Modified source engine (2017) developed by valve and leaked in 2020. Not for commercial purporses
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//========= Copyright Valve Corporation, All rights reserved. ============//
//
// Purpose:
//
// $NoKeywords: $
//
//=============================================================================//
#ifndef STDIO_H
#include <stdio.h>
#endif
#ifndef STRING_H
#include <string.h>
#endif
#ifndef QUANTIZE_H
#include <quantize.h>
#endif
#include <stdlib.h>
#include <minmax.h>
#include <math.h>
static int current_ndims;
static struct QuantizedValue *current_root;
static int current_ssize;
static uint8 *current_weights;
double SquaredError;
#define SPLIT_THEN_SORT 1
#define SQ(x) ((x)*(x))
static struct QuantizedValue *AllocQValue(void)
{
struct QuantizedValue *ret=new QuantizedValue;
ret->Samples=0;
ret->Children[0]=ret->Children[1]=0;
ret->NSamples=0;
ret->ErrorMeasure=new double[current_ndims];
ret->Mean=new uint8[current_ndims];
ret->Mins=new uint8[current_ndims];
ret->Maxs=new uint8[current_ndims];
ret->Sums=new int [current_ndims];
memset(ret->Sums,0,sizeof(int)*current_ndims);
ret->NQuant=0;
ret->sortdim=-1;
return ret;
}
void FreeQuantization(struct QuantizedValue *t)
{
if (t)
{
delete[] t->ErrorMeasure;
delete[] t->Mean;
delete[] t->Mins;
delete[] t->Maxs;
FreeQuantization(t->Children[0]);
FreeQuantization(t->Children[1]);
delete[] t->Sums;
delete[] t;
}
}
static int QNumSort(void const *a, void const *b)
{
int32 as=((struct Sample *) a)->QNum;
int32 bs=((struct Sample *) b)->QNum;
if (as==bs) return 0;
return (as>bs)?1:-1;
}
#if SPLIT_THEN_SORT
#else
static int current_sort_dim;
static int samplesort(void const *a, void const *b)
{
uint8 as=((struct Sample *) a)->Value[current_sort_dim];
uint8 bs=((struct Sample *) b)->Value[current_sort_dim];
if (as==bs) return 0;
return (as>bs)?1:-1;
}
#endif
static int sortlong(void const *a, void const *b)
{
// treat the entire vector of values as a long integer for duplicate removal.
return memcmp(((struct Sample *) a)->Value,
((struct Sample *) b)->Value,current_ndims);
}
#define NEXTSAMPLE(s) ( (struct Sample *) (((uint8 *) s)+current_ssize))
#define SAMPLE(s,i) NthSample(s,i,current_ndims)
static void SetNDims(int n)
{
current_ssize=sizeof(struct Sample)+(n-1);
current_ndims=n;
}
int CompressSamples(struct Sample *s, int nsamples, int ndims)
{
SetNDims(ndims);
qsort(s,nsamples,current_ssize,sortlong);
// now, they are all sorted by treating all dimensions as a large number.
// we may now remove duplicates.
struct Sample *src=s;
struct Sample *dst=s;
struct Sample *lastdst=dst;
dst=NEXTSAMPLE(dst); // copy first sample to get the ball rolling
src=NEXTSAMPLE(src);
int noutput=1;
while(--nsamples) // while some remain
{
if (memcmp(src->Value,lastdst->Value,current_ndims))
{
// yikes, a difference has been found!
