Multilevel-Wasserstein-Means | First compile Cython code in cython_cuturi folder

by moonfolk Python Version: Current License: No License

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kandi X-RAY | Multilevel-Wasserstein-Means Summary

Multilevel-Wasserstein-Means is a Python library. Multilevel-Wasserstein-Means has no bugs, it has no vulnerabilities and it has low support. However Multilevel-Wasserstein-Means build file is not available. You can download it from GitHub.

First compile Cython code in cython_cuturi folder. Install Anaconda and run (on Ubuntu):. It implemets Algorithm 3 of Cuturi, which is the main computational routine. algos_cuturi.py Implements Algorithm 1 and Algorithm 2 of Cuturi. W_means_class.py implements our clustering algoritms as a scikit-learn estimator. simul_example.py has some simulated examples. Implementation is designed to be used in the interactive mode (e.g. Python IDE like Spyder).

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Multilevel-Wasserstein-Means has a low active ecosystem.

It has 15 star(s) with 8 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

Multilevel-Wasserstein-Means has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Multilevel-Wasserstein-Means is current.

Quality

Multilevel-Wasserstein-Means has no bugs reported.

Security

Multilevel-Wasserstein-Means has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

Multilevel-Wasserstein-Means does not have a standard license declared.

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Without a license, all rights are reserved, and you cannot use the library in your applications.

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Multilevel-Wasserstein-Means releases are not available. You will need to build from source code and install.

Multilevel-Wasserstein-Means 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.

Top functions reviewed by kandi - BETA

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

Uses k - means clustering .
Learns a distribution of constraints
Initialize L .
Calculate the objective function
Simulate a mixture of parameters .
Initialize clustering .
Calculates the Algorithm2 algorithm .
Simulate a mixture of atoms .
Fit the model .
Sample from a matrix .

Get all kandi verified functions for this library.

Multilevel-Wasserstein-Means Key Features

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Multilevel-Wasserstein-Means Examples and Code Snippets

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Vulnerabilities

No vulnerabilities reported

Install Multilevel-Wasserstein-Means

You can download it from GitHub.
You can use Multilevel-Wasserstein-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.

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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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