| // This file is part of Eigen, a lightweight C++ template library |
| // for linear algebra. |
| // |
| // Copyright (C) 2015 Eugene Brevdo <ebrevdo@gmail.com> |
| // |
| // This Source Code Form is subject to the terms of the Mozilla |
| // Public License v. 2.0. If a copy of the MPL was not distributed |
| // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. |
| |
| #ifndef EIGEN_SPECIAL_FUNCTIONS_H |
| #define EIGEN_SPECIAL_FUNCTIONS_H |
| |
| // IWYU pragma: private |
| #include "./InternalHeaderCheck.h" |
| |
| namespace Eigen { |
| namespace internal { |
| |
| // Parts of this code are based on the Cephes Math Library. |
| // |
| // Cephes Math Library Release 2.8: June, 2000 |
| // Copyright 1984, 1987, 1992, 2000 by Stephen L. Moshier |
| // |
| // Permission has been kindly provided by the original author |
| // to incorporate the Cephes software into the Eigen codebase: |
| // |
| // From: Stephen Moshier |
| // To: Eugene Brevdo |
| // Subject: Re: Permission to wrap several cephes functions in Eigen |
| // |
| // Hello Eugene, |
| // |
| // Thank you for writing. |
| // |
| // If your licensing is similar to BSD, the formal way that has been |
| // handled is simply to add a statement to the effect that you are incorporating |
| // the Cephes software by permission of the author. |
| // |
| // Good luck with your project, |
| // Steve |
| |
| /**************************************************************************** |
| * Implementation of lgamma, requires C++11/C99 * |
| ****************************************************************************/ |
| |
| template <typename Scalar> |
| struct lgamma_impl { |
| EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false), THIS_TYPE_IS_NOT_SUPPORTED) |
| |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar run(const Scalar) { return Scalar(0); } |
| }; |
| |
| template <typename Scalar> |
| struct lgamma_retval { |
| typedef Scalar type; |
| }; |
| |
| #if EIGEN_HAS_C99_MATH |
| // Since glibc 2.19 |
| #if defined(__GLIBC__) && ((__GLIBC__ >= 2 && __GLIBC_MINOR__ >= 19) || __GLIBC__ > 2) && \ |
| (defined(_DEFAULT_SOURCE) || defined(_BSD_SOURCE) || defined(_SVID_SOURCE)) |
| #define EIGEN_HAS_LGAMMA_R |
| #endif |
| |
| // Glibc versions before 2.19 |
| #if defined(__GLIBC__) && ((__GLIBC__ == 2 && __GLIBC_MINOR__ < 19) || __GLIBC__ < 2) && \ |
| (defined(_BSD_SOURCE) || defined(_SVID_SOURCE)) |
| #define EIGEN_HAS_LGAMMA_R |
| #endif |
| |
| template <> |
| struct lgamma_impl<float> { |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE float run(float x) { |
| #if !defined(EIGEN_GPU_COMPILE_PHASE) && defined(EIGEN_HAS_LGAMMA_R) && !defined(__APPLE__) |
| int dummy; |
| return ::lgammaf_r(x, &dummy); |
| #elif defined(SYCL_DEVICE_ONLY) |
| return cl::sycl::lgamma(x); |
| #else |
| return ::lgammaf(x); |
| #endif |
| } |
| }; |
| |
| template <> |
| struct lgamma_impl<double> { |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE double run(double x) { |
| #if !defined(EIGEN_GPU_COMPILE_PHASE) && defined(EIGEN_HAS_LGAMMA_R) && !defined(__APPLE__) |
| int dummy; |
| return ::lgamma_r(x, &dummy); |
| #elif defined(SYCL_DEVICE_ONLY) |
| return cl::sycl::lgamma(x); |
| #else |
| return ::lgamma(x); |
| #endif |
| } |
| }; |
| |
| #undef EIGEN_HAS_LGAMMA_R |
| #endif |
| |
| /**************************************************************************** |
| * Implementation of digamma (psi), based on Cephes * |
| ****************************************************************************/ |
| |
| template <typename Scalar> |
| struct digamma_retval { |
| typedef Scalar type; |
| }; |
| |
| /* |
| * |
| * Polynomial evaluation helper for the Psi (digamma) function. |
| * |
| * digamma_impl_maybe_poly::run(s) evaluates the asymptotic Psi expansion for |
| * input Scalar s, assuming s is above 10.0. |
| * |
| * If s is above a certain threshold for the given Scalar type, zero |
| * is returned. Otherwise the polynomial is evaluated with enough |
| * coefficients for results matching Scalar machine precision. |
| * |
| * |
| */ |
| template <typename Scalar> |
| struct digamma_impl_maybe_poly { |
| EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false), THIS_TYPE_IS_NOT_SUPPORTED) |
| |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar run(const Scalar) { return Scalar(0); } |
| }; |
| |
| template <> |
| struct digamma_impl_maybe_poly<float> { |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE float run(const float s) { |
| constexpr float A[] = {-4.16666666666666666667E-3f, 3.96825396825396825397E-3f, -8.33333333333333333333E-3f, |
| 8.33333333333333333333E-2f}; |
| |
| float z; |
| if (s < 1.0e8f) { |
| z = 1.0f / (s * s); |
| return z * internal::ppolevl<float, 3>::run(z, A); |
| } else |
| return 0.0f; |
| } |
| }; |
| |
| template <> |
| struct digamma_impl_maybe_poly<double> { |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE double run(const double s) { |
| constexpr double A[] = {8.33333333333333333333E-2, -2.10927960927960927961E-2, 7.57575757575757575758E-3, |
| -4.16666666666666666667E-3, 3.96825396825396825397E-3, -8.33333333333333333333E-3, |
| 8.33333333333333333333E-2}; |
| |
| double z; |
| if (s < 1.0e17) { |
| z = 1.0 / (s * s); |
| return z * internal::ppolevl<double, 6>::run(z, A); |
| } else |
| return 0.0; |
| } |
| }; |
| |
| template <typename Scalar> |
| struct digamma_impl { |
| EIGEN_DEVICE_FUNC static Scalar run(Scalar x) { |
| /* |
| * |
| * Psi (digamma) function (modified for Eigen) |
| * |
| * |
| * SYNOPSIS: |
| * |
| * double x, y, psi(); |
| * |
| * y = psi( x ); |
| * |
| * |
| * DESCRIPTION: |
| * |
| * d - |
| * psi(x) = -- ln | (x) |
| * dx |
| * |
| * is the logarithmic derivative of the gamma function. |
| * For integer x, |
| * n-1 |
| * - |
| * psi(n) = -EUL + > 1/k. |
| * - |
| * k=1 |
| * |
| * If x is negative, it is transformed to a positive argument by the |
| * reflection formula psi(1-x) = psi(x) + pi cot(pi x). |
| * For general positive x, the argument is made greater than 10 |
| * using the recurrence psi(x+1) = psi(x) + 1/x. |
| * Then the following asymptotic expansion is applied: |
| * |
| * inf. B |
| * - 2k |
| * psi(x) = log(x) - 1/2x - > ------- |
| * - 2k |
| * k=1 2k x |
| * |
| * where the B2k are Bernoulli numbers. |
| * |
| * ACCURACY (float): |
| * Relative error (except absolute when |psi| < 1): |
| * arithmetic domain # trials peak rms |
| * IEEE 0,30 30000 1.3e-15 1.4e-16 |
| * IEEE -30,0 40000 1.5e-15 2.2e-16 |
| * |
| * ACCURACY (double): |
| * Absolute error, relative when |psi| > 1 : |
| * arithmetic domain # trials peak rms |
| * IEEE -33,0 30000 8.2e-7 1.2e-7 |
| * IEEE 0,33 100000 7.3e-7 7.7e-8 |
| * |
| * ERROR MESSAGES: |
| * message condition value returned |
| * psi singularity x integer <=0 INFINITY |
| */ |
| |
| Scalar p, q, nz, s, w, y; |
| bool negative = false; |
| |
| const Scalar nan = NumTraits<Scalar>::quiet_NaN(); |
| const Scalar m_pi = Scalar(EIGEN_PI); |
| |
| const Scalar zero = Scalar(0); |
| const Scalar one = Scalar(1); |
