Kernel.cpp

Go to the documentation of this file.

/*
 * EXCEPT FOR THE KERNEL CACHING FUNCTIONS WHICH ARE (W) THORSTEN JOACHIMS
 * COPYRIGHT (C) 1999  UNIVERSITAET DORTMUND - ALL RIGHTS RESERVED
 *
 * 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 "lib/config.h"
#include "lib/common.h"
#include "lib/io.h"
#include "lib/File.h"
#include "lib/Time.h"
#include "lib/Signal.h"

#include "base/Parallel.h"

#include "kernel/Kernel.h"
#include "kernel/IdentityKernelNormalizer.h"
#include "features/Features.h"

#include "classifier/svm/SVM.h"

#include <string.h>
#include <unistd.h>
#include <math.h>

#ifndef WIN32
#include <pthread.h>
#endif


#ifdef HAVE_BOOST_SERIALIZATION
#include <boost/serialization/export.hpp>
BOOST_CLASS_EXPORT(shogun::CKernel);
#endif //HAVE_BOOST_SERIALIZATION


using namespace shogun;

CKernel::CKernel()
: CSGObject(), cache_size(10), kernel_matrix(NULL), lhs(NULL),
    rhs(NULL), num_lhs(0), num_rhs(0), combined_kernel_weight(1),
    optimization_initialized(false), opt_type(FASTBUTMEMHUNGRY),
    properties(KP_NONE), normalizer(NULL)
{

#ifdef USE_SVMLIGHT
    memset(&kernel_cache, 0x0, sizeof(KERNEL_CACHE));
#endif //USE_SVMLIGHT

    set_normalizer(new CIdentityKernelNormalizer());
}

CKernel::CKernel(int32_t size)
: CSGObject(), kernel_matrix(NULL), lhs(NULL), rhs(NULL), num_lhs(0),
    num_rhs(0), combined_kernel_weight(1), optimization_initialized(false),
    opt_type(FASTBUTMEMHUNGRY), properties(KP_NONE), normalizer(NULL)
{
    if (size<10)
        size=10;

    cache_size=size;
#ifdef USE_SVMLIGHT
    memset(&kernel_cache, 0x0, sizeof(KERNEL_CACHE));
#endif //USE_SVMLIGHT

    if (get_is_initialized())
        SG_ERROR( "COptimizableKernel still initialized on destruction");

    set_normalizer(new CIdentityKernelNormalizer());
}


CKernel::CKernel(CFeatures* p_lhs, CFeatures* p_rhs, int32_t size) : CSGObject(),
    kernel_matrix(NULL), lhs(NULL), rhs(NULL), num_lhs(0), num_rhs(0),
    combined_kernel_weight(1), optimization_initialized(false),
    opt_type(FASTBUTMEMHUNGRY), properties(KP_NONE), normalizer(NULL)
{
    if (size<10)
        size=10;

    cache_size=size;
#ifdef USE_SVMLIGHT
    memset(&kernel_cache, 0x0, sizeof(KERNEL_CACHE));
#endif //USE_SVMLIGHT
    if (get_is_initialized())
        SG_ERROR("Kernel initialized on construction.\n");

    set_normalizer(new CIdentityKernelNormalizer());
    init(p_lhs, p_rhs);
}

CKernel::~CKernel()
{
    if (get_is_initialized())
        SG_ERROR("Kernel still initialized on destruction.\n");

    remove_lhs_and_rhs();
    SG_UNREF(normalizer);

    SG_INFO("Kernel deleted (%p).\n", this);
}

void CKernel::get_kernel_matrix(float64_t** dst, int32_t* m, int32_t* n)
{
    ASSERT(dst && m && n);

    float64_t* result = NULL;

    if (has_features())
    {
        int32_t num_vec1=get_num_vec_lhs();
        int32_t num_vec2=get_num_vec_rhs();
        *m=num_vec1;
        *n=num_vec2;

        int64_t total_num = ((int64_t) num_vec1) * num_vec2;
        SG_DEBUG( "allocating memory for a kernel matrix"
                " of size %dx%d\n", num_vec1, num_vec2);

        result=(float64_t*) malloc(sizeof(float64_t)*total_num);
        ASSERT(result);
        get_kernel_matrix<float64_t>(num_vec1,num_vec2, result);
    }
    else
        SG_ERROR( "no features assigned to kernel\n");

