simhash | A Python Implementation of Simhash Algorithm | Download Utils library

by leonsim Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | simhash Summary

simhash is a Python library typically used in Utilities, Download Utils applications. simhash has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has high support. You can download it from GitHub.

A Python Implementation of Simhash Algorithm

Support

Quality

Security

License

Reuse

Support

simhash has a highly active ecosystem.

It has 698 star(s) with 185 fork(s). There are 25 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 24 have been closed. On average issues are closed in 89 days. There are no pull requests.

It has a positive sentiment in the developer community.

The latest version of simhash is current.

Quality

simhash has 0 bugs and 0 code smells.

Security

simhash has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

simhash code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

simhash is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

simhash 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.

simhash saves you 117 person hours of effort in developing the same functionality from scratch.

It has 302 lines of code, 34 functions and 3 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

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simhash Key Features

No Key Features are available at this moment for simhash.

simhash Examples and Code Snippets

No Code Snippets are available at this moment for simhash.

Community Discussions

Trending Discussions on simhash

How to compare the similarity of documents with Simhash algorithm?

python simhash import issue [github.com/seomoz/simhash-py]

QUESTION

How to compare the similarity of documents with Simhash algorithm?

Asked 2019-Aug-06 at 14:32

I'm currently creating a program that can compute near-dupliate score within a corpus of text documents (+5000 docs). I'm using Simhash to generate a uniq footprint of a document (thanks to this github repo)

my datas are :

...

ANSWER

Answered 2019-Apr-10 at 09:49

Before I answer your question, it is important to keep in mind:

Simhash is useful as it detects near duplicates. This means that near duplicates will end up with the same hash.
For exact duplicates you can simply use any one way, consistent hashing mechanism (ex. md5)
The examples that you pasted here are too small and given their size, their differences are significant. The algorithm is tailored to work with large Web Documents and not small sentences.

Now, I have replied to your question on the Github issue that you raised here.

For reference though, here is some sample code you can use to print the final near duplicate documents after hashing them.

Source https://stackoverflow.com/questions/49820228

QUESTION

python simhash import issue [github.com/seomoz/simhash-py]

Asked 2017-Oct-08 at 18:15

I've installed simhash using below command

...

ANSWER

Answered 2017-Sep-16 at 14:46

I've installed it via an another method.

Source https://stackoverflow.com/questions/46253804

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install simhash

You can download it from GitHub.
You can use simhash like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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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