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Kernel.cpp
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Kernel.cpp
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/*
* 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 <shogun/base/progress.h>
#include <shogun/io/File.h>
#include <shogun/io/SGIO.h>
#include <shogun/lib/Signal.h>
#include <shogun/lib/Time.h>
#include <shogun/lib/common.h>
#include <shogun/lib/config.h>
#include <shogun/base/Parallel.h>
#include <shogun/kernel/Kernel.h>
#include <shogun/kernel/normalizer/IdentityKernelNormalizer.h>
#include <shogun/features/Features.h>
#include <shogun/base/Parameter.h>
#include <shogun/classifier/svm/SVM.h>
#include <string.h>
#ifndef _WIN32
#include <unistd.h>
#endif
#include <shogun/mathematics/Math.h>
using namespace shogun;
CKernel::CKernel() : CSGObject()
{
init();
register_params();
}
CKernel::CKernel(int32_t size) : CSGObject()
{
init();
if (size<10)
size=10;
cache_size=size;
register_params();
}
CKernel::CKernel(CFeatures* p_lhs, CFeatures* p_rhs, int32_t size) : CSGObject()
{
init();
if (size<10)
size=10;
cache_size=size;
set_normalizer(new CIdentityKernelNormalizer());
init(p_lhs, p_rhs);
register_params();
}
CKernel::~CKernel()
{
if (get_is_initialized())
SG_ERROR("Kernel still initialized on destruction.\n")
remove_lhs_and_rhs();
SG_UNREF(normalizer);
}
#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 that features are not deleted if same ones are used */
SG_REF(l);
SG_REF(r);
//make sure features were indeed supplied
REQUIRE(l, "CKernel::init(%p, %p): Left hand side features required!\n", l, r)
REQUIRE(r, "CKernel::init(%p, %p): Right hand side features required!\n", l, r)
//make sure features are compatible
if (l->support_compatible_class())
{
REQUIRE(l->get_feature_class_compatibility(r->get_feature_class()),
"Right hand side of features (%s) must be compatible with left hand side features (%s)\n",
l->get_name(), r->get_name());
}
else
{
REQUIRE(l->get_feature_class()==r->get_feature_class(),
"Right hand side of features (%s) must be compatible with left hand side features (%s)\n",
l->get_name(), r->get_name())
}
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)
lhs_equals_rhs=true;
else // 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();
/* unref "safety" refs from beginning */
SG_UNREF(r);
SG_UNREF(l);
SG_DEBUG("leaving CKernel::init(%p, %p)\n", l, r)
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 = SG_MALLOC(int32_t, totdoc);
kernel_cache.occu = SG_MALLOC(int32_t, totdoc);
kernel_cache.lru = SG_MALLOC(int32_t, totdoc);
kernel_cache.invindex = SG_MALLOC(int32_t, totdoc);
kernel_cache.active2totdoc = SG_MALLOC(int32_t, totdoc);
kernel_cache.totdoc2active = SG_MALLOC(int32_t, totdoc);
kernel_cache.buffer = SG_MALLOC(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)
{
int32_t nthreads=parallel->get_num_threads();
if (nthreads<2)
{
for(int32_t i=0;i<num_rows;i++)
cache_kernel_row(rows[i]);
}
else
{
// fill up kernel cache
int32_t* uncached_rows = SG_MALLOC(int32_t, num_rows);
KERNELCACHE_ELEM** cache = SG_MALLOC(KERNELCACHE_ELEM*, num_rows);
S_KTHREAD_PARAM params;
int32_t num_threads=nthreads-1;
int32_t num_vec=get_num_vec_lhs();
ASSERT(num_vec>0)
uint8_t* needs_computation=SG_CALLOC(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 (idx>=num_vec)
idx=2*num_vec-1-idx;
if (kernel_cache_check(idx))
continue;
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/nthreads;
if (step<1)
{
num_threads=num-1;
step=1;
}
#pragma omp parallel for private(params)
for (int32_t t=0; t<num_threads; t++)
{
params.kernel = this;
params.kernel_cache = &kernel_cache;
params.cache = cache;
params.uncached_rows = uncached_rows;
params.needs_computation = needs_computation;
params.num_uncached = num;
params.start = t*step;
params.end = (t+1)*step;
params.num_vectors = get_num_vec_lhs();
end=params.end;
cache_multiple_kernel_row_helper(¶ms);
}
}
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);
SG_FREE(needs_computation);
SG_FREE(cache);
SG_FREE(uncached_rows);
}
}
// 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)
{
ASSERT(totdoc > 0);
register int32_t i,j,jj,scount; // 0 in after.
