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Stats.h
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Stats.h
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#pragma once
#include "Types.h"
#include <math.h>
#include <vector>
#include <map>
#include <limits>
#include <climits>
#include <algorithm> // for std::sort
#include <string.h> // for memset
#include <stdio.h> // for printf
#include <assert.h>
double calcScore ( const int * bins, const int bincount, const int ballcount );
void plot ( double n );
double chooseK ( int b, int k );
double chooseUpToK ( int n, int k );
//-----------------------------------------------------------------------------
inline uint32_t f3mix ( uint32_t k )
{
k ^= k >> 16;
k *= 0x85ebca6b;
k ^= k >> 13;
k *= 0xc2b2ae35;
k ^= k >> 16;
return k;
}
static void printHash(const void* key, size_t len)
{
const unsigned char* const p = (const unsigned char*)key;
assert(len < INT_MAX);
for (int i=(int)len-1; i >= 0 ; i--) printf("%02x", p[i]);
printf(" ");
}
//-----------------------------------------------------------------------------
// Sort the hash list, count the total number of collisions and return
// the first N collisions for further processing
template< typename hashtype >
unsigned int FindCollisions ( std::vector<hashtype> & hashes,
HashSet<hashtype> & collisions,
int maxCollisions = 1000,
bool drawDiagram = false)
{
unsigned int collcount = 0;
#if 0
// sort indices instead
std::vector< std::pair<hashtype, size_t>> pairs;
pairs.resize (hashes.size());
for(size_t i = 0; i < hashes.size(); i++)
{
pairs[i] = std::make_pair(hashes[i], i);
}
std::sort(pairs.begin(),pairs.end());
for(size_t hnb = 1; hnb < pairs.size(); hnb++)
{
hashtype h1 = pairs[hnb].first;
hashtype prev = pairs[hnb-1].first;
if(h1 == prev)
{
collcount++;
if((int)collisions.size() < maxCollisions)
{
#ifdef DEBUG
printf ("\n%zu <=> %zu: ", pairs[hnb-1].second, pairs[hnb].second);
printHash(&h1, sizeof(hashtype));
#endif
collisions.insert(h1);
}
}
}
#else
std::sort(hashes.begin(),hashes.end());
for(size_t hnb = 1; hnb < hashes.size(); hnb++)
{
if(hashes[hnb] == hashes[hnb-1])
{
collcount++;
if(collcount < maxCollisions)
{
#ifdef DEBUG
printf ("\n%zu: ", hnb);
printHash(&hashes[hnb], sizeof(hashtype));
#endif
if (drawDiagram)
collisions.insert(hashes[hnb]);
}
}
}
#endif
#ifdef DEBUG
if (collcount)
printf ("\n");
#endif
return collcount;
}
// Note: with 32bit 77163 keys will get a 50% probability of one collision.
// Naive multiplication, no accuracy at all
static double ExpectedNBCollisions_Slow ( const double nbH, const double nbBits )
{
long balls = nbH;
long double bins = nbBits;
long double result = 1.0;
for (long i = 1; i < (long)nbH / 2; i++) {
// take a pair from the front and the end to minimize errors
result *= ((bins - i) / bins) * ((bins - (nbH - i)) / bins);
}
return (double)(nbH * result);
}
// TODO This only works for a low number of collisions
static inline double ExpectedCollisions ( const double balls, const double bins )
{
return balls - (bins * (1 - pow((bins - 1)/bins, balls)));
}
// Still too inaccurate: https://preshing.com/20110504/hash-collision-probabilities/
static double EstimateNbCollisions_Taylor(const double nbH, const double nbBits)
{
const long double k = nbH;
const long double b = nbBits;
return (double)(k * (1.0 - expl(-0.5 * k * (k - 1.0) / b)));
}
// demerphq: (double(count) * double(count-1)) / pow(2.0,double(sizeof(hashtype) * 8 + 1));
// the very same as our calc. pow 2 vs exp2. Just the high cutoff is missing here.
static double EstimateNbCollisions_Demerphq(const double nbH, const double nbBits)
{
return (nbH * (nbH - 1)) / pow(2.0, nbBits + 1);
}
// The currently best calculation, highly prone to inaccuracies with low results (1.0 - 10.0)
// TODO: return also the error.
