ML_CIA | Official account : All codes of Machine Learning

by gutouyu Python Version: Current License: MIT

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

kandi X-RAY | ML_CIA Summary

ML_CIA is a Python library. ML_CIA has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However ML_CIA build file is not available. You can download it from GitHub.

Official account: All codes of Machine Learning Recommendation Intelligence Bureau

Support

Quality

Security

License

Reuse

Support

ML_CIA has a low active ecosystem.

It has 545 star(s) with 292 fork(s). There are 38 watchers for this library.

It had no major release in the last 6 months.

There are 5 open issues and 0 have been closed. On average issues are closed in 565 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of ML_CIA is current.

Quality

ML_CIA has 0 bugs and 0 code smells.

Security

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

ML_CIA code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

ML_CIA 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

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

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

It has 4191 lines of code, 178 functions and 35 files.

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

Top functions reviewed by kandi - BETA

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

Build model function .
Builds a built network .
Train the model .
returns a feature dictionary
parse feat_dict from df
Play the game .
Optimize the model .
returns the result of the player
Train the model .
Performs a UCT algorithm .

Get all kandi verified functions for this library.

ML_CIA Key Features

No Key Features are available at this moment for ML_CIA.

ML_CIA Examples and Code Snippets

No Code Snippets are available at this moment for ML_CIA.

Community Discussions

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

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

Vulnerabilities

No vulnerabilities reported

Install ML_CIA

You can download it from GitHub.
You can use ML_CIA 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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