SubGradientSVM.cpp

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00001 /*
00002  * This program is free software; you can redistribute it and/or modify
00003  * it under the terms of the GNU General Public License as published by
00004  * the Free Software Foundation; either version 3 of the License, or
00005  * (at your option) any later version.
00006  *
00007  * Written (W) 2007-2009 Soeren Sonnenburg
00008  * Written (W) 2007-2008 Vojtech Franc
00009  * Copyright (C) 2007-2009 Fraunhofer Institute FIRST and Max-Planck-Society
00010  */
00011 
00012 #include "lib/config.h"
00013 #include "lib/Mathematics.h"
00014 #include "lib/Signal.h"
00015 #include "lib/Time.h"
00016 #include "classifier/LinearClassifier.h"
00017 #include "classifier/svm/SubGradientSVM.h"
00018 #include "classifier/svm/qpbsvmlib.h"
00019 #include "features/DotFeatures.h"
00020 #include "features/Labels.h"
00021 
00022 #undef DEBUG_SUBGRADIENTSVM
00023 
00024 extern float64_t sparsity;
00025 float64_t tim;
00026 
00027 CSubGradientSVM::CSubGradientSVM()
00028 : CLinearClassifier(), C1(1), C2(1), epsilon(1e-5), qpsize(42),
00029     qpsize_max(2000), use_bias(false), delta_active(0), delta_bound(0)
00030 {
00031 }
00032 
00033 CSubGradientSVM::CSubGradientSVM(
00034     float64_t C, CDotFeatures* traindat, CLabels* trainlab)
00035 : CLinearClassifier(), C1(C), C2(C), epsilon(1e-5), qpsize(42),
00036     qpsize_max(2000), use_bias(false), delta_active(0), delta_bound(0)
00037 {
00038     set_features(traindat);
00039     set_labels(trainlab);
00040 }
00041 
00042 
00043 CSubGradientSVM::~CSubGradientSVM()
00044 {
00045 }
00046 
00047 /*
00048 int32_t CSubGradientSVM::find_active(int32_t num_feat, int32_t num_vec, int32_t& num_active, int32_t& num_bound)
00049 {
00050     int32_t delta_active=0;
00051     num_active=0;
00052     num_bound=0;
00053 
00054     for (int32_t i=0; i<num_vec; i++)
00055     {
00056         active[i]=0;
00057 
00058         //within margin/wrong side
00059         if (proj[i] < 1-work_epsilon)
00060         {
00061             idx_active[num_active++]=i;
00062             active[i]=1;
00063         }
00064 
00065         //on margin
00066         if (CMath::abs(proj[i]-1) <= work_epsilon)
00067         {
00068             idx_bound[num_bound++]=i;
00069             active[i]=2;
00070         }
00071 
00072         if (active[i]!=old_active[i])
00073             delta_active++;
00074     }
00075 
00076     return delta_active;
00077 }
00078 */
00079 
00080 int32_t CSubGradientSVM::find_active(
00081     int32_t num_feat, int32_t num_vec, int32_t& num_active, int32_t& num_bound)
00082 {
00083     delta_bound=0;
00084     delta_active=0;
00085     num_active=0;
00086     num_bound=0;
00087 
00088     for (int32_t i=0; i<num_vec; i++)
00089     {
00090         active[i]=0;
00091 
00092         //within margin/wrong side
00093         if (proj[i] < 1-autoselected_epsilon)
00094         {
00095             idx_active[num_active++]=i;
00096             active[i]=1;
00097         }
00098 
00099         //on margin
00100         if (CMath::abs(proj[i]-1) <= autoselected_epsilon)
00101         {
00102             idx_bound[num_bound++]=i;
00103             active[i]=2;
00104         }
00105 
00106         if (active[i]!=old_active[i])
00107             delta_active++;
00108 
00109         if (active[i]==2 && old_active[i]==2)
00110             delta_bound++;
00111     }
00112 
00113 
00114     if (delta_active==0 && work_epsilon<=epsilon) //we converged
00115         return 0;
00116     else if (delta_active==0) //lets decrease work_epsilon
00117     {
00118         work_epsilon=CMath::min(work_epsilon/2, autoselected_epsilon);
00119         work_epsilon=CMath::max(work_epsilon, epsilon);
00120         num_bound=qpsize;
