package org.apache.mahout.clustering.syntheticcontrol.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 org.apache.mahout.clustering.dirichlet.UncommonDistributions;
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;
/**
* An implementation of the ModelDistribution interface suitable for testing the
* DirichletCluster algorithm. Uses a Normal Distribution
*/
public class NormalScModelDistribution implements ModelDistribution<Vector> {
@Override
public Model<Vector>[] sampleFromPrior(int howMany) {
Model<Vector>[] result = new NormalScModel[howMany];
for (int i = 0; i < howMany; i++) {
DenseVector mean = new DenseVector(60);
for (int j = 0; j < 60; j++)
mean.set(j, UncommonDistributions.rNorm(30, 0.5));
result[i] = new NormalScModel(mean, 1);
}
return result;
}
@Override
public Model<Vector>[] sampleFromPosterior(Model<Vector>[] posterior) {
Model<Vector>[] result = new NormalScModel[posterior.length];
for (int i = 0; i < posterior.length; i++) {
NormalScModel m = (NormalScModel) posterior[i];
result[i] = m.sample();
}
return result;
}
}