# `RectangleTree`

The `RectangleTree` class represents a generic multidimensional space
partitioning tree.  It is heavily templatized to control splitting behavior and
other behaviors, and is the actual class underlying trees such as the
[`RTree`](r_tree.md).  In general, the `RectangleTree` class is not meant to
be used directly, and instead one of the numerous variants should be used
instead:

 * [`RTree`](r_tree.md)
 * [`RStarTree`](r_star_tree.md)
 * [`XTree`](x_tree.md)
 * [`RPlusTree`](r_plus_tree.md)
 * [`RPlusPlusTree`](r_plus_plus_tree.md)
 * [`HilbertRTree`](hilbert_r_tree.md)

The `RectangleTree` and its variants are capable of inserting points and
deleting them.  This is different from [`BinarySpaceTree`](binary_space_tree.md)
and other mlpack tree types, where the tree is built entirely in batch at
construction time.  However, this capability comes with a runtime cost, and so
in general the use of `RectangleTree` with mlpack algorithms will be slower than
the batch-construction trees---but, if insert/delete functionality is required,
`RectangleTree` is the only choice.

---

For users who want to use `RectangleTree` directly or with custom behavior,
the full class is still detailed in the subsections below.  `RectangleTree`
supports the [TreeType API](../../../developer/trees.md#the-treetype-api) and
can be used with mlpack's tree-based algorithms, although using custom behavior
may require a template typedef.

 * [Template parameters](#template-parameters)
 * [Constructors](#constructors)
 * [Basic tree properties](#basic-tree-properties)
 * [Bounding distances with the tree](#bounding-distances-with-the-tree)
 * [`StatisticType`](#statistictype) template parameter
 * [`SplitType`](#splittype) template parameter
 * [`DescentType`](#descenttype) template parameter
 * [`AuxiliaryInformationType`](#auxiliaryinformationtype) template parameter
 * [Tree traversals](#tree-traversals)
 * [Example usage](#example-usage)

## See also

 * [`RTree`](r_tree.md)
 * [mlpack trees](../trees.md)
 * [`KNN`](../../methods/knn.md)
 * [mlpack geometric algorithms](../../modeling.md#geometric-algorithms)
 * [R-Tree on Wikipedia](https://en.wikipedia.org/wiki/R-tree)
 * [R-Trees: A Dynamic Index Structure for Spatial Searching (pdf)](http://www-db.deis.unibo.it/courses/SI-LS/papers/Gut84.pdf)
 * [Tree-Independent Dual-Tree Algorithms (pdf)](https://www.ratml.org/pub/pdf/2013tree.pdf)

## Template parameters

The `RectangleTree` class takes six template parameters.  The first three of
these are required by the
[TreeType API](../../../developer/trees.md#template-parameters-required-by-the-treetype-policy)
(see also
[this more detailed section](../../../developer/trees.md#template-parameters)). The
full signature of the class is:

```
template<typename DistanceType,
         typename StatisticType,
         typename MatType,
         typename SplitType,
         typename DescentType,
         template<typename> class AuxiliaryInformationType>
class RectangleTree;
```

 * `DistanceType`: the [distance metric](../distances.md) to use for distance
   computations.  `RectangleTree` requires that this is
   [`EuclideanDistance`](../distances.md#lmetric), and a compilation error will
   be thrown if any other `DistanceType` is specified.

 * `StatisticType`: this holds auxiliary information in each tree node.  By
   default, [`EmptyStatistic`](#emptystatistic) is used, which holds no
   information.
   - See the [`StatisticType`](#statistictype) section for more details.

 * `MatType`: the type of matrix used to represent points.  Must be a type
   matching the [Armadillo API](../../matrices.md).  By default, `arma::mat` is
   used, but other types such as `arma::fmat` or similar will work just fine.

 * `SplitType`: the class defining how an individual `RectangleTree` node
   should be split.  By default, [`RTreeSplit`](#rtreesplit) is used.
   - See the [`SplitType`](#splittype) section for more details.

 * `DescentType`: the class defining how a child node is chosen for point
   insertion.  By default, [`RTreeDescentHeuristic`](#rtreedescentheuristic) is
   used.
   - See the [`DescentType`](#descenttype) section for more details.

 * `AuxiliaryInformationType`: holds information specific to the variant of the
   `RectangleTree`.  By default, `NoAuxiliaryInformation` is used.

Note that the TreeType API requires trees to have only three template
parameters.  In order to use a `RectangleTree` with its six template parameters
with an mlpack algorithm that needs a TreeType, it is easiest to define a
template typedef:

```
template<typename DistanceType, typename StatisticType, typename MatType>
using CustomTree = Rectangle<DistanceType, StatisticType, MatType,
    CustomSplitType, CustomDescentType, CustomAuxiliaryInformationType>
```

Here, `CustomSplitType`, `CustomDescentType`, and
`CustomAuxiliaryInformationType` are the desired splitting and descent
strategies and auxiliary information type.  This is the way that all
`RectangleTree` variants (such as [`RTree`](r_tree.md)) are defined.

## Constructors

`RectangleTree`s are constructed by inserting points in a dataset sequentially.
The dataset is not permuted during the construction process.

---

 * `node = RectangleTree(data)`
 * `node = RectangleTree(data, maxLeafSize=20, minLeafSize=8)`
 * `node = RectangleTree(data, maxLeafSize=20, minLeafSize=8, maxNumChildren=5, minNumChildren=2)`
   - Construct a `RectangleTree` on the given `data` with the given construction
     parameters.
   - Default template parameters are used, meaning that this tree will be a
     [`RTree`](r_tree.md).
   - By default, `data` is copied.  Avoid a copy by using `std::move()` (e.g.
     `std::move(data)`); when doing this, `data` will be set to an empty matrix.

---

 * `node = RectangleTree<DistanceType, StatisticType, MatType, SplitType, DescentType, AuxiliaryInformationType>(data)`
 * `node = RectangleTree<DistanceType, StatisticType, MatType, SplitType, DescentType, AuxiliaryInformationType>(data, maxLeafSize=20, minLeafSize=8)`
 * `node = RectangleTree<DistanceType, StatisticType, MatType, SplitType, DescentType, AuxiliaryInformationType>(data, maxLeafSize=20, minLeafSize=8, maxNumChildren=5, minNumChildren=2)`
   - Construct a `RectangleTree` on the given `data`, using custom template
     parameters to control the behavior of the tree and the given construction
     parameters.
   - By default, `data` is copied.  Avoid a copy by using `std::move()` (e.g.
     `std::move(data)`); when doing this, `data` will be set to an empty matrix.

