Improve documentation

1 files changed, 15 insertions, 3 deletions

diff --git a/src/python/gudhi/sklearn/cubical_persistence.py b/src/python/gudhi/sklearn/cubical_persistence.py
index f4341bf6..251e240f 100644
--- a/src/python/gudhi/sklearn/cubical_persistence.py
+++ b/src/python/gudhi/sklearn/cubical_persistence.py
@@ -1,3 +1,12 @@
+# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT.
+# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details.
+# Author(s):       Vincent Rouvreau
+#
+# Copyright (C) 2021 Inria
+#
+# Modification(s):
+#   - YYYY/MM Author: Description of the modification
+
 from .. import CubicalComplex
 from sklearn.base import BaseEstimator, TransformerMixin
 
@@ -17,7 +26,8 @@ class CubicalPersistence(BaseEstimator, TransformerMixin):
         Constructor for the CubicalPersistence class.
 
         Parameters:
-            dimensions (list of int): A list of number of top dimensional cells.
+            dimensions (list of int): A list of number of top dimensional cells if cells filtration values will require
+                to be reshaped (cf. :func:`~gudhi.sklearn.cubical_persistence.CubicalPersistence.transform`)
             persistence_dim (int): The returned persistence diagrams dimension. Default value is `0`.
             min_persistence (float): The minimum persistence value to take into account (strictly greater than
                 `min_persistence`). Default value is `0.0`. Sets `min_persistence` to `-1.0` to see all values.
@@ -39,7 +49,7 @@ class CubicalPersistence(BaseEstimator, TransformerMixin):
 
     def fit(self, X, Y=None):
         """
-        Nothing to be done.
+        Nothing to be done, but useful when included in a scikit-learn Pipeline.
         """
         return self
 
@@ -56,7 +66,9 @@ class CubicalPersistence(BaseEstimator, TransformerMixin):
         Compute all the cubical complexes and their associated persistence diagrams.
 
         Parameters:
-            X (list of list of double OR list of numpy.ndarray): List of cells filtration values.
+            X (list of list of double OR list of numpy.ndarray): List of cells filtration values that can be flatten if
+                dimensions is set in the constructor, or already with the correct shape in a numpy.ndarray (and
+                dimensions must not be set).
 
         Returns:
             Persistence diagrams