SVR_light.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
 * Copyright (C) 1999-2009 Fraunhofer Institute FIRST and Max-Planck-Society
 */

#include "lib/config.h"

#ifdef USE_SVMLIGHT

#include "lib/io.h"
#include "lib/lapack.h"
#include "lib/Signal.h"
#include "lib/Mathematics.h"
#include "regression/svr/SVR_light.h"
#include "classifier/KernelMachine.h"
#include "kernel/CombinedKernel.h"

#include <unistd.h>

#ifdef USE_CPLEX
extern "C" {
#include <ilcplex/cplex.h>
}
#endif

#include "base/Parallel.h"

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

using namespace shogun;

#ifndef DOXYGEN_SHOULD_SKIP_THIS
struct S_THREAD_PARAM
{
    float64_t* lin;
    int32_t start, end;
    int32_t* active2dnum;
    int32_t* docs;
    CKernel* kernel;
    int32_t num_vectors;
};
#endif // DOXYGEN_SHOULD_SKIP_THIS

CSVRLight::CSVRLight(float64_t C, float64_t eps, CKernel* k, CLabels* lab)
: CSVMLight(C, k, lab)
{
    set_tube_epsilon(eps);
}

CSVRLight::CSVRLight()
: CSVMLight()
{
}

bool CSVRLight::train(CFeatures* data)
{
    //certain setup params
    verbosity=1;
    init_margin=0.15;
    init_iter=500;
    precision_violations=0;
    opt_precision=DEF_PRECISION;

    strcpy (learn_parm->predfile, "");
    learn_parm->biased_hyperplane=1;
    learn_parm->sharedslack=0;
    learn_parm->remove_inconsistent=0;
    learn_parm->skip_final_opt_check=1;
    learn_parm->svm_maxqpsize=get_qpsize();
    learn_parm->svm_newvarsinqp=learn_parm->svm_maxqpsize-1;
    learn_parm->maxiter=100000;
    learn_parm->svm_iter_to_shrink=100;
    learn_parm->svm_c=get_C1();
    learn_parm->transduction_posratio=0.33;
    learn_parm->svm_costratio=get_C2()/get_C1();
    learn_parm->svm_costratio_unlab=1.0;
    learn_parm->svm_unlabbound=1E-5;
    learn_parm->epsilon_crit=epsilon; // GU: better decrease it ... ??
    learn_parm->epsilon_a=1E-15;
    learn_parm->compute_loo=0;
    learn_parm->rho=1.0;
    learn_parm->xa_depth=0;

    if (!kernel)
    {
        SG_ERROR( "SVR_light can not proceed without kernel!\n");
        return false ;
    }

    if (!labels)
    {
        SG_ERROR( "SVR_light can not proceed without labels!\n");
        return false;
    }

    if (data)
    {
        if (labels->get_num_labels() != data->get_num_vectors())
            SG_ERROR("Number of training vectors does not match number of labels\n");
        kernel->init(data, data);
    }

    if (kernel->has_property(KP_LINADD) && get_linadd_enabled())
        kernel->clear_normal();

    // output some info
    SG_DEBUG( "qpsize = %i\n", learn_parm->svm_maxqpsize) ;
    SG_DEBUG( "epsilon = %1.1e\n", learn_parm->epsilon_crit) ;
    SG_DEBUG( "kernel->has_property(KP_LINADD) = %i\n", kernel->has_property(KP_LINADD)) ;
    SG_DEBUG( "kernel->has_property(KP_KERNCOMBINATION) = %i\n", kernel->has_property(KP_KERNCOMBINATION)) ;
    SG_DEBUG( "get_linadd_enabled() = %i\n", get_linadd_enabled()) ;
    SG_DEBUG( "kernel->get_num_subkernels() = %i\n", kernel->get_num_subkernels()) ;

    use_kernel_cache = !((kernel->get_kernel_type() == K_CUSTOM) ||
                         (get_linadd_enabled() && kernel->has_property(KP_LINADD)));

    SG_DEBUG( "use_kernel_cache = %i\n", use_kernel_cache) ;

    // train the svm
    svr_learn();

