void FilterBoostLearner::run(const nor_utils::Args& args)
{
// load the arguments
this->getArgs(args);
time_t startTime, currentTime;
time(&startTime);
// get the registered weak learner (type from name)
BaseLearner* pWeakHypothesisSource =
BaseLearner::RegisteredLearners().getLearner(_baseLearnerName);
// initialize learning options; normally it's done in the strong loop
// also, here we do it for Product learners, so input data can be created
pWeakHypothesisSource->initLearningOptions(args);
BaseLearner* pConstantWeakHypothesisSource =
BaseLearner::RegisteredLearners().getLearner("ConstantLearner");
// get the training input data, and load it
InputData* pTrainingData = pWeakHypothesisSource->createInputData();
pTrainingData->initOptions(args);
pTrainingData->load(_trainFileName, IT_TRAIN, _verbose);
const int numClasses = pTrainingData->getNumClasses();
const int numExamples = pTrainingData->getNumExamples();
//initialize the margins variable
_margins.resize( numExamples );
for( int i=0; i<numExamples; i++ )
{
_margins[i].resize( numClasses );
fill( _margins[i].begin(), _margins[i].end(), 0.0 );
}
// get the testing input data, and load it
InputData* pTestData = NULL;
if ( !_testFileName.empty() )
{
pTestData = pWeakHypothesisSource->createInputData();
pTestData->initOptions(args);
pTestData->load(_testFileName, IT_TEST, _verbose);
}
// The output information object
OutputInfo* pOutInfo = NULL;
if ( !_outputInfoFile.empty() )
{
// Baseline: constant classifier - goes into 0th iteration
BaseLearner* pConstantWeakHypothesis = pConstantWeakHypothesisSource->create() ;
pConstantWeakHypothesis->initLearningOptions(args);
pConstantWeakHypothesis->setTrainingData(pTrainingData);
AlphaReal constantEnergy = pConstantWeakHypothesis->run();
pOutInfo = new OutputInfo(args);
pOutInfo->initialize(pTrainingData);
updateMargins( pTrainingData, pConstantWeakHypothesis );
if (pTestData)
pOutInfo->initialize(pTestData);
pOutInfo->outputHeader(pTrainingData->getClassMap() );
pOutInfo->outputIteration(-1);
pOutInfo->outputCustom(pTrainingData, pConstantWeakHypothesis);
if (pTestData)
{
pOutInfo->separator();
pOutInfo->outputCustom(pTestData, pConstantWeakHypothesis);
}
pOutInfo->outputCurrentTime();
pOutInfo->endLine();
pOutInfo->initialize(pTrainingData);
if (pTestData)
pOutInfo->initialize(pTestData);
}
// reload the previously found weak learners if -resume is set.
// otherwise just return 0
int startingIteration = resumeWeakLearners(pTrainingData);
Serialization ss(_shypFileName, _isShypCompressed );
ss.writeHeader(_baseLearnerName); // this must go after resumeProcess has been called
// perform the resuming if necessary. If not it will just return
resumeProcess(ss, pTrainingData, pTestData, pOutInfo);
if (_verbose == 1)
cout << "Learning in progress..." << endl;
///////////////////////////////////////////////////////////////////////
// Starting the AdaBoost main loop
///////////////////////////////////////////////////////////////////////
for (int t = startingIteration; t < _numIterations; ++t)
{
if (_verbose > 1)
cout << "------- WORKING ON ITERATION " << (t+1) << " -------" << endl;
// create the weak learner
BaseLearner* pWeakHypothesis;
BaseLearner* pConstantWeakHypothesis;
pWeakHypothesis = pWeakHypothesisSource->create();
pWeakHypothesis->initLearningOptions(args);
//pTrainingData->clearIndexSet();
pWeakHypothesis->setTrainingData(pTrainingData);
AlphaReal edge, energy=0.0;
// create the constant learner
pConstantWeakHypothesis = pConstantWeakHypothesisSource->create() ;
pConstantWeakHypothesis->initLearningOptions(args);
pConstantWeakHypothesis->setTrainingData(pTrainingData);
AlphaReal constantEdge = -numeric_limits<AlphaReal>::max();
int currentNumberOfUsedData = static_cast<int>(_Cn * log(t+3.0));
if ( _onlineWeakLearning )
{
//check whether the weak learner is a ScalarLeaerner
try {
StochasticLearner* pStochasticLearner = dynamic_cast<StochasticLearner*>(pWeakHypothesis);
