/external/eigen/Eigen/src/Core/ |
H A D | Transpositions.h | 25 * Each transposition \f$ T_{i} \f$ applied on the left of a matrix (\f$ T_{i} M\f$) interchanges 26 * the rows \c i and \c indices[i] of the matrix \c M. 32 * To apply a sequence of transpositions to a matrix, simply use the operator * as in the following example: 38 * In this example, we detect that the matrix appears on both side, and so the transpositions 115 // might be usefull when the target matrix expression is complex, e.g.: 116 // object.matrix().block(..,..,..,..) = trans * object.matrix().block(..,..,..,..); 201 /** Constructs an uninitialized permutation matrix of given size. 327 /** \returns the \a matrix with the \a transpositions applied to the columns. 331 operator*(const MatrixBase<Derived>& matrix, argument 344 operator *(const TranspositionsBase<TranspositionDerived> &transpositions, const MatrixBase<Derived>& matrix) argument 367 transposition_matrix_product_retval(const TranspositionType& tr, const MatrixType& matrix) argument 416 operator *(const MatrixBase<Derived>& matrix, const Transpose& trt) argument [all...] |
/external/eigen/Eigen/src/Eigen2Support/ |
H A D | SVD.h | 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 53 SVD(const MatrixType& matrix) argument 54 : m_matU(matrix.rows(), (std::min)(matrix.rows(), matrix.cols())), 55 m_matV(matrix.cols(),matrix.cols()), 56 m_sigma((std::min)(matrix 94 compute(const MatrixType& matrix) argument [all...] |
/external/eigen/Eigen/src/Eigenvalues/ |
H A D | ComplexSchur.h | 28 * \brief Performs a complex Schur decomposition of a real or complex square matrix 30 * \tparam _MatrixType the type of the matrix of which we are 34 * Given a real or complex square matrix A, this class computes the 36 * complex matrix, and T is a complex upper triangular matrix. The 37 * diagonal of the matrix T corresponds to the eigenvalues of the 38 * matrix A. 41 * a given matrix. Alternatively, you can use the 78 * This is a square matrix with entries of type #ComplexScalar. 85 * \param [in] size Positive integer, size of the matrix whos 112 ComplexSchur(const MatrixType& matrix, bool computeU = true) argument 316 compute(const MatrixType& matrix, bool computeU) argument 353 run(ComplexSchur<MatrixType>& _this, const MatrixType& matrix, bool computeU) argument 364 run(ComplexSchur<MatrixType>& _this, const MatrixType& matrix, bool computeU) argument [all...] |
H A D | EigenSolver.h | 25 * \tparam _MatrixType the type of the matrix of which we are computing the 29 * The eigenvalues and eigenvectors of a matrix \f$ A \f$ are scalars 31 * \f$ D \f$ is a diagonal matrix with the eigenvalues on the diagonal, and 32 * \f$ V \f$ is a matrix with the eigenvectors as its columns, then \f$ A V = 33 * V D \f$. The matrix \f$ V \f$ is almost always invertible, in which case we 36 * The eigenvalues and eigenvectors of a matrix may be complex, even when the 37 * matrix is real. However, we can choose real matrices \f$ V \f$ and \f$ D 39 * matrix \f$ D \f$ is not required to be diagonal, but if it is allowed to 47 * a given matrix. Alternatively, you can use the 99 /** \brief Type for matrix o 146 EigenSolver(const MatrixType& matrix, bool computeEigenvectors = true) argument 365 compute(const MatrixType& matrix, bool computeEigenvectors) argument [all...] |
H A D | HessenbergDecomposition.h | 32 * \brief Reduces a square matrix to Hessenberg form by an orthogonal similarity transformation 34 * \tparam _MatrixType the type of the matrix of which we are computing the Hessenberg decomposition 36 * This class performs an Hessenberg decomposition of a matrix \f$ A \f$. In 38 * matrix \f$ Q \f$ and a Hessenberg matrix \f$ H \f$ such that \f$ A = Q H 39 * Q^T \f$. An orthogonal matrix is a matrix whose inverse equals its 40 * transpose (\f$ Q^{-1} = Q^T \f$). A Hessenberg matrix has zeros below the 42 * of a complex matrix is \f$ A = Q H Q^* \f$ with \f$ Q \f$ unitary (that is, 46 * given matrix 118 HessenbergDecomposition(const MatrixType& matrix) argument 150 compute(const MatrixType& matrix) argument [all...] |
H A D | Tridiagonalization.h | 34 * \brief Tridiagonal decomposition of a selfadjoint matrix 36 * \tparam _MatrixType the type of the matrix of which we are computing the 40 * This class performs a tridiagonal decomposition of a selfadjoint matrix \f$ A \f$ such that: 41 * \f$ A = Q T Q^* \f$ where \f$ Q \f$ is unitary and \f$ T \f$ a real symmetric tridiagonal matrix. 43 * A tridiagonal matrix is a matrix which has nonzero elements only on the 45 * decomposition of a selfadjoint matrix is in fact a tridiagonal 47 * eigenvalues and eigenvectors of a selfadjoint matrix. 50 * given matrix. Alternatively, you can use the Tridiagonalization(const MatrixType&) 103 * \param [in] size Positive integer, size of the matrix whos 129 Tridiagonalization(const MatrixType& matrix) argument 155 compute(const MatrixType& matrix) argument [all...] |
/external/eigen/Eigen/src/Geometry/ |
H A D | Homogeneous.h | 71 inline Homogeneous(const MatrixType& matrix) argument 72 : m_matrix(matrix) 135 * \returns a matrix expression of homogeneous column (or row) vectors 215 static const type& run (const TransformType& x) { return x.matrix(); }
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/external/eigen/Eigen/src/LU/ |
H A D | Inverse.h | 24 static inline void run(const MatrixType& matrix, ResultType& result) argument 26 result = matrix.partialPivLu().inverse(); 40 static inline void run(const MatrixType& matrix, ResultType& result) argument 43 result.coeffRef(0,0) = Scalar(1) / matrix.coeff(0,0); 51 const MatrixType& matrix, 59 determinant = matrix.coeff(0,0); 71 const MatrixType& matrix, const typename ResultType::Scalar& invdet, 74 result.coeffRef(0,0) = matrix.coeff(1,1) * invdet; 75 result.coeffRef(1,0) = -matrix.coeff(1,0) * invdet; 76 result.coeffRef(0,1) = -matrix 50 run( const MatrixType& matrix, const typename MatrixType::RealScalar& absDeterminantThreshold, ResultType& result, typename ResultType::Scalar& determinant, bool& invertible ) argument 70 compute_inverse_size2_helper( const MatrixType& matrix, const typename ResultType::Scalar& invdet, ResultType& result) argument 83 run(const MatrixType& matrix, ResultType& result) argument 94 run( const MatrixType& matrix, const typename MatrixType::RealScalar& absDeterminantThreshold, ResultType& inverse, typename ResultType::Scalar& determinant, bool& invertible ) argument 130 compute_inverse_size3_helper( const MatrixType& matrix, const typename ResultType::Scalar& invdet, const Matrix<typename ResultType::Scalar,3,1>& cofactors_col0, ResultType& result) argument 148 run(const MatrixType& matrix, ResultType& result) argument 164 run( const MatrixType& matrix, const typename MatrixType::RealScalar& absDeterminantThreshold, ResultType& inverse, typename ResultType::Scalar& determinant, bool& invertible ) argument 191 general_det3_helper(const MatrixBase<Derived>& matrix, int i1, int i2, int i3, int j1, int j2, int j3) argument 199 cofactor_4x4(const MatrixType& matrix) argument 217 run(const MatrixType& matrix, ResultType& result) argument 249 run( const MatrixType& matrix, const typename MatrixType::RealScalar& absDeterminantThreshold, ResultType& inverse, typename ResultType::Scalar& determinant, bool& invertible ) argument 282 inverse_impl(const MatrixType& matrix) argument [all...] |
H A D | PartialPivLU.h | 20 * \brief LU decomposition of a matrix with partial pivoting, and related features 22 * \param MatrixType the type of the matrix of which we are computing the LU decomposition 24 * This class represents a LU decomposition of a \b square \b invertible matrix, with partial pivoting: the matrix A 26 * is a permutation matrix. 29 * matrices. Thus LAPACK's dgesv and dgesvx require the matrix to be square and invertible. The present class 30 * does the same. It will assert that the matrix is square, but it won't (actually it can't) check that the 31 * matrix is invertible: it is your task to check that you only use this decomposition on invertible matrices. 41 * On the other hand, it is \b not suitable to determine whether a given matrix is invertible. 85 * \param matrix th 202 PartialPivLU(const MatrixType& matrix) argument 387 compute(const MatrixType& matrix) argument [all...] |
/external/eigen/Eigen/src/LU/arch/ |
H A D | Inverse_SSE.h | 12 // The SSE code for the 4x4 float and double matrix inverse in this file 14 // http://software.intel.com/en-us/articles/optimized-matrix-library-for-use-with-the-intel-pentiumr-4-processors-sse2-instructions/ 43 static void run(const MatrixType& matrix, ResultType& result) argument 47 // Load the full matrix into registers 48 __m128 _L1 = matrix.template packet<MatrixAlignment>( 0); 49 __m128 _L2 = matrix.template packet<MatrixAlignment>( 4); 50 __m128 _L3 = matrix.template packet<MatrixAlignment>( 8); 51 __m128 _L4 = matrix.template packet<MatrixAlignment>(12); 54 // original matrix is divide into four 2x2 sub-matrices. Since each 55 // register holds four matrix elemen 170 run(const MatrixType& matrix, ResultType& result) argument [all...] |
/external/eigen/Eigen/src/QR/ |
H A D | HouseholderQR.h | 22 * \brief Householder QR decomposition of a matrix 24 * \param MatrixType the type of the matrix of which we are computing the QR decomposition 26 * This class performs a QR decomposition of a matrix \b A into matrices \b Q and \b R 31 * by using Householder transformations. Here, \b Q a unitary matrix and \b R an upper triangular matrix. 82 /** \brief Constructs a QR factorization from a given matrix 84 * This constructor computes the QR factorization of the matrix \a matrix by calling 88 * HouseholderQR<MatrixType> qr(matrix.rows(), matrix 94 HouseholderQR(const MatrixType& matrix) argument 344 compute(const MatrixType& matrix) argument [all...] |
/external/eigen/Eigen/src/SparseCore/ |
