/********************************************************************
* *
- * THIS FILE IS PART OF THE Ogg Vorbis SOFTWARE CODEC SOURCE CODE. *
- * USE, DISTRIBUTION AND REPRODUCTION OF THIS SOURCE IS GOVERNED BY *
- * THE GNU PUBLIC LICENSE 2, WHICH IS INCLUDED WITH THIS SOURCE. *
- * PLEASE READ THESE TERMS DISTRIBUTING. *
+ * THIS FILE IS PART OF THE OggVorbis SOFTWARE CODEC SOURCE CODE. *
+ * USE, DISTRIBUTION AND REPRODUCTION OF THIS LIBRARY SOURCE IS *
+ * GOVERNED BY A BSD-STYLE SOURCE LICENSE INCLUDED WITH THIS SOURCE *
+ * IN 'COPYING'. PLEASE READ THESE TERMS BEFORE DISTRIBUTING. *
* *
- * THE OggSQUISH SOURCE CODE IS (C) COPYRIGHT 1994-1999 *
- * by 1999 Monty <monty@xiph.org> and The XIPHOPHORUS Company *
- * http://www.xiph.org/ *
+ * THE OggVorbis SOURCE CODE IS (C) COPYRIGHT 1994-2001 *
+ * by the Xiph.Org Foundation https://xiph.org/ *
* *
********************************************************************
- function: build a VQ codebook
- author: Monty <xiphmont@mit.edu>
- modifications by: Monty
- last modification date: Nov 18 1999
+ function: train a VQ codebook
********************************************************************/
pregenerated codebook that is compiled into libvorbis. It is an
expensive (but good) algorithm. Run it on big iron. */
+/* There are so many optimizations to explore in *both* stages that
+ considering the undertaking is almost withering. For now, we brute
+ force it all */
+
#include <stdlib.h>
#include <stdio.h>
#include <math.h>
#include <string.h>
-/************************************************************************
+#include "vqgen.h"
+#include "bookutil.h"
+
+/* Codebook generation happens in two steps:
+
+ 1) Train the codebook with data collected from the encoder: We use
+ one of a few error metrics (which represent the distance between a
+ given data point and a candidate point in the training set) to
+ divide the training set up into cells representing roughly equal
+ probability of occurring.
+
+ 2) Generate the codebook and auxiliary data from the trained data set
+*/
+
+/* Codebook training ****************************************************
+ *
* The basic idea here is that a VQ codebook is like an m-dimensional
* foam with n bubbles. The bubbles compete for space/volume and are
* 'pressurized' [biased] according to some metric. The basic alg
* iterates through allowing the bubbles to compete for space until
* they converge (if the damping is dome properly) on a steady-state
- * solution.
- *
- * We use the ratio of local to average 'error' as the metric to bias a
- * variable-length word codebook, and probability of occurrence within
- * that bubble as the metric to bias fixed length word
- * codebooks. Individual input points, collected from libvorbis, are
+ * solution. Individual input points, collected from libvorbis, are
* used to train the algorithm monte-carlo style. */
-typedef struct vqgen{
- int elements;
- double errspread;
+/* internal helpers *****************************************************/
+#define vN(data,i) (data+v->elements*i)
- /* point cache */
- double *pointlist;
- long *first;