| const Scalar half = Scalar(0.5); |
| nz = zero; |
| |
| if (x <= zero) { |
| negative = true; |
| q = x; |
| p = numext::floor(q); |
| if (p == q) { |
| return nan; |
| } |
| /* Remove the zeros of tan(m_pi x) |
| * by subtracting the nearest integer from x |
| */ |
| nz = q - p; |
| if (nz != half) { |
| if (nz > half) { |
| p += one; |
| nz = q - p; |
| } |
| nz = m_pi / numext::tan(m_pi * nz); |
| } else { |
| nz = zero; |
| } |
| x = one - x; |
| } |
| |
| /* use the recurrence psi(x+1) = psi(x) + 1/x. */ |
| s = x; |
| w = zero; |
| while (s < Scalar(10)) { |
| w += one / s; |
| s += one; |
| } |
| |
| y = digamma_impl_maybe_poly<Scalar>::run(s); |
| |
| y = numext::log(s) - (half / s) - y - w; |
| |
| return (negative) ? y - nz : y; |
| } |
| }; |
| |
| /**************************************************************************** |
| * Implementation of erf, requires C++11/C99 * |
| ****************************************************************************/ |
| |
| /** \internal \returns the error function of \a a (coeff-wise) |
| This uses a 11/10-degree rational interpolantand is accurate to 3 ulp for |
| normalized floats. |
| |
| This implementation works on both scalars and SIMD "packets". |
| */ |
| template <typename T> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T generic_fast_erf_float(const T& x) { |
| // The monomial coefficients of the numerator polynomial (odd). |
| constexpr float alpha[] = {2.123732201653183437883853912353515625e-06f, 2.861979592125862836837768554687500000e-04f, |
| 3.658048342913389205932617187500000000e-03f, 5.243302136659622192382812500000000000e-02f, |
| 1.874160766601562500000000000000000000e-01f, 1.128379106521606445312500000000000000e+00f}; |
| |
| // The monomial coefficients of the denominator polynomial (even). |
| constexpr float beta[] = {3.89185734093189239501953125000e-05f, 1.14329601638019084930419921875e-03f, |
| 1.47520881146192550659179687500e-02f, 1.12945675849914550781250000000e-01f, |
| 4.99425798654556274414062500000e-01f, 1.0f}; |
| |
| // Since the polynomials are odd/even, we need x^2. |
| // Since erf(4) == 1 in float, we clamp x^2 to 16 to avoid |
| // computing Inf/Inf below. |
| const T x2 = pmin(pset1<T>(16.0f), pmul(x, x)); |
| |
| // Evaluate the numerator polynomial p. |
| T p = ppolevl<T, 5>::run(x2, alpha); |
| p = pmul(x, p); |
| |
| // Evaluate the denominator polynomial p. |
| T q = ppolevl<T, 5>::run(x2, beta); |
| const T r = pdiv(p, q); |
| |
| // Clamp to [-1:1]. |
| return pmax(pmin(r, pset1<T>(1.0f)), pset1<T>(-1.0f)); |
| } |
| |
| template <typename T> |
| struct erf_impl { |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T& x) { return generic_fast_erf_float(x); } |
| }; |
| |
| template <typename Scalar> |
| struct erf_retval { |
| typedef Scalar type; |
| }; |
| |
| #if EIGEN_HAS_C99_MATH |
| template <> |
| struct erf_impl<float> { |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE float run(float x) { |
| #if defined(SYCL_DEVICE_ONLY) |
| return cl::sycl::erf(x); |
| #else |
| return generic_fast_erf_float(x); |
| #endif |
| } |
| }; |
| |
| template <> |
| struct erf_impl<double> { |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE double run(double x) { |
| #if defined(SYCL_DEVICE_ONLY) |
| return cl::sycl::erf(x); |
| #else |
| return ::erf(x); |
| #endif |
| } |
| }; |
| #endif // EIGEN_HAS_C99_MATH |
| |
| /*************************************************************************** |
| * Implementation of erfc, requires C++11/C99 * |
| ****************************************************************************/ |
| |
| template <typename Scalar> |
| struct erfc_impl { |
| EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false), THIS_TYPE_IS_NOT_SUPPORTED) |
| |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar run(const Scalar) { return Scalar(0); } |
| }; |
| |
| template <typename Scalar> |
| struct erfc_retval { |
| typedef Scalar type; |
| }; |
| |
| #if EIGEN_HAS_C99_MATH |
| template <> |
| struct erfc_impl<float> { |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE float run(const float x) { |
| #if defined(SYCL_DEVICE_ONLY) |
| return cl::sycl::erfc(x); |
| #else |
| return ::erfcf(x); |
| #endif |
| } |
| }; |
| |
| template <> |
| struct erfc_impl<double> { |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE double run(const double x) { |
| #if defined(SYCL_DEVICE_ONLY) |
| return cl::sycl::erfc(x); |
| #else |
| return ::erfc(x); |
| #endif |
| } |
| }; |
| #endif // EIGEN_HAS_C99_MATH |
| |
| /*************************************************************************** |
| * Implementation of ndtri. * |
| ****************************************************************************/ |
| |
| /* Inverse of Normal distribution function (modified for Eigen). |
| * |
| * |
| * SYNOPSIS: |
| * |
| * double x, y, ndtri(); |
| * |
| * x = ndtri( y ); |
| * |
| * |
| * |
| * DESCRIPTION: |
| * |
| * Returns the argument, x, for which the area under the |
| * Gaussian probability density function (integrated from |
| * minus infinity to x) is equal to y. |
| * |
| * |
| * For small arguments 0 < y < exp(-2), the program computes |
| * z = sqrt( -2.0 * log(y) ); then the approximation is |
| * x = z - log(z)/z - (1/z) P(1/z) / Q(1/z). |
| * There are two rational functions P/Q, one for 0 < y < exp(-32) |
| * and the other for y up to exp(-2). For larger arguments, |
| * w = y - 0.5, and x/sqrt(2pi) = w + w**3 R(w**2)/S(w**2)). |
| * |
| * |
| * ACCURACY: |
| * |
| * Relative error: |
| * arithmetic domain # trials peak rms |
| * DEC 0.125, 1 5500 9.5e-17 2.1e-17 |
| * DEC 6e-39, 0.135 3500 5.7e-17 1.3e-17 |
| * IEEE 0.125, 1 20000 7.2e-16 1.3e-16 |
| * IEEE 3e-308, 0.135 50000 4.6e-16 9.8e-17 |
| * |
| * |
| * ERROR MESSAGES: |
| * |
| * message condition value returned |
| * ndtri domain x == 0 -INF |
| * ndtri domain x == 1 INF |
| * ndtri domain x < 0, x > 1 NAN |
| */ |
| /* |
| Cephes Math Library Release 2.2: June, 1992 |
| Copyright 1985, 1987, 1992 by Stephen L. Moshier |
| Direct inquiries to 30 Frost Street, Cambridge, MA 02140 |
| */ |
| |
| // TODO: Add a cheaper approximation for float. |
| |
| template <typename T> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T flipsign(const T& should_flipsign, const T& x) { |
| typedef typename unpacket_traits<T>::type Scalar; |
| const T sign_mask = pset1<T>(Scalar(-0.0)); |
| T sign_bit = pand<T>(should_flipsign, sign_mask); |
| return pxor<T>(sign_bit, x); |
| } |
| |
| template <> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double flipsign<double>(const double& should_flipsign, const double& x) { |
| return should_flipsign == 0 ? x : -x; |
| } |
| |
| template <> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float flipsign<float>(const float& should_flipsign, const float& x) { |
| return should_flipsign == 0 ? x : -x; |
| } |
| |
| // We split this computation in to two so that in the scalar path |
| // only one branch is evaluated (due to our template specialization of pselect |
