findkit | A Python library for content-based information retrieval | Machine Learning library

by lambdaofgod Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | findkit Summary

findkit is a Python library typically used in Telecommunications, Media, Media, Entertainment, Artificial Intelligence, Machine Learning, Deep Learning applications. findkit has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Modern deep learning models can often be used for extracting features from different types of data - for example images and music. On the other hands there are methods for similarity search based on k-nearest neighbors algorithms. This library aims to provide unified interface for machine learning frameworks and nearest-neighbors indexing libraries, and to bridge the gap between them.

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Support

findkit has a low active ecosystem.

It has 7 star(s) with 0 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 719 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of findkit is current.

Quality

findkit has 0 bugs and 0 code smells.

Security

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

findkit code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

findkit 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.

Reuse

findkit 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.

Top functions reviewed by kandi - BETA

kandi has reviewed findkit and discovered the below as its top functions. This is intended to give you an instant insight into findkit implemented functionality, and help decide if they suit your requirements.

Load a wavevector from a file
Calculate the STFT
Load the wave file
Save metadata to a file
Get configuration
Validate metadata
Check that the metadata is consistent

Get all kandi verified functions for this library.

findkit Key Features

No Key Features are available at this moment for findkit.

findkit Examples and Code Snippets

No Code Snippets are available at this moment for findkit.

Community Discussions

Trending Discussions on findkit

Perl sub skips foreach within which it is called

QUESTION

Perl sub skips foreach within which it is called

Asked 2018-Jan-11 at 18:41

I'm having some problem with a subroutine that locates certain files and extracts some data out of them.

This subroutine is called inside a foreach loop, but whenever the call is made the loop skips to its next iteration. So I am wondering whether any of the next;'s are somehow escaping from the subroutine to the foreach loop where it is called?

To my knowledge the sub looks solid though so I'm hoping if anyone can see something I'm missing?

...

ANSWER

Answered 2018-Jan-11 at 18:41

To summarize comments, the refactored code:

Source https://stackoverflow.com/questions/43224346

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

Vulnerabilities

No vulnerabilities reported

Install findkit

You can download it from GitHub.
You can use findkit 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

Notes on Music Information RetrievalKeras modelsMXNetannoyNMSLib, its manual is a good source of knowledge on approximate nearest neighbors algorithmsApproximate Nearest Neighbours for Recommender Systems

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