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C++ Args::hasArgument方法代码示例

本文整理汇总了C++中nor_utils::Args::hasArgument方法的典型用法代码示例。如果您正苦于以下问题：C++ Args::hasArgument方法的具体用法？C++ Args::hasArgument怎么用？C++ Args::hasArgument使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类nor_utils::Args的用法示例。


在下文中一共展示了Args::hasArgument方法的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。

示例1: initLearningOptions

	void BaseLearner::initLearningOptions(const nor_utils::Args& args)
	{
		if ( args.hasArgument("verbose") )
			args.getValue("verbose", 0, _verbose);

		// Set the value of theta
		if ( args.hasArgument("edgeoffset") )
			args.getValue("edgeoffset", 0, _theta);   
	}
开发者ID:busarobi，项目名称:MDDAG，代码行数:9，代码来源:BaseLearner.cpp

示例2: 

	VJCascadeClassifier::VJCascadeClassifier(const nor_utils::Args &args, int verbose)
	: _verbose(verbose), _args(args), _positiveLabelIndex(-1)
	{
		// The file with the step-by-step information
		if ( args.hasArgument("outputinfo") )
			args.getValue("outputinfo", 0, _outputInfoFile);
		
		if ( args.hasArgument("positivelabel") )
		{
			args.getValue("positivelabel", 0, _positiveLabelName);
		} else {
			cout << "The name of positive label has to be given!!!" << endl;
			exit(-1);
		}				
	}
开发者ID:busarobi，项目名称:MDDAG2，代码行数:15，代码来源:VJCascadeClassifier.cpp

示例3: getArgs

    void FilterBoostLearner::getArgs(const nor_utils::Args& args)
    {
        AdaBoostMHLearner::getArgs( args );
        // Set the value of the sample size
        if ( args.hasArgument("Cn") )
        {
            args.getValue("Cn", 0, _Cn);
            if (_verbose > 1)
                cout << "--> Resampling size: " << _Cn << endl;
        }

        if ( args.hasArgument("onlinetraining") )
        {
            _onlineWeakLearning = true;                     
        }                                       
    }
开发者ID:junjiek，项目名称:cmu-exp，代码行数:16，代码来源:FilterBoostLearner.cpp

示例4: 

MDDAGClassifier::MDDAGClassifier(const nor_utils::Args &args, int verbose)
    : _verbose(verbose), _args(args)
{
    // The file with the step-by-step information
    if ( args.hasArgument("outputinfo") )
        args.getValue("outputinfo", 0, _outputInfoFile);
}
开发者ID:busarobi，项目名称:MDDAG2，代码行数:7，代码来源:MDDAGClassifier.cpp

示例5: 

//----------------------------------------------------------------
//----------------------------------------------------------------
    void Exp3::initLearningOptions(const nor_utils::Args& args) 
    {
        if ( args.hasArgument( "gamma" ) ){
            _gamma = args.getValue<double>("gamma", 0 );
        } 

    }
开发者ID:junjiek，项目名称:cmu-exp，代码行数:9，代码来源:Exp3.cpp

示例6: initLearningOptions

	void StochasticLearner::initLearningOptions(const nor_utils::Args& args)
	{
		BaseLearner::initLearningOptions(args);
		
		if (args.hasArgument("initgamma"))
			args.getValue("initgamma", 0, _initialGammat);   		
		
		if (args.hasArgument("gammdivperiod"))
			args.getValue("gammdivperiod", 0, _gammdivperiod);   		
		
		
		if (args.hasArgument("graditer"))
			args.getValue("graditer", 0, _maxIter);   		
		
		if (args.hasArgument("gradmethod"))
		{
			string gradMethod;
			args.getValue("gradmethod", 0, gradMethod);   		
			
			if ( gradMethod.compare( "sgd" ) == 0 )
				_gMethod = OPT_SGD;
			else if ( gradMethod.compare( "bgd" ) == 0 )
				_gMethod = OPT_BGD;
			else {
				cerr << "SigmoidSingleStumpLearner::Unknown update gradient method" << endl;
				exit( -1 );
			}					
		}		
		
		if (args.hasArgument("tfunc"))
		{
			string targetFunction;
			args.getValue("tfunc", 0, targetFunction);
			
			if ( targetFunction.compare( "exploss" ) == 0 )
				_tFunction = TF_EXPLOSS;
			else if ( targetFunction.compare( "edge" ) == 0 )
				_tFunction = TF_EDGE;
			else {
				cerr << "SigmoidSingleStumpLearner::Unknown target function" << endl;
				exit( -1 );				
			}					
			
		}
		
	}
开发者ID:busarobi，项目名称:MDDAG2，代码行数:46，代码来源:StochasticLearner.cpp

示例7: initLearningOptions

	void EnumLearnerSA::initLearningOptions(const nor_utils::Args& args)
	{
		BaseLearner::initLearningOptions(args);

		if ( args.hasArgument( "uoffset" ) )  
			args.getValue("uoffset", 0, _uOffset);   

