Пример #1
0
void MDDAGClassifier::run(const string& dataFileName, const string& shypFileName,
                          int numIterations, const string& outResFileName, int numRanksEnclosed)
{
    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;

    // get the results
    computeResults( pData, weakHypotheses, results, numIterations );

    const int numClasses = pData->getNumClasses();

    if (_verbose > 0)
    {
        // well.. if verbose = 0 no results are displayed! :)
        cout << "Done!" << endl;

        vector< vector<float> > rankedError(numRanksEnclosed);

        // Get the per-class error for the numRanksEnclosed-th ranks
        for (int i = 0; i < numRanksEnclosed; ++i)
            getClassError( pData, results, rankedError[i], i );

        // output it
        cout << endl;
        cout << "Error Summary" << endl;
        cout << "=============" << endl;

        for ( int l = 0; l < numClasses; ++l )
        {
            // first rank (winner): rankedError[0]
            cout << "Class '" << pData->getClassMap().getNameFromIdx(l) << "': "
                 << setprecision(4) << rankedError[0][l] * 100 << "%";

            // output the others on its side
            if (numRanksEnclosed > 1 && _verbose > 1)
            {
                cout << " (";
                for (int i = 1; i < numRanksEnclosed; ++i)
                    cout << " " << i+1 << ":[" << setprecision(4) << rankedError[i][l] * 100 << "%]";
                cout << " )";
            }

            cout << endl;
        }

        // the overall error
        cout << "\n--> Overall Error: "
             << setprecision(4) << getOverallError(pData, results, 0) * 100 << "%";

        // output the others on its side
        if (numRanksEnclosed > 1 && _verbose > 1)
        {
            cout << " (";
            for (int i = 1; i < numRanksEnclosed; ++i)
                cout << " " << i+1 << ":[" << setprecision(4) << getOverallError(pData, results, i) * 100 << "%]";
            cout << " )";
        }

        cout << endl;

    } // verbose


    // If asked output the results
    if ( !outResFileName.empty() )
    {
        const int numExamples = pData->getNumExamples();
        ofstream outRes(outResFileName.c_str());

        outRes << "Instance" << '\t' << "Forecast" << '\t' << "Labels" << '\n';

        string exampleName;

        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() )
                outRes << i << '\t';
            else
                outRes << exampleName << '\t';

            // output the predicted class
            outRes << pData->getClassMap().getNameFromIdx( results[i]->getWinner().first ) << '\t';

            outRes << '|';

            vector<Label>& labels = pData->getLabels(i);
            for (vector<Label>::iterator lIt=labels.begin(); lIt != labels.end(); ++lIt) {
                if (lIt->y>0)
                {
                    outRes << ' ' << pData->getClassMap().getNameFromIdx(lIt->idx);
                }
            }

            outRes << endl;
        }

        if (_verbose > 0)
            cout << "\nPredictions written on file <" << outResFileName << ">!" << endl;

    }


    // delete the input data file
    if (pData)
        delete pData;

    vector<ExampleResults*>::iterator it;
    for (it = results.begin(); it != results.end(); ++it)
        delete (*it);
}
Пример #2
0
	void AdaBoostMHClassifier::saveROC(const string& dataFileName, const string& shypFileName, 
		const string& outFileName, int numIterations)
	{
		InputData* pData = loadInputData(dataFileName, shypFileName);
		ofstream outFile(outFileName.c_str());
		
		if ( ! outFile.is_open() )
		{
			cout << "Cannot open outfile" << endl;
			exit( -1 );
		}

		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);
		weakHypotheses.resize( numIterations );

		// where the results go
		vector< ExampleResults* > results;

		if (_verbose > 0)
			cout << "Classifying..." << flush;

