From b25aadae6f69a5577b8d964e2418a541b5ecfbd0 Mon Sep 17 00:00:00 2001 From: krahets Date: Sun, 26 Feb 2023 02:19:40 +0800 Subject: [PATCH] Remove the file headers from the docs. --- docs/chapter_array_and_linkedlist/array.md | 4 ---- docs/chapter_array_and_linkedlist/linked_list.md | 4 ---- docs/chapter_array_and_linkedlist/list.md | 4 ---- docs/chapter_array_and_linkedlist/summary.md | 4 ---- .../performance_evaluation.md | 4 ---- docs/chapter_computational_complexity/space_complexity.md | 4 ---- docs/chapter_computational_complexity/space_time_tradeoff.md | 4 ---- docs/chapter_computational_complexity/summary.md | 4 ---- docs/chapter_computational_complexity/time_complexity.md | 4 ---- .../classification_of_data_structure.md | 4 ---- docs/chapter_data_structure/data_and_memory.md | 4 ---- docs/chapter_data_structure/summary.md | 4 ---- docs/chapter_graph/graph.md | 4 ---- docs/chapter_graph/graph_operations.md | 4 ---- docs/chapter_graph/graph_traversal.md | 4 ---- docs/chapter_hashing/hash_collision.md | 4 ---- docs/chapter_hashing/hash_map.md | 4 ---- docs/chapter_hashing/summary.md | 4 ---- docs/chapter_heap/heap.md | 4 ---- docs/chapter_introduction/algorithms_are_everywhere.md | 4 ---- docs/chapter_introduction/what_is_dsa.md | 4 ---- docs/chapter_preface/about_the_book.md | 4 ---- docs/chapter_preface/contribution.md | 4 ---- docs/chapter_preface/installation.md | 4 ---- docs/chapter_preface/suggestions.md | 4 ---- docs/chapter_searching/binary_search.md | 4 ---- docs/chapter_searching/hashing_search.md | 4 ---- docs/chapter_searching/linear_search.md | 4 ---- docs/chapter_searching/summary.md | 4 ---- docs/chapter_sorting/bubble_sort.md | 4 ---- docs/chapter_sorting/insertion_sort.md | 4 ---- docs/chapter_sorting/intro_to_sort.md | 4 ---- docs/chapter_sorting/merge_sort.md | 4 ---- docs/chapter_sorting/quick_sort.md | 4 ---- docs/chapter_sorting/summary.md | 4 ---- docs/chapter_stack_and_queue/deque.md | 4 ---- docs/chapter_stack_and_queue/queue.md | 4 ---- docs/chapter_stack_and_queue/stack.md | 4 ---- docs/chapter_stack_and_queue/summary.md | 4 ---- docs/chapter_tree/avl_tree.md | 4 ---- docs/chapter_tree/binary_search_tree.md | 4 ---- docs/chapter_tree/binary_tree.md | 4 ---- docs/chapter_tree/binary_tree_traversal.md | 4 ---- docs/chapter_tree/summary.md | 4 ---- 44 files changed, 176 deletions(-) diff --git a/docs/chapter_array_and_linkedlist/array.md b/docs/chapter_array_and_linkedlist/array.md index e2c42dfe6..6a4165433 100755 --- a/docs/chapter_array_and_linkedlist/array.md +++ b/docs/chapter_array_and_linkedlist/array.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 数组 「数组 Array」是一种将 **相同类型元素** 存储在 **连续内存空间** 的数据结构，将元素在数组中的位置称为元素的「索引 Index」。 diff --git a/docs/chapter_array_and_linkedlist/linked_list.md b/docs/chapter_array_and_linkedlist/linked_list.md index bf8c3b05e..f18327395 100755 --- a/docs/chapter_array_and_linkedlist/linked_list.md +++ b/docs/chapter_array_and_linkedlist/linked_list.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 链表 !!! note "引言" diff --git a/docs/chapter_array_and_linkedlist/list.md b/docs/chapter_array_and_linkedlist/list.md index a9285c215..d2c02406c 100755 --- a/docs/chapter_array_and_linkedlist/list.md +++ b/docs/chapter_array_and_linkedlist/list.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 列表 **由于长度不可变，数组的实用性大大降低**。在很多情况下，我们事先并不知道会输入多少数据，这就为数组长度的选择带来了很大困难。长度选小了，需要在添加数据中频繁地扩容数组；长度选大了，又造成内存空间的浪费。 