GUIClassifier.cpp

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/*
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 3 of the License, or
 * (at your option) any later version.
 *
 * Written (W) 1999-2009 Soeren Sonnenburg
 * Written (W) 1999-2008 Gunnar Raetsch
 * Copyright (C) 1999-2009 Fraunhofer Institute FIRST and Max-Planck-Society
 */
#include <shogun/ui/GUIClassifier.h>
#include <shogun/ui/SGInterface.h>

#include <shogun/lib/config.h>
#include <shogun/io/SGIO.h>

#include <shogun/features/SparseFeatures.h>
#include <shogun/features/RealFileFeatures.h>
#include <shogun/labels/Labels.h>

#include <shogun/kernel/AUCKernel.h>

#include <shogun/multiclass/KNN.h>
#include <shogun/clustering/KMeans.h>
#include <shogun/clustering/Hierarchical.h>
#include <shogun/classifier/PluginEstimate.h>

#include <shogun/classifier/LDA.h>
#include <shogun/classifier/LPM.h>
#include <shogun/classifier/LPBoost.h>
#include <shogun/classifier/Perceptron.h>

#include <shogun/machine/LinearMachine.h>

#ifdef USE_SVMLIGHT
#include <shogun/classifier/svm/SVMLight.h>
#include <shogun/classifier/svm/SVMLightOneClass.h>
#include <shogun/regression/svr/SVRLight.h>
#endif //USE_SVMLIGHT

#include <shogun/classifier/mkl/MKLClassification.h>
#include <shogun/regression/svr/MKLRegression.h>
#include <shogun/classifier/mkl/MKLOneClass.h>
#include <shogun/classifier/mkl/MKLMulticlass.h>
#include <shogun/classifier/svm/LibSVM.h>
#include <shogun/multiclass/LaRank.h>
#include <shogun/classifier/svm/GPBTSVM.h>
#include <shogun/classifier/svm/LibSVMOneClass.h>
#include <shogun/multiclass/MulticlassLibSVM.h>

#include <shogun/regression/svr/LibSVR.h>
#include <shogun/regression/KernelRidgeRegression.h>

#include <shogun/classifier/svm/LibLinear.h>
#include <shogun/classifier/svm/MPDSVM.h>
#include <shogun/classifier/svm/GNPPSVM.h>
#include <shogun/multiclass/GMNPSVM.h>
#include <shogun/multiclass/ScatterSVM.h>

#include <shogun/classifier/svm/SVMLin.h>
#include <shogun/classifier/svm/SVMOcas.h>
#include <shogun/classifier/svm/SVMSGD.h>
#include <shogun/classifier/svm/WDSVMOcas.h>

#include <shogun/io/SerializableAsciiFile.h>

using namespace shogun;

CGUIClassifier::CGUIClassifier(CSGInterface* ui_)
: CSGObject(), ui(ui_)
{
    constraint_generator=NULL;
    classifier=NULL;
    max_train_time=0;

    // Perceptron parameters
    perceptron_learnrate=0.1;
    perceptron_maxiter=1000;

    // SVM parameters
    svm_qpsize=41;
    svm_bufsize=3000;
    svm_max_qpsize=1000;
    mkl_norm=1;
    ent_lambda=0;
    mkl_block_norm=4;
    svm_C1=1;
    svm_C2=1;
    C_mkl=0;
    mkl_use_interleaved=true;
    svm_weight_epsilon=1e-5;
    svm_epsilon=1e-5;
    svm_tube_epsilon=1e-2;
    svm_nu=0.5;
    svm_use_shrinking = true ;

    svm_use_bias = true;
    svm_use_batch_computation = true ;
    svm_use_linadd = true ;
    svm_do_auc_maximization = false ;

    // KRR parameters
    krr_tau=1;

    solver_type=ST_AUTO;
}

CGUIClassifier::~CGUIClassifier()
{
    SG_UNREF(classifier);
    SG_UNREF(constraint_generator);
}

bool CGUIClassifier::new_classifier(char* name, int32_t d, int32_t from_d)
{
    if (strcmp(name,"LIBSVM_ONECLASS")==0)
    {
        SG_UNREF(classifier);
        classifier = new CLibSVMOneClass();
        SG_INFO("created SVMlibsvm object for oneclass\n")
    }
    else if (strcmp(name,"LIBSVM_MULTICLASS")==0)
    {
        SG_UNREF(classifier);
        classifier = new CMulticlassLibSVM();
        SG_INFO("created SVMlibsvm object for multiclass\n")
    }
    else if (strcmp(name,"LIBSVM_NUMULTICLASS")==0)
    {
        SG_UNREF(classifier);
        classifier= new CMulticlassLibSVM(LIBSVM_NU_SVC);
        SG_INFO("created SVMlibsvm object for multiclass\n")
    }
#ifdef USE_SVMLIGHT
    else if (strcmp(name,"SCATTERSVM_NO_BIAS_SVMLIGHT")==0)
    {
        SG_UNREF(classifier);
        classifier= new CScatterSVM(NO_BIAS_SVMLIGHT);
        SG_INFO("created ScatterSVM NO BIAS SVMLIGHT object\n")
    }
#endif //USE_SVMLIGHT
    else if (strcmp(name,"SCATTERSVM_NO_BIAS_LIBSVM")==0)
    {
        SG_UNREF(classifier);
        classifier= new CScatterSVM(NO_BIAS_LIBSVM);
        SG_INFO("created ScatterSVM NO BIAS LIBSVM object\n")
    }
    else if (strcmp(name,"SCATTERSVM_TESTRULE1")==0)
    {
        SG_UNREF(classifier);
        classifier= new CScatterSVM(TEST_RULE1);
        SG_INFO("created ScatterSVM TESTRULE1 object\n")
    }
    else if (strcmp(name,"SCATTERSVM_TESTRULE2")==0)
    {
        SG_UNREF(classifier);
        classifier= new CScatterSVM(TEST_RULE2);
        SG_INFO("created ScatterSVM TESTRULE2 object\n")
    }
    else if (strcmp(name,"LIBSVM_NU")==0)
    {
        SG_UNREF(classifier);
        classifier= new CLibSVM(LIBSVM_NU_SVC);
        SG_INFO("created SVMlibsvm object\n")
    }
    else if (strcmp(name,"LIBSVM")==0)
    {
        SG_UNREF(classifier);
        classifier= new CLibSVM();
        SG_INFO("created SVMlibsvm object\n")
    }
    else if (strcmp(name,"LARANK")==0)
    {
        SG_UNREF(classifier);
        classifier= new CLaRank();
        SG_INFO("created LaRank object\n")
    }
#ifdef USE_SVMLIGHT
    else if ((strcmp(name,"LIGHT")==0) || (strcmp(name,"SVMLIGHT")==0))
    {
        SG_UNREF(classifier);
        classifier= new CSVMLight();
        SG_INFO("created SVMLight object\n")
    }
    else if (strcmp(name,"SVMLIGHT_ONECLASS")==0)
    {
        SG_UNREF(classifier);
        classifier= new CSVMLightOneClass();
        SG_INFO("created SVMLightOneClass object\n")
    }
    else if (strcmp(name,"SVRLIGHT")==0)
    {
        SG_UNREF(classifier);
        classifier= new CSVRLight();
        SG_INFO("created SVRLight object\n")
    }
#endif //USE_SVMLIGHT
    else if (strcmp(name,"GPBTSVM")==0)
    {
        SG_UNREF(classifier);
        classifier= new CGPBTSVM();
        SG_INFO("created GPBT-SVM object\n")
    }
    else if (strcmp(name,"MPDSVM")==0)
    {
        SG_UNREF(classifier);
        classifier= new CMPDSVM();
        SG_INFO("created MPD-SVM object\n")
    }
    else if (strcmp(name,"GNPPSVM")==0)
    {
        SG_UNREF(classifier);
        classifier= new CGNPPSVM();
        SG_INFO("created GNPP-SVM object\n")
    }
    else if (strcmp(name,"GMNPSVM")==0)
    {
        SG_UNREF(classifier);
        classifier= new CGMNPSVM();
        SG_INFO("created GMNP-SVM object\n")
    }
    else if (strcmp(name,"LIBSVR")==0)
    {
        SG_UNREF(classifier);
        classifier= new CLibSVR();
        SG_INFO("created SVRlibsvm object\n")
    }
#ifdef HAVE_LAPACK
    else if (strcmp(name, "KERNELRIDGEREGRESSION")==0)
    {
        SG_UNREF(classifier);
        classifier=new CKernelRidgeRegression(krr_tau, ui->ui_kernel->get_kernel(),
            ui->ui_labels->get_train_labels());
        SG_INFO("created KernelRidgeRegression object %p\n", classifier)
    }
#endif //HAVE_LAPACK
    else if (strcmp(name,"PERCEPTRON")==0)
    {
        SG_UNREF(classifier);
        classifier= new CPerceptron();
        SG_INFO("created Perceptron object\n")
    }
#ifdef HAVE_LAPACK
    else if (strncmp(name,"LIBLINEAR",9)==0)
    {
        LIBLINEAR_SOLVER_TYPE st=L2R_LR;

