exercise_2/3rdparty/colmap-dev/lib/VLFeat/ikmeans_elkan.tc

/** @file ikmeans_elkan.tc
 ** @brief Integer K-Means - Elkan Algorithm - Definition
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#include "mathop.h"

/** @internal
 ** Update inter cluster distance table.
 **/

static void
vl_ikm_elkan_update_inter_dist (VlIKMFilt *f)
{
  vl_uindex i, k, kp ;

  /* inter cluster distances */
  for(k = 0 ; k < f->K ; ++ k) {
    for(kp = 0 ; kp < f->K ; ++ kp) {
      vl_ikmacc_t dist = 0 ;
      if (k != kp) {
        for(i = 0 ; i < f->M ; ++i) {
          vl_ikmacc_t delta  = f->centers [kp * f->M + i] - f->centers [k * f->M + i] ;
          dist  += delta * delta ;
        }
      }
      f->inter_dist [k * f->K + kp] = f->inter_dist [kp * f->K + k] = dist >> 2 ;
    }
  }
}

/** @internal
 ** @brief Helper function to initialize filter for Triangle algorithm
 ** @param f filter.
 **/

static void
vl_ikm_init_elkan (VlIKMFilt *f)
{
  if (f->inter_dist) {
    vl_free (f-> inter_dist) ;
  }
  f->inter_dist = vl_malloc (sizeof(*f->inter_dist) * f->K * f->K) ;
  vl_ikm_elkan_update_inter_dist (f) ;
}


/** @internal
 ** @brief Elkan algorithm
 ** @param f     IKM quantizer.
 ** @param data  Data to quantize.
 ** @param N     Number of data elements.
 **/

static int
vl_ikm_train_elkan (VlIKMFilt* f, vl_uint8 const* data, vl_size N)
{
  /* REMARK !! All distances are squared !! */
  vl_uindex i,pass,c,cp,x,cx ;
  vl_size dist_calc = 0 ;

  vl_ikmacc_t dist ;
  vl_ikmacc_t *m_pt = vl_malloc(sizeof(*m_pt) * f->M * f->K) ; /* new centers (temp) */
  vl_ikmacc_t *u_pt = vl_malloc(sizeof(*u_pt) * N) ; /* upper bound (may str) */
  char *r_pt = vl_malloc(sizeof(*r_pt) * 1 * N) ; /* flag: u is strict */
  vl_ikmacc_t *s_pt = vl_malloc(sizeof(*s_pt) * f->K) ; /* min cluster dist. */
  vl_ikmacc_t *l_pt = vl_malloc(sizeof(*l_pt) * N * f->K) ; /* lower bound  */
  vl_ikmacc_t *d_pt = f->inter_dist ; /* half inter clst dist  */
  vl_uint32 *asgn = vl_malloc (sizeof(*asgn) * N) ;
  vl_uint32 *counts =vl_malloc (sizeof(*counts) * N) ;

  int done = 0 ;

  /* do passes */
  vl_ikm_elkan_update_inter_dist (f) ;

  /* init */
  memset(l_pt, 0, sizeof(*l_pt) * N * f->K) ;
  memset(u_pt, 0, sizeof(*u_pt) * N) ;
  memset(r_pt, 0, sizeof(*r_pt) * N) ;
  for(x = 0 ; x < N ; ++x) {
    vl_ikmacc_t best_dist ;

    /* do first cluster `by hand' */
    dist_calc ++ ;
    for(dist = 0, i = 0 ; i < f->M ; ++i) {
      vl_ikmacc_t delta = (vl_ikmacc_t)data[x * f->M + i] - f->centers[i] ;
      dist += delta*delta ;
    }
    cx = 0 ;
    best_dist = dist ;
    l_pt[x] = dist ;

    /* do other clusters */
    for(c = 1 ; c < f->K ; ++c) {
      if(d_pt[f->K * cx + c] < best_dist) {
        /* might need to be updated */

        dist_calc++ ;
        for(dist=0, i = 0 ; i < f->M ; ++i) {
          vl_ikmacc_t delta =
          (vl_ikmacc_t)data[x * f->M + i]
          - f->centers[c * f->M + i] ;
          dist += delta * delta ;
        }

