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| Documentation and tutorial |[](https://pyepal.readthedocs.io/en/latest/?badge=latest)[](https://mybinder.org/v2/gh/kjappelbaum/pyepal/HEAD?filepath=examples)|
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| Documentation and tutorial |[](https://pyepal.readthedocs.io/en/latest/?badge=latest)[](https://mybinder.org/v2/gh/kjappelbaum/pyepal/HEAD?filepath=examples)|
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Generalized Python implementation of a modified version of the ε-PAL algorithm [[1](#1), [2](#2)].
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@@ -25,6 +24,12 @@ To install the latest stable release use
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pip install pyepal
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```
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or the conda channel (recommended)
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```(bash)
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conda install pyepal -c conda-forge
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```
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to install the latest development version from the head use
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```(bash)
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### Additional Notes
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- On MacOS you might need to install `libomp` (e.g., `brew install libomp`) for multithreading in some of the models.
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- On macOS you might need to install `libomp` (e.g., `brew install libomp`) for multithreading in some models.
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- We currently support Python 3.7 and 3.8.
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- If you want to [limit how many CPUs openblas uses](https://github.com/numpy/numpy/issues/8120), you can `export OPENBLAS_NUM_THREADS=1`
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## Usage
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2. <aname="2"></a> Zuluaga, M.; Sergent, G.; Krause, A.; Püschel, M. Active Learning for Multi-Objective Optimization; Dasgupta, S., McAllester, D., Eds.; Proceedings of machine learning research; PMLR: Atlanta, Georgia, USA, 2013; Vol. 28, pp 462–470.
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## Citation
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<aname="citation"></a>
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If you find this code useful for your work, please cite:
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