    // brain damaged svm light work around
    create_new_model(model->sv_num-1);
    set_bias(-model->b);
    for (int32_t i=0; i<model->sv_num-1; i++)
    {
        set_alpha(i, model->alpha[i+1]);
        set_support_vector(i, model->supvec[i+1]);
    }

    if (kernel->has_property(KP_LINADD) && get_linadd_enabled())
        kernel->clear_normal() ;

    return true ;
}

void CSVRLight::svr_learn()
{
    int32_t *inconsistent, i, j;
    int32_t inconsistentnum;
    int32_t upsupvecnum;
    float64_t maxdiff, *lin, *c, *a;
    int32_t runtime_start,runtime_end;
    int32_t iterations;
    float64_t *xi_fullset; /* buffer for storing xi on full sample in loo */
    float64_t *a_fullset;  /* buffer for storing alpha on full sample in loo */
    TIMING timing_profile;
    SHRINK_STATE shrink_state;
    int32_t* label;
    int32_t* docs;

    ASSERT(labels);
    int32_t totdoc=labels->get_num_labels();
    num_vectors=totdoc;

    // set up regression problem in standard form
    docs=new int32_t[2*totdoc];
    label=new int32_t[2*totdoc];
    c = new float64_t[2*totdoc];

  for(i=0;i<totdoc;i++) {
      docs[i]=i;
      j=2*totdoc-1-i;
      label[i]=+1;
      c[i]=labels->get_label(i);
      docs[j]=j;
      label[j]=-1;
      c[j]=labels->get_label(i);
  }
  totdoc*=2;

  //prepare kernel cache for regression (i.e. cachelines are twice of current size)
  kernel->resize_kernel_cache( kernel->get_cache_size(), true);

  if (kernel->get_kernel_type() == K_COMBINED)
  {
      CCombinedKernel* k      = (CCombinedKernel*) kernel;
      CKernel* kn = k->get_first_kernel();

      while (kn)
      {
          kn->resize_kernel_cache( kernel->get_cache_size(), true);
          SG_UNREF(kn);
          kn = k->get_next_kernel();
      }
  }

  runtime_start=get_runtime();
  timing_profile.time_kernel=0;
  timing_profile.time_opti=0;
  timing_profile.time_shrink=0;
  timing_profile.time_update=0;
  timing_profile.time_model=0;
  timing_profile.time_check=0;
  timing_profile.time_select=0;

    delete[] W;
    W=NULL;

    if (kernel->has_property(KP_KERNCOMBINATION) && callback)
    {
        W = new float64_t[totdoc*kernel->get_num_subkernels()];
        for (i=0; i<totdoc*kernel->get_num_subkernels(); i++)
            W[i]=0;
    }

    /* make sure -n value is reasonable */
    if((learn_parm->svm_newvarsinqp < 2)
            || (learn_parm->svm_newvarsinqp > learn_parm->svm_maxqpsize)) {
        learn_parm->svm_newvarsinqp=learn_parm->svm_maxqpsize;
    }

    init_shrink_state(&shrink_state,totdoc,(int32_t)MAXSHRINK);

    inconsistent = new int32_t[totdoc];
    a = new float64_t[totdoc];
    a_fullset = new float64_t[totdoc];
    xi_fullset = new float64_t[totdoc];
    lin = new float64_t[totdoc];
    learn_parm->svm_cost = new float64_t[totdoc];
    if (linear_term.size() > 0)
        learn_parm->eps=get_linear_term_array();
    else
    {
        learn_parm->eps=new float64_t[totdoc];      /* equivalent regression epsilon for classification */
        CMath::fill_vector(learn_parm->eps, totdoc, tube_epsilon);
    }

    delete[] model->supvec;
    delete[] model->alpha;
    delete[] model->index;
    model->supvec = new int32_t[totdoc+2];
    model->alpha = new float64_t[totdoc+2];
    model->index = new int32_t[totdoc+2];

    model->at_upper_bound=0;
    model->b=0;
    model->supvec[0]=0;  /* element 0 reserved and empty for now */
    model->alpha[0]=0;
    model->totdoc=totdoc;

    model->kernel=kernel;

    model->sv_num=1;
    model->loo_error=-1;
    model->loo_recall=-1;
    model->loo_precision=-1;
    model->xa_error=-1;
    model->xa_recall=-1;
    model->xa_precision=-1;
    inconsistentnum=0;

  for(i=0;i<totdoc;i++) {    /* various inits */
    inconsistent[i]=0;
    a[i]=0;
    lin[i]=0;

        if(label[i] > 0) {
            learn_parm->svm_cost[i]=learn_parm->svm_c*learn_parm->svm_costratio*
                fabs((float64_t)label[i]);
        }
        else if(label[i] < 0) {
            learn_parm->svm_cost[i]=learn_parm->svm_c*fabs((float64_t)label[i]);
        }
        else
            ASSERT(false);
    }

