Searched refs:ix_ (Results 1 – 6 of 6) sorted by relevance
| /external/tensorflow/tensorflow/core/util/sparse/ |
| D | dim_comparator.h | 51 : ix_(ix), order_(order), dims_(shape.size()) { in DimComparator()
63 if (ix_(i, d) < ix_(j, d)) return true; in operator()
64 if (ix_(i, d) > ix_(j, d)) return false; in operator()
88 const TTypes<int64>::Matrix ix_;
106 if (ix_(i, d) < ix_(j, d)) { in operator()
110 if (ix_(i, d) > ix_(j, d)) break; in operator()
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| D | group_iterator.h | 81 : ix_(ix), in GroupIterable()
82 ix_matrix_(ix_.matrix<int64>()), in GroupIterable()
91 CHECK(loc >= 0 && loc <= ix_.dim_size(0)) in at()
92 << "loc provided must lie between 0 and " << ix_.dim_size(0); in at()
95 IteratorStep end() { return IteratorStep(this, ix_.dim_size(0)); } in end()
131 const Tensor ix_; variable
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| D | sparse_tensor.h | 116 : ix_(ix), in SparseTensor()
132 : SparseTensor(other.ix_, other.vals_, other.shape_, other.order_) {} in SparseTensor()
135 : SparseTensor(std::move(other.ix_), std::move(other.vals_), in SparseTensor()
139 ix_ = other.ix_;
148 ix_ = std::move(other.ix_);
156 std::size_t num_entries() const { return ix_.dim_size(0); } in num_entries()
160 const Tensor& indices() const { return ix_; } in indices()
167 const auto ix_t = ix_.matrix<int64>(); in IndicesValid()
205 return GroupIterable(ix_, vals_, dims_, group_ix); in group()
369 Tensor ix_; variable
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| /external/tensorflow/tensorflow/contrib/boosted_trees/lib/utils/ |
| D | sparse_column_iterable.h | 104 : ix_(ix), example_start_(example_start), example_end_(example_end) { in SparseColumnIterable()
111 const TTypes<int64>::ConstMatrix& ix() const { return ix_; } in ix()
115 const TTypes<int64>::ConstMatrix& sparse_indices() const { return ix_; } in sparse_indices()
119 TTypes<int64>::ConstMatrix ix_;
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| D | sparse_column_iterable.cc | 117 : iter_(iter), example_idx_(example_idx), end_(iter->ix_.dimension(0)) { in Iterator()
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| /external/tensorflow/tensorflow/contrib/factorization/python/ops/ |
| D | factorization_ops_test_utils.py | 56 mat = np_matrix[np.ix_(nz_row_ids, nz_col_ids)]
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