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Removed the deprecated EIGEN2_SUPPORT, as previously announced. A compilation error is raised, if this compile-switch is defined. The documentation references to the corresponding pages from Eigen3.2 now. Also, the Eigen2 testsuite has been removed.

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This commit is contained in:
Christoph Hertzberg 2014-07-01 16:58:11 +02:00
parent 75e574275c
commit 324e7e8fc9
115 changed files with 30 additions and 12104 deletions
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11
Eigen/Array
View File

@ -1,11 +0,0 @@
#ifndef EIGEN_ARRAY_MODULE_H
#define EIGEN_ARRAY_MODULE_H
// include Core first to handle Eigen2 support macros
#include "Core"
#ifndef EIGEN2_SUPPORT
#error The Eigen/Array header does no longer exist in Eigen3. All that functionality has moved to Eigen/Core.
#endif
#endif // EIGEN_ARRAY_MODULE_H

35
Eigen/Core
View File

@ -244,34 +244,9 @@ inline static const char *SimdInstructionSetsInUse(void) {
} // end namespace Eigen
#define STAGE10_FULL_EIGEN2_API 10
#define STAGE20_RESOLVE_API_CONFLICTS 20
#define STAGE30_FULL_EIGEN3_API 30
#define STAGE40_FULL_EIGEN3_STRICTNESS 40
#define STAGE99_NO_EIGEN2_SUPPORT 99
#if defined EIGEN2_SUPPORT_STAGE40_FULL_EIGEN3_STRICTNESS
#define EIGEN2_SUPPORT
#define EIGEN2_SUPPORT_STAGE STAGE40_FULL_EIGEN3_STRICTNESS
#elif defined EIGEN2_SUPPORT_STAGE30_FULL_EIGEN3_API
#define EIGEN2_SUPPORT
#define EIGEN2_SUPPORT_STAGE STAGE30_FULL_EIGEN3_API
#elif defined EIGEN2_SUPPORT_STAGE20_RESOLVE_API_CONFLICTS
#define EIGEN2_SUPPORT
#define EIGEN2_SUPPORT_STAGE STAGE20_RESOLVE_API_CONFLICTS
#elif defined EIGEN2_SUPPORT_STAGE10_FULL_EIGEN2_API
#define EIGEN2_SUPPORT
#define EIGEN2_SUPPORT_STAGE STAGE10_FULL_EIGEN2_API
#elif defined EIGEN2_SUPPORT
// default to stage 3, that's what it's always meant
#define EIGEN2_SUPPORT_STAGE30_FULL_EIGEN3_API
#define EIGEN2_SUPPORT_STAGE STAGE30_FULL_EIGEN3_API
#else
#define EIGEN2_SUPPORT_STAGE STAGE99_NO_EIGEN2_SUPPORT
#endif
#ifdef EIGEN2_SUPPORT
#undef minor
#if defined EIGEN2_SUPPORT_STAGE40_FULL_EIGEN3_STRICTNESS || defined EIGEN2_SUPPORT_STAGE30_FULL_EIGEN3_API || defined EIGEN2_SUPPORT_STAGE20_RESOLVE_API_CONFLICTS || defined EIGEN2_SUPPORT_STAGE10_FULL_EIGEN2_API || defined EIGEN2_SUPPORT
// This will generate an error message:
#error Eigen2-support is only available up to version 3.2. Please go to "http://eigen.tuxfamily.org/index.php?title=Eigen2" for further information
#endif
// we use size_t frequently and we'll never remember to prepend it with std:: everytime just to
@ -429,8 +404,4 @@ using std::ptrdiff_t;
#include "src/Core/util/ReenableStupidWarnings.h"
#ifdef EIGEN2_SUPPORT
#include "Eigen2Support"
#endif
#endif // EIGEN_CORE_H

82
Eigen/Eigen2Support
View File

@ -1,82 +0,0 @@
// 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/.
#ifndef EIGEN2SUPPORT_H
#define EIGEN2SUPPORT_H
#if (!defined(EIGEN2_SUPPORT)) || (!defined(EIGEN_CORE_H))
#error Eigen2 support must be enabled by defining EIGEN2_SUPPORT before including any Eigen header
#endif
#include "src/Core/util/DisableStupidWarnings.h"
/** \ingroup Support_modules
* \defgroup Eigen2Support_Module Eigen2 support module
* This module provides a couple of deprecated functions improving the compatibility with Eigen2.
*
* To use it, define EIGEN2_SUPPORT before including any Eigen header
* \code
* #define EIGEN2_SUPPORT
* \endcode
*
*/
#include "src/Eigen2Support/Macros.h"
#include "src/Eigen2Support/Memory.h"
#include "src/Eigen2Support/Meta.h"
#include "src/Eigen2Support/Lazy.h"
#include "src/Eigen2Support/Cwise.h"
#include "src/Eigen2Support/CwiseOperators.h"
#include "src/Eigen2Support/TriangularSolver.h"
#include "src/Eigen2Support/Block.h"
#include "src/Eigen2Support/VectorBlock.h"
#include "src/Eigen2Support/Minor.h"
#include "src/Eigen2Support/MathFunctions.h"
#include "src/Core/util/ReenableStupidWarnings.h"
// Eigen2 used to include iostream
#include<iostream>
#define EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, SizeSuffix) \
using Eigen::Matrix##SizeSuffix##TypeSuffix; \
using Eigen::Vector##SizeSuffix##TypeSuffix; \
using Eigen::RowVector##SizeSuffix##TypeSuffix;
#define EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE(TypeSuffix) \
EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 2) \
EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 3) \
EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 4) \
EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, X) \
#define EIGEN_USING_MATRIX_TYPEDEFS \
EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE(i) \
EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE(f) \
EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE(d) \
EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE(cf) \
EIGEN_USING_MATRIX_TYPEDEFS_FOR_TYPE(cd)
#define USING_PART_OF_NAMESPACE_EIGEN \
EIGEN_USING_MATRIX_TYPEDEFS \
using Eigen::Matrix; \
using Eigen::MatrixBase; \
using Eigen::ei_random; \
using Eigen::ei_real; \
using Eigen::ei_imag; \
using Eigen::ei_conj; \
using Eigen::ei_abs; \
using Eigen::ei_abs2; \
using Eigen::ei_sqrt; \
using Eigen::ei_exp; \
using Eigen::ei_log; \
using Eigen::ei_sin; \
using Eigen::ei_cos;
#endif // EIGEN2SUPPORT_H

38
Eigen/Geometry
View File

@ -33,29 +33,23 @@
#include "src/Geometry/OrthoMethods.h"
#include "src/Geometry/EulerAngles.h"
#if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS
#include "src/Geometry/Homogeneous.h"
#include "src/Geometry/RotationBase.h"
#include "src/Geometry/Rotation2D.h"
#include "src/Geometry/Quaternion.h"
#include "src/Geometry/AngleAxis.h"
#include "src/Geometry/Transform.h"
#include "src/Geometry/Translation.h"
#include "src/Geometry/Scaling.h"
#include "src/Geometry/Hyperplane.h"
#include "src/Geometry/ParametrizedLine.h"
#include "src/Geometry/AlignedBox.h"
#include "src/Geometry/Umeyama.h"
#include "src/Geometry/Homogeneous.h"
#include "src/Geometry/RotationBase.h"
#include "src/Geometry/Rotation2D.h"
#include "src/Geometry/Quaternion.h"
#include "src/Geometry/AngleAxis.h"
#include "src/Geometry/Transform.h"
#include "src/Geometry/Translation.h"
#include "src/Geometry/Scaling.h"
#include "src/Geometry/Hyperplane.h"
#include "src/Geometry/ParametrizedLine.h"
#include "src/Geometry/AlignedBox.h"
#include "src/Geometry/Umeyama.h"
// Use the SSE optimized version whenever possible. At the moment the
// SSE version doesn't compile when AVX is enabled
#if defined EIGEN_VECTORIZE_SSE && !defined EIGEN_VECTORIZE_AVX
#include "src/Geometry/arch/Geometry_SSE.h"
#endif
#endif
#ifdef EIGEN2_SUPPORT
#include "src/Eigen2Support/Geometry/All.h"
// Use the SSE optimized version whenever possible. At the moment the
// SSE version doesn't compile when AVX is enabled
#if defined EIGEN_VECTORIZE_SSE && !defined EIGEN_VECTORIZE_AVX
#include "src/Geometry/arch/Geometry_SSE.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"

4
Eigen/LU
View File

@ -33,10 +33,6 @@
#include "src/LU/arch/Inverse_SSE.h"
#endif
#ifdef EIGEN2_SUPPORT
#include "src/Eigen2Support/LU.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_LU_MODULE_H

32
Eigen/LeastSquares
View File

@ -1,32 +0,0 @@
#ifndef EIGEN_REGRESSION_MODULE_H
#define EIGEN_REGRESSION_MODULE_H
#ifndef EIGEN2_SUPPORT
#error LeastSquares is only available in Eigen2 support mode (define EIGEN2_SUPPORT)
#endif
// exclude from normal eigen3-only documentation
#ifdef EIGEN2_SUPPORT
#include "Core"
#include "src/Core/util/DisableStupidWarnings.h"
#include "Eigenvalues"
#include "Geometry"
/** \defgroup LeastSquares_Module LeastSquares module
* This module provides linear regression and related features.
*
* \code
* #include <Eigen/LeastSquares>
* \endcode
*/
#include "src/Eigen2Support/LeastSquares.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN2_SUPPORT
#endif // EIGEN_REGRESSION_MODULE_H

8
Eigen/QR
View File

@ -33,15 +33,7 @@
#include "src/QR/ColPivHouseholderQR_MKL.h"
#endif
#ifdef EIGEN2_SUPPORT
#include "src/Eigen2Support/QR.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"
#ifdef EIGEN2_SUPPORT
#include "Eigenvalues"
#endif
#endif // EIGEN_QR_MODULE_H
/* vim: set filetype=cpp et sw=2 ts=2 ai: */

4
Eigen/SVD
View File

@ -27,10 +27,6 @@
#endif
#include "src/SVD/UpperBidiagonalization.h"
#ifdef EIGEN2_SUPPORT
#include "src/Eigen2Support/SVD.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_SVD_MODULE_H

16
Eigen/src/Cholesky/LDLT.h
View File

@ -151,13 +151,6 @@ template<typename _MatrixType, int _UpLo> class LDLT
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return m_sign == internal::PositiveSemiDef || m_sign == internal::ZeroSign;
}
#ifdef EIGEN2_SUPPORT
inline bool isPositiveDefinite() const
{
return isPositive();
}
#endif
/** \returns true if the matrix is negative (semidefinite) */
inline bool isNegative(void) const
@ -191,15 +184,6 @@ template<typename _MatrixType, int _UpLo> class LDLT
return internal::solve_retval<LDLT, Rhs>(*this, b.derived());
}
#ifdef EIGEN2_SUPPORT
template<typename OtherDerived, typename ResultType>
bool solve(const MatrixBase<OtherDerived>& b, ResultType *result) const
{
*result = this->solve(b);
return true;
}
#endif
template<typename Derived>
bool solveInPlace(MatrixBase<Derived> &bAndX) const;

11
Eigen/src/Cholesky/LLT.h
View File

@ -127,17 +127,6 @@ template<typename _MatrixType, int _UpLo> class LLT
return internal::solve_retval<LLT, Rhs>(*this, b.derived());
}
#ifdef EIGEN2_SUPPORT
template<typename OtherDerived, typename ResultType>
bool solve(const MatrixBase<OtherDerived>& b, ResultType *result) const
{
*result = this->solve(b);
return true;
}
bool isPositiveDefinite() const { return true; }
#endif
template<typename Derived>
void solveInPlace(MatrixBase<Derived> &bAndX) const;

11
Eigen/src/Core/DenseBase.h
View File

@ -506,17 +506,6 @@ template<typename Derived> class DenseBase
# endif
#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
#ifdef EIGEN2_SUPPORT
Block<Derived> corner(CornerType type, Index cRows, Index cCols);
const Block<Derived> corner(CornerType type, Index cRows, Index cCols) const;
template<int CRows, int CCols>
Block<Derived, CRows, CCols> corner(CornerType type);
template<int CRows, int CCols>
const Block<Derived, CRows, CCols> corner(CornerType type) const;
#endif // EIGEN2_SUPPORT
// disable the use of evalTo for dense objects with a nice compilation error
template<typename Dest>

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

@ -156,10 +156,8 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
EIGEN_STRONG_INLINE CoeffReturnType
operator[](Index index) const
{
#ifndef EIGEN2_SUPPORT
EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime,
THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD)
#endif
eigen_assert(index >= 0 && index < size());
return derived().coeff(index);
}
@ -388,10 +386,8 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
EIGEN_STRONG_INLINE Scalar&
operator[](Index index)
{
#ifndef EIGEN2_SUPPORT
EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime,
THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD)
#endif
eigen_assert(index >= 0 && index < size());
return derived().coeffRef(index);
}

15
Eigen/src/Core/DiagonalMatrix.h
View File

@ -95,21 +95,6 @@ class DiagonalBase : public EigenBase<Derived>
{
return other.diagonal() * scalar;
}
#ifdef EIGEN2_SUPPORT
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
bool isApprox(const DiagonalBase<OtherDerived>& other, typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) const
{
return diagonal().isApprox(other.diagonal(), precision);
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
bool isApprox(const MatrixBase<OtherDerived>& other, typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) const
{
return toDenseMatrix().isApprox(other, precision);
}
#endif
};
template<typename Derived>

28
Eigen/src/Core/Dot.h
View File

@ -76,34 +76,6 @@ MatrixBase<Derived>::dot(const MatrixBase<OtherDerived>& other) const
return internal::dot_nocheck<Derived,OtherDerived>::run(*this, other);
}
#ifdef EIGEN2_SUPPORT
/** \returns the dot product of *this with other, with the Eigen2 convention that the dot product is linear in the first variable
* (conjugating the second variable). Of course this only makes a difference in the complex case.
*
* This method is only available in EIGEN2_SUPPORT mode.
*
* \only_for_vectors
*
* \sa dot()
*/
template<typename Derived>
template<typename OtherDerived>
typename internal::traits<Derived>::Scalar
MatrixBase<Derived>::eigen2_dot(const MatrixBase<OtherDerived>& other) const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived)
EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
eigen_assert(size() == other.size());
return internal::dot_nocheck<OtherDerived,Derived>::run(other,*this);
}
#endif
//---------- implementation of L2 norm and related functions ----------
/** \returns, for vectors, the squared \em l2 norm of \c *this, and for matrices the Frobenius norm.

5
Eigen/src/Core/Map.h
View File

@ -110,14 +110,9 @@ template<typename PlainObjectType, int MapOptions, typename StrideType> class Ma
EIGEN_DENSE_PUBLIC_INTERFACE(Map)
typedef typename Base::PointerType PointerType;
#if EIGEN2_SUPPORT_STAGE <= STAGE30_FULL_EIGEN3_API
typedef const Scalar* PointerArgType;
inline PointerType cast_to_pointer_type(PointerArgType ptr) { return const_cast<PointerType>(ptr); }
#else
typedef PointerType PointerArgType;
EIGEN_DEVICE_FUNC
inline PointerType cast_to_pointer_type(PointerArgType ptr) { return ptr; }
#endif
EIGEN_DEVICE_FUNC
inline Index innerStride() const

7
Eigen/src/Core/Matrix.h
View File

@ -366,13 +366,6 @@ class Matrix
EIGEN_DEVICE_FUNC
Matrix& operator=(const RotationBase<OtherDerived,ColsAtCompileTime>& r);
#ifdef EIGEN2_SUPPORT
template<typename OtherDerived>
explicit Matrix(const eigen2_RotationBase<OtherDerived,ColsAtCompileTime>& r);
template<typename OtherDerived>
Matrix& operator=(const eigen2_RotationBase<OtherDerived,ColsAtCompileTime>& r);
#endif
// allow to extend Matrix outside Eigen
#ifdef EIGEN_MATRIX_PLUGIN
#include EIGEN_MATRIX_PLUGIN

78
Eigen/src/Core/MatrixBase.h
View File

@ -221,11 +221,6 @@ template<typename Derived> class MatrixBase
typename internal::scalar_product_traits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
dot(const MatrixBase<OtherDerived>& other) const;
#ifdef EIGEN2_SUPPORT
template<typename OtherDerived>
Scalar eigen2_dot(const MatrixBase<OtherDerived>& other) const;
#endif
EIGEN_DEVICE_FUNC RealScalar squaredNorm() const;
EIGEN_DEVICE_FUNC RealScalar norm() const;
RealScalar stableNorm() const;
@ -269,17 +264,6 @@ template<typename Derived> class MatrixBase
typename ConstDiagonalIndexReturnType<DynamicIndex>::Type diagonal(Index index) const;
#endif
#ifdef EIGEN2_SUPPORT
template<unsigned int Mode> typename internal::eigen2_part_return_type<Derived, Mode>::type part();
template<unsigned int Mode> const typename internal::eigen2_part_return_type<Derived, Mode>::type part() const;
// huuuge hack. make Eigen2's matrix.part<Diagonal>() work in eigen3. Problem: Diagonal is now a class template instead
// of an integer constant. Solution: overload the part() method template wrt template parameters list.
template<template<typename T, int N> class U>
const DiagonalWrapper<ConstDiagonalReturnType> part() const
{ return diagonal().asDiagonal(); }
#endif // EIGEN2_SUPPORT
template<unsigned int Mode> struct TriangularViewReturnType { typedef TriangularView<Derived, Mode> Type; };
template<unsigned int Mode> struct ConstTriangularViewReturnType { typedef const TriangularView<const Derived, Mode> Type; };
@ -373,24 +357,7 @@ template<typename Derived> class MatrixBase
EIGEN_DEVICE_FUNC const FullPivLU<PlainObject> fullPivLu() const;
EIGEN_DEVICE_FUNC const PartialPivLU<PlainObject> partialPivLu() const;
#if EIGEN2_SUPPORT_STAGE < STAGE20_RESOLVE_API_CONFLICTS
const LU<PlainObject> lu() const;
#endif
#ifdef EIGEN2_SUPPORT
const LU<PlainObject> eigen2_lu() const;
#endif
#if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS
const PartialPivLU<PlainObject> lu() const;
#endif
#ifdef EIGEN2_SUPPORT
template<typename ResultType>
void computeInverse(MatrixBase<ResultType> *result) const {
*result = this->inverse();
}
#endif
EIGEN_DEVICE_FUNC
const internal::inverse_impl<Derived> inverse() const;
@ -419,10 +386,6 @@ template<typename Derived> class MatrixBase
const HouseholderQR<PlainObject> householderQr() const;
const ColPivHouseholderQR<PlainObject> colPivHouseholderQr() const;
const FullPivHouseholderQR<PlainObject> fullPivHouseholderQr() const;
#ifdef EIGEN2_SUPPORT
const QR<PlainObject> qr() const;
#endif
EigenvaluesReturnType eigenvalues() const;
RealScalar operatorNorm() const;
@ -431,10 +394,6 @@ template<typename Derived> class MatrixBase
JacobiSVD<PlainObject> jacobiSvd(unsigned int computationOptions = 0) const;
#ifdef EIGEN2_SUPPORT
SVD<PlainObject> svd() const;
#endif
/////////// Geometry module ///////////
#ifndef EIGEN_PARSED_BY_DOXYGEN
@ -458,13 +417,11 @@ template<typename Derived> class MatrixBase
Matrix<Scalar,3,1> eulerAngles(Index a0, Index a1, Index a2) const;
#if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS
ScalarMultipleReturnType operator*(const UniformScaling<Scalar>& s) const;
// put this as separate enum value to work around possible GCC 4.3 bug (?)
enum { HomogeneousReturnTypeDirection = ColsAtCompileTime==1?Vertical:Horizontal };
typedef Homogeneous<Derived, HomogeneousReturnTypeDirection> HomogeneousReturnType;
HomogeneousReturnType homogeneous() const;
#endif
enum {
SizeMinusOne = SizeAtCompileTime==Dynamic ? Dynamic : SizeAtCompileTime-1
@ -513,41 +470,6 @@ template<typename Derived> class MatrixBase
const MatrixPowerReturnValue<Derived> pow(const RealScalar& p) const;
const MatrixComplexPowerReturnValue<Derived> pow(const std::complex<RealScalar>& p) const;
#ifdef EIGEN2_SUPPORT
template<typename ProductDerived, typename Lhs, typename Rhs>
Derived& operator+=(const Flagged<ProductBase<ProductDerived, Lhs,Rhs>, 0,
EvalBeforeAssigningBit>& other);
template<typename ProductDerived, typename Lhs, typename Rhs>
Derived& operator-=(const Flagged<ProductBase<ProductDerived, Lhs,Rhs>, 0,
EvalBeforeAssigningBit>& other);
/** \deprecated because .lazy() is deprecated
* Overloaded for cache friendly product evaluation */
template<typename OtherDerived>
Derived& lazyAssign(const Flagged<OtherDerived, 0, EvalBeforeAssigningBit>& other)
{ return lazyAssign(other._expression()); }
template<unsigned int Added>
const Flagged<Derived, Added, 0> marked() const;
const Flagged<Derived, 0, EvalBeforeAssigningBit> lazy() const;
inline const Cwise<Derived> cwise() const;
inline Cwise<Derived> cwise();
VectorBlock<Derived> start(Index size);
const VectorBlock<const Derived> start(Index size) const;
VectorBlock<Derived> end(Index size);
const VectorBlock<const Derived> end(Index size) const;
template<int Size> VectorBlock<Derived,Size> start();
template<int Size> const VectorBlock<const Derived,Size> start() const;
template<int Size> VectorBlock<Derived,Size> end();
template<int Size> const VectorBlock<const Derived,Size> end() const;
Minor<Derived> minor(Index row, Index col);
const Minor<Derived> minor(Index row, Index col) const;
#endif
protected:
EIGEN_DEVICE_FUNC MatrixBase() : Base() {}

7
Eigen/src/Core/NumTraits.h
View File

@ -85,13 +85,6 @@ template<typename T> struct GenericNumTraits
}
static inline T highest() { return (std::numeric_limits<T>::max)(); }
static inline T lowest() { return IsInteger ? (std::numeric_limits<T>::min)() : (-(std::numeric_limits<T>::max)()); }
#ifdef EIGEN2_SUPPORT
enum {
HasFloatingPoint = !IsInteger
};
typedef NonInteger FloatingPoint;
#endif
};
template<typename T> struct NumTraits : GenericNumTraits<T>

6
Eigen/src/Core/ProductBase.h
View File

@ -131,17 +131,13 @@ class ProductBase : public MatrixBase<Derived>
const Diagonal<FullyLazyCoeffBaseProductType,Dynamic> diagonal(Index index) const
{ return FullyLazyCoeffBaseProductType(m_lhs, m_rhs).diagonal(index); }
// restrict coeff accessors to 1x1 expressions. No need to care about mutators here since this isnt a Lvalue expression
// restrict coeff accessors to 1x1 expressions. No need to care about mutators here since this isn't an Lvalue expression
typename Base::CoeffReturnType coeff(Index row, Index col) const
{
#ifdef EIGEN2_SUPPORT
return lhs().row(row).cwiseProduct(rhs().col(col).transpose()).sum();
#else
EIGEN_STATIC_ASSERT_SIZE_1x1(Derived)
eigen_assert(this->rows() == 1 && this->cols() == 1);
Matrix<Scalar,1,1> result = *this;
return result.coeff(row,col);
#endif
}
typename Base::CoeffReturnType coeff(Index i) const

25
Eigen/src/Core/SelfAdjointView.h
View File

@ -177,31 +177,6 @@ template<typename MatrixType, unsigned int UpLo> class SelfAdjointView
EigenvaluesReturnType eigenvalues() const;
EIGEN_DEVICE_FUNC
RealScalar operatorNorm() const;
#ifdef EIGEN2_SUPPORT
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
SelfAdjointView& operator=(const MatrixBase<OtherDerived>& other)
{
enum {
OtherPart = UpLo == Upper ? StrictlyLower : StrictlyUpper
};
m_matrix.const_cast_derived().template triangularView<UpLo>() = other;
m_matrix.const_cast_derived().template triangularView<OtherPart>() = other.adjoint();
return *this;
}
template<typename OtherMatrixType, unsigned int OtherMode>
EIGEN_DEVICE_FUNC
SelfAdjointView& operator=(const TriangularView<OtherMatrixType, OtherMode>& other)
{
enum {
OtherPart = UpLo == Upper ? StrictlyLower : StrictlyUpper
};
m_matrix.const_cast_derived().template triangularView<UpLo>() = other.toDenseMatrix();
m_matrix.const_cast_derived().template triangularView<OtherPart>() = other.toDenseMatrix().adjoint();
return *this;
}
#endif
protected:
MatrixTypeNested m_matrix;

64
Eigen/src/Core/TriangularMatrix.h
View File

@ -342,35 +342,6 @@ template<typename _MatrixType, unsigned int _Mode> class TriangularView
(lhs.derived(),rhs.m_matrix);
}
#ifdef EIGEN2_SUPPORT
template<typename OtherDerived>
struct eigen2_product_return_type
{
typedef typename TriangularView<MatrixType,Mode>::DenseMatrixType DenseMatrixType;
typedef typename OtherDerived::PlainObject::DenseType OtherPlainObject;
typedef typename ProductReturnType<DenseMatrixType, OtherPlainObject>::Type ProdRetType;
typedef typename ProdRetType::PlainObject type;
};
template<typename OtherDerived>
const typename eigen2_product_return_type<OtherDerived>::type
operator*(const EigenBase<OtherDerived>& rhs) const
{
typename OtherDerived::PlainObject::DenseType rhsPlainObject;
rhs.evalTo(rhsPlainObject);
return this->toDenseMatrix() * rhsPlainObject;
}
template<typename OtherMatrixType>
bool isApprox(const TriangularView<OtherMatrixType, Mode>& other, typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) const
{
return this->toDenseMatrix().isApprox(other.toDenseMatrix(), precision);
}
template<typename OtherDerived>
bool isApprox(const MatrixBase<OtherDerived>& other, typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) const
{
return this->toDenseMatrix().isApprox(other, precision);
}
#endif // EIGEN2_SUPPORT
template<int Side, typename Other>
EIGEN_DEVICE_FUNC
inline const internal::triangular_solve_retval<Side,TriangularView, Other>
@ -780,41 +751,6 @@ void TriangularBase<Derived>::evalToLazy(MatrixBase<DenseDerived> &other) const
* Implementation of MatrixBase methods
***************************************************************************/
#ifdef EIGEN2_SUPPORT
// implementation of part<>(), including the SelfAdjoint case.
namespace internal {
template<typename MatrixType, unsigned int Mode>
struct eigen2_part_return_type
{
typedef TriangularView<MatrixType, Mode> type;
};
template<typename MatrixType>
struct eigen2_part_return_type<MatrixType, SelfAdjoint>
{
typedef SelfAdjointView<MatrixType, Upper> type;
};
}
/** \deprecated use MatrixBase::triangularView() */
template<typename Derived>
template<unsigned int Mode>
const typename internal::eigen2_part_return_type<Derived, Mode>::type MatrixBase<Derived>::part() const
{
return derived();
}
/** \deprecated use MatrixBase::triangularView() */
template<typename Derived>
template<unsigned int Mode>
typename internal::eigen2_part_return_type<Derived, Mode>::type MatrixBase<Derived>::part()
{
return derived();
}
#endif
/**
* \returns an expression of a triangular view extracted from the current matrix
*

2
Eigen/src/Core/VectorwiseOp.h
View File

@ -560,9 +560,7 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
/////////// Geometry module ///////////
#if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS
Homogeneous<ExpressionType,Direction> homogeneous() const;
#endif
typedef typename ExpressionType::PlainObject CrossReturnType;
template<typename OtherDerived>

35
Eigen/src/Core/util/ForwardDeclarations.h
View File

@ -236,35 +236,12 @@ template<typename Scalar> class Rotation2D;
template<typename Scalar> class AngleAxis;
template<typename Scalar,int Dim> class Translation;
#ifdef EIGEN2_SUPPORT
template<typename Derived, int _Dim> class eigen2_RotationBase;
template<typename Lhs, typename Rhs> class eigen2_Cross;
template<typename Scalar> class eigen2_Quaternion;
template<typename Scalar> class eigen2_Rotation2D;
template<typename Scalar> class eigen2_AngleAxis;
template<typename Scalar,int Dim> class eigen2_Transform;
template <typename _Scalar, int _AmbientDim> class eigen2_ParametrizedLine;
template <typename _Scalar, int _AmbientDim> class eigen2_Hyperplane;
template<typename Scalar,int Dim> class eigen2_Translation;
template<typename Scalar,int Dim> class eigen2_Scaling;
#endif
#if EIGEN2_SUPPORT_STAGE < STAGE20_RESOLVE_API_CONFLICTS
template<typename Scalar> class Quaternion;
template<typename Scalar,int Dim> class Transform;
template <typename _Scalar, int _AmbientDim> class ParametrizedLine;
template <typename _Scalar, int _AmbientDim> class Hyperplane;
template<typename Scalar,int Dim> class Scaling;
#endif
#if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS
template<typename Scalar, int Options = AutoAlign> class Quaternion;
template<typename Scalar,int Dim,int Mode,int _Options=AutoAlign> class Transform;
template <typename _Scalar, int _AmbientDim, int Options=AutoAlign> class ParametrizedLine;
template <typename _Scalar, int _AmbientDim, int Options=AutoAlign> class Hyperplane;
template<typename Scalar> class UniformScaling;
template<typename MatrixType,int Direction> class Homogeneous;
#endif
// MatrixFunctions module
template<typename Derived> struct MatrixExponentialReturnValue;
@ -283,18 +260,6 @@ struct stem_function
};
}
#ifdef EIGEN2_SUPPORT
template<typename ExpressionType> class Cwise;
template<typename MatrixType> class Minor;
template<typename MatrixType> class LU;
template<typename MatrixType> class QR;
template<typename MatrixType> class SVD;
namespace internal {
template<typename MatrixType, unsigned int Mode> struct eigen2_part_return_type;
}
#endif
} // end namespace Eigen
#endif // EIGEN_FORWARDDECLARATIONS_H

7
Eigen/src/Core/util/StaticAssert.h
View File

@ -168,13 +168,8 @@
) \
)
#ifdef EIGEN2_SUPPORT
#define EIGEN_STATIC_ASSERT_NON_INTEGER(TYPE) \
eigen_assert(!NumTraits<Scalar>::IsInteger);
#else
#define EIGEN_STATIC_ASSERT_NON_INTEGER(TYPE) \
#define EIGEN_STATIC_ASSERT_NON_INTEGER(TYPE) \
EIGEN_STATIC_ASSERT(!NumTraits<TYPE>::IsInteger, THIS_FUNCTION_IS_NOT_FOR_INTEGER_NUMERIC_TYPES)
#endif
// static assertion failing if it is guaranteed at compile-time that the two matrix expression types have different sizes

126
Eigen/src/Eigen2Support/Block.h
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@ -1,126 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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/.
#ifndef EIGEN_BLOCK2_H
#define EIGEN_BLOCK2_H
namespace Eigen {
/** \returns a dynamic-size expression of a corner of *this.
*
* \param type the type of corner. Can be \a Eigen::TopLeft, \a Eigen::TopRight,
* \a Eigen::BottomLeft, \a Eigen::BottomRight.
* \param cRows the number of rows in the corner
* \param cCols the number of columns in the corner
*
* Example: \include MatrixBase_corner_enum_int_int.cpp
* Output: \verbinclude MatrixBase_corner_enum_int_int.out
*
* \note Even though the returned expression has dynamic size, in the case
* when it is applied to a fixed-size matrix, it inherits a fixed maximal size,
* which means that evaluating it does not cause a dynamic memory allocation.
*
* \sa class Block, block(Index,Index,Index,Index)
*/
template<typename Derived>
inline Block<Derived> DenseBase<Derived>
::corner(CornerType type, Index cRows, Index cCols)
{
switch(type)
{
default:
eigen_assert(false && "Bad corner type.");
case TopLeft:
return Block<Derived>(derived(), 0, 0, cRows, cCols);
case TopRight:
return Block<Derived>(derived(), 0, cols() - cCols, cRows, cCols);
case BottomLeft:
return Block<Derived>(derived(), rows() - cRows, 0, cRows, cCols);
case BottomRight:
return Block<Derived>(derived(), rows() - cRows, cols() - cCols, cRows, cCols);
}
}
/** This is the const version of corner(CornerType, Index, Index).*/
template<typename Derived>
inline const Block<Derived>
DenseBase<Derived>::corner(CornerType type, Index cRows, Index cCols) const
{
switch(type)
{
default:
eigen_assert(false && "Bad corner type.");
case TopLeft:
return Block<Derived>(derived(), 0, 0, cRows, cCols);
case TopRight:
return Block<Derived>(derived(), 0, cols() - cCols, cRows, cCols);
case BottomLeft:
return Block<Derived>(derived(), rows() - cRows, 0, cRows, cCols);
case BottomRight:
return Block<Derived>(derived(), rows() - cRows, cols() - cCols, cRows, cCols);
}
}
/** \returns a fixed-size expression of a corner of *this.
*
* \param type the type of corner. Can be \a Eigen::TopLeft, \a Eigen::TopRight,
* \a Eigen::BottomLeft, \a Eigen::BottomRight.
*
* The template parameters CRows and CCols arethe number of rows and columns in the corner.
*
* Example: \include MatrixBase_template_int_int_corner_enum.cpp
* Output: \verbinclude MatrixBase_template_int_int_corner_enum.out
*
* \sa class Block, block(Index,Index,Index,Index)
*/
template<typename Derived>
template<int CRows, int CCols>
inline Block<Derived, CRows, CCols>
DenseBase<Derived>::corner(CornerType type)
{
switch(type)
{
default:
eigen_assert(false && "Bad corner type.");
case TopLeft:
return Block<Derived, CRows, CCols>(derived(), 0, 0);
case TopRight:
return Block<Derived, CRows, CCols>(derived(), 0, cols() - CCols);
case BottomLeft:
return Block<Derived, CRows, CCols>(derived(), rows() - CRows, 0);
case BottomRight:
return Block<Derived, CRows, CCols>(derived(), rows() - CRows, cols() - CCols);
}
}
/** This is the const version of corner<int, int>(CornerType).*/
template<typename Derived>
template<int CRows, int CCols>
inline const Block<Derived, CRows, CCols>
DenseBase<Derived>::corner(CornerType type) const
{
switch(type)
{
default:
eigen_assert(false && "Bad corner type.");
case TopLeft:
return Block<Derived, CRows, CCols>(derived(), 0, 0);
case TopRight:
return Block<Derived, CRows, CCols>(derived(), 0, cols() - CCols);
case BottomLeft:
return Block<Derived, CRows, CCols>(derived(), rows() - CRows, 0);
case BottomRight:
return Block<Derived, CRows, CCols>(derived(), rows() - CRows, cols() - CCols);
}
}
} // end namespace Eigen
#endif // EIGEN_BLOCK2_H

8
Eigen/src/Eigen2Support/CMakeLists.txt
View File

@ -1,8 +0,0 @@
FILE(GLOB Eigen_Eigen2Support_SRCS "*.h")
INSTALL(FILES
${Eigen_Eigen2Support_SRCS}
DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Eigen2Support COMPONENT Devel
)
ADD_SUBDIRECTORY(Geometry)

192
Eigen/src/Eigen2Support/Cwise.h
View File

@ -1,192 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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/.
#ifndef EIGEN_CWISE_H
#define EIGEN_CWISE_H
namespace Eigen {
/** \internal
* convenient macro to defined the return type of a cwise binary operation */
#define EIGEN_CWISE_BINOP_RETURN_TYPE(OP) \
CwiseBinaryOp<OP<typename internal::traits<ExpressionType>::Scalar>, ExpressionType, OtherDerived>
/** \internal
* convenient macro to defined the return type of a cwise unary operation */
#define EIGEN_CWISE_UNOP_RETURN_TYPE(OP) \
CwiseUnaryOp<OP<typename internal::traits<ExpressionType>::Scalar>, ExpressionType>
/** \internal
* convenient macro to defined the return type of a cwise comparison to a scalar */
#define EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(OP) \
CwiseBinaryOp<OP<typename internal::traits<ExpressionType>::Scalar>, ExpressionType, \
typename ExpressionType::ConstantReturnType >
/** \class Cwise
*
* \brief Pseudo expression providing additional coefficient-wise operations
*
* \param ExpressionType the type of the object on which to do coefficient-wise operations
*
* This class represents an expression with additional coefficient-wise features.
* It is the return type of MatrixBase::cwise()
* and most of the time this is the only way it is used.
*
* Example: \include MatrixBase_cwise_const.cpp
* Output: \verbinclude MatrixBase_cwise_const.out
*
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_CWISE_PLUGIN.
*
* \sa MatrixBase::cwise() const, MatrixBase::cwise()
*/
template<typename ExpressionType> class Cwise
{
public:
typedef typename internal::traits<ExpressionType>::Scalar Scalar;
typedef typename internal::conditional<internal::must_nest_by_value<ExpressionType>::ret,
ExpressionType, const ExpressionType&>::type ExpressionTypeNested;
typedef CwiseUnaryOp<internal::scalar_add_op<Scalar>, ExpressionType> ScalarAddReturnType;
inline Cwise(const ExpressionType& matrix) : m_matrix(matrix) {}
/** \internal */
inline const ExpressionType& _expression() const { return m_matrix; }
template<typename OtherDerived>
const EIGEN_CWISE_PRODUCT_RETURN_TYPE(ExpressionType,OtherDerived)
operator*(const MatrixBase<OtherDerived> &other) const;
template<typename OtherDerived>
const EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_quotient_op)
operator/(const MatrixBase<OtherDerived> &other) const;
/** \deprecated ArrayBase::min() */
template<typename OtherDerived>
const EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_min_op)
(min)(const MatrixBase<OtherDerived> &other) const
{ return EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_min_op)(_expression(), other.derived()); }
/** \deprecated ArrayBase::max() */
template<typename OtherDerived>
const EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_max_op)
(max)(const MatrixBase<OtherDerived> &other) const
{ return EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_max_op)(_expression(), other.derived()); }
const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_abs_op) abs() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_abs2_op) abs2() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_square_op) square() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_cube_op) cube() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_inverse_op) inverse() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_sqrt_op) sqrt() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_exp_op) exp() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_log_op) log() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_cos_op) cos() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_sin_op) sin() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_pow_op) pow(const Scalar& exponent) const;
const ScalarAddReturnType
operator+(const Scalar& scalar) const;
/** \relates Cwise */
friend const ScalarAddReturnType
operator+(const Scalar& scalar, const Cwise& mat)
{ return mat + scalar; }
ExpressionType& operator+=(const Scalar& scalar);
const ScalarAddReturnType
operator-(const Scalar& scalar) const;
ExpressionType& operator-=(const Scalar& scalar);
template<typename OtherDerived>
inline ExpressionType& operator*=(const MatrixBase<OtherDerived> &other);
template<typename OtherDerived>
inline ExpressionType& operator/=(const MatrixBase<OtherDerived> &other);
template<typename OtherDerived> const EIGEN_CWISE_BINOP_RETURN_TYPE(std::less)
operator<(const MatrixBase<OtherDerived>& other) const;
template<typename OtherDerived> const EIGEN_CWISE_BINOP_RETURN_TYPE(std::less_equal)
operator<=(const MatrixBase<OtherDerived>& other) const;
template<typename OtherDerived> const EIGEN_CWISE_BINOP_RETURN_TYPE(std::greater)
operator>(const MatrixBase<OtherDerived>& other) const;
template<typename OtherDerived> const EIGEN_CWISE_BINOP_RETURN_TYPE(std::greater_equal)
operator>=(const MatrixBase<OtherDerived>& other) const;
template<typename OtherDerived> const EIGEN_CWISE_BINOP_RETURN_TYPE(std::equal_to)
operator==(const MatrixBase<OtherDerived>& other) const;
template<typename OtherDerived> const EIGEN_CWISE_BINOP_RETURN_TYPE(std::not_equal_to)
operator!=(const MatrixBase<OtherDerived>& other) const;
// comparisons to a scalar value
const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::less)
operator<(Scalar s) const;
const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::less_equal)
operator<=(Scalar s) const;
const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::greater)
operator>(Scalar s) const;
const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::greater_equal)
operator>=(Scalar s) const;
const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::equal_to)
operator==(Scalar s) const;
const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::not_equal_to)
operator!=(Scalar s) const;
// allow to extend Cwise outside Eigen
#ifdef EIGEN_CWISE_PLUGIN
#include EIGEN_CWISE_PLUGIN
#endif
protected:
ExpressionTypeNested m_matrix;
};
/** \returns a Cwise wrapper of *this providing additional coefficient-wise operations
*
* Example: \include MatrixBase_cwise_const.cpp
* Output: \verbinclude MatrixBase_cwise_const.out
*
* \sa class Cwise, cwise()
*/
template<typename Derived>
inline const Cwise<Derived> MatrixBase<Derived>::cwise() const
{
return derived();
}
/** \returns a Cwise wrapper of *this providing additional coefficient-wise operations
*
* Example: \include MatrixBase_cwise.cpp
* Output: \verbinclude MatrixBase_cwise.out
*
* \sa class Cwise, cwise() const
*/
template<typename Derived>
inline Cwise<Derived> MatrixBase<Derived>::cwise()
{
return derived();
}
} // end namespace Eigen
#endif // EIGEN_CWISE_H

298
Eigen/src/Eigen2Support/CwiseOperators.h
View File

@ -1,298 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 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/.
#ifndef EIGEN_ARRAY_CWISE_OPERATORS_H
#define EIGEN_ARRAY_CWISE_OPERATORS_H
namespace Eigen {
/***************************************************************************
* The following functions were defined in Core
***************************************************************************/
/** \deprecated ArrayBase::abs() */
template<typename ExpressionType>
EIGEN_STRONG_INLINE const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_abs_op)
Cwise<ExpressionType>::abs() const
{
return _expression();
}
/** \deprecated ArrayBase::abs2() */
template<typename ExpressionType>
EIGEN_STRONG_INLINE const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_abs2_op)
Cwise<ExpressionType>::abs2() const
{
return _expression();
}
/** \deprecated ArrayBase::exp() */
template<typename ExpressionType>
inline const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_exp_op)
Cwise<ExpressionType>::exp() const
{
return _expression();
}
/** \deprecated ArrayBase::log() */
template<typename ExpressionType>
inline const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_log_op)
Cwise<ExpressionType>::log() const
{
return _expression();
}
/** \deprecated ArrayBase::operator*() */
template<typename ExpressionType>
template<typename OtherDerived>
EIGEN_STRONG_INLINE const EIGEN_CWISE_PRODUCT_RETURN_TYPE(ExpressionType,OtherDerived)
Cwise<ExpressionType>::operator*(const MatrixBase<OtherDerived> &other) const
{
return EIGEN_CWISE_PRODUCT_RETURN_TYPE(ExpressionType,OtherDerived)(_expression(), other.derived());
}
/** \deprecated ArrayBase::operator/() */
template<typename ExpressionType>
template<typename OtherDerived>
EIGEN_STRONG_INLINE const EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_quotient_op)
Cwise<ExpressionType>::operator/(const MatrixBase<OtherDerived> &other) const
{
return EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_quotient_op)(_expression(), other.derived());
}
/** \deprecated ArrayBase::operator*=() */
template<typename ExpressionType>
template<typename OtherDerived>
inline ExpressionType& Cwise<ExpressionType>::operator*=(const MatrixBase<OtherDerived> &other)
{
return m_matrix.const_cast_derived() = *this * other;
}
/** \deprecated ArrayBase::operator/=() */
template<typename ExpressionType>
template<typename OtherDerived>
inline ExpressionType& Cwise<ExpressionType>::operator/=(const MatrixBase<OtherDerived> &other)
{
return m_matrix.const_cast_derived() = *this / other;
}
/***************************************************************************
* The following functions were defined in Array
***************************************************************************/
// -- unary operators --
/** \deprecated ArrayBase::sqrt() */
template<typename ExpressionType>
inline const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_sqrt_op)
Cwise<ExpressionType>::sqrt() const
{
return _expression();
}
/** \deprecated ArrayBase::cos() */
template<typename ExpressionType>
inline const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_cos_op)
Cwise<ExpressionType>::cos() const
{
return _expression();
}
/** \deprecated ArrayBase::sin() */
template<typename ExpressionType>
inline const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_sin_op)
Cwise<ExpressionType>::sin() const
{
return _expression();
}
/** \deprecated ArrayBase::log() */
template<typename ExpressionType>
inline const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_pow_op)
Cwise<ExpressionType>::pow(const Scalar& exponent) const
{
return EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_pow_op)(_expression(), internal::scalar_pow_op<Scalar>(exponent));
}
/** \deprecated ArrayBase::inverse() */
template<typename ExpressionType>
inline const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_inverse_op)
Cwise<ExpressionType>::inverse() const
{
return _expression();
}
/** \deprecated ArrayBase::square() */
template<typename ExpressionType>
inline const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_square_op)
Cwise<ExpressionType>::square() const
{
return _expression();
}
/** \deprecated ArrayBase::cube() */
template<typename ExpressionType>
inline const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_cube_op)
Cwise<ExpressionType>::cube() const
{
return _expression();
}
// -- binary operators --
/** \deprecated ArrayBase::operator<() */
template<typename ExpressionType>
template<typename OtherDerived>
inline const EIGEN_CWISE_BINOP_RETURN_TYPE(std::less)
Cwise<ExpressionType>::operator<(const MatrixBase<OtherDerived> &other) const
{
return EIGEN_CWISE_BINOP_RETURN_TYPE(std::less)(_expression(), other.derived());
}
/** \deprecated ArrayBase::<=() */
template<typename ExpressionType>
template<typename OtherDerived>
inline const EIGEN_CWISE_BINOP_RETURN_TYPE(std::less_equal)
Cwise<ExpressionType>::operator<=(const MatrixBase<OtherDerived> &other) const
{
return EIGEN_CWISE_BINOP_RETURN_TYPE(std::less_equal)(_expression(), other.derived());
}
/** \deprecated ArrayBase::operator>() */
template<typename ExpressionType>
template<typename OtherDerived>
inline const EIGEN_CWISE_BINOP_RETURN_TYPE(std::greater)
Cwise<ExpressionType>::operator>(const MatrixBase<OtherDerived> &other) const
{
return EIGEN_CWISE_BINOP_RETURN_TYPE(std::greater)(_expression(), other.derived());
}
/** \deprecated ArrayBase::operator>=() */
template<typename ExpressionType>
template<typename OtherDerived>
inline const EIGEN_CWISE_BINOP_RETURN_TYPE(std::greater_equal)
Cwise<ExpressionType>::operator>=(const MatrixBase<OtherDerived> &other) const
{
return EIGEN_CWISE_BINOP_RETURN_TYPE(std::greater_equal)(_expression(), other.derived());
}
/** \deprecated ArrayBase::operator==() */
template<typename ExpressionType>
template<typename OtherDerived>
inline const EIGEN_CWISE_BINOP_RETURN_TYPE(std::equal_to)
Cwise<ExpressionType>::operator==(const MatrixBase<OtherDerived> &other) const
{
return EIGEN_CWISE_BINOP_RETURN_TYPE(std::equal_to)(_expression(), other.derived());
}
/** \deprecated ArrayBase::operator!=() */
template<typename ExpressionType>
template<typename OtherDerived>
inline const EIGEN_CWISE_BINOP_RETURN_TYPE(std::not_equal_to)
Cwise<ExpressionType>::operator!=(const MatrixBase<OtherDerived> &other) const
{
return EIGEN_CWISE_BINOP_RETURN_TYPE(std::not_equal_to)(_expression(), other.derived());
}
// comparisons to scalar value
/** \deprecated ArrayBase::operator<(Scalar) */
template<typename ExpressionType>
inline const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::less)
Cwise<ExpressionType>::operator<(Scalar s) const
{
return EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::less)(_expression(),
typename ExpressionType::ConstantReturnType(_expression().rows(), _expression().cols(), s));
}
/** \deprecated ArrayBase::operator<=(Scalar) */
template<typename ExpressionType>
inline const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::less_equal)
Cwise<ExpressionType>::operator<=(Scalar s) const
{
return EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::less_equal)(_expression(),
typename ExpressionType::ConstantReturnType(_expression().rows(), _expression().cols(), s));
}
/** \deprecated ArrayBase::operator>(Scalar) */
template<typename ExpressionType>
inline const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::greater)
Cwise<ExpressionType>::operator>(Scalar s) const
{
return EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::greater)(_expression(),
typename ExpressionType::ConstantReturnType(_expression().rows(), _expression().cols(), s));
}
/** \deprecated ArrayBase::operator>=(Scalar) */
template<typename ExpressionType>
inline const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::greater_equal)
Cwise<ExpressionType>::operator>=(Scalar s) const
{
return EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::greater_equal)(_expression(),
typename ExpressionType::ConstantReturnType(_expression().rows(), _expression().cols(), s));
}
/** \deprecated ArrayBase::operator==(Scalar) */
template<typename ExpressionType>
inline const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::equal_to)
Cwise<ExpressionType>::operator==(Scalar s) const
{
return EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::equal_to)(_expression(),
typename ExpressionType::ConstantReturnType(_expression().rows(), _expression().cols(), s));
}
/** \deprecated ArrayBase::operator!=(Scalar) */
template<typename ExpressionType>
inline const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::not_equal_to)
Cwise<ExpressionType>::operator!=(Scalar s) const
{
return EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::not_equal_to)(_expression(),
typename ExpressionType::ConstantReturnType(_expression().rows(), _expression().cols(), s));
}
// scalar addition
/** \deprecated ArrayBase::operator+(Scalar) */
template<typename ExpressionType>
inline const typename Cwise<ExpressionType>::ScalarAddReturnType
Cwise<ExpressionType>::operator+(const Scalar& scalar) const
{
return typename Cwise<ExpressionType>::ScalarAddReturnType(m_matrix, internal::scalar_add_op<Scalar>(scalar));
}
/** \deprecated ArrayBase::operator+=(Scalar) */
template<typename ExpressionType>
inline ExpressionType& Cwise<ExpressionType>::operator+=(const Scalar& scalar)
{
return m_matrix.const_cast_derived() = *this + scalar;
}
/** \deprecated ArrayBase::operator-(Scalar) */
template<typename ExpressionType>
inline const typename Cwise<ExpressionType>::ScalarAddReturnType
Cwise<ExpressionType>::operator-(const Scalar& scalar) const
{
return *this + (-scalar);
}
/** \deprecated ArrayBase::operator-=(Scalar) */
template<typename ExpressionType>
inline ExpressionType& Cwise<ExpressionType>::operator-=(const Scalar& scalar)
{
return m_matrix.const_cast_derived() = *this - scalar;
}
} // end namespace Eigen
#endif // EIGEN_ARRAY_CWISE_OPERATORS_H