	}
开发者ID:ShenWei，项目名称:src，代码行数:8，代码来源:EnumLearnerSA.cpp

示例8: initLearningOptions

	void FeaturewiseLearner::initLearningOptions(const nor_utils::Args& args)
	{
		AbstainableLearner::initLearningOptions(args);
		_maxNumOfDimensions = numeric_limits<int>::max();
		
		// If the sampling is required
		if ( args.hasArgument("rsample") )
			_maxNumOfDimensions = args.getValue<int>("rsample", 0);
	}
开发者ID:busarobi，项目名称:MDDAG2，代码行数:9，代码来源:FeaturewiseLearner.cpp

示例9: getArgs

void MultiMDDAGLearner::getArgs(const nor_utils::Args& args)
{
    MDDAGLearner::getArgs(args);

    // Set the value of theta
    if ( args.hasArgument("updateperc") )
        args.getValue("updateperc", 0, _randomNPercent);

}
开发者ID:busarobi，项目名称:MDDAG2，代码行数:9，代码来源:MultiMDDAGLearner.cpp

示例10: initLearningOptions

void ParasiteLearner::initLearningOptions(const nor_utils::Args& args)
{
   BaseLearner::initLearningOptions(args);

   args.getValue("pool", 0, _nameBaseLearnerFile);   
   args.getValue("pool", 1, _numBaseLearners);   

   if ( args.hasArgument("closed") )
      _closed = 1;
}
开发者ID:ShenWei，项目名称:src，代码行数:10，代码来源:ParasiteLearner.cpp

示例11: doConfusionMatrix

	void FilterBoostLearner::doConfusionMatrix(const nor_utils::Args& args)
	{
		FilterBoostClassifier classifier(args, _verbose);

		// -cmatrix <dataFile> <shypFile>
		if ( args.hasArgument("cmatrix") )
		{
			string testFileName = args.getValue<string>("cmatrix", 0);
			string shypFileName = args.getValue<string>("cmatrix", 1);

			classifier.printConfusionMatrix(testFileName, shypFileName);
		}
		// -cmatrixfile <dataFile> <shypFile> <outFile>
		else if ( args.hasArgument("cmatrixfile") )
		{
			string testFileName = args.getValue<string>("cmatrix", 0);
			string shypFileName = args.getValue<string>("cmatrix", 1);
			string outResFileName = args.getValue<string>("cmatrix", 2);

			classifier.saveConfusionMatrix(testFileName, shypFileName, outResFileName);
		}
	}
开发者ID:ShenWei，项目名称:src，代码行数:22，代码来源:FilterBoostLearner.cpp

示例12: resumeProcess

int MultiMDDAGLearner::resumeProcess(const nor_utils::Args& args, InputData* pTestData)
{
    int numPolicies = 0;

    AlphaReal policyAlpha = 0.0;

    if ( args.hasArgument("policyalpha") )
        args.getValue("policyalpha", 0, policyAlpha);

    _policy = new AdaBoostArrayOfPolicyArray(args, _actionNumber);

    return numPolicies;
}
开发者ID:busarobi，项目名称:MDDAG2，代码行数:13，代码来源:MultiMDDAGLearner.cpp

示例13: initLearningOptions

	void AbstainableLearner::initLearningOptions(const nor_utils::Args& args)
	{
		BaseLearner::initLearningOptions(args);
		
		// set abstention
		if ( args.hasArgument("abstention") )
		{
			string abstType = args.getValue<string>("abstention", 0);
			
			if (abstType == "greedy")
				_abstention = ABST_GREEDY;
			else if (abstType == "full")
				_abstention = ABST_FULL;
			else if (abstType == "real")
				_abstention = ABST_REAL;
			else if (abstType == "classwise")
				_abstention = ABST_CLASSWISE;
			else
			{
				cerr << "ERROR: Invalid type of abstention <" << abstType << ">!!" << endl;
				exit(1);
			}
		}
	}
开发者ID:busarobi，项目名称:MDDAG2，代码行数:24，代码来源:AbstainableLearner.cpp

示例14: initLearningOptions

void TreeLearnerUCT::initLearningOptions(const nor_utils::Args& args)
{
    BaseLearner::initLearningOptions(args);

    string baseLearnerName;
    args.getValue("baselearnertype", 0, baseLearnerName);
    args.getValue("baselearnertype", 1, _numBaseLearners);

