LinearRegression.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 "LinearRegression.h"

GRT_BEGIN_NAMESPACE

//Register the LinearRegression module with the Classifier base class
RegisterRegressifierModule< LinearRegression > LinearRegression::registerModule("LinearRegression");

LinearRegression::LinearRegression(bool useScaling)
{
 this->useScaling = useScaling;
 minChange = 1.0e-5;
 maxNumEpochs = 500;
 learningRate = 0.01;
 classType = "LinearRegression";
 regressifierType = classType;
 debugLog.setProceedingText("[DEBUG LinearRegression]");
 errorLog.setProceedingText("[ERROR LinearRegression]");
 trainingLog.setProceedingText("[TRAINING LinearRegression]");
 warningLog.setProceedingText("[WARNING LinearRegression]");
}

LinearRegression::~LinearRegression(void)
{
}

LinearRegression& LinearRegression::operator=(const LinearRegression &rhs){
 if( this != &rhs ){
 this->w0 = rhs.w0;
 this->w = rhs.w;
 
 //Copy the base variables
 copyBaseVariables( (Regressifier*)&rhs );
 }
 return *this;
}

bool LinearRegression::deepCopyFrom(const Regressifier *regressifier){
 
 if( regressifier == NULL ) return false;
 
 if( this->getRegressifierType() == regressifier->getRegressifierType() ){
 
 const LinearRegression *ptr = dynamic_cast<const LinearRegression*>(regressifier);
 
 this->w0 = ptr->w0;
 this->w = ptr->w;
 
 //Copy the base variables
 return copyBaseVariables( regressifier );
 }
 return false;
}

bool LinearRegression::train_(RegressionData &trainingData){
 
 const unsigned int M = trainingData.getNumSamples();
 const unsigned int N = trainingData.getNumInputDimensions();
 const unsigned int K = trainingData.getNumTargetDimensions();
 trained = false;
 trainingResults.clear();
 
 if( M == 0 ){
 errorLog << "train_(RegressionData &trainingData) - Training data has zero samples!" << std::endl;
 return false;
 }
 
 if( K == 0 ){
 errorLog << "train_(RegressionData &trainingData) - The number of target dimensions is not 1!" << std::endl;
 return false;
 }
 
 numInputDimensions = N;
 numOutputDimensions = 1; //Logistic Regression will have 1 output
 inputVectorRanges.clear();
 targetVectorRanges.clear();
 
 //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();
 
 //Scale the training data
 trainingData.scale(inputVectorRanges,targetVectorRanges,0.0,1.0);
 }
 
 //Reset the weights
 Random rand;
 w0 = rand.getRandomNumberUniform(-0.1,0.1);
 w.resize(N);
 for(UINT j=0; j<N; j++){
 w[j] = rand.getRandomNumberUniform(-0.1,0.1);
 }
 
 Float error = 0;
 Float lastError = 0;
 Float delta = 0;
 UINT iter = 0;
 bool keepTraining = true;
 Vector< UINT > randomTrainingOrder(M);
 TrainingResult result;
 trainingResults.reserve(M);
 
 //In most cases, the training data is grouped into classes (100 samples for class 1, followed by 100 samples for class 2, etc.)
 //This can cause a problem for stochastic gradient descent algorithm. To avoid this issue, we randomly shuffle the order of the
 //training samples. This random order is then used at each epoch.
 for(UINT i=0; i<M; i++){
 randomTrainingOrder[i] = i;
 }
 std::random_shuffle(randomTrainingOrder.begin(), randomTrainingOrder.end());
 
 //Run the main stochastic gradient descent training algorithm
 while( keepTraining ){
 
 //Run one epoch of training using stochastic gradient descent
 totalSquaredTrainingError = 0;
 for(UINT m=0; m<M; m++){
 
 //Select the random sample
 UINT i = randomTrainingOrder[m];
 
 //Compute the error, given the current weights
 VectorFloat x = trainingData[i].getInputVector();
 VectorFloat y = trainingData[i].getTargetVector();
 Float h = w0;
 for(UINT j=0; j<N; j++){
 h += x[j] * w[j];
 }
 error = y[0] - h;
 totalSquaredTrainingError += SQR( error );
 
 //Update the weights
 for(UINT j=0; j<N; j++){
 w[j] += learningRate * error * x[j];
 }
 w0 += learningRate * error;
 }
 
 //Compute the error
 delta = fabs( totalSquaredTrainingError-lastError );
 lastError = totalSquaredTrainingError;
 
 //Check to see if we should stop
 if( delta <= minChange ){
 keepTraining = false;
 }
 
 if( grt_isinf( totalSquaredTrainingError ) || grt_isnan( totalSquaredTrainingError ) ){
 errorLog << "train_(RegressionData &trainingData) - Training failed! Total squared training error is NAN. If scaling is not enabled then you should try to scale your data and see if this solves the issue." << std::endl;
 return false;
 }
 
 if( ++iter >= maxNumEpochs ){
 keepTraining = false;
 }
 
 //Store the training results
 rootMeanSquaredTrainingError = sqrt( totalSquaredTrainingError / Float(M) );
 result.setRegressionResult(iter,totalSquaredTrainingError,rootMeanSquaredTrainingError,this);
 trainingResults.push_back( result );
 
 //Notify any observers of the new result
 trainingResultsObserverManager.notifyObservers( result );
 
 trainingLog << "Epoch: " << iter << " SSE: " << totalSquaredTrainingError << " Delta: " << delta << std::endl;
 }
 