        if (strcmp(name,"LIBLINEAR_L2R_LR")==0)
        {
            st=L2R_LR;
            SG_INFO("created LibLinear l2 regularized logistic regression object\n")
        }
        else if (strcmp(name,"LIBLINEAR_L2R_L2LOSS_SVC_DUAL")==0)
        {
            st=L2R_L2LOSS_SVC_DUAL;
            SG_INFO("created LibLinear l2 regularized l2 loss SVM dual object\n")
        }
        else if (strcmp(name,"LIBLINEAR_L2R_L2LOSS_SVC")==0)
        {
            st=L2R_L2LOSS_SVC;
            SG_INFO("created LibLinear l2 regularized l2 loss SVM primal object\n")
        }
        else if (strcmp(name,"LIBLINEAR_L1R_L2LOSS_SVC")==0)
        {
            st=L1R_L2LOSS_SVC;
            SG_INFO("created LibLinear l1 regularized l2 loss SVM primal object\n")
        }
        else if (strcmp(name,"LIBLINEAR_L2R_L1LOSS_SVC_DUAL")==0)
        {
            st=L2R_L1LOSS_SVC_DUAL;
            SG_INFO("created LibLinear l2 regularized l1 loss dual SVM object\n")
        }
        else
            SG_ERROR("unknown liblinear type\n")

        SG_UNREF(classifier);
        classifier= new CLibLinear(st);
        ((CLibLinear*) classifier)->set_C(svm_C1, svm_C2);
        ((CLibLinear*) classifier)->set_epsilon(svm_epsilon);
        ((CLibLinear*) classifier)->set_bias_enabled(svm_use_bias);
    }
#endif //HAVE_LAPACK
#ifdef HAVE_EIGEN3
    else if (strcmp(name,"LDA")==0)
    {
        SG_UNREF(classifier);
        classifier= new CLDA();
        SG_INFO("created LDA object\n")
    }
#endif //HAVE_EIGEN3
#ifdef USE_CPLEX
    else if (strcmp(name,"LPM")==0)
    {
        SG_UNREF(classifier);
        classifier= new CLPM();
        ((CLPM*) classifier)->set_C(svm_C1, svm_C2);
        ((CLPM*) classifier)->set_epsilon(svm_epsilon);
        ((CLPM*) classifier)->set_bias_enabled(svm_use_bias);
        ((CLPM*) classifier)->set_max_train_time(max_train_time);
        SG_INFO("created LPM object\n")
    }
    else if (strcmp(name,"LPBOOST")==0)
    {
        SG_UNREF(classifier);
        classifier= new CLPBoost();
        ((CLPBoost*) classifier)->set_C(svm_C1, svm_C2);
        ((CLPBoost*) classifier)->set_epsilon(svm_epsilon);
        ((CLPBoost*) classifier)->set_bias_enabled(svm_use_bias);
        ((CLPBoost*) classifier)->set_max_train_time(max_train_time);
        SG_INFO("created LPBoost object\n")
    }
#endif //USE_CPLEX
    else if (strncmp(name,"KNN", strlen("KNN"))==0)
    {
        SG_UNREF(classifier);
        classifier= new CKNN();
        SG_INFO("created KNN object\n")
    }
    else if (strncmp(name,"KMEANS", strlen("KMEANS"))==0)
    {
        SG_UNREF(classifier);
        classifier= new CKMeans();
        SG_INFO("created KMeans object\n")
    }
    else if (strncmp(name,"HIERARCHICAL", strlen("HIERARCHICAL"))==0)
    {
        SG_UNREF(classifier);
        classifier= new CHierarchical();
        SG_INFO("created Hierarchical clustering object\n")
    }
    else if (strcmp(name,"SVMLIN")==0)
    {
        SG_UNREF(classifier);
        classifier= new CSVMLin();
        ((CSVMLin*) classifier)->set_C(svm_C1, svm_C2);
        ((CSVMLin*) classifier)->set_epsilon(svm_epsilon);
        ((CSVMLin*) classifier)->set_bias_enabled(svm_use_bias);
        SG_INFO("created SVMLin object\n")
    }
    else if (strncmp(name,"WDSVMOCAS", strlen("WDSVMOCAS"))==0)
    {
        SG_UNREF(classifier);
        classifier= new CWDSVMOcas(SVM_OCAS);

        ((CWDSVMOcas*) classifier)->set_bias_enabled(svm_use_bias);
        ((CWDSVMOcas*) classifier)->set_degree(d, from_d);
        ((CWDSVMOcas*) classifier)->set_C(svm_C1, svm_C2);
        ((CWDSVMOcas*) classifier)->set_epsilon(svm_epsilon);
        ((CWDSVMOcas*) classifier)->set_bufsize(svm_bufsize);
        SG_INFO("created Weighted Degree Kernel SVM Ocas(OCAS) object of order %d (from order:%d)\n", d, from_d)
    }
    else if (strcmp(name,"SVMOCAS")==0)
    {
        SG_UNREF(classifier);
        classifier= new CSVMOcas(SVM_OCAS);

