mirror of
https://github.com/boostorg/math.git
synced 2026-01-19 04:22:09 +00:00
91ae2bfc77
* Add parallel impl and change seq impl [CI SKIP] * Validate seq impl [CI SKIP] * Remove old impl * Add user interfaces [CI SKIP] * Floating point covariance validated [CI SKIP] * Integer covariance validated [CI SKIP] * Change correlation_coeff impl interface [CI SKIP] * Cleanup [CI SKIP] * correlation passes all parameters for par impl [CI SKIP] * Finish framework [CI SKIP] * Add correlation coefficient test cases * Add benchmark and make small changes [CI SKIP] * Update docs
80 lines
2.7 KiB
C++
80 lines
2.7 KiB
C++
// (C) Copyright Matt Borland 2021.
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// Use, modification and distribution are subject to the
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// Boost Software License, Version 1.0. (See accompanying file
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// LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
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#include <vector>
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#include <boost/math/tools/random_vector.hpp>
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#include <boost/math/statistics/bivariate_statistics.hpp>
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#include <benchmark/benchmark.h>
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using boost::math::generate_random_vector;
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template<typename T>
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void seq_covariance(benchmark::State& state)
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{
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constexpr std::size_t seed {};
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const std::size_t size = state.range(0);
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std::vector<T> u = generate_random_vector<T>(size, seed);
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std::vector<T> v = generate_random_vector<T>(size, seed);
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for(auto _ : state)
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{
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benchmark::DoNotOptimize(boost::math::statistics::covariance(std::execution::seq, u, v));
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}
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state.SetComplexityN(state.range(0));
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}
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template<typename T>
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void par_covariance(benchmark::State& state)
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{
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constexpr std::size_t seed {};
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const std::size_t size = state.range(0);
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std::vector<T> u = generate_random_vector<T>(size, seed);
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std::vector<T> v = generate_random_vector<T>(size, seed);
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for(auto _ : state)
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{
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benchmark::DoNotOptimize(boost::math::statistics::covariance(std::execution::par, u, v));
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}
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state.SetComplexityN(state.range(0));
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}
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template<typename T>
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void seq_correlation(benchmark::State& state)
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{
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constexpr std::size_t seed {};
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const std::size_t size = state.range(0);
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std::vector<T> u = generate_random_vector<T>(size, seed);
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std::vector<T> v = generate_random_vector<T>(size, seed);
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for(auto _ : state)
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{
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benchmark::DoNotOptimize(boost::math::statistics::correlation_coefficient(std::execution::seq, u, v));
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}
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state.SetComplexityN(state.range(0));
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}
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template<typename T>
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void par_correlation(benchmark::State& state)
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{
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constexpr std::size_t seed {};
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const std::size_t size = state.range(0);
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std::vector<T> u = generate_random_vector<T>(size, seed);
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std::vector<T> v = generate_random_vector<T>(size, seed);
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for(auto _ : state)
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{
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benchmark::DoNotOptimize(boost::math::statistics::correlation_coefficient(std::execution::par, u, v));
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}
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state.SetComplexityN(state.range(0));
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}
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BENCHMARK_TEMPLATE(seq_covariance, double)->RangeMultiplier(2)->Range(1 << 6, 1 << 20)->Complexity(benchmark::oN)->UseRealTime();
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BENCHMARK_TEMPLATE(par_covariance, double)->RangeMultiplier(2)->Range(1 << 6, 1 << 20)->Complexity(benchmark::oN)->UseRealTime();
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BENCHMARK_TEMPLATE(seq_correlation, double)->RangeMultiplier(2)->Range(1 << 6, 1 << 20)->Complexity(benchmark::oN)->UseRealTime();
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BENCHMARK_TEMPLATE(par_correlation, double)->RangeMultiplier(2)->Range(1 << 6, 1 << 20)->Complexity(benchmark::oN)->UseRealTime();
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BENCHMARK_MAIN();
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