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Internal linkage for outlier_variance

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Martin Hořeňovský 2023-05-01 00:51:43 +02:00
parent 10f0a58643
commit 51fdbedd13
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2 changed files with 157 additions and 143 deletions
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298
src/catch2/benchmark/detail/catch_stats.cpp
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@ -21,128 +21,172 @@
#include <future>
#endif
namespace {
namespace Catch {
namespace Benchmark {
namespace Detail {
namespace {
using Catch::Benchmark::Detail::sample;
template <typename URng, typename Estimator>
static sample
resample( URng& rng,
unsigned int resamples,
std::vector<double>::const_iterator first,
std::vector<double>::const_iterator last,
Estimator& estimator ) {
auto n = static_cast<size_t>( last - first );
std::uniform_int_distribution<decltype( n )> dist( 0,
n - 1 );
template <typename URng, typename Estimator>
static sample resample(URng& rng, unsigned int resamples,
std::vector<double>::const_iterator first,
std::vector<double>::const_iterator last,
Estimator& estimator) {
auto n = static_cast<size_t>(last - first);
std::uniform_int_distribution<decltype(n)> dist(0, n - 1);
sample out;
out.reserve( resamples );
// We allocate the vector outside the loop to avoid realloc
// per resample
std::vector<double> resampled;
resampled.reserve( n );
for ( size_t i = 0; i < resamples; ++i ) {
resampled.clear();
for ( size_t s = 0; s < n; ++s ) {
resampled.push_back(
first[static_cast<std::ptrdiff_t>(
dist( rng ) )] );
}
const auto estimate =
estimator( resampled.begin(), resampled.end() );
out.push_back( estimate );
}
std::sort( out.begin(), out.end() );
return out;
}
sample out;
out.reserve(resamples);
// We allocate the vector outside the loop to avoid realloc per resample
std::vector<double> resampled;
resampled.reserve( n );
for ( size_t i = 0; i < resamples; ++i ) {
resampled.clear();
for ( size_t s = 0; s < n; ++s ) {
resampled.push_back(
first[static_cast<std::ptrdiff_t>( dist( rng ) )] );
}
const auto estimate =
estimator( resampled.begin(), resampled.end() );
out.push_back( estimate );
}
std::sort(out.begin(), out.end());
return out;
}
static double outlier_variance( Estimate<double> mean,
Estimate<double> stddev,
int n ) {
double sb = stddev.point;
double mn = mean.point / n;
double mg_min = mn / 2.;
double sg = (std::min)( mg_min / 4., sb / std::sqrt( n ) );
double sg2 = sg * sg;
double sb2 = sb * sb;
auto c_max = [n, mn, sb2, sg2]( double x ) -> double {
double k = mn - x;
double d = k * k;
double nd = n * d;
double k0 = -n * nd;
double k1 = sb2 - n * sg2 + nd;
double det = k1 * k1 - 4 * sg2 * k0;
return static_cast<int>( -2. * k0 /
( k1 + std::sqrt( det ) ) );
};
double erf_inv(double x) {
// Code accompanying the article "Approximating the erfinv function" in GPU Computing Gems, Volume 2
double w, p;
auto var_out = [n, sb2, sg2]( double c ) {
double nc = n - c;
return ( nc / n ) * ( sb2 - nc * sg2 );
};
w = -log((1.0 - x) * (1.0 + x));
return (std::min)( var_out( 1 ),
var_out(
(std::min)( c_max( 0. ),
c_max( mg_min ) ) ) ) /
sb2;
}
if (w < 6.250000) {
w = w - 3.125000;
p = -3.6444120640178196996e-21;
p = -1.685059138182016589e-19 + p * w;
p = 1.2858480715256400167e-18 + p * w;
p = 1.115787767802518096e-17 + p * w;
p = -1.333171662854620906e-16 + p * w;
p = 2.0972767875968561637e-17 + p * w;
p = 6.6376381343583238325e-15 + p * w;
p = -4.0545662729752068639e-14 + p * w;
p = -8.1519341976054721522e-14 + p * w;
p = 2.6335093153082322977e-12 + p * w;
p = -1.2975133253453532498e-11 + p * w;
p = -5.4154120542946279317e-11 + p * w;
p = 1.051212273321532285e-09 + p * w;
p = -4.1126339803469836976e-09 + p * w;
p = -2.9070369957882005086e-08 + p * w;
p = 4.2347877827932403518e-07 + p * w;
p = -1.3654692000834678645e-06 + p * w;
p = -1.3882523362786468719e-05 + p * w;
p = 0.0001867342080340571352 + p * w;
p = -0.00074070253416626697512 + p * w;
p = -0.0060336708714301490533 + p * w;
p = 0.24015818242558961693 + p * w;
p = 1.6536545626831027356 + p * w;
} else if (w < 16.000000) {
w = sqrt(w) - 3.250000;
p = 2.2137376921775787049e-09;
p = 9.0756561938885390979e-08 + p * w;
p = -2.7517406297064545428e-07 + p * w;
p = 1.8239629214389227755e-08 + p * w;
p = 1.5027403968909827627e-06 + p * w;
p = -4.013867526981545969e-06 + p * w;
p = 2.9234449089955446044e-06 + p * w;
p = 1.2475304481671778723e-05 + p * w;
p = -4.7318229009055733981e-05 + p * w;
p = 6.8284851459573175448e-05 + p * w;
p = 2.4031110387097893999e-05 + p * w;
p = -0.0003550375203628474796 + p * w;
p = 0.00095328937973738049703 + p * w;
p = -0.0016882755560235047313 + p * w;
p = 0.0024914420961078508066 + p * w;
p = -0.0037512085075692412107 + p * w;
p = 0.005370914553590063617 + p * w;
p = 1.0052589676941592334 + p * w;
p = 3.0838856104922207635 + p * w;
} else {
w = sqrt(w) - 5.000000;
p = -2.7109920616438573243e-11;
p = -2.5556418169965252055e-10 + p * w;
p = 1.5076572693500548083e-09 + p * w;
p = -3.7894654401267369937e-09 + p * w;
p = 7.6157012080783393804e-09 + p * w;
p = -1.4960026627149240478e-08 + p * w;
p = 2.9147953450901080826e-08 + p * w;
p = -6.7711997758452339498e-08 + p * w;
p = 2.2900482228026654717e-07 + p * w;
p = -9.9298272942317002539e-07 + p * w;
p = 4.5260625972231537039e-06 + p * w;
p = -1.9681778105531670567e-05 + p * w;
p = 7.5995277030017761139e-05 + p * w;
p = -0.00021503011930044477347 + p * w;
p = -0.00013871931833623122026 + p * w;
p = 1.0103004648645343977 + p * w;
p = 4.8499064014085844221 + p * w;
}
return p * x;
}
static double erf_inv( double x ) {
// Code accompanying the article "Approximating the erfinv
// function" in GPU Computing Gems, Volume 2
double w, p;
double standard_deviation(std::vector<double>::const_iterator first,
std::vector<double>::const_iterator last) {
auto m = Catch::Benchmark::Detail::mean(first, last);
double variance = std::accumulate( first,
last,
0.,
[m]( double a, double b ) {
double diff = b - m;
return a + diff * diff;
} ) /
( last - first );
return std::sqrt( variance );
}
w = -log( ( 1.0 - x ) * ( 1.0 + x ) );
}
if ( w < 6.250000 ) {
w = w - 3.125000;
p = -3.6444120640178196996e-21;
p = -1.685059138182016589e-19 + p * w;
p = 1.2858480715256400167e-18 + p * w;
p = 1.115787767802518096e-17 + p * w;
p = -1.333171662854620906e-16 + p * w;
p = 2.0972767875968561637e-17 + p * w;
p = 6.6376381343583238325e-15 + p * w;
p = -4.0545662729752068639e-14 + p * w;
p = -8.1519341976054721522e-14 + p * w;
p = 2.6335093153082322977e-12 + p * w;
p = -1.2975133253453532498e-11 + p * w;
p = -5.4154120542946279317e-11 + p * w;
p = 1.051212273321532285e-09 + p * w;
p = -4.1126339803469836976e-09 + p * w;
p = -2.9070369957882005086e-08 + p * w;
p = 4.2347877827932403518e-07 + p * w;
p = -1.3654692000834678645e-06 + p * w;
