The Shape of Data: Intrinsic Distance for Data Distributions

This repository provides a reference implementation of IMD, a metric for comparing underlying intrinsic geometry of data manifolds.

Installation

Installation is as simple as python setup.py install.

Requirements

• Python 2.7 or Python 3.3+
• SciPy
• NumPy
• [optional] pykgraph, for Anaconda users, just conda install pykgraph

Example usage

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))