    *dst=result;
}

#ifdef USE_SVMLIGHT
void CKernel::resize_kernel_cache(KERNELCACHE_IDX size, bool regression_hack)
{
    if (size<10)
        size=10;

    kernel_cache_cleanup();
    cache_size=size;

    if (has_features() && get_num_vec_lhs())
        kernel_cache_init(cache_size, regression_hack);
}
#endif //USE_SVMLIGHT

bool CKernel::init(CFeatures* l, CFeatures* r)
{
    //make sure features were indeed supplied
    ASSERT(l);
    ASSERT(r);

    //make sure features are compatible
    ASSERT(l->get_feature_class()==r->get_feature_class());
    ASSERT(l->get_feature_type()==r->get_feature_type());

    //remove references to previous features
    remove_lhs_and_rhs();

    //increase reference counts
    SG_REF(l);
    if (l!=r)
        SG_REF(r);

    lhs=l;
    rhs=r;

    ASSERT(!num_lhs || num_lhs==l->get_num_vectors());
    ASSERT(!num_rhs || num_rhs==l->get_num_vectors());

    num_lhs=l->get_num_vectors();
    num_rhs=r->get_num_vectors();

    return true;
}

bool CKernel::set_normalizer(CKernelNormalizer* n)
{
    SG_REF(n);
    if (lhs && rhs)
        n->init(this);

    SG_UNREF(normalizer);
    normalizer=n;

    return (normalizer!=NULL);
}

CKernelNormalizer* CKernel::get_normalizer()
{
    SG_REF(normalizer)
    return normalizer;
}

bool CKernel::init_normalizer()
{
    return normalizer->init(this);
}

void CKernel::cleanup()
{
    remove_lhs_and_rhs();
}

#ifdef USE_SVMLIGHT
/****************************** Cache handling *******************************/

void CKernel::kernel_cache_init(int32_t buffsize, bool regression_hack)
{
    int32_t totdoc=get_num_vec_lhs();
    if (totdoc<=0)
    {
        SG_ERROR("kernel has zero rows: num_lhs=%d num_rhs=%d\n",
                get_num_vec_lhs(), get_num_vec_rhs());
    }
    uint64_t buffer_size=0;
    int32_t i;

    //in regression the additional constraints are made by doubling the training data
    if (regression_hack)
        totdoc*=2;

    buffer_size=((uint64_t) buffsize)*1024*1024/sizeof(KERNELCACHE_ELEM);
    if (buffer_size>((uint64_t) totdoc)*totdoc)
        buffer_size=((uint64_t) totdoc)*totdoc;

    SG_INFO( "using a kernel cache of size %lld MB (%lld bytes) for %s Kernel\n", buffer_size*sizeof(KERNELCACHE_ELEM)/1024/1024, buffer_size*sizeof(KERNELCACHE_ELEM), get_name());

    //make sure it fits in the *signed* KERNELCACHE_IDX type
    ASSERT(buffer_size < (((uint64_t) 1) << (sizeof(KERNELCACHE_IDX)*8-1)));

    kernel_cache.index = new int32_t[totdoc];
    kernel_cache.occu = new int32_t[totdoc];
    kernel_cache.lru = new int32_t[totdoc];
    kernel_cache.invindex = new int32_t[totdoc];
    kernel_cache.active2totdoc = new int32_t[totdoc];
    kernel_cache.totdoc2active = new int32_t[totdoc];
    kernel_cache.buffer = new KERNELCACHE_ELEM[buffer_size];
    kernel_cache.buffsize=buffer_size;
    kernel_cache.max_elems=(int32_t) (kernel_cache.buffsize/totdoc);

    if(kernel_cache.max_elems>totdoc) {
        kernel_cache.max_elems=totdoc;
    }

    kernel_cache.elems=0;   // initialize cache
    for(i=0;i<totdoc;i++) {
        kernel_cache.index[i]=-1;
        kernel_cache.lru[i]=0;
    }
    for(i=0;i<totdoc;i++) {
        kernel_cache.occu[i]=0;
        kernel_cache.invindex[i]=-1;
    }

    kernel_cache.activenum=totdoc;;
    for(i=0;i<totdoc;i++) {
        kernel_cache.active2totdoc[i]=i;
        kernel_cache.totdoc2active[i]=i;
    }

    kernel_cache.time=0;
}

void CKernel::get_kernel_row(
    int32_t docnum, int32_t *active2dnum, float64_t *buffer, bool full_line)
{
    int32_t i,j;
    KERNELCACHE_IDX start;

    int32_t num_vectors = get_num_vec_lhs();
    if (docnum>=num_vectors)
        docnum=2*num_vectors-1-docnum;

