tesseract/ccstruct/statistc.cpp

/**********************************************************************
 * File:        statistc.c  (Formerly stats.c)
 * Description: Simple statistical package for integer values.
 * Author:					Ray Smith
 * Created:					Mon Feb 04 16:56:05 GMT 1991
 *
 * (C) Copyright 1991, Hewlett-Packard Ltd.
 ** Licensed under the Apache License, Version 2.0 (the "License");
 ** you may not use this file except in compliance with the License.
 ** You may obtain a copy of the License at
 ** http://www.apache.org/licenses/LICENSE-2.0
 ** Unless required by applicable law or agreed to in writing, software
 ** distributed under the License is distributed on an "AS IS" BASIS,
 ** WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 ** See the License for the specific language governing permissions and
 ** limitations under the License.
 *
 **********************************************************************/

// Include automatically generated configuration file if running autoconf.
#ifdef HAVE_CONFIG_H
#include "config_auto.h"
#endif

#include          "statistc.h"
#include          <string.h>
#include          <math.h>
#include          <stdlib.h>
#include          "helpers.h"
#include          "scrollview.h"
#include          "tprintf.h"

using tesseract::KDPairInc;

/**********************************************************************
 * STATS::STATS
 *
 * Construct a new stats element by allocating and zeroing the memory.
 **********************************************************************/
STATS::STATS(inT32 min_bucket_value, inT32 max_bucket_value_plus_1) {
  if (max_bucket_value_plus_1 <= min_bucket_value) {
    min_bucket_value = 0;
    max_bucket_value_plus_1 = 1;
  }
  rangemin_ = min_bucket_value;                // setup
  rangemax_ = max_bucket_value_plus_1;
  buckets_ = new inT32[rangemax_ - rangemin_];
  clear();
}

STATS::STATS() {
  rangemax_ = 0;
  rangemin_ = 0;
  buckets_ = NULL;
}

/**********************************************************************
 * STATS::set_range
 *
 * Alter the range on an existing stats element.
 **********************************************************************/
bool STATS::set_range(inT32 min_bucket_value, inT32 max_bucket_value_plus_1) {
  if (max_bucket_value_plus_1 <= min_bucket_value) {
    return false;
  }
  if (rangemax_ - rangemin_ != max_bucket_value_plus_1 - min_bucket_value) {
    delete [] buckets_;
    buckets_ = new inT32[max_bucket_value_plus_1 - min_bucket_value];
  }
  rangemin_ = min_bucket_value;                // setup
  rangemax_ = max_bucket_value_plus_1;
  clear();                // zero it
  return true;
}

/**********************************************************************
 * STATS::clear
 *
 * Clear out the STATS class by zeroing all the buckets.
 **********************************************************************/
void STATS::clear() {  // clear out buckets
  total_count_ = 0;
  if (buckets_ != NULL)
    memset(buckets_, 0, (rangemax_ - rangemin_) * sizeof(buckets_[0]));
}

/**********************************************************************
 * STATS::~STATS
 *
 * Destructor for a stats class.
 **********************************************************************/
STATS::~STATS () {
  if (buckets_ != NULL) {
    delete [] buckets_;
    buckets_ = NULL;
  }
}

/**********************************************************************
 * STATS::add
 *
 * Add a set of samples to (or delete from) a pile.
 **********************************************************************/
void STATS::add(inT32 value, inT32 count) {
  if (buckets_ == NULL) {
    return;
  }
  value = ClipToRange(value, rangemin_, rangemax_ - 1);
  buckets_[value - rangemin_] += count;
  total_count_ += count;          // keep count of total
}

/**********************************************************************
 * STATS::mode
 *
 * Find the mode of a stats class.
 **********************************************************************/
inT32 STATS::mode() const {  // get mode of samples
  if (buckets_ == NULL) {
    return rangemin_;
  }
  inT32 max = buckets_[0];           // max cell count
  inT32 maxindex = 0;                // index of max
  for (int index = rangemax_ - rangemin_ - 1; index > 0; --index) {
    if (buckets_[index] > max) {
      max = buckets_[index];      // find biggest
      maxindex = index;
    }
  }
  return maxindex + rangemin_;    // index of biggest
}

/**********************************************************************
 * STATS::mean
 *
 * Find the mean of a stats class.
 **********************************************************************/
double STATS::mean() const {  //get mean of samples
  if (buckets_ == NULL || total_count_ <= 0) {
    return static_cast<double>(rangemin_);
  }
  inT64 sum = 0;
  for (int index = rangemax_ - rangemin_ - 1; index >= 0; --index) {
    sum += static_cast<inT64>(index) * buckets_[index];
  }
  return static_cast<double>(sum) / total_count_ + rangemin_;
}

/**********************************************************************
 * STATS::sd
 *
 * Find the standard deviation of a stats class.
 **********************************************************************/
double STATS::sd() const {  //standard deviation
  if (buckets_ == NULL || total_count_ <= 0) {
    return 0.0;
  }
  inT64 sum = 0;
  double sqsum = 0.0;
  for (int index = rangemax_ - rangemin_ - 1; index >= 0; --index) {
    sum += static_cast<inT64>(index) * buckets_[index];
    sqsum += static_cast<double>(index) * index * buckets_[index];
  }
  double variance = static_cast<double>(sum) / total_count_;
  variance = sqsum / total_count_ - variance * variance;
  if (variance > 0.0)
    return sqrt(variance);
  return 0.0;
}

/**********************************************************************
 * STATS::ile
 *
 * Returns the fractile value such that frac fraction (in [0,1]) of samples
 * has a value less than the return value.
 **********************************************************************/
double STATS::ile(double frac) const {
  if (buckets_ == NULL || total_count_ == 0) {
    return static_cast<double>(rangemin_);
  }
#if 0
  // TODO(rays) The existing code doesn't seem to be doing the right thing
  // with target a double but this substitute crashes the code that uses it.
  // Investigate and fix properly.
  int target = IntCastRounded(frac * total_count_);
  target = ClipToRange(target, 1, total_count_);
#else
  double target = frac * total_count_;
  target = ClipToRange(target, 1.0, static_cast<double>(total_count_));
#endif
  int sum = 0;
  int index = 0;
  for (index = 0; index < rangemax_ - rangemin_ && sum < target;
       sum += buckets_[index++]);
  if (index > 0) {
    ASSERT_HOST(buckets_[index - 1] > 0);
    return rangemin_ + index -
        static_cast<double>(sum - target) / buckets_[index - 1];
  } else {
    return static_cast<double>(rangemin_);
  }
}

/**********************************************************************
 * STATS::min_bucket
 *
 * Find REAL minimum bucket - ile(0.0) isnt necessarily correct
 **********************************************************************/
inT32 STATS::min_bucket() const {  // Find min
  if (buckets_ == NULL || total_count_ == 0) {
    return rangemin_;
  }
  inT32 min = 0;
  for (min = 0; (min < rangemax_ - rangemin_) && (buckets_[min] == 0); min++);
  return rangemin_ + min;
}


/**********************************************************************
 * STATS::max_bucket
 *
 * Find REAL maximum bucket - ile(1.0) isnt necessarily correct
 **********************************************************************/

inT32 STATS::max_bucket() const {  // Find max
  if (buckets_ == NULL || total_count_ == 0) {
    return rangemin_;
  }
  inT32 max;
  for (max = rangemax_ - rangemin_ - 1; max > 0 && buckets_[max] == 0; max--);
  return rangemin_ + max;
}

/**********************************************************************
 * STATS::median
 *
 * Finds a more useful estimate of median than ile(0.5).
 *
 * Overcomes a problem with ile() - if the samples are, for example,
 * 6,6,13,14 ile(0.5) return 7.0 - when a more useful value would be midway
 * between 6 and 13 = 9.5
 **********************************************************************/
double STATS::median() const {  //get median
  if (buckets_ == NULL) {
    return static_cast<double>(rangemin_);
  }
  double median = ile(0.5);
  int median_pile = static_cast<int>(floor(median));
  if ((total_count_ > 1) && (pile_count(median_pile) == 0)) {
    inT32 min_pile;
    inT32 max_pile;
    /* Find preceeding non zero pile */
    for (min_pile = median_pile; pile_count(min_pile) == 0; min_pile--);
    /* Find following non zero pile */
    for (max_pile = median_pile; pile_count(max_pile) == 0; max_pile++);
    median = (min_pile + max_pile) / 2.0;
  }
  return median;
}

/**********************************************************************
 * STATS::local_min
 *
 * Return TRUE if this point is a local min.
 **********************************************************************/
bool STATS::local_min(inT32 x) const {
  if (buckets_ == NULL) {
    return false;
  }
  x = ClipToRange(x, rangemin_, rangemax_ - 1) - rangemin_;
  if (buckets_[x] == 0)
    return true;
  inT32 index;                   // table index
  for (index = x - 1; index >= 0 && buckets_[index] == buckets_[x]; --index);
  if (index >= 0 && buckets_[index] < buckets_[x])
    return false;
  for (index = x + 1; index < rangemax_ - rangemin_ &&
       buckets_[index] == buckets_[x]; ++index);
  if (index < rangemax_ - rangemin_ && buckets_[index] < buckets_[x])
    return false;
  else
    return true;
}

/**********************************************************************
 * STATS::smooth
 *
 * Apply a triangular smoothing filter to the stats.
 * This makes the modes a bit more useful.
 * The factor gives the height of the triangle, i.e. the weight of the
 * centre.
 **********************************************************************/
void STATS::smooth(inT32 factor) {
  if (buckets_ == NULL || factor < 2) {
    return;
  }
  STATS result(rangemin_, rangemax_);
  int entrycount = rangemax_ - rangemin_;
  for (int entry = 0; entry < entrycount; entry++) {
                                 //centre weight
    int count = buckets_[entry] * factor;
    for (int offset = 1; offset < factor; offset++) {
      if (entry - offset >= 0)
        count += buckets_[entry - offset] * (factor - offset);
      if (entry + offset < entrycount)
        count += buckets_[entry + offset] * (factor - offset);
    }
    result.add(entry + rangemin_, count);
  }
  total_count_ = result.total_count_;
  memcpy(buckets_, result.buckets_, entrycount * sizeof(buckets_[0]));
}

/**********************************************************************
 * STATS::cluster
 *
 * Cluster the samples into max_cluster clusters.
 * Each call runs one iteration. The array of clusters must be
 * max_clusters+1 in size as cluster 0 is used to indicate which samples
 * have been used.
 * The return value is the current number of clusters.
 **********************************************************************/

inT32 STATS::cluster(float lower,         // thresholds
                     float upper,
                     float multiple,      // distance threshold
                     inT32 max_clusters,  // max no to make
                     STATS *clusters) {   // array of clusters
  BOOL8 new_cluster;             // added one
  float *centres;                // cluster centres
  inT32 entry;                   // bucket index
  inT32 cluster;                 // cluster index
  inT32 best_cluster;            // one to assign to
  inT32 new_centre = 0;          // residual mode
  inT32 new_mode;                // pile count of new_centre
  inT32 count;                   // pile to place
  float dist;                    // from cluster
  float min_dist;                // from best_cluster
  inT32 cluster_count;           // no of clusters

  if (buckets_ == NULL || max_clusters < 1)
    return 0;
  centres = new float[max_clusters + 1];
  for (cluster_count = 1; cluster_count <= max_clusters
       && clusters[cluster_count].buckets_ != NULL
       && clusters[cluster_count].total_count_ > 0;
       cluster_count++) {
    centres[cluster_count] =
      static_cast<float>(clusters[cluster_count].ile(0.5));
    new_centre = clusters[cluster_count].mode();
    for (entry = new_centre - 1; centres[cluster_count] - entry < lower
         && entry >= rangemin_
         && pile_count(entry) <= pile_count(entry + 1);
         entry--) {
      count = pile_count(entry) - clusters[0].pile_count(entry);
      if (count > 0) {
        clusters[cluster_count].add(entry, count);
        clusters[0].add (entry, count);
      }
    }
    for (entry = new_centre + 1; entry - centres[cluster_count] < lower
         && entry < rangemax_
         && pile_count(entry) <= pile_count(entry - 1);
         entry++) {
      count = pile_count(entry) - clusters[0].pile_count(entry);
      if (count > 0) {
        clusters[cluster_count].add(entry, count);
        clusters[0].add(entry, count);
      }
    }
  }
  cluster_count--;

  if (cluster_count == 0) {
    clusters[0].set_range(rangemin_, rangemax_);
  }
  do {
    new_cluster = FALSE;
    new_mode = 0;
    for (entry = 0; entry < rangemax_ - rangemin_; entry++) {
      count = buckets_[entry] - clusters[0].buckets_[entry];
      //remaining pile
      if (count > 0) {           //any to handle
        min_dist = static_cast<float>(MAX_INT32);
        best_cluster = 0;
        for (cluster = 1; cluster <= cluster_count; cluster++) {
          dist = entry + rangemin_ - centres[cluster];
          //find distance
          if (dist < 0)
            dist = -dist;
          if (dist < min_dist) {
            min_dist = dist;     //find least
            best_cluster = cluster;
          }
        }
        if (min_dist > upper     //far enough for new
          && (best_cluster == 0
          || entry + rangemin_ > centres[best_cluster] * multiple
        || entry + rangemin_ < centres[best_cluster] / multiple)) {
          if (count > new_mode) {
            new_mode = count;
            new_centre = entry + rangemin_;
          }
        }
      }
    }
                                 // need new and room
    if (new_mode > 0 && cluster_count < max_clusters) {
      cluster_count++;
      new_cluster = TRUE;
      if (!clusters[cluster_count].set_range(rangemin_, rangemax_))
        return 0;
      centres[cluster_count] = static_cast<float>(new_centre);
      clusters[cluster_count].add(new_centre, new_mode);
      clusters[0].add(new_centre, new_mode);
      for (entry = new_centre - 1; centres[cluster_count] - entry < lower
        && entry >= rangemin_
      && pile_count (entry) <= pile_count(entry + 1); entry--) {
        count = pile_count(entry) - clusters[0].pile_count(entry);
        if (count > 0) {
          clusters[cluster_count].add(entry, count);
          clusters[0].add(entry, count);
        }
      }
      for (entry = new_centre + 1; entry - centres[cluster_count] < lower
        && entry < rangemax_
      && pile_count (entry) <= pile_count(entry - 1); entry++) {
        count = pile_count(entry) - clusters[0].pile_count(entry);
        if (count > 0) {
          clusters[cluster_count].add(entry, count);
          clusters[0].add (entry, count);
        }
      }
      centres[cluster_count] =
        static_cast<float>(clusters[cluster_count].ile(0.5));
    }
  } while (new_cluster && cluster_count < max_clusters);
  delete [] centres;
  return cluster_count;
}

// Helper tests that the current index is still part of the peak and gathers
// the data into the peak, returning false when the peak is ended.
// src_buckets[index] - used_buckets[index] is the unused part of the histogram.
// prev_count is the histogram count of the previous index on entry and is
// updated to the current index on return.
// total_count and total_value are accumulating the mean of the peak.
static bool GatherPeak(int index, const int* src_buckets, int* used_buckets,
                       int* prev_count, int* total_count, double* total_value) {
  int pile_count = src_buckets[index] - used_buckets[index];
  if (pile_count <= *prev_count && pile_count > 0) {
    // Accumulate count and index.count product.
    *total_count += pile_count;
    *total_value += index * pile_count;
    // Mark this index as used
    used_buckets[index] = src_buckets[index];
    *prev_count = pile_count;
    return true;
  } else {
    return false;
  }
}

// Finds (at most) the top max_modes modes, well actually the whole peak around
// each mode, returning them in the given modes vector as a <mean of peak,
// total count of peak> pair in order of decreasing total count.
// Since the mean is the key and the count the data in the pair, a single call
// to sort on the output will re-sort by increasing mean of peak if that is
// more useful than decreasing total count.
// Returns the actual number of modes found.
int STATS::top_n_modes(int max_modes,
                       GenericVector<KDPairInc<float, int> >* modes) const {
  if (max_modes <= 0) return 0;
  int src_count = rangemax_ - rangemin_;
  // Used copies the counts in buckets_ as they get used.
  STATS used(rangemin_, rangemax_);
  modes->truncate(0);
  // Total count of the smallest peak found so far.
  int least_count = 1;
  // Mode that is used as a seed for each peak
  int max_count = 0;
  do {
    // Find an unused mode.
    max_count = 0;
    int max_index = 0;
    for (int src_index = 0; src_index < src_count; src_index++) {
      int pile_count = buckets_[src_index] - used.buckets_[src_index];
      if (pile_count > max_count) {
        max_count = pile_count;
        max_index = src_index;
      }
    }
    if (max_count > 0) {
      // Copy the bucket count to used so it doesn't get found again.
      used.buckets_[max_index] = max_count;
      // Get the entire peak.
      double total_value = max_index * max_count;
      int total_count = max_count;
      int prev_pile = max_count;
      for (int offset = 1; max_index + offset < src_count; ++offset) {
        if (!GatherPeak(max_index + offset, buckets_, used.buckets_,
                        &prev_pile, &total_count, &total_value))
          break;
      }
      prev_pile = buckets_[max_index];
      for (int offset = 1; max_index - offset >= 0; ++offset) {
        if (!GatherPeak(max_index - offset, buckets_, used.buckets_,
                        &prev_pile, &total_count, &total_value))
          break;
      }
      if (total_count > least_count || modes->size() < max_modes) {
        // We definitely want this mode, so if we have enough discard the least.
        if (modes->size() == max_modes)
          modes->truncate(max_modes - 1);
        int target_index = 0;
        // Linear search for the target insertion point.
        while (target_index < modes->size() &&
               (*modes)[target_index].data >= total_count)
          ++target_index;
        float peak_mean =
            static_cast<float>(total_value / total_count + rangemin_);
        modes->insert(KDPairInc<float, int>(peak_mean, total_count),
                      target_index);
        least_count = modes->back().data;
      }
    }
  } while (max_count > 0);
  return modes->size();
}

/**********************************************************************
 * STATS::print
 *
 * Prints a summary and table of the histogram.
 **********************************************************************/
void STATS::print() const {
  if (buckets_ == NULL) {
    return;
  }
  inT32 min = min_bucket() - rangemin_;
  inT32 max = max_bucket() - rangemin_;

  int num_printed = 0;
  for (int index = min; index <= max; index++) {
    if (buckets_[index] != 0) {
      tprintf("%4d:%-3d ", rangemin_ + index, buckets_[index]);
      if (++num_printed % 8 == 0)
        tprintf ("\n");
    }
  }
  tprintf ("\n");
  print_summary();
}



/**********************************************************************
 * STATS::print_summary
 *
 * Print a summary of the stats.
 **********************************************************************/
void STATS::print_summary() const {
  if (buckets_ == NULL) {
    return;
  }
  inT32 min = min_bucket();
  inT32 max = max_bucket();
  tprintf("Total count=%d\n", total_count_);
  tprintf("Min=%.2f Really=%d\n", ile(0.0), min);
  tprintf("Lower quartile=%.2f\n", ile(0.25));
  tprintf("Median=%.2f, ile(0.5)=%.2f\n", median(), ile(0.5));
  tprintf("Upper quartile=%.2f\n", ile(0.75));
  tprintf("Max=%.2f Really=%d\n", ile(1.0), max);
  tprintf("Range=%d\n", max + 1 - min);
  tprintf("Mean= %.2f\n", mean());
  tprintf("SD= %.2f\n", sd());
}


/**********************************************************************
 * STATS::plot
 *
 * Draw a histogram of the stats table.
 **********************************************************************/

#ifndef GRAPHICS_DISABLED
void STATS::plot(ScrollView* window,  // to draw in
                 float xorigin,       // bottom left
                 float yorigin,
                 float xscale,        // one x unit
                 float yscale,        // one y unit
                 ScrollView::Color colour) const {   // colour to draw in
  if (buckets_ == NULL) {
    return;
  }
  window->Pen(colour);

  for (int index = 0; index < rangemax_ - rangemin_; index++) {
    window->Rectangle( xorigin + xscale * index, yorigin,
      xorigin + xscale * (index + 1),
      yorigin + yscale * buckets_[index]);
  }
}
#endif


/**********************************************************************
 * STATS::plotline
 *
 * Draw a histogram of the stats table. (Line only)
 **********************************************************************/

#ifndef GRAPHICS_DISABLED
void STATS::plotline(ScrollView* window,  // to draw in
                     float xorigin,       // bottom left
                     float yorigin,
                     float xscale,        // one x unit
                     float yscale,        // one y unit
                     ScrollView::Color colour) const {  // colour to draw in
  if (buckets_ == NULL) {
    return;
  }
  window->Pen(colour);
  window->SetCursor(xorigin, yorigin + yscale * buckets_[0]);
  for (int index = 0; index < rangemax_ - rangemin_; index++) {
    window->DrawTo(xorigin + xscale * index,
                   yorigin + yscale * buckets_[index]);
  }
}
#endif


