mirror of
https://gitlab.com/libeigen/eigen.git
synced 2024-12-15 07:10:37 +08:00
102 lines
4.3 KiB
C++
102 lines
4.3 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
|
|
// for linear algebra.
|
|
//
|
|
// Copyright (C) 2009 Gael Guennebaud <g.gael@free.fr>
|
|
//
|
|
// Eigen is free software; you can redistribute it and/or
|
|
// modify it under the terms of the GNU Lesser General Public
|
|
// License as published by the Free Software Foundation; either
|
|
// version 3 of the License, or (at your option) any later version.
|
|
//
|
|
// Alternatively, you can redistribute it and/or
|
|
// modify it under the terms of the GNU General Public License as
|
|
// published by the Free Software Foundation; either version 2 of
|
|
// the License, or (at your option) any later version.
|
|
//
|
|
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
|
|
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
|
|
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
|
|
// GNU General Public License for more details.
|
|
//
|
|
// You should have received a copy of the GNU Lesser General Public
|
|
// License and a copy of the GNU General Public License along with
|
|
// Eigen. If not, see <http://www.gnu.org/licenses/>.
|
|
|
|
#include "main.h"
|
|
|
|
template<typename MatrixType> void stable_norm(const MatrixType& m)
|
|
{
|
|
/* this test covers the following files:
|
|
StableNorm.h
|
|
*/
|
|
|
|
typedef typename MatrixType::Scalar Scalar;
|
|
typedef typename NumTraits<Scalar>::Real RealScalar;
|
|
|
|
// Check the basic machine-dependent constants.
|
|
{
|
|
int ibeta, it, iemin, iemax;
|
|
|
|
ibeta = std::numeric_limits<RealScalar>::radix; // base for floating-point numbers
|
|
it = std::numeric_limits<RealScalar>::digits; // number of base-beta digits in mantissa
|
|
iemin = std::numeric_limits<RealScalar>::min_exponent; // minimum exponent
|
|
iemax = std::numeric_limits<RealScalar>::max_exponent; // maximum exponent
|
|
|
|
VERIFY( (!(iemin > 1 - 2*it || 1+it>iemax || (it==2 && ibeta<5) || (it<=4 && ibeta <= 3 ) || it<2))
|
|
&& "the stable norm algorithm cannot be guaranteed on this computer");
|
|
}
|
|
|
|
|
|
int rows = m.rows();
|
|
int cols = m.cols();
|
|
|
|
Scalar big = ei_abs(ei_random<Scalar>()) * (std::numeric_limits<RealScalar>::max() * RealScalar(1e-4));
|
|
Scalar small = static_cast<RealScalar>(1)/big;
|
|
|
|
MatrixType vzero = MatrixType::Zero(rows, cols),
|
|
vrand = MatrixType::Random(rows, cols),
|
|
vbig(rows, cols),
|
|
vsmall(rows,cols);
|
|
|
|
vbig.fill(big);
|
|
vsmall.fill(small);
|
|
|
|
VERIFY_IS_MUCH_SMALLER_THAN(vzero.norm(), static_cast<RealScalar>(1));
|
|
VERIFY_IS_APPROX(vrand.stableNorm(), vrand.norm());
|
|
VERIFY_IS_APPROX(vrand.blueNorm(), vrand.norm());
|
|
VERIFY_IS_APPROX(vrand.hypotNorm(), vrand.norm());
|
|
|
|
RealScalar size = static_cast<RealScalar>(m.size());
|
|
|
|
// test overflow
|
|
VERIFY_IS_NOT_APPROX(static_cast<Scalar>(vbig.norm()), ei_sqrt(size)*big); // here the default norm must fail
|
|
VERIFY_IS_APPROX(static_cast<Scalar>(vbig.stableNorm()), ei_sqrt(size)*big);
|
|
VERIFY_IS_APPROX(static_cast<Scalar>(vbig.blueNorm()), ei_sqrt(size)*big);
|
|
VERIFY_IS_APPROX(static_cast<Scalar>(vbig.hypotNorm()), ei_sqrt(size)*big);
|
|
|
|
// test underflow
|
|
VERIFY_IS_NOT_APPROX(static_cast<Scalar>(vsmall.norm()), ei_sqrt(size)*small); // here the default norm must fail
|
|
VERIFY_IS_APPROX(static_cast<Scalar>(vsmall.stableNorm()), ei_sqrt(size)*small);
|
|
VERIFY_IS_APPROX(static_cast<Scalar>(vsmall.blueNorm()), ei_sqrt(size)*small);
|
|
VERIFY_IS_APPROX(static_cast<Scalar>(vsmall.hypotNorm()), ei_sqrt(size)*small);
|
|
|
|
// Test compilation of cwise() version
|
|
VERIFY_IS_APPROX(vrand.colwise().stableNorm(), vrand.colwise().norm());
|
|
VERIFY_IS_APPROX(vrand.colwise().blueNorm(), vrand.colwise().norm());
|
|
VERIFY_IS_APPROX(vrand.colwise().hypotNorm(), vrand.colwise().norm());
|
|
VERIFY_IS_APPROX(vrand.rowwise().stableNorm(), vrand.rowwise().norm());
|
|
VERIFY_IS_APPROX(vrand.rowwise().blueNorm(), vrand.rowwise().norm());
|
|
VERIFY_IS_APPROX(vrand.rowwise().hypotNorm(), vrand.rowwise().norm());
|
|
}
|
|
|
|
void test_stable_norm()
|
|
{
|
|
for(int i = 0; i < g_repeat; i++) {
|
|
CALL_SUBTEST_1( stable_norm(Matrix<float, 1, 1>()) );
|
|
CALL_SUBTEST_2( stable_norm(Vector4d()) );
|
|
CALL_SUBTEST_3( stable_norm(VectorXd(ei_random<int>(10,2000))) );
|
|
CALL_SUBTEST_4( stable_norm(VectorXf(ei_random<int>(10,2000))) );
|
|
CALL_SUBTEST_5( stable_norm(VectorXcd(ei_random<int>(10,2000))) );
|
|
}
|
|
}
|