---

 * `node = RectangleTree(dimensionality)`
   - Construct an empty `RectangleTree` with no children, no points, and default
     template parameters.
   - Use `node.Insert()` to insert points into the tree.  All points must have
     dimensionality `dimensionality`.

---

 * `node.Insert(x)`
   - Insert the point `x` into the tree.
   - `x` should have vector type compatible with the chosen `MatType`; so, for
     default `MatType`, `arma::vec` is the expected type.
   - If a custom `MatType` is specified (e.g. `arma::fmat`), then `x` should
     have type equivalent to the corresponding column vector type (e.g.
     `arma::fvec`).
   - Due to tree rebalancing, this may change the internal structure of the
     tree; so references and pointers to children of `node` may become invalid.
   - ***Warning:*** This will throw an exception if `node` is not the root of
     the tree!

 * `node.Delete(i)
   - Delete the point with index `i` from the tree.
   - The point to be deleted from the tree will be `node.Dataset().col(i)`;
     after deleting, the column will be removed from `node.Dataset()` and all
     indexes held in all tree nodes will be updated.  (Thus, this operation can
     be expensive!)
   - Due to tree rebalancing, this may change the internal structure of the
     tree; so references and pointers to children of `node` may become invalid.
   - ***Warning:*** This will throw an exception if `node` is not the root of
     the tree!

---

***Notes:***

 - The name `node` is used here for `RectangleTree` objects instead of `tree`,
   because each `RectangleTree` object is a single node in the tree.  The
   constructor returns the node that is the root of the tree.

 - See also the
   [developer documentation on tree constructors](../../../developer/trees.md#constructors-and-destructors).

---

### Constructor parameters:

| **name** | **type** | **description** | **default** |
|----------|----------|-----------------|-------------|
| `data` | [`MatType`](../../matrices.md) | [Column-major](../../matrices.md#representing-data-in-mlpack) matrix to build the tree on. | _(N/A)_ |
| `maxLeafSize` | `size_t` | Maximum number of points to store in each leaf. | `20` |
| `minLeafSize` | `size_t` | Minimum number of points to store in each leaf. | `8` |
| `maxNumChildren` | `size_t` | Maximum number of children allowed in each non-leaf node. | `5` |
| `minNumChildren` | `size_t` | Minimum number of children in each non-leaf node. | `2` |
| `dimensionality` | `size_t` | Dimensionality of points to be held in the tree. | _(N/A)_ |
| | | |
| `x` | [`arma::vec`](../../matrices.md) | Column vector: point to insert into tree.  Should have type matching the column vector type associated with `MatType`, and must have `node.Dataset().n_rows` elements. | _(N/A)_ |
| `i` | `size_t` | Index of point in `node.Dataset()` to delete from `node`. | _(N/A)_ |

## Basic tree properties

Once a `RectangleTree` object is constructed, various properties of the tree
can be accessed or inspected.  Many of these functions are required by the
[TreeType API](../../../developer/trees.md#the-treetype-api).

### Navigating the tree

 * `node.NumChildren()` returns the number of children in `node`.  This is `0`
   if `node` is a leaf, and between the values of `node.MinNumChildren()` and
   `node.MaxNumChildren()` (inclusive) otherwise.

 * `node.IsLeaf()` returns a `bool` indicating whether or not `node` is a leaf.

 * `node.Child(i)` returns a `RectangleTree&` that is the `i`th child.
   - `i` must be less than `node.NumChildren()`.
   - This function should only be called if `node.NumChildren()` is not `0`
     (e.g. if `node` is not a leaf).  Note that this returns a valid
     `RectangleTree&` that can itself be used just like the root node of the
     tree!

 * `node.Parent()` will return a `RectangleTree*` that points to the parent of
   `node`, or `NULL` if `node` is the root of the `RectangleTree`.

---

### Accessing members of a tree

 * `node.Bound()` will return an
   [`HRectBound<DistanceType, ElemType>&`](binary_space_tree.md#hrectbound)
   object that represents the hyperrectangle bounding box of `node`.
   - `ElemType` is the element type of `MatType`; so, if default template
     parameters are used, `ElemType` is `double`.
   - `bound` is a hyperrectangle that encloses all the descendant points of
     `node`.  It may be somewhat loose (e.g. points may not be very near the
     edges).

 * `node.Stat()` will return a `StatisticType&` holding the statistics of the
   node that were computed during tree construction.

 * `node.Distance()` will return a `EuclideanDistance&`.  Since
   `EuclideanDistance` has no members, this function is not likely to be useful,
   but it is required by the TreeType API.

 * `node.AuxiliaryInfo()` returns an `AuxiliaryInformationType&` that holds any
   auxiliary information required by the node.

 * `node.MinNumChildren()` returns the minimum number of children that the
   node is required to have as a `size_t`.  If points are deleted such that the
   number of children falls below this limit, then `node` will become a leaf and
   the tree will be rebalanced.

 * `node.MaxNumChildren()` returns the maximum number of children that the
   node is required to have as a `size_t`.  If points are inserted such that the
   number of children goes above this limit, new nodes will be added and the
   tree will be rebalanced.

 * `node.MaxLeafSize()` returns the maximum number of points that the node is
   allowed to hold as a `size_t`.  If the number of points held by `node`
   exceeds this limit during insertion, then `node` will be split and the tree
   will be rebalanced.

 * `node.MinLeafSize()` returns the minimum number of points that the node is
   allowed to hold as a `size_t`.  If the number of points held by `node` goes
   under this limit during deletion, then `node` will be deleted (if possible)
   and the tree will be rebalanced.

See also the
[developer documentation](../../../developer/trees.md#basic-tree-functionality)
for basic tree functionality in mlpack.

---

### Accessing data held in a tree

 * `node.Dataset()` will return a `const MatType&` that is an internally-held
   representation of the dataset the tree was built on.

