From 1a5c45f501fec041f4077efefcf3c5534720b079 Mon Sep 17 00:00:00 2001
From: Kristoffer Carlsson <kcarlsson89@gmail.com>
Date: Sat, 29 Jun 2024 18:24:17 +0200
Subject: [PATCH] bump the default leafsize from 10 to 25 (#198)

---
 README.md        | 2 +-
 src/ball_tree.jl | 6 +++---
 src/kd_tree.jl   | 6 +++---
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/README.md b/README.md
index ac7c151..12814af 100644
--- a/README.md
+++ b/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)
 ```
diff --git a/src/ball_tree.jl b/src/ball_tree.jl
index acee950..1be8cc3 100644
--- a/src/ball_tree.jl
+++ b/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}
diff --git a/src/kd_tree.jl b/src/kd_tree.jl
index 91cb69d..5518d7d 100644
--- a/src/kd_tree.jl
+++ b/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}