gurls::ParamSelHoPrimal< T > Class Template Reference

ParamSelHoPrimal is the subclass of ParamSelection that implements hold-out cross validation with the primal formulation of RLS.

#include <hoprimal.h>

Inheritance diagram for gurls::ParamSelHoPrimal< T >:

Collaboration diagram for gurls::ParamSelHoPrimal< T >:

List of all members.

Public Member Functions
GurlsOptionsList *	execute (const gMat2D< T > &X, const gMat2D< T > &Y, const GurlsOptionsList &opt)
	Performs parameter selection when the primal formulation of RLS is used.
Static Public Member Functions
static ParamSelection< T > *	factory (const std::string &id) throw (BadParamSelectionCreation)
	Factory function returning a pointer to the newly created object.
Protected Member Functions
unsigned long	eig_function (T *A, T *L, int A_rows_cols, unsigned long d, const GurlsOptionsList &opt, unsigned long last)
	Auxiliary method used to call the right eig/svd function for this class.

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Detailed Description

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

Definition at line 72 of file hoprimal.h.

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Member Function Documentation

template<typename T >

GurlsOptionsList * gurls::ParamSelHoPrimal< T >::execute	(	const gMat2D< T > &	X,
		const gMat2D< T > &	Y,
		const GurlsOptionsList &	opt
	)		[virtual]

The hold-out approach is used. The performance measure specified by opt.hoperf is maximized.

Parameters:

X	input data matrix
Y	labels matrix
opt	options with the following: nlambda (default) hoperf (default) smallnumber (default) split (settable with the class Split and its subclasses)

Returns:

paramsel, a GurlsOptionList with the following fields:

• lambdas = array of values of the regularization parameter lambda minimizing the validation error for each class
• guesses = array of guesses for the regularization parameter lambda
• forho = matrix of validation accuracies for each lambda guess and for each class

Implements gurls::ParamSelection< T >.

Definition at line 140 of file hoprimal.h.

{
    //    [n,T]  = size(y);
    const unsigned long t = Y.cols();
    const unsigned long d = X.cols();


    GurlsOptionsList* nestedOpt = new GurlsOptionsList("nested");


    const GurlsOptionsList* split = opt.getOptAs<GurlsOptionsList>("split");
    const gMat2D< unsigned long > &indices_mat = split->getOptValue<OptMatrix<gMat2D< unsigned long > > >("indices");
    const gMat2D< unsigned long > &lasts_mat = split->getOptValue<OptMatrix<gMat2D< unsigned long > > >("lasts");

    const unsigned long n = indices_mat.cols();

    const unsigned long *lasts = lasts_mat.getData();
    const unsigned long* indices_buffer = indices_mat.getData();


    int tot = static_cast<int>(std::ceil( opt.getOptAsNumber("nlambda")));

    int nholdouts = static_cast<int>(std::ceil( opt.getOptAsNumber("nholdouts")));

    gMat2D<T> *LAMBDA = new gMat2D<T>(1, t);
    T* lambdas = LAMBDA->getData();
    set(lambdas, (T)0.0, t);


    T* Q = new T[d*d];
    T* QtXty = new T[d*t];
    T *L = new T[d];

    gMat2D<T>* perf_mat = new gMat2D<T>(nholdouts, tot*t);
    T* perf = perf_mat->getData();

    gMat2D<T>*  guesses_mat = new gMat2D<T>(nholdouts, tot);
    T* ret_guesses = guesses_mat->getData();

    gMat2D<T>* lambdas_round_mat = new gMat2D<T>(nholdouts, t);
    T* lambdas_round = lambdas_round_mat->getData();

    PredPrimal< T > primal;
    Performance<T>* perfClass = Performance<T>::factory(opt.getOptAsString("hoperf"));

    GurlsOptionsList* optimizer = new GurlsOptionsList("optimizer");
    nestedOpt->addOpt("optimizer",optimizer);

    gMat2D<T> *W = new gMat2D<T>(d, t);
    optimizer->addOpt("W", new OptMatrix<gMat2D<T> >(*W));


    bool hasXt = opt.hasOpt("kernel.XtX") && opt.hasOpt("kernel.Xty");

    for(int nh=0; nh<nholdouts; ++nh)
    {
        unsigned long last = lasts[nh];
        unsigned long* tr = new unsigned long[last];
        unsigned long* va = new unsigned long[n-last];