00121     }
00122 
00123     delta_bound=0;
00124     delta_active=0;
00125     num_active=0;
00126     num_bound=0;
00127 
00128     for (int32_t i=0; i<num_vec; i++)
00129     {
00130         tmp_proj[i]=CMath::abs(proj[i]-1);
00131         tmp_proj_idx[i]=i;
00132     }
00133 
00134     CMath::qsort_index(tmp_proj, tmp_proj_idx, num_vec);
00135 
00136     autoselected_epsilon=tmp_proj[CMath::min(qpsize,num_vec-1)];
00137 
00138 #ifdef DEBUG_SUBGRADIENTSVM
00139     //SG_PRINT("autoseleps: %15.15f\n", autoselected_epsilon);
00140 #endif
00141 
00142     if (autoselected_epsilon>work_epsilon)
00143         autoselected_epsilon=work_epsilon;
00144 
00145     if (autoselected_epsilon<epsilon)
00146     {
00147         autoselected_epsilon=epsilon;
00148 
00149         int32_t i=0;
00150         while (i < num_vec && tmp_proj[i] <= autoselected_epsilon)
00151             i++;
00152 
00153         //SG_PRINT("lower bound on epsilon requires %d variables in qp\n", i);
00154 
00155         if (i>=qpsize_max && autoselected_epsilon>epsilon) //qpsize limit
00156         {
00157             SG_INFO("qpsize limit (%d) reached\n", qpsize_max);
00158             int32_t num_in_qp=i;
00159             while (--i>=0 && num_in_qp>=qpsize_max)
00160             {
00161                 if (tmp_proj[i] < autoselected_epsilon)
00162                 {
00163                     autoselected_epsilon=tmp_proj[i];
00164                     num_in_qp--;
00165                 }
00166             }
00167 
00168             //SG_PRINT("new qpsize will be %d, autoeps:%15.15f\n", num_in_qp, autoselected_epsilon);
00169         }
00170     }
00171 
00172     for (int32_t i=0; i<num_vec; i++)
00173     {
00174         active[i]=0;
00175 
00176         //within margin/wrong side
00177         if (proj[i] < 1-autoselected_epsilon)
00178         {
00179             idx_active[num_active++]=i;
00180             active[i]=1;
00181         }
00182 
00183         //on margin
00184         if (CMath::abs(proj[i]-1) <= autoselected_epsilon)
00185         {
00186             idx_bound[num_bound++]=i;
00187             active[i]=2;
00188         }
00189 
00190         if (active[i]!=old_active[i])
00191             delta_active++;
00192 
00193         if (active[i]==2 && old_active[i]==2)
00194             delta_bound++;
00195     }
00196 
00197     //SG_PRINT("delta_bound: %d of %d (%02.2f)\n", delta_bound, num_bound, 100.0*delta_bound/num_bound);
00198     return delta_active;
00199 }
00200 
00201 
00202 void CSubGradientSVM::update_active(int32_t num_feat, int32_t num_vec)
00203 {
00204     for (int32_t i=0; i<num_vec; i++)
00205     {
00206         if (active[i]==1 && old_active[i]!=1)
00207         {
00208             features->add_to_dense_vec(C1*get_label(i), i, sum_CXy_active, num_feat);
00209             if (use_bias)
00210                 sum_Cy_active+=C1*get_label(i);
00211         }
00212         else if (old_active[i]==1 && active[i]!=1)
00213         {
00214             features->add_to_dense_vec(-C1*get_label(i), i, sum_CXy_active, num_feat);
00215             if (use_bias)
00216                 sum_Cy_active-=C1*get_label(i);
00217         }
00218     }
00219 
00220     CMath::swap(active,old_active);
00221 }
00222 
00223 float64_t CSubGradientSVM::line_search(int32_t num_feat, int32_t num_vec)
00224 {
00225     float64_t sum_B = 0;
00226     float64_t A_zero = 0.5*CMath::dot(grad_w, grad_w, num_feat);
00227     float64_t B_zero = -CMath::dot(w, grad_w, num_feat);
00228 
00229     int32_t num_hinge=0;
00230 
00231     for (int32_t i=0; i<num_vec; i++)
00232     {
00233         float64_t p=get_label(i)*(features->dense_dot(i, grad_w, num_feat)+grad_b);
00234         grad_proj[i]=p;
00235         if (p!=0)
00236         {
