Searched defs:nnz (Results 1 - 21 of 21) sorted by relevance

/external/eigen/Eigen/src/SparseCore/
H A DMappedSparseMatrix.h42 inline MappedSparseMatrix(Index rows, Index cols, Index nnz, StorageIndex* outerIndexPtr, StorageIndex* innerIndexPtr, Scalar* valuePtr, StorageIndex* innerNonZeroPtr = 0) argument
43 : Base(rows, cols, nnz, outerIndexPtr, innerIndexPtr, valuePtr, innerNonZeroPtr)
H A DConservativeSparseSparseProduct.h41 // Therefore, we have nnz(lhs*rhs) = nnz(lhs) + nnz(rhs)
51 Index nnz = 0; local
64 indices[nnz] = i;
65 ++nnz;
74 for(Index k=0; k<nnz; ++k)
87 // FIXME reserve nnz non zeros
88 // FIXME implement faster sorting algorithms for very small nnz
93 if((nnz<20
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H A DSparseMap.h124 inline SparseMapBase(Index rows, Index cols, Index nnz, IndexPointer outerIndexPtr, IndexPointer innerIndexPtr, argument
126 : m_outerSize(IsRowMajor?rows:cols), m_innerSize(IsRowMajor?cols:rows), m_zero_nnz(0,internal::convert_index<StorageIndex>(nnz)), m_outerIndex(outerIndexPtr),
131 inline SparseMapBase(Index size, Index nnz, IndexPointer innerIndexPtr, ScalarPointer valuePtr) argument
132 : m_outerSize(1), m_innerSize(size), m_zero_nnz(0,internal::convert_index<StorageIndex>(nnz)), m_outerIndex(m_zero_nnz.data()),
195 inline SparseMapBase(Index rows, Index cols, Index nnz, StorageIndex* outerIndexPtr, StorageIndex* innerIndexPtr, argument
197 : Base(rows, cols, nnz, outerIndexPtr, innerIndexPtr, valuePtr, innerNonZerosPtr)
201 inline SparseMapBase(Index size, Index nnz, StorageIndex* innerIndexPtr, Scalar* valuePtr) argument
202 : Base(size, nnz, innerIndexPtr, valuePtr)
237 /** Constructs a read-write Map to a sparse matrix of size \a rows x \a cols, containing \a nnz non-zero coefficients,
245 inline Map(Index rows, Index cols, Index nnz, StorageInde argument
270 Map(Index rows, Index cols, Index nnz, const StorageIndex* outerIndexPtr, const StorageIndex* innerIndexPtr, const Scalar* valuePtr, const StorageIndex* innerNonZerosPtr = 0) argument
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H A DSparseBlock.h46 Index nnz = 0; local
50 ++nnz;
51 return nnz;
132 Index nnz = tmp.nonZeros(); local
145 if(nnz>free_size)
148 typename SparseMatrixType::Storage newdata(m_matrix.data().allocatedSize() - block_size + nnz);
153 internal::smart_copy(tmp.valuePtr() + tmp_start, tmp.valuePtr() + tmp_start + nnz, newdata.valuePtr() + start);
154 internal::smart_copy(tmp.innerIndexPtr() + tmp_start, tmp.innerIndexPtr() + tmp_start + nnz, newdata.indexPtr() + start);
156 internal::smart_copy(matrix.valuePtr()+end, matrix.valuePtr()+end + tail_size, newdata.valuePtr()+start+nnz);
157 internal::smart_copy(matrix.innerIndexPtr()+end, matrix.innerIndexPtr()+end + tail_size, newdata.indexPtr()+start+nnz);
489 Index nnz = m_block.nonZeros(); local
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H A DSparseSelfAdjointView.h467 Index nnz = count.sum(); local
470 dest.resizeNonZeros(nnz);
/external/tensorflow/tensorflow/core/kernels/
H A Dsparse_softmax_op.cc70 const int nnz = static_cast<int>(indices_t->dim_size(0)); variable
76 OP_REQUIRES_OK(context, context->allocate_output(0, TensorShape({nnz}),
H A Dsparse_tensor_dense_matmul_op_test.cc42 static Graph* SparseTensorDenseMatmul(int nnz, int m, int k, int n, argument
45 Tensor a_values(DT_FLOAT, TensorShape({nnz}));
46 Tensor a_indices(DT_INT64, TensorShape({nnz, 2}));
57 for (int32 i = 0; i < nnz; ++i) {
H A Dreshape_util.cc54 const int64 nnz = input_indices_in.shape().dim_size(0); local
124 TensorShape({nnz, output_rank}),
128 for (int i = 0; i < nnz; ++i) {
H A Dsparse_dense_binary_op_shared.cc31 // The only output is a vector of flat values with shape [nnz], since this op
109 const int nnz = static_cast<int>(indices_t->dim_size(0)); variable
111 ctx->allocate_output(0, TensorShape({nnz}), &output_values));
113 ctx, ctx->allocate_temp(DataTypeToEnum<T>::value, TensorShape({nnz}),
132 for (int i = 0; i < nnz; ++i) { \
H A Dsparse_tensor_dense_matmul_op_gpu.cu.cc31 __global__ void SparseTensorDenseMatMulKernel(int nnz, int m, int b_rows, argument
39 CUDA_1D_KERNEL_LOOP(index, nnz * p) {
73 int nnz = a_values.size(); local
80 // TODO(ebrevdo): Should this be alpha * nnz instead of
81 // out.size()? Perhaps p * nnz ?
82 CudaLaunchConfig config = GetCudaLaunchConfig(p * nnz, d);
86 nnz, m, b_rows, b_cols, p, a_indices.data(), a_values.data(),
H A Dsparse_reduce_op.cc270 int64 nnz = 0; variable
273 nnz++;
279 0, TensorShape({nnz, reduction.reduced_shape.dims()}),
288 ctx->allocate_output(1, TensorShape({nnz}), &out_values_t));
H A Dsparse_tensor_dense_matmul_op.cc68 const int64 nnz = a_indices->shape().dim_size(0); variable
69 OP_REQUIRES(ctx, nnz == a_values->NumElements(),
111 OP_REQUIRES(ctx, FastBoundsCheck(nnz * outer_right, int32max),
113 "Cannot use GPU when output.shape[1] * nnz(a) > 2^31"));
251 const std::size_t nnz = a_values.size(); local
267 for (std::size_t i = 0; i < nnz; ++i) {
287 for (std::size_t i = 0; i < nnz; ++i) { \
303 // columns in the nnz loop.
H A Dsparse_conditional_accumulator.h164 const int64 nnz = grad_idx->dim_size(0); variable
169 accum_idx_vec_->reserve(nnz);
170 for (int i = 0; i < nnz; i++) {
186 count_element_ = new std::vector<int>(nnz, 1);
323 const int64 nnz = count_element_->size(); local
333 &accum_flat(0,0), &accum_flat(nnz,0), &accum_flat(0,0),
340 for (int64 i = 0; i < nnz; i++) {
387 const int64 nnz = grad_idx_tensor->dim_size(0); local
391 OP_REQUIRES_BOOLEAN(ctx, grad_val_tensor->dim_size(0) == nnz,
392 errors::InvalidArgument("Expected ", nnz,
424 const int64 nnz = accum_idx_vec_->size(); local
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/external/eigen/Eigen/src/OrderingMethods/
H A DOrdering.h134 StorageIndex nnz = StorageIndex(mat.nonZeros()); local
136 StorageIndex Alen = internal::colamd_recommended(nnz, m, n);
144 for(StorageIndex i=0; i < nnz; i++) A(i) = mat.innerIndexPtr()[i];
H A DEigen_Colamd.h189 the COLAMD_RECOMMENDED (nnz, n_row, n_col) macro. It returns -1 if any
190 argument is negative. 2*nnz space is required for the row and column
194 and nnz/5 more space is recommended for run time efficiency.
251 * \param nnz nonzeros in A
257 inline IndexType colamd_recommended ( IndexType nnz, IndexType n_row, IndexType n_col) argument
259 if ((nnz) < 0 || (n_row) < 0 || (n_col) < 0)
262 return (2 * (nnz) + colamd_c (n_col) + colamd_r (n_row) + (n_col) + ((nnz) / 5));
327 IndexType nnz ; /* nonzeros in A */ local
385 nnz
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/external/eigen/bench/
H A Dsparse_setter.cpp107 std::cout << "nnz = " << coords.size() << "\n";
302 const int nnz,
312 for (int n = 0; n < nnz; n++){
316 //cumsum the nnz per row to get Bp[]
322 Bp[n_row] = nnz;
325 for(int n = 0; n < nnz; n++){
384 I nnz = 0; local
397 Aj[nnz] = j;
398 Ax[nnz] = x;
399 nnz
300 coo_tocsr(const int n_row, const int n_col, const int nnz, const Coordinates Aij, const Values Ax, int Bp[], int Bj[], T Bx[]) argument
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/external/eigen/test/
H A Dsparse_basic.cpp73 Index nnz = internal::random<int>(1,int(rows)/2); local
77 m2.reserve(VectorXi::Constant(m2.outerSize(), int(nnz)));
79 m2.reserve(m2.outerSize() * nnz);
84 for (Index k=0; k<nnz; ++k)
/external/eigen/Eigen/src/IterativeLinearSolvers/
H A DIncompleteCholesky.h217 Index nnz = m_L.nonZeros(); local
218 Map<VectorSx> vals(m_L.valuePtr(), nnz); //values
219 Map<VectorIx> rowIdx(m_L.innerIndexPtr(), nnz); //Row indices
329 vals = Map<const VectorSx>(L_save.valuePtr(), nnz);
330 rowIdx = Map<const VectorIx>(L_save.innerIndexPtr(), nnz);
/external/eigen/Eigen/src/SparseLU/
H A DSparseLU.h434 // Apply the permutation and compute the nnz per column.
537 Index nnz = m_mat.nonZeros(); local
541 Index info = Base::memInit(m, n, nnz, lwork, m_perfv.fillfactor, m_perfv.panel_size, m_glu);
/external/libavc/encoder/
H A Dih264e_cavlc.c100 * total nnz.
145 /* Compute Runs of zeros for all nnz coefficients except the last 3 */
298 DEBUG("\n[%d numcoeff, %d numtrailing ones, %d nnz]",u4_total_coeff, 0, u4_nc);
359 DEBUG("\n[%d numcoeff, %d numtrailing ones, %d nnz]",u4_total_coeff, u4_trailing_ones, u4_nc);
593 * pointer to the buffer containing nnz of all the subblks to the top
596 * pointer to the buffer containing nnz of all the subblks to the left
754 /* estimate nnz for the current mb */
1233 UWORD32 *nnz; local
1244 /* set nnz to zero */
1246 nnz
1544 UWORD32 *nnz; local
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/external/eigen/Eigen/src/SuperLUSupport/
H A DSuperLUSupport.h139 union {int nnz;int lda;}; member in union:Eigen::SluMatrix::__anon6431::__anon6432
212 res.storage.nnz = internal::convert_index<int>(mat.nonZeros());
271 res.storage.nnz = mat.nonZeros();
721 m_l.resizeNonZeros(Lstore->nnz);
723 m_u.resizeNonZeros(Ustore->nnz);

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