585da50d7d
This PR adds the code of Boost.Math as of version 1.89 into the third-party directory, as discussed in a recent RFC [1]. The goal is for this code to be used as a back-end for the C++17 Math Special Functions. As explained in third-paty/README.md, this code is cleared for usage inside libc++ for the Math Special functions, however the LLVM Foundation should be consulted before using this code anywhere else in the LLVM project, due to the fact that it is under the Boost Software License (as opposed to the usual LLVM license). See the RFC [1] for more details. [1]: https://discourse.llvm.org/t/rfc-libc-taking-a-dependency-on-boost-math-for-the-c-17-math-special-functions
134 lines
5.0 KiB
C++
134 lines
5.0 KiB
C++
/*
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* Copyright Nick Thompson, 2019
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* 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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*/
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#ifndef BOOST_MATH_STATISTICS_LINEAR_REGRESSION_HPP
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#define BOOST_MATH_STATISTICS_LINEAR_REGRESSION_HPP
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#include <cmath>
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#include <algorithm>
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#include <utility>
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#include <tuple>
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#include <stdexcept>
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#include <type_traits>
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#include <boost/math/statistics/univariate_statistics.hpp>
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#include <boost/math/statistics/bivariate_statistics.hpp>
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namespace boost { namespace math { namespace statistics { namespace detail {
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template<class ReturnType, class RandomAccessContainer>
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ReturnType simple_ordinary_least_squares_impl(RandomAccessContainer const & x,
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RandomAccessContainer const & y)
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{
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using Real = typename std::tuple_element<0, ReturnType>::type;
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if (x.size() <= 1)
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{
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throw std::domain_error("At least 2 samples are required to perform a linear regression.");
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}
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if (x.size() != y.size())
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{
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throw std::domain_error("The same number of samples must be in the independent and dependent variable.");
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}
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std::tuple<Real, Real, Real> temp = boost::math::statistics::means_and_covariance(x, y);
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Real mu_x = std::get<0>(temp);
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Real mu_y = std::get<1>(temp);
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Real cov_xy = std::get<2>(temp);
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Real var_x = boost::math::statistics::variance(x);
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if (var_x <= 0) {
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throw std::domain_error("Independent variable has no variance; this breaks linear regression.");
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}
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Real c1 = cov_xy/var_x;
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Real c0 = mu_y - c1*mu_x;
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return std::make_pair(c0, c1);
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}
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template<class ReturnType, class RandomAccessContainer>
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ReturnType simple_ordinary_least_squares_with_R_squared_impl(RandomAccessContainer const & x,
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RandomAccessContainer const & y)
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{
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using Real = typename std::tuple_element<0, ReturnType>::type;
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if (x.size() <= 1)
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{
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throw std::domain_error("At least 2 samples are required to perform a linear regression.");
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}
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if (x.size() != y.size())
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{
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throw std::domain_error("The same number of samples must be in the independent and dependent variable.");
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}
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std::tuple<Real, Real, Real> temp = boost::math::statistics::means_and_covariance(x, y);
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Real mu_x = std::get<0>(temp);
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Real mu_y = std::get<1>(temp);
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Real cov_xy = std::get<2>(temp);
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Real var_x = boost::math::statistics::variance(x);
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if (var_x <= 0) {
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throw std::domain_error("Independent variable has no variance; this breaks linear regression.");
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}
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Real c1 = cov_xy/var_x;
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Real c0 = mu_y - c1*mu_x;
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Real squared_residuals = 0;
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Real squared_mean_deviation = 0;
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for(decltype(y.size()) i = 0; i < y.size(); ++i) {
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squared_mean_deviation += (y[i] - mu_y)*(y[i]-mu_y);
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Real ei = (c0 + c1*x[i]) - y[i];
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squared_residuals += ei*ei;
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}
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Real Rsquared;
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if (squared_mean_deviation == 0) {
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// Then y = constant, so the linear regression is perfect.
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Rsquared = 1;
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} else {
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Rsquared = 1 - squared_residuals/squared_mean_deviation;
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}
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return std::make_tuple(c0, c1, Rsquared);
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}
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} // namespace detail
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template<typename RandomAccessContainer, typename Real = typename RandomAccessContainer::value_type,
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typename std::enable_if<std::is_integral<Real>::value, bool>::type = true>
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inline auto simple_ordinary_least_squares(RandomAccessContainer const & x, RandomAccessContainer const & y) -> std::pair<double, double>
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{
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return detail::simple_ordinary_least_squares_impl<std::pair<double, double>>(x, y);
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}
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template<typename RandomAccessContainer, typename Real = typename RandomAccessContainer::value_type,
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typename std::enable_if<!std::is_integral<Real>::value, bool>::type = true>
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inline auto simple_ordinary_least_squares(RandomAccessContainer const & x, RandomAccessContainer const & y) -> std::pair<Real, Real>
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{
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return detail::simple_ordinary_least_squares_impl<std::pair<Real, Real>>(x, y);
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}
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template<typename RandomAccessContainer, typename Real = typename RandomAccessContainer::value_type,
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typename std::enable_if<std::is_integral<Real>::value, bool>::type = true>
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inline auto simple_ordinary_least_squares_with_R_squared(RandomAccessContainer const & x, RandomAccessContainer const & y) -> std::tuple<double, double, double>
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{
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return detail::simple_ordinary_least_squares_with_R_squared_impl<std::tuple<double, double, double>>(x, y);
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}
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template<typename RandomAccessContainer, typename Real = typename RandomAccessContainer::value_type,
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typename std::enable_if<!std::is_integral<Real>::value, bool>::type = true>
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inline auto simple_ordinary_least_squares_with_R_squared(RandomAccessContainer const & x, RandomAccessContainer const & y) -> std::tuple<Real, Real, Real>
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{
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return detail::simple_ordinary_least_squares_with_R_squared_impl<std::tuple<Real, Real, Real>>(x, y);
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}
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}}} // namespace boost::math::statistics
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#endif
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