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authorMarc Glisse <marc.glisse@inria.fr>2019-11-14 13:37:08 +0100
committerMarc Glisse <marc.glisse@inria.fr>2019-11-14 13:37:08 +0100
commit8427713bc748bc040dd696a64d81b3fe6f648a07 (patch)
tree655d53f6efe979a5b62ecaf9e0320a54e9adb9ff /src/python
parent0fa77a12509ea6d51043569b592bad855f276a45 (diff)
Remove mentions of the exact sliced Wasserstein (not implemented)
Diffstat (limited to 'src/python')
-rw-r--r--src/python/gudhi/sktda/kernel_methods.py2
-rw-r--r--src/python/gudhi/sktda/metrics.py2
2 files changed, 2 insertions, 2 deletions
diff --git a/src/python/gudhi/sktda/kernel_methods.py b/src/python/gudhi/sktda/kernel_methods.py
index 24067718..d409accd 100644
--- a/src/python/gudhi/sktda/kernel_methods.py
+++ b/src/python/gudhi/sktda/kernel_methods.py
@@ -26,7 +26,7 @@ class SlicedWassersteinKernel(BaseEstimator, TransformerMixin):
Attributes:
bandwidth (double): bandwidth of the Gaussian kernel applied to the sliced Wasserstein distance (default 1.).
- num_directions (int): number of lines evenly sampled from [-pi/2,pi/2] in order to approximate and speed up the kernel computation (default 10). If -1, the exact kernel is computed.
+ num_directions (int): number of lines evenly sampled from [-pi/2,pi/2] in order to approximate and speed up the kernel computation (default 10).
"""
self.bandwidth = bandwidth
self.sw_ = SlicedWassersteinDistance(num_directions=num_directions)
diff --git a/src/python/gudhi/sktda/metrics.py b/src/python/gudhi/sktda/metrics.py
index b8acf261..f55f553b 100644
--- a/src/python/gudhi/sktda/metrics.py
+++ b/src/python/gudhi/sktda/metrics.py
@@ -15,7 +15,7 @@ try:
USE_GUDHI = True
except ImportError:
USE_GUDHI = False
- print("Gudhi not found: BottleneckDistance will return null matrix, and exact SlicedWassersteinDistance not available")
+ print("Gudhi built without CGAL: BottleneckDistance will return a null matrix")
#############################################
# Metrics ###################################