GNN-GCP | Graph Neural Network architecture to solve the decision | Machine Learning library

by machine-reasoning-ufrgs Python Version: Current License: No License

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

kandi X-RAY | GNN-GCP Summary

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

Graph Neural Network architecture to solve the decision version of the graph coloring problem (GCP)

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Quality

Security

License

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Support

GNN-GCP has a low active ecosystem.

It has 5 star(s) with 5 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of GNN-GCP is current.

Quality

GNN-GCP has 0 bugs and 0 code smells.

Security

GNN-GCP has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

GNN-GCP code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

GNN-GCP 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

GNN-GCP releases are not available. You will need to build from source code and install.

GNN-GCP 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 1718 lines of code, 80 functions and 19 files.

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

Top functions reviewed by kandi - BETA

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

Creates a dataset
Calculate the rank of a Gaussian distribution
Generate a set of random variables
Write a dimensionacs dimacs file
Ensures that all of the datasets exist
Creates a dataset
Test a graph
Run tabucol algorithm
Yield batches of filenames
Reads a graph from a file
Parse a Glucose3 matrix
Prepare line before writing
Runs test with the given solution
Return memory usage information
Run a test
Creates a dense matrix
Summarize an epoch
Get test batches
Read a CNF
Build the network
Save weights to path
Loads previously saved weights
Create a BatchCNF
Execute the solution
Read a graph from a file
Builds a neurosat graph
Runs the solver
Run a training batch
Perform tabucol algorithm
Get memory usage

Get all kandi verified functions for this library.

GNN-GCP Key Features

No Key Features are available at this moment for GNN-GCP.

GNN-GCP Examples and Code Snippets

No Code Snippets are available at this moment for GNN-GCP.

Community Discussions

Trending Discussions on GNN-GCP

Error when running github source code from anaconda shell

QUESTION

Error when running github source code from anaconda shell

Asked 2021-Jul-23 at 12:18

Following the documentation in the source code of my interest from github, I run py dataset.py -path adversarial-training --train from the folder I have cloned repository to.

However, I receive an error

...

ANSWER

Answered 2021-Jul-23 at 11:05

I think you need to add it to the environment variables (path). For example: https://datatofish.com/add-python-to-windows-path/

It is not finding in the environment varaiables

Source https://stackoverflow.com/questions/68498118

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

Vulnerabilities

No vulnerabilities reported

Install GNN-GCP

You can download it from GitHub.
You can use GNN-GCP 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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