00001
00002
00003
00004
00005
00006
00007
00008
00009
00010
00011 #include <shogun/lib/config.h>
00012
00013 #ifdef USE_SVMLIGHT
00014
00015 #include <shogun/io/SGIO.h>
00016 #include <shogun/mathematics/lapack.h>
00017 #include <shogun/lib/Signal.h>
00018 #include <shogun/mathematics/Math.h>
00019 #include <shogun/regression/svr/SVRLight.h>
00020 #include <shogun/machine/KernelMachine.h>
00021 #include <shogun/kernel/CombinedKernel.h>
00022
00023 #include <unistd.h>
00024
00025 #ifdef USE_CPLEX
00026 extern "C" {
00027 #include <ilcplex/cplex.h>
00028 }
00029 #endif
00030
00031 #include <shogun/base/Parallel.h>
00032
00033 #ifdef HAVE_PTHREAD
00034 #include <pthread.h>
00035 #endif
00036
00037 using namespace shogun;
00038
00039 #ifndef DOXYGEN_SHOULD_SKIP_THIS
00040 struct S_THREAD_PARAM
00041 {
00042 float64_t* lin;
00043 int32_t start, end;
00044 int32_t* active2dnum;
00045 int32_t* docs;
00046 CKernel* kernel;
00047 int32_t num_vectors;
00048 };
00049 #endif // DOXYGEN_SHOULD_SKIP_THIS
00050
00051 CSVRLight::CSVRLight(float64_t C, float64_t eps, CKernel* k, CLabels* lab)
00052 : CSVMLight(C, k, lab)
00053 {
00054 set_tube_epsilon(eps);
00055 }
00056
00057 CSVRLight::CSVRLight()
00058 : CSVMLight()
00059 {
00060 }
00061
00063 CSVRLight::~CSVRLight()
00064 {
00065 }
00066
00067 EClassifierType CSVRLight::get_classifier_type()
00068 {
00069 return CT_SVRLIGHT;
00070 }
00071
00072 bool CSVRLight::train_machine(CFeatures* data)
00073 {
00074
00075 verbosity=1;
00076 init_margin=0.15;
00077 init_iter=500;
00078 precision_violations=0;
00079 opt_precision=DEF_PRECISION;
00080
00081 strcpy (learn_parm->predfile, "");
00082 learn_parm->biased_hyperplane=1;
00083 learn_parm->sharedslack=0;
00084 learn_parm->remove_inconsistent=0;
00085 learn_parm->skip_final_opt_check=1;
00086 learn_parm->svm_maxqpsize=get_qpsize();
00087 learn_parm->svm_newvarsinqp=learn_parm->svm_maxqpsize-1;
00088 learn_parm->maxiter=100000;
00089 learn_parm->svm_iter_to_shrink=100;
00090 learn_parm->svm_c=get_C1();
00091 learn_parm->transduction_posratio=0.33;
00092 learn_parm->svm_costratio=get_C2()/get_C1();
00093 learn_parm->svm_costratio_unlab=1.0;
00094 learn_parm->svm_unlabbound=1E-5;
00095 learn_parm->epsilon_crit=epsilon;
00096 learn_parm->epsilon_a=1E-15;
00097 learn_parm->compute_loo=0;
00098 learn_parm->rho=1.0;
00099 learn_parm->xa_depth=0;
00100
00101 if (!kernel)
00102 {
00103 SG_ERROR( "SVR_light can not proceed without kernel!\n");
00104 return false ;
00105 }
00106
00107 if (!labels)
00108 {
00109 SG_ERROR( "SVR_light can not proceed without labels!\n");
00110 return false;
00111 }
00112
00113 if (data)
00114 {
00115 if (labels->get_num_labels() != data->get_num_vectors())
00116 SG_ERROR("Number of training vectors does not match number of labels\n");
00117 kernel->init(data, data);
00118 }
00119
00120 if (kernel->has_property(KP_LINADD) && get_linadd_enabled())
00121 kernel->clear_normal();
00122
00123
00124 SG_DEBUG( "qpsize = %i\n", learn_parm->svm_maxqpsize) ;
00125 SG_DEBUG( "epsilon = %1.1e\n", learn_parm->epsilon_crit) ;
00126 SG_DEBUG( "kernel->has_property(KP_LINADD) = %i\n", kernel->has_property(KP_LINADD)) ;
00127 SG_DEBUG( "kernel->has_property(KP_KERNCOMBINATION) = %i\n", kernel->has_property(KP_KERNCOMBINATION)) ;
00128 SG_DEBUG( "get_linadd_enabled() = %i\n", get_linadd_enabled()) ;
00129 SG_DEBUG( "kernel->get_num_subkernels() = %i\n", kernel->get_num_subkernels()) ;
00130
00131 use_kernel_cache = !((kernel->get_kernel_type() == K_CUSTOM) ||
00132 (get_linadd_enabled() && kernel->has_property(KP_LINADD)));
00133
00134 SG_DEBUG( "use_kernel_cache = %i\n", use_kernel_cache) ;
00135
00136
00137 svr_learn();
00138
00139
00140 create_new_model(model->sv_num-1);
00141 set_bias(-model->b);
00142 for (int32_t i=0; i<model->sv_num-1; i++)
00143 {
00144 set_alpha(i, model->alpha[i+1]);
00145 set_support_vector(i, model->supvec[i+1]);
00146 }
00147
00148 if (kernel->has_property(KP_LINADD) && get_linadd_enabled())
00149 kernel->clear_normal() ;
00150
00151 return true ;
00152 }
00153
00154 void CSVRLight::svr_learn()
00155 {
00156 int32_t *inconsistent, i, j;
00157 int32_t upsupvecnum;
00158 float64_t maxdiff, *lin, *c, *a;
00159 int32_t iterations;
00160 float64_t *xi_fullset;
00161 float64_t *a_fullset;
00162 TIMING timing_profile;
00163 SHRINK_STATE shrink_state;
00164 int32_t* label;
00165 int32_t* docs;
00166
00167 ASSERT(labels);
00168 int32_t totdoc=labels->get_num_labels();
00169 num_vectors=totdoc;
00170
00171
00172 docs=SG_MALLOC(int32_t, 2*totdoc);
00173 label=SG_MALLOC(int32_t, 2*totdoc);
00174 c = SG_MALLOC(float64_t, 2*totdoc);
00175
00176 for(i=0;i<totdoc;i++) {
00177 docs[i]=i;
00178 j=2*totdoc-1-i;
00179 label[i]=+1;
00180 c[i]=labels->get_label(i);
00181 docs[j]=j;
00182 label[j]=-1;
00183 c[j]=labels->get_label(i);
00184 }
00185 totdoc*=2;
00186
00187
00188 kernel->resize_kernel_cache( kernel->get_cache_size(), true);
00189
00190 if (kernel->get_kernel_type() == K_COMBINED)
00191 {
00192 CCombinedKernel* k = (CCombinedKernel*) kernel;
00193 CKernel* kn = k->get_first_kernel();
00194
00195 while (kn)
00196 {
00197 kn->resize_kernel_cache( kernel->get_cache_size(), true);
00198 SG_UNREF(kn);
00199 kn = k->get_next_kernel();
00200 }
00201 }
00202
00203 timing_profile.time_kernel=0;
00204 timing_profile.time_opti=0;
00205 timing_profile.time_shrink=0;
00206 timing_profile.time_update=0;
00207 timing_profile.time_model=0;
00208 timing_profile.time_check=0;
00209 timing_profile.time_select=0;
00210
00211 SG_FREE(W);
00212 W=NULL;
00213
00214 if (kernel->has_property(KP_KERNCOMBINATION) && callback)
00215 {
00216 W = SG_MALLOC(float64_t, totdoc*kernel->get_num_subkernels());
00217 for (i=0; i<totdoc*kernel->get_num_subkernels(); i++)
00218 W[i]=0;
00219 }
00220
00221
00222 if((learn_parm->svm_newvarsinqp < 2)
00223 || (learn_parm->svm_newvarsinqp > learn_parm->svm_maxqpsize)) {
00224 learn_parm->svm_newvarsinqp=learn_parm->svm_maxqpsize;
00225 }
00226
00227 init_shrink_state(&shrink_state,totdoc,(int32_t)MAXSHRINK);
00228
00229 inconsistent = SG_MALLOC(int32_t, totdoc);
00230 a = SG_MALLOC(float64_t, totdoc);
00231 a_fullset = SG_MALLOC(float64_t, totdoc);
00232 xi_fullset = SG_MALLOC(float64_t, totdoc);
