685 lines
15 KiB
C++
685 lines
15 KiB
C++
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//========= Copyright <20> 1996-2005, Valve Corporation, All rights reserved. ============//
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//
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// Purpose:
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//
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// $NoKeywords: $
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//
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//=============================================================================//
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#ifndef STDIO_H
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#include <stdio.h>
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#endif
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#ifndef STRING_H
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#include <string.h>
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#endif
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#ifndef QUANTIZE_H
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#include <quantize.h>
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#endif
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#include <stdlib.h>
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#include <math.h>
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// NOTE: This has to be the last file included!
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#include "tier0/memdbgon.h"
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static int current_ndims;
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static struct QuantizedValue *current_root;
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static int current_ssize;
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static uint8 *current_weights;
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double SquaredError;
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#define SPLIT_THEN_SORT 1
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#define SQ(x) ((x)*(x))
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static struct QuantizedValue *AllocQValue(void)
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{
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struct QuantizedValue *ret=new QuantizedValue;
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ret->Samples=0;
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ret->Children[0]=ret->Children[1]=0;
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ret->NSamples=0;
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ret->ErrorMeasure=new double[current_ndims];
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ret->Mean=new uint8[current_ndims];
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ret->Mins=new uint8[current_ndims];
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ret->Maxs=new uint8[current_ndims];
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ret->Sums=new int [current_ndims];
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memset(ret->Sums,0,sizeof(int)*current_ndims);
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ret->NQuant=0;
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ret->sortdim=-1;
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return ret;
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}
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void FreeQuantization(struct QuantizedValue *t)
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{
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if (t)
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{
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delete[] t->ErrorMeasure;
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delete[] t->Mean;
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delete[] t->Mins;
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delete[] t->Maxs;
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FreeQuantization(t->Children[0]);
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FreeQuantization(t->Children[1]);
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delete[] t->Sums;
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delete[] t;
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}
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}
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static int QNumSort(void const *a, void const *b)
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{
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int32 as=((struct Sample *) a)->QNum;
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int32 bs=((struct Sample *) b)->QNum;
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if (as==bs) return 0;
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return (as>bs)?1:-1;
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}
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#if SPLIT_THEN_SORT
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#else
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static int current_sort_dim;
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static int samplesort(void const *a, void const *b)
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{
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uint8 as=((struct Sample *) a)->Value[current_sort_dim];
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uint8 bs=((struct Sample *) b)->Value[current_sort_dim];
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if (as==bs) return 0;
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return (as>bs)?1:-1;
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}
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#endif
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static int sortlong(void const *a, void const *b)
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{
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// treat the entire vector of values as a long integer for duplicate removal.
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return memcmp(((struct Sample *) a)->Value,
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((struct Sample *) b)->Value,current_ndims);
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}
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#define NEXTSAMPLE(s) ( (struct Sample *) (((uint8 *) s)+current_ssize))
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#define SAMPLE(s,i) NthSample(s,i,current_ndims)
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static void SetNDims(int n)
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{
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current_ssize=sizeof(struct Sample)+(n-1);
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current_ndims=n;
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}
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int CompressSamples(struct Sample *s, int nsamples, int ndims)
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{
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SetNDims(ndims);
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qsort(s,nsamples,current_ssize,sortlong);
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// now, they are all sorted by treating all dimensions as a large number.
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// we may now remove duplicates.
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struct Sample *src=s;
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struct Sample *dst=s;
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struct Sample *lastdst=dst;
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dst=NEXTSAMPLE(dst); // copy first sample to get the ball rolling
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src=NEXTSAMPLE(src);
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int noutput=1;
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while(--nsamples) // while some remain
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{
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if (memcmp(src->Value,lastdst->Value,current_ndims))
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{
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// yikes, a difference has been found!
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memcpy(dst,src,current_ssize);
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lastdst=dst;
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dst=NEXTSAMPLE(dst);
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noutput++;
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}
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else
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lastdst->Count++;
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src=NEXTSAMPLE(src);
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}
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return noutput;
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}
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void PrintSamples(struct Sample const *s, int nsamples, int ndims)
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{
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SetNDims(ndims);
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int cnt=0;
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while(nsamples--)
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{
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printf("sample #%d, count=%d, values=\n { ",cnt++,s->Count);
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for(int d=0;d<ndims;d++)
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printf("%02x,",s->Value[d]);
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printf("}\n");
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s=NEXTSAMPLE(s);
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}
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}
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void PrintQTree(struct QuantizedValue const *p,int idlevel)
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{
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int i;
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if (p)
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{
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for(i=0;i<idlevel;i++)
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printf(" ");
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printf("node=%p NSamples=%d value=%d Mean={",p,p->NSamples,p->value);
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for(i=0;i<current_ndims;i++)
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printf("%x,",p->Mean[i]);
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printf("}\n");
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for(i=0;i<idlevel;i++)
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printf(" ");
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printf("Errors={");
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for(i=0;i<current_ndims;i++)
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printf("%f,",p->ErrorMeasure[i]);
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printf("}\n");
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for(i=0;i<idlevel;i++)
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printf(" ");
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printf("Mins={");
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for(i=0;i<current_ndims;i++)
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printf("%d,",p->Mins[i]);
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printf("} Maxs={");
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for(i=0;i<current_ndims;i++)
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printf("%d,",p->Maxs[i]);
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printf("}\n");
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PrintQTree(p->Children[0],idlevel+2);
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PrintQTree(p->Children[1],idlevel+2);
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}
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}
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static void UpdateStats(struct QuantizedValue *v)
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{
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// first, find mean
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int32 Means[MAXDIMS];
