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Description: 雜湊法(Hashing)的搜尋與一般的搜尋法(searching)是不一樣的。在雜湊法中，鍵值(key value)或識別字(identifier)在記憶體的位址是經由函數(function)轉換而得的。此種函數，一般稱之為雜湊函數(Hashing function)或鍵值對應位址轉換(key to address transformation)。對於有限的儲存空間，能夠有效使用且在加入或刪除時也能快的完成，利用雜湊法是最適當不過了。因為雜湊表搜尋在沒有碰撞(collision)及溢位(overflow)的情況下，只要一次就可擷取到。
-hash (Hashing) search and search for a general law (searching) is not the same. The hash law, the pair (key value) or identification characters (identifier) in the memory address of the sender is by function (function) generated from the conversion. Such functions, commonly known as hash functions (Hashing function) keys corresponding addresses or conversion (key to address transformation). For the limited storage space, which can be effectively used in acceding to or deleted can be completed faster, using hash method is the most appropriate. Because hash table search in the absence of collisions (collision) and overflow (overflow) circumstances, as long as one can capture that.
Platform: |
Size: 24590 |
Author: HCJ |
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Description: 輕易學好C++編程技巧 - 進楷 (香港科技大學筆記 19課) 內容包括
1) base C++ review,
2) Pointers and Dynamic Objects,
3) Recursion,Linked Lists,
4) Stacks and Queues,
5) Algorithm Analysis,
6) Insertion Sort and Mergesort,
7) Quicksort,
8) Heaps and Heapsort,
9) Lower Bound of Sorting and Radix Sort,
10) Binary Trees and Binary Search Trees
11) AVL Trees,
12) B+ Trees
13) Graphs and Breadth-First Search
14) Depth-First Search
15) Connected Components, Directed Graphs,
16) Topological Sort
17) Hashing
18) Pattern Matching
19) Additional Review
Platform: |
Size: 3752380 |
Author: chan kindfish |
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Description: Libgist is an implementation of the Generalized Search Tree, a template index structure that makes it easy to implement any type of hierarchical access method (AM).
-Libgist is an implementation of the Generalized Search Tree, a template index structure that makes it easy to implement any type of hierarchical access method (AM).
Platform: |
Size: 2267136 |
Author: hu |
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Description: 雜湊法(Hashing)的搜尋與一般的搜尋法(searching)是不一樣的。在雜湊法中，鍵值(key value)或識別字(identifier)在記憶體的位址是經由函數(function)轉換而得的。此種函數，一般稱之為雜湊函數(Hashing function)或鍵值對應位址轉換(key to address transformation)。對於有限的儲存空間，能夠有效使用且在加入或刪除時也能快的完成，利用雜湊法是最適當不過了。因為雜湊表搜尋在沒有碰撞(collision)及溢位(overflow)的情況下，只要一次就可擷取到。
-hash (Hashing) search and search for a general law (searching) is not the same. The hash law, the pair (key value) or identification characters (identifier) in the memory address of the sender is by function (function) generated from the conversion. Such functions, commonly known as hash functions (Hashing function) keys corresponding addresses or conversion (key to address transformation). For the limited storage space, which can be effectively used in acceding to or deleted can be completed faster, using hash method is the most appropriate. Because hash table search in the absence of collisions (collision) and overflow (overflow) circumstances, as long as one can capture that.
Platform: |
Size: 24576 |
Author: HCJ |
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Description: 雜湊法(Hashing)的搜尋與一般的搜尋法(searching)是不一樣的。在雜湊法中，鍵值(key value)或識別字(identifier)在記憶體的位址是經由函數(function)轉換而得的。此種函數，一般稱之為雜湊函數(Hashing function)或鍵值對應位址轉換(key to address transformation)。對於有限的儲存空間，能夠有效使用且在加入或刪除時也能快的完成，利用雜湊法是最適當不過了。因為雜湊表搜尋在沒有碰撞(collision)及溢位(overflow)的情況下，只要一次就可擷取到。-Hash method (Hashing) search and general search method (searching) is not the same. In the hash law, the key (key value) or identifier (identifier) address in memory by the function (function) derived from the conversion. Such a function, commonly known as hash function (Hashing function) or key corresponds to the address converter (key to address transformation). For a limited storage space, can be used effectively and in time to add or delete can be completed faster, the use of hash is the most appropriate of all. Because the hash table search in the absence of a collision (collision) and overflow (overflow) circumstances, as long as one can be retrieved.