    if(verbosity==1) {
        SG_DEBUG( "Optimizing...\n");
    }

    /* train the svm */
        SG_DEBUG( "num_train: %d\n", totdoc);
  iterations=optimize_to_convergence(docs,label,totdoc,
                     &shrink_state,inconsistent,a,lin,
                     c,&timing_profile,
                     &maxdiff,(int32_t)-1,
                     (int32_t)1);


    if(verbosity>=1) {
        SG_DONE();
        SG_INFO("(%ld iterations)\n",iterations);
        SG_INFO( "Optimization finished (maxdiff=%.8f).\n",maxdiff);
        SG_INFO( "obj = %.16f, rho = %.16f\n",get_objective(),model->b);

        runtime_end=get_runtime();
        upsupvecnum=0;

        SG_DEBUG( "num sv: %d\n", model->sv_num);
        for(i=1;i<model->sv_num;i++)
        {
            if(fabs(model->alpha[i]) >=
                    (learn_parm->svm_cost[model->supvec[i]]-
                     learn_parm->epsilon_a))
                upsupvecnum++;
        }
        SG_INFO( "Number of SV: %ld (including %ld at upper bound)\n",
                model->sv_num-1,upsupvecnum);
    }

  /* this makes sure the model we return does not contain pointers to the
     temporary documents */
  for(i=1;i<model->sv_num;i++) {
    j=model->supvec[i];
    if(j >= (totdoc/2)) {
      j=totdoc-j-1;
    }
    model->supvec[i]=j;
  }

  shrink_state_cleanup(&shrink_state);
    delete[] label;
    delete[] inconsistent;
    delete[] c;
    delete[] a;
    delete[] a_fullset;
    delete[] xi_fullset;
    delete[] lin;
    delete[] learn_parm->svm_cost;
    delete[] docs;
}

float64_t CSVRLight::compute_objective_function(
    float64_t *a, float64_t *lin, float64_t *c, float64_t* eps, int32_t *label,
    int32_t totdoc)
{
  /* calculate value of objective function */
  float64_t criterion=0;

  for(int32_t i=0;i<totdoc;i++)
      criterion+=(eps[i]-(float64_t)label[i]*c[i])*a[i]+0.5*a[i]*label[i]*lin[i];

  /* float64_t check=0;
  for(int32_t i=0;i<totdoc;i++)
  {
      check+=a[i]*eps-a[i]*label[i]*c[i];
      for(int32_t j=0;j<totdoc;j++)
          check+= 0.5*a[i]*label[i]*a[j]*label[j]*compute_kernel(i,j);

  }

  SG_INFO("REGRESSION OBJECTIVE %f vs. CHECK %f (diff %f)\n", criterion, check, criterion-check); */

  return(criterion);
}

void* CSVRLight::update_linear_component_linadd_helper(void *params_)
{
    S_THREAD_PARAM * params = (S_THREAD_PARAM*) params_ ;

    int32_t jj=0, j=0 ;

    for(jj=params->start;(jj<params->end) && (j=params->active2dnum[jj])>=0;jj++)
        params->lin[j]+=params->kernel->compute_optimized(CSVRLight::regression_fix_index2(params->docs[j], params->num_vectors));

    return NULL ;
}


void CSVRLight::update_linear_component(
    int32_t* docs, int32_t* label, int32_t *active2dnum, float64_t *a,
    float64_t *a_old, int32_t *working2dnum, int32_t totdoc, float64_t *lin,
    float64_t *aicache, float64_t* c)
     /* keep track of the linear component */
     /* lin of the gradient etc. by updating */
     /* based on the change of the variables */
     /* in the current working set */
{
    register int32_t i=0,ii=0,j=0,jj=0;

    if (kernel->has_property(KP_LINADD) && get_linadd_enabled())
    {
        if (callback)
        {
            update_linear_component_mkl_linadd(docs, label, active2dnum, a, a_old, working2dnum,
                                               totdoc,  lin, aicache, c) ;
        }
        else
        {
            kernel->clear_normal();

            int32_t num_working=0;
            for(ii=0;(i=working2dnum[ii])>=0;ii++) {
                if(a[i] != a_old[i]) {
                    kernel->add_to_normal(regression_fix_index(docs[i]), (a[i]-a_old[i])*(float64_t)label[i]);
                    num_working++;
                }
            }

