FilterHash | An open addressing based hash table implementation
kandi X-RAY | FilterHash Summary
kandi X-RAY | FilterHash Summary
FilterHash is a Java library. FilterHash has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.
An open addressing based hash table implementation. Terminology We are hashing n elements into k locations. The load factor is α=n/k. Description of the algorithm The idea is to partition the set of indexes (i.e. the underlying array) into i subsets. Notice that the partition is purely conceptual: In practice there is only one array, but our algorithm will operate on the array as if it was split into i subarrays. Order the subsets by descending size. Each of these subsets will be mapped by a hash table with the following closed addressing collision strategy: Rehash the colliding item into the next hash table in the ordered sequence of hash tables. Thus, the first (and biggest) hash table will receive all of the n items we are hashing. And the expected number of collisions in one hash table will be the input for the next hash table. We can achieve any desired load factor α=n/k in a given hash table, by choosing k small enough relative to n. And if all i hash tables have an expected load factor of α, the whole data structure as such will have a load factor of α.
An open addressing based hash table implementation. Terminology We are hashing n elements into k locations. The load factor is α=n/k. Description of the algorithm The idea is to partition the set of indexes (i.e. the underlying array) into i subsets. Notice that the partition is purely conceptual: In practice there is only one array, but our algorithm will operate on the array as if it was split into i subarrays. Order the subsets by descending size. Each of these subsets will be mapped by a hash table with the following closed addressing collision strategy: Rehash the colliding item into the next hash table in the ordered sequence of hash tables. Thus, the first (and biggest) hash table will receive all of the n items we are hashing. And the expected number of collisions in one hash table will be the input for the next hash table. We can achieve any desired load factor α=n/k in a given hash table, by choosing k small enough relative to n. And if all i hash tables have an expected load factor of α, the whole data structure as such will have a load factor of α.
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Support
FilterHash has a low active ecosystem.
It has 0 star(s) with 0 fork(s). There are 1 watchers for this library.
It had no major release in the last 6 months.
FilterHash has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of FilterHash is current.
Quality
FilterHash has no bugs reported.
Security
FilterHash has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
FilterHash does not have a standard license declared.
Check the repository for any license declaration and review the terms closely.
Without a license, all rights are reserved, and you cannot use the library in your applications.
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FilterHash releases are not available. You will need to build from source code and install.
Build file is available. You can build the component from source.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of FilterHash
FilterHash Key Features
No Key Features are available at this moment for FilterHash.
FilterHash Examples and Code Snippets
No Code Snippets are available at this moment for FilterHash.
Community Discussions
No Community Discussions are available at this moment for FilterHash.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install FilterHash
You can download it from GitHub.
You can use FilterHash like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the FilterHash component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
You can use FilterHash like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the FilterHash component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
Support
For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .
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