- long *second;
- long points;
- long allocated;
+/* default metric; squared 'distance' from desired value. */
+float _dist(vqgen *v,float *a, float *b){
+ int i;
+ int el=v->elements;
+ float acc=0.f;
+ for(i=0;i<el;i++){
+ float val=(a[i]-b[i]);
+ acc+=val*val;
+ }
+ return sqrt(acc);
+}
- /* entries */
- double *entry_now;
- double *entry_next;
- long *assigned;
- double *metric;
- double *bias;
- long entries;
+float *_weight_null(vqgen *v,float *a){
+ return a;
+}
- double (*metric_func) (struct vqgen *v,double *a,double *b);
-} vqgen;
+/* *must* be beefed up. */
+void _vqgen_seed(vqgen *v){
+ long i;
+ for(i=0;i<v->entries;i++)
+ memcpy(_now(v,i),_point(v,i),sizeof(float)*v->elements);
+ v->seeded=1;
+}
-/* internal helpers *****************************************************/
-double *_point(vqgen *v,long ptr){
- return v->pointlist+(v->elements*ptr);
+int directdsort(const void *a, const void *b){
+ float av=*((float *)a);
+ float bv=*((float *)b);
+ return (av<bv)-(av>bv);
}
-double *_now(vqgen *v,long ptr){
- return v->entry_now+(v->elements*ptr);
+void vqgen_cellmetric(vqgen *v){
+ int j,k;
+ float min=-1.f,max=-1.f,mean=0.f,acc=0.f;
+ long dup=0,unused=0;
+ #ifdef NOISY
+ int i;
+ char buff[80];
+ float spacings[v->entries];
+ int count=0;
+ FILE *cells;
+ sprintf(buff,"cellspace%d.m",v->it);
+ cells=fopen(buff,"w");
+#endif
+
+ /* minimum, maximum, cell spacing */
+ for(j=0;j<v->entries;j++){
+ float localmin=-1.;
+
+ for(k=0;k<v->entries;k++){
+ if(j!=k){
+ float this=_dist(v,_now(v,j),_now(v,k));
+ if(this>0){
+ if(v->assigned[k] && (localmin==-1 || this<localmin))
+ localmin=this;
+ }else{
+ if(k<j){
+ dup++;
+ break;
+ }
+ }
+ }
+ }
+ if(k<v->entries)continue;
+
+ if(v->assigned[j]==0){
+ unused++;
+ continue;
+ }
+
+ localmin=v->max[j]+localmin/2; /* this gives us rough diameter */
+ if(min==-1 || localmin<min)min=localmin;
+ if(max==-1 || localmin>max)max=localmin;
+ mean+=localmin;
+ acc++;
+#ifdef NOISY
+ spacings[count++]=localmin;
+#endif
+ }
+
+ fprintf(stderr,"cell diameter: %.03g::%.03g::%.03g (%ld unused/%ld dup)\n",
+ min,mean/acc,max,unused,dup);
+
+#ifdef NOISY
+ qsort(spacings,count,sizeof(float),directdsort);
+ for(i=0;i<count;i++)
+ fprintf(cells,"%g\n",spacings[i]);
+ fclose(cells);
+#endif
+
}
-double *_next(vqgen *v,long ptr){
- return v->entry_next+(v->elements*ptr);
+/* External calls *******************************************************/
+
+/* We have two forms of quantization; in the first, each vector
+ element in the codebook entry is orthogonal. Residues would use this
+ quantization for example.
+
+ In the second, we have a sequence of monotonically increasing
+ values that we wish to quantize as deltas (to save space). We
+ still need to quantize so that absolute values are accurate. For
+ example, LSP quantizes all absolute values, but the book encodes
+ distance between values because each successive value is larger
+ than the preceeding value. Thus the desired quantibits apply to
+ the encoded (delta) values, not abs positions. This requires minor
+ additional encode-side trickery. */
+
+void vqgen_quantize(vqgen *v,quant_meta *q){