        ((CSVMOcas*) classifier)->set_C(svm_C1, svm_C2);
        ((CSVMOcas*) classifier)->set_epsilon(svm_epsilon);
        ((CSVMOcas*) classifier)->set_bufsize(svm_bufsize);
        ((CSVMOcas*) classifier)->set_bias_enabled(svm_use_bias);
        SG_INFO("created SVM Ocas(OCAS) object\n")
    }
    else if (strcmp(name,"SVMSGD")==0)
    {
        SG_UNREF(classifier);
        classifier= new CSVMSGD(svm_C1);
        ((CSVMSGD*) classifier)->set_bias_enabled(svm_use_bias);
        SG_INFO("created SVM SGD object\n")
    }
    else if (strcmp(name,"SVMBMRM")==0 || (strcmp(name,"SVMPERF")==0))
    {
        SG_UNREF(classifier);
        classifier= new CSVMOcas(SVM_BMRM);

        ((CSVMOcas*) classifier)->set_C(svm_C1, svm_C2);
        ((CSVMOcas*) classifier)->set_epsilon(svm_epsilon);
        ((CSVMOcas*) classifier)->set_bufsize(svm_bufsize);
        ((CSVMOcas*) classifier)->set_bias_enabled(svm_use_bias);
        SG_INFO("created SVM Ocas(BMRM/PERF) object\n")
    }
    else if (strcmp(name,"MKL_CLASSIFICATION")==0)
    {
        SG_UNREF(classifier);
        classifier= new CMKLClassification();
    }
    else if (strcmp(name,"MKL_ONECLASS")==0)
    {
        SG_UNREF(classifier);
        classifier= new CMKLOneClass();
    }
    else if (strcmp(name,"MKL_MULTICLASS")==0)
    {
        SG_UNREF(classifier);
        classifier= new CMKLMulticlass();
    }
    else if (strcmp(name,"MKL_REGRESSION")==0)
    {
        SG_UNREF(classifier);
        classifier= new CMKLRegression();
    }
    else
    {
        SG_ERROR("Unknown classifier %s.\n", name)
        return false;
    }
    SG_REF(classifier);

    return (classifier!=NULL);
}

bool CGUIClassifier::train_mkl_multiclass()
{
    CMKLMulticlass* mkl= (CMKLMulticlass*) classifier;
    if (!mkl)
        SG_ERROR("No MKL available.\n")

    CLabels* trainlabels=ui->ui_labels->get_train_labels();
    if (!trainlabels)
        SG_ERROR("No trainlabels available.\n")

    CKernel* kernel=ui->ui_kernel->get_kernel();
    if (!kernel)
        SG_ERROR("No kernel available.\n")

    bool success=ui->ui_kernel->init_kernel("TRAIN");

    if (!success || !ui->ui_kernel->is_initialized() || !kernel->has_features())
        SG_ERROR("Kernel not initialized / no train features available.\n")

    int32_t num_vec=kernel->get_num_vec_lhs();
    if (trainlabels->get_num_labels() != num_vec)
        SG_ERROR("Number of train labels (%d) and training vectors (%d) differs!\n", trainlabels->get_num_labels(), num_vec)

    SG_INFO("Starting MC-MKL training on %ld vectors using C1=%lf C2=%lf epsilon=%lf\n", num_vec, svm_C1, svm_C2, svm_epsilon)

    mkl->set_mkl_epsilon(svm_weight_epsilon);
    mkl->set_mkl_norm(mkl_norm);
    //mkl->set_max_num_mkliters(-1);
    mkl->set_solver_type(solver_type);
    mkl->set_bias_enabled(svm_use_bias);
    mkl->set_epsilon(svm_epsilon);
    mkl->set_max_train_time(max_train_time);
    mkl->set_tube_epsilon(svm_tube_epsilon);
    mkl->set_nu(svm_nu);
    mkl->set_C(svm_C1);
    mkl->set_qpsize(svm_qpsize);
    mkl->set_shrinking_enabled(svm_use_shrinking);
    mkl->set_linadd_enabled(svm_use_linadd);
    mkl->set_batch_computation_enabled(svm_use_batch_computation);

    ((CKernelMulticlassMachine*) mkl)->set_labels(trainlabels);
    ((CKernelMulticlassMachine*) mkl)->set_kernel(kernel);

    return mkl->train();
}

bool CGUIClassifier::train_mkl()
{
    CMKL* mkl= (CMKL*) classifier;
    if (!mkl)
        SG_ERROR("No SVM available.\n")

    bool oneclass=(mkl->get_classifier_type()==CT_LIBSVMONECLASS);
    CLabels* trainlabels=NULL;
    if(!oneclass)
        trainlabels=ui->ui_labels->get_train_labels();
    else
        SG_INFO("Training one class mkl.\n")
    if (!trainlabels && !oneclass)
        SG_ERROR("No trainlabels available.\n")

    CKernel* kernel=ui->ui_kernel->get_kernel();
    if (!kernel)
        SG_ERROR("No kernel available.\n")

    bool success=ui->ui_kernel->init_kernel("TRAIN");
    if (!success || !ui->ui_kernel->is_initialized() || !kernel->has_features())
        SG_ERROR("Kernel not initialized.\n")

    int32_t num_vec=kernel->get_num_vec_lhs();
    if (!oneclass && trainlabels->get_num_labels() != num_vec)
        SG_ERROR("Number of train labels (%d) and training vectors (%d) differs!\n", trainlabels->get_num_labels(), num_vec)

    SG_INFO("Starting SVM training on %ld vectors using C1=%lf C2=%lf epsilon=%lf\n", num_vec, svm_C1, svm_C2, svm_epsilon)

    if (constraint_generator)
        mkl->set_constraint_generator(constraint_generator);
    mkl->set_solver_type(solver_type);
    mkl->set_bias_enabled(svm_use_bias);
    mkl->set_epsilon(svm_epsilon);
    mkl->set_max_train_time(max_train_time);
    mkl->set_tube_epsilon(svm_tube_epsilon);
    mkl->set_nu(svm_nu);
    mkl->set_C(svm_C1, svm_C2);
    mkl->set_qpsize(svm_qpsize);
    mkl->set_shrinking_enabled(svm_use_shrinking);
    mkl->set_linadd_enabled(svm_use_linadd);
    mkl->set_batch_computation_enabled(svm_use_batch_computation);
    mkl->set_mkl_epsilon(svm_weight_epsilon);
    mkl->set_mkl_norm(mkl_norm);
    mkl->set_elasticnet_lambda(ent_lambda);
    mkl->set_mkl_block_norm(mkl_block_norm);
    mkl->set_C_mkl(C_mkl);
    mkl->set_interleaved_optimization_enabled(mkl_use_interleaved);