p = -1.3882523362786468719e-05 + p * w;
p = 0.0001867342080340571352 + p * w;
p = -0.00074070253416626697512 + p * w;
p = -0.0060336708714301490533 + p * w;
p = 0.24015818242558961693 + p * w;
p = 1.6536545626831027356 + p * w;
} else if ( w < 16.000000 ) {
w = sqrt( w ) - 3.250000;
p = 2.2137376921775787049e-09;
p = 9.0756561938885390979e-08 + p * w;
p = -2.7517406297064545428e-07 + p * w;
p = 1.8239629214389227755e-08 + p * w;
p = 1.5027403968909827627e-06 + p * w;
p = -4.013867526981545969e-06 + p * w;
p = 2.9234449089955446044e-06 + p * w;
p = 1.2475304481671778723e-05 + p * w;
p = -4.7318229009055733981e-05 + p * w;
p = 6.8284851459573175448e-05 + p * w;
p = 2.4031110387097893999e-05 + p * w;
p = -0.0003550375203628474796 + p * w;
p = 0.00095328937973738049703 + p * w;
p = -0.0016882755560235047313 + p * w;
p = 0.0024914420961078508066 + p * w;
p = -0.0037512085075692412107 + p * w;
p = 0.005370914553590063617 + p * w;
p = 1.0052589676941592334 + p * w;
p = 3.0838856104922207635 + p * w;
} else {
w = sqrt( w ) - 5.000000;
p = -2.7109920616438573243e-11;
p = -2.5556418169965252055e-10 + p * w;
p = 1.5076572693500548083e-09 + p * w;
p = -3.7894654401267369937e-09 + p * w;
p = 7.6157012080783393804e-09 + p * w;
p = -1.4960026627149240478e-08 + p * w;
p = 2.9147953450901080826e-08 + p * w;
p = -6.7711997758452339498e-08 + p * w;
p = 2.2900482228026654717e-07 + p * w;
p = -9.9298272942317002539e-07 + p * w;
p = 4.5260625972231537039e-06 + p * w;
p = -1.9681778105531670567e-05 + p * w;
p = 7.5995277030017761139e-05 + p * w;
p = -0.00021503011930044477347 + p * w;
p = -0.00013871931833623122026 + p * w;
p = 1.0103004648645343977 + p * w;
p = 4.8499064014085844221 + p * w;
}
return p * x;
}
static double
standard_deviation( std::vector<double>::const_iterator first,
std::vector<double>::const_iterator last ) {
auto m = Catch::Benchmark::Detail::mean( first, last );
double variance =
std::accumulate( first,
last,
0.,
[m]( double a, double b ) {
double diff = b - m;
return a + diff * diff;
} ) /
( last - first );
return std::sqrt( variance );
}
} // namespace
} // namespace Detail
} // namespace Benchmark
} // namespace Catch
namespace Catch {
namespace Benchmark {
@ -234,34 +278,6 @@ namespace Catch {
return result;
}
double outlier_variance(Estimate<double> mean, Estimate<double> stddev, int n) {
double sb = stddev.point;
double mn = mean.point / n;
double mg_min = mn / 2.;
double sg = (std::min)(mg_min / 4., sb / std::sqrt(n));
double sg2 = sg * sg;
double sb2 = sb * sb;
auto c_max = [n, mn, sb2, sg2](double x) -> double {
double k = mn - x;
double d = k * k;
double nd = n * d;
double k0 = -n * nd;
double k1 = sb2 - n * sg2 + nd;
double det = k1 * k1 - 4 * sg2 * k0;
return static_cast<int>(-2. * k0 / (k1 + std::sqrt(det)));
};
auto var_out = [n, sb2, sg2](double c) {
double nc = n - c;
return (nc / n) * (sb2 - nc * sg2);
};
return (std::min)(var_out(1), var_out((std::min)(c_max(0.), c_max(mg_min)))) / sb2;
}
bootstrap_analysis analyse_samples(double confidence_level,
unsigned int n_resamples,
std::vector<double>::iterator first,

2
src/catch2/benchmark/detail/catch_stats.hpp
View File

@ -108,8 +108,6 @@ namespace Catch {
return { point, resample[lo], resample[hi], confidence_level };
}
double outlier_variance(Estimate<double> mean, Estimate<double> stddev, int n);
struct bootstrap_analysis {
Estimate<double> mean;
Estimate<double> standard_deviation;