move note

1 files changed, 4 insertions, 4 deletions

diff --git a/src/python/gudhi/point_cloud/dtm.py b/src/python/gudhi/point_cloud/dtm.py
index 2f30908d..f8cca2c1 100644
--- a/src/python/gudhi/point_cloud/dtm.py
+++ b/src/python/gudhi/point_cloud/dtm.py
@@ -76,6 +76,10 @@ class DTMDensity:
     Density estimator based on the distance to the empirical measure defined by a point set, as defined
     in :cite:`dtmdensity`. Note that this implementation only renormalizes when asked, and the renormalization
     only works for a Euclidean metric, so in other cases the total measure may not be 1.
+
+    .. note:: When the dimension is high, using it as an exponent can quickly lead to under- or overflows.
+        We recommend using a small fixed value instead in those cases, even if it won't have the same nice
+        theoretical properties as the dimension.
     """
 
     def __init__(self, k=None, weights=None, q=None, dim=None, normalize=False, n_samples=None, **kwargs):
@@ -86,10 +90,6 @@ class DTMDensity:
             q (float): order used to compute the distance to measure. Defaults to dim.
             dim (float): final exponent representing the dimension. Defaults to the dimension, and must be specified
                 when the dimension cannot be read from the input (metric="neighbors" or metric="precomputed").
-
-                .. note:: when the dimension is high, using it as an exponent can quickly lead to under- or overflows.
-                    We recommend using a small fixed value instead in those cases, even if it won't have the same nice
-                    theoretical properties as the dimension.
             normalize (bool): normalize the density so it corresponds to a probability measure on ℝᵈ.
                 Only available for the Euclidean metric, defaults to False.
             n_samples (int): number of sample points used for fitting. Only needed if `normalize` is True and