diff --git a/src/catch2/benchmark/detail/catch_stats.cpp b/src/catch2/benchmark/detail/catch_stats.cpp index a7863224..ea483a30 100644 --- a/src/catch2/benchmark/detail/catch_stats.cpp +++ b/src/catch2/benchmark/detail/catch_stats.cpp @@ -21,128 +21,172 @@ #include #endif -namespace { +namespace Catch { + namespace Benchmark { + namespace Detail { + namespace { -using Catch::Benchmark::Detail::sample; + template + static sample + resample( URng& rng, + unsigned int resamples, + std::vector::const_iterator first, + std::vector::const_iterator last, + Estimator& estimator ) { + auto n = static_cast( last - first ); + std::uniform_int_distribution dist( 0, + n - 1 ); - template - static sample resample(URng& rng, unsigned int resamples, - std::vector::const_iterator first, - std::vector::const_iterator last, - Estimator& estimator) { - auto n = static_cast(last - first); - std::uniform_int_distribution dist(0, n - 1); + sample out; + out.reserve( resamples ); + // We allocate the vector outside the loop to avoid realloc + // per resample + std::vector 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( + 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 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( 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 mean, + Estimate 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( -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::const_iterator first, - std::vector::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::const_iterator first, + std::vector::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 mean, Estimate 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(-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::iterator first, diff --git a/src/catch2/benchmark/detail/catch_stats.hpp b/src/catch2/benchmark/detail/catch_stats.hpp index 62ca80f2..c1ce5664 100644 --- a/src/catch2/benchmark/detail/catch_stats.hpp +++ b/src/catch2/benchmark/detail/catch_stats.hpp @@ -108,8 +108,6 @@ namespace Catch { return { point, resample[lo], resample[hi], confidence_level }; } - double outlier_variance(Estimate mean, Estimate stddev, int n); - struct bootstrap_analysis { Estimate mean; Estimate standard_deviation;