/**
 * 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.clustering.dirichlet.UncommonDistributions;
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];
    Vector prototype = getModelPrototype().get().like();
    for (int i = 0; i < prototype.size(); i++) {
      prototype.setQuick(i, UncommonDistributions.rNorm(0, 1));
    }
    for (int i = 0; i < howMany; i++) {
      result[i] = new DistanceMeasureCluster(prototype, 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;
  }

}