00237             hinge_point[num_hinge]=(proj[i]-1)/p;
00238             hinge_idx[num_hinge]=i;
00239             num_hinge++;
00240 
00241             if (p<0)
00242                 sum_B+=p;
00243         }
00244     }
00245     sum_B*=C1;
00246 
00247     CMath::qsort_index(hinge_point, hinge_idx, num_hinge);
00248 
00249 
00250     float64_t alpha = hinge_point[0];
00251     float64_t grad_val = 2*A_zero*alpha + B_zero + sum_B;
00252 
00253     //CMath::display_vector(grad_w, num_feat, "grad_w");
00254     //CMath::display_vector(grad_proj, num_vec, "grad_proj");
00255     //CMath::display_vector(hinge_point, num_vec, "hinge_point");
00256     //SG_PRINT("A_zero=%f\n", A_zero);
00257     //SG_PRINT("B_zero=%f\n", B_zero);
00258     //SG_PRINT("sum_B=%f\n", sum_B);
00259     //SG_PRINT("alpha=%f\n", alpha);
00260     //SG_PRINT("grad_val=%f\n", grad_val);
00261 
00262     float64_t old_grad_val = grad_val;
00263     float64_t old_alpha = alpha;
00264 
00265     for (int32_t i=1; i < num_hinge && grad_val < 0; i++)
00266     {
00267         alpha = hinge_point[i];
00268         grad_val = 2*A_zero*alpha + B_zero + sum_B;
00269 
00270         if (grad_val > 0)
00271         {
00272             ASSERT(old_grad_val-grad_val != 0);
00273             float64_t gamma = -grad_val/(old_grad_val-grad_val);
00274             alpha = old_alpha*gamma + (1-gamma)*alpha;
00275         }
00276         else
00277         {
00278             old_grad_val = grad_val;
00279             old_alpha = alpha;
00280 
00281             sum_B = sum_B + CMath::abs(C1*grad_proj[hinge_idx[i]]);
00282             grad_val = 2*A_zero*alpha + B_zero + sum_B;
00283         }
00284     }
00285 
00286     return alpha;
00287 }
00288 
00289 float64_t CSubGradientSVM::compute_min_subgradient(
00290     int32_t num_feat, int32_t num_vec, int32_t num_active, int32_t num_bound)
00291 {
00292     float64_t dir_deriv=0;
00293 
00294     if (num_bound > 0)
00295     {
00296 
00297             CTime t2;
00298         CMath::add(v, 1.0, w, -1.0, sum_CXy_active, num_feat);
00299 
00300         if (num_bound>=qpsize_max && num_it_noimprovement!=10) // if qp gets to large, lets just choose a random beta
00301         {
00302             //SG_PRINT("qpsize too large  (%d>=%d) choosing random subgradient/beta\n", num_bound, qpsize_max);
00303             for (int32_t i=0; i<num_bound; i++)
00304                 beta[i]=CMath::random(0.0,1.0);
00305         }
00306         else
00307         {
00308             memset(beta, 0, sizeof(float64_t)*num_bound);
00309 
00310             float64_t bias_const=0;
00311 
00312             if (use_bias)
00313                 bias_const=1;
00314 
00315             for (int32_t i=0; i<num_bound; i++)
00316             {
00317                 for (int32_t j=i; j<num_bound; j++)
00318                 {
00319                     Z[i*num_bound+j]= 2.0*C1*C1*get_label(idx_bound[i])*get_label(idx_bound[j])* 
00320                         (features->dot(idx_bound[i], idx_bound[j]) + bias_const);
00321 
00322                     Z[j*num_bound+i]=Z[i*num_bound+j];
00323 
00324                 }
00325 
00326                 Zv[i]=-2.0*C1*get_label(idx_bound[i])* 
00327                     (features->dense_dot(idx_bound[i], v, num_feat)-sum_Cy_active);
00328             }
00329 
00330             //CMath::display_matrix(Z, num_bound, num_bound, "Z");
00331             //CMath::display_vector(Zv, num_bound, "Zv");
00332             t2.stop();
00333             t2.time_diff_sec(true);
00334 
00335             CTime t;
00336             CQPBSVMLib solver(Z,num_bound, Zv,num_bound, 1.0);
00337             //solver.set_solver(QPB_SOLVER_GRADDESC);
00338             //solver.set_solver(QPB_SOLVER_GS);
00339 #ifdef USE_CPLEX
