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

 by   1e0ng Python Version: 2.1.2 License: MIT

kandi X-RAY | simhash Summary

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 medium support. You can install using 'pip install simhash' or download it from GitHub, PyPI.

A Python Implementation of Simhash Algorithm
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            kandi-support Support

              simhash has a medium active ecosystem.
              It has 898 star(s) with 222 fork(s). There are 23 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 2 open issues and 40 have been closed. On average issues are closed in 136 days. There are 2 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of simhash is 2.1.2

            kandi-Quality Quality

              simhash has 0 bugs and 0 code smells.

            kandi-Security 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.

            kandi-License 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.

            kandi-Reuse Reuse

              simhash releases are not available. You will need to build from source code and install.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              It has 302 lines of code, 34 functions and 3 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed simhash and discovered the below as its top functions. This is intended to give you an instant insight into simhash implemented functionality, and help decide if they suit your requirements.
            • Build a score from the given text
            • Build a hash based on features
            • Tokenize content
            • Sum the digests
            • Slide the content of the given content
            • Convert a byte array into an array
            Get all kandi verified functions for this library.

            simhash Key Features

            No Key Features are available at this moment for simhash.

            simhash Examples and Code Snippets

            python simhash import issue [github.com/seomoz/simhash-py]
            Pythondot img1Lines of Code : 6dot img1License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            git clone https://github.com/seomoz/simhash-py.git
            cd simhash-py
            git submodule update --init --recursive
            
            sudo python setup.py install
            

            Community Discussions

            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:

            1. Simhash is useful as it detects near duplicates. This means that near duplicates will end up with the same hash.
            2. For exact duplicates you can simply use any one way, consistent hashing mechanism (ex. md5)
            3. 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 install using 'pip install simhash' or download it from GitHub, PyPI.
            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 .
            Find more information at:

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            Install
          • PyPI

            pip install simhash

          • CLONE
          • HTTPS

            https://github.com/1e0ng/simhash.git

          • CLI

            gh repo clone 1e0ng/simhash

          • sshUrl

            git@github.com:1e0ng/simhash.git

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