Atol: Vector Representation: Atol

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

Vector Representation: Atol

Description

Computes measure vectorization (e.g. point clouds, persistence diagrams, etc.) after a quantisation step according to the Atol algorithm \insertCiteroyer2021atolrgudhi.

References

\insertCited

Super classes

rgudhi::PythonClass -> rgudhi::SKLearnClass -> rgudhi::VectorRepresentationStep -> Atol

Methods

Public methods

• Atol$new()

• Atol$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 Atol constructor.

Usage

Atol$new(
  quantiser,
  weighting_method = c("cloud", "iidproba"),
  contrast = c("gaussian", "laplacian", "indicator")
)

Arguments

quantiser

An object of class BaseClustering specifying any clustering algorithm from the sklearn.cluster module. It will be fitted when the ⁠$fit()⁠ method is called.

weighting_method

A string specifying the constant generic function for weighting the measure points. Choices are either "cloud" or "iidproba". Defaults to "cloud", i.e. the measure is seen as a point cloud. This will have no impact if weights are provided along with measures all the way, i.e. at ⁠$fit()⁠ and ⁠$transform()⁠ calls, through the optional argument sample_weight.

contrast

A string specifying the constant function for evaluating proximity of a measure with respect to centers. Choices are either "gaussian" or "laplacian" or "indicator". Defaults to "gaussian" (see page 3 in \insertCiteroyer2021atol;textualrgudhi).

Returns

An object of class Atol.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

Atol$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)
km <- KMeans$new(n_clusters = 2, random_state = 202006)
vr <- Atol$new(quantiser = km)
# vr$apply(dgm) # TODO: needs fix in python
vr$fit_transform(list(dgm))

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