/**********************************************************************
 * choose_nth_item
 *
 * Returns the index of what would b the nth item in the array
 * if the members were sorted, without actually sorting.
 **********************************************************************/

inT32 choose_nth_item(inT32 index, float *array, inT32 count) {
  inT32 next_sample;             // next one to do
  inT32 next_lesser;             // space for new
  inT32 prev_greater;            // last one saved
  inT32 equal_count;             // no of equal ones
  float pivot;                   // proposed median
  float sample;                  // current sample

  if (count <= 1)
    return 0;
  if (count == 2) {
    if (array[0] < array[1]) {
      return index >= 1 ? 1 : 0;
    }
    else {
      return index >= 1 ? 0 : 1;
    }
  }
  else {
    if (index < 0)
      index = 0;                 // ensure legal
    else if (index >= count)
      index = count - 1;
    equal_count = (inT32) (rand() % count);
    pivot = array[equal_count];
                                 // fill gap
    array[equal_count] = array[0];
    next_lesser = 0;
    prev_greater = count;
    equal_count = 1;
    for (next_sample = 1; next_sample < prev_greater;) {
      sample = array[next_sample];
      if (sample < pivot) {
                                 // shuffle
        array[next_lesser++] = sample;
        next_sample++;
      }
      else if (sample > pivot) {
        prev_greater--;
                                 // juggle
        array[next_sample] = array[prev_greater];
        array[prev_greater] = sample;
      }
      else {
        equal_count++;
        next_sample++;
      }
    }
    for (next_sample = next_lesser; next_sample < prev_greater;)
      array[next_sample++] = pivot;
    if (index < next_lesser)
      return choose_nth_item (index, array, next_lesser);
    else if (index < prev_greater)
      return next_lesser;        // in equal bracket
    else
      return choose_nth_item (index - prev_greater,
        array + prev_greater,
        count - prev_greater) + prev_greater;
  }
}

/**********************************************************************
 * choose_nth_item
 *
 * Returns the index of what would be the nth item in the array
 * if the members were sorted, without actually sorting.
 **********************************************************************/
inT32 choose_nth_item(inT32 index, void *array, inT32 count, size_t size,
                      int (*compar)(const void*, const void*)) {
  int result;                    // of compar
  inT32 next_sample;             // next one to do
  inT32 next_lesser;             // space for new
  inT32 prev_greater;            // last one saved
  inT32 equal_count;             // no of equal ones
  inT32 pivot;                   // proposed median

  if (count <= 1)
    return 0;
  if (count == 2) {
    if (compar (array, (char *) array + size) < 0) {
      return index >= 1 ? 1 : 0;
    }
    else {
      return index >= 1 ? 0 : 1;
    }
  }
  if (index < 0)
    index = 0;                   // ensure legal
  else if (index >= count)
    index = count - 1;
  pivot = (inT32) (rand () % count);
  swap_entries (array, size, pivot, 0);
  next_lesser = 0;
  prev_greater = count;
  equal_count = 1;
  for (next_sample = 1; next_sample < prev_greater;) {
    result =
      compar ((char *) array + size * next_sample,
      (char *) array + size * next_lesser);
    if (result < 0) {
      swap_entries (array, size, next_lesser++, next_sample++);
      // shuffle
    }
    else if (result > 0) {
      prev_greater--;
      swap_entries(array, size, prev_greater, next_sample);
    }
    else {
      equal_count++;
      next_sample++;
    }
  }
  if (index < next_lesser)
    return choose_nth_item (index, array, next_lesser, size, compar);
  else if (index < prev_greater)
    return next_lesser;          // in equal bracket
  else
    return choose_nth_item (index - prev_greater,
      (char *) array + size * prev_greater,
      count - prev_greater, size,
      compar) + prev_greater;
}

/**********************************************************************
 * swap_entries
 *
 * Swap 2 entries of arbitrary size in-place in a table.
 **********************************************************************/
void swap_entries(void *array,   // array of entries
                  size_t size,   // size of entry
                  inT32 index1,  // entries to swap
                  inT32 index2) {
  char tmp;
  char *ptr1;                    // to entries
  char *ptr2;
  size_t count;                  // of bytes

  ptr1 = reinterpret_cast<char*>(array) + index1 * size;
  ptr2 = reinterpret_cast<char*>(array) + index2 * size;
  for (count = 0; count < size; count++) {
    tmp = *ptr1;
    *ptr1++ = *ptr2;
    *ptr2++ = tmp;               // tedious!
  }
}