+
+ float maxdel;
+ float mindel;
+
+ float delta;
+ float maxquant=((1<<q->quant)-1);
+
+ int j,k;
+
+ mindel=maxdel=_now(v,0)[0];
+
+ for(j=0;j<v->entries;j++){
+ float last=0.f;
+ for(k=0;k<v->elements;k++){
+ if(mindel>_now(v,j)[k]-last)mindel=_now(v,j)[k]-last;
+ if(maxdel<_now(v,j)[k]-last)maxdel=_now(v,j)[k]-last;
+ if(q->sequencep)last=_now(v,j)[k];
+ }
+ }
+
+
+ /* first find the basic delta amount from the maximum span to be
+ encoded. Loosen the delta slightly to allow for additional error
+ during sequence quantization */
+
+ delta=(maxdel-mindel)/((1<<q->quant)-1.5f);
+
+ q->min=_float32_pack(mindel);
+ q->delta=_float32_pack(delta);
+
+ mindel=_float32_unpack(q->min);
+ delta=_float32_unpack(q->delta);
+
+ for(j=0;j<v->entries;j++){
+ float last=0;
+ for(k=0;k<v->elements;k++){
+ float val=_now(v,j)[k];
+ float now=rint((val-last-mindel)/delta);
+
+ _now(v,j)[k]=now;
+ if(now<0){
+ /* be paranoid; this should be impossible */
+ fprintf(stderr,"fault; quantized value<0\n");
+ exit(1);
+ }
+
+ if(now>maxquant){
+ /* be paranoid; this should be impossible */
+ fprintf(stderr,"fault; quantized value>max\n");
+ exit(1);
+ }
+ if(q->sequencep)last=(now*delta)+mindel+last;
+ }
+ }
}
-double _dist_sq(vqgen *v,double *a, double *b){
- int i;
- int el=v->elements;
- double acc=0.;
- for(i=0;i<el;i++){
- double val=(a[i]-b[i]);
- acc+=val*val;
+/* much easier :-). Unlike in the codebook, we don't un-log log
+ scales; we just make sure they're properly offset. */
+void vqgen_unquantize(vqgen *v,quant_meta *q){
+ long j,k;
+ float mindel=_float32_unpack(q->min);
+ float delta=_float32_unpack(q->delta);
+
+ for(j=0;j<v->entries;j++){
+ float last=0.f;
+ for(k=0;k<v->elements;k++){
+ float now=_now(v,j)[k];
+ now=fabs(now)*delta+last+mindel;
+ if(q->sequencep)last=now;
+ _now(v,j)[k]=now;
+ }
}
- return acc;
}
-void vqgen_init(vqgen *v,int elements,int entries,
- double (*metric)(vqgen *,double *, double *),
- double spread){
+void vqgen_init(vqgen *v,int elements,int aux,int entries,float mindist,
+ float (*metric)(vqgen *,float *, float *),
+ float *(*weight)(vqgen *,float *),int centroid){
memset(v,0,sizeof(vqgen));
+ v->centroid=centroid;
v->elements=elements;
- v->errspread=spread;
+ v->aux=aux;
+ v->mindist=mindist;
v->allocated=32768;
- v->pointlist=malloc(v->allocated*v->elements*sizeof(double));
- v->first=malloc(v->allocated*sizeof(long));
- v->second=malloc(v->allocated*sizeof(long));
+ v->pointlist=_ogg_malloc(v->allocated*(v->elements+v->aux)*sizeof(float));
v->entries=entries;
- v->entry_now=malloc(v->entries*v->elements*sizeof(double));
- v->entry_next=malloc(v->entries*v->elements*sizeof(double));
- v->assigned=malloc(v->entries*sizeof(long));
- v->metric=malloc(v->entries*sizeof(double));
- v->bias=calloc(v->entries,sizeof(double));
+ v->entrylist=_ogg_malloc(v->entries*v->elements*sizeof(float));
+ v->assigned=_ogg_malloc(v->entries*sizeof(long));
+ v->bias=_ogg_calloc(v->entries,sizeof(float));
+ v->max=_ogg_calloc(v->entries,sizeof(float));
if(metric)
v->metric_func=metric;
else
- v->metric_func=_dist_sq;
-}
+ v->metric_func=_dist;
+ if(weight)
