From 4da2fe71052b75b30c701fa783f64f42e7993b4d Mon Sep 17 00:00:00 2001
From: Olivia Coffey <ocoffey@protonmail.com>
Date: Mon, 20 Jan 2020 15:43:01 -0500
Subject: [PATCH] edits for proper rendering

---
 README.md | 32 ++++++++++++++++++++++----------
 1 file changed, 22 insertions(+), 10 deletions(-)

diff --git a/README.md b/README.md
index e53d840..792d913 100644
--- a/README.md
+++ b/README.md
@@ -1,10 +1,11 @@
-Ruby k-d tree
-=============
+# Ruby k-d tree
 
 Ruby implementation of "K-Dimensional Tree" and "N-Nearest Neighbors" based on [http://en.wikipedia.org/wiki/Kd-tree](http://en.wikipedia.org/wiki/Kd-tree).
 
-##API
-#####Initialize
+## API
+
+### Initialize
+
 Build a new tree on a set of (2 or higher dimensional) coordinates:
 
 ```ruby
@@ -12,13 +13,17 @@ points = [[2,3], [5,4], [9,6], [4,7], [8,1], [7,2]]
 kdtree = KDTree.new points
 ```
 
-#####Flatten
+### Flatten
+
 Get an inorder representation as an array
+
 ```ruby
 kdtree.to_a
 => [[2, 3], [5, 4], [4, 7], [7, 2], [8, 1], [9, 6]]
 ```
-#####Print
+
+### Print
+
 ```ruby
 kdtree.print
 =>
@@ -29,16 +34,23 @@ kdtree.print
     [5, 4]
         [2, 3]
 ```
-#####Nearest Neighbors
+
+### Nearest Neighbors
+
 Current implementation uses squared euclidean as distance metric.
-That is, for any 2 points a and b in D-dimensional space:<br>
+That is, for any 2 points a and b in D-dimensional space:
+
 ![equation](http://latex.codecogs.com/gif.latex?%5Csum_%7Bd%3D1%7D%5E%7BD%7D%28a_d-b_d%29%5E2)
-#####1-NN
+
+### 1-NN
+
 ```ruby
 kdtree.nnearest([1,1])
 => [[2, 3]]
 ```
-#####k-NN
+
+### k-NN
+
 ```ruby
 kdtree.nnearest([1,1],3)
 => [[7, 2], [5, 4], [2, 3]]