SuperNNova | The official repository

by tdeboissiere Python Version: Current License: No License

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

kandi X-RAY | SuperNNova Summary

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

The official repository has been changed to: supernnova/SuperNNova.

Support

Quality

Security

License

Reuse

Support

SuperNNova has a low active ecosystem.

It has 11 star(s) with 5 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

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

It has a neutral sentiment in the developer community.

The latest version of SuperNNova is current.

Quality

SuperNNova has no bugs reported.

Security

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

License

SuperNNova 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

SuperNNova 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.

Top functions reviewed by kandi - BETA

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

Make an early prediction
Set PyTorch model name
Load a PlasticC test set
Loads predictions from RNN
Train the model
Evaluate accuracy
Format MNIST dataset
Plot the prediction distribution
Plot the light curve distributions
Forward computation
Calculate quantity for a given variable
Run Bayesian Estimator
Create a pandas dataset
Run cyclic benchmark
Run the BASELINE HPM
Run the variational best model
Load a FITRES file
Run the speed calculation
Calculates predictions for a speed benchmark
Train cyclic model
Perform forward computation
Load database
Get metrics for a single model
Calculate evaluation metrics
Load PTB dataset
Train baseline models on SALT

Get all kandi verified functions for this library.

SuperNNova Key Features

No Key Features are available at this moment for SuperNNova.

SuperNNova Examples and Code Snippets

No Code Snippets are available at this moment for SuperNNova.

Community Discussions

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

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

Vulnerabilities

No vulnerabilities reported

Install SuperNNova

You can download it from GitHub.
You can use SuperNNova 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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