📦 Installation#
adaptive works with Python 3.7 and higher on Linux, Windows, or Mac, and provides optional extensions for working with the Jupyter/IPython Notebook.
The recommended way to install adaptive is using conda:
conda install -c conda-forge adaptive
adaptive is also available on PyPI:
pip install "adaptive[notebook]"
The [notebook] above will also install the optional dependencies for running adaptive inside a Jupyter notebook.
To use Adaptive in Jupyterlab, you need to install the following labextensions.
jupyter labextension install @jupyter-widgets/jupyterlab-manager
jupyter labextension install @pyviz/jupyterlab_pyviz
🔧 Development#
Clone the repository and run pip install -e ".[notebook,testing,other]" to add a link to the cloned repo into your Python path:
git clone git@github.com:python-adaptive/adaptive.git
cd adaptive
pip install -e ".[notebook,testing,other]"
We recommend using a Conda environment or a virtualenv for package management during Adaptive development.
To avoid polluting the history with notebook output, set up the git filter by running:
python ipynb_filter.py
in the repository.
To maintain consistent code style, we use pre-commit. Install it by running:
pre-commit install
in the repository.
📚 Citing#
If you used Adaptive in a scientific work, please cite it as follows.
@misc{Nijholt2019,
doi = {10.5281/zenodo.1182437},
author = {Bas Nijholt and Joseph Weston and Jorn Hoofwijk and Anton Akhmerov},
title = {\textit{Adaptive}: parallel active learning of mathematical functions},
publisher = {Zenodo},
year = {2019}
}
📄 Draft Paper#
If you’re interested in the scientific background and principles behind Adaptive, we recommend taking a look at the draft paper that is currently being written. This paper provides a comprehensive overview of the concepts, algorithms, and applications of the Adaptive library.
✨ Credits#
We would like to give credits to the following people:
Pedro Gonnet for his implementation of CQUAD, “Algorithm 4” as described in “Increasing the Reliability of Adaptive Quadrature Using Explicit Interpolants”, P. Gonnet, ACM Transactions on Mathematical Software, 37 (3), art. no. 26, 2010.
Pauli Virtanen for his
AdaptiveTriSamplingscript (no longer available online since SciPy Central went down) which served as inspiration for theadaptive.Learner2D.