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| author | Marc Glisse <marc.glisse@inria.fr> | 2020-03-28 12:45:00 +0100 |
|---|---|---|
| committer | Marc Glisse <marc.glisse@inria.fr> | 2020-03-28 12:45:00 +0100 |
| commit | 7f323484acdeafca93efdd9bdd20ed428f8fb95b (patch) | |
| tree | cc1b69d50936d6cb028feb150b47a507653412bd /src/python/gudhi/point_cloud/dtm.py | |
| parent | 40f4b6fb1fe20c3843b1fd80f99996e6d25c9426 (diff) | |
Optional sort_results
Diffstat (limited to 'src/python/gudhi/point_cloud/dtm.py')
| -rw-r--r-- | src/python/gudhi/point_cloud/dtm.py | 4 |
1 files changed, 1 insertions, 3 deletions
diff --git a/src/python/gudhi/point_cloud/dtm.py b/src/python/gudhi/point_cloud/dtm.py index ba011eaf..678524f2 100644 --- a/src/python/gudhi/point_cloud/dtm.py +++ b/src/python/gudhi/point_cloud/dtm.py @@ -35,9 +35,7 @@ class DTM: X (numpy.array): coordinates for mass points. """ if self.params.setdefault("metric", "euclidean") != "neighbors": - # KNN gives sorted distances, which is unnecessary here. - # Maybe add a parameter to say we don't need sorting? - self.knn = KNN(self.k, return_index=False, return_distance=True, **self.params) + self.knn = KNN(self.k, return_index=False, return_distance=True, sort_results=False, **self.params) self.knn.fit(X) return self |