TGAN | Generative adversarial training | Machine Learning library

by sdv-dev Python Version: v0.1.0 License: MIT

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

kandi X-RAY | TGAN Summary

TGAN is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Generative adversarial networks applications. TGAN 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.

TGAN is a tabular data synthesizer. It can generate fully synthetic data from real data. Currently, TGAN can generate numerical columns and categorical columns.

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Quality

Security

License

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Support

TGAN has a low active ecosystem.

It has 184 star(s) with 60 fork(s). There are 25 watchers for this library.

It had no major release in the last 12 months.

There are 27 open issues and 29 have been closed. On average issues are closed in 16 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of TGAN is v0.1.0

Quality

TGAN has 0 bugs and 0 code smells.

Security

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

TGAN code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

TGAN 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

TGAN releases are available to install and integrate.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

TGAN saves you 732 person hours of effort in developing the same functionality from scratch.

It has 1689 lines of code, 92 functions and 15 files.

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

Top functions reviewed by kandi - BETA

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

Runs the experiments
Fit a TFAN model
Prepare kwargs for training
Compute the transformed data
Fit the GAN model
Sample from the model
Run a score model
Evaluate the classification
Generate features and labels
Transform the data into a Pandas DataFrame
Transform data
Inverse transform function
Prepare sampling
Return a model builder
Check that the given metadata contains unsupported types
Save the model to path
Create a tar file from the output directory
Argument parser
Load a TANAN model from a tar archive
Fit the model to data

Get all kandi verified functions for this library.

TGAN Key Features

No Key Features are available at this moment for TGAN.

TGAN Examples and Code Snippets

No Code Snippets are available at this moment for TGAN.

Community Discussions

Trending Discussions on TGAN

iOS How can I share PDF or DOC from one app to another?

QUESTION

iOS How can I share PDF or DOC from one app to another?

Asked 2017-Aug-21 at 03:59

I wanna share a PDF file from APP1 by the function of share extension， but I just got a URL path from method of loadItemForTypeIdentifier,which is a path of APP1 sandbox, I can not get the PDF file by URL,so how can I get the PDF or Doc through the method loadItemForTypeIdentifier?

I tried many types but I can only get NSURL.

...

ANSWER

Answered 2017-Aug-21 at 03:59

When I assign item to NSData, I get a NSData.

Source https://stackoverflow.com/questions/42804695

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

Vulnerabilities

No vulnerabilities reported

Install TGAN

The simplest and recommended way to install TGAN is using pip:. Alternatively, you can also clone the repository and install it from sources. For development, you can use make install-develop instead in order to install all the required dependencies for testing and code linting.
In this short tutorial we will guide you through a series of steps that will help you getting started with the most basic usage of TGAN in order to generate samples from a given dataset.

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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