diff --git a/docs/chapter_array_and_linkedlist/summary.md b/docs/chapter_array_and_linkedlist/summary.md index 60fe604f0..0c3318ab9 100644 --- a/docs/chapter_array_and_linkedlist/summary.md +++ b/docs/chapter_array_and_linkedlist/summary.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 小结 - 数组和链表是两种基本数据结构，代表了数据在计算机内存中的两种存储方式，即连续空间存储和离散空间存储。两者的优点与缺点呈现出此消彼长的关系。 diff --git a/docs/chapter_computational_complexity/performance_evaluation.md b/docs/chapter_computational_complexity/performance_evaluation.md index 449c6bfed..fe1f27682 100644 --- a/docs/chapter_computational_complexity/performance_evaluation.md +++ b/docs/chapter_computational_complexity/performance_evaluation.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 算法效率评估 ## 算法评价维度 diff --git a/docs/chapter_computational_complexity/space_complexity.md b/docs/chapter_computational_complexity/space_complexity.md index e0225e939..fc0de8bbb 100755 --- a/docs/chapter_computational_complexity/space_complexity.md +++ b/docs/chapter_computational_complexity/space_complexity.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 空间复杂度 「空间复杂度 Space Complexity」统计 **算法使用内存空间随着数据量变大时的增长趋势**。这个概念与时间复杂度很类似。 diff --git a/docs/chapter_computational_complexity/space_time_tradeoff.md b/docs/chapter_computational_complexity/space_time_tradeoff.md index 1c4827c05..42cab0d5c 100755 --- a/docs/chapter_computational_complexity/space_time_tradeoff.md +++ b/docs/chapter_computational_complexity/space_time_tradeoff.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 权衡时间与空间 理想情况下，我们希望算法的时间复杂度和空间复杂度都能够达到最优，而实际上，同时优化时间复杂度和空间复杂度是非常困难的。 diff --git a/docs/chapter_computational_complexity/summary.md b/docs/chapter_computational_complexity/summary.md index 0a1ee80be..e96e0addf 100644 --- a/docs/chapter_computational_complexity/summary.md +++ b/docs/chapter_computational_complexity/summary.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 小结 ### 算法效率评估 diff --git a/docs/chapter_computational_complexity/time_complexity.md b/docs/chapter_computational_complexity/time_complexity.md index 162bcbd57..97fb3b440 100755 --- a/docs/chapter_computational_complexity/time_complexity.md +++ b/docs/chapter_computational_complexity/time_complexity.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 时间复杂度 ## 统计算法运行时间 diff --git a/docs/chapter_data_structure/classification_of_data_structure.md b/docs/chapter_data_structure/classification_of_data_structure.md index a2f5221b8..b9948497b 100644 --- a/docs/chapter_data_structure/classification_of_data_structure.md +++ b/docs/chapter_data_structure/classification_of_data_structure.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 数据结构分类 数据结构主要可根据「逻辑结构」和「物理结构」两种角度进行分类。 diff --git a/docs/chapter_data_structure/data_and_memory.md b/docs/chapter_data_structure/data_and_memory.md index 78f4f7e87..1b9a85540 100644 --- a/docs/chapter_data_structure/data_and_memory.md +++ b/docs/chapter_data_structure/data_and_memory.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 数据与内存 ## 基本数据类型 diff --git a/docs/chapter_data_structure/summary.md b/docs/chapter_data_structure/summary.md index e51e4dc5b..0133fb452 100644 --- a/docs/chapter_data_structure/summary.md +++ b/docs/chapter_data_structure/summary.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 小结 - 整数 byte, short, int, long 、浮点数 float, double 、字符 char 、布尔 boolean 是计算机中的基本数据类型，占用空间的大小决定了它们的取值范围。 