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Diffstat (limited to 'examples/sliced-wasserstein/plot_variance.py')
| -rw-r--r-- | examples/sliced-wasserstein/plot_variance.py | 16 |
1 files changed, 9 insertions, 7 deletions
diff --git a/examples/sliced-wasserstein/plot_variance.py b/examples/sliced-wasserstein/plot_variance.py index f3deeff..27df656 100644 --- a/examples/sliced-wasserstein/plot_variance.py +++ b/examples/sliced-wasserstein/plot_variance.py @@ -4,9 +4,11 @@ 2D Sliced Wasserstein Distance ============================== -This example illustrates the computation of the sliced Wasserstein Distance as proposed in [31]. +This example illustrates the computation of the sliced Wasserstein Distance as +proposed in [31]. -[31] Bonneel, Nicolas, et al. "Sliced and radon wasserstein barycenters of measures." Journal of Mathematical Imaging and Vision 51.1 (2015): 22-45 +[31] Bonneel, Nicolas, et al. "Sliced and radon wasserstein barycenters of +measures." Journal of Mathematical Imaging and Vision 51.1 (2015): 22-45 """ @@ -50,9 +52,9 @@ pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.legend(loc=0) pl.title('Source and target distributions') -################################################################################### -# Compute Sliced Wasserstein distance for different seeds and number of projections -# ----------- +############################################################################### +# Sliced Wasserstein distance for different seeds and number of projections +# ------------------------------------------------------------------------- n_seed = 50 n_projections_arr = np.logspace(0, 3, 25, dtype=int) @@ -66,9 +68,9 @@ for seed in range(n_seed): res_mean = np.mean(res, axis=0) res_std = np.std(res, axis=0) -################################################################################### +############################################################################### # Plot Sliced Wasserstein Distance -# ----------- +# -------------------------------- pl.figure(2) pl.plot(n_projections_arr, res_mean, label="SWD") |