| /external/autotest/client/deps/webgl_mpd/src/resources/ |
| D | J3DIMath.js | 31 J3DIMatrix4 - A 4x4 Matrix
37 This class implements a 4x4 matrix. It has functions which duplicate the
38 functionality of the OpenGL matrix stack and glut functions. On browsers
44 … Constructor(in J3DIMatrix4 matrix), // copy passed matrix into new J3DIMatrix4
46 … Constructor() // create new J3DIMatrix4 with identity matrix
49 … void load(in J3DIMatrix4 matrix); // copy the values from the passed matrix
50 void load(in sequence<float> array); // copy 16 floats into the matrix
51 … sequence<float> getAsArray(); // return the matrix as an array of 16 floats
52 …Float32Array getAsFloat32Array(); // return the matrix as a Float32Array with 16 values
53 …void setUniform(in WebGLRenderingContext ctx, // Send the matrix to the passed uniform locat…
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| /external/deqp/framework/common/ |
| D | tcuMatrix.hpp | 23 * \brief Templatized matrix class.
33 // Templated matrix class.
35 class Matrix class
48 Matrix (void);
49 explicit Matrix (const T& src);
50 explicit Matrix (const T src[Rows*Cols]);
51 Matrix (const Vector<T, Rows>& src);
52 Matrix (const Matrix<T, Rows, Cols>& src);
53 ~Matrix (void);
55 Matrix<T, Rows, Cols>& operator= (const Matrix<T, Rows, Cols>& src);
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| /external/swiftshader/src/Renderer/ |
| D | Matrix.hpp | 24 struct Matrix struct
26 Matrix();
27 Matrix(const int i);
28 Matrix(const float m[16]);
29 Matrix(const float m[4][4]);
30 Matrix(float m11, float m12, float m13,
33 Matrix(float m11, float m12, float m13, float m14,
37 Matrix(const Vector &v1, const Vector &v2, const Vector &v3); // Column vectors
39 Matrix &operator=(const Matrix &N);
44 static Matrix diag(float m11, float m22, float m33, float m44);
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| D | Matrix.cpp | 15 #include "Matrix.hpp"
22 Matrix Matrix::diag(float m11, float m22, float m33, float m44) in diag()
24 return Matrix(m11, 0, 0, 0, in diag()
30 Matrix::operator float*() in operator float*()
35 Matrix Matrix::operator+() const in operator +()
40 Matrix Matrix::operator-() const in operator -()
42 const Matrix &M = *this; in operator -()
44 return Matrix(-M(1, 1), -M(1, 2), -M(1, 3), -M(1, 4), in operator -()
50 Matrix Matrix::operator!() const in operator !()
52 const Matrix &M = *this; in operator !()
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| /external/vulkan-validation-layers/libs/glm/gtc/ |
| D | matrix_integer.hpp | 60 /// High-precision signed integer 2x2 matrix.
64 /// High-precision signed integer 3x3 matrix.
68 /// High-precision signed integer 4x4 matrix.
72 /// High-precision signed integer 2x2 matrix.
76 /// High-precision signed integer 2x3 matrix.
80 /// High-precision signed integer 2x4 matrix.
84 /// High-precision signed integer 3x2 matrix.
88 /// High-precision signed integer 3x3 matrix.
92 /// High-precision signed integer 3x4 matrix.
96 /// High-precision signed integer 4x2 matrix.
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| /external/eigen/unsupported/Eigen/ |
| D | MatrixFunctions | 22 * \defgroup MatrixFunctions_Module Matrix functions module
24 * matrix functions.