00233 lin = SG_MALLOC(float64_t, totdoc);
00234 learn_parm->svm_cost = SG_MALLOC(float64_t, totdoc);
00235 if (m_linear_term.vlen>0)
00236 learn_parm->eps=get_linear_term_array();
00237 else
00238 {
00239 learn_parm->eps=SG_MALLOC(float64_t, totdoc);
00240 CMath::fill_vector(learn_parm->eps, totdoc, tube_epsilon);
00241 }
00242
00243 SG_FREE(model->supvec);
00244 SG_FREE(model->alpha);
00245 SG_FREE(model->index);
00246 model->supvec = SG_MALLOC(int32_t, totdoc+2);
00247 model->alpha = SG_MALLOC(float64_t, totdoc+2);
00248 model->index = SG_MALLOC(int32_t, totdoc+2);
00249
00250 model->at_upper_bound=0;
00251 model->b=0;
00252 model->supvec[0]=0;
00253 model->alpha[0]=0;
00254 model->totdoc=totdoc;
00255
00256 model->kernel=kernel;
00257
00258 model->sv_num=1;
00259 model->loo_error=-1;
00260 model->loo_recall=-1;
00261 model->loo_precision=-1;
00262 model->xa_error=-1;
00263 model->xa_recall=-1;
00264 model->xa_precision=-1;
00265
00266 for(i=0;i<totdoc;i++) {
00267 inconsistent[i]=0;
00268 a[i]=0;
00269 lin[i]=0;
00270
00271 if(label[i] > 0) {
00272 learn_parm->svm_cost[i]=learn_parm->svm_c*learn_parm->svm_costratio*
00273 fabs((float64_t)label[i]);
00274 }
00275 else if(label[i] < 0) {
00276 learn_parm->svm_cost[i]=learn_parm->svm_c*fabs((float64_t)label[i]);
00277 }
00278 else
00279 ASSERT(false);
00280 }
00281
00282 if(verbosity==1) {
00283 SG_DEBUG( "Optimizing...\n");
00284 }
00285
00286
00287 SG_DEBUG( "num_train: %d\n", totdoc);
00288 iterations=optimize_to_convergence(docs,label,totdoc,
00289 &shrink_state,inconsistent,a,lin,
00290 c,&timing_profile,
00291 &maxdiff,(int32_t)-1,
00292 (int32_t)1);
00293
00294
00295 if(verbosity>=1) {
00296 SG_DONE();
00297 SG_INFO("(%ld iterations)\n",iterations);
00298 SG_INFO( "Optimization finished (maxdiff=%.8f).\n",maxdiff);
00299 SG_INFO( "obj = %.16f, rho = %.16f\n",get_objective(),model->b);
00300
00301 upsupvecnum=0;
00302
00303 SG_DEBUG( "num sv: %d\n", model->sv_num);
00304 for(i=1;i<model->sv_num;i++)
00305 {
00306 if(fabs(model->alpha[i]) >=
00307 (learn_parm->svm_cost[model->supvec[i]]-
00308 learn_parm->epsilon_a))
00309 upsupvecnum++;
00310 }
00311 SG_INFO( "Number of SV: %ld (including %ld at upper bound)\n",
00312 model->sv_num-1,upsupvecnum);
00313 }
00314
00315
00316
00317 for(i=1;i<model->sv_num;i++) {
00318 j=model->supvec[i];
00319 if(j >= (totdoc/2)) {
00320 j=totdoc-j-1;
00321 }
00322 model->supvec[i]=j;
00323 }
00324
00325 shrink_state_cleanup(&shrink_state);
00326 SG_FREE(label);
00327 SG_FREE(inconsistent);
00328 SG_FREE(c);
00329 SG_FREE(a);
00330 SG_FREE(a_fullset);
00331 SG_FREE(xi_fullset);
00332 SG_FREE(lin);
00333 SG_FREE(learn_parm->svm_cost);
00334 SG_FREE(docs);
00335 }
00336
00337 float64_t CSVRLight::compute_objective_function(
00338 float64_t *a, float64_t *lin, float64_t *c, float64_t* eps, int32_t *label,
00339 int32_t totdoc)
00340 {
00341
00342 float64_t criterion=0;
00343
00344 for(int32_t i=0;i<totdoc;i++)
00345 criterion+=(eps[i]-(float64_t)label[i]*c[i])*a[i]+0.5*a[i]*label[i]*lin[i];
00346
00347
00348
00349
00350
00351
00352
00353
