diff options
Diffstat (limited to 'src/python/gudhi/point_cloud/dtm.py')
-rw-r--r-- | src/python/gudhi/point_cloud/dtm.py | 11 |
1 files changed, 0 insertions, 11 deletions
diff --git a/src/python/gudhi/point_cloud/dtm.py b/src/python/gudhi/point_cloud/dtm.py index 96a9e7bf..55ac58e6 100644 --- a/src/python/gudhi/point_cloud/dtm.py +++ b/src/python/gudhi/point_cloud/dtm.py @@ -9,7 +9,6 @@ from .knn import KNearestNeighbors import numpy as np -import warnings __author__ = "Marc Glisse" __copyright__ = "Copyright (C) 2020 Inria" @@ -67,11 +66,6 @@ class DistanceToMeasure: distances = distances ** self.q dtm = distances.sum(-1) / self.k dtm = dtm ** (1.0 / self.q) - with warnings.catch_warnings(): - import torch - if isinstance(dtm, torch.Tensor): - if not(torch.isfinite(dtm).all()): - warnings.warn("Overflow/infinite value encountered while computing 'dtm'", RuntimeWarning) # We compute too many powers, 1/p in knn then q in dtm, 1/q in dtm then q or some log in the caller. # Add option to skip the final root? return dtm @@ -169,11 +163,6 @@ class DTMDensity: distances = self.knn.transform(X) distances = distances ** q dtm = (distances * weights).sum(-1) - with warnings.catch_warnings(): - import torch - if isinstance(dtm, torch.Tensor): - if not(torch.isfinite(dtm).all()): - warnings.warn("Overflow/infinite value encountered while computing 'dtm' for density", RuntimeWarning) if self.normalize: dtm /= (np.arange(1, k + 1) ** (q / dim) * weights).sum() density = dtm ** (-dim / q) |