StochasticLearner* pStochasticConstantWeakHypothesis = dynamic_cast<StochasticLearner*> (pConstantWeakHypothesis);
pStochasticLearner->initLearning();
pStochasticConstantWeakHypothesis->initLearning();
if (_verbose>1)
cout << "Number of random instances: \t" << currentNumberOfUsedData << endl;
// set the weights
setWeightToMargins(pTrainingData);
//learning
for (int i=0; i<currentNumberOfUsedData; ++i )
{
int randomIndex = (rand() % pTrainingData->getNumExamples());
//int randomIndex = getRandomIndex();
pStochasticLearner->update(randomIndex);
pStochasticConstantWeakHypothesis->update(randomIndex);
}
pStochasticLearner->finishLearning();
pStochasticConstantWeakHypothesis->finishLearning();
}
catch (bad_cast& e) {
cerr << "The weak learner must be a StochasticLearner!!!" << endl;
exit(-1);
}
}
else
{
filter( pTrainingData, currentNumberOfUsedData );
if ( pTrainingData->getNumExamples() < 2 )
{
filter( pTrainingData, currentNumberOfUsedData, false );
}
if (_verbose > 1)
{
cout << "--> Size of training data = " << pTrainingData->getNumExamples() << endl;
}
energy = pWeakHypothesis->run();
pConstantWeakHypothesis->run();
}
//estimate edge
filter( pTrainingData, currentNumberOfUsedData, false );
edge = pWeakHypothesis->getEdge(true) / 2.0;
constantEdge = pConstantWeakHypothesis->getEdge() / 2.0;
if ( constantEdge > edge )
{
delete pWeakHypothesis;
pWeakHypothesis = pConstantWeakHypothesis;
edge = constantEdge;
} else {
delete pConstantWeakHypothesis;
}
// calculate alpha
AlphaReal alpha = 0.0;
alpha = 0.5 * log( ( 1 + edge ) / ( 1 - edge ) );
pWeakHypothesis->setAlpha( alpha );
_sumAlpha += alpha;
if (_verbose > 1)
cout << "Weak learner: " << pWeakHypothesis->getName()<< endl;
// Output the step-by-step information
pTrainingData->clearIndexSet();
printOutputInfo(pOutInfo, t, pTrainingData, pTestData, pWeakHypothesis);
// Updates the weights and returns the edge
//AlphaReal gamma = updateWeights(pTrainingData, pWeakHypothesis);
if (_verbose > 1)
{
cout << setprecision(5)
<< "--> Alpha = " << pWeakHypothesis->getAlpha() << endl
<< "--> Edge = " << edge << endl
<< "--> Energy = " << energy << endl
// << "--> ConstantEnergy = " << constantEnergy << endl
// << "--> difference = " << (energy - constantEnergy) << endl
;
}
// update the margins
//saveMargins();
updateMargins( pTrainingData, pWeakHypothesis );
// append the current weak learner to strong hypothesis file,
// that is, serialize it.
ss.appendHypothesis(t, pWeakHypothesis);
// Add it to the internal list of weak hypotheses
_foundHypotheses.push_back(pWeakHypothesis);
// check if the time limit has been reached
if (_maxTime > 0)
{
time( ¤tTime );
float diff = difftime(currentTime, startTime); // difftime is in seconds
diff /= 60; // = minutes
if (diff > _maxTime)
{
if (_verbose > 0)
cout << "Time limit of " << _maxTime
<< " minutes has been reached!" << endl;
break;
}
} // check for maxtime
delete pWeakHypothesis;
} // loop on iterations
/////////////////////////////////////////////////////////
// write the footer of the strong hypothesis file
ss.writeFooter();
// Free the two input data objects
if (pTrainingData)
delete pTrainingData;
if (pTestData)
delete pTestData;
if (pOutInfo)
delete pOutInfo;
if (_verbose > 0)
cout << "Learning completed." << endl;
}
void FilterBoostLearner::run(const nor_utils::Args& args)
{
// load the arguments
this->getArgs(args);
time_t startTime, currentTime;
time(&startTime);
// get the registered weak learner (type from name)
BaseLearner* pWeakHypothesisSource =
BaseLearner::RegisteredLearners().getLearner(_baseLearnerName);
// initialize learning options; normally it's done in the strong loop
// also, here we do it for Product learners, so input data can be created
pWeakHypothesisSource->initLearningOptions(args);
BaseLearner* pConstantWeakHypothesisSource =
BaseLearner::RegisteredLearners().getLearner("ConstantLearner");