    /* is cached? */
    if(kernel_cache.index[docnum] != -1)
    {
        kernel_cache.lru[kernel_cache.index[docnum]]=kernel_cache.time; /* lru */
        start=((KERNELCACHE_IDX) kernel_cache.activenum)*kernel_cache.index[docnum];

        if (full_line)
        {
            for(j=0;j<get_num_vec_lhs();j++)
            {
                if(kernel_cache.totdoc2active[j] >= 0)
                    buffer[j]=kernel_cache.buffer[start+kernel_cache.totdoc2active[j]];
                else
                    buffer[j]=(float64_t) kernel(docnum, j);
            }
        }
        else
        {
            for(i=0;(j=active2dnum[i])>=0;i++)
            {
                if(kernel_cache.totdoc2active[j] >= 0)
                    buffer[j]=kernel_cache.buffer[start+kernel_cache.totdoc2active[j]];
                else
                {
                    int32_t k=j;
                    if (k>=num_vectors)
                        k=2*num_vectors-1-k;
                    buffer[j]=(float64_t) kernel(docnum, k);
                }
            }
        }
    }
    else
    {
        if (full_line)
        {
            for(j=0;j<get_num_vec_lhs();j++)
                buffer[j]=(KERNELCACHE_ELEM) kernel(docnum, j);
        }
        else
        {
            for(i=0;(j=active2dnum[i])>=0;i++)
            {
                int32_t k=j;
                if (k>=num_vectors)
                    k=2*num_vectors-1-k;
                buffer[j]=(KERNELCACHE_ELEM) kernel(docnum, k);
            }
        }
    }
}


// Fills cache for the row m
void CKernel::cache_kernel_row(int32_t m)
{
    register int32_t j,k,l;
    register KERNELCACHE_ELEM *cache;

    int32_t num_vectors = get_num_vec_lhs();

    if (m>=num_vectors)
        m=2*num_vectors-1-m;

    if(!kernel_cache_check(m))   // not cached yet
    {
        cache = kernel_cache_clean_and_malloc(m);
        if(cache) {
            l=kernel_cache.totdoc2active[m];

            for(j=0;j<kernel_cache.activenum;j++)  // fill cache
            {
                k=kernel_cache.active2totdoc[j];

                if((kernel_cache.index[k] != -1) && (l != -1) && (k != m)) {
                    cache[j]=kernel_cache.buffer[((KERNELCACHE_IDX) kernel_cache.activenum)
                        *kernel_cache.index[k]+l];
                }
                else
                {
                    if (k>=num_vectors)
                        k=2*num_vectors-1-k;

                    cache[j]=kernel(m, k);
                }
            }
        }
        else
            perror("Error: Kernel cache full! => increase cache size");
    }
}


void* CKernel::cache_multiple_kernel_row_helper(void* p)
{
    int32_t j,k,l;
    S_KTHREAD_PARAM* params = (S_KTHREAD_PARAM*) p;

    for (int32_t i=params->start; i<params->end; i++)
    {
        KERNELCACHE_ELEM* cache=params->cache[i];
        int32_t m = params->uncached_rows[i];
        l=params->kernel_cache->totdoc2active[m];

        for(j=0;j<params->kernel_cache->activenum;j++)  // fill cache
        {
            k=params->kernel_cache->active2totdoc[j];

            if((params->kernel_cache->index[k] != -1) && (l != -1) && (!params->needs_computation[k])) {
                cache[j]=params->kernel_cache->buffer[((KERNELCACHE_IDX) params->kernel_cache->activenum)
                    *params->kernel_cache->index[k]+l];
            }
            else
                {
                    if (k>=params->num_vectors)
                        k=2*params->num_vectors-1-k;

                    cache[j]=params->kernel->kernel(m, k);
                }
        }

        //now line m is cached
        params->needs_computation[m]=0;
    }
    return NULL;
}

