Merge branch 'trekhleb:master' into master

README.md

@@ -301,7 +301,8 @@ npm test -- 'playground'
 
 ### References
 
-[▶ Data Structures and Algorithms on YouTube](https://www.youtube.com/playlist?list=PLLXdhg_r2hKA7DPDsunoDZ-Z769jWn4R8)
+- [▶ Data Structures and Algorithms on YouTube](https://www.youtube.com/playlist?list=PLLXdhg_r2hKA7DPDsunoDZ-Z769jWn4R8)
+- [✍🏻 Data Structure Sketches](https://okso.app/showcase/data-structures)
 
 ### Big O Notation
 

src/data-structures/graph/README.fr-FR.md

@@ -1,23 +1,25 @@
 # Graph
 
 En informatique, un **graphe** est une structure de
-données abstraite qui implémente les concepts de 
+données abstraite qui implémente les concepts de
 graphe orienté et de graphe non-orienté venant
 des mathématiques, plus précisément du domaine de
 la théorie des graphes.
 
-La structure de données abstraite de graphe consiste 
-en un ensemble fini, éventuellement mutable de sommets 
-ou nœuds ou points, avec un ensemble de paires ordonnées 
-ou non de tels éléments. Ces paires sont des arêtes, arcs 
+La structure de données abstraite de graphe consiste
+en un ensemble fini, éventuellement mutable de sommets
+ou nœuds ou points, avec un ensemble de paires ordonnées
+ou non de tels éléments. Ces paires sont des arêtes, arcs
 non orientés, ou lignes pour un graphe non orienté, et
-flèches, arêtes orientées , arcs, ou lignes orientées 
-dans le cas orienté. Les sommets peuvent faire partie 
-de la structure, ou être des entités extérieures, 
+flèches, arêtes orientées , arcs, ou lignes orientées
+dans le cas orienté. Les sommets peuvent faire partie
+de la structure, ou être des entités extérieures,
 représentées par des entiers ou des références.
 
-![Graph](https://www.tutorialspoint.com/data_structures_algorithms/images/graph.jpg)
+![Graph](./images/graph.jpeg)
+
+*Made with [okso.app](https://okso.app)*
 
 ## References
 
-- [Wikipedia](https://fr.wikipedia.org/wiki/Graphe_(type_abstrait))
+- [Wikipedia](https://fr.wikipedia.org/wiki/Graphe_(type_abstrait))

src/data-structures/graph/README.md

@@ -6,23 +6,25 @@ _Read this in other languages:_
 [_Français_](README.fr-FR.md),
 [_Português_](README.pt-BR.md)
 
-In computer science, a **graph** is an abstract data type 
-that is meant to implement the undirected graph and 
+In computer science, a **graph** is an abstract data type
+that is meant to implement the undirected graph and
 directed graph concepts from mathematics, specifically
 the field of graph theory
 
-A graph data structure consists of a finite (and possibly 
-mutable) set of vertices or nodes or points, together 
-with a set of unordered pairs of these vertices for an 
-undirected graph or a set of ordered pairs for a 
-directed graph. These pairs are known as edges, arcs, 
-or lines for an undirected graph and as arrows, 
-directed edges, directed arcs, or directed lines 
-for a directed graph. The vertices may be part of 
-the graph structure, or may be external entities 
+A graph data structure consists of a finite (and possibly
+mutable) set of vertices or nodes or points, together
+with a set of unordered pairs of these vertices for an
+undirected graph or a set of ordered pairs for a
+directed graph. These pairs are known as edges, arcs,
+or lines for an undirected graph and as arrows,
+directed edges, directed arcs, or directed lines
+for a directed graph. The vertices may be part of
+the graph structure, or may be external entities
 represented by integer indices or references.
 
-![Graph](https://www.tutorialspoint.com/data_structures_algorithms/images/graph.jpg)
+![Graph](./images/graph.jpeg)
+
+*Made with [okso.app](https://okso.app)*
 
 ## References
 

src/data-structures/graph/README.pt-BR.md

@@ -17,7 +17,9 @@ para um grafo direcionado.
 Os vértices podem fazer parte a estrutura do grafo, ou podem
 ser entidades externas representadas por índices inteiros ou referências.
 
