/**
 * 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;

import org.apache.mahout.clustering.dirichlet.UncommonDistributions;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.SequentialAccessSparseVector;
import org.apache.mahout.math.Vector;

public class DirichletClusteringPolicy implements ClusteringPolicy {
  
  public DirichletClusteringPolicy(int k, double alpha0) {
    this.totalCounts = new DenseVector(k);
    this.alpha0 = alpha0;
    this.mixture = UncommonDistributions.rDirichlet(totalCounts, alpha0);
  }
  
  // The mixture is the Dirichlet distribution of the total Cluster counts over
  // all iterations
  private Vector mixture;
  
  // Alpha_0 primes the Dirichlet distribution
  private final double alpha0;
  
  // Total observed over all time
  private final Vector totalCounts;
  
  @Override
  public Vector select(Vector probabilities) {
    int rMultinom = UncommonDistributions.rMultinom(probabilities.times(mixture));
    Vector weights = new SequentialAccessSparseVector(probabilities.size());
    weights.set(rMultinom, 1.0);
    return weights;
  }
  
  // update the total counts and then the mixture
  @Override
  public void update(ClusterClassifier prior) {
    for (int i = 0; i < totalCounts.size(); i++) {
      long nObserved = prior.getModels().get(i).getNumPoints();
      totalCounts.set(i, totalCounts.get(i) + nObserved);
    }
    mixture = UncommonDistributions.rDirichlet(totalCounts, alpha0);
  }
}