    if (svm_do_auc_maximization)
    {
        CAUCKernel* auc_kernel = new CAUCKernel(10, kernel);
        CLabels* auc_labels= auc_kernel->setup_auc_maximization(trainlabels);
        ((CKernelMachine*) mkl)->set_labels(auc_labels);
        ((CKernelMachine*) mkl)->set_kernel(auc_kernel);
        SG_UNREF(auc_labels);
    }
    else
    {
        if(!oneclass)
            ((CKernelMachine*) mkl)->set_labels(trainlabels);
        ((CKernelMachine*) mkl)->set_kernel(kernel);
    }

    bool result=mkl->train();

    return result;
}

bool CGUIClassifier::train_svm()
{
    EMachineType type = classifier->get_classifier_type();

    if (!classifier)
        SG_ERROR("No SVM available.\n")

    bool oneclass=(type==CT_LIBSVMONECLASS);
    CLabels* trainlabels=NULL;
    if(!oneclass)
        trainlabels=ui->ui_labels->get_train_labels();
    else
        SG_INFO("Training one class svm.\n")
    if (!trainlabels && !oneclass)
        SG_ERROR("No trainlabels available.\n")

    CKernel* kernel=ui->ui_kernel->get_kernel();
    if (!kernel)
        SG_ERROR("No kernel available.\n")

    bool success=ui->ui_kernel->init_kernel("TRAIN");

    if (!success || !ui->ui_kernel->is_initialized() || !kernel->has_features())
        SG_ERROR("Kernel not initialized / no train features available.\n")

    int32_t num_vec=kernel->get_num_vec_lhs();
    if (!oneclass && trainlabels->get_num_labels() != num_vec)
        SG_ERROR("Number of train labels (%d) and training vectors (%d) differs!\n", trainlabels->get_num_labels(), num_vec)

    SG_INFO("Starting SVM training on %ld vectors using C1=%lf C2=%lf epsilon=%lf\n", num_vec, svm_C1, svm_C2, svm_epsilon)

    if (type==CT_LARANK || type==CT_GMNPSVM || type==CT_LIBSVMMULTICLASS)
    {
        CMulticlassSVM* svm = (CMulticlassSVM*)classifier;
        svm->set_solver_type(solver_type);
        svm->set_bias_enabled(svm_use_bias);
        svm->set_epsilon(svm_epsilon);
        svm->set_max_train_time(max_train_time);
        svm->set_tube_epsilon(svm_tube_epsilon);
        svm->set_nu(svm_nu);
        svm->set_C(svm_C1);
        svm->set_qpsize(svm_qpsize);
        svm->set_shrinking_enabled(svm_use_shrinking);
        svm->set_linadd_enabled(svm_use_linadd);
        svm->set_batch_computation_enabled(svm_use_batch_computation);
    }
    else
    {
        CSVM* svm = (CSVM*)classifier;
        svm->set_solver_type(solver_type);
        svm->set_bias_enabled(svm_use_bias);
        svm->set_epsilon(svm_epsilon);
        svm->set_max_train_time(max_train_time);
        svm->set_tube_epsilon(svm_tube_epsilon);
        svm->set_nu(svm_nu);
        svm->set_C(svm_C1, svm_C2);
        svm->set_qpsize(svm_qpsize);
        svm->set_shrinking_enabled(svm_use_shrinking);
        svm->set_linadd_enabled(svm_use_linadd);
        svm->set_batch_computation_enabled(svm_use_batch_computation);
    }

    if (type==CT_MKLMULTICLASS)
    {
        ((CMKLMulticlass *)classifier)->set_mkl_epsilon(svm_weight_epsilon);
    }

    if (svm_do_auc_maximization)
    {
        CAUCKernel* auc_kernel = new CAUCKernel(10, kernel);
        CLabels* auc_labels = auc_kernel->setup_auc_maximization(trainlabels);
        ((CKernelMachine*)classifier)->set_labels(auc_labels);
        ((CKernelMachine*)classifier)->set_kernel(auc_kernel);
        SG_UNREF(auc_labels);
    }
    else
    {
        if (type==CT_LARANK || type==CT_GMNPSVM || type==CT_LIBSVMMULTICLASS)
        {
            ((CKernelMulticlassMachine*)classifier)->set_labels(trainlabels);
            ((CKernelMulticlassMachine*)classifier)->set_kernel(kernel);
        }
        else
        {
            if(!oneclass)
                ((CKernelMachine*)classifier)->set_labels(trainlabels);

            ((CKernelMachine*)classifier)->set_kernel(kernel);
        }
    }

    bool result = classifier->train();

    return result;
}

bool CGUIClassifier::train_clustering(int32_t k, int32_t max_iter)
{
    bool result=false;
    CDistance* distance=ui->ui_distance->get_distance();

    if (!distance)
        SG_ERROR("No distance available\n")

    if (!ui->ui_distance->init_distance("TRAIN"))
        SG_ERROR("Initializing distance with train features failed.\n")

    ((CDistanceMachine*) classifier)->set_distance(distance);

    EMachineType type=classifier->get_classifier_type();
    switch (type)
    {
        case CT_KMEANS:
        {
            ((CKMeans*) classifier)->set_k(k);
            ((CKMeans*) classifier)->set_max_iter(max_iter);
            result=((CKMeans*) classifier)->train();
            break;
        }
        case CT_HIERARCHICAL:
        {
            ((CHierarchical*) classifier)->set_merges(k);
            result=((CHierarchical*) classifier)->train();
            break;
        }
        default:
            SG_ERROR("Unknown clustering type %d\n", type)
    }

    return result;
}

bool CGUIClassifier::train_knn(int32_t k)
{
    CLabels* trainlabels=ui->ui_labels->get_train_labels();
    CDistance* distance=ui->ui_distance->get_distance();

    bool result=false;

    if (trainlabels)
    {
        if (distance)
        {
            if (!ui->ui_distance->init_distance("TRAIN"))
                SG_ERROR("Initializing distance with train features failed.\n")
            ((CKNN*) classifier)->set_labels(trainlabels);
            ((CKNN*) classifier)->set_distance(distance);
            ((CKNN*) classifier)->set_k(k);
            result=((CKNN*) classifier)->train();
        }
        else
            SG_ERROR("No distance available.\n")
    }
    else
        SG_ERROR("No labels available\n")

    return result;
}

bool CGUIClassifier::train_krr()
{
#ifdef HAVE_LAPACK
    CKernelRidgeRegression* krr= (CKernelRidgeRegression*) classifier;
    if (!krr)
        SG_ERROR("No SVM available.\n")

    CLabels* trainlabels=NULL;
    trainlabels=ui->ui_labels->get_train_labels();
    if (!trainlabels)
        SG_ERROR("No trainlabels available.\n")

