added image for barycenter in the /img repository

3 files changed, 20 insertions, 19 deletions

diff --git a/src/python/doc/barycenter_sum.inc b/src/python/doc/barycenter_sum.inc
index 7801a845..afac07d7 100644
--- a/src/python/doc/barycenter_sum.inc
+++ b/src/python/doc/barycenter_sum.inc
@@ -3,7 +3,7 @@
 
    +-----------------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------+
    | .. figure::                                                     | A Frechet mean (or barycenter) is a generalization of the arithmetic | :Author: Theo Lacombe                                            |
-   |      ../../doc/Barycenter/barycenter.png                        | mean in a non linear space such as the one of persistence diagrams.  |                                                                  |
+   |      ./img/barycenter.png                                       | mean in a non linear space such as the one of persistence diagrams.  |                                                                  |
    |      :figclass: align-center                                    | Given a set of persistence diagrams :math:`\mu_1 \dots \mu_n`, it is | :Introduced in: GUDHI 3.1.0                                      |
    |                                                                 | defined as a minimizer of the variance functional, that is of        |                                                                  |
    |      Illustration of Frechet mean between persistence           | :math:`\mu \mapsto \sum_{i=1}^n d_2(\mu,\mu_i)^2`.                   | :Copyright: MIT                                                  |
@@ -14,7 +14,9 @@
    |                                                                 | -lable is based on [Turner et al, 2014], and uses an EM-scheme to    |                                                                  |
    |                                                                 | provide a local minimum of the variance functional (somewhat similar |                                                                  |
    |                                                                 | to the Lloyd algorithm to estimate a solution to the k-means         |                                                                  |
-   |                                                                 | problem). The combinatorial structure of the algorithm limits its    |                                                                  |
+   |                                                                 | problem). The local minimum returned depends on the initialization of|                                                                  |
+   |                                                                 | the barycenter.                                                      |                                                                  |
+   |                                                                 | The combinatorial structure of the algorithm limits its              |                                                                  |
    |                                                                 | scaling on large scale problems (thousands of diagrams and of points |                                                                  |
    |                                                                 | per diagram).                                                        |                                                                  |
    +-----------------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------+
diff --git a/src/python/doc/img/barycenter.png b/src/python/doc/img/barycenter.png
new file mode 100644
index 00000000..cad6af70
--- /dev/null
+++ b/src/python/doc/img/barycenter.png
diff --git a/src/python/gudhi/barycenter.py b/src/python/gudhi/barycenter.py
index 3cd214a7..b4afdb6a 100644
--- a/src/python/gudhi/barycenter.py
+++ b/src/python/gudhi/barycenter.py
@@ -293,13 +293,12 @@ def _test_perf():
 
 
 def _sanity_check(verbose):
-    #dg1 = np.array([[0.2, 0.5]])
-    #dg2 = np.array([[0.2, 0.7]])
-    #dg3 = np.array([[0.3, 0.6], [0.7, 0.8], [0.2, 0.3]])
-    #dg4 = np.array([[0.72, 0.82]])
-    #X = [dg1, dg2, dg3, dg4]
-    #Y, a = lagrangian_barycenter(X, verbose=verbose)
-    #_plot_barycenter(X, Y, a)
+    dg1 = np.array([[0.2, 0.5]])
+    dg2 = np.array([[0.2, 0.7], [0.73, 0.88]])
+    dg3 = np.array([[0.3, 0.6], [0.7, 0.85], [0.2, 0.3]])
+    X = [dg1, dg2, dg3]
+    Y, a = lagrangian_barycenter(X, verbose=verbose)
+    _plot_barycenter(X, Y, a)
 
     #dg1 = np.array([[0.2, 0.5]])
     #dg2 = np.array([]) # The empty diagram
@@ -323,16 +322,16 @@ def _sanity_check(verbose):
     #_plot_barycenter(X, Y, a)
  
     
-    dg1 = np.array([[0.2, 0.5]])
-    dg2 = np.array([[0.2, 0.7]])
-    dg3 = np.array([[0.3, 0.6], [0.7, 0.8], [0.2, 0.3]])
-    dg4 = np.array([])
-    
-    bary = lagrangian_barycenter(pdiagset=[dg1, dg2, dg3, dg4],init=3)
-
-    message = "Wasserstein barycenter estimated:"    
-    print(message)
-    print(bary)
+    #dg1 = np.array([[0.2, 0.5]])
+    #dg2 = np.array([[0.2, 0.7]])
+    #dg3 = np.array([[0.3, 0.6], [0.7, 0.8], [0.2, 0.3]])
+    #dg4 = np.array([])
+    #
+    #bary, a = lagrangian_barycenter(pdiagset=[dg1, dg2, dg3, dg4],init=3, verbose=True)
+    #_plot_barycenter([dg1, dg2, dg3, dg4], bary, a)
+    #message = "Wasserstein barycenter estimated:"    
+    #print(message)
+    #print(bary)
 
 if __name__=="__main__":
     _sanity_check(verbose = True)