void MDDAGClassifier::saveLikelihoods(const string& dataFileName, const string& shypFileName,
const string& outFileName, int numIterations)
{
InputData* pData = loadInputData(dataFileName, shypFileName);
if (_verbose > 0)
cout << "Loading strong hypothesis..." << flush;
// The class that loads the weak hypotheses
UnSerialization us;
// Where to put the weak hypotheses
vector<BaseLearner*> weakHypotheses;
// loads them
us.loadHypotheses(shypFileName, weakHypotheses, pData);
// where the results go
vector< ExampleResults* > results;
if (_verbose > 0)
cout << "Classifying..." << flush;
const int numClasses = pData->getNumClasses();
const int numExamples = pData->getNumExamples();
ofstream outFile(outFileName.c_str());
string exampleName;
if (_verbose > 0)
cout << "Output likelihoods..." << flush;
// get the results
/////////////////////////////////////////////////////////////////////
// computeResults( pData, weakHypotheses, results, numIterations );
assert( !weakHypotheses.empty() );
// Initialize the output info
OutputInfo* pOutInfo = NULL;
if ( !_outputInfoFile.empty() )
pOutInfo = new OutputInfo(_outputInfoFile, "err");
// Creating the results structures. See file Structures.h for the
// PointResults structure
results.clear();
results.reserve(numExamples);
for (int i = 0; i < numExamples; ++i)
results.push_back( new ExampleResults(i, numClasses) );
// sum votes for classes
vector< AlphaReal > votesForExamples( numClasses );
vector< AlphaReal > expVotesForExamples( numClasses );
// iterator over all the weak hypotheses
vector<BaseLearner*>::const_iterator whyIt;
int t;
pOutInfo->initialize( pData );
// for every feature: 1..T
for (whyIt = weakHypotheses.begin(), t = 0;
whyIt != weakHypotheses.end() && t < numIterations; ++whyIt, ++t)
{
BaseLearner* currWeakHyp = *whyIt;
AlphaReal alpha = currWeakHyp->getAlpha();
// for every point
for (int i = 0; i < numExamples; ++i)
{
// a reference for clarity and speed
vector<AlphaReal>& currVotesVector = results[i]->getVotesVector();
// for every class
for (int l = 0; l < numClasses; ++l)
currVotesVector[l] += alpha * currWeakHyp->classify(pData, i, l);
}
// if needed output the step-by-step information
if ( pOutInfo )
{
pOutInfo->outputIteration(t);
pOutInfo->outputCustom(pData, currWeakHyp);
// Margins and edge requires an update of the weight,
// therefore I keep them out for the moment
//outInfo.outputMargins(pData, currWeakHyp);
//outInfo.outputEdge(pData, currWeakHyp);
pOutInfo->endLine();
} // for (int i = 0; i < numExamples; ++i)
// calculate likelihoods from votes
fill( votesForExamples.begin(), votesForExamples.end(), 0.0 );
AlphaReal lLambda = 0.0;
for (int i = 0; i < numExamples; ++i)
{
// a reference for clarity and speed
vector<AlphaReal>& currVotesVector = results[i]->getVotesVector();
AlphaReal sumExp = 0.0;
// for every class
for (int l = 0; l < numClasses; ++l)
{
expVotesForExamples[l] = exp( currVotesVector[l] ) ;
sumExp += expVotesForExamples[l];
}
if ( sumExp > numeric_limits<AlphaReal>::epsilon() )
{
for (int l = 0; l < numClasses; ++l)
{
expVotesForExamples[l] /= sumExp;
}
}
Example ex = pData->getExample( results[i]->getIdx() );
vector<Label> labs = ex.getLabels();
AlphaReal m = numeric_limits<AlphaReal>::infinity();
for (int l = 0; l < numClasses; ++l)
{
if ( labs[l].y > 0 )
{
if ( expVotesForExamples[l] > numeric_limits<AlphaReal>::epsilon() )
{
AlphaReal logVal = log( expVotesForExamples[l] );
if ( logVal != m ) {
