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-rw-r--r--Eigen/src/Core/util/Memory.h27
-rw-r--r--Eigen/src/Geometry/AlignedBox.h1
-rw-r--r--Eigen/src/QR/EigenSolver.h1
-rw-r--r--Eigen/src/QR/HessenbergDecomposition.h1
-rw-r--r--Eigen/src/QR/QR.h1
-rw-r--r--Eigen/src/QR/SelfAdjointEigenSolver.h3
-rw-r--r--Eigen/src/QR/Tridiagonalization.h9
-rw-r--r--Eigen/src/SVD/SVD.h1
-rw-r--r--doc/Doxyfile.in3
-rw-r--r--doc/TutorialGeometry.dox6
10 files changed, 29 insertions, 24 deletions
diff --git a/Eigen/src/Core/util/Memory.h b/Eigen/src/Core/util/Memory.h
index caf1d48ce..0d2c432eb 100644
--- a/Eigen/src/Core/util/Memory.h
+++ b/Eigen/src/Core/util/Memory.h
@@ -44,7 +44,7 @@ template <typename T, int Size> struct ei_aligned_array<T,Size,false>
T array[Size];
};
-/** \internal allocates \a size * sizeof(\a T) bytes with a 16 bytes based alignment */
+/** \internal allocates \a size * sizeof(\a T) bytes with 16 bytes alignment */
template<typename T>
inline T* ei_aligned_malloc(size_t size)
{
@@ -91,7 +91,7 @@ inline static int ei_alignmentOffset(const Scalar* ptr, int maxOffset)
/** \internal
* ei_alloc_stack(TYPE,SIZE) allocates sizeof(TYPE)*SIZE bytes on the stack if sizeof(TYPE)*SIZE is
* smaller than EIGEN_STACK_ALLOCATION_LIMIT. Otherwise the memory is allocated using the operator new.
- * Data allocated with ei_alloc_stack \b must be freed calling ei_free_stack(PTR,TYPE,SIZE).
+ * Data allocated with ei_alloc_stack \b must be freed by calling ei_free_stack(PTR,TYPE,SIZE).
* \code
* float * data = ei_alloc_stack(float,array.size());
* // ...
@@ -108,15 +108,15 @@ inline static int ei_alignmentOffset(const Scalar* ptr, int maxOffset)
/** \class WithAlignedOperatorNew
*
- * \brief Enforces inherited classes to be 16 bytes aligned when dynamicalled allocated with operator new
+ * \brief Enforces instances of inherited classes to be 16 bytes aligned when allocated with operator new
*
* When Eigen's explicit vectorization is enabled, Eigen assumes that some fixed sizes types are aligned
- * on a 16 bytes boundary. Such types include:
+ * on a 16 bytes boundary. Those include all Matrix types having a sizeof multiple of 16 bytes, e.g.:
* - Vector2d, Vector4f, Vector4i, Vector4d,
* - Matrix2d, Matrix4f, Matrix4i, Matrix4d,
* - etc.
- * When objects are statically allocated, the compiler will automatically and always enforces 16 bytes
- * alignment of the data. However some troubles might appear when data are dynamically allocated.
+ * When an object is statically allocated, the compiler will automatically and always enforces 16 bytes
+ * alignment of the data when needed. However some troubles might appear when data are dynamically allocated.
* Let's pick an example:
* \code
* struct Foo {
@@ -130,8 +130,8 @@ inline static int ei_alignmentOffset(const Scalar* ptr, int maxOffset)
* pObj2->some_vector = Vector4f(..); // => !! might segfault !!
* \endcode
* Here, the problem is that operator new is not aware of the compile time alignment requirement of the
- * type Vector4f (and hence of the type Foo). Therefore "new Foo" does not necessarily returned a 16 bytes
- * aligned pointer. The purpose of the class WithAlignedOperatorNew is exactly to overcome this issue, by
+ * type Vector4f (and hence of the type Foo). Therefore "new Foo" does not necessarily returns a 16 bytes
+ * aligned pointer. The purpose of the class WithAlignedOperatorNew is exactly to overcome this issue by
* overloading the operator new to return aligned data when the vectorization is enabled.
* Here is a similar safe example:
* \code
@@ -139,12 +139,9 @@ inline static int ei_alignmentOffset(const Scalar* ptr, int maxOffset)
* char dummy;
* Vector4f some_vector;
* };
- * Foo obj1; // static allocation
- * obj1.some_vector = Vector4f(..); // => OK
- *
* Foo *pObj2 = new Foo; // dynamic allocation
* pObj2->some_vector = Vector4f(..); // => SAFE !