memcpy(dst,src,current_ssize);
lastdst=dst;
dst=NEXTSAMPLE(dst);
noutput++;
}
else
lastdst->Count++;
src=NEXTSAMPLE(src);
}
return noutput;
}
void PrintSamples(struct Sample const *s, int nsamples, int ndims)
{
SetNDims(ndims);
int cnt=0;
while(nsamples--)
{
printf("sample #%d, count=%d, values=\n { ",cnt++,s->Count);
for(int d=0;d<ndims;d++)
printf("%02x,",s->Value[d]);
printf("}\n");
s=NEXTSAMPLE(s);
}
}
void PrintQTree(struct QuantizedValue const *p,int idlevel)
{
int i;
if (p)
{
for(i=0;i<idlevel;i++)
printf(" ");
printf("node=%p NSamples=%d value=%d Mean={",p,p->NSamples,p->value);
for(i=0;i<current_ndims;i++)
printf("%x,",p->Mean[i]);
printf("}\n");
for(i=0;i<idlevel;i++)
printf(" ");
printf("Errors={");
for(i=0;i<current_ndims;i++)
printf("%f,",p->ErrorMeasure[i]);
printf("}\n");
for(i=0;i<idlevel;i++)
printf(" ");
printf("Mins={");
for(i=0;i<current_ndims;i++)
printf("%d,",p->Mins[i]);
printf("} Maxs={");
for(i=0;i<current_ndims;i++)
printf("%d,",p->Maxs[i]);
printf("}\n");
PrintQTree(p->Children[0],idlevel+2);
PrintQTree(p->Children[1],idlevel+2);
}
}
static void UpdateStats(struct QuantizedValue *v)
{
// first, find mean
int32 Means[MAXDIMS];
double Errors[MAXDIMS];
double WorstError[MAXDIMS];
int i,j;
memset(Means,0,sizeof(Means));
int N=0;
for(i=0;i<v->NSamples;i++)
{
struct Sample *s=SAMPLE(v->Samples,i);
N+=s->Count;
for(j=0;j<current_ndims;j++)
{
uint8 val=s->Value[j];
Means[j]+=val*s->Count;
}
}
for(j=0;j<current_ndims;j++)
{
if (N) v->Mean[j]=(uint8) (Means[j]/N);
Errors[j]=WorstError[j]=0.;
}
for(i=0;i<v->NSamples;i++)
{
struct Sample *s=SAMPLE(v->Samples,i);
double c=s->Count;
for(j=0;j<current_ndims;j++)
{
double diff=SQ(s->Value[j]-v->Mean[j]);
Errors[j]+=c*diff; // charles uses abs not sq()
if (diff>WorstError[j])
WorstError[j]=diff;
}
}
v->TotalError=0.;
double ErrorScale=1.; // /sqrt((double) (N));
for(j=0;j<current_ndims;j++)
{
v->ErrorMeasure[j]=(ErrorScale*Errors[j]*current_weights[j]);
v->TotalError+=v->ErrorMeasure[j];
#if SPLIT_THEN_SORT
v->ErrorMeasure[j]*=WorstError[j];
#endif
}
v->TotSamples=N;
}
static int ErrorDim;
static double ErrorVal;
static struct QuantizedValue *ErrorNode;
static void UpdateWorst(struct QuantizedValue *q)
{
if (q->Children[0])
{
// not a leaf node
UpdateWorst(q->Children[0]);
UpdateWorst(q->Children[1]);
}
else
{
if (q->TotalError>ErrorVal)
{
ErrorVal=q->TotalError;
ErrorNode=q;
ErrorDim=0;
for(int d=0;d<current_ndims;d++)
if (q->ErrorMeasure[d]>q->ErrorMeasure[ErrorDim])
ErrorDim=d;
}
}
}
static int FindWorst(void)
{
ErrorVal=-1.;
UpdateWorst(current_root);
return (ErrorVal>0);
}
static void SubdivideNode(struct QuantizedValue *n, int whichdim)
{
int NAdded=0;
int i;
#if SPLIT_THEN_SORT
// we will try the "split then sort" method. This works by finding the
// means for all samples above and below the mean along the given axis.
// samples are then split into two groups, with the selection based upon
// which of the n-dimensional means the sample is closest to.
double LocalMean[MAXDIMS][2];
int totsamps[2];
for(i=0;i<current_ndims;i++)
LocalMean[i][0]=LocalMean[i][1]=0.;
totsamps[0]=totsamps[1]=0;
uint8 minv=255;
uint8 maxv=0;
struct Sample *minS=0,*maxS=0;
for(i=0;i<n->NSamples;i++)
{
uint8 v;
int whichside=1;
struct Sample *sl;
sl=SAMPLE(n->Samples,i);
v=sl->Value[whichdim];
if (v<minv) { minv=v; minS=sl; }
if (v>maxv) { maxv=v; maxS=sl; }
if (v<n->Mean[whichdim])
whichside=0;
totsamps[whichside]+=sl->Count;
for(int d=0;d<current_ndims;d++)
LocalMean[d][whichside]+=
sl->Count*sl->Value[d];
}
if (totsamps[0] && totsamps[1])
for(i=0;i<current_ndims;i++)
{
LocalMean[i][0]/=totsamps[0];
LocalMean[i][1]/=totsamps[1];
}
else
{
// it is possible that the clustering failed to split the samples.
