/**
* 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.dirichlet.models;
import org.apache.mahout.clustering.DistanceMeasureCluster;
import org.apache.mahout.clustering.Model;
import org.apache.mahout.common.distance.DistanceMeasure;
import org.apache.mahout.common.distance.ManhattanDistanceMeasure;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.VectorWritable;
/**
* An implementation of the ModelDistribution interface suitable for testing the DirichletCluster algorithm.
* Models use a DistanceMeasure to calculate pdf values.
*/
public class DistanceMeasureClusterDistribution extends AbstractVectorModelDistribution {
private DistanceMeasure measure;
public DistanceMeasureClusterDistribution() {
}
public DistanceMeasureClusterDistribution(VectorWritable modelPrototype) {
super(modelPrototype);
this.measure = new ManhattanDistanceMeasure();
}
public DistanceMeasureClusterDistribution(VectorWritable modelPrototype, DistanceMeasure measure) {
super(modelPrototype);
this.measure = measure;
}
@Override
public Model<VectorWritable>[] sampleFromPrior(int howMany) {
Model<VectorWritable>[] result = new DistanceMeasureCluster[howMany];
for (int i = 0; i < howMany; i++) {
Vector prototype = getModelPrototype().get();
result[i] = new DistanceMeasureCluster(prototype.like(), i, measure);
}
return result;
}
@Override
public Model<VectorWritable>[] sampleFromPosterior(Model<VectorWritable>[] posterior) {
Model<VectorWritable>[] result = new DistanceMeasureCluster[posterior.length];
for (int i = 0; i < posterior.length; i++) {
result[i] = posterior[i].sampleFromPosterior();
}
return result;
}
public void setMeasure(DistanceMeasure measure) {
this.measure = measure;
}
public DistanceMeasure getMeasure() {
return measure;
}
}