 * `node.NumPoints()` returns a `size_t` indicating the number of points held
   directly in `node`.
   - If `node` is not a leaf, this will return `0`, as `RectangleTree` only
     holds points directly in its leaves.
   - If `node` is a leaf, then this will return values between
     `node.MinLeafSize()` and `node.MaxLeafSize()` (inclusive).
   - If the tree has fewer than `node.MinLeafSize()` points total, then
     `node.NumPoints()` will return a value less than `node.MinLeafSize()`.

 * `node.Point(i)` returns a `size_t` indicating the index of the `i`'th point
   in `node.Dataset()`.
   - `i` must be in the range `[0, node.NumPoints() - 1]` (inclusive).
   - `node` must be a leaf (as non-leaves do not hold any points).
   - The `i`'th point in `node` can then be accessed as
     `node.Dataset().col(node.Point(i))`.
   - Accessing the actual `i`'th point itself can be done with, e.g.,
     `node.Dataset().col(node.Point(i))`.
   - Point indices are not necessarily contiguous for `RectangleTree`s; that is,
     `node.Point(i) + 1` is not necessarily `node.Point(i + 1)`.

 * `node.NumDescendants()` returns a `size_t` indicating the number of points
   held in all descendant leaves of `node`.
   - If `node` is the root of the tree, then `node.NumDescendants()` will be
     equal to `node.Dataset().n_cols`.

 * `node.Descendant(i)` returns a `size_t` indicating the index of the `i`'th
   descendant point in `node.Dataset()`.
   - `i` must be in the range `[0, node.NumDescendants() - 1]` (inclusive).
   - `node` does not need to be a leaf.
   - The `i`'th descendant point in `node` can then be accessed as
     `node.Dataset().col(node.Descendant(i))`.
   - Accessing the actual `i`'th descendant itself can be done with, e.g.,
     `node.Dataset().col(node.Descendant(i))`.
   - Descendant point indices are not necessarily contiguous for
     `RectangleTree`s; that is, `node.Descendant(i) + 1` is not necessarily
     `node.Descendant(i + 1)`.

---

### Accessing computed bound quantities of a tree

The following quantities are cached for each node in a `RectangleTree`, and so
accessing them does not require any computation.  In the documentation below,
`ElemType` is the element type of the given `MatType`; e.g., if `MatType` is
`arma::mat`, then `ElemType` is `double`.

 * `node.FurthestPointDistance()` returns an `ElemType` representing the
   distance between the center of the bound of `node` and the furthest point
   held by `node`.
   - If `node` is not a leaf, this returns 0 (because `node` does not hold any
     points).

 * `node.FurthestDescendantDistance()` returns an `ElemType` representing the
   distance between the center of the bound of `node` and the furthest
   descendant point held by `node`.

 * `node.MinimumBoundDistance()` returns an `ElemType` representing the minimum
   possible distance from the center of the node to any edge of its bound.

 * `node.ParentDistance()` returns an `ElemType` representing the distance
   between the center of the bound of `node` and the center of the bound of its
   parent.
   - If `node` is the root of the tree, `0` is returned.

***Note:*** for more details on each bound quantity, see the [developer
documentation](../../../developer/trees.md#complex-tree-functionality-and-bounds)
on bound quantities for trees.

---

### Other functionality

 * `node.Center(center)` computes the center of the hyperrectangle bounding box
   of `node` and stores it in `center`.
   - `center` should be of type `arma::Col<ElemType>&`, where `ElemType` is the
     element type of the specified `MatType`.
   - `center` will be set to have size equivalent to the dimensionality of the
     dataset held by `node`.
   - This is equivalent to calling `node.Bound().Center(center)`.

 * A `RectangleTree` can be serialized with
   [`Save()` and `Load()`](../../load_save.md#mlpack-models-and-objects).

## Bounding distances with the tree

The primary use of trees in mlpack is bounding distances to points or other tree
nodes.  The following functions can be used for these tasks.

 * `node.GetNearestChild(point)`
 * `node.GetFurthestChild(point)`
   - Return a `size_t` indicating the index of the child that is closest to (or
     furthest from) `point`, with respect to the `MinDistance()` (or
     `MaxDistance()`) function.
   - If there is a tie, the node with the lowest index is returned.
   - If `node` is a leaf, `0` is returned.
   - `point` should be a column vector type of the same type as `MatType`.
     (e.g., if `MatType` is `arma::mat`, then `point` should be an `arma::vec`.)

 * `node.GetNearestChild(other)`
 * `node.GetFurthestChild(other)`
   - Return a `size_t` indicating the index of the child that is closest to (or
     furthest from) the `RectangleTree` node `other`, with respect to the
     `MinDistance()` (or `MaxDistance()`) function.
   - If there is a tie, the node with the lowest index is returned.
   - If `node` is a leaf, `0` is returned.

---

 * `node.MinDistance(point)`
 * `node.MinDistance(other)`
   - Return a `double` indicating the minimum possible distance between `node`
     and `point`, or the `RectangleTree` node `other`.
   - This is equivalent to the minimum possible distance between any point
     contained in the bounding hyperrectangle of `node` and `point`, or between
     any point contained in the bounding hyperrectangle of `node` and any point
     contained in the bounding hyperrectangle of `other`.
   - `point` should be a column vector type of the same type as `MatType`.
     (e.g., if `MatType` is `arma::mat`, then `point` should be an `arma::vec`.)

 * `node.MaxDistance(point)`
 * `node.MaxDistance(other)`
   - Return a `double` indicating the maximum possible distance between `node`
     and `point`, or the `RectangleTree` node `other`.
   - This is equivalent to the maximum possible distance between any point
     contained in the bounding hyperrectangle of `node` and `point`, or between
     any point contained in the bounding hyperrectangle of `node` and any point
     contained in the bounding hyperrectangle of `other`.
   - `point` should be a column vector type of the same type as `MatType`.
     (e.g., if `MatType` is `arma::mat`, then `point` should be an `arma::vec`.)

 * `node.RangeDistance(point)`
 * `node.RangeDistance(other)`
   - Return a [`RangeType<ElemType>`](../math.md#range) whose lower bound is
     `node.MinDistance(point)` or `node.MinDistance(other)`, and whose upper
      bound is `node.MaxDistance(point)` or `node.MaxDistance(other)`.
   - `ElemType` is the element type of `MatType`.
   - `point` should be a column vector type of the same type as `MatType`.
     (e.g., if `MatType` is `arma::mat`, then `point` should be an `arma::vec`.)

## Tree traversals

Like every mlpack tree, the `RectangleTree` class provides a [single-tree and
dual-tree traversal](../../../developer/trees.md#traversals) that can be paired
with a [`RuleType` class](../../../developer/trees.md#rules) to implement a
single-tree or dual-tree algorithm.

 * `RectangleTree::SingleTreeTraverser`
   - Implements a depth-first single-tree traverser.

 * `RectangleTree::DualTreeTraverser`
   - Implements a dual-depth-first dual-tree traverser.

## `StatisticType`

Each node in a `RectangleTree` holds an instance of the `StatisticType` class.
This class can be used to store additional bounding information or other cached
quantities that a `RectangleTree` does not already compute.

mlpack provides a few existing `StatisticType` classes, and a custom
`StatisticType` can also be easily implemented:

 * [`EmptyStatistic`](#emptystatistic): an empty statistic class that does not
   hold any information
 * [Custom `StatisticType`s](#custom-statistictypes): implement a fully custom
   `StatisticType`

*Note:* this section is still under construction---not all statistic types are
documented yet.

### `EmptyStatistic`

The `EmptyStatistic` class is an empty placeholder class that is used as the
default `StatisticType` template parameter for mlpack trees.

The class ***does not hold any members and provides no functionality***.
[See the implementation.](/src/mlpack/core/tree/statistic.hpp)

### Custom `StatisticType`s

A custom `StatisticType` is trivial to implement.  Only a default constructor
and a constructor taking a `RectangleTree` is necessary.

```
class CustomStatistic
{
 public:
  // Default constructor required by the StatisticType policy.
  CustomStatistic();

  // Construct a CustomStatistic for the given fully-constructed
  // `RectangleTree` node.  Here we have templatized the tree type to make it
  // easy to handle any type of `RectangleTree`.
  template<typename TreeType>
  StatisticType(TreeType& node);

  //
  // Adding any additional precomputed bound quantities can be done; these
  // quantities should be computed in the constructor.  They can then be
  // accessed from the tree with `node.Stat()`.
  //
};
```

*Example*: suppose we wanted to know, for each node, the exact time at which it
was created.  A `StatisticType` could be created that has a
[`std::time_t`](https://en.cppreference.com/w/cpp/chrono/c/time_t) member,
whose value is computed in the constructor.

## `SplitType`

The `SplitType` template parameter controls the algorithm used to split each
node of a `RectangleTree` while building.  The splitting strategy used can be
entirely arbitrary---the `SplitType` simply needs to split a leaf node and a
non-leaf node into children.

mlpack provides several drop-in choices for `SplitType`, and it is also possible
to write a fully custom split:

 * [`RTreeSplit`](#rtreesplit): splits according to a simple binary heuristic
 * [`RStarTreeSplit`](#rstartreesplit): finds the best possible binary split
   that minimizes the volume of the two children and maximizes the margin
   between them
 * [`XTreeSplit`](#xtreesplit): an improved splitting strategy that minimizes
   overlap of sibling nodes
 * [`RPlusTreeSplit`](#rplustreesplit): split by partitioning two nodes along
   the dimension that minimizes overall node volume
 * [`RPlusPlusTreeSplit`](#rplusplustreesplit): split using maximum bounding
   rectangles to ensure zero overlap between sibling nodes
 * [`HilbertRTreeSplit<>`](#hilbertrtreesplit): use deferred splitting and
   Z-ordering values of points to decide the split
 * [Custom `SplitType`s](#custom-splittypes): implement a fully custom
   `SplitType` class

*Note:* this section is still under construction---not all split types are
documented yet.

### `RTreeSplit`

The `RTreeSplit` class implements the original R-tree splitting strategy and can
be used with the [`RectangleTree`](#rectangletree) class.  This is the splitting
strategy used for the [`RTree`](r_tree.md) class, and is the same strategy
proposed in the [original paper
(pdf)](http://www-db.deis.unibo.it/courses/SI-LS/papers/Gut84.pdf).  The
strategy works as follows:

 * Find the two furthest-apart points (or children if the node is not a leaf).
 * Create two children with each point (or child) as the only point (or child).
 * Iteratively add each remaining point (or child) to the new child whose
   hyperrectangle bound volume increases the least.

For implementation details, see
[the source code](/src/mlpack/core/tree/rectangle_tree/r_tree_split_impl.hpp).

### `RStarTreeSplit`

The `RStarTreeSplit` class implements the improved R\*-tree splitting strategy
and can be used with the [`RectangleTree`](#rectangletree) class.  This is the
splitting strategy used for the [`RStarTree`](r_star_tree.md) class, and is the
strategy proposed in the
[R\*-tree paper (pdf)](https://infolab.usc.edu/csci599/Fall2001/paper/rstar-tree.pdf).
The strategy computes, for each possible binary split in each dimension,

 * The combined volume of the two child nodes,
 * The size of the margin between the two child nodes, and
 * The size of the overlap between the two child nodes.

The split that minimizes the combined volume and maximizes the overlap is
chosen.

In addition, the `RStarTreeSplit` will sometimes perform *forced reinsertion*,
where points are removed from a node during the splitting process and reinserted
into the tree.  This can help decrease the overlap between adjacent nodes in the
tree, which in turn improves the quality of the tree for search and other tasks.

For implementation details, see
[the source code](/src/mlpack/core/tree/rectangle_tree/r_star_tree_split_impl.hpp).

### `XTreeSplit`

The `XTreeSplit` class implements the improved splitting strategy for the
[`XTree`](x_tree.md) as described in the
[X-tree paper (pdf)](https://www.vldb.org/conf/1996/P028.PDF).  This strategy is
an improved version of the standard [`RTreeSplit`](#rtreesplit), where the
overlap of sibling nodes is minimized.

When overlap cannot be prevented, `XTreeSplit` will instead create
"super-nodes" with more children than typically allowed.  The split is then
deferred until a later time when overlap can be more effectively avoided.