// this can happen with a heavily biased sample (i.e. all black
// with a few stars). If this happens, we will cluster around the
// extrema instead. LocalMean[i][0] will be the point with the lowest
// value on the dimension and LocalMean[i][1] the one with the lowest
// value.
for(i=0;i<current_ndims;i++)
{
LocalMean[i][0]=minS->Value[i];
LocalMean[i][1]=maxS->Value[i];
}
}
// now, we have 2 n-dimensional means. We will label each sample
// for which one it is nearer to by using the QNum field.
for(i=0;i<n->NSamples;i++)
{
double dist[2];
dist[0]=dist[1]=0.;
struct Sample *s=SAMPLE(n->Samples,i);
for(int d=0;d<current_ndims;d++)
for(int w=0;w<2;w++)
dist[w]+=current_weights[d]*SQ(LocalMean[d][w]-s->Value[d]);
s->QNum=(dist[0]<dist[1]);
}
// hey ho! we have now labelled each one with a candidate bin. Let's
// sort the array by moving the 0-labelled ones to the head of the array.
n->sortdim=-1;
qsort(n->Samples,n->NSamples,current_ssize,QNumSort);
for(i=0;i<n->NSamples;i++,NAdded++)
if (SAMPLE(n->Samples,i)->QNum)
break;
#else
if (whichdim != n->sortdim)
{
current_sort_dim=whichdim;
qsort(n->Samples,n->NSamples,current_ssize,samplesort);
n->sortdim=whichdim;
}
// now, the samples are sorted along the proper dimension. we need
// to find the place to cut in order to split the node. this is
// complicated by the fact that each sample entry can represent many
// samples. What we will do is start at the beginning of the array,
// adding samples to the first node, until either the number added
// is >=TotSamples/2, or there is only one left.
int TotAdded=0;
for(;;)
{
if (NAdded==n->NSamples-1)
break;
if (TotAdded>=n->TotSamples/2)
break;
TotAdded+=SAMPLE(n->Samples,NAdded)->Count;
NAdded++;
}
#endif
struct QuantizedValue *a=AllocQValue();
a->sortdim=n->sortdim;
a->Samples=n->Samples;
a->NSamples=NAdded;
n->Children[0]=a;
UpdateStats(a);
a=AllocQValue();
a->Samples=SAMPLE(n->Samples,NAdded);
a->NSamples=n->NSamples-NAdded;
a->sortdim=n->sortdim;
n->Children[1]=a;
UpdateStats(a);
}
static int colorid=0;
static void Label(struct QuantizedValue *q, int updatecolor)
{
// fill in max/min values for tree, etc.
if (q)
{
Label(q->Children[0],updatecolor);
Label(q->Children[1],updatecolor);
if (! q->Children[0]) // leaf node?
{
if (updatecolor)
{
q->value=colorid++;
for(int j=0;j<q->NSamples;j++)
{
SAMPLE(q->Samples,j)->QNum=q->value;
SAMPLE(q->Samples,j)->qptr=q;
}
}
for(int i=0;i<current_ndims;i++)
{
q->Mins[i]=q->Mean[i];
q->Maxs[i]=q->Mean[i];
}
}
else
for(int i=0;i<current_ndims;i++)
{
q->Mins[i]=min(q->Children[0]->Mins[i],q->Children[1]->Mins[i]);
q->Maxs[i]=max(q->Children[0]->Maxs[i],q->Children[1]->Maxs[i]);
}
}
}
struct QuantizedValue *FindQNode(struct QuantizedValue const *q, int32 code)
{
if (! (q->Children[0]))
if (code==q->value) return (struct QuantizedValue *) q;
else return 0;
else
{
struct QuantizedValue *found=FindQNode(q->Children[0],code);
if (! found) found=FindQNode(q->Children[1],code);
return found;
}
}
void CheckInRange(struct QuantizedValue *q, uint8 *max, uint8 *min)
{
if (q)
{
if (q->Children[0])
{
// non-leaf node
CheckInRange(q->Children[0],q->Maxs, q->Mins);
CheckInRange(q->Children[1],q->Maxs, q->Mins);
CheckInRange(q->Children[0],max, min);
CheckInRange(q->Children[1],max, min);
}
for (int i=0;i<current_ndims;i++)
{
if (q->Maxs[i]>max[i]) printf("error1\n");
if (q->Mins[i]<min[i]) printf("error2\n");
}
}
}
struct QuantizedValue *Quantize(struct Sample *s, int nsamples, int ndims,
int nvalues, uint8 *weights, int firstvalue)
{
SetNDims(ndims);
current_weights=weights;
current_root=AllocQValue();
current_root->Samples=s;
current_root->NSamples=nsamples;
UpdateStats(current_root);
while(--nvalues)
{
if (! FindWorst())
break; // if <n unique ones, stop now
SubdivideNode(ErrorNode,ErrorDim);
}
colorid=firstvalue;
Label(current_root,1);
return current_root;
}
double MinimumError(struct QuantizedValue const *q, uint8 const *sample,
int ndims, uint8 const *weights)
{
double err=0;
for(int i=0;i<ndims;i++)
{
int val1;
int val2=sample[i];
if ((q->Mins[i]<=val2) && (q->Maxs[i]>=val2)) val1=val2;
else
{
val1=(val2<=q->Mins[i])?q->Mins[i]:q->Maxs[i];
}
err+=weights[i]*SQ(val1-val2);
}
return err;
}
double MaximumError(struct QuantizedValue const *q, uint8 const *sample,
int ndims, uint8 const *weights)
{
double err=0;
for(int i=0;i<ndims;i++)
{
int val2=sample[i];
int val1=(abs(val2-q->Mins[i])>abs(val2-q->Maxs[i]))?
