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author | Marc Glisse <marc.glisse@inria.fr> | 2020-03-27 20:27:10 +0100 |
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committer | Marc Glisse <marc.glisse@inria.fr> | 2020-03-27 20:27:10 +0100 |
commit | 03376ffe0f6060864ee8908893297f8800b7b8d1 (patch) | |
tree | 3298d62022a102fa12b4e452fec1544fd7fb9601 /src/python/gudhi/point_cloud/dtm.py | |
parent | f74c71ca8e474ff927cae029ea63329d30293582 (diff) |
doc
Diffstat (limited to 'src/python/gudhi/point_cloud/dtm.py')
-rw-r--r-- | src/python/gudhi/point_cloud/dtm.py | 6 |
1 files changed, 5 insertions, 1 deletions
diff --git a/src/python/gudhi/point_cloud/dtm.py b/src/python/gudhi/point_cloud/dtm.py index 541b74a6..e4096c5e 100644 --- a/src/python/gudhi/point_cloud/dtm.py +++ b/src/python/gudhi/point_cloud/dtm.py @@ -11,11 +11,15 @@ from .knn import KNN class DTM: + """ + Class to compute the distance to the empirical measure defined by a point set. + """ + def __init__(self, k, q=2, **kwargs): """ Args: q (float): order used to compute the distance to measure. Defaults to the dimension, or 2 if input_type is 'distance_matrix'. - kwargs: Same parameters as KNN, except that metric="neighbors" means that transform() expects an array with the distances to the k nearest neighbors. + kwargs: Same parameters as :class:`~gudhi.point_cloud.knn.KNN`, except that metric="neighbors" means that :func:`transform` expects an array with the distances to the k nearest neighbors. """ self.k = k self.q = q |