From 39ed87570bdb2f86969d4be821c94b722dc71179 Mon Sep 17 00:00:00 2001
From: Joe Ludwig <joe@valvesoftware.com>
Date: Wed, 26 Jun 2013 15:22:04 -0700
Subject: First version of the SOurce SDK 2013

---
 mp/src/mathlib/quantize.cpp | 679 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 679 insertions(+)
 create mode 100644 mp/src/mathlib/quantize.cpp

(limited to 'mp/src/mathlib/quantize.cpp')

diff --git a/mp/src/mathlib/quantize.cpp b/mp/src/mathlib/quantize.cpp
new file mode 100644
index 00000000..e1fd88dc
--- /dev/null
+++ b/mp/src/mathlib/quantize.cpp
@@ -0,0 +1,679 @@
+//========= 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 v=s->Value[j];
+			Means[j]+=v*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(int 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);
+}
-- 
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