6 files changed, 29 insertions, 29 deletions
diff --git a/examples/gromov/plot_barycenter_fgw.py b/examples/gromov/plot_barycenter_fgw.py
index dc3c6aa..3b5db8b 100644
--- a/examples/gromov/plot_barycenter_fgw.py
+++ b/examples/gromov/plot_barycenter_fgw.py
@@ -34,8 +34,8 @@ from ot.gromov import fgw_barycenters
 
 def find_thresh(C, inf=0.5, sup=3, step=10):
     """ Trick to find the adequate thresholds from where value of the C matrix are considered close enough to say that nodes are connected
-        Tthe threshold is found by a linesearch between values "inf" and "sup" with "step" thresholds tested.
-        The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjency matrix
+        The threshold is found by a linesearch between values "inf" and "sup" with "step" thresholds tested.
+        The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjacency matrix
         and the original matrix.
     Parameters
     ----------
@@ -51,15 +51,15 @@ def find_thresh(C, inf=0.5, sup=3, step=10):
     dist = []
     search = np.linspace(inf, sup, step)
     for thresh in search:
-        Cprime = sp_to_adjency(C, 0, thresh)
+        Cprime = sp_to_adjacency(C, 0, thresh)
         SC = shortest_path(Cprime, method='D')
         SC[SC == float('inf')] = 100
         dist.append(np.linalg.norm(SC - C))
     return search[np.argmin(dist)], dist
 
 
-def sp_to_adjency(C, threshinf=0.2, threshsup=1.8):
-    """ Thresholds the structure matrix in order to compute an adjency matrix.
+def sp_to_adjacency(C, threshinf=0.2, threshsup=1.8):
+    """ Thresholds the structure matrix in order to compute an adjacency matrix.
     All values between threshinf and threshsup are considered representing connected nodes and set to 1. Else are set to 0
     Parameters
     ----------
@@ -174,7 +174,7 @@ A, C, log = fgw_barycenters(sizebary, Ys, Cs, ps, lambdas, alpha=0.95, log=True)
 # -------------------------
 
 #%% Create the barycenter
-bary = nx.from_numpy_array(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0]))
+bary = nx.from_numpy_array(sp_to_adjacency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0]))
 for i, v in enumerate(A.ravel()):
     bary.add_node(i, attr_name=v)
 
diff --git a/examples/gromov/plot_fgw.py b/examples/gromov/plot_fgw.py
index 5475fb3..bf10de6 100644
--- a/examples/gromov/plot_fgw.py
+++ b/examples/gromov/plot_fgw.py
@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 """
 ==============================
-Plot Fused-gromov-Wasserstein
+Plot Fused-Gromov-Wasserstein
 ==============================
 
 This example illustrates the computation of FGW for 1D measures [18].
diff --git a/examples/gromov/plot_gromov.py b/examples/gromov/plot_gromov.py
index 05074dc..afb5bdc 100644
--- a/examples/gromov/plot_gromov.py
+++ b/examples/gromov/plot_gromov.py
@@ -3,7 +3,7 @@
 ==========================
 Gromov-Wasserstein example
 ==========================
-This example is designed to show how to use the Gromov-Wassertsein distance
+This example is designed to show how to use the Gromov-Wasserstein distance
 computation in POT.
 """
 
diff --git a/examples/gromov/plot_gromov_barycenter.py b/examples/gromov/plot_gromov_barycenter.py
index 08ec610..1b9abbf 100755
--- a/examples/gromov/plot_gromov_barycenter.py
+++ b/examples/gromov/plot_gromov_barycenter.py
@@ -36,7 +36,7 @@ import ot
 def smacof_mds(C, dim, max_iter=3000, eps=1e-9):
     """
     Returns an interpolated point cloud following the dissimilarity matrix C
-    using SMACOF multidimensional scaling (MDS) in specific dimensionned
+    using SMACOF multidimensional scaling (MDS) in specific dimensioned
     target space
 
     Parameters
diff --git a/examples/gromov/plot_gromov_wasserstein_dictionary_learning.py b/examples/gromov/plot_gromov_wasserstein_dictionary_learning.py
index 7585944..8cccf88 100755
--- a/examples/gromov/plot_gromov_wasserstein_dictionary_learning.py
+++ b/examples/gromov/plot_gromov_wasserstein_dictionary_learning.py
@@ -1,11 +1,11 @@
 # -*- coding: utf-8 -*-
 
 r"""
-=================================
+=====================================================
 (Fused) Gromov-Wasserstein Linear Dictionary Learning
-=================================
+=====================================================
 
-In this exemple, we illustrate how to learn a Gromov-Wasserstein dictionary on
+In this example, we illustrate how to learn a Gromov-Wasserstein dictionary on
 a dataset of structured data such as graphs, denoted
 :math:`\{ \mathbf{C_s} \}_{s \in [S]}` where every nodes have uniform weights.
 Given a dictionary :math:`\mathbf{C_{dict}}` composed of D structures of a fixed
@@ -49,7 +49,7 @@ from networkx.generators.community import stochastic_block_model as sbm
 #############################################################################
 #
 # Generate a dataset composed of graphs following Stochastic Block models of 1, 2 and 3 clusters.
-# ---------------------------------------------
+# -----------------------------------------------------------------------------------------------
 
 np.random.seed(42)
 
