From 039bc676f7e09b32dd34a59891bd9dc4932b8f54 Mon Sep 17 00:00:00 2001 From: bt3gl <138340846+bt3gl-cryptographer@users.noreply.github.com> Date: Tue, 8 Aug 2023 17:42:57 -0700 Subject: [PATCH] Update README.md --- heaps/README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/heaps/README.md b/heaps/README.md index 3612fe3..e85cc8e 100644 --- a/heaps/README.md +++ b/heaps/README.md @@ -10,7 +10,7 @@
-* heap is a data structure capable of giving you the smallest (or the largest) element in constant time, while adding or removing the smallest (or the largest) element on logarithmic time. +* heap is a data structure capable of giving you the smallest (or the largest) element in constant time, while adding or removing the smallest (or the largest) element in logarithmic time. * heaps guarantees both insertion and deletion to have time complexity `O(log N)` (while maintaining get_max/get_min at `O(1)`). @@ -93,13 +93,13 @@ class Heap:
-* it's cheaper to heapify an array of data (`O(N)`) than creating an empty heap and inserting each element (`O(N log(N))`). - * heapify means create a binary tree and then comparing each nodes with their child (and swapping when necessary). +* it's cheaper to heapify an array of data (`O(N)`) than create an empty heap and inserting each element (`O(N log(N))`). + * heapify means create a binary tree and then comparing each nodes with their children (and swapping when necessary). * parents node can simply exchange with their smallest child (so the max number of exchanges is `N/2`) and leaves are left out. * python's built-in heap differs from the standard implementation of a heap in two ways: * firstly, it uses zero-based indexing, so it stores the root node at index zero instead of the size of the heap. - * secondly, the built-in module does not offer a direct way to create a max heap, instead, we must modify the values of each eelement when inserting in the heap, and when removing it from the heap. + * secondly, the built-in module does not offer a direct way to create a max heap, instead, we must modify the values of each element when inserting in the heap, and when removing it from the heap.
@@ -155,7 +155,7 @@ size_max_heap = len(max_heap) - then compare this node to each parent, exchanging them until the tree's properties are correct. * `extract_min`: - - first, remove/return the top and then replace the tree's top with its latest element (the bottom most rightmost). + - first, remove/return the top and then replace the tree's top with its latest element (the bottom-most rightmost). - then bubble down, swapping it with one of its children until the min-heap is properly restored - there is no need for order between right and left, so this operation would only take `O(log N)` runtime.