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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009-2014 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"
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;
bool complex_real_product_ok = true;
// 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");
Scalar inf = std::numeric_limits<RealScalar>::infinity();
if(NumTraits<Scalar>::IsComplex && (numext::isnan)(inf*RealScalar(1)) )
{
complex_real_product_ok = false;
static bool first = true;
if(first)
std::cerr << "WARNING: compiler mess up complex*real product, " << inf << " * " << 1.0 << " = " << inf*RealScalar(1) << std::endl;
first = false;
}
}
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 numext::isfinite
VERIFY(!(numext::isfinite)( std::numeric_limits<RealScalar>::infinity()));
VERIFY(!(numext::isfinite)(sqrt(-abs(big))));
// test overflow
VERIFY((numext::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((numext::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());
// test NaN, +inf, -inf
MatrixType v;
Index i = internal::random<Index>(0,rows-1);
Index j = internal::random<Index>(0,cols-1);
// NaN
{
v = vrand;
v(i,j) = std::numeric_limits<RealScalar>::quiet_NaN();
VERIFY(!(numext::isfinite)(v.squaredNorm())); VERIFY((numext::isnan)(v.squaredNorm()));
VERIFY(!(numext::isfinite)(v.norm())); VERIFY((numext::isnan)(v.norm()));
VERIFY(!(numext::isfinite)(v.stableNorm())); VERIFY((numext::isnan)(v.stableNorm()));
VERIFY(!(numext::isfinite)(v.blueNorm())); VERIFY((numext::isnan)(v.blueNorm()));
VERIFY(!(numext::isfinite)(v.hypotNorm())); VERIFY((numext::isnan)(v.hypotNorm()));
}
// +inf
{
v = vrand;
v(i,j) = std::numeric_limits<RealScalar>::infinity();
VERIFY(!(numext::isfinite)(v.squaredNorm())); VERIFY(isPlusInf(v.squaredNorm()));
VERIFY(!(numext::isfinite)(v.norm())); VERIFY(isPlusInf(v.norm()));
VERIFY(!(numext::isfinite)(v.stableNorm()));
if(complex_real_product_ok){
VERIFY(isPlusInf(v.stableNorm()));
}
VERIFY(!(numext::isfinite)(v.blueNorm())); VERIFY(isPlusInf(v.blueNorm()));
VERIFY(!(numext::isfinite)(v.hypotNorm())); VERIFY(isPlusInf(v.hypotNorm()));
}
// -inf
{
v = vrand;
v(i,j) = -std::numeric_limits<RealScalar>::infinity();
VERIFY(!(numext::isfinite)(v.squaredNorm())); VERIFY(isPlusInf(v.squaredNorm()));
VERIFY(!(numext::isfinite)(v.norm())); VERIFY(isPlusInf(v.norm()));
VERIFY(!(numext::isfinite)(v.stableNorm()));
if(complex_real_product_ok) {
VERIFY(isPlusInf(v.stableNorm()));
}
VERIFY(!(numext::isfinite)(v.blueNorm())); VERIFY(isPlusInf(v.blueNorm()));
VERIFY(!(numext::isfinite)(v.hypotNorm())); VERIFY(isPlusInf(v.hypotNorm()));
}
// mix
{
Index i2 = internal::random<Index>(0,rows-1);
Index j2 = internal::random<Index>(0,cols-1);
v = vrand;
v(i,j) = -std::numeric_limits<RealScalar>::infinity();
v(i2,j2) = std::numeric_limits<RealScalar>::quiet_NaN();
VERIFY(!(numext::isfinite)(v.squaredNorm())); VERIFY((numext::isnan)(v.squaredNorm()));
VERIFY(!(numext::isfinite)(v.norm())); VERIFY((numext::isnan)(v.norm()));
VERIFY(!(numext::isfinite)(v.stableNorm())); VERIFY((numext::isnan)(v.stableNorm()));
VERIFY(!(numext::isfinite)(v.blueNorm())); VERIFY((numext::isnan)(v.blueNorm()));
VERIFY(!(numext::isfinite)(v.hypotNorm())); VERIFY((numext::isnan)(v.hypotNorm()));
}
// stableNormalize[d]
{
VERIFY_IS_APPROX(vrand.stableNormalized(), vrand.normalized());
MatrixType vcopy(vrand);
vcopy.stableNormalize();
VERIFY_IS_APPROX(vcopy, vrand.normalized());
VERIFY_IS_APPROX((vrand.stableNormalized()).norm(), RealScalar(1));
VERIFY_IS_APPROX(vcopy.norm(), RealScalar(1));
VERIFY_IS_APPROX((vbig.stableNormalized()).norm(), RealScalar(1));
VERIFY_IS_APPROX((vsmall.stableNormalized()).norm(), RealScalar(1));
RealScalar big_scaling = ((std::numeric_limits<RealScalar>::max)() * RealScalar(1e-4));
VERIFY_IS_APPROX(vbig/big_scaling, (vbig.stableNorm() * vbig.stableNormalized()).eval()/big_scaling);
VERIFY_IS_APPROX(vsmall, vsmall.stableNorm() * vsmall.stableNormalized());
}
}
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))) );
}
}
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