DOIAbstract
This release features two new types of lifting functions: radial basis functions, and random Fourier features. Click the links for examples, or check them out on Binder! You can now also use almost any scikit-learn regressor as a backend for EDMD with EdmdMeta. You can find a cool example of sparse regression with the lasso here. Finally, two quality-of-life changes are introduced in this update. You can access your lifting function feature names with KoopmanLiftingFn.get_feature_names_out(), and you can quickly plot Koopman predictions and Koopman operator properties with a bunch of plot_*() methods scattered throughout the library. See below for more details. Note that in this release, we are dropping official Python 3.7 support, thoug...
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