Preprocessing Step

Preprocessing Step

Preprocessing Step

Author

Mathieu Carrière

Super classes

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

Methods

Public methods

• PreprocessingStep$apply()

• PreprocessingStep$fit()

• PreprocessingStep$transform()

• PreprocessingStep$fit_transform()

• PreprocessingStep$clone()

Inherited methods

• rgudhi::PythonClass$get_python_class()
• rgudhi::PythonClass$set_python_class()
• rgudhi::SKLearnClass$get_params()
• rgudhi::SKLearnClass$set_params()

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Method apply()

Applies the class on a single persistence diagram and outputs the result.

Usage

PreprocessingStep$apply(diag)

Arguments

diag

A 2-column tibble::tibble specifying a persistence diagram.

Returns

A 2-column tibble::tibble storing the preprocessed persistence diagram.

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Method fit()

Fits the class on a sample of persistence diagrams.

Usage

PreprocessingStep$fit(X, y = NULL)

Arguments

X

A list of 2-column tibble::tibbles specifying a sample of persistence diagrams.

y

An integer vector specifying persistence diagram labels (unused for now).

Returns

The class itself invisibly.

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Method transform()

Applies the class on a sample of persistence diagrams.

Usage

PreprocessingStep$transform(X)

Arguments

X

A list of 2-column tibble::tibbles specifying a sample of persistence diagrams.

Returns

A list of 2-column tibble::tibbles storing the preprocessed persistence diagrams.

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Method fit_transform()

Applies sequentially the $fit() and $transform() methods on a sample of persistence diagrams in a more efficient way than calling them directly.

Usage

PreprocessingStep$fit_transform(X, y = NULL)

Arguments

X

A list of 2-column tibble::tibbles specifying a sample of persistence diagrams.

y

An integer vector specifying persistence diagram labels (unused for now).

Returns

A list of 2-column tibble::tibbles storing the preprocessed persistence diagrams.

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Method clone()

The objects of this class are cloneable with this method.

Usage

PreprocessingStep$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.