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add a stable_norm unit test

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Gael Guennebaud
2009-09-07 12:46:16 +02:00
parent bdcc0bc157
commit b56bb441dd
4 changed files with 91 additions and 17 deletions
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test/stable_norm.cpp Normal file
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// 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;
int rows = m.rows();
int cols = m.cols();
Scalar big = ei_random<Scalar>() * std::numeric_limits<RealScalar>::max() * 1e-4;
Scalar small = 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 = m.size();
// test overflow
VERIFY_IS_NOT_APPROX(vbig.norm(), ei_sqrt(size)*big); // here the default norm must fail
VERIFY_IS_APPROX(vbig.stableNorm(), ei_sqrt(size)*big);
VERIFY_IS_APPROX(vbig.blueNorm(), ei_sqrt(size)*big);
VERIFY_IS_APPROX(vbig.hypotNorm(), ei_sqrt(size)*big);
// test underflow
VERIFY_IS_NOT_APPROX(vsmall.norm(), ei_sqrt(size)*small); // here the default norm must fail
VERIFY_IS_APPROX(vsmall.stableNorm(), ei_sqrt(size)*small);
VERIFY_IS_APPROX(vsmall.blueNorm(), ei_sqrt(size)*small);
VERIFY_IS_APPROX(vsmall.hypotNorm(), ei_sqrt(size)*small);
}
void test_stable_norm()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST( stable_norm(Matrix<float, 1, 1>()) );
CALL_SUBTEST( stable_norm(Vector4d()) );
CALL_SUBTEST( stable_norm(VectorXd(ei_random<int>(10,2000))) );
CALL_SUBTEST( stable_norm(VectorXf(ei_random<int>(10,2000))) );
CALL_SUBTEST( stable_norm(VectorXcd(ei_random<int>(10,2000))) );
}
}