        /* lower bound */
        l_pt[N*c + x] = dist ;

        if(dist < best_dist) {
          best_dist = dist ;
          cx        = c ;
        }
      }
    }

    asgn[x] = (vl_uint32)cx ;
    u_pt[x] = best_dist ;
  }

  /* --------------------------------------------------------------------
   *                                                               Passes
   * ------------------------------------------------------------------ */

  for (pass = 0 ; 1 ; ++ pass) {

    /* ------------------------------------------------------------------
     *                                                 Re-calculate means
     * ---------------------------------------------------------------- */
    memset(m_pt, 0, sizeof(*m_pt) * f->M * f->K) ;
    memset(counts, 0, sizeof(*counts) * f->K) ;

    /* accumulate */
    for(x = 0 ; x < N ; ++x) {
      int cx = asgn[x] ;
      ++ counts[ cx ] ;
      for(i = 0 ; i < f->M ; ++i) {
        m_pt[cx * f->M + i] += data[x * f->M + i] ;
      }
    }

    /* normalize */
    for(c = 0 ; c < f->K ; ++c) {
      vl_ikmacc_t n = counts[c] ;
      if(n > 0) {
        for(i = 0 ; i < f->M ; ++i) {
          m_pt[c * f->M + i] /= n ;
        }
      } else {
        for(i = 0 ; i < f->M ; ++i) {
          /*m_pt[c*M + i] = data[pairs_pt[c].j*M + i] ;*/
        }
      }
    }

    /* ------------------------------------------------------------------
     *                                                      Update bounds
     * --------------------------------------------------------------- */
    for(c = 0 ; c < f->K ; ++c) {

      /* distance d(m(c),c) and update c */
      dist_calc++ ;
      for(dist = 0, i = 0 ; i < f->M ; ++i) {
        vl_ikmacc_t delta =
        (vl_ikmacc_t)m_pt[c * f->M + i]
        - f->centers[c * f->M + i] ;
        f->centers[c * f->M + i] = m_pt[c * f->M +i] ;
        dist += delta * delta ;
      }
      for(x = 0 ; x < N ; ++x) {
        vl_ikmacc_t lxc = l_pt[c * N + x] ;
        vl_uindex cx  = (int) asgn[x] ;

        /* lower bound */
        if(dist < lxc) {
          lxc = (vl_ikmacc_t) (lxc + dist - 2*(vl_fast_sqrt_ui64(lxc)+1)*(vl_fast_sqrt_ui64(dist)+1)) ;
        } else {
          lxc = 0 ;
        }
        l_pt[c*N + x]  = lxc ;

        /* upper bound */
        if(c == cx) {
          vl_ikmacc_t ux = u_pt[x] ;
          u_pt[x] = (vl_ikmacc_t) (ux + dist + 2 * (vl_fast_sqrt_ui64(ux)+1)*(vl_fast_sqrt_ui64(dist)+1)) ;
          r_pt[x] = 1 ;
        }
      }
    }

    /* inter cluster distances */
    for(c = 0 ; c < f->K ; ++c) {
      for(cp = 0 ; cp < f->K ; ++cp) {
        dist = 0 ;
        if( c != cp ) {
          dist_calc++;
          for(i = 0 ; i < f->M ; ++i) {
            vl_ikmacc_t delta = f->centers[cp * f->M + i] - f->centers[ c * f->M + i] ;
            dist += delta*delta ;
          }
        }
        d_pt[c * f->K + cp] = d_pt[cp * f->K + c] = dist>>2 ;
      }
    }

    /* closest cluster distance */
    for(c = 0  ; c < f->K ; ++c) {
      vl_ikmacc_t best_dist = VL_IKMACC_MAX ;
      for(cp = 0 ; cp < f->K ; ++cp) {
        dist = d_pt[c * f->K + cp] ;
        if(c != cp && dist < best_dist) best_dist = dist ;
      }
      s_pt[c] = best_dist >> 2 ;
    }


    /* ------------------------------------------------------------------
     * Assign data to centers
     * ---------------------------------------------------------------- */
    done = 1 ;
    for(x = 0 ; x < N ; ++x) {
      vl_uindex cx = (vl_uindex) asgn[x] ;
      vl_ikmacc_t ux = u_pt[x] ;

