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Construction & inference in Java

package com.bayesserver.examples;

import com.bayesserver.*;
import com.bayesserver.inference.*;


public class NetworkExample {

public static void main(String[] args) throws IOException, XMLStreamException, InconsistentEvidenceException {

// In this example we programatically create a simple Bayesian network.
// Note that you can automatically define nodes from data using
// classes in BayesServer.Data.Discovery,
// and you can automatically learn the parameters using classes in
// BayesServer.Learning.Parameters,
// however here we build a Bayesian network from scratch.

Network network = new Network("Demo");

// add the nodes (variables)

State aTrue = new State("True");
State aFalse = new State("False");
Node a = new Node("A", aTrue, aFalse);

State bTrue = new State("True");
State bFalse = new State("False");
Node b = new Node("B", bTrue, bFalse);

State cTrue = new State("True");
State cFalse = new State("False");
Node c = new Node("C", cTrue, cFalse);

State dTrue = new State("True");
State dFalse = new State("False");
Node d = new Node("D", dTrue, dFalse);


// add some directed links

network.getLinks().add(new Link(a, b));
network.getLinks().add(new Link(a, c));
network.getLinks().add(new Link(b, d));
network.getLinks().add(new Link(c, d));

// at this point we have fully specified the structural (graphical) specification of the Bayesian Network.

// We must define the necessary probability distributions for each node.

// Each node in a Bayesian Network requires a probability distribution conditioned on it's parents.

// newDistribution() can be called on a Node to create the appropriate probability distribution for a node
// or it can be created manually.

// The interface Distribution has been designed to represent both discrete and continuous variables,

// As we are currently dealing with discrete distributions, we will use the
// Table class.

// To access the discrete part of a distribution, we use Distribution.Table.

// The Table class is used to define distributions over a number of discrete variables.

Table tableA = a.newDistribution().getTable(); // access the table property of the Distribution

// Note that calling Node.newDistribution() does NOT assign the distribution to the node.
// A distribution cannot be assigned to a node until it is correctly specified.
// If a distribution becomes invalid (e.g. a parent node is added), it is automatically set to null.

// as node A has no parents there is no ambiguity about the order of variables in the distribution
tableA.set(0.1, aTrue);
tableA.set(0.9, aFalse);

// now tableA is correctly specified we can assign it to Node A;

// node B has node A as a parent, therefore its distribution will be P(B|A)

Table tableB = b.newDistribution().getTable();
tableB.set(0.2, aTrue, bTrue);
tableB.set(0.8, aTrue, bFalse);
tableB.set(0.15, aFalse, bTrue);
tableB.set(0.85, aFalse, bFalse);

// specify P(C|A)
Table tableC = c.newDistribution().getTable();
tableC.set(0.3, aTrue, cTrue);
tableC.set(0.7, aTrue, cFalse);
tableC.set(0.4, aFalse, cTrue);
tableC.set(0.6, aFalse, cFalse);

// specify P(D|B,C)
Table tableD = d.newDistribution().getTable();

// we could specify the values individually as above, or we can use a TableIterator as follows
TableIterator iteratorD = new TableIterator(tableD, new Node[]{b, c, d});
iteratorD.copyFrom(new double[]{0.4, 0.6, 0.55, 0.45, 0.32, 0.68, 0.01, 0.99});

// The network is now fully specified

// If required the network can be saved...

if (false) // change this to true to save the network
{"fileName.bayes"); // replace 'fileName.bayes' with your own path

// Now we will calculate P(A|D=True), i.e. the probability of A given the evidence that D is true

// use the factory design pattern to create the necessary inference related objects
InferenceFactory factory = new RelevanceTreeInferenceFactory();
Inference inference = factory.createInferenceEngine(network);
QueryOptions queryOptions = factory.createQueryOptions();
QueryOutput queryOutput = factory.createQueryOutput();

// we could have created these objects explicitly instead, but as the number of algorithms grows
// this makes it easier to switch between them

inference.getEvidence().setState(dTrue); // set D = True

Table queryA = new Table(a);
inference.query(queryOptions, queryOutput); // note that this can raise an exception (see help for details)

System.out.println("P(A|D=True) = {" + queryA.get(aTrue) + "," + queryA.get(aFalse) + "}.");

// Expected output ...
// P(A|D=True) = {0.0980748663101604,0.90192513368984}

// to perform another query we reuse all the objects

// now lets calculate P(A|D=True, C=True)

// we will also return the log-likelihood of the case
queryOptions.setLogLikelihood(true); // only request the log-likelihood if you really need it, as extra computation is involved

inference.query(queryOptions, queryOutput);
System.out.println(String.format("P(A|D=True, C=True) = {%s,%s}, log-likelihood = %s.", queryA.get(aTrue), queryA.get(aFalse), queryOutput.getLogLikelihood()));

// Expected output ...
// P(A|D=True, C=True) = {0.0777777777777778,0.922222222222222}, log-likelihood = -2.04330249506396.

// Note that we can also calculate joint queries such as P(A,B|D=True,C=True)