/** * Licensed to the Apache Software Foundation (ASF) under one or more * contributor license agreements. See the NOTICE file distributed with * this work for additional information regarding copyright ownership. * The ASF licenses this file to You under the Apache License, Version 2.0 * (the "License"); you may not use this file except in compliance with * the License. You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ package org.apache.mahout.clustering.iterator; import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; import org.apache.mahout.clustering.Cluster; import org.apache.mahout.clustering.classify.ClusterClassifier; /** * This is a simple maximum likelihood clustering policy, suitable for k-means * clustering * */ public class KMeansClusteringPolicy extends AbstractClusteringPolicy { public KMeansClusteringPolicy() { } public KMeansClusteringPolicy(double convergenceDelta) { this.convergenceDelta = convergenceDelta; } private double convergenceDelta = 0.001; @Override public void write(DataOutput out) throws IOException { out.writeDouble(convergenceDelta); } @Override public void readFields(DataInput in) throws IOException { this.convergenceDelta = in.readDouble(); } @Override public void close(ClusterClassifier posterior) { boolean allConverged = true; for (Cluster cluster : posterior.getModels()) { org.apache.mahout.clustering.kmeans.Kluster kluster = (org.apache.mahout.clustering.kmeans.Kluster) cluster; boolean converged = kluster.calculateConvergence(convergenceDelta); allConverged = allConverged && converged; cluster.computeParameters(); } } }