WassersteinDistance | R Documentation |
Computes the Wasserstein distance matrix from a list of persistence diagrams.
rgudhi::PythonClass
-> rgudhi::SKLearnClass
-> rgudhi::MetricStep
-> WassersteinDistance
new()
The WassersteinDistance
constructor.
WassersteinDistance$new( order = 1, internal_p = Inf, mode = c("hera", "pot"), delta = 0.01, n_jobs = 1 )
order
An integer value specifying the exponent of the Wasserstein
distance. Defaults to 1.0
.
internal_p
An integer value specifying the ground metric on the
(upper-half) plane (i.e. the norm \ell_p
in R^2
). Defaults
to Inf
.
mode
A string specifying the method for computing the Wasserstein
distance. Choices are either "pot"
or "hera"
. Defaults to "hera"
.
delta
A numeric value specifying the relative error
1+\delta
. Defaults to 0.01
. Used only if mode == "hera"
.
n_jobs
An integer value specifying the number of jobs to use for
the computation. Defaults to 1L
.
An object of class WassersteinDistance
.
clone()
The objects of this class are cloneable with this method.
WassersteinDistance$clone(deep = FALSE)
deep
Whether to make a deep clone.
Mathieu Carrière
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 <- WassersteinDistance$new()
dis$apply(dgm, dgm)
dis$fit_transform(list(dgm))
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