| // being an if statement.) |
| |
| template <typename T, typename ScalarType> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T generic_ndtri_gt_exp_neg_two(const T& b) { |
| const ScalarType p0[] = {ScalarType(-5.99633501014107895267e1), ScalarType(9.80010754185999661536e1), |
| ScalarType(-5.66762857469070293439e1), ScalarType(1.39312609387279679503e1), |
| ScalarType(-1.23916583867381258016e0)}; |
| const ScalarType q0[] = {ScalarType(1.0), |
| ScalarType(1.95448858338141759834e0), |
| ScalarType(4.67627912898881538453e0), |
| ScalarType(8.63602421390890590575e1), |
| ScalarType(-2.25462687854119370527e2), |
| ScalarType(2.00260212380060660359e2), |
| ScalarType(-8.20372256168333339912e1), |
| ScalarType(1.59056225126211695515e1), |
| ScalarType(-1.18331621121330003142e0)}; |
| const T sqrt2pi = pset1<T>(ScalarType(2.50662827463100050242e0)); |
| const T half = pset1<T>(ScalarType(0.5)); |
| T c, c2, ndtri_gt_exp_neg_two; |
| |
| c = psub(b, half); |
| c2 = pmul(c, c); |
| ndtri_gt_exp_neg_two = |
| pmadd(c, pmul(c2, pdiv(internal::ppolevl<T, 4>::run(c2, p0), internal::ppolevl<T, 8>::run(c2, q0))), c); |
| return pmul(ndtri_gt_exp_neg_two, sqrt2pi); |
| } |
| |
| template <typename T, typename ScalarType> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T generic_ndtri_lt_exp_neg_two(const T& b, const T& should_flipsign) { |
| /* Approximation for interval z = sqrt(-2 log a ) between 2 and 8 |
| * i.e., a between exp(-2) = .135 and exp(-32) = 1.27e-14. |
| */ |
| const ScalarType p1[] = {ScalarType(4.05544892305962419923e0), ScalarType(3.15251094599893866154e1), |
| ScalarType(5.71628192246421288162e1), ScalarType(4.40805073893200834700e1), |
| ScalarType(1.46849561928858024014e1), ScalarType(2.18663306850790267539e0), |
| ScalarType(-1.40256079171354495875e-1), ScalarType(-3.50424626827848203418e-2), |
| ScalarType(-8.57456785154685413611e-4)}; |
| const ScalarType q1[] = {ScalarType(1.0), |
| ScalarType(1.57799883256466749731e1), |
| ScalarType(4.53907635128879210584e1), |
| ScalarType(4.13172038254672030440e1), |
| ScalarType(1.50425385692907503408e1), |
| ScalarType(2.50464946208309415979e0), |
| ScalarType(-1.42182922854787788574e-1), |
| ScalarType(-3.80806407691578277194e-2), |
| ScalarType(-9.33259480895457427372e-4)}; |
| /* Approximation for interval z = sqrt(-2 log a ) between 8 and 64 |
| * i.e., a between exp(-32) = 1.27e-14 and exp(-2048) = 3.67e-890. |
| */ |
| const ScalarType p2[] = {ScalarType(3.23774891776946035970e0), ScalarType(6.91522889068984211695e0), |
| ScalarType(3.93881025292474443415e0), ScalarType(1.33303460815807542389e0), |
| ScalarType(2.01485389549179081538e-1), ScalarType(1.23716634817820021358e-2), |
| ScalarType(3.01581553508235416007e-4), ScalarType(2.65806974686737550832e-6), |
| ScalarType(6.23974539184983293730e-9)}; |
| const ScalarType q2[] = {ScalarType(1.0), |
| ScalarType(6.02427039364742014255e0), |
| ScalarType(3.67983563856160859403e0), |
| ScalarType(1.37702099489081330271e0), |
| ScalarType(2.16236993594496635890e-1), |
| ScalarType(1.34204006088543189037e-2), |
| ScalarType(3.28014464682127739104e-4), |
| ScalarType(2.89247864745380683936e-6), |
| ScalarType(6.79019408009981274425e-9)}; |
| const T eight = pset1<T>(ScalarType(8.0)); |
| const T neg_two = pset1<T>(ScalarType(-2)); |
| T x, x0, x1, z; |
| |
| x = psqrt(pmul(neg_two, plog(b))); |
| x0 = psub(x, pdiv(plog(x), x)); |
| z = preciprocal(x); |
| x1 = |
| pmul(z, pselect(pcmp_lt(x, eight), pdiv(internal::ppolevl<T, 8>::run(z, p1), internal::ppolevl<T, 8>::run(z, q1)), |
| pdiv(internal::ppolevl<T, 8>::run(z, p2), internal::ppolevl<T, 8>::run(z, q2)))); |
| return flipsign(should_flipsign, psub(x0, x1)); |
| } |
| |
| template <typename T, typename ScalarType> |
| EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T generic_ndtri(const T& a) { |
| const T maxnum = pset1<T>(NumTraits<ScalarType>::infinity()); |
| const T neg_maxnum = pset1<T>(-NumTraits<ScalarType>::infinity()); |
| |
| const T zero = pset1<T>(ScalarType(0)); |
| const T one = pset1<T>(ScalarType(1)); |
| // exp(-2) |
| const T exp_neg_two = pset1<T>(ScalarType(0.13533528323661269189)); |
| T b, ndtri, should_flipsign; |
| |
| should_flipsign = pcmp_le(a, psub(one, exp_neg_two)); |
| b = pselect(should_flipsign, a, psub(one, a)); |
| |
| ndtri = pselect(pcmp_lt(exp_neg_two, b), generic_ndtri_gt_exp_neg_two<T, ScalarType>(b), |
| generic_ndtri_lt_exp_neg_two<T, ScalarType>(b, should_flipsign)); |
| |
| return pselect(pcmp_eq(a, zero), neg_maxnum, pselect(pcmp_eq(one, a), maxnum, ndtri)); |
| } |
| |
| template <typename Scalar> |
| struct ndtri_retval { |
| typedef Scalar type; |
| }; |
| |
| #if !EIGEN_HAS_C99_MATH |
| |
| template <typename Scalar> |
| struct ndtri_impl { |
| EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false), THIS_TYPE_IS_NOT_SUPPORTED) |
| |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar run(const Scalar) { return Scalar(0); } |
| }; |
| |
| #else |
| |
| template <typename Scalar> |
| struct ndtri_impl { |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar run(const Scalar x) { return generic_ndtri<Scalar, Scalar>(x); } |
| }; |
| |
| #endif // EIGEN_HAS_C99_MATH |
| |
| /************************************************************************************************************** |
| * Implementation of igammac (complemented incomplete gamma integral), based on Cephes but requires C++11/C99 * |
| **************************************************************************************************************/ |
| |
| template <typename Scalar> |
| struct igammac_retval { |
| typedef Scalar type; |
| }; |
| |
| // NOTE: cephes_helper is also used to implement zeta |
| template <typename Scalar> |
| struct cephes_helper { |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar machep() { |
| eigen_assert(false && "machep not supported for this type"); |
| return 0.0; |
| } |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar big() { |
| eigen_assert(false && "big not supported for this type"); |
| return 0.0; |
| } |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar biginv() { |
| eigen_assert(false && "biginv not supported for this type"); |
| return 0.0; |
| } |
| }; |
| |
| template <> |
| struct cephes_helper<float> { |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE float machep() { |
| return NumTraits<float>::epsilon() / 2; // 1.0 - machep == 1.0 |
| } |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE float big() { |
| // use epsneg (1.0 - epsneg == 1.0) |
| return 1.0f / (NumTraits<float>::epsilon() / 2); |
| } |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE float biginv() { |
| // epsneg |
| return machep(); |
| } |
| }; |
| |
| template <> |
| struct cephes_helper<double> { |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE double machep() { |
| return NumTraits<double>::epsilon() / 2; // 1.0 - machep == 1.0 |
| } |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE double big() { return 1.0 / NumTraits<double>::epsilon(); } |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE double biginv() { |