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double Errors[MAXDIMS];
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double WorstError[MAXDIMS];
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int i,j;
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memset(Means,0,sizeof(Means));
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int N=0;
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for(i=0;i<v->NSamples;i++)
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{
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struct Sample *s=SAMPLE(v->Samples,i);
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N+=s->Count;
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for(j=0;j<current_ndims;j++)
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{
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uint8 v=s->Value[j];
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Means[j]+=v*s->Count;
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}
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}
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for(j=0;j<current_ndims;j++)
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{
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if (N) v->Mean[j]=(uint8) (Means[j]/N);
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Errors[j]=WorstError[j]=0.;
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}
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for(i=0;i<v->NSamples;i++)
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{
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struct Sample *s=SAMPLE(v->Samples,i);
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double c=s->Count;
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for(j=0;j<current_ndims;j++)
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{
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double diff=SQ(s->Value[j]-v->Mean[j]);
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Errors[j]+=c*diff; // charles uses abs not sq()
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if (diff>WorstError[j])
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WorstError[j]=diff;
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}
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}
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v->TotalError=0.;
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double ErrorScale=1.; // /sqrt((double) (N));
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for(j=0;j<current_ndims;j++)
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{
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v->ErrorMeasure[j]=(ErrorScale*Errors[j]*current_weights[j]);
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v->TotalError+=v->ErrorMeasure[j];
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#if SPLIT_THEN_SORT
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v->ErrorMeasure[j]*=WorstError[j];
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#endif
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}
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v->TotSamples=N;
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}
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static int ErrorDim;
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static double ErrorVal;
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static struct QuantizedValue *ErrorNode;
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static void UpdateWorst(struct QuantizedValue *q)
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{
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if (q->Children[0])
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{
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// not a leaf node
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UpdateWorst(q->Children[0]);
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UpdateWorst(q->Children[1]);
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}
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else
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{
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if (q->TotalError>ErrorVal)
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{
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ErrorVal=q->TotalError;
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ErrorNode=q;
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ErrorDim=0;
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for(int d=0;d<current_ndims;d++)
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if (q->ErrorMeasure[d]>q->ErrorMeasure[ErrorDim])
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ErrorDim=d;
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}
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}
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}
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static int FindWorst(void)
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{
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ErrorVal=-1.;
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UpdateWorst(current_root);
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return (ErrorVal>0);
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}
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static void SubdivideNode(struct QuantizedValue *n, int whichdim)
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{
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int NAdded=0;
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int i;
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#if SPLIT_THEN_SORT
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// we will try the "split then sort" method. This works by finding the
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// means for all samples above and below the mean along the given axis.
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// samples are then split into two groups, with the selection based upon
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// which of the n-dimensional means the sample is closest to.
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double LocalMean[MAXDIMS][2];
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int totsamps[2];
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for(i=0;i<current_ndims;i++)
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LocalMean[i][0]=LocalMean[i][1]=0.;
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totsamps[0]=totsamps[1]=0;
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uint8 minv=255;
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uint8 maxv=0;
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struct Sample *minS=0,*maxS=0;
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for(i=0;i<n->NSamples;i++)
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{
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uint8 v;
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int whichside=1;
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struct Sample *sl;
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sl=SAMPLE(n->Samples,i);
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v=sl->Value[whichdim];
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if (v<minv) { minv=v; minS=sl; }
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if (v>maxv) { maxv=v; maxS=sl; }
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if (v<n->Mean[whichdim])
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whichside=0;
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totsamps[whichside]+=sl->Count;
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for(int d=0;d<current_ndims;d++)
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LocalMean[d][whichside]+=
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sl->Count*sl->Value[d];
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}
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if (totsamps[0] && totsamps[1])
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for(i=0;i<current_ndims;i++)
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{
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LocalMean[i][0]/=totsamps[0];
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LocalMean[i][1]/=totsamps[1];
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}
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else
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{
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// it is possible that the clustering failed to split the samples.
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// this can happen with a heavily biased sample (i.e. all black
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// with a few stars). If this happens, we will cluster around the
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// extrema instead. LocalMean[i][0] will be the point with the lowest
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// value on the dimension and LocalMean[i][1] the one with the lowest
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// value.
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for(int i=0;i<current_ndims;i++)
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{
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LocalMean[i][0]=minS->Value[i];
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LocalMean[i][1]=maxS->Value[i];
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}
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}
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// now, we have 2 n-dimensional means. We will label each sample
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// for which one it is nearer to by using the QNum field.
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for(i=0;i<n->NSamples;i++)
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{
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double dist[2];
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dist[0]=dist[1]=0.;
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struct Sample *s=SAMPLE(n->Samples,i);
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for(int d=0;d<current_ndims;d++)
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for(int w=0;w<2;w++)
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dist[w]+=current_weights[d]*SQ(LocalMean[d][w]-s->Value[d]);
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s->QNum=(dist[0]<dist[1]);
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}
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// hey ho! we have now labelled each one with a candidate bin. Let's
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// sort the array by moving the 0-labelled ones to the head of the array.
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n->sortdim=-1;
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qsort(n->Samples,n->NSamples,current_ssize,QNumSort);
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for(i=0;i<n->NSamples;i++,NAdded++)
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if (SAMPLE(n->Samples,i)->QNum)
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break;
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#else
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if (whichdim != n->sortdim)
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{
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current_sort_dim=whichdim;
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qsort(n->Samples,n->NSamples,current_ssize,samplesort);
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n->sortdim=whichdim;
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}
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// now, the samples are sorted along the proper dimension. we need
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// to find the place to cut in order to split the node. this is
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// complicated by the fact that each sample entry can represent many
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// samples. What we will do is start at the beginning of the array,
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// adding samples to the first node, until either the number added
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|
|
// 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);
|
|||
|
|
}
|