00354
00355
00356
00357
00358 return(criterion);
00359 }
00360
00361 void* CSVRLight::update_linear_component_linadd_helper(void *params_)
00362 {
00363 S_THREAD_PARAM * params = (S_THREAD_PARAM*) params_ ;
00364
00365 int32_t jj=0, j=0 ;
00366
00367 for(jj=params->start;(jj<params->end) && (j=params->active2dnum[jj])>=0;jj++)
00368 params->lin[j]+=params->kernel->compute_optimized(CSVRLight::regression_fix_index2(params->docs[j], params->num_vectors));
00369
00370 return NULL ;
00371 }
00372
00373 int32_t CSVRLight::regression_fix_index(int32_t i)
00374 {
00375 if (i>=num_vectors)
00376 i=2*num_vectors-1-i;
00377
00378 return i;
00379 }
00380
00381 int32_t CSVRLight::regression_fix_index2(
00382 int32_t i, int32_t num_vectors)
00383 {
00384 if (i>=num_vectors)
00385 i=2*num_vectors-1-i;
00386
00387 return i;
00388 }
00389
00390 float64_t CSVRLight::compute_kernel(int32_t i, int32_t j)
00391 {
00392 i=regression_fix_index(i);
00393 j=regression_fix_index(j);
00394 return kernel->kernel(i, j);
00395 }
00396
00397 void CSVRLight::update_linear_component(
00398 int32_t* docs, int32_t* label, int32_t *active2dnum, float64_t *a,
00399 float64_t *a_old, int32_t *working2dnum, int32_t totdoc, float64_t *lin,
00400 float64_t *aicache, float64_t* c)
00401
00402
00403
00404
00405 {
00406 register int32_t i=0,ii=0,j=0,jj=0;
00407
00408 if (kernel->has_property(KP_LINADD) && get_linadd_enabled())
00409 {
00410 if (callback)
00411 {
00412 update_linear_component_mkl_linadd(docs, label, active2dnum, a, a_old, working2dnum,
00413 totdoc, lin, aicache, c) ;
00414 }
00415 else
00416 {
00417 kernel->clear_normal();
00418
00419 int32_t num_working=0;
00420 for(ii=0;(i=working2dnum[ii])>=0;ii++) {
00421 if(a[i] != a_old[i]) {
00422 kernel->add_to_normal(regression_fix_index(docs[i]), (a[i]-a_old[i])*(float64_t)label[i]);
00423 num_working++;
00424 }
00425 }
00426
00427 if (num_working>0)
00428 {
00429 if (parallel->get_num_threads() < 2)
00430 {
00431 for(jj=0;(j=active2dnum[jj])>=0;jj++) {
00432 lin[j]+=kernel->compute_optimized(regression_fix_index(docs[j]));
00433 }
00434 }
00435 #ifdef HAVE_PTHREAD
00436 else
00437 {
00438 int32_t num_elem = 0 ;
00439 for(jj=0;(j=active2dnum[jj])>=0;jj++) num_elem++ ;
00440
00441 pthread_t* threads = SG_MALLOC(pthread_t, parallel->get_num_threads()-1);
00442 S_THREAD_PARAM* params = SG_MALLOC(S_THREAD_PARAM, parallel->get_num_threads()-1);
00443 int32_t start = 0 ;
00444 int32_t step = num_elem/parallel->get_num_threads() ;
00445 int32_t end = step ;
00446
00447 for (int32_t t=0; t<parallel->get_num_threads()-1; t++)
00448 {
00449 params[t].kernel = kernel ;
00450 params[t].lin = lin ;
00451 params[t].docs = docs ;
00452 params[t].active2dnum=active2dnum ;
00453 params[t].start = start ;
00454 params[t].end = end ;
00455 params[t].num_vectors=num_vectors ;
00456
00457 start=end ;
00458 end+=step ;
00459 pthread_create(&threads[t], NULL, update_linear_component_linadd_helper, (void*)¶ms[t]) ;
00460 }
00461
00462 for(jj=params[parallel->get_num_threads()-2].end;(j=active2dnum[jj])>=0;jj++) {
00463 lin[j]+=kernel->compute_optimized(regression_fix_index(docs[j]));
