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Parameter learning in Java

package com.bayesserver.examples;

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

import java.util.Arrays;

public class ParameterLearningNonTemporal {

public static void main(String[] args) {

Network network = CreateNetworkStructure(); // we manually construct the network here, but it could be loaded from a file
Variable x = network.getVariables().get("X");
Variable y = network.getVariables().get("Y");

// now learn the parameters from the data in Walkthrough 2 - Mixture model

// This example uses Sql Server as the data source and assumes the data has been copied to
// a table called MixtureModel
// 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();

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

DataReaderCommand dataReaderCommand = new DatabaseDataReaderCommand(
"Select X, Y From MixtureModel"); // note that if a case column was added we would need to ORDER BY the case column here

ReaderOptions readerOptions = new ReaderOptions(null); // we do not have a case column in this example

// here we map variables to database columns
// in this case the variables and database columns have the same name
VariableReference[] variableReferences = new VariableReference[]
new VariableReference(x, ColumnValueType.VALUE, x.getName()),
new VariableReference(y, ColumnValueType.VALUE, y.getName())

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

EvidenceReaderCommand evidenceReaderCommand = new DefaultEvidenceReaderCommand(

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

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


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

Node nodeCluster = new Node("Cluster", new String[]{"Cluster1", "Cluster2", "Cluster3"});

Variable x = new Variable("X", VariableValueType.CONTINUOUS);
Variable y = new Variable("Y", VariableValueType.CONTINUOUS);

Node nodePosition = new Node("Position", new Variable[]{x, y});

network.getLinks().add(new Link(nodeCluster, nodePosition));

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

return network;