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| author | Hind-M <hind.montassif@gmail.com> | 2021-09-08 18:01:11 +0200 |
|---|---|---|
| committer | Hind-M <hind.montassif@gmail.com> | 2021-09-08 18:01:11 +0200 |
| commit | 145fcba2de5f174b8fcdeab5ac1997978ffcdc0d (patch) | |
| tree | 29e63938deab34d7508b7a51e86a7f95ab3477a4 /src/python/gudhi/point_cloud/knn.py | |
| parent | 7ea4e020af2fa8bf2fdfefe85ca24a1bcc2d08e1 (diff) | |
Set the warning filter to "always"
Add test for dtm overflow warning
Diffstat (limited to 'src/python/gudhi/point_cloud/knn.py')
| -rw-r--r-- | src/python/gudhi/point_cloud/knn.py | 6 |
1 files changed, 6 insertions, 0 deletions
diff --git a/src/python/gudhi/point_cloud/knn.py b/src/python/gudhi/point_cloud/knn.py index dec5f88f..0724ce94 100644 --- a/src/python/gudhi/point_cloud/knn.py +++ b/src/python/gudhi/point_cloud/knn.py @@ -259,9 +259,11 @@ class KNearestNeighbors: neighbors, distances = self.graph.knn_query(X, k, num_threads=self.params["num_threads"]) if numpy.any(numpy.isnan(distances)): import warnings + warnings.simplefilter("always") warnings.warn("NaN value encountered while computing 'distances'", RuntimeWarning) if numpy.any(numpy.isinf(distances)): import warnings + warnings.simplefilter("always") warnings.warn("Overflow value encountered while computing 'distances'", RuntimeWarning) # The k nearest neighbors are always sorted. I couldn't find it in the doc, but the code calls searchKnn, # which returns a priority_queue, and then fills the return array backwards with top/pop on the queue. @@ -298,9 +300,11 @@ class KNearestNeighbors: distances, neighbors = mat.Kmin_argKmin(k, dim=1) if torch.isnan(distances).any(): import warnings + warnings.simplefilter("always") warnings.warn("NaN value encountered while computing 'distances'", RuntimeWarning) if torch.isinf(distances).any(): import warnings + warnings.simplefilter("always") warnings.warn("Overflow encountered while computing 'distances'", RuntimeWarning) if p != numpy.inf: distances = distances ** (1.0 / p) @@ -312,9 +316,11 @@ class KNearestNeighbors: distances = mat.Kmin(k, dim=1) if torch.isnan(distances).any(): import warnings + warnings.simplefilter("always") warnings.warn("NaN value encountered while computing 'distances'", RuntimeWarning) if torch.isinf(distances).any(): import warnings + warnings.simplefilter("always") warnings.warn("Overflow encountered while computing 'distances'", RuntimeWarning) if p != numpy.inf: distances = distances ** (1.0 / p) |