33 * - \ref matrixbase_cos "MatrixBase::cos()", for computing the matrix cosine
34 * - \ref matrixbase_cosh "MatrixBase::cosh()", for computing the matrix hyperbolic cosine
35 * - \ref matrixbase_exp "MatrixBase::exp()", for computing the matrix exponential
36 * - \ref matrixbase_log "MatrixBase::log()", for computing the matrix logarithm
37 * - \ref matrixbase_pow "MatrixBase::pow()", for computing the matrix power
38 …* - \ref matrixbase_matrixfunction "MatrixBase::matrixFunction()", for computing general matrix f…
39 * - \ref matrixbase_sin "MatrixBase::sin()", for computing the matrix sine
40 * - \ref matrixbase_sinh "MatrixBase::sinh()", for computing the matrix hyperbolic sine
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| /external/eigen/Eigen/src/LU/ |
| D | InverseImpl.h | 26 static inline void run(const MatrixType& matrix, ResultType& result) in run()
28 result = matrix.partialPivLu().inverse(); in run()
43 static inline void run(const MatrixType& matrix, ResultType& result)
46 internal::evaluator<MatrixType> matrixEval(matrix);
56 const MatrixType& matrix,
64 determinant = matrix.coeff(0,0);
77 const MatrixType& matrix, const typename ResultType::Scalar& invdet,
80 result.coeffRef(0,0) = matrix.coeff(1,1) * invdet;
81 result.coeffRef(1,0) = -matrix.coeff(1,0) * invdet;
82 result.coeffRef(0,1) = -matrix.coeff(0,1) * invdet;
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| /external/eigen/test/ |
| D | geo_transformations.cpp | 16 Matrix<T,2,1> angleToVec(T a) in angleToVec()
18 return Matrix<T,2,1>(std::cos(a), std::sin(a)); in angleToVec()
31 typedef Matrix<Scalar,3,1> Vector3; in non_projective_only()
50 VERIFY_IS_APPROX(t0.matrix(), Transform3::MatrixType::Identity()); in non_projective_only()
69 VERIFY((t0 * t1).matrix().isIdentity(test_precision<Scalar>())); in non_projective_only()
72 VERIFY_IS_APPROX(t1.matrix(), t0.matrix()); in non_projective_only()
93 typedef Matrix<Scalar,3,3> Matrix3; in transformations()
94 typedef Matrix<Scalar,4,4> Matrix4; in transformations()
95 typedef Matrix<Scalar,2,1> Vector2; in transformations()
96 typedef Matrix<Scalar,3,1> Vector3; in transformations()
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| D | corners.cpp | 13 VERIFY_IS_EQUAL(matrix.A, matrix.B); \
25 MatrixType matrix = MatrixType::Random(rows,cols); in corners() local
48 MatrixType matrix = MatrixType::Random(); in corners_fixedsize() local
60 VERIFY_IS_EQUAL((matrix.template topLeftCorner<r,c>()), (matrix.template block<r,c>(0,0))); in corners_fixedsize()
61 VERIFY_IS_EQUAL((matrix.template topRightCorner<r,c>()), (matrix.template block<r,c>(0,cols-c))); in corners_fixedsize()
62 …VERIFY_IS_EQUAL((matrix.template bottomLeftCorner<r,c>()), (matrix.template block<r,c>(rows-r,0))); in corners_fixedsize()
63 …VERIFY_IS_EQUAL((matrix.template bottomRightCorner<r,c>()), (matrix.template block<r,c>(rows-r,col… in corners_fixedsize()
65 …VERIFY_IS_EQUAL((matrix.template topLeftCorner<r,c>()), (matrix.template topLeftCorner<r,Dynamic>(… in corners_fixedsize()
66 …VERIFY_IS_EQUAL((matrix.template topRightCorner<r,c>()), (matrix.template topRightCorner<r,Dynamic… in corners_fixedsize()
67 …VERIFY_IS_EQUAL((matrix.template bottomLeftCorner<r,c>()), (matrix.template bottomLeftCorner<r,Dyn… in corners_fixedsize()
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| /external/pdfium/xfa/fxbarcode/qrcode/ |
| D | BC_QRCoderMatrixUtil.cpp | 78 void CBC_QRCoderMatrixUtil::ClearMatrix(CBC_CommonByteMatrix* matrix, in ClearMatrix() argument
80 if (!matrix) { in ClearMatrix()
84 matrix->clear((uint8_t)-1); in ClearMatrix()
91 CBC_CommonByteMatrix* matrix, in BuildMatrix() argument
93 if (!matrix) { in BuildMatrix()
97 ClearMatrix(matrix, e); in BuildMatrix()
100 EmbedBasicPatterns(version, matrix, e); in BuildMatrix()
103 EmbedTypeInfo(ecLevel, maskPattern, matrix, e); in BuildMatrix()
106 MaybeEmbedVersionInfo(version, matrix, e); in BuildMatrix()
109 EmbedDataBits(dataBits, maskPattern, matrix, e); in BuildMatrix()
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| /external/webrtc/webrtc/modules/audio_processing/beamformer/ |
| D | matrix.h | 41 // Matrix is a class for doing standard matrix operations on 2 dimensional
42 // matrices of any size. Results of matrix operations are stored in the
49 // 'In-place' operations that inherently change the size of the matrix (eg.