static double EstimateNbCollisions(const int nbH, const int nbBits)
{
#if 0
//return ExpectedNBCollisions_Slow((const double)nbH, (const double)nbBits);
return EstimateNbCollisions_Demerphq((const double)nbH, (const double)nbBits);
//return EstimateNbCollisions_Taylor((const double)nbH, (const double)nbBits);
//return ExpectedCollisions((const double)nbH, (const double)nbBits);
#else
double exp = exp2((double)nbBits); // 2 ^ bits
double result = (double(nbH) * double(nbH-1)) / (2.0 * exp);
if (result > (double)nbH)
result = (double)nbH;
// improved floating point accuracy
if (result <= exp || nbBits > 32)
return result;
return result - exp;
#endif
}
template< typename hashtype >
bool CountLowbitsCollisions ( std::vector<hashtype> & revhashes, int nbLBits)
{
const int origBits = sizeof(hashtype) * 8;
int shiftBy = origBits - nbLBits;
if (shiftBy <= 0) return true;
size_t const nbH = revhashes.size();
double expected = EstimateNbCollisions(nbH, nbLBits);
printf("Testing collisions (low %2i-bit) - Expected %12.1f, ", nbLBits, expected);
int collcount = 0;
for (size_t hnb = 1; hnb < nbH; hnb++)
{
#ifdef DEBUG
hashtype const h1x = revhashes[hnb-1];
hashtype const h2x = revhashes[hnb];
#endif
hashtype const h1 = revhashes[hnb-1] >> shiftBy;
hashtype const h2 = revhashes[hnb] >> shiftBy;
if(h1 == h2)
collcount++;
}
double ratio = double(collcount) / expected;
printf("actual %6i (%.2fx)", collcount, expected > 0.0 ? ratio : (double)collcount);
if (ratio > 0.98 && collcount != (int)expected)
printf(" (%i)", collcount - (int)expected);
// low estimation values are too inaccurate
if (expected >= 0.1 && expected <= 10.0)
{
if (ratio > 4.0)
{
printf(" !!!!!\n");
return false;
}
else if (ratio > 2.0)
printf(" !");
}
// allow expected 0.3 and actual 1
else if (ratio > 2.0 && collcount > 1)
{
printf(" !!!!!\n");
return false;
}
printf("\n");
return true;
}
template< typename hashtype >
bool CountHighbitsCollisions ( std::vector<hashtype> & hashes, int nbHBits)
{
int origBits = sizeof(hashtype) * 8;
int shiftBy = origBits - nbHBits;
if (shiftBy <= 0) return true;
size_t const nbH = hashes.size();
double expected = EstimateNbCollisions(nbH, nbHBits);
printf("Testing collisions (high %2i-bit) - Expected %12.1f, ", nbHBits, expected);
int collcount = 0;
for (size_t hnb = 1; hnb < nbH; hnb++)
{
#ifdef DEBUG
hashtype const h1x = hashes[hnb-1];
hashtype const h2x = hashes[hnb];
#endif
hashtype const h1 = hashes[hnb-1] >> shiftBy;
hashtype const h2 = hashes[hnb] >> shiftBy;
if(h1 == h2)
collcount++;
}
double ratio = double(collcount) / expected;
printf("actual %6i (%.2fx)", collcount, expected > 0.0 ? ratio : (double)collcount);
if (ratio > 0.98 && collcount != (int)expected)
printf(" (%i)", collcount - (int)expected);
// low estimation values are too inaccurate
if (expected >= 0.1 && expected <= 10.0)
{
if (ratio > 4.0)
{
printf(" !!!!!\n");
return false;
}
else if (ratio > 2.0)
printf(" !");
}
// allow expected 0.3 and actual 1
else if (ratio > 2.0 && collcount > 1)
{
printf(" !!!!!\n");
return false;
}
printf("\n");
return true;
}
static int FindMinBits_TargetCollisionShare(int nbHashes, double share)
{
int nb;
for (nb=2; nb<64; nb++) {
double const maxColls = (double)(1ULL << nb) * share;
double const nbColls = EstimateNbCollisions(nbHashes, nb);
if (nbColls < maxColls) return nb;
}
assert(0);
return nb;
}
static int FindMaxBits_TargetCollisionNb(int nbHashes, int minCollisions)
{
int nb;
for (nb=63; nb>2; nb--) {
double const nbColls = EstimateNbCollisions(nbHashes, nb);
if (nbColls > minCollisions) return nb;
}
assert(0);
return nb;
}
template< typename hashtype >
int CountNbCollisions ( std::vector<hashtype> & hashes, int nbHBits)
{
const int origBits = sizeof(hashtype) * 8;
const int shiftBy = origBits - nbHBits;
assert(shiftBy > 0);