For implementation details, see
[the source code](/src/mlpack/core/tree/rectangle_tree/x_tree_split_impl.hpp).

### `RPlusTreeSplit`

The `RPlusTreeSplit` class implements the splitting policy of the
[R+-tree](r_plus_tree.md).  The strategy splits nodes (leaves and non-leaves) by
partitioning along the dimension that results in the two children with minimum
volume, similar to the [kd-tree](kdtree.md).

For implementation details, see
[the source code](/src/mlpack/core/tree/rectangle_tree/r_plus_tree_split_impl.hpp).
Note that `RPlusTreeSplit` is a template typedef of the general
`RPlusTreeSplitType<>` class.

### `RPlusPlusTreeSplit`

The `RPlusPlusTreeSplit` class implements the splitting policy of the
[R++-tree](r_plus_plus_tree.md).  This class can only be used in a tree that
uses [`RPlusPlusTreeAuxiliaryInformation`](#rplusplustreeauxiliaryinformation)
as the [`AuxiliaryInformationType`](#auxiliaryinformationtype).

The splitting strategy splits leaf nodes along an arbitrarily-chosen dimension,
and splits non-leaf nodes along the dimension that minimizes the number of
descendant nodes that also must be split along that dimension.

For implementation details, see
[the source code](/src/mlpack/core/tree/rectangle_tree/r_plus_tree_split_impl.hpp).
Note that `RPlusPlusTreeSplit` is a template typedef of the general
`RPlusTreeSplitType<>` class.

### `HilbertRTreeSplit<>`

The `HilbertRTreeSplit<>` class is an implementation of the
[`HilbertRTree`](hilbert_r_tree.md) splitting strategy.  This strategy, proposed
in [the original paper (pdf)](https://www.vldb.org/conf/1994/P500.PDF), has two
main differences from the standard [`RTreeSplit`](#rtreesplit) strategy:

 * The idea of space-filling curves is used to order points for insertion.
 * Instead of one node splitting into two, the `HilbertRTreeSplit<>` class
   defers splitting, and re-splits a group of two nodes into three nodes.
   - *Note*: this behavior is configurable, see below.

When inserting a point, one cooperating sibling node is found.  If both the node
and its cooperating sibling are full, then all points in the two nodes as well
as the point being inserted are ordered by Z-ordering value (also known as
Morton ordering), and split evenly into three nodes.

***Notes:***

 - `HilbertRTreeSplit<>` has one template parameter, which controls the number
   of sibling nodes to split.  This is why the class must be specified as
   `HilbertRTreeSplit<>` and not `HilbertRTreeSplit`.

 - By default, `HilbertRTreeSplit<>` splits two sibling nodes into three new
   nodes; but this is configurable: `HilbertRTreeSplit<N>` will split `N`
   sibling nodes into `N + 1` new nodes.

 - The concept of splitting based on Z-ordering is also used in the
   [`UBTreeSplit`](binary_space_tree.md#ubtreesplit) strategy for the
   [`UBTree`](ub_tree.md), a variant of the
   [`BinarySpaceTree`](binary_space_tree.md) class.

### Custom `SplitType`s

Custom split strategies for a `RectangleTree` can be implemented via the
`SplitType` template parameter.  By default, the [`RTreeSplit`](#rtreesplit)
splitting strategy is used, but it is also possible to implement and use a
custom `SplitType`.  Any custom `SplitType` class must implement the following
signature:

```c++
class SplitType
{
 public:
  // Given the leaf node `tree`, split into multiple nodes.  `TreeType` will be
  // the relevant `RectangleTree` type.  `tree` should be modified directly.
  //
  // `relevels` is an auxiliary array used by some splitting strategies, such as
  // the `RStarTreeSplit`, to indicate whether a node needs to be reinserted
  // into the tree.
  template<typename TreeType>
  static void SplitLeafNode(TreeType* tree, std::vector<bool>& relevels);

  // Given the non-leaf node `tree`, split into multiple nodes.  `TreeType` will
  // be the relevant `RectangleTree` type.  `tree` should be modified directly.
  //
  // `relevels` is an auxiliary array used by some splitting strategies, such as
  // the `RStarTreeSplit`, to indicate whether a node needs to be reinserted
  // into the tree.
  template<typename TreeType>
  static void SplitNonLeafNode(TreeType* tree, std::vector<bool>& relevels);
};
```

## `DescentType`

The `DescentType` template parameter controls the algorithm used to assign child
points and child nodes to nodes in a `RectangleTree`.  The strategy used can be
arbitrary: the `DescentType` simply needs to return an index of a child to
insert a point or node into.

mlpack provides several drop-in choices for `DescentType`, and it is also
possible to write a fully custom split:

 * [`RTreeDescentHeuristic`](#rtreedescentheuristic): selects the closest child,
   which is the child whose volume will increase the least.
 * [`RStarTreeDescentHeuristic`](#rstartreedescentheuristic): selects a child
   such that overlap is minimized and volume increase is minimized
 * [`RPlusTreeDescentHeuristic`](#rplustreedescentheuristic): selects a child
   that does not cause overlap; if not possible, creates a new child
 * [`RPlusPlusTreeDescentHeuristic`](#rplusplustreedescentheuristic): selects
   the child whose maximum bounding box contains the point
   such that overlap is minimized and volume increase is minimized.
 * [`HilbertRTreeDescentHeuristic`](#hilbertrtreedescentheuristic): select the
   first child with minimum Z-order value greater than the point or node to be
   inserted.
 * [Custom `SplitType`s](#custom-splittypes): implement a fully custom
   `SplitType` class

*Note:* this section is still under construction---not all split types are
documented yet.

### `RTreeDescentHeuristic`

The `RTreeDescentHeuristic` is the default descent strategy for the
`RectangleTree` and is used by the [`RTree`](r_tree.md).  The strategy is
simple: the child node whose volume will increase the least is chosen as the
child to insert a point or other node into.

For implementation details, see
[the source code](/src/mlpack/core/tree/rectangle_tree/r_tree_descent_heuristic.hpp).