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Eigen/src/Eigen2Support/Geometry/AlignedBox.h
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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/.
// no include guard, we'll include this twice from All.h from Eigen2Support, and it's internal anyway
namespace Eigen {
/** \geometry_module \ingroup Geometry_Module
* \nonstableyet
*
* \class AlignedBox
*
* \brief An axis aligned box
*
* \param _Scalar the type of the scalar coefficients
* \param _AmbientDim the dimension of the ambient space, can be a compile time value or Dynamic.
*
* This class represents an axis aligned box as a pair of the minimal and maximal corners.
*/
template <typename _Scalar, int _AmbientDim>
class AlignedBox
{
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim==Dynamic ? Dynamic : _AmbientDim+1)
enum { AmbientDimAtCompileTime = _AmbientDim };
typedef _Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar,AmbientDimAtCompileTime,1> VectorType;
/** Default constructor initializing a null box. */
inline AlignedBox()
{ if (AmbientDimAtCompileTime!=Dynamic) setNull(); }
/** Constructs a null box with \a _dim the dimension of the ambient space. */
inline explicit AlignedBox(int _dim) : m_min(_dim), m_max(_dim)
{ setNull(); }
/** Constructs a box with extremities \a _min and \a _max. */
inline AlignedBox(const VectorType& _min, const VectorType& _max) : m_min(_min), m_max(_max) {}
/** Constructs a box containing a single point \a p. */
inline explicit AlignedBox(const VectorType& p) : m_min(p), m_max(p) {}
~AlignedBox() {}
/** \returns the dimension in which the box holds */
inline int dim() const { return AmbientDimAtCompileTime==Dynamic ? m_min.size()-1 : AmbientDimAtCompileTime; }
/** \returns true if the box is null, i.e, empty. */
inline bool isNull() const { return (m_min.cwise() > m_max).any(); }
/** Makes \c *this a null/empty box. */
inline void setNull()
{
m_min.setConstant( (std::numeric_limits<Scalar>::max)());
m_max.setConstant(-(std::numeric_limits<Scalar>::max)());
}
/** \returns the minimal corner */
inline const VectorType& (min)() const { return m_min; }
/** \returns a non const reference to the minimal corner */
inline VectorType& (min)() { return m_min; }
/** \returns the maximal corner */
inline const VectorType& (max)() const { return m_max; }
/** \returns a non const reference to the maximal corner */
inline VectorType& (max)() { return m_max; }
/** \returns true if the point \a p is inside the box \c *this. */
inline bool contains(const VectorType& p) const
{ return (m_min.cwise()<=p).all() && (p.cwise()<=m_max).all(); }
/** \returns true if the box \a b is entirely inside the box \c *this. */
inline bool contains(const AlignedBox& b) const
{ return (m_min.cwise()<=(b.min)()).all() && ((b.max)().cwise()<=m_max).all(); }
/** Extends \c *this such that it contains the point \a p and returns a reference to \c *this. */
inline AlignedBox& extend(const VectorType& p)
{ m_min = (m_min.cwise().min)(p); m_max = (m_max.cwise().max)(p); return *this; }
/** Extends \c *this such that it contains the box \a b and returns a reference to \c *this. */
inline AlignedBox& extend(const AlignedBox& b)
{ m_min = (m_min.cwise().min)(b.m_min); m_max = (m_max.cwise().max)(b.m_max); return *this; }
/** Clamps \c *this by the box \a b and returns a reference to \c *this. */
inline AlignedBox& clamp(const AlignedBox& b)
{ m_min = (m_min.cwise().max)(b.m_min); m_max = (m_max.cwise().min)(b.m_max); return *this; }
/** Translate \c *this by the vector \a t and returns a reference to \c *this. */
inline AlignedBox& translate(const VectorType& t)
{ m_min += t; m_max += t; return *this; }
/** \returns the squared distance between the point \a p and the box \c *this,
* and zero if \a p is inside the box.
* \sa exteriorDistance()
*/
inline Scalar squaredExteriorDistance(const VectorType& p) const;
/** \returns the distance between the point \a p and the box \c *this,
* and zero if \a p is inside the box.
* \sa squaredExteriorDistance()
*/
inline Scalar exteriorDistance(const VectorType& p) const
{ return ei_sqrt(squaredExteriorDistance(p)); }
/** \returns \c *this with scalar type casted to \a NewScalarType
*
* Note that if \a NewScalarType is equal to the current scalar type of \c *this
* then this function smartly returns a const reference to \c *this.
*/
template<typename NewScalarType>
inline typename internal::cast_return_type<AlignedBox,
AlignedBox<NewScalarType,AmbientDimAtCompileTime> >::type cast() const
{
return typename internal::cast_return_type<AlignedBox,
AlignedBox<NewScalarType,AmbientDimAtCompileTime> >::type(*this);
}
/** Copy constructor with scalar type conversion */
template<typename OtherScalarType>
inline explicit AlignedBox(const AlignedBox<OtherScalarType,AmbientDimAtCompileTime>& other)
{
m_min = (other.min)().template cast<Scalar>();
m_max = (other.max)().template cast<Scalar>();
}
/** \returns \c true if \c *this is approximately equal to \a other, within the precision
* determined by \a prec.
*
* \sa MatrixBase::isApprox() */
bool isApprox(const AlignedBox& other, typename NumTraits<Scalar>::Real prec = precision<Scalar>()) const
{ return m_min.isApprox(other.m_min, prec) && m_max.isApprox(other.m_max, prec); }
protected:
VectorType m_min, m_max;
};
template<typename Scalar,int AmbiantDim>
inline Scalar AlignedBox<Scalar,AmbiantDim>::squaredExteriorDistance(const VectorType& p) const
{
Scalar dist2(0);
Scalar aux;
for (int k=0; k<dim(); ++k)
{
if ((aux = (p[k]-m_min[k]))<Scalar(0))
dist2 += aux*aux;
else if ( (aux = (m_max[k]-p[k]))<Scalar(0))
dist2 += aux*aux;
}
return dist2;
}
} // end namespace Eigen

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Eigen/src/Eigen2Support/Geometry/All.h
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#ifndef EIGEN2_GEOMETRY_MODULE_H
#define EIGEN2_GEOMETRY_MODULE_H
#include <limits>
#ifndef M_PI
#define M_PI 3.14159265358979323846
#endif
#if EIGEN2_SUPPORT_STAGE < STAGE20_RESOLVE_API_CONFLICTS
#include "RotationBase.h"
#include "Rotation2D.h"
#include "Quaternion.h"
#include "AngleAxis.h"
#include "Transform.h"
#include "Translation.h"
#include "Scaling.h"
#include "AlignedBox.h"
#include "Hyperplane.h"
#include "ParametrizedLine.h"
#endif
#define RotationBase eigen2_RotationBase
#define Rotation2D eigen2_Rotation2D
#define Rotation2Df eigen2_Rotation2Df
#define Rotation2Dd eigen2_Rotation2Dd
#define Quaternion eigen2_Quaternion
#define Quaternionf eigen2_Quaternionf
#define Quaterniond eigen2_Quaterniond
#define AngleAxis eigen2_AngleAxis
#define AngleAxisf eigen2_AngleAxisf
#define AngleAxisd eigen2_AngleAxisd
#define Transform eigen2_Transform
#define Transform2f eigen2_Transform2f
#define Transform2d eigen2_Transform2d
#define Transform3f eigen2_Transform3f
#define Transform3d eigen2_Transform3d
#define Translation eigen2_Translation
#define Translation2f eigen2_Translation2f
#define Translation2d eigen2_Translation2d
#define Translation3f eigen2_Translation3f
#define Translation3d eigen2_Translation3d
#define Scaling eigen2_Scaling
#define Scaling2f eigen2_Scaling2f
#define Scaling2d eigen2_Scaling2d
#define Scaling3f eigen2_Scaling3f
#define Scaling3d eigen2_Scaling3d
#define AlignedBox eigen2_AlignedBox
#define Hyperplane eigen2_Hyperplane
#define ParametrizedLine eigen2_ParametrizedLine
#define ei_toRotationMatrix eigen2_ei_toRotationMatrix
#define ei_quaternion_assign_impl eigen2_ei_quaternion_assign_impl
#define ei_transform_product_impl eigen2_ei_transform_product_impl
#include "RotationBase.h"
#include "Rotation2D.h"
#include "Quaternion.h"
#include "AngleAxis.h"
#include "Transform.h"
#include "Translation.h"
#include "Scaling.h"
#include "AlignedBox.h"
#include "Hyperplane.h"
#include "ParametrizedLine.h"
#undef ei_toRotationMatrix
#undef ei_quaternion_assign_impl
#undef ei_transform_product_impl
#undef RotationBase
#undef Rotation2D
#undef Rotation2Df
#undef Rotation2Dd
#undef Quaternion
#undef Quaternionf
#undef Quaterniond
#undef AngleAxis
#undef AngleAxisf
#undef AngleAxisd
#undef Transform
#undef Transform2f
#undef Transform2d
#undef Transform3f
#undef Transform3d
#undef Translation
#undef Translation2f
#undef Translation2d
#undef Translation3f
#undef Translation3d
#undef Scaling
#undef Scaling2f
#undef Scaling2d
#undef Scaling3f
#undef Scaling3d
#undef AlignedBox
#undef Hyperplane
#undef ParametrizedLine
#endif // EIGEN2_GEOMETRY_MODULE_H

228
Eigen/src/Eigen2Support/Geometry/AngleAxis.h
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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/.
// no include guard, we'll include this twice from All.h from Eigen2Support, and it's internal anyway
namespace Eigen {
/** \geometry_module \ingroup Geometry_Module
*
* \class AngleAxis
*
* \brief Represents a 3D rotation as a rotation angle around an arbitrary 3D axis
*
* \param _Scalar the scalar type, i.e., the type of the coefficients.
*
* The following two typedefs are provided for convenience:
* \li \c AngleAxisf for \c float
* \li \c AngleAxisd for \c double
*
* \addexample AngleAxisForEuler \label How to define a rotation from Euler-angles
*
* Combined with MatrixBase::Unit{X,Y,Z}, AngleAxis can be used to easily
* mimic Euler-angles. Here is an example:
* \include AngleAxis_mimic_euler.cpp
* Output: \verbinclude AngleAxis_mimic_euler.out
*
* \note This class is not aimed to be used to store a rotation transformation,
* but rather to make easier the creation of other rotation (Quaternion, rotation Matrix)
* and transformation objects.
*
* \sa class Quaternion, class Transform, MatrixBase::UnitX()
*/
template<typename _Scalar> struct ei_traits<AngleAxis<_Scalar> >
{
typedef _Scalar Scalar;
};
template<typename _Scalar>
class AngleAxis : public RotationBase<AngleAxis<_Scalar>,3>
{
typedef RotationBase<AngleAxis<_Scalar>,3> Base;
public:
using Base::operator*;
enum { Dim = 3 };
/** the scalar type of the coefficients */
typedef _Scalar Scalar;
typedef Matrix<Scalar,3,3> Matrix3;
typedef Matrix<Scalar,3,1> Vector3;
typedef Quaternion<Scalar> QuaternionType;
protected:
Vector3 m_axis;
Scalar m_angle;
public:
/** Default constructor without initialization. */
AngleAxis() {}
/** Constructs and initialize the angle-axis rotation from an \a angle in radian
* and an \a axis which must be normalized. */
template<typename Derived>
inline AngleAxis(Scalar angle, const MatrixBase<Derived>& axis) : m_axis(axis), m_angle(angle)
{
using std::sqrt;
using std::abs;
// since we compare against 1, this is equal to computing the relative error
eigen_assert( abs(m_axis.derived().squaredNorm() - 1) < sqrt( NumTraits<Scalar>::dummy_precision() ) );
}
/** Constructs and initialize the angle-axis rotation from a quaternion \a q. */
inline AngleAxis(const QuaternionType& q) { *this = q; }
/** Constructs and initialize the angle-axis rotation from a 3x3 rotation matrix. */
template<typename Derived>
inline explicit AngleAxis(const MatrixBase<Derived>& m) { *this = m; }
Scalar angle() const { return m_angle; }
Scalar& angle() { return m_angle; }
const Vector3& axis() const { return m_axis; }
Vector3& axis() { return m_axis; }
/** Concatenates two rotations */
inline QuaternionType operator* (const AngleAxis& other) const
{ return QuaternionType(*this) * QuaternionType(other); }
/** Concatenates two rotations */
inline QuaternionType operator* (const QuaternionType& other) const
{ return QuaternionType(*this) * other; }
/** Concatenates two rotations */
friend inline QuaternionType operator* (const QuaternionType& a, const AngleAxis& b)
{ return a * QuaternionType(b); }
/** Concatenates two rotations */
inline Matrix3 operator* (const Matrix3& other) const
{ return toRotationMatrix() * other; }
/** Concatenates two rotations */
inline friend Matrix3 operator* (const Matrix3& a, const AngleAxis& b)
{ return a * b.toRotationMatrix(); }
/** Applies rotation to vector */
inline Vector3 operator* (const Vector3& other) const
{ return toRotationMatrix() * other; }
/** \returns the inverse rotation, i.e., an angle-axis with opposite rotation angle */
AngleAxis inverse() const
{ return AngleAxis(-m_angle, m_axis); }
AngleAxis& operator=(const QuaternionType& q);
template<typename Derived>
AngleAxis& operator=(const MatrixBase<Derived>& m);
template<typename Derived>
AngleAxis& fromRotationMatrix(const MatrixBase<Derived>& m);
Matrix3 toRotationMatrix(void) const;
/** \returns \c *this with scalar type casted to \a NewScalarType
*
* Note that if \a NewScalarType is equal to the current scalar type of \c *this
* then this function smartly returns a const reference to \c *this.
*/
template<typename NewScalarType>
inline typename internal::cast_return_type<AngleAxis,AngleAxis<NewScalarType> >::type cast() const
{ return typename internal::cast_return_type<AngleAxis,AngleAxis<NewScalarType> >::type(*this); }
/** Copy constructor with scalar type conversion */
template<typename OtherScalarType>
inline explicit AngleAxis(const AngleAxis<OtherScalarType>& other)
{
m_axis = other.axis().template cast<Scalar>();
m_angle = Scalar(other.angle());
}
/** \returns \c true if \c *this is approximately equal to \a other, within the precision
* determined by \a prec.
*
* \sa MatrixBase::isApprox() */
bool isApprox(const AngleAxis& other, typename NumTraits<Scalar>::Real prec = precision<Scalar>()) const
{ return m_axis.isApprox(other.m_axis, prec) && ei_isApprox(m_angle,other.m_angle, prec); }
};
/** \ingroup Geometry_Module
* single precision angle-axis type */
typedef AngleAxis<float> AngleAxisf;
/** \ingroup Geometry_Module
* double precision angle-axis type */
typedef AngleAxis<double> AngleAxisd;
/** Set \c *this from a quaternion.
* The axis is normalized.
*/
template<typename Scalar>
AngleAxis<Scalar>& AngleAxis<Scalar>::operator=(const QuaternionType& q)
{
Scalar n2 = q.vec().squaredNorm();
if (n2 < precision<Scalar>()*precision<Scalar>())
{
m_angle = 0;
m_axis << 1, 0, 0;
}
else
{
m_angle = 2*std::acos(q.w());
m_axis = q.vec() / ei_sqrt(n2);
using std::sqrt;
using std::abs;
// since we compare against 1, this is equal to computing the relative error
eigen_assert( abs(m_axis.derived().squaredNorm() - 1) < sqrt( NumTraits<Scalar>::dummy_precision() ) );
}
return *this;
}
/** Set \c *this from a 3x3 rotation matrix \a mat.
*/
template<typename Scalar>
template<typename Derived>
AngleAxis<Scalar>& AngleAxis<Scalar>::operator=(const MatrixBase<Derived>& mat)
{
// Since a direct conversion would not be really faster,
// let's use the robust Quaternion implementation:
return *this = QuaternionType(mat);
}
/** Constructs and \returns an equivalent 3x3 rotation matrix.
*/
template<typename Scalar>
typename AngleAxis<Scalar>::Matrix3
AngleAxis<Scalar>::toRotationMatrix(void) const
{
Matrix3 res;
Vector3 sin_axis = ei_sin(m_angle) * m_axis;
Scalar c = ei_cos(m_angle);
Vector3 cos1_axis = (Scalar(1)-c) * m_axis;
Scalar tmp;
tmp = cos1_axis.x() * m_axis.y();
res.coeffRef(0,1) = tmp - sin_axis.z();
res.coeffRef(1,0) = tmp + sin_axis.z();
tmp = cos1_axis.x() * m_axis.z();
res.coeffRef(0,2) = tmp + sin_axis.y();
res.coeffRef(2,0) = tmp - sin_axis.y();
tmp = cos1_axis.y() * m_axis.z();
res.coeffRef(1,2) = tmp - sin_axis.x();
res.coeffRef(2,1) = tmp + sin_axis.x();
res.diagonal() = (cos1_axis.cwise() * m_axis).cwise() + c;
return res;
}
} // end namespace Eigen

6
Eigen/src/Eigen2Support/Geometry/CMakeLists.txt
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FILE(GLOB Eigen_Eigen2Support_Geometry_SRCS "*.h")
INSTALL(FILES
${Eigen_Eigen2Support_Geometry_SRCS}
DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Eigen2Support/Geometry
)

254
Eigen/src/Eigen2Support/Geometry/Hyperplane.h
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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/.
// no include guard, we'll include this twice from All.h from Eigen2Support, and it's internal anyway
namespace Eigen {
/** \geometry_module \ingroup Geometry_Module
*
* \class Hyperplane
*
* \brief A hyperplane
*
* A hyperplane is an affine subspace of dimension n-1 in a space of dimension n.
* For example, a hyperplane in a plane is a line; a hyperplane in 3-space is a plane.
*
* \param _Scalar the scalar type, i.e., the type of the coefficients
* \param _AmbientDim the dimension of the ambient space, can be a compile time value or Dynamic.
* Notice that the dimension of the hyperplane is _AmbientDim-1.
*
* This class represents an hyperplane as the zero set of the implicit equation
* \f$ n \cdot x + d = 0 \f$ where \f$ n \f$ is a unit normal vector of the plane (linear part)
* and \f$ d \f$ is the distance (offset) to the origin.
*/
template <typename _Scalar, int _AmbientDim>
class Hyperplane
{
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim==Dynamic ? Dynamic : _AmbientDim+1)
enum { AmbientDimAtCompileTime = _AmbientDim };
typedef _Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar,AmbientDimAtCompileTime,1> VectorType;
typedef Matrix<Scalar,int(AmbientDimAtCompileTime)==Dynamic
? Dynamic
: int(AmbientDimAtCompileTime)+1,1> Coefficients;
typedef Block<Coefficients,AmbientDimAtCompileTime,1> NormalReturnType;
/** Default constructor without initialization */
inline Hyperplane() {}
/** Constructs a dynamic-size hyperplane with \a _dim the dimension
* of the ambient space */
inline explicit Hyperplane(int _dim) : m_coeffs(_dim+1) {}
/** Construct a plane from its normal \a n and a point \a e onto the plane.
* \warning the vector normal is assumed to be normalized.
*/
inline Hyperplane(const VectorType& n, const VectorType& e)
: m_coeffs(n.size()+1)
{
normal() = n;
offset() = -e.eigen2_dot(n);
}
/** Constructs a plane from its normal \a n and distance to the origin \a d
* such that the algebraic equation of the plane is \f$ n \cdot x + d = 0 \f$.
* \warning the vector normal is assumed to be normalized.
*/
inline Hyperplane(const VectorType& n, Scalar d)
: m_coeffs(n.size()+1)
{
normal() = n;
offset() = d;
}
/** Constructs a hyperplane passing through the two points. If the dimension of the ambient space
* is greater than 2, then there isn't uniqueness, so an arbitrary choice is made.
*/
static inline Hyperplane Through(const VectorType& p0, const VectorType& p1)
{
Hyperplane result(p0.size());
result.normal() = (p1 - p0).unitOrthogonal();
result.offset() = -result.normal().eigen2_dot(p0);
return result;
}
/** Constructs a hyperplane passing through the three points. The dimension of the ambient space
* is required to be exactly 3.
*/
static inline Hyperplane Through(const VectorType& p0, const VectorType& p1, const VectorType& p2)
{
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(VectorType, 3)
Hyperplane result(p0.size());
result.normal() = (p2 - p0).cross(p1 - p0).normalized();
result.offset() = -result.normal().eigen2_dot(p0);
return result;
}
/** Constructs a hyperplane passing through the parametrized line \a parametrized.
* If the dimension of the ambient space is greater than 2, then there isn't uniqueness,
* so an arbitrary choice is made.
*/
// FIXME to be consitent with the rest this could be implemented as a static Through function ??
explicit Hyperplane(const ParametrizedLine<Scalar, AmbientDimAtCompileTime>& parametrized)
{
normal() = parametrized.direction().unitOrthogonal();
offset() = -normal().eigen2_dot(parametrized.origin());
}
~Hyperplane() {}
/** \returns the dimension in which the plane holds */
inline int dim() const { return int(AmbientDimAtCompileTime)==Dynamic ? m_coeffs.size()-1 : int(AmbientDimAtCompileTime); }
/** normalizes \c *this */
void normalize(void)
{
m_coeffs /= normal().norm();
}
/** \returns the signed distance between the plane \c *this and a point \a p.
* \sa absDistance()
*/
inline Scalar signedDistance(const VectorType& p) const { return p.eigen2_dot(normal()) + offset(); }
/** \returns the absolute distance between the plane \c *this and a point \a p.
* \sa signedDistance()
*/
inline Scalar absDistance(const VectorType& p) const { return ei_abs(signedDistance(p)); }
/** \returns the projection of a point \a p onto the plane \c *this.
*/
inline VectorType projection(const VectorType& p) const { return p - signedDistance(p) * normal(); }
/** \returns a constant reference to the unit normal vector of the plane, which corresponds
* to the linear part of the implicit equation.
*/
inline const NormalReturnType normal() const { return NormalReturnType(*const_cast<Coefficients*>(&m_coeffs),0,0,dim(),1); }
/** \returns a non-constant reference to the unit normal vector of the plane, which corresponds
* to the linear part of the implicit equation.
*/
inline NormalReturnType normal() { return NormalReturnType(m_coeffs,0,0,dim(),1); }
/** \returns the distance to the origin, which is also the "constant term" of the implicit equation
* \warning the vector normal is assumed to be normalized.
*/
inline const Scalar& offset() const { return m_coeffs.coeff(dim()); }
/** \returns a non-constant reference to the distance to the origin, which is also the constant part
* of the implicit equation */
inline Scalar& offset() { return m_coeffs(dim()); }
/** \returns a constant reference to the coefficients c_i of the plane equation:
* \f$ c_0*x_0 + ... + c_{d-1}*x_{d-1} + c_d = 0 \f$
*/
inline const Coefficients& coeffs() const { return m_coeffs; }
/** \returns a non-constant reference to the coefficients c_i of the plane equation:
* \f$ c_0*x_0 + ... + c_{d-1}*x_{d-1} + c_d = 0 \f$
*/
inline Coefficients& coeffs() { return m_coeffs; }
/** \returns the intersection of *this with \a other.
*
* \warning The ambient space must be a plane, i.e. have dimension 2, so that \c *this and \a other are lines.
*
* \note If \a other is approximately parallel to *this, this method will return any point on *this.
*/
VectorType intersection(const Hyperplane& other)
{
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(VectorType, 2)
Scalar det = coeffs().coeff(0) * other.coeffs().coeff(1) - coeffs().coeff(1) * other.coeffs().coeff(0);
// since the line equations ax+by=c are normalized with a^2+b^2=1, the following tests
// whether the two lines are approximately parallel.
if(ei_isMuchSmallerThan(det, Scalar(1)))
{ // special case where the two lines are approximately parallel. Pick any point on the first line.
if(ei_abs(coeffs().coeff(1))>ei_abs(coeffs().coeff(0)))
return VectorType(coeffs().coeff(1), -coeffs().coeff(2)/coeffs().coeff(1)-coeffs().coeff(0));
else
return VectorType(-coeffs().coeff(2)/coeffs().coeff(0)-coeffs().coeff(1), coeffs().coeff(0));
}
else
{ // general case
Scalar invdet = Scalar(1) / det;
return VectorType(invdet*(coeffs().coeff(1)*other.coeffs().coeff(2)-other.coeffs().coeff(1)*coeffs().coeff(2)),
invdet*(other.coeffs().coeff(0)*coeffs().coeff(2)-coeffs().coeff(0)*other.coeffs().coeff(2)));
}
}
/** Applies the transformation matrix \a mat to \c *this and returns a reference to \c *this.
*
* \param mat the Dim x Dim transformation matrix
* \param traits specifies whether the matrix \a mat represents an Isometry
* or a more generic Affine transformation. The default is Affine.
*/
template<typename XprType>
inline Hyperplane& transform(const MatrixBase<XprType>& mat, TransformTraits traits = Affine)
{
if (traits==Affine)
normal() = mat.inverse().transpose() * normal();
else if (traits==Isometry)
normal() = mat * normal();
else
{
ei_assert("invalid traits value in Hyperplane::transform()");
}
return *this;
}
/** Applies the transformation \a t to \c *this and returns a reference to \c *this.
*
* \param t the transformation of dimension Dim
* \param traits specifies whether the transformation \a t represents an Isometry
* or a more generic Affine transformation. The default is Affine.
* Other kind of transformations are not supported.
*/
inline Hyperplane& transform(const Transform<Scalar,AmbientDimAtCompileTime>& t,
TransformTraits traits = Affine)
{
transform(t.linear(), traits);
offset() -= t.translation().eigen2_dot(normal());
return *this;
}
/** \returns \c *this with scalar type casted to \a NewScalarType
*
* Note that if \a NewScalarType is equal to the current scalar type of \c *this
* then this function smartly returns a const reference to \c *this.
*/
template<typename NewScalarType>
inline typename internal::cast_return_type<Hyperplane,
Hyperplane<NewScalarType,AmbientDimAtCompileTime> >::type cast() const
{
return typename internal::cast_return_type<Hyperplane,
Hyperplane<NewScalarType,AmbientDimAtCompileTime> >::type(*this);
}
/** Copy constructor with scalar type conversion */
template<typename OtherScalarType>
inline explicit Hyperplane(const Hyperplane<OtherScalarType,AmbientDimAtCompileTime>& other)
{ m_coeffs = other.coeffs().template cast<Scalar>(); }
/** \returns \c true if \c *this is approximately equal to \a other, within the precision
* determined by \a prec.
*
* \sa MatrixBase::isApprox() */
bool isApprox(const Hyperplane& other, typename NumTraits<Scalar>::Real prec = precision<Scalar>()) const
{ return m_coeffs.isApprox(other.m_coeffs, prec); }
protected:
Coefficients m_coeffs;
};
} // end namespace Eigen

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Eigen/src/Eigen2Support/Geometry/ParametrizedLine.h
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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/.
// no include guard, we'll include this twice from All.h from Eigen2Support, and it's internal anyway
namespace Eigen {
/** \geometry_module \ingroup Geometry_Module
*
* \class ParametrizedLine
*
* \brief A parametrized line
*
* A parametrized line is defined by an origin point \f$ \mathbf{o} \f$ and a unit
* direction vector \f$ \mathbf{d} \f$ such that the line corresponds to
* the set \f$ l(t) = \mathbf{o} + t \mathbf{d} \f$, \f$ l \in \mathbf{R} \f$.
*
* \param _Scalar the scalar type, i.e., the type of the coefficients
* \param _AmbientDim the dimension of the ambient space, can be a compile time value or Dynamic.
*/
template <typename _Scalar, int _AmbientDim>
class ParametrizedLine
{
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim)
enum { AmbientDimAtCompileTime = _AmbientDim };
typedef _Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar,AmbientDimAtCompileTime,1> VectorType;
/** Default constructor without initialization */
inline ParametrizedLine() {}
/** Constructs a dynamic-size line with \a _dim the dimension
* of the ambient space */
inline explicit ParametrizedLine(int _dim) : m_origin(_dim), m_direction(_dim) {}
/** Initializes a parametrized line of direction \a direction and origin \a origin.
* \warning the vector direction is assumed to be normalized.
*/
ParametrizedLine(const VectorType& origin, const VectorType& direction)
: m_origin(origin), m_direction(direction) {}
explicit ParametrizedLine(const Hyperplane<_Scalar, _AmbientDim>& hyperplane);
/** Constructs a parametrized line going from \a p0 to \a p1. */
static inline ParametrizedLine Through(const VectorType& p0, const VectorType& p1)
{ return ParametrizedLine(p0, (p1-p0).normalized()); }
~ParametrizedLine() {}
/** \returns the dimension in which the line holds */
inline int dim() const { return m_direction.size(); }
const VectorType& origin() const { return m_origin; }
VectorType& origin() { return m_origin; }
const VectorType& direction() const { return m_direction; }
VectorType& direction() { return m_direction; }
/** \returns the squared distance of a point \a p to its projection onto the line \c *this.
* \sa distance()
*/
RealScalar squaredDistance(const VectorType& p) const
{
VectorType diff = p-origin();
return (diff - diff.eigen2_dot(direction())* direction()).squaredNorm();
}
/** \returns the distance of a point \a p to its projection onto the line \c *this.
* \sa squaredDistance()
*/
RealScalar distance(const VectorType& p) const { return ei_sqrt(squaredDistance(p)); }
/** \returns the projection of a point \a p onto the line \c *this. */
VectorType projection(const VectorType& p) const
{ return origin() + (p-origin()).eigen2_dot(direction()) * direction(); }
Scalar intersection(const Hyperplane<_Scalar, _AmbientDim>& hyperplane);
/** \returns \c *this with scalar type casted to \a NewScalarType
*
* Note that if \a NewScalarType is equal to the current scalar type of \c *this
* then this function smartly returns a const reference to \c *this.
*/
template<typename NewScalarType>
inline typename internal::cast_return_type<ParametrizedLine,
ParametrizedLine<NewScalarType,AmbientDimAtCompileTime> >::type cast() const
{
return typename internal::cast_return_type<ParametrizedLine,
ParametrizedLine<NewScalarType,AmbientDimAtCompileTime> >::type(*this);
}
/** Copy constructor with scalar type conversion */
template<typename OtherScalarType>
inline explicit ParametrizedLine(const ParametrizedLine<OtherScalarType,AmbientDimAtCompileTime>& other)
{
m_origin = other.origin().template cast<Scalar>();
m_direction = other.direction().template cast<Scalar>();
}
/** \returns \c true if \c *this is approximately equal to \a other, within the precision
* determined by \a prec.
*
* \sa MatrixBase::isApprox() */
bool isApprox(const ParametrizedLine& other, typename NumTraits<Scalar>::Real prec = precision<Scalar>()) const
{ return m_origin.isApprox(other.m_origin, prec) && m_direction.isApprox(other.m_direction, prec); }
protected:
VectorType m_origin, m_direction;
};
/** Constructs a parametrized line from a 2D hyperplane
*
* \warning the ambient space must have dimension 2 such that the hyperplane actually describes a line
*/
template <typename _Scalar, int _AmbientDim>
inline ParametrizedLine<_Scalar, _AmbientDim>::ParametrizedLine(const Hyperplane<_Scalar, _AmbientDim>& hyperplane)
{
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(VectorType, 2)
direction() = hyperplane.normal().unitOrthogonal();
origin() = -hyperplane.normal()*hyperplane.offset();
}
/** \returns the parameter value of the intersection between \c *this and the given hyperplane
*/
template <typename _Scalar, int _AmbientDim>
inline _Scalar ParametrizedLine<_Scalar, _AmbientDim>::intersection(const Hyperplane<_Scalar, _AmbientDim>& hyperplane)
{
return -(hyperplane.offset()+origin().eigen2_dot(hyperplane.normal()))
/(direction().eigen2_dot(hyperplane.normal()));
}
} // end namespace Eigen

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Eigen/src/Eigen2Support/Geometry/Quaternion.h
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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/.
// no include guard, we'll include this twice from All.h from Eigen2Support, and it's internal anyway
namespace Eigen {
template<typename Other,
int OtherRows=Other::RowsAtCompileTime,
int OtherCols=Other::ColsAtCompileTime>
struct ei_quaternion_assign_impl;
/** \geometry_module \ingroup Geometry_Module
*
* \class Quaternion
*
* \brief The quaternion class used to represent 3D orientations and rotations
*
* \param _Scalar the scalar type, i.e., the type of the coefficients
*
* This class represents a quaternion \f$ w+xi+yj+zk \f$ that is a convenient representation of
* orientations and rotations of objects in three dimensions. Compared to other representations
* like Euler angles or 3x3 matrices, quatertions offer the following advantages:
* \li \b compact storage (4 scalars)
* \li \b efficient to compose (28 flops),
* \li \b stable spherical interpolation
*
* The following two typedefs are provided for convenience:
* \li \c Quaternionf for \c float
* \li \c Quaterniond for \c double
*
* \sa class AngleAxis, class Transform
*/
template<typename _Scalar> struct ei_traits<Quaternion<_Scalar> >
{
typedef _Scalar Scalar;
};
template<typename _Scalar>
class Quaternion : public RotationBase<Quaternion<_Scalar>,3>
{
typedef RotationBase<Quaternion<_Scalar>,3> Base;
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,4)
using Base::operator*;
/** the scalar type of the coefficients */
typedef _Scalar Scalar;
/** the type of the Coefficients 4-vector */
typedef Matrix<Scalar, 4, 1> Coefficients;
/** the type of a 3D vector */
typedef Matrix<Scalar,3,1> Vector3;
/** the equivalent rotation matrix type */
typedef Matrix<Scalar,3,3> Matrix3;
/** the equivalent angle-axis type */
typedef AngleAxis<Scalar> AngleAxisType;
/** \returns the \c x coefficient */
inline Scalar x() const { return m_coeffs.coeff(0); }
/** \returns the \c y coefficient */
inline Scalar y() const { return m_coeffs.coeff(1); }
/** \returns the \c z coefficient */
inline Scalar z() const { return m_coeffs.coeff(2); }
/** \returns the \c w coefficient */
inline Scalar w() const { return m_coeffs.coeff(3); }
/** \returns a reference to the \c x coefficient */
inline Scalar& x() { return m_coeffs.coeffRef(0); }
/** \returns a reference to the \c y coefficient */
inline Scalar& y() { return m_coeffs.coeffRef(1); }
/** \returns a reference to the \c z coefficient */
inline Scalar& z() { return m_coeffs.coeffRef(2); }
/** \returns a reference to the \c w coefficient */
inline Scalar& w() { return m_coeffs.coeffRef(3); }
/** \returns a read-only vector expression of the imaginary part (x,y,z) */
inline const Block<const Coefficients,3,1> vec() const { return m_coeffs.template start<3>(); }
/** \returns a vector expression of the imaginary part (x,y,z) */
inline Block<Coefficients,3,1> vec() { return m_coeffs.template start<3>(); }
/** \returns a read-only vector expression of the coefficients (x,y,z,w) */
inline const Coefficients& coeffs() const { return m_coeffs; }
/** \returns a vector expression of the coefficients (x,y,z,w) */
inline Coefficients& coeffs() { return m_coeffs; }
/** Default constructor leaving the quaternion uninitialized. */
inline Quaternion() {}
/** Constructs and initializes the quaternion \f$ w+xi+yj+zk \f$ from
* its four coefficients \a w, \a x, \a y and \a z.
*
* \warning Note the order of the arguments: the real \a w coefficient first,
* while internally the coefficients are stored in the following order:
* [\c x, \c y, \c z, \c w]
*/
inline Quaternion(Scalar w, Scalar x, Scalar y, Scalar z)
{ m_coeffs << x, y, z, w; }
/** Copy constructor */
inline Quaternion(const Quaternion& other) { m_coeffs = other.m_coeffs; }
/** Constructs and initializes a quaternion from the angle-axis \a aa */
explicit inline Quaternion(const AngleAxisType& aa) { *this = aa; }
/** Constructs and initializes a quaternion from either:
* - a rotation matrix expression,
* - a 4D vector expression representing quaternion coefficients.
* \sa operator=(MatrixBase<Derived>)
*/
template<typename Derived>
explicit inline Quaternion(const MatrixBase<Derived>& other) { *this = other; }
Quaternion& operator=(const Quaternion& other);
Quaternion& operator=(const AngleAxisType& aa);
template<typename Derived>
Quaternion& operator=(const MatrixBase<Derived>& m);
/** \returns a quaternion representing an identity rotation
* \sa MatrixBase::Identity()
*/
static inline Quaternion Identity() { return Quaternion(1, 0, 0, 0); }
/** \sa Quaternion::Identity(), MatrixBase::setIdentity()
*/
inline Quaternion& setIdentity() { m_coeffs << 0, 0, 0, 1; return *this; }
/** \returns the squared norm of the quaternion's coefficients
* \sa Quaternion::norm(), MatrixBase::squaredNorm()
*/
inline Scalar squaredNorm() const { return m_coeffs.squaredNorm(); }
/** \returns the norm of the quaternion's coefficients
* \sa Quaternion::squaredNorm(), MatrixBase::norm()
*/
inline Scalar norm() const { return m_coeffs.norm(); }
/** Normalizes the quaternion \c *this
* \sa normalized(), MatrixBase::normalize() */
inline void normalize() { m_coeffs.normalize(); }
/** \returns a normalized version of \c *this
* \sa normalize(), MatrixBase::normalized() */
inline Quaternion normalized() const { return Quaternion(m_coeffs.normalized()); }
/** \returns the dot product of \c *this and \a other
* Geometrically speaking, the dot product of two unit quaternions
* corresponds to the cosine of half the angle between the two rotations.
* \sa angularDistance()
*/
inline Scalar eigen2_dot(const Quaternion& other) const { return m_coeffs.eigen2_dot(other.m_coeffs); }
inline Scalar angularDistance(const Quaternion& other) const;
Matrix3 toRotationMatrix(void) const;
template<typename Derived1, typename Derived2>
Quaternion& setFromTwoVectors(const MatrixBase<Derived1>& a, const MatrixBase<Derived2>& b);
inline Quaternion operator* (const Quaternion& q) const;
inline Quaternion& operator*= (const Quaternion& q);
Quaternion inverse(void) const;
Quaternion conjugate(void) const;
Quaternion slerp(Scalar t, const Quaternion& other) const;
template<typename Derived>
Vector3 operator* (const MatrixBase<Derived>& vec) const;
/** \returns \c *this with scalar type casted to \a NewScalarType
*
* Note that if \a NewScalarType is equal to the current scalar type of \c *this
* then this function smartly returns a const reference to \c *this.
*/
template<typename NewScalarType>
inline typename internal::cast_return_type<Quaternion,Quaternion<NewScalarType> >::type cast() const
{ return typename internal::cast_return_type<Quaternion,Quaternion<NewScalarType> >::type(*this); }
/** Copy constructor with scalar type conversion */
template<typename OtherScalarType>
inline explicit Quaternion(const Quaternion<OtherScalarType>& other)
{ m_coeffs = other.coeffs().template cast<Scalar>(); }
/** \returns \c true if \c *this is approximately equal to \a other, within the precision
* determined by \a prec.
*
* \sa MatrixBase::isApprox() */
bool isApprox(const Quaternion& other, typename NumTraits<Scalar>::Real prec = precision<Scalar>()) const
{ return m_coeffs.isApprox(other.m_coeffs, prec); }
protected:
Coefficients m_coeffs;
};
/** \ingroup Geometry_Module
* single precision quaternion type */
typedef Quaternion<float> Quaternionf;
/** \ingroup Geometry_Module
* double precision quaternion type */
typedef Quaternion<double> Quaterniond;
// Generic Quaternion * Quaternion product
template<typename Scalar> inline Quaternion<Scalar>
ei_quaternion_product(const Quaternion<Scalar>& a, const Quaternion<Scalar>& b)
{
return Quaternion<Scalar>
(
a.w() * b.w() - a.x() * b.x() - a.y() * b.y() - a.z() * b.z(),
a.w() * b.x() + a.x() * b.w() + a.y() * b.z() - a.z() * b.y(),
a.w() * b.y() + a.y() * b.w() + a.z() * b.x() - a.x() * b.z(),
a.w() * b.z() + a.z() * b.w() + a.x() * b.y() - a.y() * b.x()
);
}
/** \returns the concatenation of two rotations as a quaternion-quaternion product */
template <typename Scalar>
inline Quaternion<Scalar> Quaternion<Scalar>::operator* (const Quaternion& other) const
{
return ei_quaternion_product(*this,other);
}
/** \sa operator*(Quaternion) */
template <typename Scalar>
inline Quaternion<Scalar>& Quaternion<Scalar>::operator*= (const Quaternion& other)
{
return (*this = *this * other);
}
/** Rotation of a vector by a quaternion.
* \remarks If the quaternion is used to rotate several points (>1)
* then it is much more efficient to first convert it to a 3x3 Matrix.
* Comparison of the operation cost for n transformations:
* - Quaternion: 30n
* - Via a Matrix3: 24 + 15n
*/
template <typename Scalar>
template<typename Derived>
inline typename Quaternion<Scalar>::Vector3
Quaternion<Scalar>::operator* (const MatrixBase<Derived>& v) const
{
// Note that this algorithm comes from the optimization by hand
// of the conversion to a Matrix followed by a Matrix/Vector product.
// It appears to be much faster than the common algorithm found
// in the litterature (30 versus 39 flops). It also requires two
// Vector3 as temporaries.
Vector3 uv;
uv = 2 * this->vec().cross(v);
return v + this->w() * uv + this->vec().cross(uv);
}
template<typename Scalar>
inline Quaternion<Scalar>& Quaternion<Scalar>::operator=(const Quaternion& other)
{
m_coeffs = other.m_coeffs;
return *this;
}
/** Set \c *this from an angle-axis \a aa and returns a reference to \c *this
*/
template<typename Scalar>
inline Quaternion<Scalar>& Quaternion<Scalar>::operator=(const AngleAxisType& aa)
{
Scalar ha = Scalar(0.5)*aa.angle(); // Scalar(0.5) to suppress precision loss warnings
this->w() = ei_cos(ha);
this->vec() = ei_sin(ha) * aa.axis();
return *this;
}
/** Set \c *this from the expression \a xpr:
* - if \a xpr is a 4x1 vector, then \a xpr is assumed to be a quaternion
* - if \a xpr is a 3x3 matrix, then \a xpr is assumed to be rotation matrix
* and \a xpr is converted to a quaternion
*/
template<typename Scalar>
template<typename Derived>
inline Quaternion<Scalar>& Quaternion<Scalar>::operator=(const MatrixBase<Derived>& xpr)
{
ei_quaternion_assign_impl<Derived>::run(*this, xpr.derived());
return *this;
}
/** Convert the quaternion to a 3x3 rotation matrix */
template<typename Scalar>
inline typename Quaternion<Scalar>::Matrix3
Quaternion<Scalar>::toRotationMatrix(void) const
{
// NOTE if inlined, then gcc 4.2 and 4.4 get rid of the temporary (not gcc 4.3 !!)
// if not inlined then the cost of the return by value is huge ~ +35%,
// however, not inlining this function is an order of magnitude slower, so
// it has to be inlined, and so the return by value is not an issue
Matrix3 res;
const Scalar tx = Scalar(2)*this->x();
const Scalar ty = Scalar(2)*this->y();
const Scalar tz = Scalar(2)*this->z();
const Scalar twx = tx*this->w();
const Scalar twy = ty*this->w();
const Scalar twz = tz*this->w();
const Scalar txx = tx*this->x();
const Scalar txy = ty*this->x();
const Scalar txz = tz*this->x();
const Scalar tyy = ty*this->y();
const Scalar tyz = tz*this->y();
const Scalar tzz = tz*this->z();
res.coeffRef(0,0) = Scalar(1)-(tyy+tzz);
res.coeffRef(0,1) = txy-twz;
res.coeffRef(0,2) = txz+twy;
res.coeffRef(1,0) = txy+twz;
res.coeffRef(1,1) = Scalar(1)-(txx+tzz);
res.coeffRef(1,2) = tyz-twx;
res.coeffRef(2,0) = txz-twy;
res.coeffRef(2,1) = tyz+twx;
res.coeffRef(2,2) = Scalar(1)-(txx+tyy);
return res;
}
/** Sets *this to be a quaternion representing a rotation sending the vector \a a to the vector \a b.
*
* \returns a reference to *this.
*
* Note that the two input vectors do \b not have to be normalized.
*/
template<typename Scalar>
template<typename Derived1, typename Derived2>
inline Quaternion<Scalar>& Quaternion<Scalar>::setFromTwoVectors(const MatrixBase<Derived1>& a, const MatrixBase<Derived2>& b)
{
Vector3 v0 = a.normalized();
Vector3 v1 = b.normalized();
Scalar c = v0.eigen2_dot(v1);
// if dot == 1, vectors are the same
if (ei_isApprox(c,Scalar(1)))
{
// set to identity
this->w() = 1; this->vec().setZero();
return *this;
}
// if dot == -1, vectors are opposites
if (ei_isApprox(c,Scalar(-1)))
{
this->vec() = v0.unitOrthogonal();
this->w() = 0;
return *this;
}
Vector3 axis = v0.cross(v1);
Scalar s = ei_sqrt((Scalar(1)+c)*Scalar(2));
Scalar invs = Scalar(1)/s;
this->vec() = axis * invs;
this->w() = s * Scalar(0.5);
return *this;
}
/** \returns the multiplicative inverse of \c *this
* Note that in most cases, i.e., if you simply want the opposite rotation,
* and/or the quaternion is normalized, then it is enough to use the conjugate.
*
* \sa Quaternion::conjugate()
*/
template <typename Scalar>
inline Quaternion<Scalar> Quaternion<Scalar>::inverse() const
{
// FIXME should this function be called multiplicativeInverse and conjugate() be called inverse() or opposite() ??
Scalar n2 = this->squaredNorm();
if (n2 > 0)
return Quaternion(conjugate().coeffs() / n2);
else
{
// return an invalid result to flag the error
return Quaternion(Coefficients::Zero());
}
}
/** \returns the conjugate of the \c *this which is equal to the multiplicative inverse
* if the quaternion is normalized.
* The conjugate of a quaternion represents the opposite rotation.
*
* \sa Quaternion::inverse()
*/
template <typename Scalar>
inline Quaternion<Scalar> Quaternion<Scalar>::conjugate() const
{
return Quaternion(this->w(),-this->x(),-this->y(),-this->z());
}
/** \returns the angle (in radian) between two rotations
* \sa eigen2_dot()
*/
template <typename Scalar>
inline Scalar Quaternion<Scalar>::angularDistance(const Quaternion& other) const
{
double d = ei_abs(this->eigen2_dot(other));
if (d>=1.0)
return 0;
return Scalar(2) * std::acos(d);
}
/** \returns the spherical linear interpolation between the two quaternions
* \c *this and \a other at the parameter \a t
*/
template <typename Scalar>
Quaternion<Scalar> Quaternion<Scalar>::slerp(Scalar t, const Quaternion& other) const
{
static const Scalar one = Scalar(1) - machine_epsilon<Scalar>();
Scalar d = this->eigen2_dot(other);
Scalar absD = ei_abs(d);
Scalar scale0;
Scalar scale1;
if (absD>=one)
{
scale0 = Scalar(1) - t;
scale1 = t;
}
else
{
// theta is the angle between the 2 quaternions
Scalar theta = std::acos(absD);
Scalar sinTheta = ei_sin(theta);
scale0 = ei_sin( ( Scalar(1) - t ) * theta) / sinTheta;
scale1 = ei_sin( ( t * theta) ) / sinTheta;
if (d<0)
scale1 = -scale1;
}
return Quaternion<Scalar>(scale0 * coeffs() + scale1 * other.coeffs());
}
// set from a rotation matrix
template<typename Other>
struct ei_quaternion_assign_impl<Other,3,3>
{
typedef typename Other::Scalar Scalar;
static inline void run(Quaternion<Scalar>& q, const Other& mat)
{
// This algorithm comes from "Quaternion Calculus and Fast Animation",
// Ken Shoemake, 1987 SIGGRAPH course notes
Scalar t = mat.trace();
if (t > 0)
{
t = ei_sqrt(t + Scalar(1.0));
q.w() = Scalar(0.5)*t;
t = Scalar(0.5)/t;
q.x() = (mat.coeff(2,1) - mat.coeff(1,2)) * t;
q.y() = (mat.coeff(0,2) - mat.coeff(2,0)) * t;
q.z() = (mat.coeff(1,0) - mat.coeff(0,1)) * t;
}
else
{
int i = 0;
if (mat.coeff(1,1) > mat.coeff(0,0))
i = 1;
if (mat.coeff(2,2) > mat.coeff(i,i))
i = 2;
int j = (i+1)%3;
int k = (j+1)%3;
t = ei_sqrt(mat.coeff(i,i)-mat.coeff(j,j)-mat.coeff(k,k) + Scalar(1.0));
q.coeffs().coeffRef(i) = Scalar(0.5) * t;
t = Scalar(0.5)/t;
q.w() = (mat.coeff(k,j)-mat.coeff(j,k))*t;
q.coeffs().coeffRef(j) = (mat.coeff(j,i)+mat.coeff(i,j))*t;
q.coeffs().coeffRef(k) = (mat.coeff(k,i)+mat.coeff(i,k))*t;
}
}
};
// set from a vector of coefficients assumed to be a quaternion
template<typename Other>
struct ei_quaternion_assign_impl<Other,4,1>
{
typedef typename Other::Scalar Scalar;
static inline void run(Quaternion<Scalar>& q, const Other& vec)
{
q.coeffs() = vec;
}
};
} // end namespace Eigen

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Eigen/src/Eigen2Support/Geometry/Rotation2D.h
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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/.
// no include guard, we'll include this twice from All.h from Eigen2Support, and it's internal anyway
namespace Eigen {
/** \geometry_module \ingroup Geometry_Module
*
* \class Rotation2D
*
* \brief Represents a rotation/orientation in a 2 dimensional space.
*
* \param _Scalar the scalar type, i.e., the type of the coefficients
*
* This class is equivalent to a single scalar representing a counter clock wise rotation
* as a single angle in radian. It provides some additional features such as the automatic
* conversion from/to a 2x2 rotation matrix. Moreover this class aims to provide a similar
* interface to Quaternion in order to facilitate the writing of generic algorithms
* dealing with rotations.
*
* \sa class Quaternion, class Transform
*/
template<typename _Scalar> struct ei_traits<Rotation2D<_Scalar> >
{
typedef _Scalar Scalar;
};
template<typename _Scalar>
class Rotation2D : public RotationBase<Rotation2D<_Scalar>,2>
{
typedef RotationBase<Rotation2D<_Scalar>,2> Base;
public:
using Base::operator*;
enum { Dim = 2 };
/** the scalar type of the coefficients */
typedef _Scalar Scalar;
typedef Matrix<Scalar,2,1> Vector2;
typedef Matrix<Scalar,2,2> Matrix2;
protected:
Scalar m_angle;
public:
/** Construct a 2D counter clock wise rotation from the angle \a a in radian. */
inline Rotation2D(Scalar a) : m_angle(a) {}
/** \returns the rotation angle */
inline Scalar angle() const { return m_angle; }
/** \returns a read-write reference to the rotation angle */
inline Scalar& angle() { return m_angle; }
/** \returns the inverse rotation */
inline Rotation2D inverse() const { return -m_angle; }
/** Concatenates two rotations */
inline Rotation2D operator*(const Rotation2D& other) const
{ return m_angle + other.m_angle; }
/** Concatenates two rotations */
inline Rotation2D& operator*=(const Rotation2D& other)
{ return m_angle += other.m_angle; return *this; }
/** Applies the rotation to a 2D vector */
Vector2 operator* (const Vector2& vec) const
{ return toRotationMatrix() * vec; }
template<typename Derived>
Rotation2D& fromRotationMatrix(const MatrixBase<Derived>& m);
Matrix2 toRotationMatrix(void) const;
/** \returns the spherical interpolation between \c *this and \a other using
* parameter \a t. It is in fact equivalent to a linear interpolation.
*/
inline Rotation2D slerp(Scalar t, const Rotation2D& other) const
{ return m_angle * (1-t) + other.angle() * t; }
/** \returns \c *this with scalar type casted to \a NewScalarType
*
* Note that if \a NewScalarType is equal to the current scalar type of \c *this
* then this function smartly returns a const reference to \c *this.
*/
template<typename NewScalarType>
inline typename internal::cast_return_type<Rotation2D,Rotation2D<NewScalarType> >::type cast() const
{ return typename internal::cast_return_type<Rotation2D,Rotation2D<NewScalarType> >::type(*this); }
/** Copy constructor with scalar type conversion */
template<typename OtherScalarType>
inline explicit Rotation2D(const Rotation2D<OtherScalarType>& other)
{
m_angle = Scalar(other.angle());
}
/** \returns \c true if \c *this is approximately equal to \a other, within the precision
* determined by \a prec.
*
* \sa MatrixBase::isApprox() */
bool isApprox(const Rotation2D& other, typename NumTraits<Scalar>::Real prec = precision<Scalar>()) const
{ return ei_isApprox(m_angle,other.m_angle, prec); }
};
/** \ingroup Geometry_Module
* single precision 2D rotation type */
typedef Rotation2D<float> Rotation2Df;
/** \ingroup Geometry_Module
* double precision 2D rotation type */
typedef Rotation2D<double> Rotation2Dd;
/** Set \c *this from a 2x2 rotation matrix \a mat.
* In other words, this function extract the rotation angle
* from the rotation matrix.
*/
template<typename Scalar>
template<typename Derived>
Rotation2D<Scalar>& Rotation2D<Scalar>::fromRotationMatrix(const MatrixBase<Derived>& mat)
{
EIGEN_STATIC_ASSERT(Derived::RowsAtCompileTime==2 && Derived::ColsAtCompileTime==2,YOU_MADE_A_PROGRAMMING_MISTAKE)
m_angle = ei_atan2(mat.coeff(1,0), mat.coeff(0,0));
return *this;
}
/** Constructs and \returns an equivalent 2x2 rotation matrix.
*/
template<typename Scalar>
typename Rotation2D<Scalar>::Matrix2
Rotation2D<Scalar>::toRotationMatrix(void) const
{
Scalar sinA = ei_sin(m_angle);
Scalar cosA = ei_cos(m_angle);
return (Matrix2() << cosA, -sinA, sinA, cosA).finished();
}
} // end namespace Eigen

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Eigen/src/Eigen2Support/Geometry/RotationBase.h
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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/.
// no include guard, we'll include this twice from All.h from Eigen2Support, and it's internal anyway
namespace Eigen {
// this file aims to contains the various representations of rotation/orientation
// in 2D and 3D space excepted Matrix and Quaternion.
/** \class RotationBase
*
* \brief Common base class for compact rotation representations
*
* \param Derived is the derived type, i.e., a rotation type
* \param _Dim the dimension of the space
*/
template<typename Derived, int _Dim>
class RotationBase
{
public:
enum { Dim = _Dim };
/** the scalar type of the coefficients */
typedef typename ei_traits<Derived>::Scalar Scalar;
/** corresponding linear transformation matrix type */
typedef Matrix<Scalar,Dim,Dim> RotationMatrixType;
inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
inline Derived& derived() { return *static_cast<Derived*>(this); }
/** \returns an equivalent rotation matrix */
inline RotationMatrixType toRotationMatrix() const { return derived().toRotationMatrix(); }
/** \returns the inverse rotation */
inline Derived inverse() const { return derived().inverse(); }
/** \returns the concatenation of the rotation \c *this with a translation \a t */
inline Transform<Scalar,Dim> operator*(const Translation<Scalar,Dim>& t) const
{ return toRotationMatrix() * t; }
/** \returns the concatenation of the rotation \c *this with a scaling \a s */
inline RotationMatrixType operator*(const Scaling<Scalar,Dim>& s) const
{ return toRotationMatrix() * s; }
/** \returns the concatenation of the rotation \c *this with an affine transformation \a t */
inline Transform<Scalar,Dim> operator*(const Transform<Scalar,Dim>& t) const
{ return toRotationMatrix() * t; }
};
/** \geometry_module
*
* Constructs a Dim x Dim rotation matrix from the rotation \a r
*/
template<typename _Scalar, int _Rows, int _Cols, int _Storage, int _MaxRows, int _MaxCols>
template<typename OtherDerived>
Matrix<_Scalar, _Rows, _Cols, _Storage, _MaxRows, _MaxCols>
::Matrix(const RotationBase<OtherDerived,ColsAtCompileTime>& r)
{
EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(Matrix,int(OtherDerived::Dim),int(OtherDerived::Dim))
*this = r.toRotationMatrix();
}
/** \geometry_module
*
* Set a Dim x Dim rotation matrix from the rotation \a r
*/
template<typename _Scalar, int _Rows, int _Cols, int _Storage, int _MaxRows, int _MaxCols>
template<typename OtherDerived>
Matrix<_Scalar, _Rows, _Cols, _Storage, _MaxRows, _MaxCols>&
Matrix<_Scalar, _Rows, _Cols, _Storage, _MaxRows, _MaxCols>
::operator=(const RotationBase<OtherDerived,ColsAtCompileTime>& r)
{
EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(Matrix,int(OtherDerived::Dim),int(OtherDerived::Dim))
return *this = r.toRotationMatrix();
}
/** \internal
*
* Helper function to return an arbitrary rotation object to a rotation matrix.
*
* \param Scalar the numeric type of the matrix coefficients
* \param Dim the dimension of the current space
*
* It returns a Dim x Dim fixed size matrix.
*
* Default specializations are provided for:
* - any scalar type (2D),
* - any matrix expression,
* - any type based on RotationBase (e.g., Quaternion, AngleAxis, Rotation2D)
*
* Currently ei_toRotationMatrix is only used by Transform.
*
* \sa class Transform, class Rotation2D, class Quaternion, class AngleAxis
*/
template<typename Scalar, int Dim>
static inline Matrix<Scalar,2,2> ei_toRotationMatrix(const Scalar& s)
{
EIGEN_STATIC_ASSERT(Dim==2,YOU_MADE_A_PROGRAMMING_MISTAKE)
return Rotation2D<Scalar>(s).toRotationMatrix();
}
template<typename Scalar, int Dim, typename OtherDerived>
static inline Matrix<Scalar,Dim,Dim> ei_toRotationMatrix(const RotationBase<OtherDerived,Dim>& r)
{
return r.toRotationMatrix();
}
template<typename Scalar, int Dim, typename OtherDerived>
static inline const MatrixBase<OtherDerived>& ei_toRotationMatrix(const MatrixBase<OtherDerived>& mat)
{
EIGEN_STATIC_ASSERT(OtherDerived::RowsAtCompileTime==Dim && OtherDerived::ColsAtCompileTime==Dim,
YOU_MADE_A_PROGRAMMING_MISTAKE)
return mat;
}
} // end namespace Eigen

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Eigen/src/Eigen2Support/Geometry/Scaling.h
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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/.
// no include guard, we'll include this twice from All.h from Eigen2Support, and it's internal anyway
namespace Eigen {
/** \geometry_module \ingroup Geometry_Module
*
* \class Scaling
*
* \brief Represents a possibly non uniform scaling transformation
*
* \param _Scalar the scalar type, i.e., the type of the coefficients.
* \param _Dim the dimension of the space, can be a compile time value or Dynamic
*
* \note This class is not aimed to be used to store a scaling transformation,
* but rather to make easier the constructions and updates of Transform objects.
*
* \sa class Translation, class Transform
*/
template<typename _Scalar, int _Dim>
class Scaling
{
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_Dim)
/** dimension of the space */
enum { Dim = _Dim };
/** the scalar type of the coefficients */
typedef _Scalar Scalar;
/** corresponding vector type */
typedef Matrix<Scalar,Dim,1> VectorType;
/** corresponding linear transformation matrix type */
typedef Matrix<Scalar,Dim,Dim> LinearMatrixType;
/** corresponding translation type */
typedef Translation<Scalar,Dim> TranslationType;
/** corresponding affine transformation type */
typedef Transform<Scalar,Dim> TransformType;
protected:
VectorType m_coeffs;
public:
/** Default constructor without initialization. */
Scaling() {}
/** Constructs and initialize a uniform scaling transformation */
explicit inline Scaling(const Scalar& s) { m_coeffs.setConstant(s); }
/** 2D only */
inline Scaling(const Scalar& sx, const Scalar& sy)
{
ei_assert(Dim==2);
m_coeffs.x() = sx;
m_coeffs.y() = sy;
}
/** 3D only */
inline Scaling(const Scalar& sx, const Scalar& sy, const Scalar& sz)
{
ei_assert(Dim==3);
m_coeffs.x() = sx;
m_coeffs.y() = sy;
m_coeffs.z() = sz;
}
/** Constructs and initialize the scaling transformation from a vector of scaling coefficients */
explicit inline Scaling(const VectorType& coeffs) : m_coeffs(coeffs) {}
const VectorType& coeffs() const { return m_coeffs; }
VectorType& coeffs() { return m_coeffs; }
/** Concatenates two scaling */
inline Scaling operator* (const Scaling& other) const
{ return Scaling(coeffs().cwise() * other.coeffs()); }
/** Concatenates a scaling and a translation */
inline TransformType operator* (const TranslationType& t) const;
/** Concatenates a scaling and an affine transformation */
inline TransformType operator* (const TransformType& t) const;
/** Concatenates a scaling and a linear transformation matrix */
// TODO returns an expression
inline LinearMatrixType operator* (const LinearMatrixType& other) const
{ return coeffs().asDiagonal() * other; }
/** Concatenates a linear transformation matrix and a scaling */
// TODO returns an expression
friend inline LinearMatrixType operator* (const LinearMatrixType& other, const Scaling& s)
{ return other * s.coeffs().asDiagonal(); }
template<typename Derived>
inline LinearMatrixType operator*(const RotationBase<Derived,Dim>& r) const
{ return *this * r.toRotationMatrix(); }
/** Applies scaling to vector */
inline VectorType operator* (const VectorType& other) const
{ return coeffs().asDiagonal() * other; }
/** \returns the inverse scaling */
inline Scaling inverse() const
{ return Scaling(coeffs().cwise().inverse()); }
inline Scaling& operator=(const Scaling& other)
{
m_coeffs = other.m_coeffs;
return *this;
}
/** \returns \c *this with scalar type casted to \a NewScalarType
*
* Note that if \a NewScalarType is equal to the current scalar type of \c *this
* then this function smartly returns a const reference to \c *this.
*/
template<typename NewScalarType>
inline typename internal::cast_return_type<Scaling,Scaling<NewScalarType,Dim> >::type cast() const
{ return typename internal::cast_return_type<Scaling,Scaling<NewScalarType,Dim> >::type(*this); }
/** Copy constructor with scalar type conversion */
template<typename OtherScalarType>
inline explicit Scaling(const Scaling<OtherScalarType,Dim>& other)
{ m_coeffs = other.coeffs().template cast<Scalar>(); }
/** \returns \c true if \c *this is approximately equal to \a other, within the precision
* determined by \a prec.
*
* \sa MatrixBase::isApprox() */
bool isApprox(const Scaling& other, typename NumTraits<Scalar>::Real prec = precision<Scalar>()) const
{ return m_coeffs.isApprox(other.m_coeffs, prec); }
};
/** \addtogroup Geometry_Module */
//@{
typedef Scaling<float, 2> Scaling2f;
typedef Scaling<double,2> Scaling2d;
typedef Scaling<float, 3> Scaling3f;
typedef Scaling<double,3> Scaling3d;
//@}
template<typename Scalar, int Dim>
inline typename Scaling<Scalar,Dim>::TransformType
Scaling<Scalar,Dim>::operator* (const TranslationType& t) const
{
TransformType res;
res.matrix().setZero();
res.linear().diagonal() = coeffs();
res.translation() = m_coeffs.cwise() * t.vector();
res(Dim,Dim) = Scalar(1);
return res;
}
template<typename Scalar, int Dim>
inline typename Scaling<Scalar,Dim>::TransformType
Scaling<Scalar,Dim>::operator* (const TransformType& t) const
{
TransformType res = t;
res.prescale(m_coeffs);
return res;
}
} // end namespace Eigen

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Eigen/src/Eigen2Support/Geometry/Transform.h
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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/.
// no include guard, we'll include this twice from All.h from Eigen2Support, and it's internal anyway
namespace Eigen {
// Note that we have to pass Dim and HDim because it is not allowed to use a template
// parameter to define a template specialization. To be more precise, in the following
// specializations, it is not allowed to use Dim+1 instead of HDim.
template< typename Other,
int Dim,
int HDim,
int OtherRows=Other::RowsAtCompileTime,
int OtherCols=Other::ColsAtCompileTime>
struct ei_transform_product_impl;
/** \geometry_module \ingroup Geometry_Module
*
* \class Transform
*
* \brief Represents an homogeneous transformation in a N dimensional space
*
* \param _Scalar the scalar type, i.e., the type of the coefficients
* \param _Dim the dimension of the space
*
* The homography is internally represented and stored as a (Dim+1)^2 matrix which
* is available through the matrix() method.
*
* Conversion methods from/to Qt's QMatrix and QTransform are available if the
* preprocessor token EIGEN_QT_SUPPORT is defined.
*
* \sa class Matrix, class Quaternion
*/
template<typename _Scalar, int _Dim>
class Transform
{
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_Dim==Dynamic ? Dynamic : (_Dim+1)*(_Dim+1))
enum {
Dim = _Dim, ///< space dimension in which the transformation holds
HDim = _Dim+1 ///< size of a respective homogeneous vector
};
/** the scalar type of the coefficients */
typedef _Scalar Scalar;
/** type of the matrix used to represent the transformation */
typedef Matrix<Scalar,HDim,HDim> MatrixType;
/** type of the matrix used to represent the linear part of the transformation */
typedef Matrix<Scalar,Dim,Dim> LinearMatrixType;
/** type of read/write reference to the linear part of the transformation */
typedef Block<MatrixType,Dim,Dim> LinearPart;
/** type of read/write reference to the linear part of the transformation */
typedef const Block<const MatrixType,Dim,Dim> ConstLinearPart;
/** type of a vector */
typedef Matrix<Scalar,Dim,1> VectorType;
/** type of a read/write reference to the translation part of the rotation */
typedef Block<MatrixType,Dim,1> TranslationPart;
/** type of a read/write reference to the translation part of the rotation */
typedef const Block<const MatrixType,Dim,1> ConstTranslationPart;
/** corresponding translation type */
typedef Translation<Scalar,Dim> TranslationType;
/** corresponding scaling transformation type */
typedef Scaling<Scalar,Dim> ScalingType;
protected:
MatrixType m_matrix;
public:
/** Default constructor without initialization of the coefficients. */
inline Transform() { }
inline Transform(const Transform& other)
{
m_matrix = other.m_matrix;
}
inline explicit Transform(const TranslationType& t) { *this = t; }
inline explicit Transform(const ScalingType& s) { *this = s; }
template<typename Derived>
inline explicit Transform(const RotationBase<Derived, Dim>& r) { *this = r; }
inline Transform& operator=(const Transform& other)
{ m_matrix = other.m_matrix; return *this; }
template<typename OtherDerived, bool BigMatrix> // MSVC 2005 will commit suicide if BigMatrix has a default value
struct construct_from_matrix
{
static inline void run(Transform *transform, const MatrixBase<OtherDerived>& other)
{
transform->matrix() = other;
}
};
template<typename OtherDerived> struct construct_from_matrix<OtherDerived, true>
{
static inline void run(Transform *transform, const MatrixBase<OtherDerived>& other)
{
transform->linear() = other;
transform->translation().setZero();
transform->matrix()(Dim,Dim) = Scalar(1);
transform->matrix().template block<1,Dim>(Dim,0).setZero();
}
};
/** Constructs and initializes a transformation from a Dim^2 or a (Dim+1)^2 matrix. */
template<typename OtherDerived>
inline explicit Transform(const MatrixBase<OtherDerived>& other)
{
construct_from_matrix<OtherDerived, int(OtherDerived::RowsAtCompileTime) == Dim>::run(this, other);
}
/** Set \c *this from a (Dim+1)^2 matrix. */
template<typename OtherDerived>
inline Transform& operator=(const MatrixBase<OtherDerived>& other)
{ m_matrix = other; return *this; }
#ifdef EIGEN_QT_SUPPORT
inline Transform(const QMatrix& other);
inline Transform& operator=(const QMatrix& other);
inline QMatrix toQMatrix(void) const;
inline Transform(const QTransform& other);
inline Transform& operator=(const QTransform& other);
inline QTransform toQTransform(void) const;
#endif
/** shortcut for m_matrix(row,col);
* \sa MatrixBase::operaror(int,int) const */
inline Scalar operator() (int row, int col) const { return m_matrix(row,col); }
/** shortcut for m_matrix(row,col);
* \sa MatrixBase::operaror(int,int) */
inline Scalar& operator() (int row, int col) { return m_matrix(row,col); }
/** \returns a read-only expression of the transformation matrix */
inline const MatrixType& matrix() const { return m_matrix; }
/** \returns a writable expression of the transformation matrix */
inline MatrixType& matrix() { return m_matrix; }
/** \returns a read-only expression of the linear (linear) part of the transformation */
inline ConstLinearPart linear() const { return m_matrix.template block<Dim,Dim>(0,0); }
/** \returns a writable expression of the linear (linear) part of the transformation */
inline LinearPart linear() { return m_matrix.template block<Dim,Dim>(0,0); }
/** \returns a read-only expression of the translation vector of the transformation */
inline ConstTranslationPart translation() const { return m_matrix.template block<Dim,1>(0,Dim); }
/** \returns a writable expression of the translation vector of the transformation */
inline TranslationPart translation() { return m_matrix.template block<Dim,1>(0,Dim); }
/** \returns an expression of the product between the transform \c *this and a matrix expression \a other
*
* The right hand side \a other might be either:
* \li a vector of size Dim,
* \li an homogeneous vector of size Dim+1,
* \li a transformation matrix of size Dim+1 x Dim+1.
*/
// note: this function is defined here because some compilers cannot find the respective declaration
template<typename OtherDerived>
inline const typename ei_transform_product_impl<OtherDerived,_Dim,_Dim+1>::ResultType
operator * (const MatrixBase<OtherDerived> &other) const
{ return ei_transform_product_impl<OtherDerived,Dim,HDim>::run(*this,other.derived()); }
/** \returns the product expression of a transformation matrix \a a times a transform \a b
* The transformation matrix \a a must have a Dim+1 x Dim+1 sizes. */
template<typename OtherDerived>
friend inline const typename ProductReturnType<OtherDerived,MatrixType>::Type
operator * (const MatrixBase<OtherDerived> &a, const Transform &b)
{ return a.derived() * b.matrix(); }
/** Contatenates two transformations */
inline const Transform
operator * (const Transform& other) const
{ return Transform(m_matrix * other.matrix()); }
/** \sa MatrixBase::setIdentity() */
void setIdentity() { m_matrix.setIdentity(); }
static const typename MatrixType::IdentityReturnType Identity()
{
return MatrixType::Identity();
}
template<typename OtherDerived>
inline Transform& scale(const MatrixBase<OtherDerived> &other);
template<typename OtherDerived>
inline Transform& prescale(const MatrixBase<OtherDerived> &other);
inline Transform& scale(Scalar s);
inline Transform& prescale(Scalar s);
template<typename OtherDerived>
inline Transform& translate(const MatrixBase<OtherDerived> &other);
template<typename OtherDerived>
inline Transform& pretranslate(const MatrixBase<OtherDerived> &other);
template<typename RotationType>
inline Transform& rotate(const RotationType& rotation);
template<typename RotationType>
inline Transform& prerotate(const RotationType& rotation);
Transform& shear(Scalar sx, Scalar sy);
Transform& preshear(Scalar sx, Scalar sy);
inline Transform& operator=(const TranslationType& t);
inline Transform& operator*=(const TranslationType& t) { return translate(t.vector()); }
inline Transform operator*(const TranslationType& t) const;
inline Transform& operator=(const ScalingType& t);
inline Transform& operator*=(const ScalingType& s) { return scale(s.coeffs()); }
inline Transform operator*(const ScalingType& s) const;
friend inline Transform operator*(const LinearMatrixType& mat, const Transform& t)
{
Transform res = t;
res.matrix().row(Dim) = t.matrix().row(Dim);
res.matrix().template block<Dim,HDim>(0,0) = (mat * t.matrix().template block<Dim,HDim>(0,0)).lazy();
return res;
}
template<typename Derived>
inline Transform& operator=(const RotationBase<Derived,Dim>& r);
template<typename Derived>
inline Transform& operator*=(const RotationBase<Derived,Dim>& r) { return rotate(r.toRotationMatrix()); }
template<typename Derived>
inline Transform operator*(const RotationBase<Derived,Dim>& r) const;
LinearMatrixType rotation() const;
template<typename RotationMatrixType, typename ScalingMatrixType>
void computeRotationScaling(RotationMatrixType *rotation, ScalingMatrixType *scaling) const;
template<typename ScalingMatrixType, typename RotationMatrixType>
void computeScalingRotation(ScalingMatrixType *scaling, RotationMatrixType *rotation) const;
template<typename PositionDerived, typename OrientationType, typename ScaleDerived>
Transform& fromPositionOrientationScale(const MatrixBase<PositionDerived> &position,
const OrientationType& orientation, const MatrixBase<ScaleDerived> &scale);
inline const MatrixType inverse(TransformTraits traits = Affine) const;
/** \returns a const pointer to the column major internal matrix */
const Scalar* data() const { return m_matrix.data(); }
/** \returns a non-const pointer to the column major internal matrix */
Scalar* data() { return m_matrix.data(); }
/** \returns \c *this with scalar type casted to \a NewScalarType
*
* Note that if \a NewScalarType is equal to the current scalar type of \c *this
* then this function smartly returns a const reference to \c *this.
*/
template<typename NewScalarType>
inline typename internal::cast_return_type<Transform,Transform<NewScalarType,Dim> >::type cast() const
{ return typename internal::cast_return_type<Transform,Transform<NewScalarType,Dim> >::type(*this); }
/** Copy constructor with scalar type conversion */
template<typename OtherScalarType>
inline explicit Transform(const Transform<OtherScalarType,Dim>& other)
{ m_matrix = other.matrix().template cast<Scalar>(); }
/** \returns \c true if \c *this is approximately equal to \a other, within the precision
* determined by \a prec.
*
* \sa MatrixBase::isApprox() */
bool isApprox(const Transform& other, typename NumTraits<Scalar>::Real prec = precision<Scalar>()) const
{ return m_matrix.isApprox(other.m_matrix, prec); }
#ifdef EIGEN_TRANSFORM_PLUGIN
#include EIGEN_TRANSFORM_PLUGIN
#endif
protected:
};
/** \ingroup Geometry_Module */
typedef Transform<float,2> Transform2f;
/** \ingroup Geometry_Module */
typedef Transform<float,3> Transform3f;
/** \ingroup Geometry_Module */
typedef Transform<double,2> Transform2d;
/** \ingroup Geometry_Module */
typedef Transform<double,3> Transform3d;
/**************************
*** Optional QT support ***
**************************/
#ifdef EIGEN_QT_SUPPORT
/** Initialises \c *this from a QMatrix assuming the dimension is 2.
*
* This function is available only if the token EIGEN_QT_SUPPORT is defined.
*/
template<typename Scalar, int Dim>
Transform<Scalar,Dim>::Transform(const QMatrix& other)
{
*this = other;
}
/** Set \c *this from a QMatrix assuming the dimension is 2.
*
* This function is available only if the token EIGEN_QT_SUPPORT is defined.
*/
template<typename Scalar, int Dim>
Transform<Scalar,Dim>& Transform<Scalar,Dim>::operator=(const QMatrix& other)
{
EIGEN_STATIC_ASSERT(Dim==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
m_matrix << other.m11(), other.m21(), other.dx(),
other.m12(), other.m22(), other.dy(),
0, 0, 1;
return *this;
}
/** \returns a QMatrix from \c *this assuming the dimension is 2.
*
* \warning this convertion might loss data if \c *this is not affine
*
* This function is available only if the token EIGEN_QT_SUPPORT is defined.
*/
template<typename Scalar, int Dim>
QMatrix Transform<Scalar,Dim>::toQMatrix(void) const
{
EIGEN_STATIC_ASSERT(Dim==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
return QMatrix(m_matrix.coeff(0,0), m_matrix.coeff(1,0),
m_matrix.coeff(0,1), m_matrix.coeff(1,1),
m_matrix.coeff(0,2), m_matrix.coeff(1,2));
}
/** Initialises \c *this from a QTransform assuming the dimension is 2.
*
* This function is available only if the token EIGEN_QT_SUPPORT is defined.
*/
template<typename Scalar, int Dim>
Transform<Scalar,Dim>::Transform(const QTransform& other)
{
*this = other;
}
/** Set \c *this from a QTransform assuming the dimension is 2.
*
* This function is available only if the token EIGEN_QT_SUPPORT is defined.
*/
template<typename Scalar, int Dim>
Transform<Scalar,Dim>& Transform<Scalar,Dim>::operator=(const QTransform& other)
{
EIGEN_STATIC_ASSERT(Dim==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
m_matrix << other.m11(), other.m21(), other.dx(),
other.m12(), other.m22(), other.dy(),
other.m13(), other.m23(), other.m33();
return *this;
}
/** \returns a QTransform from \c *this assuming the dimension is 2.
*
* This function is available only if the token EIGEN_QT_SUPPORT is defined.
*/
template<typename Scalar, int Dim>
QTransform Transform<Scalar,Dim>::toQTransform(void) const
{
EIGEN_STATIC_ASSERT(Dim==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
return QTransform(m_matrix.coeff(0,0), m_matrix.coeff(1,0), m_matrix.coeff(2,0),
m_matrix.coeff(0,1), m_matrix.coeff(1,1), m_matrix.coeff(2,1),
m_matrix.coeff(0,2), m_matrix.coeff(1,2), m_matrix.coeff(2,2));
}
#endif
/*********************
*** Procedural API ***
*********************/
/** Applies on the right the non uniform scale transformation represented
* by the vector \a other to \c *this and returns a reference to \c *this.
* \sa prescale()
*/
template<typename Scalar, int Dim>
template<typename OtherDerived>
Transform<Scalar,Dim>&
Transform<Scalar,Dim>::scale(const MatrixBase<OtherDerived> &other)
{
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim))
linear() = (linear() * other.asDiagonal()).lazy();
return *this;
}
/** Applies on the right a uniform scale of a factor \a c to \c *this
* and returns a reference to \c *this.
* \sa prescale(Scalar)
*/
template<typename Scalar, int Dim>
inline Transform<Scalar,Dim>& Transform<Scalar,Dim>::scale(Scalar s)
{
linear() *= s;
return *this;
}
/** Applies on the left the non uniform scale transformation represented
* by the vector \a other to \c *this and returns a reference to \c *this.
* \sa scale()
*/
template<typename Scalar, int Dim>
template<typename OtherDerived>
Transform<Scalar,Dim>&
Transform<Scalar,Dim>::prescale(const MatrixBase<OtherDerived> &other)
{
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim))
m_matrix.template block<Dim,HDim>(0,0) = (other.asDiagonal() * m_matrix.template block<Dim,HDim>(0,0)).lazy();
return *this;
}
/** Applies on the left a uniform scale of a factor \a c to \c *this
* and returns a reference to \c *this.
* \sa scale(Scalar)
*/
template<typename Scalar, int Dim>
inline Transform<Scalar,Dim>& Transform<Scalar,Dim>::prescale(Scalar s)
{
m_matrix.template corner<Dim,HDim>(TopLeft) *= s;
return *this;
}
/** Applies on the right the translation matrix represented by the vector \a other
* to \c *this and returns a reference to \c *this.
* \sa pretranslate()
*/
template<typename Scalar, int Dim>
template<typename OtherDerived>
Transform<Scalar,Dim>&
Transform<Scalar,Dim>::translate(const MatrixBase<OtherDerived> &other)
{
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim))
translation() += linear() * other;
return *this;
}
/** Applies on the left the translation matrix represented by the vector \a other
* to \c *this and returns a reference to \c *this.
* \sa translate()
*/
template<typename Scalar, int Dim>
template<typename OtherDerived>
Transform<Scalar,Dim>&
Transform<Scalar,Dim>::pretranslate(const MatrixBase<OtherDerived> &other)
{
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim))
translation() += other;
return *this;
}
/** Applies on the right the rotation represented by the rotation \a rotation
* to \c *this and returns a reference to \c *this.
*
* The template parameter \a RotationType is the type of the rotation which
* must be known by ei_toRotationMatrix<>.
*
* Natively supported types includes:
* - any scalar (2D),
* - a Dim x Dim matrix expression,
* - a Quaternion (3D),
* - a AngleAxis (3D)
*
* This mechanism is easily extendable to support user types such as Euler angles,
* or a pair of Quaternion for 4D rotations.
*
* \sa rotate(Scalar), class Quaternion, class AngleAxis, prerotate(RotationType)
*/
template<typename Scalar, int Dim>
template<typename RotationType>
Transform<Scalar,Dim>&
Transform<Scalar,Dim>::rotate(const RotationType& rotation)
{
linear() *= ei_toRotationMatrix<Scalar,Dim>(rotation);
return *this;
}
/** Applies on the left the rotation represented by the rotation \a rotation
* to \c *this and returns a reference to \c *this.
*
* See rotate() for further details.
*
* \sa rotate()
*/
template<typename Scalar, int Dim>
template<typename RotationType>
Transform<Scalar,Dim>&
Transform<Scalar,Dim>::prerotate(const RotationType& rotation)
{
m_matrix.template block<Dim,HDim>(0,0) = ei_toRotationMatrix<Scalar,Dim>(rotation)
* m_matrix.template block<Dim,HDim>(0,0);
return *this;
}
/** Applies on the right the shear transformation represented
* by the vector \a other to \c *this and returns a reference to \c *this.
* \warning 2D only.
* \sa preshear()
*/
template<typename Scalar, int Dim>
Transform<Scalar,Dim>&
Transform<Scalar,Dim>::shear(Scalar sx, Scalar sy)
{
EIGEN_STATIC_ASSERT(int(Dim)==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
VectorType tmp = linear().col(0)*sy + linear().col(1);
linear() << linear().col(0) + linear().col(1)*sx, tmp;
return *this;
}
/** Applies on the left the shear transformation represented
* by the vector \a other to \c *this and returns a reference to \c *this.
* \warning 2D only.
* \sa shear()
*/
template<typename Scalar, int Dim>
Transform<Scalar,Dim>&
Transform<Scalar,Dim>::preshear(Scalar sx, Scalar sy)
{
EIGEN_STATIC_ASSERT(int(Dim)==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
m_matrix.template block<Dim,HDim>(0,0) = LinearMatrixType(1, sx, sy, 1) * m_matrix.template block<Dim,HDim>(0,0);
return *this;
}
/******************************************************
*** Scaling, Translation and Rotation compatibility ***
******************************************************/
template<typename Scalar, int Dim>
inline Transform<Scalar,Dim>& Transform<Scalar,Dim>::operator=(const TranslationType& t)
{
linear().setIdentity();
translation() = t.vector();
m_matrix.template block<1,Dim>(Dim,0).setZero();
m_matrix(Dim,Dim) = Scalar(1);
return *this;
}
template<typename Scalar, int Dim>
inline Transform<Scalar,Dim> Transform<Scalar,Dim>::operator*(const TranslationType& t) const
{
Transform res = *this;
res.translate(t.vector());
return res;
}
template<typename Scalar, int Dim>
inline Transform<Scalar,Dim>& Transform<Scalar,Dim>::operator=(const ScalingType& s)
{
m_matrix.setZero();
linear().diagonal() = s.coeffs();
m_matrix.coeffRef(Dim,Dim) = Scalar(1);
return *this;
}
template<typename Scalar, int Dim>
inline Transform<Scalar,Dim> Transform<Scalar,Dim>::operator*(const ScalingType& s) const
{
Transform res = *this;
res.scale(s.coeffs());
return res;
}
template<typename Scalar, int Dim>
template<typename Derived>
inline Transform<Scalar,Dim>& Transform<Scalar,Dim>::operator=(const RotationBase<Derived,Dim>& r)
{
linear() = ei_toRotationMatrix<Scalar,Dim>(r);
translation().setZero();
m_matrix.template block<1,Dim>(Dim,0).setZero();
m_matrix.coeffRef(Dim,Dim) = Scalar(1);
return *this;
}
template<typename Scalar, int Dim>
template<typename Derived>
inline Transform<Scalar,Dim> Transform<Scalar,Dim>::operator*(const RotationBase<Derived,Dim>& r) const
{
Transform res = *this;
res.rotate(r.derived());
return res;
}
/************************
*** Special functions ***
************************/
/** \returns the rotation part of the transformation
* \nonstableyet
*
* \svd_module
*
* \sa computeRotationScaling(), computeScalingRotation(), class SVD
*/
template<typename Scalar, int Dim>
typename Transform<Scalar,Dim>::LinearMatrixType
Transform<Scalar,Dim>::rotation() const
{
LinearMatrixType result;
computeRotationScaling(&result, (LinearMatrixType*)0);
return result;
}
/** decomposes the linear part of the transformation as a product rotation x scaling, the scaling being
* not necessarily positive.
*
* If either pointer is zero, the corresponding computation is skipped.
*
* \nonstableyet
*
* \svd_module
*
* \sa computeScalingRotation(), rotation(), class SVD
*/
template<typename Scalar, int Dim>
template<typename RotationMatrixType, typename ScalingMatrixType>
void Transform<Scalar,Dim>::computeRotationScaling(RotationMatrixType *rotation, ScalingMatrixType *scaling) const
{
JacobiSVD<LinearMatrixType> svd(linear(), ComputeFullU|ComputeFullV);
Scalar x = (svd.matrixU() * svd.matrixV().adjoint()).determinant(); // so x has absolute value 1
Matrix<Scalar, Dim, 1> sv(svd.singularValues());
sv.coeffRef(0) *= x;
if(scaling)
{
scaling->noalias() = svd.matrixV() * sv.asDiagonal() * svd.matrixV().adjoint();
}
if(rotation)
{
LinearMatrixType m(svd.matrixU());
m.col(0) /= x;
rotation->noalias() = m * svd.matrixV().adjoint();
}
}
/** decomposes the linear part of the transformation as a product rotation x scaling, the scaling being
* not necessarily positive.
*
* If either pointer is zero, the corresponding computation is skipped.
*
* \nonstableyet
*
* \svd_module
*
* \sa computeRotationScaling(), rotation(), class SVD
*/
template<typename Scalar, int Dim>
template<typename ScalingMatrixType, typename RotationMatrixType>
void Transform<Scalar,Dim>::computeScalingRotation(ScalingMatrixType *scaling, RotationMatrixType *rotation) const
{
JacobiSVD<LinearMatrixType> svd(linear(), ComputeFullU|ComputeFullV);
Scalar x = (svd.matrixU() * svd.matrixV().adjoint()).determinant(); // so x has absolute value 1
Matrix<Scalar, Dim, 1> sv(svd.singularValues());
sv.coeffRef(0) *= x;
if(scaling)
{
scaling->noalias() = svd.matrixU() * sv.asDiagonal() * svd.matrixU().adjoint();
}
if(rotation)
{
LinearMatrixType m(svd.matrixU());
m.col(0) /= x;
rotation->noalias() = m * svd.matrixV().adjoint();
}
}
/** Convenient method to set \c *this from a position, orientation and scale
* of a 3D object.
*/
template<typename Scalar, int Dim>
template<typename PositionDerived, typename OrientationType, typename ScaleDerived>
Transform<Scalar,Dim>&
Transform<Scalar,Dim>::fromPositionOrientationScale(const MatrixBase<PositionDerived> &position,
const OrientationType& orientation, const MatrixBase<ScaleDerived> &scale)
{
linear() = ei_toRotationMatrix<Scalar,Dim>(orientation);
linear() *= scale.asDiagonal();
translation() = position;
m_matrix.template block<1,Dim>(Dim,0).setZero();
m_matrix(Dim,Dim) = Scalar(1);
return *this;
}
/** \nonstableyet
*
* \returns the inverse transformation matrix according to some given knowledge
* on \c *this.
*
* \param traits allows to optimize the inversion process when the transformion
* is known to be not a general transformation. The possible values are:
* - Projective if the transformation is not necessarily affine, i.e., if the
* last row is not guaranteed to be [0 ... 0 1]
* - Affine is the default, the last row is assumed to be [0 ... 0 1]
* - Isometry if the transformation is only a concatenations of translations
* and rotations.
*
* \warning unless \a traits is always set to NoShear or NoScaling, this function
* requires the generic inverse method of MatrixBase defined in the LU module. If
* you forget to include this module, then you will get hard to debug linking errors.
*
* \sa MatrixBase::inverse()
*/
template<typename Scalar, int Dim>
inline const typename Transform<Scalar,Dim>::MatrixType
Transform<Scalar,Dim>::inverse(TransformTraits traits) const
{
if (traits == Projective)
{
return m_matrix.inverse();
}
else
{
MatrixType res;
if (traits == Affine)
{
res.template corner<Dim,Dim>(TopLeft) = linear().inverse();
}
else if (traits == Isometry)
{
res.template corner<Dim,Dim>(TopLeft) = linear().transpose();
}
else
{
ei_assert("invalid traits value in Transform::inverse()");
}
// translation and remaining parts
res.template corner<Dim,1>(TopRight) = - res.template corner<Dim,Dim>(TopLeft) * translation();
res.template corner<1,Dim>(BottomLeft).setZero();
res.coeffRef(Dim,Dim) = Scalar(1);
return res;
}
}
/*****************************************************
*** Specializations of operator* with a MatrixBase ***
*****************************************************/
template<typename Other, int Dim, int HDim>
struct ei_transform_product_impl<Other,Dim,HDim, HDim,HDim>
{
typedef Transform<typename Other::Scalar,Dim> TransformType;
typedef typename TransformType::MatrixType MatrixType;
typedef typename ProductReturnType<MatrixType,Other>::Type ResultType;
static ResultType run(const TransformType& tr, const Other& other)
{ return tr.matrix() * other; }
};
template<typename Other, int Dim, int HDim>
struct ei_transform_product_impl<Other,Dim,HDim, Dim,Dim>
{
typedef Transform<typename Other::Scalar,Dim> TransformType;
typedef typename TransformType::MatrixType MatrixType;
typedef TransformType ResultType;
static ResultType run(const TransformType& tr, const Other& other)
{
TransformType res;
res.translation() = tr.translation();
res.matrix().row(Dim) = tr.matrix().row(Dim);
res.linear() = (tr.linear() * other).lazy();
return res;
}
};
template<typename Other, int Dim, int HDim>
struct ei_transform_product_impl<Other,Dim,HDim, HDim,1>
{
typedef Transform<typename Other::Scalar,Dim> TransformType;
typedef typename TransformType::MatrixType MatrixType;
typedef typename ProductReturnType<MatrixType,Other>::Type ResultType;
static ResultType run(const TransformType& tr, const Other& other)
{ return tr.matrix() * other; }
};
template<typename Other, int Dim, int HDim>
struct ei_transform_product_impl<Other,Dim,HDim, Dim,1>
{
typedef typename Other::Scalar Scalar;
typedef Transform<Scalar,Dim> TransformType;
typedef Matrix<Scalar,Dim,1> ResultType;
static ResultType run(const TransformType& tr, const Other& other)
{ return ((tr.linear() * other) + tr.translation())
* (Scalar(1) / ( (tr.matrix().template block<1,Dim>(Dim,0) * other).coeff(0) + tr.matrix().coeff(Dim,Dim))); }
};
} // end namespace Eigen

