imd
imd copied to clipboard
xgfs
Code for MSID, a Multi-Scale Intrinsic Distance for comparing generative models, studying neural networks, and more!
trafficstars
=============================== Intrinsic Multi-scale Evaluation of Generative Models
This repository provides a reference implementation of MSID, a metric for comparing underlying intrinsic geometry of data manifolds (paper <https://arxiv.org/abs/1905.11141>_).
Installation
Installation is as simple as python setup.py install.
Requirements
- Python 2.7 or Python 3.3+
SciPy <http://www.scipy.org/install.html/>_NumPy <http://www.numpy.org/>_- [optional]
pykgraph <https://github.com/aaalgo/kgraph/>_, for Anaconda users, justconda install pykgraph
Example usage
.. code-block:: python
import numpy as np
from msid import msid_score
np.random.seed(1)
x0 = np.random.randn(1000, 10)
x1 = np.random.randn(1000, 9) # MSID can compare two data distributions with different dimensionalities
y0 = np.random.beta(0.5, 0.5, (1000, 10))
print('x0=N(0, 1), shape=', x0.shape)
print('x1=N(0, 1), shape=', x1.shape)
print('y0=beta(0.5, 0.5), shape=', y0.shape)
print('MSID(x0, x1)', msid_score(x0, x1))
print('MSID(x0, y0)', msid_score(x0, y0))
Contact
echo "%7=87@=<2=<>5.27" | tr '#-)/->' '_-|'
xgfs