C++ BaseLearner::createInputData方法代码示例

	void DataReader::loadInputData(const string& dataFileName, const string& testDataFileName, const string& testDataFileName2, const string& shypFileName)
	{
		// open file
		ifstream inFile(shypFileName.c_str());
		if (!inFile.is_open())
		{
			cerr << "ERROR: Cannot open strong hypothesis file <" << shypFileName << ">!" << endl;
			exit(1);
		}
		
		// Declares the stream tokenizer
		nor_utils::StreamTokenizer st(inFile, "<>\n\r\t");
		
		// Move until it finds the multiboost tag
		if ( !UnSerialization::seekSimpleTag(st, "multiboost") )
		{
			// no multiboost tag found: this is not the correct file!
			cerr << "ERROR: Not a valid MultiBoost Strong Hypothesis file: " << shypFileName << endl;
			exit(1);
		}
		
		// Move until it finds the algo tag
		string basicLearnerName = UnSerialization::seekAndParseEnclosedValue<string>(st, "algo");
		
		// Check if the weak learner exists
		if ( !BaseLearner::RegisteredLearners().hasLearner(basicLearnerName) )
		{
			cerr << "ERROR: Weak learner <" << basicLearnerName << "> not registered!!" << endl;
			exit(1);
		}
		
		// get the training input data, and load it
		BaseLearner* baseLearner = BaseLearner::RegisteredLearners().getLearner(basicLearnerName);
		baseLearner->initLearningOptions(_args);
		_pTrainData = baseLearner->createInputData();
		
		// set the non-default arguments of the input data
		_pTrainData->initOptions(_args);
		// load the data
		_pTrainData->load(dataFileName, IT_TEST, _verbose);				
		_pCurrentData = _pTrainData;
		
		_pTestData = baseLearner->createInputData();
		
		// set the non-default arguments of the input data
		_pTestData->initOptions(_args);
		// load the data
		_pTestData->load(testDataFileName, IT_TEST, _verbose);				
        
        
        _pTestData2 = NULL;
        if (!testDataFileName2.empty()) {
            _pTestData2 = baseLearner->createInputData();
            
            // set the non-default arguments of the input data
            _pTestData2->initOptions(_args);
            // load the data
            _pTestData2->load(testDataFileName2, IT_TEST, _verbose);				            
        }        
	}				

    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
//.........这里部分代码省略.........

	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);
//.........这里部分代码省略.........

	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
//.........这里部分代码省略.........

    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;
//.........这里部分代码省略.........