-![Graph](https://www.tutorialspoint.com/data_structures_algorithms/images/graph.jpg)
+![Graph](./images/graph.jpeg)
+
+*Made with [okso.app](https://okso.app)*
 
 ## Referências
 

src/data-structures/graph/README.ru-RU.md

@@ -3,7 +3,7 @@
 **Граф** в информатике - абстрактный тип данных, который должен реализовывать концепции направленного и ненаправленного
 графа в математике, особенно в области теории графов.
 
-Структура данных графа состоит из конечного (и возможно изменяющегося) набора вершин или узлов, или точек, совместно с 
+Структура данных графа состоит из конечного (и возможно изменяющегося) набора вершин или узлов, или точек, совместно с
 набором ненаправленных пар этих вершин для ненаправленного графа или с набором направленных пар для направленного графа.
 Эти пары известны как рёбра, арки или линии для ненаправленного графа и как стрелки, направленные рёбра, направленные
 арки или направленные линии для направленного графа. Эти вершины могут быть частью структуры графа, или внешними
@@ -14,7 +14,9 @@
 информатике, могут быть представлены графами. Например, строение Википедии можно смоделировать при помощи
 ориентированного графа, в котором вершины — это статьи, а дуги (ориентированные рёбра) — гиперссылки.
 
-![Граф](https://www.tutorialspoint.com/data_structures_algorithms/images/graph.jpg)
+![Граф](./images/graph.jpeg)
+
+*Made with [okso.app](https://okso.app)*
 
 ## Ссылки
 

src/data-structures/graph/README.zh-CN.md

@@ -11,9 +11,9 @@
 
 如果顶点对之间的边是有权重的,该图可称为加权图。
 
+![Graph](./images/graph.jpeg)
 
-
-![Graph](https://www.tutorialspoint.com/data_structures_algorithms/images/graph.jpg)
+*Made with [okso.app](https://okso.app)*
 
 ## 参考
 

src/data-structures/hash-table/README.fr-FR.md

@@ -17,11 +17,13 @@ hachage où la fonction de hachage génère le même index
 pour plusieurs clés. De telles collisions doivent être
 accommodées d'une manière ou d'une autre.
 
-![Hash Table](https://upload.wikimedia.org/wikipedia/commons/7/7d/Hash_table_3_1_1_0_1_0_0_SP.svg)
+![Hash Table](./images/hash-table.jpeg)
 
 Collision de hachage résolue par chaînage séparé.
 
-![Hash Collision](https://upload.wikimedia.org/wikipedia/commons/d/d0/Hash_table_5_0_1_1_1_1_1_LL.svg)
+![Hash Collision](./images/collision-resolution.jpeg)
+
+*Made with [okso.app](https://okso.app)*
 
 ## Références
 

src/data-structures/hash-table/README.ja-JP.md

@@ -1,14 +1,16 @@
 # ハッシュテーブル
- 
+
 コンピュータサイエンスにおいて、**ハッシュテーブル**（ハッシュマップ)は*キーを値にマッピング*できる*連想配列*の機能を持ったデータ構造です。ハッシュテーブルは*ハッシュ関数*を使ってバケットやスロットの配列へのインデックスを計算し、そこから目的の値を見つけることができます。
 
 理想的には、ハッシュ関数は各キーを一意のバケットに割り当てますが、ほとんどのハッシュテーブルは不完全なハッシュ関数を採用しているため、複数のキーに対して同じインデックスを生成した時にハッシュの衝突が起こります。このような衝突は何らかの方法で対処する必要があります。
 
-![Hash Table](https://upload.wikimedia.org/wikipedia/commons/7/7d/Hash_table_3_1_1_0_1_0_0_SP.svg)
+![Hash Table](./images/hash-table.jpeg)
 
 チェイン法によるハッシュの衝突の解決例
 
-![Hash Collision](https://upload.wikimedia.org/wikipedia/commons/d/d0/Hash_table_5_0_1_1_1_1_1_LL.svg)
+![Hash Collision](./images/collision-resolution.jpeg)
+
+*Made with [okso.app](https://okso.app)*
 
 ## 参考
 

src/data-structures/hash-table/README.ko-KR.md

@@ -11,13 +11,17 @@ _Read this in other languages:_
 
 이상적으로, 해시 함수는 각 키들을 고유 버킷에 할당하지만 대부분의 해시 테이블은 불완전한 해시 함수를 사용하기 때문에 해시 함수를 통해 두 개 이상의 키에 대해 동일한 인덱스를 생성하는 해시 충돌이 발생할 수 있습니다. 이러한 해시 충돌은 어떠한 방법으로든 해결되어야 합니다.
 