size_t const nbH = hashes.size();
int collcount = 0;
for (size_t hnb = 1; hnb < nbH; hnb++)
{
hashtype const h1 = hashes[hnb-1] >> shiftBy;
hashtype const h2 = hashes[hnb] >> shiftBy;
if(h1 == h2)
{
collcount++;
}
}
return collcount;
}
template< typename hashtype >
bool TestLowbitsCollisions ( std::vector<hashtype> & revhashes)
{
int origBits = sizeof(hashtype) * 8;
size_t const nbH = revhashes.size();
int const minBits = FindMinBits_TargetCollisionShare(nbH, 0.01);
int const maxBits = FindMaxBits_TargetCollisionNb(nbH, 20);
if (maxBits <= 0 || maxBits >= origBits) return true;
printf("Testing collisions (low %2i-%2i bits) - ", minBits, maxBits);
double maxCollDev = 0.0;
int maxCollDevBits = 0;
int maxCollDevNb = 0;
double maxCollDevExp = 1.0;
for (int b = minBits; b <= maxBits; b++) {
int const nbColls = CountNbCollisions(revhashes, b);
double const expected = EstimateNbCollisions(nbH, b);
assert(expected > 0.0);
double const dev = (double)nbColls / expected;
if (dev > maxCollDev) {
maxCollDev = dev;
maxCollDevBits = b;
maxCollDevNb = nbColls;
maxCollDevExp = expected;
}
}
printf("Worst is %2i bits: %2i/%2i (%.2fx)",
maxCollDevBits, maxCollDevNb, (int)maxCollDevExp, maxCollDev);
if (maxCollDev > 2.0) {
printf(" !!!!!\n");
return false;
}
printf("\n");
return true;
}
template< typename hashtype >
bool TestHighbitsCollisions ( std::vector<hashtype> & hashes)
{
int origBits = sizeof(hashtype) * 8;
size_t const nbH = hashes.size();
int const minBits = FindMinBits_TargetCollisionShare(nbH, 0.01);
int const maxBits = FindMaxBits_TargetCollisionNb(nbH, 20);
if (maxBits >= origBits) return true;
printf("Testing collisions (high %2i-%2i bits) - ", minBits, maxBits);
double maxCollDev = 0.0;
int maxCollDevBits = 0;
int maxCollDevNb = 0;
double maxCollDevExp = 1.0;
for (int b = minBits; b <= maxBits; b++) {
int const nbColls = CountNbCollisions(hashes, b);
double const expected = EstimateNbCollisions(nbH, b);
assert(expected > 0.0);
double const dev = (double)nbColls / expected;
if (dev > maxCollDev) {
maxCollDev = dev;
maxCollDevBits = b;
maxCollDevNb = nbColls;
maxCollDevExp = expected;
}
}
printf("Worst is %2i bits: %2i/%2i (%.2fx)",
maxCollDevBits, maxCollDevNb, (int)maxCollDevExp, maxCollDev);
if (maxCollDev > 2.0) {
printf(" !!!!!\n");
return false;
}
printf("\n");
return true;
}
//-----------------------------------------------------------------------------
template < typename hashtype >
int PrintCollisions ( HashSet<hashtype> & collisions )
{
printf("\nCollisions:\n");
for (typename HashSet<hashtype>::iterator it = collisions.begin();
it != collisions.end(); ++it)
{
const hashtype &hash = *it;
printhex(&hash, sizeof(hashtype));
printf("\n");
}
return 0;
}
//----------------------------------------------------------------------------
// Measure the distribution "score" for each possible N-bit span up to 20 bits
template< typename hashtype >
bool TestDistribution ( std::vector<hashtype> & hashes, bool drawDiagram )
{
printf("Testing distribution - ");
if(drawDiagram) printf("\n");
const int hashbits = sizeof(hashtype) * 8;
int maxwidth = 20;
// We need at least 5 keys per bin to reliably test distribution biases
// down to 1%, so don't bother to test sparser distributions than that
while(double(hashes.size()) / double(1 << maxwidth) < 5.0)
{
maxwidth--;
}
std::vector<int> bins;
bins.resize(1 << maxwidth);
double worst = 0;
int worstStart = -1;
int worstWidth = -1;
for(int start = 0; start < hashbits; start++)
{
int width = maxwidth;
int bincount = (1 << width);
memset(&bins[0],0,sizeof(int)*bincount);
for(size_t j = 0; j < hashes.size(); j++)
{
hashtype & hash = hashes[j];
uint32_t index = window(&hash,sizeof(hash),start,width);
bins[index]++;
}
// Test the distribution, then fold the bins in half,
// repeat until we're down to 256 bins