lLambda += ( ( 1.0/(AlphaReal)numExamples ) * logVal );
}
}
}
}
}
outFile << t << "\t" << lLambda ;
outFile << '\n';
outFile.flush();
}
if (pOutInfo)
delete pOutInfo;
// computeResults( pData, weakHypotheses, results, numIterations );
///////////////////////////////////////////////////////////////////////////////////
/*
for (int i = 0; i < numExamples; ++i)
{
// output the name if it exists, otherwise the number
// of the example
exampleName = pData->getExampleName(i);
if ( !exampleName.empty() )
outFile << exampleName << ',';
// output the posteriors
outFile << results[i]->getVotesVector()[0];
for (int l = 1; l < numClasses; ++l)
outFile << ',' << results[i]->getVotesVector()[l];
outFile << '\n';
}
*/
if (_verbose > 0)
cout << "Done!" << endl;
if (_verbose > 1)
{
cout << "\nClass order (You can change it in the header of the data file):" << endl;
for (int l = 0; l < numClasses; ++l)
cout << "- " << pData->getClassMap().getNameFromIdx(l) << endl;
}
// delete the input data file
if (pData)
delete pData;
vector<ExampleResults*>::iterator it;
for (it = results.begin(); it != results.end(); ++it)
delete (*it);
}
// Returns the results into ptRes
void MDDAGClassifier::computeResults(InputData* pData, vector<BaseLearner*>& weakHypotheses,
vector< ExampleResults* >& results, int numIterations)
{
assert( !weakHypotheses.empty() );
const int numClasses = pData->getNumClasses();
const int numExamples = pData->getNumExamples();
// Initialize the output info
OutputInfo* pOutInfo = NULL;
if ( !_outputInfoFile.empty() )
{
if ( _args.getNumValues("outputinfo") > 1 )
{
pOutInfo = new OutputInfo(_args);;
}
else
{
pOutInfo = new OutputInfo(_outputInfoFile, "e01hamauc", false);
}
}
// Creating the results structures. See file Structures.h for the
// PointResults structure
results.clear();
results.reserve(numExamples);
for (int i = 0; i < numExamples; ++i)
results.push_back( new ExampleResults(i, numClasses) );
// iterator over all the weak hypotheses
vector<BaseLearner*>::const_iterator whyIt;
int t;
if ( pOutInfo )
{
pOutInfo->initialize( pData );
pOutInfo->outputHeader(pData->getClassMap(),
true, // output iterations
false, // output time
true // endline
);
}
// for every feature: 1..T
for (whyIt = weakHypotheses.begin(), t = 0;
whyIt != weakHypotheses.end() && t < numIterations; ++whyIt, ++t)
{
BaseLearner* currWeakHyp = *whyIt;
AlphaReal alpha = currWeakHyp->getAlpha();
// for every point
for (int i = 0; i < numExamples; ++i)
{
// a reference for clarity and speed
vector<AlphaReal>& currVotesVector = results[i]->getVotesVector();
// for every class
for (int l = 0; l < numClasses; ++l)
currVotesVector[l] += alpha * currWeakHyp->classify(pData, i, l);
}
// if needed output the step-by-step information
if ( pOutInfo )
{
pOutInfo->outputIteration(t);
// pOutInfo->outputError(pData, currWeakHyp);
// pOutInfo->outTPRFPR(pData);
//pOutInfo->outputBalancedError(pData, currWeakHyp);
// if ( ( t % 1 ) == 0 ) {
// pOutInfo->outputROC(pData);
// }
pOutInfo->outputCustom(pData, currWeakHyp);
// Margins and edge requires an update of the weight,
// therefore I keep them out for the moment
//outInfo.outputMargins(pData, currWeakHyp);
//outInfo.outputEdge(pData, currWeakHyp);
pOutInfo->endLine();
}
}
if (pOutInfo)
delete pOutInfo;
}
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;
}
/**
* The main function. Everything starts here!
* \param argc The number of arguments.
* \param argv The arguments.
* \date 11/11/2005
*/
int main(int argc, const char* argv[])
{
// initializing the random number generator
srand ( time(NULL) );
// no need to synchronize with C style stream
std::ios_base::sync_with_stdio(false);
#if STABLE_SORT
cerr << "WARNING: Stable sort active! It might be slower!!" << endl;
#endif
//////////////////////////////////////////////////////////////////////////
// Standard arguments
nor_utils::Args args;
args.setArgumentDiscriminator("--");
args.declareArgument("help");
args.declareArgument("static");
args.declareArgument("h", "Help", 1, "<optiongroup>");
//////////////////////////////////////////////////////////////////////////
// Basic Arguments
args.setGroup("Parameters");
args.declareArgument("train", "Performs training.", 2, "<dataFile> <nInterations>");
args.declareArgument("traintest", "Performs training and test at the same time.", 3, "<trainingDataFile> <testDataFile> <nInterations>");
args.declareArgument("trainvalidtest", "Performs training and test at the same time.", 4, "<trainingDataFile> <validDataFile> <testDataFile> <nInterations>");
args.declareArgument("test", "Test the model.", 3, "<dataFile> <numIters> <shypFile>");
args.declareArgument("test", "Test the model and output the results", 4, "<datafile> <shypFile> <numIters> <outFile>");
args.declareArgument("cmatrix", "Print the confusion matrix for the given model.", 2, "<dataFile> <shypFile>");
args.declareArgument("cmatrixfile", "Print the confusion matrix with the class names to a file.", 3, "<dataFile> <shypFile> <outFile>");
args.declareArgument("posteriors", "Output the posteriors for each class, that is the vector-valued discriminant function for the given dataset and model.", 4, "<dataFile> <shypFile> <outFile> <numIters>");
args.declareArgument("posteriors", "Output the posteriors for each class, that is the vector-valued discriminant function for the given dataset and model periodically.", 5, "<dataFile> <shypFile> <outFile> <numIters> <period>");
args.declareArgument("encode", "Save the coefficient vector of boosting individually on each point using ParasiteLearner", 6, "<inputDataFile> <autoassociativeDataFile> <outputDataFile> <nIterations> <poolFile> <nBaseLearners>");
args.declareArgument("ssfeatures", "Print matrix data for SingleStump-Based weak learners (if numIters=0 it means all of them).", 4, "<dataFile> <shypFile> <outFile> <numIters>");
args.declareArgument( "fileformat", "Defines the type of intput file. Available types are:\n"
"* simple: each line has attributes separated by whitespace and class at the end (DEFAULT!)\n"
"* arff: arff filetype. The header file can be specified using --headerfile option\n"
"* arffbzip: bziped arff filetype. The header file can be specified using --headerfile option\n"
"* svmlight: \n"
"(Example: --fileformat simple)",
1, "<fileFormat>" );
args.declareArgument("headerfile", "The header file for arff and SVMLight and arff formats.", 1, "header.txt");
args.declareArgument("constant", "Check constant learner in each iteration.", 0, "");
args.declareArgument("timelimit", "Time limit in minutes", 1, "<minutes>" );
args.declareArgument("stronglearner", "Available strong learners:\n"
"AdaBoost (default)\n"
"FilterBoost\n"
"SoftCascade\n"
"VJcascade\n", 1, "<stronglearner>" );
args.declareArgument("slowresumeprocess", "Computes every statitstic in each iteration (slow resume)\n"
"Computes only the statistics in the last iteration (fast resume, default)\n", 0, "" );
args.declareArgument("weights", "Outputs the weights of instances at the end of the learning process", 1, "<filename>" );
args.declareArgument("Cn", "Resampling size for FilterBoost (default=300)", 1, "<value>" );
args.declareArgument("onlinetraining", "The weak learner will be trained online\n", 0, "" );
//// ignored for the moment!
//args.declareArgument("arffheader", "Specify the arff header.", 1, "<arffHeaderFile>");
// for MDDAG
//args.setGroup("MDDAG");
args.declareArgument("traintestmddag", "Performs training and test at the same time using mddag.", 5, "<trainingDataFile> <testDataFile> <modelFile> <nIterations> <baseIter>");
args.declareArgument("policytrainingiter", "The iteration number the policy learner takes.", 1, "<iternum>");
args.declareArgument("rollouts", "The number of rollouts.", 1, "<num>");
args.declareArgument("rollouttype", "Rollout type (montecarlo or szatymaz)", 1, "<rollouttype>");
args.declareArgument("beta", "Trade-off parameter", 1, "<beta>");
args.declareArgument("outdir", "Output directory.", 1, "<outdir>");
args.declareArgument("policyalpha", "Alpha for policy array.", 1, "<alpha>");
args.declareArgument("succrewardtype", "Rewrd type (e01 or hammng)", 1, "<rward_type");
args.declareArgument("outtrainingerror", "Output training error", 0, "");
args.declareArgument("epsilon", "Exploration term", 1, "<epsilon>");
args.declareArgument("updateperc", "Number of component in the policy are updated", 1, "<perc>");
// for VJ cascade
VJCascadeLearner::declareBaseArguments(args);
// for SoftCascade
SoftCascadeLearner::declareBaseArguments(args);
//////////////////////////////////////////////////////////////////////////
// Options
args.setGroup("I/O Options");
/////////////////////////////////////////////
// these are valid only for .txt input!