diff --git a/docs/chapter_graph/graph.md b/docs/chapter_graph/graph.md index 2b5ca43d8..a996e50e8 100644 --- a/docs/chapter_graph/graph.md +++ b/docs/chapter_graph/graph.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 图 「图 Graph」是一种非线性数据结构，由「顶点 Vertex」和「边 Edge」组成。我们可将图 $G$ 抽象地表示为一组顶点 $V$ 和一组边 $E$ 的集合。例如，以下表示一个包含 5 个顶点和 7 条边的图 diff --git a/docs/chapter_graph/graph_operations.md b/docs/chapter_graph/graph_operations.md index 933e9b843..9c9cfe6fe 100644 --- a/docs/chapter_graph/graph_operations.md +++ b/docs/chapter_graph/graph_operations.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 图基础操作 图的基础操作分为对「边」的操作和对「顶点」的操作，在「邻接矩阵」和「邻接表」这两种表示下的实现方式不同。 diff --git a/docs/chapter_graph/graph_traversal.md b/docs/chapter_graph/graph_traversal.md index 173b703a9..6710a9616 100644 --- a/docs/chapter_graph/graph_traversal.md +++ b/docs/chapter_graph/graph_traversal.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 图的遍历 !!! note "图与树的关系" diff --git a/docs/chapter_hashing/hash_collision.md b/docs/chapter_hashing/hash_collision.md index d17bb5911..dffff9108 100644 --- a/docs/chapter_hashing/hash_collision.md +++ b/docs/chapter_hashing/hash_collision.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 哈希冲突 理想情况下，哈希函数应该为每个输入产生唯一的输出，使得 key 和 value 一一对应。而实际上，往往存在向哈希函数输入不同的 key 而产生相同输出的情况，这种情况被称为「哈希冲突 Hash Collision」。哈希冲突会导致查询结果错误，从而严重影响哈希表的可用性。 diff --git a/docs/chapter_hashing/hash_map.md b/docs/chapter_hashing/hash_map.md index 3ad00dd58..3a2214a7e 100755 --- a/docs/chapter_hashing/hash_map.md +++ b/docs/chapter_hashing/hash_map.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 哈希表 哈希表通过建立「键 key」和「值 value」之间的映射，实现高效的元素查找。具体地，输入一个 key ，在哈希表中查询并获取 value ，时间复杂度为 $O(1)$ 。 diff --git a/docs/chapter_hashing/summary.md b/docs/chapter_hashing/summary.md index 5f58b7594..2e79e520a 100644 --- a/docs/chapter_hashing/summary.md +++ b/docs/chapter_hashing/summary.md @@ -1,5 +1 @@ ---- -comments: true ---- - # 小结 diff --git a/docs/chapter_heap/heap.md b/docs/chapter_heap/heap.md index 1b016aa7e..1dec57f51 100644 --- a/docs/chapter_heap/heap.md +++ b/docs/chapter_heap/heap.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 堆 「堆 Heap」是一棵限定条件下的「完全二叉树」。根据成立条件，堆主要分为两种类型： diff --git a/docs/chapter_introduction/algorithms_are_everywhere.md b/docs/chapter_introduction/algorithms_are_everywhere.md index dcf70805b..4ecea1614 100644 --- a/docs/chapter_introduction/algorithms_are_everywhere.md +++ b/docs/chapter_introduction/algorithms_are_everywhere.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 算法无处不在 听到“算法”这个词，我们一般会联想到数学。但实际上，大多数算法并不包含复杂的数学，而更像是在考察基本逻辑，而这些逻辑在我们日常生活中处处可见。 diff --git a/docs/chapter_introduction/what_is_dsa.md b/docs/chapter_introduction/what_is_dsa.md index 10ea25133..ab90280ab 100644 --- a/docs/chapter_introduction/what_is_dsa.md +++ b/docs/chapter_introduction/what_is_dsa.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 算法是什么 ## 算法定义 diff --git a/docs/chapter_preface/about_the_book.md b/docs/chapter_preface/about_the_book.md index a7c219f92..1c1890767 100644 --- a/docs/chapter_preface/about_the_book.md +++ b/docs/chapter_preface/about_the_book.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 关于本书 五年前发生的一件事，成为了我职业生涯的重要转折点。当时的我在交大读研，对互联网求职一无所知，但仍然硬着头皮申请了 Microsoft 软件工程师实习。面试官让我在白板上写出“快速排序”代码，我畏畏缩缩地写了一个“冒泡排序”，并且还写错了` (ToT) ` 。从面试官的表情上，我看到了一个大大的 "GG" 。 diff --git a/docs/chapter_preface/contribution.md b/docs/chapter_preface/contribution.md index 964c247f9..75abd2ad9 100644 --- a/docs/chapter_preface/contribution.md +++ b/docs/chapter_preface/contribution.