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Diffstat (limited to 'src/third_party/boost-1.69.0/boost/math/special_functions/detail/t_distribution_inv.hpp')
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diff --git a/src/third_party/boost-1.69.0/boost/math/special_functions/detail/t_distribution_inv.hpp b/src/third_party/boost-1.69.0/boost/math/special_functions/detail/t_distribution_inv.hpp new file mode 100644 index 00000000000..ab5a8fbca69 --- /dev/null +++ b/src/third_party/boost-1.69.0/boost/math/special_functions/detail/t_distribution_inv.hpp @@ -0,0 +1,549 @@ +// Copyright John Maddock 2007. +// Copyright Paul A. Bristow 2007 +// 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) + +#ifndef BOOST_MATH_SF_DETAIL_INV_T_HPP +#define BOOST_MATH_SF_DETAIL_INV_T_HPP + +#ifdef _MSC_VER +#pragma once +#endif + +#include <boost/math/special_functions/cbrt.hpp> +#include <boost/math/special_functions/round.hpp> +#include <boost/math/special_functions/trunc.hpp> + +namespace boost{ namespace math{ namespace detail{ + +// +// The main method used is due to Hill: +// +// G. W. Hill, Algorithm 396, Student's t-Quantiles, +// Communications of the ACM, 13(10): 619-620, Oct., 1970. +// +template <class T, class Policy> +T inverse_students_t_hill(T ndf, T u, const Policy& pol) +{ + BOOST_MATH_STD_USING + BOOST_ASSERT(u <= 0.5); + + T a, b, c, d, q, x, y; + + if (ndf > 1e20f) + return -boost::math::erfc_inv(2 * u, pol) * constants::root_two<T>(); + + a = 1 / (ndf - 0.5f); + b = 48 / (a * a); + c = ((20700 * a / b - 98) * a - 16) * a + 96.36f; + d = ((94.5f / (b + c) - 3) / b + 1) * sqrt(a * constants::pi<T>() / 2) * ndf; + y = pow(d * 2 * u, 2 / ndf); + + if (y > (0.05f + a)) + { + // + // Asymptotic inverse expansion about normal: + // + x = -boost::math::erfc_inv(2 * u, pol) * constants::root_two<T>(); + y = x * x; + + if (ndf < 5) + c += 0.3f * (ndf - 4.5f) * (x + 0.6f); + c += (((0.05f * d * x - 5) * x - 7) * x - 2) * x + b; + y = (((((0.4f * y + 6.3f) * y + 36) * y + 94.5f) / c - y - 3) / b + 1) * x; + y = boost::math::expm1(a * y * y, pol); + } + else + { + y = static_cast<T>(((1 / (((ndf + 6) / (ndf * y) - 0.089f * d - 0.822f) + * (ndf + 2) * 3) + 0.5 / (ndf + 4)) * y - 1) + * (ndf + 1) / (ndf + 2) + 1 / y); + } + q = sqrt(ndf * y); + + return -q; +} +// +// Tail and body series are due to Shaw: +// +// www.mth.kcl.ac.uk/~shaww/web_page/papers/Tdistribution06.pdf +// +// Shaw, W.T., 2006, "Sampling Student's T distribution - use of +// the inverse cumulative distribution function." +// Journal of Computational Finance, Vol 9 Issue 4, pp 37-73, Summer 2006 +// +template <class T, class Policy> +T inverse_students_t_tail_series(T df, T v, const Policy& pol) +{ + BOOST_MATH_STD_USING + // Tail series expansion, see section 6 of Shaw's paper. + // w is calculated using Eq 60: + T w = boost::math::tgamma_delta_ratio(df / 2, constants::half<T>(), pol) + * sqrt(df * constants::pi<T>()) * v; + // define some variables: + T np2 = df + 2; + T np4 = df + 4; + T np6 = df + 6; + // + // Calculate the coefficients d(k), these depend only on the + // number of degrees of freedom df, so at least in theory + // we could tabulate these for fixed df, see p15 of Shaw: + // + T d[7] = { 1, }; + d[1] = -(df + 1) / (2 * np2); + np2 *= (df + 2); + d[2] = -df * (df + 1) * (df + 3) / (8 * np2 * np4); + np2 *= df + 2; + d[3] = -df * (df + 1) * (df + 5) * (((3 * df) + 7) * df -2) / (48 * np2 * np4 * np6); + np2 *= (df + 2); + np4 *= (df + 4); + d[4] = -df * (df + 1) * (df + 7) * + ( (((((15 * df) + 154) * df + 465) * df + 286) * df - 336) * df + 64 ) + / (384 * np2 * np4 * np6 * (df + 8)); + np2 *= (df + 2); + d[5] = -df * (df + 1) * (df + 3) * (df + 9) + * (((((((35 * df + 452) * df + 1573) * df + 600) * df - 2020) * df) + 928) * df -128) + / (1280 * np2 * np4 * np6 * (df + 8) * (df + 10)); + np2 *= (df + 2); + np4 *= (df + 4); + np6 *= (df + 6); + d[6] = -df * (df + 1) * (df + 11) + * ((((((((((((945 * df) + 31506) * df + 425858) * df + 2980236) * df + 11266745) * df + 20675018) * df + 7747124) * df - 22574632) * df - 8565600) * df + 18108416) * df - 7099392) * df + 884736) + / (46080 * np2 * np4 * np6 * (df + 8) * (df + 10) * (df +12)); + // + // Now bring everthing together to provide the result, + // this is Eq 62 of Shaw: + // + T rn = sqrt(df); + T div = pow(rn * w, 1 / df); + T power = div * div; + T result = tools::evaluate_polynomial<7, T, T>(d, power); + result *= rn; + result /= div; + return -result; +} + +template <class T, class Policy> +T inverse_students_t_body_series(T df, T u, const Policy& pol) +{ + BOOST_MATH_STD_USING + // + // Body series for small N: + // + // Start with Eq 56 of Shaw: + // + T v = boost::math::tgamma_delta_ratio(df / 2, constants::half<T>(), pol) + * sqrt(df * constants::pi<T>()) * (u - constants::half<T>()); + // + // Workspace for the polynomial coefficients: + // + T c[11] = { 0, 1, }; + // + // Figure out what the coefficients are, note these depend + // only on the degrees of freedom (Eq 57 of Shaw): + // + T in = 1 / df; + c[2] = static_cast<T>(0.16666666666666666667 + 0.16666666666666666667 * in); + c[3] = static_cast<T>((0.0083333333333333333333 * in + + 0.066666666666666666667) * in + + 0.058333333333333333333); + c[4] = static_cast<T>(((0.00019841269841269841270 * in + + 0.0017857142857142857143) * in + + 0.026785714285714285714) * in + + 0.025198412698412698413); + c[5] = static_cast<T>((((2.7557319223985890653e-6 * in + + 0.00037477954144620811287) * in + - 0.0011078042328042328042) * in + + 0.010559964726631393298) * in + + 0.012039792768959435626); + c[6] = static_cast<T>(((((2.5052108385441718775e-8 * in + - 0.000062705427288760622094) * in + + 0.00059458674042007375341) * in + - 0.0016095979637646304313) * in + + 0.0061039211560044893378) * in + + 0.0038370059724226390893); + c[7] = static_cast<T>((((((1.6059043836821614599e-10 * in + + 0.000015401265401265401265) * in + - 0.00016376804137220803887) * in + + 0.00069084207973096861986) * in + - 0.0012579159844784844785) * in + + 0.0010898206731540064873) * in + + 0.0032177478835464946576); + c[8] = static_cast<T>(((((((7.6471637318198164759e-13 * in + - 3.9851014346715404916e-6) * in + + 0.000049255746366361445727) * in + - 0.00024947258047043099953) * in + + 0.00064513046951456342991) * in + - 0.00076245135440323932387) * in + + 0.000033530976880017885309) * in + + 0.0017438262298340009980); + c[9] = static_cast<T>((((((((2.8114572543455207632e-15 * in + + 1.0914179173496789432e-6) * in + - 0.000015303004486655377567) * in + + 0.000090867107935219902229) * in + - 0.00029133414466938067350) * in + + 0.00051406605788341121363) * in + - 0.00036307660358786885787) * in + - 0.00031101086326318780412) * in + + 0.00096472747321388644237); + c[10] = static_cast<T>(((((((((8.2206352466243297170e-18 * in + - 3.1239569599829868045e-7) * in + + 4.8903045291975346210e-6) * in + - 0.000033202652391372058698) * in + + 0.00012645437628698076975) * in + - 0.00028690924218514613987) * in + + 0.00035764655430568632777) * in + - 0.00010230378073700412687) * in + - 0.00036942667800009661203) * in + + 0.00054229262813129686486); + // + // The result is then a polynomial in v (see Eq 56 of Shaw): + // + return tools::evaluate_odd_polynomial<11, T, T>(c, v); +} + +template <class T, class Policy> +T inverse_students_t(T df, T u, T v, const Policy& pol, bool* pexact = 0) +{ + // + // df = number of degrees of freedom. + // u = probablity. + // v = 1 - u. + // l = lanczos type to use. + // + BOOST_MATH_STD_USING + bool invert = false; + T result = 0; + if(pexact) + *pexact = false; + if(u > v) + { + // function is symmetric, invert it: + std::swap(u, v); + invert = true; + } + if((floor(df) == df) && (df < 20)) + { + // + // we have integer degrees of freedom, try for the special + // cases first: + // + T tolerance = ldexp(1.0f, (2 * policies::digits<T, Policy>()) / 3); + + switch(itrunc(df, Policy())) + { + case 1: + { + // + // df = 1 is the same as the Cauchy distribution, see + // Shaw Eq 35: + // + if(u == 0.5) + result = 0; + else + result = -cos(constants::pi<T>() * u) / sin(constants::pi<T>() * u); + if(pexact) + *pexact = true; + break; + } + case 2: + { + // + // df = 2 has an exact result, see Shaw Eq 36: + // + result =(2 * u - 1) / sqrt(2 * u * v); + if(pexact) + *pexact = true; + break; + } + case 4: + { + // + // df = 4 has an exact result, see Shaw Eq 38 & 39: + // + T alpha = 4 * u * v; + T root_alpha = sqrt(alpha); + T r = 4 * cos(acos(root_alpha) / 3) / root_alpha; + T x = sqrt(r - 4); + result = u - 0.5f < 0 ? (T)-x : x; + if(pexact) + *pexact = true; + break; + } + case 6: + { + // + // We get numeric overflow in this area: + // + if(u < 1e-150) + return (invert ? -1 : 1) * inverse_students_t_hill(df, u, pol); + // + // Newton-Raphson iteration of a polynomial case, + // choice of seed value is taken from Shaw's online + // supplement: + // + T a = 4 * (u - u * u);//1 - 4 * (u - 0.5f) * (u - 0.5f); + T b = boost::math::cbrt(a); + static const T c = static_cast<T>(0.85498797333834849467655443627193); + T p = 6 * (1 + c * (1 / b - 1)); + T p0; + do{ + T p2 = p * p; + T p4 = p2 * p2; + T p5 = p * p4; + p0 = p; + // next term is given by Eq 41: + p = 2 * (8 * a * p5 - 270 * p2 + 2187) / (5 * (4 * a * p4 - 216 * p - 243)); + }while(fabs((p - p0) / p) > tolerance); + // + // Use Eq 45 to extract the result: + // + p = sqrt(p - df); + result = (u - 0.5f) < 0 ? (T)-p : p; + break; + } +#if 0 + // + // These are Shaw's "exact" but iterative solutions + // for even df, the numerical accuracy of these is + // rather less than Hill's method, so these are disabled + // for now, which is a shame because they are reasonably + // quick to evaluate... + // + case 8: + { + // + // Newton-Raphson iteration of a polynomial case, + // choice of seed value is taken from Shaw's online + // supplement: + // + static const T c8 = 0.85994765706259820318168359251872L; + T a = 4 * (u - u * u); //1 - 4 * (u - 0.5f) * (u - 0.5f); + T b = pow(a, T(1) / 4); + T p = 8 * (1 + c8 * (1 / b - 1)); + T p0 = p; + do{ + T p5 = p * p; + p5 *= p5 * p; + p0 = p; + // Next term is given by Eq 42: + p = 2 * (3 * p + (640 * (160 + p * (24 + p * (p + 4)))) / (-5120 + p * (-2048 - 960 * p + a * p5))) / 7; + }while(fabs((p - p0) / p) > tolerance); + // + // Use Eq 45 to extract the result: + // + p = sqrt(p - df); + result = (u - 0.5f) < 0 ? -p : p; + break; + } + case 10: + { + // + // Newton-Raphson iteration of a polynomial case, + // choice of seed value is taken from Shaw's online + // supplement: + // + static const T c10 = 0.86781292867813396759105692122285L; + T a = 4 * (u - u * u); //1 - 4 * (u - 0.5f) * (u - 0.5f); + T b = pow(a, T(1) / 5); + T p = 10 * (1 + c10 * (1 / b - 1)); + T p0; + do{ + T p6 = p * p; + p6 *= p6 * p6; + p0 = p; + // Next term given by Eq 43: + p = (8 * p) / 9 + (218750 * (21875 + 4 * p * (625 + p * (75 + 2 * p * (5 + p))))) / + (9 * (-68359375 + 8 * p * (-2343750 + p * (-546875 - 175000 * p + 8 * a * p6)))); + }while(fabs((p - p0) / p) > tolerance); + // + // Use Eq 45 to extract the result: + // + p = sqrt(p - df); + result = (u - 0.5f) < 0 ? -p : p; + break; + } +#endif + default: + goto calculate_real; + } + } + else + { +calculate_real: + if(df > 0x10000000) + { + result = -boost::math::erfc_inv(2 * u, pol) * constants::root_two<T>(); + if((pexact) && (df >= 1e20)) + *pexact = true; + } + else if(df < 3) + { + // + // Use a roughly linear scheme to choose between Shaw's + // tail series and body series: + // + T crossover = 0.2742f - df * 0.0242143f; + if(u > crossover) + { + result = boost::math::detail::inverse_students_t_body_series(df, u, pol); + } + else + { + result = boost::math::detail::inverse_students_t_tail_series(df, u, pol); + } + } + else + { + // + // Use Hill's method except in the exteme tails + // where we use Shaw's tail