// they might be removed!
args.declareArgument("d", "The separation characters between the fields (default: whitespaces).\nExample: -d \"\\t,.-\"\nNote: new-line is always included!", 1, "<separators>");
args.declareArgument("classend", "The class is the last column instead of the first (or second if -examplelabel is active).");
args.declareArgument("examplename", "The data file has an additional column (the very first) which contains the 'name' of the example.");
/////////////////////////////////////////////
args.setGroup("Basic Algorithm Options");
args.declareArgument("weightpolicy", "Specify the type of weight initialization. The user specified weights (if available) are used inside the policy which can be:\n"
"* sharepoints Share the weight equally among data points and between positiv and negative labels (DEFAULT)\n"
"* sharelabels Share the weight equally among data points\n"
"* proportional Share the weights freely", 1, "<weightType>");
args.setGroup("General Options");
args.declareArgument("verbose", "Set the verbose level 0, 1 or 2 (0=no messages, 1=default, 2=all messages).", 1, "<val>");
args.declareArgument("outputinfo", "Output informations on the algorithm performances during training, on file <filename>.", 1, "<filename>");
args.declareArgument("outputinfo", "Output specific informations on the algorithm performances during training, on file <filename> <outputlist>. <outputlist> must be a concatenated list of three characters abreviation (ex: err for error, fpr for false positive rate)", 2, "<filename> <outputlist>");
args.declareArgument("seed", "Defines the seed for the random operations.", 1, "<seedval>");
//////////////////////////////////////////////////////////////////////////
// Shows the list of available learners
string learnersComment = "Available learners are:";
vector<string> learnersList;
BaseLearner::RegisteredLearners().getList(learnersList);
vector<string>::const_iterator it;
for (it = learnersList.begin(); it != learnersList.end(); ++it)
{
learnersComment += "\n ** " + *it;
// defaultLearner is defined in Defaults.h
if ( *it == defaultLearner )
learnersComment += " (DEFAULT)";
}
args.declareArgument("learnertype", "Change the type of weak learner. " + learnersComment, 1, "<learner>");
//////////////////////////////////////////////////////////////////////////
//// Declare arguments that belongs to all weak learners
BaseLearner::declareBaseArguments(args);
////////////////////////////////////////////////////////////////////////////
//// Weak learners (and input data) arguments
for (it = learnersList.begin(); it != learnersList.end(); ++it)
{
args.setGroup(*it + " Options");
// add weaklearner-specific options
BaseLearner::RegisteredLearners().getLearner(*it)->declareArguments(args);
}
//////////////////////////////////////////////////////////////////////////
//// Declare arguments that belongs to all bandit learner
GenericBanditAlgorithm::declareBaseArguments(args);
//////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////
switch ( args.readArguments(argc, argv) )
{
case nor_utils::AOT_NO_ARGUMENTS:
showBase();
break;
case nor_utils::AOT_UNKOWN_ARGUMENT:
exit(1);
break;
case nor_utils::AOT_INCORRECT_VALUES_NUMBER:
exit(1);
break;
case nor_utils::AOT_OK:
break;
}
//////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////
if ( args.hasArgument("help") )
showHelp(args, learnersList);
if ( args.hasArgument("static") )
showStaticConfig();
//////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////
if ( args.hasArgument("h") )
showOptionalHelp(args);
//////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////
int verbose = 1;
if ( args.hasArgument("verbose") )
args.getValue("verbose", 0, verbose);
//////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////
