HyperbolicNF | ICML 2020 Paper : Latent Variable

by joeybose Python Version: Current License: No License

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

kandi X-RAY | HyperbolicNF Summary

HyperbolicNF is a Python library. HyperbolicNF has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

ICML 2020 Paper: Latent Variable Modelling with Hyperbolic Normalizing Flows

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HyperbolicNF has a low active ecosystem.

It has 42 star(s) with 5 fork(s). There are 2 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 1 days. There are 14 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of HyperbolicNF is current.

Quality

HyperbolicNF has 0 bugs and 0 code smells.

Security

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

HyperbolicNF code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

HyperbolicNF 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

HyperbolicNF 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, examples and code snippets are available.

HyperbolicNF saves you 2342 person hours of effort in developing the same functionality from scratch.

It has 5112 lines of code, 406 functions and 33 files.

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

Top functions reviewed by kandi - BETA

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

Generate a set of synthetic graphs
Construct a directed graph from a graph
Draw an nx graph
Computes the log - likelihood of the model
Create tensor with zeros
Reparametrize hyperboloid
Get training data
Removes rows that are less than the last experiment
Aggregate gradients for each experiment
Truncates a list of experiments
Perform the forward computation
Return the BST structure
This function cleans up the graph
Calculate the density of a given radius
Forward computation
Encodes the model into the network
Creates a VGAE
Render a matplotlib figure
Computes the ranking metric
Encode the model
Plot a figure
Generate a matplotlib figure
Plots a mixture of the data
Create a Dataset
Calculate the log - likelihood of the model
Calculate the gaussian model

Get all kandi verified functions for this library.

HyperbolicNF Key Features

No Key Features are available at this moment for HyperbolicNF.

HyperbolicNF Examples and Code Snippets

No Code Snippets are available at this moment for HyperbolicNF.

Community Discussions

No Community Discussions are available at this moment for HyperbolicNF.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install HyperbolicNF

Other packages can be found in Requirements.txt but not all from that list are needed.
Pytorch Geometric: https://github.com/rusty1s/pytorch_geometric Follow the installation instructions carefully for this package! Make sure all your environment Path variables are exactly as outlined otherwise you will get weird symbol errors
Pytorch 1.5
WandB for logging

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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