From 06e19d394b7c6e9e9914141975eb8b9bfa219054 Mon Sep 17 00:00:00 2001
From: marina <138340846+bt3gl-cryptographer@users.noreply.github.com>
Date: Mon, 7 Aug 2023 20:31:27 -0700
Subject: [PATCH] Update README.md

---
 sets/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sets/README.md b/sets/README.md
index 8c3967d..5797a73 100644
--- a/sets/README.md
+++ b/sets/README.md
@@ -13,7 +13,7 @@
 * this type of structure widely used in statistical algorithms such as markov chain monte carlo and metropolis-hastings algorithms, which needs sampling from a probability distribution when it's difficult to compute the distribution itself.
   
 * candidates for `O(1)` average insert time are:
-    * **hashmaps (or hashsets)**: to be able to implement `get_random()` at `O(1)` (choose a random index and to retrieve narandom element), we would have to convert hashmap keys in a list, which is `O(N)`. a solution is to build a list of keys aside and use this list to compute `get_random` in `O(1)`.
+    * **hashmaps (or hashsets)**: to be able to implement `get_random` at `O(1)` (choose a random index and to retrieve narandom element), we would have to convert hashmap keys in a list, which is `O(N)`. a solution is to build a list of keys aside and use this list to compute `get_random` in `O(1)`.
     * **array lists**: we would have `O(N)` with `delete`. the solution would be delete the last value (first swap the element to delete with the last one, then pop the last element out). for that, we need to compute an index of each element in `O(N)`, and we need a hashmap that stores `element -> index`.
 
 * either way, we need the same combination of data structures: a hashmap and an array.