add test free support barycenter algorithm + cleaning

1 files changed, 14 insertions, 15 deletions

diff --git a/examples/plot_free_support_barycenter.py b/examples/plot_free_support_barycenter.py
index 5b08507..b2e62c8 100644
--- a/examples/plot_free_support_barycenter.py
+++ b/examples/plot_free_support_barycenter.py
@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 """
 ====================================================
-2D Wasserstein barycenters of distributions
+2D free support Wasserstein barycenters of distributions
 ====================================================
 
 Illustration of 2D Wasserstein barycenters if discributions that are weighted
@@ -15,7 +15,8 @@ sum of diracs.
 
 import numpy as np
 import matplotlib.pylab as pl
-import ot.plot
+import ot
+
 
 ##############################################################################
 # Generate data
@@ -28,16 +29,16 @@ measures_weights = []
 
 for i in range(N):
 
-    n = np.random.randint(low=1, high=20)  # nb samples
+    n_i = np.random.randint(low=1, high=20)  # nb samples
 
-    mu = np.random.normal(0., 4., (d,))
+    mu_i = np.random.normal(0., 4., (d,))  # Gaussian mean
 
-    A = np.random.rand(d, d)
-    cov = np.dot(A, A.transpose())
+    A_i = np.random.rand(d, d)
+    cov_i = np.dot(A_i, A_i.transpose())  # Gaussian covariance matrix
 
-    x_i = ot.datasets.make_2D_samples_gauss(n, mu, cov)
-    b_i = np.random.uniform(0., 1., (n,))
-    b_i = b_i / np.sum(b_i)
+    x_i = ot.datasets.make_2D_samples_gauss(n_i, mu_i, cov_i)  # Dirac locations
+    b_i = np.random.uniform(0., 1., (n_i,))
+    b_i = b_i / np.sum(b_i)  # Dirac weights
 
     measures_locations.append(x_i)
     measures_weights.append(b_i)
@@ -47,19 +48,17 @@ for i in range(N):
 # Compute free support barycenter
 # -------------
 
-k = 10
-X_init = np.random.normal(0., 1., (k, d))
-b = np.ones((k,)) / k
+k = 10 # number of Diracs of the barycenter
+X_init = np.random.normal(0., 1., (k, d)) # initial Dirac locations
+b = np.ones((k,)) / k # weights of the barycenter (it will not be optimized, only the locations are optimized)
 
-X = ot.lp.cvx.free_support_barycenter(measures_locations, measures_weights, X_init, b)
+X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, b)
 
 
 ##############################################################################
 # Plot data
 # ---------
 
-#%% plot samples
-
 pl.figure(1)
 for (x_i, b_i) in zip(measures_locations, measures_weights):
     color = np.random.randint(low=1, high=10 * N)