diff options
Diffstat (limited to 'examples/gromov/plot_semirelaxed_fgw.py')
| -rw-r--r-- | examples/gromov/plot_semirelaxed_fgw.py | 16 |
1 files changed, 8 insertions, 8 deletions
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 |