import catch2 3.8.1vendor/catch2/3.8.1vendor/catch2

1 files changed, 393 insertions, 0 deletions

diff --git a/src/catch2/benchmark/detail/catch_stats.cpp b/src/catch2/benchmark/detail/catch_stats.cpp
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+++ b/src/catch2/benchmark/detail/catch_stats.cpp
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+
+//              Copyright Catch2 Authors
+// Distributed under the Boost Software License, Version 1.0.
+//   (See accompanying file LICENSE.txt or copy at
+//        https://www.boost.org/LICENSE_1_0.txt)
+
+// SPDX-License-Identifier: BSL-1.0
+// Adapted from donated nonius code.
+
+#include <catch2/benchmark/detail/catch_stats.hpp>
+
+#include <catch2/internal/catch_compiler_capabilities.hpp>
+#include <catch2/internal/catch_floating_point_helpers.hpp>
+#include <catch2/internal/catch_random_number_generator.hpp>
+#include <catch2/internal/catch_uniform_integer_distribution.hpp>
+
+#include <algorithm>
+#include <cassert>
+#include <cmath>
+#include <cstddef>
+#include <numeric>
+#include <random>
+
+
+#if defined(CATCH_CONFIG_USE_ASYNC)
+#include <future>
+#endif
+
+namespace Catch {
+    namespace Benchmark {
+        namespace Detail {
+            namespace {
+
+                template <typename URng, typename Estimator>
+                static sample
+                resample( URng& rng,
+                          unsigned int resamples,
+                          double const* first,
+                          double const* last,
+                          Estimator& estimator ) {
+                    auto n = static_cast<size_t>( last - first );
+                    Catch::uniform_integer_distribution<size_t> dist( 0, n - 1 );
+
+                    sample out;
+                    out.reserve( resamples );
+                    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[dist( rng )] );
+                        }
+                        const auto estimate =
+                            estimator( resampled.data(), resampled.data() + resampled.size() );
+                        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 ) ) );
+                    };
+
+                    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;
+                }
+
+                static double erf_inv( double x ) {
+                    // Code accompanying the article "Approximating the erfinv
+                    // function" in GPU Computing Gems, Volume 2
+                    double w, p;
+
+                    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( double const* first, double const* 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;
+                                         } ) /
+                        static_cast<double>( last - first );
+                    return std::sqrt( variance );
+                }
+
+                static sample jackknife( double ( *estimator )( double const*,
+                                                                double const* ),
+                                         double* first,
+                                         double* last ) {
+                    const auto second = first + 1;
+                    sample results;
+                    results.reserve( static_cast<size_t>( last - first ) );
+
+                    for ( auto it = first; it != last; ++it ) {
+                        std::iter_swap( it, first );
+                        results.push_back( estimator( second, last ) );
+                    }
+
+                    return results;
+                }
+
+
+            } // namespace
+        }     // namespace Detail
+    }         // namespace Benchmark
+} // namespace Catch
+
+namespace Catch {
+    namespace Benchmark {
+        namespace Detail {
+
+            double weighted_average_quantile( int k,
+                                              int q,
+                                              double* first,
+                                              double* last ) {
+                auto count = last - first;
+                double idx = static_cast<double>((count - 1) * k) / static_cast<double>(q);
+                int j = static_cast<int>(idx);
+                double g = idx - j;
+                std::nth_element(first, first + j, last);
+                auto xj = first[j];
+                if ( Catch::Detail::directCompare( g, 0 ) ) {
+                    return xj;
+                }
+
+                auto xj1 = *std::min_element(first + (j + 1), last);
+                return xj + g * (xj1 - xj);
+            }
+
+            OutlierClassification
+            classify_outliers( double const* first, double const* last ) {
+                std::vector<double> copy( first, last );
+
+                auto q1 = weighted_average_quantile( 1, 4, copy.data(), copy.data() + copy.size() );
+                auto q3 = weighted_average_quantile( 3, 4, copy.data(), copy.data() + copy.size() );
+                auto iqr = q3 - q1;
+                auto los = q1 - ( iqr * 3. );
+                auto lom = q1 - ( iqr * 1.5 );
+                auto him = q3 + ( iqr * 1.5 );
+                auto his = q3 + ( iqr * 3. );
+
+                OutlierClassification o;
+                for ( ; first != last; ++first ) {
+                    const double t = *first;
+                    if ( t < los ) {
+                        ++o.low_severe;
+                    } else if ( t < lom ) {
+                        ++o.low_mild;
+                    } else if ( t > his ) {
+                        ++o.high_severe;
+                    } else if ( t > him ) {
+                        ++o.high_mild;
+                    }
+                    ++o.samples_seen;
+                }
+                return o;
+            }
+
+            double mean( double const* first, double const* last ) {
+                auto count = last - first;
+                double sum = 0.;
+                while (first != last) {
+                    sum += *first;
+                    ++first;
+                }
+                return sum / static_cast<double>(count);
+            }
+
+            double normal_cdf( double x ) {
+                return std::erfc( -x / std::sqrt( 2.0 ) ) / 2.0;
+            }
+
+            double erfc_inv(double x) {
+                return erf_inv(1.0 - x);
+            }
+
+            double normal_quantile(double p) {
+                static const double ROOT_TWO = std::sqrt(2.0);
+
+                double result = 0.0;
+                assert(p >= 0 && p <= 1);
+                if (p < 0 || p > 1) {
+                    return result;
+                }
+
+                result = -erfc_inv(2.0 * p);
+                // result *= normal distribution standard deviation (1.0) * sqrt(2)
+                result *= /*sd * */ ROOT_TWO;
+                // result += normal disttribution mean (0)
+                return result;
+            }
+
+            Estimate<double>
+            bootstrap( double confidence_level,
+                       double* first,
+                       double* last,
+                       sample const& resample,
+                       double ( *estimator )( double const*, double const* ) ) {
+                auto n_samples = last - first;
+
+                double point = estimator( first, last );
+                // Degenerate case with a single sample
+                if ( n_samples == 1 )
+                    return { point, point, point, confidence_level };
+
+                sample jack = jackknife( estimator, first, last );
+                double jack_mean =
+                    mean( jack.data(), jack.data() + jack.size() );
+                double sum_squares = 0, sum_cubes = 0;
+                for ( double x : jack ) {
+                    auto difference = jack_mean - x;
+                    auto square = difference * difference;
+                    auto cube = square * difference;
+                    sum_squares += square;
+                    sum_cubes += cube;
+                }
+
+                double accel = sum_cubes / ( 6 * std::pow( sum_squares, 1.5 ) );
+                long n = static_cast<long>( resample.size() );
+                double prob_n = static_cast<double>(
+                    std::count_if( resample.begin(),
+                                   resample.end(),
+                                   [point]( double x ) { return x < point; } )) /
+                    static_cast<double>( n );
+                // degenerate case with uniform samples
+                if ( Catch::Detail::directCompare( prob_n, 0. ) ) {
+                    return { point, point, point, confidence_level };
+                }
+
+                double bias = normal_quantile( prob_n );
+                double z1 = normal_quantile( ( 1. - confidence_level ) / 2. );
+
+                auto cumn = [n]( double x ) -> long {
+                    return std::lround( normal_cdf( x ) *
+                                        static_cast<double>( n ) );
+                };
+                auto a = [bias, accel]( double b ) {
+                    return bias + b / ( 1. - accel * b );
+                };
+                double b1 = bias + z1;
+                double b2 = bias - z1;
+                double a1 = a( b1 );
+                double a2 = a( b2 );
+                auto lo = static_cast<size_t>( (std::max)( cumn( a1 ), 0l ) );
+                auto hi =
+                    static_cast<size_t>( (std::min)( cumn( a2 ), n - 1 ) );
+
+                return { point, resample[lo], resample[hi], confidence_level };
+            }
+
+            bootstrap_analysis analyse_samples(double confidence_level,
+                                               unsigned int n_resamples,
+                                               double* first,
+                                               double* last) {
+                auto mean = &Detail::mean;
+                auto stddev = &standard_deviation;
+
+#if defined(CATCH_CONFIG_USE_ASYNC)
+                auto Estimate = [=](double(*f)(double const*, double const*)) {
+                    std::random_device rd;
+                    auto seed = rd();
+                    return std::async(std::launch::async, [=] {
+                        SimplePcg32 rng( seed );
+                        auto resampled = resample(rng, n_resamples, first, last, f);
+                        return bootstrap(confidence_level, first, last, resampled, f);
+                    });
+                };
+
+                auto mean_future = Estimate(mean);
+                auto stddev_future = Estimate(stddev);
+
+                auto mean_estimate = mean_future.get();
+                auto stddev_estimate = stddev_future.get();
+#else
+                auto Estimate = [=](double(*f)(double const* , double const*)) {
+                    std::random_device rd;
+                    auto seed = rd();
+                    SimplePcg32 rng( seed );
+                    auto resampled = resample(rng, n_resamples, first, last, f);
+                    return bootstrap(confidence_level, first, last, resampled, f);
+                };
+
+                auto mean_estimate = Estimate(mean);
+                auto stddev_estimate = Estimate(stddev);
+#endif // CATCH_USE_ASYNC
+
+                auto n = static_cast<int>(last - first); // seriously, one can't use integral types without hell in C++
+                double outlier_variance = Detail::outlier_variance(mean_estimate, stddev_estimate, n);
+
+                return { mean_estimate, stddev_estimate, outlier_variance };
+            }
+        } // namespace Detail
+    } // namespace Benchmark
+} // namespace Catch