KERNELCACHE_IDX from=0,to=0;
int32_t *keep;
keep=SG_MALLOC(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;
if (kernel_cache.activenum>0)
kernel_cache.buffsize/=kernel_cache.activenum;
if(kernel_cache.max_elems>totdoc)
kernel_cache.max_elems=totdoc;
SG_FREE(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()
{
SG_FREE(kernel_cache.index);
SG_FREE(kernel_cache.occu);
SG_FREE(kernel_cache.lru);
SG_FREE(kernel_cache.invindex);
SG_FREE(kernel_cache.active2totdoc);
SG_FREE(kernel_cache.totdoc2active);
SG_FREE(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)
{
SG_SET_LOCALE_C;
SG_RESET_LOCALE;
}
void CKernel::save(CFile* writer)
{
SGMatrix<float64_t> k_matrix=get_kernel_matrix<float64_t>();
SG_SET_LOCALE_C;
writer->set_matrix(k_matrix.matrix, k_matrix.num_rows, k_matrix.num_cols);
SG_RESET_LOCALE;
}
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;
lhs_equals_rhs=false;
#ifdef USE_SVMLIGHT
cache_reset();
#endif //USE_SVMLIGHT
}
void CKernel::remove_lhs()
{
if (rhs==lhs)
rhs=NULL;
SG_UNREF(lhs);
lhs = NULL;
num_lhs=0;
lhs_equals_rhs=false;
#ifdef USE_SVMLIGHT
cache_reset();
#endif //USE_SVMLIGHT
}
/// takes all necessary steps if the rhs is removed from kernel
void CKernel::remove_rhs()
{
if (rhs!=lhs)
SG_UNREF(rhs);
rhs = NULL;
num_rhs=0;
lhs_equals_rhs=false;
#ifdef USE_SVMLIGHT
cache_reset();
#endif //USE_SVMLIGHT
}
#define ENUM_CASE(n) case n: SG_INFO(#n " ") break;
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())
{
ENUM_CASE(K_UNKNOWN)
ENUM_CASE(K_LINEAR)
ENUM_CASE(K_POLY)
ENUM_CASE(K_GAUSSIAN)
ENUM_CASE(K_GAUSSIANSHIFT)
ENUM_CASE(K_GAUSSIANMATCH)
ENUM_CASE(K_GAUSSIANCOMPACT)
ENUM_CASE(K_HISTOGRAM)
ENUM_CASE(K_SALZBERG)
ENUM_CASE(K_LOCALITYIMPROVED)
ENUM_CASE(K_SIMPLELOCALITYIMPROVED)
ENUM_CASE(K_FIXEDDEGREE)
ENUM_CASE(K_WEIGHTEDDEGREE)
ENUM_CASE(K_WEIGHTEDDEGREEPOS)
ENUM_CASE(K_WEIGHTEDDEGREERBF)
ENUM_CASE(K_WEIGHTEDCOMMWORDSTRING)
ENUM_CASE(K_POLYMATCH)
ENUM_CASE(K_ALIGNMENT)
ENUM_CASE(K_COMMWORDSTRING)
ENUM_CASE(K_COMMULONGSTRING)
ENUM_CASE(K_SPECTRUMRBF)
ENUM_CASE(K_COMBINED)
ENUM_CASE(K_AUC)
ENUM_CASE(K_CUSTOM)
ENUM_CASE(K_SIGMOID)
ENUM_CASE(K_CHI2)
ENUM_CASE(K_DIAG)
ENUM_CASE(K_CONST)
ENUM_CASE(K_DISTANCE)
ENUM_CASE(K_LOCALALIGNMENT)
ENUM_CASE(K_PYRAMIDCHI2)
ENUM_CASE(K_OLIGO)
ENUM_CASE(K_MATCHWORD)
ENUM_CASE(K_TPPK)
ENUM_CASE(K_REGULATORYMODULES)
ENUM_CASE(K_SPARSESPATIALSAMPLE)