if(drawDiagram) printf("[");
while(bincount >= 256)
{
double n = calcScore(&bins[0],bincount,(int)hashes.size());
if(drawDiagram) plot(n);
if(n > worst)
{
worst = n;
worstStart = start;
worstWidth = width;
}
width--;
bincount /= 2;
if(width < 8) break;
for(int i = 0; i < bincount; i++)
{
bins[i] += bins[i+bincount];
}
}
if(drawDiagram) printf("]\n");
}
double pct = worst * 100.0;
printf("Worst bias is the %2d-bit window at bit %2d - %.3f%%",
worstWidth, worstStart, pct);
if(pct >= 1.0) {
printf(" !!!!!\n");
return false;
}
else {
printf("\n");
return true;
}
}
//----------------------------------------------------------------------------
static int FindNbBitsForCollisionTarget(int targetNbCollisions, int nbHashes)
{
int nb;
double const target = (double)targetNbCollisions;
for (nb=2; nb<64; nb++) {
double nbColls = EstimateNbCollisions(nbHashes, nb);
if (nbColls < target) break;
}
if ((EstimateNbCollisions(nbHashes, nb)) > targetNbCollisions/5)
return nb;
return nb-1;
}
// 0xf00f1001 => 0x8008f00f
template <typename hashtype>
hashtype bitreverse(hashtype n, size_t b = sizeof(hashtype) * 8)
{
assert(b <= std::numeric_limits<hashtype>::digits);
hashtype rv = 0;
for (size_t i = 0; i < b; i += 8) {
rv <<= 8;
rv |= bitrev(n & 0xff); // ensure overloaded |= op for Blob not underflowing
n >>= 8;
}
return rv;
}
template < typename hashtype >
bool TestHashList ( std::vector<hashtype> & hashes, bool drawDiagram,
bool testCollision = true, bool testDist = true,
bool testHighBits = true, bool testLowBits = true)
{
bool result = true;
if (testCollision)
{
size_t const count = hashes.size();
double const expected = EstimateNbCollisions(count, sizeof(hashtype) * 8);
printf("Testing collisions (%3i-bit) - Expected %6.1f, ",
(int)sizeof(hashtype)*8, expected);
const int i_expected = (int)expected;
int collcount = 0;
HashSet<hashtype> collisions;
collcount = FindCollisions(hashes, collisions, 1000, drawDiagram);
double ratio = double(collcount) / expected;
printf("actual %6i (%.2fx)", (int)collcount, expected > 0.0 ? ratio : (double)collcount);
if (ratio > 0.98 && collcount != i_expected)
printf(" (%i)", collcount - i_expected);
if (sizeof(hashtype) <= sizeof(uint32_t))
{
// fail with >= 2x expected collisions
// TODO - collision failure cutoff needs to be expressed as a standard deviation instead
// of a scale factor, otherwise we fail erroneously if there are a small expected number
// of collisions
// low estimation values are too inaccurate
if (expected >= 0.1 && expected <= 10.0)
{
if (ratio > 4.0)
{
printf(" !!!!!\n");
return false;
}
else if (ratio > 2.0)
printf(" !");
}
// allow expected 0.3 and actual 1
else if (ratio > 2.0 && collcount > 1)
{
printf(" !!!!!\n");
return false;
}
}
else
{
// For all hashes larger than 32 bits, _any_ collisions are a failure.
if (collcount > 0 && expected < 1.0)
{
printf(" !!!!!");
result = false;
if(drawDiagram)
{
PrintCollisions(collisions);
//printf("Mapping collisions\n");
//CollisionMap<uint128_t,ByteVec> cmap;
//CollisionCallback<uint128_t> c2(hash,collisions,cmap);
////TwoBytesKeygen(20,c2);
//printf("Dumping collisions\n");
//DumpCollisionMap(cmap);
}
}
}
printf("\n");
fflush(NULL);
if (testHighBits) {
result &= CountHighbitsCollisions(hashes, 224);
result &= CountHighbitsCollisions(hashes, 160);
result &= CountHighbitsCollisions(hashes, 128);
result &= CountHighbitsCollisions(hashes, 64);
result &= CountHighbitsCollisions(hashes, 32);
/*
int const optimalNbBits = FindNbBitsForCollisionTarget(100, count);
result &= CountHighbitsCollisions(hashes, optimalNbBits);
*/
result &= TestHighbitsCollisions(hashes);
/* Following tests are too small : tables are necessarily saturated.
* It would be better to count the nb of collisions per Cell,
* and compared the distribution of values against a random source.
