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
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.
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
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.
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.
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
Acknowledgements
National Science Foundation #1520825
American Heart Association #18EIA33900046
Credit
TODO.
Release files for gaussian-proc 0.12.6
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| gaussian_proc-0.12.6.tar.gz | 434.2 kB | Details |
Built distributions (wheels)
Total release size: 12.0 MB
Release files / gaussian_proc-0.12.6.tar.gz
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Release files / gaussian_proc-0.12.6-cp39-cp39-win_amd64.whl
| Download URL | gaussian_proc-0.12.6-cp39-cp39-win_amd64.whl |
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Release files / gaussian_proc-0.12.6-cp39-cp39-manylinux2014_x86_64.whl
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Release files / gaussian_proc-0.12.6-cp39-cp39-macosx_10_9_x86_64.whl
| Download URL | gaussian_proc-0.12.6-cp39-cp39-macosx_10_9_x86_64.whl |
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Release files / gaussian_proc-0.12.6-cp38-cp38-win_amd64.whl
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Release files / gaussian_proc-0.12.6-cp38-cp38-manylinux2014_x86_64.whl
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Release files / gaussian_proc-0.12.6-cp38-cp38-macosx_10_9_x86_64.whl
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Release files / gaussian_proc-0.12.6-cp37-cp37m-win_amd64.whl
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Release files / gaussian_proc-0.12.6-cp37-cp37m-manylinux2014_x86_64.whl
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Release files / gaussian_proc-0.12.6-cp37-cp37m-macosx_10_9_x86_64.whl
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Release files / gaussian_proc-0.12.6-cp36-cp36m-win_amd64.whl
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Release files / gaussian_proc-0.12.6-cp36-cp36m-manylinux2014_x86_64.whl
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Release files / gaussian_proc-0.12.6-cp36-cp36m-macosx_10_9_x86_64.whl
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