ENUM_CASE(K_HISTOGRAMINTERSECTION)
ENUM_CASE(K_WAVELET)
ENUM_CASE(K_WAVE)
ENUM_CASE(K_CAUCHY)
ENUM_CASE(K_TSTUDENT)
ENUM_CASE(K_MULTIQUADRIC)
ENUM_CASE(K_EXPONENTIAL)
ENUM_CASE(K_RATIONAL_QUADRATIC)
ENUM_CASE(K_POWER)
ENUM_CASE(K_SPHERICAL)
ENUM_CASE(K_LOG)
ENUM_CASE(K_SPLINE)
ENUM_CASE(K_ANOVA)
ENUM_CASE(K_CIRCULAR)
ENUM_CASE(K_INVERSEMULTIQUADRIC)
ENUM_CASE(K_SPECTRUMMISMATCHRBF)
ENUM_CASE(K_DISTANTSEGMENTS)
ENUM_CASE(K_BESSEL)
ENUM_CASE(K_JENSENSHANNON)
ENUM_CASE(K_DIRECTOR)
ENUM_CASE(K_PRODUCT)
ENUM_CASE(K_EXPONENTIALARD)
ENUM_CASE(K_GAUSSIANARD)
ENUM_CASE(K_GAUSSIANARDSPARSE)
ENUM_CASE(K_STREAMING)
ENUM_CASE(K_PERIODIC)
}
switch (get_feature_class())
{
ENUM_CASE(C_UNKNOWN)
ENUM_CASE(C_DENSE)
ENUM_CASE(C_SPARSE)
ENUM_CASE(C_STRING)
ENUM_CASE(C_STREAMING_DENSE)
ENUM_CASE(C_STREAMING_SPARSE)
ENUM_CASE(C_STREAMING_STRING)
ENUM_CASE(C_STREAMING_VW)
ENUM_CASE(C_COMBINED)
ENUM_CASE(C_COMBINED_DOT)
ENUM_CASE(C_WD)
ENUM_CASE(C_SPEC)
ENUM_CASE(C_WEIGHTEDSPEC)
ENUM_CASE(C_POLY)
ENUM_CASE(C_BINNED_DOT)
ENUM_CASE(C_DIRECTOR_DOT)
ENUM_CASE(C_LATENT)
ENUM_CASE(C_MATRIX)
ENUM_CASE(C_FACTOR_GRAPH)
ENUM_CASE(C_INDEX)
ENUM_CASE(C_SUB_SAMPLES_DENSE)
ENUM_CASE(C_ANY)
}
switch (get_feature_type())
{
ENUM_CASE(F_UNKNOWN)
ENUM_CASE(F_BOOL)
ENUM_CASE(F_CHAR)
ENUM_CASE(F_BYTE)
ENUM_CASE(F_SHORT)
ENUM_CASE(F_WORD)
ENUM_CASE(F_INT)
ENUM_CASE(F_UINT)
ENUM_CASE(F_LONG)
ENUM_CASE(F_ULONG)
ENUM_CASE(F_SHORTREAL)
ENUM_CASE(F_DREAL)
ENUM_CASE(F_LONGREAL)
ENUM_CASE(F_ANY)
}
SG_INFO("\n")
}
#undef ENUM_CASE
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 ;
}
SGVector<float64_t> CKernel::get_subkernel_weights()
{
int num_weights = 1;
const float64_t* weight = get_subkernel_weights(num_weights);
return SGVector<float64_t>(const_cast<float64_t*>(weight),1,false);
}
void CKernel::set_subkernel_weights(const SGVector<float64_t> weights)
{
ASSERT(weights.vector)
if (weights.vlen!=1)
SG_ERROR("number of subkernel weights should be one ...\n")
combined_kernel_weight = weights.vector[0] ;
}
CKernel* CKernel::obtain_from_generic(CSGObject* kernel)
{
if (kernel)
{
CKernel* casted=dynamic_cast<CKernel*>(kernel);
REQUIRE(casted, "CKernel::obtain_from_generic(): Error, provided object"
" of class \"%s\" is not a subclass of CKernel!\n",
kernel->get_name());
return casted;
}
else
return NULL;
}
bool CKernel::init_optimization_svm(CSVM * svm)
