x/data-structure-typed
1
0
Fork 0
mirror of https://github.com/zrwusa/data-structure-typed.git synced 2024-11-23 12:54:04 +00:00
Code Issues Actions Packages Projects Releases Wiki Activity

docs: refined the table of deferences of data structures

Browse source
This commit is contained in:
Revone 2024-07-31 14:33:55 +12:00
parent 7f32fee19a
commit 5d146cbfc2
1 changed files with 3 additions and 3 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

6
README.md
View file

@ -169,17 +169,17 @@ Performance surpasses that of native JS/TS
</tr>
<tr>
<td>Binary Search Tree (BST)</td>
<td>A bunny group in the form of a tree, where each bunny can grow at most 2 tails (Doubly Linked List). The most important data structure in a binary tree (the core is that the time complexity for insertion, deletion, modification, and search is O(log n)). The data stored in a BST is structured and ordered, not in strict order like 1, 2, 3, 4, 5, but maintaining that all nodes in the left subtree are less than the node, and all nodes in the right subtree are greater than the node. This order provides O(log n) time complexity for insertion, deletion, modification, and search. Reducing O(n) to O(log n) is the most common algorithm complexity optimization in the computer field, an exponential improvement in efficiency. It's also the most efficient way to organize unordered data into ordered data (most sorting algorithms only maintain O(n log n)). Of course, the binary search trees we provide support organizing data in both ascending and descending order. Remember that basic BSTs do not have self-balancing capabilities, and if you sequentially add sorted data to this data structure, it will degrade into a list, thus losing the O(log n) capability. Of course, our addMany method is specially handled to prevent degradation. However, for practical applications, please use Red-black Tree or AVL Tree as much as possible, as they inherently have self-balancing functions.</td>
<td>A tree-like rabbit colony composed of doubly linked lists where each rabbit has at most two tails. These rabbits are disciplined and obedient, arranged in their positions according to a certain order. The most important data structure in a binary tree (the core is that the time complexity for insertion, deletion, modification, and search is O(log n)). The data stored in a BST is structured and ordered, not in strict order like 1, 2, 3, 4, 5, but maintaining that all nodes in the left subtree are less than the node, and all nodes in the right subtree are greater than the node. This order provides O(log n) time complexity for insertion, deletion, modification, and search. Reducing O(n) to O(log n) is the most common algorithm complexity optimization in the computer field, an exponential improvement in efficiency. It's also the most efficient way to organize unordered data into ordered data (most sorting algorithms only maintain O(n log n)). Of course, the binary search trees we provide support organizing data in both ascending and descending order. Remember that basic BSTs do not have self-balancing capabilities, and if you sequentially add sorted data to this data structure, it will degrade into a list, thus losing the O(log n) capability. Of course, our addMany method is specially handled to prevent degradation. However, for practical applications, please use Red-black Tree or AVL Tree as much as possible, as they inherently have self-balancing functions.</td>
<td><img alt="binary search tree" src="https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/assets/binary-search-tree.png"></td>
</tr>
<tr>
<td>Red-black Tree</td>
<td>A self-balancing binary search tree. Each node is marked with a red-black label. Ensuring that no path is more than twice as long as any other (maintaining a certain balance to improve the speed of search, addition, and deletion).</td>
<td>A tree-like rabbit colony composed of doubly linked lists, where each rabbit has at most two tails. These rabbits are not only obedient but also intelligent, automatically arranging their positions in a certain order. A self-balancing binary search tree. Each node is marked with a red-black label. Ensuring that no path is more than twice as long as any other (maintaining a certain balance to improve the speed of search, addition, and deletion).</td>
<td><img alt="red-black tree" src="https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/assets/red-black tree.png"></td>
</tr>
<tr>
<td>AVL Tree</td>
<td>A self-balancing binary search tree. Each node is marked with a balance factor, representing the height difference between its left and right subtrees. The absolute value of the balance factor does not exceed 1 (maintaining stricter balance, which makes search efficiency higher than Red-black Tree, but insertion and deletion operations will be more complex and relatively less efficient).</td>
<td>A tree-like rabbit colony composed of doubly linked lists, where each rabbit has at most two tails. These rabbits are not only obedient but also intelligent, automatically arranging their positions in a certain order, and they follow very strict rules. A self-balancing binary search tree. Each node is marked with a balance factor, representing the height difference between its left and right subtrees. The absolute value of the balance factor does not exceed 1 (maintaining stricter balance, which makes search efficiency higher than Red-black Tree, but insertion and deletion operations will be more complex and relatively less efficient).</td>
<td><img alt="avl tree" src="https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/assets/avl-tree.png"></td>
</tr>
<tr>