| /external/tensorflow/tensorflow/compiler/tf2xla/ |
| shape_util.cc | 35 for (int i = 0; i < xla::ShapeUtil::Rank(shape); ++i) {
44 int rank = tensor_shape.dims(); local
45 std::vector<int64> dimensions(rank);
46 std::vector<int64> layout(rank);
47 for (int d = 0; d < rank; ++d) {
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| /external/tensorflow/tensorflow/contrib/mpi_collectives/python/ops/ |
| mpi_ops.py | 47 def rank(name=None): function
48 """An op which returns the MPI rank of the calling process.
51 rank of the current process in the global communicator.
54 An integer scalar with the MPI rank of the calling process.
75 """An op which returns the local MPI rank of the calling process, within the
83 An integer scalar with the local MPI rank of the calling process.
113 different processes must have the same rank and shape, except for the first
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| /external/apache-commons-math/src/main/java/org/apache/commons/math/stat/descriptive/rank/ |
| Median.java | 17 package org.apache.commons.math.stat.descriptive.rank;
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| Max.java | 17 package org.apache.commons.math.stat.descriptive.rank;
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| Min.java | 17 package org.apache.commons.math.stat.descriptive.rank;
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| /external/apache-commons-math/src/main/java/org/apache/commons/math/stat/ranking/ |
| RankingAlgorithm.java | 21 * Interface representing a rank transformation.
28 * <p>Performs a rank transformation on the input data, returning an array
40 double[] rank (double[] data); method in interface:RankingAlgorithm
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| /external/tensorflow/tensorflow/core/ops/ |
| control_flow_ops.cc | 96 int32 rank = c->Rank(out); local
99 if (!c->RankKnown(input) || c->Rank(input) != rank) {
104 for (int d = 0; d < rank; ++d) {
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| resource_variable_ops.cc | 133 int64 rank = c->RankKnown(var_shape) ? c->Rank(var_shape) local
135 c->set_output(0, c->Vector(rank));
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| /external/clang/test/Analysis/ |
| MemRegion.cpp | 8 int rank = 0; local
9 MPI_Comm_rank(MPI_COMM_WORLD, &rank);
15 int rank = 0; local
16 MPI_Comm_rank(MPI_COMM_WORLD, &rank);
22 int rank = 0; local
23 MPI_Comm_rank(MPI_COMM_WORLD, &rank);
31 int rank = 0; local
32 MPI_Comm_rank(MPI_COMM_WORLD, &rank);
40 int rank = 0; local
41 MPI_Comm_rank(MPI_COMM_WORLD, &rank);
[all...] |
| /external/libxcam/modules/ocl/ |
| priority_buffer_queue.h | 34 uint32_t rank; member in struct:XCam::PriorityBuffer
39 : rank (0)
50 // when change to next rank
52 ++rank;
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| /external/mesa3d/src/gallium/drivers/llvmpipe/ |
| lp_fence.h | 50 unsigned rank; member in struct:lp_fence
56 lp_fence_create(unsigned rank);
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| /external/tensorflow/tensorflow/compiler/xla/ |
| index_util.cc | 154 int64 rank = ShapeUtil::Rank(shape); local
155 if (rank != index.size()) {
158 for (int64 d = 0; d < rank; ++d) {
169 int64 rank = lhs.size(); local
170 CHECK_EQ(rhs.size(), rank);
171 for (int64 dim = 0; dim < rank; ++dim) {
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| sparse_index_array.h | 32 // sparse index is an array of `rank` int64 value that gives the location of a
34 // checked (except for the rank). To avoid confusion, we refer to the position
45 // indices, with an initial contents obtained from the given array. The rank
64 SparseIndexArray(int64 max_indices, int64 rank,
66 SparseIndexArray(int64 max_indices, int64 rank,
94 int64 rank() const { return rank_; } function in class:xla::SparseIndexArray
142 tensorflow::gtl::InlinedVector<int64, 8> saved_index(rank());
153 std::copy_n(At(i).begin(), rank(), saved_index.begin());
158 std::copy_n(saved_index.begin(), rank(), At(j).begin());
164 std::copy_n(At(sort_order[j]).begin(), rank(), At(j).begin())
[all...] |
