Batch query Java

package com.bayesserver.examples;
import com.bayesserver.*;
import com.bayesserver.data.*;
import com.bayesserver.inference.*;

import java.util.Arrays;

public class Main {

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

        var network = new Network();

        // TODO download the network from the Bayes Server User Interface (or Bayes Server Online)
        // and adjust the following path
        network.load("Waste.bayes");

        var variables = network.getVariables();
        // discrete
        var burningRegimen = variables.get("Burning Regimen", true);
        var wasteType = variables.get("Waste type", true);
        var filterState = variables.get("Filter state", true);

        var burningRegimenStable = burningRegimen.getStates().get("Stable", true);

        // continuous
        var filterEfficiency = variables.get("Filter efficiency", true);
        var dustEmission = variables.get("Dust emission", true);
        var metalsInWaste = variables.get("Metals in waste", true);
        var co2Concentration = variables.get("CO2 concentration", true);
        var lightPenetrability = variables.get("Light penetrability", true);
        var metalsEmission = variables.get("Metals emission", true);

        // Create an inference engine to calculate the queries.
        // The same inference engine can be re-used for multiple rows of data.
        // Although not shown here, you can also create multiple inference engines,
        // to take advantage of multiple threads.

        var factory = new RelevanceTreeInferenceFactory();
        var inference = factory.createInferenceEngine(network);
        var queryOptions = factory.createQueryOptions();
        var queryOutput = factory.createQueryOutput();

        // add some marginal queries
        var queries = inference.getQueryDistributions();
        var queryBurningRegimen = new Table(burningRegimen);
        queries.add(queryBurningRegimen);
        var queryLightPenetrability = new CLGaussian(lightPenetrability);
        queries.add(queryLightPenetrability);
        // You can also add joint queries over > 1 variable
        var queryJoint = new CLGaussian(new Variable[]{filterEfficiency, burningRegimen});
        queries.add(queryJoint);

        // we will also query the log-likelihood
        queryOptions.setLogLikelihood(true);


        var evidenceReaderCommand = createEvidenceReaderCommand(network);

        var readOptions = new DefaultReadOptions();

        // Iterate through the data, perform queries for each row.
        // In this example, the output is simply written to the console, but the output
        // could be streamed into a database or another data store or endpoint.

        try(var evidenceReader = evidenceReaderCommand.executeReader())
        {
            while (evidenceReader.read(inference.getEvidence(), readOptions)) {
                inference.query(queryOptions, queryOutput);

                var logLikelihood = queryOutput.getLogLikelihood();
                var probStable = queryBurningRegimen.get(burningRegimenStable);
                var meanLightPenetrability = queryLightPenetrability.getMean(lightPenetrability);
                var varianceLightPenetrability = queryLightPenetrability.getVariance(lightPenetrability);
                var jointElement = queryJoint.getMean(filterEfficiency, burningRegimenStable);

                System.out.printf(
                        "{%s}\t{%s}\t{%s}\t{%s}\t{%s}%n",
                        logLikelihood,
                        probStable,
                        meanLightPenetrability,
                        varianceLightPenetrability,
                        jointElement);
            }
        }
    }

    /**
    Create an evidence reader command, than maps data to variables.
    
    This can connect to a database, a DataTable or you can create a custom reader.
    **/
    private static EvidenceReaderCommand createEvidenceReaderCommand(Network network) {

        var dataReaderCommand = createDataReaderCommand();

        var wasteType = network.getVariables().get("Waste type", true);
        var co2Concentration = network.getVariables().get("CO2 concentration", true);

        return new DefaultEvidenceReaderCommand(
                dataReaderCommand,
                Arrays.asList(

                                new VariableReference(wasteType, ColumnValueType.NAME, wasteType.getName()),
                                new VariableReference(co2Concentration, ColumnValueType.VALUE, co2Concentration.getName())
                        ),
                new ReaderOptions()
        );

    }

    /**
    Manually create some data here to keep the example self contained.
    This could also be queried from a database,
    or another data source.
    **/
    private static DataReaderCommand createDataReaderCommand() {
        var table = new DataTable();
        var wasteType = table.getColumns().add("Waste type", String.class);
        var co2Concentration = table.getColumns().add("CO2 concentration", Double.class);

        // you can use null for missing data

        var rows = table.getRows();
        rows.add("Industrial", -1.7);
        rows.add("Household", -2.0);
        rows.add(null, -1.7);
        rows.add("Industrial", null);
        rows.add("Industrial", 0.5);

        return new DataTableDataReaderCommand(table);
    }

    // Expected output ...
    //-1.56469966974624       0.934046351645668       1.57952317582283        0.419529880001972       -3.725
    //-0.247575914981347      0.98111967122024        1.52305983561012        0.351488920422928       -3.065
    //-0.311935701250374      0.934046351645668       1.52238029582283        0.380432496848107       -3.25357124
    //-1.25276396849587       0.85    1.5375  0.43747475      -3.725
    //-7.21683608441226       1.11889682547702E-11    1.11250000000559        0.420474750002601       -3.725
}