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package de.lmu.ifi.dbs.elki.algorithm.clustering.em;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2015
Ludwig-Maximilians-Universität München
Lehr- und Forschungseinheit für Datenbanksysteme
ELKI Development Team
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.data.model.MeanModel;
/**
* Models useable in EM clustering.
*
* @author Erich Schubert
* @since 0.3
*/
public interface EMClusterModel<M extends MeanModel> {
/**
* Begin the E step.
*/
void beginEStep();
/**
* Update the
*
* @param vec Vector to process
* @param weight Weight
*/
void updateE(NumberVector vec, double weight);
/**
* Finalize the E step.
*/
void finalizeEStep();
/**
* Estimate the likelihood of a vector.
*
* @param vec Vector
* @return Likelihood.
*/
double estimateDensity(NumberVector vec);
/**
* Finalize a cluster model.
*
* @return Cluster model
*/
M finalizeCluster();
/**
* Get the cluster weight.
*
* @return Cluster weight
*/
double getWeight();
/**
* Set the cluster weight.
*
* @param weight Cluster weight
*/
void setWeight(double weight);
}
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