H A D | SparseBlock.h | 127 _NestedMatrixType& matrix = const_cast<_NestedMatrixType&>(m_matrix);; local 129 // and/or it is not at the end of the nonzeros of the underlying matrix. 136 Index start = m_outerStart==0 ? 0 : matrix.outerIndexPtr()[m_outerStart]; // starting position of the current block 142 ? Index(matrix.data().allocatedSize()) + block_size 156 std::memcpy(&newdata.value(start+nnz), &matrix.data().value(end), tail_size*sizeof(Scalar)); 157 std::memcpy(&newdata.index(start+nnz), &matrix.data().index(end), tail_size*sizeof(Index)); 161 matrix.data().swap(newdata); 166 matrix.data().resize(start + nnz + tail_size); 168 std::memmove(&matrix.data().value(start+nnz), &matrix [all...] |
/external/eigen/blas/ |
H A D | common.h | 103 matrix(T* data, int rows, int cols, int stride) function
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/external/eigen/unsupported/Eigen/src/IterativeSolvers/ |
H A D | IncompleteCholesky.h | 23 * \tparam _MatrixType The type of the sparse matrix. It should be a symmetric 24 * matrix. It is advised to give a row-oriented sparse matrix 25 * \tparam _UpLo The triangular part of the matrix to reference. 44 IncompleteCholesky(const MatrixType& matrix) : m_shift(1),m_factorizationIsOk(false) argument 46 compute(matrix); 57 * \c NumericalIssue if the matrix appears to be negative. 85 void compute (const MatrixType& matrix) argument 87 analyzePattern(matrix); 88 factorize(matrix); [all...] |
/external/eigen/unsupported/Eigen/src/Skyline/ |
H A D | SkylineInplaceLU.h | 19 * \brief Inplace LU decomposition of a skyline matrix and associated features 21 * \param MatrixType the type of the matrix of which we are computing the LU factorization 34 /** Creates a LU object and compute the respective factorization of \a matrix using 36 SkylineInplaceLU(MatrixType& matrix, int flags = 0) argument 37 : /*m_matrix(matrix.rows(), matrix.cols()),*/ m_flags(flags), m_status(0), m_lu(matrix) { 92 /** \returns the lower triangular matrix L */ 95 /** \returns the upper triangular matrix U */ 130 //Lower matrix Column [all...] |
/external/eigen/unsupported/Eigen/src/SparseExtra/ |
H A D | MatrixMarketIterator.h | 27 * and matname_SPD.mtx if the matrix is Symmetric and positive definite (or Hermitian) 30 * Note that the right hand side for a SPD matrix is named as matname_SPD_b.mtx 75 /** Return the sparse matrix corresponding to the current file */ 76 inline MatrixType& matrix() function in class:Eigen::MatrixMarketIterator 78 // Read the matrix 90 { // Store the upper part of the matrix. It is needed by the solvers dealing with nonsymmetric matrices ?? 98 /** Return the right hand side corresponding to the current matrix. 117 if (!m_matIsLoaded) this->matrix(); 130 * where A and b are the matrix and the rhs. 175 // Here, we return with the next valid matrix i [all...] |
/external/freetype/src/base/ |
H A D | ftcalc.c | 983 FT_Matrix_Invert( FT_Matrix* matrix ) 988 if ( !matrix ) 992 delta = FT_MulFix( matrix->xx, matrix->yy ) - 993 FT_MulFix( matrix->xy, matrix->yx ); 996 return FT_THROW( Invalid_Argument ); /* matrix can't be inverted */ 998 matrix->xy = - FT_DivFix( matrix->xy, delta ); 999 matrix 1039 FT_Vector_Transform_Scaled( FT_Vector* vector, const FT_Matrix* matrix, FT_Long scaling ) argument [all...] |
/external/freetype/src/smooth/ |
H A D | ftsmooth.c | 61 const FT_Matrix* matrix, 73 if ( matrix ) 74 FT_Outline_Transform( &slot->outline, matrix ); 59 ft_smooth_transform( FT_Renderer render, FT_GlyphSlot slot, const FT_Matrix* matrix, const FT_Vector* delta ) argument
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/external/kernel-headers/original/uapi/sound/ |
H A D | hdsp.h | 89 unsigned short matrix[HDSP_MATRIX_MIXER_SIZE]; member in struct:hdsp_mixer