        //copy int tr indices_ from n*nh to last
        copy< unsigned long >(tr,indices_buffer + n*nh,last,1,1);

        //copy int va indices_ from n*nh+last to n*nh+n
        copy< unsigned long >(va,(indices_buffer+ n*nh+last), n-last,1,1);


        gMat2D<T> Xva(n-last, d);
        gMat2D<T> yva(n-last, t);

        subMatrixFromRows(X.getData(), X.rows(), d, va, n-last, Xva.getData());
        subMatrixFromRows(Y.getData(), Y.rows(), t, va, n-last, yva.getData());

        T* Xtr = NULL;
        if(hasXt)
        {
            T* XvatXva = new T[d*d];
            dot(Xva.getData(), Xva.getData(), XvatXva, n-last, d, n-last, d, d, d, CblasTrans, CblasNoTrans, CblasColMajor);

            const gMat2D<T>&XtX = opt.getOptValue<OptMatrix<gMat2D<T> > >("kernel.XtX");

            // Q = XtX - XvatXva
            copy(Q, XtX.getData(), XtX.getSize());
            axpy(d*d, (T)-1.0, XvatXva, 1, Q, 1);

            delete [] XvatXva;
        }
        else
        {
            //       K = X(tr,:)'*X(tr,:);
            Xtr = new T[last*d];
            subMatrixFromRows(X.getData(), n, d, tr, last, Xtr);

            dot(Xtr, Xtr, Q, last, d, last, d, d, d, CblasTrans, CblasNoTrans, CblasColMajor);
        }

        unsigned long k = eig_function(Q, L, d, d, opt, last);

        T* guesses = lambdaguesses(L, d, k, last, tot, (T)(opt.getOptAsNumber("smallnumber")));

        T* ap = new T[tot*t];


        if(hasXt)
        {
            T* Xvatyva = new T[d*t];
            dot(Xva.getData(), yva.getData(), Xvatyva, n-last, d, n-last, t, d, t, CblasTrans, CblasNoTrans, CblasColMajor);

            const gMat2D<T>&Xty = opt.getOptValue<OptMatrix<gMat2D<T> > >("kernel.Xty");


            // QtXty = Q'*(Xty - XvatXva)

            T* Xtrtytr = new T[d*t];

            copy(Xtrtytr, Xty.getData(), Xty.getSize());
            axpy(d*t, (T)-1.0, Xvatyva, 1, Xtrtytr, 1);

            dot(Q, Xtrtytr, QtXty, d, d, d, t, d, t, CblasTrans, CblasNoTrans, CblasColMajor);

            delete [] Xvatyva;
            delete [] Xtrtytr;
        }
        else
        {
            T* ytr = new T[last*t];
            subMatrixFromRows(Y.getData(), n, t, tr, last, ytr);


            T* Xtrtytr = new T[d*t];
            dot(Xtr, ytr, Xtrtytr, last, d, last, t, d, t, CblasTrans, CblasNoTrans, CblasColMajor);
            delete [] Xtr;

            dot(Q, Xtrtytr, QtXty, d, d, d, t, d, t, CblasTrans, CblasNoTrans, CblasColMajor);

            delete [] ytr;
            delete [] Xtrtytr;
        }


        T* work = new T[d*(d+1)];

        for(int i=0; i<tot; ++i)
        {
            rls_eigen(Q, L, QtXty, W->getData(), guesses[i], last, d, d, d, d, t, work);

            OptMatrix<gMat2D<T> > *ret_pred = primal.execute(Xva, yva, *nestedOpt);

            nestedOpt->removeOpt("pred");
            nestedOpt->addOpt("pred", ret_pred);

            GurlsOptionsList* ret_perf = perfClass->execute(Xva, yva, *nestedOpt);

            gMat2D<T> &forho_vec = ret_perf->getOptValue<OptMatrix<gMat2D<T> > >("forho");

            copy(ap+i, forho_vec.getData(), t, tot, 1);

            delete ret_perf;
        }

        delete [] va;
        delete [] tr;
        delete [] work;