| // inverse of eps |
| return NumTraits<double>::epsilon(); |
| } |
| }; |
| |
| enum IgammaComputationMode { VALUE, DERIVATIVE, SAMPLE_DERIVATIVE }; |
| |
| template <typename Scalar> |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar main_igamma_term(Scalar a, Scalar x) { |
| /* Compute x**a * exp(-x) / gamma(a) */ |
| Scalar logax = a * numext::log(x) - x - lgamma_impl<Scalar>::run(a); |
| if (logax < -numext::log(NumTraits<Scalar>::highest()) || |
| // Assuming x and a aren't Nan. |
| (numext::isnan)(logax)) { |
| return Scalar(0); |
| } |
| return numext::exp(logax); |
| } |
| |
| template <typename Scalar, IgammaComputationMode mode> |
| EIGEN_DEVICE_FUNC int igamma_num_iterations() { |
| /* Returns the maximum number of internal iterations for igamma computation. |
| */ |
| if (mode == VALUE) { |
| return 2000; |
| } |
| |
| if (internal::is_same<Scalar, float>::value) { |
| return 200; |
| } else if (internal::is_same<Scalar, double>::value) { |
| return 500; |
| } else { |
| return 2000; |
| } |
| } |
| |
| template <typename Scalar, IgammaComputationMode mode> |
| struct igammac_cf_impl { |
| /* Computes igamc(a, x) or derivative (depending on the mode) |
| * using the continued fraction expansion of the complementary |
| * incomplete Gamma function. |
| * |
| * Preconditions: |
| * a > 0 |
| * x >= 1 |
| * x >= a |
| */ |
| EIGEN_DEVICE_FUNC static Scalar run(Scalar a, Scalar x) { |
| const Scalar zero = 0; |
| const Scalar one = 1; |
| const Scalar two = 2; |
| const Scalar machep = cephes_helper<Scalar>::machep(); |
| const Scalar big = cephes_helper<Scalar>::big(); |
| const Scalar biginv = cephes_helper<Scalar>::biginv(); |
| |
| if ((numext::isinf)(x)) { |
| return zero; |
| } |
| |
| Scalar ax = main_igamma_term<Scalar>(a, x); |
| // This is independent of mode. If this value is zero, |
| // then the function value is zero. If the function value is zero, |
| // then we are in a neighborhood where the function value evaluates to zero, |
| // so the derivative is zero. |
| if (ax == zero) { |
| return zero; |
| } |
| |
| // continued fraction |
| Scalar y = one - a; |
| Scalar z = x + y + one; |
| Scalar c = zero; |
| Scalar pkm2 = one; |
| Scalar qkm2 = x; |
| Scalar pkm1 = x + one; |
| Scalar qkm1 = z * x; |
| Scalar ans = pkm1 / qkm1; |
| |
| Scalar dpkm2_da = zero; |
| Scalar dqkm2_da = zero; |
| Scalar dpkm1_da = zero; |
| Scalar dqkm1_da = -x; |
| Scalar dans_da = (dpkm1_da - ans * dqkm1_da) / qkm1; |
| |
| for (int i = 0; i < igamma_num_iterations<Scalar, mode>(); i++) { |
| c += one; |
| y += one; |
| z += two; |
| |
| Scalar yc = y * c; |
| Scalar pk = pkm1 * z - pkm2 * yc; |
| Scalar qk = qkm1 * z - qkm2 * yc; |
| |
| Scalar dpk_da = dpkm1_da * z - pkm1 - dpkm2_da * yc + pkm2 * c; |
| Scalar dqk_da = dqkm1_da * z - qkm1 - dqkm2_da * yc + qkm2 * c; |
| |
| if (qk != zero) { |
| Scalar ans_prev = ans; |
| ans = pk / qk; |
| |
| Scalar dans_da_prev = dans_da; |
| dans_da = (dpk_da - ans * dqk_da) / qk; |
| |
| if (mode == VALUE) { |
| if (numext::abs(ans_prev - ans) <= machep * numext::abs(ans)) { |
| break; |
| } |
| } else { |
| if (numext::abs(dans_da - dans_da_prev) <= machep) { |
| break; |
| } |
| } |
| } |
| |
| pkm2 = pkm1; |
| pkm1 = pk; |
| qkm2 = qkm1; |
| qkm1 = qk; |
| |
| dpkm2_da = dpkm1_da; |
| dpkm1_da = dpk_da; |
| dqkm2_da = dqkm1_da; |
| dqkm1_da = dqk_da; |
| |
| if (numext::abs(pk) > big) { |
| pkm2 *= biginv; |
| pkm1 *= biginv; |
| qkm2 *= biginv; |
| qkm1 *= biginv; |
| |
| dpkm2_da *= biginv; |
| dpkm1_da *= biginv; |
| dqkm2_da *= biginv; |
| dqkm1_da *= biginv; |
| } |
| } |
| |
| /* Compute x**a * exp(-x) / gamma(a) */ |
| Scalar dlogax_da = numext::log(x) - digamma_impl<Scalar>::run(a); |
| Scalar dax_da = ax * dlogax_da; |
| |
| switch (mode) { |
| case VALUE: |
| return ans * ax; |
| case DERIVATIVE: |
| return ans * dax_da + dans_da * ax; |
| case SAMPLE_DERIVATIVE: |
| default: // this is needed to suppress clang warning |
| return -(dans_da + ans * dlogax_da) * x; |
| } |
| } |
| }; |
| |
| template <typename Scalar, IgammaComputationMode mode> |
| struct igamma_series_impl { |
| /* Computes igam(a, x) or its derivative (depending on the mode) |
| * using the series expansion of the incomplete Gamma function. |
| * |
| * Preconditions: |
| * x > 0 |
| * a > 0 |
| * !(x > 1 && x > a) |
| */ |
| EIGEN_DEVICE_FUNC static Scalar run(Scalar a, Scalar x) { |
| const Scalar zero = 0; |
| const Scalar one = 1; |
| const Scalar machep = cephes_helper<Scalar>::machep(); |
| |
| Scalar ax = main_igamma_term<Scalar>(a, x); |
| |
| // This is independent of mode. If this value is zero, |
| // then the function value is zero. If the function value is zero, |
| // then we are in a neighborhood where the function value evaluates to zero, |
| // so the derivative is zero. |
| if (ax == zero) { |
| return zero; |
| } |
| |
| ax /= a; |
| |
| /* power series */ |
| Scalar r = a; |
| Scalar c = one; |
| Scalar ans = one; |
| |
| Scalar dc_da = zero; |
| Scalar dans_da = zero; |
| |
| for (int i = 0; i < igamma_num_iterations<Scalar, mode>(); i++) { |
| r += one; |
| Scalar term = x / r; |
| Scalar dterm_da = -x / (r * r); |
| dc_da = term * dc_da + dterm_da * c; |
| dans_da += dc_da; |
| c *= term; |
| ans += c; |
| |
| if (mode == VALUE) { |
| if (c <= machep * ans) { |
| break; |
| } |
| } else { |
| if (numext::abs(dc_da) <= machep * numext::abs(dans_da)) { |
| break; |
| } |
| } |
| } |
| |
| Scalar dlogax_da = numext::log(x) - digamma_impl<Scalar>::run(a + one); |
| Scalar dax_da = ax * dlogax_da; |
| |
| switch (mode) { |
| case VALUE: |
| return ans * ax; |
| case DERIVATIVE: |
| return ans * dax_da + dans_da * ax; |
| case SAMPLE_DERIVATIVE: |
| default: // this is needed to suppress clang warning |
| return -(dans_da + ans * dlogax_da) * x / a; |
| } |
| } |
| }; |
| |
| #if !EIGEN_HAS_C99_MATH |
| |
| template <typename Scalar> |
| struct igammac_impl { |
| EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false), THIS_TYPE_IS_NOT_SUPPORTED) |
| |
| EIGEN_DEVICE_FUNC static Scalar run(Scalar a, Scalar x) { return Scalar(0); } |
| }; |
| |
| #else |
| |
| template <typename Scalar> |
| struct igammac_impl { |
| EIGEN_DEVICE_FUNC static Scalar run(Scalar a, Scalar x) { |
| /* igamc() |
| * |
| * Incomplete gamma integral (modified for Eigen) |
| * |
| * |
| * |
| * SYNOPSIS: |
| * |
| * double a, x, y, igamc(); |
| * |
| * y = igamc( a, x ); |
| * |
| * DESCRIPTION: |
| * |
| * The function is defined by |
| * |
| * |
| * igamc(a,x) = 1 - igam(a,x) |
| * |
| * inf. |
| * - |
| * 1 | | -t a-1 |
| * = ----- | e t dt. |
| * - | | |
| * | (a) - |
| * x |
| * |
| * |
| * In this implementation both arguments must be positive. |
| * The integral is evaluated by either a power series or |
| * continued fraction expansion, depending on the relative |
| * values of a and x. |
| * |
| * ACCURACY (float): |
| * |
| * Relative error: |
| * arithmetic domain # trials peak rms |
| * IEEE 0,30 30000 7.8e-6 5.9e-7 |
