/external/eigen/doc/snippets/ |
TopicAliasing_mult1.cpp | 1 MatrixXf matA(2,2); 2 matA << 2, 0, 0, 2; 3 matA = matA * matA; 4 cout << matA;
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TopicAliasing_mult3.cpp | 1 MatrixXf matA(2,2); 2 matA << 2, 0, 0, 2; 3 matA.noalias() = matA * matA; 4 cout << matA;
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TopicAliasing_mult2.cpp | 1 MatrixXf matA(2,2), matB(2,2); 2 matA << 2, 0, 0, 2; 5 matB = matA * matA; 9 matB.noalias() = matA * matA;
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Tutorial_AdvancedInitialization_Block.cpp | 1 MatrixXf matA(2, 2); 2 matA << 1, 2, 3, 4; 4 matB << matA, matA/10, matA/10, matA;
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/external/eigen/Eigen/src/Eigenvalues/ |
GeneralizedSelfAdjointEigenSolver.h | 83 * \param[in] matA Selfadjoint matrix in matrix pencil. 92 * generalized eigenproblem \f$ Ax = \lambda B x \f$ with \a matA the 107 GeneralizedSelfAdjointEigenSolver(const MatrixType& matA, const MatrixType& matB, 109 : Base(matA.cols()) 111 compute(matA, matB, options); 116 * \param[in] matA Selfadjoint matrix in matrix pencil. 130 * with \a matA the selfadjoint matrix \f$ A \f$ and \a matB the positive definite 154 GeneralizedSelfAdjointEigenSolver& compute(const MatrixType& matA, const MatrixType& matB, 164 compute(const MatrixType& matA, const MatrixType& matB, int options) 166 eigen_assert(matA.cols()==matA.rows() && matB.rows()==matA.rows() && matB.cols()==matB.rows()) [all...] |
HessenbergDecomposition.h | 270 static void _compute(MatrixType& matA, CoeffVectorType& hCoeffs, VectorType& temp); 280 * Performs a tridiagonal decomposition of \a matA in place. 282 * \param matA the input selfadjoint matrix 285 * The result is written in the lower triangular part of \a matA. 292 void HessenbergDecomposition<MatrixType>::_compute(MatrixType& matA, CoeffVectorType& hCoeffs, VectorType& temp) 294 assert(matA.rows()==matA.cols()); 295 Index n = matA.rows(); 303 matA.col(i).tail(remainingSize).makeHouseholderInPlace(h, beta); 304 matA.col(i).coeffRef(i+1) = beta [all...] |
Tridiagonalization.h | 26 void tridiagonalization_inplace(MatrixType& matA, CoeffVectorType& hCoeffs); 323 * Performs a tridiagonal decomposition of the selfadjoint matrix \a matA in-place. 325 * \param[in,out] matA On input the selfadjoint matrix. Only the \b lower triangular part is referenced. 331 * and lower sub-diagonal of the matrix \a matA. 339 * \f$ v_i = [ 0, \ldots, 0, 1, matA(i+2,i), \ldots, matA(N-1,i) ]^T \f$. 346 void tridiagonalization_inplace(MatrixType& matA, CoeffVectorType& hCoeffs) 351 Index n = matA.rows(); 352 eigen_assert(n==matA.cols()); 360 matA.col(i).tail(remainingSize).makeHouseholderInPlace(h, beta) [all...] |
SelfAdjointEigenSolver.h | 333 SelfAdjointEigenSolver(const MatrixType& matA, const MatrixType& matB, bool computeEigenvectors = true) 334 : m_eivec(matA.cols(), matA.cols()), 335 m_eivalues(matA.cols()), 336 m_subdiag(matA.cols() > 1 ? matA.cols() - 1 : 1), 339 static_cast<GeneralizedSelfAdjointEigenSolver<MatrixType>*>(this)->compute(matA, matB, computeEigenvectors ? ComputeEigenvectors : EigenvaluesOnly); 347 void compute(const MatrixType& matA, const MatrixType& matB, bool computeEigenvectors = true) 349 compute(matA, matB, computeEigenvectors ? ComputeEigenvectors : EigenvaluesOnly); 369 * \param matA the input selfadjoint matri [all...] |
