smile.feature

Interface FeatureTransform

All Superinterfaces:

java.io.Serializable

All Known Implementing Classes:

MaxAbsScaler, Normalizer, RobustStandardizer, Scaler, Standardizer, WinsorScaler

public interface FeatureTransform
extends java.io.Serializable

Feature transformation. In general, learning algorithms benefit from standardization of the data set. If some outliers are present in the set, robust transformers are more appropriate.

Method Summary

Modifier and Type	Method and Description
smile.data.DataFrame	transform(smile.data.DataFrame data) Transform a data frame.
double[]	transform(double[] x) Transform a feature vector.
default double[][]	transform(double[][] data) Transform a data frame.
smile.data.Tuple	transform(smile.data.Tuple x) Transform a feature vector.

Method Detail

transform

double[] transform(double[] x)

Transform a feature vector.

Parameters:

x - a feature vector.

Returns:

the transformed feature value.

transform

default double[][] transform(double[][] data)

Transform a data frame.

Parameters:

data - a data frame.

Returns:

the transformed data frame.

transform

smile.data.Tuple transform(smile.data.Tuple x)

Transform a feature vector.

Parameters:

x - a feature vector.

Returns:

the transformed feature value.

transform

smile.data.DataFrame transform(smile.data.DataFrame data)

Transform a data frame.

Parameters:

data - a data frame.

Returns:

the transformed data frame.