runlmc | Structurally efficient multi-output linearly coregionalized

by vlad17 Python Version: Current License: Non-SPDX

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

kandi X-RAY | runlmc Summary

runlmc is a Python library. runlmc has no bugs, it has no vulnerabilities, it has build file available and it has low support. However runlmc has a Non-SPDX License. You can download it from GitHub.

Do you like to apply Bayesian nonparameteric methods to your regressions? Are you frequently tempted by the flexibility that kernel-based learning provides? Do you have trouble getting structured kernel interpolation or various training conditional inducing point approaches to work in a non-stationary multi-output setting?. If so, this package is for you.

Support

Quality

Security

License

Reuse

Support

runlmc has a low active ecosystem.

It has 28 star(s) with 8 fork(s). There are 6 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 2 have been closed. On average issues are closed in 1 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of runlmc is current.

Quality

runlmc has 0 bugs and 0 code smells.

Security

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

runlmc code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

runlmc 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

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

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

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

runlmc saves you 2387 person hours of effort in developing the same functionality from scratch.

It has 5204 lines of code, 595 functions and 96 files.

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

Top functions reviewed by kandi - BETA

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

Run the krylov test
Calculates the total rank for the given active dimension
Computes the error between two vectors
Generate a grid kernel
Calculate gradients
Print the epilog
Prepare climin
Solve the covariance matrix
Returns the sparse linear operator
Convert to numpy array
Updates the gradient with the given gradients
Generate a sum matrix
Normalize the input vector
Sets up the test - run
Generate a block - wise block matrix
Unset prior
Generate a tensorflow tensorflow matrix
Calculate the derivative of a discrete objective function
Generate RBF kernels
Set the prior
Print image for given column
Set the input dimension
Predict for the model
Compute the kernel for a given functional kernel
Perform pre - interpolation
Generate a stochastic derivative

Get all kandi verified functions for this library.

runlmc Key Features

No Key Features are available at this moment for runlmc.

runlmc Examples and Code Snippets

No Code Snippets are available at this moment for runlmc.

Community Discussions

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

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

Vulnerabilities

No vulnerabilities reported

Install runlmc

You can download it from GitHub.
You can use runlmc 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 .

Find more information at: