/*
* 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 3 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, see <http://www.gnu.org/licenses/>.
*/
/*
* BayesNet.java
* Copyright (C) 2003-2012 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.bayes.net;
import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;
import weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes;
import weka.core.Attribute;
import weka.core.DenseInstance;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
import weka.core.Utils;
import weka.estimators.Estimator;
/**
<!-- globalinfo-start -->
* Bayes Network learning using various search algorithms and quality measures.<br/>
* Base class for a Bayes Network classifier. Provides datastructures (network structure, conditional probability distributions, etc.) and facilities common to Bayes Network learning algorithms like K2 and B.<br/>
* <br/>
* For more information see:<br/>
* <br/>
* http://www.cs.waikato.ac.nz/~remco/weka.pdf
* <p/>
<!-- globalinfo-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -B
* Generate network (instead of instances)
* </pre>
*
* <pre> -N <integer>
* Nr of nodes
* </pre>
*
* <pre> -A <integer>
* Nr of arcs
* </pre>
*
* <pre> -M <integer>
* Nr of instances
* </pre>
*
* <pre> -C <integer>
* Cardinality of the variables
* </pre>
*
* <pre> -S <integer>
* Seed for random number generator
* </pre>
*
* <pre> -F <file>
* The BIF file to obtain the structure from.
* </pre>
*
<!-- options-end -->
*
* @author Remco Bouckaert (rrb@xm.co.nz)
* @version $Revision: 8034 $
*/
public class BayesNetGenerator extends EditableBayesNet {
/** the seed value */
int m_nSeed = 1;
/** the random number generator */
Random random;
/** for serialization */
static final long serialVersionUID = -7462571170596157720L;
/**
* Constructor for BayesNetGenerator.
*/
public BayesNetGenerator() {
super();
} // c'tor
/**
* Generate random connected Bayesian network with discrete nodes
* having all the same cardinality.
*
* @throws Exception if something goes wrong
*/
public void generateRandomNetwork () throws Exception {
if (m_otherBayesNet == null) {
// generate from scratch
Init(m_nNrOfNodes, m_nCardinality);
generateRandomNetworkStructure(m_nNrOfNodes, m_nNrOfArcs);
generateRandomDistributions(m_nNrOfNodes, m_nCardinality);
} else {
// read from file, just copy parent sets and distributions
m_nNrOfNodes = m_otherBayesNet.getNrOfNodes();
m_ParentSets = m_otherBayesNet.getParentSets();
m_Distributions = m_otherBayesNet.getDistributions();
random = new Random(m_nSeed);
// initialize m_Instances
FastVector attInfo = new FastVector(m_nNrOfNodes);
// generate value strings
for (int iNode = 0; iNode < m_nNrOfNodes; iNode++) {
int nValues = m_otherBayesNet.getCardinality(iNode);
FastVector nomStrings = new FastVector(nValues + 1);
for (int iValue = 0; iValue < nValues; iValue++) {
nomStrings.addElement(m_otherBayesNet.getNodeValue(iNode, iValue));
}
Attribute att = new Attribute(m_otherBayesNet.getNodeName(iNode), nomStrings);
attInfo.addElement(att);
}
m_Instances = new Instances(m_otherBayesNet.getName(), attInfo, 100);
m_Instances.setClassIndex(m_nNrOfNodes - 1);
}
} // GenerateRandomNetwork
/**
* Init defines a minimal Bayes net with no arcs
* @param nNodes number of nodes in the Bayes net
* @param nValues number of values each of the nodes can take
* @throws Exception if something goes wrong
*/
public void Init(int nNodes, int nValues) throws Exception {
random = new Random(m_nSeed);
// initialize structure
FastVector attInfo = new FastVector(nNodes);
// generate value strings
FastVector nomStrings = new FastVector(nValues + 1);
for (int iValue = 0; iValue < nValues; iValue++) {
nomStrings.addElement("Value" + (iValue + 1));
}
for (int iNode = 0; iNode < nNodes; iNode++) {
Attribute att = new Attribute("Node" + (iNode + 1), nomStrings);
attInfo.addElement(att);
}
m_Instances = new Instances("RandomNet", attInfo, 100);
m_Instances.setClassIndex(nNodes - 1);
setUseADTree(false);
// m_bInitAsNaiveBayes = false;