| * |
| * |
| * ACCURACY (double): |
| * |
| * Tested at random a, x. |
| * a x Relative error: |
| * arithmetic domain domain # trials peak rms |
| * IEEE 0.5,100 0,100 200000 1.9e-14 1.7e-15 |
| * IEEE 0.01,0.5 0,100 200000 1.4e-13 1.6e-15 |
| * |
| */ |
| /* |
| Cephes Math Library Release 2.2: June, 1992 |
| Copyright 1985, 1987, 1992 by Stephen L. Moshier |
| Direct inquiries to 30 Frost Street, Cambridge, MA 02140 |
| */ |
| const Scalar zero = 0; |
| const Scalar one = 1; |
| const Scalar nan = NumTraits<Scalar>::quiet_NaN(); |
| |
| if ((x < zero) || (a <= zero)) { |
| // domain error |
| return nan; |
| } |
| |
| if ((numext::isnan)(a) || (numext::isnan)(x)) { // propagate nans |
| return nan; |
| } |
| |
| if ((x < one) || (x < a)) { |
| return (one - igamma_series_impl<Scalar, VALUE>::run(a, x)); |
| } |
| |
| return igammac_cf_impl<Scalar, VALUE>::run(a, x); |
| } |
| }; |
| |
| #endif // EIGEN_HAS_C99_MATH |
| |
| /************************************************************************************************ |
| * Implementation of igamma (incomplete gamma integral), based on Cephes but requires C++11/C99 * |
| ************************************************************************************************/ |
| |
| #if !EIGEN_HAS_C99_MATH |
| |
| template <typename Scalar, IgammaComputationMode mode> |
| struct igamma_generic_impl { |
| EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false), THIS_TYPE_IS_NOT_SUPPORTED) |
| |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar run(Scalar a, Scalar x) { return Scalar(0); } |
| }; |
| |
| #else |
| |
| template <typename Scalar, IgammaComputationMode mode> |
| struct igamma_generic_impl { |
| EIGEN_DEVICE_FUNC static Scalar run(Scalar a, Scalar x) { |
| /* Depending on the mode, returns |
| * - VALUE: incomplete Gamma function igamma(a, x) |
| * - DERIVATIVE: derivative of incomplete Gamma function d/da igamma(a, x) |
| * - SAMPLE_DERIVATIVE: implicit derivative of a Gamma random variable |
| * x ~ Gamma(x | a, 1), dx/da = -1 / Gamma(x | a, 1) * d igamma(a, x) / dx |
| * |
| * Derivatives are implemented by forward-mode differentiation. |
| */ |
| const Scalar zero = 0; |
| const Scalar one = 1; |
| const Scalar nan = NumTraits<Scalar>::quiet_NaN(); |
| |
| if (x == zero) return zero; |
| |
| if ((x < zero) || (a <= zero)) { // domain error |
| return nan; |
| } |
| |
| if ((numext::isnan)(a) || (numext::isnan)(x)) { // propagate nans |
| return nan; |
| } |
| |
| if ((x > one) && (x > a)) { |
| Scalar ret = igammac_cf_impl<Scalar, mode>::run(a, x); |
| if (mode == VALUE) { |
| return one - ret; |
| } else { |
| return -ret; |
| } |
| } |
| |
| return igamma_series_impl<Scalar, mode>::run(a, x); |
| } |
| }; |
| |
| #endif // EIGEN_HAS_C99_MATH |
| |
| template <typename Scalar> |
| struct igamma_retval { |
| typedef Scalar type; |
| }; |
| |
| template <typename Scalar> |
| struct igamma_impl : igamma_generic_impl<Scalar, VALUE> { |
| /* igam() |
| * Incomplete gamma integral. |
| * |
| * The CDF of Gamma(a, 1) random variable at the point x. |
| * |
| * Accuracy estimation. For each a in [10^-2, 10^-1...10^3] we sample |
| * 50 Gamma random variables x ~ Gamma(x | a, 1), a total of 300 points. |
| * The ground truth is computed by mpmath. Mean absolute error: |
| * float: 1.26713e-05 |
| * double: 2.33606e-12 |
| * |
| * Cephes documentation below. |
| * |
| * SYNOPSIS: |
| * |
| * double a, x, y, igam(); |
| * |
| * y = igam( a, x ); |
| * |
| * DESCRIPTION: |
| * |
| * The function is defined by |
| * |
| * x |
| * - |
| * 1 | | -t a-1 |
| * igam(a,x) = ----- | e t dt. |
| * - | | |
| * | (a) - |
| * 0 |
| * |
| * |
| * In this implementation both arguments must be positive. |
| * The integral is evaluated by either a power series or |
| * continued fraction expansion, depending on the relative |
| * values of a and x. |
| * |
| * ACCURACY (double): |
| * |
| * Relative error: |
| * arithmetic domain # trials peak rms |
| * IEEE 0,30 200000 3.6e-14 2.9e-15 |
| * IEEE 0,100 300000 9.9e-14 1.5e-14 |
| * |
| * |
| * ACCURACY (float): |
| * |
| * Relative error: |
| * arithmetic domain # trials peak rms |
| * IEEE 0,30 20000 7.8e-6 5.9e-7 |
| * |
| */ |
| /* |
| Cephes Math Library Release 2.2: June, 1992 |
| Copyright 1985, 1987, 1992 by Stephen L. Moshier |
| Direct inquiries to 30 Frost Street, Cambridge, MA 02140 |
| */ |
| |
| /* left tail of incomplete gamma function: |
| * |
| * inf. k |
| * a -x - x |
| * x e > ---------- |
| * - - |
| * k=0 | (a+k+1) |
| * |
| */ |
| }; |
| |
| template <typename Scalar> |
| struct igamma_der_a_retval : igamma_retval<Scalar> {}; |
| |
| template <typename Scalar> |
| struct igamma_der_a_impl : igamma_generic_impl<Scalar, DERIVATIVE> { |
| /* Derivative of the incomplete Gamma function with respect to a. |
| * |
| * Computes d/da igamma(a, x) by forward differentiation of the igamma code. |
| * |
| * Accuracy estimation. For each a in [10^-2, 10^-1...10^3] we sample |
| * 50 Gamma random variables x ~ Gamma(x | a, 1), a total of 300 points. |
| * The ground truth is computed by mpmath. Mean absolute error: |
| * float: 6.17992e-07 |
| * double: 4.60453e-12 |
| * |
| * Reference: |
| * R. Moore. "Algorithm AS 187: Derivatives of the incomplete gamma |
| * integral". Journal of the Royal Statistical Society. 1982 |
| */ |
| }; |
| |
| template <typename Scalar> |
| struct gamma_sample_der_alpha_retval : igamma_retval<Scalar> {}; |
| |
| template <typename Scalar> |
| struct gamma_sample_der_alpha_impl : igamma_generic_impl<Scalar, SAMPLE_DERIVATIVE> { |
| /* Derivative of a Gamma random variable sample with respect to alpha. |
| * |
| * Consider a sample of a Gamma random variable with the concentration |
| * parameter alpha: sample ~ Gamma(alpha, 1). The reparameterization |
| * derivative that we want to compute is dsample / dalpha = |
| * d igammainv(alpha, u) / dalpha, where u = igamma(alpha, sample). |
| * However, this formula is numerically unstable and expensive, so instead |
| * we use implicit differentiation: |
| * |
| * igamma(alpha, sample) = u, where u ~ Uniform(0, 1). |
| * Apply d / dalpha to both sides: |
| * d igamma(alpha, sample) / dalpha |
| * + d igamma(alpha, sample) / dsample * dsample/dalpha = 0 |
| * d igamma(alpha, sample) / dalpha |
| * + Gamma(sample | alpha, 1) dsample / dalpha = 0 |
| * dsample/dalpha = - (d igamma(alpha, sample) / dalpha) |
| * / Gamma(sample | alpha, 1) |
| * |
| * Here Gamma(sample | alpha, 1) is the PDF of the Gamma distribution |
| * (note that the derivative of the CDF w.r.t. sample is the PDF). |
| * See the reference below for more details. |
| * |
| * The derivative of igamma(alpha, sample) is computed by forward |
| * differentiation of the igamma code. Division by the Gamma PDF is performed |
| * in the same code, increasing the accuracy and speed due to cancellation |
| * of some terms. |
| * |
| * Accuracy estimation. For each alpha in [10^-2, 10^-1...10^3] we sample |
| * 50 Gamma random variables sample ~ Gamma(sample | alpha, 1), a total of 300 |