* But this is a different test */
result &= CountHighbitsCollisions(hashes, 12);
result &= CountHighbitsCollisions(hashes, 8);
}
if (testLowBits) {
// reverse: bitwise flip the hashes. lowest bits first
std::vector<hashtype> revhashes = hashes;
for (size_t i = 0; i < revhashes.size(); i++) {
revhashes[i] = bitreverse(hashes[i]);
}
std::sort(revhashes.begin(), revhashes.end());
result &= CountLowbitsCollisions(revhashes, 224);
result &= CountLowbitsCollisions(revhashes, 160);
result &= CountLowbitsCollisions(revhashes, 128);
result &= CountLowbitsCollisions(revhashes, 64);
result &= CountLowbitsCollisions(revhashes, 32);
/*
int const optimalNbBits = FindNbBitsForCollisionTarget(100, count);
result &= CountLowbitsCollisions(hashes, optimalNbBits);
*/
result &= TestLowbitsCollisions(revhashes);
/* Following tests are too small : tables are necessarily saturated.
* It would be better to count the nb of collisions per Cell,
* and compared the distribution of values against a random source.
* But this is a different test */
/* rurban: No, these tests are for non-prime hash tables, using only
the lower 5-10 bits */
result &= CountLowbitsCollisions(revhashes, 12);
result &= CountLowbitsCollisions(revhashes, 8);
//std::vector<hashtype>().swap(revhashes);
//revhashes.clear();
}
}
//----------
if(testDist)
{
result &= TestDistribution(hashes,drawDiagram);
}
return result;
}
//-----------------------------------------------------------------------------
template < class keytype, typename hashtype >
bool TestKeyList ( hashfunc<hashtype> hash, std::vector<keytype> & keys,
bool drawDiagram, bool testColl, bool testDist )
{
int keycount = (int)keys.size();
std::vector<hashtype> hashes;
hashes.resize(keycount);
printf("Hashing");
for(int i = 0; i < keycount; i++)
{
if(i % (keycount / 10) == 0) printf(".");
keytype & k = keys[i];
hash(&k,sizeof(k),0,&hashes[i]);
}
printf("\n");
bool result = TestHashList(hashes,drawDiagram,testColl,testDist);
printf("\n");
return result;
}
//-----------------------------------------------------------------------------
// Bytepair test - generate 16-bit indices from all possible non-overlapping
// 8-bit sections of the hash value, check distribution on all of them.
// This is a very good test for catching weak intercorrelations between bits -
// much harder to pass than the normal distribution test. However, it doesn't
// really model the normal usage of hash functions in hash table lookup, so
// I'm not sure it's that useful (and hash functions that fail this test but
// pass the normal distribution test still work well in practice)
template < typename hashtype >
double TestDistributionBytepairs ( std::vector<hashtype> & hashes, bool drawDiagram )
{
const int nbytes = sizeof(hashtype);
const int hashbits = nbytes * 8;
const int nbins = 65536;
std::vector<int> bins(nbins,0);
double worst = 0;
for(int a = 0; a < hashbits; a++)
{
if(drawDiagram) if((a % 8 == 0) && (a > 0)) printf("\n");
if(drawDiagram) printf("[");
for(int b = 0; b < hashbits; b++)
{
if(drawDiagram) if((b % 8 == 0) && (b > 0)) printf(" ");
bins.clear();
bins.resize(nbins,0);
for(size_t i = 0; i < hashes.size(); i++)
{
hashtype & hash = hashes[i];
uint32_t pa = window(&hash,sizeof(hash),a,8);
uint32_t pb = window(&hash,sizeof(hash),b,8);
bins[pa | (pb << 8)]++;
}
double s = calcScore(bins,bins.size(),hashes.size());
if(drawDiagram) plot(s);
if(s > worst)
{
worst = s;
}
}
if(drawDiagram) printf("]\n");
}
return worst;
}
//-----------------------------------------------------------------------------
// Simplified test - only check 64k distributions, and only on byte boundaries
template < typename hashtype >
void TestDistributionFast ( std::vector<hashtype> & hashes, double & dworst, double & davg )
{
const int hashbits = sizeof(hashtype) * 8;
const int nbins = 65536;
std::vector<int> bins(nbins,0);
dworst = -1.0e90;
davg = 0;
for(int start = 0; start < hashbits; start += 8)
{
bins.clear();
bins.resize(nbins,0);
for(size_t j = 0; j < hashes.size(); j++)
{
hashtype & hash = hashes[j];
uint32_t index = window(&hash,sizeof(hash),start,16);
bins[index]++;
}
double n = calcScore(&bins.front(),(int)bins.size(),(int)hashes.size());
davg += n;
if(n > dworst) dworst = n;
}
davg /= double(hashbits/8);
}
//-----------------------------------------------------------------------------