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 一起参与创作 !!! success "开源的魅力" diff --git a/docs/chapter_preface/installation.md b/docs/chapter_preface/installation.md index b74125bbd..aba6de2d1 100644 --- a/docs/chapter_preface/installation.md +++ b/docs/chapter_preface/installation.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 编程环境安装 （TODO 视频教程） diff --git a/docs/chapter_preface/suggestions.md b/docs/chapter_preface/suggestions.md index 4c7743564..7b506b753 100644 --- a/docs/chapter_preface/suggestions.md +++ b/docs/chapter_preface/suggestions.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 如何使用本书 ## 图文搭配学 diff --git a/docs/chapter_searching/binary_search.md b/docs/chapter_searching/binary_search.md index 1b489d746..f499573df 100755 --- a/docs/chapter_searching/binary_search.md +++ b/docs/chapter_searching/binary_search.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 二分查找 「二分查找 Binary Search」利用数据的有序性，通过每轮缩小一半搜索区间来查找目标元素。 diff --git a/docs/chapter_searching/hashing_search.md b/docs/chapter_searching/hashing_search.md index 8ee456d7a..8e7a36630 100755 --- a/docs/chapter_searching/hashing_search.md +++ b/docs/chapter_searching/hashing_search.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 哈希查找 !!! question diff --git a/docs/chapter_searching/linear_search.md b/docs/chapter_searching/linear_search.md index 4c079794f..a3421eae9 100755 --- a/docs/chapter_searching/linear_search.md +++ b/docs/chapter_searching/linear_search.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 线性查找 「线性查找 Linear Search」是一种最基础的查找方法，其从数据结构的一端开始，依次访问每个元素，直到另一端后停止。 diff --git a/docs/chapter_searching/summary.md b/docs/chapter_searching/summary.md index 066d09af5..6d2fb0b3f 100644 --- a/docs/chapter_searching/summary.md +++ b/docs/chapter_searching/summary.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 小结 - 线性查找是一种最基础的查找方法，通过遍历数据结构 + 判断条件实现查找。 diff --git a/docs/chapter_sorting/bubble_sort.md b/docs/chapter_sorting/bubble_sort.md index 3b22c33ba..7ec50f128 100755 --- a/docs/chapter_sorting/bubble_sort.md +++ b/docs/chapter_sorting/bubble_sort.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 冒泡排序 「冒泡排序 Bubble Sort」是一种最基础的排序算法，非常适合作为第一个学习的排序算法。顾名思义，「冒泡」是该算法的核心操作。 diff --git a/docs/chapter_sorting/insertion_sort.md b/docs/chapter_sorting/insertion_sort.md index e660e3e0b..8b17106c2 100755 --- a/docs/chapter_sorting/insertion_sort.md +++ b/docs/chapter_sorting/insertion_sort.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 插入排序 「插入排序 Insertion Sort」是一种基于 **数组插入操作** 的排序算法。 diff --git a/docs/chapter_sorting/intro_to_sort.md b/docs/chapter_sorting/intro_to_sort.md index 52cef38cc..f92a45364 100644 --- a/docs/chapter_sorting/intro_to_sort.md +++ b/docs/chapter_sorting/intro_to_sort.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 排序简介 「排序算法 Sorting Algorithm」使得列表中的所有元素按照从小到大的顺序排列。 diff --git a/docs/chapter_sorting/merge_sort.md b/docs/chapter_sorting/merge_sort.md index 57e1c18ec..cceb7b078 100755 --- a/docs/chapter_sorting/merge_sort.md +++ b/docs/chapter_sorting/merge_sort.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 归并排序 「归并排序 Merge Sort」是算法中“分治思想”的典型体现，其有「划分」和「合并」两个阶段： diff --git a/docs/chapter_sorting/quick_sort.md b/docs/chapter_sorting/quick_sort.md index ef299d450..23c2c1efa 100755 --- a/docs/chapter_sorting/quick_sort.md +++ b/docs/chapter_sorting/quick_sort.