SlicedWassersteinDistance: Metrics: Sliced Wasserstein Distance

In rgudhi: An Interface to the GUDHI Library for Topological Data Analysis

Metrics: Sliced Wasserstein Distance

Description

Computes the sliced Wasserstein distance matrix from a list of persistence diagrams. The Sliced Wasserstein distance is computed by projecting the persistence diagrams onto lines, comparing the projections with the 1-norm, and finally integrating over all possible lines. See http://proceedings.mlr.press/v70/carriere17a.html for more details.

Super classes

rgudhi::PythonClass -> rgudhi::SKLearnClass -> rgudhi::MetricStep -> SlicedWassersteinDistance

Methods

Public methods

• SlicedWassersteinDistance$new()

• SlicedWassersteinDistance$clone()

Inherited methods

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

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Method new()

The SlicedWassersteinDistance constructor.

Usage

SlicedWassersteinDistance$new(num_directions = 10, n_jobs = 1)

Arguments

num_directions

An integer value specifying the number of lines evenly sampled from [-\pi/2,\pi/2] in order to approximate and speed up the kernel computation. Defaults to 10L.

n_jobs

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

Returns

An object of class SlicedWassersteinDistance.

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Method clone()

The objects of this class are cloneable with this method.

Usage

SlicedWassersteinDistance$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

Mathieu Carrière

Examples

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)
dis <- SlicedWassersteinDistance$new()
dis$apply(dgm, dgm)
dis$fit_transform(list(dgm))

rgudhi documentation built on March 31, 2023, 11:38 p.m.