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bug #1193: fix lpNorm<Infinity> for empty input.

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This commit is contained in:
Gael Guennebaud 2016-06-02 15:29:59 +02:00
parent d616a81294
commit 8b6f53222b
3 changed files with 24 additions and 3 deletions
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7
Eigen/src/Core/Dot.h
View File

@ -227,9 +227,12 @@ struct lpNorm_selector<Derived, 2>
template<typename Derived>
struct lpNorm_selector<Derived, Infinity>
{
typedef typename NumTraits<typename traits<Derived>::Scalar>::Real RealScalar;
EIGEN_DEVICE_FUNC
static inline typename NumTraits<typename traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
static inline RealScalar run(const MatrixBase<Derived>& m)
{
if(Derived::SizeAtCompileTime==0 || (Derived::SizeAtCompileTime==Dynamic && m.size()==0))
return RealScalar(0);
return m.cwiseAbs().maxCoeff();
}
};
@ -240,6 +243,8 @@ struct lpNorm_selector<Derived, Infinity>
* of the coefficients of \c *this. If \a p is the special value \a Eigen::Infinity, this function returns the \f$ \ell^\infty \f$
* norm, that is the maximum of the absolute values of the coefficients of \c *this.
*
* In all cases, if \c *this is empty, then the value 0 is returned.
*
* \note For matrices, this function does not compute the <a href="https://en.wikipedia.org/wiki/Operator_norm">operator-norm</a>. That is, if \c *this is a matrix, then its coefficients are interpreted as a 1D vector. Nonetheless, you can easily compute the 1-norm and \f$\infty\f$-norm matrix operator norms using \link TutorialReductionsVisitorsBroadcastingReductionsNorm partial reductions \endlink.
*
* \sa norm()

4
Eigen/src/Core/Redux.h
View File

@ -438,7 +438,9 @@ DenseBase<Derived>::maxCoeff() const
return derived().redux(Eigen::internal::scalar_max_op<Scalar>());
}
/** \returns the sum of all coefficients of *this
/** \returns the sum of all coefficients of \c *this
*
* If \c *this is empty, then the value 0 is returned.
*
* \sa trace(), prod(), mean()
*/

16
test/array_for_matrix.cpp
View File

@ -144,9 +144,21 @@ template<typename MatrixType> void comparisons(const MatrixType& m)
template<typename VectorType> void lpNorm(const VectorType& v)
{
using std::sqrt;
typedef typename VectorType::RealScalar RealScalar;
VectorType u = VectorType::Random(v.size());
VERIFY_IS_APPROX(u.template lpNorm<Infinity>(), u.cwiseAbs().maxCoeff());
if(v.size()==0)
{
VERIFY_IS_APPROX(u.template lpNorm<Infinity>(), RealScalar(0));
VERIFY_IS_APPROX(u.template lpNorm<1>(), RealScalar(0));
VERIFY_IS_APPROX(u.template lpNorm<2>(), RealScalar(0));
VERIFY_IS_APPROX(u.template lpNorm<5>(), RealScalar(0));
}
else
{
VERIFY_IS_APPROX(u.template lpNorm<Infinity>(), u.cwiseAbs().maxCoeff());
}
VERIFY_IS_APPROX(u.template lpNorm<1>(), u.cwiseAbs().sum());
VERIFY_IS_APPROX(u.template lpNorm<2>(), sqrt(u.array().abs().square().sum()));
VERIFY_IS_APPROX(numext::pow(u.template lpNorm<5>(), typename VectorType::RealScalar(5)), u.array().abs().pow(5).sum());
@ -255,6 +267,8 @@ void test_array_for_matrix()
CALL_SUBTEST_5( lpNorm(VectorXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_4( lpNorm(VectorXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
}
CALL_SUBTEST_5( lpNorm(VectorXf(0)) );
CALL_SUBTEST_4( lpNorm(VectorXcf(0)) );
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_4( resize(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_5( resize(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );