removed barycenters specific doc files as those are included in wasserstein distance now

2 files changed, 0 insertions, 84 deletions

diff --git a/src/python/doc/barycenter_sum.inc b/src/python/doc/barycenter_sum.inc
deleted file mode 100644
index da2bdd84..00000000
--- a/src/python/doc/barycenter_sum.inc
+++ /dev/null
@@ -1,24 +0,0 @@
-.. table::
-   :widths: 30 50 20
-
-   +-----------------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------+
-   | .. figure::                                                     | A Frechet mean (or barycenter) is a generalization of the arithmetic | :Author: Theo Lacombe                                            |
-   |      ./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                                                  |
-   |      diagrams.                                                  | where :math:`d_2` denotes the Wasserstein-2 distance between         |                                                                  |
-   |                                                                 | persistence diagrams.                                                |                                                                  |
-   |                                                                 | It is known to exist and is generically unique. However, an exact    |                                                                  |
-   |                                                                 | computation is in general untractable. Current implementation        | :Requires: Python Optimal Transport (POT) :math:`\geq` 0.5.1     |
-   |                                                                 | available 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 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).                                                        |                                                                  |
-   +-----------------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------+
-   | * :doc:`barycenter_user`                                        |                                                                                                                                         |
-   +-----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------+
diff --git a/src/python/doc/barycenter_user.rst b/src/python/doc/barycenter_user.rst
deleted file mode 100644
index 83e9bebb..00000000
--- a/src/python/doc/barycenter_user.rst
+++ /dev/null
@@ -1,60 +0,0 @@
-:orphan:
-
-.. To get rid of WARNING: document isn't included in any toctree
-
-Barycenter user manual
-================================
-Definition
-----------
-
-.. include:: barycenter_sum.inc
-
-This implementation is based on ideas from "Frechet means for distribution of 
-persistence diagrams", Turner et al. 2014.
-
-Function
---------
-.. autofunction:: gudhi.barycenter.lagrangian_barycenter
-
-
-Basic example
--------------
-
-This example computes the Frechet mean (aka Wasserstein barycenter) between 
-four persistence diagrams.
-It is initialized on the 4th diagram.
-As the algorithm is not convex, its output depends on the initialization and 
-is only a local minimum of the objective function.
-Initialization can be either given as an integer (in which case the i-th 
-diagram of the list is used as initial estimate) or as a diagram. 
-If None, it will randomly select one of the diagram of the list 
-as initial estimate.
-Note that persistence diagrams must be submitted as 
-(n x 2) numpy arrays and must not contain inf values.
-
-.. testcode::
-
-    import gudhi.barycenter
-    import numpy as np
-
-    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([])
-    pdiagset = [dg1, dg2, dg3, dg4]
-    bary = gudhi.barycenter.lagrangian_barycenter(pdiagset=pdiagset,init=3)
-
-    message = "Wasserstein barycenter estimated:"    
-    print(message)
-    print(bary)
-
-The output is:
-
-.. testoutput::
-
-    Wasserstein barycenter estimated:
-    [[0.27916667 0.55416667]
-     [0.7375     0.7625    ]
-     [0.2375     0.2625    ]]
-
-