116 lines
4.3 KiB
C++
116 lines
4.3 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
|
|
// for linear algebra.
|
|
//
|
|
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
|
|
//
|
|
// This Source Code Form is subject to the terms of the Mozilla
|
|
// Public License v. 2.0. If a copy of the MPL was not distributed
|
|
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
|
|
|
|
#include "main.h"
|
|
|
|
// workaround aggressive optimization in ICC
|
|
template<typename T> EIGEN_DONT_INLINE T sub(T a, T b) { return a - b; }
|
|
|
|
template<typename T> bool isFinite(const T& x)
|
|
{
|
|
return isNotNaN(sub(x,x));
|
|
}
|
|
|
|
template<typename T> EIGEN_DONT_INLINE T copy(const T& x)
|
|
{
|
|
return x;
|
|
}
|
|
|
|
template<typename MatrixType> void stable_norm(const MatrixType& m)
|
|
{
|
|
/* this test covers the following files:
|
|
StableNorm.h
|
|
*/
|
|
using std::sqrt;
|
|
using std::abs;
|
|
typedef typename MatrixType::Index Index;
|
|
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");
|
|
}
|
|
|
|
|
|
Index rows = m.rows();
|
|
Index cols = m.cols();
|
|
|
|
// get a non-zero random factor
|
|
Scalar factor = internal::random<Scalar>();
|
|
while(numext::abs2(factor)<RealScalar(1e-4))
|
|
factor = internal::random<Scalar>();
|
|
Scalar big = factor * ((std::numeric_limits<RealScalar>::max)() * RealScalar(1e-4));
|
|
|
|
factor = internal::random<Scalar>();
|
|
while(numext::abs2(factor)<RealScalar(1e-4))
|
|
factor = internal::random<Scalar>();
|
|
Scalar small = factor * ((std::numeric_limits<RealScalar>::min)() * RealScalar(1e4));
|
|
|
|
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 isFinite
|
|
VERIFY(!isFinite( std::numeric_limits<RealScalar>::infinity()));
|
|
VERIFY(!isFinite(sqrt(-abs(big))));
|
|
|
|
// test overflow
|
|
VERIFY(isFinite(sqrt(size)*abs(big)));
|
|
VERIFY_IS_NOT_APPROX(sqrt(copy(vbig.squaredNorm())), abs(sqrt(size)*big)); // here the default norm must fail
|
|
VERIFY_IS_APPROX(vbig.stableNorm(), sqrt(size)*abs(big));
|
|
VERIFY_IS_APPROX(vbig.blueNorm(), sqrt(size)*abs(big));
|
|
VERIFY_IS_APPROX(vbig.hypotNorm(), sqrt(size)*abs(big));
|
|
|
|
// test underflow
|
|
VERIFY(isFinite(sqrt(size)*abs(small)));
|
|
VERIFY_IS_NOT_APPROX(sqrt(copy(vsmall.squaredNorm())), abs(sqrt(size)*small)); // here the default norm must fail
|
|
VERIFY_IS_APPROX(vsmall.stableNorm(), sqrt(size)*abs(small));
|
|
VERIFY_IS_APPROX(vsmall.blueNorm(), sqrt(size)*abs(small));
|
|
VERIFY_IS_APPROX(vsmall.hypotNorm(), sqrt(size)*abs(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(internal::random<int>(10,2000))) );
|
|
CALL_SUBTEST_4( stable_norm(VectorXf(internal::random<int>(10,2000))) );
|
|
CALL_SUBTEST_5( stable_norm(VectorXcd(internal::random<int>(10,2000))) );
|
|
}
|
|
}
|