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849f2848bd
Previously, each warning suppression was self-contained, with its own pair of `SUPPRESS_X_WARNING` and `UNSUPPRESS_X_WARNING` macros. This had the obvious advantage of being self-containing, but it also meant that if we needed to suppress more than one warning in a single place, then we would manipulate the compiler's warning state multiple times, even though logically we would only need one layer. The new way of suppressing warnings in macros is to push compiler's warning state with `CATCH_INTERNAL_START_WARNINGS_SUPPRESSION` macro, then disable whatever macros we need with the `CATCH_INTERNAL_SUPPRESS_X_WARNINGS` macro, and then return to the previous state using `CATCH_INTERNAL_STOP_WARNINGS_SUPPRESSION`.
225 lines
8.7 KiB
C++
225 lines
8.7 KiB
C++
/*
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* Created by Martin on 15/06/2019.
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* Adapted from donated nonius code.
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*
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* Distributed under the Boost Software License, Version 1.0. (See accompanying
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* file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
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*/
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// Statistical analysis tools
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#if defined(CATCH_CONFIG_ENABLE_BENCHMARKING)
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#include "catch_stats.hpp"
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#include "../../catch_compiler_capabilities.h"
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#include <cassert>
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#include <random>
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#if defined(CATCH_CONFIG_USE_ASYNC)
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#include <future>
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#endif
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namespace {
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double erf_inv(double x) {
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// Code accompanying the article "Approximating the erfinv function" in GPU Computing Gems, Volume 2
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double w, p;
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w = -log((1.0 - x) * (1.0 + x));
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if (w < 6.250000) {
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w = w - 3.125000;
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p = -3.6444120640178196996e-21;
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p = -1.685059138182016589e-19 + p * w;
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p = 1.2858480715256400167e-18 + p * w;
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p = 1.115787767802518096e-17 + p * w;
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p = -1.333171662854620906e-16 + p * w;
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p = 2.0972767875968561637e-17 + p * w;
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p = 6.6376381343583238325e-15 + p * w;
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p = -4.0545662729752068639e-14 + p * w;
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p = -8.1519341976054721522e-14 + p * w;
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p = 2.6335093153082322977e-12 + p * w;
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p = -1.2975133253453532498e-11 + p * w;
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p = -5.4154120542946279317e-11 + p * w;
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p = 1.051212273321532285e-09 + p * w;
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p = -4.1126339803469836976e-09 + p * w;
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p = -2.9070369957882005086e-08 + p * w;
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p = 4.2347877827932403518e-07 + p * w;
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p = -1.3654692000834678645e-06 + p * w;
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p = -1.3882523362786468719e-05 + p * w;
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p = 0.0001867342080340571352 + p * w;
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p = -0.00074070253416626697512 + p * w;
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p = -0.0060336708714301490533 + p * w;
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p = 0.24015818242558961693 + p * w;
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p = 1.6536545626831027356 + p * w;
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} else if (w < 16.000000) {
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w = sqrt(w) - 3.250000;
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p = 2.2137376921775787049e-09;
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p = 9.0756561938885390979e-08 + p * w;
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p = -2.7517406297064545428e-07 + p * w;
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p = 1.8239629214389227755e-08 + p * w;
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p = 1.5027403968909827627e-06 + p * w;
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p = -4.013867526981545969e-06 + p * w;
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p = 2.9234449089955446044e-06 + p * w;
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p = 1.2475304481671778723e-05 + p * w;
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p = -4.7318229009055733981e-05 + p * w;
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p = 6.8284851459573175448e-05 + p * w;
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p = 2.4031110387097893999e-05 + p * w;
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p = -0.0003550375203628474796 + p * w;
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p = 0.00095328937973738049703 + p * w;
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p = -0.0016882755560235047313 + p * w;
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p = 0.0024914420961078508066 + p * w;
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p = -0.0037512085075692412107 + p * w;
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p = 0.005370914553590063617 + p * w;
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p = 1.0052589676941592334 + p * w;
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p = 3.0838856104922207635 + p * w;
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} else {
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w = sqrt(w) - 5.000000;
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p = -2.7109920616438573243e-11;
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p = -2.5556418169965252055e-10 + p * w;
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p = 1.5076572693500548083e-09 + p * w;
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p = -3.7894654401267369937e-09 + p * w;
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p = 7.6157012080783393804e-09 + p * w;
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p = -1.4960026627149240478e-08 + p * w;
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p = 2.9147953450901080826e-08 + p * w;
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p = -6.7711997758452339498e-08 + p * w;
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p = 2.2900482228026654717e-07 + p * w;
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p = -9.9298272942317002539e-07 + p * w;
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p = 4.5260625972231537039e-06 + p * w;
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p = -1.9681778105531670567e-05 + p * w;
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p = 7.5995277030017761139e-05 + p * w;
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p = -0.00021503011930044477347 + p * w;
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p = -0.00013871931833623122026 + p * w;
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p = 1.0103004648645343977 + p * w;
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p = 4.8499064014085844221 + p * w;
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}
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return p * x;
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}
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double standard_deviation(std::vector<double>::iterator first, std::vector<double>::iterator last) {
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auto m = Catch::Benchmark::Detail::mean(first, last);
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double variance = std::accumulate(first, last, 0., [m](double a, double b) {
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double diff = b - m;
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return a + diff * diff;
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}) / (last - first);
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return std::sqrt(variance);
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}
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}
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namespace Catch {
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namespace Benchmark {
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namespace Detail {
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double weighted_average_quantile(int k, int q, std::vector<double>::iterator first, std::vector<double>::iterator last) {
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auto count = last - first;
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double idx = (count - 1) * k / static_cast<double>(q);
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int j = static_cast<int>(idx);
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double g = idx - j;
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std::nth_element(first, first + j, last);
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auto xj = first[j];
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if (g == 0) return xj;
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auto xj1 = *std::min_element(first + (j + 1), last);
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return xj + g * (xj1 - xj);
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}
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double erfc_inv(double x) {
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return erf_inv(1.0 - x);
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}
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double normal_quantile(double p) {
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static const double ROOT_TWO = std::sqrt(2.0);
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double result = 0.0;
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assert(p >= 0 && p <= 1);
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if (p < 0 || p > 1) {
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return result;
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}
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result = -erfc_inv(2.0 * p);
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// result *= normal distribution standard deviation (1.0) * sqrt(2)
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result *= /*sd * */ ROOT_TWO;
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// result += normal disttribution mean (0)
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return result;
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}
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double outlier_variance(Estimate<double> mean, Estimate<double> stddev, int n) {
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double sb = stddev.point;
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double mn = mean.point / n;
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double mg_min = mn / 2.;
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double sg = std::min(mg_min / 4., sb / std::sqrt(n));
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double sg2 = sg * sg;
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double sb2 = sb * sb;
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auto c_max = [n, mn, sb2, sg2](double x) -> double {
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double k = mn - x;
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double d = k * k;
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double nd = n * d;
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double k0 = -n * nd;
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double k1 = sb2 - n * sg2 + nd;
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double det = k1 * k1 - 4 * sg2 * k0;
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return (int)(-2. * k0 / (k1 + std::sqrt(det)));
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};
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auto var_out = [n, sb2, sg2](double c) {
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double nc = n - c;
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return (nc / n) * (sb2 - nc * sg2);
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};
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return std::min(var_out(1), var_out(std::min(c_max(0.), c_max(mg_min)))) / sb2;
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}
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bootstrap_analysis analyse_samples(double confidence_level, int n_resamples, std::vector<double>::iterator first, std::vector<double>::iterator last) {
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CATCH_INTERNAL_START_WARNINGS_SUPPRESSION
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CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS
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static std::random_device entropy;
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CATCH_INTERNAL_STOP_WARNINGS_SUPPRESSION
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auto n = static_cast<int>(last - first); // seriously, one can't use integral types without hell in C++
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auto mean = &Detail::mean<std::vector<double>::iterator>;
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auto stddev = &standard_deviation;
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#if defined(CATCH_CONFIG_USE_ASYNC)
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auto Estimate = [=](double(*f)(std::vector<double>::iterator, std::vector<double>::iterator)) {
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auto seed = entropy();
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return std::async(std::launch::async, [=] {
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std::mt19937 rng(seed);
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auto resampled = resample(rng, n_resamples, first, last, f);
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return bootstrap(confidence_level, first, last, resampled, f);
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});
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};
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auto mean_future = Estimate(mean);
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auto stddev_future = Estimate(stddev);
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auto mean_estimate = mean_future.get();
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auto stddev_estimate = stddev_future.get();
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#else
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auto Estimate = [=](double(*f)(std::vector<double>::iterator, std::vector<double>::iterator)) {
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auto seed = entropy();
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std::mt19937 rng(seed);
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auto resampled = resample(rng, n_resamples, first, last, f);
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return bootstrap(confidence_level, first, last, resampled, f);
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};
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auto mean_estimate = Estimate(mean);
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auto stddev_estimate = Estimate(stddev);
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#endif // CATCH_USE_ASYNC
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double outlier_variance = Detail::outlier_variance(mean_estimate, stddev_estimate, n);
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return { mean_estimate, stddev_estimate, outlier_variance };
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}
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} // namespace Detail
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} // namespace Benchmark
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} // namespace Catch
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#endif // CATCH_CONFIG_ENABLE_BENCHMARKING
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