@@ -112,8 +112,8 @@ pl.show()
 
 #############################################################################
 #
-# Estimate the gromov-wasserstein dictionary from the dataset
-# ---------------------------------------------
+# Estimate the Gromov-Wasserstein dictionary from the dataset
+# -----------------------------------------------------------
 
 
 np.random.seed(0)
@@ -144,7 +144,7 @@ pl.show()
 #############################################################################
 #
 # Visualization of the estimated dictionary atoms
-# ---------------------------------------------
+# -----------------------------------------------
 
 
 # Continuous connections between nodes of the atoms are colored in shades of grey (1: dark / 2: white)
@@ -169,7 +169,7 @@ pl.show()
 #############################################################################
 #
 # Visualization of the embedding space
-# ---------------------------------------------
+# ------------------------------------
 
 unmixings = []
 reconstruction_errors = []
@@ -217,7 +217,7 @@ pl.show()
 #############################################################################
 #
 # Endow the dataset with node features
-# ---------------------------------------------
+# ------------------------------------
 # We follow this feature assignment on all nodes of a graph depending on its label/number of clusters
 # 1 cluster --> 0 as nodes feature
 # 2 clusters --> 1 as nodes feature
@@ -257,7 +257,7 @@ pl.show()
 #############################################################################
 #
 # Estimate a Fused Gromov-Wasserstein dictionary from the dataset of attributed graphs
-# ---------------------------------------------
+# ------------------------------------------------------------------------------------
 np.random.seed(0)
 ps = [ot.unif(C.shape[0]) for C in dataset]
 D = 3  # 6 atoms instead of 3
@@ -286,7 +286,7 @@ pl.show()
 #############################################################################
 #
 # Visualization of the estimated dictionary atoms
-# ---------------------------------------------
+# -----------------------------------------------
 
 pl.figure(7, (12, 8))
 pl.clf()
@@ -313,7 +313,7 @@ pl.show()
 #############################################################################
 #
 # Visualization of the embedding space
-# ---------------------------------------------
+# ------------------------------------
 
 unmixings = []
 reconstruction_errors = []
diff --git a/examples/gromov/plot_semirelaxed_fgw.py b/examples/gromov/plot_semirelaxed_fgw.py
index ef4b286..579f23d 100644
--- a/examples/gromov/plot_semirelaxed_fgw.py
+++ b/examples/gromov/plot_semirelaxed_fgw.py
@@ -1,8 +1,8 @@
 # -*- coding: utf-8 -*-
 """
-==========================
+===============================================
 Semi-relaxed (Fused) Gromov-Wasserstein example
-==========================
+===============================================
 
 This example is designed to show how to use the semi-relaxed Gromov-Wasserstein
 and the semi-relaxed Fused Gromov-Wasserstein divergences.
@@ -34,7 +34,7 @@ from networkx.generators.community import stochastic_block_model as sbm
 #############################################################################
 #
 # Generate two graphs following Stochastic Block models of 2 and 3 clusters.
-# ---------------------------------------------
+# --------------------------------------------------------------------------
 
 
 N2 = 20  # 2 communities
@@ -85,7 +85,7 @@ for i, j in G3.edges():
 #############################################################################
 #
 # Compute their semi-relaxed Gromov-Wasserstein divergences
-# ---------------------------------------------
+# ---------------------------------------------------------
 
 # 0) GW(C2, h2, C3, h3) for reference
 OT, log = gromov_wasserstein(C2, C3, h2, h3, symmetric=True, log=True)
@@ -110,7 +110,7 @@ print('srGW(C3, h3, C2) = ', srgw_32)
 #############################################################################
 #
 # Visualization of the semi-relaxed Gromov-Wasserstein matchings
-# ---------------------------------------------
+# --------------------------------------------------------------
 #
 # We color nodes of the graph on the right - then project its node colors
 # based on the optimal transport plan from the srGW matching
@@ -226,7 +226,7 @@ pl.show()
 #############################################################################
 #
 # Add node features
-# ---------------------------------------------
+# -----------------
 
 # We add node features with given mean - by clusters
 # and inversely proportional to clusters' intra-connectivity
@@ -242,7 +242,7 @@ for i, c in enumerate(part_G3):
 #############################################################################
 #
 # Compute their semi-relaxed Fused Gromov-Wasserstein divergences
-# ---------------------------------------------
+# ---------------------------------------------------------------
 
 alpha = 0.5
 # Compute pairwise euclidean distance between node features
@@ -272,7 +272,7 @@ print('srGW(C3, F3, h3, C2, F2) = ', srfgw_32)
 #############################################################################
 #
 # Visualization of the semi-relaxed Fused Gromov-Wasserstein matchings
-# ---------------------------------------------
+# --------------------------------------------------------------------
 #
 # We color nodes of the graph on the right - then project its node colors
 # based on the optimal transport plan from the srFGW matching