- * \endcode
+ * \endcode
*
* \sa class ei_new_allocator
*/
@@ -172,7 +169,7 @@ struct WithAlignedOperatorNew
void operator delete(void * ptr) { free(ptr); }
void operator delete[](void * ptr) { free(ptr); }
-
+
#endif
};
@@ -190,14 +187,14 @@ struct ei_with_aligned_operator_new<T,SizeAtCompileTime,false> {};
* STL allocator simply wrapping operators new[] and delete[]. Unlike GCC's default new_allocator,
* ei_new_allocator call operator new on the type \a T and not the general new operator ignoring
* overloaded version of operator new.
- *
+ *
* Example:
* \code
* // Vector4f requires 16 bytes alignment:
* std::vector<Vector4f,ei_new_allocator<Vector4f> > dataVec4;
* // Vector3f does not require 16 bytes alignment, no need to use Eigen's allocator:
* std::vector<Vector3f> dataVec3;
- *
+ *
* struct Foo : WithAlignedOperatorNew {
* char dummy;
* Vector4f some_vector;
diff --git a/Eigen/src/Geometry/AlignedBox.h b/Eigen/src/Geometry/AlignedBox.h
index fb6d3d0a7..3d4608e73 100644
--- a/Eigen/src/Geometry/AlignedBox.h
+++ b/Eigen/src/Geometry/AlignedBox.h
@@ -26,6 +26,7 @@
#define EIGEN_ALIGNEDBOX_H
/** \geometry_module \ingroup GeometryModule
+ * \nonstableyet
*
* \class AlignedBox
*
diff --git a/Eigen/src/QR/EigenSolver.h b/Eigen/src/QR/EigenSolver.h
index 38a383f14..33dcd6daa 100644
--- a/Eigen/src/QR/EigenSolver.h
+++ b/Eigen/src/QR/EigenSolver.h
@@ -26,6 +26,7 @@
#define EIGEN_EIGENSOLVER_H
/** \ingroup QR_Module
+ * \nonstableyet
*
* \class EigenSolver
*
diff --git a/Eigen/src/QR/HessenbergDecomposition.h b/Eigen/src/QR/HessenbergDecomposition.h
index 30541670c..21597bb02 100644
--- a/Eigen/src/QR/HessenbergDecomposition.h
+++ b/Eigen/src/QR/HessenbergDecomposition.h
@@ -26,6 +26,7 @@
#define EIGEN_HESSENBERGDECOMPOSITION_H
/** \ingroup QR_Module
+ * \nonstableyet
*
* \class HessenbergDecomposition
*
diff --git a/Eigen/src/QR/QR.h b/Eigen/src/QR/QR.h
index e584ee120..c3fe96718 100644
--- a/Eigen/src/QR/QR.h
+++ b/Eigen/src/QR/QR.h
@@ -26,6 +26,7 @@
#define EIGEN_QR_H
/** \ingroup QR_Module
+ * \nonstableyet
*
* \class QR
*
diff --git a/Eigen/src/QR/SelfAdjointEigenSolver.h b/Eigen/src/QR/SelfAdjointEigenSolver.h
index fdb2764c4..e57b52ed5 100644
--- a/Eigen/src/QR/SelfAdjointEigenSolver.h
+++ b/Eigen/src/QR/SelfAdjointEigenSolver.h
@@ -26,6 +26,7 @@
#define EIGEN_SELFADJOINTEIGENSOLVER_H
/** \qr_module \ingroup QR_Module
+ * \nonstableyet
*
* \class SelfAdjointEigenSolver
*
@@ -225,7 +226,7 @@ void SelfAdjointEigenSolver<MatrixType>::
compute(const MatrixType& matA, const MatrixType& matB, bool computeEigenvectors)
{
ei_assert(matA.cols()==matA.rows() && matB.rows()==matA.rows() && matB.cols()==matB.rows());
-
+
// Compute the cholesky decomposition of matB = L L'
LLT<MatrixType> cholB(matB);
diff --git a/Eigen/src/QR/Tridiagonalization.h b/Eigen/src/QR/Tridiagonalization.h
index a9635c961..a4fa32ed4 100644
--- a/Eigen/src/QR/Tridiagonalization.h
+++ b/Eigen/src/QR/Tridiagonalization.h
@@ -26,6 +26,7 @@
#define EIGEN_TRIDIAGONALIZATION_H
/** \ingroup QR_Module
+ * \nonstableyet
*
* \class Tridiagonalization
*
@@ -219,7 +220,7 @@ void Tridiagonalization<MatrixType>::_compute(MatrixType& matA, CoeffVectorType&
// i.e., A = H' A H where H = I - h v v' and v = matA.col(i).end(n-i-1)
matA.col(i).coeffRef(i+1) = 1;
-
+
/* This is the initial algorithm which minimize operation counts and maximize
* the use of Eigen's expression. Unfortunately, the first matrix-vector product
* using Part<Lower|Selfadjoint> is very very slow */
@@ -284,7 +285,7 @@ void Tridiagonalization<MatrixType>::_compute(MatrixType& matA, CoeffVectorType&
hCoeffs.end(n-i-1) += (h * Scalar(-0.5) * matA.col(i).end(n-i-1).dot(hCoeffs.end(n-i-1)))
