Landscape: Vector Representation: Landscape

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

Vector Representation: Landscape

Description

Computes persistence landscapes from a list of persistence diagrams. A persistence landscape is a collection of 1D piecewise-linear functions computed from the rank function associated to the persistence diagram. These piecewise-linear functions are then sampled evenly on a given range and the corresponding vectors of samples are concatenated and returned. See http://jmlr.org/papers/v16/bubenik15a.html for more details.

Super classes

rgudhi::PythonClass -> rgudhi::SKLearnClass -> rgudhi::VectorRepresentationStep -> Landscape

Methods

Public methods

• Landscape$new()

• Landscape$clone()

Inherited methods

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

---

Method new()

The Landscape constructor.

Usage

Landscape$new(
  num_landscapes = 5,
  resolution = 100,
  sample_range = rep(NA_real_, 2)
)

Arguments

num_landscapes

An integer value specifying the number of piecewise linear functions to output. Defaults to 5L.

resolution

An integer value specifying the grid size for the landscapes. Defaults to 100L.

sample_range

A length-2 numeric vector specifying the domain for the entropy summary function, of the form [x_{\min}, x_{\max}]. Defaults to rep(NA, 2). It is the interval on which samples will be drawn evenly. If one of the values is NA, it can be computed from the persistence diagrams with the ⁠$fit()⁠ method.

Returns

An object of class Landscape.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

Landscape$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)
lds <- Landscape$new()
lds$apply(dgm)
lds$fit_transform(list(dgm))

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