Preprocessing: Prominent Points

This is a class or removing points that are close or far from the diagonal in persistence diagrams. If persistence diagrams are 2-column tibble::tibbles (i.e. persistence diagrams with ordinary features), points are ordered and thresholded by distance-to-diagonal. If persistence diagrams are 1-column tibble::tibbles (i.e. persistence diagrams with essential features), points are not ordered and thresholded by first coordinate.

Author

Mathieu Carrière

Super classes

rgudhi::PythonClass -> rgudhi::SKLearnClass -> rgudhi::PreprocessingStep -> ProminentPoints

Methods

Public methods

• ProminentPoints$new()

• ProminentPoints$clone()

Inherited methods

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

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Method new()

The ProminentPoints constructor.

Usage

ProminentPoints$new(
  use = FALSE,
  num_pts = 10,
  threshold = -1,
  location = c("upper", "lower")
)

Arguments

use

A boolean value specifying whether to use the class. Defaults to FALSE.

num_pts

An integer value specifying the cardinality threshold. Defaults to 10L. If location == "upper", keeps the top num_pts points that are the farthest away from the diagonal. If location == "lower", keeps the top num_pts points that are the closest to the diagonal.

threshold

A numeric value specifying the distance-to-diagonal threshold. Defaults to -1.0. If location == "upper", keeps the points that are at least at a distance threshold from the diagonal. If location == "lower", keeps the points that are at most at a distance threshold from the diagonal.

location

A string specifying whether to keep the points that are far away ("upper") or close ("lower") to the diagonal. Defaults to "upper".

Returns

An object of class ProminentPoints.

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Method clone()

The objects of this class are cloneable with this method.

Usage

ProminentPoints$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)
pp <- ProminentPoints$new()
pp$apply(dgm)
pp$fit_transform(list(dgm))
}