// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2014 Pedro Gonnet (pedro.gonnet@gmail.com) // Copyright (C) 2016 Gael Guennebaud // // 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_MATHFUNCTIONSIMPL_H #define EIGEN_MATHFUNCTIONSIMPL_H namespace Eigen { namespace internal { /** \internal \returns the hyperbolic tan of \a a (coeff-wise) Doesn't do anything fancy, just a 13/6-degree rational interpolant which is accurate up to a couple of ulps in the (approximate) range [-8, 8], outside of which tanh(x) = +/-1 in single precision. The input is clamped to the range [-c, c]. The value c is chosen as the smallest value where the approximation evaluates to exactly 1. In the reange [-0.0004, 0.0004] the approxmation tanh(x) ~= x is used for better accuracy as x tends to zero. This implementation works on both scalars and packets. */ template T generic_fast_tanh_float(const T& a_x) { // Clamp the inputs to the range [-c, c] #ifdef EIGEN_VECTORIZE_FMA const T plus_clamp = pset1(7.99881172180175781f); const T minus_clamp = pset1(-7.99881172180175781f); #else const T plus_clamp = pset1(7.90531110763549805f); const T minus_clamp = pset1(-7.90531110763549805f); #endif const T tiny = pset1(0.0004f); const T x = pmax(pmin(a_x, plus_clamp), minus_clamp); const T tiny_mask = pcmp_lt(pabs(a_x), tiny); // The monomial coefficients of the numerator polynomial (odd). const T alpha_1 = pset1(4.89352455891786e-03f); const T alpha_3 = pset1(6.37261928875436e-04f); const T alpha_5 = pset1(1.48572235717979e-05f); const T alpha_7 = pset1(5.12229709037114e-08f); const T alpha_9 = pset1(-8.60467152213735e-11f); const T alpha_11 = pset1(2.00018790482477e-13f); const T alpha_13 = pset1(-2.76076847742355e-16f); // The monomial coefficients of the denominator polynomial (even). const T beta_0 = pset1(4.89352518554385e-03f); const T beta_2 = pset1(2.26843463243900e-03f); const T beta_4 = pset1(1.18534705686654e-04f); const T beta_6 = pset1(1.19825839466702e-06f); // Since the polynomials are odd/even, we need x^2. const T x2 = pmul(x, x); // Evaluate the numerator polynomial p. T p = pmadd(x2, alpha_13, alpha_11); p = pmadd(x2, p, alpha_9); p = pmadd(x2, p, alpha_7); p = pmadd(x2, p, alpha_5); p = pmadd(x2, p, alpha_3); p = pmadd(x2, p, alpha_1); p = pmul(x, p); // Evaluate the denominator polynomial q. T q = pmadd(x2, beta_6, beta_4); q = pmadd(x2, q, beta_2); q = pmadd(x2, q, beta_0); // Divide the numerator by the denominator. return pselect(tiny_mask, x, pdiv(p, q)); } template EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE RealScalar positive_real_hypot(const RealScalar& x, const RealScalar& y) { EIGEN_USING_STD_MATH(sqrt); RealScalar p, qp; p = numext::maxi(x,y); if(p==RealScalar(0)) return RealScalar(0); qp = numext::mini(y,x) / p; return p * sqrt(RealScalar(1) + qp*qp); } template struct hypot_impl { typedef typename NumTraits::Real RealScalar; static EIGEN_DEVICE_FUNC inline RealScalar run(const Scalar& x, const Scalar& y) { EIGEN_USING_STD_MATH(abs); return positive_real_hypot(abs(x), abs(y)); } }; } // end namespace internal } // end namespace Eigen #endif // EIGEN_MATHFUNCTIONSIMPL_H