XMLC | Probabilistic Label Tree for Extreme Classification | Testing library

by busarobi Java Version: Current License: GPL-3.0

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

kandi X-RAY | XMLC Summary

XMLC is a Java library typically used in Institutions, Learning, Administration, Public Services, Testing applications. XMLC has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

Probabilistic Label Tree for Extreme Classification

Support

Quality

Security

License

Reuse

Support

XMLC has a low active ecosystem.

It has 24 star(s) with 4 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

There are 10 open issues and 2 have been closed. On average issues are closed in 55 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of XMLC is current.

Quality

XMLC has 0 bugs and 0 code smells.

Security

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

XMLC code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

XMLC 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

XMLC 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 available. Examples and code snippets are not available.

XMLC saves you 2376 person hours of effort in developing the same functionality from scratch.

It has 5181 lines of code, 325 functions and 52 files.

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

Top functions reviewed by kandi - BETA

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

Validate the EXU
Set the thresholds
Returns the number of labels in the AVTable
Validate the EXU data
Set the thresholds
Returns the number of labels in the AVTable
Run the training threshold
Saves the posteriors
Calculate the threshold
Saves the posteriors
Validates the sample
Validates the dataset
Takes out the exu
Test program
Allocates the frequent frequencies
Main entry point
The main test function
Main test function
Gets the top estimates for each node in the given dataset
Given an array of nodes compute the top k labels
Triggers the training algorithm
Trains the training dataset
Allocate classifiers
Main method for testing
Validate the prediction
Compute the F - measure F - measure
Trains the train algorithm
Main entry point for test case
Build Huffman tree

Get all kandi verified functions for this library.

XMLC Key Features

No Key Features are available at this moment for XMLC.

XMLC Examples and Code Snippets

No Code Snippets are available at this moment for XMLC.

Community Discussions

Trending Discussions on XMLC

Getting XML node names without duplicating in simplexml_load_file

QUESTION

Getting XML node names without duplicating in simplexml_load_file

Asked 2021-Apr-06 at 21:02

I'm getting the node names of an XML using this code:

...

ANSWER

Answered 2021-Apr-06 at 21:02

i used recursive function this is simple

Source https://stackoverflow.com/questions/66976196

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

Vulnerabilities

No vulnerabilities reported

Install XMLC

Having executed these two commands, you should find a jar called XMLC_PLT-jar-with-dependencies.jar in the root directory of git project.
"-train" Train PLT
"-eval" Evaluate the model on a given test file
"-posteriors" Output posteriors based on a model
"-tune" Tune thresholds for optimizing the Macro F-measure
"-test" Compute the prediction based on a model and corresponding thresholds that were validated for macro F-measure
Train PLT

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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