deep-clustering-kingdra | Official implementation of ICLR 2020 paper

by divamgupta Python Version: Current License: No License

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

kandi X-RAY | deep-clustering-kingdra Summary

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

Official implementation of ICLR 2020 paper Unsupervised Clustering using Pseudo-semi-supervised Learning

Support

Quality

Security

License

Reuse

Support

deep-clustering-kingdra has a low active ecosystem.

It has 40 star(s) with 15 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 7 have been closed. On average issues are closed in 11 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of deep-clustering-kingdra is current.

Quality

deep-clustering-kingdra has 0 bugs and 0 code smells.

Security

deep-clustering-kingdra has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

deep-clustering-kingdra code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

deep-clustering-kingdra 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

deep-clustering-kingdra 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.

deep-clustering-kingdra saves you 272 person hours of effort in developing the same functionality from scratch.

It has 658 lines of code, 43 functions and 9 files.

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

Top functions reviewed by kandi - BETA

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

Fit the Linear Gradient Gradient model
R Compute the encliptic graph
Get training data from buckets
Given a list of buckets and model indices compute the indices for each bucket
Assigns two cls
Compute the ACCESS coefficient
Compute the mut - inf loss
Removes all indices from the graph
Compute the KL divergence between two logits
R Create a multi - layer model for a given layer
Build the guass
Define a weight matrix
Calculates the Shannon squared error loss
Normalize l2
Compute self - dot loss
A self - dot loss
Compute the model for a given dataset
Calculate the ACCESS coefficient
Predict for each model
Generate noise loss

Get all kandi verified functions for this library.

deep-clustering-kingdra Key Features

No Key Features are available at this moment for deep-clustering-kingdra.

deep-clustering-kingdra Examples and Code Snippets

No Code Snippets are available at this moment for deep-clustering-kingdra.

Community Discussions

No Community Discussions are available at this moment for deep-clustering-kingdra.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install deep-clustering-kingdra

You can download it from GitHub.
You can use deep-clustering-kingdra 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 .

Find more information at: