Python implementation of the algorithms from the paper. If you use this algorithm in your research we kindly ask you to cite our work
@article{khrulkov2018geometry,
title={Geometry {S}core: {A} {M}ethod {F}or {C}omparing {G}enerative {A}dversarial {N}etworks},
author={Khrulkov, Valentin and Oseledets, Ivan},
journal={arXiv preprint arXiv:1802.02664},
year={2018}
}
- Python 2.7 or Python 3.3+
- SciPy
- NumPy
- matplotlib
- GUDHI
- Cython
import numpy as np
import gs
X = np.random.rand(1000, 2)
rlt = gs.rlts(X, L_0=32, gamma=1.0/8, i_max=100, n=100)
mrlt = np.mean(rlt, axis=0)
For more details see the MNIST example and toy examples .