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 快速排序 「快速排序 Quick Sort」是一种基于“分治思想”的排序算法，速度很快、应用很广。 diff --git a/docs/chapter_sorting/summary.md b/docs/chapter_sorting/summary.md index df0b7a700..b65d06938 100644 --- a/docs/chapter_sorting/summary.md +++ b/docs/chapter_sorting/summary.md @@ -1,6 +1,2 @@ ---- -comments: true ---- - # 小结 diff --git a/docs/chapter_stack_and_queue/deque.md b/docs/chapter_stack_and_queue/deque.md index e7d6462c5..ab1a5b4dc 100644 --- a/docs/chapter_stack_and_queue/deque.md +++ b/docs/chapter_stack_and_queue/deque.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 双向队列 对于队列，我们只能在头部删除或在尾部添加元素，而「双向队列 Deque」更加灵活，在其头部和尾部都能执行元素添加或删除操作。 diff --git a/docs/chapter_stack_and_queue/queue.md b/docs/chapter_stack_and_queue/queue.md index 5f7912d6c..885a555bb 100755 --- a/docs/chapter_stack_and_queue/queue.md +++ b/docs/chapter_stack_and_queue/queue.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 队列 「队列 Queue」是一种遵循「先入先出 first in, first out」数据操作规则的线性数据结构。顾名思义，队列模拟的是排队现象，即外面的人不断加入队列尾部，而处于队列头部的人不断地离开。 diff --git a/docs/chapter_stack_and_queue/stack.md b/docs/chapter_stack_and_queue/stack.md index 14f065385..1fca9530a 100755 --- a/docs/chapter_stack_and_queue/stack.md +++ b/docs/chapter_stack_and_queue/stack.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 栈 「栈 Stack」是一种遵循「先入后出 first in, last out」数据操作规则的线性数据结构。我们可以将栈类比为放在桌面上的一摞盘子，如果需要拿出底部的盘子，则需要先将上面的盘子依次取出。 diff --git a/docs/chapter_stack_and_queue/summary.md b/docs/chapter_stack_and_queue/summary.md index d6066f0f1..1e13b6e14 100644 --- a/docs/chapter_stack_and_queue/summary.md +++ b/docs/chapter_stack_and_queue/summary.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 小结 - 栈是一种遵循先入后出的数据结构，可以使用数组或链表实现。 diff --git a/docs/chapter_tree/avl_tree.md b/docs/chapter_tree/avl_tree.md index 5d2c7e0d9..49287f74e 100644 --- a/docs/chapter_tree/avl_tree.md +++ b/docs/chapter_tree/avl_tree.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # AVL 树 * 在「二叉搜索树」章节中提到，在进行多次插入与删除操作后，二叉搜索树可能会退化为链表。此时所有操作的时间复杂度都会由 $O(\log n)$ 劣化至 $O(n)$ 。 diff --git a/docs/chapter_tree/binary_search_tree.md b/docs/chapter_tree/binary_search_tree.md index 8c6dbb829..d8c3a94b8 100755 --- a/docs/chapter_tree/binary_search_tree.md +++ b/docs/chapter_tree/binary_search_tree.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 二叉搜索树 「二叉搜索树 Binary Search Tree」满足以下条件： diff --git a/docs/chapter_tree/binary_tree.md b/docs/chapter_tree/binary_tree.md index 0fcd32222..46c147b6f 100644 --- a/docs/chapter_tree/binary_tree.md +++ b/docs/chapter_tree/binary_tree.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 二叉树 「二叉树 Binary Tree」是一种非线性数据结构，代表着祖先与后代之间的派生关系，体现着“一分为二”的分治逻辑。类似于链表，二叉树也是以结点为单位存储的，结点包含「值」和两个「指针」。 diff --git a/docs/chapter_tree/binary_tree_traversal.md b/docs/chapter_tree/binary_tree_traversal.md index 9a03c59f7..f07355fc0 100755 --- a/docs/chapter_tree/binary_tree_traversal.md +++ b/docs/chapter_tree/binary_tree_traversal.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 二叉树遍历 从物理结构角度看，树是一种基于链表的数据结构，因此遍历方式也是通过指针（即引用）逐个遍历结点。同时，树还是一种非线性数据结构，这导致遍历树比遍历链表更加复杂，需要使用搜索算法来实现。 diff --git a/docs/chapter_tree/summary.md b/docs/chapter_tree/summary.md index 15dc2f6d3..c9d9f6f89 100644 --- a/docs/chapter_tree/summary.md +++ b/docs/chapter_tree/summary.md @@ -1,7 +1,3 @@ ---- -comments: true ---- - # 小结 - 二叉树是一种非线性数据结构，代表着“一分为二”的分治逻辑。二叉树的结点包含「值」和两个「指针」，分别指向左子结点和右子结点。