57 // Memory for storage is allocated when a matrix is resized only if the new
62 // matrix. TODO(claguna): albeit tricky, allow for data to be referenced
65 class Matrix {
67 Matrix() : num_rows_(0), num_columns_(0) {} in Matrix() function
70 Matrix(size_t num_rows, size_t num_columns) in Matrix() function
77 // Copies |data| into the new Matrix.
78 Matrix(const T* data, size_t num_rows, size_t num_columns) in Matrix() function
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| D | matrix_unittest.cc | 14 #include "webrtc/modules/audio_processing/beamformer/matrix.h"
30 Matrix<float> lh_mat(*kValuesLeft, kNumRows, kNumCols); in TEST()
31 Matrix<float> rh_mat(*kValuesRight, kNumRows, kNumCols); in TEST()
32 Matrix<float> expected_result(*kValuesExpected, kNumRows, kNumCols); in TEST()
33 Matrix<float> actual_result(kNumRows, kNumCols); in TEST()
54 Matrix<int> lh_mat(*kValuesLeft, kNumRowsLeft, kNumColsLeft); in TEST()
55 Matrix<int> rh_mat(*kValuesRight, kNumRowsRight, kNumColsRight); in TEST()
56 Matrix<int> expected_result(*kValuesExpected, kNumRowsLeft, kNumColsRight); in TEST()
57 Matrix<int> actual_result(kNumRowsLeft, kNumColsRight); in TEST()
76 Matrix<float> initial_mat(*kValuesInitial, kNumInitialRows, kNumInitialCols); in TEST()
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| /external/eigen/Eigen/src/Core/ |
| D | Matrix.h | 18 struct traits<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
55 /** \class Matrix
58 * \brief The matrix class, also used for vectors and row-vectors
60 …* The %Matrix class is the work-horse for all \em dense (\ref dense "note") matrices and vectors w…
63 …* The %Matrix class encompasses \em both fixed-size and dynamic-size objects (\ref fixedsize "note…
81 * \li \c Matrix2d is a 2x2 square matrix of doubles (\c Matrix<double, 2, 2>)
82 * \li \c Vector4f is a vector of 4 floats (\c Matrix<float, 4, 1>)
83 * \li \c RowVector3i is a row-vector of 3 ints (\c Matrix<int, 1, 3>)
85 * \li \c MatrixXf is a dynamic-size matrix of floats (\c Matrix<float, Dynamic, Dynamic>)
86 * \li \c VectorXf is a dynamic-size vector of floats (\c Matrix<float, Dynamic, 1>)
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| /external/opencv/cxcore/include/ |
| D | cxcore.hpp | 213 CvMatrix() : matrix(0) {} in CvMatrix()
215 { matrix = cvCreateMat( rows, cols, type ); } in CvMatrix()
219 { matrix = cvInitMatHeader( hdr, rows, cols, type, data, step ); } in CvMatrix()
224 { matrix = cvCreateMatHeader( rows, cols, type ); in CvMatrix()
225 cvSetData( matrix, data, step ); } in CvMatrix()
228 { matrix = m; } in CvMatrix()
232 matrix = m.matrix; in CvMatrix()
236 CvMatrix( const char* filename, const char* matname=0, int color=-1 ) : matrix(0) in CvMatrix()
239 CvMatrix( CvFileStorage* fs, const char* mapname, const char* matname ) : matrix(0) in CvMatrix()
242 CvMatrix( CvFileStorage* fs, const char* seqname, int idx ) : matrix(0) in CvMatrix()
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| /external/eigen/Eigen/src/Eigenvalues/ |
| 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. Alternatively, you can use the
82 … typedef Matrix<Scalar, SizeMinusOne, 1, Options & ~RowMajor, MaxSizeMinusOne, 1> CoeffVectorType;
91 * \param [in] size The size of the matrix whose Hessenberg decomposition will be computed.