            if (num_working>0)
            {
                if (parallel->get_num_threads() < 2)
                {
                    for(jj=0;(j=active2dnum[jj])>=0;jj++) {
                        lin[j]+=kernel->compute_optimized(regression_fix_index(docs[j]));
                    }
                }
#ifndef WIN32
                else
                {
                    int32_t num_elem = 0 ;
                    for(jj=0;(j=active2dnum[jj])>=0;jj++) num_elem++ ;

                    pthread_t* threads = new pthread_t[parallel->get_num_threads()-1] ;
                    S_THREAD_PARAM* params = new S_THREAD_PARAM[parallel->get_num_threads()-1] ;
                    int32_t start = 0 ;
                    int32_t step = num_elem/parallel->get_num_threads() ;
                    int32_t end = step ;

                    for (int32_t t=0; t<parallel->get_num_threads()-1; t++)
                    {
                        params[t].kernel = kernel ;
                        params[t].lin = lin ;
                        params[t].docs = docs ;
                        params[t].active2dnum=active2dnum ;
                        params[t].start = start ;
                        params[t].end = end ;
                        params[t].num_vectors=num_vectors ;

                        start=end ;
                        end+=step ;
                        pthread_create(&threads[t], NULL, update_linear_component_linadd_helper, (void*)&params[t]) ;
                    }

                    for(jj=params[parallel->get_num_threads()-2].end;(j=active2dnum[jj])>=0;jj++) {
                        lin[j]+=kernel->compute_optimized(regression_fix_index(docs[j]));
                    }
                    void* ret;
                    for (int32_t t=0; t<parallel->get_num_threads()-1; t++)
                        pthread_join(threads[t], &ret) ;

                    delete[] params;
                    delete[] threads;
                }
#endif
            }
        }
    }
    else
    {
        if (callback)
        {
            update_linear_component_mkl(docs, label, active2dnum,
                    a, a_old, working2dnum, totdoc, lin, aicache, c) ;
        }
        else {
            for(jj=0;(i=working2dnum[jj])>=0;jj++) {
                if(a[i] != a_old[i]) {
                    kernel->get_kernel_row(i,active2dnum,aicache);
                    for(ii=0;(j=active2dnum[ii])>=0;ii++)
                        lin[j]+=(a[i]-a_old[i])*aicache[j]*(float64_t)label[i];
                }
            }
        }
    }
}

void CSVRLight::update_linear_component_mkl(
    int32_t* docs, int32_t* label, int32_t *active2dnum, float64_t *a,
    float64_t *a_old, int32_t *working2dnum, int32_t totdoc, float64_t *lin,
    float64_t *aicache, float64_t* c)
{
    int32_t num         = totdoc;
    int32_t num_weights = -1;
    int32_t num_kernels = kernel->get_num_subkernels() ;
    const float64_t* old_beta  = kernel->get_subkernel_weights(num_weights);

    ASSERT(num_weights==num_kernels);

    if ((kernel->get_kernel_type()==K_COMBINED) &&
             (!((CCombinedKernel*)kernel)->get_append_subkernel_weights()))// for combined kernel
    {
        CCombinedKernel* k      = (CCombinedKernel*) kernel;
        CKernel* kn = k->get_first_kernel() ;
        int32_t n = 0, i, j ;

        while (kn!=NULL)
        {
            for(i=0;i<num;i++)
            {
                if(a[i] != a_old[i])
                {
                    kn->get_kernel_row(i,NULL,aicache, true);
                    for(j=0;j<num;j++)
                        W[j*num_kernels+n]+=(a[i]-a_old[i])*aicache[regression_fix_index(j)]*(float64_t)label[i];
                }
            }
            SG_UNREF(kn);
            kn = k->get_next_kernel();
            n++ ;
        }
    }
    else // hope the kernel is fast ...
    {
        float64_t* w_backup = new float64_t[num_kernels] ;
        float64_t* w1 = new float64_t[num_kernels] ;

        // backup and set to zero
        for (int32_t i=0; i<num_kernels; i++)
        {
            w_backup[i] = old_beta[i] ;
            w1[i]=0.0 ;
        }
        for (int32_t n=0; n<num_kernels; n++)
        {
            w1[n]=1.0 ;
            kernel->set_subkernel_weights(w1, num_weights) ;

            for(int32_t i=0;i<num;i++)
            {
                if(a[i] != a_old[i])
                {
                    for(int32_t j=0;j<num;j++)
                        W[j*num_kernels+n]+=(a[i]-a_old[i])*compute_kernel(i,j)*(float64_t)label[i];
                }
            }
            w1[n]=0.0 ;
        }

