gaussian-proc 0.12.6

Gaussian process regression

A python package for machine learning with Gaussian process regression.

This package is under development and has not been released.

Features

• TODO

Interactive Tutorial

Launch an online interactive tutorial in Jupyter notebook.

Links

• Documentation

• Package on Anaconda Cloud

• Package on PyPi

Install

Supported Platforms

Successful installation and tests have been performed on the following platforms and Python/PyPy versions shown in the table below.

Platform	Python version	PyPy version	Status
2.7	3.6	3.7	3.8	3.9	2.7	3.6	3.7
Linux	✔	✔	✔	✔	✔	✔	✔	✔
macOS	✔	✔	✔	✔	✔	✖	✖	✖
Windows	✖	✔	✔	✔	✔	✖	✖	✖

• For the Python/PyPy versions indicated by ✔ in the above, this package can be installed using either pip or conda (see Install Package below.)

• This package cannot be installed via pip or conda on the Python/PyPy versions indicated by ✖ in the above table.

• To install on the older Python 3 versions that are not listed in the above (for example Python 3.5), you should build this package from the source code (see Build and Install from Source Code).

Dependencies

• At runtime: TODO

• For tests: To run Test, scipy package is required and can be installed by

  python -m pip install -r tests/requirements.txt

Install Package

Either Install from PyPi, Install from Anaconda Cloud, or Build and Install from Source Code.

Install from PyPi

The recommended installation method is through the package available at PyPi using pip.

1. Ensure pip is installed within Python and upgrade the existing pip by

   python -m ensurepip
   python -m pip install --upgrade pip

   If you are using PyPy instead of Python, ensure pip is installed and upgrade the existing pip by

   pypy -m ensurepip
   pypy -m pip install --upgrade pip

2. Install this package in Python by

   python -m pip install gaussian_process

   or, in PyPy by

   pypy -m pip install gaussian_process

Install from Anaconda Cloud

Alternatively, the package can be installed through Anaconda could.

• In Linux and Windows:

  conda install -c s-ameli gaussian_process

• In macOS:

  conda install -c s-ameli -c conda-forge gaussian_process

Build and Install from Source Code

Build dependencies: To build the package from the source code, numpy and cython are required. These dependencies are installed automatically during the build process and no action is needed.

1. Install both C and Fortran compilers as follows.

   • Linux: Install gcc, for instance, by apt (or any other package manager on your Linux distro)

     sudo apt install gcc

   • macOS: Install gcc via Homebrew:

     sudo brew install gcc

     Note: If gcc is already installed, but Fortran compiler is yet not available on macOS, you may resolve this issue by reinstalling:

     sudo brew reinstall gcc

   • Windows: Install both Microsoft Visual C++ compiler and Intel Fortran compiler (Intel oneAPI). Open the command prompt (where you will enter the installation commands in the next step) and load the Intel compiler variables by

     C:\Program Files (x86)\Intel\oneAPI\setvars.bat

     Here, we assumed the Intel Fortran compiler is installed in C:\Program Files (x86)\Intel\oneAPI. You may set this directory accordingly to the directory of your Intel compiler.

2. Clone the source code and install this package by

   git clone https://github.com/ameli/gaussian_process.git
   cd gaussian_process
   python -m pip install .

Warning: After the package is built and installed from the source code, the package cannot be imported properly if the current working directory is the same as the source code directory. To properly import the package, change the current working directory to a directory anywhere else outside of the source code directory. For instance:

cd ..
python
>>> import gaussian_process

Test

To test package, install tox:

python -m pip install tox

and test the package with

tox

Modules

Syntax	User guide
todo(nu, z, n)	Module name todo <https://ameli.github.io/gaussian_process/module_name.html>`_

Typed Arguments:

Argument	Type	Description
nu	double	Parameter

Examples

Related Packages

• TODO

Acknowledgements

• National Science Foundation #1520825

• American Heart Association #18EIA33900046

Credit

• TODO.