kandi X-RAY | MLMC Summary
kandi X-RAY | MLMC Summary
MLMC is a Python library. MLMC has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can install using 'pip install MLMC' or download it from GitHub, PyPI.
Multi-level Monte Carlo method with approximation of distribution function and quantiles. It is meant as part of GeoMop project in particular Analysis component.
Multi-level Monte Carlo method with approximation of distribution function and quantiles. It is meant as part of GeoMop project in particular Analysis component.
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Quality
Security
License
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Support
MLMC has a low active ecosystem.
It has 4 star(s) with 1 fork(s). There are 2 watchers for this library.
It had no major release in the last 12 months.
There are 66 open issues and 57 have been closed. On average issues are closed in 142 days. There are 5 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of MLMC is 1.0.2
Quality
MLMC has no bugs reported.
Security
MLMC has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
MLMC does not have a standard license declared.
Check the repository for any license declaration and review the terms closely.
Without a license, all rights are reserved, and you cannot use the library in your applications.
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MLMC 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 not available. Examples and code snippets are available.
Top functions reviewed by kandi - BETA
kandi has reviewed MLMC and discovered the below as its top functions. This is intended to give you an instant insight into MLMC implemented functionality, and help decide if they suit your requirements.
- Read mesh .
- Constructs the autogonal moments from a covariance matrix .
- Create a level simulation instance .
- Initialize the plot .
- Calculates the saved samples .
- Compute the SVD of the covariance matrix .
- Estimate the mean of a quantity .
- Add the variance to the plot .
- Get the results files from the results directory
- Generate samples .
Get all kandi verified functions for this library.
MLMC Key Features
No Key Features are available at this moment for MLMC.
MLMC Examples and Code Snippets
No Code Snippets are available at this moment for MLMC.
Community Discussions
No Community Discussions are available at this moment for MLMC.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install MLMC
You can install using 'pip install MLMC' or download it from GitHub, PyPI.
You can use MLMC 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.
You can use MLMC 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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