Kernel Representation: Persistence Fisher Kernel

Computes 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.

Author

Mathieu Carrière

Super classes

rgudhi::PythonClass -> rgudhi::SKLearnClass -> rgudhi::KernelRepresentationStep -> PersistenceFisherKernel

Methods

Public methods

• PersistenceFisherKernel$new()

• PersistenceFisherKernel$clone()

Inherited methods

• rgudhi::PythonClass$get_python_class()
• rgudhi::PythonClass$set_python_class()
• rgudhi::SKLearnClass$get_params()
• rgudhi::SKLearnClass$set_params()
• rgudhi::KernelRepresentationStep$apply()
• rgudhi::KernelRepresentationStep$fit()
• rgudhi::KernelRepresentationStep$fit_transform()
• rgudhi::KernelRepresentationStep$transform()

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Method new()

The PersistenceFisherKernel constructor.

Usage

PersistenceFisherKernel$new(
  bandwidth_fisher = 1,
  bandwidth = 1,
  kernel_approx = NULL,
  n_jobs = 1
)

Arguments

bandwidth_fisher

A numeric value specifying the bandwidth of the Gaussian kernel used to turn persistence diagrams into probability distributions by the PersistenceFisherDistance class. Defaults to 1.0.

bandwidth

A numeric value specifying the bandwidth of the Gaussian kernel applied to the persistence Fisher distance. Defaults to 1.0.

kernel_approx

A Python class specifying the kernel approximation class used to speed up computation. Defaults to NULL. Common kernel approximations classes can be found in the scikit-learn library (such as RBFSampler for instance).

n_jobs

An integer value specifying the number of jobs to use for the computation. Defaults to 1.

Returns

An object of class PersistenceFisherKernel.

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Method clone()

The objects of this class are cloneable with this method.

Usage

PersistenceFisherKernel$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (FALSE) { # reticulate::py_module_available("gudhi")
X <- seq_circle(10)
ac <- AlphaComplex$new(points = X)
st <- ac$create_simplex_tree()
dgm <- st$compute_persistence()$persistence_intervals_in_dimension(0)
ds <- DiagramSelector$new(use = TRUE)
dgm <- ds$apply(dgm)
pfk <- PersistenceFisherKernel$new()
pfk$apply(dgm, dgm)
pfk$fit_transform(list(dgm))
}