184
Eigen/src/Eigen2Support/Geometry/Translation.h
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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/.
// no include guard, we'll include this twice from All.h from Eigen2Support, and it's internal anyway
namespace Eigen {
/** \geometry_module \ingroup Geometry_Module
*
* \class Translation
*
* \brief Represents a translation transformation
*
* \param _Scalar the scalar type, i.e., the type of the coefficients.
* \param _Dim the dimension of the space, can be a compile time value or Dynamic
*
* \note This class is not aimed to be used to store a translation transformation,
* but rather to make easier the constructions and updates of Transform objects.
*
* \sa class Scaling, class Transform
*/
template<typename _Scalar, int _Dim>
class Translation
{
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_Dim)
/** dimension of the space */
enum { Dim = _Dim };
/** the scalar type of the coefficients */
typedef _Scalar Scalar;
/** corresponding vector type */
typedef Matrix<Scalar,Dim,1> VectorType;
/** corresponding linear transformation matrix type */
typedef Matrix<Scalar,Dim,Dim> LinearMatrixType;
/** corresponding scaling transformation type */
typedef Scaling<Scalar,Dim> ScalingType;
/** corresponding affine transformation type */
typedef Transform<Scalar,Dim> TransformType;
protected:
VectorType m_coeffs;
public:
/** Default constructor without initialization. */
Translation() {}
/** */
inline Translation(const Scalar& sx, const Scalar& sy)
{
ei_assert(Dim==2);
m_coeffs.x() = sx;
m_coeffs.y() = sy;
}
/** */
inline Translation(const Scalar& sx, const Scalar& sy, const Scalar& sz)
{
ei_assert(Dim==3);
m_coeffs.x() = sx;
m_coeffs.y() = sy;
m_coeffs.z() = sz;
}
/** Constructs and initialize the scaling transformation from a vector of scaling coefficients */
explicit inline Translation(const VectorType& vector) : m_coeffs(vector) {}
const VectorType& vector() const { return m_coeffs; }
VectorType& vector() { return m_coeffs; }
/** Concatenates two translation */
inline Translation operator* (const Translation& other) const
{ return Translation(m_coeffs + other.m_coeffs); }
/** Concatenates a translation and a scaling */
inline TransformType operator* (const ScalingType& other) const;
/** Concatenates a translation and a linear transformation */
inline TransformType operator* (const LinearMatrixType& linear) const;
template<typename Derived>
inline TransformType operator*(const RotationBase<Derived,Dim>& r) const
{ return *this * r.toRotationMatrix(); }
/** Concatenates a linear transformation and a translation */
// its a nightmare to define a templated friend function outside its declaration
friend inline TransformType operator* (const LinearMatrixType& linear, const Translation& t)
{
TransformType res;
res.matrix().setZero();
res.linear() = linear;
res.translation() = linear * t.m_coeffs;
res.matrix().row(Dim).setZero();
res(Dim,Dim) = Scalar(1);
return res;
}
/** Concatenates a translation and an affine transformation */
inline TransformType operator* (const TransformType& t) const;
/** Applies translation to vector */
inline VectorType operator* (const VectorType& other) const
{ return m_coeffs + other; }
/** \returns the inverse translation (opposite) */
Translation inverse() const { return Translation(-m_coeffs); }
Translation& operator=(const Translation& other)
{
m_coeffs = other.m_coeffs;
return *this;
}
/** \returns \c *this with scalar type casted to \a NewScalarType
*
* Note that if \a NewScalarType is equal to the current scalar type of \c *this
* then this function smartly returns a const reference to \c *this.
*/
template<typename NewScalarType>
inline typename internal::cast_return_type<Translation,Translation<NewScalarType,Dim> >::type cast() const
{ return typename internal::cast_return_type<Translation,Translation<NewScalarType,Dim> >::type(*this); }
/** Copy constructor with scalar type conversion */
template<typename OtherScalarType>
inline explicit Translation(const Translation<OtherScalarType,Dim>& other)
{ m_coeffs = other.vector().template cast<Scalar>(); }
/** \returns \c true if \c *this is approximately equal to \a other, within the precision
* determined by \a prec.
*
* \sa MatrixBase::isApprox() */
bool isApprox(const Translation& other, typename NumTraits<Scalar>::Real prec = precision<Scalar>()) const
{ return m_coeffs.isApprox(other.m_coeffs, prec); }
};
/** \addtogroup Geometry_Module */
//@{
typedef Translation<float, 2> Translation2f;
typedef Translation<double,2> Translation2d;
typedef Translation<float, 3> Translation3f;
typedef Translation<double,3> Translation3d;
//@}
template<typename Scalar, int Dim>
inline typename Translation<Scalar,Dim>::TransformType
Translation<Scalar,Dim>::operator* (const ScalingType& other) const
{
TransformType res;
res.matrix().setZero();
res.linear().diagonal() = other.coeffs();
res.translation() = m_coeffs;
res(Dim,Dim) = Scalar(1);
return res;
}
template<typename Scalar, int Dim>
inline typename Translation<Scalar,Dim>::TransformType
Translation<Scalar,Dim>::operator* (const LinearMatrixType& linear) const
{
TransformType res;
res.matrix().setZero();
res.linear() = linear;
res.translation() = m_coeffs;
res.matrix().row(Dim).setZero();
res(Dim,Dim) = Scalar(1);
return res;
}
template<typename Scalar, int Dim>
inline typename Translation<Scalar,Dim>::TransformType
Translation<Scalar,Dim>::operator* (const TransformType& t) const
{
TransformType res = t;
res.pretranslate(m_coeffs);
return res;
}
} // end namespace Eigen

120
Eigen/src/Eigen2Support/LU.h
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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/.
#ifndef EIGEN2_LU_H
#define EIGEN2_LU_H
namespace Eigen {
template<typename MatrixType>
class LU : public FullPivLU<MatrixType>
{
public:
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
typedef Matrix<int, 1, MatrixType::ColsAtCompileTime, MatrixType::Options, 1, MatrixType::MaxColsAtCompileTime> IntRowVectorType;
typedef Matrix<int, MatrixType::RowsAtCompileTime, 1, MatrixType::Options, MatrixType::MaxRowsAtCompileTime, 1> IntColVectorType;
typedef Matrix<Scalar, 1, MatrixType::ColsAtCompileTime, MatrixType::Options, 1, MatrixType::MaxColsAtCompileTime> RowVectorType;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1, MatrixType::Options, MatrixType::MaxRowsAtCompileTime, 1> ColVectorType;
typedef Matrix<typename MatrixType::Scalar,
MatrixType::ColsAtCompileTime, // the number of rows in the "kernel matrix" is the number of cols of the original matrix
// so that the product "matrix * kernel = zero" makes sense
Dynamic, // we don't know at compile-time the dimension of the kernel
MatrixType::Options,
MatrixType::MaxColsAtCompileTime, // see explanation for 2nd template parameter
MatrixType::MaxColsAtCompileTime // the kernel is a subspace of the domain space, whose dimension is the number
// of columns of the original matrix
> KernelResultType;
typedef Matrix<typename MatrixType::Scalar,
MatrixType::RowsAtCompileTime, // the image is a subspace of the destination space, whose dimension is the number
// of rows of the original matrix
Dynamic, // we don't know at compile time the dimension of the image (the rank)
MatrixType::Options,
MatrixType::MaxRowsAtCompileTime, // the image matrix will consist of columns from the original matrix,
MatrixType::MaxColsAtCompileTime // so it has the same number of rows and at most as many columns.
> ImageResultType;
typedef FullPivLU<MatrixType> Base;
template<typename T>
explicit LU(const T& t) : Base(t), m_originalMatrix(t) {}
template<typename OtherDerived, typename ResultType>
bool solve(const MatrixBase<OtherDerived>& b, ResultType *result) const
{
*result = static_cast<const Base*>(this)->solve(b);
return true;
}
template<typename ResultType>
inline void computeInverse(ResultType *result) const
{
solve(MatrixType::Identity(this->rows(), this->cols()), result);
}
template<typename KernelMatrixType>
void computeKernel(KernelMatrixType *result) const
{
*result = static_cast<const Base*>(this)->kernel();
}
template<typename ImageMatrixType>
void computeImage(ImageMatrixType *result) const
{
*result = static_cast<const Base*>(this)->image(m_originalMatrix);
}
const ImageResultType image() const
{
return static_cast<const Base*>(this)->image(m_originalMatrix);
}
const MatrixType& m_originalMatrix;
};
#if EIGEN2_SUPPORT_STAGE < STAGE20_RESOLVE_API_CONFLICTS
/** \lu_module
*
* Synonym of partialPivLu().
*
* \return the partial-pivoting LU decomposition of \c *this.
*
* \sa class PartialPivLU
*/
template<typename Derived>
inline const LU<typename MatrixBase<Derived>::PlainObject>
MatrixBase<Derived>::lu() const
{
return LU<PlainObject>(eval());
}
#endif
#ifdef EIGEN2_SUPPORT
/** \lu_module
*
* Synonym of partialPivLu().
*
* \return the partial-pivoting LU decomposition of \c *this.
*
* \sa class PartialPivLU
*/
template<typename Derived>
inline const LU<typename MatrixBase<Derived>::PlainObject>
MatrixBase<Derived>::eigen2_lu() const
{
return LU<PlainObject>(eval());
}
#endif
} // end namespace Eigen
#endif // EIGEN2_LU_H

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Eigen/src/Eigen2Support/Lazy.h
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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/.
#ifndef EIGEN_LAZY_H
#define EIGEN_LAZY_H
namespace Eigen {
/** \deprecated it is only used by lazy() which is deprecated
*
* \returns an expression of *this with added flags
*
* Example: \include MatrixBase_marked.cpp
* Output: \verbinclude MatrixBase_marked.out
*
* \sa class Flagged, extract(), part()
*/
template<typename Derived>
template<unsigned int Added>
inline const Flagged<Derived, Added, 0>
MatrixBase<Derived>::marked() const
{
return derived();
}
/** \deprecated use MatrixBase::noalias()
*
* \returns an expression of *this with the EvalBeforeAssigningBit flag removed.
*
* Example: \include MatrixBase_lazy.cpp
* Output: \verbinclude MatrixBase_lazy.out
*
* \sa class Flagged, marked()
*/
template<typename Derived>
inline const Flagged<Derived, 0, EvalBeforeAssigningBit>
MatrixBase<Derived>::lazy() const
{
return derived();
}
/** \internal
* Overloaded to perform an efficient C += (A*B).lazy() */
template<typename Derived>
template<typename ProductDerived, typename Lhs, typename Rhs>
Derived& MatrixBase<Derived>::operator+=(const Flagged<ProductBase<ProductDerived, Lhs,Rhs>, 0,
EvalBeforeAssigningBit>& other)
{
other._expression().derived().addTo(derived()); return derived();
}
/** \internal
* Overloaded to perform an efficient C -= (A*B).lazy() */
template<typename Derived>
template<typename ProductDerived, typename Lhs, typename Rhs>
Derived& MatrixBase<Derived>::operator-=(const Flagged<ProductBase<ProductDerived, Lhs,Rhs>, 0,
EvalBeforeAssigningBit>& other)
{
other._expression().derived().subTo(derived()); return derived();
}
} // end namespace Eigen
#endif // EIGEN_LAZY_H

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Eigen/src/Eigen2Support/LeastSquares.h
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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/.
#ifndef EIGEN2_LEASTSQUARES_H
#define EIGEN2_LEASTSQUARES_H
namespace Eigen {
/** \ingroup LeastSquares_Module
*
* \leastsquares_module
*
* For a set of points, this function tries to express
* one of the coords as a linear (affine) function of the other coords.
*
* This is best explained by an example. This function works in full
* generality, for points in a space of arbitrary dimension, and also over
* the complex numbers, but for this example we will work in dimension 3
* over the real numbers (doubles).
*
* So let us work with the following set of 5 points given by their
* \f$(x,y,z)\f$ coordinates:
* @code
Vector3d points[5];
points[0] = Vector3d( 3.02, 6.89, -4.32 );
points[1] = Vector3d( 2.01, 5.39, -3.79 );
points[2] = Vector3d( 2.41, 6.01, -4.01 );
points[3] = Vector3d( 2.09, 5.55, -3.86 );
points[4] = Vector3d( 2.58, 6.32, -4.10 );
* @endcode
* Suppose that we want to express the second coordinate (\f$y\f$) as a linear
* expression in \f$x\f$ and \f$z\f$, that is,
* \f[ y=ax+bz+c \f]
* for some constants \f$a,b,c\f$. Thus, we want to find the best possible
* constants \f$a,b,c\f$ so that the plane of equation \f$y=ax+bz+c\f$ fits
* best the five above points. To do that, call this function as follows:
* @code
Vector3d coeffs; // will store the coefficients a, b, c
linearRegression(
5,
&points,
&coeffs,
1 // the coord to express as a function of
// the other ones. 0 means x, 1 means y, 2 means z.
);
* @endcode
* Now the vector \a coeffs is approximately
* \f$( 0.495 , -1.927 , -2.906 )\f$.
* Thus, we get \f$a=0.495, b = -1.927, c = -2.906\f$. Let us check for
* instance how near points[0] is from the plane of equation \f$y=ax+bz+c\f$.
* Looking at the coords of points[0], we see that:
* \f[ax+bz+c = 0.495 * 3.02 + (-1.927) * (-4.32) + (-2.906) = 6.91.\f]
* On the other hand, we have \f$y=6.89\f$. We see that the values
* \f$6.91\f$ and \f$6.89\f$
* are near, so points[0] is very near the plane of equation \f$y=ax+bz+c\f$.
*
* Let's now describe precisely the parameters:
* @param numPoints the number of points
* @param points the array of pointers to the points on which to perform the linear regression
* @param result pointer to the vector in which to store the result.
This vector must be of the same type and size as the
data points. The meaning of its coords is as follows.
For brevity, let \f$n=Size\f$,
\f$r_i=result[i]\f$,
and \f$f=funcOfOthers\f$. Denote by
\f$x_0,\ldots,x_{n-1}\f$
the n coordinates in the n-dimensional space.
Then the resulting equation is:
\f[ x_f = r_0 x_0 + \cdots + r_{f-1}x_{f-1}
+ r_{f+1}x_{f+1} + \cdots + r_{n-1}x_{n-1} + r_n. \f]
* @param funcOfOthers Determines which coord to express as a function of the
others. Coords are numbered starting from 0, so that a
value of 0 means \f$x\f$, 1 means \f$y\f$,
2 means \f$z\f$, ...
*
* \sa fitHyperplane()
*/
template<typename VectorType>
void linearRegression(int numPoints,
VectorType **points,
VectorType *result,
int funcOfOthers )
{
typedef typename VectorType::Scalar Scalar;
typedef Hyperplane<Scalar, VectorType::SizeAtCompileTime> HyperplaneType;
const int size = points[0]->size();
result->resize(size);
HyperplaneType h(size);
fitHyperplane(numPoints, points, &h);
for(int i = 0; i < funcOfOthers; i++)
result->coeffRef(i) = - h.coeffs()[i] / h.coeffs()[funcOfOthers];
for(int i = funcOfOthers; i < size; i++)
result->coeffRef(i) = - h.coeffs()[i+1] / h.coeffs()[funcOfOthers];
}
/** \ingroup LeastSquares_Module
*
* \leastsquares_module
*
* This function is quite similar to linearRegression(), so we refer to the
* documentation of this function and only list here the differences.
*
* The main difference from linearRegression() is that this function doesn't
* take a \a funcOfOthers argument. Instead, it finds a general equation
* of the form
* \f[ r_0 x_0 + \cdots + r_{n-1}x_{n-1} + r_n = 0, \f]
* where \f$n=Size\f$, \f$r_i=retCoefficients[i]\f$, and we denote by
* \f$x_0,\ldots,x_{n-1}\f$ the n coordinates in the n-dimensional space.
*
* Thus, the vector \a retCoefficients has size \f$n+1\f$, which is another
* difference from linearRegression().
*
* In practice, this function performs an hyper-plane fit in a total least square sense
* via the following steps:
* 1 - center the data to the mean
* 2 - compute the covariance matrix
* 3 - pick the eigenvector corresponding to the smallest eigenvalue of the covariance matrix
* The ratio of the smallest eigenvalue and the second one gives us a hint about the relevance
* of the solution. This value is optionally returned in \a soundness.
*
* \sa linearRegression()
*/
template<typename VectorType, typename HyperplaneType>
void fitHyperplane(int numPoints,
VectorType **points,
HyperplaneType *result,
typename NumTraits<typename VectorType::Scalar>::Real* soundness = 0)
{
typedef typename VectorType::Scalar Scalar;
typedef Matrix<Scalar,VectorType::SizeAtCompileTime,VectorType::SizeAtCompileTime> CovMatrixType;
EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorType)
ei_assert(numPoints >= 1);
int size = points[0]->size();
ei_assert(size+1 == result->coeffs().size());
// compute the mean of the data
VectorType mean = VectorType::Zero(size);
for(int i = 0; i < numPoints; ++i)
mean += *(points[i]);
mean /= numPoints;
// compute the covariance matrix
CovMatrixType covMat = CovMatrixType::Zero(size, size);
VectorType remean = VectorType::Zero(size);
for(int i = 0; i < numPoints; ++i)
{
VectorType diff = (*(points[i]) - mean).conjugate();
covMat += diff * diff.adjoint();
}
// now we just have to pick the eigen vector with smallest eigen value
SelfAdjointEigenSolver<CovMatrixType> eig(covMat);
result->normal() = eig.eigenvectors().col(0);
if (soundness)
*soundness = eig.eigenvalues().coeff(0)/eig.eigenvalues().coeff(1);
// let's compute the constant coefficient such that the
// plane pass trough the mean point:
result->offset() = - (result->normal().cwise()* mean).sum();
}
} // end namespace Eigen
#endif // EIGEN2_LEASTSQUARES_H

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Eigen/src/Eigen2Support/Macros.h
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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/.
#ifndef EIGEN2_MACROS_H
#define EIGEN2_MACROS_H
#define ei_assert eigen_assert
#define ei_internal_assert eigen_internal_assert
#define EIGEN_ALIGN_128 EIGEN_ALIGN16
#define EIGEN_ARCH_WANTS_ALIGNMENT EIGEN_ALIGN_STATICALLY
#endif // EIGEN2_MACROS_H

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Eigen/src/Eigen2Support/MathFunctions.h
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2010 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/.
#ifndef EIGEN2_MATH_FUNCTIONS_H
#define EIGEN2_MATH_FUNCTIONS_H
namespace Eigen {
template<typename T> inline typename NumTraits<T>::Real ei_real(const T& x) { return numext::real(x); }
template<typename T> inline typename NumTraits<T>::Real ei_imag(const T& x) { return numext::imag(x); }
template<typename T> inline T ei_conj(const T& x) { return numext::conj(x); }
template<typename T> inline typename NumTraits<T>::Real ei_abs (const T& x) { using std::abs; return abs(x); }
template<typename T> inline typename NumTraits<T>::Real ei_abs2(const T& x) { return numext::abs2(x); }
template<typename T> inline T ei_sqrt(const T& x) { using std::sqrt; return sqrt(x); }
template<typename T> inline T ei_exp (const T& x) { using std::exp; return exp(x); }
template<typename T> inline T ei_log (const T& x) { using std::log; return log(x); }
template<typename T> inline T ei_sin (const T& x) { using std::sin; return sin(x); }
template<typename T> inline T ei_cos (const T& x) { using std::cos; return cos(x); }
template<typename T> inline T ei_atan2(const T& x,const T& y) { using std::atan2; return atan2(x,y); }
template<typename T> inline T ei_pow (const T& x,const T& y) { return numext::pow(x,y); }
template<typename T> inline T ei_random () { return internal::random<T>(); }
template<typename T> inline T ei_random (const T& x, const T& y) { return internal::random(x, y); }
template<typename T> inline T precision () { return NumTraits<T>::dummy_precision(); }
template<typename T> inline T machine_epsilon () { return NumTraits<T>::epsilon(); }
template<typename Scalar, typename OtherScalar>
inline bool ei_isMuchSmallerThan(const Scalar& x, const OtherScalar& y,
typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision())
{
return internal::isMuchSmallerThan(x, y, precision);
}
template<typename Scalar>
inline bool ei_isApprox(const Scalar& x, const Scalar& y,
typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision())
{
return internal::isApprox(x, y, precision);
}
template<typename Scalar>
inline bool ei_isApproxOrLessThan(const Scalar& x, const Scalar& y,
typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision())
{
return internal::isApproxOrLessThan(x, y, precision);
}
} // end namespace Eigen
#endif // EIGEN2_MATH_FUNCTIONS_H

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Eigen/src/Eigen2Support/Memory.h
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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/.
#ifndef EIGEN2_MEMORY_H
#define EIGEN2_MEMORY_H
namespace Eigen {
inline void* ei_aligned_malloc(size_t size) { return internal::aligned_malloc(size); }
inline void ei_aligned_free(void *ptr) { internal::aligned_free(ptr); }
inline void* ei_aligned_realloc(void *ptr, size_t new_size, size_t old_size) { return internal::aligned_realloc(ptr, new_size, old_size); }
inline void* ei_handmade_aligned_malloc(size_t size) { return internal::handmade_aligned_malloc(size); }
inline void ei_handmade_aligned_free(void *ptr) { internal::handmade_aligned_free(ptr); }
template<bool Align> inline void* ei_conditional_aligned_malloc(size_t size)
{
return internal::conditional_aligned_malloc<Align>(size);
}
template<bool Align> inline void ei_conditional_aligned_free(void *ptr)
{
internal::conditional_aligned_free<Align>(ptr);
}
template<bool Align> inline void* ei_conditional_aligned_realloc(void* ptr, size_t new_size, size_t old_size)
{
return internal::conditional_aligned_realloc<Align>(ptr, new_size, old_size);
}
template<typename T> inline T* ei_aligned_new(size_t size)
{
return internal::aligned_new<T>(size);
}
template<typename T> inline void ei_aligned_delete(T *ptr, size_t size)
{
return internal::aligned_delete(ptr, size);
}
} // end namespace Eigen
#endif // EIGEN2_MACROS_H

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Eigen/src/Eigen2Support/Meta.h
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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/.
#ifndef EIGEN2_META_H
#define EIGEN2_META_H
namespace Eigen {
template<typename T>
struct ei_traits : internal::traits<T>
{};
struct ei_meta_true { enum { ret = 1 }; };
struct ei_meta_false { enum { ret = 0 }; };
template<bool Condition, typename Then, typename Else>
struct ei_meta_if { typedef Then ret; };
template<typename Then, typename Else>
struct ei_meta_if <false, Then, Else> { typedef Else ret; };
template<typename T, typename U> struct ei_is_same_type { enum { ret = 0 }; };
template<typename T> struct ei_is_same_type<T,T> { enum { ret = 1 }; };
template<typename T> struct ei_unref { typedef T type; };
template<typename T> struct ei_unref<T&> { typedef T type; };
template<typename T> struct ei_unpointer { typedef T type; };
template<typename T> struct ei_unpointer<T*> { typedef T type; };
template<typename T> struct ei_unpointer<T*const> { typedef T type; };
template<typename T> struct ei_unconst { typedef T type; };
template<typename T> struct ei_unconst<const T> { typedef T type; };
template<typename T> struct ei_unconst<T const &> { typedef T & type; };
template<typename T> struct ei_unconst<T const *> { typedef T * type; };
template<typename T> struct ei_cleantype { typedef T type; };
template<typename T> struct ei_cleantype<const T> { typedef typename ei_cleantype<T>::type type; };
template<typename T> struct ei_cleantype<const T&> { typedef typename ei_cleantype<T>::type type; };
template<typename T> struct ei_cleantype<T&> { typedef typename ei_cleantype<T>::type type; };
template<typename T> struct ei_cleantype<const T*> { typedef typename ei_cleantype<T>::type type; };
template<typename T> struct ei_cleantype<T*> { typedef typename ei_cleantype<T>::type type; };
/** \internal In short, it computes int(sqrt(\a Y)) with \a Y an integer.
* Usage example: \code ei_meta_sqrt<1023>::ret \endcode
*/
template<int Y,
int InfX = 0,
int SupX = ((Y==1) ? 1 : Y/2),
bool Done = ((SupX-InfX)<=1 ? true : ((SupX*SupX <= Y) && ((SupX+1)*(SupX+1) > Y))) >
// use ?: instead of || just to shut up a stupid gcc 4.3 warning
class ei_meta_sqrt
{
enum {
MidX = (InfX+SupX)/2,
TakeInf = MidX*MidX > Y ? 1 : 0,
NewInf = int(TakeInf) ? InfX : int(MidX),
NewSup = int(TakeInf) ? int(MidX) : SupX
};
public:
enum { ret = ei_meta_sqrt<Y,NewInf,NewSup>::ret };
};
template<int Y, int InfX, int SupX>
class ei_meta_sqrt<Y, InfX, SupX, true> { public: enum { ret = (SupX*SupX <= Y) ? SupX : InfX }; };
} // end namespace Eigen
#endif // EIGEN2_META_H

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Eigen/src/Eigen2Support/Minor.h
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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/.
#ifndef EIGEN_MINOR_H
#define EIGEN_MINOR_H
namespace Eigen {
/**
* \class Minor
*
* \brief Expression of a minor
*
* \param MatrixType the type of the object in which we are taking a minor
*
* This class represents an expression of a minor. It is the return
* type of MatrixBase::minor() and most of the time this is the only way it
* is used.
*
* \sa MatrixBase::minor()
*/
namespace internal {
template<typename MatrixType>
struct traits<Minor<MatrixType> >
: traits<MatrixType>
{
typedef typename nested<MatrixType>::type MatrixTypeNested;
typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
typedef typename MatrixType::StorageKind StorageKind;
enum {
RowsAtCompileTime = (MatrixType::RowsAtCompileTime != Dynamic) ?
int(MatrixType::RowsAtCompileTime) - 1 : Dynamic,
ColsAtCompileTime = (MatrixType::ColsAtCompileTime != Dynamic) ?
int(MatrixType::ColsAtCompileTime) - 1 : Dynamic,
MaxRowsAtCompileTime = (MatrixType::MaxRowsAtCompileTime != Dynamic) ?
int(MatrixType::MaxRowsAtCompileTime) - 1 : Dynamic,
MaxColsAtCompileTime = (MatrixType::MaxColsAtCompileTime != Dynamic) ?
int(MatrixType::MaxColsAtCompileTime) - 1 : Dynamic,
Flags = _MatrixTypeNested::Flags & (HereditaryBits | LvalueBit),
CoeffReadCost = _MatrixTypeNested::CoeffReadCost // minor is used typically on tiny matrices,
// where loops are unrolled and the 'if' evaluates at compile time
};
};
}
template<typename MatrixType> class Minor
: public MatrixBase<Minor<MatrixType> >
{
public:
typedef MatrixBase<Minor> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Minor)
inline Minor(const MatrixType& matrix,
Index row, Index col)
: m_matrix(matrix), m_row(row), m_col(col)
{
eigen_assert(row >= 0 && row < matrix.rows()
&& col >= 0 && col < matrix.cols());
}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Minor)
inline Index rows() const { return m_matrix.rows() - 1; }
inline Index cols() const { return m_matrix.cols() - 1; }
inline Scalar& coeffRef(Index row, Index col)
{
return m_matrix.const_cast_derived().coeffRef(row + (row >= m_row), col + (col >= m_col));
}
inline const Scalar coeff(Index row, Index col) const
{
return m_matrix.coeff(row + (row >= m_row), col + (col >= m_col));
}
protected:
const typename MatrixType::Nested m_matrix;
const Index m_row, m_col;
};
/**
* \return an expression of the (\a row, \a col)-minor of *this,
* i.e. an expression constructed from *this by removing the specified
* row and column.
*
* Example: \include MatrixBase_minor.cpp
* Output: \verbinclude MatrixBase_minor.out
*
* \sa class Minor
*/
template<typename Derived>
inline Minor<Derived>
MatrixBase<Derived>::minor(Index row, Index col)
{
return Minor<Derived>(derived(), row, col);
}
/**
* This is the const version of minor(). */
template<typename Derived>
inline const Minor<Derived>
MatrixBase<Derived>::minor(Index row, Index col) const
{
return Minor<Derived>(derived(), row, col);
}
} // end namespace Eigen
#endif // EIGEN_MINOR_H

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Eigen/src/Eigen2Support/QR.h
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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/.
#ifndef EIGEN2_QR_H
#define EIGEN2_QR_H
namespace Eigen {
template<typename MatrixType>
class QR : public HouseholderQR<MatrixType>
{
public:
typedef HouseholderQR<MatrixType> Base;
typedef Block<const MatrixType, MatrixType::ColsAtCompileTime, MatrixType::ColsAtCompileTime> MatrixRBlockType;
QR() : Base() {}
template<typename T>
explicit QR(const T& t) : Base(t) {}
template<typename OtherDerived, typename ResultType>
bool solve(const MatrixBase<OtherDerived>& b, ResultType *result) const
{
*result = static_cast<const Base*>(this)->solve(b);
return true;
}
MatrixType matrixQ(void) const {
MatrixType ret = MatrixType::Identity(this->rows(), this->cols());
ret = this->householderQ() * ret;
return ret;
}
bool isFullRank() const {
return true;
}
const TriangularView<MatrixRBlockType, UpperTriangular>
matrixR(void) const
{
int cols = this->cols();
return MatrixRBlockType(this->matrixQR(), 0, 0, cols, cols).template triangularView<UpperTriangular>();
}
};
/** \return the QR decomposition of \c *this.
*
* \sa class QR
*/
template<typename Derived>
const QR<typename MatrixBase<Derived>::PlainObject>
MatrixBase<Derived>::qr() const
{
return QR<PlainObject>(eval());
}
} // end namespace Eigen
#endif // EIGEN2_QR_H

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Eigen/src/Eigen2Support/SVD.h
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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/.
#ifndef EIGEN2_SVD_H
#define EIGEN2_SVD_H
namespace Eigen {
/** \ingroup SVD_Module
* \nonstableyet
*
* \class SVD
*
* \brief Standard SVD decomposition of a matrix and associated features
*
* \param MatrixType the type of the matrix of which we are computing the SVD decomposition
*
* This class performs a standard SVD decomposition of a real matrix A of size \c M x \c N
* with \c M \>= \c N.
*
*
* \sa MatrixBase::SVD()
*/
template<typename MatrixType> class SVD
{
private:
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
enum {
PacketSize = internal::packet_traits<Scalar>::size,
AlignmentMask = int(PacketSize)-1,
MinSize = EIGEN_SIZE_MIN_PREFER_DYNAMIC(MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime)
};
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> ColVector;
typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> RowVector;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MinSize> MatrixUType;
typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, MatrixType::ColsAtCompileTime> MatrixVType;
typedef Matrix<Scalar, MinSize, 1> SingularValuesType;
public:
SVD() {} // a user who relied on compiler-generated default compiler reported problems with MSVC in 2.0.7
SVD(const MatrixType& matrix)
: m_matU(matrix.rows(), (std::min)(matrix.rows(), matrix.cols())),
m_matV(matrix.cols(),matrix.cols()),
m_sigma((std::min)(matrix.rows(),matrix.cols()))
{
compute(matrix);
}
template<typename OtherDerived, typename ResultType>
bool solve(const MatrixBase<OtherDerived> &b, ResultType* result) const;
const MatrixUType& matrixU() const { return m_matU; }
const SingularValuesType& singularValues() const { return m_sigma; }
const MatrixVType& matrixV() const { return m_matV; }
void compute(const MatrixType& matrix);
SVD& sort();
template<typename UnitaryType, typename PositiveType>
void computeUnitaryPositive(UnitaryType *unitary, PositiveType *positive) const;
template<typename PositiveType, typename UnitaryType>
void computePositiveUnitary(PositiveType *positive, UnitaryType *unitary) const;
template<typename RotationType, typename ScalingType>
void computeRotationScaling(RotationType *unitary, ScalingType *positive) const;
template<typename ScalingType, typename RotationType>
void computeScalingRotation(ScalingType *positive, RotationType *unitary) const;
protected:
/** \internal */
MatrixUType m_matU;
/** \internal */
MatrixVType m_matV;
/** \internal */
SingularValuesType m_sigma;
};
/** Computes / recomputes the SVD decomposition A = U S V^* of \a matrix
*
* \note this code has been adapted from JAMA (public domain)
*/
template<typename MatrixType>
void SVD<MatrixType>::compute(const MatrixType& matrix)
{
const int m = matrix.rows();
const int n = matrix.cols();
const int nu = (std::min)(m,n);
ei_assert(m>=n && "In Eigen 2.0, SVD only works for MxN matrices with M>=N. Sorry!");
ei_assert(m>1 && "In Eigen 2.0, SVD doesn't work on 1x1 matrices");
m_matU.resize(m, nu);
m_matU.setZero();
m_sigma.resize((std::min)(m,n));
m_matV.resize(n,n);
RowVector e(n);
ColVector work(m);
MatrixType matA(matrix);
const bool wantu = true;
const bool wantv = true;
int i=0, j=0, k=0;
// Reduce A to bidiagonal form, storing the diagonal elements
// in s and the super-diagonal elements in e.
int nct = (std::min)(m-1,n);
int nrt = (std::max)(0,(std::min)(n-2,m));
for (k = 0; k < (std::max)(nct,nrt); ++k)
{
if (k < nct)
{
// Compute the transformation for the k-th column and
// place the k-th diagonal in m_sigma[k].
m_sigma[k] = matA.col(k).end(m-k).norm();
if (m_sigma[k] != 0.0) // FIXME
{
if (matA(k,k) < 0.0)
m_sigma[k] = -m_sigma[k];
matA.col(k).end(m-k) /= m_sigma[k];
matA(k,k) += 1.0;
}
m_sigma[k] = -m_sigma[k];
}
for (j = k+1; j < n; ++j)
{
if ((k < nct) && (m_sigma[k] != 0.0))
{
// Apply the transformation.
Scalar t = matA.col(k).end(m-k).eigen2_dot(matA.col(j).end(m-k)); // FIXME dot product or cwise prod + .sum() ??
t = -t/matA(k,k);
matA.col(j).end(m-k) += t * matA.col(k).end(m-k);
}
// Place the k-th row of A into e for the
// subsequent calculation of the row transformation.
e[j] = matA(k,j);
}
// Place the transformation in U for subsequent back multiplication.
if (wantu & (k < nct))
m_matU.col(k).end(m-k) = matA.col(k).end(m-k);
if (k < nrt)
{
// Compute the k-th row transformation and place the
// k-th super-diagonal in e[k].
e[k] = e.end(n-k-1).norm();
if (e[k] != 0.0)
{
if (e[k+1] < 0.0)
e[k] = -e[k];
e.end(n-k-1) /= e[k];
e[k+1] += 1.0;
}
e[k] = -e[k];
if ((k+1 < m) & (e[k] != 0.0))
{
// Apply the transformation.
work.end(m-k-1) = matA.corner(BottomRight,m-k-1,n-k-1) * e.end(n-k-1);
for (j = k+1; j < n; ++j)
matA.col(j).end(m-k-1) += (-e[j]/e[k+1]) * work.end(m-k-1);
}
// Place the transformation in V for subsequent back multiplication.
if (wantv)
m_matV.col(k).end(n-k-1) = e.end(n-k-1);
}
}
// Set up the final bidiagonal matrix or order p.
int p = (std::min)(n,m+1);
if (nct < n)
m_sigma[nct] = matA(nct,nct);
if (m < p)
m_sigma[p-1] = 0.0;
if (nrt+1 < p)
e[nrt] = matA(nrt,p-1);
e[p-1] = 0.0;
// If required, generate U.
if (wantu)
{
for (j = nct; j < nu; ++j)
{
m_matU.col(j).setZero();
m_matU(j,j) = 1.0;
}
for (k = nct-1; k >= 0; k--)
{
if (m_sigma[k] != 0.0)
{
for (j = k+1; j < nu; ++j)
{
Scalar t = m_matU.col(k).end(m-k).eigen2_dot(m_matU.col(j).end(m-k)); // FIXME is it really a dot product we want ?
t = -t/m_matU(k,k);
m_matU.col(j).end(m-k) += t * m_matU.col(k).end(m-k);
}
m_matU.col(k).end(m-k) = - m_matU.col(k).end(m-k);
m_matU(k,k) = Scalar(1) + m_matU(k,k);
if (k-1>0)
m_matU.col(k).start(k-1).setZero();
}
else
{
m_matU.col(k).setZero();
m_matU(k,k) = 1.0;
}
}
}
// If required, generate V.
if (wantv)
{
for (k = n-1; k >= 0; k--)
{
if ((k < nrt) & (e[k] != 0.0))
{
for (j = k+1; j < nu; ++j)
{
Scalar t = m_matV.col(k).end(n-k-1).eigen2_dot(m_matV.col(j).end(n-k-1)); // FIXME is it really a dot product we want ?
t = -t/m_matV(k+1,k);
m_matV.col(j).end(n-k-1) += t * m_matV.col(k).end(n-k-1);
}
}
m_matV.col(k).setZero();
m_matV(k,k) = 1.0;
}
}
// Main iteration loop for the singular values.
int pp = p-1;
int iter = 0;
Scalar eps = ei_pow(Scalar(2),ei_is_same_type<Scalar,float>::ret ? Scalar(-23) : Scalar(-52));
while (p > 0)
{
int k=0;
int kase=0;
// Here is where a test for too many iterations would go.
// This section of the program inspects for
// negligible elements in the s and e arrays. On
// completion the variables kase and k are set as follows.
// kase = 1 if s(p) and e[k-1] are negligible and k<p
// kase = 2 if s(k) is negligible and k<p
// kase = 3 if e[k-1] is negligible, k<p, and
// s(k), ..., s(p) are not negligible (qr step).
// kase = 4 if e(p-1) is negligible (convergence).
for (k = p-2; k >= -1; --k)
{
if (k == -1)
break;
if (ei_abs(e[k]) <= eps*(ei_abs(m_sigma[k]) + ei_abs(m_sigma[k+1])))
{
e[k] = 0.0;
break;
}
}
if (k == p-2)
{
kase = 4;
}
else
{
int ks;
for (ks = p-1; ks >= k; --ks)
{
if (ks == k)
break;
Scalar t = (ks != p ? ei_abs(e[ks]) : Scalar(0)) + (ks != k+1 ? ei_abs(e[ks-1]) : Scalar(0));
if (ei_abs(m_sigma[ks]) <= eps*t)
{
m_sigma[ks] = 0.0;
break;
}
}
if (ks == k)
{
kase = 3;
}
else if (ks == p-1)
{
kase = 1;
}
else
{
kase = 2;
k = ks;
}
}
++k;
// Perform the task indicated by kase.
switch (kase)
{
// Deflate negligible s(p).
case 1:
{
Scalar f(e[p-2]);
e[p-2] = 0.0;
for (j = p-2; j >= k; --j)
{
Scalar t(numext::hypot(m_sigma[j],f));
Scalar cs(m_sigma[j]/t);
Scalar sn(f/t);
m_sigma[j] = t;
if (j != k)
{
f = -sn*e[j-1];
e[j-1] = cs*e[j-1];
}
if (wantv)
{
for (i = 0; i < n; ++i)
{
t = cs*m_matV(i,j) + sn*m_matV(i,p-1);
m_matV(i,p-1) = -sn*m_matV(i,j) + cs*m_matV(i,p-1);
m_matV(i,j) = t;
}
}
}
}
break;
// Split at negligible s(k).
case 2:
{
Scalar f(e[k-1]);
e[k-1] = 0.0;
for (j = k; j < p; ++j)
{
Scalar t(numext::hypot(m_sigma[j],f));
Scalar cs( m_sigma[j]/t);
Scalar sn(f/t);
m_sigma[j] = t;
f = -sn*e[j];
e[j] = cs*e[j];
if (wantu)
{
for (i = 0; i < m; ++i)
{
t = cs*m_matU(i,j) + sn*m_matU(i,k-1);
m_matU(i,k-1) = -sn*m_matU(i,j) + cs*m_matU(i,k-1);
m_matU(i,j) = t;
}
}
}
}
break;
// Perform one qr step.
case 3:
{
// Calculate the shift.
Scalar scale = (std::max)((std::max)((std::max)((std::max)(
ei_abs(m_sigma[p-1]),ei_abs(m_sigma[p-2])),ei_abs(e[p-2])),
ei_abs(m_sigma[k])),ei_abs(e[k]));
Scalar sp = m_sigma[p-1]/scale;
Scalar spm1 = m_sigma[p-2]/scale;
Scalar epm1 = e[p-2]/scale;
Scalar sk = m_sigma[k]/scale;
Scalar ek = e[k]/scale;
Scalar b = ((spm1 + sp)*(spm1 - sp) + epm1*epm1)/Scalar(2);
Scalar c = (sp*epm1)*(sp*epm1);
Scalar shift(0);
if ((b != 0.0) || (c != 0.0))
{
shift = ei_sqrt(b*b + c);
if (b < 0.0)
shift = -shift;
shift = c/(b + shift);
}
Scalar f = (sk + sp)*(sk - sp) + shift;
Scalar g = sk*ek;
// Chase zeros.
for (j = k; j < p-1; ++j)
{
Scalar t = numext::hypot(f,g);
Scalar cs = f/t;
Scalar sn = g/t;
if (j != k)
e[j-1] = t;
f = cs*m_sigma[j] + sn*e[j];
e[j] = cs*e[j] - sn*m_sigma[j];
g = sn*m_sigma[j+1];
m_sigma[j+1] = cs*m_sigma[j+1];
if (wantv)
{
for (i = 0; i < n; ++i)
{
t = cs*m_matV(i,j) + sn*m_matV(i,j+1);
m_matV(i,j+1) = -sn*m_matV(i,j) + cs*m_matV(i,j+1);
m_matV(i,j) = t;
}
}
t = numext::hypot(f,g);
cs = f/t;
sn = g/t;
m_sigma[j] = t;
f = cs*e[j] + sn*m_sigma[j+1];
m_sigma[j+1] = -sn*e[j] + cs*m_sigma[j+1];
g = sn*e[j+1];
e[j+1] = cs*e[j+1];
if (wantu && (j < m-1))
{
for (i = 0; i < m; ++i)
{
t = cs*m_matU(i,j) + sn*m_matU(i,j+1);
m_matU(i,j+1) = -sn*m_matU(i,j) + cs*m_matU(i,j+1);
m_matU(i,j) = t;
}
}
}
e[p-2] = f;
iter = iter + 1;
}
break;
// Convergence.
case 4:
{
// Make the singular values positive.
if (m_sigma[k] <= 0.0)
{
m_sigma[k] = m_sigma[k] < Scalar(0) ? -m_sigma[k] : Scalar(0);
if (wantv)
m_matV.col(k).start(pp+1) = -m_matV.col(k).start(pp+1);
}
// Order the singular values.
while (k < pp)
{
if (m_sigma[k] >= m_sigma[k+1])
break;
Scalar t = m_sigma[k];
m_sigma[k] = m_sigma[k+1];
m_sigma[k+1] = t;
if (wantv && (k < n-1))
m_matV.col(k).swap(m_matV.col(k+1));
if (wantu && (k < m-1))
m_matU.col(k).swap(m_matU.col(k+1));
++k;
}
iter = 0;
p--;
}
break;
} // end big switch
} // end iterations
}
template<typename MatrixType>
SVD<MatrixType>& SVD<MatrixType>::sort()
{
int mu = m_matU.rows();
int mv = m_matV.rows();
int n = m_matU.cols();
for (int i=0; i<n; ++i)
{
int k = i;
Scalar p = m_sigma.coeff(i);
for (int j=i+1; j<n; ++j)
{
if (m_sigma.coeff(j) > p)
{
k = j;
p = m_sigma.coeff(j);
}
}
if (k != i)
{
m_sigma.coeffRef(k) = m_sigma.coeff(i); // i.e.
m_sigma.coeffRef(i) = p; // swaps the i-th and the k-th elements
int j = mu;
for(int s=0; j!=0; ++s, --j)
std::swap(m_matU.coeffRef(s,i), m_matU.coeffRef(s,k));
j = mv;
for (int s=0; j!=0; ++s, --j)
std::swap(m_matV.coeffRef(s,i), m_matV.coeffRef(s,k));
}
}
return *this;
}
/** \returns the solution of \f$ A x = b \f$ using the current SVD decomposition of A.
* The parts of the solution corresponding to zero singular values are ignored.
*
* \sa MatrixBase::svd(), LU::solve(), LLT::solve()
*/
template<typename MatrixType>
template<typename OtherDerived, typename ResultType>
bool SVD<MatrixType>::solve(const MatrixBase<OtherDerived> &b, ResultType* result) const
{
ei_assert(b.rows() == m_matU.rows());
Scalar maxVal = m_sigma.cwise().abs().maxCoeff();
for (int j=0; j<b.cols(); ++j)
{
Matrix<Scalar,MatrixUType::RowsAtCompileTime,1> aux = m_matU.transpose() * b.col(j);
for (int i = 0; i <m_matU.cols(); ++i)
{
Scalar si = m_sigma.coeff(i);
if (ei_isMuchSmallerThan(ei_abs(si),maxVal))
aux.coeffRef(i) = 0;
else
aux.coeffRef(i) /= si;
}
result->col(j) = m_matV * aux;
}
return true;
}
/** Computes the polar decomposition of the matrix, as a product unitary x positive.
*
* If either pointer is zero, the corresponding computation is skipped.
*
* Only for square matrices.
*
* \sa computePositiveUnitary(), computeRotationScaling()
*/
template<typename MatrixType>
template<typename UnitaryType, typename PositiveType>
void SVD<MatrixType>::computeUnitaryPositive(UnitaryType *unitary,
PositiveType *positive) const
{
ei_assert(m_matU.cols() == m_matV.cols() && "Polar decomposition is only for square matrices");
if(unitary) *unitary = m_matU * m_matV.adjoint();
if(positive) *positive = m_matV * m_sigma.asDiagonal() * m_matV.adjoint();
}
/** Computes the polar decomposition of the matrix, as a product positive x unitary.
*
* If either pointer is zero, the corresponding computation is skipped.
*
* Only for square matrices.
*
* \sa computeUnitaryPositive(), computeRotationScaling()
*/
template<typename MatrixType>
template<typename UnitaryType, typename PositiveType>
void SVD<MatrixType>::computePositiveUnitary(UnitaryType *positive,
PositiveType *unitary) const
{
ei_assert(m_matU.rows() == m_matV.rows() && "Polar decomposition is only for square matrices");
if(unitary) *unitary = m_matU * m_matV.adjoint();
if(positive) *positive = m_matU * m_sigma.asDiagonal() * m_matU.adjoint();
}
/** decomposes the matrix as a product rotation x scaling, the scaling being
* not necessarily positive.
*
* If either pointer is zero, the corresponding computation is skipped.
*
* This method requires the Geometry module.
*
* \sa computeScalingRotation(), computeUnitaryPositive()
*/
template<typename MatrixType>
template<typename RotationType, typename ScalingType>
void SVD<MatrixType>::computeRotationScaling(RotationType *rotation, ScalingType *scaling) const
{
ei_assert(m_matU.rows() == m_matV.rows() && "Polar decomposition is only for square matrices");
Scalar x = (m_matU * m_matV.adjoint()).determinant(); // so x has absolute value 1
Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> sv(m_sigma);
sv.coeffRef(0) *= x;
if(scaling) scaling->lazyAssign(m_matV * sv.asDiagonal() * m_matV.adjoint());
if(rotation)
{
MatrixType m(m_matU);
m.col(0) /= x;
rotation->lazyAssign(m * m_matV.adjoint());
}
}
/** decomposes the matrix as a product scaling x rotation, the scaling being
* not necessarily positive.
*
* If either pointer is zero, the corresponding computation is skipped.
*
* This method requires the Geometry module.
*
* \sa computeRotationScaling(), computeUnitaryPositive()
*/
template<typename MatrixType>
template<typename ScalingType, typename RotationType>
void SVD<MatrixType>::computeScalingRotation(ScalingType *scaling, RotationType *rotation) const
{
ei_assert(m_matU.rows() == m_matV.rows() && "Polar decomposition is only for square matrices");
Scalar x = (m_matU * m_matV.adjoint()).determinant(); // so x has absolute value 1
Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> sv(m_sigma);
sv.coeffRef(0) *= x;
if(scaling) scaling->lazyAssign(m_matU * sv.asDiagonal() * m_matU.adjoint());
if(rotation)
{
MatrixType m(m_matU);
m.col(0) /= x;
rotation->lazyAssign(m * m_matV.adjoint());
}
}
/** \svd_module
* \returns the SVD decomposition of \c *this
*/
template<typename Derived>
inline SVD<typename MatrixBase<Derived>::PlainObject>
MatrixBase<Derived>::svd() const
{
return SVD<PlainObject>(derived());
}
} // end namespace Eigen
#endif // EIGEN2_SVD_H

42
Eigen/src/Eigen2Support/TriangularSolver.h
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@ -1,42 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2010 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/.
#ifndef EIGEN_TRIANGULAR_SOLVER2_H
#define EIGEN_TRIANGULAR_SOLVER2_H
namespace Eigen {
const unsigned int UnitDiagBit = UnitDiag;
const unsigned int SelfAdjointBit = SelfAdjoint;
const unsigned int UpperTriangularBit = Upper;
const unsigned int LowerTriangularBit = Lower;
const unsigned int UpperTriangular = Upper;
const unsigned int LowerTriangular = Lower;
const unsigned int UnitUpperTriangular = UnitUpper;
const unsigned int UnitLowerTriangular = UnitLower;
template<typename ExpressionType, unsigned int Added, unsigned int Removed>
template<typename OtherDerived>
typename ExpressionType::PlainObject
Flagged<ExpressionType,Added,Removed>::solveTriangular(const MatrixBase<OtherDerived>& other) const
{
return m_matrix.template triangularView<Added>().solve(other.derived());
}
template<typename ExpressionType, unsigned int Added, unsigned int Removed>
template<typename OtherDerived>
void Flagged<ExpressionType,Added,Removed>::solveTriangularInPlace(const MatrixBase<OtherDerived>& other) const
{
m_matrix.template triangularView<Added>().solveInPlace(other.derived());
}
} // end namespace Eigen
#endif // EIGEN_TRIANGULAR_SOLVER2_H

94
Eigen/src/Eigen2Support/VectorBlock.h
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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/.
#ifndef EIGEN2_VECTORBLOCK_H
#define EIGEN2_VECTORBLOCK_H
namespace Eigen {
/** \deprecated use DenseMase::head(Index) */
template<typename Derived>
inline VectorBlock<Derived>
MatrixBase<Derived>::start(Index size)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return VectorBlock<Derived>(derived(), 0, size);
}
/** \deprecated use DenseMase::head(Index) */
template<typename Derived>
inline const VectorBlock<const Derived>
MatrixBase<Derived>::start(Index size) const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return VectorBlock<const Derived>(derived(), 0, size);
}
/** \deprecated use DenseMase::tail(Index) */
template<typename Derived>
inline VectorBlock<Derived>
MatrixBase<Derived>::end(Index size)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return VectorBlock<Derived>(derived(), this->size() - size, size);
}
/** \deprecated use DenseMase::tail(Index) */
template<typename Derived>
inline const VectorBlock<const Derived>
MatrixBase<Derived>::end(Index size) const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return VectorBlock<const Derived>(derived(), this->size() - size, size);
}
/** \deprecated use DenseMase::head() */
template<typename Derived>
template<int Size>
inline VectorBlock<Derived,Size>
MatrixBase<Derived>::start()
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return VectorBlock<Derived,Size>(derived(), 0);
}
/** \deprecated use DenseMase::head() */
template<typename Derived>
template<int Size>
inline const VectorBlock<const Derived,Size>
MatrixBase<Derived>::start() const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return VectorBlock<const Derived,Size>(derived(), 0);
}
/** \deprecated use DenseMase::tail() */
template<typename Derived>
template<int Size>
inline VectorBlock<Derived,Size>
MatrixBase<Derived>::end()
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return VectorBlock<Derived, Size>(derived(), size() - Size);
}
/** \deprecated use DenseMase::tail() */
template<typename Derived>
template<int Size>
inline const VectorBlock<const Derived,Size>
MatrixBase<Derived>::end() const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return VectorBlock<const Derived, Size>(derived(), size() - Size);
}
} // end namespace Eigen
#endif // EIGEN2_VECTORBLOCK_H

34
Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
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@ -346,40 +346,6 @@ template<typename _MatrixType> class SelfAdjointEigenSolver
*/
static const int m_maxIterations = 30;
#ifdef EIGEN2_SUPPORT
EIGEN_DEVICE_FUNC
SelfAdjointEigenSolver(const MatrixType& matrix, bool computeEigenvectors)
: m_eivec(matrix.rows(), matrix.cols()),
m_eivalues(matrix.cols()),
m_subdiag(matrix.rows() > 1 ? matrix.rows() - 1 : 1),
m_isInitialized(false)
{
compute(matrix, computeEigenvectors);
}
EIGEN_DEVICE_FUNC
SelfAdjointEigenSolver(const MatrixType& matA, const MatrixType& matB, bool computeEigenvectors = true)
: m_eivec(matA.cols(), matA.cols()),
m_eivalues(matA.cols()),
m_subdiag(matA.cols() > 1 ? matA.cols() - 1 : 1),
m_isInitialized(false)
{
static_cast<GeneralizedSelfAdjointEigenSolver<MatrixType>*>(this)->compute(matA, matB, computeEigenvectors ? ComputeEigenvectors : EigenvaluesOnly);
}
EIGEN_DEVICE_FUNC
void compute(const MatrixType& matrix, bool computeEigenvectors)
{
compute(matrix, computeEigenvectors ? ComputeEigenvectors : EigenvaluesOnly);
}
EIGEN_DEVICE_FUNC
void compute(const MatrixType& matA, const MatrixType& matB, bool computeEigenvectors = true)
{
compute(matA, matB, computeEigenvectors ? ComputeEigenvectors : EigenvaluesOnly);
}
#endif // EIGEN2_SUPPORT
protected:
MatrixType m_eivec;
RealVectorType m_eivalues;

3
Eigen/src/LU/PartialPivLU.h
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@ -481,7 +481,6 @@ MatrixBase<Derived>::partialPivLu() const
}
#endif
#if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS
/** \lu_module
*
* Synonym of partialPivLu().
@ -499,8 +498,6 @@ MatrixBase<Derived>::lu() const
}
#endif
#endif
} // end namespace Eigen
#endif // EIGEN_PARTIALLU_H

11
Eigen/src/SparseCore/SparseMatrixBase.h
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@ -369,17 +369,6 @@ template<typename Derived> class SparseMatrixBase : public EigenBase<Derived>
template<typename OtherDerived>
Derived& operator*=(const SparseMatrixBase<OtherDerived>& other);
#ifdef EIGEN2_SUPPORT
// deprecated
template<typename OtherDerived>
typename internal::plain_matrix_type_column_major<OtherDerived>::type
solveTriangular(const MatrixBase<OtherDerived>& other) const;
// deprecated
template<typename OtherDerived>
void solveTriangularInPlace(MatrixBase<OtherDerived>& other) const;
#endif // EIGEN2_SUPPORT
template<int Mode>
inline const SparseTriangularView<Derived, Mode> triangularView() const;

24
Eigen/src/SparseCore/TriangularSolver.h
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@ -305,30 +305,6 @@ void SparseTriangularView<ExpressionType,Mode>::solveInPlace(SparseMatrixBase<Ot
// other = otherCopy;
}
#ifdef EIGEN2_SUPPORT
// deprecated stuff:
/** \deprecated */
template<typename Derived>
template<typename OtherDerived>
void SparseMatrixBase<Derived>::solveTriangularInPlace(MatrixBase<OtherDerived>& other) const
{
this->template triangular<Flags&(Upper|Lower)>().solveInPlace(other);
}
/** \deprecated */
template<typename Derived>
template<typename OtherDerived>
typename internal::plain_matrix_type_column_major<OtherDerived>::type
SparseMatrixBase<Derived>::solveTriangular(const MatrixBase<OtherDerived>& other) const
{
typename internal::plain_matrix_type_column_major<OtherDerived>::type res(other);
derived().solveTriangularInPlace(res);
return res;
}
#endif // EIGEN2_SUPPORT
} // end namespace Eigen
#endif // EIGEN_SPARSETRIANGULARSOLVER_H

7
doc/A05_PortingFrom2To3.dox
View File

@ -9,11 +9,8 @@ and gives tips to help porting your application from Eigen2 to Eigen3.
\section CompatibilitySupport Eigen2 compatibility support
In order to ease the switch from Eigen2 to Eigen3, Eigen3 features \subpage Eigen2SupportModes "Eigen2 support modes".
The quick way to enable this is to define the \c EIGEN2_SUPPORT preprocessor token \b before including any Eigen header (typically it should be set in your project options).
A more powerful, \em staged migration path is also provided, which may be useful to migrate larger projects from Eigen2 to Eigen3. This is explained in the \ref Eigen2SupportModes "Eigen 2 support modes" page.
Up to version 3.2 %Eigen provides <a href="http://eigen.tuxfamily.org/dox/Eigen2SupportModes.html">Eigen2 support modes</a>. These are removed now, because they were barely used anymore and became hard to maintain after internal re-designs.
You can still use them by first <a href="http://eigen.tuxfamily.org/dox/Eigen2ToEigen3.html">porting your code to Eigen 3.2</a>.
\section Using The USING_PART_OF_NAMESPACE_EIGEN macro

93
doc/A10_Eigen2SupportModes.dox
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@ -1,93 +0,0 @@
namespace Eigen {
/** \page Eigen2SupportModes Eigen 2 support modes
This page documents the Eigen2 support modes, a powerful tool to help migrating your project from Eigen 2 to Eigen 3.
Don't miss our page on \ref Eigen2ToEigen3 "API changes" between Eigen 2 and Eigen 3.
\eigenAutoToc
\section EIGEN2_SUPPORT_Macro The quick way: define EIGEN2_SUPPORT
By defining EIGEN2_SUPPORT before including any Eigen 3 header, you get back a large part of the Eigen 2 API, while keeping the Eigen 3 API and ABI unchanged.
This defaults to the \ref Stage30 "stage 30" described below.
The rest of this page describes an optional, more powerful \em staged migration path.
\section StagedMigrationPathOverview Overview of the staged migration path
The primary reason why EIGEN2_SUPPORT alone may not be enough to migrate a large project from Eigen 2 to Eigen 3 is that some of the Eigen 2 API is inherently incompatible with the Eigen 3 API. This happens when the same identifier is used in Eigen 2 and in Eigen 3 with different meanings. To help migrate projects that rely on such API, we provide a staged migration path allowing to perform the migration \em incrementally.
It goes as follows:
\li Step 0: start with a project using Eigen 2.
\li Step 1: build your project against Eigen 3 with \ref Stage10 "Eigen 2 support stage 10". This mode enables maximum compatibility with the Eigen 2 API, with just a few exceptions.
\li Step 2: build your project against Eigen 3 with \ref Stage20 "Eigen 2 support stage 20". This mode forces you to add eigen2_ prefixes to the Eigen2 identifiers that conflict with Eigen 3 API.
\li Step 3: build your project against Eigen 3 with \ref Stage30 "Eigen 2 support stage 30". This mode enables the full Eigen 3 API.
\li Step 4: build your project against Eigen 3 with \ref Stage40 "Eigen 2 support stage 40". This mode enables the full Eigen 3 strictness on matters, such as const-correctness, where Eigen 2 was looser.
\li Step 5: build your project against Eigen 3 without any Eigen 2 support mode.
\section Stage10 Stage 10: define EIGEN2_SUPPORT_STAGE10_FULL_EIGEN2_API
Enable this mode by defining the EIGEN2_SUPPORT_STAGE10_FULL_EIGEN2_API preprocessor macro before including any Eigen 3 header.
This mode maximizes support for the Eigen 2 API. As a result, it does not offer the full Eigen 3 API. Also, it doesn't offer quite 100% of the Eigen 2 API.
The part of the Eigen 3 API that is not present in this mode, is Eigen 3's Geometry module. Indeed, this mode completely replaces it by a copy of Eigen 2's Geometry module.
The parts of the API that are still not 100% Eigen 2 compatible in this mode are:
\li Dot products over complex numbers. Eigen 2's dot product was linear in the first variable. Eigen 3's dot product is linear in the second variable. In other words, the Eigen 2 code \code x.dot(y) \endcode is equivalent to the Eigen 3 code \code y.dot(x) \endcode In yet other words, dot products are complex-conjugated in Eigen 3 compared to Eigen 2. The switch to the new convention was commanded by common usage, especially with the notation \f$ x^Ty \f$ for dot products of column-vectors.
\li The Sparse module.
\li Certain fine details of linear algebraic decompositions. For example, LDLT decomposition is now pivoting in Eigen 3 whereas it wasn't in Eigen 2, so code that was relying on its underlying matrix structure will break.
\li Usage of Eigen types in STL containers, \ref Eigen2ToEigen3 "as explained on this page".
\section Stage20 Stage 20: define EIGEN2_SUPPORT_STAGE20_RESOLVE_API_CONFLICTS
Enable this mode by defining the EIGEN2_SUPPORT_STAGE10_FULL_EIGEN2_API preprocessor macro before including any Eigen 3 header.
This mode removes the Eigen 2 API that is directly conflicting with Eigen 3 API. Instead, these bits of Eigen 2 API remain available with eigen2_ prefixes. The main examples of such API are:
\li the whole Geometry module. For example, replace \c Quaternion by \c eigen2_Quaternion, replace \c Transform3f by \c eigen2_Transform3f, etc.
\li the lu() method to obtain a LU decomposition. Replace by eigen2_lu().
There is also one more eigen2_-prefixed identifier that you should know about, even though its use is not checked at compile time by this mode: the dot() method. As was discussed above, over complex numbers, its meaning is different between Eigen 2 and Eigen 3. You can use eigen2_dot() to get the Eigen 2 behavior.
\section Stage30 Stage 30: define EIGEN2_SUPPORT_STAGE30_FULL_EIGEN3_API
Enable this mode by defining the EIGEN2_SUPPORT_STAGE30_FULL_EIGEN3_API preprocessor macro before including any Eigen 3 header. Also, this mode is what you get by default when you just define EIGEN2_SUPPORT.
This mode gives you the full unaltered Eigen 3 API, while still keeping as much support as possible for the Eigen 2 API.
The eigen2_-prefixed identifiers are still available, but at this stage you should now replace them by Eigen 3 identifiers. Have a look at our page on \ref Eigen2ToEigen3 "API changes" between Eigen 2 and Eigen 3.
\section Stage40 Stage 40: define EIGEN2_SUPPORT_STAGE40_FULL_EIGEN3_STRICTNESS
Enable this mode by defining the EIGEN2_SUPPORT_STAGE40_FULL_EIGEN3_STRICTNESS preprocessor macro before including any Eigen 3 header.
This mode tightens the last bits of strictness, especially const-correctness, that had to be loosened to support what Eigen 2 allowed. For example, this code compiled in Eigen 2:
\code
const float array[4];
x = Map<Vector4f>(array);
\endcode
That allowed to circumvent constness. This is no longer allowed in Eigen 3. If you have to map const data in Eigen 3, map it as a const-qualified type. However, rather than explictly constructing Map objects, we strongly encourage you to use the static Map methods instead, as they take care of all of this for you:
\code
const float array[4];
x = Vector4f::Map(array);
\endcode
This lets Eigen do the right thing for you and works equally well in Eigen 2 and in Eigen 3.
\section FinallyDropAllEigen2Support Finally drop all Eigen 2 support
Stage 40 is the first where it's "comfortable" to stay for a little longer period, since it preserves 100% Eigen 3 compatibility. However, we still encourage you to complete your migration as quickly as possible. While we do run the Eigen 2 test suite against Eigen 3's stage 10 support mode, we can't guarantee the same level of support and quality assurance for Eigen 2 support as we do for Eigen 3 itself, especially not in the long term. \ref Eigen2ToEigen3 "This page" describes a large part of the changes that you may need to perform.
\section ABICompatibility What about ABI compatibility?
It goes as follows:
\li Stage 10 already is ABI compatible with Eigen 3 for the basic (Matrix, Array, SparseMatrix...) types. However, since this stage uses a copy of Eigen 2's Geometry module instead of Eigen 3's own Geometry module, the ABI in the Geometry module is not Eigen 3 compatible.
\li Stage 20 removes the Eigen 3-incompatible Eigen 2 Geometry module (it remains available with eigen2_ prefix). So at this stage, all the identifiers that exist in Eigen 3 have the Eigen 3 ABI (and API).
\li Stage 30 introduces the remaining Eigen 3 identifiers. So at this stage, you have the full Eigen 3 ABI.
\li Stage 40 is no different than Stage 30 in these matters.
*/
}

4
doc/Doxyfile.in
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@ -1584,7 +1584,6 @@ PREDEFINED = EIGEN_EMPTY_STRUCT \
EIGEN_QT_SUPPORT \
EIGEN_STRONG_INLINE=inline \
EIGEN_DEVICE_FUNC= \
"EIGEN2_SUPPORT_STAGE=99" \
"EIGEN_MAKE_CWISE_BINARY_OP(METHOD,FUNCTOR)=template<typename OtherDerived> const CwiseBinaryOp<FUNCTOR<Scalar>, const Derived, const OtherDerived> METHOD(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const;" \
"EIGEN_CWISE_PRODUCT_RETURN_TYPE(LHS,RHS)=CwiseBinaryOp<internal::scalar_product_op<typename LHS::Scalar, typename RHS::Scalar >, const LHS, const RHS>"
@ -1601,8 +1600,7 @@ EXPAND_AS_DEFINED = EIGEN_MAKE_TYPEDEFS \
EIGEN_CWISE_BINOP_RETURN_TYPE \
EIGEN_CURRENT_STORAGE_BASE_CLASS \
EIGEN_MATHFUNC_IMPL \
_EIGEN_GENERIC_PUBLIC_INTERFACE \
EIGEN2_SUPPORT
_EIGEN_GENERIC_PUBLIC_INTERFACE
# If the SKIP_FUNCTION_MACROS tag is set to YES (the default) then
# doxygen's preprocessor will remove all references to function-like macros

11
doc/PreprocessorDirectives.dox
View File

@ -5,7 +5,7 @@ namespace Eigen {
You can control some aspects of %Eigen by defining the preprocessor tokens using \c \#define. These macros
should be defined before any %Eigen headers are included. Often they are best set in the project options.
This page lists the preprocesor tokens recognised by %Eigen.
This page lists the preprocessor tokens recognized by %Eigen.
\eigenAutoToc
@ -18,10 +18,9 @@ one option, and other parts (or libraries that you use) are compiled with anothe
fail to link or exhibit subtle bugs. Nevertheless, these options can be useful for people who know what they
are doing.
- \b EIGEN2_SUPPORT - if defined, enables the Eigen2 compatibility mode. This is meant to ease the transition
of Eigen2 to Eigen3 (see \ref Eigen2ToEigen3). Not defined by default.
- \b EIGEN2_SUPPORT_STAGEnn_xxx (for various values of nn and xxx) - staged migration path from Eigen2 to
Eigen3; see \ref Eigen2SupportModes.
- \b EIGEN2_SUPPORT and \b EIGEN2_SUPPORT_STAGEnn_xxx are disabled starting from the 3.3 release.
Defining one of these will raise a compile-error. If you need to compile Eigen2 code,
<a href="http://eigen.tuxfamily.org/index.php?title=Eigen2">check this site</a>.
- \b EIGEN_DEFAULT_DENSE_INDEX_TYPE - the type for column and row indices in matrices, vectors and array
(DenseBase::Index). Set to \c std::ptrdiff_t by default.
- \b EIGEN_DEFAULT_IO_FORMAT - the IOFormat to use when printing a matrix if no %IOFormat is specified.
@ -55,7 +54,7 @@ run time. However, these assertions do cost time and can thus be turned off.
\section TopicPreprocessorDirectivesPerformance Alignment, vectorization and performance tweaking
- \b EIGEN_MALLOC_ALREADY_ALIGNED - Can be set to 0 or 1 to tell whether default system malloc already
- \b EIGEN_MALLOC_ALREADY_ALIGNED - Can be set to 0 or 1 to tell whether default system \c malloc already
returns aligned buffers. In not defined, then this information is automatically deduced from the compiler
and system preprocessor tokens.
- \b EIGEN_DONT_ALIGN - disables alignment completely. %Eigen will not try to align its objects and does not

18
doc/examples/MatrixBase_cwise_const.cpp
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@ -1,18 +0,0 @@
#define EIGEN2_SUPPORT
#include <Eigen/Core>
#include <iostream>
using namespace Eigen;
using namespace std;
int main()
{
Matrix3i m = Matrix3i::Random();
cout << "Here is the matrix m:" << endl << m << endl;
Matrix3i n = Matrix3i::Random();
cout << "And here is the matrix n:" << endl << n << endl;
cout << "The coefficient-wise product of m and n is:" << endl;
cout << m.cwise() * n << endl;
cout << "Taking the cube of the coefficients of m yields:" << endl;
cout << m.cwise().pow(3) << endl;
}

5
test/CMakeLists.txt
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@ -157,7 +157,6 @@ ei_add_test(vectorization_logic)
ei_add_test(basicstuff)
ei_add_test(linearstructure)
ei_add_test(integer_types)
ei_add_test(cwiseop)
ei_add_test(unalignedcount)
ei_add_test(exceptions)
ei_add_test(redux)
@ -258,8 +257,6 @@ if(QT4_FOUND)
ei_add_test(qtvector "" "${QT_QTCORE_LIBRARY}")
endif(QT4_FOUND)
ei_add_test(eigen2support)
if(UMFPACK_FOUND)
ei_add_test(umfpack_support "" "${UMFPACK_ALL_LIBS}")
endif()
@ -303,7 +300,7 @@ ei_add_property(EIGEN_TESTING_SUMMARY "Sparse lib flags: ${SPARSE_LIBS}\n")
option(EIGEN_TEST_EIGEN2 "Run whole Eigen2 test suite against EIGEN2_SUPPORT" OFF)
if(EIGEN_TEST_EIGEN2)
add_subdirectory(eigen2)
message(WARNING "The Eigen2 test suite has been removed")
endif()

182
test/cwiseop.cpp
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@ -1,182 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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/.
#define EIGEN2_SUPPORT
#define EIGEN_NO_STATIC_ASSERT
#include "main.h"
#include <functional>
#ifdef min
#undef min
#endif
#ifdef max
#undef max
#endif
using namespace std;
template<typename Scalar> struct AddIfNull {
const Scalar operator() (const Scalar a, const Scalar b) const {return a<=1e-3 ? b : a;}
enum { Cost = NumTraits<Scalar>::AddCost };
};
template<typename MatrixType>
typename Eigen::internal::enable_if<!NumTraits<typename MatrixType::Scalar>::IsInteger,typename MatrixType::Scalar>::type
cwiseops_real_only(MatrixType& m1, MatrixType& m2, MatrixType& m3, MatrixType& mones)
{
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
VERIFY_IS_APPROX(m1.cwise() / m2, m1.cwise() * (m2.cwise().inverse()));
m3 = m1.cwise().abs().cwise().sqrt();
VERIFY_IS_APPROX(m3.cwise().square(), m1.cwise().abs());
VERIFY_IS_APPROX(m1.cwise().square().cwise().sqrt(), m1.cwise().abs());
VERIFY_IS_APPROX(m1.cwise().abs().cwise().log().cwise().exp() , m1.cwise().abs());
VERIFY_IS_APPROX(m1.cwise().pow(2), m1.cwise().square());
m3 = (m1.cwise().abs().cwise()<=RealScalar(0.01)).select(mones,m1);
VERIFY_IS_APPROX(m3.cwise().pow(-1), m3.cwise().inverse());
m3 = m1.cwise().abs();
VERIFY_IS_APPROX(m3.cwise().pow(RealScalar(0.5)), m3.cwise().sqrt());
// VERIFY_IS_APPROX(m1.cwise().tan(), m1.cwise().sin().cwise() / m1.cwise().cos());
VERIFY_IS_APPROX(mones, m1.cwise().sin().cwise().square() + m1.cwise().cos().cwise().square());
m3 = m1;
m3.cwise() /= m2;
VERIFY_IS_APPROX(m3, m1.cwise() / m2);
return Scalar(0);
}
template<typename MatrixType>
typename Eigen::internal::enable_if<NumTraits<typename MatrixType::Scalar>::IsInteger,typename MatrixType::Scalar>::type
cwiseops_real_only(MatrixType& , MatrixType& , MatrixType& , MatrixType& )
{
return 0;
}
template<typename MatrixType> void cwiseops(const MatrixType& m)
{
typedef typename MatrixType::Index Index;
typedef typename MatrixType::Scalar Scalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
Index rows = m.rows();
Index cols = m.cols();
MatrixType m1 = MatrixType::Random(rows, cols),
m1bis = m1,
m2 = MatrixType::Random(rows, cols),
m3(rows, cols),
m4(rows, cols),
mzero = MatrixType::Zero(rows, cols),
mones = MatrixType::Ones(rows, cols),
identity = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime>
::Identity(rows, rows);
VectorType vzero = VectorType::Zero(rows),
vones = VectorType::Ones(rows),
v3(rows);
Index r = internal::random<Index>(0, rows-1),
c = internal::random<Index>(0, cols-1);
Scalar s1 = internal::random<Scalar>();
// test Zero, Ones, Constant, and the set* variants
m3 = MatrixType::Constant(rows, cols, s1);
for (int j=0; j<cols; ++j)
for (int i=0; i<rows; ++i)
{
VERIFY_IS_APPROX(mzero(i,j), Scalar(0));
VERIFY_IS_APPROX(mones(i,j), Scalar(1));
VERIFY_IS_APPROX(m3(i,j), s1);
}
VERIFY(mzero.isZero());
VERIFY(mones.isOnes());
VERIFY(m3.isConstant(s1));
VERIFY(identity.isIdentity());
VERIFY_IS_APPROX(m4.setConstant(s1), m3);
VERIFY_IS_APPROX(m4.setConstant(rows,cols,s1), m3);
VERIFY_IS_APPROX(m4.setZero(), mzero);
VERIFY_IS_APPROX(m4.setZero(rows,cols), mzero);
VERIFY_IS_APPROX(m4.setOnes(), mones);
VERIFY_IS_APPROX(m4.setOnes(rows,cols), mones);
m4.fill(s1);
VERIFY_IS_APPROX(m4, m3);
VERIFY_IS_APPROX(v3.setConstant(rows, s1), VectorType::Constant(rows,s1));
VERIFY_IS_APPROX(v3.setZero(rows), vzero);
VERIFY_IS_APPROX(v3.setOnes(rows), vones);
m2 = m2.template binaryExpr<AddIfNull<Scalar> >(mones);
VERIFY_IS_APPROX(m1.cwise().pow(2), m1.cwise().abs2());
VERIFY_IS_APPROX(m1.cwise().pow(2), m1.cwise().square());
VERIFY_IS_APPROX(m1.cwise().pow(3), m1.cwise().cube());
VERIFY_IS_APPROX(m1 + mones, m1.cwise()+Scalar(1));
VERIFY_IS_APPROX(m1 - mones, m1.cwise()-Scalar(1));
m3 = m1; m3.cwise() += 1;
VERIFY_IS_APPROX(m1 + mones, m3);
m3 = m1; m3.cwise() -= 1;
VERIFY_IS_APPROX(m1 - mones, m3);
VERIFY_IS_APPROX(m2, m2.cwise() * mones);
VERIFY_IS_APPROX(m1.cwise() * m2, m2.cwise() * m1);
m3 = m1;
m3.cwise() *= m2;
VERIFY_IS_APPROX(m3, m1.cwise() * m2);
VERIFY_IS_APPROX(mones, m2.cwise()/m2);
// check min
VERIFY_IS_APPROX( m1.cwise().min(m2), m2.cwise().min(m1) );
VERIFY_IS_APPROX( m1.cwise().min(m1+mones), m1 );
VERIFY_IS_APPROX( m1.cwise().min(m1-mones), m1-mones );
// check max
VERIFY_IS_APPROX( m1.cwise().max(m2), m2.cwise().max(m1) );
VERIFY_IS_APPROX( m1.cwise().max(m1-mones), m1 );
VERIFY_IS_APPROX( m1.cwise().max(m1+mones), m1+mones );
VERIFY( (m1.cwise() == m1).all() );
VERIFY( (m1.cwise() != m2).any() );
VERIFY(!(m1.cwise() == (m1+mones)).any() );
if (rows*cols>1)
{
m3 = m1;
m3(r,c) += 1;
VERIFY( (m1.cwise() == m3).any() );
VERIFY( !(m1.cwise() == m3).all() );
}
VERIFY( (m1.cwise().min(m2).cwise() <= m2).all() );
VERIFY( (m1.cwise().max(m2).cwise() >= m2).all() );
VERIFY( (m1.cwise().min(m2).cwise() < (m1+mones)).all() );
VERIFY( (m1.cwise().max(m2).cwise() > (m1-mones)).all() );
VERIFY( (m1.cwise()<m1.unaryExpr(bind2nd(plus<Scalar>(), Scalar(1)))).all() );
VERIFY( !(m1.cwise()<m1bis.unaryExpr(bind2nd(minus<Scalar>(), Scalar(1)))).all() );
VERIFY( !(m1.cwise()>m1bis.unaryExpr(bind2nd(plus<Scalar>(), Scalar(1)))).any() );
cwiseops_real_only(m1, m2, m3, mones);
}
void test_cwiseop()
{
for(int i = 0; i < g_repeat ; i++) {
CALL_SUBTEST_1( cwiseops(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( cwiseops(Matrix4d()) );
CALL_SUBTEST_3( cwiseops(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_4( cwiseops(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_5( cwiseops(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_6( cwiseops(MatrixXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
}
}

65
test/eigen2/CMakeLists.txt
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@ -1,65 +0,0 @@
add_custom_target(eigen2_buildtests)
add_custom_target(eigen2_check COMMAND "ctest -R eigen2")
add_dependencies(eigen2_check eigen2_buildtests)
add_dependencies(buildtests eigen2_buildtests)
add_definitions("-DEIGEN2_SUPPORT_STAGE10_FULL_EIGEN2_API")
# Disable unused warnings for this module
# As EIGEN2 support is deprecated, it is not really worth fixing them
ei_add_cxx_compiler_flag("-Wno-unused-local-typedefs")
ei_add_cxx_compiler_flag("-Wno-unused-but-set-variable")
ei_add_test(eigen2_meta)
ei_add_test(eigen2_sizeof)
ei_add_test(eigen2_dynalloc)
ei_add_test(eigen2_nomalloc)
#ei_add_test(eigen2_first_aligned)
ei_add_test(eigen2_mixingtypes)
#ei_add_test(eigen2_packetmath)
ei_add_test(eigen2_unalignedassert)
#ei_add_test(eigen2_vectorization_logic)
ei_add_test(eigen2_basicstuff)
ei_add_test(eigen2_linearstructure)
ei_add_test(eigen2_cwiseop)
ei_add_test(eigen2_sum)
ei_add_test(eigen2_product_small)
ei_add_test(eigen2_product_large ${EI_OFLAG})
ei_add_test(eigen2_adjoint)
ei_add_test(eigen2_submatrices)
ei_add_test(eigen2_miscmatrices)
ei_add_test(eigen2_commainitializer)
ei_add_test(eigen2_smallvectors)
ei_add_test(eigen2_map)
ei_add_test(eigen2_array)
ei_add_test(eigen2_triangular)
ei_add_test(eigen2_cholesky " " "${GSL_LIBRARIES}")
ei_add_test(eigen2_lu ${EI_OFLAG})
ei_add_test(eigen2_determinant ${EI_OFLAG})
ei_add_test(eigen2_inverse)
ei_add_test(eigen2_qr)
ei_add_test(eigen2_eigensolver " " "${GSL_LIBRARIES}")
ei_add_test(eigen2_svd)
ei_add_test(eigen2_geometry)
ei_add_test(eigen2_geometry_with_eigen2_prefix)
ei_add_test(eigen2_hyperplane)
ei_add_test(eigen2_parametrizedline)
ei_add_test(eigen2_alignedbox)
ei_add_test(eigen2_regression)
ei_add_test(eigen2_stdvector)
ei_add_test(eigen2_newstdvector)
if(QT4_FOUND)
ei_add_test(eigen2_qtvector " " "${QT_QTCORE_LIBRARY}")
endif(QT4_FOUND)
# no support for eigen2 sparse module
# if(NOT EIGEN_DEFAULT_TO_ROW_MAJOR)
# ei_add_test(eigen2_sparse_vector)
# ei_add_test(eigen2_sparse_basic)
# ei_add_test(eigen2_sparse_solvers " " "${SPARSE_LIBS}")
# ei_add_test(eigen2_sparse_product)
# endif()
ei_add_test(eigen2_swap)
ei_add_test(eigen2_visitor)
ei_add_test(eigen2_bug_132)
ei_add_test(eigen2_prec_inverse_4x4 ${EI_OFLAG})

101
test/eigen2/eigen2_adjoint.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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 MatrixType> void adjoint(const MatrixType& m)
{
/* this test covers the following files:
Transpose.h Conjugate.h Dot.h
*/
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> SquareMatrixType;
int rows = m.rows();
int cols = m.cols();
RealScalar largerEps = test_precision<RealScalar>();
if (ei_is_same_type<RealScalar,float>::ret)
largerEps = RealScalar(1e-3f);
MatrixType m1 = MatrixType::Random(rows, cols),
m2 = MatrixType::Random(rows, cols),
m3(rows, cols),
mzero = MatrixType::Zero(rows, cols),
identity = SquareMatrixType::Identity(rows, rows),
square = SquareMatrixType::Random(rows, rows);
VectorType v1 = VectorType::Random(rows),
v2 = VectorType::Random(rows),
v3 = VectorType::Random(rows),
vzero = VectorType::Zero(rows);
Scalar s1 = ei_random<Scalar>(),
s2 = ei_random<Scalar>();
// check basic compatibility of adjoint, transpose, conjugate
VERIFY_IS_APPROX(m1.transpose().conjugate().adjoint(), m1);
VERIFY_IS_APPROX(m1.adjoint().conjugate().transpose(), m1);
// check multiplicative behavior
VERIFY_IS_APPROX((m1.adjoint() * m2).adjoint(), m2.adjoint() * m1);
VERIFY_IS_APPROX((s1 * m1).adjoint(), ei_conj(s1) * m1.adjoint());
// check basic properties of dot, norm, norm2
typedef typename NumTraits<Scalar>::Real RealScalar;
VERIFY(ei_isApprox((s1 * v1 + s2 * v2).eigen2_dot(v3), s1 * v1.eigen2_dot(v3) + s2 * v2.eigen2_dot(v3), largerEps));
VERIFY(ei_isApprox(v3.eigen2_dot(s1 * v1 + s2 * v2), ei_conj(s1)*v3.eigen2_dot(v1)+ei_conj(s2)*v3.eigen2_dot(v2), largerEps));
VERIFY_IS_APPROX(ei_conj(v1.eigen2_dot(v2)), v2.eigen2_dot(v1));
VERIFY_IS_APPROX(ei_real(v1.eigen2_dot(v1)), v1.squaredNorm());
if(NumTraits<Scalar>::HasFloatingPoint)
VERIFY_IS_APPROX(v1.squaredNorm(), v1.norm() * v1.norm());
VERIFY_IS_MUCH_SMALLER_THAN(ei_abs(vzero.eigen2_dot(v1)), static_cast<RealScalar>(1));
if(NumTraits<Scalar>::HasFloatingPoint)
VERIFY_IS_MUCH_SMALLER_THAN(vzero.norm(), static_cast<RealScalar>(1));
// check compatibility of dot and adjoint
VERIFY(ei_isApprox(v1.eigen2_dot(square * v2), (square.adjoint() * v1).eigen2_dot(v2), largerEps));
// like in testBasicStuff, test operator() to check const-qualification
int r = ei_random<int>(0, rows-1),
c = ei_random<int>(0, cols-1);
VERIFY_IS_APPROX(m1.conjugate()(r,c), ei_conj(m1(r,c)));
VERIFY_IS_APPROX(m1.adjoint()(c,r), ei_conj(m1(r,c)));
if(NumTraits<Scalar>::HasFloatingPoint)
{
// check that Random().normalized() works: tricky as the random xpr must be evaluated by
// normalized() in order to produce a consistent result.
VERIFY_IS_APPROX(VectorType::Random(rows).normalized().norm(), RealScalar(1));
}
// check inplace transpose
m3 = m1;
m3.transposeInPlace();
VERIFY_IS_APPROX(m3,m1.transpose());
m3.transposeInPlace();
VERIFY_IS_APPROX(m3,m1);
}
void test_eigen2_adjoint()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( adjoint(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( adjoint(Matrix3d()) );
CALL_SUBTEST_3( adjoint(Matrix4f()) );
CALL_SUBTEST_4( adjoint(MatrixXcf(4, 4)) );
CALL_SUBTEST_5( adjoint(MatrixXi(8, 12)) );
CALL_SUBTEST_6( adjoint(MatrixXf(21, 21)) );
}
// test a large matrix only once
CALL_SUBTEST_7( adjoint(Matrix<float, 100, 100>()) );
}

60
test/eigen2/eigen2_alignedbox.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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"
#include <Eigen/Geometry>
#include <Eigen/LU>
#include <Eigen/QR>
template<typename BoxType> void alignedbox(const BoxType& _box)
{
/* this test covers the following files:
AlignedBox.h
*/
const int dim = _box.dim();
typedef typename BoxType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar, BoxType::AmbientDimAtCompileTime, 1> VectorType;
VectorType p0 = VectorType::Random(dim);
VectorType p1 = VectorType::Random(dim);
RealScalar s1 = ei_random<RealScalar>(0,1);
BoxType b0(dim);
BoxType b1(VectorType::Random(dim),VectorType::Random(dim));
BoxType b2;
b0.extend(p0);
b0.extend(p1);
VERIFY(b0.contains(p0*s1+(Scalar(1)-s1)*p1));
VERIFY(!b0.contains(p0 + (1+s1)*(p1-p0)));
(b2 = b0).extend(b1);
VERIFY(b2.contains(b0));
VERIFY(b2.contains(b1));
VERIFY_IS_APPROX(b2.clamp(b0), b0);
// casting
const int Dim = BoxType::AmbientDimAtCompileTime;
typedef typename GetDifferentType<Scalar>::type OtherScalar;
AlignedBox<OtherScalar,Dim> hp1f = b0.template cast<OtherScalar>();
VERIFY_IS_APPROX(hp1f.template cast<Scalar>(),b0);
AlignedBox<Scalar,Dim> hp1d = b0.template cast<Scalar>();
VERIFY_IS_APPROX(hp1d.template cast<Scalar>(),b0);
}
void test_eigen2_alignedbox()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( alignedbox(AlignedBox<float,2>()) );
CALL_SUBTEST_2( alignedbox(AlignedBox<float,3>()) );
CALL_SUBTEST_3( alignedbox(AlignedBox<double,4>()) );
}
}

142
test/eigen2/eigen2_array.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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"
#include <Eigen/Array>
template<typename MatrixType> void array(const MatrixType& m)
{
/* this test covers the following files:
Array.cpp
*/
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
int rows = m.rows();
int cols = m.cols();
MatrixType m1 = MatrixType::Random(rows, cols),
m2 = MatrixType::Random(rows, cols),
m3(rows, cols);
Scalar s1 = ei_random<Scalar>(),
s2 = ei_random<Scalar>();
// scalar addition
VERIFY_IS_APPROX(m1.cwise() + s1, s1 + m1.cwise());
VERIFY_IS_APPROX(m1.cwise() + s1, MatrixType::Constant(rows,cols,s1) + m1);
VERIFY_IS_APPROX((m1*Scalar(2)).cwise() - s2, (m1+m1) - MatrixType::Constant(rows,cols,s2) );
m3 = m1;
m3.cwise() += s2;
VERIFY_IS_APPROX(m3, m1.cwise() + s2);
m3 = m1;
m3.cwise() -= s1;
VERIFY_IS_APPROX(m3, m1.cwise() - s1);
// reductions
VERIFY_IS_APPROX(m1.colwise().sum().sum(), m1.sum());
VERIFY_IS_APPROX(m1.rowwise().sum().sum(), m1.sum());
if (!ei_isApprox(m1.sum(), (m1+m2).sum()))
VERIFY_IS_NOT_APPROX(((m1+m2).rowwise().sum()).sum(), m1.sum());
VERIFY_IS_APPROX(m1.colwise().sum(), m1.colwise().redux(internal::scalar_sum_op<Scalar>()));
}
template<typename MatrixType> void comparisons(const MatrixType& m)
{
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
int rows = m.rows();
int cols = m.cols();
int r = ei_random<int>(0, rows-1),
c = ei_random<int>(0, cols-1);
MatrixType m1 = MatrixType::Random(rows, cols),
m2 = MatrixType::Random(rows, cols),
m3(rows, cols);
VERIFY(((m1.cwise() + Scalar(1)).cwise() > m1).all());
VERIFY(((m1.cwise() - Scalar(1)).cwise() < m1).all());
if (rows*cols>1)
{
m3 = m1;
m3(r,c) += 1;
VERIFY(! (m1.cwise() < m3).all() );
VERIFY(! (m1.cwise() > m3).all() );
}
// comparisons to scalar
VERIFY( (m1.cwise() != (m1(r,c)+1) ).any() );
VERIFY( (m1.cwise() > (m1(r,c)-1) ).any() );
VERIFY( (m1.cwise() < (m1(r,c)+1) ).any() );
VERIFY( (m1.cwise() == m1(r,c) ).any() );
// test Select
VERIFY_IS_APPROX( (m1.cwise()<m2).select(m1,m2), m1.cwise().min(m2) );
VERIFY_IS_APPROX( (m1.cwise()>m2).select(m1,m2), m1.cwise().max(m2) );
Scalar mid = (m1.cwise().abs().minCoeff() + m1.cwise().abs().maxCoeff())/Scalar(2);
for (int j=0; j<cols; ++j)
for (int i=0; i<rows; ++i)
m3(i,j) = ei_abs(m1(i,j))<mid ? 0 : m1(i,j);
VERIFY_IS_APPROX( (m1.cwise().abs().cwise()<MatrixType::Constant(rows,cols,mid))
.select(MatrixType::Zero(rows,cols),m1), m3);
// shorter versions:
VERIFY_IS_APPROX( (m1.cwise().abs().cwise()<MatrixType::Constant(rows,cols,mid))
.select(0,m1), m3);
VERIFY_IS_APPROX( (m1.cwise().abs().cwise()>=MatrixType::Constant(rows,cols,mid))
.select(m1,0), m3);
// even shorter version:
VERIFY_IS_APPROX( (m1.cwise().abs().cwise()<mid).select(0,m1), m3);
// count
VERIFY(((m1.cwise().abs().cwise()+1).cwise()>RealScalar(0.1)).count() == rows*cols);
VERIFY_IS_APPROX(((m1.cwise().abs().cwise()+1).cwise()>RealScalar(0.1)).colwise().count().template cast<int>(), RowVectorXi::Constant(cols,rows));
VERIFY_IS_APPROX(((m1.cwise().abs().cwise()+1).cwise()>RealScalar(0.1)).rowwise().count().template cast<int>(), VectorXi::Constant(rows, cols));
}
template<typename VectorType> void lpNorm(const VectorType& v)
{
VectorType u = VectorType::Random(v.size());
VERIFY_IS_APPROX(u.template lpNorm<Infinity>(), u.cwise().abs().maxCoeff());
VERIFY_IS_APPROX(u.template lpNorm<1>(), u.cwise().abs().sum());
VERIFY_IS_APPROX(u.template lpNorm<2>(), ei_sqrt(u.cwise().abs().cwise().square().sum()));
VERIFY_IS_APPROX(ei_pow(u.template lpNorm<5>(), typename VectorType::RealScalar(5)), u.cwise().abs().cwise().pow(5).sum());
}
void test_eigen2_array()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( array(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( array(Matrix2f()) );
CALL_SUBTEST_3( array(Matrix4d()) );
CALL_SUBTEST_4( array(MatrixXcf(3, 3)) );
CALL_SUBTEST_5( array(MatrixXf(8, 12)) );
CALL_SUBTEST_6( array(MatrixXi(8, 12)) );
}
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( comparisons(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( comparisons(Matrix2f()) );
CALL_SUBTEST_3( comparisons(Matrix4d()) );
CALL_SUBTEST_5( comparisons(MatrixXf(8, 12)) );
CALL_SUBTEST_6( comparisons(MatrixXi(8, 12)) );
}
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( lpNorm(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( lpNorm(Vector2f()) );
CALL_SUBTEST_3( lpNorm(Vector3d()) );
CALL_SUBTEST_4( lpNorm(Vector4f()) );
CALL_SUBTEST_5( lpNorm(VectorXf(16)) );
CALL_SUBTEST_7( lpNorm(VectorXcd(10)) );
}
}

108
test/eigen2/eigen2_basicstuff.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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 MatrixType> void basicStuff(const MatrixType& m)
{
typedef typename MatrixType::Scalar Scalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
int rows = m.rows();
int cols = m.cols();
// this test relies a lot on Random.h, and there's not much more that we can do
// to test it, hence I consider that we will have tested Random.h
MatrixType m1 = MatrixType::Random(rows, cols),
m2 = MatrixType::Random(rows, cols),
m3(rows, cols),
mzero = MatrixType::Zero(rows, cols),
identity = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime>
::Identity(rows, rows),
square = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime>::Random(rows, rows);
VectorType v1 = VectorType::Random(rows),
v2 = VectorType::Random(rows),
vzero = VectorType::Zero(rows);
Scalar x = ei_random<Scalar>();
int r = ei_random<int>(0, rows-1),
c = ei_random<int>(0, cols-1);
m1.coeffRef(r,c) = x;
VERIFY_IS_APPROX(x, m1.coeff(r,c));
m1(r,c) = x;
VERIFY_IS_APPROX(x, m1(r,c));
v1.coeffRef(r) = x;
VERIFY_IS_APPROX(x, v1.coeff(r));
v1(r) = x;
VERIFY_IS_APPROX(x, v1(r));
v1[r] = x;
VERIFY_IS_APPROX(x, v1[r]);
VERIFY_IS_APPROX( v1, v1);
VERIFY_IS_NOT_APPROX( v1, 2*v1);
VERIFY_IS_MUCH_SMALLER_THAN( vzero, v1);
if(NumTraits<Scalar>::HasFloatingPoint)
VERIFY_IS_MUCH_SMALLER_THAN( vzero, v1.norm());
VERIFY_IS_NOT_MUCH_SMALLER_THAN(v1, v1);
VERIFY_IS_APPROX( vzero, v1-v1);
VERIFY_IS_APPROX( m1, m1);
VERIFY_IS_NOT_APPROX( m1, 2*m1);
VERIFY_IS_MUCH_SMALLER_THAN( mzero, m1);
VERIFY_IS_NOT_MUCH_SMALLER_THAN(m1, m1);
VERIFY_IS_APPROX( mzero, m1-m1);
// always test operator() on each read-only expression class,
// in order to check const-qualifiers.
// indeed, if an expression class (here Zero) is meant to be read-only,
// hence has no _write() method, the corresponding MatrixBase method (here zero())
// should return a const-qualified object so that it is the const-qualified
// operator() that gets called, which in turn calls _read().
VERIFY_IS_MUCH_SMALLER_THAN(MatrixType::Zero(rows,cols)(r,c), static_cast<Scalar>(1));
// now test copying a row-vector into a (column-)vector and conversely.
square.col(r) = square.row(r).eval();
Matrix<Scalar, 1, MatrixType::RowsAtCompileTime> rv(rows);
Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> cv(rows);
rv = square.row(r);
cv = square.col(r);
VERIFY_IS_APPROX(rv, cv.transpose());
if(cols!=1 && rows!=1 && MatrixType::SizeAtCompileTime!=Dynamic)
{
VERIFY_RAISES_ASSERT(m1 = (m2.block(0,0, rows-1, cols-1)));
}
VERIFY_IS_APPROX(m3 = m1,m1);
MatrixType m4;
VERIFY_IS_APPROX(m4 = m1,m1);
// test swap
m3 = m1;
m1.swap(m2);
VERIFY_IS_APPROX(m3, m2);
if(rows*cols>=3)
{
VERIFY_IS_NOT_APPROX(m3, m1);
}
}
void test_eigen2_basicstuff()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( basicStuff(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( basicStuff(Matrix4d()) );
CALL_SUBTEST_3( basicStuff(MatrixXcf(3, 3)) );
CALL_SUBTEST_4( basicStuff(MatrixXi(8, 12)) );
CALL_SUBTEST_5( basicStuff(MatrixXcd(20, 20)) );
CALL_SUBTEST_6( basicStuff(Matrix<float, 100, 100>()) );
CALL_SUBTEST_7( basicStuff(Matrix<long double,Dynamic,Dynamic>(10,10)) );
}
}

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test/eigen2/eigen2_bug_132.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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"
void test_eigen2_bug_132() {
int size = 100;
MatrixXd A(size, size);
VectorXd b(size), c(size);
{
VectorXd y = A.transpose() * (b-c); // bug 132: infinite recursion in coeffRef
VectorXd z = (b-c).transpose() * A; // bug 132: infinite recursion in coeffRef
}
// the following ones weren't failing, but let's include them for completeness:
{
VectorXd y = A * (b-c);
VectorXd z = (b-c).transpose() * A.transpose();
}
}

113
test/eigen2/eigen2_cholesky.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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/.
#define EIGEN_NO_ASSERTION_CHECKING
#include "main.h"
#include <Eigen/Cholesky>
#include <Eigen/LU>
#ifdef HAS_GSL
#include "gsl_helper.h"
#endif
template<typename MatrixType> void cholesky(const MatrixType& m)
{
/* this test covers the following files:
LLT.h LDLT.h
*/
int rows = m.rows();
int cols = m.cols();
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> SquareMatrixType;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
MatrixType a0 = MatrixType::Random(rows,cols);
VectorType vecB = VectorType::Random(rows), vecX(rows);
MatrixType matB = MatrixType::Random(rows,cols), matX(rows,cols);
SquareMatrixType symm = a0 * a0.adjoint();
// let's make sure the matrix is not singular or near singular
MatrixType a1 = MatrixType::Random(rows,cols);
symm += a1 * a1.adjoint();
#ifdef HAS_GSL
if (ei_is_same_type<RealScalar,double>::ret)
{
typedef GslTraits<Scalar> Gsl;
typename Gsl::Matrix gMatA=0, gSymm=0;
typename Gsl::Vector gVecB=0, gVecX=0;
convert<MatrixType>(symm, gSymm);
convert<MatrixType>(symm, gMatA);
convert<VectorType>(vecB, gVecB);
convert<VectorType>(vecB, gVecX);
Gsl::cholesky(gMatA);
Gsl::cholesky_solve(gMatA, gVecB, gVecX);
VectorType vecX(rows), _vecX, _vecB;
convert(gVecX, _vecX);
symm.llt().solve(vecB, &vecX);
Gsl::prod(gSymm, gVecX, gVecB);
convert(gVecB, _vecB);
// test gsl itself !
VERIFY_IS_APPROX(vecB, _vecB);
VERIFY_IS_APPROX(vecX, _vecX);
Gsl::free(gMatA);
Gsl::free(gSymm);
Gsl::free(gVecB);
Gsl::free(gVecX);
}
#endif
{
LDLT<SquareMatrixType> ldlt(symm);
VERIFY(ldlt.isPositiveDefinite());
// in eigen3, LDLT is pivoting
//VERIFY_IS_APPROX(symm, ldlt.matrixL() * ldlt.vectorD().asDiagonal() * ldlt.matrixL().adjoint());
ldlt.solve(vecB, &vecX);
VERIFY_IS_APPROX(symm * vecX, vecB);
ldlt.solve(matB, &matX);
VERIFY_IS_APPROX(symm * matX, matB);
}
{
LLT<SquareMatrixType> chol(symm);
VERIFY(chol.isPositiveDefinite());
VERIFY_IS_APPROX(symm, chol.matrixL() * chol.matrixL().adjoint());
chol.solve(vecB, &vecX);
VERIFY_IS_APPROX(symm * vecX, vecB);
chol.solve(matB, &matX);
VERIFY_IS_APPROX(symm * matX, matB);
}
#if 0 // cholesky is not rank-revealing anyway
// test isPositiveDefinite on non definite matrix
if (rows>4)
{
SquareMatrixType symm = a0.block(0,0,rows,cols-4) * a0.block(0,0,rows,cols-4).adjoint();
LLT<SquareMatrixType> chol(symm);
VERIFY(!chol.isPositiveDefinite());
LDLT<SquareMatrixType> cholnosqrt(symm);
VERIFY(!cholnosqrt.isPositiveDefinite());
}
#endif
}
void test_eigen2_cholesky()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( cholesky(Matrix<double,1,1>()) );
CALL_SUBTEST_2( cholesky(Matrix2d()) );
CALL_SUBTEST_3( cholesky(Matrix3f()) );
CALL_SUBTEST_4( cholesky(Matrix4d()) );
CALL_SUBTEST_5( cholesky(MatrixXcd(7,7)) );
CALL_SUBTEST_6( cholesky(MatrixXf(17,17)) );
CALL_SUBTEST_7( cholesky(MatrixXd(33,33)) );
}
}

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test/eigen2/eigen2_commainitializer.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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"
void test_eigen2_commainitializer()
{
Matrix3d m3;
Matrix4d m4;
VERIFY_RAISES_ASSERT( (m3 << 1, 2, 3, 4, 5, 6, 7, 8) );
#ifndef _MSC_VER
VERIFY_RAISES_ASSERT( (m3 << 1, 2, 3, 4, 5, 6, 7, 8, 9, 10) );
#endif
double data[] = {1, 2, 3, 4, 5, 6, 7, 8, 9};
Matrix3d ref = Map<Matrix<double,3,3,RowMajor> >(data);
m3 = Matrix3d::Random();
m3 << 1, 2, 3, 4, 5, 6, 7, 8, 9;
VERIFY_IS_APPROX(m3, ref );
Vector3d vec[3];
vec[0] << 1, 4, 7;
vec[1] << 2, 5, 8;
vec[2] << 3, 6, 9;
m3 = Matrix3d::Random();
m3 << vec[0], vec[1], vec[2];
VERIFY_IS_APPROX(m3, ref);
vec[0] << 1, 2, 3;
vec[1] << 4, 5, 6;
vec[2] << 7, 8, 9;
m3 = Matrix3d::Random();
m3 << vec[0].transpose(),
4, 5, 6,
vec[2].transpose();
VERIFY_IS_APPROX(m3, ref);
}

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test/eigen2/eigen2_cwiseop.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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"
#include <functional>
#include <Eigen/Array>
using namespace std;
template<typename Scalar> struct AddIfNull {
const Scalar operator() (const Scalar a, const Scalar b) const {return a<=1e-3 ? b : a;}
enum { Cost = NumTraits<Scalar>::AddCost };
};
template<typename MatrixType> void cwiseops(const MatrixType& m)
{
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
int rows = m.rows();
int cols = m.cols();
MatrixType m1 = MatrixType::Random(rows, cols),
m2 = MatrixType::Random(rows, cols),
m3(rows, cols),
m4(rows, cols),
mzero = MatrixType::Zero(rows, cols),
mones = MatrixType::Ones(rows, cols),
identity = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime>
::Identity(rows, rows),
square = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime>::Random(rows, rows);
VectorType v1 = VectorType::Random(rows),
v2 = VectorType::Random(rows),
vzero = VectorType::Zero(rows),
vones = VectorType::Ones(rows),
v3(rows);
int r = ei_random<int>(0, rows-1),
c = ei_random<int>(0, cols-1);
Scalar s1 = ei_random<Scalar>();
// test Zero, Ones, Constant, and the set* variants
m3 = MatrixType::Constant(rows, cols, s1);
for (int j=0; j<cols; ++j)
for (int i=0; i<rows; ++i)
{
VERIFY_IS_APPROX(mzero(i,j), Scalar(0));
VERIFY_IS_APPROX(mones(i,j), Scalar(1));
VERIFY_IS_APPROX(m3(i,j), s1);
}
VERIFY(mzero.isZero());
VERIFY(mones.isOnes());
VERIFY(m3.isConstant(s1));
VERIFY(identity.isIdentity());
VERIFY_IS_APPROX(m4.setConstant(s1), m3);
VERIFY_IS_APPROX(m4.setConstant(rows,cols,s1), m3);
VERIFY_IS_APPROX(m4.setZero(), mzero);
VERIFY_IS_APPROX(m4.setZero(rows,cols), mzero);
VERIFY_IS_APPROX(m4.setOnes(), mones);
VERIFY_IS_APPROX(m4.setOnes(rows,cols), mones);
m4.fill(s1);
VERIFY_IS_APPROX(m4, m3);
VERIFY_IS_APPROX(v3.setConstant(rows, s1), VectorType::Constant(rows,s1));
VERIFY_IS_APPROX(v3.setZero(rows), vzero);
VERIFY_IS_APPROX(v3.setOnes(rows), vones);
m2 = m2.template binaryExpr<AddIfNull<Scalar> >(mones);
VERIFY_IS_APPROX(m1.cwise().pow(2), m1.cwise().abs2());
VERIFY_IS_APPROX(m1.cwise().pow(2), m1.cwise().square());
VERIFY_IS_APPROX(m1.cwise().pow(3), m1.cwise().cube());
VERIFY_IS_APPROX(m1 + mones, m1.cwise()+Scalar(1));
VERIFY_IS_APPROX(m1 - mones, m1.cwise()-Scalar(1));
m3 = m1; m3.cwise() += 1;
VERIFY_IS_APPROX(m1 + mones, m3);
m3 = m1; m3.cwise() -= 1;
VERIFY_IS_APPROX(m1 - mones, m3);
VERIFY_IS_APPROX(m2, m2.cwise() * mones);
VERIFY_IS_APPROX(m1.cwise() * m2, m2.cwise() * m1);
m3 = m1;
m3.cwise() *= m2;
VERIFY_IS_APPROX(m3, m1.cwise() * m2);
VERIFY_IS_APPROX(mones, m2.cwise()/m2);
if(NumTraits<Scalar>::HasFloatingPoint)
{
VERIFY_IS_APPROX(m1.cwise() / m2, m1.cwise() * (m2.cwise().inverse()));
m3 = m1.cwise().abs().cwise().sqrt();
VERIFY_IS_APPROX(m3.cwise().square(), m1.cwise().abs());
VERIFY_IS_APPROX(m1.cwise().square().cwise().sqrt(), m1.cwise().abs());
VERIFY_IS_APPROX(m1.cwise().abs().cwise().log().cwise().exp() , m1.cwise().abs());
VERIFY_IS_APPROX(m1.cwise().pow(2), m1.cwise().square());
m3 = (m1.cwise().abs().cwise()<=RealScalar(0.01)).select(mones,m1);
VERIFY_IS_APPROX(m3.cwise().pow(-1), m3.cwise().inverse());
m3 = m1.cwise().abs();
VERIFY_IS_APPROX(m3.cwise().pow(RealScalar(0.5)), m3.cwise().sqrt());
// VERIFY_IS_APPROX(m1.cwise().tan(), m1.cwise().sin().cwise() / m1.cwise().cos());
VERIFY_IS_APPROX(mones, m1.cwise().sin().cwise().square() + m1.cwise().cos().cwise().square());
m3 = m1;
m3.cwise() /= m2;
VERIFY_IS_APPROX(m3, m1.cwise() / m2);
}
// check min
VERIFY_IS_APPROX( m1.cwise().min(m2), m2.cwise().min(m1) );
VERIFY_IS_APPROX( m1.cwise().min(m1+mones), m1 );
VERIFY_IS_APPROX( m1.cwise().min(m1-mones), m1-mones );
// check max
VERIFY_IS_APPROX( m1.cwise().max(m2), m2.cwise().max(m1) );
VERIFY_IS_APPROX( m1.cwise().max(m1-mones), m1 );
VERIFY_IS_APPROX( m1.cwise().max(m1+mones), m1+mones );
VERIFY( (m1.cwise() == m1).all() );
VERIFY( (m1.cwise() != m2).any() );
VERIFY(!(m1.cwise() == (m1+mones)).any() );
if (rows*cols>1)
{
m3 = m1;
m3(r,c) += 1;
VERIFY( (m1.cwise() == m3).any() );
VERIFY( !(m1.cwise() == m3).all() );
}
VERIFY( (m1.cwise().min(m2).cwise() <= m2).all() );
VERIFY( (m1.cwise().max(m2).cwise() >= m2).all() );
VERIFY( (m1.cwise().min(m2).cwise() < (m1+mones)).all() );
VERIFY( (m1.cwise().max(m2).cwise() > (m1-mones)).all() );
VERIFY( (m1.cwise()<m1.unaryExpr(bind2nd(plus<Scalar>(), Scalar(1)))).all() );
VERIFY( !(m1.cwise()<m1.unaryExpr(bind2nd(minus<Scalar>(), Scalar(1)))).all() );
VERIFY( !(m1.cwise()>m1.unaryExpr(bind2nd(plus<Scalar>(), Scalar(1)))).any() );
}
void test_eigen2_cwiseop()
{
for(int i = 0; i < g_repeat ; i++) {
CALL_SUBTEST_1( cwiseops(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( cwiseops(Matrix4d()) );
CALL_SUBTEST_3( cwiseops(MatrixXf(3, 3)) );
CALL_SUBTEST_3( cwiseops(MatrixXf(22, 22)) );
CALL_SUBTEST_4( cwiseops(MatrixXi(8, 12)) );
CALL_SUBTEST_5( cwiseops(MatrixXd(20, 20)) );
}
}

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test/eigen2/eigen2_determinant.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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"
#include <Eigen/LU>
template<typename MatrixType> void determinant(const MatrixType& m)
{
/* this test covers the following files:
Determinant.h
*/
int size = m.rows();
MatrixType m1(size, size), m2(size, size);
m1.setRandom();
m2.setRandom();
typedef typename MatrixType::Scalar Scalar;
Scalar x = ei_random<Scalar>();
VERIFY_IS_APPROX(MatrixType::Identity(size, size).determinant(), Scalar(1));
VERIFY_IS_APPROX((m1*m2).determinant(), m1.determinant() * m2.determinant());
if(size==1) return;
int i = ei_random<int>(0, size-1);
int j;
do {
j = ei_random<int>(0, size-1);
} while(j==i);
m2 = m1;
m2.row(i).swap(m2.row(j));
VERIFY_IS_APPROX(m2.determinant(), -m1.determinant());
m2 = m1;
m2.col(i).swap(m2.col(j));
VERIFY_IS_APPROX(m2.determinant(), -m1.determinant());
VERIFY_IS_APPROX(m2.determinant(), m2.transpose().determinant());
VERIFY_IS_APPROX(ei_conj(m2.determinant()), m2.adjoint().determinant());
m2 = m1;
m2.row(i) += x*m2.row(j);
VERIFY_IS_APPROX(m2.determinant(), m1.determinant());
m2 = m1;
m2.row(i) *= x;
VERIFY_IS_APPROX(m2.determinant(), m1.determinant() * x);
}
void test_eigen2_determinant()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( determinant(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( determinant(Matrix<double, 2, 2>()) );
CALL_SUBTEST_3( determinant(Matrix<double, 3, 3>()) );
CALL_SUBTEST_4( determinant(Matrix<double, 4, 4>()) );
CALL_SUBTEST_5( determinant(Matrix<std::complex<double>, 10, 10>()) );
CALL_SUBTEST_6( determinant(MatrixXd(20, 20)) );
}
CALL_SUBTEST_6( determinant(MatrixXd(200, 200)) );
}

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test/eigen2/eigen2_dynalloc.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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"
#if EIGEN_ARCH_WANTS_ALIGNMENT
#define ALIGNMENT 16
#else
#define ALIGNMENT 1
#endif
void check_handmade_aligned_malloc()
{
for(int i = 1; i < 1000; i++)
{
char *p = (char*)ei_handmade_aligned_malloc(i);
VERIFY(std::size_t(p)%ALIGNMENT==0);
// if the buffer is wrongly allocated this will give a bad write --> check with valgrind
for(int j = 0; j < i; j++) p[j]=0;
ei_handmade_aligned_free(p);
}
}
void check_aligned_malloc()
{
for(int i = 1; i < 1000; i++)
{
char *p = (char*)ei_aligned_malloc(i);
VERIFY(std::size_t(p)%ALIGNMENT==0);
// if the buffer is wrongly allocated this will give a bad write --> check with valgrind
for(int j = 0; j < i; j++) p[j]=0;
ei_aligned_free(p);
}
}
void check_aligned_new()
{
for(int i = 1; i < 1000; i++)
{
float *p = ei_aligned_new<float>(i);
VERIFY(std::size_t(p)%ALIGNMENT==0);
// if the buffer is wrongly allocated this will give a bad write --> check with valgrind
for(int j = 0; j < i; j++) p[j]=0;
ei_aligned_delete(p,i);
}
}
void check_aligned_stack_alloc()
{
for(int i = 1; i < 1000; i++)
{
ei_declare_aligned_stack_constructed_variable(float, p, i, 0);
VERIFY(std::size_t(p)%ALIGNMENT==0);
// if the buffer is wrongly allocated this will give a bad write --> check with valgrind
for(int j = 0; j < i; j++) p[j]=0;
}
}
// test compilation with both a struct and a class...
struct MyStruct
{
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
char dummychar;
Vector4f avec;
};
class MyClassA
{
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
char dummychar;
Vector4f avec;
};
template<typename T> void check_dynaligned()
{
T* obj = new T;
VERIFY(std::size_t(obj)%ALIGNMENT==0);
delete obj;
}
void test_eigen2_dynalloc()
{
// low level dynamic memory allocation
CALL_SUBTEST(check_handmade_aligned_malloc());
CALL_SUBTEST(check_aligned_malloc());
CALL_SUBTEST(check_aligned_new());
CALL_SUBTEST(check_aligned_stack_alloc());
for (int i=0; i<g_repeat*100; ++i)
{
CALL_SUBTEST( check_dynaligned<Vector4f>() );
CALL_SUBTEST( check_dynaligned<Vector2d>() );
CALL_SUBTEST( check_dynaligned<Matrix4f>() );
CALL_SUBTEST( check_dynaligned<Vector4d>() );
CALL_SUBTEST( check_dynaligned<Vector4i>() );
}
// check static allocation, who knows ?
{
MyStruct foo0; VERIFY(std::size_t(foo0.avec.data())%ALIGNMENT==0);
MyClassA fooA; VERIFY(std::size_t(fooA.avec.data())%ALIGNMENT==0);
}
// dynamic allocation, single object
for (int i=0; i<g_repeat*100; ++i)
{
MyStruct *foo0 = new MyStruct(); VERIFY(std::size_t(foo0->avec.data())%ALIGNMENT==0);
MyClassA *fooA = new MyClassA(); VERIFY(std::size_t(fooA->avec.data())%ALIGNMENT==0);
delete foo0;
delete fooA;
}
// dynamic allocation, array
const int N = 10;
for (int i=0; i<g_repeat*100; ++i)
{
MyStruct *foo0 = new MyStruct[N]; VERIFY(std::size_t(foo0->avec.data())%ALIGNMENT==0);
MyClassA *fooA = new MyClassA[N]; VERIFY(std::size_t(fooA->avec.data())%ALIGNMENT==0);
delete[] foo0;
delete[] fooA;
}
}

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test/eigen2/eigen2_eigensolver.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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"
#include <Eigen/QR>
#ifdef HAS_GSL
#include "gsl_helper.h"
#endif
template<typename MatrixType> void selfadjointeigensolver(const MatrixType& m)
{
/* this test covers the following files:
EigenSolver.h, SelfAdjointEigenSolver.h (and indirectly: Tridiagonalization.h)
*/
int rows = m.rows();
int cols = m.cols();
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
typedef Matrix<RealScalar, MatrixType::RowsAtCompileTime, 1> RealVectorType;
typedef typename std::complex<typename NumTraits<typename MatrixType::Scalar>::Real> Complex;
RealScalar largerEps = 10*test_precision<RealScalar>();
MatrixType a = MatrixType::Random(rows,cols);
MatrixType a1 = MatrixType::Random(rows,cols);
MatrixType symmA = a.adjoint() * a + a1.adjoint() * a1;
MatrixType b = MatrixType::Random(rows,cols);
MatrixType b1 = MatrixType::Random(rows,cols);
MatrixType symmB = b.adjoint() * b + b1.adjoint() * b1;
SelfAdjointEigenSolver<MatrixType> eiSymm(symmA);
// generalized eigen pb
SelfAdjointEigenSolver<MatrixType> eiSymmGen(symmA, symmB);
#ifdef HAS_GSL
if (ei_is_same_type<RealScalar,double>::ret)
{
typedef GslTraits<Scalar> Gsl;
typename Gsl::Matrix gEvec=0, gSymmA=0, gSymmB=0;
typename GslTraits<RealScalar>::Vector gEval=0;
RealVectorType _eval;
MatrixType _evec;
convert<MatrixType>(symmA, gSymmA);
convert<MatrixType>(symmB, gSymmB);
convert<MatrixType>(symmA, gEvec);
gEval = GslTraits<RealScalar>::createVector(rows);
Gsl::eigen_symm(gSymmA, gEval, gEvec);
convert(gEval, _eval);
convert(gEvec, _evec);
// test gsl itself !
VERIFY((symmA * _evec).isApprox(_evec * _eval.asDiagonal(), largerEps));
// compare with eigen
VERIFY_IS_APPROX(_eval, eiSymm.eigenvalues());
VERIFY_IS_APPROX(_evec.cwise().abs(), eiSymm.eigenvectors().cwise().abs());
// generalized pb
Gsl::eigen_symm_gen(gSymmA, gSymmB, gEval, gEvec);
convert(gEval, _eval);
convert(gEvec, _evec);
// test GSL itself:
VERIFY((symmA * _evec).isApprox(symmB * (_evec * _eval.asDiagonal()), largerEps));
// compare with eigen
MatrixType normalized_eivec = eiSymmGen.eigenvectors()*eiSymmGen.eigenvectors().colwise().norm().asDiagonal().inverse();
VERIFY_IS_APPROX(_eval, eiSymmGen.eigenvalues());
VERIFY_IS_APPROX(_evec.cwiseAbs(), normalized_eivec.cwiseAbs());
Gsl::free(gSymmA);
Gsl::free(gSymmB);
GslTraits<RealScalar>::free(gEval);
Gsl::free(gEvec);
}
#endif
VERIFY((symmA * eiSymm.eigenvectors()).isApprox(
eiSymm.eigenvectors() * eiSymm.eigenvalues().asDiagonal(), largerEps));
// generalized eigen problem Ax = lBx
VERIFY((symmA * eiSymmGen.eigenvectors()).isApprox(
symmB * (eiSymmGen.eigenvectors() * eiSymmGen.eigenvalues().asDiagonal()), largerEps));
MatrixType sqrtSymmA = eiSymm.operatorSqrt();
VERIFY_IS_APPROX(symmA, sqrtSymmA*sqrtSymmA);
VERIFY_IS_APPROX(sqrtSymmA, symmA*eiSymm.operatorInverseSqrt());
}
template<typename MatrixType> void eigensolver(const MatrixType& m)
{
/* this test covers the following files:
EigenSolver.h
*/
int rows = m.rows();
int cols = m.cols();
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
typedef Matrix<RealScalar, MatrixType::RowsAtCompileTime, 1> RealVectorType;
typedef typename std::complex<typename NumTraits<typename MatrixType::Scalar>::Real> Complex;
// RealScalar largerEps = 10*test_precision<RealScalar>();
MatrixType a = MatrixType::Random(rows,cols);
MatrixType a1 = MatrixType::Random(rows,cols);
MatrixType symmA = a.adjoint() * a + a1.adjoint() * a1;
EigenSolver<MatrixType> ei0(symmA);
VERIFY_IS_APPROX(symmA * ei0.pseudoEigenvectors(), ei0.pseudoEigenvectors() * ei0.pseudoEigenvalueMatrix());
VERIFY_IS_APPROX((symmA.template cast<Complex>()) * (ei0.pseudoEigenvectors().template cast<Complex>()),
(ei0.pseudoEigenvectors().template cast<Complex>()) * (ei0.eigenvalues().asDiagonal()));
EigenSolver<MatrixType> ei1(a);
VERIFY_IS_APPROX(a * ei1.pseudoEigenvectors(), ei1.pseudoEigenvectors() * ei1.pseudoEigenvalueMatrix());
VERIFY_IS_APPROX(a.template cast<Complex>() * ei1.eigenvectors(),
ei1.eigenvectors() * ei1.eigenvalues().asDiagonal());
}
void test_eigen2_eigensolver()
{
for(int i = 0; i < g_repeat; i++) {
// very important to test a 3x3 matrix since we provide a special path for it
CALL_SUBTEST_1( selfadjointeigensolver(Matrix3f()) );
CALL_SUBTEST_2( selfadjointeigensolver(Matrix4d()) );
CALL_SUBTEST_3( selfadjointeigensolver(MatrixXf(7,7)) );
CALL_SUBTEST_4( selfadjointeigensolver(MatrixXcd(5,5)) );
CALL_SUBTEST_5( selfadjointeigensolver(MatrixXd(19,19)) );
CALL_SUBTEST_6( eigensolver(Matrix4f()) );
CALL_SUBTEST_5( eigensolver(MatrixXd(17,17)) );
}
}

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test/eigen2/eigen2_first_aligned.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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 Scalar>
void test_eigen2_first_aligned_helper(Scalar *array, int size)
{
const int packet_size = sizeof(Scalar) * ei_packet_traits<Scalar>::size;
VERIFY(((std::size_t(array) + sizeof(Scalar) * ei_alignmentOffset(array, size)) % packet_size) == 0);
}
template<typename Scalar>
void test_eigen2_none_aligned_helper(Scalar *array, int size)
{
VERIFY(ei_packet_traits<Scalar>::size == 1 || ei_alignmentOffset(array, size) == size);
}
struct some_non_vectorizable_type { float x; };
void test_eigen2_first_aligned()
{
EIGEN_ALIGN_128 float array_float[100];
test_first_aligned_helper(array_float, 50);
test_first_aligned_helper(array_float+1, 50);
test_first_aligned_helper(array_float+2, 50);
test_first_aligned_helper(array_float+3, 50);
test_first_aligned_helper(array_float+4, 50);
test_first_aligned_helper(array_float+5, 50);
EIGEN_ALIGN_128 double array_double[100];
test_first_aligned_helper(array_double, 50);
test_first_aligned_helper(array_double+1, 50);
test_first_aligned_helper(array_double+2, 50);
double *array_double_plus_4_bytes = (double*)(std::size_t(array_double)+4);
test_none_aligned_helper(array_double_plus_4_bytes, 50);
test_none_aligned_helper(array_double_plus_4_bytes+1, 50);
some_non_vectorizable_type array_nonvec[100];
test_first_aligned_helper(array_nonvec, 100);
test_none_aligned_helper(array_nonvec, 100);
}

431
test/eigen2/eigen2_geometry.cpp
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@ -1,431 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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"
#include <Eigen/Geometry>
#include <Eigen/LU>
#include <Eigen/SVD>
template<typename Scalar> void geometry(void)
{
/* this test covers the following files:
Cross.h Quaternion.h, Transform.cpp
*/
typedef Matrix<Scalar,2,2> Matrix2;
typedef Matrix<Scalar,3,3> Matrix3;
typedef Matrix<Scalar,4,4> Matrix4;
typedef Matrix<Scalar,2,1> Vector2;
typedef Matrix<Scalar,3,1> Vector3;
typedef Matrix<Scalar,4,1> Vector4;
typedef Quaternion<Scalar> Quaternionx;
typedef AngleAxis<Scalar> AngleAxisx;
typedef Transform<Scalar,2> Transform2;
typedef Transform<Scalar,3> Transform3;
typedef Scaling<Scalar,2> Scaling2;
typedef Scaling<Scalar,3> Scaling3;
typedef Translation<Scalar,2> Translation2;
typedef Translation<Scalar,3> Translation3;
Scalar largeEps = test_precision<Scalar>();
if (ei_is_same_type<Scalar,float>::ret)
largeEps = 1e-2f;
Vector3 v0 = Vector3::Random(),
v1 = Vector3::Random(),
v2 = Vector3::Random();
Vector2 u0 = Vector2::Random();
Matrix3 matrot1;
Scalar a = ei_random<Scalar>(-Scalar(M_PI), Scalar(M_PI));
// cross product
VERIFY_IS_MUCH_SMALLER_THAN(v1.cross(v2).eigen2_dot(v1), Scalar(1));
Matrix3 m;
m << v0.normalized(),
(v0.cross(v1)).normalized(),
(v0.cross(v1).cross(v0)).normalized();
VERIFY(m.isUnitary());
// Quaternion: Identity(), setIdentity();
Quaternionx q1, q2;
q2.setIdentity();
VERIFY_IS_APPROX(Quaternionx(Quaternionx::Identity()).coeffs(), q2.coeffs());
q1.coeffs().setRandom();
VERIFY_IS_APPROX(q1.coeffs(), (q1*q2).coeffs());
// unitOrthogonal
VERIFY_IS_MUCH_SMALLER_THAN(u0.unitOrthogonal().eigen2_dot(u0), Scalar(1));
VERIFY_IS_MUCH_SMALLER_THAN(v0.unitOrthogonal().eigen2_dot(v0), Scalar(1));
VERIFY_IS_APPROX(u0.unitOrthogonal().norm(), Scalar(1));
VERIFY_IS_APPROX(v0.unitOrthogonal().norm(), Scalar(1));
VERIFY_IS_APPROX(v0, AngleAxisx(a, v0.normalized()) * v0);
VERIFY_IS_APPROX(-v0, AngleAxisx(Scalar(M_PI), v0.unitOrthogonal()) * v0);
VERIFY_IS_APPROX(ei_cos(a)*v0.squaredNorm(), v0.eigen2_dot(AngleAxisx(a, v0.unitOrthogonal()) * v0));
m = AngleAxisx(a, v0.normalized()).toRotationMatrix().adjoint();
VERIFY_IS_APPROX(Matrix3::Identity(), m * AngleAxisx(a, v0.normalized()));
VERIFY_IS_APPROX(Matrix3::Identity(), AngleAxisx(a, v0.normalized()) * m);
q1 = AngleAxisx(a, v0.normalized());
q2 = AngleAxisx(a, v1.normalized());
// angular distance
Scalar refangle = ei_abs(AngleAxisx(q1.inverse()*q2).angle());
if (refangle>Scalar(M_PI))
refangle = Scalar(2)*Scalar(M_PI) - refangle;
if((q1.coeffs()-q2.coeffs()).norm() > 10*largeEps)
{
VERIFY(ei_isApprox(q1.angularDistance(q2), refangle, largeEps));
}
// rotation matrix conversion
VERIFY_IS_APPROX(q1 * v2, q1.toRotationMatrix() * v2);
VERIFY_IS_APPROX(q1 * q2 * v2,
q1.toRotationMatrix() * q2.toRotationMatrix() * v2);
VERIFY( (q2*q1).isApprox(q1*q2, largeEps) || !(q2 * q1 * v2).isApprox(
q1.toRotationMatrix() * q2.toRotationMatrix() * v2));
q2 = q1.toRotationMatrix();
VERIFY_IS_APPROX(q1*v1,q2*v1);
matrot1 = AngleAxisx(Scalar(0.1), Vector3::UnitX())
* AngleAxisx(Scalar(0.2), Vector3::UnitY())
* AngleAxisx(Scalar(0.3), Vector3::UnitZ());
VERIFY_IS_APPROX(matrot1 * v1,
AngleAxisx(Scalar(0.1), Vector3(1,0,0)).toRotationMatrix()
* (AngleAxisx(Scalar(0.2), Vector3(0,1,0)).toRotationMatrix()
* (AngleAxisx(Scalar(0.3), Vector3(0,0,1)).toRotationMatrix() * v1)));
// angle-axis conversion
AngleAxisx aa = q1;
VERIFY_IS_APPROX(q1 * v1, Quaternionx(aa) * v1);
VERIFY_IS_NOT_APPROX(q1 * v1, Quaternionx(AngleAxisx(aa.angle()*2,aa.axis())) * v1);
// from two vector creation
VERIFY_IS_APPROX(v2.normalized(),(q2.setFromTwoVectors(v1,v2)*v1).normalized());
VERIFY_IS_APPROX(v2.normalized(),(q2.setFromTwoVectors(v1,v2)*v1).normalized());
// inverse and conjugate
VERIFY_IS_APPROX(q1 * (q1.inverse() * v1), v1);
VERIFY_IS_APPROX(q1 * (q1.conjugate() * v1), v1);
// AngleAxis
VERIFY_IS_APPROX(AngleAxisx(a,v1.normalized()).toRotationMatrix(),
Quaternionx(AngleAxisx(a,v1.normalized())).toRotationMatrix());
AngleAxisx aa1;
m = q1.toRotationMatrix();
aa1 = m;
VERIFY_IS_APPROX(AngleAxisx(m).toRotationMatrix(),
Quaternionx(m).toRotationMatrix());
// Transform
// TODO complete the tests !
a = 0;
while (ei_abs(a)<Scalar(0.1))
a = ei_random<Scalar>(-Scalar(0.4)*Scalar(M_PI), Scalar(0.4)*Scalar(M_PI));
q1 = AngleAxisx(a, v0.normalized());
Transform3 t0, t1, t2;
// first test setIdentity() and Identity()
t0.setIdentity();
VERIFY_IS_APPROX(t0.matrix(), Transform3::MatrixType::Identity());
t0.matrix().setZero();
t0 = Transform3::Identity();
VERIFY_IS_APPROX(t0.matrix(), Transform3::MatrixType::Identity());
t0.linear() = q1.toRotationMatrix();
t1.setIdentity();
t1.linear() = q1.toRotationMatrix();
v0 << 50, 2, 1;//= ei_random_matrix<Vector3>().cwiseProduct(Vector3(10,2,0.5));
t0.scale(v0);
t1.prescale(v0);
VERIFY_IS_APPROX( (t0 * Vector3(1,0,0)).norm(), v0.x());
//VERIFY(!ei_isApprox((t1 * Vector3(1,0,0)).norm(), v0.x()));
t0.setIdentity();
t1.setIdentity();
v1 << 1, 2, 3;
t0.linear() = q1.toRotationMatrix();
t0.pretranslate(v0);
t0.scale(v1);
t1.linear() = q1.conjugate().toRotationMatrix();
t1.prescale(v1.cwise().inverse());
t1.translate(-v0);
VERIFY((t0.matrix() * t1.matrix()).isIdentity(test_precision<Scalar>()));
t1.fromPositionOrientationScale(v0, q1, v1);
VERIFY_IS_APPROX(t1.matrix(), t0.matrix());
VERIFY_IS_APPROX(t1*v1, t0*v1);
t0.setIdentity(); t0.scale(v0).rotate(q1.toRotationMatrix());
t1.setIdentity(); t1.scale(v0).rotate(q1);
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
t0.setIdentity(); t0.scale(v0).rotate(AngleAxisx(q1));
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
VERIFY_IS_APPROX(t0.scale(a).matrix(), t1.scale(Vector3::Constant(a)).matrix());
VERIFY_IS_APPROX(t0.prescale(a).matrix(), t1.prescale(Vector3::Constant(a)).matrix());
// More transform constructors, operator=, operator*=
Matrix3 mat3 = Matrix3::Random();
Matrix4 mat4;
mat4 << mat3 , Vector3::Zero() , Vector4::Zero().transpose();
Transform3 tmat3(mat3), tmat4(mat4);
tmat4.matrix()(3,3) = Scalar(1);
VERIFY_IS_APPROX(tmat3.matrix(), tmat4.matrix());
Scalar a3 = ei_random<Scalar>(-Scalar(M_PI), Scalar(M_PI));
Vector3 v3 = Vector3::Random().normalized();
AngleAxisx aa3(a3, v3);
Transform3 t3(aa3);
Transform3 t4;
t4 = aa3;
VERIFY_IS_APPROX(t3.matrix(), t4.matrix());
t4.rotate(AngleAxisx(-a3,v3));
VERIFY_IS_APPROX(t4.matrix(), Matrix4::Identity());
t4 *= aa3;
VERIFY_IS_APPROX(t3.matrix(), t4.matrix());
v3 = Vector3::Random();
Translation3 tv3(v3);
Transform3 t5(tv3);
t4 = tv3;
VERIFY_IS_APPROX(t5.matrix(), t4.matrix());
t4.translate(-v3);
VERIFY_IS_APPROX(t4.matrix(), Matrix4::Identity());
t4 *= tv3;
VERIFY_IS_APPROX(t5.matrix(), t4.matrix());
Scaling3 sv3(v3);
Transform3 t6(sv3);
t4 = sv3;
VERIFY_IS_APPROX(t6.matrix(), t4.matrix());
t4.scale(v3.cwise().inverse());
VERIFY_IS_APPROX(t4.matrix(), Matrix4::Identity());
t4 *= sv3;
VERIFY_IS_APPROX(t6.matrix(), t4.matrix());
// matrix * transform
VERIFY_IS_APPROX(Transform3(t3.matrix()*t4).matrix(), Transform3(t3*t4).matrix());
// chained Transform product
VERIFY_IS_APPROX(((t3*t4)*t5).matrix(), (t3*(t4*t5)).matrix());
// check that Transform product doesn't have aliasing problems
t5 = t4;
t5 = t5*t5;
VERIFY_IS_APPROX(t5, t4*t4);
// 2D transformation
Transform2 t20, t21;
Vector2 v20 = Vector2::Random();
Vector2 v21 = Vector2::Random();
for (int k=0; k<2; ++k)
if (ei_abs(v21[k])<Scalar(1e-3)) v21[k] = Scalar(1e-3);
t21.setIdentity();
t21.linear() = Rotation2D<Scalar>(a).toRotationMatrix();
VERIFY_IS_APPROX(t20.fromPositionOrientationScale(v20,a,v21).matrix(),
t21.pretranslate(v20).scale(v21).matrix());
t21.setIdentity();
t21.linear() = Rotation2D<Scalar>(-a).toRotationMatrix();
VERIFY( (t20.fromPositionOrientationScale(v20,a,v21)
* (t21.prescale(v21.cwise().inverse()).translate(-v20))).matrix().isIdentity(test_precision<Scalar>()) );
// Transform - new API
// 3D
t0.setIdentity();
t0.rotate(q1).scale(v0).translate(v0);
// mat * scaling and mat * translation
t1 = (Matrix3(q1) * Scaling3(v0)) * Translation3(v0);
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// mat * transformation and scaling * translation
t1 = Matrix3(q1) * (Scaling3(v0) * Translation3(v0));
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
t0.setIdentity();
t0.prerotate(q1).prescale(v0).pretranslate(v0);
// translation * scaling and transformation * mat
t1 = (Translation3(v0) * Scaling3(v0)) * Matrix3(q1);
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// scaling * mat and translation * mat
t1 = Translation3(v0) * (Scaling3(v0) * Matrix3(q1));
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
t0.setIdentity();
t0.scale(v0).translate(v0).rotate(q1);
// translation * mat and scaling * transformation
t1 = Scaling3(v0) * (Translation3(v0) * Matrix3(q1));
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// transformation * scaling
t0.scale(v0);
t1 = t1 * Scaling3(v0);
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// transformation * translation
t0.translate(v0);
t1 = t1 * Translation3(v0);
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// translation * transformation
t0.pretranslate(v0);
t1 = Translation3(v0) * t1;
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// transform * quaternion
t0.rotate(q1);
t1 = t1 * q1;
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// translation * quaternion
t0.translate(v1).rotate(q1);
t1 = t1 * (Translation3(v1) * q1);
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// scaling * quaternion
t0.scale(v1).rotate(q1);
t1 = t1 * (Scaling3(v1) * q1);
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// quaternion * transform
t0.prerotate(q1);
t1 = q1 * t1;
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// quaternion * translation
t0.rotate(q1).translate(v1);
t1 = t1 * (q1 * Translation3(v1));
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// quaternion * scaling
t0.rotate(q1).scale(v1);
t1 = t1 * (q1 * Scaling3(v1));
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// translation * vector
t0.setIdentity();
t0.translate(v0);
VERIFY_IS_APPROX(t0 * v1, Translation3(v0) * v1);
// scaling * vector
t0.setIdentity();
t0.scale(v0);
VERIFY_IS_APPROX(t0 * v1, Scaling3(v0) * v1);
// test transform inversion
t0.setIdentity();
t0.translate(v0);
t0.linear().setRandom();
VERIFY_IS_APPROX(t0.inverse(Affine), t0.matrix().inverse());
t0.setIdentity();
t0.translate(v0).rotate(q1);
VERIFY_IS_APPROX(t0.inverse(Isometry), t0.matrix().inverse());
// test extract rotation and scaling
t0.setIdentity();
t0.translate(v0).rotate(q1).scale(v1);
VERIFY_IS_APPROX(t0.rotation() * v1, Matrix3(q1) * v1);
Matrix3 mat_rotation, mat_scaling;
t0.setIdentity();
t0.translate(v0).rotate(q1).scale(v1);
t0.computeRotationScaling(&mat_rotation, &mat_scaling);
VERIFY_IS_APPROX(t0.linear(), mat_rotation * mat_scaling);
VERIFY_IS_APPROX(mat_rotation*mat_rotation.adjoint(), Matrix3::Identity());
VERIFY_IS_APPROX(mat_rotation.determinant(), Scalar(1));
t0.computeScalingRotation(&mat_scaling, &mat_rotation);
VERIFY_IS_APPROX(t0.linear(), mat_scaling * mat_rotation);
VERIFY_IS_APPROX(mat_rotation*mat_rotation.adjoint(), Matrix3::Identity());
VERIFY_IS_APPROX(mat_rotation.determinant(), Scalar(1));
// test casting
Transform<float,3> t1f = t1.template cast<float>();
VERIFY_IS_APPROX(t1f.template cast<Scalar>(),t1);
Transform<double,3> t1d = t1.template cast<double>();
VERIFY_IS_APPROX(t1d.template cast<Scalar>(),t1);
Translation3 tr1(v0);
Translation<float,3> tr1f = tr1.template cast<float>();
VERIFY_IS_APPROX(tr1f.template cast<Scalar>(),tr1);
Translation<double,3> tr1d = tr1.template cast<double>();
VERIFY_IS_APPROX(tr1d.template cast<Scalar>(),tr1);
Scaling3 sc1(v0);
Scaling<float,3> sc1f = sc1.template cast<float>();
VERIFY_IS_APPROX(sc1f.template cast<Scalar>(),sc1);
Scaling<double,3> sc1d = sc1.template cast<double>();
VERIFY_IS_APPROX(sc1d.template cast<Scalar>(),sc1);
Quaternion<float> q1f = q1.template cast<float>();
VERIFY_IS_APPROX(q1f.template cast<Scalar>(),q1);
Quaternion<double> q1d = q1.template cast<double>();
VERIFY_IS_APPROX(q1d.template cast<Scalar>(),q1);
AngleAxis<float> aa1f = aa1.template cast<float>();
VERIFY_IS_APPROX(aa1f.template cast<Scalar>(),aa1);
AngleAxis<double> aa1d = aa1.template cast<double>();
VERIFY_IS_APPROX(aa1d.template cast<Scalar>(),aa1);
Rotation2D<Scalar> r2d1(ei_random<Scalar>());
Rotation2D<float> r2d1f = r2d1.template cast<float>();
VERIFY_IS_APPROX(r2d1f.template cast<Scalar>(),r2d1);
Rotation2D<double> r2d1d = r2d1.template cast<double>();
VERIFY_IS_APPROX(r2d1d.template cast<Scalar>(),r2d1);
m = q1;
// m.col(1) = Vector3(0,ei_random<Scalar>(),ei_random<Scalar>()).normalized();
// m.col(0) = Vector3(-1,0,0).normalized();
// m.col(2) = m.col(0).cross(m.col(1));
#define VERIFY_EULER(I,J,K, X,Y,Z) { \
Vector3 ea = m.eulerAngles(I,J,K); \
Matrix3 m1 = Matrix3(AngleAxisx(ea[0], Vector3::Unit##X()) * AngleAxisx(ea[1], Vector3::Unit##Y()) * AngleAxisx(ea[2], Vector3::Unit##Z())); \
VERIFY_IS_APPROX(m, Matrix3(AngleAxisx(ea[0], Vector3::Unit##X()) * AngleAxisx(ea[1], Vector3::Unit##Y()) * AngleAxisx(ea[2], Vector3::Unit##Z()))); \
}
VERIFY_EULER(0,1,2, X,Y,Z);
VERIFY_EULER(0,1,0, X,Y,X);
VERIFY_EULER(0,2,1, X,Z,Y);
VERIFY_EULER(0,2,0, X,Z,X);
VERIFY_EULER(1,2,0, Y,Z,X);
VERIFY_EULER(1,2,1, Y,Z,Y);
VERIFY_EULER(1,0,2, Y,X,Z);
VERIFY_EULER(1,0,1, Y,X,Y);
VERIFY_EULER(2,0,1, Z,X,Y);
VERIFY_EULER(2,0,2, Z,X,Z);
VERIFY_EULER(2,1,0, Z,Y,X);
VERIFY_EULER(2,1,2, Z,Y,Z);
// colwise/rowwise cross product
mat3.setRandom();
Vector3 vec3 = Vector3::Random();
Matrix3 mcross;
int i = ei_random<int>(0,2);
mcross = mat3.colwise().cross(vec3);
VERIFY_IS_APPROX(mcross.col(i), mat3.col(i).cross(vec3));
mcross = mat3.rowwise().cross(vec3);
VERIFY_IS_APPROX(mcross.row(i), mat3.row(i).cross(vec3));
}
void test_eigen2_geometry()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( geometry<float>() );
CALL_SUBTEST_2( geometry<double>() );
}
}

