@Test public void test() throws Exception { MaximimumLikelihoodByBatchExample.main(null); }
public static void main(String[] args) throws Exception { DataStream<DataInstance> data = DataStreamLoader.open("datasets/simulated/WasteIncineratorSample.arff"); ParameterLearningAlgorithm parameterLearningAlgorithm = new ParallelMaximumLikelihood(); parameterLearningAlgorithm.setDAG(getNaiveBayesStructure(data,0)); parameterLearningAlgorithm.initLearning(); for (DataOnMemory<DataInstance> batch : data.iterableOverBatches(100)){ parameterLearningAlgorithm.updateModel(batch); } BayesianNetwork bnModel = parameterLearningAlgorithm.getLearntBayesianNetwork(); System.out.println(bnModel.toString()); }
MaximimumLikelihoodByBatchExample { public static void main(String[] args) throws Exception { DataStream<DataInstance> data = DataStreamLoader.open("datasets/simulated/WasteIncineratorSample.arff"); ParameterLearningAlgorithm parameterLearningAlgorithm = new ParallelMaximumLikelihood(); parameterLearningAlgorithm.setDAG(getNaiveBayesStructure(data,0)); parameterLearningAlgorithm.initLearning(); for (DataOnMemory<DataInstance> batch : data.iterableOverBatches(100)){ parameterLearningAlgorithm.updateModel(batch); } BayesianNetwork bnModel = parameterLearningAlgorithm.getLearntBayesianNetwork(); System.out.println(bnModel.toString()); } }
MaximimumLikelihoodByBatchExample { public static void main(String[] args) throws Exception { DataStream<DataInstance> data = DataStreamLoader.open("datasets/simulated/WasteIncineratorSample.arff"); ParameterLearningAlgorithm parameterLearningAlgorithm = new ParallelMaximumLikelihood(); parameterLearningAlgorithm.setDAG(getNaiveBayesStructure(data,0)); parameterLearningAlgorithm.initLearning(); for (DataOnMemory<DataInstance> batch : data.iterableOverBatches(100)){ parameterLearningAlgorithm.updateModel(batch); } BayesianNetwork bnModel = parameterLearningAlgorithm.getLearntBayesianNetwork(); System.out.println(bnModel.toString()); }  }
MaximimumLikelihoodByBatchExample { public static void main(String[] args) throws Exception { DataStream<DataInstance> data = DataStreamLoader.open("datasets/simulated/WasteIncineratorSample.arff"); ParameterLearningAlgorithm parameterLearningAlgorithm = new ParallelMaximumLikelihood(); parameterLearningAlgorithm.setDAG(getNaiveBayesStructure(data,0)); parameterLearningAlgorithm.initLearning(); for (DataOnMemory<DataInstance> batch : data.iterableOverBatches(100)){ parameterLearningAlgorithm.updateModel(batch); } BayesianNetwork bnModel = parameterLearningAlgorithm.getLearntBayesianNetwork(); System.out.println(bnModel.toString()); }  static DAG getNaiveBayesStructure(DataStream<DataInstance> dataStream, int classIndex); static void main(String[] args); }
MaximimumLikelihoodByBatchExample { public static void main(String[] args) throws Exception { DataStream<DataInstance> data = DataStreamLoader.open("datasets/simulated/WasteIncineratorSample.arff"); ParameterLearningAlgorithm parameterLearningAlgorithm = new ParallelMaximumLikelihood(); parameterLearningAlgorithm.setDAG(getNaiveBayesStructure(data,0)); parameterLearningAlgorithm.initLearning(); for (DataOnMemory<DataInstance> batch : data.iterableOverBatches(100)){ parameterLearningAlgorithm.updateModel(batch); } BayesianNetwork bnModel = parameterLearningAlgorithm.getLearntBayesianNetwork(); System.out.println(bnModel.toString()); }  static DAG getNaiveBayesStructure(DataStream<DataInstance> dataStream, int classIndex); static void main(String[] args);  }