        //[dummy,idx] = max(ap,[],1);
        work = NULL;
        unsigned long* idx = new unsigned long[t];
        indicesOfMax(ap, tot, t, idx, work, 1);

        //vout.lambdas_round{nh} = guesses(idx);
        T* lambdas_nh = new T[t];
        copyLocations(idx, guesses, t, tot, lambdas_nh);

        copy(lambdas_round+nh, lambdas_nh, t, nholdouts, 1);

        //add lambdas_nh to lambdas
        axpy(t, (T)1, lambdas_nh, 1, lambdas, 1);

        delete [] lambdas_nh;
        delete [] idx;

        //  vout.perf{nh} = ap;
        copy(perf + nh, ap, tot*t, nholdouts, 1);

        //  vout.guesses{nh} = guesses;
        copy(ret_guesses + nh, guesses, tot, nholdouts, 1);

        delete [] guesses;
        delete [] ap;

    }

    delete nestedOpt;

    delete perfClass;
    delete [] Q;
    delete [] QtXty;
    delete [] L;

    GurlsOptionsList* paramsel;

    if(opt.hasOpt("paramsel"))
    {
        GurlsOptionsList* tmp_opt = new GurlsOptionsList("tmp");
        tmp_opt->copyOpt("paramsel", opt);

        paramsel = GurlsOptionsList::dynacast(tmp_opt->getOpt("paramsel"));
        tmp_opt->removeOpt("paramsel", false);
        delete tmp_opt;

        paramsel->removeOpt("guesses");
        paramsel->removeOpt("lambdas");
        paramsel->removeOpt("perf");
        paramsel->removeOpt("lambdas_round");
    }
    else
        paramsel = new GurlsOptionsList("paramsel");


    paramsel->addOpt("guesses", new OptMatrix<gMat2D<T> >(*guesses_mat));

    if(nholdouts>1)
        scal(t, (T)1.0/nholdouts, lambdas, 1);

    paramsel->addOpt("lambdas", new OptMatrix<gMat2D<T> >(*LAMBDA));
    paramsel->addOpt("perf", new OptMatrix<gMat2D<T> >(*perf_mat));
    paramsel->addOpt("lambdas_round", new OptMatrix<gMat2D<T> >(*lambdas_round_mat));

    return paramsel;
}

template<typename T>

static ParamSelection<T>* gurls::ParamSelection< T >::factory

(

const std::string &

id

)

throw (BadParamSelectionCreation) [inline, static, inherited]

Warning:

The returned pointer is a plain, un-managed pointer. The calling function is responsible of deallocating the object.

Definition at line 146 of file paramsel.h.

    {
        if(id == "loocvprimal")
            return new ParamSelLoocvPrimal<T>;
        if(id == "loocvdual")
            return new ParamSelLoocvDual<T>;
        if(id == "fixlambda")
            return new ParamSelFixLambda<T>;
        if(id == "calibratesgd")
            return new ParamSelCalibrateSGD<T>;
        if(id == "siglam")
            return new ParamSelSiglam<T>;
        if(id == "siglamho")
            return new ParamSelSiglamHo<T>;
        if(id == "hodual")
            return new ParamSelHoDual<T>;
        if(id == "hodualr")
            return new ParamSelHoDualr<T>;
        if(id == "hoprimal")
            return new ParamSelHoPrimal<T>;
        if(id == "hoprimalr")
            return new ParamSelHoPrimalr<T>;
        if(id == "fixsiglam")
            return new ParamSelFixSigLam<T>;
        if(id == "loogpregr")
            return new ParamSelLooGPRegr<T>;
        if(id == "hogpregr")
            return new ParamSelHoGPRegr<T>;
        if(id == "siglamloogpregr")
            return new ParamSelSiglamLooGPRegr<T>;
        if(id == "siglamhogpregr")
            return new ParamSelSiglamHoGPRegr<T>;

        throw BadParamSelectionCreation(id);
    }

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

• hoprimal.h