Clean up iterator usage in benchmarks

Specifically we turned `mean`, `classify_outliers`, `jackknife`,
into concrete functions that take only `const_iterator` from vecs,
instead of generic iterators over anything.

I also changed `resample` to take `const_iterator` instead of
plain `iterator`, and similar for `standard_deviation`, and
`analyse_samples`.

src/catch2/benchmark/detail/catch_stats.cpp

@@ -26,7 +26,10 @@ namespace {
 using Catch::Benchmark::Detail::sample;
 
      template <typename URng, typename Estimator>
-     sample resample(URng& rng, unsigned int resamples, std::vector<double>::iterator first, std::vector<double>::iterator last, Estimator& 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);
 
@@ -118,7 +121,8 @@ using Catch::Benchmark::Detail::sample;
         return p * x;
     }
 
-    double standard_deviation(std::vector<double>::iterator first, std::vector<double>::iterator last) {
+    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,
@@ -161,6 +165,47 @@ namespace Catch {
                 return xj + g * (xj1 - xj);
             }
 
+            OutlierClassification
+            classify_outliers( std::vector<double>::const_iterator first,
+                               std::vector<double>::const_iterator last ) {
+                std::vector<double> copy( first, last );
+
+                auto q1 = weighted_average_quantile( 1, 4, copy.begin(), copy.end() );
+                auto q3 = weighted_average_quantile( 3, 4, copy.begin(), copy.end() );
+                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( std::vector<double>::const_iterator first,
+                         std::vector<double>::const_iterator last ) {
+                auto count = last - first;
+                double sum = 0.;
+                while (first != last) {
+                    sum += *first;
+                    ++first;
+                }
+                return sum / static_cast<double>(count);
+            }
+
 
             double erfc_inv(double x) {
                 return erf_inv(1.0 - x);
@@ -210,7 +255,10 @@ namespace Catch {
             }
 
 
-            bootstrap_analysis analyse_samples(double confidence_level, unsigned int n_resamples, std::vector<double>::iterator first, std::vector<double>::iterator last) {
+            bootstrap_analysis analyse_samples(double confidence_level,
+                                               unsigned int n_resamples,
+                                               std::vector<double>::iterator first,
+                                               std::vector<double>::iterator last) {
                 CATCH_INTERNAL_START_WARNINGS_SUPPRESSION
                 CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS
                 static std::random_device entropy;
@@ -218,11 +266,12 @@ namespace Catch {
 
                 auto n = static_cast<int>(last - first); // seriously, one can't use integral types without hell in C++
 
-                auto mean = &Detail::mean<std::vector<double>::iterator>;
+                auto mean = &Detail::mean;
                 auto stddev = &standard_deviation;
 
 #if defined(CATCH_CONFIG_USE_ASYNC)
-                auto Estimate = [=](double(*f)(std::vector<double>::iterator, std::vector<double>::iterator)) {
+                auto Estimate = [=](double(*f)(std::vector<double>::const_iterator,
+                                               std::vector<double>::const_iterator)) {
                     auto seed = entropy();
                     return std::async(std::launch::async, [=] {
                         std::mt19937 rng(seed);
@@ -237,7 +286,8 @@ namespace Catch {
                 auto mean_estimate = mean_future.get();
                 auto stddev_estimate = stddev_future.get();
 #else
-                auto Estimate = [=](double(*f)(std::vector<double>::iterator, std::vector<double>::iterator)) {
+                auto Estimate = [=](double(*f)(std::vector<double>::const_iterator,
+                                               std::vector<double>::const_iterator)) {
                     auto seed = entropy();
                     std::mt19937 rng(seed);
                     auto resampled = resample(rng, n_resamples, first, last, f);

src/catch2/benchmark/detail/catch_stats.hpp

@@ -30,39 +30,17 @@ namespace Catch {
 
             double weighted_average_quantile(int k, int q, std::vector<double>::iterator first, std::vector<double>::iterator last);
 
-            template <typename Iterator>
+            OutlierClassification
-            OutlierClassification classify_outliers(Iterator first, Iterator last) {
+            classify_outliers( std::vector<double>::const_iterator first,
-                std::vector<double> copy(first, last);
+                               std::vector<double>::const_iterator last );
 
-                auto q1 = weighted_average_quantile(1, 4, copy.begin(), copy.end());
+            double mean( std::vector<double>::const_iterator first,
-                auto q3 = weighted_average_quantile(3, 4, copy.begin(), copy.end());
+                         std::vector<double>::const_iterator last );
-                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;
+            template <typename Estimator>
-                for (; first != last; ++first) {
+            sample jackknife(Estimator&& estimator,
-                    auto&& t = *first;
+                             std::vector<double>::iterator first,
-                    if (t < los) ++o.low_severe;
+                             std::vector<double>::iterator last) {
-                    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;
-            }
-
-            template <typename Iterator>
-            double mean(Iterator first, Iterator last) {
-                auto count = last - first;
-                double sum = std::accumulate(first, last, 0.);
-                return sum / static_cast<double>(count);
-            }
-
-            template <typename Estimator, typename Iterator>
-            sample jackknife(Estimator&& estimator, Iterator first, Iterator last) {
                 auto n = static_cast<size_t>(last - first);
                 auto second = first;
                 ++second;
@@ -85,8 +63,12 @@ namespace Catch {
 
             double normal_quantile(double p);
 
-            template <typename Iterator, typename Estimator>
+            template <typename Estimator>
-            Estimate<double> bootstrap(double confidence_level, Iterator first, Iterator last, sample const& resample, Estimator&& estimator) {
+            Estimate<double> bootstrap( double confidence_level,
+                                        std::vector<double>::iterator first,
+                                        std::vector<double>::iterator last,
+                                        sample const& resample,
+                                        Estimator&& estimator ) {
                 auto n_samples = last - first;
 
                 double point = estimator(first, last);
@@ -136,7 +118,10 @@ namespace Catch {
                 double outlier_variance;
             };
 
-            bootstrap_analysis analyse_samples(double confidence_level, unsigned int n_resamples, std::vector<double>::iterator first, std::vector<double>::iterator last);
+            bootstrap_analysis analyse_samples(double confidence_level,
+                                               unsigned int n_resamples,
+                                               std::vector<double>::iterator first,
+                                               std::vector<double>::iterator last);
         } // namespace Detail
     } // namespace Benchmark
 } // namespace Catch