### `RStarTreeDescentHeuristic`

The `RStarTreeDescentHeuristic` is a descent strategy for the
[`RectangleTree`](#rectangletree) and is used by the
[`RStarTree`](r_star_tree.md).  The heuristic will always prefer to insert a
point or node into a child node whose hyperrectangle bound already contains the
point or node to be inserted.

When inserting a point or node into a node whose children are leaves, the
strategy will choose to insert into the child where the overall overlap of
children's volumes after insertion is minimized.

When inserting a point or node into a node whose children are not leaves, the
strategy will choose to insert into the child whose volume is the smallest after
insertion.

For implementation details, see [the source
code](/src/mlpack/core/tree/rectangle_tree/r_star_tree_descent_heuristic.hpp).

### `RPlusTreeDescentHeuristic`

The `RPlusTreeDescentHeuristic` is the descent strategy used by the
[`RPlusTree`](r_plus_tree.md).  When determining which node to insert a point
into, the following heuristic is used:

 * If the point to be inserted already falls within the bounding hyperrectangle
   of a child, select that child.
 * If the point to be inserted does not fall within the bounding
   hyperrectangle of any child, but a child's volume can be expanded to
   encompass the point *without* causing any children to overlap, select that
   child.
 * If neither of the conditions above are true, insert the point into a new
   child node.  This child node will likely be rebalanced or modified later by
   [`RPlusTreeSplit`](#rplustreesplit).

### `RPlusPlusTreeDescentHeuristic`

The `RPlusPlusTreeDescentHeuristic` is the descent strategy used by the
[`RPlusPlusTree`](r_plus_plus_tree.md).  The strategy chooses the child whose
outer bound (held by
[`RPlusPlusTreeAuxiliaryInformation`](#rplusplustreeauxiliaryinformation))
contains the point to be inserted.

For implementation details, see [the source
code](/src/mlpack/core/tree/rectangle_tree/r_plus_plus_tree_descent_heuristic.hpp).

### `HilbertRTreeDescentHeuristic`

The `HilbertRTreeDescentHeuristic` is the descent strategy used by the
[`HilbertRTree`](hilbert_r_tree.md).  The strategy depends on the concept of
Z-ordering (or Morton ordering): the child node whose minimum Z-ordering value
is closest to but greater than the Z-ordering value of the point to be inserted
is chosen.

For implementation details, see
[the source code](/src/mlpack/core/tree/rectangle_tree/hilbert_r_tree_descent_heuristic_impl.hpp).

### Custom `DescentType`s

Custom descent strategies for a `RectangleTree` can be implemented via the
`DescentType` template parameter.  By default, the
[`RTreeDescentHeuristic`](#rtreedescentheuristic) descent strategy is used,
but it is also possible to implement and use a custom `DescentType`.  Any custom
`DescentType` class must implement the following signature:

```c++
class DescentType
{
 public:
  // Return a `size_t` indicating which child of `node` should be chosen to
  // insert `point` in.
  //
  // `TreeType` will be the relevant `RectangleTree` type.
  template<typename TreeType>
  static size_t ChooseDescentNode(const TreeType* node, const size_t point);

  // Return a `size_t` indicating which child of `node` should be chosen to
  // insert `insertedNode` in.
  //
  // `TreeType` will be the relevant `RectangleTree` type.
  template<typename TreeType>
  static size_t ChooseDescentNode(const TreeType* node,
                                  const TreeType* insertedNode);
};
```

## `AuxiliaryInformationType`

The `AuxiliaryInformationType` template parameter holds any auxiliary
information required by the `SplitType` or `DescentType` strategies.  By
default, the `NoAuxiliaryInformation` class is used, which holds nothing.
Different variants of `RectangleTree`s may use other predefined types for their
`AuxiliaryInformationType`s:

 * [`XTreeAuxiliaryInformation`](#xtreeauxiliaryinformation): used for the
   [`XTree`](x_tree.md).
 * [`RPlusPlusTreeAuxiliaryInformation`](#rplusplustreeauxiliaryinformation):
   used for the [`RPlusPlusTree`](r_plus_plus_tree.md).
 * [`DiscreteHilbertRTreeAuxiliaryInformation`](#discretehilbertrtreeauxiliaryinformation):
   used for the [`HilbertRTree`](hilbert_r_tree.md).

### `XTreeAuxiliaryInformation`

The `XTreeAuxiliaryInformation` class is the auxiliary information type used by
the [`XTree`](x_tree.md) class, and is meant to be used with the
[`XTreeSplit`](#xtreesplit) splitting strategy.  It holds information required
to construct super-nodes (a concept specific to X-trees), where splitting is
being deferred.

For implementation details, see [the source
code](/src/mlpack/core/tree/rectangle_tree/x_tree_auxiliary_information.hpp).

### `RPlusPlusTreeAuxiliaryInformation`

The `RPlusPlusTreeAuxiliaryInformation` class is used by the
[`RPlusPlusTree`](r_plus_plus_tree.md) to store information required for tree
building.  In addition to the regular
[`HRectBound`](binary_space_tree.md#hrectbound) that is used to maintain the
minimum bounding rectangle of each node, each R++-tree node also maintains an
'outer bound' that represents the *maximum* bounding rectangle.  This maximum
bounding rectangle is used for splitting, instead of the minimum bounding
rectangle; this helps prevent overlap in nodes.

For an object `auxInfo`, the function `auxInfo.OuterBound()` will return an
[`HRectBound&`](binary_space_tree.md#hrectbound).  If the tree was built with a
[non-standard `MatType`](#template-parameters), then the type returned will be
`HRectBound<EuclideanDistance, ElemType>`, where `ElemType` is the element type
of the given `MatType`.

For implementation details, see [the source
code](/src/mlpack/core/tree/rectangle_tree/r_plus_plus_tree_auxiliary_information.hpp).

### `DiscreteHilbertRTreeAuxiliaryInformation`

The `DiscreteHilbertRTreeAuxiliaryInformation` class is used by the
[`HilbertRTree`](hilbert_r_tree.md).  It stores the largest Z-ordering value of
any descendant point of a node.  (This can be accessed with the `HilbertValue()`
method.)

For more details, see
[the source code](/src/mlpack/core/tree/rectangle_tree/hilbert_r_tree_auxiliary_information_impl.hpp).