/external/neven/Embedded/common/src/b_TensorEm/ |
Int32Mat.h | 106 * matA: the square matrix, array of size ( matWidthA * matWidthA ) 111 * tmpMatA: matrix of same size as matA 115 const int32* matA, 123 /** same as _solve(), but matA gets overwritten, and tmpMatA is not needed: 124 * saves memory when matA is large; 129 int32* matA,
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Int32Mat.c | 210 const int32* matA, 218 bbs_memcpy32( tmpMatA, matA, ( matWidthA * matWidthA ) * bbs_SIZEOF32( int32 ) ); 232 int32* matA, 246 int32* matL = matA;
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/frameworks/base/graphics/java/android/graphics/ |
ColorMatrix.java | 151 * as applying matB and then applying matA. It is legal for either matA or 154 public void setConcat(ColorMatrix matA, ColorMatrix matB) { 157 if (matA == this || matB == this) { 164 final float[] a = matA.mArray;
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/external/eigen/Eigen/src/Eigen2Support/ |
SVD.h | 109 MatrixType matA(matrix); 124 m_sigma[k] = matA.col(k).end(m-k).norm(); 127 if (matA(k,k) < 0.0) 129 matA.col(k).end(m-k) /= m_sigma[k]; 130 matA(k,k) += 1.0; 140 Scalar t = matA.col(k).end(m-k).eigen2_dot(matA.col(j).end(m-k)); // FIXME dot product or cwise prod + .sum() ?? 141 t = -t/matA(k,k); 142 matA.col(j).end(m-k) += t * matA.col(k).end(m-k) [all...] |
/external/opencv/cv/src/ |
cvcornersubpix.cpp | 233 CvMat matA, matInvA; 239 cvInitMatHeader( &matA, 2, 2, CV_64F, A ); 242 cvInvert( &matA, &matInvA, CV_SVD );
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/external/skia/legacy/src/utils/ |
SkColorMatrix.cpp | 70 void SkColorMatrix::setConcat(const SkColorMatrix& matA, 75 if (&matA == this || &matB == this) { 79 const SkScalar* a = matA.fMat;
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/external/skia/src/effects/ |
SkColorMatrix.cpp | 71 void SkColorMatrix::setConcat(const SkColorMatrix& matA, 76 if (&matA == this || &matB == this) { 80 const SkScalar* a = matA.fMat;
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/external/eigen/test/ |
cholesky.cpp | 257 MatrixType matA; 258 matA << 1, 1, 1, 1; 261 VectorType vecX = matA.ldlt().solve(vecB); 262 VERIFY_IS_APPROX(matA * vecX, vecB);
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/external/eigen/blas/ |
level3_impl.h | 272 Matrix<Scalar,Dynamic,Dynamic,ColMajor> matA(size,size); 275 matA.triangularView<Upper>() = matrix(a,size,size,*lda); 276 matA.triangularView<Lower>() = matrix(a,size,size,*lda).transpose(); 280 matA.triangularView<Lower>() = matrix(a,size,size,*lda); 281 matA.triangularView<Upper>() = matrix(a,size,size,*lda).transpose(); 284 matrix(c, *m, *n, *ldc) += alpha * matA * matrix(b, *m, *n, *ldb); 286 matrix(c, *m, *n, *ldc) += alpha * matrix(b, *m, *n, *ldb) * matA; [all...] |
/external/opencv/cvaux/src/ |
cvepilines.cpp | [all...] |
/prebuilts/sdk/10/ |
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/prebuilts/sdk/14/ |
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/prebuilts/sdk/15/ |
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/prebuilts/sdk/17/ |
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/prebuilts/sdk/4/ |
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/prebuilts/sdk/6/ |
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