From 7bbbe63ffa2a812dc49c37c77b4f4a4be46b2a49 Mon Sep 17 00:00:00 2001 From: Marc Glisse Date: Mon, 11 May 2020 23:34:23 +0200 Subject: move note --- src/python/gudhi/point_cloud/dtm.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) (limited to 'src/python/gudhi/point_cloud') 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 -- cgit v1.2.3