gaussian-process-regression | 高斯过程回归 | Analytics library

by Ulti-Dreisteine Python Version: Current License: No License

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

kandi X-RAY | gaussian-process-regression Summary

gaussian-process-regression is a Python library typically used in Analytics applications. gaussian-process-regression has no bugs, it has no vulnerabilities and it has low support. However gaussian-process-regression build file is not available. You can download it from GitHub.

高斯过程回归

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Quality

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Support

gaussian-process-regression has a low active ecosystem.

It has 52 star(s) with 15 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

gaussian-process-regression has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of gaussian-process-regression is current.

Quality

gaussian-process-regression has no bugs reported.

Security

gaussian-process-regression has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

gaussian-process-regression 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.

Reuse

gaussian-process-regression releases are not available. You will need to build from source code and install.

gaussian-process-regression has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

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

Calculate covariance matrix
Calculate the kernel function
Calculate the kernel
Compute a linear kernel
Compute the kernel
Generate Gaussian process samples
Execute the GPRSampling
Loads environ from config files
Load yaml file
Multivariate Gaussian gaussian distribution
Univariate Gaussian distribution
Load model configuration
Load test params

Get all kandi verified functions for this library.

gaussian-process-regression Key Features

No Key Features are available at this moment for gaussian-process-regression.

gaussian-process-regression Examples and Code Snippets

No Code Snippets are available at this moment for gaussian-process-regression.

Community Discussions

Trending Discussions on gaussian-process-regression

Error in using Gaussian Process regression in sklearn python

QUESTION

Error in using Gaussian Process regression in sklearn python

Asked 2020-Jul-05 at 14:30

I am started learning python and trying to implement Gaussian regression using Sklearn library. I tried to follow the examples available here for my own data points. However, I am getting the following example when I am trying to run y_pred, std = model.predict(X_te, return_std=True) this line of code of my problem. The error I got 'XA and XB must have the same number of columns (i.e. feature dimension.)'.

I don't know where I made my mistake, please help and thanks in advance.

The sample of input and output data is given as follows

...

ANSWER

Answered 2020-Jul-05 at 14:30

This works. I changed your data from pandas to numpy arrays and fixed your reshapeing issues from which your error resulted.

Source https://stackoverflow.com/questions/62741602

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

Vulnerabilities

No vulnerabilities reported

Install gaussian-process-regression

You can download it from GitHub.
You can use gaussian-process-regression 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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