add all files for doc

1 files changed, 21 insertions, 21 deletions

diff --git a/examples/plot_gromov_barycenter.py b/examples/plot_gromov_barycenter.py
index 93533c0..180b0cf 100755
--- a/examples/plot_gromov_barycenter.py
+++ b/examples/plot_gromov_barycenter.py
@@ -24,12 +24,12 @@ from sklearn.decomposition import PCA
 

 import ot

 

-"""

-Smacof MDS

-==========

-This function allows to find an embedding of points given a dissimilarity matrix

-that will be given by the output of the algorithm

-"""

+##############################################################################

+# Smacof MDS

+# ----------

+#

+# This function allows to find an embedding of points given a dissimilarity matrix

+# that will be given by the output of the algorithm

 

 

 def smacof_mds(C, dim, max_iter=3000, eps=1e-9):

@@ -78,11 +78,11 @@ def smacof_mds(C, dim, max_iter=3000, eps=1e-9):
     return npos

 

 

-"""

-Data preparation

-================

-The four distributions are constructed from 4 simple images

-"""

+##############################################################################

+# Data preparation

+# ----------------

+#

+# The four distributions are constructed from 4 simple images

 

 

 def im2mat(I):

@@ -110,12 +110,11 @@ for nb in range(4):
 xs = np.array([np.array(xs[0]), np.array(xs[1]),

                np.array(xs[2]), np.array(xs[3])])

 

+##############################################################################

+# Barycenter computation

+# ----------------------

+

 

-"""

-Barycenter computation

-======================

-The four distributions are constructed from 4 simple images

-"""

 ns = [len(xs[s]) for s in range(S)]

 n_samples = 30

 

@@ -157,12 +156,13 @@ for i in range(2):
                                             ], p, lambdast[i], 'square_loss', 5e-4,

                                            max_iter=100, tol=1e-3)

 

-"""

-Visualization

-=============

-"""

 

-"""The PCA helps in getting consistency between the rotations"""

+##############################################################################

+# Visualization

+# -------------

+#

+# The PCA helps in getting consistency between the rotations

+

 

 clf = PCA(n_components=2)

 npos = [0, 0, 0, 0]