fuzzy-c-means | Fuzzy c-means | Machine Learning library

by oeg-upm Python Version: v1.0 License: Apache-2.0

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

kandi X-RAY | fuzzy-c-means Summary

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

This implementation is based on the paper FCM: The fuzzy c-means clustering algorithm.

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Quality

Security

License

Reuse

Support

fuzzy-c-means has a highly active ecosystem.

It has 23 star(s) with 17 fork(s). There are 1 watchers for this library.

It had no major release in the last 12 months.

fuzzy-c-means has no issues reported. There are 1 open pull requests and 0 closed requests.

It has a positive sentiment in the developer community.

The latest version of fuzzy-c-means is v1.0

Quality

fuzzy-c-means has 0 bugs and 23 code smells.

Security

fuzzy-c-means has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

fuzzy-c-means code analysis shows 0 unresolved vulnerabilities.

There are 2 security hotspots that need review.

License

fuzzy-c-means is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

fuzzy-c-means releases are available to install and integrate.

Deployable package is available in PyPI.

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

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

It has 328 lines of code, 22 functions and 7 files.

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

Top functions reviewed by kandi - BETA

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

Example example
Compute cluster centers
Performs clustering
Compute the membership of each cluster
Calculate the membership from a hard cluster
Draws an FCM model
Predicate the class
Compute the membership for each cluster
Compute the distance squared distance between two vectors
Draw a 2d figure
Example example of a single cluster
Example example for a single zero filled feature

Get all kandi verified functions for this library.

fuzzy-c-means Key Features

No Key Features are available at this moment for fuzzy-c-means.

fuzzy-c-means Examples and Code Snippets

No Code Snippets are available at this moment for fuzzy-c-means.

Community Discussions

Trending Discussions on fuzzy-c-means

Table variable name duplication error when trying to plot iris dataset

QUESTION

Table variable name duplication error when trying to plot iris dataset

Asked 2021-Mar-05 at 20:30

I've been trying to plot the iris data points of each combination of two dimensions of the four dimension dataset which contains the sepal length, sepal width, petal length and petal width. I tried to follow the documentation written in Fuzzy C-Means Clustering for Iris Data though, I've been getting errors such as 'Error using load Unknown text on line number 1 of ASCII file iris.dat "Iris-setosa".' when I tried to type load iris.dat in the command window.

This is what I've written so far:

...

ANSWER

Answered 2021-Mar-05 at 20:30

While I don't have the Fuzzy Logic Toolbox, I do have the Statistics & ML Toolbox, which provides a file called 'fisheriris.csv' that can be opened with readtable(). I suspect all you need to do in your case is to invoke table2array() before concatenating columns with the same variable name from different table arrays (i.e. before doing stuff like [setosa(...,n) versicolor(...,n) ...]):

Source https://stackoverflow.com/questions/66498185

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

Vulnerabilities

No vulnerabilities reported

Install fuzzy-c-means

You can install using 'pip install fuzzy-c-means' or download it from GitHub, PyPI.
You can use fuzzy-c-means 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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