series. + // The crossover point is roughly exponential in -df: + // + T crossover = ldexp(1.0f, iround(T(df / -0.654f), typename policies::normalise<Policy, policies::rounding_error<policies::ignore_error> >::type())); + if(u > crossover) + { + result = boost::math::detail::inverse_students_t_hill(df, u, pol); + } + else + { + result = boost::math::detail::inverse_students_t_tail_series(df, u, pol); + } + } + } + return invert ? (T)-result : result; +} + +template <class T, class Policy> +inline T find_ibeta_inv_from_t_dist(T a, T p, T /*q*/, T* py, const Policy& pol) +{ + T u = p / 2; + T v = 1 - u; + T df = a * 2; + T t = boost::math::detail::inverse_students_t(df, u, v, pol); + *py = t * t / (df + t * t); + return df / (df + t * t); +} + +template <class T, class Policy> +inline T fast_students_t_quantile_imp(T df, T p, const Policy& pol, const mpl::false_*) +{ + BOOST_MATH_STD_USING + // + // Need to use inverse incomplete beta to get + // required precision so not so fast: + // + T probability = (p > 0.5) ? 1 - p : p; + T t, x, y(0); + x = ibeta_inv(df / 2, T(0.5), 2 * probability, &y, pol); + if(df * y > tools::max_value<T>() * x) + t = policies::raise_overflow_error<T>("boost::math::students_t_quantile<%1%>(%1%,%1%)", 0, pol); + else + t = sqrt(df * y / x); + // + // Figure out sign based on the size of p: + // + if(p < 0.5) + t = -t; + return t; +} + +template <class T, class Policy> +T fast_students_t_quantile_imp(T df, T p, const Policy& pol, const mpl::true_*) +{ + BOOST_MATH_STD_USING + bool invert = false; + if((df < 2) && (floor(df) != df)) + return boost::math::detail::fast_students_t_quantile_imp(df, p, pol, static_cast<mpl::false_*>(0)); + if(p > 0.5) + { + p = 1 - p; + invert = true; + } + // + // Get an estimate of the result: + // + bool exact; + T t = inverse_students_t(df, p, T(1-p), pol, &exact); + if((t == 0) || exact) + return invert ? -t : t; // can't do better! + // + // Change variables to inverse incomplete beta: + // + T t2 = t * t; + T xb = df / (df + t2); + T y = t2 / (df + t2); + T a = df / 2; + // + // t can be so large that x underflows, + // just return our estimate in that case: + // + if(xb == 0) + return t; + // + // Get incomplete beta and it's derivative: + // + T f1; + T f0 = xb < y ? ibeta_imp(a, constants::half<T>(), xb, pol, false, true, &f1) + : ibeta_imp(constants::half<T>(), a, y, pol, true, true, &f1); + + // Get cdf from incomplete beta result: + T p0 = f0 / 2 - p; + // Get pdf from derivative: + T p1 = f1 * sqrt(y * xb * xb * xb / df); + // + // Second derivative divided by p1: + // + // yacas gives: + // + // In> PrettyForm(Simplify(D(t) (1 + t^2/v) ^ (-(v+1)/2))) + // + // | | v + 1 | | + // | -| ----- + 1 | | + // | | 2 | | + // -| | 2 | | + // | | t | | + // | | -- + 1 | | + // | ( v + 1 ) * | v | * t | + // --------------------------------------------- + // v + // + // Which after some manipulation is: + // + // -p1 * t * (df + 1) / (t^2 + df) + // + T p2 = t * (df + 1) / (t * t + df); + // Halley step: + t = fabs(t); + t += p0 / (p1 + p0 * p2 / 2); + return !invert ? -t : t; +} + +template <class T, class Policy> +inline T fast_students_t_quantile(T df, T p, const Policy& pol) +{ + typedef typename policies::evaluation<T, Policy>::type value_type; + typedef typename policies::normalise< + Policy, + policies::promote_float<false>, + policies::promote_double<false>, + policies::discrete_quantile<>, + policies::assert_undefined<> >::type forwarding_policy; + + typedef mpl::bool_< + (std::numeric_limits<T>::digits <= 53) + && + (std::numeric_limits<T>::is_specialized) + && + (std::numeric_limits<T>::radix == 2) + > tag_type; + return policies::checked_narrowing_cast<T, forwarding_policy>(fast_students_t_quantile_imp(static_cast<value_type>(df), static_cast<value_type>(p), pol, static_cast<tag_type*>(0)), "boost::math::students_t_quantile<%1%>(%1%,%1%,%1%)"); +} + +}}} // namespaces + +#endif // BOOST_MATH_SF_DETAIL_INV_T_HPP + + + |