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