ProminentPoints: Preprocessing: Prominent Points

ProminentPointsR Documentation

Preprocessing: Prominent Points

Description

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.

Super classes

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

Methods

Public methods

Inherited methods

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.


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.

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


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