mirror of
https://gitlab.com/libeigen/eigen.git
synced 2024-12-21 07:19:46 +08:00
bug #977: avoid division by 0 in normalize() and normalized().
This commit is contained in:
parent
7cae8918c0
commit
ee37eb4eed
@ -102,7 +102,10 @@ inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real Matr
|
||||
return numext::sqrt(squaredNorm());
|
||||
}
|
||||
|
||||
/** \returns an expression of the quotient of *this by its own norm.
|
||||
/** \returns an expression of the quotient of \c *this by its own norm.
|
||||
*
|
||||
* \warning If the input vector is too small (i.e., this->norm()==0),
|
||||
* then this function returns a copy of the input.
|
||||
*
|
||||
* \only_for_vectors
|
||||
*
|
||||
@ -114,19 +117,29 @@ MatrixBase<Derived>::normalized() const
|
||||
{
|
||||
typedef typename internal::nested_eval<Derived,2>::type _Nested;
|
||||
_Nested n(derived());
|
||||
return n / n.norm();
|
||||
RealScalar z = n.squaredNorm();
|
||||
// NOTE: after extensive benchmarking, this conditional does not impact performance, at least on recent x86 CPU
|
||||
if(z>RealScalar(0))
|
||||
return n / numext::sqrt(z);
|
||||
else
|
||||
return n;
|
||||
}
|
||||
|
||||
/** Normalizes the vector, i.e. divides it by its own norm.
|
||||
*
|
||||
* \only_for_vectors
|
||||
*
|
||||
* \warning If the input vector is too small (i.e., this->norm()==0), then \c *this is left unchanged.
|
||||
*
|
||||
* \sa norm(), normalized()
|
||||
*/
|
||||
template<typename Derived>
|
||||
inline void MatrixBase<Derived>::normalize()
|
||||
{
|
||||
*this /= norm();
|
||||
RealScalar z = squaredNorm();
|
||||
// NOTE: after extensive benchmarking, this conditional does not impact performance, at least on recent x86 CPU
|
||||
if(z>RealScalar(0))
|
||||
derived() /= numext::sqrt(z);
|
||||
}
|
||||
|
||||
//---------- implementation of other norms ----------
|
||||
|
@ -42,6 +42,15 @@ template<> struct adjoint_specific<false> {
|
||||
VERIFY_IS_APPROX(v1, v1.norm() * v3);
|
||||
VERIFY_IS_APPROX(v3, v1.normalized());
|
||||
VERIFY_IS_APPROX(v3.norm(), RealScalar(1));
|
||||
|
||||
// check null inputs
|
||||
VERIFY_IS_APPROX((v1*0).normalized(), (v1*0));
|
||||
RealScalar very_small = (std::numeric_limits<RealScalar>::min)();
|
||||
VERIFY( (v1*very_small).norm() == 0 );
|
||||
VERIFY_IS_APPROX((v1*very_small).normalized(), (v1*very_small));
|
||||
v3 = v1*very_small;
|
||||
v3.normalize();
|
||||
VERIFY_IS_APPROX(v3, (v1*very_small));
|
||||
|
||||
// check compatibility of dot and adjoint
|
||||
ref = NumTraits<Scalar>::IsInteger ? 0 : (std::max)((std::max)(v1.norm(),v2.norm()),(std::max)((square * v2).norm(),(square.adjoint() * v1).norm()));
|
||||
|
Loading…
Reference in New Issue
Block a user