RLSWrapper is the sub-class of GurlsWrapper that implements Regularized Least Squares with a linear model. More...

#include <rlswrapper.h>

Inheritance diagram for gurls::RLSWrapper< T >:

Collaboration diagram for gurls::RLSWrapper< T >:

Public Types
enum	ProblemType { CLASSIFICATION, REGRESSION }
Public Member Functions
gMat2D< T > *	eval (const gMat2D< T > &X)
	Estimates label for an input matrix.
virtual T	eval (const gVec< T > &X, unsigned long *index=NULL)
	Estimates label for a new input point.
virtual const GurlsOptionsList &	getOpt () const
	Returns a const reference to the options structure.
virtual void	loadModel (const std::string &fileName)
	Loads a computed model from a file.
	RLSWrapper (const std::string &name)
	Constructor.
virtual void	saveModel (const std::string &fileName)
	Saves the computed model to file.
virtual void	setNparams (unsigned long value)
virtual void	setParam (double value)
virtual void	setProblemType (ProblemType value)
virtual void	setSplitProportion (double value)
void	train (const gMat2D< T > &X, const gMat2D< T > &y)
	Initial parameter selection and training.
Protected Member Functions
virtual bool	trainedModel ()
	Checks if model has already been trained.
Protected Attributes
std::string	name
	Name of the options structure.
GurlsOptionsList *	opt
	Options structure where information about initial training is stored.
ProblemType	probType
	Problem type.

Detailed Description

template<typename T>
class gurls::RLSWrapper< T >

The regularization parameter is estimated by the wrapper (default) or explicitely given in input by the user via method train(). The eval() method estimates the output for new data.

Definition at line 58 of file rlswrapper.h.

Constructor & Destructor Documentation

template<typename T >

gurls::RLSWrapper< T >::RLSWrapper ( const std::string & name )

Parameters:

name	Name of the option's structure that will be initialized

Definition at line 12 of file rlswrapper.hpp.

: GurlsWrapper<T>(name) { }

Member Function Documentation

template<typename T >

gMat2D< T > * gurls::RLSWrapper< T >::eval ( const gMat2D< T > & X ) [virtual]

Parameters:

[in] X Input matrix

Returns:: Matrix of predicted labels

Implements gurls::GurlsWrapper< T >.

Reimplemented in gurls::RandomFeaturesWrapper< T >.

Definition at line 42 of file rlswrapper.hpp.

{
    PredPrimal<T> predTask;
    gMat2D<T> empty;
    OptMatrix<gMat2D<T> >* result = predTask.execute(X, empty, *(this->opt));

    gMat2D<T>* pred = &(result->getValue());

    result->detachValue();
    delete result;

    return pred;
}

template<typename T >

T gurls::GurlsWrapper< T >::eval	(	const gVec< T > &	X,
		unsigned long *	index = `NULL`
	)		`[virtual, inherited]`

Parameters:

[in]	X	Input point
[out]	index	Index of the estimated label

Returns:: Estimated label

Definition at line 26 of file wrapper.hpp.

{
    if(!trainedModel())
        throw gException("Error, Train Model First");

    gMat2D<T>X_mat(1, X.getSize());
    copy(X_mat.getData(), X.getData(), X.getSize());

    gMat2D<T>* pred_mat = eval(X_mat);

    const T* pred = pred_mat->getData();
    const unsigned long size = pred_mat->getSize();

    const T* max = std::max_element(pred, pred+size);
    T ret = *max;
    if(index != NULL)
        *index = max-pred;

    delete pred_mat;
    return ret;
}

template<typename T >

void gurls::GurlsWrapper< T >::loadModel ( const std::string & fileName ) [virtual, inherited]

Parameters:

fileName name of the file containing the data to load

Definition at line 61 of file wrapper.hpp.

{
    opt->load(fileName);
}

template<typename T >

void gurls::GurlsWrapper< T >::saveModel ( const std::string & fileName ) [virtual, inherited]

Parameters:

fileName name of the file where data will be saved

Definition at line 55 of file wrapper.hpp.

