From dd3546baf9c59733b2109a971293eba48d2eaed3 Mon Sep 17 00:00:00 2001
From: Rémi Flamary <remi.flamary@gmail.com>
Date: Fri, 15 Sep 2017 13:57:01 +0200
Subject: add all files for doc

---
 examples/plot_gromov_barycenter.py | 42 +++++++++++++++++++-------------------
 1 file changed, 21 insertions(+), 21 deletions(-)

(limited to 'examples/plot_gromov_barycenter.py')

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]
-- 
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