// m_bMarkovBlanketClassifier = false;
initStructure();
// initialize conditional distribution tables
m_Distributions = new Estimator[nNodes][1];
for (int iNode = 0; iNode < nNodes; iNode++) {
m_Distributions[iNode][0] =
new DiscreteEstimatorBayes(nValues, getEstimator().getAlpha());
}
m_nEvidence = new FastVector(nNodes);
for (int i = 0; i < nNodes; i++) {
m_nEvidence.addElement(-1);
}
m_fMarginP = new FastVector(nNodes);
for (int i = 0; i < nNodes; i++) {
double[] P = new double[getCardinality(i)];
m_fMarginP.addElement(P);
}
m_nPositionX = new FastVector(nNodes);
m_nPositionY = new FastVector(nNodes);
for (int iNode = 0; iNode < nNodes; iNode++) {
m_nPositionX.addElement(iNode%10 * 50);
m_nPositionY.addElement(((int)(iNode/10)) * 50);
}
} // DefineNodes
/**
* GenerateRandomNetworkStructure generate random connected Bayesian network
* @param nNodes number of nodes in the Bayes net to generate
* @param nArcs number of arcs to generate. Must be between nNodes - 1 and nNodes * (nNodes-1) / 2
* @throws Exception if number of arcs is incorrect
*/
public void generateRandomNetworkStructure(int nNodes, int nArcs)
throws Exception
{
if (nArcs < nNodes - 1) {
throw new Exception("Number of arcs should be at least (nNodes - 1) = " + (nNodes - 1) + " instead of " + nArcs);
}
if (nArcs > nNodes * (nNodes - 1) / 2) {
throw new Exception("Number of arcs should be at most nNodes * (nNodes - 1) / 2 = "+ (nNodes * (nNodes - 1) / 2) + " instead of " + nArcs);
}
if (nArcs == 0) {return;} // deal with patalogical case for nNodes = 1
// first generate tree connecting all nodes
generateTree(nNodes);
// The tree contains nNodes - 1 arcs, so there are
// nArcs - (nNodes-1) to add at random.
// All arcs point from lower to higher ordered nodes
// so that acyclicity is ensured.
for (int iArc = nNodes - 1; iArc < nArcs; iArc++) {
boolean bDone = false;
while (!bDone) {
int nNode1 = random.nextInt(nNodes);
int nNode2 = random.nextInt(nNodes);
if (nNode1 == nNode2) {nNode2 = (nNode1 + 1) % nNodes;}
if (nNode2 < nNode1) {int h = nNode1; nNode1 = nNode2; nNode2 = h;}
if (!m_ParentSets[nNode2].contains(nNode1)) {
m_ParentSets[nNode2].addParent(nNode1, m_Instances);
bDone = true;
}
}
}
} // GenerateRandomNetworkStructure
/**
* GenerateTree creates a tree-like network structure (actually a
* forest) by starting with a randomly selected pair of nodes, add
* an arc between. Then keep on selecting one of the connected nodes
* and one of the unconnected ones and add an arrow between them,
* till all nodes are connected.
* @param nNodes number of nodes in the Bayes net to generate
*/
void generateTree(int nNodes) {
boolean [] bConnected = new boolean [nNodes];
// start adding an arc at random
int nNode1 = random.nextInt(nNodes);
int nNode2 = random.nextInt(nNodes);
if (nNode1 == nNode2) {nNode2 = (nNode1 + 1) % nNodes;}
if (nNode2 < nNode1) {int h = nNode1; nNode1 = nNode2; nNode2 = h;}
m_ParentSets[nNode2].addParent(nNode1, m_Instances);
bConnected[nNode1] = true;
bConnected[nNode2] = true;
// Repeatedly, select one of the connected nodes, and one of
// the unconnected nodes and add an arc.
// All arcs point from lower to higher ordered nodes
// so that acyclicity is ensured.
for (int iArc = 2; iArc < nNodes; iArc++ ) {
int nNode = random.nextInt(nNodes);
nNode1 = 0; // one of the connected nodes
while (nNode >= 0) {
nNode1 = (nNode1 + 1) % nNodes;
while (!bConnected[nNode1]) {
nNode1 = (nNode1 + 1) % nNodes;
}
nNode--;
}
nNode = random.nextInt(nNodes);
nNode2 = 0; // one of the unconnected nodes
while (nNode >= 0) {
nNode2 = (nNode2 + 1) % nNodes;
while (bConnected[nNode2]) {
nNode2 = (nNode2 + 1) % nNodes;
}
nNode--;
}
if (nNode2 < nNode1) {int h = nNode1; nNode1 = nNode2; nNode2 = h;}
m_ParentSets[nNode2].addParent(nNode1, m_Instances);
bConnected[nNode1] = true;
bConnected[nNode2] = true;
}
} // GenerateTree
/**
* GenerateRandomDistributions generates discrete conditional distribution tables
* for all nodes of a Bayes network once a network structure has been determined.
