/**
 * 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.Model;
import org.apache.mahout.clustering.dirichlet.UncommonDistributions;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.VectorWritable;

/**
 * An implementation of the ModelDistribution interface suitable for testing the DirichletCluster algorithm.
 * Uses a Normal Distribution to sample the prior model values. Model values have a vector standard deviation,
 * allowing assymetrical regions to be covered by a model.
 */
public class GaussianClusterDistribution extends AbstractVectorModelDistribution {

  public GaussianClusterDistribution() {
  }

  public GaussianClusterDistribution(VectorWritable modelPrototype) {
    super(modelPrototype);
  }

  @Override
  public Model<VectorWritable>[] sampleFromPrior(int howMany) {
    Model<VectorWritable>[] result = new GaussianCluster[howMany];
    for (int i = 0; i < howMany; i++) {
      Vector prototype = getModelPrototype().get();
      Vector mean = prototype.like();
      for (int j = 0; j < prototype.size(); j++) {
        mean.set(j, UncommonDistributions.rNorm(0, 1));
      }
      Vector sd = prototype.like();
      for (int j = 0; j < prototype.size(); j++) {
        sd.set(j, UncommonDistributions.rNorm(1, 1));
      }
      result[i] = new GaussianCluster(mean, sd, i);
    }
    return result;
  }

  @Override
  public Model<VectorWritable>[] sampleFromPosterior(Model<VectorWritable>[] posterior) {
    Model<VectorWritable>[] result = new GaussianCluster[posterior.length];
    for (int i = 0; i < posterior.length; i++) {
      result[i] = posterior[i].sampleFromPosterior();
    }
    return result;
  }

}