00464 }
00465 void* ret;
00466 for (int32_t t=0; t<parallel->get_num_threads()-1; t++)
00467 pthread_join(threads[t], &ret) ;
00468
00469 SG_FREE(params);
00470 SG_FREE(threads);
00471 }
00472 #endif
00473 }
00474 }
00475 }
00476 else
00477 {
00478 if (callback)
00479 {
00480 update_linear_component_mkl(docs, label, active2dnum,
00481 a, a_old, working2dnum, totdoc, lin, aicache, c) ;
00482 }
00483 else {
00484 for(jj=0;(i=working2dnum[jj])>=0;jj++) {
00485 if(a[i] != a_old[i]) {
00486 kernel->get_kernel_row(i,active2dnum,aicache);
00487 for(ii=0;(j=active2dnum[ii])>=0;ii++)
00488 lin[j]+=(a[i]-a_old[i])*aicache[j]*(float64_t)label[i];
00489 }
00490 }
00491 }
00492 }
00493 }
00494
00495 void CSVRLight::update_linear_component_mkl(
00496 int32_t* docs, int32_t* label, int32_t *active2dnum, float64_t *a,
00497 float64_t *a_old, int32_t *working2dnum, int32_t totdoc, float64_t *lin,
00498 float64_t *aicache, float64_t* c)
00499 {
00500 int32_t num = totdoc;
00501 int32_t num_weights = -1;
00502 int32_t num_kernels = kernel->get_num_subkernels() ;
00503 const float64_t* old_beta = kernel->get_subkernel_weights(num_weights);
00504
00505 ASSERT(num_weights==num_kernels);
00506
00507 if ((kernel->get_kernel_type()==K_COMBINED) &&
00508 (!((CCombinedKernel*)kernel)->get_append_subkernel_weights()))
00509 {
00510 CCombinedKernel* k = (CCombinedKernel*) kernel;
00511 CKernel* kn = k->get_first_kernel() ;
00512 int32_t n = 0, i, j ;
00513
00514 while (kn!=NULL)
00515 {
00516 for(i=0;i<num;i++)
00517 {
00518 if(a[i] != a_old[i])
00519 {
00520 kn->get_kernel_row(i,NULL,aicache, true);
00521 for(j=0;j<num;j++)
00522 W[j*num_kernels+n]+=(a[i]-a_old[i])*aicache[regression_fix_index(j)]*(float64_t)label[i];
00523 }
00524 }
00525 SG_UNREF(kn);
00526 kn = k->get_next_kernel();
00527 n++ ;
00528 }
00529 }
00530 else
00531 {
00532 float64_t* w_backup = SG_MALLOC(float64_t, num_kernels);
00533 float64_t* w1 = SG_MALLOC(float64_t, num_kernels);
00534
00535
00536 for (int32_t i=0; i<num_kernels; i++)
00537 {
00538 w_backup[i] = old_beta[i] ;
00539 w1[i]=0.0 ;
00540 }
00541 for (int32_t n=0; n<num_kernels; n++)
00542 {
00543 w1[n]=1.0 ;
00544 kernel->set_subkernel_weights(SGVector<float64_t>(w1, num_weights)) ;
00545
00546 for(int32_t i=0;i<num;i++)
00547 {
00548 if(a[i] != a_old[i])
00549 {
00550 for(int32_t j=0;j<num;j++)
00551 W[j*num_kernels+n]+=(a[i]-a_old[i])*compute_kernel(i,j)*(float64_t)label[i];
00552 }
00553 }
00554 w1[n]=0.0 ;
00555 }
00556
00557
00558 kernel->set_subkernel_weights(SGVector<float64_t>(w_backup,num_weights));
00559
00560 SG_FREE(w_backup);
00561 SG_FREE(w1);
00562 }
00563
00564 call_mkl_callback(a, label, lin, c, totdoc);
00565 }
00566
00567
00568 void CSVRLight::update_linear_component_mkl_linadd(
00569 int32_t* docs, int32_t* label, int32_t *active2dnum, float64_t *a,
00570 float64_t *a_old, int32_t *working2dnum, int32_t totdoc, float64_t *lin,
00571 float64_t *aicache, float64_t* c)
00572 {
00573
00574
00575 int32_t num = totdoc;
00576 int32_t num_weights = -1;
00577 int32_t num_kernels = kernel->get_num_subkernels() ;