// get the training input data, and load it
InputData* pTrainingData = pWeakHypothesisSource->createInputData();
pTrainingData->initOptions(args);
pTrainingData->load(_trainFileName, IT_TRAIN, _verbose);
const int numClasses = pTrainingData->getNumClasses();
const int numExamples = pTrainingData->getNumExamples();
//initialize the margins variable
_margins.resize( numExamples );
for( int i=0; i<numExamples; i++ )
{
_margins[i].resize( numClasses );
fill( _margins[i].begin(), _margins[i].end(), 0.0 );
}
// get the testing input data, and load it
InputData* pTestData = NULL;
if ( !_testFileName.empty() )
{
pTestData = pWeakHypothesisSource->createInputData();
pTestData->initOptions(args);
pTestData->load(_testFileName, IT_TEST, _verbose);
}
// The output information object
OutputInfo* pOutInfo = NULL;
if ( !_outputInfoFile.empty() )
{
// Baseline: constant classifier - goes into 0th iteration
BaseLearner* pConstantWeakHypothesis = pConstantWeakHypothesisSource->create() ;
pConstantWeakHypothesis->initLearningOptions(args);
pConstantWeakHypothesis->setTrainingData(pTrainingData);
float constantEnergy = pConstantWeakHypothesis->run();
pOutInfo = new OutputInfo(_outputInfoFile);
pOutInfo->initialize(pTrainingData);
updateMargins( pTrainingData, pConstantWeakHypothesis );
if (pTestData)
pOutInfo->initialize(pTestData);
pOutInfo->outputHeader();
pOutInfo->outputIteration(-1);
pOutInfo->outputError(pTrainingData, pConstantWeakHypothesis);
if (pTestData)
pOutInfo->outputError(pTestData, pConstantWeakHypothesis);
/*
pOutInfo->outputMargins(pTrainingData, pConstantWeakHypothesis);
pOutInfo->outputEdge(pTrainingData, pConstantWeakHypothesis);
if (pTestData)
pOutInfo->outputMargins(pTestData, pConstantWeakHypothesis);
pOutInfo->outputMAE(pTrainingData);
if (pTestData)
pOutInfo->outputMAE(pTestData);
*/
pOutInfo->outputCurrentTime();
pOutInfo->endLine();
pOutInfo->initialize(pTrainingData);
if (pTestData)
pOutInfo->initialize(pTestData);
}
// reload the previously found weak learners if -resume is set.
// otherwise just return 0
int startingIteration = resumeWeakLearners(pTrainingData);
Serialization ss(_shypFileName, _isShypCompressed );
ss.writeHeader(_baseLearnerName); // this must go after resumeProcess has been called
// perform the resuming if necessary. If not it will just return
resumeProcess(ss, pTrainingData, pTestData, pOutInfo);
if (_verbose == 1)
cout << "Learning in progress..." << endl;
///////////////////////////////////////////////////////////////////////
// Starting the AdaBoost main loop
///////////////////////////////////////////////////////////////////////
for (int t = startingIteration; t < _numIterations; ++t)
{
if (_verbose > 1)
cout << "------- WORKING ON ITERATION " << (t+1) << " -------" << endl;
filter( pTrainingData, (int)(_Cn * log(t+2.0)) );
if ( pTrainingData->getNumExamples() < 2 )
{
filter( pTrainingData, (int)(_Cn * log(t+2.0)), false );
}
if (_verbose > 1)
{
cout << "--> Size of training data = " << pTrainingData->getNumExamples() << endl;
}
BaseLearner* pWeakHypothesis = pWeakHypothesisSource->create();
pWeakHypothesis->initLearningOptions(args);
//pTrainingData->clearIndexSet();
pWeakHypothesis->setTrainingData(pTrainingData);
float energy = pWeakHypothesis->run();
BaseLearner* pConstantWeakHypothesis;
if (_withConstantLearner) // check constant learner if user wants it
{
pConstantWeakHypothesis = pConstantWeakHypothesisSource->create() ;
pConstantWeakHypothesis->initLearningOptions(args);
pConstantWeakHypothesis->setTrainingData(pTrainingData);
float constantEnergy = pConstantWeakHypothesis->run();
}
//estimate edge
filter( pTrainingData, (int)(_Cn * log(t+2.0)), false );
float edge = pWeakHypothesis->getEdge() / 2.0;
if (_withConstantLearner) // check constant learner if user wants it
{
float constantEdge = pConstantWeakHypothesis->getEdge() / 2.0;
if ( constantEdge > edge )
{
delete pWeakHypothesis;
pWeakHypothesis = pConstantWeakHypothesis;
