Hello Azim, Since we are manipulating empirical probability distributions, p and q are vectors of probability masses per samples in your distribution. Indeed, it is generally a common choice to consider them as equal and set them as 1/(number of samples in the distribution). Hope this helps, Best regards, Nicolas

Le 8 août 2019 à 21:53, Azim Dehghani Amirabad a écrit :

Hi,

I'm using OT package for GW, and I have a question regarding two arguments of the ot.gromov.gromov_wasserstein function. What is p and q? I couldn't see them in the objective function of the GW.

p = ot.unif(n_samples) q = ot.unif(n_samples)

gw0, log0 = ot.gromov.gromov_wasserstein( C1, C2, p, q, 'square_loss', verbose=True, log=True)

gw, log = ot.gromov.entropic_gromov_wasserstein( C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True)

print('Gromov-Wasserstein distances: ' + str(log0['gw_dist'])) print('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist'])) _______________________________________________ POT mailing list -- pot@python.org To unsubscribe send an email to pot-leave@python.org https://mail.python.org/mailman3/lists/pot.python.org/