Preprocessing: Diagram Scaler

This is a class for preprocessing persistence diagrams with a given list of scalers, such as those included in scikit-learn.

Author

Mathieu Carrière

Super classes

rgudhi::PythonClass -> rgudhi::SKLearnClass -> rgudhi::PreprocessingStep -> DiagramScaler

Methods

Public methods

• DiagramScaler$new()

• DiagramScaler$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()

---

Method new()

The DiagramScaler constructor.

Usage

DiagramScaler$new(use = FALSE, scalers = list())

Arguments

use

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

scalers

A list of scalers to be fit on the persistence diagrams. Defaults to list() which is an empty list. Each element of the list is a length-2 base::list with two elements:

• the first one is a list of coordinates;

• the second one is an instantiated scaler class. Choices are MaxAbsScaler, MinMaxScaler, RobustScaler or StandardScaler.

Returns

An object of class DiagramScaler.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

DiagramScaler$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)
ds <- DiagramScaler$new()
ds$apply(dgm)
ds$fit_transform(list(dgm))
}