Time Series Parameter learning in Java

package com.bayesserver.examples;

import com.bayesserver.*;
import com.bayesserver.data.*;
import com.bayesserver.inference.*;
import com.bayesserver.learning.parameters.*;

import java.util.Arrays;

public class ParameterLearningTemporal {

    public static void main(String[] args) {

        // we manually construct the network here, but it could be loaded from a file
        Network network = createNetworkStructure();
        Variable x1 = network.getVariables().get("X1");
        Variable x2 = network.getVariables().get("X2");

        // now learn the parameters from the data in Walkthrough 3 - Time Series network

        // This example uses Sql Server as the data source and assumes the data has been copied to
        // a table called TimeSeriesWalkthrough
        // We will use the RelevanceTree algorithm here, as it is optimized for parameter learning
        ParameterLearning learning = new ParameterLearning(network, new RelevanceTreeInferenceFactory());
        ParameterLearningOptions learningOptions = new ParameterLearningOptions();
        learningOptions.setTimeSeriesMode(TimeSeriesMode.ROLLING);

        String connectionUrl = "<connection url to database goes here>";

        DataReaderCommand temporalDataReaderCommand = new DatabaseDataReaderCommand(
                connectionUrl,
                "Select [Case], Time, X1, X2 From TimeSeriesWalkthrough ORDER BY [Case], Time");

        TemporalReaderOptions temporalReaderOptions = new TemporalReaderOptions("Case", "Time", TimeValueType.INDEX);

        // here we map variables to database columns
        // in this case the variables and database columns have the same name
        VariableReference[] temporalVariableReferences = new VariableReference[]
                {
                        new VariableReference(x1, ColumnValueType.VALUE, x1.getName()),
                        new VariableReference(x2, ColumnValueType.VALUE, x2.getName())
                };

        // note that although this example only has temporal data
        // we could have included additional non temporal variables and data

        EvidenceReaderCommand evidenceReaderCommand = new DefaultEvidenceReaderCommand(
                temporalDataReaderCommand,
                Arrays.asList(temporalVariableReferences),
                temporalReaderOptions);

        ParameterLearningOutput result = learning.learn(evidenceReaderCommand, learningOptions);

        System.out.println("Log likelihood = " + result.getLogLikelihood());

    }

    private static Network createNetworkStructure() {
        Network network = new Network();

        Variable x1 = new Variable("X1", VariableValueType.CONTINUOUS);
        Variable x2 = new Variable("X2", VariableValueType.CONTINUOUS);

        // add a temporal (time series) node, with two continuous variables
        Node nodeX = new Node("X", new Variable[]{x1, x2});
        nodeX.setTemporalType(TemporalType.TEMPORAL);

        network.getNodes().add(nodeX);

        // add temporal links
        for (int order = 1; order <= 4; order++) {
            network.getLinks().add(new Link(nodeX, nodeX, order));
        }

        // at this point the Dynamic Bayesian network structure is fully specified

        return network;
    }
}