        // restore old weights
        kernel->set_subkernel_weights(w_backup,num_weights) ;

        delete[] w_backup ;
        delete[] w1 ;
    }

    call_mkl_callback(a, label, lin, c, totdoc);
}


void CSVRLight::update_linear_component_mkl_linadd(
    int32_t* docs, int32_t* label, int32_t *active2dnum, float64_t *a,
    float64_t *a_old, int32_t *working2dnum, int32_t totdoc, float64_t *lin,
    float64_t *aicache, float64_t* c)
{
    // kernel with LP_LINADD property is assumed to have
    // compute_by_subkernel functions
    int32_t num         = totdoc;
    int32_t num_weights = -1;
    int32_t num_kernels = kernel->get_num_subkernels() ;
    const float64_t* old_beta   = kernel->get_subkernel_weights(num_weights);

    ASSERT(num_weights==num_kernels);

    float64_t* w_backup=new float64_t[num_kernels];
    float64_t* w1=new float64_t[num_kernels];

    // backup and set to one
    for (int32_t i=0; i<num_kernels; i++)
    {
        w_backup[i] = old_beta[i] ;
        w1[i]=1.0 ;
    }
    // set the kernel weights
    kernel->set_subkernel_weights(w1, num_weights) ;

    // create normal update (with changed alphas only)
    kernel->clear_normal();
    for(int32_t ii=0, i=0;(i=working2dnum[ii])>=0;ii++) {
        if(a[i] != a_old[i]) {
            kernel->add_to_normal(regression_fix_index(docs[i]), (a[i]-a_old[i])*(float64_t)label[i]);
        }
    }

    // determine contributions of different kernels
    for (int32_t i=0; i<num; i++)
        kernel->compute_by_subkernel(i,&W[i*num_kernels]) ;

    // restore old weights
    kernel->set_subkernel_weights(w_backup,num_weights) ;

    delete[] w_backup ;
    delete[] w1 ;

    call_mkl_callback(a, label, lin, c, totdoc);
}

void CSVRLight::call_mkl_callback(float64_t* a, int32_t* label, float64_t* lin, float64_t* c, int32_t totdoc)
{
    int32_t num = totdoc;
    int32_t num_kernels = kernel->get_num_subkernels() ;
    int nk = (int) num_kernels; // calling external lib
    float64_t sumalpha = 0;
    float64_t* sumw=new float64_t[num_kernels];

    for (int32_t i=0; i<num; i++)
        sumalpha-=a[i]*(learn_parm->eps[i]-label[i]*c[i]);

#ifdef HAVE_LAPACK
    double* alphay  = new double[num];
    for (int32_t i=0; i<num; i++)
        alphay[i]=a[i]*label[i];

    for (int32_t i=0; i<num_kernels; i++)
        sumw[i]=0;

    cblas_dgemv(CblasColMajor, CblasNoTrans, nk, (int) num, 0.5, (double*) W,
        nk, (double*) alphay, 1, 1.0, (double*) sumw, 1);

    delete[] alphay;
#else
    for (int32_t d=0; d<num_kernels; d++)
    {
        sumw[d]=0;
        for(int32_t i=0; i<num; i++)
            sumw[d] += 0.5*a[i]*label[i]*W[i*num_kernels+d];
    }
#endif

    if (callback)
        mkl_converged=callback(mkl, sumw, sumalpha);

    const float64_t* new_beta   = kernel->get_subkernel_weights(num_kernels);

    // update lin
#ifdef HAVE_LAPACK
    cblas_dgemv(CblasColMajor, CblasTrans, nk, (int) num, 1.0, (double*) W,
        nk, (double*) new_beta, 1, 0.0, (double*) lin, 1);
#else
    for(int32_t i=0; i<num; i++)
        lin[i]=0 ;
    for (int32_t d=0; d<num_kernels; d++)
        if (new_beta[d]!=0)
            for(int32_t i=0; i<num; i++)
                lin[i] += new_beta[d]*W[i*num_kernels+d] ;
#endif