00578 const float64_t* old_beta = kernel->get_subkernel_weights(num_weights);
00579
00580 ASSERT(num_weights==num_kernels);
00581
00582 float64_t* w_backup=SG_MALLOC(float64_t, num_kernels);
00583 float64_t* w1=SG_MALLOC(float64_t, num_kernels);
00584
00585
00586 for (int32_t i=0; i<num_kernels; i++)
00587 {
00588 w_backup[i] = old_beta[i] ;
00589 w1[i]=1.0 ;
00590 }
00591
00592 kernel->set_subkernel_weights(SGVector<float64_t>(w1, num_weights));
00593
00594
00595 kernel->clear_normal();
00596 for(int32_t ii=0, i=0;(i=working2dnum[ii])>=0;ii++) {
00597 if(a[i] != a_old[i]) {
00598 kernel->add_to_normal(regression_fix_index(docs[i]), (a[i]-a_old[i])*(float64_t)label[i]);
00599 }
00600 }
00601
00602
00603 for (int32_t i=0; i<num; i++)
00604 kernel->compute_by_subkernel(i,&W[i*num_kernels]) ;
00605
00606
00607 kernel->set_subkernel_weights(SGVector<float64_t>(w_backup,num_weights));
00608
00609 SG_FREE(w_backup);
00610 SG_FREE(w1);
00611
00612 call_mkl_callback(a, label, lin, c, totdoc);
00613 }
00614
00615 void CSVRLight::call_mkl_callback(float64_t* a, int32_t* label, float64_t* lin, float64_t* c, int32_t totdoc)
00616 {
00617 int32_t num = totdoc;
00618 int32_t num_kernels = kernel->get_num_subkernels() ;
00619 int nk = (int) num_kernels;
00620 float64_t sumalpha = 0;
00621 float64_t* sumw=SG_MALLOC(float64_t, num_kernels);
00622
00623 for (int32_t i=0; i<num; i++)
00624 sumalpha-=a[i]*(learn_parm->eps[i]-label[i]*c[i]);
00625
00626 #ifdef HAVE_LAPACK
00627 double* alphay = SG_MALLOC(double, num);
00628 for (int32_t i=0; i<num; i++)
00629 alphay[i]=a[i]*label[i];
00630
00631 for (int32_t i=0; i<num_kernels; i++)
00632 sumw[i]=0;
00633
00634 cblas_dgemv(CblasColMajor, CblasNoTrans, nk, (int) num, 0.5, (double*) W,
00635 nk, (double*) alphay, 1, 1.0, (double*) sumw, 1);
00636
00637 SG_FREE(alphay);
00638 #else
00639 for (int32_t d=0; d<num_kernels; d++)
00640 {
00641 sumw[d]=0;
00642 for(int32_t i=0; i<num; i++)
00643 sumw[d] += 0.5*a[i]*label[i]*W[i*num_kernels+d];
00644 }
00645 #endif
00646
00647 if (callback)
00648 mkl_converged=callback(mkl, sumw, sumalpha);
00649
00650 const float64_t* new_beta = kernel->get_subkernel_weights(num_kernels);
00651
00652
00653 #ifdef HAVE_LAPACK
00654 cblas_dgemv(CblasColMajor, CblasTrans, nk, (int) num, 1.0, (double*) W,
00655 nk, (double*) new_beta, 1, 0.0, (double*) lin, 1);
00656 #else
00657 for(int32_t i=0; i<num; i++)
00658 lin[i]=0 ;
00659 for (int32_t d=0; d<num_kernels; d++)
00660 if (new_beta[d]!=0)
00661 for(int32_t i=0; i<num; i++)
00662 lin[i] += new_beta[d]*W[i*num_kernels+d] ;
00663 #endif
00664
00665
00666 SG_FREE(sumw);
00667 }
00668
00669
00670 void CSVRLight::reactivate_inactive_examples(
00671 int32_t* label, float64_t *a, SHRINK_STATE *shrink_state, float64_t *lin,
00672 float64_t *c, int32_t totdoc, int32_t iteration, int32_t *inconsistent,
00673 int32_t* docs, float64_t *aicache, float64_t *maxdiff)
00674
00675
00676
00677 {
00678 register int32_t i=0,j,ii=0,jj,t,*changed2dnum,*inactive2dnum;
00679 int32_t *changed,*inactive;
00680 register float64_t *a_old,dist;