    CKernel* kernel=ui->ui_kernel->get_kernel();
    if (!kernel)
        SG_ERROR("No kernel available.\n")

    bool success=ui->ui_kernel->init_kernel("TRAIN");

    if (!success || !ui->ui_kernel->is_initialized() || !kernel->has_features())
        SG_ERROR("Kernel not initialized / no train features available.\n")

    int32_t num_vec=kernel->get_num_vec_lhs();
    if (trainlabels->get_num_labels() != num_vec)
        SG_ERROR("Number of train labels (%d) and training vectors (%d) differs!\n", trainlabels->get_num_labels(), num_vec)


    // Set training labels and kernel
    krr->set_labels(trainlabels);
    krr->set_kernel(kernel);

    bool result=krr->train();
    return result;
#else
    return false;
#endif
}

bool CGUIClassifier::train_linear(float64_t gamma)
{
    ASSERT(classifier)
    EMachineType ctype = classifier->get_classifier_type();
    CFeatures* trainfeatures=ui->ui_features->get_train_features();
    CLabels* trainlabels=ui->ui_labels->get_train_labels();
    bool result=false;

    if (!trainfeatures)
        SG_ERROR("No trainfeatures available.\n")

    if (!trainfeatures->has_property(FP_DOT))
        SG_ERROR("Trainfeatures not based on DotFeatures.\n")

    if (!trainlabels)
        SG_ERROR("No labels available\n")

    if (ctype==CT_PERCEPTRON)
    {
        ((CPerceptron*) classifier)->set_learn_rate(perceptron_learnrate);
        ((CPerceptron*) classifier)->set_max_iter(perceptron_maxiter);
    }

#ifdef HAVE_EIGEN3
    if (ctype==CT_LDA)
    {
        if (trainfeatures->get_feature_type()!=F_DREAL ||
                trainfeatures->get_feature_class()!=C_DENSE)
        SG_ERROR("LDA requires train features of class SIMPLE type REAL.\n")
        ((CLDA*) classifier)->set_gamma(gamma);
    }
#endif //HAVE_EIGEN3

    if (ctype==CT_SVMOCAS)
        ((CSVMOcas*) classifier)->set_C(svm_C1, svm_C2);
#ifdef HAVE_LAPACK
    else if (ctype==CT_LIBLINEAR)
        ((CLibLinear*) classifier)->set_C(svm_C1, svm_C2);
#endif
    else if (ctype==CT_SVMLIN)
        ((CSVMLin*) classifier)->set_C(svm_C1, svm_C2);
    else if (ctype==CT_SVMSGD)
        ((CSVMSGD*) classifier)->set_C(svm_C1, svm_C2);
    else if (ctype==CT_LPM || ctype==CT_LPBOOST)
    {
        if (trainfeatures->get_feature_class()!=C_SPARSE ||
                trainfeatures->get_feature_type()!=F_DREAL)
            SG_ERROR("LPM and LPBOOST require trainfeatures of class SPARSE type REAL.\n")
    }

    ((CLinearMachine*) classifier)->set_labels(trainlabels);
    ((CLinearMachine*) classifier)->set_features((CDenseFeatures<float64_t>*) trainfeatures);
    result=((CLinearMachine*) classifier)->train();

    return result;
}

bool CGUIClassifier::train_wdocas()
{
    CFeatures* trainfeatures=ui->ui_features->get_train_features();
    CLabels* trainlabels=ui->ui_labels->get_train_labels();

    bool result=false;

    if (!trainfeatures)
        SG_ERROR("No trainfeatures available.\n")

    if (trainfeatures->get_feature_class()!=C_STRING ||
            trainfeatures->get_feature_type()!=F_BYTE )
        SG_ERROR("Trainfeatures are not of class STRING type BYTE.\n")

    if (!trainlabels)
        SG_ERROR("No labels available.\n")

    ((CWDSVMOcas*) classifier)->set_labels(trainlabels);
    ((CWDSVMOcas*) classifier)->set_features((CStringFeatures<uint8_t>*) trainfeatures);
    result=((CWDSVMOcas*) classifier)->train();

    return result;
}

bool CGUIClassifier::load(char* filename, char* type)
{
    bool result=false;

    if (new_classifier(type))
    {
        FILE* model_file=fopen(filename, "r");
        REQUIRE(model_file != NULL, "SVM/Classifier loading failed on file %s.\n", filename);

        CSerializableAsciiFile* ascii_file = new CSerializableAsciiFile(model_file,'r');

        if (ascii_file)
        {
            if (classifier && classifier->load_serializable(ascii_file))
            {
                SG_DEBUG("file successfully read.\n")
                result=true;
            }
            else
                SG_ERROR("SVM/Classifier creation/loading failed on file %s.\n", filename)

            delete ascii_file;
        }
        else
            SG_ERROR("Opening file %s failed.\n", filename)

        return result;
    }
    else
        SG_ERROR("Type %s of SVM/Classifier unknown.\n", type)

    return false;
}

bool CGUIClassifier::save(char* param)
{
    bool result=false;
    param=SGIO::skip_spaces(param);

    if (classifier)
    {
        FILE* file=fopen(param, "w");
        CSerializableAsciiFile* ascii_file = new CSerializableAsciiFile(file,'w');

        if ((!ascii_file) || (!classifier->save_serializable(ascii_file)))
            printf("writing to file %s failed!\n", param);
        else
        {
            printf("successfully written classifier into \"%s\" !\n", param);
            result=true;
        }

        if (ascii_file)
            delete ascii_file;
    }
    else
        SG_ERROR("create classifier first\n")

    return result;
}

bool CGUIClassifier::set_perceptron_parameters(
    float64_t learnrate, int32_t maxiter)
{
    if (learnrate<=0)
        perceptron_learnrate=0.01;
    else
        perceptron_learnrate=learnrate;

    if (maxiter<=0)
        perceptron_maxiter=1000;
    else
        perceptron_maxiter=maxiter;
    SG_INFO("Setting to perceptron parameters (learnrate %f and maxiter: %d\n", perceptron_learnrate, perceptron_maxiter)

    return true;
}

bool CGUIClassifier::set_svm_epsilon(float64_t epsilon)
{
    if (epsilon<0)
        svm_epsilon=1e-4;
    else
        svm_epsilon=epsilon;
    SG_INFO("Set to svm_epsilon=%f.\n", svm_epsilon)

    return true;
}

bool CGUIClassifier::set_max_train_time(float64_t max)
{
    if (max>0)
    {
        max_train_time=max;
        SG_INFO("Set to max_train_time=%f.\n", max_train_time)
    }
    else
        SG_INFO("Disabling max_train_time.\n")

    return true;
}

bool CGUIClassifier::set_svr_tube_epsilon(float64_t tube_epsilon)
{
    if (!classifier)
        SG_ERROR("No regression method allocated\n")

    if (classifier->get_classifier_type() != CT_LIBSVR &&
            classifier->get_classifier_type() != CT_SVRLIGHT &&
            classifier->get_classifier_type() != CT_MKLREGRESSION )
    {
        SG_ERROR("Underlying method not capable of SV-regression\n")
    }

    if (tube_epsilon<0)
        svm_tube_epsilon=1e-2;
    svm_tube_epsilon=tube_epsilon;