    delete[] sumw;
}


void CSVRLight::reactivate_inactive_examples(
    int32_t* label, float64_t *a, SHRINK_STATE *shrink_state, float64_t *lin,
    float64_t *c, int32_t totdoc, int32_t iteration, int32_t *inconsistent,
    int32_t* docs, float64_t *aicache, float64_t *maxdiff)
     /* Make all variables active again which had been removed by
        shrinking. */
     /* Computes lin for those variables from scratch. */
{
  register int32_t i=0,j,ii=0,jj,t,*changed2dnum,*inactive2dnum;
  int32_t *changed,*inactive;
  register float64_t *a_old,dist;
  float64_t ex_c,target;

  if (kernel->has_property(KP_LINADD) && get_linadd_enabled()) { /* special linear case */
      a_old=shrink_state->last_a;

      kernel->clear_normal();
      int32_t num_modified=0;
      for(i=0;i<totdoc;i++) {
          if(a[i] != a_old[i]) {
              kernel->add_to_normal(regression_fix_index(docs[i]), ((a[i]-a_old[i])*(float64_t)label[i]));
              a_old[i]=a[i];
              num_modified++;
          }
      }

      if (num_modified>0)
      {
          for(i=0;i<totdoc;i++) {
              if(!shrink_state->active[i]) {
                  lin[i]=shrink_state->last_lin[i]+kernel->compute_optimized(regression_fix_index(docs[i]));
              }
              shrink_state->last_lin[i]=lin[i];
          }
      }
  }
  else
  {
      changed=new int32_t[totdoc];
      changed2dnum=new int32_t[totdoc+11];
      inactive=new int32_t[totdoc];
      inactive2dnum=new int32_t[totdoc+11];
      for(t=shrink_state->deactnum-1;(t>=0) && shrink_state->a_history[t];t--) {
          if(verbosity>=2) {
              SG_INFO( "%ld..",t);
          }
          a_old=shrink_state->a_history[t];
          for(i=0;i<totdoc;i++) {
              inactive[i]=((!shrink_state->active[i])
                      && (shrink_state->inactive_since[i] == t));
              changed[i]= (a[i] != a_old[i]);
          }
          compute_index(inactive,totdoc,inactive2dnum);
          compute_index(changed,totdoc,changed2dnum);

          for(ii=0;(i=changed2dnum[ii])>=0;ii++) {
              CKernelMachine::kernel->get_kernel_row(i,inactive2dnum,aicache);
              for(jj=0;(j=inactive2dnum[jj])>=0;jj++)
                  lin[j]+=(a[i]-a_old[i])*aicache[j]*(float64_t)label[i];
          }
      }
      delete[] changed;
      delete[] changed2dnum;
      delete[] inactive;
      delete[] inactive2dnum;
  }

  (*maxdiff)=0;
  for(i=0;i<totdoc;i++) {
    shrink_state->inactive_since[i]=shrink_state->deactnum-1;
    if(!inconsistent[i]) {
      dist=(lin[i]-model->b)*(float64_t)label[i];
      target=-(learn_parm->eps[i]-(float64_t)label[i]*c[i]);
      ex_c=learn_parm->svm_cost[i]-learn_parm->epsilon_a;
      if((a[i]>learn_parm->epsilon_a) && (dist > target)) {
    if((dist-target)>(*maxdiff))  /* largest violation */
      (*maxdiff)=dist-target;
      }
      else if((a[i]<ex_c) && (dist < target)) {
    if((target-dist)>(*maxdiff))  /* largest violation */
      (*maxdiff)=target-dist;
      }
      if((a[i]>(0+learn_parm->epsilon_a))
     && (a[i]<ex_c)) {
    shrink_state->active[i]=1;                         /* not at bound */
      }
      else if((a[i]<=(0+learn_parm->epsilon_a)) && (dist < (target+learn_parm->epsilon_shrink))) {
    shrink_state->active[i]=1;
      }
      else if((a[i]>=ex_c)
          && (dist > (target-learn_parm->epsilon_shrink))) {
    shrink_state->active[i]=1;
      }
      else if(learn_parm->sharedslack) { /* make all active when sharedslack */
    shrink_state->active[i]=1;
      }
    }
  }
  if (use_kernel_cache) { /* update history for non-linear */
      for(i=0;i<totdoc;i++) {
          (shrink_state->a_history[shrink_state->deactnum-1])[i]=a[i];
      }
      for(t=shrink_state->deactnum-2;(t>=0) && shrink_state->a_history[t];t--) {
          delete[] shrink_state->a_history[t];
          shrink_state->a_history[t]=0;
      }
  }
}
#endif //USE_SVMLIGHT