Add rvalue reference constructor.

src/mlpack/core/tree/rectangle_tree/rectangle_tree.hpp

@@ -144,6 +144,27 @@ class RectangleTree
                 const size_t minNumChildren = 2,
                 const size_t firstDataIndex = 0);
 
+  /**
+   * Construct this as the root node of a rectangle tree type using the given
+   * dataset, and taking ownership of the given dataset.
+   *
+   * @param data Dataset from which to create the tree.
+   * @param maxLeafSize Maximum size of each leaf in the tree.
+   * @param minLeafSize Minimum size of each leaf in the tree.
+   * @param maxNumChildren The maximum number of child nodes a non-leaf node may
+   *      have.
+   * @param minNumChildren The minimum number of child nodes a non-leaf node may
+   *      have.
+   * @param firstDataIndex The index of the first data point.  UNUSED UNLESS WE
+   *      ADD SUPPORT FOR HAVING A "CENTERAL" DATA MATRIX.
+   */
+  RectangleTree(MatType&& data,
+                const size_t maxLeafSize = 20,
+                const size_t minLeafSize = 8,
+                const size_t maxNumChildren = 5,
+                const size_t minNumChildren = 2,
+                const size_t firstDataIndex = 0);
+
   /**
    * Construct this as an empty node with the specified parent.  Copying the
    * parameters (maxLeafSize, minLeafSize, maxNumChildren, minNumChildren,

src/mlpack/core/tree/rectangle_tree/rectangle_tree_impl.hpp

@@ -56,6 +56,45 @@ RectangleTree(const MatType& data,
     root->InsertPoint(i);
 }
 
+template<typename MetricType,
+         typename StatisticType,
+         typename MatType,
+         typename SplitType,
+         typename DescentType>
+RectangleTree<MetricType, StatisticType, MatType, SplitType, DescentType>::
+RectangleTree(MatType&& data,
+              const size_t maxLeafSize,
+              const size_t minLeafSize,
+              const size_t maxNumChildren,
+              const size_t minNumChildren,
+              const size_t firstDataIndex) :
+    maxNumChildren(maxNumChildren),
+    minNumChildren(minNumChildren),
+    numChildren(0),
+    children(maxNumChildren + 1), // Add one to make splitting the node simpler.
+    parent(NULL),
+    begin(0),
+    count(0),
+    maxLeafSize(maxLeafSize),
+    minLeafSize(minLeafSize),
+    bound(data.n_rows),
+    splitHistory(bound.Dim()),
+    parentDistance(0),
+    dataset(new MatType(std::move(data))),
+    ownsDataset(true),
+    points(maxLeafSize + 1), // Add one to make splitting the node simpler.
+    localDataset(new MatType(arma::zeros<MatType>(data.n_rows,
+                                                  maxLeafSize + 1)))
+{
+  stat = StatisticType(*this);
+
+  // For now, just insert the points in order.
+  RectangleTree* root = this;
+
+  for (size_t i = firstDataIndex; i < data.n_cols; i++)
+    root->InsertPoint(i);
+}
+
 template<typename MetricType,
          typename StatisticType,
          typename MatType,

src/mlpack/tests/rectangle_tree_test.cpp

@@ -823,4 +823,16 @@ BOOST_AUTO_TEST_CASE(RStarTreeSplitTest)
       0.9, 1e-15);
 }
 
+BOOST_AUTO_TEST_CASE(RectangleTreeMoveDatasetTest)
+{
+  arma::mat dataset = arma::randu<arma::mat>(3, 1000);
+  typedef RTree<EuclideanDistance, EmptyStatistic, arma::mat> TreeType;
+
+  TreeType tree(std::move(dataset));
+
+  BOOST_REQUIRE_EQUAL(dataset.n_elem, 0);
+  BOOST_REQUIRE_EQUAL(tree.Dataset().n_rows, 3);
+  BOOST_REQUIRE_EQUAL(tree.Dataset().n_cols, 1000);
+}
+
 BOOST_AUTO_TEST_SUITE_END();