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

Examples of org.apache.mahout.clustering.dirichlet.models.NormalModel

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  @Test
  public void testDirichletNormalModelClusterAsFormatString() {
    double[] d = { 1.1, 2.2, 3.3 };
    Vector m = new DenseVector(d);
    NormalModel model = new NormalModel(5, m, 0.75);
    Cluster cluster = new DirichletCluster(model, 35.0);
    String format = cluster.asFormatString(null);
    assertEquals("format", "C-5: nm{n=0 m=[1.100, 2.200, 3.300] sd=0.75}", format);
  }
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  @Test
  public void testDirichletNormalModelClusterAsJsonString() {
    double[] d = { 1.1, 2.2, 3.3 };
    Vector m = new DenseVector(d);
    NormalModel model = new NormalModel(5, m, 0.75);
    Cluster cluster = new DirichletCluster(model, 35.0);
    String json = cluster.asJsonString();
    GsonBuilder builder = new GsonBuilder();
    builder.registerTypeAdapter(Cluster.class, new JsonClusterModelAdapter());
    Gson gson = builder.create();
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  }

  @Test
  public void testNormalModelWritableSerialization() throws Exception {
    double[] m = { 1.1, 2.2, 3.3 };
    Model<?> model = new NormalModel(5, new DenseVector(m), 3.3);
    DataOutputBuffer out = new DataOutputBuffer();
    model.write(out);
    Model<?> model2 = new NormalModel();
    DataInputBuffer in = new DataInputBuffer();
    in.reset(out.getData(), out.getLength());
    model2.readFields(in);
    assertEquals("models", model.toString(), model2.toString());
  }
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  }

  @Test
  public void testClusterWritableSerialization() throws Exception {
    double[] m = { 1.1, 2.2, 3.3 };
    DirichletCluster cluster = new DirichletCluster(new NormalModel(5, new DenseVector(m), 4), 10);
    DataOutputBuffer out = new DataOutputBuffer();
    cluster.write(out);
    DirichletCluster cluster2 = new DirichletCluster();
    DataInputBuffer in = new DataInputBuffer();
    in.reset(out.getData(), out.getLength());
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    int i = result.size() - 1;
    for (Model<Vector>[] models : result) {
      g2.setStroke(new BasicStroke(i == 0 ? 3 : 1));
      g2.setColor(colors[Math.min(colors.length - 1, i--)]);
      for (Model<Vector> m : models) {
        NormalModel mm = (NormalModel) m;
        dv.assign(mm.sd * 3);
        if (isSignificant(mm))
          plotEllipse(g2, mm.mean, dv);
      }
    }
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    int i = result.size() - 1;
    for (Model<Vector>[] models : result) {
      g2.setStroke(new BasicStroke(i == 0 ? 3 : 1));
      g2.setColor(colors[Math.min(colors.length - 1, i--)]);
      for (Model<Vector> m : models) {
        NormalModel mm = (NormalModel) m;
        dv.assign(mm.sd * 3);
        if (isSignificant(mm))
          plotEllipse(g2, mm.mean, dv);
      }
    }
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    int i = result.size() - 1;
    for (Model<Vector>[] models : result) {
      g2.setStroke(new BasicStroke(i == 0 ? 3 : 1));
      g2.setColor(colors[Math.min(colors.length - 1, i--)]);
      for (Model<Vector> m : models) {
        NormalModel mm = (NormalModel) m;
        dv.assign(mm.sd * 3);
        if (isSignificant(mm))
          plotEllipse(g2, mm.mean, dv);
      }
    }
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  }

  @SuppressWarnings("unchecked")
  public void testNormalModelSerialization() {
    double[] m = { 1.1, 2.2 };
    Model model = new NormalModel(new DenseVector(m), 3.3);
    GsonBuilder builder = new GsonBuilder();
    builder.registerTypeAdapter(Vector.class, new JsonVectorAdapter());
    Gson gson = builder.create();
    String jsonString = gson.toJson(model);
    Model model2 = gson.fromJson(jsonString, NormalModel.class);
    assertEquals("models", model.toString(), model2.toString());
  }
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    builder.registerTypeAdapter(Vector.class, new JsonVectorAdapter());
    builder
        .registerTypeAdapter(ModelHolder.class, new JsonModelHolderAdapter());
    Gson gson = builder.create();
    double[] d = { 1.1, 2.2 };
    ModelHolder mh = new ModelHolder(new NormalModel(new DenseVector(d), 3.3));
    String format = gson.toJson(mh);
    System.out.println(format);
    ModelHolder mh2 = gson.fromJson(format, ModelHolder.class);
    assertEquals("mh", mh.model.toString(), mh2.model.toString());
  }
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    int i = result.size() - 1;
    for (Model<Vector>[] models : result) {
      g2.setStroke(new BasicStroke(i == 0 ? 3 : 1));
      g2.setColor(colors[Math.min(colors.length - 1, i--)]);
      for (Model<Vector> m : models) {
        NormalModel mm = (NormalModel) m;
        dv.assign(mm.getStdDev() * 3);
        if (isSignificant(mm))
          plotEllipse(g2, mm.getMean(), dv);
      }
    }
  }
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Related Classes of org.apache.mahout.clustering.dirichlet.models.NormalModel

  • com.google.gson.Gson
  • com.google.gson.GsonBuilder
  • org.apache.mahout.clustering.dirichlet.DisplayNDirichlet
  • org.apache.mahout.clustering.dirichlet.DisplayOutputState
  • org.apache.mahout.clustering.dirichlet.DisplaySNDirichlet
  • org.apache.mahout.clustering.dirichlet.JsonModelAdapter
  • org.apache.mahout.clustering.dirichlet.TestMapReduce
  • org.apache.mahout.clustering.JsonModelAdapter
  • org.apache.mahout.clustering.syntheticcontrol.dirichlet.NormalScModelDistribution
  • org.apache.mahout.clustering.TestClusterInterface
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