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test/eigen2/eigen2_geometry_with_eigen2_prefix.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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/.
#define EIGEN2_SUPPORT_STAGE15_RESOLVE_API_CONFLICTS_WARN
#include "main.h"
#include <Eigen/Geometry>
#include <Eigen/LU>
#include <Eigen/SVD>
template<typename Scalar> void geometry(void)
{
/* this test covers the following files:
Cross.h Quaternion.h, Transform.cpp
*/
typedef Matrix<Scalar,2,2> Matrix2;
typedef Matrix<Scalar,3,3> Matrix3;
typedef Matrix<Scalar,4,4> Matrix4;
typedef Matrix<Scalar,2,1> Vector2;
typedef Matrix<Scalar,3,1> Vector3;
typedef Matrix<Scalar,4,1> Vector4;
typedef eigen2_Quaternion<Scalar> Quaternionx;
typedef eigen2_AngleAxis<Scalar> AngleAxisx;
typedef eigen2_Transform<Scalar,2> Transform2;
typedef eigen2_Transform<Scalar,3> Transform3;
typedef eigen2_Scaling<Scalar,2> Scaling2;
typedef eigen2_Scaling<Scalar,3> Scaling3;
typedef eigen2_Translation<Scalar,2> Translation2;
typedef eigen2_Translation<Scalar,3> Translation3;
Scalar largeEps = test_precision<Scalar>();
if (ei_is_same_type<Scalar,float>::ret)
largeEps = 1e-2f;
Vector3 v0 = Vector3::Random(),
v1 = Vector3::Random(),
v2 = Vector3::Random();
Vector2 u0 = Vector2::Random();
Matrix3 matrot1;
Scalar a = ei_random<Scalar>(-Scalar(M_PI), Scalar(M_PI));
// cross product
VERIFY_IS_MUCH_SMALLER_THAN(v1.cross(v2).eigen2_dot(v1), Scalar(1));
Matrix3 m;
m << v0.normalized(),
(v0.cross(v1)).normalized(),
(v0.cross(v1).cross(v0)).normalized();
VERIFY(m.isUnitary());
// Quaternion: Identity(), setIdentity();
Quaternionx q1, q2;
q2.setIdentity();
VERIFY_IS_APPROX(Quaternionx(Quaternionx::Identity()).coeffs(), q2.coeffs());
q1.coeffs().setRandom();
VERIFY_IS_APPROX(q1.coeffs(), (q1*q2).coeffs());
// unitOrthogonal
VERIFY_IS_MUCH_SMALLER_THAN(u0.unitOrthogonal().eigen2_dot(u0), Scalar(1));
VERIFY_IS_MUCH_SMALLER_THAN(v0.unitOrthogonal().eigen2_dot(v0), Scalar(1));
VERIFY_IS_APPROX(u0.unitOrthogonal().norm(), Scalar(1));
VERIFY_IS_APPROX(v0.unitOrthogonal().norm(), Scalar(1));
VERIFY_IS_APPROX(v0, AngleAxisx(a, v0.normalized()) * v0);
VERIFY_IS_APPROX(-v0, AngleAxisx(Scalar(M_PI), v0.unitOrthogonal()) * v0);
VERIFY_IS_APPROX(ei_cos(a)*v0.squaredNorm(), v0.eigen2_dot(AngleAxisx(a, v0.unitOrthogonal()) * v0));
m = AngleAxisx(a, v0.normalized()).toRotationMatrix().adjoint();
VERIFY_IS_APPROX(Matrix3::Identity(), m * AngleAxisx(a, v0.normalized()));
VERIFY_IS_APPROX(Matrix3::Identity(), AngleAxisx(a, v0.normalized()) * m);
q1 = AngleAxisx(a, v0.normalized());
q2 = AngleAxisx(a, v1.normalized());
// angular distance
Scalar refangle = ei_abs(AngleAxisx(q1.inverse()*q2).angle());
if (refangle>Scalar(M_PI))
refangle = Scalar(2)*Scalar(M_PI) - refangle;
if((q1.coeffs()-q2.coeffs()).norm() > 10*largeEps)
{
VERIFY(ei_isApprox(q1.angularDistance(q2), refangle, largeEps));
}
// rotation matrix conversion
VERIFY_IS_APPROX(q1 * v2, q1.toRotationMatrix() * v2);
VERIFY_IS_APPROX(q1 * q2 * v2,
q1.toRotationMatrix() * q2.toRotationMatrix() * v2);
VERIFY( (q2*q1).isApprox(q1*q2, largeEps) || !(q2 * q1 * v2).isApprox(
q1.toRotationMatrix() * q2.toRotationMatrix() * v2));
q2 = q1.toRotationMatrix();
VERIFY_IS_APPROX(q1*v1,q2*v1);
matrot1 = AngleAxisx(Scalar(0.1), Vector3::UnitX())
* AngleAxisx(Scalar(0.2), Vector3::UnitY())
* AngleAxisx(Scalar(0.3), Vector3::UnitZ());
VERIFY_IS_APPROX(matrot1 * v1,
AngleAxisx(Scalar(0.1), Vector3(1,0,0)).toRotationMatrix()
* (AngleAxisx(Scalar(0.2), Vector3(0,1,0)).toRotationMatrix()
* (AngleAxisx(Scalar(0.3), Vector3(0,0,1)).toRotationMatrix() * v1)));
// angle-axis conversion
AngleAxisx aa = q1;
VERIFY_IS_APPROX(q1 * v1, Quaternionx(aa) * v1);
VERIFY_IS_NOT_APPROX(q1 * v1, Quaternionx(AngleAxisx(aa.angle()*2,aa.axis())) * v1);
// from two vector creation
VERIFY_IS_APPROX(v2.normalized(),(q2.setFromTwoVectors(v1,v2)*v1).normalized());
VERIFY_IS_APPROX(v2.normalized(),(q2.setFromTwoVectors(v1,v2)*v1).normalized());
// inverse and conjugate
VERIFY_IS_APPROX(q1 * (q1.inverse() * v1), v1);
VERIFY_IS_APPROX(q1 * (q1.conjugate() * v1), v1);
// AngleAxis
VERIFY_IS_APPROX(AngleAxisx(a,v1.normalized()).toRotationMatrix(),
Quaternionx(AngleAxisx(a,v1.normalized())).toRotationMatrix());
AngleAxisx aa1;
m = q1.toRotationMatrix();
aa1 = m;
VERIFY_IS_APPROX(AngleAxisx(m).toRotationMatrix(),
Quaternionx(m).toRotationMatrix());
// Transform
// TODO complete the tests !
a = 0;
while (ei_abs(a)<Scalar(0.1))
a = ei_random<Scalar>(-Scalar(0.4)*Scalar(M_PI), Scalar(0.4)*Scalar(M_PI));
q1 = AngleAxisx(a, v0.normalized());
Transform3 t0, t1, t2;
// first test setIdentity() and Identity()
t0.setIdentity();
VERIFY_IS_APPROX(t0.matrix(), Transform3::MatrixType::Identity());
t0.matrix().setZero();
t0 = Transform3::Identity();
VERIFY_IS_APPROX(t0.matrix(), Transform3::MatrixType::Identity());
t0.linear() = q1.toRotationMatrix();
t1.setIdentity();
t1.linear() = q1.toRotationMatrix();
v0 << 50, 2, 1;//= ei_random_matrix<Vector3>().cwiseProduct(Vector3(10,2,0.5));
t0.scale(v0);
t1.prescale(v0);
VERIFY_IS_APPROX( (t0 * Vector3(1,0,0)).norm(), v0.x());
//VERIFY(!ei_isApprox((t1 * Vector3(1,0,0)).norm(), v0.x()));
t0.setIdentity();
t1.setIdentity();
v1 << 1, 2, 3;
t0.linear() = q1.toRotationMatrix();
t0.pretranslate(v0);
t0.scale(v1);
t1.linear() = q1.conjugate().toRotationMatrix();
t1.prescale(v1.cwise().inverse());
t1.translate(-v0);
VERIFY((t0.matrix() * t1.matrix()).isIdentity(test_precision<Scalar>()));
t1.fromPositionOrientationScale(v0, q1, v1);
VERIFY_IS_APPROX(t1.matrix(), t0.matrix());
VERIFY_IS_APPROX(t1*v1, t0*v1);
t0.setIdentity(); t0.scale(v0).rotate(q1.toRotationMatrix());
t1.setIdentity(); t1.scale(v0).rotate(q1);
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
t0.setIdentity(); t0.scale(v0).rotate(AngleAxisx(q1));
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
VERIFY_IS_APPROX(t0.scale(a).matrix(), t1.scale(Vector3::Constant(a)).matrix());
VERIFY_IS_APPROX(t0.prescale(a).matrix(), t1.prescale(Vector3::Constant(a)).matrix());
// More transform constructors, operator=, operator*=
Matrix3 mat3 = Matrix3::Random();
Matrix4 mat4;
mat4 << mat3 , Vector3::Zero() , Vector4::Zero().transpose();
Transform3 tmat3(mat3), tmat4(mat4);
tmat4.matrix()(3,3) = Scalar(1);
VERIFY_IS_APPROX(tmat3.matrix(), tmat4.matrix());
Scalar a3 = ei_random<Scalar>(-Scalar(M_PI), Scalar(M_PI));
Vector3 v3 = Vector3::Random().normalized();
AngleAxisx aa3(a3, v3);
Transform3 t3(aa3);
Transform3 t4;
t4 = aa3;
VERIFY_IS_APPROX(t3.matrix(), t4.matrix());
t4.rotate(AngleAxisx(-a3,v3));
VERIFY_IS_APPROX(t4.matrix(), Matrix4::Identity());
t4 *= aa3;
VERIFY_IS_APPROX(t3.matrix(), t4.matrix());
v3 = Vector3::Random();
Translation3 tv3(v3);
Transform3 t5(tv3);
t4 = tv3;
VERIFY_IS_APPROX(t5.matrix(), t4.matrix());
t4.translate(-v3);
VERIFY_IS_APPROX(t4.matrix(), Matrix4::Identity());
t4 *= tv3;
VERIFY_IS_APPROX(t5.matrix(), t4.matrix());
Scaling3 sv3(v3);
Transform3 t6(sv3);
t4 = sv3;
VERIFY_IS_APPROX(t6.matrix(), t4.matrix());
t4.scale(v3.cwise().inverse());
VERIFY_IS_APPROX(t4.matrix(), Matrix4::Identity());
t4 *= sv3;
VERIFY_IS_APPROX(t6.matrix(), t4.matrix());
// matrix * transform
VERIFY_IS_APPROX(Transform3(t3.matrix()*t4).matrix(), Transform3(t3*t4).matrix());
// chained Transform product
VERIFY_IS_APPROX(((t3*t4)*t5).matrix(), (t3*(t4*t5)).matrix());
// check that Transform product doesn't have aliasing problems
t5 = t4;
t5 = t5*t5;
VERIFY_IS_APPROX(t5, t4*t4);
// 2D transformation
Transform2 t20, t21;
Vector2 v20 = Vector2::Random();
Vector2 v21 = Vector2::Random();
for (int k=0; k<2; ++k)
if (ei_abs(v21[k])<Scalar(1e-3)) v21[k] = Scalar(1e-3);
t21.setIdentity();
t21.linear() = Rotation2D<Scalar>(a).toRotationMatrix();
VERIFY_IS_APPROX(t20.fromPositionOrientationScale(v20,a,v21).matrix(),
t21.pretranslate(v20).scale(v21).matrix());
t21.setIdentity();
t21.linear() = Rotation2D<Scalar>(-a).toRotationMatrix();
VERIFY( (t20.fromPositionOrientationScale(v20,a,v21)
* (t21.prescale(v21.cwise().inverse()).translate(-v20))).matrix().isIdentity(test_precision<Scalar>()) );
// Transform - new API
// 3D
t0.setIdentity();
t0.rotate(q1).scale(v0).translate(v0);
// mat * scaling and mat * translation
t1 = (Matrix3(q1) * Scaling3(v0)) * Translation3(v0);
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// mat * transformation and scaling * translation
t1 = Matrix3(q1) * (Scaling3(v0) * Translation3(v0));
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
t0.setIdentity();
t0.prerotate(q1).prescale(v0).pretranslate(v0);
// translation * scaling and transformation * mat
t1 = (Translation3(v0) * Scaling3(v0)) * Matrix3(q1);
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// scaling * mat and translation * mat
t1 = Translation3(v0) * (Scaling3(v0) * Matrix3(q1));
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
t0.setIdentity();
t0.scale(v0).translate(v0).rotate(q1);
// translation * mat and scaling * transformation
t1 = Scaling3(v0) * (Translation3(v0) * Matrix3(q1));
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// transformation * scaling
t0.scale(v0);
t1 = t1 * Scaling3(v0);
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// transformation * translation
t0.translate(v0);
t1 = t1 * Translation3(v0);
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// translation * transformation
t0.pretranslate(v0);
t1 = Translation3(v0) * t1;
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// transform * quaternion
t0.rotate(q1);
t1 = t1 * q1;
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// translation * quaternion
t0.translate(v1).rotate(q1);
t1 = t1 * (Translation3(v1) * q1);
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// scaling * quaternion
t0.scale(v1).rotate(q1);
t1 = t1 * (Scaling3(v1) * q1);
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// quaternion * transform
t0.prerotate(q1);
t1 = q1 * t1;
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// quaternion * translation
t0.rotate(q1).translate(v1);
t1 = t1 * (q1 * Translation3(v1));
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// quaternion * scaling
t0.rotate(q1).scale(v1);
t1 = t1 * (q1 * Scaling3(v1));
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// translation * vector
t0.setIdentity();
t0.translate(v0);
VERIFY_IS_APPROX(t0 * v1, Translation3(v0) * v1);
// scaling * vector
t0.setIdentity();
t0.scale(v0);
VERIFY_IS_APPROX(t0 * v1, Scaling3(v0) * v1);
// test transform inversion
t0.setIdentity();
t0.translate(v0);
t0.linear().setRandom();
VERIFY_IS_APPROX(t0.inverse(Affine), t0.matrix().inverse());
t0.setIdentity();
t0.translate(v0).rotate(q1);
VERIFY_IS_APPROX(t0.inverse(Isometry), t0.matrix().inverse());
// test extract rotation and scaling
t0.setIdentity();
t0.translate(v0).rotate(q1).scale(v1);
VERIFY_IS_APPROX(t0.rotation() * v1, Matrix3(q1) * v1);
Matrix3 mat_rotation, mat_scaling;
t0.setIdentity();
t0.translate(v0).rotate(q1).scale(v1);
t0.computeRotationScaling(&mat_rotation, &mat_scaling);
VERIFY_IS_APPROX(t0.linear(), mat_rotation * mat_scaling);
VERIFY_IS_APPROX(mat_rotation*mat_rotation.adjoint(), Matrix3::Identity());
VERIFY_IS_APPROX(mat_rotation.determinant(), Scalar(1));
t0.computeScalingRotation(&mat_scaling, &mat_rotation);
VERIFY_IS_APPROX(t0.linear(), mat_scaling * mat_rotation);
VERIFY_IS_APPROX(mat_rotation*mat_rotation.adjoint(), Matrix3::Identity());
VERIFY_IS_APPROX(mat_rotation.determinant(), Scalar(1));
// test casting
eigen2_Transform<float,3> t1f = t1.template cast<float>();
VERIFY_IS_APPROX(t1f.template cast<Scalar>(),t1);
eigen2_Transform<double,3> t1d = t1.template cast<double>();
VERIFY_IS_APPROX(t1d.template cast<Scalar>(),t1);
Translation3 tr1(v0);
eigen2_Translation<float,3> tr1f = tr1.template cast<float>();
VERIFY_IS_APPROX(tr1f.template cast<Scalar>(),tr1);
eigen2_Translation<double,3> tr1d = tr1.template cast<double>();
VERIFY_IS_APPROX(tr1d.template cast<Scalar>(),tr1);
Scaling3 sc1(v0);
eigen2_Scaling<float,3> sc1f = sc1.template cast<float>();
VERIFY_IS_APPROX(sc1f.template cast<Scalar>(),sc1);
eigen2_Scaling<double,3> sc1d = sc1.template cast<double>();
VERIFY_IS_APPROX(sc1d.template cast<Scalar>(),sc1);
eigen2_Quaternion<float> q1f = q1.template cast<float>();
VERIFY_IS_APPROX(q1f.template cast<Scalar>(),q1);
eigen2_Quaternion<double> q1d = q1.template cast<double>();
VERIFY_IS_APPROX(q1d.template cast<Scalar>(),q1);
eigen2_AngleAxis<float> aa1f = aa1.template cast<float>();
VERIFY_IS_APPROX(aa1f.template cast<Scalar>(),aa1);
eigen2_AngleAxis<double> aa1d = aa1.template cast<double>();
VERIFY_IS_APPROX(aa1d.template cast<Scalar>(),aa1);
eigen2_Rotation2D<Scalar> r2d1(ei_random<Scalar>());
eigen2_Rotation2D<float> r2d1f = r2d1.template cast<float>();
VERIFY_IS_APPROX(r2d1f.template cast<Scalar>(),r2d1);
eigen2_Rotation2D<double> r2d1d = r2d1.template cast<double>();
VERIFY_IS_APPROX(r2d1d.template cast<Scalar>(),r2d1);
m = q1;
// m.col(1) = Vector3(0,ei_random<Scalar>(),ei_random<Scalar>()).normalized();
// m.col(0) = Vector3(-1,0,0).normalized();
// m.col(2) = m.col(0).cross(m.col(1));
#define VERIFY_EULER(I,J,K, X,Y,Z) { \
Vector3 ea = m.eulerAngles(I,J,K); \
Matrix3 m1 = Matrix3(AngleAxisx(ea[0], Vector3::Unit##X()) * AngleAxisx(ea[1], Vector3::Unit##Y()) * AngleAxisx(ea[2], Vector3::Unit##Z())); \
VERIFY_IS_APPROX(m, Matrix3(AngleAxisx(ea[0], Vector3::Unit##X()) * AngleAxisx(ea[1], Vector3::Unit##Y()) * AngleAxisx(ea[2], Vector3::Unit##Z()))); \
}
VERIFY_EULER(0,1,2, X,Y,Z);
VERIFY_EULER(0,1,0, X,Y,X);
VERIFY_EULER(0,2,1, X,Z,Y);
VERIFY_EULER(0,2,0, X,Z,X);
VERIFY_EULER(1,2,0, Y,Z,X);
VERIFY_EULER(1,2,1, Y,Z,Y);
VERIFY_EULER(1,0,2, Y,X,Z);
VERIFY_EULER(1,0,1, Y,X,Y);
VERIFY_EULER(2,0,1, Z,X,Y);
VERIFY_EULER(2,0,2, Z,X,Z);
VERIFY_EULER(2,1,0, Z,Y,X);
VERIFY_EULER(2,1,2, Z,Y,Z);
// colwise/rowwise cross product
mat3.setRandom();
Vector3 vec3 = Vector3::Random();
Matrix3 mcross;
int i = ei_random<int>(0,2);
mcross = mat3.colwise().cross(vec3);
VERIFY_IS_APPROX(mcross.col(i), mat3.col(i).cross(vec3));
mcross = mat3.rowwise().cross(vec3);
VERIFY_IS_APPROX(mcross.row(i), mat3.row(i).cross(vec3));
}
void test_eigen2_geometry_with_eigen2_prefix()
{
std::cout << "eigen2 support: " << EIGEN2_SUPPORT_STAGE << std::endl;
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( geometry<float>() );
CALL_SUBTEST_2( geometry<double>() );
}
}

126
test/eigen2/eigen2_hyperplane.cpp
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@ -1,126 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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"
#include <Eigen/Geometry>
#include <Eigen/LU>
#include <Eigen/QR>
template<typename HyperplaneType> void hyperplane(const HyperplaneType& _plane)
{
/* this test covers the following files:
Hyperplane.h
*/
const int dim = _plane.dim();
typedef typename HyperplaneType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar, HyperplaneType::AmbientDimAtCompileTime, 1> VectorType;
typedef Matrix<Scalar, HyperplaneType::AmbientDimAtCompileTime,
HyperplaneType::AmbientDimAtCompileTime> MatrixType;
VectorType p0 = VectorType::Random(dim);
VectorType p1 = VectorType::Random(dim);
VectorType n0 = VectorType::Random(dim).normalized();
VectorType n1 = VectorType::Random(dim).normalized();
HyperplaneType pl0(n0, p0);
HyperplaneType pl1(n1, p1);
HyperplaneType pl2 = pl1;
Scalar s0 = ei_random<Scalar>();
Scalar s1 = ei_random<Scalar>();
VERIFY_IS_APPROX( n1.eigen2_dot(n1), Scalar(1) );
VERIFY_IS_MUCH_SMALLER_THAN( pl0.absDistance(p0), Scalar(1) );
VERIFY_IS_APPROX( pl1.signedDistance(p1 + n1 * s0), s0 );
VERIFY_IS_MUCH_SMALLER_THAN( pl1.signedDistance(pl1.projection(p0)), Scalar(1) );
VERIFY_IS_MUCH_SMALLER_THAN( pl1.absDistance(p1 + pl1.normal().unitOrthogonal() * s1), Scalar(1) );
// transform
if (!NumTraits<Scalar>::IsComplex)
{
MatrixType rot = MatrixType::Random(dim,dim).qr().matrixQ();
Scaling<Scalar,HyperplaneType::AmbientDimAtCompileTime> scaling(VectorType::Random());
Translation<Scalar,HyperplaneType::AmbientDimAtCompileTime> translation(VectorType::Random());
pl2 = pl1;
VERIFY_IS_MUCH_SMALLER_THAN( pl2.transform(rot).absDistance(rot * p1), Scalar(1) );
pl2 = pl1;
VERIFY_IS_MUCH_SMALLER_THAN( pl2.transform(rot,Isometry).absDistance(rot * p1), Scalar(1) );
pl2 = pl1;
VERIFY_IS_MUCH_SMALLER_THAN( pl2.transform(rot*scaling).absDistance((rot*scaling) * p1), Scalar(1) );
pl2 = pl1;
VERIFY_IS_MUCH_SMALLER_THAN( pl2.transform(rot*scaling*translation)
.absDistance((rot*scaling*translation) * p1), Scalar(1) );
pl2 = pl1;
VERIFY_IS_MUCH_SMALLER_THAN( pl2.transform(rot*translation,Isometry)
.absDistance((rot*translation) * p1), Scalar(1) );
}
// casting
const int Dim = HyperplaneType::AmbientDimAtCompileTime;
typedef typename GetDifferentType<Scalar>::type OtherScalar;
Hyperplane<OtherScalar,Dim> hp1f = pl1.template cast<OtherScalar>();
VERIFY_IS_APPROX(hp1f.template cast<Scalar>(),pl1);
Hyperplane<Scalar,Dim> hp1d = pl1.template cast<Scalar>();
VERIFY_IS_APPROX(hp1d.template cast<Scalar>(),pl1);
}
template<typename Scalar> void lines()
{
typedef Hyperplane<Scalar, 2> HLine;
typedef ParametrizedLine<Scalar, 2> PLine;
typedef Matrix<Scalar,2,1> Vector;
typedef Matrix<Scalar,3,1> CoeffsType;
for(int i = 0; i < 10; i++)
{
Vector center = Vector::Random();
Vector u = Vector::Random();
Vector v = Vector::Random();
Scalar a = ei_random<Scalar>();
while (ei_abs(a-1) < 1e-4) a = ei_random<Scalar>();
while (u.norm() < 1e-4) u = Vector::Random();
while (v.norm() < 1e-4) v = Vector::Random();
HLine line_u = HLine::Through(center + u, center + a*u);
HLine line_v = HLine::Through(center + v, center + a*v);
// the line equations should be normalized so that a^2+b^2=1
VERIFY_IS_APPROX(line_u.normal().norm(), Scalar(1));
VERIFY_IS_APPROX(line_v.normal().norm(), Scalar(1));
Vector result = line_u.intersection(line_v);
// the lines should intersect at the point we called "center"
VERIFY_IS_APPROX(result, center);
// check conversions between two types of lines
PLine pl(line_u); // gcc 3.3 will commit suicide if we don't name this variable
CoeffsType converted_coeffs(HLine(pl).coeffs());
converted_coeffs *= line_u.coeffs()(0)/converted_coeffs(0);
VERIFY(line_u.coeffs().isApprox(converted_coeffs));
}
}
void test_eigen2_hyperplane()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( hyperplane(Hyperplane<float,2>()) );
CALL_SUBTEST_2( hyperplane(Hyperplane<float,3>()) );
CALL_SUBTEST_3( hyperplane(Hyperplane<double,4>()) );
CALL_SUBTEST_4( hyperplane(Hyperplane<std::complex<double>,5>()) );
CALL_SUBTEST_5( lines<float>() );
CALL_SUBTEST_6( lines<double>() );
}
}

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test/eigen2/eigen2_inverse.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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"
#include <Eigen/LU>
template<typename MatrixType> void inverse(const MatrixType& m)
{
/* this test covers the following files:
Inverse.h
*/
int rows = m.rows();
int cols = m.cols();
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> VectorType;
MatrixType m1 = MatrixType::Random(rows, cols),
m2(rows, cols),
mzero = MatrixType::Zero(rows, cols),
identity = MatrixType::Identity(rows, rows);
while(ei_abs(m1.determinant()) < RealScalar(0.1) && rows <= 8)
{
m1 = MatrixType::Random(rows, cols);
}
m2 = m1.inverse();
VERIFY_IS_APPROX(m1, m2.inverse() );
m1.computeInverse(&m2);
VERIFY_IS_APPROX(m1, m2.inverse() );
VERIFY_IS_APPROX((Scalar(2)*m2).inverse(), m2.inverse()*Scalar(0.5));
VERIFY_IS_APPROX(identity, m1.inverse() * m1 );
VERIFY_IS_APPROX(identity, m1 * m1.inverse() );
VERIFY_IS_APPROX(m1, m1.inverse().inverse() );
// since for the general case we implement separately row-major and col-major, test that
VERIFY_IS_APPROX(m1.transpose().inverse(), m1.inverse().transpose());
}
void test_eigen2_inverse()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( inverse(Matrix<double,1,1>()) );
CALL_SUBTEST_2( inverse(Matrix2d()) );
CALL_SUBTEST_3( inverse(Matrix3f()) );
CALL_SUBTEST_4( inverse(Matrix4f()) );
CALL_SUBTEST_5( inverse(MatrixXf(8,8)) );
CALL_SUBTEST_6( inverse(MatrixXcd(7,7)) );
}
}

84
test/eigen2/eigen2_linearstructure.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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 MatrixType> void linearStructure(const MatrixType& m)
{
/* this test covers the following files:
Sum.h Difference.h Opposite.h ScalarMultiple.h
*/
typedef typename MatrixType::Scalar Scalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
int rows = m.rows();
int cols = m.cols();
// this test relies a lot on Random.h, and there's not much more that we can do
// to test it, hence I consider that we will have tested Random.h
MatrixType m1 = MatrixType::Random(rows, cols),
m2 = MatrixType::Random(rows, cols),
m3(rows, cols),
mzero = MatrixType::Zero(rows, cols);
Scalar s1 = ei_random<Scalar>();
while (ei_abs(s1)<1e-3) s1 = ei_random<Scalar>();
int r = ei_random<int>(0, rows-1),
c = ei_random<int>(0, cols-1);
VERIFY_IS_APPROX(-(-m1), m1);
VERIFY_IS_APPROX(m1+m1, 2*m1);
VERIFY_IS_APPROX(m1+m2-m1, m2);
VERIFY_IS_APPROX(-m2+m1+m2, m1);
VERIFY_IS_APPROX(m1*s1, s1*m1);
VERIFY_IS_APPROX((m1+m2)*s1, s1*m1+s1*m2);
VERIFY_IS_APPROX((-m1+m2)*s1, -s1*m1+s1*m2);
m3 = m2; m3 += m1;
VERIFY_IS_APPROX(m3, m1+m2);
m3 = m2; m3 -= m1;
VERIFY_IS_APPROX(m3, m2-m1);
m3 = m2; m3 *= s1;
VERIFY_IS_APPROX(m3, s1*m2);
if(NumTraits<Scalar>::HasFloatingPoint)
{
m3 = m2; m3 /= s1;
VERIFY_IS_APPROX(m3, m2/s1);
}
// again, test operator() to check const-qualification
VERIFY_IS_APPROX((-m1)(r,c), -(m1(r,c)));
VERIFY_IS_APPROX((m1-m2)(r,c), (m1(r,c))-(m2(r,c)));
VERIFY_IS_APPROX((m1+m2)(r,c), (m1(r,c))+(m2(r,c)));
VERIFY_IS_APPROX((s1*m1)(r,c), s1*(m1(r,c)));
VERIFY_IS_APPROX((m1*s1)(r,c), (m1(r,c))*s1);
if(NumTraits<Scalar>::HasFloatingPoint)
VERIFY_IS_APPROX((m1/s1)(r,c), (m1(r,c))/s1);
// use .block to disable vectorization and compare to the vectorized version
VERIFY_IS_APPROX(m1+m1.block(0,0,rows,cols), m1+m1);
VERIFY_IS_APPROX(m1.cwise() * m1.block(0,0,rows,cols), m1.cwise() * m1);
VERIFY_IS_APPROX(m1 - m1.block(0,0,rows,cols), m1 - m1);
VERIFY_IS_APPROX(m1.block(0,0,rows,cols) * s1, m1 * s1);
}
void test_eigen2_linearstructure()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( linearStructure(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( linearStructure(Matrix2f()) );
CALL_SUBTEST_3( linearStructure(Vector3d()) );
CALL_SUBTEST_4( linearStructure(Matrix4d()) );
CALL_SUBTEST_5( linearStructure(MatrixXcf(3, 3)) );
CALL_SUBTEST_6( linearStructure(MatrixXf(8, 12)) );
CALL_SUBTEST_7( linearStructure(MatrixXi(8, 12)) );
CALL_SUBTEST_8( linearStructure(MatrixXcd(20, 20)) );
}
}

122
test/eigen2/eigen2_lu.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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"
#include <Eigen/LU>
template<typename Derived>
void doSomeRankPreservingOperations(Eigen::MatrixBase<Derived>& m)
{
typedef typename Derived::RealScalar RealScalar;
for(int a = 0; a < 3*(m.rows()+m.cols()); a++)
{
RealScalar d = Eigen::ei_random<RealScalar>(-1,1);
int i = Eigen::ei_random<int>(0,m.rows()-1); // i is a random row number
int j;
do {
j = Eigen::ei_random<int>(0,m.rows()-1);
} while (i==j); // j is another one (must be different)
m.row(i) += d * m.row(j);
i = Eigen::ei_random<int>(0,m.cols()-1); // i is a random column number
do {
j = Eigen::ei_random<int>(0,m.cols()-1);
} while (i==j); // j is another one (must be different)
m.col(i) += d * m.col(j);
}
}
template<typename MatrixType> void lu_non_invertible()
{
/* this test covers the following files:
LU.h
*/
// NOTE there seems to be a problem with too small sizes -- could easily lie in the doSomeRankPreservingOperations function
int rows = ei_random<int>(20,200), cols = ei_random<int>(20,200), cols2 = ei_random<int>(20,200);
int rank = ei_random<int>(1, std::min(rows, cols)-1);
MatrixType m1(rows, cols), m2(cols, cols2), m3(rows, cols2), k(1,1);
m1 = MatrixType::Random(rows,cols);
if(rows <= cols)
for(int i = rank; i < rows; i++) m1.row(i).setZero();
else
for(int i = rank; i < cols; i++) m1.col(i).setZero();
doSomeRankPreservingOperations(m1);
LU<MatrixType> lu(m1);
typename LU<MatrixType>::KernelResultType m1kernel = lu.kernel();
typename LU<MatrixType>::ImageResultType m1image = lu.image();
VERIFY(rank == lu.rank());
VERIFY(cols - lu.rank() == lu.dimensionOfKernel());
VERIFY(!lu.isInjective());
VERIFY(!lu.isInvertible());
VERIFY(lu.isSurjective() == (lu.rank() == rows));
VERIFY((m1 * m1kernel).isMuchSmallerThan(m1));
VERIFY(m1image.lu().rank() == rank);
MatrixType sidebyside(m1.rows(), m1.cols() + m1image.cols());
sidebyside << m1, m1image;
VERIFY(sidebyside.lu().rank() == rank);
m2 = MatrixType::Random(cols,cols2);
m3 = m1*m2;
m2 = MatrixType::Random(cols,cols2);
lu.solve(m3, &m2);
VERIFY_IS_APPROX(m3, m1*m2);
/* solve now always returns true
m3 = MatrixType::Random(rows,cols2);
VERIFY(!lu.solve(m3, &m2));
*/
}
template<typename MatrixType> void lu_invertible()
{
/* this test covers the following files:
LU.h
*/
typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
int size = ei_random<int>(10,200);
MatrixType m1(size, size), m2(size, size), m3(size, size);
m1 = MatrixType::Random(size,size);
if (ei_is_same_type<RealScalar,float>::ret)
{
// let's build a matrix more stable to inverse
MatrixType a = MatrixType::Random(size,size*2);
m1 += a * a.adjoint();
}
LU<MatrixType> lu(m1);
VERIFY(0 == lu.dimensionOfKernel());
VERIFY(size == lu.rank());
VERIFY(lu.isInjective());
VERIFY(lu.isSurjective());
VERIFY(lu.isInvertible());
VERIFY(lu.image().lu().isInvertible());
m3 = MatrixType::Random(size,size);
lu.solve(m3, &m2);
VERIFY_IS_APPROX(m3, m1*m2);
VERIFY_IS_APPROX(m2, lu.inverse()*m3);
m3 = MatrixType::Random(size,size);
VERIFY(lu.solve(m3, &m2));
}
void test_eigen2_lu()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( lu_non_invertible<MatrixXf>() );
CALL_SUBTEST_2( lu_non_invertible<MatrixXd>() );
CALL_SUBTEST_3( lu_non_invertible<MatrixXcf>() );
CALL_SUBTEST_4( lu_non_invertible<MatrixXcd>() );
CALL_SUBTEST_1( lu_invertible<MatrixXf>() );
CALL_SUBTEST_2( lu_invertible<MatrixXd>() );
CALL_SUBTEST_3( lu_invertible<MatrixXcf>() );
CALL_SUBTEST_4( lu_invertible<MatrixXcd>() );
}
}

114
test/eigen2/eigen2_map.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2007-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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 VectorType> void map_class_vector(const VectorType& m)
{
typedef typename VectorType::Scalar Scalar;
int size = m.size();
// test Map.h
Scalar* array1 = ei_aligned_new<Scalar>(size);
Scalar* array2 = ei_aligned_new<Scalar>(size);
Scalar* array3 = new Scalar[size+1];
Scalar* array3unaligned = std::size_t(array3)%16 == 0 ? array3+1 : array3;
Map<VectorType, Aligned>(array1, size) = VectorType::Random(size);
Map<VectorType>(array2, size) = Map<VectorType>(array1, size);
Map<VectorType>(array3unaligned, size) = Map<VectorType>((const Scalar*)array1, size); // test non-const-correctness support in eigen2
VectorType ma1 = Map<VectorType>(array1, size);
VectorType ma2 = Map<VectorType, Aligned>(array2, size);
VectorType ma3 = Map<VectorType>(array3unaligned, size);
VERIFY_IS_APPROX(ma1, ma2);
VERIFY_IS_APPROX(ma1, ma3);
ei_aligned_delete(array1, size);
ei_aligned_delete(array2, size);
delete[] array3;
}
template<typename MatrixType> void map_class_matrix(const MatrixType& m)
{
typedef typename MatrixType::Scalar Scalar;
int rows = m.rows(), cols = m.cols(), size = rows*cols;
// test Map.h
Scalar* array1 = ei_aligned_new<Scalar>(size);
for(int i = 0; i < size; i++) array1[i] = Scalar(1);
Scalar* array2 = ei_aligned_new<Scalar>(size);
for(int i = 0; i < size; i++) array2[i] = Scalar(1);
Scalar* array3 = new Scalar[size+1];
for(int i = 0; i < size+1; i++) array3[i] = Scalar(1);
Scalar* array3unaligned = std::size_t(array3)%16 == 0 ? array3+1 : array3;
Map<MatrixType, Aligned>(array1, rows, cols) = MatrixType::Ones(rows,cols);
Map<MatrixType>(array2, rows, cols) = Map<MatrixType>((const Scalar*)array1, rows, cols); // test non-const-correctness support in eigen2
Map<MatrixType>(array3unaligned, rows, cols) = Map<MatrixType>(array1, rows, cols);
MatrixType ma1 = Map<MatrixType>(array1, rows, cols);
MatrixType ma2 = Map<MatrixType, Aligned>(array2, rows, cols);
VERIFY_IS_APPROX(ma1, ma2);
MatrixType ma3 = Map<MatrixType>(array3unaligned, rows, cols);
VERIFY_IS_APPROX(ma1, ma3);
ei_aligned_delete(array1, size);
ei_aligned_delete(array2, size);
delete[] array3;
}
template<typename VectorType> void map_static_methods(const VectorType& m)
{
typedef typename VectorType::Scalar Scalar;
int size = m.size();
// test Map.h
Scalar* array1 = ei_aligned_new<Scalar>(size);
Scalar* array2 = ei_aligned_new<Scalar>(size);
Scalar* array3 = new Scalar[size+1];
Scalar* array3unaligned = std::size_t(array3)%16 == 0 ? array3+1 : array3;
VectorType::MapAligned(array1, size) = VectorType::Random(size);
VectorType::Map(array2, size) = VectorType::Map(array1, size);
VectorType::Map(array3unaligned, size) = VectorType::Map(array1, size);
VectorType ma1 = VectorType::Map(array1, size);
VectorType ma2 = VectorType::MapAligned(array2, size);
VectorType ma3 = VectorType::Map(array3unaligned, size);
VERIFY_IS_APPROX(ma1, ma2);
VERIFY_IS_APPROX(ma1, ma3);
ei_aligned_delete(array1, size);
ei_aligned_delete(array2, size);
delete[] array3;
}
void test_eigen2_map()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( map_class_vector(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( map_class_vector(Vector4d()) );
CALL_SUBTEST_3( map_class_vector(RowVector4f()) );
CALL_SUBTEST_4( map_class_vector(VectorXcf(8)) );
CALL_SUBTEST_5( map_class_vector(VectorXi(12)) );
CALL_SUBTEST_1( map_class_matrix(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( map_class_matrix(Matrix4d()) );
CALL_SUBTEST_6( map_class_matrix(Matrix<float,3,5>()) );
CALL_SUBTEST_4( map_class_matrix(MatrixXcf(ei_random<int>(1,10),ei_random<int>(1,10))) );
CALL_SUBTEST_5( map_class_matrix(MatrixXi(ei_random<int>(1,10),ei_random<int>(1,10))) );
CALL_SUBTEST_1( map_static_methods(Matrix<double, 1, 1>()) );
CALL_SUBTEST_2( map_static_methods(Vector3f()) );
CALL_SUBTEST_7( map_static_methods(RowVector3d()) );
CALL_SUBTEST_4( map_static_methods(VectorXcd(8)) );
CALL_SUBTEST_5( map_static_methods(VectorXf(12)) );
}
}

60
test/eigen2/eigen2_meta.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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"
void test_eigen2_meta()
{
typedef float & FloatRef;
typedef const float & ConstFloatRef;
VERIFY((ei_meta_if<(3<4),ei_meta_true, ei_meta_false>::ret::ret));
VERIFY(( ei_is_same_type<float,float>::ret));
VERIFY((!ei_is_same_type<float,double>::ret));
VERIFY((!ei_is_same_type<float,float&>::ret));
VERIFY((!ei_is_same_type<float,const float&>::ret));
VERIFY(( ei_is_same_type<float,ei_cleantype<const float&>::type >::ret));
VERIFY(( ei_is_same_type<float,ei_cleantype<const float*>::type >::ret));
VERIFY(( ei_is_same_type<float,ei_cleantype<const float*&>::type >::ret));
VERIFY(( ei_is_same_type<float,ei_cleantype<float**>::type >::ret));
VERIFY(( ei_is_same_type<float,ei_cleantype<float**&>::type >::ret));
VERIFY(( ei_is_same_type<float,ei_cleantype<float* const *&>::type >::ret));
VERIFY(( ei_is_same_type<float,ei_cleantype<float* const>::type >::ret));
VERIFY(( ei_is_same_type<float*,ei_unconst<const float*>::type >::ret));
VERIFY(( ei_is_same_type<float&,ei_unconst<const float&>::type >::ret));
VERIFY(( ei_is_same_type<float&,ei_unconst<ConstFloatRef>::type >::ret));
VERIFY(( ei_is_same_type<float&,ei_unconst<float&>::type >::ret));
VERIFY(( ei_is_same_type<float,ei_unref<float&>::type >::ret));
VERIFY(( ei_is_same_type<const float,ei_unref<const float&>::type >::ret));
VERIFY(( ei_is_same_type<float,ei_unpointer<float*>::type >::ret));
VERIFY(( ei_is_same_type<const float,ei_unpointer<const float*>::type >::ret));
VERIFY(( ei_is_same_type<float,ei_unpointer<float* const >::type >::ret));
VERIFY(ei_meta_sqrt<1>::ret == 1);
#define VERIFY_META_SQRT(X) VERIFY(ei_meta_sqrt<X>::ret == int(ei_sqrt(double(X))))
VERIFY_META_SQRT(2);
VERIFY_META_SQRT(3);
VERIFY_META_SQRT(4);
VERIFY_META_SQRT(5);
VERIFY_META_SQRT(6);
VERIFY_META_SQRT(8);
VERIFY_META_SQRT(9);
VERIFY_META_SQRT(15);
VERIFY_META_SQRT(16);
VERIFY_META_SQRT(17);
VERIFY_META_SQRT(255);
VERIFY_META_SQRT(256);
VERIFY_META_SQRT(257);
VERIFY_META_SQRT(1023);
VERIFY_META_SQRT(1024);
VERIFY_META_SQRT(1025);
}

48
test/eigen2/eigen2_miscmatrices.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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 MatrixType> void miscMatrices(const MatrixType& m)
{
/* this test covers the following files:
DiagonalMatrix.h Ones.h
*/
typedef typename MatrixType::Scalar Scalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
typedef Matrix<Scalar, 1, MatrixType::ColsAtCompileTime> RowVectorType;
int rows = m.rows();
int cols = m.cols();
int r = ei_random<int>(0, rows-1), r2 = ei_random<int>(0, rows-1), c = ei_random<int>(0, cols-1);
VERIFY_IS_APPROX(MatrixType::Ones(rows,cols)(r,c), static_cast<Scalar>(1));
MatrixType m1 = MatrixType::Ones(rows,cols);
VERIFY_IS_APPROX(m1(r,c), static_cast<Scalar>(1));
VectorType v1 = VectorType::Random(rows);
v1[0];
Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime>
square = v1.asDiagonal();
if(r==r2) VERIFY_IS_APPROX(square(r,r2), v1[r]);
else VERIFY_IS_MUCH_SMALLER_THAN(square(r,r2), static_cast<Scalar>(1));
square = MatrixType::Zero(rows, rows);
square.diagonal() = VectorType::Ones(rows);
VERIFY_IS_APPROX(square, MatrixType::Identity(rows, rows));
}
void test_eigen2_miscmatrices()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( miscMatrices(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( miscMatrices(Matrix4d()) );
CALL_SUBTEST_3( miscMatrices(MatrixXcf(3, 3)) );
CALL_SUBTEST_4( miscMatrices(MatrixXi(8, 12)) );
CALL_SUBTEST_5( miscMatrices(MatrixXcd(20, 20)) );
}
}

77
test/eigen2/eigen2_mixingtypes.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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/.
#ifndef EIGEN_NO_STATIC_ASSERT
#define EIGEN_NO_STATIC_ASSERT // turn static asserts into runtime asserts in order to check them
#endif
#ifndef EIGEN_DONT_VECTORIZE
#define EIGEN_DONT_VECTORIZE // SSE intrinsics aren't designed to allow mixing types
#endif
#include "main.h"
template<int SizeAtCompileType> void mixingtypes(int size = SizeAtCompileType)
{
typedef Matrix<float, SizeAtCompileType, SizeAtCompileType> Mat_f;
typedef Matrix<double, SizeAtCompileType, SizeAtCompileType> Mat_d;
typedef Matrix<std::complex<float>, SizeAtCompileType, SizeAtCompileType> Mat_cf;
typedef Matrix<std::complex<double>, SizeAtCompileType, SizeAtCompileType> Mat_cd;
typedef Matrix<float, SizeAtCompileType, 1> Vec_f;
typedef Matrix<double, SizeAtCompileType, 1> Vec_d;
typedef Matrix<std::complex<float>, SizeAtCompileType, 1> Vec_cf;
typedef Matrix<std::complex<double>, SizeAtCompileType, 1> Vec_cd;
Mat_f mf(size,size);
Mat_d md(size,size);
Mat_cf mcf(size,size);
Mat_cd mcd(size,size);
Vec_f vf(size,1);
Vec_d vd(size,1);
Vec_cf vcf(size,1);
Vec_cd vcd(size,1);
mf+mf;
VERIFY_RAISES_ASSERT(mf+md);
VERIFY_RAISES_ASSERT(mf+mcf);
VERIFY_RAISES_ASSERT(vf=vd);
VERIFY_RAISES_ASSERT(vf+=vd);
VERIFY_RAISES_ASSERT(mcd=md);
mf*mf;
md*mcd;
mcd*md;
mf*vcf;
mcf*vf;
mcf *= mf;
vcd = md*vcd;
vcf = mcf*vf;
#if 0
// these are know generating hard build errors in eigen3
VERIFY_RAISES_ASSERT(mf*md);
VERIFY_RAISES_ASSERT(mcf*mcd);
VERIFY_RAISES_ASSERT(mcf*vcd);
VERIFY_RAISES_ASSERT(vcf = mf*vf);
vf.eigen2_dot(vf);
VERIFY_RAISES_ASSERT(vd.eigen2_dot(vf));
VERIFY_RAISES_ASSERT(vcf.eigen2_dot(vf)); // yeah eventually we should allow this but i'm too lazy to make that change now in Dot.h
// especially as that might be rewritten as cwise product .sum() which would make that automatic.
#endif
}
void test_eigen2_mixingtypes()
{
// check that our operator new is indeed called:
CALL_SUBTEST_1(mixingtypes<3>());
CALL_SUBTEST_2(mixingtypes<4>());
CALL_SUBTEST_3(mixingtypes<Dynamic>(20));
}

149
test/eigen2/eigen2_newstdvector.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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/.
#define EIGEN_USE_NEW_STDVECTOR
#include "main.h"
#include <Eigen/StdVector>
#include <Eigen/Geometry>
template<typename MatrixType>
void check_stdvector_matrix(const MatrixType& m)
{
int rows = m.rows();
int cols = m.cols();
MatrixType x = MatrixType::Random(rows,cols), y = MatrixType::Random(rows,cols);
std::vector<MatrixType,Eigen::aligned_allocator<MatrixType> > v(10, MatrixType(rows,cols)), w(20, y);
v[5] = x;
w[6] = v[5];
VERIFY_IS_APPROX(w[6], v[5]);
v = w;
for(int i = 0; i < 20; i++)
{
VERIFY_IS_APPROX(w[i], v[i]);
}
v.resize(21);
v[20] = x;
VERIFY_IS_APPROX(v[20], x);
v.resize(22,y);
VERIFY_IS_APPROX(v[21], y);
v.push_back(x);
VERIFY_IS_APPROX(v[22], x);
VERIFY((std::size_t)&(v[22]) == (std::size_t)&(v[21]) + sizeof(MatrixType));
// do a lot of push_back such that the vector gets internally resized
// (with memory reallocation)
MatrixType* ref = &w[0];
for(int i=0; i<30 || ((ref==&w[0]) && i<300); ++i)
v.push_back(w[i%w.size()]);
for(unsigned int i=23; i<v.size(); ++i)
{
VERIFY(v[i]==w[(i-23)%w.size()]);
}
}
template<typename TransformType>
void check_stdvector_transform(const TransformType&)
{
typedef typename TransformType::MatrixType MatrixType;
TransformType x(MatrixType::Random()), y(MatrixType::Random());
std::vector<TransformType,Eigen::aligned_allocator<TransformType> > v(10), w(20, y);
v[5] = x;
w[6] = v[5];
VERIFY_IS_APPROX(w[6], v[5]);
v = w;
for(int i = 0; i < 20; i++)
{
VERIFY_IS_APPROX(w[i], v[i]);
}
v.resize(21);
v[20] = x;
VERIFY_IS_APPROX(v[20], x);
v.resize(22,y);
VERIFY_IS_APPROX(v[21], y);
v.push_back(x);
VERIFY_IS_APPROX(v[22], x);
VERIFY((std::size_t)&(v[22]) == (std::size_t)&(v[21]) + sizeof(TransformType));
// do a lot of push_back such that the vector gets internally resized
// (with memory reallocation)
TransformType* ref = &w[0];
for(int i=0; i<30 || ((ref==&w[0]) && i<300); ++i)
v.push_back(w[i%w.size()]);
for(unsigned int i=23; i<v.size(); ++i)
{
VERIFY(v[i].matrix()==w[(i-23)%w.size()].matrix());
}
}
template<typename QuaternionType>
void check_stdvector_quaternion(const QuaternionType&)
{
typedef typename QuaternionType::Coefficients Coefficients;
QuaternionType x(Coefficients::Random()), y(Coefficients::Random());
std::vector<QuaternionType,Eigen::aligned_allocator<QuaternionType> > v(10), w(20, y);
v[5] = x;
w[6] = v[5];
VERIFY_IS_APPROX(w[6], v[5]);
v = w;
for(int i = 0; i < 20; i++)
{
VERIFY_IS_APPROX(w[i], v[i]);
}
v.resize(21);
v[20] = x;
VERIFY_IS_APPROX(v[20], x);
v.resize(22,y);
VERIFY_IS_APPROX(v[21], y);
v.push_back(x);
VERIFY_IS_APPROX(v[22], x);
VERIFY((std::size_t)&(v[22]) == (std::size_t)&(v[21]) + sizeof(QuaternionType));
// do a lot of push_back such that the vector gets internally resized
// (with memory reallocation)
QuaternionType* ref = &w[0];
for(int i=0; i<30 || ((ref==&w[0]) && i<300); ++i)
v.push_back(w[i%w.size()]);
for(unsigned int i=23; i<v.size(); ++i)
{
VERIFY(v[i].coeffs()==w[(i-23)%w.size()].coeffs());
}
}
void test_eigen2_newstdvector()
{
// some non vectorizable fixed sizes
CALL_SUBTEST_1(check_stdvector_matrix(Vector2f()));
CALL_SUBTEST_1(check_stdvector_matrix(Matrix3f()));
CALL_SUBTEST_1(check_stdvector_matrix(Matrix3d()));
// some vectorizable fixed sizes
CALL_SUBTEST_2(check_stdvector_matrix(Matrix2f()));
CALL_SUBTEST_2(check_stdvector_matrix(Vector4f()));
CALL_SUBTEST_2(check_stdvector_matrix(Matrix4f()));
CALL_SUBTEST_2(check_stdvector_matrix(Matrix4d()));
// some dynamic sizes
CALL_SUBTEST_3(check_stdvector_matrix(MatrixXd(1,1)));
CALL_SUBTEST_3(check_stdvector_matrix(VectorXd(20)));
CALL_SUBTEST_3(check_stdvector_matrix(RowVectorXf(20)));
CALL_SUBTEST_3(check_stdvector_matrix(MatrixXcf(10,10)));
// some Transform
CALL_SUBTEST_4(check_stdvector_transform(Transform2f()));
CALL_SUBTEST_4(check_stdvector_transform(Transform3f()));
CALL_SUBTEST_4(check_stdvector_transform(Transform3d()));
//CALL_SUBTEST(check_stdvector_transform(Transform4d()));
// some Quaternion
CALL_SUBTEST_5(check_stdvector_quaternion(Quaternionf()));
CALL_SUBTEST_5(check_stdvector_quaternion(Quaterniond()));
}