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| 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
32 * instantiation of the Matrix class template.
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.
81 …typedef Matrix<ComplexScalar, RowsAtCompileTime, ColsAtCompileTime, Options, MaxRowsAtCompileTime,…
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| D | Tridiagonalization.h | 36 * \brief Tridiagonal decomposition of a selfadjoint matrix
38 * \tparam _MatrixType the type of the matrix of which we are computing the
40 * Matrix class template.
42 * This class performs a tridiagonal decomposition of a selfadjoint matrix \f$ A \f$ such that:
43 …* \f$ A = Q T Q^* \f$ where \f$ Q \f$ is unitary and \f$ T \f$ a real symmetric tridiagonal matrix.
45 * A tridiagonal matrix is a matrix which has nonzero elements only on the
47 * decomposition of a selfadjoint matrix is in fact a tridiagonal
49 * eigenvalues and eigenvectors of a selfadjoint matrix.
52 * given matrix. Alternatively, you can use the Tridiagonalization(const MatrixType&)
82 … typedef Matrix<Scalar, SizeMinusOne, 1, Options & ~RowMajor, MaxSizeMinusOne, 1> CoeffVectorType;
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| D | ComplexEigenSolver.h | 26 * \tparam _MatrixType the type of the matrix of which we are
28 * instantiation of the Matrix class template.
30 * The eigenvalues and eigenvectors of a matrix \f$ A \f$ are scalars
32 * \f$. If \f$ D \f$ is a diagonal matrix with the eigenvalues on
33 * the diagonal, and \f$ V \f$ is a matrix with the eigenvectors as
34 * its columns, then \f$ A V = V D \f$. The matrix \f$ V \f$ is
78 …typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options&(~RowMajor), MaxColsAtCompileTime, 1> …
80 /** \brief Type for matrix of eigenvectors as returned by eigenvectors().
82 * This is a square matrix with entries of type #ComplexScalar.
85 …typedef Matrix<ComplexScalar, RowsAtCompileTime, ColsAtCompileTime, Options, MaxRowsAtCompileTime,…
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| /external/llvm/include/llvm/CodeGen/PBQP/ |
| D | Math.h | 1 //===------ Math.h - PBQP Vector and Matrix classes -------------*- C++ -*-===//
162 /// \brief PBQP Matrix class
163 class Matrix {
165 friend hash_code hash_value(const Matrix &);
168 /// \brief Construct a PBQP Matrix with the given dimensions.
169 Matrix(unsigned Rows, unsigned Cols) : in Matrix() function
173 /// \brief Construct a PBQP Matrix with the given dimensions and initial
175 Matrix(unsigned Rows, unsigned Cols, PBQPNum InitVal) in Matrix() function
180 /// \brief Copy construct a PBQP matrix.
181 Matrix(const Matrix &M) in Matrix() function
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| /external/apache-commons-math/src/main/java/org/apache/commons/math/stat/correlation/ |
| D | Covariance.java | 27 * Computes covariances for pairs of arrays or columns of a matrix.
48 /** covariance matrix */
52 * Create an empty covariance matrix.
67 * Create a Covariance matrix from a rectangular array
86 * Create a Covariance matrix from a rectangular array
101 * Create a covariance matrix from a matrix whose columns
107 * <p>The matrix must have at least two columns and two rows</p>
109 * @param matrix matrix with columns representing covariates
111 * @throws IllegalArgumentException if the input matrix does not have
114 public Covariance(RealMatrix matrix, boolean biasCorrected) { in Covariance() argument
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| D | PearsonsCorrelation.java | 33 * or columns of a matrix.