+ v->weight_func=weight;
+ else
+ v->weight_func=_weight_null;
+
+ v->asciipoints=tmpfile();
-/* *must* be beefed up. Perhaps a Floyd-Steinberg like scattering? */
-void _vqgen_seed(vqgen *v){
- memcpy(v->entry_now,v->pointlist,sizeof(double)*v->entries*v->elements);
}
-void vqgen_addpoint(vqgen *v, double *p){
+void vqgen_addpoint(vqgen *v, float *p,float *a){
+ int k;
+ for(k=0;k<v->elements;k++)
+ fprintf(v->asciipoints,"%.12g\n",p[k]);
+ for(k=0;k<v->aux;k++)
+ fprintf(v->asciipoints,"%.12g\n",a[k]);
+
if(v->points>=v->allocated){
v->allocated*=2;
- v->pointlist=realloc(v->pointlist,v->allocated*v->elements*sizeof(double));
- v->first=realloc(v->first,v->allocated*sizeof(long));
- v->second=realloc(v->second,v->allocated*sizeof(long));
+ v->pointlist=_ogg_realloc(v->pointlist,v->allocated*(v->elements+v->aux)*
+ sizeof(float));
+ }
+
+ memcpy(_point(v,v->points),p,sizeof(float)*v->elements);
+ if(v->aux)memcpy(_point(v,v->points)+v->elements,a,sizeof(float)*v->aux);
+
+ /* quantize to the density mesh if it's selected */
+ if(v->mindist>0.f){
+ /* quantize to the mesh */
+ for(k=0;k<v->elements+v->aux;k++)
+ _point(v,v->points)[k]=
+ rint(_point(v,v->points)[k]/v->mindist)*v->mindist;
}
-
- memcpy(_point(v,v->points),p,sizeof(double)*v->elements);
v->points++;
- if(v->points==v->entries)_vqgen_seed(v);
+ if(!(v->points&0xff))spinnit("loading... ",v->points);
}
-double *sort_t_vals;
-int sort_t_compare(const void *ap,const void *bp){
- long a=*(long *)ap;
- long b=*(long *)bp;
- double val=sort_t_vals[a]-sort_t_vals[b];
- if(val<0)return(1);
- if(val>0)return(-1);
- return(0);
+/* yes, not threadsafe. These utils aren't */
+static int sortit=0;
+static int sortsize=0;
+static int meshcomp(const void *a,const void *b){
+ if(((sortit++)&0xfff)==0)spinnit("sorting mesh...",sortit);
+ return(memcmp(a,b,sortsize));
}
-void vqgen_iterate(vqgen *v,int biasp){
- static int iteration=0;
- long i,j;
- double avmetric=0.;
-
- FILE *as;
- FILE *graph;
- FILE *err;
- FILE *bias;
- char name[80];
-
+void vqgen_sortmesh(vqgen *v){
+ sortit=0;
+ if(v->mindist>0.f){
+ long i,march=1;
+
+ /* sort to make uniqueness detection trivial */
+ sortsize=(v->elements+v->aux)*sizeof(float);
+ qsort(v->pointlist,v->points,sortsize,meshcomp);
+
+ /* now march through and eliminate dupes */
+ for(i=1;i<v->points;i++){
+ if(memcmp(_point(v,i),_point(v,i-1),sortsize)){
+ /* a new, unique entry. march it down */
+ if(i>march)memcpy(_point(v,march),_point(v,i),sortsize);
+ march++;
+ }
+ spinnit("eliminating density... ",v->points-i);
+ }
- sprintf(name,"space%d.m",iteration);
- graph=fopen(name,"w");
+ /* we're done */
+ fprintf(stderr,"\r%ld training points remining out of %ld"
+ " after density mesh (%ld%%)\n",march,v->points,march*100/v->points);
+ v->points=march;
- sprintf(name,"err%d.m",iteration);
- err=fopen(name,"w");
+ }
+ v->sorted=1;
+}
- sprintf(name,"bias%d.m",iteration);
- bias=fopen(name,"w");
+float vqgen_iterate(vqgen *v,int biasp){
+ long i,j,k;
+
+ float fdesired;
+ long desired;
+ long desired2;
+
+ float asserror=0.f;
+ float meterror=0.f;
+ float *new;
+ float *new2;
+ long *nearcount;
