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@ -12,12 +12,11 @@
* 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)`).
* a heap is a binary tree with these properties:
* a heap is like a binary tree with these properties:
* it must have all of **its nodes in a specific order**, and
* its shape must be **complete** (all the levels of the tree must be completely filled except maybe for the last one and the last level must have the left-most nodes filled, always).
* a max heap's **root node must** have all its children either **greater than or equal** to its children. a min heap is the opposite. duplicate values are allowed.
* a max heap's **root node must** have all its children either **greater than or equal** to its children.
* a min heap is the opposite. duplicate values are allowed.
* heaps can be represented with linked lists, queues (arrays), or binary trees.
@ -251,7 +250,7 @@ class MinHeap:
* insert the element at the bottom, at the leftmost node.
* then compare the node to each parent, exchanging them until the tree's properties are correct.
* `extreact_max`:
* `extract_max`:
* remove/return the top and then replace the tree's top with its bottom rightmost element.
* swap up until the max element is on the top.
@ -409,7 +408,7 @@ def compare_two_tops(array) -> int:
<br>
* given an array of `intervals[i] = [start_i, end_i]`, return the minimum the non-overlapping intervals
* given an array of `intervals[i] = [start_i, end_i]`, return the minimum the non-overlapping intervals:
<br>