Merge remote-tracking branch 'origin/master' into dtmdensity

2 files changed, 6 insertions, 3 deletions

diff --git a/src/python/gudhi/point_cloud/knn.py b/src/python/gudhi/point_cloud/knn.py
index 65896847..4652fe80 100644
--- a/src/python/gudhi/point_cloud/knn.py
+++ b/src/python/gudhi/point_cloud/knn.py
@@ -19,6 +19,10 @@ __license__ = "MIT"
 class KNearestNeighbors:
     """
     Class wrapping several implementations for computing the k nearest neighbors in a point set.
+
+    :Requires: `PyKeOps <installation.html#pykeops>`_, `SciPy <installation.html#scipy>`_,
+        `Scikit-learn <installation.html#scikit-learn>`_, and/or `Hnswlib <installation.html#hnswlib>`_
+        in function of the selected `implementation`.
     """
 
     def __init__(self, k, return_index=True, return_distance=False, metric="euclidean", **kwargs):
diff --git a/src/python/gudhi/point_cloud/timedelay.py b/src/python/gudhi/point_cloud/timedelay.py
index f01df442..5292e752 100644
--- a/src/python/gudhi/point_cloud/timedelay.py
+++ b/src/python/gudhi/point_cloud/timedelay.py
@@ -10,9 +10,8 @@ import numpy as np
 
 
 class TimeDelayEmbedding:
-    """Point cloud transformation class.
-    Embeds time-series data in the R^d according to [Takens' Embedding Theorem]
-    (https://en.wikipedia.org/wiki/Takens%27s_theorem) and obtains the 
+    """Point cloud transformation class. Embeds time-series data in the R^d according to
+    `Takens' Embedding Theorem <https://en.wikipedia.org/wiki/Takens%27s_theorem>`_ and obtains the
     coordinates of each point.
 
     Parameters