Scales each feature by its maximum absolute value

This estimator scales and translates each feature individually such that the maximal absolute value of each feature in the training set will be 1.0. It does not shift/center the data, and thus does not destroy any sparsity.

This scaler can also be applied to sparse CSR or CSC matrices.

Super classes

rgudhi::PythonClass -> rgudhi::SKLearnClass -> rgudhi::BaseScaler -> MaxAbsScaler

Methods

Public methods

• MaxAbsScaler$new()

• MaxAbsScaler$clone()

Inherited methods

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

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Method new()

The MaxAbsScaler class constructor.

Usage

MaxAbsScaler$new(copy = TRUE)

Arguments

copy

A boolean value specifying whether to perform in-place scaling and avoid a copy (if the input is already a numpy array). Defaults to TRUE.

Returns

An object of class MaxAbsScaler.

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Method clone()

The objects of this class are cloneable with this method.

Usage

MaxAbsScaler$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (FALSE) { # reticulate::py_module_available("sklearn.preprocessing")
mas <- MaxAbsScaler$new()
}