@Test public void test() throws Exception { ParallelMaximumLikelihoodExample.main(null); }
public static void main(String[] args) throws Exception { DataStream<DataInstance> data = DataStreamLoader.open("datasets/simulated/WasteIncineratorSample.arff"); ParallelMaximumLikelihood parameterLearningAlgorithm = new ParallelMaximumLikelihood(); parameterLearningAlgorithm.setParallelMode(true); parameterLearningAlgorithm.setDebug(false); parameterLearningAlgorithm.setDAG(MaximimumLikelihoodByBatchExample.getNaiveBayesStructure(data, 0)); parameterLearningAlgorithm.setWindowsSize(100); parameterLearningAlgorithm.setDataStream(data); parameterLearningAlgorithm.runLearning(); BayesianNetwork bnModel = parameterLearningAlgorithm.getLearntBayesianNetwork(); System.out.println(bnModel.toString()); }
ParallelMaximumLikelihoodExample { public static void main(String[] args) throws Exception { DataStream<DataInstance> data = DataStreamLoader.open("datasets/simulated/WasteIncineratorSample.arff"); ParallelMaximumLikelihood parameterLearningAlgorithm = new ParallelMaximumLikelihood(); parameterLearningAlgorithm.setParallelMode(true); parameterLearningAlgorithm.setDebug(false); parameterLearningAlgorithm.setDAG(MaximimumLikelihoodByBatchExample.getNaiveBayesStructure(data, 0)); parameterLearningAlgorithm.setWindowsSize(100); parameterLearningAlgorithm.setDataStream(data); parameterLearningAlgorithm.runLearning(); BayesianNetwork bnModel = parameterLearningAlgorithm.getLearntBayesianNetwork(); System.out.println(bnModel.toString()); } }
ParallelMaximumLikelihoodExample { public static void main(String[] args) throws Exception { DataStream<DataInstance> data = DataStreamLoader.open("datasets/simulated/WasteIncineratorSample.arff"); ParallelMaximumLikelihood parameterLearningAlgorithm = new ParallelMaximumLikelihood(); parameterLearningAlgorithm.setParallelMode(true); parameterLearningAlgorithm.setDebug(false); parameterLearningAlgorithm.setDAG(MaximimumLikelihoodByBatchExample.getNaiveBayesStructure(data, 0)); parameterLearningAlgorithm.setWindowsSize(100); parameterLearningAlgorithm.setDataStream(data); parameterLearningAlgorithm.runLearning(); BayesianNetwork bnModel = parameterLearningAlgorithm.getLearntBayesianNetwork(); System.out.println(bnModel.toString()); }  }
ParallelMaximumLikelihoodExample { public static void main(String[] args) throws Exception { DataStream<DataInstance> data = DataStreamLoader.open("datasets/simulated/WasteIncineratorSample.arff"); ParallelMaximumLikelihood parameterLearningAlgorithm = new ParallelMaximumLikelihood(); parameterLearningAlgorithm.setParallelMode(true); parameterLearningAlgorithm.setDebug(false); parameterLearningAlgorithm.setDAG(MaximimumLikelihoodByBatchExample.getNaiveBayesStructure(data, 0)); parameterLearningAlgorithm.setWindowsSize(100); parameterLearningAlgorithm.setDataStream(data); parameterLearningAlgorithm.runLearning(); BayesianNetwork bnModel = parameterLearningAlgorithm.getLearntBayesianNetwork(); System.out.println(bnModel.toString()); }  static void main(String[] args); }
ParallelMaximumLikelihoodExample { public static void main(String[] args) throws Exception { DataStream<DataInstance> data = DataStreamLoader.open("datasets/simulated/WasteIncineratorSample.arff"); ParallelMaximumLikelihood parameterLearningAlgorithm = new ParallelMaximumLikelihood(); parameterLearningAlgorithm.setParallelMode(true); parameterLearningAlgorithm.setDebug(false); parameterLearningAlgorithm.setDAG(MaximimumLikelihoodByBatchExample.getNaiveBayesStructure(data, 0)); parameterLearningAlgorithm.setWindowsSize(100); parameterLearningAlgorithm.setDataStream(data); parameterLearningAlgorithm.runLearning(); BayesianNetwork bnModel = parameterLearningAlgorithm.getLearntBayesianNetwork(); System.out.println(bnModel.toString()); }  static void main(String[] args);  }