Package org.apache.mahout.clustering.dirichlet.models

Source Code of org.apache.mahout.clustering.dirichlet.models.NormalModelDistribution

/**
* 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.math.Vector;
import org.apache.mahout.math.VectorWritable;

/**
* An implementation of the ModelDistribution interface suitable for testing the DirichletCluster algorithm.
* Uses a Normal Distribution
*/
public class NormalModelDistribution extends AbstractVectorModelDistribution {
 
  public NormalModelDistribution(VectorWritable modelPrototype) {
    super(modelPrototype);
  }
 
  public NormalModelDistribution() {
  }
 
  @Override
  public Model<VectorWritable>[] sampleFromPrior(int howMany) {
    Model<VectorWritable>[] result = new NormalModel[howMany];
    for (int i = 0; i < howMany; i++) {
      Vector prototype = getModelPrototype().get();
      result[i] = new NormalModel(i, prototype.like(), 1);
    }
    return result;
  }
 
  @Override
  public Model<VectorWritable>[] sampleFromPosterior(Model<VectorWritable>[] posterior) {
    Model<VectorWritable>[] result = new NormalModel[posterior.length];
    for (int i = 0; i < posterior.length; i++) {
      NormalModel m = (NormalModel) posterior[i];
      result[i] = m.sampleFromPosterior();
    }
    return result;
  }
}
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