| /external/eigen/test/ |
| linearstructure.cpp | 27 Index cols = m.cols(); local
31 MatrixType m1 = MatrixType::Random(rows, cols),
32 m2 = MatrixType::Random(rows, cols),
33 m3(rows, cols);
39 c = internal::random<Index>(0, cols-1);
70 VERIFY_IS_APPROX(m1+m1.block(0,0,rows,cols), m1+m1);
71 VERIFY_IS_APPROX(m1.cwiseProduct(m1.block(0,0,rows,cols)), m1.cwiseProduct(m1));
72 VERIFY_IS_APPROX(m1 - m1.block(0,0,rows,cols), m1 - m1);
73 VERIFY_IS_APPROX(m1.block(0,0,rows,cols) * s1, m1 * s1)
[all...] |
| jacobi.cpp | 19 Index cols = m.cols(); local
28 const MatrixType a(MatrixType::Random(rows, cols));
48 Index p = internal::random<Index>(0, cols-1);
51 q = internal::random<Index>(0, cols-1);
|
| triangular.cpp | 23 typename MatrixType::Index cols = m.cols(); local
25 MatrixType m1 = MatrixType::Random(rows, cols),
26 m2 = MatrixType::Random(rows, cols),
27 m3(rows, cols),
28 m4(rows, cols),
29 r1(rows, cols),
30 r2(rows, cols);
36 if (rows*cols>1)
66 m1 = MatrixType::Random(rows, cols);
143 Index cols = m.cols(); local
[all...] |
| eigensolver_generalized_real.cpp | 23 Index cols = m.cols(); local
29 MatrixType a = MatrixType::Random(rows,cols);
30 MatrixType b = MatrixType::Random(rows,cols);
31 MatrixType a1 = MatrixType::Random(rows,cols);
32 MatrixType b1 = MatrixType::Random(rows,cols);
60 for(Index k=0; k<cols; ++k)
|
| sparse_block.cpp | 29 const Index cols = ref.cols(); local
36 double density = (std::max)(8./(rows*cols), 0.01);
44 SparseMatrixType m(rows, cols);
45 DenseMatrix refMat = DenseMatrix::Zero(rows, cols);
53 Index j = internal::random<Index>(0,cols-2);
55 Index w = internal::random<Index>(1,cols-j);
108 for(Index c=0; c<cols; c++)
123 DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
124 SparseMatrixType m2(rows, cols);
[all...] |
| /external/libvpx/libvpx/test/ |
| active_map_test.cc | 55 map.cols = (kWidth + 15) / 16;
57 ASSERT_EQ(map.cols, 13u);
63 map.cols = (kWidth + 15) / 16;
|
| /external/webrtc/webrtc/system_wrappers/include/ |
| aligned_array.h | 23 AlignedArray(size_t rows, size_t cols, size_t alignment)
25 cols_(cols) {
74 size_t cols() const { function in class:webrtc::AlignedArray
|
| /packages/apps/Launcher3/tests/src/com/android/launcher3/util/ |
| GridOccupancyTest.java | 59 int cols = cells.length / rows; local
61 GridOccupancy grid = new GridOccupancy(cols, rows);
63 for (int x = 0; x < cols; x++) {
|
| /external/tensorflow/tensorflow/core/kernels/ |
| cwise_ops_test.cc | 131 Graph* BiasAdd(int rows, int cols, DataType type) {
133 Tensor lhs(type, TensorShape({rows, cols}));
136 rhs_shape = TensorShape({cols});
147 const int cols = ColsFromArg(arg); \
148 const int64 tot = static_cast<int64>(iters) * rows * cols; \
151 test::Benchmark(#DEVICE, BiasAdd<C_TYPE>(rows, cols, TF_TYPE)).Run(iters); \
175 Graph* BiasAddGrad(int rows, int cols, int channels, DataType type,
180 lhs_shape = TensorShape({channels, rows, cols});
182 lhs_shape = TensorShape({rows, cols, channels});
198 const int cols = ColsFromArg(arg);
[all...] |
| eigen_attention_test.cc | 32 const ptrdiff_t cols = 48; local
36 Tensor<float, 4> input(depth, rows, cols, batch);
52 c + ((1.0f + offsets[b].second) * cols - glimpse_cols) / 2;
68 const ptrdiff_t cols = 48; local
72 Tensor<float, 4> input(depth, rows, cols, batch);
88 c + ((1.0f + offsets[b].second) * cols - glimpse_cols) / 2;
89 if (source_c < glimpse_cols / 2 || source_c >= cols - glimpse_cols / 2) {
|
| matrix_solve_ls_op_impl.h | 87 const int64 cols = matrix.cols(); variable
88 if (rows == 0 || cols == 0) {
99 if (matrix.rows() >= matrix.cols()) {
