| /external/autotest/client/site_tests/firmware_TouchMTB/tests/ |
| mtb_unittest.py | 427 filename = 'two_finger_tracking.diagonal.slow.dat'
528 'drag_edge_thumb.diagonal.dat',
558 'drag_edge_thumb.diagonal.dat': {AXIS.X: 84, AXIS.Y: 58},
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| /external/eigen/Eigen/src/Core/ |
| TriangularMatrix.h | 375 return m_matrix.diagonal().prod();
573 dst.diagonal().setOnes();
593 dst.diagonal().setOnes();
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| SolveTriangular.h | 199 * diagonal must be non zero). It works as a forward (resp. backward) substitution if \c *this
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| /external/eigen/test/ |
| sparse_basic.cpp | 483 // test diagonal
488 VERIFY_IS_APPROX(m2.diagonal(), refMat2.diagonal().eval());
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| /external/opencv3/modules/core/include/opencv2/core/ |
| mat.hpp | 596 diagonal. Such operations are also O(1) because the new header references the same data. You can
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| /external/ceres-solver/docs/source/ |
| solving.rst | 162 The matrix :math:`D(x)` is a non-negative diagonal matrix, typically
163 the square root of the diagonal of the matrix :math:`J(x)^\top J(x)`.
526 \mathbb{R}^{qs\times qs}` is a block diagonal matrix with :math:`q` blocks
540 a block diagonal matrix, with small diagonal blocks of size
567 inversion of the block diagonal matrix :math:`C`, a few matrix-matrix
697 The simplest of all preconditioners is the diagonal or Jacobi
703 diagonal preconditioners for :math:`S`. The block diagonal of the
704 matrix :math:`B` [Mandel]_ and the block diagonal :math:`S`, i.e, th
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| /external/opencv3/3rdparty/openexr/Imath/ |
| ImathMatrixAlgo.h | 698 // check the diagonal
709 // diagonal is negative
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| /external/eigen/Eigen/src/Geometry/ |
| Transform.h | 221 /** The return type of the product between a diagonal matrix and a transform */
416 /** \returns The product expression of a transform \a a times a diagonal matrix \a b
418 * The rhs diagonal matrix is interpreted as an affine scaling transformation. The
431 /** \returns The product expression of a diagonal matrix \a a times a transform \a b
433 * The lhs diagonal matrix is interpreted as an affine scaling transformation. The
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| /external/opencv/ml/src/ |
| mlem.cpp | 765 cov_eigen_values[k] are diagonal matrices (represented by 1D vectors) of eigen values.
842 contains the diagonal elements (variations). In the case of
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| /external/ImageMagick/ImageMagick/api/ |
| feature.html | 141 <p>Use HoughLineImage() in conjunction with any binary edge extracted image (we recommand Canny) to identify lines in the image. The algorithm accumulates counts for every white pixel for every possible orientation (for angles from 0 to 179 in 1 degree increments) and distance from the center of the image to the corner (in 1 px increments) and stores the counts in an accumulator matrix of angle vs distance. The size of the accumulator is 180x(diagonal/2). Next it searches this space for peaks in counts and converts the locations of the peaks to slope and intercept in the normal x,y input image space. Use the slope/intercepts to find the endpoints clipped to the bounds of the image. The lines are then drawn. The counts are a measure of the length of the lines</p>
|
| /external/ImageMagick/www/api/ |
| feature.html | 145 <p>Use HoughLineImage() in conjunction with any binary edge extracted image (we recommand Canny) to identify lines in the image. The algorithm accumulates counts for every white pixel for every possible orientation (for angles from 0 to 179 in 1 degree increments) and distance from the center of the image to the corner (in 1 px increments) and stores the counts in an accumulator matrix of angle vs distance. The size of the accumulator is 180x(diagonal/2). Next it searches this space for peaks in counts and converts the locations of the peaks to slope and intercept in the normal x,y input image space. Use the slope/intercepts to find the endpoints clipped to the bounds of the image. The lines are then drawn. The counts are a measure of the length of the lines</p>
|
| feature.php | 141 <p>Use HoughLineImage() in conjunction with any binary edge extracted image (we recommand Canny) to identify lines in the image. The algorithm accumulates counts for every white pixel for every possible orientation (for angles from 0 to 179 in 1 degree increments) and distance from the center of the image to the corner (in 1 px increments) and stores the counts in an accumulator matrix of angle vs distance. The size of the accumulator is 180x(diagonal/2). Next it searches this space for peaks in counts and converts the locations of the peaks to slope and intercept in the normal x,y input image space. Use the slope/intercepts to find the endpoints clipped to the bounds of the image. The lines are then drawn. The counts are a measure of the length of the lines</p>
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| /external/apache-commons-math/src/main/java/org/apache/commons/math/linear/ |
| BigMatrix.java | 283 * trace</a> of the matrix (the sum of the elements on the main diagonal).
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| BigMatrixImpl.java | [all...] |
| LUDecompositionImpl.java | 155 // Divide the lower elements by the "winning" diagonal elt.
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| SingularValueDecompositionImpl.java | 30 * p × p diagonal matrix with positive or null elements, V is a p ×
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| /external/ceres-solver/internal/ceres/ |
| linear_solver.h | 180 // Given a matrix A, an optional diagonal matrix D as a vector,
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| program_evaluator.h | 273 // storage can be reserved for additional diagonal elements if
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| sparse_normal_cholesky_solver.cc | 89 // Temporarily append a diagonal block to the A matrix, but undo
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| /external/clang/utils/ABITest/ |
| Enumeration.py | 105 # Conceptually we want to slide a diagonal line across a
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| /external/eigen/Eigen/src/Core/util/ |
| ForwardDeclarations.h | 99 template<typename MatrixType, int Index = 0> class Diagonal;
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| /external/eigen/Eigen/src/SparseCore/ |
| SparseMatrix.h | 61 struct traits<Diagonal<const SparseMatrix<_Scalar, _Options, _Index>, DiagIndex> >
625 /** \returns a const expression of the diagonal coefficients */
626 const Diagonal<const SparseMatrix> diagonal() const { return *this; } function in class:Eigen::SparseMatrix
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| /external/eigen/blas/fortran/ |
| chpmv.f | 58 * Note that the imaginary parts of the diagonal elements need
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| zhpmv.f | 58 * Note that the imaginary parts of the diagonal elements need
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| /external/eigen/doc/ |
| TutorialLinearAlgebra.dox | 239 on the decomposition but is typically the diagonal size times machine epsilon. While this is the best default we
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