{
    opt->save(fileName);
}

template<typename T >

void gurls::GurlsWrapper< T >::setNparams ( unsigned long value ) [virtual, inherited]

Parameters:

[in] value

Definition at line 67 of file wrapper.hpp.

{
    opt->getOptValue<OptNumber>("nlambda") = value;

    if(opt->hasOpt("paramsel.lambdas") && value > 1.0)
    {
        std::cout << "Warning: ignoring previous values of the regularization parameter" << std::endl;
        opt->getOptAs<GurlsOptionsList>("paramsel")->removeOpt("lambdas");
    }
}

template<typename T >

void gurls::GurlsWrapper< T >::setParam ( double value ) [virtual, inherited]

Parameters:

[in] value

Reimplemented in gurls::NystromWrapper< T >.

Definition at line 79 of file wrapper.hpp.

{
    if(!opt->hasOpt("paramsel"))
        opt->addOpt("paramsel", new GurlsOptionsList("paramsel"));

    if(opt->hasOpt("paramsel.lambdas"))
        opt->getOptValue<OptMatrix<gMat2D<T> > >("paramsel.lambdas").getData()[0] = (T)value;
    else
    {
        gMat2D<T> * lambdas = new gMat2D<T>(1,1);
        lambdas->getData()[0] = (T)value;
        opt->getOptAs<GurlsOptionsList>("paramsel")->addOpt("lambdas", new OptMatrix<gMat2D<T> >(*lambdas));
    }

    setNparams(1);
}

template<typename T >

void gurls::GurlsWrapper< T >::setProblemType ( typename GurlsWrapper< T >::ProblemType value ) [virtual, inherited]

Parameters:

[in] value

Definition at line 103 of file wrapper.hpp.

{
    probType = value;

    opt->getOptValue<OptString>("hoperf") = (value == CLASSIFICATION)? "macroavg": "rmse";
}

template<typename T >

void gurls::GurlsWrapper< T >::setSplitProportion ( double value ) [virtual, inherited]

Parameters:

[in] value

Definition at line 97 of file wrapper.hpp.

{
    opt->getOptValue<OptNumber>("hoproportion") = value;
}

template<typename T >

void gurls::RLSWrapper< T >::train	(	const gMat2D< T > &	X,
		const gMat2D< T > &	y
	)		`[virtual]`

Parameters:

[in]	X	Input data matrix
[in]	Y	Labels matrix

Implements gurls::GurlsWrapper< T >.

Reimplemented in gurls::RandomFeaturesWrapper< T >.

Definition at line 15 of file rlswrapper.hpp.

{
    this->opt->removeOpt("split");
    this->opt->removeOpt("optimizer");

    const unsigned long nlambda = static_cast<unsigned long>(this->opt->getOptAsNumber("nlambda"));
    if(nlambda != 1)
    {
        SplitHo<T> splitTask;
        this->opt->addOpt("split", splitTask.execute(X, y, *(this->opt)));

        ParamSelHoPrimal<T> paramselTask;
        GurlsOption * result = paramselTask.execute(X, y, *(this->opt));
        this->opt->removeOpt("paramsel");
        this->opt->addOpt("paramsel", result);
    }
    else
    {
        if(!this->opt->hasOpt("paramsel.lambdas"))
            throw gException("Please set a valid value for the regularization parameter, calling setParam(value)");
    }

    RLSPrimal<T> optimizerTask;
    this->opt->addOpt("optimizer", optimizerTask.execute(X, y, *(this->opt)));
}

template<typename T >

bool gurls::GurlsWrapper< T >::trainedModel ( ) [protected, virtual, inherited]

Parameters:

value
value

Definition at line 111 of file wrapper.hpp.

{
    return opt->hasOpt("optimizer");
}

The documentation for this class was generated from the following files:

Public Types

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

template<typename T> class gurls::RLSWrapper< T >

Constructor & Destructor Documentation

Member Function Documentation

template<typename T>
class gurls::RLSWrapper< T >