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tesseract/ccstruct/statistc.cpp
joregan 5c8ad7ee72 add config_auto.h anywhere #ifndef GRAPHICS_DISABLED is used 
git-svn-id: https://tesseract-ocr.googlecode.com/svn/trunk@384 d0cd1f9f-072b-0410-8dd7-cf729c803f20
2010-05-28 12:03:45 +00:00

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/**********************************************************************
* 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 "mfcpch.h" //precompiled headers
#include <string.h>
#include <math.h>
#include <stdlib.h>
#include "memry.h"
//#include "ipeerr.h"
#include "tprintf.h"
#include "statistc.h"
#define SEED1 0x1234 //default seeds
#define SEED2 0x5678
#define SEED3 0x9abc
/**********************************************************************
* STATS::STATS
*
* Construct a new stats element by allocating and zeroing the memory.
**********************************************************************/
STATS::STATS( //constructor
inT32 min, //min of range
inT32 max //max of range
) {
if (max <= min) {
/* err.log(RESULT_LOGICAL_ERROR,E_LOC,ERR_PRIMITIVES,
ERR_SCROLLING,ERR_CONTINUE,ERR_ERROR,
"Illegal range for stats, Min=%d, Max=%d",min,max);*/
min = 0;
max = 1;
}
rangemin = min; //setup
rangemax = max;
buckets = (inT32 *) alloc_mem ((max - min) * sizeof (inT32));
if (buckets != NULL)
this->clear (); //zero it
/* else
err.log(RESULT_NO_MEMORY,E_LOC,ERR_PRIMITIVES,
ERR_SCROLLING,ERR_CONTINUE,ERR_ERROR,
"No memory for stats, Min=%d, Max=%d",min,max); */
}
STATS::STATS() { //constructor
rangemax = 0; //empty
rangemin = 0;
buckets = NULL;
}
/**********************************************************************
* STATS::set_range
*
* Alter the range on an existing stats element.
**********************************************************************/
bool STATS::set_range( //constructor
inT32 min, //min of range
inT32 max //max of range
) {
if (max <= min) {
return false;
}
rangemin = min; //setup
rangemax = max;
if (buckets != NULL)
free_mem(buckets); //no longer want it
buckets = (inT32 *) alloc_mem ((max - min) * sizeof (inT32));
/* if (buckets==NULL)
return err.log(RESULT_NO_MEMORY,E_LOC,ERR_PRIMITIVES,
ERR_SCROLLING,ERR_CONTINUE,ERR_ERROR,
"No memory for stats, Min=%d, Max=%d",min,max);*/
this->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 (inT32));
//zero it
}
/**********************************************************************
* STATS::~STATS
*
* Destructor for a stats class.
**********************************************************************/
STATS::~STATS ( //destructor
) {
if (buckets != NULL) {
free_mem(buckets);
buckets = NULL;
}
}
/**********************************************************************
* STATS::add
*
* Add a set of samples to (or delete from) a pile.
**********************************************************************/
void STATS::add( //add sample
inT32 value, //bucket
inT32 count //no to add
) {
if (buckets == NULL) {
/* err.log(RESULT_LOGICAL_ERROR,E_LOC,ERR_PRIMITIVES,
ERR_SCROLLING,ERR_CONTINUE,ERR_ERROR,
"Empty stats");*/
return;
}
if (value <= rangemin)
buckets[0] += count; //silently clip to range
else if (value >= rangemax)
buckets[rangemax - rangemin - 1] += count;
else
//add count to cell
buckets[value - rangemin] += count;
total_count += count; //keep count of total
}
/**********************************************************************
* STATS::mode
*
* Find the mode of a stats class.
**********************************************************************/
inT32 STATS::mode() { //get mode of samples
inT32 index; //current index
inT32 max; //max cell count
inT32 maxindex; //index of max
if (buckets == NULL) {
/* err.log(RESULT_LOGICAL_ERROR,E_LOC,ERR_PRIMITIVES,
ERR_SCROLLING,ERR_CONTINUE,ERR_ERROR,
"Empty stats");*/
return rangemin;
}
for (max = 0, maxindex = 0, 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.
**********************************************************************/
float STATS::mean() { //get mean of samples
inT32 index; //current index
inT32 sum; //sum of cells
if (buckets == NULL) {
/* err.log(RESULT_LOGICAL_ERROR,E_LOC,ERR_PRIMITIVES,
ERR_SCROLLING,ERR_CONTINUE,ERR_ERROR,
"Empty stats");*/
return (float) rangemin;
}
for (sum = 0, index = rangemax - rangemin - 1; index >= 0; index--) {
//sum all buckets
sum += index * buckets[index];
}
if (total_count > 0)
//mean value
return (float) sum / total_count + rangemin;
else
return (float) rangemin; //no mean
}
/**********************************************************************
* STATS::sd
*
* Find the standard deviation of a stats class.
**********************************************************************/
float STATS::sd() { //standard deviation
inT32 index; //current index
inT32 sum; //sum of cells
inT32 sqsum; //sum of squares
float variance;
if (buckets == NULL) {
/* err.log(RESULT_LOGICAL_ERROR,E_LOC,ERR_PRIMITIVES,
ERR_SCROLLING,ERR_CONTINUE,ERR_ERROR,
"Empty stats"); */
return (float) 0.0;
}
for (sum = 0, sqsum = 0, index = rangemax - rangemin - 1; index >= 0;
index--) {
//sum all buckets
sum += index * buckets[index];
//and squares
sqsum += index * index * buckets[index];
}
if (total_count > 0) {
variance = sum / ((float) total_count);
variance = sqsum / ((float) total_count) - variance * variance;
return (float) sqrt (variance);
}
else
return (float) 0.0;
}
/**********************************************************************
* STATS::ile
*
* Find an arbitrary %ile of a stats class.
**********************************************************************/
float STATS::ile( //percentile
float frac //fraction to find
) {
inT32 index; //current index
inT32 sum; //sum of cells
float target; //target value
if (buckets == NULL) {
/* err.log(RESULT_LOGICAL_ERROR,E_LOC,ERR_PRIMITIVES,
ERR_SCROLLING,ERR_CONTINUE,ERR_ERROR,
"Empty stats"); */
return (float) rangemin;
}
target = frac * total_count;
if (target <= 0)
target = (float) 1;
if (target > total_count)
target = (float) total_count;
for (sum = 0, index = 0; index < rangemax - rangemin
&& sum < target; sum += buckets[index], index++);
if (index > 0)
return rangemin + index - (sum - target) / buckets[index - 1];
//better than just ints
else
return (float) rangemin;
}
/**********************************************************************
* STATS::median
*
* Finds a more usefule 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
**********************************************************************/
float STATS::median() { //get median
float median;
inT32 min_pile;
inT32 median_pile;
inT32 max_pile;
if (buckets == NULL) {
/* err.log(RESULT_LOGICAL_ERROR,E_LOC,ERR_PRIMITIVES,
ERR_SCROLLING,ERR_CONTINUE,ERR_ERROR,
"Empty stats");*/
return (float) rangemin;
}
median = (float) ile ((float) 0.5);
median_pile = (inT32) floor (median);
if ((total_count > 1) && (pile_count (median_pile) == 0)) {
/* 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 = (float) ((min_pile + max_pile) / 2.0);
}
return median;
}
/**********************************************************************
* 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( //smooth samples
inT32 factor //size of triangle
) {
inT32 entry; //bucket index
inT32 offset; //from entry
inT32 entrycount; //no of entries
inT32 bucket; //new smoothed pile
//output stats
STATS result(rangemin, rangemax);
if (buckets == NULL) {
/* err.log(RESULT_LOGICAL_ERROR,E_LOC,ERR_PRIMITIVES,
ERR_SCROLLING,ERR_CONTINUE,ERR_ERROR,
"Empty stats"); */
return;
}
if (factor < 2)
return; //is a no-op
entrycount = rangemax - rangemin;
for (entry = 0; entry < entrycount; entry++) {
//centre weight
bucket = buckets[entry] * factor;
for (offset = 1; offset < factor; offset++) {
if (entry - offset >= 0)
bucket += buckets[entry - offset] * (factor - offset);
if (entry + offset < entrycount)
bucket += buckets[entry + offset] * (factor - offset);
}
result.add (entry + rangemin, bucket);
}
total_count = result.total_count;
memcpy (buckets, result.buckets, entrycount * sizeof (inT32));
}
/**********************************************************************
* 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( //cluster samples
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 (max_clusters < 1)
return 0;
if (buckets == NULL) {
/* err.log(RESULT_LOGICAL_ERROR,E_LOC,ERR_PRIMITIVES,
ERR_SCROLLING,ERR_CONTINUE,ERR_ERROR,
"Empty stats");*/
return 0;
}
centres = (float *) alloc_mem ((max_clusters + 1) * sizeof (float));
if (centres == NULL) {
/* err.log(RESULT_NO_MEMORY,E_LOC,ERR_PRIMITIVES,
ERR_SCROLLING,ERR_CONTINUE,ERR_ERROR,
"No memory for centres"); */
return 0;
}
for (cluster_count = 1; cluster_count <= max_clusters
&& clusters[cluster_count].buckets != NULL
&& clusters[cluster_count].total_count > 0; cluster_count++) {
centres[cluster_count] =
(float) clusters[cluster_count].ile ((float) 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 = (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] = (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] =
(float) clusters[cluster_count].ile ((float) 0.5);
}
}
while (new_cluster && cluster_count < max_clusters);
free_mem(centres);
return cluster_count;
}
/**********************************************************************
* STATS::local_min
*
* Return TRUE if this point is a local min.
**********************************************************************/
BOOL8 STATS::local_min( //test minness
inT32 x //of x
) {
inT32 index; //table index
if (buckets == NULL) {
/* err.log(RESULT_LOGICAL_ERROR,E_LOC,ERR_PRIMITIVES,
ERR_SCROLLING,ERR_CONTINUE,ERR_ERROR,
"Empty stats");*/
return FALSE;
}
if (x < rangemin)
x = rangemin;
if (x >= rangemax)
x = rangemax - 1;
x -= rangemin;
if (buckets[x] == 0)
return TRUE;
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::print
*
* Print a summary of the stats and optionally a dump of the table.
**********************************************************************/
void STATS::print( //print stats table
FILE *, //Now uses tprintf instead
BOOL8 dump //dump full table
) {
inT32 index; //table index
if (buckets == NULL) {
/* err.log(RESULT_LOGICAL_ERROR,E_LOC,ERR_PRIMITIVES,
ERR_SCROLLING,ERR_CONTINUE,ERR_ERROR,
"Empty stats"); */
return;
}
if (dump) {
for (index = 0; index < rangemax - rangemin; index++) {
tprintf ("%4d:%-3d ", rangemin + index, buckets[index]);
if (index % 8 == 7)
tprintf ("\n");
}
tprintf ("\n");
}
tprintf ("Total count=%d\n", total_count);
tprintf ("Min=%d\n", (inT32) (ile ((float) 0.0)));
tprintf ("Lower quartile=%.2f\n", ile ((float) 0.25));
tprintf ("Median=%.2f\n", ile ((float) 0.5));
tprintf ("Upper quartile=%.2f\n", ile ((float) 0.75));
tprintf ("Max=%d\n", (inT32) (ile ((float) 0.99999)));
tprintf ("Mean= %.2f\n", mean ());
tprintf ("SD= %.2f\n", sd ());
}
/**********************************************************************
* STATS::min_bucket
*
* Find REAL minimum bucket - ile(0.0) isnt necessarily correct
**********************************************************************/
inT32 STATS::min_bucket() { //Find min
inT32 min;
if (buckets == NULL) {
/* err.log(RESULT_LOGICAL_ERROR,E_LOC,ERR_PRIMITIVES,
ERR_SCROLLING,ERR_CONTINUE,ERR_ERROR,
"Empty stats");*/
return rangemin;
}
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() { //Find max
inT32 max;
if (buckets == NULL) {
/* err.log(RESULT_LOGICAL_ERROR,E_LOC,ERR_PRIMITIVES,
ERR_SCROLLING,ERR_CONTINUE,ERR_ERROR,
"Empty stats");*/
return rangemin;
}
for (max = rangemax - rangemin - 1;
(max > 0) && (buckets[max] == 0); max--);
return rangemin + max;
}
/**********************************************************************
* STATS::short_print
*
* Print a summary of the stats and optionally a dump of the table.