edge = constantEdge;
} else {
delete pConstantWeakHypothesis;
}
}
// calculate alpha
float alpha = 0.0;
alpha = 0.5 * log( ( 0.5 + edge ) / ( 0.5 - edge ) );
pWeakHypothesis->setAlpha( alpha );
if (_verbose > 1)
cout << "Weak learner: " << pWeakHypothesis->getName()<< endl;
// Output the step-by-step information
pTrainingData->clearIndexSet();
printOutputInfo(pOutInfo, t, pTrainingData, pTestData, pWeakHypothesis);
// Updates the weights and returns the edge
float gamma = updateWeights(pTrainingData, pWeakHypothesis);
if (_verbose > 1)
{
cout << setprecision(5)
<< "--> Alpha = " << pWeakHypothesis->getAlpha() << endl
<< "--> Edge = " << gamma << endl
<< "--> Energy = " << energy << endl
// << "--> ConstantEnergy = " << constantEnergy << endl
// << "--> difference = " << (energy - constantEnergy) << endl
;
}
// update the margins
updateMargins( pTrainingData, pWeakHypothesis );
// append the current weak learner to strong hypothesis file,
// that is, serialize it.
ss.appendHypothesis(t, pWeakHypothesis);
// Add it to the internal list of weak hypotheses
_foundHypotheses.push_back(pWeakHypothesis);
// check if the time limit has been reached
if (_maxTime > 0)
{
time( ¤tTime );
float diff = difftime(currentTime, startTime); // difftime is in seconds
diff /= 60; // = minutes
if (diff > _maxTime)
{
if (_verbose > 0)
cout << "Time limit of " << _maxTime
<< " minutes has been reached!" << endl;
break;
}
} // check for maxtime
delete pWeakHypothesis;
} // loop on iterations
/////////////////////////////////////////////////////////
// write the footer of the strong hypothesis file
ss.writeFooter();
// Free the two input data objects
if (pTrainingData)
delete pTrainingData;
if (pTestData)
delete pTestData;
if (pOutInfo)
delete pOutInfo;
if (_verbose > 0)
cout << "Learning completed." << endl;
}
void AdaBoostMHLearner::run(const nor_utils::Args& args)
{
// load the arguments
this->getArgs(args);
// get the registered weak learner (type from name)
BaseLearner* pWeakHypothesisSource =
BaseLearner::RegisteredLearners().getLearner(_baseLearnerName);
// initialize learning options; normally it's done in the strong loop
// also, here we do it for Product learners, so input data can be created
pWeakHypothesisSource->initLearningOptions(args);
BaseLearner* pConstantWeakHypothesisSource =
BaseLearner::RegisteredLearners().getLearner("ConstantLearner");
// get the training input data, and load it
InputData* pTrainingData = pWeakHypothesisSource->createInputData();
pTrainingData->initOptions(args);
pTrainingData->load(_trainFileName, IT_TRAIN, _verbose);
// get the testing input data, and load it
InputData* pTestData = NULL;
if ( !_testFileName.empty() )
{
pTestData = pWeakHypothesisSource->createInputData();
pTestData->initOptions(args);
pTestData->load(_testFileName, IT_TEST, _verbose);
}
// The output information object
OutputInfo* pOutInfo = NULL;
if ( !_outputInfoFile.empty() )
{
// Baseline: constant classifier - goes into 0th iteration
BaseLearner* pConstantWeakHypothesis = pConstantWeakHypothesisSource->create() ;
pConstantWeakHypothesis->initLearningOptions(args);
pConstantWeakHypothesis->setTrainingData(pTrainingData);
AlphaReal constantEnergy = pConstantWeakHypothesis->run();
//pOutInfo = new OutputInfo(_outputInfoFile);
pOutInfo = new OutputInfo(args);
pOutInfo->initialize(pTrainingData);
if (pTestData)
pOutInfo->initialize(pTestData);
pOutInfo->outputHeader(pTrainingData->getClassMap());
pOutInfo->outputIteration(-1);
pOutInfo->outputCustom(pTrainingData, pConstantWeakHypothesis);
if (pTestData != NULL)
{
pOutInfo->separator();
pOutInfo->outputCustom(pTestData, pConstantWeakHypothesis);
}
pOutInfo->outputCurrentTime();
pOutInfo->endLine();
pOutInfo->initialize(pTrainingData);
if (pTestData)
pOutInfo->initialize(pTestData);
}
//cout << "Before serialization" << endl;
// reload the previously found weak learners if -resume is set.