{
int32_t num_suppvec=svm->get_num_support_vectors();
int32_t* sv_idx=SG_MALLOC(int32_t, num_suppvec);
float64_t* sv_weight=SG_MALLOC(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);
SG_FREE(sv_idx);
SG_FREE(sv_weight);
return ret;
}
void CKernel::load_serializable_post() throw (ShogunException)
{
CSGObject::load_serializable_post();
if (lhs_equals_rhs)
rhs=lhs;
}
void CKernel::save_serializable_pre() throw (ShogunException)
{
CSGObject::save_serializable_pre();
if (lhs_equals_rhs)
rhs=NULL;
}
void CKernel::save_serializable_post() throw (ShogunException)
{
CSGObject::save_serializable_post();
if (lhs_equals_rhs)
rhs=lhs;
}
void CKernel::register_params() {
SG_ADD(&cache_size, "cache_size",
"Cache size in MB.", MS_NOT_AVAILABLE);
SG_ADD(
&lhs, "lhs", "Feature vectors to occur on left hand side.",
MS_NOT_AVAILABLE);
SG_ADD(
&rhs, "rhs", "Feature vectors to occur on right hand side.",
MS_NOT_AVAILABLE);
SG_ADD(&lhs_equals_rhs, "lhs_equals_rhs",
"If features on lhs are the same as on rhs.", MS_NOT_AVAILABLE);
SG_ADD(&num_lhs, "num_lhs", "Number of feature vectors on left hand side.",
MS_NOT_AVAILABLE);
SG_ADD(&num_rhs, "num_rhs", "Number of feature vectors on right hand side.",
MS_NOT_AVAILABLE);
SG_ADD(&combined_kernel_weight, "combined_kernel_weight",
"Combined kernel weight.", MS_AVAILABLE);
SG_ADD(&optimization_initialized, "optimization_initialized",
"Optimization is initialized.", MS_NOT_AVAILABLE);
SG_ADD((machine_int_t*) &opt_type, "opt_type",
"Optimization type.", MS_NOT_AVAILABLE);
SG_ADD(&properties, "properties", "Kernel properties.", MS_NOT_AVAILABLE);
SG_ADD((CSGObject**) &normalizer, "normalizer", "Normalize the kernel.",
MS_AVAILABLE);
}
void CKernel::init()
{
cache_size=10;
kernel_matrix=NULL;
lhs=NULL;
rhs=NULL;
num_lhs=0;
num_rhs=0;
lhs_equals_rhs=false;
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());
}
namespace shogun
{
/** kernel thread parameters */
template <class T> struct K_THREAD_PARAM
{
/** kernel */
CKernel* kernel;
/** start (unit row) */
int32_t start;
/** end (unit row) */
int32_t end;
/** start (unit number of elements) */
int64_t total_start;
/** m */
int32_t m;
/** n */
int32_t n;
/** result */
T* result;
/** kernel matrix k(i,j)=k(j,i) */
bool symmetric;
/** output progress */
bool verbose;
/* Progress bar*/
PRange<int64_t>* pb;
};
}