somber | Recursive Self-Organizing Map/Neural Gas | Machine Learning library

by stephantul Python Version: 2.0.1 License: MIT

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

kandi X-RAY | somber Summary

somber is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Numpy applications. somber has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install somber' or download it from GitHub, PyPI.

Recursive Self-Organizing Map/Neural Gas.

Support

Quality

Security

License

Reuse

Support

somber has a low active ecosystem.

It has 44 star(s) with 16 fork(s). There are 6 watchers for this library.

It had no major release in the last 12 months.

There are 2 open issues and 2 have been closed. On average issues are closed in 106 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of somber is 2.0.1

Quality

somber has 0 bugs and 0 code smells.

Security

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

somber code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

somber 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

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

Deployable package is available in PyPI.

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

somber saves you 332 person hours of effort in developing the same functionality from scratch.

It has 796 lines of code, 63 functions and 11 files.

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

Top functions reviewed by kandi - BETA

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

Compute the receptive field
Compute the model
Predict the output of the model
Check if input is a 2D matrix
Fit the model on X
Propagate the activations
Runs the training epoch
Fit the model
Performs a training epoch
Update the params
Calculate the influence matrix
Backward computation
Calculate the BMU
Propagate activations
Forward computation
Calculate the distance between neighbors
Returns a list of the nearest neighbor neighbors
Compute the topographic error
Inverse of the inverse projection
Predict the distance between the features
Calculates the average spread across multiple neurons
Compute the quantization error

Get all kandi verified functions for this library.

somber Key Features

No Key Features are available at this moment for somber.

somber Examples and Code Snippets

No Code Snippets are available at this moment for somber.

Community Discussions

Trending Discussions on somber

How to read contents from a web page and then output it using urllib

QUESTION

How to read contents from a web page and then output it using urllib

Asked 2020-May-17 at 06:40

I am trying to get random text from a website. My code:

...

ANSWER

Answered 2020-May-16 at 11:26

You need to parse the html code, either manually or using a library that does it for you as BeautifulSoup: https://www.crummy.com/software/BeautifulSoup/bs4/doc/

Source https://stackoverflow.com/questions/61835940

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

Vulnerabilities

No vulnerabilities reported

Install somber

You can install using 'pip install somber' or download it from GitHub, PyPI.
You can use somber 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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