// otherwise just return 0
int startingIteration = resumeWeakLearners(pTrainingData);
Serialization ss(_shypFileName, _isShypCompressed );
ss.writeHeader(_baseLearnerName); // this must go after resumeProcess has been called
// perform the resuming if necessary. If not it will just return
resumeProcess(ss, pTrainingData, pTestData, pOutInfo);
if (_verbose == 1)
cout << "Learning in progress..." << endl;
//I put here the starting time, but it may take very long time to load the saved model
time_t startTime, currentTime;
time(&startTime);
///////////////////////////////////////////////////////////////////////
// Starting the AdaBoost main loop
///////////////////////////////////////////////////////////////////////
for (int t = startingIteration; t < _numIterations; ++t)
{
if (_verbose > 1)
cout << "------- WORKING ON ITERATION " << (t+1) << " -------" << endl;
BaseLearner* pWeakHypothesis = pWeakHypothesisSource->create();
pWeakHypothesis->initLearningOptions(args);
//pTrainingData->clearIndexSet();
pWeakHypothesis->setTrainingData(pTrainingData);
AlphaReal energy = pWeakHypothesis->run();
//float gamma = pWeakHypothesis->getEdge();
//cout << gamma << endl;
if ( (_withConstantLearner) || ( energy != energy ) ) // check constant learner if user wants it (if energi is nan, then we chose constant learner
{
BaseLearner* pConstantWeakHypothesis = pConstantWeakHypothesisSource->create() ;
pConstantWeakHypothesis->initLearningOptions(args);
pConstantWeakHypothesis->setTrainingData(pTrainingData);
AlphaReal constantEnergy = pConstantWeakHypothesis->run();
if ( (constantEnergy <= energy) || ( energy != energy ) ) {
delete pWeakHypothesis;
pWeakHypothesis = pConstantWeakHypothesis;
}
}
if (_verbose > 1)
cout << "Weak learner: " << pWeakHypothesis->getName()<< endl;
// Output the step-by-step information
printOutputInfo(pOutInfo, t, pTrainingData, pTestData, pWeakHypothesis);
// Updates the weights and returns the edge
AlphaReal gamma = updateWeights(pTrainingData, pWeakHypothesis);
if (_verbose > 1)
{
cout << setprecision(5)
<< "--> Alpha = " << pWeakHypothesis->getAlpha() << endl
<< "--> Edge = " << gamma << endl
<< "--> Energy = " << energy << endl
// << "--> ConstantEnergy = " << constantEnergy << endl
// << "--> difference = " << (energy - constantEnergy) << endl
;
}
// If gamma <= theta the algorithm must stop.
// If theta == 0 and gamma is 0, it means that the weak learner is no better than chance
// and no further training is possible.
if (gamma <= _theta)
{
if (_verbose > 0)
{
cout << "Can't train any further: edge = " << gamma
<< " (with and edge offset (theta)=" << _theta << ")" << endl;
}
// delete pWeakHypothesis;
// break;
}
// append the current weak learner to strong hypothesis file,
// that is, serialize it.
