Public Member Functions | |
def | __init__ (self, bandwidth_fisher=1., bandwidth=1., kernel_approx=None, n_jobs=None) |
def | fit (self, X, y=None) |
def | transform (self, X) |
def | __call__ (self, diag1, diag2) |
This is a class for computing the persistence Fisher kernel matrix from a list of persistence diagrams. The persistence Fisher kernel is computed by exponentiating the corresponding persistence Fisher distance with a Gaussian kernel. See papers.nips.cc/paper/8205-persistence-fisher-kernel-a-riemannian-manifold-kernel-for-persistence-diagrams for more details.
def gudhi.representations.kernel_methods.PersistenceFisherKernel.__init__ | ( | self, | |
bandwidth_fisher = 1. , |
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bandwidth = 1. , |
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kernel_approx = None , |
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n_jobs = None |
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) |
Constructor for the PersistenceFisherKernel class. 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). n_jobs (int): number of jobs to use for the computation. See :func:`pairwise_persistence_diagram_kernels` for details.
def gudhi.representations.kernel_methods.PersistenceFisherKernel.__call__ | ( | self, | |
diag1, | |||
diag2 | |||
) |
Apply PersistenceFisherKernel on a single pair of persistence diagrams and outputs the result. Parameters: diag1 (n x 2 numpy array): first input persistence diagram. diag2 (n x 2 numpy array): second input persistence diagram. Returns: float: persistence Fisher kernel value.
def gudhi.representations.kernel_methods.PersistenceFisherKernel.fit | ( | self, | |
X, | |||
y = None |
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) |
Fit the PersistenceFisherKernel class on a list of persistence diagrams: an instance of the PersistenceFisherDistance class is fitted on the diagrams and then stored. Parameters: X (list of n x 2 numpy arrays): input persistence diagrams. y (n x 1 array): persistence diagram labels (unused).
def gudhi.representations.kernel_methods.PersistenceFisherKernel.transform | ( | self, | |
X | |||
) |
Compute all persistence Fisher kernel values between the persistence diagrams that were stored after calling the fit() method, and a given list of (possibly different) persistence diagrams. Parameters: X (list of n x 2 numpy arrays): input persistence diagrams. Returns: numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise persistence Fisher kernel values.
GUDHI Version 3.3.0 - C++ library for Topological Data Analysis (TDA) and Higher Dimensional Geometry Understanding. - Copyright : MIT | Generated on Tue Aug 11 2020 11:58:59 for GUDHI by Doxygen 1.8.18 |