 //Flag that the algorithm has been trained
 regressionData.resize(1,0);
 trained = true;
 return trained;
}

bool LinearRegression::predict_(VectorFloat &inputVector){
 
 if( !trained ){
 errorLog << "predict_(VectorFloat &inputVector) - Model Not Trained!" << std::endl;
 return false;
 }
 
 if( !trained ) return false;
 
 if( inputVector.size() != numInputDimensions ){
 errorLog << "predict_(VectorFloat &inputVector) - The size of the input Vector (" << int( inputVector.size() ) << ") does not match the num features in the model (" << numInputDimensions << std::endl;
 return false;
 }
 
 if( useScaling ){
 for(UINT n=0; n<numInputDimensions; n++){
 inputVector[n] = scale(inputVector[n], inputVectorRanges[n].minValue, inputVectorRanges[n].maxValue, 0, 1);
 }
 }
 
 regressionData[0] = w0;
 for(UINT j=0; j<numInputDimensions; j++){
 regressionData[0] += inputVector[j] * w[j];
 }
 
 if( useScaling ){
 for(UINT n=0; n<numOutputDimensions; n++){
 regressionData[n] = scale(regressionData[n], 0, 1, targetVectorRanges[n].minValue, targetVectorRanges[n].maxValue);
 }
 }
 
 return true;
}

bool LinearRegression::save( std::fstream &file ) const{
 
 if(!file.is_open())
 {
 errorLog << "save(fstream &file) - The file is not open!" << std::endl;
 return false;
 }
 
 //Write the header info
 file<<"GRT_LINEAR_REGRESSION_MODEL_FILE_V2.0\n";
 
 //Write the regressifier settings to the file
 if( !Regressifier::saveBaseSettingsToFile(file) ){
 errorLog <<"save(fstream &file) - Failed to save Regressifier base settings to file!" << std::endl;
 return false;
 }
 
 if( trained ){
 file << "Weights: ";
 file << w0;
 for(UINT j=0; j<numInputDimensions; j++){
 file << " " << w[j];
 }
 file << std::endl;
 }
 
 return true;
}

bool LinearRegression::load( std::fstream &file ){
 
 clear();
 
 if(!file.is_open())
 {
 errorLog << "load( fstream &file ) - Could not open file to load model" << std::endl;
 return false;
 }
 
 std::string word;
 
 //Find the file type header
 file >> word;
 
 //Check to see if we should load a legacy file
 if( word == "GRT_LINEAR_REGRESSION_MODEL_FILE_V1.0" ){
 return loadLegacyModelFromFile( file );
 }
 
 if( word != "GRT_LINEAR_REGRESSION_MODEL_FILE_V2.0" ){
 errorLog << "load( fstream &file ) - Could not find Model File Header" << std::endl;
 return false;
 }
 
 //Load the regressifier settings from the file
 if( !Regressifier::loadBaseSettingsFromFile(file) ){
 errorLog <<"load( fstream &file ) - Failed to save Regressifier base settings to file!" << std::endl;
 return false;
 }
 
 if( trained ){
 
 //Resize the weights
 w.resize(numInputDimensions);
 
 //Load the weights
 file >> word;
 if(word != "Weights:"){
 errorLog << "load( fstream &file ) - Could not find the Weights!" << std::endl;
 return false;
 }
 
 file >> w0;
 for(UINT j=0; j<numInputDimensions; j++){
 file >> w[j];
 
 }
 }
 
 return true;
}

bool LinearRegression::setMaxNumIterations(const UINT maxNumIterations){
 return setMaxNumEpochs( maxNumIterations );
}

UINT LinearRegression::getMaxNumIterations() const{
 return getMaxNumEpochs();
}

bool LinearRegression::loadLegacyModelFromFile( std::fstream &file ){
 
 std::string word;
 
 file >> word;
 if(word != "NumFeatures:"){
 errorLog << "loadLegacyModelFromFile( fstream &file ) - Could not find NumFeatures!" << std::endl;
 return false;
 }
 file >> numInputDimensions;
 
 file >> word;
 if(word != "NumOutputDimensions:"){
 errorLog << "loadLegacyModelFromFile( fstream &file ) - Could not find NumOutputDimensions!" << std::endl;
 return false;
 }
 file >> numOutputDimensions;
 
 file >> word;
 if(word != "UseScaling:"){
 errorLog << "loadLegacyModelFromFile( fstream &file ) - Could not find UseScaling!" << std::endl;
 return false;
 }
 file >> useScaling;
 
 if( useScaling ){
 //Resize the ranges buffer
 inputVectorRanges.resize(numInputDimensions);
 targetVectorRanges.resize(numOutputDimensions);
 
 //Load the ranges
 file >> word;
 if(word != "InputVectorRanges:"){
 file.close();
 errorLog << "loadLegacyModelFromFile( 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 << "loadLegacyModelFromFile( 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;
 }
 }
 
 //Resize the weights
 w.resize(numInputDimensions);
 
 //Load the weights
 file >> word;
 if(word != "Weights:"){
 errorLog << "loadLegacyModelFromFile( fstream &file ) - Could not find the Weights!" << std::endl;
 return false;
 }
 
 file >> w0;
 for(UINT j=0; j<numInputDimensions; j++){
 file >> w[j];
 
 }
 
 //Resize the regression data Vector
 regressionData.resize(1,0);
 
 //Flag that the model has been trained
 trained = true;
 
 return true;
}

GRT_END_NAMESPACE