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/*
* Copyright (C) 2020 University of Pittsburgh.
*
* This library is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* This library is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with this library; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,
* MA 02110-1301 USA
*/
package edu.cmu.tetrad.bayes;
import edu.cmu.tetrad.graph.Dag;
import edu.cmu.tetrad.graph.Graph;
import edu.cmu.tetrad.graph.Node;
import java.io.IOException;
import java.io.ObjectInputStream;
import java.util.List;
/**
* Jan 21, 2020 11:03:09 AM
*
* @author Kevin V. Bui ([email protected])
*/
public class JunctionTreeUpdater implements ManipulatingBayesUpdater {
private static final long serialVersionUID = 23L;
/**
* The BayesIm which this updater modifies.
*
* @serial Cannot be null.
*/
private final BayesIm bayesIm;
/**
* Stores evidence for all variables.
*
* @serial Cannot be null.
*/
private Evidence evidence;
/**
* The last manipulated BayesIm.
*
* @serial Can be null.
*/
private BayesIm manipulatedBayesIm;
/**
* The BayesIm after update, if this was calculated.
*
* @serial Can be null.
*/
private BayesIm updatedBayesIm;
/**
* Calculates probabilities from the manipulated Bayes IM.
*
* @serial Can be null.
*/
// private BayesImProbs bayesImProbs;
private JunctionTreeAlgorithm jta;
public JunctionTreeUpdater(BayesIm bayesIm) {
this(bayesIm, Evidence.tautology(bayesIm));
}
public JunctionTreeUpdater(BayesIm bayesIm, Evidence evidence) {
if (bayesIm == null) {
throw new NullPointerException();
}
this.bayesIm = bayesIm;
setEvidence(evidence);
}
@Override
public BayesIm getManipulatedBayesIm() {
return this.manipulatedBayesIm;
}
@Override
public Graph getManipulatedGraph() {
return getManipulatedBayesIm().getDag();
}
@Override
public Evidence getEvidence() {
return new Evidence(this.evidence);
}
@Override
public void setEvidence(Evidence evidence) {
if (evidence == null) {
throw new NullPointerException();
}
if (evidence.isIncompatibleWith(this.bayesIm)) {
throw new IllegalArgumentException("The variable list for the "
+ "given bayesIm must be compatible with the variable list "
+ "for this evidence.");
}
this.evidence = evidence;
Graph graph = this.bayesIm.getBayesPm().getDag();
Dag manipulatedGraph = createManipulatedGraph(graph);
BayesPm manipulatedPm = createUpdatedBayesPm(manipulatedGraph);
this.manipulatedBayesIm = createdUpdatedBayesIm(manipulatedPm);
Evidence evidence2 = new Evidence(evidence, this.manipulatedBayesIm);
this.updatedBayesIm = new UpdatedBayesIm(this.manipulatedBayesIm, evidence2);
this.jta = new JunctionTreeAlgorithm(this.updatedBayesIm);
}
@Override
public BayesIm getUpdatedBayesIm() {
if (this.updatedBayesIm == null) {
updateAll();
}
return this.updatedBayesIm;
}
@Override
public double getMarginal(int variable, int category) {
Proposition assertion = Proposition.tautology(this.manipulatedBayesIm);
Proposition condition
= new Proposition(this.manipulatedBayesIm, this.evidence.getProposition());
assertion.setCategory(variable, category);
if (condition.existsCombination()) {
return this.jta.getMarginalProbability(variable, category);
} else {
return Double.NaN;
}
}
@Override
public boolean isJointMarginalSupported() {
return true;
}
@Override
public double getJointMarginal(int[] variables, int[] values) {
if (variables.length != values.length) {
throw new IllegalArgumentException("Values must match variables.");
}
Proposition assertion = Proposition.tautology(this.manipulatedBayesIm);
Proposition condition
= new Proposition(this.manipulatedBayesIm, this.evidence.getProposition());
for (int i = 0; i < variables.length; i++) {
assertion.setCategory(variables[i], values[i]);
}
if (condition.existsCombination()) {
double joint = 1.0;
for (int i = 0; i < variables.length; i++) {
joint *= this.jta.getMarginalProbability(variables[i], values[i]);