// Fills cache for the rows in key
void CKernel::cache_multiple_kernel_rows(int32_t* rows, int32_t num_rows)
{
#ifndef WIN32
    if (parallel->get_num_threads()<2)
    {
#endif
        for(int32_t i=0;i<num_rows;i++)
            cache_kernel_row(rows[i]);
#ifndef WIN32
    }
    else
    {
        // fill up kernel cache
        int32_t* uncached_rows = new int32_t[num_rows];
        KERNELCACHE_ELEM** cache = new KERNELCACHE_ELEM*[num_rows];
        pthread_t* threads = new pthread_t[parallel->get_num_threads()-1];
        S_KTHREAD_PARAM* params = new S_KTHREAD_PARAM[parallel->get_num_threads()-1];
        int32_t num_threads=parallel->get_num_threads()-1;
        int32_t num_vec=get_num_vec_lhs();
        ASSERT(num_vec>0);
        uint8_t* needs_computation=new uint8_t[num_vec];
        memset(needs_computation, 0, sizeof(uint8_t)*num_vec);
        int32_t step=0;
        int32_t num=0;
        int32_t end=0;

        // allocate cachelines if necessary
        for (int32_t i=0; i<num_rows; i++)
        {
            int32_t idx=rows[i];
            if (kernel_cache_check(idx))
                continue;

            if (idx>=num_vec)
                idx=2*num_vec-1-idx;

            needs_computation[idx]=1;
            uncached_rows[num]=idx;
            cache[num]= kernel_cache_clean_and_malloc(idx);

            if (!cache[num])
                SG_ERROR("Kernel cache full! => increase cache size\n");

            num++;
        }

        if (num>0)
        {
            step= num/parallel->get_num_threads();

            if (step<1)
            {
                num_threads=num-1;
                step=1;
            }

            for (int32_t t=0; t<num_threads; t++)
            {
                params[t].kernel = this;
                params[t].kernel_cache = &kernel_cache;
                params[t].cache = cache;
                params[t].uncached_rows = uncached_rows;
                params[t].needs_computation = needs_computation;
                params[t].num_uncached = num;
                params[t].start = t*step;
                params[t].end = (t+1)*step;
                params[t].num_vectors = get_num_vec_lhs();
                end=params[t].end;

                if (pthread_create(&threads[t], NULL, CKernel::cache_multiple_kernel_row_helper, (void*)&params[t]) != 0)
                {
                    num_threads=t;
                    end=t*step;
                    SG_WARNING("thread creation failed\n");
                    break;
                }
            }
        }
        else
            num_threads=-1;


        S_KTHREAD_PARAM last_param;
        last_param.kernel = this;
        last_param.kernel_cache = &kernel_cache;
        last_param.cache = cache;
        last_param.uncached_rows = uncached_rows;
        last_param.needs_computation = needs_computation;
        last_param.start = end;
        last_param.num_uncached = num;
        last_param.end = num;
        last_param.num_vectors = get_num_vec_lhs();

        cache_multiple_kernel_row_helper(&last_param);


        for (int32_t t=0; t<num_threads; t++)
        {
            if (pthread_join(threads[t], NULL) != 0)
                SG_WARNING( "pthread_join failed\n");
        }

        delete[] needs_computation;
        delete[] params;
        delete[] threads;
        delete[] cache;
        delete[] uncached_rows;
    }
#endif
}

// remove numshrink columns in the cache
// which correspond to examples marked
void CKernel::kernel_cache_shrink(
    int32_t totdoc, int32_t numshrink, int32_t *after)
{
    register int32_t i,j,jj,scount;     // 0 in after.
    KERNELCACHE_IDX from=0,to=0;
    int32_t *keep;

    keep=new int32_t[totdoc];
    for(j=0;j<totdoc;j++) {
        keep[j]=1;
    }
    scount=0;
    for(jj=0;(jj<kernel_cache.activenum) && (scount<numshrink);jj++) {
        j=kernel_cache.active2totdoc[jj];
        if(!after[j]) {
            scount++;
            keep[j]=0;
        }
    }

