diff options
Diffstat (limited to 'src/python/gudhi/sktda/kernel_methods.py')
| -rw-r--r-- | src/python/gudhi/sktda/kernel_methods.py | 8 |
1 files changed, 4 insertions, 4 deletions
diff --git a/src/python/gudhi/sktda/kernel_methods.py b/src/python/gudhi/sktda/kernel_methods.py index d409accd..e93138e6 100644 --- a/src/python/gudhi/sktda/kernel_methods.py +++ b/src/python/gudhi/sktda/kernel_methods.py @@ -50,7 +50,7 @@ class SlicedWassersteinKernel(BaseEstimator, TransformerMixin): X (list of n x 2 numpy arrays): input persistence diagrams. Returns: - Xfit (numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X)): matrix of pairwise sliced Wasserstein kernel values. + numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise sliced Wasserstein kernel values. """ return np.exp(-self.sw_.transform(X)/self.bandwidth) @@ -92,7 +92,7 @@ class PersistenceWeightedGaussianKernel(BaseEstimator, TransformerMixin): X (list of n x 2 numpy arrays): input persistence diagrams. Returns: - Xfit (numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X)): matrix of pairwise persistence weighted Gaussian kernel values. + numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise persistence weighted Gaussian kernel values. """ Xp = list(X) Xfit = np.zeros((len(Xp), len(self.diagrams_))) @@ -157,7 +157,7 @@ class PersistenceScaleSpaceKernel(BaseEstimator, TransformerMixin): X (list of n x 2 numpy arrays): input persistence diagrams. Returns: - Xfit (numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X)): matrix of pairwise persistence scale space kernel values. + numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise persistence scale space kernel values. """ Xp = list(X) for i in range(len(Xp)): @@ -200,7 +200,7 @@ class PersistenceFisherKernel(BaseEstimator, TransformerMixin): X (list of n x 2 numpy arrays): input persistence diagrams. Returns: - Xfit (numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X)): matrix of pairwise persistence Fisher kernel values. + numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise persistence Fisher kernel values. """ return np.exp(-self.pf_.transform(X)/self.bandwidth) |