====== M-LOOP ====== The Machine-Learning Online Optimization Package is designed to automatically and rapidly optimize the parameters of a scientific experiment or computer controller system. .. figure:: _static/M-LOOPandBEC.png :alt: M-LOOP optimizing a BEC. M-LOOP in control of an ultra-cold atom experiment. M-LOOP was able to find an optimal set of ramps to evaporatively cool a thermal gas and form a Bose-Einstein Condensate. Using M-LOOP is simple, once the parameters of your experiment are computer controlled, all you need to do is determine a cost function that quantifies the performance of an experiment after a single run. You can then hand over control of the experiment to M-LOOP which will attempt to find a global optimal set of parameters that minimize the cost function, by performing experiments and testing different parameters. M-LOOP uses machine learning to predict how the parameters of the experiment relate to the cost; it uses this model to pick the next best parameters to test to find an optimum as quickly as possible. This approach is known as Bayesian optimization. M-LOOP not only finds an optimal set of parameters for the experiment it also provides a model of how the parameters are related to the costs which can be used to improve the experiment. If you use M-LOOP please cite our publication where we first used the package to optimize the production of a Bose-Einstein Condensate: Fast Machine-Learning Online Optimization of Ultra-Cold-Atom Experiments. *Scientific Reports* **6**, 25890 (2016). DOI: `Link 10.1038/srep25890 `_ http://www.nature.com/articles/srep25890 Quick Start =========== To get M-LOOP running follow the :ref:`sec-installation` instructions and :ref:`sec-tutorial`. Contents ======== .. toctree:: install tutorials interfaces data visualizations examples contributing changelog api/index Indices ======= * :ref:`genindex` * :ref:`modindex` * :ref:`search`