100 // Overdetermined case (rows >= cols): Solves the ordinary (possibly
106 Matrix gramian(cols, cols);
111 (Scalar(l2_regularizer) * Matrix::Ones(cols, 1)).asDiagonal();
122 // Underdetermined case (rows < cols): Solves the minimum-norm problem
|
| /external/opencv/cv/src/ |
| cvkdtree.cpp | 90 assert(d->cols == tr->dims());
93 assert(results->cols == k);
94 assert(dist->cols == k);
120 int rn = results->rows * results->cols;
146 (&tmp[0], &tmp[0] + tmp.size(), mat->cols,
174 assert(bounds_min->rows * bounds_min->cols == dims());
175 assert(bounds_max->rows * bounds_max->cols == dims());
216 if (desc->cols != dims)
218 if (results->rows != desc->rows && results->cols != k)
220 if (dist->rows != desc->rows && dist->cols != k
[all...] |
| cvadapthresh.cpp | 53 int i, j, rows, cols; local
67 cols = src->cols;
72 CV_CALL( mean = cvCreateMat( rows, cols, CV_8UC1 ));
92 for( j = 0; j < cols; j++ )
|
| /external/eigen/Eigen/src/Core/ |
| CwiseNullaryOp.h | 68 CwiseNullaryOp(Index rows, Index cols, const NullaryOp& func = NullaryOp())
69 : m_rows(rows), m_cols(cols), m_functor(func)
73 && cols >= 0
74 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols));
80 EIGEN_STRONG_INLINE Index cols() const { return m_cols.value(); } function in class:Eigen::CwiseNullaryOp
95 * The parameters \a rows and \a cols are the number of rows and of columns of
99 * it is redundant to pass \a rows and \a cols as arguments, so Zero() should be used
109 DenseBase<Derived>::NullaryExpr(Index rows, Index cols, const CustomNullaryOp& func)
111 return CwiseNullaryOp<CustomNullaryOp, PlainObject>(rows, cols, func);
161 * The parameters \a rows and \a cols are the number of rows and of columns o
[all...] |
| EigenBase.h | 57 /** \returns the number of rows. \sa cols(), RowsAtCompileTime */
62 inline Index cols() const { return derived().cols(); } function in struct:Eigen::EigenBase
63 /** \returns the number of coefficients, which is rows()*cols().
64 * \sa rows(), cols(), SizeAtCompileTime. */
66 inline Index size() const { return rows() * cols(); }
81 typename Dest::PlainObject res(rows(),cols());
93 typename Dest::PlainObject res(rows(),cols());
|
| DenseStorage.h | 203 EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols) {
205 eigen_internal_assert(size==rows*cols && rows==_Rows && cols==_Cols);
208 EIGEN_UNUSED_VARIABLE(cols);
212 EIGEN_DEVICE_FUNC static Index cols(void) {return _Cols;} function in class:Eigen::DenseStorage
230 EIGEN_DEVICE_FUNC static Index cols(void) {return _Cols;} function in class:Eigen::DenseStorage
268 EIGEN_DEVICE_FUNC DenseStorage(Index, Index rows, Index cols) : m_rows(rows), m_cols(cols) {}
272 EIGEN_DEVICE_FUNC Index cols() const {return m_cols;} function in class:Eigen::DenseStorage
273 EIGEN_DEVICE_FUNC void conservativeResize(Index, Index rows, Index cols) { m_rows = rows; m_cols = cols;
301 EIGEN_DEVICE_FUNC Index cols(void) const {return _Cols;} function in class:Eigen::DenseStorage
330 EIGEN_DEVICE_FUNC Index cols(void) const {return m_cols;} function in class:Eigen::DenseStorage
395 EIGEN_DEVICE_FUNC Index cols(void) const {return m_cols;} function in class:Eigen::DenseStorage
471 EIGEN_DEVICE_FUNC Index cols(void) const {return m_cols;} function in class:Eigen::DenseStorage
545 EIGEN_DEVICE_FUNC static Index cols(void) {return _Cols;} function in class:Eigen::DenseStorage
[all...] |
| Reverse.h | 93 EIGEN_DEVICE_FUNC inline Index cols() const { return m_matrix.cols(); } function in class:Eigen::Reverse
141 if(cols()>rows())
143 Index half = cols()/2;
145 if((cols()%2)==1)
157 Index half2 = cols()/2;
185 Index half = xpr.cols()/2;
