diff options
Diffstat (limited to 'src/python/gudhi/point_cloud')
| -rw-r--r-- | src/python/gudhi/point_cloud/dtm.py | 11 | ||||
| -rw-r--r-- | src/python/gudhi/point_cloud/knn.py | 10 |
2 files changed, 21 insertions, 0 deletions
diff --git a/src/python/gudhi/point_cloud/dtm.py b/src/python/gudhi/point_cloud/dtm.py index 55ac58e6..96a9e7bf 100644 --- a/src/python/gudhi/point_cloud/dtm.py +++ b/src/python/gudhi/point_cloud/dtm.py @@ -9,6 +9,7 @@ from .knn import KNearestNeighbors import numpy as np +import warnings __author__ = "Marc Glisse" __copyright__ = "Copyright (C) 2020 Inria" @@ -66,6 +67,11 @@ 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 @@ -163,6 +169,11 @@ 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) diff --git a/src/python/gudhi/point_cloud/knn.py b/src/python/gudhi/point_cloud/knn.py index 829bf1bf..de5844f9 100644 --- a/src/python/gudhi/point_cloud/knn.py +++ b/src/python/gudhi/point_cloud/knn.py @@ -8,6 +8,7 @@ # - YYYY/MM Author: Description of the modification import numpy +import warnings # TODO: https://github.com/facebookresearch/faiss @@ -257,6 +258,9 @@ class KNearestNeighbors: if ef is not None: self.graph.set_ef(ef) neighbors, distances = self.graph.knn_query(X, k, num_threads=self.params["num_threads"]) + with warnings.catch_warnings(): + if not(numpy.all(numpy.isfinite(distances))): + warnings.warn("Overflow/infinite 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. if self.return_index: @@ -290,6 +294,9 @@ class KNearestNeighbors: if self.return_index: if self.return_distance: distances, neighbors = mat.Kmin_argKmin(k, dim=1) + with warnings.catch_warnings(): + if not(torch.isfinite(distances).all()): + warnings.warn("Overflow/infinite value encountered while computing 'distances'", RuntimeWarning) if p != numpy.inf: distances = distances ** (1.0 / p) return neighbors, distances @@ -298,6 +305,9 @@ class KNearestNeighbors: return neighbors if self.return_distance: distances = mat.Kmin(k, dim=1) + with warnings.catch_warnings(): + if not(torch.isfinite(distances).all()): + warnings.warn("Overflow/infinite value encountered while computing 'distances'", RuntimeWarning) if p != numpy.inf: distances = distances ** (1.0 / p) return distances |