| * points. The ground truth is computed by mpmath. Mean absolute error: |
| * float: 2.1686e-06 |
| * double: 1.4774e-12 |
| * |
| * Reference: |
| * M. Figurnov, S. Mohamed, A. Mnih "Implicit Reparameterization Gradients". |
| * 2018 |
| */ |
| }; |
| |
| /***************************************************************************** |
| * Implementation of Riemann zeta function of two arguments, based on Cephes * |
| *****************************************************************************/ |
| |
| template <typename Scalar> |
| struct zeta_retval { |
| typedef Scalar type; |
| }; |
| |
| template <typename Scalar> |
| struct zeta_impl_series { |
| EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false), THIS_TYPE_IS_NOT_SUPPORTED) |
| |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar run(const Scalar) { return Scalar(0); } |
| }; |
| |
| template <> |
| struct zeta_impl_series<float> { |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE bool run(float& a, float& b, float& s, const float x, |
| const float machep) { |
| int i = 0; |
| while (i < 9) { |
| i += 1; |
| a += 1.0f; |
| b = numext::pow(a, -x); |
| s += b; |
| if (numext::abs(b / s) < machep) return true; |
| } |
| |
| // Return whether we are done |
| return false; |
| } |
| }; |
| |
| template <> |
| struct zeta_impl_series<double> { |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE bool run(double& a, double& b, double& s, const double x, |
| const double machep) { |
| int i = 0; |
| while ((i < 9) || (a <= 9.0)) { |
| i += 1; |
| a += 1.0; |
| b = numext::pow(a, -x); |
| s += b; |
| if (numext::abs(b / s) < machep) return true; |
| } |
| |
| // Return whether we are done |
| return false; |
| } |
| }; |
| |
| template <typename Scalar> |
| struct zeta_impl { |
| EIGEN_DEVICE_FUNC static Scalar run(Scalar x, Scalar q) { |
| /* zeta.c |
| * |
| * Riemann zeta function of two arguments |
| * |
| * |
| * |
| * SYNOPSIS: |
| * |
| * double x, q, y, zeta(); |
| * |
| * y = zeta( x, q ); |
| * |
| * |
| * |
| * DESCRIPTION: |
| * |
| * |
| * |
| * inf. |
| * - -x |
| * zeta(x,q) = > (k+q) |
| * - |
| * k=0 |
| * |
| * where x > 1 and q is not a negative integer or zero. |
| * The Euler-Maclaurin summation formula is used to obtain |
| * the expansion |
| * |
| * n |
| * - -x |
| * zeta(x,q) = > (k+q) |
| * - |
| * k=1 |
| * |
| * 1-x inf. B x(x+1)...(x+2j) |
| * (n+q) 1 - 2j |
| * + --------- - ------- + > -------------------- |
| * x-1 x - x+2j+1 |
| * 2(n+q) j=1 (2j)! (n+q) |
| * |
| * where the B2j are Bernoulli numbers. Note that (see zetac.c) |
| * zeta(x,1) = zetac(x) + 1. |
| * |
| * |
| * |
| * ACCURACY: |
| * |
| * Relative error for single precision: |
| * arithmetic domain # trials peak rms |
| * IEEE 0,25 10000 6.9e-7 1.0e-7 |
| * |
| * Large arguments may produce underflow in powf(), in which |
| * case the results are inaccurate. |
| * |
| * REFERENCE: |
| * |
| * Gradshteyn, I. S., and I. M. Ryzhik, Tables of Integrals, |
| * Series, and Products, p. 1073; Academic Press, 1980. |
| * |
| */ |
| |
| int i; |
| Scalar p, r, a, b, k, s, t, w; |
| |
| const Scalar A[] = { |
| Scalar(12.0), |
| Scalar(-720.0), |
| Scalar(30240.0), |
| Scalar(-1209600.0), |
| Scalar(47900160.0), |
| Scalar(-1.8924375803183791606e9), /*1.307674368e12/691*/ |
| Scalar(7.47242496e10), |
| Scalar(-2.950130727918164224e12), /*1.067062284288e16/3617*/ |
| Scalar(1.1646782814350067249e14), /*5.109094217170944e18/43867*/ |
| Scalar(-4.5979787224074726105e15), /*8.028576626982912e20/174611*/ |
| Scalar(1.8152105401943546773e17), /*1.5511210043330985984e23/854513*/ |
| Scalar(-7.1661652561756670113e18) /*1.6938241367317436694528e27/236364091*/ |
| }; |
| |
| const Scalar maxnum = NumTraits<Scalar>::infinity(); |
| const Scalar zero = Scalar(0.0), half = Scalar(0.5), one = Scalar(1.0); |
| const Scalar machep = cephes_helper<Scalar>::machep(); |
| const Scalar nan = NumTraits<Scalar>::quiet_NaN(); |
| |
| if (x == one) return maxnum; |
| |
| if (x < one) { |
| return nan; |
| } |
| |
| if (q <= zero) { |
| if (q == numext::floor(q)) { |
| if (x == numext::floor(x) && long(x) % 2 == 0) { |
| return maxnum; |
| } else { |
| return nan; |
| } |
| } |
| p = x; |
| r = numext::floor(p); |
| if (p != r) return nan; |
| } |
| |
| /* Permit negative q but continue sum until n+q > +9 . |
| * This case should be handled by a reflection formula. |
| * If q<0 and x is an integer, there is a relation to |
| * the polygamma function. |
| */ |
| s = numext::pow(q, -x); |
| a = q; |
| b = zero; |
| // Run the summation in a helper function that is specific to the floating precision |
| if (zeta_impl_series<Scalar>::run(a, b, s, x, machep)) { |
| return s; |
| } |
| |
| // If b is zero, then the tail sum will also end up being zero. |
| // Exiting early here can prevent NaNs for some large inputs, where |
| // the tail sum computed below has term `a` which can overflow to `inf`. |
| if (numext::equal_strict(b, zero)) { |
| return s; |
| } |
| |
| w = a; |
| s += b * w / (x - one); |
| s -= half * b; |
| a = one; |
| k = zero; |
| |
| for (i = 0; i < 12; i++) { |
| a *= x + k; |
| b /= w; |
| t = a * b / A[i]; |
| s = s + t; |
| t = numext::abs(t / s); |
| if (t < machep) { |
| break; |
| } |
| k += one; |
| a *= x + k; |
| b /= w; |
| k += one; |
| } |
| return s; |
| } |
| }; |
| |
| /**************************************************************************** |
| * Implementation of polygamma function, requires C++11/C99 * |
| ****************************************************************************/ |
| |
| template <typename Scalar> |
| struct polygamma_retval { |
| typedef Scalar type; |
| }; |
| |
| #if !EIGEN_HAS_C99_MATH |
| |
| template <typename Scalar> |
| struct polygamma_impl { |
| EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false), THIS_TYPE_IS_NOT_SUPPORTED) |
| |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar run(Scalar n, Scalar x) { return Scalar(0); } |
| }; |
| |
| #else |
| |
| template <typename Scalar> |
| struct polygamma_impl { |
| EIGEN_DEVICE_FUNC static Scalar run(Scalar n, Scalar x) { |
| Scalar zero = 0.0, one = 1.0; |
| Scalar nplus = n + one; |
| const Scalar nan = NumTraits<Scalar>::quiet_NaN(); |
| |
| // Check that n is a non-negative integer |
| if (numext::floor(n) != n || n < zero) { |
| return nan; |
| } |
| // Just return the digamma function for n = 0 |
| else if (n == zero) { |
| return digamma_impl<Scalar>::run(x); |
| } |
| // Use the same implementation as scipy |
| else { |
| Scalar factorial = numext::exp(lgamma_impl<Scalar>::run(nplus)); |
| return numext::pow(-one, nplus) * factorial * zeta_impl<Scalar>::run(nplus, x); |
| } |
| } |
| }; |
| |
| #endif // EIGEN_HAS_C99_MATH |
| |
| /************************************************************************************************ |
| * Implementation of betainc (incomplete beta integral), based on Cephes but requires C++11/C99 * |
| ************************************************************************************************/ |