00340             solver.set_solver(QPB_SOLVER_CPLEX);
00341 #else
00342             solver.set_solver(QPB_SOLVER_SCAS);
00343 #endif
00344 
00345             solver.solve_qp(beta, num_bound);
00346 
00347             t.stop();
00348             tim+=t.time_diff_sec(true);
00349 
00350             //CMath::display_vector(beta, num_bound, "beta gs");
00351             //solver.set_solver(QPB_SOLVER_CPLEX);
00352             //solver.solve_qp(beta, num_bound);
00353             //CMath::display_vector(beta, num_bound, "beta cplex");
00354 
00355             //CMath::display_vector(grad_w, num_feat, "grad_w");
00356             //SG_PRINT("grad_b:%f\n", grad_b);
00357         }
00358 
00359         CMath::add(grad_w, 1.0, w, -1.0, sum_CXy_active, num_feat);
00360         grad_b = -sum_Cy_active;
00361 
00362         for (int32_t i=0; i<num_bound; i++)
00363         {
00364             features->add_to_dense_vec(-C1*beta[i]*get_label(idx_bound[i]), idx_bound[i], grad_w, num_feat);
00365             if (use_bias)
00366                 grad_b -=  C1 * get_label(idx_bound[i])*beta[i];
00367         }
00368 
00369         dir_deriv = CMath::dot(grad_w, v, num_feat) - grad_b*sum_Cy_active;
00370         for (int32_t i=0; i<num_bound; i++)
00371         {
00372             float64_t val= C1*get_label(idx_bound[i])*(features->dense_dot(idx_bound[i], grad_w, num_feat)+grad_b);
00373             dir_deriv += CMath::max(0.0, val);
00374         }
00375     }
00376     else
00377     {
00378         CMath::add(grad_w, 1.0, w, -1.0, sum_CXy_active, num_feat);
00379         grad_b = -sum_Cy_active;
00380 
00381         dir_deriv = CMath::dot(grad_w, grad_w, num_feat)+ grad_b*grad_b;
00382     }
00383 
00384     return dir_deriv;
00385 }
00386 
00387 float64_t CSubGradientSVM::compute_objective(int32_t num_feat, int32_t num_vec)
00388 {
00389     float64_t result= 0.5 * CMath::dot(w,w, num_feat);
00390     
00391     for (int32_t i=0; i<num_vec; i++)
00392     {
00393         if (proj[i]<1.0)
00394             result += C1 * (1.0-proj[i]);
00395     }
00396 
00397     return result;
00398 }
00399 
00400 void CSubGradientSVM::compute_projection(int32_t num_feat, int32_t num_vec)
00401 {
00402     for (int32_t i=0; i<num_vec; i++)
00403         proj[i]=get_label(i)*(features->dense_dot(i, w, num_feat)+bias);
00404 }
00405 
00406 void CSubGradientSVM::update_projection(float64_t alpha, int32_t num_vec)
00407 {
00408     CMath::vec1_plus_scalar_times_vec2(proj,-alpha, grad_proj, num_vec);
00409 }
00410 
00411 void CSubGradientSVM::init(int32_t num_vec, int32_t num_feat)
00412 {
00413     // alloc normal and bias inited with 0
00414     delete[] w;
00415     w=new float64_t[num_feat];
00416     memset(w,0,sizeof(float64_t)*num_feat);
00417     //CMath::random_vector(w, num_feat, -1.0, 1.0);
00418     bias=0;
00419     num_it_noimprovement=0;
00420     grad_b=0;
00421     set_w(w, num_feat);
00422     qpsize_limit=5000;
00423 
00424     grad_w=new float64_t[num_feat];
00425     memset(grad_w,0,sizeof(float64_t)*num_feat);
00426 
00427     sum_CXy_active=new float64_t[num_feat];
00428     memset(sum_CXy_active,0,sizeof(float64_t)*num_feat);
00429 
00430     v=new float64_t[num_feat];
00431     memset(v,0,sizeof(float64_t)*num_feat);
00432 
00433     old_v=new float64_t[num_feat];
00434     memset(old_v,0,sizeof(float64_t)*num_feat);
00435 
00436     sum_Cy_active=0;
00437 
00438     proj= new float64_t[num_vec];
00439     memset(proj,0,sizeof(float64_t)*num_vec);
00440 
00441     tmp_proj=new float64_t[num_vec];
00442     memset(proj,0,sizeof(float64_t)*num_vec);
00443 
00444     tmp_proj_idx= new int32_t[num_vec];
00445     memset(tmp_proj_idx,0,sizeof(int32_t)*num_vec);
00446 
00447     grad_proj= new float64_t[num_vec];
00448     memset(grad_proj,0,sizeof(float64_t)*num_vec);