// defines the seed
if (args.hasArgument("seed"))
{
unsigned int seed = args.getValue<unsigned int>("seed", 0);
srand(seed);
}
//////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////
GenericStrongLearner* pModel = NULL;
if ( args.hasArgument("train") ||
args.hasArgument("traintest") ||
args.hasArgument("trainvalidtest") ) // for Viola-Jones Cascade
{
// get the name of the learner
string baseLearnerName = defaultLearner;
if ( args.hasArgument("learnertype") )
args.getValue("learnertype", 0, baseLearnerName);
checkBaseLearner(baseLearnerName);
if (verbose > 1)
cout << "--> Using learner: " << baseLearnerName << endl;
// This hould be changed: the user decides the strong learner
BaseLearner* pWeakHypothesisSource = BaseLearner::RegisteredLearners().getLearner(baseLearnerName);
pModel = pWeakHypothesisSource->createGenericStrongLearner( args );
pModel->run(args);
}
//////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////
else if ( args.hasArgument("traintestmddag") )
{
// -test <dataFile> <shypFile> <numIters>
string shypFileName = args.getValue<string>("traintestmddag", 2);
string baseLearnerName = UnSerialization::getWeakLearnerName(shypFileName);
BaseLearner* pWeakHypothesisSource = BaseLearner::RegisteredLearners().getLearner(baseLearnerName);
pModel = pWeakHypothesisSource->createGenericStrongLearner( args );
pModel->run(args);
}
//////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////
else if ( args.hasArgument("test") )
{
// -test <dataFile> <shypFile> <numIters>
string shypFileName = args.getValue<string>("test", 1);
string baseLearnerName = UnSerialization::getWeakLearnerName(shypFileName);
BaseLearner* pWeakHypothesisSource = BaseLearner::RegisteredLearners().getLearner(baseLearnerName);
pModel = pWeakHypothesisSource->createGenericStrongLearner( args );
pModel->classify(args);
}
//////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////
else if ( args.hasArgument("cmatrix") )
{
// -cmatrix <dataFile> <shypFile>
string shypFileName = args.getValue<string>("cmatrix", 1);
string baseLearnerName = UnSerialization::getWeakLearnerName(shypFileName);
BaseLearner* pWeakHypothesisSource = BaseLearner::RegisteredLearners().getLearner(baseLearnerName);
pModel = pWeakHypothesisSource->createGenericStrongLearner( args );
pModel->doConfusionMatrix(args);
}
//////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////
else if ( args.hasArgument("posteriors") )
{
// -posteriors <dataFile> <shypFile> <outFileName>
string shypFileName = args.getValue<string>("posteriors", 1);
string baseLearnerName = UnSerialization::getWeakLearnerName(shypFileName);
BaseLearner* pWeakHypothesisSource = BaseLearner::RegisteredLearners().getLearner(baseLearnerName);
pModel = pWeakHypothesisSource->createGenericStrongLearner( args );
pModel->doPosteriors(args);
}
//////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////
else if ( args.hasArgument("ssfeatures") )
{
// ONLY for AdaBoostMH classifiers
// -ssfeatures <dataFile> <shypFile> <outFile> <numIters>
string testFileName = args.getValue<string>("ssfeatures", 0);
string shypFileName = args.getValue<string>("ssfeatures", 1);
string outFileName = args.getValue<string>("ssfeatures", 2);
int numIterations = args.getValue<int>("ssfeatures", 3);
cerr << "ERROR: ssfeatures has been deactivated for the moment!" << endl;
//classifier.saveSingleStumpFeatureData(testFileName, shypFileName, outFileName, numIterations);
}
//////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////
else if ( args.hasArgument("encode") )
{
// --encode <inputDataFile> <outputDataFile> <nIterations> <poolFile> <nBaseLearners>
string labelsFileName = args.getValue<string>("encode", 0);
string autoassociativeFileName = args.getValue<string>("encode", 1);