ss.appendHypothesis(t, pWeakHypothesis);
// Add it to the internal list of weak hypotheses
_foundHypotheses.push_back(pWeakHypothesis);
// check if the time limit has been reached
if (_maxTime > 0)
{
time( ¤tTime );
float diff = difftime(currentTime, startTime); // difftime is in seconds
diff /= 60; // = minutes
if (diff > _maxTime)
{
if (_verbose > 0)
cout << "Time limit of " << _maxTime
<< " minutes has been reached!" << endl;
break;
}
} // check for maxtime
delete pWeakHypothesis;
} // loop on iterations
/////////////////////////////////////////////////////////
// write the footer of the strong hypothesis file
ss.writeFooter();
// write the weights of the instances if the name of weights file isn't empty
printOutWeights( pTrainingData );
// Free the two input data objects
if (pTrainingData)
delete pTrainingData;
if (pTestData)
delete pTestData;
if (pOutInfo)
delete pOutInfo;
if (_verbose > 0)
cout << "Learning completed." << endl;
}
// -------------------------------------------------------------------------
void AdaBoostMHLearner::run( const nor_utils::Args& args, InputData* pTrainingData, const string baseLearnerName, const int numIterations, vector<BaseLearner*>& foundHypotheses )
{
// get the registered weak learner (type from name)
BaseLearner* pWeakHypothesisSource =
BaseLearner::RegisteredLearners().getLearner(baseLearnerName);
// initialize learning options; normally it's done in the strong loop
// also, here we do it for Product learners, so input data can be created
pWeakHypothesisSource->initLearningOptions(args);
BaseLearner* pConstantWeakHypothesisSource =
BaseLearner::RegisteredLearners().getLearner("ConstantLearner");
if (_verbose == 1)
cout << "Learning in progress..." << endl;
///////////////////////////////////////////////////////////////////////
// Starting the AdaBoost main loop
///////////////////////////////////////////////////////////////////////
for (int t = 0; t < numIterations; ++t)
{
if ((_verbose > 0)&&((t%100)==0))
cout << "--------------[ Boosting iteration " << (t+1) << " ]--------------" << endl;
BaseLearner* pWeakHypothesis = pWeakHypothesisSource->create();
pWeakHypothesis->initLearningOptions(args);
//pTrainingData->clearIndexSet();
pWeakHypothesis->setTrainingData(pTrainingData);
AlphaReal energy = pWeakHypothesis->run();
//float gamma = pWeakHypothesis->getEdge();
//cout << gamma << endl;
if ( (_withConstantLearner) || ( energy != energy ) ) // check constant learner if user wants it (if energi is nan, then we chose constant learner
{
BaseLearner* pConstantWeakHypothesis = pConstantWeakHypothesisSource->create() ;
pConstantWeakHypothesis->initLearningOptions(args);
pConstantWeakHypothesis->setTrainingData(pTrainingData);
AlphaReal constantEnergy = pConstantWeakHypothesis->run();
if ( (constantEnergy <= energy) || ( energy != energy ) ) {
delete pWeakHypothesis;
pWeakHypothesis = pConstantWeakHypothesis;
}
}
if (_verbose > 1)
cout << "Weak learner: " << pWeakHypothesis->getName()<< endl;
// Updates the weights and returns the edge
AlphaReal gamma = updateWeights(pTrainingData, pWeakHypothesis);
if (_verbose > 1)
{
cout << setprecision(5)
<< "--> Alpha = " << pWeakHypothesis->getAlpha() << endl
<< "--> Edge = " << gamma << endl
<< "--> Energy = " << energy << endl
// << "--> ConstantEnergy = " << constantEnergy << endl
// << "--> difference = " << (energy - constantEnergy) << endl
;
}
// If gamma <= theta the algorithm must stop.
// If theta == 0 and gamma is 0, it means that the weak learner is no better than chance
// and no further training is possible.
if (gamma <= _theta)
{
if (_verbose > 0)
{
cout << "Can't train any further: edge = " << gamma
<< " (with and edge offset (theta)=" << _theta << ")" << endl;
}
// delete pWeakHypothesis;
// break;
}
// Add it to the internal list of weak hypotheses
foundHypotheses.push_back(pWeakHypothesis);
} // loop on iterations
/////////////////////////////////////////////////////////
if (_verbose > 0)
cout << "--------------[ AdaBoost Learning completed. ]--------------" << endl;
}
float BanditProductLearner::run()
{
if ( ! this->_banditAlgo->isInitialized() ) {
init();
}
