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Ballerina math vector Library

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This package provides functions for doing vector operations including calculating the L1 and L2 norm, dot product, cosine similarity, Euclidean distance, and Manhattan distance.

Issues and projects

The Issues and Projects tabs are disabled for this repository as this is part of the Ballerina Standard Library. To report bugs, request new features, start new discussions, view project boards, etc., go to the Ballerina Standard Library parent repository.

This repository contains only the source code of the package.

Build from the source

Set up the prerequisites

  • Download and install Java SE Development Kit (JDK) version 17 (from one of the following locations).

    • Oracle

    • OpenJDK

      Note: Set the JAVA_HOME environment variable to the path name of the directory into which you installed JDK.

Build the source

Execute the commands below to build from the source.

  1. To build the library:

     ./gradlew clean build
    
  2. To run the integration tests:

     ./gradlew clean test
    
  3. To build the package without the tests:

     ./gradlew clean build -x test
    
  4. To debug the tests:

     ./gradlew clean build -Pdebug=<port>
    
  5. To debug the package with Ballerina language:

     ./gradlew clean build -PbalJavaDebug=<port>
    
  6. Publish ZIP artifact to the local .m2 repository:

     ./gradlew clean build publishToMavenLocal
    
  7. Publish the generated artifacts to the local Ballerina central repository:

     ./gradlew clean build -PpublishToLocalCentral=true
    
  8. Publish the generated artifacts to the Ballerina central repository:

     ./gradlew clean build -PpublishToCentral=true
    

Contribute to Ballerina

As an open source project, Ballerina welcomes contributions from the community.

For more information, go to the contribution guidelines.

Code of conduct

All contributors are encouraged to read the Ballerina Code of Conduct.

Useful links

  • Chat live with us on our Discord server.
  • Technical questions should be posted on Stack Overflow with the #ballerina tag.