		// get the results
		computeResults( pData, weakHypotheses, results, weakHypotheses.size());

		const int numClasses = pData->getNumClasses();
		const int numExamples = pData->getNumExamples();

		if (_verbose > 0)
			cout << "Done!" << endl;		

		vector< pair< int, double> > sortedExample( numExamples );
		
		for( int i=0; i<numExamples; i++ )
		{
			sortedExample[i].first = i;
			sortedExample[i].second = results[i]->getVotesVector()[0];
		}
		sort( sortedExample.begin(), sortedExample.end(), nor_utils::comparePair< 2, int, double, greater<double> >() );

		vector<double> positiveWeights( numExamples );
		double sumOfPositiveWeights = 0.0;

		vector<double>  negativeWeights( numExamples );
		double sumOfNegativeWeights = 0.0;
		
		fill( positiveWeights.begin(), positiveWeights.end(), 0.0 );
		fill( negativeWeights.begin(), negativeWeights.end(), 0.0 );

		string className = pData->getClassMap().getNameFromIdx( 0 );

		vector<Label>& labels = pData->getLabels( sortedExample[0].first );
		vector<Label>::iterator labIt = find( labels.begin(), labels.end(), 0);
		
		if ( labIt != labels.end() )
		{
			if ( labIt->y > 0.0 )
			{
				positiveWeights[0] = labIt->initialWeight;
				sumOfPositiveWeights += labIt->initialWeight;
			} else
			{
				negativeWeights[0] = labIt->initialWeight;
				sumOfNegativeWeights += labIt->initialWeight;
			}
		}
		
		for( int i=1; i<numExamples; i++ )
		{
			labels = pData->getLabels( sortedExample[i].first );
			labIt = find( labels.begin(), labels.end(), 0);
			if ( labIt != labels.end() )
			{
				if ( labIt->y > 0.0 )
				{
					negativeWeights[i] = negativeWeights[i-1];
					positiveWeights[i] = positiveWeights[i-1] + labIt->initialWeight;
					sumOfPositiveWeights += labIt->initialWeight;
				} else
				{
					positiveWeights[i] = positiveWeights[i-1];
					negativeWeights[i] = negativeWeights[i-1] + labIt->initialWeight;
					sumOfNegativeWeights += labIt->initialWeight;
				}
			} else {
				positiveWeights[i] = positiveWeights[i-1];
				negativeWeights[i] = negativeWeights[i-1];
			}
		}

		outFile << "Class name: " << className << endl;
		for( int i=0; i<numExamples; i++ )
		{
			outFile <<  sortedExample[i].first << " ";
			// false positive rate
			outFile << ( positiveWeights[i] / sumOfPositiveWeights ) << " ";
			//true negative rate
			outFile << ( negativeWeights[i] / sumOfNegativeWeights ) << endl;
		}		

		outFile.close();

		// delete the input data file
		if (pData) 
			delete pData;

		vector<ExampleResults*>::iterator it;
		for (it = results.begin(); it != results.end(); ++it)
			delete (*it);
	}
Пример #3
0
	void VJCascadeClassifier::savePosteriors(const string& dataFileName, const string& shypFileName, 
											  const string& outFileName, int numIterations)
	{
		// loading data
		InputData* pData = loadInputData(dataFileName, shypFileName);
		const int numOfExamples = pData->getNumExamples();
		
		//get the index of positive label		
		const NameMap& namemap = pData->getClassMap();
		_positiveLabelIndex = namemap.getIdxFromName( _positiveLabelName );
		
		
		if (_verbose > 0)
			cout << "Loading strong hypothesis..." << flush;
		
		
		// open outfile
		ofstream outRes(outFileName.c_str());
		if (!outRes.is_open())
		{
			cout << "Cannot open outfile!!! " << outFileName << endl;
		}
				
		
		// The class that loads the weak hypotheses
		UnSerialization us;
		
		// Where to put the weak hypotheses
		vector<vector<BaseLearner*> > weakHypotheses;
        		