00449 
00450     hinge_point= new float64_t[num_vec];
00451     memset(hinge_point,0,sizeof(float64_t)*num_vec);
00452 
00453     hinge_idx= new int32_t[num_vec];
00454     memset(hinge_idx,0,sizeof(int32_t)*num_vec);
00455 
00456     active=new uint8_t[num_vec];
00457     memset(active,0,sizeof(uint8_t)*num_vec);
00458 
00459     old_active=new uint8_t[num_vec];
00460     memset(old_active,0,sizeof(uint8_t)*num_vec);
00461 
00462     idx_bound=new int32_t[num_vec];
00463     memset(idx_bound,0,sizeof(int32_t)*num_vec);
00464 
00465     idx_active=new int32_t[num_vec];
00466     memset(idx_active,0,sizeof(int32_t)*num_vec);
00467 
00468     Z=new float64_t[qpsize_limit*qpsize_limit];
00469     memset(Z,0,sizeof(float64_t)*qpsize_limit*qpsize_limit);
00470 
00471     Zv=new float64_t[qpsize_limit];
00472     memset(Zv,0,sizeof(float64_t)*qpsize_limit);
00473 
00474     beta=new float64_t[qpsize_limit];
00475     memset(beta,0,sizeof(float64_t)*qpsize_limit);
00476 
00477     old_Z=new float64_t[qpsize_limit*qpsize_limit];
00478     memset(old_Z,0,sizeof(float64_t)*qpsize_limit*qpsize_limit);
00479 
00480     old_Zv=new float64_t[qpsize_limit];
00481     memset(old_Zv,0,sizeof(float64_t)*qpsize_limit);
00482 
00483     old_beta=new float64_t[qpsize_limit];
00484     memset(old_beta,0,sizeof(float64_t)*qpsize_limit);
00485 
00486 }
00487 
00488 void CSubGradientSVM::cleanup()
00489 {
00490     delete[] hinge_idx;
00491     delete[] hinge_point;
00492     delete[] grad_proj;
00493     delete[] proj;
00494     delete[] tmp_proj;
00495     delete[] tmp_proj_idx;
00496     delete[] active;
00497     delete[] old_active;
00498     delete[] idx_bound;
00499     delete[] idx_active;
00500     delete[] sum_CXy_active;
00501     delete[] grad_w;
00502     delete[] v;
00503     delete[] Z;
00504     delete[] Zv;
00505     delete[] beta;
00506     delete[] old_v;
00507     delete[] old_Z;
00508     delete[] old_Zv;
00509     delete[] old_beta;
00510 
00511     hinge_idx=NULL;
00512     proj=NULL;
00513     active=NULL;
00514     old_active=NULL;
00515     idx_bound=NULL;
00516     idx_active=NULL;
00517     sum_CXy_active=NULL;
00518     grad_w=NULL;
00519     v=NULL;
00520     Z=NULL;
00521     Zv=NULL;
00522     beta=NULL;
00523 }
00524 
00525 bool CSubGradientSVM::train()
00526 {
00527     tim=0;
00528     SG_INFO("C=%f epsilon=%f\n", C1, epsilon);
00529     ASSERT(labels);
00530     ASSERT(get_features());
00531 
00532     int32_t num_iterations=0;
00533     int32_t num_train_labels=labels->get_num_labels();
00534     int32_t num_feat=features->get_dim_feature_space();
00535     int32_t num_vec=features->get_num_vectors();
00536 
00537     ASSERT(num_vec==num_train_labels);
00538 
00539     init(num_vec, num_feat);
00540 
00541     int32_t num_active=0;
00542     int32_t num_bound=0;
00543     float64_t alpha=0;
00544     float64_t dir_deriv=0;
00545     float64_t obj=0;
00546     delta_active=num_vec;
00547     last_it_noimprovement=-1;
00548 
00549     work_epsilon=0.99;
00550     autoselected_epsilon=work_epsilon;
00551 
00552     compute_projection(num_feat, num_vec);
00553 
00554     CTime time;
00555     float64_t loop_time=0;
00556     while (!(CSignal::cancel_computations()))
00557     {
00558         CTime t;
00559         delta_active=find_active(num_feat, num_vec, num_active, num_bound);
00560 
00561         update_active(num_feat, num_vec);
00562 
00563 #ifdef DEBUG_SUBGRADIENTSVM
00564         SG_PRINT("==================================================\niteration: %d ", num_iterations);
00565         obj=compute_objective(num_feat, num_vec);
00566         SG_PRINT("objective:%.10f alpha: %.10f dir_deriv: %f num_bound: %d num_active: %d work_eps: %10.10f eps: %10.10f auto_eps: %10.10f time:%f\n",
