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reword readme #139

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Feb 8, 2022
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21 changes: 12 additions & 9 deletions README.md
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Expand Up @@ -13,10 +13,19 @@

## Creating a tree

The abstract tree type that the trees in this package are a subtype of is called a `NNTree`. A `NNTree`
is created by the function:
There are currently three types of trees available:

* `BruteTree`: Not actually a tree. It linearly searches all points in a brute force fashion. Works with any `Metric`.
* `KDTree`: In a kd tree the points are recursively split into groups using hyper-planes.
Therefore a `KDTree` only works with axis aligned metrics which are: `Euclidean`, `Chebyshev`, `Minkowski` and `Cityblock`.
* `BallTree`: Points are recursively split into groups bounded by hyper-spheres. Works with any `Metric`.

These trees are created with the following syntax:

```jl
NNTree(data, metric; leafsize, reorder)
BruteTree(data, metric; leafsize, reorder)
KDTree(data, metric; leafsize, reorder)
BallTree(data, metric; leafsize, reorder)
```

* `data`: The data, i.e., the points to build up the tree from. It can either be
Expand All @@ -27,12 +36,6 @@ NNTree(data, metric; leafsize, reorder)
* `leafsize` (keyword argument): Determines at what number of points to stop splitting the tree further. There is a trade-off between traversing the tree and having to evaluate the metric function for increasing number of points.
* `reorder` (keyword argument): While building the tree this will put points close in distance close in memory since this helps with cache locality. In this case, a copy of the original data will be made so that the original data is left unmodified. This can have a significant impact on performance and is by default set to `true`.

There are currently three types of trees available:

* `BruteTree`: Not actually a tree. It linearly searches all points in a brute force fashion. Works with any `Metric`.
* `KDTree`: In a kd tree the points are recursively split into groups using hyper-planes.
Therefore a `KDTree` only works with axis aligned metrics which are: `Euclidean`, `Chebyshev`, `Minkowski` and `Cityblock`.
* `BallTree`: Points are recursively split into groups bounded by hyper-spheres. Works with any `Metric`.

All trees in `NearestNeighbors.jl` are static which means that points can not be added or removed from an already created tree.

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