| |
| template <typename Scalar> |
| struct betainc_retval { |
| typedef Scalar type; |
| }; |
| |
| #if !EIGEN_HAS_C99_MATH |
| |
| template <typename Scalar> |
| struct betainc_impl { |
| EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false), THIS_TYPE_IS_NOT_SUPPORTED) |
| |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar run(Scalar a, Scalar b, Scalar x) { return Scalar(0); } |
| }; |
| |
| #else |
| |
| template <typename Scalar> |
| struct betainc_impl { |
| EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false), THIS_TYPE_IS_NOT_SUPPORTED) |
| |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar run(Scalar, Scalar, Scalar) { |
| /* betaincf.c |
| * |
| * Incomplete beta integral |
| * |
| * |
| * SYNOPSIS: |
| * |
| * float a, b, x, y, betaincf(); |
| * |
| * y = betaincf( a, b, x ); |
| * |
| * |
| * DESCRIPTION: |
| * |
| * Returns incomplete beta integral of the arguments, evaluated |
| * from zero to x. The function is defined as |
| * |
| * x |
| * - - |
| * | (a+b) | | a-1 b-1 |
| * ----------- | t (1-t) dt. |
| * - - | | |
| * | (a) | (b) - |
| * 0 |
| * |
| * The domain of definition is 0 <= x <= 1. In this |
| * implementation a and b are restricted to positive values. |
| * The integral from x to 1 may be obtained by the symmetry |
| * relation |
| * |
| * 1 - betainc( a, b, x ) = betainc( b, a, 1-x ). |
| * |
| * The integral is evaluated by a continued fraction expansion. |
| * If a < 1, the function calls itself recursively after a |
| * transformation to increase a to a+1. |
| * |
| * ACCURACY (float): |
| * |
| * Tested at random points (a,b,x) with a and b in the indicated |
| * interval and x between 0 and 1. |
| * |
| * arithmetic domain # trials peak rms |
| * Relative error: |
| * IEEE 0,30 10000 3.7e-5 5.1e-6 |
| * IEEE 0,100 10000 1.7e-4 2.5e-5 |
| * The useful domain for relative error is limited by underflow |
| * of the single precision exponential function. |
| * Absolute error: |
| * IEEE 0,30 100000 2.2e-5 9.6e-7 |
| * IEEE 0,100 10000 6.5e-5 3.7e-6 |
| * |
| * Larger errors may occur for extreme ratios of a and b. |
| * |
| * ACCURACY (double): |
| * arithmetic domain # trials peak rms |
| * IEEE 0,5 10000 6.9e-15 4.5e-16 |
| * IEEE 0,85 250000 2.2e-13 1.7e-14 |
| * IEEE 0,1000 30000 5.3e-12 6.3e-13 |
| * IEEE 0,10000 250000 9.3e-11 7.1e-12 |
| * IEEE 0,100000 10000 8.7e-10 4.8e-11 |
| * Outputs smaller than the IEEE gradual underflow threshold |
| * were excluded from these statistics. |
| * |
| * ERROR MESSAGES: |
| * message condition value returned |
| * incbet domain x<0, x>1 nan |
| * incbet underflow nan |
| */ |
| return Scalar(0); |
| } |
| }; |
| |
| /* Continued fraction expansion #1 for incomplete beta integral (small_branch = True) |
| * Continued fraction expansion #2 for incomplete beta integral (small_branch = False) |
| */ |
| template <typename Scalar> |
| struct incbeta_cfe { |
| EIGEN_STATIC_ASSERT((internal::is_same<Scalar, float>::value || internal::is_same<Scalar, double>::value), |
| THIS_TYPE_IS_NOT_SUPPORTED) |
| |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar run(Scalar a, Scalar b, Scalar x, bool small_branch) { |
| const Scalar big = cephes_helper<Scalar>::big(); |
| const Scalar machep = cephes_helper<Scalar>::machep(); |
| const Scalar biginv = cephes_helper<Scalar>::biginv(); |
| |
| const Scalar zero = 0; |
| const Scalar one = 1; |
| const Scalar two = 2; |
| |
| Scalar xk, pk, pkm1, pkm2, qk, qkm1, qkm2; |
| Scalar k1, k2, k3, k4, k5, k6, k7, k8, k26update; |
| Scalar ans; |
| int n; |
| |
| const int num_iters = (internal::is_same<Scalar, float>::value) ? 100 : 300; |
| const Scalar thresh = (internal::is_same<Scalar, float>::value) ? machep : Scalar(3) * machep; |
| Scalar r = (internal::is_same<Scalar, float>::value) ? zero : one; |
| |
| if (small_branch) { |
| k1 = a; |
| k2 = a + b; |
| k3 = a; |
| k4 = a + one; |
| k5 = one; |
| k6 = b - one; |
| k7 = k4; |
| k8 = a + two; |
| k26update = one; |
| } else { |
| k1 = a; |
| k2 = b - one; |
| k3 = a; |
| k4 = a + one; |
| k5 = one; |
| k6 = a + b; |
| k7 = a + one; |
| k8 = a + two; |
| k26update = -one; |
| x = x / (one - x); |
| } |
| |
| pkm2 = zero; |
| qkm2 = one; |
| pkm1 = one; |
| qkm1 = one; |
| ans = one; |
| n = 0; |
| |
| do { |
| xk = -(x * k1 * k2) / (k3 * k4); |
| pk = pkm1 + pkm2 * xk; |
| qk = qkm1 + qkm2 * xk; |
| pkm2 = pkm1; |
| pkm1 = pk; |
| qkm2 = qkm1; |
| qkm1 = qk; |
| |
| xk = (x * k5 * k6) / (k7 * k8); |
| pk = pkm1 + pkm2 * xk; |
| qk = qkm1 + qkm2 * xk; |
| pkm2 = pkm1; |
| pkm1 = pk; |
| qkm2 = qkm1; |
| qkm1 = qk; |
| |
| if (qk != zero) { |
| r = pk / qk; |
| if (numext::abs(ans - r) < numext::abs(r) * thresh) { |
| return r; |
| } |
| ans = r; |
| } |
| |
| k1 += one; |
| k2 += k26update; |
| k3 += two; |
| k4 += two; |
| k5 += one; |
| k6 -= k26update; |
| k7 += two; |
| k8 += two; |
| |
| if ((numext::abs(qk) + numext::abs(pk)) > big) { |
| pkm2 *= biginv; |
| pkm1 *= biginv; |
| qkm2 *= biginv; |
| qkm1 *= biginv; |
| } |
| if ((numext::abs(qk) < biginv) || (numext::abs(pk) < biginv)) { |
| pkm2 *= big; |
| pkm1 *= big; |
| qkm2 *= big; |
| qkm1 *= big; |
| } |
| } while (++n < num_iters); |
| |
| return ans; |
| } |
| }; |
| |
| /* Helper functions depending on the Scalar type */ |
| template <typename Scalar> |
| struct betainc_helper {}; |
| |
| template <> |
| struct betainc_helper<float> { |
| /* Core implementation, assumes a large (> 1.0) */ |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE float incbsa(float aa, float bb, float xx) { |
| float ans, a, b, t, x, onemx; |
| bool reversed_a_b = false; |
| |
| onemx = 1.0f - xx; |
| |
| /* see if x is greater than the mean */ |
| if (xx > (aa / (aa + bb))) { |
| reversed_a_b = true; |
| a = bb; |
| b = aa; |
| t = xx; |
| x = onemx; |
| } else { |
| a = aa; |
| b = bb; |
| t = onemx; |
| x = xx; |
| } |
| |
| /* Choose expansion for optimal convergence */ |
| if (b > 10.0f) { |
| if (numext::abs(b * x / a) < 0.3f) { |
| t = betainc_helper<float>::incbps(a, b, x); |
| if (reversed_a_b) t = 1.0f - t; |
| return t; |
| } |
| } |
| |
| ans = x * (a + b - 2.0f) / (a - 1.0f); |
| if (ans < 1.0f) { |
| ans = incbeta_cfe<float>::run(a, b, x, true /* small_branch */); |
| t = b * numext::log(t); |
| } else { |
| ans = incbeta_cfe<float>::run(a, b, x, false /* small_branch */); |
| t = (b - 1.0f) * numext::log(t); |
| } |
| |
| t += a * numext::log(x) + lgamma_impl<float>::run(a + b) - lgamma_impl<float>::run(a) - lgamma_impl<float>::run(b); |
| t += numext::log(ans / a); |
| t = numext::exp(t); |
| |
| if (reversed_a_b) t = 1.0f - t; |
| return t; |
| } |
| |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE float incbps(float a, float b, float x) { |
| float t, u, y, s; |
| const float machep = cephes_helper<float>::machep(); |
| |
| y = a * numext::log(x) + (b - 1.0f) * numext::log1p(-x) - numext::log(a); |
| y -= lgamma_impl<float>::run(a) + lgamma_impl<float>::run(b); |
| y += lgamma_impl<float>::run(a + b); |
| |
| t = x / (1.0f - x); |
| s = 0.0f; |