q->Mins[i]:
q->Maxs[i];
err+=weights[i]*SQ(val2-val1);
}
return err;
}
// heap (priority queue) routines used for nearest-neghbor searches
struct FHeap {
int heap_n;
double *heap[MAXQUANT];
};
void InitHeap(struct FHeap *h)
{
h->heap_n=0;
}
void UpHeap(int k, struct FHeap *h)
{
double *tmpk=h->heap[k];
double tmpkn=*tmpk;
while((k>1) && (tmpkn <= *(h->heap[k/2])))
{
h->heap[k]=h->heap[k/2];
k/=2;
}
h->heap[k]=tmpk;
}
void HeapInsert(struct FHeap *h,double *elem)
{
h->heap_n++;
h->heap[h->heap_n]=elem;
UpHeap(h->heap_n,h);
}
void DownHeap(int k, struct FHeap *h)
{
double *v=h->heap[k];
while(k<=h->heap_n/2)
{
int j=2*k;
if (j<h->heap_n)
if (*(h->heap[j]) >= *(h->heap[j+1]))
j++;
if (*v < *(h->heap[j]))
{
h->heap[k]=v;
return;
}
h->heap[k]=h->heap[j]; k=j;
}
h->heap[k]=v;
}
void *RemoveHeapItem(struct FHeap *h)
{
void *ret=0;
if (h->heap_n!=0)
{
ret=h->heap[1];
h->heap[1]=h->heap[h->heap_n];
h->heap_n--;
DownHeap(1,h);
}
return ret;
}
// now, nearest neighbor finder. Use a heap to traverse the tree, stopping
// when there are no nodes with a minimum error < the current error.
struct FHeap TheQueue;
#define PUSHNODE(a) { \
(a)->MinError=MinimumError(a,sample,ndims,weights); \
if ((a)->MinError < besterror) HeapInsert(&TheQueue,&(a)->MinError); \
}
struct QuantizedValue *FindMatch(uint8 const *sample, int ndims,
uint8 *weights, struct QuantizedValue *q)
{
InitHeap(&TheQueue);
struct QuantizedValue *bestmatch=0;
double besterror=1.0e63;
PUSHNODE(q);
for(;;)
{
struct QuantizedValue *test=(struct QuantizedValue *)
RemoveHeapItem(&TheQueue);
if (! test) break; // heap empty
// printf("got pop node =%p minerror=%f\n",test,test->MinError);
if (test->MinError>besterror) break;
if (test->Children[0])
{
// it's a parent node. put the children on the queue
struct QuantizedValue *c1=test->Children[0];
struct QuantizedValue *c2=test->Children[1];
c1->MinError=MinimumError(c1,sample,ndims,weights);
if (c1->MinError < besterror)
HeapInsert(&TheQueue,&(c1->MinError));
c2->MinError=MinimumError(c2,sample,ndims,weights);
if (c2->MinError < besterror)
HeapInsert(&TheQueue,&(c2->MinError));
}
else
{
// it's a leaf node. This must be a new minimum or the MinError
// test would have failed.
if (test->MinError < besterror)
{
bestmatch=test;
besterror=test->MinError;
}
}
}
if (bestmatch)
{
SquaredError+=besterror;
bestmatch->NQuant++;
for(int i=0;i<ndims;i++)
bestmatch->Sums[i]+=sample[i];
}
return bestmatch;
}
static void RecalcMeans(struct QuantizedValue *q)
{
if (q)
{
if (q->Children[0])
{
// not a leaf, invoke recursively.
RecalcMeans(q->Children[0]);
RecalcMeans(q->Children[0]);
}
else
{
// it's a leaf. Set the means
if (q->NQuant)
{
for(int i=0;i<current_ndims;i++)
{
q->Mean[i]=(uint8) (q->Sums[i]/q->NQuant);
q->Sums[i]=0;
}
q->NQuant=0;
}
}
}
}
void OptimizeQuantizer(struct QuantizedValue *q, int ndims)
{
SetNDims(ndims);
RecalcMeans(q); // reset q values
Label(q,0); // update max/mins
}
static void RecalcStats(struct QuantizedValue *q)
{
if (q)
{
UpdateStats(q);
RecalcStats(q->Children[0]);
RecalcStats(q->Children[1]);
}
}
void RecalculateValues(struct QuantizedValue *q, int ndims)
{
SetNDims(ndims);
RecalcStats(q);
Label(q,0);
}