-![Hash Table](https://upload.wikimedia.org/wikipedia/commons/7/7d/Hash_table_3_1_1_0_1_0_0_SP.svg)
+![Hash Table](./images/hash-table.jpeg)
+
+*Made with [okso.app](https://okso.app)*
 
 다음은 분리 연결법을 통해 해시 충돌을 해결한 예시입니다.
 
-![Hash Collision](https://upload.wikimedia.org/wikipedia/commons/d/d0/Hash_table_5_0_1_1_1_1_1_LL.svg)
+![Hash Collision](./images/collision-resolution.jpeg)
+
+*Made with [okso.app](https://okso.app)*
 
 ## 참고
 
 - [Wikipedia](https://en.wikipedia.org/wiki/Hash_table)
-- [YouTube](https://www.youtube.com/watch?v=shs0KM3wKv8&index=4&list=PLLXdhg_r2hKA7DPDsunoDZ-Z769jWn4R8)
+- [YouTube](https://www.youtube.com/watch?v=shs0KM3wKv8&index=4&list=PLLXdhg_r2hKA7DPDsunoDZ-Z769jWn4R8)

src/data-structures/hash-table/README.md

@@ -22,11 +22,13 @@ collisions where the hash function generates the same
 index for more than one key. Such collisions must be
 accommodated in some way.
 
-![Hash Table](https://upload.wikimedia.org/wikipedia/commons/7/7d/Hash_table_3_1_1_0_1_0_0_SP.svg)
+![Hash Table](./images/hash-table.jpeg)
 
 Hash collision resolved by separate chaining.
 
-![Hash Collision](https://upload.wikimedia.org/wikipedia/commons/d/d0/Hash_table_5_0_1_1_1_1_1_LL.svg)
+![Hash Collision](./images/collision-resolution.jpeg)
+
+*Made with [okso.app](https://okso.app)*
 
 ## References
 

src/data-structures/hash-table/README.pt-BR.md

@@ -13,11 +13,13 @@ imperfeita, pela qual poderá causar colisões de hashes onde a função de hash
 gera o mesmo índice para mais de uma chave.Tais colisões devem ser
 acomodados de alguma forma.
 
-![Hash Table](https://upload.wikimedia.org/wikipedia/commons/7/7d/Hash_table_3_1_1_0_1_0_0_SP.svg)
+![Hash Table](./images/hash-table.jpeg)
 
 Colisão de hash resolvida por encadeamento separado.
 
-![Hash Collision](https://upload.wikimedia.org/wikipedia/commons/d/d0/Hash_table_5_0_1_1_1_1_1_LL.svg)
+![Hash Collision](./images/collision-resolution.jpeg)
+
+*Made with [okso.app](https://okso.app)*
 
 ## Referências
 

src/data-structures/hash-table/README.ru-RU.md

@@ -2,10 +2,10 @@
 
 **Хеш-таблица** - структура данных, реализующая абстрактный тип данных *ассоциативный массив*, т.е. структура, которая
 *связывает ключи со значениями*. Хеш-таблица использует *хеш-функцию* для вычисления индекса в массиве, в котором может
-быть найдено желаемое значение. Ниже представлена хеш-таблица, в которой ключом выступает имя человека, а значениями 
+быть найдено желаемое значение. Ниже представлена хеш-таблица, в которой ключом выступает имя человека, а значениями
 являются телефонные номера. Хеш-функция преобразует ключ-имя в индекс массива с телефонными номерами.
 
-![Хеш-таблица](https://upload.wikimedia.org/wikipedia/commons/7/7d/Hash_table_3_1_1_0_1_0_0_SP.svg)
+![Hash Table](./images/hash-table.jpeg)
 
 В идеале хеш-функция будет присваивать элементу массива уникальный ключ. Однако большинство реальных хеш-таблиц
 используют несовершенные хеш-функции. Это может привести к ситуациям, когда хеш-функция генерирует одинаковый индекс для
@@ -14,9 +14,13 @@
 Существует два варианта решения коллизий - хеш-таблица с цепочками и с открытой адресацией.
 
 Метод цепочек подразумевает хранение значений, соответствующих одному и тому же индексу в виде связного списка(цепочки).
-![Хеш цепочки](https://upload.wikimedia.org/wikipedia/commons/d/d0/Hash_table_5_0_1_1_1_1_1_LL.svg)
+
+![Hash Collision](./images/collision-resolution.jpeg)
+
+*Made with [okso.app](https://okso.app)*
 
 Метод открытой адресации помещает значение, для которого получен дублирующий индекс, в первую свободную ячейку.
+
 ![Хеш открытая адресация](https://upload.wikimedia.org/wikipedia/commons/thumb/b/bf/Hash_table_5_0_1_1_1_1_0_SP.svg/380px-Hash_table_5_0_1_1_1_1_0_SP.svg.png)
 
 ## Ссылки

src/data-structures/hash-table/README.zh-CN.md

@@ -1,6 +1,6 @@
 # 哈希表
 
-在计算中, 一个  **哈希表(hash table 或hash map)**  是一种实现 *关联数组(associative array)* 
+在计算中, 一个  **哈希表(hash table 或hash map)**  是一种实现 *关联数组(associative array)*
 的抽象数据类型, 该结构可以将 *键映射到值*。
 
 哈希表使用 *哈希函数/散列函数* 来计算一个值在数组或桶(buckets)中或槽(slots)中对应的索引,可使用该索引找到所需的值。
@@ -9,11 +9,15 @@
 以某种方式进行处理。
 
 
-![Hash Table](https://upload.wikimedia.org/wikipedia/commons/7/7d/Hash_table_3_1_1_0_1_0_0_SP.svg)
+![Hash Table](./images/hash-table.jpeg)
+
+*Made with [okso.app](https://okso.app)*
 
 通过单独的链接解决哈希冲突
 
-![Hash Collision](https://upload.wikimedia.org/wikipedia/commons/d/d0/Hash_table_5_0_1_1_1_1_1_LL.svg)
+![Hash Collision](./images/collision-resolution.jpeg)
+
+*Made with [okso.app](https://okso.app)*
 
 ## 参考
 

src/data-structures/tree/binary-search-tree/README.md

@@ -3,32 +3,34 @@
 _Read this in other languages:_
 [_Português_](README.pt-BR.md)
 
-In computer science, **binary search trees** (BST), sometimes called 
-ordered or sorted binary trees, are a particular type of container: 
-data structures that store "items" (such as numbers, names etc.) 
-in memory. They allow fast lookup, addition and removal of 
-items, and can be used to implement either dynamic sets of 
-items, or lookup tables that allow finding an item by its key 
+In computer science, **binary search trees** (BST), sometimes called
+ordered or sorted binary trees, are a particular type of container:
+data structures that store "items" (such as numbers, names etc.)
+in memory. They allow fast lookup, addition and removal of
+items, and can be used to implement either dynamic sets of
+items, or lookup tables that allow finding an item by its key
 (e.g., finding the phone number of a person by name).
 
-Binary search trees keep their keys in sorted order, so that lookup 
-and other operations can use the principle of binary search: 
-when looking for a key in a tree (or a place to insert a new key), 
-they traverse the tree from root to leaf, making comparisons to 
-keys stored in the nodes of the tree and deciding, on the basis 
-of the comparison, to continue searching in the left or right 
-subtrees. On average, this means that each comparison allows 
-the operations to skip about half of the tree, so that each 
-lookup, insertion or deletion takes time proportional to the 
-logarithm of the number of items stored in the tree. This is 
-much better than the linear time required to find items by key 
-in an (unsorted) array, but slower than the corresponding 
+Binary search trees keep their keys in sorted order, so that lookup
+and other operations can use the principle of binary search:
+when looking for a key in a tree (or a place to insert a new key),
+they traverse the tree from root to leaf, making comparisons to
+keys stored in the nodes of the tree and deciding, on the basis
+of the comparison, to continue searching in the left or right
+subtrees. On average, this means that each comparison allows
+the operations to skip about half of the tree, so that each
+lookup, insertion or deletion takes time proportional to the
+logarithm of the number of items stored in the tree. This is
+much better than the linear time required to find items by key
+in an (unsorted) array, but slower than the corresponding
 operations on hash tables.
 
 A binary search tree of size 9 and depth 3, with 8 at the root.
 The leaves are not drawn.
 
-![Binary Search Tree](https://upload.wikimedia.org/wikipedia/commons/d/da/Binary_search_tree.svg)
+![Trie](./images/binary-search-tree.jpg)
+
+*Made with [okso.app](https://okso.app)*
 
 ## Pseudocode for Basic Operations
 
@@ -45,7 +47,7 @@ insert(value)
   end if
 end insert
 ```
-    
+
 ```text
 insertNode(current, value)
   Pre: current is the node to start from
@@ -84,8 +86,8 @@ contains(root, value)
   end if
 end contains
 ```
-    
-     
+
+
 ### Deletion
 
 ```text
@@ -186,7 +188,7 @@ findNode(root, value)
   end if
 end findNode
 ```
-    
+
 ### Find Minimum
 
 ```text
@@ -200,7 +202,7 @@ findMin(root)
   findMin(root.left)
 end findMin
 ```
-    
+
 ### Find Maximum
 
 ```text
@@ -214,7 +216,7 @@ findMax(root)
   findMax(root.right)
 end findMax
 ```
-    
+
 ### Traversal
 
 #### InOrder Traversal
@@ -244,7 +246,7 @@ preorder(root)
   end if
 end preorder
 ```
-   
+
 #### PostOrder Traversal
 
 ```text
@@ -258,7 +260,7 @@ postorder(root)
   end if
 end postorder
 ```
-     
+
 ## Complexities
 
 ### Time Complexity

src/data-structures/tree/binary-search-tree/README.pt-BR.md

@@ -26,8 +26,9 @@ Uma pesquisa de árvore binária de tamanho 9 e profundidade 3, com valor 8
 na raíz.
 As folhas não foram desenhadas.
 
+![Trie](./images/binary-search-tree.jpg)
 
-![Binary Search Tree](https://upload.wikimedia.org/wikipedia/commons/d/da/Binary_search_tree.svg)
+*Made with [okso.app](https://okso.app)*
 
 ## Pseudocódigo para Operações Básicas
 
@@ -44,7 +45,7 @@ insert(value)
   end if
 end insert
 ```
-    
+
 ```text
 insertNode(current, value)
   Pre: current is the node to start from
@@ -83,8 +84,8 @@ contains(root, value)
   end if
 end contains
 ```
-    
-     
+
+
 ### Remoção
 
 ```text
@@ -185,7 +186,7 @@ findNode(root, value)
   end if
 end findNode
 ```
-    
+
 ### Encontrar Mínimo
 
 ```text
@@ -199,7 +200,7 @@ findMin(root)
   findMin(root.left)
 end findMin
 ```
-    
+
 ### Encontrar Máximo
 
 ```text
@@ -213,7 +214,7 @@ findMax(root)
   findMax(root.right)
 end findMax
 ```
-    
+
 ### Traversal
 
 #### Na Ordem Traversal (InOrder Traversal)
@@ -243,7 +244,7 @@ preorder(root)
   end if
 end preorder
 ```
-   
+
 #### Pós Ordem Traversal (PostOrder Traversal)
 
 ```text
@@ -257,7 +258,7 @@ postorder(root)
   end if
 end postorder
 ```
-     
+
 ## Complexidades
 
 ### Complexidade de Tempo

src/data-structures/trie/README.md

@@ -5,22 +5,24 @@ _Read this in other languages:_
 [_Русский_](README.ru-RU.md),
 [_Português_](README.pt-BR.md)
 