### Custom `AuxiliaryInformationType`s

Custom `AuxiliaryInformationType`s can be implemented and used with the
`AuxiliaryInformationType` template parameter.  Any custom
`AuxiliaryInformationType` class must implement the following signature:

```c++
// TreeType will be the type of RectangleTree that the auxiliary information
// type is being used in.
template<typename TreeType>
class CustomAuxiliaryInformationType
{
 public:
  // Default constructor is required.
  CustomAuxiliaryInformationType();
  // Construct the object with a tree node that may not yet be constructed.
  CustomAuxiliaryInformationType(TreeType* node);
  // Construct the object with another object and another tree node, optionally
  // making a 'deep copy' instead of just copying pointers where relevant.
  CustomAuxiliaryInformationType(const CustomAuxiliaryInformationType& other,
                                 TreeType* node,
                                 const bool deepCopy = true);

  // Just before a point is inserted into a node, this is called.
  // `node` is the node that will have `node.Dataset().col(point)` inserted into
  // it.
  //
  // Optionally, this method can manipulate `node`.  If so, `true` should be
  // returned to indicate that `node` was changed.  Otherwise, return `false`
  // and the RectangleTree will perform its default behavior.
  bool HandlePointInsertion(TreeType* node, const size_t point);

  // Just before a child node is inserted into a node, this is called.
  // `node` is the node that will have `nodeToInsert` inserted into it as a
  // child.
  //
  // Optionally, this method can manipulate `node`.  If so, `true` should be
  // returned to indicate that `node` was changed.  Otherwise, return `false`
  // and the RectangleTree will perform its default behavior.
  bool HandleNodeInsertion(TreeType* node,
                           TreeType* nodeToInsert,
                           const bool atMaxDepth);

  // Just before a point is deleted from a node, this is called.
  // `node` is the node that will have `node.Dataset().col(point)` deleted from
  // it.
  //
  // Optionally, this method can manipulate `node`.  If so, `true` should be
  // returned to indicate that `node` was changed.  Otherwise, return `false`
  // and the RectangleTree will perform its default behavior.
  bool HandlePointDeletion(TreeType* node, const size_t point);

  // Just before a child node is deleted from a node, this is called.
  // `node` is the node that will have `node.Child(nodeIndex)` deleted from it.
  //
  // Optionally, this method can manipulate `node`.  If so, `true` should be
  // returned to indicate that `node` was changed.  Otherwise, return `false`
  // and the RectangleTree will perform its default behavior.
  bool HandleNodeRemoval(TreeType* node, const size_t nodeIndex);

  // When `node` is changed, this is called so that the auxiliary information
  // can be updated.  If information needs to be propagated upward, return
  // `true` and then `UpdateAuxiliaryInfo(node->Parent())` will be called.
  bool UpdateAuxiliaryInfo(TreeType* node);
};
```

## Example usage

The `RectangleTree` class is only really necessary when a custom split type or
custom descent strategy is intended to be used.  For simpler use cases, one of
the typedefs of `RectangleTree` (such as [`RTree`](r_tree.md)) will suffice.

For this reason, all of the examples below explicitly specify all six template
parameters of `RectangleTree`.
[Writing a custom splitting strategy](#custom-splittypes),
[writing a custom descent strategy](#custom-descenttypes),
and [writing a custom auxiliary information
type](#custom-auxiliaryinformationtypes) are discussed in the previous sections.
Each of the parameters in the examples below can be trivially changed for
different behavior.

---

Build a `RectangleTree` on the `cloud` dataset and print basic statistics
about the tree.

```c++
// See https://datasets.mlpack.org/cloud.csv.
arma::mat dataset;
mlpack::Load("cloud.csv", dataset, mlpack::Fatal);

// Build the rectangle tree with a leaf size of 10.  (This means that leaf nodes
// cannot contain more than 10 points.)
//
// The std::move() means that `dataset` will be empty after this call, and no
// data will be copied during tree building.
mlpack::RectangleTree<mlpack::EuclideanDistance,
                      mlpack::EmptyStatistic,
                      arma::mat,
                      mlpack::RTreeSplit,
                      mlpack::RTreeDescentHeuristic,
                      mlpack::NoAuxiliaryInformation> tree(std::move(dataset));

// Print the bounding box of the root node.
std::cout << "Bounding box of root node:" << std::endl;
for (size_t i = 0; i < tree.Bound().Dim(); ++i)
{
  std::cout << " - Dimension " << i << ": [" << tree.Bound()[i].Lo() << ", "
      << tree.Bound()[i].Hi() << "]." << std::endl;
}
std::cout << std::endl;

// Print the number of children in the root, and the allowable range.
std::cout << "Number of children of root: " << tree.NumChildren()
    << "; allowable range: [" << tree.MinNumChildren() << ", "
    << tree.MaxNumChildren() << "]." << std::endl;

// Print the number of descendant points of the root, and of each of its
// children.
std::cout << "Descendant points of root:        "
    << tree.NumDescendants() << "." << std::endl;
for (size_t i = 0; i < tree.NumChildren(); ++i)
{
  std::cout << "Descendant points of child " << i << ":  "
      << tree.Child(i).NumDescendants() << "." << std::endl;
}
std::cout << std::endl;

// Compute the center of the RectangleTree.
arma::vec center;
tree.Center(center);
std::cout << "Center of tree: " << center.t();
```