    for(i=0;i<kernel_cache.max_elems;i++) {
        for(jj=0;jj<kernel_cache.activenum;jj++) {
            j=kernel_cache.active2totdoc[jj];
            if(!keep[j]) {
                from++;
            }
            else {
                kernel_cache.buffer[to]=kernel_cache.buffer[from];
                to++;
                from++;
            }
        }
    }

    kernel_cache.activenum=0;
    for(j=0;j<totdoc;j++) {
        if((keep[j]) && (kernel_cache.totdoc2active[j] != -1)) {
            kernel_cache.active2totdoc[kernel_cache.activenum]=j;
            kernel_cache.totdoc2active[j]=kernel_cache.activenum;
            kernel_cache.activenum++;
        }
        else {
            kernel_cache.totdoc2active[j]=-1;
        }
    }

    kernel_cache.max_elems=
        (int32_t)(kernel_cache.buffsize/kernel_cache.activenum);
    if(kernel_cache.max_elems>totdoc) {
        kernel_cache.max_elems=totdoc;
    }

    delete[] keep;

}

void CKernel::kernel_cache_reset_lru()
{
    int32_t maxlru=0,k;

    for(k=0;k<kernel_cache.max_elems;k++) {
        if(maxlru < kernel_cache.lru[k])
            maxlru=kernel_cache.lru[k];
    }
    for(k=0;k<kernel_cache.max_elems;k++) {
        kernel_cache.lru[k]-=maxlru;
    }
}

void CKernel::kernel_cache_cleanup()
{
    delete[] kernel_cache.index;
    delete[] kernel_cache.occu;
    delete[] kernel_cache.lru;
    delete[] kernel_cache.invindex;
    delete[] kernel_cache.active2totdoc;
    delete[] kernel_cache.totdoc2active;
    delete[] kernel_cache.buffer;
    memset(&kernel_cache, 0x0, sizeof(KERNEL_CACHE));
}

int32_t CKernel::kernel_cache_malloc()
{
  int32_t i;

  if(kernel_cache_space_available()) {
    for(i=0;i<kernel_cache.max_elems;i++) {
      if(!kernel_cache.occu[i]) {
    kernel_cache.occu[i]=1;
    kernel_cache.elems++;
    return(i);
      }
    }
  }
  return(-1);
}

void CKernel::kernel_cache_free(int32_t cacheidx)
{
    kernel_cache.occu[cacheidx]=0;
    kernel_cache.elems--;
}

// remove least recently used cache
// element
int32_t CKernel::kernel_cache_free_lru()
{
  register int32_t k,least_elem=-1,least_time;

  least_time=kernel_cache.time+1;
  for(k=0;k<kernel_cache.max_elems;k++) {
    if(kernel_cache.invindex[k] != -1) {
      if(kernel_cache.lru[k]<least_time) {
    least_time=kernel_cache.lru[k];
    least_elem=k;
      }
    }
  }

  if(least_elem != -1) {
    kernel_cache_free(least_elem);
    kernel_cache.index[kernel_cache.invindex[least_elem]]=-1;
    kernel_cache.invindex[least_elem]=-1;
    return(1);
  }
  return(0);
}

// Get a free cache entry. In case cache is full, the lru
// element is removed.
KERNELCACHE_ELEM* CKernel::kernel_cache_clean_and_malloc(int32_t cacheidx)
{
    int32_t result;
    if((result = kernel_cache_malloc()) == -1) {
        if(kernel_cache_free_lru()) {
            result = kernel_cache_malloc();
        }
    }
    kernel_cache.index[cacheidx]=result;
    if(result == -1) {
        return(0);
    }
    kernel_cache.invindex[result]=cacheidx;
    kernel_cache.lru[kernel_cache.index[cacheidx]]=kernel_cache.time; // lru
    return &kernel_cache.buffer[((KERNELCACHE_IDX) kernel_cache.activenum)*kernel_cache.index[cacheidx]];
}
#endif //USE_SVMLIGHT

void CKernel::load(CFile* loader)
{
}

void CKernel::save(CFile* writer)
{
    int32_t m,n;
    float64_t* km=get_kernel_matrix<float64_t>(m,n, NULL);
    writer->set_real_matrix(km, m,n);
    delete[] km;
}

void CKernel::remove_lhs_and_rhs()
{
    if (rhs!=lhs)
        SG_UNREF(rhs);
    rhs = NULL;
    num_rhs=0;