    ((CSVM*) classifier)->set_tube_epsilon(svm_tube_epsilon);
    SG_INFO("Set to svr_tube_epsilon=%f.\n", svm_tube_epsilon)

    return true;
}

bool CGUIClassifier::set_svm_nu(float64_t nu)
{
    if (nu<0 || nu>1)
        nu=0.5;

    svm_nu=nu;
    SG_INFO("Set to nu=%f.\n", svm_nu)

    return true;
}

bool CGUIClassifier::set_svm_mkl_parameters(
    float64_t weight_epsilon, float64_t C, float64_t norm)
{
    if (weight_epsilon<0)
        weight_epsilon=1e-4;
    if (C<0)
        C=0;
    if (norm<0)
        SG_ERROR("MKL norm >= 0\n")

    svm_weight_epsilon=weight_epsilon;
    C_mkl=C;
    mkl_norm=norm;

    SG_INFO("Set to weight_epsilon=%f.\n", svm_weight_epsilon)
    SG_INFO("Set to C_mkl=%f.\n", C_mkl)
    SG_INFO("Set to mkl_norm=%f.\n", mkl_norm)

    return true;
}

bool CGUIClassifier::set_elasticnet_lambda(float64_t lambda)
{
  if (lambda<0 || lambda>1)
    SG_ERROR("0 <= ent_lambda <= 1\n")

  ent_lambda = lambda;
  return true;
}

bool CGUIClassifier::set_mkl_block_norm(float64_t mkl_bnorm)
{
  if (mkl_bnorm<1)
    SG_ERROR("1 <= mkl_block_norm <= inf\n")

  mkl_block_norm=mkl_bnorm;
  return true;
}


bool CGUIClassifier::set_svm_C(float64_t C1, float64_t C2)
{
    if (C1<0)
        svm_C1=1.0;
    else
        svm_C1=C1;

    if (C2<0)
        svm_C2=svm_C1;
    else
        svm_C2=C2;

    SG_INFO("Set to C1=%f C2=%f.\n", svm_C1, svm_C2)

    return true;
}

bool CGUIClassifier::set_svm_qpsize(int32_t qpsize)
{
    if (qpsize<2)
        svm_qpsize=41;
    else
        svm_qpsize=qpsize;
    SG_INFO("Set qpsize to svm_qpsize=%d.\n", svm_qpsize)

    return true;
}

bool CGUIClassifier::set_svm_max_qpsize(int32_t max_qpsize)
{
    if (max_qpsize<50)
        svm_max_qpsize=50;
    else
        svm_max_qpsize=max_qpsize;
    SG_INFO("Set max qpsize to svm_max_qpsize=%d.\n", svm_max_qpsize)

    return true;
}

bool CGUIClassifier::set_svm_bufsize(int32_t bufsize)
{
    if (svm_bufsize<0)
        svm_bufsize=3000;
    else
        svm_bufsize=bufsize;
    SG_INFO("Set bufsize to svm_bufsize=%d.\n", svm_bufsize)

    return true ;
}

bool CGUIClassifier::set_svm_shrinking_enabled(bool enabled)
{
    svm_use_shrinking=enabled;
    if (svm_use_shrinking)
        SG_INFO("Enabling shrinking optimization.\n")
    else
        SG_INFO("Disabling shrinking optimization.\n")

    return true;
}

bool CGUIClassifier::set_svm_batch_computation_enabled(bool enabled)
{
    svm_use_batch_computation=enabled;
    if (svm_use_batch_computation)
        SG_INFO("Enabling batch computation.\n")
    else
        SG_INFO("Disabling batch computation.\n")

    return true;
}

bool CGUIClassifier::set_svm_linadd_enabled(bool enabled)
{
    svm_use_linadd=enabled;
    if (svm_use_linadd)
        SG_INFO("Enabling LINADD optimization.\n")
    else
        SG_INFO("Disabling LINADD optimization.\n")

    return true;
}

bool CGUIClassifier::set_svm_bias_enabled(bool enabled)
{
    svm_use_bias=enabled;
    if (svm_use_bias)
        SG_INFO("Enabling svm bias.\n")
    else
        SG_INFO("Disabling svm bias.\n")

    return true;
}

bool CGUIClassifier::set_mkl_interleaved_enabled(bool enabled)
{
    mkl_use_interleaved=enabled;
    if (mkl_use_interleaved)
        SG_INFO("Enabling mkl interleaved optimization.\n")
    else
        SG_INFO("Disabling mkl interleaved optimization.\n")

    return true;
}

bool CGUIClassifier::set_do_auc_maximization(bool do_auc)
{
    svm_do_auc_maximization=do_auc;

    if (svm_do_auc_maximization)
        SG_INFO("Enabling AUC maximization.\n")
    else
        SG_INFO("Disabling AUC maximization.\n")

    return true;
}


CLabels* CGUIClassifier::classify()
{
    ASSERT(classifier)

    switch (classifier->get_classifier_type())
    {
        case CT_LIGHT:
        case CT_LIGHTONECLASS:
        case CT_LIBSVM:
        case CT_SCATTERSVM:
        case CT_MPD:
        case CT_GPBT:
        case CT_CPLEXSVM:
        case CT_GMNPSVM:
        case CT_GNPPSVM:
        case CT_LIBSVR:
        case CT_LIBSVMMULTICLASS:
        case CT_LIBSVMONECLASS:
        case CT_SVRLIGHT:
        case CT_MKLCLASSIFICATION:
        case CT_MKLMULTICLASS:
        case CT_MKLREGRESSION:
        case CT_MKLONECLASS:
        case CT_KERNELRIDGEREGRESSION:
            return classify_kernelmachine();
        case CT_KNN:
            return classify_distancemachine();
        case CT_PERCEPTRON:
        case CT_LDA:
            return classify_linear();
        case CT_SVMLIN:
        case CT_SVMPERF:
        case CT_SVMOCAS:
        case CT_SVMSGD:
        case CT_LPM:
        case CT_LPBOOST:
        case CT_LIBLINEAR:
            return classify_linear();
        case CT_WDSVMOCAS:
            return classify_byte_linear();
        default:
            SG_ERROR("unknown classifier type\n")
            break;
    };

    return NULL;
}

CLabels* CGUIClassifier::classify_kernelmachine()
{
    CFeatures* trainfeatures=ui->ui_features->get_train_features();
    CFeatures* testfeatures=ui->ui_features->get_test_features();

    if (!classifier)
        SG_ERROR("No kernelmachine available.\n")

    bool success=true;

    REQUIRE(ui->ui_kernel->get_kernel(), "No kernel set");
    if (ui->ui_kernel->get_kernel()->get_kernel_type()!=K_CUSTOM)
    {
        if (ui->ui_kernel->get_kernel()->get_kernel_type()==K_COMBINED
                && ( !trainfeatures || !testfeatures ))
        {
            SG_DEBUG("skipping initialisation of combined kernel "
                    "as train/test features are unavailable\n")
        }
        else
        {
            if (!trainfeatures)
                SG_ERROR("No training features available.\n")
            if (!testfeatures)
                SG_ERROR("No test features available.\n")

            success=ui->ui_kernel->init_kernel("TEST");
        }
    }

    if (!success || !ui->ui_kernel->is_initialized())
        SG_ERROR("Kernel not initialized.\n")