		// For stagewise thresholds 
		vector<AlphaReal> thresholds(0);
		// loads them
		//us.loadHypotheses(shypFileName, weakHypotheses, pData);
		us.loadCascadeHypotheses(shypFileName, weakHypotheses, thresholds, pData);
		
		// output the number of stages
		outRes << "StageNum " << weakHypotheses.size() << endl;
		
		// output original labels
		outRes << "Labels";
		for(int i=0; i<numOfExamples; ++i )
		{		
			vector<Label>& labels = pData->getLabels(i);
			if (labels[_positiveLabelIndex].y>0) // pos label				
				outRes << " 1";
			else
				outRes << " 0";
		}				
		outRes << endl;
		
		// store result
		vector<CascadeOutputInformation> cascadeData(0);
		vector<CascadeOutputInformation>::iterator it;
		
		cascadeData.resize(numOfExamples);		
		for( it=cascadeData.begin(); it != cascadeData.end(); ++it )
		{
			it->active=true;
		}										
		
		for(int stagei=0; stagei < weakHypotheses.size(); ++stagei )
		{
			// for posteriors
			vector<AlphaReal> posteriors(0);		
			
			// calculate the posteriors after stage
			VJCascadeLearner::calculatePosteriors( pData, weakHypotheses[stagei], posteriors, _positiveLabelIndex );			
			
			// update the data (posteriors, active element index etc.)
			//VJCascadeLearner::forecastOverAllCascade( pData, posteriors, activeInstances, thresholds[stagei] );
			updateCascadeData(pData, weakHypotheses, stagei, posteriors, thresholds, _positiveLabelIndex, cascadeData);
			
			
			int numberOfActiveInstance = 0;
			for( int i = 0; i < numOfExamples; ++i )
				if (cascadeData[i].active) numberOfActiveInstance++;
			
			if (_verbose > 0 )
				cout << "Number of active instances: " << numberOfActiveInstance << "(" << numOfExamples << ")" << endl;									
			
			// output stats
			outRes << "Stage " << stagei << " " << weakHypotheses[stagei].size() << endl; 

			outRes << "Forecast";
			for(int i=0; i<numOfExamples; ++i )
			{	
				outRes << " " << cascadeData[i].forecast;
			}				
			outRes << endl;

			outRes << "Active";
			for(int i=0; i<numOfExamples; ++i )
			{	
				if( cascadeData[i].active)
					outRes << " 1";
				else
					outRes << " 0";
			}				
			outRes << endl;

			outRes << "Posteriors";
			for(int i=0; i<numOfExamples; ++i )
			{	
				outRes << " " << cascadeData[i].score;
			}				
			outRes << endl;
			
		}						
		
		outRes.close();
		
		// free memory allocation
		vector<vector<BaseLearner*> >::iterator bvIt;
		for( bvIt = weakHypotheses.begin(); bvIt != weakHypotheses.end(); ++bvIt )
		{
			vector<BaseLearner* >::iterator bIt;
			for( bIt = (*bvIt).begin(); bIt != (*bvIt).end(); ++bIt )
				delete *bIt;
		}
	}
Пример #4
0
	void VJCascadeClassifier::run(const string& dataFileName, const string& shypFileName, 
								   int numIterations, const string& outResFileName )
	{
		// loading data
		InputData* pData = loadInputData(dataFileName, shypFileName);
		const int numOfExamples = pData->getNumExamples();
				
		//get the index of positive label		
		const NameMap& namemap = pData->getClassMap();
		_positiveLabelIndex = namemap.getIdxFromName( _positiveLabelName );				
		
		if (_verbose > 0)
			cout << "Loading strong hypothesis..." << flush;
		
		
		
		// The class that loads the weak hypotheses
		UnSerialization us;
		
		// Where to put the weak hypotheses
		vector<vector<BaseLearner*> > weakHypotheses;
		
		// For stagewise thresholds 
		vector<AlphaReal> thresholds(0);
        
