package eu.amidst.core.examples.learning;


import eu.amidst.core.datastream.DataInstance;
import eu.amidst.core.datastream.DataStream;
import eu.amidst.core.io.DataStreamLoader;
import eu.amidst.core.learning.parametric.ParallelMaximumLikelihood;
import eu.amidst.core.models.BayesianNetwork;

/**
 *
 * This example shows how to learn in parallel the parameters of a Bayesian network from a stream of data using maximum
 * likelihood.
 *
 * Created by andresmasegosa on 18/6/15.
 */
public class ParallelMaximumLikelihoodExample {


    public static void main(String[] args) throws Exception {

        //We can open the data stream using the static class DataStreamLoader
        DataStream<DataInstance> data = DataStreamLoader.open("datasets/simulated/WasteIncineratorSample.arff");

        //We create a ParallelMaximumLikelihood object with the MaximumLikehood builder
        ParallelMaximumLikelihood parameterLearningAlgorithm = new ParallelMaximumLikelihood();

        //We activate the parallel mode.
        parameterLearningAlgorithm.setParallelMode(true);

        //We desactivate the debug mode.
        parameterLearningAlgorithm.setDebug(false);

        //We fix the DAG structure
        parameterLearningAlgorithm.setDAG(MaximimumLikelihoodByBatchExample.getNaiveBayesStructure(data, 0));

        //We set the batch size which will be employed to learn the model in parallel
        parameterLearningAlgorithm.setWindowsSize(100);

        //We set the data which is going to be used for leaning the parameters
        parameterLearningAlgorithm.setDataStream(data);

        //We perform the learning
        parameterLearningAlgorithm.runLearning();

        //And we get the model
        BayesianNetwork bnModel = parameterLearningAlgorithm.getLearntBayesianNetwork();

        //We print the model
        System.out.println(bnModel.toString());

    }

}

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Update ParallelMaximumLikelihoodExample.java