    SG_UNREF(lhs);
    lhs = NULL;
    num_lhs=0;

#ifdef USE_SVMLIGHT
    cache_reset();
#endif //USE_SVMLIGHT
}

void CKernel::remove_lhs()
{
    if (rhs==lhs)
        rhs=NULL;
    SG_UNREF(lhs);
    lhs = NULL;
    num_lhs=NULL;

#ifdef USE_SVMLIGHT
    cache_reset();
#endif //USE_SVMLIGHT
}

void CKernel::remove_rhs()
{
    if (rhs!=lhs)
        SG_UNREF(rhs);
    rhs = NULL;
    num_rhs=NULL;

#ifdef USE_SVMLIGHT
    cache_reset();
#endif //USE_SVMLIGHT
}


void CKernel::list_kernel()
{
    SG_INFO( "%p - \"%s\" weight=%1.2f OPT:%s", this, get_name(),
            get_combined_kernel_weight(),
            get_optimization_type()==FASTBUTMEMHUNGRY ? "FASTBUTMEMHUNGRY" :
            "SLOWBUTMEMEFFICIENT");

    switch (get_kernel_type())
    {
        case K_UNKNOWN:
            SG_INFO( "K_UNKNOWN ");
            break;
        case K_LINEAR:
            SG_INFO( "K_LINEAR ");
            break;
        case K_SPARSELINEAR:
            SG_INFO( "K_SPARSELINEAR ");
            break;
        case K_POLY:
            SG_INFO( "K_POLY ");
            break;
        case K_GAUSSIAN:
            SG_INFO( "K_GAUSSIAN ");
            break;
        case K_SPARSEGAUSSIAN:
            SG_INFO( "K_SPARSEGAUSSIAN ");
            break;
        case K_GAUSSIANSHIFT:
            SG_INFO( "K_GAUSSIANSHIFT ");
            break;
        case K_HISTOGRAM:
            SG_INFO( "K_HISTOGRAM ");
            break;
        case K_SALZBERG:
            SG_INFO( "K_SALZBERG ");
            break;
        case K_LOCALITYIMPROVED:
            SG_INFO( "K_LOCALITYIMPROVED ");
            break;
        case K_SIMPLELOCALITYIMPROVED:
            SG_INFO( "K_SIMPLELOCALITYIMPROVED ");
            break;
        case K_FIXEDDEGREE:
            SG_INFO( "K_FIXEDDEGREE ");
            break;
        case K_WEIGHTEDDEGREE:
            SG_INFO( "K_WEIGHTEDDEGREE ");
            break;
        case K_WEIGHTEDDEGREEPOS:
            SG_INFO( "K_WEIGHTEDDEGREEPOS ");
            break;
        case K_WEIGHTEDDEGREERBF:
            SG_INFO( "K_WEIGHTEDDEGREERBF ");
            break;
        case K_WEIGHTEDCOMMWORDSTRING:
            SG_INFO( "K_WEIGHTEDCOMMWORDSTRING ");
            break;
        case K_POLYMATCH:
            SG_INFO( "K_POLYMATCH ");
            break;
        case K_ALIGNMENT:
            SG_INFO( "K_ALIGNMENT ");
            break;
        case K_COMMWORDSTRING:
            SG_INFO( "K_COMMWORDSTRING ");
            break;
        case K_COMMULONGSTRING:
            SG_INFO( "K_COMMULONGSTRING ");
            break;
        case K_SPECTRUMMISMATCHRBF:
            SG_INFO( "K_SPECTRUMMISMATCHRBF ");
            break;
        case K_COMBINED:
            SG_INFO( "K_COMBINED ");
            break;
        case K_AUC:
            SG_INFO( "K_AUC ");
            break;
        case K_CUSTOM:
            SG_INFO( "K_CUSTOM ");
            break;
        case K_SIGMOID:
            SG_INFO( "K_SIGMOID ");
            break;
        case K_CHI2:
            SG_INFO( "K_CHI2 ");
            break;
        case K_DIAG:
            SG_INFO( "K_DIAG ");
            break;
        case K_CONST:
            SG_INFO( "K_CONST ");
            break;
        case K_DISTANCE:
            SG_INFO( "K_DISTANCE ");
            break;
        case K_LOCALALIGNMENT:
            SG_INFO( "K_LOCALALIGNMENT ");
            break;
        case K_TPPK:
            SG_INFO( "K_TPPK ");
            break;
        default:
         SG_ERROR( "ERROR UNKNOWN KERNEL TYPE");
            break;
    }