		// loads them
		//us.loadHypotheses(shypFileName, weakHypotheses, pData);
		us.loadCascadeHypotheses(shypFileName, weakHypotheses, thresholds, pData);
		

		// store result
		vector<CascadeOutputInformation> cascadeData(0);
		vector<CascadeOutputInformation>::iterator it;
		
		cascadeData.resize(numOfExamples);		
		for( it=cascadeData.begin(); it != cascadeData.end(); ++it )
		{
			it->active=true;
		}										
		
		if (!_outputInfoFile.empty())
		{
			outputHeader();
		}
		
		for(int stagei=0; stagei < weakHypotheses.size(); ++stagei )
		{
			// for posteriors
			vector<AlphaReal> posteriors(0);		
			
			// calculate the posteriors after stage
			VJCascadeLearner::calculatePosteriors( pData, weakHypotheses[stagei], posteriors, _positiveLabelIndex );			
			
			// update the data (posteriors, active element index etc.)
			updateCascadeData(pData, weakHypotheses, stagei, posteriors, thresholds, _positiveLabelIndex, cascadeData);
			
			if (!_outputInfoFile.empty())
			{
				_output << stagei + 1 << "\t";
				_output << weakHypotheses[stagei].size() << "\t";
				outputCascadeResult( pData, cascadeData );
			}
			
			int numberOfActiveInstance = 0;
			for( int i = 0; i < numOfExamples; ++i )
				if (cascadeData[i].active) numberOfActiveInstance++;
			
			if (_verbose > 0 )
				cout << "Number of active instances: " << numberOfActiveInstance << "(" << numOfExamples << ")" << endl;									
		}
				
		vector<vector<int> > confMatrix(2);
		confMatrix[0].resize(2);
		fill( confMatrix[0].begin(), confMatrix[0].end(), 0 );
		confMatrix[1].resize(2);
		fill( confMatrix[1].begin(), confMatrix[1].end(), 0 );
		
	    // print accuracy
		for(int i=0; i<numOfExamples; ++i )
		{		
			vector<Label>& labels = pData->getLabels(i);
			if (labels[_positiveLabelIndex].y>0) // pos label				
				if (cascadeData[i].forecast==1)
					confMatrix[1][1]++;
				else
					confMatrix[1][0]++;
			else // negative label
				if (cascadeData[i].forecast==0)
					confMatrix[0][0]++;
				else
					confMatrix[0][1]++;
		}			
		
		double acc = 100.0 * (confMatrix[0][0] + confMatrix[1][1]) / ((double) numOfExamples);
		// output it
		cout << endl;
		cout << "Error Summary" << endl;
		cout << "=============" << endl;
		
		cout << "Accuracy: " << setprecision(4) << acc << endl;
		cout << setw(10) << "\t" << setw(10) << namemap.getNameFromIdx(1-_positiveLabelIndex) << setw(10) << namemap.getNameFromIdx(_positiveLabelIndex) << endl;
		cout << setw(10) << namemap.getNameFromIdx(1-_positiveLabelIndex) << setw(10) << confMatrix[0][0] << setw(10) << confMatrix[0][1] << endl;
		cout << setw(10) << namemap.getNameFromIdx(_positiveLabelIndex) << setw(10) << confMatrix[1][0] << setw(10) << confMatrix[1][1] << endl;		
		
		// output forecast 
		if (!outResFileName.empty() ) outputForecast(pData, outResFileName, cascadeData );
						
		// free memory allocation
		vector<vector<BaseLearner*> >::iterator bvIt;
		for( bvIt = weakHypotheses.begin(); bvIt != weakHypotheses.end(); ++bvIt )
		{
			vector<BaseLearner* >::iterator bIt;
			for( bIt = (*bvIt).begin(); bIt != (*bvIt).end(); ++bIt )
				delete *bIt;
		}
	}
Пример #5
0
/**
 * 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;
}