Package org.encog.ml.bayesian.training

Examples of org.encog.ml.bayesian.training.TrainBayesian

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    }
    else {
      throw new BayesianError("Invalid init type: " + initStr);
    }
   
    return new TrainBayesian((BayesianNetwork) method, training, maxParents, init, search, estimator);
  }
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    BayesianNetwork network = new BayesianNetwork();
    BayesianEvent x1 = network.createEvent("x1", labels);
    BayesianEvent x2 = network.createEvent("x2", labels);
    BayesianEvent x3 = network.createEvent("x3", labels);
    network.finalizeStructure();
    TrainBayesian train = new TrainBayesian(network,data,10);
    train.setInitNetwork(BayesianInit.InitEmpty);
    while(!train.isTrainingDone()) {
      train.iteration();
    }
    train.iteration();
    Assert.assertTrue(x1.getParents().size()==0);
    Assert.assertTrue(x2.getParents().size()==1);
    Assert.assertTrue(x3.getParents().size()==1);
    Assert.assertTrue(x2.getParents().contains(x1));
    Assert.assertTrue(x3.getParents().contains(x2));
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    BayesianNetwork network = new BayesianNetwork();
    BayesianEvent x1 = network.createEvent("x1", labels);
    BayesianEvent x2 = network.createEvent("x2", labels);
    BayesianEvent x3 = network.createEvent("x3", labels);
    network.finalizeStructure();
    TrainBayesian train = new TrainBayesian(network,data,10);
    SearchK2 search = (SearchK2)train.getSearch();
   
    double p = search.calculateG(network, x1, x1.getParents());
    Assert.assertEquals(3.607503E-4, p, 0.0001);
   
    network.createDependency(x1, x2);
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Related Classes of org.encog.ml.bayesian.training.TrainBayesian

  • org.encog.ml.bayesian.BayesianEvent
  • org.encog.ml.bayesian.TestK2
  • org.encog.ml.bayesian.training.estimator.SimpleEstimator
  • org.encog.ml.bayesian.training.search.k2.SearchK2
  • org.encog.ml.factory.train.TrainBayesianFactory
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