* ( BUT ONLY THE PART OF THE TABLE BETWEEN MIN AND MAX)
**********************************************************************/
void STATS::short_print( //print stats table
FILE *, //Now uses tprintf instead
BOOL8 dump //dump full table
) {
inT32 index; //table index
inT32 min = min_bucket ();
inT32 max = max_bucket ();
if (buckets == NULL) {
/* err.log(RESULT_LOGICAL_ERROR,E_LOC,ERR_PRIMITIVES,
ERR_SCROLLING,ERR_CONTINUE,ERR_ERROR,
"Empty stats"); */
return;
}
if (dump) {
for (index = min; index <= max; index++) {
tprintf ("%4d:%-3d ", rangemin + index, buckets[index]);
if ((index - min) % 8 == 7)
tprintf ("\n");
}
tprintf ("\n");
}
tprintf ("Total count=%d\n", total_count);
tprintf ("Min=%d Really=%d\n", (inT32) (ile ((float) 0.0)), min);
tprintf ("Max=%d Really=%d\n", (inT32) (ile ((float) 1.1)), max);
tprintf ("Range=%d\n", max + 1 - min);
tprintf ("Lower quartile=%.2f\n", ile ((float) 0.25));
tprintf ("Median=%.2f\n", ile ((float) 0.5));
tprintf ("Upper quartile=%.2f\n", ile ((float) 0.75));
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( //plot stats table
ScrollView* window, //to draw in
float xorigin, //bottom left
float yorigin,
float xscale, //one x unit
float yscale, //one y unit
ScrollView::Color colour //colour to draw in
) {
inT32 index; //table index
if (buckets == NULL) {
/* err.log(RESULT_LOGICAL_ERROR,E_LOC,ERR_PRIMITIVES,
ERR_SCROLLING,ERR_CONTINUE,ERR_ERROR,
"Empty stats");*/
return;
}
window->Pen(colour);
for (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( //plot stats table
ScrollView* window, //to draw in
float xorigin, //bottom left
float yorigin,
float xscale, //one x unit
float yscale, //one y unit
ScrollView::Color colour //colour to draw in
) {
inT32 index; //table index
if (buckets == NULL) {
/* err.log(RESULT_LOGICAL_ERROR,E_LOC,ERR_PRIMITIVES,
ERR_SCROLLING,ERR_CONTINUE,ERR_ERROR,
"Empty stats"); */
return;
}
window->Pen(colour);
window->SetCursor(xorigin, yorigin + yscale * buckets[0]);
for (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.
**********************************************************************/
DLLSYM inT32 choose_nth_item( //fast median
inT32 index, //index to choose
float *array, //array of items
inT32 count //no of items
) {
static uinT16 seeds[3] = { SEED1, SEED2, SEED3 };
//for nrand
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 lergal
else if (index >= count)
index = count - 1;
#ifdef __UNIX__
equal_count = (inT32) (nrand48 (seeds) % count);
#else
equal_count = (inT32) (rand () % count);
#endif
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 b the nth item in the array
* if the members were sorted, without actually sorting.
**********************************************************************/
DLLSYM inT32
choose_nth_item ( //fast median
inT32 index, //index to choose
void *array, //array of items
inT32 count, //no of items
size_t size, //element size
//comparator
int (*compar) (const void *, const void *)
) {
static uinT16 seeds[3] = { SEED1, SEED2, SEED3 };
//for nrand
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 lergal
else if (index >= count)
index = count - 1;
#ifdef __UNIX__
pivot = (inT32) (nrand48 (seeds) % count);
#else
pivot = (inT32) (rand () % count);
#endif
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 abitrary size in-place in a table.
**********************************************************************/
void swap_entries( //swap in place
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 = (char *) array + index1 * size;
ptr2 = (char *) array + index2 * size;
for (count = 0; count < size; count++) {
tmp = *ptr1;
*ptr1++ = *ptr2;
*ptr2++ = tmp; //tedious!
}
}
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