deepred | A PyTorch implementation of DeepRed | Machine Learning library

by zekarias-tilahun Python Version: Current License: MIT

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

kandi X-RAY | deepred Summary

deepred is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. deepred has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However deepred build file is not available. You can download it from GitHub.

A PyTorch implementation of DeePRed, an algorithm for next item prediction in temporal interaction networks.

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Support

deepred has a low active ecosystem.

It has 2 star(s) with 0 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

deepred has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of deepred is current.

Quality

deepred has 0 bugs and 0 code smells.

Security

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

deepred code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

deepred 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

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

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

Installation instructions are not available. Examples and code snippets are available.

It has 1907 lines of code, 183 functions and 14 files.

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

Top functions reviewed by kandi - BETA

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

Predict the model for the given dataset .
Train the model .
Compute the similarity between two users .
Generate sample samples for given node .
Load data .
Run DeepRedRed .
Reads a file .
Read interactions from a text file .
Evaluate nipy s nipy distance .
Parse command line arguments .

Get all kandi verified functions for this library.

deepred Key Features

No Key Features are available at this moment for deepred.

deepred Examples and Code Snippets

No Code Snippets are available at this moment for deepred.

Community Discussions

Trending Discussions on deepred

How to place 2 algorithms side by side?

QUESTION

How to place 2 algorithms side by side?

Asked 2020-May-12 at 12:55

I'm trying to place two versions of an algorithm side by side. Using depth first search as an example, I'd like to put the recursive version on the left column and iterative on the right side. I'm using this stack overflow post for python formating and minipage. The aforementioned commands don't work in minipage however as shown below. How do I center the python code blocks in each minipage?

...

ANSWER

Answered 2020-May-12 at 12:55

Automatically rescaling of content that contains text is not a good idea, this will give you suboptimal font shapes. Instead you can

save a lot of space by not adding unnecessary indention from the tex source and
allow line breaks

In case this is not enough, you could manually choose a smaller font.

Some other comments:

two minipages with each .5\textwidth won't fit besides each other if you use this within an enumerate environment. The remaining line after the numeration is only \linewidth wide, so even if there is no other text in the line, the minipages must not be wider than .5\linewidth each
please don't load the same package more than once.
hyperref should (with very few exceptions) be loaded last
don't use two-letter font switches like \bf, they are deprecated

Source https://stackoverflow.com/questions/61742072

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

Vulnerabilities

No vulnerabilities reported

Install deepred

You can download it from GitHub.
You can use deepred 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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