| u = 1.0f; |
| do { |
| b -= 1.0f; |
| if (b == 0.0f) { |
| break; |
| } |
| a += 1.0f; |
| u *= t * b / a; |
| s += u; |
| } while (numext::abs(u) > machep); |
| |
| return numext::exp(y) * (1.0f + s); |
| } |
| }; |
| |
| template <> |
| struct betainc_impl<float> { |
| EIGEN_DEVICE_FUNC static float run(float a, float b, float x) { |
| const float nan = NumTraits<float>::quiet_NaN(); |
| float ans, t; |
| |
| if (a <= 0.0f) return nan; |
| if (b <= 0.0f) return nan; |
| if ((x <= 0.0f) || (x >= 1.0f)) { |
| if (x == 0.0f) return 0.0f; |
| if (x == 1.0f) return 1.0f; |
| // mtherr("betaincf", DOMAIN); |
| return nan; |
| } |
| |
| /* transformation for small aa */ |
| if (a <= 1.0f) { |
| ans = betainc_helper<float>::incbsa(a + 1.0f, b, x); |
| t = a * numext::log(x) + b * numext::log1p(-x) + lgamma_impl<float>::run(a + b) - |
| lgamma_impl<float>::run(a + 1.0f) - lgamma_impl<float>::run(b); |
| return (ans + numext::exp(t)); |
| } else { |
| return betainc_helper<float>::incbsa(a, b, x); |
| } |
| } |
| }; |
| |
| template <> |
| struct betainc_helper<double> { |
| EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE double incbps(double a, double b, double x) { |
| const double machep = cephes_helper<double>::machep(); |
| |
| double s, t, u, v, n, t1, z, ai; |
| |
| ai = 1.0 / a; |
| u = (1.0 - b) * x; |
| v = u / (a + 1.0); |
| t1 = v; |
| t = u; |
| n = 2.0; |
| s = 0.0; |
| z = machep * ai; |
| while (numext::abs(v) > z) { |
| u = (n - b) * x / n; |
| t *= u; |
| v = t / (a + n); |
| s += v; |
| n += 1.0; |
| } |
| s += t1; |
| s += ai; |
| |
| u = a * numext::log(x); |
| // TODO: gamma() is not directly implemented in Eigen. |
| /* |
| if ((a + b) < maxgam && numext::abs(u) < maxlog) { |
| t = gamma(a + b) / (gamma(a) * gamma(b)); |
| s = s * t * pow(x, a); |
| } |
| */ |
| t = lgamma_impl<double>::run(a + b) - lgamma_impl<double>::run(a) - lgamma_impl<double>::run(b) + u + |
| numext::log(s); |
| return s = numext::exp(t); |
| } |
| }; |
| |
| template <> |
| struct betainc_impl<double> { |
| EIGEN_DEVICE_FUNC static double run(double aa, double bb, double xx) { |
| const double nan = NumTraits<double>::quiet_NaN(); |
| const double machep = cephes_helper<double>::machep(); |
| // const double maxgam = 171.624376956302725; |
| |
| double a, b, t, x, xc, w, y; |
| bool reversed_a_b = false; |
| |
| if (aa <= 0.0 || bb <= 0.0) { |
| return nan; // goto domerr; |
| } |
| |
| if ((xx <= 0.0) || (xx >= 1.0)) { |
| if (xx == 0.0) return (0.0); |
| if (xx == 1.0) return (1.0); |
| // mtherr("incbet", DOMAIN); |
| return nan; |
| } |
| |
| if ((bb * xx) <= 1.0 && xx <= 0.95) { |
| return betainc_helper<double>::incbps(aa, bb, xx); |
| } |
| |
| w = 1.0 - xx; |
| |
| /* Reverse a and b if x is greater than the mean. */ |
| if (xx > (aa / (aa + bb))) { |
| reversed_a_b = true; |
| a = bb; |
| b = aa; |
| xc = xx; |
| x = w; |
| } else { |
| a = aa; |
| b = bb; |
| xc = w; |
| x = xx; |
| } |
| |
| if (reversed_a_b && (b * x) <= 1.0 && x <= 0.95) { |
| t = betainc_helper<double>::incbps(a, b, x); |
| if (t <= machep) { |
| t = 1.0 - machep; |
| } else { |
| t = 1.0 - t; |
| } |
| return t; |
| } |
| |
| /* Choose expansion for better convergence. */ |
| y = x * (a + b - 2.0) - (a - 1.0); |
| if (y < 0.0) { |
| w = incbeta_cfe<double>::run(a, b, x, true /* small_branch */); |
| } else { |
| w = incbeta_cfe<double>::run(a, b, x, false /* small_branch */) / xc; |
| } |
| |
| /* Multiply w by the factor |
| a b _ _ _ |
| x (1-x) | (a+b) / ( a | (a) | (b) ) . */ |
| |
| y = a * numext::log(x); |
| t = b * numext::log(xc); |
| // TODO: gamma is not directly implemented in Eigen. |
| /* |
| if ((a + b) < maxgam && numext::abs(y) < maxlog && numext::abs(t) < maxlog) |
| { |
| t = pow(xc, b); |
| t *= pow(x, a); |
| t /= a; |
| t *= w; |
| t *= gamma(a + b) / (gamma(a) * gamma(b)); |
| } else { |
| */ |
| /* Resort to logarithms. */ |
| y += t + lgamma_impl<double>::run(a + b) - lgamma_impl<double>::run(a) - lgamma_impl<double>::run(b); |
| y += numext::log(w / a); |
| t = numext::exp(y); |
| |
| /* } */ |
| // done: |
| |
| if (reversed_a_b) { |
| if (t <= machep) { |
| t = 1.0 - machep; |
| } else { |
| t = 1.0 - t; |
| } |
| } |
| return t; |
| } |
| }; |
| |
| #endif // EIGEN_HAS_C99_MATH |
| |
| } // end namespace internal |
| |
| namespace numext { |
| |
| template <typename Scalar> |
| EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(lgamma, Scalar) lgamma(const Scalar& x) { |
| return EIGEN_MATHFUNC_IMPL(lgamma, Scalar)::run(x); |
| } |
| |
| template <typename Scalar> |
| EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(digamma, Scalar) digamma(const Scalar& x) { |
| return EIGEN_MATHFUNC_IMPL(digamma, Scalar)::run(x); |
| } |
| |
| template <typename Scalar> |
| EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(zeta, Scalar) zeta(const Scalar& x, const Scalar& q) { |
| return EIGEN_MATHFUNC_IMPL(zeta, Scalar)::run(x, q); |
| } |
| |
| template <typename Scalar> |
| EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(polygamma, Scalar) polygamma(const Scalar& n, const Scalar& x) { |
| return EIGEN_MATHFUNC_IMPL(polygamma, Scalar)::run(n, x); |
| } |
| |
| template <typename Scalar> |
| EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(erf, Scalar) erf(const Scalar& x) { |
| return EIGEN_MATHFUNC_IMPL(erf, Scalar)::run(x); |
| } |
| |
| template <typename Scalar> |
| EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(erfc, Scalar) erfc(const Scalar& x) { |
| return EIGEN_MATHFUNC_IMPL(erfc, Scalar)::run(x); |
| } |
| |
| template <typename Scalar> |
| EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(ndtri, Scalar) ndtri(const Scalar& x) { |
| return EIGEN_MATHFUNC_IMPL(ndtri, Scalar)::run(x); |
| } |
| |
| template <typename Scalar> |
| EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(igamma, Scalar) igamma(const Scalar& a, const Scalar& x) { |
| return EIGEN_MATHFUNC_IMPL(igamma, Scalar)::run(a, x); |
| } |
| |
| template <typename Scalar> |
| EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(igamma_der_a, Scalar) igamma_der_a(const Scalar& a, const Scalar& x) { |
| return EIGEN_MATHFUNC_IMPL(igamma_der_a, Scalar)::run(a, x); |
| } |
| |
| template <typename Scalar> |
| EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(gamma_sample_der_alpha, Scalar) |
| gamma_sample_der_alpha(const Scalar& a, const Scalar& x) { |
| return EIGEN_MATHFUNC_IMPL(gamma_sample_der_alpha, Scalar)::run(a, x); |
| } |
| |
| template <typename Scalar> |
| EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(igammac, Scalar) igammac(const Scalar& a, const Scalar& x) { |
| return EIGEN_MATHFUNC_IMPL(igammac, Scalar)::run(a, x); |
| } |
| |
| template <typename Scalar> |
| EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(betainc, Scalar) |
| betainc(const Scalar& a, const Scalar& b, const Scalar& x) { |
| return EIGEN_MATHFUNC_IMPL(betainc, Scalar)::run(a, b, x); |
| } |
| |
| } // end namespace numext |
| } // end namespace Eigen |
| |
| #endif // EIGEN_SPECIAL_FUNCTIONS_H |