+ float *nearbias;
+ #ifdef NOISY
+ char buff[80];
+ FILE *assig;
+ FILE *bias;
+ FILE *cells;
+ sprintf(buff,"cells%d.m",v->it);
+ cells=fopen(buff,"w");
+ sprintf(buff,"assig%d.m",v->it);
+ assig=fopen(buff,"w");
+ sprintf(buff,"bias%d.m",v->it);
+ bias=fopen(buff,"w");
+ #endif
+
+
+ if(v->entries<2){
+ fprintf(stderr,"generation requires at least two entries\n");
+ exit(1);
+ }
- sprintf(name,"as%d.m",iteration);
- as=fopen(name,"w");
+ if(!v->sorted)vqgen_sortmesh(v);
+ if(!v->seeded)_vqgen_seed(v);
+ fdesired=(float)v->points/v->entries;
+ desired=fdesired;
+ desired2=desired*2;
+ new=_ogg_malloc(sizeof(float)*v->entries*v->elements);
+ new2=_ogg_malloc(sizeof(float)*v->entries*v->elements);
+ nearcount=_ogg_malloc(v->entries*sizeof(long));
+ nearbias=_ogg_malloc(v->entries*desired2*sizeof(float));
- /* init */
- memset(v->entry_next,0,sizeof(double)*v->elements*v->entries);
+ /* fill in nearest points for entry biasing */
+ /*memset(v->bias,0,sizeof(float)*v->entries);*/
+ memset(nearcount,0,sizeof(long)*v->entries);
memset(v->assigned,0,sizeof(long)*v->entries);
- memset(v->metric,0,sizeof(double)*v->entries);
-
- if(v->entries<2)exit(1);
+ if(biasp){
+ for(i=0;i<v->points;i++){
+ float *ppt=v->weight_func(v,_point(v,i));
+ float firstmetric=v->metric_func(v,_now(v,0),ppt)+v->bias[0];
+ float secondmetric=v->metric_func(v,_now(v,1),ppt)+v->bias[1];
+ long firstentry=0;
+ long secondentry=1;
+
+ if(!(i&0xff))spinnit("biasing... ",v->points+v->points+v->entries-i);
+
+ if(firstmetric>secondmetric){
+ float temp=firstmetric;
+ firstmetric=secondmetric;
+ secondmetric=temp;
+ firstentry=1;
+ secondentry=0;
+ }
- /* assign all the points, accumulate metric */
- for(i=0;i<v->points;i++){
- double firstmetric=v->metric_func(v,_now(v,0),_point(v,i))+v->bias[0];
- double secondmetric=v->metric_func(v,_now(v,1),_point(v,i))+v->bias[1];
- long firstentry=0;
- long secondentry=0;
-
- if(firstmetric>secondmetric){
- double tempmetric=firstmetric;
- firstmetric=secondmetric;
- secondmetric=tempmetric;
- firstentry=1;
- secondentry=1;
- }
+ for(j=2;j<v->entries;j++){
+ float thismetric=v->metric_func(v,_now(v,j),ppt)+v->bias[j];
+ if(thismetric<secondmetric){
+ if(thismetric<firstmetric){
+ secondmetric=firstmetric;
+ secondentry=firstentry;
+ firstmetric=thismetric;
+ firstentry=j;
+ }else{
+ secondmetric=thismetric;
+ secondentry=j;
+ }
+ }
+ }
- for(j=2;j<v->entries;j++){
- double thismetric=v->metric_func(v,_now(v,j),_point(v,i))+v->bias[j];
- if(thismetric<secondmetric){
- if(thismetric<firstmetric){
- secondmetric=firstmetric;
- secondentry=firstentry;
- firstmetric=thismetric;
- firstentry=j;
- }else{
- secondmetric=thismetric;
- secondentry=j;
- }
+ j=firstentry;
+ for(j=0;j<v->entries;j++){
+
+ float thismetric,localmetric;
+ float *nearbiasptr=nearbias+desired2*j;
+ long k=nearcount[j];
+
+ localmetric=v->metric_func(v,_now(v,j),ppt);
+ /* 'thismetric' is to be the bias value necessary in the current
+ arrangement for entry j to capture point i */
+ if(firstentry==j){
+ /* use the secondary entry as the threshhold */
+ thismetric=secondmetric-localmetric;
+ }else{