    // get the registered weak learner (type from name)
    BaseLearner* pWeakHypothesisSource =
        BaseLearner::RegisteredLearners().getLearner(baseLearnerName);

    for( int ib = 0; ib < _numBaseLearners; ++ib ) {
        _baseLearners.push_back(pWeakHypothesisSource->create());
        _baseLearners[ib]->initLearningOptions(args);

        vector< int > tmpVector( 2, -1 );
        _idxPairs.push_back( tmpVector );
    }

    string updateRule = "";
    if ( args.hasArgument( "updaterule" ) )
        args.getValue("updaterule", 0, updateRule );

    if ( updateRule.compare( "edge" ) == 0 )
        _updateRule = EDGE_SQUARE;
    else if ( updateRule.compare( "alphas" ) == 0 )
        _updateRule = ALPHAS;
    else if ( updateRule.compare( "edgesquare" ) == 0 )
        _updateRule = ESQUARE;
    else {
        cerr << "Unknown update rule in ProductLearnerUCT (set to default [edge]" << endl;
        _updateRule = EDGE_SQUARE;
    }

}
开发者ID:junjiek，项目名称:cmu-exp，代码行数:36，代码来源:TreeLearnerUCT.cpp

示例15: getArgs

    void SoftCascadeLearner::getArgs(const nor_utils::Args& args)
    {
        if ( args.hasArgument("verbose") )
            args.getValue("verbose", 0, _verbose);

        ///////////////////////////////////////////////////
        // get the output strong hypothesis file name, if given
        if ( args.hasArgument("shypname") )
            args.getValue("shypname", 0, _shypFileName);
        else
            _shypFileName = string(SHYP_NAME);

        _shypFileName = nor_utils::addAndCheckExtension(_shypFileName, SHYP_EXTENSION);


        ///////////////////////////////////////////////////

        //TODO : create an abstract classe for cascade compliant base learners and accept only its offspring!
        // get the name of the learner
        _baseLearnerName = defaultLearner;
        if ( args.hasArgument("learnertype") )
            args.getValue("learnertype", 0, _baseLearnerName);
//            cout << "! Only HaarSingleStumpeLearner is allowed.\n";
        
        // -train <dataFile> <nInterations>
        if ( args.hasArgument("train") )
        {
            args.getValue("train", 0, _trainFileName);
            args.getValue("train", 1, _numIterations);
        }
        // -traintest <trainingDataFile> <testDataFile> <nInterations>
        else if ( args.hasArgument("traintest") ) 
        {
            args.getValue("traintest", 0, _trainFileName);
            args.getValue("traintest", 1, _testFileName);
            args.getValue("traintest", 2, _numIterations);
        }

        // The file with the step-by-step information
        if ( args.hasArgument("outputinfo") )
            args.getValue("outputinfo", 0, _outputInfoFile);
        else
            _outputInfoFile = OUTPUT_NAME;
        
        // --constant: check constant learner in each iteration
        if ( args.hasArgument("constant") )
            _withConstantLearner = true;
        
        if ( args.hasArgument("positivelabel") )
        {
            args.getValue("positivelabel", 0, _positiveLabelName);
        } else {
            cout << "Error : The name of positive label must to given. \n Type --h softcascade to know the mandatory options." << endl;
            exit(-1);
        }
        
        if (args.hasArgument("trainposteriors")) {
            args.getValue("trainposteriors", 0, _trainPosteriorsFileName);
        }

        if (args.hasArgument("testposteriors")) {
            args.getValue("testposteriors", 0, _testPosteriorsFileName);
        }

        if (args.hasArgument("detectionrate")) {
            args.getValue("detectionrate", 0, _targetDetectionRate);
        }
        else {
            cout << "Error : the target detection rate must be given. \n Type --h softcascade to know the mandatory options.";
            exit(-1);
        }

        
        if (args.hasArgument("expalpha")) {
            args.getValue("expalpha", 0, _alphaExponentialParameter);
        }
        else {
            cout << "Error : the parameter used to initialize the rejection distribution vector must be given. \n Type --h softcascade to know the mandatory options.";
            exit(-1);
        }

        if (args.hasArgument("calibrate")) {
            args.getValue("calibrate", 0, _unCalibratedShypFileName);
            if (args.getNumValues("calibrate") > 1) {
                args.getValue("calibrate", 0, _inShypLimit);
            }
        }
        else {
            _fullRun = true;
            _unCalibratedShypFileName = "shypToBeCalibrated.xml";
            cout << "The strong hypothesis file will be seved into the file " << _unCalibratedShypFileName;
            //cout << "Error : the shyp file of the uncalibrated trained classifier must be given ! \n";
            //exit(-1);

        }
        
        if (args.hasArgument("bootstrap")) {
            cout << "Warning ! The bootstrapping set and the training set must come from the same superset. \n";
            args.getValue("bootstrap", 0, _bootstrapFileName);
            args.getValue("bootstrap", 1, _bootstrapRate);
//.........这里部分代码省略.........
开发者ID:junjiek，项目名称:cmu-exp，代码行数:101，代码来源:SoftCascadeLearner.cpp


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