// the bandit algorithm selects the subset the tree learner is allowed to use
// the armindexes will be stored in _armsForPulling
getArms();
const int numClasses = _pTrainingData->getNumClasses();
const int numExamples = _pTrainingData->getNumExamples();
// Backup original labels
for (int i = 0; i < numExamples; ++i) {
const vector<Label>& labels = _pTrainingData->getLabels(i);
vector<char> exampleLabels;
for (int l = 0; l < numClasses; ++l)
exampleLabels.push_back(labels[l].y);
_savedLabels.push_back(exampleLabels);
}
for(int ib = 0; ib < _numBaseLearners; ++ib)
_baseLearners[ib]->setTrainingData(_pTrainingData);
float energy = numeric_limits<float>::max();
float previousEnergy, hx, previousAlpha;
BaseLearner* pPreviousBaseLearner = 0;
bool firstLoop = true;
int ib = -1;
while (1) {
ib += 1;
if (ib >= _numBaseLearners) {
ib = 0;
firstLoop = false;
}
previousEnergy = energy;
previousAlpha = _alpha;
if (pPreviousBaseLearner)
delete pPreviousBaseLearner;
if ( !firstLoop ) {
// take the old learner off the labels
for (int i = 0; i < numExamples; ++i) {
vector<Label>& labels = _pTrainingData->getLabels(i);
for (int l = 0; l < numClasses; ++l) {
// Here we could have the option of using confidence rated setting so the
// real valued output of classify instead of its sign
hx = _baseLearners[ib]->classify(_pTrainingData,i,l);
if ( hx < 0 )
labels[l].y *= -1;
else if ( hx == 0 ) { // have to redo the multiplications, haven't been tested
for(int ib1 = 0; ib1 < _numBaseLearners && labels[l].y != 0; ++ib1) {
if (ib != ib1) {
hx = _baseLearners[ib1]->classify(_pTrainingData,i,l);
if (hx < 0)
labels[l].y *= -1;
else if (hx == 0)
labels[l].y = 0;
}
}
}
}
}
}
pPreviousBaseLearner = _baseLearners[ib]->copyState();
energy = dynamic_cast< FeaturewiseLearner* >(_baseLearners[ib])->run(_armsForPulling );
// check if it is signailing_nan
if ( energy != energy )
{
if (_verbose > 2) {
cout << "Cannot find weak hypothesis, constant learner is used!!" << endl;
}
BaseLearner* pConstantWeakHypothesisSource =
BaseLearner::RegisteredLearners().getLearner("ConstantLearner");
BaseLearner* pConstantWeakHypothesis = pConstantWeakHypothesisSource->create() ;
pConstantWeakHypothesis->setTrainingData( _pTrainingData );
energy = pConstantWeakHypothesis->run();
delete _baseLearners[ib];
_baseLearners[ib] = pConstantWeakHypothesis;
}
_alpha = _baseLearners[ib]->getAlpha();
if (_verbose > 2) {
cout << "E[" << (ib+1) << "] = " << energy << endl << flush;
cout << "alpha[" << (ib+1) << "] = " << _alpha << endl << flush;
}
for (int i = 0; i < numExamples; ++i) {
vector<Label>& labels = _pTrainingData->getLabels(i);
for (int l = 0; l < numClasses; ++l) {
// Here we could have the option of using confidence rated setting so the
// real valued output of classify instead of its sign
if (labels[l].y != 0) { // perhaps replace it by nor_utils::is_zero(labels[l].y)
hx = _baseLearners[ib]->classify(_pTrainingData,i,l);
if ( hx < 0 )
labels[l].y *= -1;
else if ( hx == 0 )
labels[l].y = 0;
}
}
}
// We have to do at least one full iteration. For real it's not guaranteed
// Alternatively we could initialize all of them to constant
// if ( !firstLoop && energy >= previousEnergy ) {
// if (energy > previousEnergy) {
// _baseLearners[ib] = pPreviousBaseLearner->copyState();
// delete pPreviousBaseLearner;
// energy = previousEnergy;
// _alpha = _baseLearners[ib]->getAlpha();
// }
// break;
// }
if ( energy >= previousEnergy ) {
_alpha = previousAlpha;
energy = previousEnergy;
if (firstLoop) {
for(int ib2 = ib; ib2 < _numBaseLearners; ++ib2)
delete _baseLearners[ib2];
_numBaseLearners = ib;
}
else {
_baseLearners[ib] = pPreviousBaseLearner->copyState();
}
delete pPreviousBaseLearner;
break;
}
}
// Restore original labels
for (int i = 0; i < numExamples; ++i) {
vector<Label>& labels = _pTrainingData->getLabels(i);
for (int l = 0; l < numClasses; ++l)
labels[l].y = _savedLabels[i][l];
}
_id = _baseLearners[0]->getId();
for(int ib = 1; ib < _numBaseLearners; ++ib)
_id += "_x_" + _baseLearners[ib]->getId();
//bandit part we calculate the reward
_reward = getRewardFromEdge( getEdge() );
provideRewardForBanditAlgo();
return energy;
}