+ /* use the primary entry as the threshhold */
+ thismetric=firstmetric-localmetric;
+ }
+
+ /* support the idea of 'minimum distance'... if we want the
+ cells in a codebook to be roughly some minimum size (as with
+ the low resolution residue books) */
+
+ /* a cute two-stage delayed sorting hack */
+ if(k<desired){
+ nearbiasptr[k]=thismetric;
+ k++;
+ if(k==desired){
+ spinnit("biasing... ",v->points+v->points+v->entries-i);
+ qsort(nearbiasptr,desired,sizeof(float),directdsort);
+ }
+
+ }else if(thismetric>nearbiasptr[desired-1]){
+ nearbiasptr[k]=thismetric;
+ k++;
+ if(k==desired2){
+ spinnit("biasing... ",v->points+v->points+v->entries-i);
+ qsort(nearbiasptr,desired2,sizeof(float),directdsort);
+ k=desired;
+ }
+ }
+ nearcount[j]=k;
}
}
-
- v->first[i]=firstentry;
- v->second[i]=secondentry;
- v->metric[firstentry]+=firstmetric-v->bias[firstentry];
- v->assigned[firstentry]++;
-
- {
- double *no=_now(v,firstentry);
- double *ne=_next(v,firstentry);
- double *p=_point(v,i);
- for(j=0;j<v->elements;j++)
- ne[j]+=p[j];
- fprintf(graph,"%g %g\n%g %g\n\n",p[0],p[1],no[0],no[1]);
- }
- }
- /* new midpoints */
- for(i=0;i<v->entries;i++){
- double *next=_next(v,i);
- double *now=_now(v,i);
- if(v->assigned[i]){
- for(j=0;j<v->elements;j++)
- next[j]/=v->assigned[i];
- }else{
- for(j=0;j<v->elements;j++)
- next[j]=now[j];
- }
- }
-
- /* average global metric */
- for(i=0;i<v->entries;i++){
- avmetric+=v->metric[i];
- }
- avmetric/=v->entries;
- fprintf(stderr,"%ld... ",iteration);
+ /* inflate/deflate */
- /* positive/negative 'pressure' */
- if(biasp){
- /* Another round of n*m. Above we had to index by point. Now, we
- have to index by entry */
- long *index=alloca(v->points*sizeof(long));
- double *vals =alloca(v->points*sizeof(double));
- double *met =alloca(v->points*sizeof(double));
for(i=0;i<v->entries;i++){
+ float *nearbiasptr=nearbias+desired2*i;
+
+ spinnit("biasing... ",v->points+v->entries-i);
+
+ /* due to the delayed sorting, we likely need to finish it off....*/
+ if(nearcount[i]>desired)
+ qsort(nearbiasptr,nearcount[i],sizeof(float),directdsort);
+
+ v->bias[i]=nearbiasptr[desired-1];
- /* sort all n-thousand points by effective metric */
-
- for(j=0;j<v->points;j++){
- int threshentry;
- index[j]=j;
- if(v->first[j]==i){
- /* use the secondary entry as the threshhold */
- threshentry=v->second[j];
- }else{
- /* use the primary entry as the threshhold */
- threshentry=v->first[j];
- }
-
- /* bias value right at the threshhold to grab this point */
- met[j]=v->metric_func(v,_now(v,i),_point(v,j));
- vals[j]=
- v->metric_func(v,_now(v,threshentry),_point(v,j))+
- v->bias[threshentry]-
- v->metric_func(v,_now(v,i),_point(v,j));
- }
-
- sort_t_vals=vals;
- qsort(index,v->points,sizeof(long),sort_t_compare);
-
- {
- /* find the desired number of points and level of bias. We
- nominally want all entries to represent the same number of
- points, but we may also have a metric range restriction */
- int desired=(v->points+v->entries-1)/v->entries;
-
- if(v->errspread>=1.){
- double acc=0.;
- for(j=0;j<desired;j++){
- acc+=met[index[j]];
- if(acc*v->errspread>avmetric)break;
