| /external/eigen/doc/snippets/ |
| JacobiSVD_basic.cpp | 3 JacobiSVD<MatrixXf> svd(m, ComputeThinU | ComputeThinV);
4 cout << "Its singular values are:" << endl << svd.singularValues() << endl;
5 cout << "Its left singular vectors are the columns of the thin U matrix:" << endl << svd.matrixU() << endl;
6 cout << "Its right singular vectors are the columns of the thin V matrix:" << endl << svd.matrixV() << endl;
9 cout << "A least-squares solution of m*x = rhs is:" << endl << svd.solve(rhs) << endl;
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| /external/eigen/test/eigen2/ |
| eigen2_svd.cpp | 11 #include <Eigen/SVD>
13 template<typename MatrixType> void svd(const MatrixType& m) function
16 SVD.h
33 SVD<MatrixType> svd(a);
36 sigma.block(0,0,cols,cols) = svd.singularValues().asDiagonal();
37 matU.block(0,0,rows,cols) = svd.matrixU();
38 VERIFY_IS_APPROX(a, matU * sigma * svd.matrixV().transpose());
49 SVD<MatrixType> svd(a)
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| /external/eigen/Eigen/src/SVD/ |
| JacobiSVD.h | 22 /*** QR preconditioners (R-SVD)
24 *** Their role is to reduce the problem of computing the SVD to the case of a square matrix.
25 *** This approach, known as R-SVD, is an optimization for rectangular-enough matrices, and is a requirement for
77 void allocate(const JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner>& svd)
79 if (svd.rows() != m_qr.rows() || svd.cols() != m_qr.cols())
81 m_qr = FullPivHouseholderQR<MatrixType>(svd.rows(), svd.cols());
83 if (svd.m_computeFullU) m_workspace.resize(svd.rows())
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| /external/eigen/test/ |
| jacobisvd.cpp | 15 #include <Eigen/SVD>
18 void jacobisvd_check_full(const MatrixType& m, const JacobiSVD<MatrixType, QRPreconditioner>& svd)
37 sigma.diagonal() = svd.singularValues().template cast<Scalar>();
38 MatrixUType u = svd.matrixU();
39 MatrixVType v = svd.matrixV();
56 JacobiSVD<MatrixType, QRPreconditioner> svd(m, computationOptions);
58 VERIFY_IS_APPROX(svd.singularValues(), referenceSvd.singularValues());
60 VERIFY_IS_APPROX(svd.matrixU(), referenceSvd.matrixU());
62 VERIFY_IS_APPROX(svd.matrixU(), referenceSvd.matrixU().leftCols(diagSize));
64 VERIFY_IS_APPROX(svd.matrixV(), referenceSvd.matrixV())
155 JacobiSVD<MatrixType> svd; local
219 JacobiSVD<MatrixType> svd; local
253 JacobiSVD<Matrix2d> svd; local
264 JacobiSVD<MatrixXf> svd; local
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| /external/eigen/Eigen/src/Geometry/ |
| Umeyama.h | 16 // * Eigen/SVD
74 * The analysis is involving the SVD having a complexity of \f$O(d^3)\f$
132 JacobiSVD<MatrixType> svd(sigma, ComputeFullU | ComputeFullV);
142 const VectorType& d = svd.singularValues();
145 if ( svd.matrixU().determinant() * svd.matrixV().determinant() > 0 ) {
146 Rt.block(0,0,m,m).noalias() = svd.matrixU()*svd.matrixV().transpose();
149 Rt.block(0,0,m,m).noalias() = svd.matrixU() * S.asDiagonal() * svd.matrixV().transpose()
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| Transform.h | [all...] |
| Quaternion.h | 600 JacobiSVD<Matrix<Scalar,2,3> > svd(m, ComputeFullV);
601 Vector3 axis = svd.matrixV().col(2);
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| /external/eigen/Eigen/src/Eigen2Support/Geometry/ |
| Transform.h | 594 * \sa computeRotationScaling(), computeScalingRotation(), class SVD
615 * \sa computeScalingRotation(), rotation(), class SVD
621 JacobiSVD<LinearMatrixType> svd(linear(), ComputeFullU|ComputeFullV);
622 Scalar x = (svd.matrixU() * svd.matrixV().adjoint()).determinant(); // so x has absolute value 1
623 Matrix<Scalar, Dim, 1> sv(svd.singularValues());
627 scaling->noalias() = svd.matrixV() * sv.asDiagonal() * svd.matrixV().adjoint();
631 LinearMatrixType m(svd.matrixU());
633 rotation->noalias() = m * svd.matrixV().adjoint()
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| /external/ceres-solver/internal/ceres/ |
| covariance_impl.cc | 40 #include "Eigen/SVD"
777 Eigen::JacobiSVD<Matrix> svd(dense_jacobian,
782 const Vector singular_values = svd.singularValues();
824 svd.matrixV() *
826 svd.matrixV().transpose();
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| /external/eigen/Eigen/src/Core/ |
| MatrixBase.h | 380 /////////// SVD module ///////////
385 SVD<PlainObject> svd() const;
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| /external/eigen/Eigen/src/Eigen2Support/ |
| SVD.h | 18 * \class SVD
20 * \brief Standard SVD decomposition of a matrix and associated features
22 * \param MatrixType the type of the matrix of which we are computing the SVD decomposition
24 * This class performs a standard SVD decomposition of a real matrix A of size \c M x \c N
28 * \sa MatrixBase::SVD()
30 template<typename MatrixType> class SVD
51 SVD() {} // a user who relied on compiler-generated default compiler reported problems with MSVC in 2.0.7
53 SVD(const MatrixType& matrix)
69 SVD& sort();
89 /** Computes / recomputes the SVD decomposition A = U S V^* of \a matri
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