Platform: |
Size: 35840 |
Author: 張建良 |
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Description: 輕易學好C++編程技巧 - 進楷 (香港科技大學筆記 19課) 內容包括
1) base C++ review,
2) Pointers and Dynamic Objects,
3) Recursion,Linked Lists,
4) Stacks and Queues,
5) Algorithm Analysis,
6) Insertion Sort and Mergesort,
7) Quicksort,
8) Heaps and Heapsort,
9) Lower Bound of Sorting and Radix Sort,
10) Binary Trees and Binary Search Trees
11) AVL Trees,
12) B+ Trees
13) Graphs and Breadth-First Search
14) Depth-First Search
15) Connected Components, Directed Graphs,
16) Topological Sort
17) Hashing
18) Pattern Matching
19) Additional Review
-Easily learn C++ Programming skills- Jin Kai (Hong Kong University of Science and Technology Division notes 19) will include 1) base C++ Review, 2) Pointers and Dynamic Objects, 3) Recursion, Linked Lists, 4) Stacks and Queues , 5) Algorithm Analysis, 6) Insertion Sort and Mergesort, 7) Quicksort, 8) Heaps and Heapsort, 9) Lower Bound of Sorting and Radix Sort, 10) Binary Trees and Binary Search Trees 11) AVL Trees, 12) B+ Trees 13) Graphs and Breadth-First Search 14) Depth-First Search 15) Connected Components, Directed Graphs, 16) Topological Sort 17) Hashing18) Pattern Matching 19) Additional Review
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Size: 3751936 |
Author: chan kindfish |
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Description: This an implementation of double-array structure for representing trie,
as proposed by Junichi Aoe [1].
Trie is a kind of digital search tree, an efficient indexing method with
O(1) time complexity for searching. Comparably as efficient as hashing,
trie also provides flexibility on incremental matching and key spelling
manipulation. This makes it ideal for lexical analyzers, as well as spelling
dictionaries.
See the details of the implementation at [2]:
http://linux.thai.net/~thep/datrie/datrie.html
Historically, this was first implemented as C++ classes in a library called
midatrie [2], but later simplified and rewritten from scratch in C.-This is an implementation of double-array structure for representing trie,
as proposed by Junichi Aoe [1].
Trie is a kind of digital search tree, an efficient indexing method with
O(1) time complexity for searching. Comparably as efficient as hashing,
trie also provides flexibility on incremental matching and key spelling
manipulation. This makes it ideal for lexical analyzers, as well as spelling
dictionaries.
See the details of the implementation at [2]:
http://linux.thai.net/~thep/datrie/datrie.html
Historically, this was first implemented as C++ classes in a library called
midatrie [2], but later simplified and rewritten from scratch in C.
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Size: 34816 |
Author: lucoy |
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Description: program which uses hashing techniques for storing and retrieving the data.
Input to the program: Some random numbers. Input will be from a file.
Output: First the program reads all the input and store it properly using some hash techniques.
Once your program reads the complete input,it waits for the user input (prompt to the user).
User now enters some number.program displays if this number exists in the list or not.
it also display the time taken in the search operation.
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Size: 7168 |
Author: Abhiram |
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Description: 哈希表的设计
[问题描述]
真对你所在的集体（比如你所在的班级）中的人名设计一个哈希表，使得平均查找长度不超过R，完成相应的建表和查表的程序。
[基本要求]
假设人名为中国人姓名的汉语拼音形式，待填入的哈希表的人名共有30个，取平均查找长度的上限为2。哈希函数用除留余数方法构造，用伪随机探测再散列处理冲突。
-Hash table design [description of the problem] really right in your collective (such as in your class) in the design of a hash table of names, bringing the average search length of less than R, the completion of construction of the corresponding tables and look-up table procedures. [Basic requirements] assumed the names of people known as the Chinese pinyin form, fill in the hash table to be a total of 30 names, taking the average search length of the upper limit of 2. Hash function with the addition of more than a few ways to stay construction, using pseudo-random probing re-hashing to resolve disputes.
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Size: 115712 |
Author: 艾萌 |
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Description: 1. 本程序是针对“人名”设计的哈希表。 2. 程序中人名为汉语拼音形式，共30个人名，取平均查找长度的上限为2.哈希函数用除留余数法构造，用伪随机探测再散列法处理冲突。 3. 本程序以用户和计算机的对话方式执行，即在计算机终端上显示“提示信息”之后，由用户在键盘上输入程序中规定的运算命令；相应的输入数据（滤去输入中的非法字符）和运算结果显示在其后。4.本程序是清华严蔚敏配套哈希表程序-1. This program is for " names" design of the hash table. 2. Program human form, called pinyin, a total of 30 names, taking the average search length of the upper limit of 2. Hash function to remain with the addition of more than a few law structure, with the pseudo-random hashing method to detect and then deal with conflict. 3. The procedure for the user and the computer implementation of dialogue, that is displayed on the computer terminal, " tips" after the keyboard input by the user specified in the computing process orders the corresponding input data (filtered input of illegal characters ) and the results showed that in a subsequent operation. 4. This procedure is Tsinghua Yan Wei-min matching hash table program
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Size: 7168 |
Author: lt |
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Description: 1．定义哈希表数据结构。
2．除留余数法做为哈希函数、
H(key) = key P
用线性探测再散列解决冲突方法，编写函数，实现哈希造表的过程，并输出哈希表。
3．编写函数，求查找成功时的平均查找长度（ASL）。-1. Define hash table data structure. 2. In addition to leaving the remainder as the hash function method, H (key) = key P re-hashing with linear probing methods of conflict resolution, write a function to achieve the hash table making process and output of the hash table. 3. Write function, seeking to find success, the average search length (ASL).
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Size: 1024 |
Author: Yvonne |
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Description: 为班级30个人的姓名设计一个哈希表，假设姓名用汉语拼音表示。要求用除留余数法构造哈希函数，用线性探测再散列法处理冲突，平均查找长度上限为2.-The names of 30 people for the class to design a hash table, assuming that the name in pinyin. Required to remain with the addition of more than a few method to construct hash functions, hashing by linear probing further conflict, the average search length limit to 2.
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Size: 14336 |
Author: sammie |
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Description: The Rabin–Karp algorithm is a string searching algorithm created by Michael O. Rabin and Richard M. Karp in 1987 that uses hashing to find any one of a set of pattern strings in a text. For text of length n and p patterns of combined length m, its average and best case running time is O(n+m) in space O(p), but its worst-case time is O(nm). In contrast, the Aho–Corasick string matching algorithm has asymptotic worst-time complexity O(n+m) in space O(m).
A practical application of Rabin–Karp is detecting plagiarism. Given source material, Rabin–Karp can rapidly search through a paper for instances of sentences from the source material, ignoring details such as case and punctuation. Because of the abundance of the sought strings, single-string searching algorithms are impractical.
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Size: 32768 |
Author: Tanya |
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Description: locality-sensitive hashing（局部敏感哈希），实现高位数据搜索平台，具有快速简便的优点。解压无密码-locality-sensitive hashing , to achieve high data search platform with the advantages of quick and easy. No password extract
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Size: 20480 |
Author: 何学文 |
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Description: 1．定义哈希表数据结构。
2．除留余数法做为哈希函数、
H(key) = key P
用线性探测再散列解决冲突方法，编写函数，实现哈希造表的过程，并输出哈希表。
3．编写函数，求查找成功时的平均查找长度（ASL）。-1. Define the hash table data structure. 2. In addition to leaving the remainder as the hash function method, H (key) = key P re-hashing with linear probing method of conflict resolution, write functions, making the hash table implementation process, and output the hash table. 3. Write a function, seeking to find the average successful search length (ASL).
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Size: 1024 |
Author: finejade |
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Description: We’ll discuss the hash table ADT which supports only a subset of the operations allowed by binary search trees.
The implementation of hash tables is called hashing.
Hashing is a technique used for performing insertions, deletions and finds in constant average time (i.e. O(1))
This data structure, however, is not efficient in operations that require any ordering information among the elements, such as findMin, findMax and printing the entire table in sorted order.
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Size: 384000 |
Author: ashlafir |
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Description: 哈希法实现小型搜索引擎,可以在一段文字内搜索关键词出现的句子，是哈希表的基础应用-Hashing achieve small search engine, you can search for key words in a sentence paragraph of text, is the basis for the application of the hash table
Platform: |
Size: 1024 |
Author: 任碧奴 |
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Description: 哈希查找，
根据文本输入构建哈希表，
输入字符串输出对应字符串在文本中出现的位置(Hash lookup,
Build a hash table based on text input,
The input string outputs the position in which the corresponding string appears in the text)
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Size: 2048 |
Author: hyhop150
|
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Description: 多媒体检索中的线性跨模态哈希算法，旨在降低算法的复杂度，变为线性复杂度。(Linear Cross-Modal Hashing for Efficient Multimedia
Search)
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Size: 23110656 |
Author: 1091904405
|
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Description: Weighted minwise hashing (WMH) is one of the fundamental subroutine,
required by many celebrated approximation algorithms, commonly
adopted in industrial practice for large -scale search and learning. The
resource bottleneck with WMH is the computation of multiple (typically a
few hundreds to thousands) independent hashes of the data. We propose
a simple rejection type sampling scheme based on a carefully designed
red-green map, where we show that the number of rejected sample has
exactly the same distribution as weighted minwise sampling.
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Size: 463872 |
Author: nextwang |
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