LinUCB | Contextual bandit algorithm called LinUCB / Linear Upper | Reinforcement Learning library

by thunfischtoast Java Version: 1.0 License: GPL-3.0

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

kandi X-RAY | LinUCB Summary

LinUCB is a Java library typically used in Artificial Intelligence, Reinforcement Learning applications. LinUCB has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. However LinUCB has 1 bugs. You can download it from GitHub.

Contextual bandit algorithm called LinUCB / Linear Upper Confidence Bounds as proposed by Li, Langford and Schapire. We implemented the two version, one with disjoint and and one with hybrid linear models, as mentioned in the paper. See src/de/thunfischtoast/BanditTest.java for basic usage example as inspired by .

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Quality

Security

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Support

LinUCB has a low active ecosystem.

It has 25 star(s) with 10 fork(s). There are 1 watchers for this library.

It had no major release in the last 12 months.

There are 1 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of LinUCB is 1.0

Quality

LinUCB has 1 bugs (0 blocker, 1 critical, 0 major, 0 minor) and 24 code smells.

Security

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

LinUCB code analysis shows 0 unresolved vulnerabilities.

There are 2 security hotspots that need review.

License

LinUCB is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

LinUCB releases are available to install and integrate.

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

Installation instructions are not available. Examples and code snippets are available.

LinUCB saves you 107 person hours of effort in developing the same functionality from scratch.

It has 272 lines of code, 12 functions and 4 files.

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

Top functions reviewed by kandi - BETA

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

Returns the viable arm
Return the best payoff for each arm
Receive a multiple rewards
Receive a reward

Get all kandi verified functions for this library.

LinUCB Key Features

No Key Features are available at this moment for LinUCB.

LinUCB Examples and Code Snippets

LinUCB

Java

Lines of Code : 8

License : Strong Copyleft (GPL-3.0)

Copy

@inproceedings{li2010contextual,
  title={A contextual-bandit approach to personalized news article recommendation},
  author={Li, Lihong and Chu, Wei and Langford, John and Schapire, Robert E},
  booktitle={Proceedings of the 19th international conf

Community Discussions

Trending Discussions on LinUCB

Contextual-Bandit Approach: Algorithm 1 LinUCB with disjoint linear models

QUESTION

Contextual-Bandit Approach: Algorithm 1 LinUCB with disjoint linear models

Asked 2019-Feb-25 at 01:34

I am trying to implement the algorithm called LinUCB with disjoint linear models from this paper "A Contextual-Bandit Approach to Personalized News Article Recommendation" http://rob.schapire.net/papers/www10.pdf

This is the algorithm: Algorithm 1 LinUCB with disjoint linear models

I am confused about the features vector Xt,a (I highlighted on the algorithm). Is the feature vector related to information (context) of the article(arm) or the user?

I would appreciate your help. Thank you

...

ANSWER

Answered 2018-Jul-13 at 22:53

The feature vector x_t,a applies to both the user and the arm.

The vector xt,a summarizes information of both the user ut and arm a, and will be referred to as the context.

Source https://stackoverflow.com/questions/51333710

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

Vulnerabilities

No vulnerabilities reported

Install LinUCB

You can download it from GitHub.
You can use LinUCB like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the LinUCB component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

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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