From 94391b1cc232c5f66ae3cdadf865554c57f1308a Mon Sep 17 00:00:00 2001 From: Marc Glisse Date: Tue, 5 Nov 2019 23:01:31 +0100 Subject: Create GUDHI_PYTHON_MODULES_EXTRA without auto-import Put Wasserstein in it. --- src/python/doc/wasserstein_distance_user.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) (limited to 'src/python/doc/wasserstein_distance_user.rst') diff --git a/src/python/doc/wasserstein_distance_user.rst b/src/python/doc/wasserstein_distance_user.rst index bcb7f19d..fc7bd82c 100644 --- a/src/python/doc/wasserstein_distance_user.rst +++ b/src/python/doc/wasserstein_distance_user.rst @@ -13,7 +13,7 @@ This implementation is based on ideas from "Large Scale Computation of Means and Function -------- -.. autofunction:: gudhi.wasserstein_distance +.. autofunction:: gudhi.wasserstein.wasserstein_distance Basic example @@ -24,13 +24,13 @@ Note that persistence diagrams must be submitted as (n x 2) numpy arrays and mus .. testcode:: - import gudhi + import gudhi.wasserstein import numpy as np diag1 = np.array([[2.7, 3.7],[9.6, 14.],[34.2, 34.974]]) diag2 = np.array([[2.8, 4.45],[9.5, 14.1]]) - message = "Wasserstein distance value = " + '%.2f' % gudhi.wasserstein_distance(diag1, diag2, q=2., p=1.) + message = "Wasserstein distance value = " + '%.2f' % gudhi.wasserstein.wasserstein_distance(diag1, diag2, q=2., p=1.) print(message) The output is: -- cgit v1.2.3