MathFunctions.h revision c981c48f5bc9aefeffc0bcb0cc3934c2fae179dd
1c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This file is part of Eigen, a lightweight C++ template library 2c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// for linear algebra. 3c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// 4c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr> 5c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// 6c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This Source Code Form is subject to the terms of the Mozilla 7c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Public License v. 2.0. If a copy of the MPL was not distributed 8c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. 9c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 10c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#ifndef EIGEN2_MATH_FUNCTIONS_H 11c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#define EIGEN2_MATH_FUNCTIONS_H 12c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathnamespace Eigen { 14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename T> inline typename NumTraits<T>::Real ei_real(const T& x) { return internal::real(x); } 16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename T> inline typename NumTraits<T>::Real ei_imag(const T& x) { return internal::imag(x); } 17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename T> inline T ei_conj(const T& x) { return internal::conj(x); } 18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename T> inline typename NumTraits<T>::Real ei_abs (const T& x) { return internal::abs(x); } 19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename T> inline typename NumTraits<T>::Real ei_abs2(const T& x) { return internal::abs2(x); } 20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename T> inline T ei_sqrt(const T& x) { return internal::sqrt(x); } 21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename T> inline T ei_exp (const T& x) { return internal::exp(x); } 22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename T> inline T ei_log (const T& x) { return internal::log(x); } 23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename T> inline T ei_sin (const T& x) { return internal::sin(x); } 24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename T> inline T ei_cos (const T& x) { return internal::cos(x); } 25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename T> inline T ei_atan2(const T& x,const T& y) { return internal::atan2(x,y); } 26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename T> inline T ei_pow (const T& x,const T& y) { return internal::pow(x,y); } 27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename T> inline T ei_random () { return internal::random<T>(); } 28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename T> inline T ei_random (const T& x, const T& y) { return internal::random(x, y); } 29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename T> inline T precision () { return NumTraits<T>::dummy_precision(); } 31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename T> inline T machine_epsilon () { return NumTraits<T>::epsilon(); } 32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Scalar, typename OtherScalar> 35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathinline bool ei_isMuchSmallerThan(const Scalar& x, const OtherScalar& y, 36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) 37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return internal::isMuchSmallerThan(x, y, precision); 39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Scalar> 42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathinline bool ei_isApprox(const Scalar& x, const Scalar& y, 43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) 44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return internal::isApprox(x, y, precision); 46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Scalar> 49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathinline bool ei_isApproxOrLessThan(const Scalar& x, const Scalar& y, 50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) 51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return internal::isApproxOrLessThan(x, y, precision); 53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} // end namespace Eigen 56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#endif // EIGEN2_MATH_FUNCTIONS_H 58