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
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