---

Build two `RectangleTree`s on subsets of the corel dataset and compute minimum
and maximum distances between different nodes in the tree.

```c++
// See https://datasets.mlpack.org/corel-histogram.csv.
arma::mat dataset;
mlpack::Load("corel-histogram.csv", dataset, mlpack::Fatal);

// Convenience typedef for the tree type.
using TreeType = mlpack::RectangleTree<mlpack::EuclideanDistance,
                                       mlpack::EmptyStatistic,
                                       arma::mat,
                                       mlpack::RTreeSplit,
                                       mlpack::RTreeDescentHeuristic,
                                       mlpack::NoAuxiliaryInformation>;

// Build trees on the first half and the second half of points.
TreeType tree1(dataset.cols(0, dataset.n_cols / 2));
TreeType tree2(dataset.cols(dataset.n_cols / 2 + 1, dataset.n_cols - 1));

// Compute the maximum distance between the trees.
std::cout << "Maximum distance between tree root nodes: "
    << tree1.MaxDistance(tree2) << "." << std::endl;

// Get the leftmost grandchild of the first tree's root---if it exists.
if (!tree1.IsLeaf() && !tree1.Child(0).IsLeaf())
{
  TreeType& node1 = tree1.Child(0).Child(0);

  // Get the leftmost grandchild of the second tree's root---if it exists.
  if (!tree2.IsLeaf() && !tree2.Child(0).IsLeaf())
  {
    TreeType& node2 = tree2.Child(0).Child(0);

    // Print the minimum and maximum distance between the nodes.
    mlpack::Range dists = node1.RangeDistance(node2);
    std::cout << "Possible distances between two grandchild nodes: ["
        << dists.Lo() << ", " << dists.Hi() << "]." << std::endl;

    // Print the minimum distance between the first node and the first
    // descendant point of the second node.
    const size_t descendantIndex = node2.Descendant(0);
    const double descendantMinDist =
        node1.MinDistance(node2.Dataset().col(descendantIndex));
    std::cout << "Minimum distance between grandchild node and descendant "
        << "point: " << descendantMinDist << "." << std::endl;

    // Which child of node2 is closer to node1?
    const size_t closestIndex = node2.GetNearestChild(node1);
    std::cout << "Child " << closestIndex << " is closest to node1."
        << std::endl;

    // And which child of node1 is further from node2?
    const size_t furthestIndex = node1.GetFurthestChild(node2);
    std::cout << "Child " << furthestIndex << " is furthest from node2."
        << std::endl;
  }
}
```

---

Build a `RectangleTree` on 32-bit floating point data and save it to disk.

```c++
// See https://datasets.mlpack.org/corel-histogram.csv.
arma::fmat dataset;
mlpack::Load("corel-histogram.csv", dataset);

// Build the RectangleTree using 32-bit floating point data as the matrix
// type.  We will still use the default EmptyStatistic and EuclideanDistance
// parameters.  A leaf size of 100 is used here.
mlpack::RectangleTree<mlpack::EuclideanDistance,
                      mlpack::EmptyStatistic,
                      arma::fmat,
                      mlpack::RTreeSplit,
                      mlpack::RTreeDescentHeuristic,
                      mlpack::NoAuxiliaryInformation> tree(
    std::move(dataset), 100);

// Save the tree to disk with the name 'tree'.
mlpack::Save("tree.bin", tree);

std::cout << "Saved tree with " << tree.Dataset().n_cols << " points to "
    << "'tree.bin'." << std::endl;
```

---

Load a 32-bit floating point `RectangleTree` from disk, then traverse it
manually and find the number of leaf nodes with less than 10 points.

```c++
// This assumes the tree has already been saved to 'tree.bin' (as in the example
// above).

// This convenient typedef saves us a long type name!
using TreeType = mlpack::RectangleTree<mlpack::EuclideanDistance,
                                       mlpack::EmptyStatistic,
                                       arma::fmat,
                                       mlpack::RTreeSplit,
                                       mlpack::RTreeDescentHeuristic,
                                       mlpack::NoAuxiliaryInformation>;

TreeType tree;
mlpack::Load("tree.bin", tree);
std::cout << "Tree loaded with " << tree.NumDescendants() << " points."
    << std::endl;

// Recurse in a depth-first manner.  Count both the total number of leaves, and
// the number of leaves with less than 10 points.
size_t leafCount = 0;
size_t totalLeafCount = 0;
std::stack<TreeType*> stack;
stack.push(&tree);
while (!stack.empty())
{
  TreeType* node = stack.top();
  stack.pop();

  if (node->NumPoints() < 10)
    ++leafCount;
  ++totalLeafCount;

  for (size_t i = 0; i < node->NumChildren(); ++i)
    stack.push(&node->Child(i));
}

// Note that it would be possible to use TreeType::SingleTreeTraverser to
// perform the recursion above, but that is more well-suited for more complex
// tasks that require pruning and other non-trivial behavior; so using a simple
// stack is the better option here.

// Print the results.
std::cout << leafCount << " out of " << totalLeafCount << " leaves have fewer "
    << "than 10 points." << std::endl;
```

---

Build a `RectangleTree` by iteratively inserting points from the corel dataset,
print some information, and then remove a few randomly chosen points.

```c++
// See https://datasets.mlpack.org/corel-histogram.csv.
arma::mat dataset;
mlpack::Load("corel-histogram.csv", dataset, mlpack::Fatal);

// This convenient typedef saves us a long type name!
using TreeType = mlpack::RectangleTree<mlpack::EuclideanDistance,
                                       mlpack::EmptyStatistic,
                                       arma::mat,
                                       mlpack::RTreeSplit,
                                       mlpack::RTreeDescentHeuristic,
                                       mlpack::NoAuxiliaryInformation>;

// Create an empty tree of the right dimensionality.
TreeType t(dataset.n_rows);

// Insert points one by one for the first half of the dataset.
for (size_t i = 0; i < dataset.n_cols / 2; ++i)
  t.Insert(dataset.col(i));

std::cout << "After inserting half the points, the root node has "
    << t.NumDescendants() << " descendant points and "
    << t.NumChildren() << " child nodes." << std::endl;

// For the second half, insert the points backwards.
for (size_t i = dataset.n_cols - 1; i >= dataset.n_cols / 2; --i)
  t.Insert(dataset.col(i));

std::cout << "After inserting all the points, the root node has "
    << t.NumDescendants() << " descendant points and "
    << t.NumChildren() << " child nodes." << std::endl;

// Remove three random points.
t.Delete(mlpack::math::RandInt(0, t.NumDescendants()));
std::cout << "After removing 1 point, the root node has " << t.NumDescendants()
    << " descendant points." << std::endl;
t.Delete(mlpack::math::RandInt(0, t.NumDescendants()));
std::cout << "After removing 2 points, the root node has " << t.NumDescendants()
    << " descendant points." << std::endl;
t.Delete(mlpack::math::RandInt(0, t.NumDescendants()));
std::cout << "After removing 3 points, the root node has " << t.NumDescendants()
    << " descendant points." << std::endl;
```
