C++ nor_utils::Args类代码示例

	void BanditSingleStumpLearner::declareArguments(nor_utils::Args& args)
	{		
		FeaturewiseLearner::declareArguments(args);
		
		args.declareArgument("updaterule", 
			"The update weights in the UCT can be the 1-sqrt( 1- edge^2 ) [edge]\n"
			"  or the alpha [alphas]\n"
			"  Default is the first one\n",
			1, "<type>");

		args.declareArgument("rsample", 
			"Number of features to be considered\n"
			"  Default is one\n",
			1, "<K>");

		args.declareArgument("banditalgo", 
			"The bandit algorithm (UCBK, UCBKRandomized, EXP3 )\n"
			"Default is UCBK\n",
			1, "<algoname>");

		args.declareArgument("percent", 
			"The percent of database will be used for estimating the payoffs(EXP3G)\n"
			"  Default is 10%\n",
			1, "<p>");

	}

	void StochasticLearner::declareArguments(nor_utils::Args& args)
	{
		BaseLearner::declareArguments(args);
		args.declareArgument("graditer",
							 "Declares the number of randomly drawn training size for SGD"
							 "whereas it declares the number of iteration for the Batch Gradiend Descend"							 
							 " size <num> of training set. "
							 "Example: --graditer 50 -> Uses only 50 randomly chosen training instance",
							 1, "<num>");
		
		args.declareArgument("gradmethod",
							 "Declares the gradient method: "
							 " (sgd) Stochastic Gradient Descent, (bgd) Batch Gradient Descent"
							 "Example: --gradmethod sgd -> Uses stochastic gradient method",
							 1, "<method>");
		
		args.declareArgument("tfunc",
							 "Target function: "
							 "exploss: Exponential Loss, edge: max. edge"
							 "Example: --tfunc exploss -> Uses exponantial loss for minimizing",
							 1, "<function>");
		
		args.declareArgument("initgamma",
							 "The initial learning rate in gradient descent"
							 "Default values is 10.0",
							 1, "<gamma>");
		
		args.declareArgument("gammdivperiod",
							 "The periodicity of decreasing the learning rate \\gamma"
							 "Default values is 1",
							 1, "<period>");
		
				
	}

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);
}

	void TreeLearner::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);
		
		//check whether the weak learner is a ScalarLeaerner
		try {
			_pScalaWeakHypothesisSource = dynamic_cast<ScalarLearner*>(pWeakHypothesisSource);
		}
		catch (bad_cast& e) {
			cerr << "The weak hypothesis must be a ScalarLearner!!!" << endl;
			exit(-1);
		}
		
		_pScalaWeakHypothesisSource->initLearningOptions(args);
		
		/*
		 for( int ib = 0; ib < _numBaseLearners; ++ib ) {			
		 vector< int > tmpVector( 2, -1 );
		 _idxPairs.push_back( tmpVector );
		 }
		 */
	}

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

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

	}

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);

}

	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);   
	}

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;
}

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

	}

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;
}

 void SoftCascadeLearner::classify(const nor_utils::Args& args)
 {
     SoftCascadeClassifier classifier(args, _verbose);
             
     string testFileName = args.getValue<string>("test", 0);
     string shypFileName = args.getValue<string>("test", 1);
     int numIterations = args.getValue<int>("test", 2);
             
     string outResFileName = "";
     if ( args.getNumValues("test") > 3 )
         args.getValue("test", 3, outResFileName);
             
     classifier.run(testFileName, shypFileName, numIterations, outResFileName);
 }

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

   args.declareArgument("pool", 
                        "The name of the shyp file containing the pool of\n"
                        "  weak learners, followed by the number of desired\n"
                        "  weak learners. If -1 or more than the number of \n"
                        "  weak learners, we use all of them",
                        2, "<fileName> <nBaseLearners>");
         
   args.declareArgument("closed", "Include negatives of weak learners (default = false).");

}

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

		// -test <dataFile> <shypFile>
		string testFileName = args.getValue<string>("test", 0);
		string shypFileName = args.getValue<string>("test", 1);
		int numIterations = args.getValue<int>("test", 2);

		string outResFileName;
		if ( args.getNumValues("test") > 3 )
			args.getValue("test", 3, outResFileName);

		classifier.run(testFileName, shypFileName, numIterations, outResFileName);
	}

	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);
		}				
	}

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

    }