    EMachineType type = classifier->get_classifier_type();
    if (type==CT_LARANK || type==CT_GMNPSVM || type==CT_LIBSVMMULTICLASS ||
        type==CT_MKLMULTICLASS)
    {
        CKernelMulticlassMachine* kmcm = (CKernelMulticlassMachine*) classifier;
        kmcm->set_kernel(ui->ui_kernel->get_kernel());
    }
    else
    {
        CKernelMachine* km=(CKernelMachine*) classifier;
        km->set_kernel(ui->ui_kernel->get_kernel());
        km->set_batch_computation_enabled(svm_use_batch_computation);
    }

    SG_INFO("Starting kernel machine testing.\n")
    return classifier->apply();
}

bool CGUIClassifier::get_trained_classifier(
    float64_t* &weights, int32_t &rows, int32_t &cols, float64_t*& bias,
    int32_t& brows, int32_t& bcols,
    int32_t idx) // which SVM for Multiclass
{
    ASSERT(classifier)

    switch (classifier->get_classifier_type())
    {
        case CT_SCATTERSVM:
        case CT_GNPPSVM:
        case CT_LIBSVMMULTICLASS:
        case CT_LIGHT:
        case CT_LIGHTONECLASS:
        case CT_LIBSVM:
        case CT_MPD:
        case CT_GPBT:
        case CT_CPLEXSVM:
        case CT_GMNPSVM:
        case CT_LIBSVR:
        case CT_LIBSVMONECLASS:
        case CT_SVRLIGHT:
        case CT_MKLCLASSIFICATION:
        case CT_MKLREGRESSION:
        case CT_MKLONECLASS:
        case CT_MKLMULTICLASS:
        case CT_KERNELRIDGEREGRESSION:
            return get_svm(weights, rows, cols, bias, brows, bcols, idx);
            break;
        case CT_PERCEPTRON:
        case CT_LDA:
        case CT_LPM:
        case CT_LPBOOST:
        case CT_SVMOCAS:
        case CT_SVMSGD:
        case CT_SVMLIN:
        case CT_SVMPERF:
        case CT_LIBLINEAR:
            return get_linear(weights, rows, cols, bias, brows, bcols);
            break;
        case CT_KMEANS:
        case CT_HIERARCHICAL:
            return get_clustering(weights, rows, cols, bias, brows, bcols);
            break;
        case CT_KNN:
            SG_ERROR("not implemented")
            break;
        default:
            SG_ERROR("unknown classifier type\n")
            break;
    };
    return false;
}


int32_t CGUIClassifier::get_num_svms()
{
    ASSERT(classifier)
    return ((CMulticlassSVM*) classifier)->get_num_machines();
}

bool CGUIClassifier::get_svm(
    float64_t* &weights, int32_t& rows, int32_t& cols, float64_t*& bias,
    int32_t& brows, int32_t& bcols, int32_t idx)
{
    CSVM* svm=(CSVM*) classifier;

    if (idx>-1) // should be MulticlassSVM
        svm=((CMulticlassSVM*) svm)->get_svm(idx);

    if (svm)
    {
        brows=1;
        bcols=1;
        bias=SG_MALLOC(float64_t, 1);
        *bias=svm->get_bias();

        rows=svm->get_num_support_vectors();
        cols=2;
        weights=SG_MALLOC(float64_t, rows*cols);

        for (int32_t i=0; i<rows; i++)
        {
            weights[i]=svm->get_alpha(i);
            weights[i+rows]=svm->get_support_vector(i);
        }

        return true;
    }

    return false;
}

bool CGUIClassifier::get_clustering(
    float64_t* &centers, int32_t& rows, int32_t& cols, float64_t*& radi,
    int32_t& brows, int32_t& bcols)
{
    if (!classifier)
        return false;

    switch (classifier->get_classifier_type())
    {
        case CT_KMEANS:
        {
            CKMeans* clustering=(CKMeans*) classifier;

            bcols=1;
            SGVector<float64_t> r=clustering->get_radiuses();
            brows=r.vlen;
            radi=SG_MALLOC(float64_t, brows);
            memcpy(radi, r.vector, sizeof(float64_t)*brows);

            cols=1;
            SGMatrix<float64_t> c=clustering->get_cluster_centers();
            rows=c.num_rows;
            cols=c.num_cols;
            centers=SG_MALLOC(float64_t, rows*cols);
            memcpy(centers, c.matrix, sizeof(float64_t)*rows*cols);
            break;
        }

        case CT_HIERARCHICAL:
        {
            CHierarchical* clustering=(CHierarchical*) classifier;

            // radi == merge_distances, centers == pairs
            bcols=1;
            SGVector<float64_t> r=clustering->get_merge_distances();
            brows=r.vlen;
            radi=SG_MALLOC(float64_t, brows);
            memcpy(radi, r.vector, sizeof(float64_t)*brows);

            SGMatrix<int32_t> p=clustering->get_cluster_pairs();
            rows=p.num_rows;
            cols=p.num_cols;
            centers=SG_MALLOC(float64_t, rows*cols);
            for (int32_t i=0; i<rows*cols; i++)
                centers[i]=(float64_t) p.matrix[i];

            break;
        }

        default:
            SG_ERROR("internal error - unknown clustering type\n")
    }

    return true;
}

bool CGUIClassifier::get_linear(
    float64_t* &weights, int32_t& rows, int32_t& cols, float64_t*& bias,
    int32_t& brows, int32_t& bcols)
{
    CLinearMachine* linear=(CLinearMachine*) classifier;

    if (!linear)
        return false;

    bias=SG_MALLOC(float64_t, 1);
    *bias=linear->get_bias();
    brows=1;
    bcols=1;

    SGVector<float64_t> w=linear->get_w();
    cols=1;
    rows=w.vlen;

    weights= SG_MALLOC(float64_t, w.vlen);
    memcpy(weights, w.vector, sizeof(float64_t)*w.vlen);

    return true;
}

CLabels* CGUIClassifier::classify_distancemachine()
{
    CFeatures* trainfeatures=ui->ui_features->get_train_features();
    CFeatures* testfeatures=ui->ui_features->get_test_features();

    if (!classifier)
    {
        SG_ERROR("no kernelmachine available\n")
        return NULL;
    }
    if (!trainfeatures)
    {
        SG_ERROR("no training features available\n")
        return NULL;
    }

    if (!testfeatures)
    {
        SG_ERROR("no test features available\n")
        return NULL;
    }

    bool success=ui->ui_distance->init_distance("TEST");

    if (!success || !ui->ui_distance->is_initialized())
    {
        SG_ERROR("distance not initialized\n")
        return NULL;
    }

    ((CDistanceMachine*) classifier)->set_distance(
        ui->ui_distance->get_distance());
    SG_INFO("starting distance machine testing\n")
    return classifier->apply();
}


