hybrid_LDA | Hybrid distance metrics combined using Linear Discriminant

by dsmbgu8 Python Version: Current License: Non-SPDX

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kandi X-RAY | hybrid_LDA Summary

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

Implementation of hybrid metric learning algorithm described in:. Please cite the above reference if you publish work that uses this code.

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Support

hybrid_LDA has a low active ecosystem.

It has 0 star(s) with 0 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

hybrid_LDA has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of hybrid_LDA is current.

Quality

hybrid_LDA has no bugs reported.

Security

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

License

hybrid_LDA has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

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

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

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

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hybrid_LDA Key Features

No Key Features are available at this moment for hybrid_LDA.

hybrid_LDA Examples and Code Snippets

No Code Snippets are available at this moment for hybrid_LDA.

Community Discussions

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

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

Vulnerabilities

No vulnerabilities reported

Install hybrid_LDA

You can download it from GitHub.
You can use hybrid_LDA 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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