* matA.col(i).end(n-i-1);
-
+
const Scalar* EIGEN_RESTRICT pb = &matA.coeffRef(0,i);
const Scalar* EIGEN_RESTRICT pa = (&hCoeffs.coeffRef(0)) - 1;
for (int j1=i+1; j1<n; ++j1)
@@ -295,11 +296,11 @@ void Tridiagonalization<MatrixType>::_compute(MatrixType& matA, CoeffVectorType&
{
int alignedStart = (starti) + ei_alignmentOffset(&matA.coeffRef(starti,j1), n-starti);
alignedEnd = alignedStart + ((n-alignedStart)/PacketSize)*PacketSize;
-
+
for (int i1=starti; i1<alignedStart; ++i1)
matA.coeffRef(i1,j1) -= matA.coeff(i1,i)*ei_conj(hCoeffs.coeff(j1-1))
+ hCoeffs.coeff(i1-1)*ei_conj(matA.coeff(j1,i));
-
+
Packet tmp0 = ei_pset1(hCoeffs.coeff(j1-1));
Packet tmp1 = ei_pset1(matA.coeff(j1,i));
Scalar* pc = &matA.coeffRef(0,j1);
diff --git a/Eigen/src/SVD/SVD.h b/Eigen/src/SVD/SVD.h
index 100ca9310..debdc7606 100644
--- a/Eigen/src/SVD/SVD.h
+++ b/Eigen/src/SVD/SVD.h
@@ -26,6 +26,7 @@
#define EIGEN_SVD_H
/** \ingroup SVD_Module
+ * \nonstableyet
*
* \class SVD
*
diff --git a/doc/Doxyfile.in b/doc/Doxyfile.in
index f5e7a7100..7e3966c65 100644
--- a/doc/Doxyfile.in
+++ b/doc/Doxyfile.in
@@ -207,7 +207,8 @@ ALIASES = "only_for_vectors=This is only for vectors (either row-
"regression_module=This is defined in the %Regression module. \code #include <Eigen/Regression> \endcode" \
"addexample=\anchor" \
"label=\bug" \
- "redstar=<a href='#warningarraymodule' style='color:red;text-decoration: none;'><span style='color:red'>*</span></a>"
+ "redstar=<a href='#warningarraymodule' style='color:red;text-decoration: none;'><span style='color:red'>*</span></a>" \
+ "nonstableyet=\warning This class/function is not considered to be part of the stable public API yet. Some (minor) changes might happen in future releases."
# Set the OPTIMIZE_OUTPUT_FOR_C tag to YES if your project consists of C
# sources only. Doxygen will then generate output that is more tailored for C.
diff --git a/doc/TutorialGeometry.dox b/doc/TutorialGeometry.dox
index 8742341f5..5a1a3e06e 100644
--- a/doc/TutorialGeometry.dox
+++ b/doc/TutorialGeometry.dox
@@ -67,7 +67,7 @@ might still be interesting to write generic and efficient algorithms taking as i
kind of transformations.
Any of the above transformation types can be converted to any other types of the same nature,
-or to a more generic type. Here are come additional examples:
+or to a more generic type. Here are some additional examples:
<table class="tutorial_code">
<tr><td>\code
Rotation2Df r = Matrix2f(..); // assumes a pure rotation matrix
@@ -176,7 +176,7 @@ t.pretranslate(Vector_(tx,ty,..));
t *= Translation_(tx,ty,..);
t = Translation_(tx,ty,..) * t;
\endcode</td></tr>
-<tr><td>\b Rotation \n <em class="note">In 2D, any_rotation can also \n be an angle in radian</em></td><td>\code
+<tr><td>\b Rotation \n <em class="note">In 2D and for the procedural API, any_rotation can also \n be an angle in radian</em></td><td>\code
t.rotate(any_rotation);
t.prerotate(any_rotation);
\endcode</td><td>\code
@@ -216,7 +216,7 @@ t = Translation_(..) * t * RotationType(..) * Translation_(..) * Scaling_(..);
<table class="tutorial_code">
<tr><td style="max-width:30em;">
Euler angles might be convenient to create rotation objects.
-On the other hand, since there exist 24 differents convensions,they are pretty confusing to use. This example shows how
+On the other hand, since there exist 24 differents convension,they are pretty confusing to use. This example shows how
to create a rotation matrix according to the 2-1-2 convention.</td><td>\code
Matrix3f m;
m = AngleAxisf(angle1, Vector3f::UnitZ())
generated by cgit v1.2.3 (git 2.39.1) at 2024-05-26 10:57:31 +0000