Internal linkage for outlier_variance

2 files changed, 166 insertions, 152 deletions

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 <future>
 #endif
 
-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);
-
-         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;
-     }
-
-
-    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;
-    }
-
-    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 Catch {
+    namespace Benchmark {
+        namespace Detail {
+            namespace {
+
+                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;
+                }
+
+                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( 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,
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<double> mean, Estimate<double> stddev, int n);
-
             struct bootstrap_analysis {
                 Estimate<double> mean;
                 Estimate<double> standard_deviation;