Document-similarity-K-shingles-minhashing-LSH-python | many big-data problems
kandi X-RAY | Document-similarity-K-shingles-minhashing-LSH-python Summary
kandi X-RAY | Document-similarity-K-shingles-minhashing-LSH-python Summary
Document-similarity-K-shingles-minhashing-LSH-python is a Python library. Document-similarity-K-shingles-minhashing-LSH-python has no vulnerabilities and it has low support. However Document-similarity-K-shingles-minhashing-LSH-python has 1 bugs and it build file is not available. You can download it from GitLab, GitHub.
many big-data problems can be expressed as finding "similar" items. in this project we will investigate similarities among 21578 documents from a cleanup collection of documents were made available by reuters and cgi for research purposes. the collection appeared in 1987 and after processing in 1996 the data set had the form we know today with 21578 text categorization collection. as the name indicates, this collection contains 21578 text documents from reuters ltd. το be more precise, the collection consists of 22 data files, an sgml dtd file describing the data file format, and six files describing the categories used to index the data. each of the first 21 files (reut2-000.sgm through reut2-020.sgm) contain 1000 documents, while the last (reut2-021.sgm) contains 578 documents. the aim of this assignment is to discover relationships between these texts, using kshingles, jaccard similarities through minhashing and locality sensitive hashing. we are interested to investigate how similar the texts are. for this purpose we think data as "sets" of "strings" and convert shingles into minhash signatures. for the whole analysis we used python 2.7. for graphs we used microsoft excel. in more detail. the documents used for this project appeared on the reuters newswire in
many big-data problems can be expressed as finding "similar" items. in this project we will investigate similarities among 21578 documents from a cleanup collection of documents were made available by reuters and cgi for research purposes. the collection appeared in 1987 and after processing in 1996 the data set had the form we know today with 21578 text categorization collection. as the name indicates, this collection contains 21578 text documents from reuters ltd. το be more precise, the collection consists of 22 data files, an sgml dtd file describing the data file format, and six files describing the categories used to index the data. each of the first 21 files (reut2-000.sgm through reut2-020.sgm) contain 1000 documents, while the last (reut2-021.sgm) contains 578 documents. the aim of this assignment is to discover relationships between these texts, using kshingles, jaccard similarities through minhashing and locality sensitive hashing. we are interested to investigate how similar the texts are. for this purpose we think data as "sets" of "strings" and convert shingles into minhash signatures. for the whole analysis we used python 2.7. for graphs we used microsoft excel. in more detail. the documents used for this project appeared on the reuters newswire in
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Document-similarity-K-shingles-minhashing-LSH-python has a low active ecosystem.
It has 26 star(s) with 10 fork(s). There are 4 watchers for this library.
It had no major release in the last 6 months.
Document-similarity-K-shingles-minhashing-LSH-python has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of Document-similarity-K-shingles-minhashing-LSH-python is current.
Quality
Document-similarity-K-shingles-minhashing-LSH-python has 1 bugs (1 blocker, 0 critical, 0 major, 0 minor) and 93 code smells.
Security
Document-similarity-K-shingles-minhashing-LSH-python has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
Document-similarity-K-shingles-minhashing-LSH-python code analysis shows 0 unresolved vulnerabilities.
There are 3 security hotspots that need review.
License
Document-similarity-K-shingles-minhashing-LSH-python does not have a standard license declared.
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Document-similarity-K-shingles-minhashing-LSH-python releases are not available. You will need to build from source code and install.
Document-similarity-K-shingles-minhashing-LSH-python has no build file. You will be need to create the build yourself to build the component from source.
It has 400 lines of code, 5 functions and 1 files.
It has low code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed Document-similarity-K-shingles-minhashing-LSH-python and discovered the below as its top functions. This is intended to give you an instant insight into Document-similarity-K-shingles-minhashing-LSH-python implemented functionality, and help decide if they suit your requirements.
- Generate a list of similar documents .
- Test if a number is a prime number .
- Get the index of a triangle matrix .
- Returns a list of k random values from k .
- Generate a list of band hashes for minhash .
Get all kandi verified functions for this library.
Document-similarity-K-shingles-minhashing-LSH-python Key Features
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Install Document-similarity-K-shingles-minhashing-LSH-python
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You can use Document-similarity-K-shingles-minhashing-LSH-python 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.
You can use Document-similarity-K-shingles-minhashing-LSH-python 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.
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