THU-HyperG | THU-HyperG is a python toolbox for hypergraph learning

by iMoonLab Python Version: Current License: MIT

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kandi X-RAY | THU-HyperG Summary

THU-HyperG is a Python library. THU-HyperG has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

[license-image]: [license-url]: thu-hyperg: a python toolbox for hypergraph learning ===. introduction --- thu-hyperg is a python toolbox for hypergraph learning. hypergraph is a generalization of graph, which is composed of a set of nodes and a set of hyperedges. different from simple graph, in which each edge connect a pair of nodes, each hyperedge can connect any number of nodes in hypergraph. the flexible edge degree in hypergraph enables the hypergraph model to formulate the high-order correlation of data. hypergraph learning is mainly consisted of two procedures, hypergraph generation and learning on hypergraph. thus, in this toolbox, we provide several hypergraph generation methods

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THU-HyperG has a low active ecosystem.

It has 78 star(s) with 17 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 6 open issues and 0 have been closed. On average issues are closed in 252 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of THU-HyperG is current.

Quality

THU-HyperG has 0 bugs and 0 code smells.

Security

THU-HyperG has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

THU-HyperG code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

THU-HyperG 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

THU-HyperG 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.

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

It has 1055 lines of code, 59 functions and 35 files.

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

Top functions reviewed by kandi - BETA

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

Estimate the inductive objective function
Theta matrix
Inverse of edge degrees
The laplacian matrix
Performs hyperedges using hyperedge weighting
Compute the transductive single step
Update the hyperedge weights
Compute the transition matrix for a tensor hypergraph
Prints a log message
Perform a dynamic transition on a directed graph
Updates the identity matrix
Fuse a mutli sub - hg
Loads the SGResture 3D dataset
Load modelnet files
Calculate the IOU correlation coefficient
Concatenate multiple HyperGroups
Transfer the predicted value of the hypergraph
Compute the prediction of a set of hypergraphs
Generate the neighbors of a grid
Load myocardium
Gather a 2 - dimensional array of patches
Partition a spectral hg
Cross - diffusion integration
Generate k - neighbors hg
Performs multi - hyperparameterization on each hypergraph
Postprocessing post processing

Get all kandi verified functions for this library.

THU-HyperG Key Features

No Key Features are available at this moment for THU-HyperG.

THU-HyperG Examples and Code Snippets

No Code Snippets are available at this moment for THU-HyperG.

Community Discussions

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

Vulnerabilities

No vulnerabilities reported

Install THU-HyperG

You can download it from GitHub.
You can use THU-HyperG 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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