|
| /external/eigen/bench/ |
| BenchUtil.h | 24 mat.setRandom();// = MatrixType::random(mat.rows(), mat.cols());
51 dst.resize(src.rows(),src.cols());
52 for (int j=0; j<src.cols(); ++j)
66 for (int j=0; j<src.cols(); ++j)
78 dst.resize(src.rows(),src.cols());
79 for (int j=0; j<src.cols(); ++j)
|
| /external/gemmlowp/doc/ |
| quantization_example.cc | 30 for (int j = 0; j < m.cols(); j++) {
47 for (int j = 0; j < m.cols(); j++) {
120 assert(lhs.cols() == rhs.rows());
122 assert(rhs.cols() == result->cols());
124 for (int k = 0; k < rhs.cols(); k++) {
126 for (int j = 0; j < lhs.cols(); j++) {
156 MatrixWithStorage(int rows, int cols)
157 : storage(rows * cols), matrix_map(storage.data(), rows, cols) {}
[all...] |
| /external/libvpx/libvpx/vp9/common/ |
| vp9_debugmodes.c | 30 int cols = cm->mi_cols; local
36 for (mi_col = 0; mi_col < cols; mi_col++) {
52 int cols = cm->mi_cols; local
64 for (mi_col = 0; mi_col < cols; mi_col++) {
78 for (mi_col = 0; mi_col < cols; mi_col++) {
|
| /external/libxcam/modules/ocl/ |
| cv_wiener_filter.cpp | 42 int padx = padded.cols - known.cols;
57 denominator_splitted[0] = denominator_splitted[0] (cv::Rect (0, 0, blurred_image.cols, blurred_image.rows));
64 numerator_splitted[0] = numerator_splitted[0] (cv::Rect (0, 0, blurred_image.cols, blurred_image.rows));
65 numerator_splitted[1] = numerator_splitted[1] (cv::Rect (0, 0, blurred_image.cols, blurred_image.rows));
|
| /external/mesa3d/src/gallium/auxiliary/util/ |
| u_linear.c | 42 size_t bytes = t->cols * t->block.size;
66 size_t bytes = t->cols * t->block.size;
93 t->cols = t->tile.width / t->block.width;
95 t->tile.size = t->cols * t->rows * t->block.size;
99 t->stride = t->cols * t->tiles_x * t->block.size;
|
| /external/tensorflow/tensorflow/compiler/xla/service/cpu/ |
| runtime_matvec.cc | 29 void MatVec(T* out_buf, T* mat_buf, T* x_buf, int64 rows, int64 cols,
40 auto x = VectorMap(x_buf, cols);
44 int64 mat_cols = cols;
70 const int64 cols = k; local
91 MatVec<T>(out, mat, vec, rows, cols, transpose_mat);
|
| /external/eigen/unsupported/Eigen/src/Skyline/ |
| SkylineMatrixBase.h | 38 * \sa MatrixBase::rows(), MatrixBase::cols(), ColsAtCompileTime, SizeAtCompileTime */
44 * \sa MatrixBase::rows(), MatrixBase::cols(), RowsAtCompileTime, SizeAtCompileTime */
104 /** \returns the number of rows. \sa cols(), RowsAtCompileTime */
110 inline Index cols() const { function in class:Eigen::SkylineMatrixBase
111 return derived().cols();
114 /** \returns the number of coefficients, which is \a rows()*cols().
115 * \sa rows(), cols(), SizeAtCompileTime. */
117 return rows() * cols();
129 return (int(Flags) & RowMajorBit) ? this->rows() : this->cols();
133 * i.e., the number of rows for a columns major matrix, and the number of cols otherwise *
[all...] |
| /external/eigen/Eigen/src/SparseLU/ |
| SparseLU.h | 133 inline Index cols() const { return m_mat.cols(); } function in class:Eigen::SparseLU
226 X.resize(B.rows(),B.cols());
229 for(Index j = 0; j < B.cols(); ++j)
237 for (Index j = 0; j < B.cols(); ++j)
261 for (Index j = 0; j < this->cols(); ++j)
290 for (Index j = 0; j < this->cols(); ++j)
316 for (Index j = 0; j < this->cols(); ++j)
344 for (Index j = 0; j < this->cols(); ++j)
428 ei_declare_aligned_stack_constructed_variable(StorageIndex,outerIndexPtr,mat.cols()+1,mat.isCompressed()?const_cast<StorageIndex*>(mat.outerIndexPtr()):0)
710 Index cols() { return m_mapL.cols(); } function in struct:Eigen::SparseLUMatrixLReturnType
727 Index cols() { return m_mapL.cols(); } function in struct:Eigen::SparseLUMatrixUReturnType
[all...] |
