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| author | MathieuCarriere <mathieu.carriere3@gmail.com> | 2019-11-04 17:41:24 -0500 |
|---|---|---|
| committer | MathieuCarriere <mathieu.carriere3@gmail.com> | 2019-11-04 17:41:24 -0500 |
| commit | 77e7ee6e197aa8f0cf0fc0065c8d12e7c543e21f (patch) | |
| tree | d38e6f612d0032a7fcdd0a9bb3666e6ea980eacf /src/python | |
| parent | 7eb50adc342a5d2757cd209b3a8f2f297a4ad2fe (diff) | |
fixed typos
Diffstat (limited to 'src/python')
| -rw-r--r-- | src/python/gudhi/sktda/kernel_methods.py | 2 | ||||
| -rw-r--r-- | src/python/gudhi/sktda/vector_methods.py | 2 |
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 b49bdf60..20cda49b 100644 --- a/src/python/gudhi/sktda/kernel_methods.py +++ b/src/python/gudhi/sktda/kernel_methods.py @@ -22,7 +22,7 @@ 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 on [-pi,pi] 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 on [-pi/2,pi/2] in order to approximate and speed up the kernel computation (default 10). If -1, the exact kernel is computed. """ self.bandwidth = bandwidth self.sw_ = SlicedWassersteinDistance(num_directions=num_directions) diff --git a/src/python/gudhi/sktda/vector_methods.py b/src/python/gudhi/sktda/vector_methods.py index d767a952..1f304eaf 100644 --- a/src/python/gudhi/sktda/vector_methods.py +++ b/src/python/gudhi/sktda/vector_methods.py @@ -376,7 +376,7 @@ class TopologicalVector(BaseEstimator, TransformerMixin): Constructor for the TopologicalVector class. Attributes: - threshold (int): number of distances to keep (default 10). This is the dimension of the topological vector. If , this threshold is computed from the list of persistence diagrams by considering the one with the largest number of points and using the dimension of its corresponding topological vector as threshold. + threshold (int): number of distances to keep (default 10). This is the dimension of the topological vector. If -1, this threshold is computed from the list of persistence diagrams by considering the one with the largest number of points and using the dimension of its corresponding topological vector as threshold. """ self.threshold = threshold |