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/external/mesa3d/src/gallium/state_trackers/vega/ |
H A D | vgu.c | 30 #include "matrix.h" 364 VGfloat * matrix) 366 struct matrix mat; 368 if (!matrix || !is_aligned(matrix)) 381 memcpy(matrix, mat.m, sizeof(VGfloat) * 9); 390 VGfloat * matrix) 392 struct matrix mat; 394 if (!matrix || !is_aligned(matrix)) 360 vguComputeWarpQuadToSquare(VGfloat sx0, VGfloat sy0, VGfloat sx1, VGfloat sy1, VGfloat sx2, VGfloat sy2, VGfloat sx3, VGfloat sy3, VGfloat * matrix) argument 386 vguComputeWarpSquareToQuad(VGfloat dx0, VGfloat dy0, VGfloat dx1, VGfloat dy1, VGfloat dx2, VGfloat dy2, VGfloat dx3, VGfloat dy3, VGfloat * matrix) argument 412 vguComputeWarpQuadToQuad(VGfloat dx0, VGfloat dy0, VGfloat dx1, VGfloat dy1, VGfloat dx2, VGfloat dy2, VGfloat dx3, VGfloat dy3, VGfloat sx0, VGfloat sy0, VGfloat sx1, VGfloat sy1, VGfloat sx2, VGfloat sy2, VGfloat sx3, VGfloat sy3, VGfloat * matrix) argument [all...] |
/external/mesa3d/src/mesa/main/ |
H A D | nvprogram.c | 310 * Get matrix tracking information. 748 GLenum matrix, GLenum transform) 762 switch (matrix) { 780 _mesa_error(ctx, GL_INVALID_ENUM, "glTrackMatrixNV(matrix)"); 796 ctx->VertexProgram.TrackMatrix[address / 4] = matrix; 747 _mesa_TrackMatrixNV(GLenum target, GLuint address, GLenum matrix, GLenum transform) argument
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/external/mesa3d/src/mesa/program/ |
H A D | prog_statevars.c | 46 * but matrix queries may return as many as 16 values. 286 /* state[1] = which texture matrix or program matrix */ 290 const GLmatrix *matrix; local 303 matrix = ctx->ModelviewMatrixStack.Top; 306 matrix = ctx->ProjectionMatrixStack.Top; 309 matrix = &ctx->_ModelProjectMatrix; 313 matrix = ctx->TextureMatrixStack[index].Top; 317 matrix = ctx->ProgramMatrixStack[index].Top; 320 _mesa_problem(ctx, "Bad matrix nam [all...] |
/external/mp4parser/isoparser/src/main/java/com/coremedia/iso/boxes/ |
H A D | MovieHeaderBox.java | 42 private long[] matrix = new long[]{0x00010000, 0, 0, 0, 0x00010000, 0, 0, 0, 0x40000000}; field in class:MovieHeaderBox 84 return matrix; 121 matrix = new long[9]; 123 matrix[i] = IsoTypeReader.readUInt32(content); 151 for (int i = 0; i < matrix.length; i++) { 153 result.append("matrix").append(i).append("=").append(matrix[i]); 184 IsoTypeWriter.writeUInt32(byteBuffer, matrix[i]); 223 public void setMatrix(long[] matrix) { argument 224 this.matrix [all...] |
H A D | TrackHeaderBox.java | 46 private long[] matrix = new long[]{0x00010000, 0, 0, 0, 0x00010000, 0, 0, 0, 0x40000000}; field in class:TrackHeaderBox 85 return matrix; 129 matrix = new long[9]; 131 matrix[i] = IsoTypeReader.readUInt32(content); 159 IsoTypeWriter.writeUInt32(byteBuffer, matrix[i]); 181 for (int i = 0; i < matrix.length; i++) { 183 result.append("matrix").append(i).append("=").append(matrix[i]); 221 public void setMatrix(long[] matrix) { argument 222 this.matrix [all...] |
/external/pdfium/core/src/fxcodec/fx_libopenjpeg/libopenjpeg20/ |
H A D | invert.c | 37 static OPJ_BOOL opj_lupDecompose(OPJ_FLOAT32 * matrix, 106 OPJ_BOOL opj_lupDecompose(OPJ_FLOAT32 * matrix,OPJ_UINT32 * permutations, argument 118 OPJ_FLOAT32 * lTmpMatrix = matrix; 180 /* matrix[i][k] /= matrix[k][k]; */ 181 /* p = matrix[i][k] */ 186 /* matrix[i][j] -= matrix[i][k] * matrix[k][j]; */ 237 /* sum += matrix[ [all...] |