00567                 obj, alpha, dir_deriv, num_bound, num_active, work_epsilon, epsilon, autoselected_epsilon, loop_time);
00568 #else
00569       SG_ABS_PROGRESS(work_epsilon, -CMath::log10(work_epsilon), -CMath::log10(0.99999999), -CMath::log10(epsilon), 6);
00570 #endif
00571         //CMath::display_vector(w, w_dim, "w");
00572         //SG_PRINT("bias: %f\n", bias);
00573         //CMath::display_vector(proj, num_vec, "proj");
00574         //CMath::display_vector(idx_active, num_active, "idx_active");
00575         //SG_PRINT("num_active: %d\n", num_active);
00576         //CMath::display_vector(idx_bound, num_bound, "idx_bound");
00577         //SG_PRINT("num_bound: %d\n", num_bound);
00578         //CMath::display_vector(sum_CXy_active, num_feat, "sum_CXy_active");
00579         //SG_PRINT("sum_Cy_active: %f\n", sum_Cy_active);
00580         //CMath::display_vector(grad_w, num_feat, "grad_w");
00581         //SG_PRINT("grad_b:%f\n", grad_b);
00582         
00583         dir_deriv=compute_min_subgradient(num_feat, num_vec, num_active, num_bound);
00584 
00585         alpha=line_search(num_feat, num_vec);
00586 
00587         if (num_it_noimprovement==10 || num_bound<qpsize_max)
00588         {
00589             float64_t norm_grad=CMath::dot(grad_w, grad_w, num_feat) +
00590                 grad_b*grad_b;
00591 
00592 #ifdef DEBUG_SUBGRADIENTSVM
00593             SG_PRINT("CHECKING OPTIMALITY CONDITIONS: "
00594                     "work_epsilon: %10.10f delta_active:%d alpha: %10.10f norm_grad: %10.10f a*norm_grad:%10.16f\n",
00595                     work_epsilon, delta_active, alpha, norm_grad, CMath::abs(alpha*norm_grad));
00596 #else
00597             SG_ABS_PROGRESS(work_epsilon, -CMath::log10(work_epsilon), -CMath::log10(0.99999999), -CMath::log10(epsilon), 6);
00598 #endif
00599 
00600             if (work_epsilon<=epsilon && delta_active==0 && CMath::abs(alpha*norm_grad)<1e-6)
00601                 break;
00602             else
00603                 num_it_noimprovement=0;
00604         }
00605 
00606         if ((dir_deriv<0 || alpha==0) && (work_epsilon<=epsilon && delta_active==0))
00607         {
00608             if (last_it_noimprovement==num_iterations-1)
00609             {
00610                 SG_PRINT("no improvement...\n");
00611                 num_it_noimprovement++;
00612             }
00613             else
00614                 num_it_noimprovement=0;
00615 
00616             last_it_noimprovement=num_iterations;
00617         }
00618 
00619         CMath::vec1_plus_scalar_times_vec2(w, -alpha, grad_w, num_feat);
00620         bias-=alpha*grad_b;
00621 
00622         update_projection(alpha, num_vec);
00623         //compute_projection(num_feat, num_vec);
00624         //CMath::display_vector(w, w_dim, "w");
00625         //SG_PRINT("bias: %f\n", bias);
00626         //CMath::display_vector(proj, num_vec, "proj");
00627 
00628         t.stop();
00629         loop_time=t.time_diff_sec();
00630         num_iterations++;
00631 
00632         if (get_max_train_time()>0 && time.cur_time_diff()>get_max_train_time())
00633             break;
00634     }
00635 
00636     SG_INFO("converged after %d iterations\n", num_iterations);
00637 
00638     obj=compute_objective(num_feat, num_vec);
00639     SG_INFO("objective: %f alpha: %f dir_deriv: %f num_bound: %d num_active: %d sparsity: %f\n",
00640             obj, alpha, dir_deriv, num_bound, num_active, sparsity/num_iterations);
00641 
00642 #ifdef DEBUG_SUBGRADIENTSVM
00643     CMath::display_vector(w, w_dim, "w");
00644     SG_PRINT("bias: %f\n", bias);
00645 #endif
00646     SG_INFO("solver time:%f s\n", tim);
00647 
00648     cleanup();
00649 
00650     return true;
00651 }

SHOGUN Machine Learning Toolbox - Documentation