smile.feature

Class RobustStandardizer

java.lang.Object

smile.feature.FeatureTransform

smile.feature.Standardizer

smile.feature.RobustStandardizer

public class RobustStandardizer
extends Standardizer

Robustly standardizes numeric feature by subtracting the median and dividing by the IQR.

Field Summary

Fields inherited from class smile.feature.FeatureTransform

copy

Constructor Summary

Constructors

Constructor and Description
RobustStandardizer() Constructor.
RobustStandardizer(boolean copy) Constructor.

Method Summary

Modifier and Type	Method and Description
void	learn(smile.data.Attribute[] attributes, double[][] data) Learns transformation parameters from a dataset.
java.lang.String	toString()

Methods inherited from class smile.feature.Standardizer

transform

Methods inherited from class smile.feature.FeatureTransform

learn, transform

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

RobustStandardizer

public RobustStandardizer()

Constructor.

RobustStandardizer

public RobustStandardizer(boolean copy)

Constructor.

Parameters:

copy - If false, try to avoid a copy and do inplace scaling instead.

Method Detail

learn

public void learn(smile.data.Attribute[] attributes,
                  double[][] data)

Description copied from class: FeatureTransform

Learns transformation parameters from a dataset.

Overrides:

learn in class Standardizer

Parameters:

attributes - The variable attributes. Of which, numeric variables will be standardized.

data - The training data to learn scaling parameters. The data will not be modified.

toString

public java.lang.String toString()

Overrides:

toString in class Standardizer