package org.apache.mahout.clustering.dirichlet;
/**
* 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.
*/
import java.util.ArrayList;
import java.util.List;
import org.apache.mahout.clustering.dirichlet.models.Model;
import org.apache.mahout.clustering.dirichlet.models.ModelDistribution;
import org.apache.mahout.matrix.DenseVector;
import org.apache.mahout.matrix.Vector;
public class DirichletState<Observation> {
public int numClusters; // the number of clusters
public ModelDistribution<Observation> modelFactory; // the factory for models
public List<DirichletCluster<Observation>> clusters; // the clusters for this iteration
public Vector mixture; // the mixture vector
public double offset; // alpha_0 / numClusters
@SuppressWarnings("unchecked")
public DirichletState(ModelDistribution<Observation> modelFactory,
int numClusters, double alpha_0, int thin, int burnin) {
this.numClusters = numClusters;
this.modelFactory = modelFactory;
// initialize totalCounts
offset = alpha_0 / numClusters;
// sample initial prior models
clusters = new ArrayList<DirichletCluster<Observation>>();
for (Model<?> m : modelFactory.sampleFromPrior(numClusters))
clusters.add(new DirichletCluster(m, offset));
// sample the mixture parameters from a Dirichlet distribution on the totalCounts
mixture = UncommonDistributions.rDirichlet(totalCounts());
}
public DirichletState() {
}
public Vector totalCounts() {
Vector result = new DenseVector(numClusters);
for (int i = 0; i < numClusters; i++)
result.set(i, clusters.get(i).totalCount);
return result;
}
/**
* Update the receiver with the new models
*
* @param newModels a Model<Observation>[] of new models
*/
public void update(Model<Observation>[] newModels) {
// compute new model parameters based upon observations and update models
for (int i = 0; i < newModels.length; i++) {
newModels[i].computeParameters();
clusters.get(i).setModel(newModels[i]);
}
// update the mixture
mixture = UncommonDistributions.rDirichlet(totalCounts());
}
/**
* return the adjusted probability that x is described by the kth model
* @param x an Observation
* @param k an int index of a model
* @return the double probability
*/
public double adjustedProbability(Observation x, int k) {
double pdf = clusters.get(k).model.pdf(x);
double mix = mixture.get(k);
double result = mix * pdf;
//if (result < 0 || result > 1)
// System.out.print("");
return result;
}
@SuppressWarnings("unchecked")
public Model<Observation>[] getModels() {
Model<Observation>[] result = new Model[numClusters];
for (int i = 0; i < numClusters; i++)
result[i] = clusters.get(i).model;
return result;
}
}