string outputFileName = args.getValue<string>("encode", 2);
int numIterations = args.getValue<int>("encode", 3);
string poolFileName = args.getValue<string>("encode", 4);
int numBaseLearners = args.getValue<int>("encode", 5);
string outputInfoFile;
const char* tmpArgv1[] = {"bla", // for ParasiteLearner
"--pool",
args.getValue<string>("encode", 4).c_str(),
args.getValue<string>("encode", 5).c_str()};
args.readArguments(4,tmpArgv1);
InputData* pAutoassociativeData = new InputData();
pAutoassociativeData->initOptions(args);
pAutoassociativeData->load(autoassociativeFileName,IT_TRAIN,verbose);
// for the original labels
InputData* pLabelsData = new InputData();
pLabelsData->initOptions(args);
pLabelsData->load(labelsFileName,IT_TRAIN,verbose);
// set up all the InputData members identically to pAutoassociativeData
EncodeData* pOnePoint = new EncodeData();
pOnePoint->initOptions(args);
pOnePoint->load(autoassociativeFileName,IT_TRAIN,verbose);
const int numExamples = pAutoassociativeData->getNumExamples();
BaseLearner* pWeakHypothesisSource =
BaseLearner::RegisteredLearners().getLearner("ParasiteLearner");
pWeakHypothesisSource->declareArguments(args);
ParasiteLearner* pWeakHypothesis;
ofstream outFile(outputFileName.c_str());
if (!outFile.is_open())
{
cerr << "ERROR: Cannot open strong hypothesis file <" << outputFileName << ">!" << endl;
exit(1);
}
for (int i = 0; i < numExamples ; ++i)
{
vector<float> alphas;
alphas.resize(numBaseLearners);
fill(alphas.begin(), alphas.end(), 0);
if (verbose >= 1)
cout << "--> Encoding example no " << (i+1) << endl;
pOnePoint->resetData();
pOnePoint->addExample( pAutoassociativeData->getExample(i) );
AlphaReal energy = 1;
OutputInfo* pOutInfo = NULL;
if ( args.hasArgument("outputinfo") )
{
args.getValue("outputinfo", 0, outputInfoFile);
pOutInfo = new OutputInfo(args);
pOutInfo->initialize(pOnePoint);
}
for (int t = 0; t < numIterations; ++t)
{
pWeakHypothesis = (ParasiteLearner*)pWeakHypothesisSource->create();
pWeakHypothesis->initLearningOptions(args);
pWeakHypothesis->setTrainingData(pOnePoint);
energy *= pWeakHypothesis->run();
// if (verbose >= 2)
// cout << "energy = " << energy << endl << flush;
AdaBoostMHLearner adaBoostMHLearner;
if (i == 0 && t == 0)
{
if ( pWeakHypothesis->getBaseLearners().size() < numBaseLearners )
numBaseLearners = pWeakHypothesis->getBaseLearners().size();
outFile << "%Hidden representation using autoassociative boosting" << endl << endl;
outFile << "@RELATION " << outputFileName << endl << endl;
outFile << "% numBaseLearners" << endl;
for (int j = 0; j < numBaseLearners; ++j)
outFile << "@ATTRIBUTE " << j << "_" <<
pWeakHypothesis->getBaseLearners()[j]->getId() << " NUMERIC" << endl;
outFile << "@ATTRIBUTE class {" << pLabelsData->getClassMap().getNameFromIdx(0);
for (int l = 1; l < pLabelsData->getClassMap().getNumNames(); ++l)
outFile << ", " << pLabelsData->getClassMap().getNameFromIdx(l);
outFile << "}" << endl<< endl<< "@DATA" << endl;
}
alphas[pWeakHypothesis->getSelectedIndex()] +=
pWeakHypothesis->getAlpha() * pWeakHypothesis->getSignOfAlpha();
if ( pOutInfo )
adaBoostMHLearner.printOutputInfo(pOutInfo, t, pOnePoint, NULL, pWeakHypothesis);
adaBoostMHLearner.updateWeights(pOnePoint,pWeakHypothesis);
}
float sumAlphas = 0;
for (int j = 0; j < numBaseLearners; ++j)
sumAlphas += alphas[j];
for (int j = 0; j < numBaseLearners; ++j)
outFile << alphas[j]/sumAlphas << ",";
const vector<Label>& labels = pLabelsData->getLabels(i);
for (int l = 0; l < labels.size(); ++l)
if (labels[l].y > 0)
outFile << pLabelsData->getClassMap().getNameFromIdx(labels[l].idx) << endl;
delete pOutInfo;
}
outFile.close();
}
if (pModel)
delete pModel;
return 0;
}
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 SoftCascadeLearner::run(const nor_utils::Args& args)
{
// load the arguments
this->getArgs(args);
//print cascade properties
if (_verbose > 0) {
cout << "[+] Softcascade parameters :" << endl
<< "\t --> target detection rate = " << _targetDetectionRate << endl
<< "\t --> alpha (exp param) = " << _alphaExponentialParameter << endl
<< "\t --> bootstrap rate = " << _bootstrapRate << endl
<< endl;
}
// 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);
// get the training input data, and load it
InputData* pTrainingData = pWeakHypothesisSource->createInputData();
pTrainingData->initOptions(args);
pTrainingData->load(_trainFileName, IT_TRAIN, 5);
InputData* pBootstrapData = NULL;
if (!_bootstrapFileName.empty()) {
pBootstrapData = pWeakHypothesisSource->createInputData();
pBootstrapData->initOptions(args);
pBootstrapData->load(_bootstrapFileName, IT_TRAIN, 5);
}
// 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, 5);
}
Serialization ss(_shypFileName, false );
ss.writeHeader(_baseLearnerName);
// outputHeader();
// The output information object
OutputInfo* pOutInfo = NULL;
if ( !_outputInfoFile.empty() )
{
pOutInfo = new OutputInfo(args, true);
pOutInfo->setOutputList("sca", &args);
pOutInfo->initialize(pTrainingData);
if (pTestData)
pOutInfo->initialize(pTestData);
pOutInfo->outputHeader(pTrainingData->getClassMap(), true, true, false);
pOutInfo->outputUserHeader("thresh");
pOutInfo->headerEndLine();
}
// ofstream trainPosteriorsFile;
// ofstream testPosteriorsFile;
const NameMap& namemap = pTrainingData->getClassMap();
_positiveLabelIndex = namemap.getIdxFromName(_positiveLabelName);
// FIXME: output posteriors
// OutputInfo* pTrainPosteriorsOut = NULL;
// OutputInfo* pTestPosteriorsOut = NULL;
// if (! _trainPosteriorsFileName.empty()) {
// pTrainPosteriorsOut = new OutputInfo(_trainPosteriorsFileName, "pos", true);
// pTrainPosteriorsOut->initialize(pTrainingData);
// dynamic_cast<PosteriorsOutput*>( pTrainPosteriorsOut->getOutputInfoObject("pos") )->addClassIndex(_positiveLabelIndex );
// }
// if (! _testPosteriorsFileName.empty() && !_testFileName.empty() ) {
// pTestPosteriorsOut = new OutputInfo(_testPosteriorsFileName, "pos", true);
// pTestPosteriorsOut->initialize(pTestData);
// dynamic_cast<PosteriorsOutput*>( pTestPosteriorsOut->getOutputInfoObject("pos") )->addClassIndex(_positiveLabelIndex );
// }
const int numExamples = pTrainingData->getNumExamples();
vector<BaseLearner*> inWeakHypotheses;
if (_fullRun) {
// TODO : the full training is implementet, testing is needed
AdaBoostMHLearner* sHypothesis = new AdaBoostMHLearner();
sHypothesis->run(args, pTrainingData, _baseLearnerName, _numIterations, inWeakHypotheses );
delete sHypothesis;
}
else {
cout << "[+] Loading uncalibrated shyp file... ";
//read the shyp file of the trained classifier
UnSerialization us;
us.loadHypotheses(_unCalibratedShypFileName, inWeakHypotheses, pTrainingData);
if (_inShypLimit > 0 && _inShypLimit < inWeakHypotheses.size() ) {
inWeakHypotheses.resize(_inShypLimit);
}
if (_numIterations > inWeakHypotheses.size()) {
_numIterations = inWeakHypotheses.size();
}
cout << "weak hypotheses loaded, " << inWeakHypotheses.size() << " retained.\n";
}
// some initializations
_foundHypotheses.resize(0);
double faceRejectionFraction = 0.;
double estimatedExecutionTime = 0.;
vector<double> rejectionDistributionVector;
_rejectionThresholds.resize(0);
set<int> trainingIndices;
for (int i = 0; i < numExamples; i++) {
trainingIndices.insert(pTrainingData->getRawIndex(i) );
}
// init v_t (see the paper)
initializeRejectionDistributionVector(_numIterations, rejectionDistributionVector);
if (_verbose == 1)
cout << "Learning in progress..." << endl;
///////////////////////////////////////////////////////////////////////
// Starting the SoftCascade main loop
