gmmn | Generative moment matching networks | Machine Learning library

by yujiali Python Version: Current License: No License

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

kandi X-RAY | gmmn Summary

gmmn is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Generative adversarial networks, Neural Network applications. gmmn has no bugs, it has no vulnerabilities and it has low support. However gmmn build file is not available. You can download it from GitHub.

#Generative Moment Matching Networks (GMMNs) This is the code we used for the following paper:. If you use this code in your research you should cite the above paper.

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gmmn has a low active ecosystem.

It has 141 star(s) with 33 fork(s). There are 11 watchers for this library.

It had no major release in the last 6 months.

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

It has a neutral sentiment in the developer community.

The latest version of gmmn is current.

Quality

gmmn has 0 bugs and 246 code smells.

Security

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

gmmn code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

gmmn 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

gmmn releases are not available. You will need to build from source code and install.

gmmn has no build file. You will be need to create the build yourself to build the component from source.

gmmn saves you 1106 person hours of effort in developing the same functionality from scratch.

It has 2503 lines of code, 266 functions and 13 files.

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

Top functions reviewed by kandi - BETA

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

Load a fold file
Gets the image for a given fold
Load a single fold
Get the proper fold for a given fold
Returns a fixed random permutation
Compute the kernel matrix
Compute the kernel transformation

Get all kandi verified functions for this library.

gmmn Key Features

No Key Features are available at this moment for gmmn.

gmmn Examples and Code Snippets

No Code Snippets are available at this moment for gmmn.

Community Discussions

Trending Discussions on gmmn

adding extra variable inside fetched data

QUESTION

adding extra variable inside fetched data

Asked 2020-Sep-22 at 22:22

As you can see below I'm fetching the idsOfSenders and with that I'm executing another query to fetch info of that user. So far this works fine.

But as you can see, I also have another variable $msgDate and I want to put that in the output of every user. How can I do that?

...

ANSWER

Answered 2020-Sep-22 at 17:39

This is where a JOIN comes in handy. What you're doing with two queries can be done in one, and as a plus, there's no need for that horrendous implode injection:

Source https://stackoverflow.com/questions/64014964

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

Vulnerabilities

No vulnerabilities reported

Install gmmn

You can download it from GitHub.
You can use gmmn 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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