}
return joint;
} else {
return Double.NaN;
}
}
@Override
public BayesIm getBayesIm() {
return this.bayesIm;
}
@Override
public double[] calculatePriorMarginals(int nodeIndex) {
Evidence evidence = getEvidence();
setEvidence(Evidence.tautology(evidence.getVariableSource()));
double[] marginals = new double[evidence.getNumCategories(nodeIndex)];
for (int i = 0; i < getBayesIm().getNumColumns(nodeIndex); i++) {
marginals[i] = getMarginal(nodeIndex, i);
}
setEvidence(evidence);
return marginals;
}
@Override
public double[] calculateUpdatedMarginals(int nodeIndex) {
double[] marginals = new double[this.evidence.getNumCategories(nodeIndex)];
for (int i = 0; i < getBayesIm().getNumColumns(nodeIndex); i++) {
marginals[i] = getMarginal(nodeIndex, i);
}
return marginals;
}
@Override
public String toString() {
return "Junction tree updater, evidence = " + this.evidence;
}
private void updateAll() {
this.updatedBayesIm = new MlBayesIm(this.manipulatedBayesIm);
int numNodes = this.manipulatedBayesIm.getNumNodes();
Proposition assertion = Proposition.tautology(this.manipulatedBayesIm);
Proposition condition = Proposition.tautology(this.manipulatedBayesIm);
Evidence evidence2 = new Evidence(this.evidence, this.manipulatedBayesIm);
for (int node = 0; node < numNodes; node++) {
int numRows = this.manipulatedBayesIm.getNumRows(node);
int numCols = this.manipulatedBayesIm.getNumColumns(node);
int[] parents = this.manipulatedBayesIm.getParents(node);
for (int row = 0; row < numRows; row++) {
int[] parentValues
= this.manipulatedBayesIm.getParentValues(node, row);
for (int col = 0; col < numCols; col++) {
assertion.setToTautology();
condition.setToTautology();
for (int i = 0; i < numNodes; i++) {
for (int j = 0; j < evidence2.getNumCategories(i); j++) {
if (!evidence2.getProposition().isAllowed(i, j)) {
condition.removeCategory(i, j);
}
}
}
assertion.disallowComplement(node, col);
for (int k = 0; k < parents.length; k++) {
condition.disallowComplement(parents[k],
parentValues[k]);
}
if (condition.existsCombination()) {
double p = (parents.length > 0)
? this.jta.getConditionalProbability(node, col, parents, parentValues)
: this.jta.getMarginalProbability(node, col);
this.updatedBayesIm.setProbability(node, row, col, p);
} else {
this.updatedBayesIm.setProbability(node, row, col,
Double.NaN);
}
}
}
}
}
private BayesIm createdUpdatedBayesIm(BayesPm updatedBayesPm) {
return new MlBayesIm(updatedBayesPm, this.bayesIm, MlBayesIm.MANUAL);
}
private BayesPm createUpdatedBayesPm(Dag updatedGraph) {
return new BayesPm(updatedGraph, this.bayesIm.getBayesPm());
}
private Dag createManipulatedGraph(Graph graph) {
Dag updatedGraph = new Dag(graph);
// alters graph for manipulated evidenceItems
for (int i = 0; i < this.evidence.getNumNodes(); ++i) {
if (this.evidence.isManipulated(i)) {
Node node = updatedGraph.getNode(this.evidence.getNode(i).getName());
List parents = updatedGraph.getParents(node);
for (Node parent1 : parents) {
updatedGraph.removeEdge(node, parent1);
}
}
}
return updatedGraph;
}
/**
* Adds semantic checks to the default deserialization method. This method must have the standard signature for a
* readObject method, and the body of the method must begin with "s.defaultReadObject();". Other than that, any
* semantic checks can be specified and do not need to stay the same from version to version. A readObject method of
* this form may be added to any class, even if Tetrad sessions were previously saved out using a version of the
* class that didn't include it. (That's what the "s.defaultReadObject();" is for. See J. Bloch, Effective Java, for
* help.
*/
private void readObject(ObjectInputStream s)
throws IOException, ClassNotFoundException {
s.defaultReadObject();
if (this.bayesIm == null) {
throw new NullPointerException();
}
if (this.evidence == null) {
throw new NullPointerException();
}
}
}
