--- /dev/null
+// 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 = ((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] = 0.16666666666666666667 + 0.16666666666666666667 * in;
+ c[3] = (0.0083333333333333333333 * in
+ + 0.066666666666666666667) * in
+ + 0.058333333333333333333;
+ c[4] = ((0.00019841269841269841270 * in
+ + 0.0017857142857142857143) * in
+ + 0.026785714285714285714) * in
+ + 0.025198412698412698413;
+ c[5] = (((2.7557319223985890653e-6 * in
+ + 0.00037477954144620811287) * in
+ - 0.0011078042328042328042) * in
+ + 0.010559964726631393298) * in
+ + 0.012039792768959435626;
+ c[6] = ((((2.5052108385441718775e-8 * in
+ - 0.000062705427288760622094) * in
+ + 0.00059458674042007375341) * in
+ - 0.0016095979637646304313) * in
+ + 0.0061039211560044893378) * in
+ + 0.0038370059724226390893;
+ c[7] = (((((1.6059043836821614599e-10 * in
+ + 0.000015401265401265401265) * in
+ - 0.00016376804137220803887) * in
+ + 0.00069084207973096861986) * in
+ - 0.0012579159844784844785) * in
+ + 0.0010898206731540064873) * in
+ + 0.0032177478835464946576;
+ c[8] = ((((((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] = (((((((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] = ((((((((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 = 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
+
+
+
projects / rsem.git / blobdiff