63
test/eigen2/eigen2_nomalloc.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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/.
// this hack is needed to make this file compiles with -pedantic (gcc)
#ifdef __GNUC__
#define throw(X)
#endif
// discard stack allocation as that too bypasses malloc
#define EIGEN_STACK_ALLOCATION_LIMIT 0
// any heap allocation will raise an assert
#define EIGEN_NO_MALLOC
#include "main.h"
template<typename MatrixType> void nomalloc(const MatrixType& m)
{
/* this test check no dynamic memory allocation are issued with fixed-size matrices
*/
typedef typename MatrixType::Scalar Scalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
int rows = m.rows();
int cols = m.cols();
MatrixType m1 = MatrixType::Random(rows, cols),
m2 = MatrixType::Random(rows, cols),
m3(rows, cols),
mzero = MatrixType::Zero(rows, cols),
identity = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime>
::Identity(rows, rows),
square = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime>
::Random(rows, rows);
VectorType v1 = VectorType::Random(rows),
v2 = VectorType::Random(rows),
vzero = VectorType::Zero(rows);
Scalar s1 = ei_random<Scalar>();
int r = ei_random<int>(0, rows-1),
c = ei_random<int>(0, cols-1);
VERIFY_IS_APPROX((m1+m2)*s1, s1*m1+s1*m2);
VERIFY_IS_APPROX((m1+m2)(r,c), (m1(r,c))+(m2(r,c)));
VERIFY_IS_APPROX(m1.cwise() * m1.block(0,0,rows,cols), m1.cwise() * m1);
VERIFY_IS_APPROX((m1*m1.transpose())*m2, m1*(m1.transpose()*m2));
}
void test_eigen2_nomalloc()
{
// check that our operator new is indeed called:
VERIFY_RAISES_ASSERT(MatrixXd dummy = MatrixXd::Random(3,3));
CALL_SUBTEST_1( nomalloc(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( nomalloc(Matrix4d()) );
CALL_SUBTEST_3( nomalloc(Matrix<float,32,32>()) );
}

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test/eigen2/eigen2_packetmath.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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"
// using namespace Eigen;
template<typename Scalar> bool areApprox(const Scalar* a, const Scalar* b, int size)
{
for (int i=0; i<size; ++i)
if (!ei_isApprox(a[i],b[i])) return false;
return true;
}
#define CHECK_CWISE(REFOP, POP) { \
for (int i=0; i<PacketSize; ++i) \
ref[i] = REFOP(data1[i], data1[i+PacketSize]); \
ei_pstore(data2, POP(ei_pload(data1), ei_pload(data1+PacketSize))); \
VERIFY(areApprox(ref, data2, PacketSize) && #POP); \
}
#define REF_ADD(a,b) ((a)+(b))
#define REF_SUB(a,b) ((a)-(b))
#define REF_MUL(a,b) ((a)*(b))
#define REF_DIV(a,b) ((a)/(b))
namespace std {
template<> const complex<float>& min(const complex<float>& a, const complex<float>& b)
{ return a.real() < b.real() ? a : b; }
template<> const complex<float>& max(const complex<float>& a, const complex<float>& b)
{ return a.real() < b.real() ? b : a; }
}
template<typename Scalar> void packetmath()
{
typedef typename ei_packet_traits<Scalar>::type Packet;
const int PacketSize = ei_packet_traits<Scalar>::size;
const int size = PacketSize*4;
EIGEN_ALIGN_128 Scalar data1[ei_packet_traits<Scalar>::size*4];
EIGEN_ALIGN_128 Scalar data2[ei_packet_traits<Scalar>::size*4];
EIGEN_ALIGN_128 Packet packets[PacketSize*2];
EIGEN_ALIGN_128 Scalar ref[ei_packet_traits<Scalar>::size*4];
for (int i=0; i<size; ++i)
{
data1[i] = ei_random<Scalar>();
data2[i] = ei_random<Scalar>();
}
ei_pstore(data2, ei_pload(data1));
VERIFY(areApprox(data1, data2, PacketSize) && "aligned load/store");
for (int offset=0; offset<PacketSize; ++offset)
{
ei_pstore(data2, ei_ploadu(data1+offset));
VERIFY(areApprox(data1+offset, data2, PacketSize) && "ei_ploadu");
}
for (int offset=0; offset<PacketSize; ++offset)
{
ei_pstoreu(data2+offset, ei_pload(data1));
VERIFY(areApprox(data1, data2+offset, PacketSize) && "ei_pstoreu");
}
for (int offset=0; offset<PacketSize; ++offset)
{
packets[0] = ei_pload(data1);
packets[1] = ei_pload(data1+PacketSize);
if (offset==0) ei_palign<0>(packets[0], packets[1]);
else if (offset==1) ei_palign<1>(packets[0], packets[1]);
else if (offset==2) ei_palign<2>(packets[0], packets[1]);
else if (offset==3) ei_palign<3>(packets[0], packets[1]);
ei_pstore(data2, packets[0]);
for (int i=0; i<PacketSize; ++i)
ref[i] = data1[i+offset];
typedef Matrix<Scalar, PacketSize, 1> Vector;
VERIFY(areApprox(ref, data2, PacketSize) && "ei_palign");
}
CHECK_CWISE(REF_ADD, ei_padd);
CHECK_CWISE(REF_SUB, ei_psub);
CHECK_CWISE(REF_MUL, ei_pmul);
#ifndef EIGEN_VECTORIZE_ALTIVEC
if (!ei_is_same_type<Scalar,int>::ret)
CHECK_CWISE(REF_DIV, ei_pdiv);
#endif
CHECK_CWISE(std::min, ei_pmin);
CHECK_CWISE(std::max, ei_pmax);
for (int i=0; i<PacketSize; ++i)
ref[i] = data1[0];
ei_pstore(data2, ei_pset1(data1[0]));
VERIFY(areApprox(ref, data2, PacketSize) && "ei_pset1");
VERIFY(ei_isApprox(data1[0], ei_pfirst(ei_pload(data1))) && "ei_pfirst");
ref[0] = 0;
for (int i=0; i<PacketSize; ++i)
ref[0] += data1[i];
VERIFY(ei_isApprox(ref[0], ei_predux(ei_pload(data1))) && "ei_predux");
for (int j=0; j<PacketSize; ++j)
{
ref[j] = 0;
for (int i=0; i<PacketSize; ++i)
ref[j] += data1[i+j*PacketSize];
packets[j] = ei_pload(data1+j*PacketSize);
}
ei_pstore(data2, ei_preduxp(packets));
VERIFY(areApprox(ref, data2, PacketSize) && "ei_preduxp");
}
void test_eigen2_packetmath()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( packetmath<float>() );
CALL_SUBTEST_2( packetmath<double>() );
CALL_SUBTEST_3( packetmath<int>() );
CALL_SUBTEST_4( packetmath<std::complex<float> >() );
}
}

62
test/eigen2/eigen2_parametrizedline.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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"
#include <Eigen/Geometry>
#include <Eigen/LU>
#include <Eigen/QR>
template<typename LineType> void parametrizedline(const LineType& _line)
{
/* this test covers the following files:
ParametrizedLine.h
*/
const int dim = _line.dim();
typedef typename LineType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar, LineType::AmbientDimAtCompileTime, 1> VectorType;
typedef Matrix<Scalar, LineType::AmbientDimAtCompileTime,
LineType::AmbientDimAtCompileTime> MatrixType;
VectorType p0 = VectorType::Random(dim);
VectorType p1 = VectorType::Random(dim);
VectorType d0 = VectorType::Random(dim).normalized();
LineType l0(p0, d0);
Scalar s0 = ei_random<Scalar>();
Scalar s1 = ei_abs(ei_random<Scalar>());
VERIFY_IS_MUCH_SMALLER_THAN( l0.distance(p0), RealScalar(1) );
VERIFY_IS_MUCH_SMALLER_THAN( l0.distance(p0+s0*d0), RealScalar(1) );
VERIFY_IS_APPROX( (l0.projection(p1)-p1).norm(), l0.distance(p1) );
VERIFY_IS_MUCH_SMALLER_THAN( l0.distance(l0.projection(p1)), RealScalar(1) );
VERIFY_IS_APPROX( Scalar(l0.distance((p0+s0*d0) + d0.unitOrthogonal() * s1)), s1 );
// casting
const int Dim = LineType::AmbientDimAtCompileTime;
typedef typename GetDifferentType<Scalar>::type OtherScalar;
ParametrizedLine<OtherScalar,Dim> hp1f = l0.template cast<OtherScalar>();
VERIFY_IS_APPROX(hp1f.template cast<Scalar>(),l0);
ParametrizedLine<Scalar,Dim> hp1d = l0.template cast<Scalar>();
VERIFY_IS_APPROX(hp1d.template cast<Scalar>(),l0);
}
void test_eigen2_parametrizedline()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( parametrizedline(ParametrizedLine<float,2>()) );
CALL_SUBTEST_2( parametrizedline(ParametrizedLine<float,3>()) );
CALL_SUBTEST_3( parametrizedline(ParametrizedLine<double,4>()) );
CALL_SUBTEST_4( parametrizedline(ParametrizedLine<std::complex<double>,5>()) );
}
}

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test/eigen2/eigen2_prec_inverse_4x4.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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"
#include <Eigen/LU>
#include <algorithm>
template<typename T> std::string type_name() { return "other"; }
template<> std::string type_name<float>() { return "float"; }
template<> std::string type_name<double>() { return "double"; }
template<> std::string type_name<int>() { return "int"; }
template<> std::string type_name<std::complex<float> >() { return "complex<float>"; }
template<> std::string type_name<std::complex<double> >() { return "complex<double>"; }
template<> std::string type_name<std::complex<int> >() { return "complex<int>"; }
#define EIGEN_DEBUG_VAR(x) std::cerr << #x << " = " << x << std::endl;
template<typename T> inline typename NumTraits<T>::Real epsilon()
{
return std::numeric_limits<typename NumTraits<T>::Real>::epsilon();
}
template<typename MatrixType> void inverse_permutation_4x4()
{
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
Vector4i indices(0,1,2,3);
for(int i = 0; i < 24; ++i)
{
MatrixType m = MatrixType::Zero();
m(indices(0),0) = 1;
m(indices(1),1) = 1;
m(indices(2),2) = 1;
m(indices(3),3) = 1;
MatrixType inv = m.inverse();
double error = double( (m*inv-MatrixType::Identity()).norm() / epsilon<Scalar>() );
VERIFY(error == 0.0);
std::next_permutation(indices.data(),indices.data()+4);
}
}
template<typename MatrixType> void inverse_general_4x4(int repeat)
{
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
double error_sum = 0., error_max = 0.;
for(int i = 0; i < repeat; ++i)
{
MatrixType m;
RealScalar absdet;
do {
m = MatrixType::Random();
absdet = ei_abs(m.determinant());
} while(absdet < 10 * epsilon<Scalar>());
MatrixType inv = m.inverse();
double error = double( (m*inv-MatrixType::Identity()).norm() * absdet / epsilon<Scalar>() );
error_sum += error;
error_max = std::max(error_max, error);
}
std::cerr << "inverse_general_4x4, Scalar = " << type_name<Scalar>() << std::endl;
double error_avg = error_sum / repeat;
EIGEN_DEBUG_VAR(error_avg);
EIGEN_DEBUG_VAR(error_max);
VERIFY(error_avg < (NumTraits<Scalar>::IsComplex ? 8.0 : 1.25));
VERIFY(error_max < (NumTraits<Scalar>::IsComplex ? 64.0 : 20.0));
}
void test_eigen2_prec_inverse_4x4()
{
CALL_SUBTEST_1((inverse_permutation_4x4<Matrix4f>()));
CALL_SUBTEST_1(( inverse_general_4x4<Matrix4f>(200000 * g_repeat) ));
CALL_SUBTEST_2((inverse_permutation_4x4<Matrix<double,4,4,RowMajor> >()));
CALL_SUBTEST_2(( inverse_general_4x4<Matrix<double,4,4,RowMajor> >(200000 * g_repeat) ));
CALL_SUBTEST_3((inverse_permutation_4x4<Matrix4cf>()));
CALL_SUBTEST_3((inverse_general_4x4<Matrix4cf>(50000 * g_repeat)));
}

45
test/eigen2/eigen2_product_large.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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 "product.h"
void test_eigen2_product_large()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( product(MatrixXf(ei_random<int>(1,320), ei_random<int>(1,320))) );
CALL_SUBTEST_2( product(MatrixXd(ei_random<int>(1,320), ei_random<int>(1,320))) );
CALL_SUBTEST_3( product(MatrixXi(ei_random<int>(1,320), ei_random<int>(1,320))) );
CALL_SUBTEST_4( product(MatrixXcf(ei_random<int>(1,50), ei_random<int>(1,50))) );
CALL_SUBTEST_5( product(Matrix<float,Dynamic,Dynamic,RowMajor>(ei_random<int>(1,320), ei_random<int>(1,320))) );
}
#ifdef EIGEN_TEST_PART_6
{
// test a specific issue in DiagonalProduct
int N = 1000000;
VectorXf v = VectorXf::Ones(N);
MatrixXf m = MatrixXf::Ones(N,3);
m = (v+v).asDiagonal() * m;
VERIFY_IS_APPROX(m, MatrixXf::Constant(N,3,2));
}
{
// test deferred resizing in Matrix::operator=
MatrixXf a = MatrixXf::Random(10,4), b = MatrixXf::Random(4,10), c = a;
VERIFY_IS_APPROX((a = a * b), (c * b).eval());
}
{
MatrixXf mat1(10,10); mat1.setRandom();
MatrixXf mat2(32,10); mat2.setRandom();
MatrixXf result = mat1.row(2)*mat2.transpose();
VERIFY_IS_APPROX(result, (mat1.row(2)*mat2.transpose()).eval());
}
#endif
}

22
test/eigen2/eigen2_product_small.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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/.
#define EIGEN_NO_STATIC_ASSERT
#include "product.h"
void test_eigen2_product_small()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( product(Matrix<float, 3, 2>()) );
CALL_SUBTEST_2( product(Matrix<int, 3, 5>()) );
CALL_SUBTEST_3( product(Matrix3d()) );
CALL_SUBTEST_4( product(Matrix4d()) );
CALL_SUBTEST_5( product(Matrix4f()) );
}
}

69
test/eigen2/eigen2_qr.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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"
#include <Eigen/QR>
template<typename MatrixType> void qr(const MatrixType& m)
{
/* this test covers the following files:
QR.h
*/
int rows = m.rows();
int cols = m.cols();
typedef typename MatrixType::Scalar Scalar;
typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, MatrixType::ColsAtCompileTime> SquareMatrixType;
typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> VectorType;
MatrixType a = MatrixType::Random(rows,cols);
QR<MatrixType> qrOfA(a);
VERIFY_IS_APPROX(a, qrOfA.matrixQ() * qrOfA.matrixR());
VERIFY_IS_NOT_APPROX(a+MatrixType::Identity(rows, cols), qrOfA.matrixQ() * qrOfA.matrixR());
#if 0 // eigenvalues module not yet ready
SquareMatrixType b = a.adjoint() * a;
// check tridiagonalization
Tridiagonalization<SquareMatrixType> tridiag(b);
VERIFY_IS_APPROX(b, tridiag.matrixQ() * tridiag.matrixT() * tridiag.matrixQ().adjoint());
// check hessenberg decomposition
HessenbergDecomposition<SquareMatrixType> hess(b);
VERIFY_IS_APPROX(b, hess.matrixQ() * hess.matrixH() * hess.matrixQ().adjoint());
VERIFY_IS_APPROX(tridiag.matrixT(), hess.matrixH());
b = SquareMatrixType::Random(cols,cols);
hess.compute(b);
VERIFY_IS_APPROX(b, hess.matrixQ() * hess.matrixH() * hess.matrixQ().adjoint());
#endif
}
void test_eigen2_qr()
{
for(int i = 0; i < 1; i++) {
CALL_SUBTEST_1( qr(Matrix2f()) );
CALL_SUBTEST_2( qr(Matrix4d()) );
CALL_SUBTEST_3( qr(MatrixXf(12,8)) );
CALL_SUBTEST_4( qr(MatrixXcd(5,5)) );
CALL_SUBTEST_4( qr(MatrixXcd(7,3)) );
}
#ifdef EIGEN_TEST_PART_5
// small isFullRank test
{
Matrix3d mat;
mat << 1, 45, 1, 2, 2, 2, 1, 2, 3;
VERIFY(mat.qr().isFullRank());
mat << 1, 1, 1, 2, 2, 2, 1, 2, 3;
//always returns true in eigen2support
//VERIFY(!mat.qr().isFullRank());
}
#endif
}

158
test/eigen2/eigen2_qtvector.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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/.
#define EIGEN_WORK_AROUND_QT_BUG_CALLING_WRONG_OPERATOR_NEW_FIXED_IN_QT_4_5
#include "main.h"
#include <Eigen/Geometry>
#include <Eigen/QtAlignedMalloc>
#include <QtCore/QVector>
template<typename MatrixType>
void check_qtvector_matrix(const MatrixType& m)
{
int rows = m.rows();
int cols = m.cols();
MatrixType x = MatrixType::Random(rows,cols), y = MatrixType::Random(rows,cols);
QVector<MatrixType> v(10, MatrixType(rows,cols)), w(20, y);
for(int i = 0; i < 20; i++)
{
VERIFY_IS_APPROX(w[i], y);
}
v[5] = x;
w[6] = v[5];
VERIFY_IS_APPROX(w[6], v[5]);
v = w;
for(int i = 0; i < 20; i++)
{
VERIFY_IS_APPROX(w[i], v[i]);
}
v.resize(21);
v[20] = x;
VERIFY_IS_APPROX(v[20], x);
v.fill(y,22);
VERIFY_IS_APPROX(v[21], y);
v.push_back(x);
VERIFY_IS_APPROX(v[22], x);
VERIFY((size_t)&(v[22]) == (size_t)&(v[21]) + sizeof(MatrixType));
// do a lot of push_back such that the vector gets internally resized
// (with memory reallocation)
MatrixType* ref = &w[0];
for(int i=0; i<30 || ((ref==&w[0]) && i<300); ++i)
v.push_back(w[i%w.size()]);
for(int i=23; i<v.size(); ++i)
{
VERIFY(v[i]==w[(i-23)%w.size()]);
}
}
template<typename TransformType>
void check_qtvector_transform(const TransformType&)
{
typedef typename TransformType::MatrixType MatrixType;
TransformType x(MatrixType::Random()), y(MatrixType::Random());
QVector<TransformType> v(10), w(20, y);
v[5] = x;
w[6] = v[5];
VERIFY_IS_APPROX(w[6], v[5]);
v = w;
for(int i = 0; i < 20; i++)
{
VERIFY_IS_APPROX(w[i], v[i]);
}
v.resize(21);
v[20] = x;
VERIFY_IS_APPROX(v[20], x);
v.fill(y,22);
VERIFY_IS_APPROX(v[21], y);
v.push_back(x);
VERIFY_IS_APPROX(v[22], x);
VERIFY((size_t)&(v[22]) == (size_t)&(v[21]) + sizeof(TransformType));
// do a lot of push_back such that the vector gets internally resized
// (with memory reallocation)
TransformType* ref = &w[0];
for(int i=0; i<30 || ((ref==&w[0]) && i<300); ++i)
v.push_back(w[i%w.size()]);
for(unsigned int i=23; int(i)<v.size(); ++i)
{
VERIFY(v[i].matrix()==w[(i-23)%w.size()].matrix());
}
}
template<typename QuaternionType>
void check_qtvector_quaternion(const QuaternionType&)
{
typedef typename QuaternionType::Coefficients Coefficients;
QuaternionType x(Coefficients::Random()), y(Coefficients::Random());
QVector<QuaternionType> v(10), w(20, y);
v[5] = x;
w[6] = v[5];
VERIFY_IS_APPROX(w[6], v[5]);
v = w;
for(int i = 0; i < 20; i++)
{
VERIFY_IS_APPROX(w[i], v[i]);
}
v.resize(21);
v[20] = x;
VERIFY_IS_APPROX(v[20], x);
v.fill(y,22);
VERIFY_IS_APPROX(v[21], y);
v.push_back(x);
VERIFY_IS_APPROX(v[22], x);
VERIFY((size_t)&(v[22]) == (size_t)&(v[21]) + sizeof(QuaternionType));
// do a lot of push_back such that the vector gets internally resized
// (with memory reallocation)
QuaternionType* ref = &w[0];
for(int i=0; i<30 || ((ref==&w[0]) && i<300); ++i)
v.push_back(w[i%w.size()]);
for(unsigned int i=23; int(i)<v.size(); ++i)
{
VERIFY(v[i].coeffs()==w[(i-23)%w.size()].coeffs());
}
}
void test_eigen2_qtvector()
{
// some non vectorizable fixed sizes
CALL_SUBTEST_1(check_qtvector_matrix(Vector2f()));
CALL_SUBTEST_1(check_qtvector_matrix(Matrix3f()));
CALL_SUBTEST_1(check_qtvector_matrix(Matrix3d()));
// some vectorizable fixed sizes
CALL_SUBTEST_2(check_qtvector_matrix(Matrix2f()));
CALL_SUBTEST_2(check_qtvector_matrix(Vector4f()));
CALL_SUBTEST_2(check_qtvector_matrix(Matrix4f()));
CALL_SUBTEST_2(check_qtvector_matrix(Matrix4d()));
// some dynamic sizes
CALL_SUBTEST_3(check_qtvector_matrix(MatrixXd(1,1)));
CALL_SUBTEST_3(check_qtvector_matrix(VectorXd(20)));
CALL_SUBTEST_3(check_qtvector_matrix(RowVectorXf(20)));
CALL_SUBTEST_3(check_qtvector_matrix(MatrixXcf(10,10)));
// some Transform
CALL_SUBTEST_4(check_qtvector_transform(Transform2f()));
CALL_SUBTEST_4(check_qtvector_transform(Transform3f()));
CALL_SUBTEST_4(check_qtvector_transform(Transform3d()));
//CALL_SUBTEST_4(check_qtvector_transform(Transform4d()));
// some Quaternion
CALL_SUBTEST_5(check_qtvector_quaternion(Quaternionf()));
CALL_SUBTEST_5(check_qtvector_quaternion(Quaternionf()));
}

136
test/eigen2/eigen2_regression.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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"
#include <Eigen/LeastSquares>
template<typename VectorType,
typename HyperplaneType>
void makeNoisyCohyperplanarPoints(int numPoints,
VectorType **points,
HyperplaneType *hyperplane,
typename VectorType::Scalar noiseAmplitude)
{
typedef typename VectorType::Scalar Scalar;
const int size = points[0]->size();
// pick a random hyperplane, store the coefficients of its equation
hyperplane->coeffs().resize(size + 1);
for(int j = 0; j < size + 1; j++)
{
do {
hyperplane->coeffs().coeffRef(j) = ei_random<Scalar>();
} while(ei_abs(hyperplane->coeffs().coeff(j)) < 0.5);
}
// now pick numPoints random points on this hyperplane
for(int i = 0; i < numPoints; i++)
{
VectorType& cur_point = *(points[i]);
do
{
cur_point = VectorType::Random(size)/*.normalized()*/;
// project cur_point onto the hyperplane
Scalar x = - (hyperplane->coeffs().start(size).cwise()*cur_point).sum();
cur_point *= hyperplane->coeffs().coeff(size) / x;
} while( cur_point.norm() < 0.5
|| cur_point.norm() > 2.0 );
}
// add some noise to these points
for(int i = 0; i < numPoints; i++ )
*(points[i]) += noiseAmplitude * VectorType::Random(size);
}
template<typename VectorType>
void check_linearRegression(int numPoints,
VectorType **points,
const VectorType& original,
typename VectorType::Scalar tolerance)
{
int size = points[0]->size();
assert(size==2);
VectorType result(size);
linearRegression(numPoints, points, &result, 1);
typename VectorType::Scalar error = (result - original).norm() / original.norm();
VERIFY(ei_abs(error) < ei_abs(tolerance));
}
template<typename VectorType,
typename HyperplaneType>
void check_fitHyperplane(int numPoints,
VectorType **points,
const HyperplaneType& original,
typename VectorType::Scalar tolerance)
{
int size = points[0]->size();
HyperplaneType result(size);
fitHyperplane(numPoints, points, &result);
result.coeffs() *= original.coeffs().coeff(size)/result.coeffs().coeff(size);
typename VectorType::Scalar error = (result.coeffs() - original.coeffs()).norm() / original.coeffs().norm();
std::cout << ei_abs(error) << " xxx " << ei_abs(tolerance) << std::endl;
VERIFY(ei_abs(error) < ei_abs(tolerance));
}
void test_eigen2_regression()
{
for(int i = 0; i < g_repeat; i++)
{
#ifdef EIGEN_TEST_PART_1
{
Vector2f points2f [1000];
Vector2f *points2f_ptrs [1000];
for(int i = 0; i < 1000; i++) points2f_ptrs[i] = &(points2f[i]);
Vector2f coeffs2f;
Hyperplane<float,2> coeffs3f;
makeNoisyCohyperplanarPoints(1000, points2f_ptrs, &coeffs3f, 0.01f);
coeffs2f[0] = -coeffs3f.coeffs()[0]/coeffs3f.coeffs()[1];
coeffs2f[1] = -coeffs3f.coeffs()[2]/coeffs3f.coeffs()[1];
CALL_SUBTEST(check_linearRegression(10, points2f_ptrs, coeffs2f, 0.05f));
CALL_SUBTEST(check_linearRegression(100, points2f_ptrs, coeffs2f, 0.01f));
CALL_SUBTEST(check_linearRegression(1000, points2f_ptrs, coeffs2f, 0.002f));
}
#endif
#ifdef EIGEN_TEST_PART_2
{
Vector2f points2f [1000];
Vector2f *points2f_ptrs [1000];
for(int i = 0; i < 1000; i++) points2f_ptrs[i] = &(points2f[i]);
Hyperplane<float,2> coeffs3f;
makeNoisyCohyperplanarPoints(1000, points2f_ptrs, &coeffs3f, 0.01f);
CALL_SUBTEST(check_fitHyperplane(10, points2f_ptrs, coeffs3f, 0.05f));
CALL_SUBTEST(check_fitHyperplane(100, points2f_ptrs, coeffs3f, 0.01f));
CALL_SUBTEST(check_fitHyperplane(1000, points2f_ptrs, coeffs3f, 0.002f));
}
#endif
#ifdef EIGEN_TEST_PART_3
{
Vector4d points4d [1000];
Vector4d *points4d_ptrs [1000];
for(int i = 0; i < 1000; i++) points4d_ptrs[i] = &(points4d[i]);
Hyperplane<double,4> coeffs5d;
makeNoisyCohyperplanarPoints(1000, points4d_ptrs, &coeffs5d, 0.01);
CALL_SUBTEST(check_fitHyperplane(10, points4d_ptrs, coeffs5d, 0.05));
CALL_SUBTEST(check_fitHyperplane(100, points4d_ptrs, coeffs5d, 0.01));
CALL_SUBTEST(check_fitHyperplane(1000, points4d_ptrs, coeffs5d, 0.002));
}
#endif
#ifdef EIGEN_TEST_PART_4
{
VectorXcd *points11cd_ptrs[1000];
for(int i = 0; i < 1000; i++) points11cd_ptrs[i] = new VectorXcd(11);
Hyperplane<std::complex<double>,Dynamic> *coeffs12cd = new Hyperplane<std::complex<double>,Dynamic>(11);
makeNoisyCohyperplanarPoints(1000, points11cd_ptrs, coeffs12cd, 0.01);
CALL_SUBTEST(check_fitHyperplane(100, points11cd_ptrs, *coeffs12cd, 0.025));
CALL_SUBTEST(check_fitHyperplane(1000, points11cd_ptrs, *coeffs12cd, 0.006));
delete coeffs12cd;
for(int i = 0; i < 1000; i++) delete points11cd_ptrs[i];
}
#endif
}
}

31
test/eigen2/eigen2_sizeof.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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 MatrixType> void verifySizeOf(const MatrixType&)
{
typedef typename MatrixType::Scalar Scalar;
if (MatrixType::RowsAtCompileTime!=Dynamic && MatrixType::ColsAtCompileTime!=Dynamic)
VERIFY(sizeof(MatrixType)==sizeof(Scalar)*MatrixType::SizeAtCompileTime);
else
VERIFY(sizeof(MatrixType)==sizeof(Scalar*) + 2 * sizeof(typename MatrixType::Index));
}
void test_eigen2_sizeof()
{
CALL_SUBTEST( verifySizeOf(Matrix<float, 1, 1>()) );
CALL_SUBTEST( verifySizeOf(Matrix4d()) );
CALL_SUBTEST( verifySizeOf(Matrix<double, 4, 2>()) );
CALL_SUBTEST( verifySizeOf(Matrix<bool, 7, 5>()) );
CALL_SUBTEST( verifySizeOf(MatrixXcf(3, 3)) );
CALL_SUBTEST( verifySizeOf(MatrixXi(8, 12)) );
CALL_SUBTEST( verifySizeOf(MatrixXcd(20, 20)) );
CALL_SUBTEST( verifySizeOf(Matrix<float, 100, 100>()) );
}

42
test/eigen2/eigen2_smallvectors.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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 Scalar> void smallVectors()
{
typedef Matrix<Scalar, 1, 2> V2;
typedef Matrix<Scalar, 3, 1> V3;
typedef Matrix<Scalar, 1, 4> V4;
Scalar x1 = ei_random<Scalar>(),
x2 = ei_random<Scalar>(),
x3 = ei_random<Scalar>(),
x4 = ei_random<Scalar>();
V2 v2(x1, x2);
V3 v3(x1, x2, x3);
V4 v4(x1, x2, x3, x4);
VERIFY_IS_APPROX(x1, v2.x());
VERIFY_IS_APPROX(x1, v3.x());
VERIFY_IS_APPROX(x1, v4.x());
VERIFY_IS_APPROX(x2, v2.y());
VERIFY_IS_APPROX(x2, v3.y());
VERIFY_IS_APPROX(x2, v4.y());
VERIFY_IS_APPROX(x3, v3.z());
VERIFY_IS_APPROX(x3, v4.z());
VERIFY_IS_APPROX(x4, v4.w());
}
void test_eigen2_smallvectors()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST( smallVectors<int>() );
CALL_SUBTEST( smallVectors<float>() );
CALL_SUBTEST( smallVectors<double>() );
}
}

317
test/eigen2/eigen2_sparse_basic.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Daniel Gomez Ferro <dgomezferro@gmail.com>
//
// 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 "sparse.h"
template<typename SetterType,typename DenseType, typename Scalar, int Options>
bool test_random_setter(SparseMatrix<Scalar,Options>& sm, const DenseType& ref, const std::vector<Vector2i>& nonzeroCoords)
{
typedef SparseMatrix<Scalar,Options> SparseType;
{
sm.setZero();
SetterType w(sm);
std::vector<Vector2i> remaining = nonzeroCoords;
while(!remaining.empty())
{
int i = ei_random<int>(0,remaining.size()-1);
w(remaining[i].x(),remaining[i].y()) = ref.coeff(remaining[i].x(),remaining[i].y());
remaining[i] = remaining.back();
remaining.pop_back();
}
}
return sm.isApprox(ref);
}
template<typename SetterType,typename DenseType, typename T>
bool test_random_setter(DynamicSparseMatrix<T>& sm, const DenseType& ref, const std::vector<Vector2i>& nonzeroCoords)
{
sm.setZero();
std::vector<Vector2i> remaining = nonzeroCoords;
while(!remaining.empty())
{
int i = ei_random<int>(0,remaining.size()-1);
sm.coeffRef(remaining[i].x(),remaining[i].y()) = ref.coeff(remaining[i].x(),remaining[i].y());
remaining[i] = remaining.back();
remaining.pop_back();
}
return sm.isApprox(ref);
}
template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& ref)
{
const int rows = ref.rows();
const int cols = ref.cols();
typedef typename SparseMatrixType::Scalar Scalar;
enum { Flags = SparseMatrixType::Flags };
double density = std::max(8./(rows*cols), 0.01);
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
typedef Matrix<Scalar,Dynamic,1> DenseVector;
Scalar eps = 1e-6;
SparseMatrixType m(rows, cols);
DenseMatrix refMat = DenseMatrix::Zero(rows, cols);
DenseVector vec1 = DenseVector::Random(rows);
Scalar s1 = ei_random<Scalar>();
std::vector<Vector2i> zeroCoords;
std::vector<Vector2i> nonzeroCoords;
initSparse<Scalar>(density, refMat, m, 0, &zeroCoords, &nonzeroCoords);
if (zeroCoords.size()==0 || nonzeroCoords.size()==0)
return;
// test coeff and coeffRef
for (int i=0; i<(int)zeroCoords.size(); ++i)
{
VERIFY_IS_MUCH_SMALLER_THAN( m.coeff(zeroCoords[i].x(),zeroCoords[i].y()), eps );
if(ei_is_same_type<SparseMatrixType,SparseMatrix<Scalar,Flags> >::ret)
VERIFY_RAISES_ASSERT( m.coeffRef(zeroCoords[0].x(),zeroCoords[0].y()) = 5 );
}
VERIFY_IS_APPROX(m, refMat);
m.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
refMat.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
VERIFY_IS_APPROX(m, refMat);
/*
// test InnerIterators and Block expressions
for (int t=0; t<10; ++t)
{
int j = ei_random<int>(0,cols-1);
int i = ei_random<int>(0,rows-1);
int w = ei_random<int>(1,cols-j-1);
int h = ei_random<int>(1,rows-i-1);
// VERIFY_IS_APPROX(m.block(i,j,h,w), refMat.block(i,j,h,w));
for(int c=0; c<w; c++)
{
VERIFY_IS_APPROX(m.block(i,j,h,w).col(c), refMat.block(i,j,h,w).col(c));
for(int r=0; r<h; r++)
{
// VERIFY_IS_APPROX(m.block(i,j,h,w).col(c).coeff(r), refMat.block(i,j,h,w).col(c).coeff(r));
}
}
// for(int r=0; r<h; r++)
// {
// VERIFY_IS_APPROX(m.block(i,j,h,w).row(r), refMat.block(i,j,h,w).row(r));
// for(int c=0; c<w; c++)
// {
// VERIFY_IS_APPROX(m.block(i,j,h,w).row(r).coeff(c), refMat.block(i,j,h,w).row(r).coeff(c));
// }
// }
}
for(int c=0; c<cols; c++)
{
VERIFY_IS_APPROX(m.col(c) + m.col(c), (m + m).col(c));
VERIFY_IS_APPROX(m.col(c) + m.col(c), refMat.col(c) + refMat.col(c));
}
for(int r=0; r<rows; r++)
{
VERIFY_IS_APPROX(m.row(r) + m.row(r), (m + m).row(r));
VERIFY_IS_APPROX(m.row(r) + m.row(r), refMat.row(r) + refMat.row(r));
}
*/
// test SparseSetters
// coherent setter
// TODO extend the MatrixSetter
// {
// m.setZero();
// VERIFY_IS_NOT_APPROX(m, refMat);
// SparseSetter<SparseMatrixType, FullyCoherentAccessPattern> w(m);
// for (int i=0; i<nonzeroCoords.size(); ++i)
// {
// w->coeffRef(nonzeroCoords[i].x(),nonzeroCoords[i].y()) = refMat.coeff(nonzeroCoords[i].x(),nonzeroCoords[i].y());
// }
// }
// VERIFY_IS_APPROX(m, refMat);
// random setter
// {
// m.setZero();
// VERIFY_IS_NOT_APPROX(m, refMat);
// SparseSetter<SparseMatrixType, RandomAccessPattern> w(m);
// std::vector<Vector2i> remaining = nonzeroCoords;
// while(!remaining.empty())
// {
// int i = ei_random<int>(0,remaining.size()-1);
// w->coeffRef(remaining[i].x(),remaining[i].y()) = refMat.coeff(remaining[i].x(),remaining[i].y());
// remaining[i] = remaining.back();
// remaining.pop_back();
// }
// }
// VERIFY_IS_APPROX(m, refMat);
VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, StdMapTraits> >(m,refMat,nonzeroCoords) ));
#ifdef EIGEN_UNORDERED_MAP_SUPPORT
VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, StdUnorderedMapTraits> >(m,refMat,nonzeroCoords) ));
#endif
#ifdef _DENSE_HASH_MAP_H_
VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, GoogleDenseHashMapTraits> >(m,refMat,nonzeroCoords) ));
#endif
#ifdef _SPARSE_HASH_MAP_H_
VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, GoogleSparseHashMapTraits> >(m,refMat,nonzeroCoords) ));
#endif
// test fillrand
{
DenseMatrix m1(rows,cols);
m1.setZero();
SparseMatrixType m2(rows,cols);
m2.startFill();
for (int j=0; j<cols; ++j)
{
for (int k=0; k<rows/2; ++k)
{
int i = ei_random<int>(0,rows-1);
if (m1.coeff(i,j)==Scalar(0))
m2.fillrand(i,j) = m1(i,j) = ei_random<Scalar>();
}
}
m2.endFill();
VERIFY_IS_APPROX(m2,m1);
}
// test RandomSetter
/*{
SparseMatrixType m1(rows,cols), m2(rows,cols);
DenseMatrix refM1 = DenseMatrix::Zero(rows, rows);
initSparse<Scalar>(density, refM1, m1);
{
Eigen::RandomSetter<SparseMatrixType > setter(m2);
for (int j=0; j<m1.outerSize(); ++j)
for (typename SparseMatrixType::InnerIterator i(m1,j); i; ++i)
setter(i.index(), j) = i.value();
}
VERIFY_IS_APPROX(m1, m2);
}*/
// std::cerr << m.transpose() << "\n\n" << refMat.transpose() << "\n\n";
// VERIFY_IS_APPROX(m, refMat);
// test basic computations
{
DenseMatrix refM1 = DenseMatrix::Zero(rows, rows);
DenseMatrix refM2 = DenseMatrix::Zero(rows, rows);
DenseMatrix refM3 = DenseMatrix::Zero(rows, rows);
DenseMatrix refM4 = DenseMatrix::Zero(rows, rows);
SparseMatrixType m1(rows, rows);
SparseMatrixType m2(rows, rows);
SparseMatrixType m3(rows, rows);
SparseMatrixType m4(rows, rows);
initSparse<Scalar>(density, refM1, m1);
initSparse<Scalar>(density, refM2, m2);
initSparse<Scalar>(density, refM3, m3);
initSparse<Scalar>(density, refM4, m4);
VERIFY_IS_APPROX(m1+m2, refM1+refM2);
VERIFY_IS_APPROX(m1+m2+m3, refM1+refM2+refM3);
VERIFY_IS_APPROX(m3.cwise()*(m1+m2), refM3.cwise()*(refM1+refM2));
VERIFY_IS_APPROX(m1*s1-m2, refM1*s1-refM2);
VERIFY_IS_APPROX(m1*=s1, refM1*=s1);
VERIFY_IS_APPROX(m1/=s1, refM1/=s1);
VERIFY_IS_APPROX(m1+=m2, refM1+=refM2);
VERIFY_IS_APPROX(m1-=m2, refM1-=refM2);
VERIFY_IS_APPROX(m1.col(0).eigen2_dot(refM2.row(0)), refM1.col(0).eigen2_dot(refM2.row(0)));
refM4.setRandom();
// sparse cwise* dense
VERIFY_IS_APPROX(m3.cwise()*refM4, refM3.cwise()*refM4);
// VERIFY_IS_APPROX(m3.cwise()/refM4, refM3.cwise()/refM4);
}
// test innerVector()
{
DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
SparseMatrixType m2(rows, rows);
initSparse<Scalar>(density, refMat2, m2);
int j0 = ei_random(0,rows-1);
int j1 = ei_random(0,rows-1);
VERIFY_IS_APPROX(m2.innerVector(j0), refMat2.col(j0));
VERIFY_IS_APPROX(m2.innerVector(j0)+m2.innerVector(j1), refMat2.col(j0)+refMat2.col(j1));
//m2.innerVector(j0) = 2*m2.innerVector(j1);
//refMat2.col(j0) = 2*refMat2.col(j1);
//VERIFY_IS_APPROX(m2, refMat2);
}
// test innerVectors()
{
DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
SparseMatrixType m2(rows, rows);
initSparse<Scalar>(density, refMat2, m2);
int j0 = ei_random(0,rows-2);
int j1 = ei_random(0,rows-2);
int n0 = ei_random<int>(1,rows-std::max(j0,j1));
VERIFY_IS_APPROX(m2.innerVectors(j0,n0), refMat2.block(0,j0,rows,n0));
VERIFY_IS_APPROX(m2.innerVectors(j0,n0)+m2.innerVectors(j1,n0),
refMat2.block(0,j0,rows,n0)+refMat2.block(0,j1,rows,n0));
//m2.innerVectors(j0,n0) = m2.innerVectors(j0,n0) + m2.innerVectors(j1,n0);
//refMat2.block(0,j0,rows,n0) = refMat2.block(0,j0,rows,n0) + refMat2.block(0,j1,rows,n0);
}
// test transpose
{
DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
SparseMatrixType m2(rows, rows);
initSparse<Scalar>(density, refMat2, m2);
VERIFY_IS_APPROX(m2.transpose().eval(), refMat2.transpose().eval());
VERIFY_IS_APPROX(m2.transpose(), refMat2.transpose());
}
// test prune
{
SparseMatrixType m2(rows, rows);
DenseMatrix refM2(rows, rows);
refM2.setZero();
int countFalseNonZero = 0;
int countTrueNonZero = 0;
m2.startFill();
for (int j=0; j<m2.outerSize(); ++j)
for (int i=0; i<m2.innerSize(); ++i)
{
float x = ei_random<float>(0,1);
if (x<0.1)
{
// do nothing
}
else if (x<0.5)
{
countFalseNonZero++;
m2.fill(i,j) = Scalar(0);
}
else
{
countTrueNonZero++;
m2.fill(i,j) = refM2(i,j) = Scalar(1);
}
}
m2.endFill();
VERIFY(countFalseNonZero+countTrueNonZero == m2.nonZeros());
VERIFY_IS_APPROX(m2, refM2);
m2.prune(1);
VERIFY(countTrueNonZero==m2.nonZeros());
VERIFY_IS_APPROX(m2, refM2);
}
}
void test_eigen2_sparse_basic()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( sparse_basic(SparseMatrix<double>(8, 8)) );
CALL_SUBTEST_2( sparse_basic(SparseMatrix<std::complex<double> >(16, 16)) );
CALL_SUBTEST_1( sparse_basic(SparseMatrix<double>(33, 33)) );
CALL_SUBTEST_3( sparse_basic(DynamicSparseMatrix<double>(8, 8)) );
}
}

115
test/eigen2/eigen2_sparse_product.cpp
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@ -1,115 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Daniel Gomez Ferro <dgomezferro@gmail.com>
//
// 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 "sparse.h"
template<typename SparseMatrixType> void sparse_product(const SparseMatrixType& ref)
{
const int rows = ref.rows();
const int cols = ref.cols();
typedef typename SparseMatrixType::Scalar Scalar;
enum { Flags = SparseMatrixType::Flags };
double density = std::max(8./(rows*cols), 0.01);
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
typedef Matrix<Scalar,Dynamic,1> DenseVector;
// test matrix-matrix product
{
DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
DenseMatrix refMat3 = DenseMatrix::Zero(rows, rows);
DenseMatrix refMat4 = DenseMatrix::Zero(rows, rows);
DenseMatrix dm4 = DenseMatrix::Zero(rows, rows);
SparseMatrixType m2(rows, rows);
SparseMatrixType m3(rows, rows);
SparseMatrixType m4(rows, rows);
initSparse<Scalar>(density, refMat2, m2);
initSparse<Scalar>(density, refMat3, m3);
initSparse<Scalar>(density, refMat4, m4);
VERIFY_IS_APPROX(m4=m2*m3, refMat4=refMat2*refMat3);
VERIFY_IS_APPROX(m4=m2.transpose()*m3, refMat4=refMat2.transpose()*refMat3);
VERIFY_IS_APPROX(m4=m2.transpose()*m3.transpose(), refMat4=refMat2.transpose()*refMat3.transpose());
VERIFY_IS_APPROX(m4=m2*m3.transpose(), refMat4=refMat2*refMat3.transpose());
// sparse * dense
VERIFY_IS_APPROX(dm4=m2*refMat3, refMat4=refMat2*refMat3);
VERIFY_IS_APPROX(dm4=m2*refMat3.transpose(), refMat4=refMat2*refMat3.transpose());
VERIFY_IS_APPROX(dm4=m2.transpose()*refMat3, refMat4=refMat2.transpose()*refMat3);
VERIFY_IS_APPROX(dm4=m2.transpose()*refMat3.transpose(), refMat4=refMat2.transpose()*refMat3.transpose());
// dense * sparse
VERIFY_IS_APPROX(dm4=refMat2*m3, refMat4=refMat2*refMat3);
VERIFY_IS_APPROX(dm4=refMat2*m3.transpose(), refMat4=refMat2*refMat3.transpose());
VERIFY_IS_APPROX(dm4=refMat2.transpose()*m3, refMat4=refMat2.transpose()*refMat3);
VERIFY_IS_APPROX(dm4=refMat2.transpose()*m3.transpose(), refMat4=refMat2.transpose()*refMat3.transpose());
VERIFY_IS_APPROX(m3=m3*m3, refMat3=refMat3*refMat3);
}
// test matrix - diagonal product
if(false) // it compiles, but the precision is terrible. probably doesn't matter in this branch....
{
DenseMatrix refM2 = DenseMatrix::Zero(rows, rows);
DenseMatrix refM3 = DenseMatrix::Zero(rows, rows);
DiagonalMatrix<DenseVector> d1(DenseVector::Random(rows));
SparseMatrixType m2(rows, rows);
SparseMatrixType m3(rows, rows);
initSparse<Scalar>(density, refM2, m2);
initSparse<Scalar>(density, refM3, m3);
VERIFY_IS_APPROX(m3=m2*d1, refM3=refM2*d1);
VERIFY_IS_APPROX(m3=m2.transpose()*d1, refM3=refM2.transpose()*d1);
VERIFY_IS_APPROX(m3=d1*m2, refM3=d1*refM2);
VERIFY_IS_APPROX(m3=d1*m2.transpose(), refM3=d1 * refM2.transpose());
}
// test self adjoint products
{
DenseMatrix b = DenseMatrix::Random(rows, rows);
DenseMatrix x = DenseMatrix::Random(rows, rows);
DenseMatrix refX = DenseMatrix::Random(rows, rows);
DenseMatrix refUp = DenseMatrix::Zero(rows, rows);
DenseMatrix refLo = DenseMatrix::Zero(rows, rows);
DenseMatrix refS = DenseMatrix::Zero(rows, rows);
SparseMatrixType mUp(rows, rows);
SparseMatrixType mLo(rows, rows);
SparseMatrixType mS(rows, rows);
do {
initSparse<Scalar>(density, refUp, mUp, ForceRealDiag|/*ForceNonZeroDiag|*/MakeUpperTriangular);
} while (refUp.isZero());
refLo = refUp.transpose().conjugate();
mLo = mUp.transpose().conjugate();
refS = refUp + refLo;
refS.diagonal() *= 0.5;
mS = mUp + mLo;
for (int k=0; k<mS.outerSize(); ++k)
for (typename SparseMatrixType::InnerIterator it(mS,k); it; ++it)
if (it.index() == k)
it.valueRef() *= 0.5;
VERIFY_IS_APPROX(refS.adjoint(), refS);
VERIFY_IS_APPROX(mS.transpose().conjugate(), mS);
VERIFY_IS_APPROX(mS, refS);
VERIFY_IS_APPROX(x=mS*b, refX=refS*b);
VERIFY_IS_APPROX(x=mUp.template marked<UpperTriangular|SelfAdjoint>()*b, refX=refS*b);
VERIFY_IS_APPROX(x=mLo.template marked<LowerTriangular|SelfAdjoint>()*b, refX=refS*b);
VERIFY_IS_APPROX(x=mS.template marked<SelfAdjoint>()*b, refX=refS*b);
}
}
void test_eigen2_sparse_product()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( sparse_product(SparseMatrix<double>(8, 8)) );
CALL_SUBTEST_2( sparse_product(SparseMatrix<std::complex<double> >(16, 16)) );
CALL_SUBTEST_1( sparse_product(SparseMatrix<double>(33, 33)) );
CALL_SUBTEST_3( sparse_product(DynamicSparseMatrix<double>(8, 8)) );
}
}

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