FISTA | Python implementation of the Fast Iterative Shrinkage | Learning library

by JeanKossaifi Python Version: Current License: No License

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

kandi X-RAY | FISTA Summary

FISTA is a Python library typically used in Tutorial, Learning, Example Codes applications. FISTA has no vulnerabilities and it has low support. However FISTA has 3 bugs and it build file is not available. You can download it from GitHub.

This is a Fast version of the Iterative Shrinkage/Thresholding Algorithm (ISTA).

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Quality

Security

License

Reuse

Support

FISTA has a low active ecosystem.

It has 58 star(s) with 14 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. On average issues are closed in 2 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of FISTA is current.

Quality

FISTA has 3 bugs (0 blocker, 0 critical, 0 major, 3 minor) and 68 code smells.

Security

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

FISTA code analysis shows 0 unresolved vulnerabilities.

There are 5 security hotspots that need review.

License

FISTA 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

FISTA releases are not available. You will need to build from source code and install.

FISTA has no build file. You will be need to create the build yourself to build the component from source.

FISTA saves you 246 person hours of effort in developing the same functionality from scratch.

It has 599 lines of code, 45 functions and 8 files.

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

Top functions reviewed by kandi - BETA

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

Fit the Lipschitz model
Compute the M
Proximal operator
Projection onto l21
Computes the dual - norm of a mixture of coefs
R Compute the mixed norm
Load lipschitz constant matrix
Projection onto l22
Projection onto L11
Returns a dictionary containing information about the kernel
Apply mixed_norm
Compute the loss of the model
Predict the loss function

Get all kandi verified functions for this library.

FISTA Key Features

No Key Features are available at this moment for FISTA.

FISTA Examples and Code Snippets

No Code Snippets are available at this moment for FISTA.

Community Discussions

Trending Discussions on FISTA

Error "undeclared '_finite'" when compiling the SPAMS toolbox for MATLAB on Win10

QUESTION

Error "undeclared '_finite'" when compiling the SPAMS toolbox for MATLAB on Win10

Asked 2019-Jan-02 at 09:12

I am trying to import the SPAMS toolbox for sparse representations in MATLAB, using SPAMS v2.6 (downloaded from http://spams-devel.gforge.inria.fr/downloads.html), MATLAB R2018a, and under Win10. I have already compiled MEX-files with MinGW64.

Errors occur when I ran compile.m, it seems that some of the functions/modules (I'm not sure what to call them) can be compiled while others cannot. I have tried to comment those functions caused errors (about half of the total functions), the rest ran well but they cannot pass the tests in SPAMS. I guess those functions were somehow interactive.

...

ANSWER

Answered 2019-Jan-02 at 07:52

In the file linalg/misc.h that comes with SPAMS, near the top of the file, there is the following bit of code:

Source https://stackoverflow.com/questions/54001670

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

Vulnerabilities

No vulnerabilities reported

Install FISTA

You can download it from GitHub.
You can use FISTA 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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