      /* ux is an upper bound of the distance of x to its
         current center cx. s_pt[cx] is half of the minum distance
         between the cluster cx and any other cluster center.  If
         ux <= s_pt[cx] then x remains attached to cx. */

      if(ux <= s_pt[cx])  continue ;

      for(c = 0 ; c < f->K ; ++c) {
        vl_ikmacc_t dist = 0 ;
        /* so x might need to be re-associated from cx to c. We can
           exclude c if

           1 - cx = c (trivial) or
           2 - u(x) <= l(x,c)    as this implies d(x,cx) <= d(x,c) or
           3 - u(x) <= d(cx,c)/2 as this implies d(x,cx) <= d(x,c).
        */
        if(c  == cx ||
           ux <= l_pt[N * c +  x] ||
           ux <= d_pt[f->K * c + cx])
          continue ;

        /* we need to make a true comparison */

        /* if u_pt[x] is stale (i.e. not strictly equal to
           d(x,cx)), then re-calcualte it. */
        if( r_pt[x] ) {
          dist_calc++;
          for(dist = 0, i = 0 ; i < f->M ; ++i) {
            vl_ikmacc_t delta = (vl_ikmacc_t)data[x * f->M + i] - f->centers[cx * f->M + i] ;
            dist += delta*delta ;
          }
          ux = u_pt[x] = dist ;
          r_pt[x] = 0 ;

          /* now that u_pt[x] is updated, we check the conditions
             again */
          if(
             ux <= l_pt[N * c +  x]  ||
             ux <= d_pt[f->K * c + cx]  )
            continue ;
        }

        /* no way... we need to compute the distance d(x,c) */
        dist_calc++ ;
        for(dist = 0, i = 0 ; i < f->M ; ++i) {
          vl_ikmacc_t delta = (vl_ikmacc_t)data[ x * f->M + i] - f->centers[c * f->M + i] ;
          dist += delta * delta ;
        }

        l_pt[N * c + x] =  dist ;

        if (dist < ux) {
          ux = u_pt[x] = dist ;
          /* r_pt[x] already 0 */
          asgn[x] = (vl_uint32)c ;
          done = 0 ;
        }
      }
    } /* next data point */

      /* stopping condition */
    if(done || pass == f->max_niters) {
      break ;
    }
  }

  vl_free (counts) ;
  vl_free (asgn) ;
  vl_free (l_pt) ;
  vl_free (s_pt) ;
  vl_free (r_pt) ;
  vl_free (u_pt) ;
  vl_free (m_pt) ;

  if (f-> verb) {
    VL_PRINTF ("ikm: Elkan algorithm: total iterations: %d\n", pass) ;
    VL_PRINTF ("ikm: Elkan algorithm: distance calculations: %d (speedup: %.2f)\n",
               dist_calc, (float)N * f->K * (pass+2) / dist_calc - 1) ;
  }
  return 0 ;
}

/** @internal
 ** @brief Elkan algorithm
 ** @param f    IKM quantizer.
 ** @param asgn Assignment of data to centers (out).
 ** @param data Data to quantize.
 ** @param N    Number of data elements.
 **/

static void
vl_ikm_push_elkan (VlIKMFilt *f, vl_uint32 *asgn, vl_uint8 const *data, vl_size N)
{
  vl_uindex i,c,cx,x ;
  vl_size dist_calc = 0 ;
  vl_ikmacc_t dist, best_dist ;
  vl_ikmacc_t *d_pt = f->inter_dist ;

  /* assign data to centers */
  for(x = 0 ; x < N ; ++x) {
    best_dist = VL_IKMACC_MAX ;
    cx = 0 ;

    for(c = 0 ; c < f->K ; ++c) {
      if(d_pt[f->K * cx + c] < best_dist) {
        /* might need to be updated */
        dist_calc ++ ;
        for(dist=0, i = 0 ; i < f->M ; ++i) {
          vl_ikmacc_t delta = data[x * f->M + i] - f->centers[c * f->M + i] ;
          dist += delta * delta ;
        }

        /* u_pt is strict at the beginning */
        if(dist < best_dist) {
          best_dist = dist ;
          cx        = c ;
        }
      }
    }
    asgn[x] = (vl_uint32)cx ;
  }
}

/*
 * Local Variables: *
 * mode: C *
 * End: *
 */