CLabels* CGUIClassifier::classify_linear()
{
    CFeatures* testfeatures=ui->ui_features->get_test_features();

    if (!classifier)
    {
        SG_ERROR("no classifier available\n")
        return NULL;
    }
    if (!testfeatures)
    {
        SG_ERROR("no test features available\n")
        return NULL;
    }
    if (!(testfeatures->has_property(FP_DOT)))
    {
        SG_ERROR("testfeatures not based on DotFeatures\n")
        return NULL;
    }

    ((CLinearMachine*) classifier)->set_features((CDotFeatures*) testfeatures);
    SG_INFO("starting linear classifier testing\n")
    return classifier->apply();
}

CLabels* CGUIClassifier::classify_byte_linear()
{
    CFeatures* testfeatures=ui->ui_features->get_test_features();

    if (!classifier)
    {
        SG_ERROR("no svm available\n")
        return NULL;
    }
    if (!testfeatures)
    {
        SG_ERROR("no test features available\n")
        return NULL;
    }
    if (testfeatures->get_feature_class() != C_STRING ||
            testfeatures->get_feature_type() != F_BYTE )
    {
        SG_ERROR("testfeatures not of class STRING type BYTE\n")
        return NULL;
    }

    ((CWDSVMOcas*) classifier)->set_features((CStringFeatures<uint8_t>*) testfeatures);
    SG_INFO("starting linear classifier testing\n")
    return classifier->apply();
}

bool CGUIClassifier::classify_example(int32_t idx, float64_t &result)
{
    CFeatures* trainfeatures=ui->ui_features->get_train_features();
    CFeatures* testfeatures=ui->ui_features->get_test_features();

    if (!classifier)
    {
        SG_ERROR("no svm available\n")
        return false;
    }

    if (!ui->ui_kernel->is_initialized())
    {
        SG_ERROR("kernel not initialized\n")
        return false;
    }

    if (!ui->ui_kernel->get_kernel() ||
        ui->ui_kernel->get_kernel()->get_kernel_type()!=K_CUSTOM)
    {
        if (!trainfeatures)
        {
            SG_ERROR("no training features available\n")
            return false;
        }

        if (!testfeatures)
        {
            SG_ERROR("no test features available\n")
            return false;
        }
    }

    ((CKernelMachine*) classifier)->set_kernel(
        ui->ui_kernel->get_kernel());

    result=((CKernelMachine*)classifier)->apply_one(idx);
    return true ;
}


bool CGUIClassifier::set_krr_tau(float64_t tau)
{
#ifdef HAVE_LAPACK
    krr_tau=tau;
    ((CKernelRidgeRegression*) classifier)->set_tau(krr_tau);
    SG_INFO("Set to krr_tau=%f.\n", krr_tau)

    return true;
#else
    return false;
#endif
}

bool CGUIClassifier::set_solver(char* solver)
{
    ESolverType s=ST_AUTO;

    if (strncmp(solver,"NEWTON", 6)==0)
    {
        SG_INFO("Using NEWTON solver.\n")
        s=ST_NEWTON;
    }
    else if (strncmp(solver,"DIRECT", 6)==0)
    {
        SG_INFO("Using DIRECT solver\n")
        s=ST_DIRECT;
    }
    else if (strncmp(solver,"BLOCK_NORM", 9)==0)
    {
        SG_INFO("Using BLOCK_NORM solver\n")
        s=ST_BLOCK_NORM;
    }
    else if (strncmp(solver,"ELASTICNET", 10)==0)
    {
        SG_INFO("Using ELASTICNET solver\n")
        s=ST_ELASTICNET;
    }
    else if (strncmp(solver,"AUTO", 4)==0)
    {
        SG_INFO("Automagically determining solver.\n")
        s=ST_AUTO;
    }
#ifdef USE_CPLEX
    else if (strncmp(solver, "CPLEX", 5)==0)
    {
        SG_INFO("USING CPLEX METHOD selected\n")
        s=ST_CPLEX;
    }
#endif
#ifdef USE_GLPK
    else if (strncmp(solver,"GLPK", 4)==0)
    {
        SG_INFO("Using GLPK solver\n")
        s=ST_GLPK;
    }
#endif
    else
        SG_ERROR("Unknown solver type, %s (not compiled in?)\n", solver)


    solver_type=s;
    return true;
}

bool CGUIClassifier::set_constraint_generator(char* name)
{
    if (strcmp(name,"LIBSVM_ONECLASS")==0)
    {
        SG_UNREF(constraint_generator);
        constraint_generator = new CLibSVMOneClass();
        SG_INFO("created SVMlibsvm object for oneclass\n")
    }
    else if (strcmp(name,"LIBSVM_NU")==0)
    {
        SG_UNREF(constraint_generator);
        constraint_generator= new CLibSVM(LIBSVM_NU_SVC);
        SG_INFO("created SVMlibsvm object\n")
    }
    else if (strcmp(name,"LIBSVM")==0)
    {
        SG_UNREF(constraint_generator);
        constraint_generator= new CLibSVM();
        SG_INFO("created SVMlibsvm object\n")
    }
#ifdef USE_SVMLIGHT
    else if ((strcmp(name,"LIGHT")==0) || (strcmp(name,"SVMLIGHT")==0))
    {
        SG_UNREF(constraint_generator);
        constraint_generator= new CSVMLight();
        SG_INFO("created SVMLight object\n")
    }
    else if (strcmp(name,"SVMLIGHT_ONECLASS")==0)
    {
        SG_UNREF(constraint_generator);
        constraint_generator= new CSVMLightOneClass();
        SG_INFO("created SVMLightOneClass object\n")
    }
    else if (strcmp(name,"SVRLIGHT")==0)
    {
        SG_UNREF(constraint_generator);
        constraint_generator= new CSVRLight();
        SG_INFO("created SVRLight object\n")
    }
#endif //USE_SVMLIGHT
    else if (strcmp(name,"GPBTSVM")==0)
    {
        SG_UNREF(constraint_generator);
        constraint_generator= new CGPBTSVM();
        SG_INFO("created GPBT-SVM object\n")
    }
    else if (strcmp(name,"MPDSVM")==0)
    {
        SG_UNREF(constraint_generator);
        constraint_generator= new CMPDSVM();
        SG_INFO("created MPD-SVM object\n")
    }
    else if (strcmp(name,"GNPPSVM")==0)
    {
        SG_UNREF(constraint_generator);
        constraint_generator= new CGNPPSVM();
        SG_INFO("created GNPP-SVM object\n")
    }
    else if (strcmp(name,"LIBSVR")==0)
    {
        SG_UNREF(constraint_generator);
        constraint_generator= new CLibSVR();
        SG_INFO("created SVRlibsvm object\n")
    }
    else
    {
        SG_ERROR("Unknown SV-classifier %s.\n", name)
        return false;
    }
    SG_REF(constraint_generator);

    return (constraint_generator!=NULL);
}