48 /** correlation matrix */
79 * @param matrix matrix with columns representing variables to correlate
81 public PearsonsCorrelation(RealMatrix matrix) { in PearsonsCorrelation() argument
82 checkSufficientData(matrix); in PearsonsCorrelation()
83 nObs = matrix.getRowDimension(); in PearsonsCorrelation()
84 correlationMatrix = computeCorrelationMatrix(matrix); in PearsonsCorrelation()
89 * matrix is computed by scaling the Covariance's covariance matrix.
90 * The Covariance instance must have been created from a data matrix with
105 * Create a PearsonsCorrelation from a covariance matrix. The correlation
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| /external/libtextclassifier/common/ |
| D | embedding-network-params.h | 45 // Simple representation of a matrix. This small struct that doesn't own any
47 struct Matrix { struct
56 // Pointer to matrix elements, in row-major order
74 // Returns embedding matrix for the i-th embedding space.
78 Matrix GetEmbeddingMatrix(int i) const { in GetEmbeddingMatrix() argument
80 Matrix matrix; in GetEmbeddingMatrix() local
81 matrix.rows = embeddings_num_rows(i); in GetEmbeddingMatrix()
82 matrix.cols = embeddings_num_cols(i); in GetEmbeddingMatrix()
83 matrix.elements = embeddings_weights(i); in GetEmbeddingMatrix()
84 matrix.quant_type = embeddings_quant_type(i); in GetEmbeddingMatrix()
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| /external/eigen/Eigen/src/PaStiXSupport/ |
| D | PaStiXSupport.h | 27 * The matrix can be either real or complex, symmetric or not.
95 // Convert the matrix to Fortran-style Numbering
142 typedef Matrix<Scalar,Dynamic,1> Vector;
208 * \c InvalidInput if the input matrix is invalid
220 // Initialize the Pastix data structure, check the matrix
249 mutable Matrix<StorageIndex,Dynamic,1> m_perm; // Permutation vector
250 mutable Matrix<StorageIndex,Dynamic,1> m_invp; // Inverse permutation vector
251 mutable int m_size; // Size of the matrix
297 eigen_assert(mat.rows() == mat.cols() && "The input matrix should be squared");
369 eigen_assert(m_isInitialized && "The matrix should be factorized first");
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| /external/apache-commons-math/src/main/java/org/apache/commons/math/linear/ |
| D | SingularValueDecomposition.java | 24 * Singular Value Decomposition of a real matrix.
26 * The Singular Value Decomposition of matrix A is a set of three matrices: U,
28 * a m × n matrix, then U is a m × p orthogonal matrix, Σ is a
29 * p × p diagonal matrix with positive or null elements, V is a p ×
30 * n orthogonal matrix (hence V<sup>T</sup> is also orthogonal) where
56 * Returns the matrix U of the decomposition.
57 * <p>U is an orthogonal matrix, i.e. its transpose is also its inverse.</p>
58 * @return the U matrix
64 * Returns the transpose of the matrix U of the decomposition.
65 * <p>U is an orthogonal matrix, i.e. its transpose is also its inverse.</p>
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| /external/eigen/bench/btl/data/ |
| D | action_settings.txt | 1 aat ; "{/*1.5 A x A^T}" ; "matrix size" ; 4:5000
2 ata ; "{/*1.5 A^T x A}" ; "matrix size" ; 4:5000
3 atv ; "{/*1.5 matrix^T x vector}" ; "matrix size" ; 4:5000
6 matrix_matrix ; "{/*1.5 matrix matrix product}" ; "matrix size" ; 4:5000
7 matrix_vector ; "{/*1.5 matrix vector product}" ; "matrix size" ; 4:5000
8 trmm ; "{/*1.5 triangular matrix matrix product}" ; "matrix size" ; 4:5000
10 trisolve_matrix ; "{/*1.5 triangular solver - matrix (M = inv(L) M)}" ; "size" ; 4:5000
11 cholesky ; "{/*1.5 Cholesky decomposition}" ; "matrix size" ; 4:5000
12 complete_lu_decomp ; "{/*1.5 Complete LU decomposition}" ; "matrix size" ; 4:5000
13 partial_lu_decomp ; "{/*1.5 Partial LU decomposition}" ; "matrix size" ; 4:5000
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