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Bivariate Stats Policies (#503)

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* 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
This commit is contained in:
Matt Borland
2021-01-30 19:09:12 +03:00
committed by GitHub
parent 8618b4a37b
commit 91ae2bfc77
5 changed files with 913 additions and 46 deletions
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79
reporting/performance/bivariate_statistics_performance.cpp Normal file
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// (C) Copyright Matt Borland 2021.
// Use, modification and distribution are subject to the
// Boost Software License, Version 1.0. (See accompanying file
// LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
#include <vector>
#include <boost/math/tools/random_vector.hpp>
#include <boost/math/statistics/bivariate_statistics.hpp>
#include <benchmark/benchmark.h>
using boost::math::generate_random_vector;
template<typename T>
void seq_covariance(benchmark::State& state)
{
constexpr std::size_t seed {};
const std::size_t size = state.range(0);
std::vector<T> u = generate_random_vector<T>(size, seed);
std::vector<T> v = generate_random_vector<T>(size, seed);
for(auto _ : state)
{
benchmark::DoNotOptimize(boost::math::statistics::covariance(std::execution::seq, u, v));
}
state.SetComplexityN(state.range(0));
}
template<typename T>
void par_covariance(benchmark::State& state)
{
constexpr std::size_t seed {};
const std::size_t size = state.range(0);
std::vector<T> u = generate_random_vector<T>(size, seed);
std::vector<T> v = generate_random_vector<T>(size, seed);
for(auto _ : state)
{
benchmark::DoNotOptimize(boost::math::statistics::covariance(std::execution::par, u, v));
}
state.SetComplexityN(state.range(0));
}
template<typename T>
void seq_correlation(benchmark::State& state)
{
constexpr std::size_t seed {};
const std::size_t size = state.range(0);
std::vector<T> u = generate_random_vector<T>(size, seed);
std::vector<T> v = generate_random_vector<T>(size, seed);
for(auto _ : state)
{
benchmark::DoNotOptimize(boost::math::statistics::correlation_coefficient(std::execution::seq, u, v));
}
state.SetComplexityN(state.range(0));
}
template<typename T>
void par_correlation(benchmark::State& state)
{
constexpr std::size_t seed {};
const std::size_t size = state.range(0);
std::vector<T> u = generate_random_vector<T>(size, seed);
std::vector<T> v = generate_random_vector<T>(size, seed);
for(auto _ : state)
{
benchmark::DoNotOptimize(boost::math::statistics::correlation_coefficient(std::execution::par, u, v));
}
state.SetComplexityN(state.range(0));
}
BENCHMARK_TEMPLATE(seq_covariance, double)->RangeMultiplier(2)->Range(1 << 6, 1 << 20)->Complexity(benchmark::oN)->UseRealTime();
BENCHMARK_TEMPLATE(par_covariance, double)->RangeMultiplier(2)->Range(1 << 6, 1 << 20)->Complexity(benchmark::oN)->UseRealTime();
BENCHMARK_TEMPLATE(seq_correlation, double)->RangeMultiplier(2)->Range(1 << 6, 1 << 20)->Complexity(benchmark::oN)->UseRealTime();
BENCHMARK_TEMPLATE(par_correlation, double)->RangeMultiplier(2)->Range(1 << 6, 1 << 20)->Complexity(benchmark::oN)->UseRealTime();
BENCHMARK_MAIN();