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///////////////////////////////////////////////////////////////////////////////
// For information as to what this class does, see the Javadoc, below. //
// Copyright (C) 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, //
// 2007, 2008, 2009, 2010, 2014, 2015, 2022 by Peter Spirtes, Richard //
// Scheines, Joseph Ramsey, and Clark Glymour. //
// //
// This program is free software; you can redistribute it and/or modify //
// it under the terms of the GNU General Public License as published by //
// the Free Software Foundation; either version 2 of the License, or //
// (at your option) any later version. //
// //
// This program 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 General Public License for more details. //
// //
// You should have received a copy of the GNU General Public License //
// along with this program; if not, write to the Free Software //
// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA //
///////////////////////////////////////////////////////////////////////////////
package edu.cmu.tetrad.bayes;
import edu.cmu.tetrad.data.DiscreteVariable;
import edu.cmu.tetrad.graph.Dag;
import edu.cmu.tetrad.graph.Node;
import edu.cmu.tetrad.graph.NodeType;
import nu.xom.Element;
import nu.xom.Elements;
import nu.xom.Text;
import java.util.*;
/**
* Parses Bayes elements back to objects.
*
* @author josephramsey
*/
public final class BayesXmlParser {
private Map namesToVars;
private static BayesIm makeBayesIm(BayesPm bayesPm, Element element2) {
if (!"cpts".equals(element2.getQualifiedName())) {
throw new IllegalArgumentException("Expecting 'cpts' element.");
}
MlBayesIm bayesIm = new MlBayesIm(bayesPm);
Elements elements2 = element2.getChildElements();
for (int nodeIndex = 0; nodeIndex < elements2.size(); nodeIndex++) {
Element e1 = elements2.get(nodeIndex);
if (!"cpt".equals(e1.getQualifiedName())) {
throw new IllegalArgumentException("Expecting 'cpt' element.");
}
String numRowsString = e1.getAttributeValue("numRows");
String numColsString = e1.getAttributeValue("numCols");
int numRows = Integer.parseInt(numRowsString);
int numCols = Integer.parseInt(numColsString);
Elements e1Elements = e1.getChildElements();
if (e1Elements.size() != numRows) {
throw new IllegalArgumentException("Element cpt claimed " +
numRows + " rows, but there are only " +
e1Elements.size() + " rows in the file.");
}
for (int rowIndex = 0; rowIndex < numRows; rowIndex++) {
Element e2 = e1Elements.get(rowIndex);
if (!"row".equals(e2.getQualifiedName())) {
throw new IllegalArgumentException(
"Expecting 'parent' element.");
}
Text rowNode = (Text) e2.getChild(0);
String rowString = rowNode.getValue();
StringTokenizer t = new StringTokenizer(rowString);
for (int colIndex = 0; colIndex < numCols; colIndex++) {
String token = t.nextToken();
try {
double value = Double.parseDouble(token);
bayesIm.setProbability(nodeIndex, rowIndex, colIndex,
value);
} catch (NumberFormatException e) {
// Skip.
}
}
if (t.hasMoreTokens()) {
throw new IllegalArgumentException("Element cpt claimed " +
numCols +
" columnns , but there are more that that " +
"in the file.");
}
}
}
return bayesIm;
}
public BayesIm getBayesIm(Element element) {
if (!"bayesNet".equals(element.getQualifiedName())) {
throw new IllegalArgumentException("Expecting 'bayesNet' element.");
}
Elements elements = element.getChildElements();
Element element0 = elements.get(0);
Element element1 = elements.get(1);
Element element2 = elements.get(2);
List variables = getVariables(element0);
BayesPm bayesPm = makeBayesPm(variables, element1);
return BayesXmlParser.makeBayesIm(bayesPm, element2);
}
private List getVariables(Element element0) {
if (!"bnVariables".equals(element0.getQualifiedName())) {
throw new IllegalArgumentException(
"Expecting 'bnVariables' element.");
}
List variables = new LinkedList<>();
Elements elements = element0.getChildElements();
for (int i = 0; i < elements.size(); i++) {
Element e1 = elements.get(i);
Elements e2Elements = e1.getChildElements();
if (!"discreteVariable".equals(e1.getQualifiedName())) {
throw new IllegalArgumentException(
"Expecting 'discreteVariable' " + "element.");
}
String name = e1.getAttributeValue("name");
String isLatentVal = e1.getAttributeValue("latent");
boolean isLatent =
"yes".equals(isLatentVal);
int x = Integer.parseInt(e1.getAttributeValue("x"));
int y = Integer.parseInt(e1.getAttributeValue("y"));
int numCategories = e2Elements.size();
List categories = new LinkedList<>();
for (int j = 0; j < numCategories; j++) {
Element e2 = e2Elements.get(j);
if (!"category".equals(e2.getQualifiedName())) {
throw new IllegalArgumentException(
"Expecting 'category' " + "element.");
}
categories.add(e2.getAttributeValue("name"));
}
DiscreteVariable var = new DiscreteVariable(name, categories);
if (isLatent) {
var.setNodeType(NodeType.LATENT);
}
var.setCenterX(x);
var.setCenterY(y);
variables.add(var);
}
this.namesToVars = new HashMap<>();
for (Node v : variables) {
String name = v.getName();
this.namesToVars.put(name, v);
}
return variables;
}
private BayesPm makeBayesPm(List variables, Element element1) {
if (!"parents".equals(element1.getQualifiedName())) {
throw new IllegalArgumentException("Expecting 'parents' element.");
}
Dag graph = new Dag();
for (Node variable : variables) {
graph.addNode(variable);
}
Elements elements = element1.getChildElements();
for (int i = 0; i < elements.size(); i++) {
Element e1 = elements.get(i);
if (!"parentsFor".equals(e1.getQualifiedName())) {
throw new IllegalArgumentException(
"Expecting 'parentsFor' element.");
}
String varName = e1.getAttributeValue("name");
Node var = this.namesToVars.get(varName);
Elements elements1 = e1.getChildElements();
for (int j = 0; j < elements1.size(); j++) {
Element e2 = elements1.get(j);
if (!"parent".equals(e2.getQualifiedName())) {
throw new IllegalArgumentException(
"Expecting 'parent' element.");
}
String parentName = e2.getAttributeValue("name");
Node parent = this.namesToVars.get(parentName);
graph.addDirectedEdge(parent, var);
}
}
BayesPm bayesPm = new BayesPm(graph);
for (Node variable1 : variables) {
DiscreteVariable graphVariable = (DiscreteVariable) variable1;
List categories = graphVariable.getCategories();
bayesPm.setCategories(graphVariable, categories);
}
return bayesPm;
}
}