    switch (get_feature_class())
    {
        case C_UNKNOWN:
            SG_INFO( "C_UNKNOWN ");
            break;
        case C_SIMPLE:
            SG_INFO( "C_SIMPLE ");
            break;
        case C_SPARSE:
            SG_INFO( "C_SPARSE ");
            break;
        case C_STRING:
            SG_INFO( "C_STRING ");
            break;
        case C_COMBINED:
            SG_INFO( "C_COMBINED ");
            break;
        case C_ANY:
            SG_INFO( "C_ANY ");
            break;
        default:
         SG_ERROR( "ERROR UNKNOWN FEATURE CLASS");
    }

    switch (get_feature_type())
    {
        case F_UNKNOWN:
            SG_INFO( "F_UNKNOWN ");
            break;
        case F_DREAL:
            SG_INFO( "F_REAL ");
            break;
        case F_SHORT:
            SG_INFO( "F_SHORT ");
            break;
        case F_CHAR:
            SG_INFO( "F_CHAR ");
            break;
        case F_INT:
            SG_INFO( "F_INT ");
            break;
        case F_BYTE:
            SG_INFO( "F_BYTE ");
            break;
        case F_WORD:
            SG_INFO( "F_WORD ");
            break;
        case F_ULONG:
            SG_INFO( "F_ULONG ");
            break;
        case F_ANY:
            SG_INFO( "F_ANY ");
            break;
        default:
         SG_ERROR( "ERROR UNKNOWN FEATURE TYPE");
            break;
    }
    SG_INFO( "\n");
}

bool CKernel::init_optimization(
    int32_t count, int32_t *IDX, float64_t * weights)
{
   SG_ERROR( "kernel does not support linadd optimization\n");
    return false ;
}

bool CKernel::delete_optimization()
{
   SG_ERROR( "kernel does not support linadd optimization\n");
    return false;
}

float64_t CKernel::compute_optimized(int32_t vector_idx)
{
   SG_ERROR( "kernel does not support linadd optimization\n");
    return 0;
}

void CKernel::compute_batch(
    int32_t num_vec, int32_t* vec_idx, float64_t* target, int32_t num_suppvec,
    int32_t* IDX, float64_t* weights, float64_t factor)
{
   SG_ERROR( "kernel does not support batch computation\n");
}

void CKernel::add_to_normal(int32_t vector_idx, float64_t weight)
{
   SG_ERROR( "kernel does not support linadd optimization, add_to_normal not implemented\n");
}

void CKernel::clear_normal()
{
   SG_ERROR( "kernel does not support linadd optimization, clear_normal not implemented\n");
}

int32_t CKernel::get_num_subkernels()
{
    return 1;
}

void CKernel::compute_by_subkernel(
    int32_t vector_idx, float64_t * subkernel_contrib)
{
   SG_ERROR( "kernel compute_by_subkernel not implemented\n");
}

const float64_t* CKernel::get_subkernel_weights(int32_t &num_weights)
{
    num_weights=1 ;
    return &combined_kernel_weight ;
}

void CKernel::set_subkernel_weights(float64_t* weights, int32_t num_weights)
{
    combined_kernel_weight = weights[0] ;
    if (num_weights!=1)
      SG_ERROR( "number of subkernel weights should be one ...\n");
}

bool CKernel::init_optimization_svm(CSVM * svm)
{
    int32_t num_suppvec=svm->get_num_support_vectors();
    int32_t* sv_idx=new int32_t[num_suppvec];
    float64_t* sv_weight=new float64_t[num_suppvec];

    for (int32_t i=0; i<num_suppvec; i++)
    {
        sv_idx[i]    = svm->get_support_vector(i);
        sv_weight[i] = svm->get_alpha(i);
    }
    bool ret = init_optimization(num_suppvec, sv_idx, sv_weight);

    delete[] sv_idx;
    delete[] sv_weight;
    return ret;
}