| /external/tensorflow/tensorflow/compiler/xla/service/ |
| hlo_evaluator.cc | 177 const int64 rank = ShapeUtil::Rank(base_shape); local
178 DimensionVector window_index(rank);
181 std::vector<int64> base_index(rank);
183 for (int64 i = 0; i < rank; ++i) {
295 ShapeUtil::Rank(broadcast->operand(0)->shape()), 0);
298 ShapeUtil::Rank(operand_to_broadcast.shape()))
300 << " and rank of operand_to_broadcast is: "
301 << ShapeUtil::Rank(operand_to_broadcast.shape());
[all...] |
| /external/eigen/Eigen/src/misc/ |
| Image.h | 28 Dynamic, // we don't know at compile time the dimension of the image (the rank)
43 : m_dec(dec), m_rank(dec.rank()),
50 inline Index rank() const { return m_rank; } function in struct:Eigen::internal::image_retval_base
74 using Base::rank; \
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| Kernel.h | 45 m_rank(dec.rank()),
51 inline Index rank() const { return m_rank; } function in struct:Eigen::internal::kernel_retval_base
72 using Base::rank; \
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| /external/eigen/unsupported/Eigen/CXX11/src/Tensor/ |
| TensorIO.h | 18 template <typename Tensor, int Rank>
69 static const int rank = internal::array_size<Dimensions>::value; local
70 internal::TensorPrinter<Evaluator, rank>::run(os, tensor);
|
| TensorDimensions.h | 97 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t rank() const { function in struct:Eigen::Sizes
159 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t rank() const {
265 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t rank() const { function in struct:Eigen::DSizes
|
| /external/guava/guava/src/com/google/common/collect/ |
| ExplicitOrdering.java | 40 return rank(left) - rank(right); // safe because both are nonnegative
43 private int rank(T value) { method in class:ExplicitOrdering
44 Integer rank = rankMap.get(value); local
45 if (rank == null) {
48 return rank;
54 int rank = 0; local
56 builder.put(value, rank++);
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| /external/tensorflow/tensorflow/compiler/tf2xla/kernels/ |
| matrix_set_diag_op.cc | 31 const int rank = input_shape.dims(); variable
41 const int64 m = input_shape.dim_size(rank - 2);
42 const int64 n = input_shape.dim_size(rank - 1);
75 std::vector<int64> diag_broadcast_dims(rank - 1);
78 diag_broadcast_dims.back() = rank - 1;
|
| /external/tensorflow/tensorflow/compiler/xla/service/llvm_ir/ |
| ops.cc | 47 const int64 rank = ShapeUtil::Rank(output_shape); local
48 IrArray::Index start_index(rank);
49 for (int64 i = 0; i < rank; ++i) {
60 IrArray::Index output_index(rank);
61 for (int64 i = 0; i < rank; ++i) {
|
| /external/tensorflow/tensorflow/core/kernels/ |
| sparse_softmax_op.cc | 63 "Input should have rank >= 2, but received shape: ",
71 const int rank = static_cast<int>(indices_t->dim_size(1)); variable
85 gtl::InlinedVector<int64, 4> dims(rank);
87 // { 0, ..., rank-1 }.
90 const ArraySlice<int64> kGroupByDims(kReorderDims, 0, rank - 1);
96 // Therefore we group by the first (rank - 1) dimensions.
|
| /frameworks/base/core/java/android/provider/ |
| SearchIndexableData.java | 52 * The rank for the data. This is application specific.
54 public int rank; field in class:SearchIndexableData
154 sb.append("rank: ");
155 sb.append(rank);
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| /external/eigen/test/ |
| qr_fullpivoting.cpp | 23 rank = internal::random<Index>(1, (std::min)(rows, cols)-1); local
28 createRandomPIMatrixOfRank(rank,rows,cols,m1);
30 VERIFY_IS_EQUAL(rank, qr.rank());
31 VERIFY_IS_EQUAL(cols - qr.rank(), qr.dimensionOfKernel());
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| /external/eigen/unsupported/Eigen/src/LevenbergMarquardt/ |
| LMpar.h | 58 /* jacobian is rank-deficient, obtain a least squares solution. */
60 // const Index rank = qr.nonzeroPivots(); // exactly double(0.)
61 const Index rank = qr.rank(); // use a threshold local
63 wa1.tail(n-rank).setZero();
65 wa1.head(rank) = s.topLeftCorner(rank,rank).template triangularView<Upper>().solve(qtb.head(rank));
81 /* if the jacobian is not rank deficient, the newton *
[all...] |