-In computer science, a **trie**, also called digital tree and sometimes 
-radix tree or prefix tree (as they can be searched by prefixes), 
-is a kind of search tree—an ordered tree data structure that is 
-used to store a dynamic set or associative array where the keys 
-are usually strings. Unlike a binary search tree, no node in the 
-tree stores the key associated with that node; instead, its 
+In computer science, a **trie**, also called digital tree and sometimes
+radix tree or prefix tree (as they can be searched by prefixes),
+is a kind of search tree—an ordered tree data structure that is
+used to store a dynamic set or associative array where the keys
+are usually strings. Unlike a binary search tree, no node in the
+tree stores the key associated with that node; instead, its
 position in the tree defines the key with which it is associated.
 All the descendants of a node have a common prefix of the string
-associated with that node, and the root is associated with the 
-empty string. Values are not necessarily associated with every 
-node. Rather, values tend only to be associated with leaves, 
-and with some inner nodes that correspond to keys of interest. 
-For the space-optimized presentation of prefix tree, see compact 
+associated with that node, and the root is associated with the
+empty string. Values are not necessarily associated with every
+node. Rather, values tend only to be associated with leaves,
+and with some inner nodes that correspond to keys of interest.
+For the space-optimized presentation of prefix tree, see compact
 prefix tree.
 
-![Trie](https://upload.wikimedia.org/wikipedia/commons/b/be/Trie_example.svg)
+![Trie](./images/trie.jpg)
+
+*Made with [okso.app](https://okso.app)*
 
 ## References
 

src/data-structures/trie/README.pt-BR.md

@@ -3,7 +3,7 @@
 Na ciência da computação, uma **trie**, também chamada de árvore digital (digital tree)
 e algumas vezes de _radix tree_ ou _prefix tree_ (tendo em vista que eles
 podem ser pesquisados por prefixos), é um tipo de árvore de pesquisa, uma
-uma estrutura de dados de árvore ordenada que é usado para armazenar um 
+uma estrutura de dados de árvore ordenada que é usado para armazenar um
 conjunto dinâmico ou matriz associativa onde as chaves são geralmente _strings_.
 Ao contrário de uma árvore de pesquisa binária (binary search tree),
 nenhum nó na árvore armazena a chave associada a esse nó; em vez disso,
@@ -17,7 +17,9 @@ internos que correspondem a chaves de interesse.
 Para a apresentação otimizada do espaço da árvore de prefixo (_prefix tree_),
 veja árvore de prefixo compacto.
 
-![Trie](https://upload.wikimedia.org/wikipedia/commons/b/be/Trie_example.svg)
+![Trie](./images/trie.jpg)
+
+*Made with [okso.app](https://okso.app)*
 
 ## Referências
 

src/data-structures/trie/README.ru-RU.md

@@ -16,7 +16,9 @@
 дополнительную информацию, связанную с ключом, и обычно выделенными являются только листья и, возможно, некоторые
 внутренние узлы.
 
-![Префиксное дерево](https://upload.wikimedia.org/wikipedia/commons/b/be/Trie_example.svg)
+![Префиксное дерево](./images/trie.jpg)
+
+*Made with [okso.app](https://okso.app)*
 
 На рисунке представлено префиксное дерево, содержащее ключи «A», «to», «tea», «ted», «ten», «i», «in», «inn».
 

src/data-structures/trie/README.zh-CN.md

@@ -2,13 +2,15 @@
 
 在计算机科学中, **字典树(trie,中文又被称为”单词查找树“或 ”键树“)**, 也称为数字树,有时候也被称为基数树或前缀树（因为它们可以通过前缀搜索）,它是一种搜索树--一种已排序的数据结构,通常用于存储动态集或键为字符串的关联数组。
 
-与二叉搜索树不同, 树上没有节点存储与该节点关联的键; 相反,节点在树上的位置定义了与之关联的键。一个节点的全部后代节点都有一个与该节点关联的通用的字符串前缀, 与根节点关联的是空字符串。 
+与二叉搜索树不同, 树上没有节点存储与该节点关联的键; 相反,节点在树上的位置定义了与之关联的键。一个节点的全部后代节点都有一个与该节点关联的通用的字符串前缀, 与根节点关联的是空字符串。
 
 值对于字典树中关联的节点来说,不是必需的,相反,值往往和相关的叶子相关,以及与一些键相关的内部节点相关。
 
 有关字典树的空间优化示意,请参阅紧凑前缀树
 
-![Trie](https://upload.wikimedia.org/wikipedia/commons/b/be/Trie_example.svg)
+![Trie](./images/trie.jpg)
+
+*Made with [okso.app](https://okso.app)*
 
 ## 参考