- }
- }else
- j=desired;
- v->bias[i]=vals[index[j-1]];
- }
}
}else{
- memset(v->bias,0,sizeof(double)*v->entries);
+ memset(v->bias,0,v->entries*sizeof(float));
}
- /* dump state, report metric */
- for(i=0;i<v->entries;i++){
- for(j=0;j<v->assigned[i];j++)
- fprintf(err,"%g\n",v->metric[i]/v->assigned[i]);
- fprintf(bias,"%g\n",v->bias[i]);
- fprintf(as,"%ld\n",v->assigned[i]);
- }
-
- fprintf(stderr,"average metric: %g\n",avmetric/v->elements/v->points*v->entries);
-
- fclose(as);
- fclose(err);
- fclose(bias);
- fclose(graph);
- memcpy(v->entry_now,v->entry_next,sizeof(double)*v->entries*v->elements);
- iteration++;
-}
+ /* Now assign with new bias and find new midpoints */
+ for(i=0;i<v->points;i++){
+ float *ppt=v->weight_func(v,_point(v,i));
+ float firstmetric=v->metric_func(v,_now(v,0),ppt)+v->bias[0];
+ long firstentry=0;
-int main(int argc,char *argv[]){
- FILE *in=fopen(argv[1],"r");
- vqgen v;
- char buffer[160];
- int i;
+ if(!(i&0xff))spinnit("centering... ",v->points-i);
- vqgen_init(&v,4,24,_dist_sq,1.);
+ for(j=0;j<v->entries;j++){
+ float thismetric=v->metric_func(v,_now(v,j),ppt)+v->bias[j];
+ if(thismetric<firstmetric){
+ firstmetric=thismetric;
+ firstentry=j;
+ }
+ }
- while(fgets(buffer,160,in)){
- double a[8];
- if(sscanf(buffer,"%lf %lf %lf %lf",
- a,a+1,a+2,a+3)==4)
- vqgen_addpoint(&v,a);
+ j=firstentry;
+
+#ifdef NOISY
+ fprintf(cells,"%g %g\n%g %g\n\n",
+ _now(v,j)[0],_now(v,j)[1],
+ ppt[0],ppt[1]);
+#endif
+
+ firstmetric-=v->bias[j];
+ meterror+=firstmetric;
+
+ if(v->centroid==0){
+ /* set up midpoints for next iter */
+ if(v->assigned[j]++){
+ for(k=0;k<v->elements;k++)
+ vN(new,j)[k]+=ppt[k];
+ if(firstmetric>v->max[j])v->max[j]=firstmetric;
+ }else{
+ for(k=0;k<v->elements;k++)
+ vN(new,j)[k]=ppt[k];
+ v->max[j]=firstmetric;
+ }
+ }else{
+ /* centroid */
+ if(v->assigned[j]++){
+ for(k=0;k<v->elements;k++){
+ if(vN(new,j)[k]>ppt[k])vN(new,j)[k]=ppt[k];
+ if(vN(new2,j)[k]<ppt[k])vN(new2,j)[k]=ppt[k];
+ }
+ if(firstmetric>v->max[firstentry])v->max[j]=firstmetric;
+ }else{
+ for(k=0;k<v->elements;k++){
+ vN(new,j)[k]=ppt[k];
+ vN(new2,j)[k]=ppt[k];
+ }
+ v->max[firstentry]=firstmetric;
+ }
+ }
}
- fclose(in);
- for(i=0;i<20;i++)
- vqgen_iterate(&v,0);
+ /* assign midpoints */
+
+ for(j=0;j<v->entries;j++){
+#ifdef NOISY
+ fprintf(assig,"%ld\n",v->assigned[j]);
+ fprintf(bias,"%g\n",v->bias[j]);
+#endif
+ asserror+=fabs(v->assigned[j]-fdesired);
+ if(v->assigned[j]){
+ if(v->centroid==0){
+ for(k=0;k<v->elements;k++)
+ _now(v,j)[k]=vN(new,j)[k]/v->assigned[j];
+ }else{
+ for(k=0;k<v->elements;k++)
+ _now(v,j)[k]=(vN(new,j)[k]+vN(new2,j)[k])/2.f;
+ }
+ }
+ }
- for(i=0;i<100;i++)
- vqgen_iterate(&v,1);
+ asserror/=(v->entries*fdesired);
- for(i=0;i<5;i++)
- vqgen_iterate(&v,0);
+ fprintf(stderr,"Pass #%d... ",v->it);
+ fprintf(stderr,": dist %g(%g) metric error=%g \n",
+ asserror,fdesired,meterror/v->points);
+ v->it++;
- return(0);
+ free(new);
+ free(nearcount);
+ free(nearbias);
+#ifdef NOISY
+ fclose(assig);
+ fclose(bias);
+ fclose(cells);
+#endif
+ return(asserror);
}
-
projects / platform / upstream / libvorbis.git / blobdiff