bump the default leafsize from 10 to 25 (#198)

README.md

@@ -39,7 +39,7 @@ using NearestNeighbors
 data = rand(3, 10^4)
 
 # Create trees
-kdtree = KDTree(data; leafsize = 10)
+kdtree = KDTree(data; leafsize = 25)
 balltree = BallTree(data, Minkowski(3.5); reorder = false)
 brutetree = BruteTree(data)
 ```

src/ball_tree.jl

@@ -14,13 +14,13 @@ end
 
 
 """
- BallTree(data [, metric = Euclidean(); leafsize = 10, reorder = true]) -> balltree
+ BallTree(data [, metric = Euclidean(); leafsize = 25, reorder = true]) -> balltree
 
 Creates a `BallTree` from the data using the given `metric` and `leafsize`.
 """
 function BallTree(data::AbstractVector{V},
  metric::Metric = Euclidean();
- leafsize::Int = 10,
+ leafsize::Int = 25,
  reorder::Bool = true,
  storedata::Bool = true,
  reorderbuffer::Vector{V} = Vector{V}()) where {V <: AbstractArray}
@@ -70,7 +70,7 @@ end
 
 function BallTree(data::AbstractVecOrMat{T},
  metric::Metric = Euclidean();
- leafsize::Int = 10,
+ leafsize::Int = 25,
  storedata::Bool = true,
  reorder::Bool = true,
  reorderbuffer::Matrix{T} = Matrix{T}(undef, 0, 0)) where {T <: AbstractFloat}

src/kd_tree.jl

@@ -11,14 +11,14 @@ end
 
 
 """
- KDTree(data [, metric = Euclidean(); leafsize = 10, reorder = true]) -> kdtree
+ KDTree(data [, metric = Euclidean(); leafsize = 25, reorder = true]) -> kdtree
 
 Creates a `KDTree` from the data using the given `metric` and `leafsize`.
 The `metric` must be a `MinkowskiMetric`.
 """
 function KDTree(data::AbstractVector{V},
  metric::M = Euclidean();
- leafsize::Int = 10,
+ leafsize::Int = 25,
  storedata::Bool = true,
  reorder::Bool = true,
  reorderbuffer::Vector{V} = Vector{V}()) where {V <: AbstractArray, M <: MinkowskiMetric}
@@ -76,7 +76,7 @@ end
 
  function KDTree(data::AbstractVecOrMat{T},
  metric::M = Euclidean();
- leafsize::Int = 10,
+ leafsize::Int = 25,
  storedata::Bool = true,
  reorder::Bool = true,
  reorderbuffer::Matrix{T} = Matrix{T}(undef, 0, 0)) where {T <: AbstractFloat, M <: MinkowskiMetric}