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 e93138e6..c855d2be 100644 --- a/src/python/gudhi/sktda/kernel_methods.py +++ b/src/python/gudhi/sktda/kernel_methods.py @@ -24,7 +24,7 @@ class SlicedWassersteinKernel(BaseEstimator, TransformerMixin): """ Constructor for the SlicedWassersteinKernel class. - Attributes: + Parameters: 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). """ @@ -62,7 +62,7 @@ class PersistenceWeightedGaussianKernel(BaseEstimator, TransformerMixin): """ Constructor for the PersistenceWeightedGaussianKernel class. - Attributes: + Parameters: bandwidth (double): bandwidth of the Gaussian kernel with which persistence diagrams will be convolved (default 1.) weight (function): weight function for the persistence diagram points (default constant function, ie lambda x: 1). This function must be defined on 2D points, ie lists or numpy arrays of the form [p_x,p_y]. kernel_approx (class): kernel approximation class used to speed up computation (default None). Common kernel approximations classes can be found in the scikit-learn library (such as RBFSampler for instance). @@ -128,7 +128,7 @@ class PersistenceScaleSpaceKernel(BaseEstimator, TransformerMixin): """ Constructor for the PersistenceScaleSpaceKernel class. - Attributes: + Parameters: bandwidth (double): bandwidth of the Gaussian kernel with which persistence diagrams will be convolved (default 1.) kernel_approx (class): kernel approximation class used to speed up computation (default None). Common kernel approximations classes can be found in the scikit-learn library (such as RBFSampler for instance). """ @@ -173,7 +173,7 @@ class PersistenceFisherKernel(BaseEstimator, TransformerMixin): """ Constructor for the PersistenceFisherKernel class. - Attributes: + Parameters: bandwidth (double): bandwidth of the Gaussian kernel applied to the persistence Fisher distance (default 1.). bandwidth_fisher (double): bandwidth of the Gaussian kernel used to turn persistence diagrams into probability distributions by PersistenceFisherDistance class (default 1.). kernel_approx (class): kernel approximation class used to speed up computation (default None). Common kernel approximations classes can be found in the scikit-learn library (such as RBFSampler for instance). |