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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 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;
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 AsymmetricSampledNormalDistribution implements
  public Model<Vector>[] sampleFromPrior(int howMany) {
    Model<Vector>[] result = new AsymmetricSampledNormalModel[howMany];
    for (int i = 0; i < howManyi++) {
      double[] m = {UncommonDistributions.rNorm(0, 1),
          UncommonDistributions.rNorm(0, 1)};
      DenseVector mean = new DenseVector(m);
      double[] s = {UncommonDistributions.rNorm(1, 1),
          UncommonDistributions.rNorm(1, 1)};
      DenseVector sd = new DenseVector(s);
      result[i] = new AsymmetricSampledNormalModel(meansd);
    return result;
  public Model<Vector>[] sampleFromPosterior(Model<Vector>[] posterior) {
    Model<Vector>[] result = new AsymmetricSampledNormalModel[posterior.length];
    for (int i = 0; i < posterior.lengthi++) {
      result[i] = m.sample();
    return result;
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