00681 float64_t ex_c,target;
00682
00683 if (kernel->has_property(KP_LINADD) && get_linadd_enabled()) {
00684 a_old=shrink_state->last_a;
00685
00686 kernel->clear_normal();
00687 int32_t num_modified=0;
00688 for(i=0;i<totdoc;i++) {
00689 if(a[i] != a_old[i]) {
00690 kernel->add_to_normal(regression_fix_index(docs[i]), ((a[i]-a_old[i])*(float64_t)label[i]));
00691 a_old[i]=a[i];
00692 num_modified++;
00693 }
00694 }
00695
00696 if (num_modified>0)
00697 {
00698 for(i=0;i<totdoc;i++) {
00699 if(!shrink_state->active[i]) {
00700 lin[i]=shrink_state->last_lin[i]+kernel->compute_optimized(regression_fix_index(docs[i]));
00701 }
00702 shrink_state->last_lin[i]=lin[i];
00703 }
00704 }
00705 }
00706 else
00707 {
00708 changed=SG_MALLOC(int32_t, totdoc);
00709 changed2dnum=SG_MALLOC(int32_t, totdoc+11);
00710 inactive=SG_MALLOC(int32_t, totdoc);
00711 inactive2dnum=SG_MALLOC(int32_t, totdoc+11);
00712 for(t=shrink_state->deactnum-1;(t>=0) && shrink_state->a_history[t];t--) {
00713 if(verbosity>=2) {
00714 SG_INFO( "%ld..",t);
00715 }
00716 a_old=shrink_state->a_history[t];
00717 for(i=0;i<totdoc;i++) {
00718 inactive[i]=((!shrink_state->active[i])
00719 && (shrink_state->inactive_since[i] == t));
00720 changed[i]= (a[i] != a_old[i]);
00721 }
00722 compute_index(inactive,totdoc,inactive2dnum);
00723 compute_index(changed,totdoc,changed2dnum);
00724
00725 for(ii=0;(i=changed2dnum[ii])>=0;ii++) {
00726 CKernelMachine::kernel->get_kernel_row(i,inactive2dnum,aicache);
00727 for(jj=0;(j=inactive2dnum[jj])>=0;jj++)
00728 lin[j]+=(a[i]-a_old[i])*aicache[j]*(float64_t)label[i];
00729 }
00730 }
00731 SG_FREE(changed);
00732 SG_FREE(changed2dnum);
00733 SG_FREE(inactive);
00734 SG_FREE(inactive2dnum);
00735 }
00736
00737 (*maxdiff)=0;
00738 for(i=0;i<totdoc;i++) {
00739 shrink_state->inactive_since[i]=shrink_state->deactnum-1;
00740 if(!inconsistent[i]) {
00741 dist=(lin[i]-model->b)*(float64_t)label[i];
00742 target=-(learn_parm->eps[i]-(float64_t)label[i]*c[i]);
00743 ex_c=learn_parm->svm_cost[i]-learn_parm->epsilon_a;
00744 if((a[i]>learn_parm->epsilon_a) && (dist > target)) {
00745 if((dist-target)>(*maxdiff))
00746 (*maxdiff)=dist-target;
00747 }
00748 else if((a[i]<ex_c) && (dist < target)) {
00749 if((target-dist)>(*maxdiff))
00750 (*maxdiff)=target-dist;
00751 }
00752 if((a[i]>(0+learn_parm->epsilon_a))
00753 && (a[i]<ex_c)) {
00754 shrink_state->active[i]=1;
00755 }
00756 else if((a[i]<=(0+learn_parm->epsilon_a)) && (dist < (target+learn_parm->epsilon_shrink))) {
00757 shrink_state->active[i]=1;
00758 }
00759 else if((a[i]>=ex_c)
00760 && (dist > (target-learn_parm->epsilon_shrink))) {
00761 shrink_state->active[i]=1;
00762 }
00763 else if(learn_parm->sharedslack) {
00764 shrink_state->active[i]=1;
00765 }
00766 }
00767 }
00768 if (use_kernel_cache) {
00769 for(i=0;i<totdoc;i++) {
00770 (shrink_state->a_history[shrink_state->deactnum-1])[i]=a[i];
00771 }
00772 for(t=shrink_state->deactnum-2;(t>=0) && shrink_state->a_history[t];t--) {
00773 SG_FREE(shrink_state->a_history[t]);
00774 shrink_state->a_history[t]=0;
00775 }
00776 }
00777 }
00778 #endif //USE_SVMLIGHT