smile.validation.metric

Class AdjustedRandIndex

java.lang.Object

smile.validation.metric.AdjustedRandIndex

All Implemented Interfaces:

java.io.Serializable, ClusteringMetric

public class AdjustedRandIndex
extends java.lang.Object
implements ClusteringMetric

Adjusted Rand Index. Rand index is defined as the number of pairs of objects that are either in the same group or in different groups in both partitions divided by the total number of pairs of objects. The Rand index lies between 0 and 1. When two partitions agree perfectly, the Rand index achieves the maximum value 1. A problem with Rand index is that the expected value of the Rand index between two random partitions is not a constant. This problem is corrected by the adjusted Rand index that assumes the generalized hyper-geometric distribution as the model of randomness. The adjusted Rand index has the maximum value 1, and its expected value is 0 in the case of random clusters. A larger adjusted Rand index means a higher agreement between two partitions. The adjusted Rand index is recommended for measuring agreement even when the partitions compared have different numbers of clusters.

See Also:

RandIndex, Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static AdjustedRandIndex	instance Default instance.

Constructor Summary

Constructors

Constructor and Description
AdjustedRandIndex()

Method Summary

Modifier and Type	Method and Description
static double	of(int[] y1, int[] y2) Calculates the adjusted rand index.
double	score(int[] y1, int[] y2) Returns a score to measure the quality of clustering.
java.lang.String	toString()

Methods inherited from class java.lang.Object

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

Field Detail

instance

public static final AdjustedRandIndex instance

Default instance.

Constructor Detail

AdjustedRandIndex

public AdjustedRandIndex()

Method Detail

score

public double score(int[] y1,
                    int[] y2)

Description copied from interface: ClusteringMetric

Returns a score to measure the quality of clustering.

Specified by:

score in interface ClusteringMetric

Parameters:

y1 - the cluster labels.

y2 - the alternative cluster labels.

of

public static double of(int[] y1,
                        int[] y2)

Calculates the adjusted rand index.

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object