| MetricStep | R Documentation |
Metric Step
Metric Step
rgudhi::PythonClass -> rgudhi::SKLearnClass -> MetricStep
apply()Applies the class on a single persistence diagram and outputs the result.
MetricStep$apply(diag1, diag2)
diag1A 2-column tibble::tibble specifying a persistence diagram.
diag2A 2-column tibble::tibble specifying a persistence diagram.
A numeric value storing the distance between the two input diagrams.
fit()Fits the class on a sample of persistence diagrams.
MetricStep$fit(X, y = NULL)
XA list of 2-column tibble::tibbles specifying a sample of persistence diagrams.
yAn integer vector specifying persistence diagram labels (unused for now).
The class itself invisibly.
transform()Applies the class on a sample of persistence diagrams.
MetricStep$transform(X)
XA list of 2-column tibble::tibbles specifying a sample of persistence diagrams.
A numeric matrix of shape n_\mathrm{out} \times
n_\mathrm{in} storing the distances between the n_\mathrm{out}
persistence diagrams passed to the $transform() method and the
n_\mathrm{in} persistence diagrams passed to the $fit() method.
fit_transform()Applies sequentially the $fit() and $transform() methods
on a sample of persistence diagrams in a more efficient way than
calling them directly.
MetricStep$fit_transform(X, y = NULL)
XA list of 2-column tibble::tibbles specifying a sample of persistence diagrams.
yAn integer vector specifying persistence diagram labels (unused for now).
A numeric matrix of shape n \times n storing the distance
between the n persistence diagrams passed to both the $fit()
and $transform() methods.
clone()The objects of this class are cloneable with this method.
MetricStep$clone(deep = FALSE)
deepWhether to make a deep clone.
Mathieu Carrière
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