Package smile.feature.imputation

Class KMedoidsImputer

java.lang.Object

smile.feature.imputation.KMedoidsImputer

All Implemented Interfaces:

Serializable, Function<smile.data.Tuple,smile.data.Tuple>, smile.data.transform.Transform

public class KMedoidsImputer
extends Object
implements smile.data.transform.Transform

Missing value imputation by K-Medoids clustering. The k-medoids algorithm is an adaptation of the k-means algorithm. Rather than calculate the mean of the items in each cluster, a representative item, or medoid, is chosen for each cluster at each iteration. The missing values of an instance are replaced the corresponding ones of the nearest medoid.

See Also:

• Serialized Form

Constructor Summary

Constructors

Constructor	Description
KMedoidsImputer(CLARANS<smile.data.Tuple> kmedoids)	Constructor.

Method Summary

Modifier and Type	Method	Description
smile.data.Tuple	apply(smile.data.Tuple x)
static KMedoidsImputer	fit(smile.data.DataFrame data, smile.math.distance.Distance<smile.data.Tuple> distance, int k)	Fits the missing value imputation values.

Methods inherited from class java.lang.Object

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

Methods inherited from interface java.util.function.Function

andThen, compose

Methods inherited from interface smile.data.transform.Transform

andThen, apply, compose

Constructor Details

KMedoidsImputer

public KMedoidsImputer(CLARANS<smile.data.Tuple> kmedoids)

Constructor.

Parameters:

kmedoids - the K-Medoids clustering.

Method Details

apply

public smile.data.Tuple apply(smile.data.Tuple x)

Specified by:

apply in interface Function<smile.data.Tuple,smile.data.Tuple>

fit

public static KMedoidsImputer fit(smile.data.DataFrame data,
 smile.math.distance.Distance<smile.data.Tuple> distance,
 int k)

Fits the missing value imputation values.

Parameters:

data - the training data.

k - the number of clusters.

distance - the lambda of distance measure.

Returns:

the imputer.