Package org.apache.spark.ml.clustering

Class ExpectationAggregator

Object

org.apache.spark.ml.clustering.ExpectationAggregator

All Implemented Interfaces:

Serializable

public class ExpectationAggregator
extends Object
implements Serializable

ExpectationAggregator computes the partial expectation results.

param: numFeatures The number of features. param: bcWeights The broadcast weights for each Gaussian distribution in the mixture. param: bcGaussians The broadcast array of Multivariate Gaussian (Normal) Distribution in the mixture. Note only upper triangular part of the covariance matrix of each distribution is stored as dense vector (column major) in order to reduce shuffled data size.

See Also:

• Serialized Form

Constructor Summary

Constructors

Constructor	Description
ExpectationAggregator(int numFeatures, Broadcast<double[]> bcWeights, Broadcast<scala.Tuple2<DenseVector,DenseVector>[]> bcGaussians)

Method Summary

Modifier and Type	Method	Description
ExpectationAggregator	add(scala.Tuple2<Vector,Object> instance)	Add a new training instance to this ExpectationAggregator, update the weights, means and covariances for each distributions, and update the log likelihood.
long	count()
DenseVector[]	covs()
double	logLikelihood()
DenseVector[]	means()
double[]	weights()

Methods inherited from class java.lang.Object

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

Constructor Details

ExpectationAggregator

public ExpectationAggregator(int numFeatures,
 Broadcast<double[]> bcWeights,
 Broadcast<scala.Tuple2<DenseVector,DenseVector>[]> bcGaussians)

Method Details

add

public ExpectationAggregator add(scala.Tuple2<Vector,Object> instance)

Add a new training instance to this ExpectationAggregator, update the weights, means and covariances for each distributions, and update the log likelihood.

Parameters:

instance - The instance of data point to be added.

Returns:

This ExpectationAggregator object.

count

public long count()

covs

public DenseVector[] covs()

logLikelihood

public double logLikelihood()

means

public DenseVector[] means()

weights

public double[] weights()