This code reproduces the figures from the paper: "Manifold learning with arbitrary norms" by Joe Kileel, Amit Moscovich, Nathan Zelesko, Amit Singer. https://link.springer.com/article/10.1007/s00041-021-09879-2 (published), https://arxiv.org/abs/2012.14172 (arXiv version)
The numerical section of the above paper extends the following conference paper by the same authors: "Earthmover-based manifold learning for analyzing molecular conformation spaces" IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020. https://ieeexplore.ieee.org/document/9098723 (published), https://arxiv.org/abs/1911.06107 (arXiv)
Python 3 is required with the following packages:
- NumPy
- SciPy
- scikit-learn
- mrcfile
The easiest way to install these is to download the Anaconda Python distribution (https://www.anaconda.com/products/individual) and then run "pip install mrcfile".
Since the figures use latex rendering for the labels, you need:
- TeXLive (the
latexbinary must be in the command path); - dvipdf and dvipng. Or you can just remove the TeX code from the labels used in plotting the figures.
Running produce_all_figures.py performs all the computations and produces all figures into the figures/ directory.
It takes a few minutes.
If you have any questions, feel free to email: [email protected]