDataAsMatrixFloat();
 return train_( data );
}

bool SOMQuantizer::train_(TimeSeriesClassificationData &trainingData){
 MatrixFloat data = trainingData.getDataAsMatrixFloat();
 return train_( data );
}

bool SOMQuantizer::train_(ClassificationDataStream &trainingData){
 MatrixFloat data = trainingData.getDataAsMatrixFloat();
 return train_( data );
}

bool SOMQuantizer::train_(UnlabelledData &trainingData){
 MatrixFloat data = trainingData.getDataAsMatrixFloat();
 return train_( data );
}

bool SOMQuantizer::train_(MatrixFloat &trainingData){
 
 //Clear any previous model
 clear();
 
 if( trainingData.getNumRows() == 0 ){
 errorLog << "train_(MatrixFloat &trainingData) - Failed to train quantizer, the training data is empty!" << std::endl;
 return false;
 }
 
 //Train the SOM model
 som.setNetworkSize( numClusters );
 som.setNetworkTypology( SelfOrganizingMap::RANDOM_NETWORK );
 som.setAlphaStart( 0.5 );
 som.setAlphaEnd( 0.1 );
 som.setMaxNumEpochs( 1000 );
 
 if( !som.train_( trainingData ) ){
 errorLog << "train(MatrixFloat &trainingData) - Failed to train quantizer!" << std::endl;
 return false;
 }
 
 //Flag that the feature extraction module is now initialized
 initialized = true;
 trained = true;
 numInputDimensions = trainingData.getNumCols();
 numOutputDimensions = 1; //This is always 1 for the SOMQuantizer
 featureVector.resize(numOutputDimensions,0);
 quantizationDistances.resize(numClusters,0);
 
 return true;
}

UINT SOMQuantizer::quantize(const Float inputValue){
 return quantize( VectorFloat(1,inputValue) );
}

UINT SOMQuantizer::quantize(const VectorFloat &inputVector){
 
 if( !trained ){
 errorLog << "computeFeatures(const VectorFloat &inputVector) - The quantizer model has not been trained!" << std::endl;
 return 0;
 }
 
 if( inputVector.getSize() != numInputDimensions ){
 errorLog << "computeFeatures(const VectorFloat &inputVector) - The size of the inputVector (" << inputVector.getSize() << ") does not match that of the filter (" << numInputDimensions << ")!" << std::endl;
 return 0;
 }
 
 //Pass the input data through the map
 if( !som.predict( inputVector ) ){
 errorLog << "computeFeatures(const VectorFloat &inputVector) - Failed to perform map!" << std::endl;
 return 0;
 }
 quantizationDistances = som.getMappedData();
 
 //Search for the neuron with the maximum output
 UINT quantizedValue = 0;
 Float maxValue = 0;
 for(UINT k=0; k<numClusters; k++){
 if( quantizationDistances[k] > maxValue ){
 maxValue = quantizationDistances[k];
 quantizedValue = k;
 }
 }
 
 featureVector[0] = quantizedValue;
 featureDataReady = true;
 
 return quantizedValue;
}

bool SOMQuantizer::getQuantizerTrained() const {
 return trained;
}

UINT SOMQuantizer::getNumClusters() const{
 return numClusters;
}

UINT SOMQuantizer::getQuantizedValue() const {
 return (trained ? static_cast<UINT>(featureVector[0]) : 0);
}

VectorFloat SOMQuantizer::getQuantizationDistances() const{
 return quantizationDistances;
}

SelfOrganizingMap SOMQuantizer::getSelfOrganizingMap() const{
 return som;
}

bool SOMQuantizer::setNumClusters(const UINT numClusters){
 clear();
 this->numClusters = numClusters;
 return true;
}

GRT_END_NAMESPACE