///////////////////////////////////////////////////////////////////////
for (int t = 0; t < _numIterations; ++t)
{
if (_verbose > 0)
cout << "--------------[ iteration " << (t+1) << " ]--------------" << endl;
faceRejectionFraction += rejectionDistributionVector[t];
cout << "[+] Face rejection tolerated : " << faceRejectionFraction << " | v[t] = " << rejectionDistributionVector[t] << endl;
int numberOfNegatives = pTrainingData->getNumExamplesPerClass(1 - _positiveLabelIndex);
//vector<BaseLearner*>::const_iterator whyIt;
int selectedIndex = 0;
AlphaReal bestGap = 0;
vector<AlphaReal> posteriors;
computePosteriors(pTrainingData, _foundHypotheses, posteriors, _positiveLabelIndex);
//should use an iterator instead of i
vector<BaseLearner*>::iterator whyIt;
int i;
for (i = 0, whyIt = inWeakHypotheses.begin(); whyIt != inWeakHypotheses.end(); ++whyIt, ++i) {
vector<AlphaReal> temporaryPosteriors = posteriors;
vector<BaseLearner*> temporaryWeakHyp = _foundHypotheses;
temporaryWeakHyp.push_back(*whyIt);
updatePosteriors(pTrainingData, *whyIt, temporaryPosteriors, _positiveLabelIndex);
AlphaReal gap = computeSeparationSpan(pTrainingData, temporaryPosteriors, _positiveLabelIndex );
if (gap > bestGap) {
bestGap = gap;
selectedIndex = i;
}
}
BaseLearner* selectedWeakHypothesis = inWeakHypotheses[selectedIndex];
cout << "[+] Rank of the selected weak hypothesis : " << selectedIndex << endl
<< "\t ---> edge gap = " << bestGap << endl
<< "\t ---> alpha = " << selectedWeakHypothesis->getAlpha() << endl;
//update the stages
_foundHypotheses.push_back(selectedWeakHypothesis);
updatePosteriors(pTrainingData, selectedWeakHypothesis, posteriors, _positiveLabelIndex);
double missesFraction;
AlphaReal r = findBestRejectionThreshold(pTrainingData, posteriors, faceRejectionFraction, missesFraction);
_rejectionThresholds.push_back(r);
// update the output info object
dynamic_cast<SoftCascadeOutput*>( pOutInfo->getOutputInfoObject("sca") )->appendRejectionThreshold(r);
cout << "[+] Rejection threshold = " << r << endl;
//some updates
ss.appendHypothesisWithThreshold(t, selectedWeakHypothesis, r);
faceRejectionFraction -= missesFraction;
inWeakHypotheses.erase(inWeakHypotheses.begin() + selectedIndex);
double whypCost = 1; //just in case there are different costs for each whyp
estimatedExecutionTime += whypCost * numberOfNegatives;
// output perf in file
vector< vector< AlphaReal> > scores(0);
_output << t + 1 << setw(_sepWidth + 1) << r << setw(_sepWidth);
// update OutputInfo with the new whyp
// updateOutputInfo(pOutInfo, pTrainingData, selectedWeakHypothesis);
// if (pTestData) {
// updateOutputInfo(pOutInfo, pTestData, selectedWeakHypothesis);
// }
// output the iteration results
printOutputInfo(pOutInfo, t, pTrainingData, pTestData, selectedWeakHypothesis, r);
// if (pTrainPosteriorsOut) {
// pTrainPosteriorsOut->setTable(pTrainingData, pOutInfo->getTable(pTrainingData));
// pTrainPosteriorsOut->outputCustom(pTrainingData);
// }
//
// if (pTestPosteriorsOut) {
// pTestPosteriorsOut->setTable(pTestData, pOutInfo->getTable(pTestData));
// pTestPosteriorsOut->outputCustom(pTestData);
// }
int leftNegatives = filterDataset(pTrainingData, posteriors, r, trainingIndices);
if (leftNegatives == 0) {
cout << endl << "[+] No more negatives.\n";
break;
}
if (_bootstrapRate != 0) {
bootstrapTrainingSet(pTrainingData, pBootstrapData, trainingIndices);
}
} // loop on iterations
/////////////////////////////////////////////////////////
// write the footer of the strong hypothesis file
ss.writeFooter();
// Free the two input data objects
if (pTrainingData)
delete pTrainingData;
if (pBootstrapData) {
delete pBootstrapData;
}
if (pTestData)
delete pTestData;
if (_verbose > 0)
cout << "Learning completed." << endl;
}