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// Copyright (c) 2016-2020 CNRS INRIA |
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#ifndef __pinocchio_math_matrix_hpp__ |
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#define __pinocchio_math_matrix_hpp__ |
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#include "pinocchio/macros.hpp" |
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#include "pinocchio/math/fwd.hpp" |
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#include <Eigen/Core> |
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#include <boost/type_traits.hpp> |
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namespace pinocchio |
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{ |
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template<typename Derived> |
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inline bool hasNaN(const Eigen::DenseBase<Derived> & m) |
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{ |
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✓✗✓✗ ✓✗ |
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return !((m.derived().array()==m.derived().array()).all()); |
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} |
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namespace internal |
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{ |
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template<typename MatrixLike, bool value = is_floating_point<typename MatrixLike::Scalar>::value> |
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struct isZeroAlgo |
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{ |
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typedef typename MatrixLike::Scalar Scalar; |
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typedef typename MatrixLike::RealScalar RealScalar; |
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static bool run(const Eigen::MatrixBase<MatrixLike> & mat, |
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const RealScalar & prec = |
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Eigen::NumTraits< Scalar >::dummy_precision()) |
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{ |
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return mat.isZero(prec); |
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} |
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}; |
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template<typename MatrixLike> |
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struct isZeroAlgo<MatrixLike,false> |
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{ |
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typedef typename MatrixLike::Scalar Scalar; |
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typedef typename MatrixLike::RealScalar RealScalar; |
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static bool run(const Eigen::MatrixBase<MatrixLike> & /*vec*/, |
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const RealScalar & prec = |
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Eigen::NumTraits< Scalar >::dummy_precision()) |
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{ |
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PINOCCHIO_UNUSED_VARIABLE(prec); |
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return true; |
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} |
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}; |
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} |
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template<typename MatrixLike> |
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inline bool isZero(const Eigen::MatrixBase<MatrixLike> & m, |
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const typename MatrixLike::RealScalar & prec = |
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Eigen::NumTraits< typename MatrixLike::Scalar >::dummy_precision()) |
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{ |
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return internal::isZeroAlgo<MatrixLike>::run(m,prec); |
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} |
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template<typename M1, typename M2> |
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struct MatrixMatrixProduct |
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{ |
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#if EIGEN_VERSION_AT_LEAST(3,2,90) |
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typedef typename Eigen::Product<M1,M2> type; |
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#else |
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typedef typename Eigen::ProductReturnType<M1,M2>::Type type; |
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#endif |
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}; |
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template<typename Scalar, typename Matrix> |
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struct ScalarMatrixProduct |
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{ |
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#if EIGEN_VERSION_AT_LEAST(3,3,0) |
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typedef Eigen::CwiseBinaryOp<EIGEN_CAT(EIGEN_CAT(Eigen::internal::scalar_,product),_op)<Scalar,typename Eigen::internal::traits<Matrix>::Scalar>, |
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const typename Eigen::internal::plain_constant_type<Matrix,Scalar>::type, const Matrix> type; |
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#elif EIGEN_VERSION_AT_LEAST(3,2,90) |
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typedef Eigen::CwiseUnaryOp<Eigen::internal::scalar_multiple_op<Scalar>, const Matrix> type; |
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#else |
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typedef const Eigen::CwiseUnaryOp<Eigen::internal::scalar_multiple_op<Scalar>, const Matrix> type; |
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#endif |
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}; |
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template<typename Matrix, typename Scalar> |
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struct MatrixScalarProduct |
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{ |
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#if EIGEN_VERSION_AT_LEAST(3,3,0) |
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typedef Eigen::CwiseBinaryOp<EIGEN_CAT(EIGEN_CAT(Eigen::internal::scalar_,product),_op)<typename Eigen::internal::traits<Matrix>::Scalar,Scalar>, |
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const Matrix, const typename Eigen::internal::plain_constant_type<Matrix,Scalar>::type> type; |
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#elif EIGEN_VERSION_AT_LEAST(3,2,90) |
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typedef Eigen::CwiseUnaryOp<Eigen::internal::scalar_multiple_op<Scalar>, const Matrix> type; |
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#else |
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typedef const Eigen::CwiseUnaryOp<Eigen::internal::scalar_multiple_op<Scalar>, const Matrix> type; |
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#endif |
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}; |
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namespace internal |
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{ |
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template<typename MatrixLike, bool value = is_floating_point<typename MatrixLike::Scalar>::value> |
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struct isUnitaryAlgo |
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{ |
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typedef typename MatrixLike::Scalar Scalar; |
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typedef typename MatrixLike::RealScalar RealScalar; |
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static bool run(const Eigen::MatrixBase<MatrixLike> & mat, |
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const RealScalar & prec = |
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Eigen::NumTraits< Scalar >::dummy_precision()) |
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{ |
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return mat.isUnitary(prec); |
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} |
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}; |
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template<typename MatrixLike> |
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struct isUnitaryAlgo<MatrixLike,false> |
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{ |
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typedef typename MatrixLike::Scalar Scalar; |
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typedef typename MatrixLike::RealScalar RealScalar; |
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static bool run(const Eigen::MatrixBase<MatrixLike> & /*vec*/, |
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const RealScalar & prec = |
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Eigen::NumTraits< Scalar >::dummy_precision()) |
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{ |
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PINOCCHIO_UNUSED_VARIABLE(prec); |
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return true; |
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} |
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}; |
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} |
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/// |
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/// \brief Check whether the input matrix is Unitary within the given precision. |
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/// |
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/// \param[in] mat Input matrix |
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/// \param[in] prec Required precision |
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/// |
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/// \returns true if mat is unitary within the precision prec |
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/// |
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template<typename MatrixLike> |
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inline bool isUnitary(const Eigen::MatrixBase<MatrixLike> & mat, |
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const typename MatrixLike::RealScalar & prec = |
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Eigen::NumTraits< typename MatrixLike::Scalar >::dummy_precision()) |
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{ |
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return internal::isUnitaryAlgo<MatrixLike>::run(mat,prec); |
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} |
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namespace internal |
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{ |
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template<typename VectorLike, bool value = is_floating_point<typename VectorLike::Scalar>::value> |
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struct isNormalizedAlgo |
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{ |
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typedef typename VectorLike::Scalar Scalar; |
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typedef typename VectorLike::RealScalar RealScalar; |
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static bool run(const Eigen::MatrixBase<VectorLike> & vec, |
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const RealScalar & prec = |
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Eigen::NumTraits<RealScalar>::dummy_precision()) |
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{ |
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return math::fabs(static_cast<RealScalar>(vec.norm() - RealScalar(1))) <= prec; |
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} |
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}; |
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template<typename VectorLike> |
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struct isNormalizedAlgo<VectorLike,false> |
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{ |
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typedef typename VectorLike::Scalar Scalar; |
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typedef typename VectorLike::RealScalar RealScalar; |
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static bool run(const Eigen::MatrixBase<VectorLike> & /*vec*/, |
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const RealScalar & prec = |
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Eigen::NumTraits<RealScalar>::dummy_precision()) |
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{ |
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PINOCCHIO_UNUSED_VARIABLE(prec); |
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return true; |
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} |
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}; |
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} |
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/// |
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/// \brief Check whether the input vector is Normalized within the given precision. |
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/// |
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/// \param[in] vec Input vector |
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/// \param[in] prec Required precision |
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/// |
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/// \returns true if vec is normalized within the precision prec. |
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/// |
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template<typename VectorLike> |
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inline bool isNormalized(const Eigen::MatrixBase<VectorLike> & vec, |
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const typename VectorLike::RealScalar & prec = |
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Eigen::NumTraits< typename VectorLike::Scalar >::dummy_precision()) |
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{ |
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EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorLike); |
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return internal::isNormalizedAlgo<VectorLike>::run(vec,prec); |
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} |
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namespace internal |
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{ |
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template<typename Scalar> |
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struct CallCorrectMatrixInverseAccordingToScalar |
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{ |
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template<typename MatrixIn, typename MatrixOut> |
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static void run(const Eigen::MatrixBase<MatrixIn> & m_in, |
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const Eigen::MatrixBase<MatrixOut> & dest) |
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{ |
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MatrixOut & dest_ = PINOCCHIO_EIGEN_CONST_CAST(MatrixOut,dest); |
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dest_.noalias() = m_in.inverse(); |
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} |
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}; |
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} |
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template<typename MatrixIn, typename MatrixOut> |
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inline void inverse(const Eigen::MatrixBase<MatrixIn> & m_in, |
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const Eigen::MatrixBase<MatrixOut> & dest) |
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{ |
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MatrixOut & dest_ = PINOCCHIO_EIGEN_CONST_CAST(MatrixOut,dest); |
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internal::CallCorrectMatrixInverseAccordingToScalar<typename MatrixIn::Scalar>::run(m_in,dest_); |
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} |
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} |
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#endif //#ifndef __pinocchio_math_matrix_hpp__ |