* @param nNodes number of nodes in the Bayes net
* @param nValues number of values each of the nodes can take
*/
void generateRandomDistributions(int nNodes, int nValues) {
// Reserve space for CPTs
int nMaxParentCardinality = 1;
for (int iAttribute = 0; iAttribute < nNodes; iAttribute++) {
if (m_ParentSets[iAttribute].getCardinalityOfParents() > nMaxParentCardinality) {
nMaxParentCardinality = m_ParentSets[iAttribute].getCardinalityOfParents();
}
}
// Reserve plenty of memory
m_Distributions = new Estimator[m_Instances.numAttributes()][nMaxParentCardinality];
// estimate CPTs
for (int iAttribute = 0; iAttribute < nNodes; iAttribute++) {
int [] nPs = new int [nValues + 1];
nPs[0] = 0;
nPs[nValues] = 1000;
for (int iParent = 0; iParent < m_ParentSets[iAttribute].getCardinalityOfParents(); iParent++) {
// fill array with random nr's
for (int iValue = 1; iValue < nValues; iValue++) {
nPs[iValue] = random.nextInt(1000);
}
// sort
for (int iValue = 1; iValue < nValues; iValue++) {
for (int iValue2 = iValue + 1; iValue2 < nValues; iValue2++) {
if (nPs[iValue2] < nPs[iValue]) {
int h = nPs[iValue2]; nPs[iValue2] = nPs[iValue]; nPs[iValue] = h;
}
}
}
// assign to probability tables
DiscreteEstimatorBayes d = new DiscreteEstimatorBayes(nValues, getEstimator().getAlpha());
for (int iValue = 0; iValue < nValues; iValue++) {
d.addValue(iValue, nPs[iValue + 1] - nPs[iValue]);
}
m_Distributions[iAttribute][iParent] = d;
}
}
} // GenerateRandomDistributions
/**
* GenerateInstances generates random instances sampling from the
* distribution represented by the Bayes network structure. It assumes
* a Bayes network structure has been initialized
*
* @throws Exception if something goes wrong
*/
public void generateInstances () throws Exception {
int [] order = getOrder();
for (int iInstance = 0; iInstance < m_nNrOfInstances; iInstance++) {
int nNrOfAtts = m_Instances.numAttributes();
Instance instance = new DenseInstance(nNrOfAtts);
instance.setDataset(m_Instances);
for (int iAtt2 = 0; iAtt2 < nNrOfAtts; iAtt2++) {
int iAtt = order[iAtt2];
double iCPT = 0;
for (int iParent = 0; iParent < m_ParentSets[iAtt].getNrOfParents(); iParent++) {
int nParent = m_ParentSets[iAtt].getParent(iParent);
iCPT = iCPT * m_Instances.attribute(nParent).numValues() + instance.value(nParent);
}
double fRandom = random.nextInt(1000) / 1000.0f;
int iValue = 0;
while (fRandom > m_Distributions[iAtt][(int) iCPT].getProbability(iValue)) {
fRandom = fRandom - m_Distributions[iAtt][(int) iCPT].getProbability(iValue);
iValue++ ;
}
instance.setValue(iAtt, iValue);
}
m_Instances.add(instance);
}
} // GenerateInstances
/**
* @throws Exception if there's a cycle in the graph
*/
int [] getOrder() throws Exception {
int nNrOfAtts = m_Instances.numAttributes();
int [] order = new int[nNrOfAtts];
boolean [] bDone = new boolean[nNrOfAtts];
for (int iAtt = 0; iAtt < nNrOfAtts; iAtt++) {
int iAtt2 = 0;
boolean allParentsDone = false;
while (!allParentsDone && iAtt2 < nNrOfAtts) {
if (!bDone[iAtt2]) {
allParentsDone = true;
int iParent = 0;
while (allParentsDone && iParent < m_ParentSets[iAtt2].getNrOfParents()) {
allParentsDone = bDone[m_ParentSets[iAtt2].getParent(iParent++)];
}
if (allParentsDone && iParent == m_ParentSets[iAtt2].getNrOfParents()) {
order[iAtt] = iAtt2;
bDone[iAtt2] = true;
} else {
iAtt2++;
}
} else {
iAtt2++;
}
}
if (!allParentsDone && iAtt2 == nNrOfAtts) {
throw new Exception("There appears to be a cycle in the graph");
}
}
return order;
} // getOrder
/**
* Returns either the net (if BIF format) or the generated instances
*
* @return either the net or the generated instances
*/
public String toString() {
if (m_bGenerateNet) {
return toXMLBIF03();
}
return m_Instances.toString();
} // toString
boolean m_bGenerateNet = false;
int m_nNrOfNodes = 10;
int m_nNrOfArcs = 10;
int m_nNrOfInstances = 10;
int m_nCardinality = 2;
String m_sBIFFile = "";
void setNrOfNodes(int nNrOfNodes) {m_nNrOfNodes = nNrOfNodes;}
void setNrOfArcs(int nNrOfArcs) {m_nNrOfArcs = nNrOfArcs;}
void setNrOfInstances(int nNrOfInstances) {m_nNrOfInstances = nNrOfInstances;}
void setCardinality(int nCardinality) {m_nCardinality = nCardinality;}
void setSeed(int nSeed) {m_nSeed = nSeed;}
/**
* Returns an enumeration describing the available options
*
* @return an enumeration of all the available options
*/
public Enumeration listOptions() {
Vector newVector = new Vector(6);
newVector.addElement(new Option("\tGenerate network (instead of instances)\n", "B", 0, "-B"));
newVector.addElement(new Option("\tNr of nodes\n", "N", 1, "-N <integer>"));
newVector.addElement(new Option("\tNr of arcs\n", "A", 1, "-A <integer>"));
newVector.addElement(new Option("\tNr of instances\n", "M", 1, "-M <integer>"));
newVector.addElement(new Option("\tCardinality of the variables\n", "C", 1, "-C <integer>"));
newVector.addElement(new Option("\tSeed for random number generator\n", "S", 1, "-S <integer>"));
newVector.addElement(new Option("\tThe BIF file to obtain the structure from.\n", "F", 1, "-F <file>"));
return newVector.elements();
} // listOptions
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -B
* Generate network (instead of instances)
* </pre>
*
* <pre> -N <integer>
* Nr of nodes
* </pre>
*
* <pre> -A <integer>
* Nr of arcs
* </pre>
*
* <pre> -M <integer>
* Nr of instances
* </pre>
*
* <pre> -C <integer>
* Cardinality of the variables
* </pre>
*
* <pre> -S <integer>
* Seed for random number generator
* </pre>
*
* <pre> -F <file>
* The BIF file to obtain the structure from.
* </pre>
*
<!-- options-end -->
*
* @param options the list of options as an array of strings
* @exception Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
m_bGenerateNet = Utils.getFlag('B', options);
String sNrOfNodes = Utils.getOption('N', options);
if (sNrOfNodes.length() != 0) {
setNrOfNodes(Integer.parseInt(sNrOfNodes));
} else {
setNrOfNodes(10);
}
String sNrOfArcs = Utils.getOption('A', options);
if (sNrOfArcs.length() != 0) {
setNrOfArcs(Integer.parseInt(sNrOfArcs));
} else {
setNrOfArcs(10);
}
String sNrOfInstances = Utils.getOption('M', options);
if (sNrOfInstances.length() != 0) {
setNrOfInstances(Integer.parseInt(sNrOfInstances));
} else {
setNrOfInstances(10);
}
String sCardinality = Utils.getOption('C', options);
if (sCardinality.length() != 0) {
setCardinality(Integer.parseInt(sCardinality));
} else {
setCardinality(2);
}
String sSeed = Utils.getOption('S', options);
if (sSeed.length() != 0) {
setSeed(Integer.parseInt(sSeed));
} else {
setSeed(1);
}
String sBIFFile = Utils.getOption('F', options);
if ((sBIFFile != null) && (sBIFFile != "")) {
setBIFFile(sBIFFile);
}
} // setOptions
/**
* Gets the current settings of the classifier.
*
* @return an array of strings suitable for passing to setOptions
*/
public String[] getOptions() {
String[] options = new String[13];
int current = 0;
if (m_bGenerateNet) {
options[current++] = "-B";
}
options[current++] = "-N";
options[current++] = "" + m_nNrOfNodes;
options[current++] = "-A";
options[current++] = "" + m_nNrOfArcs;
options[current++] = "-M";
options[current++] = "" + m_nNrOfInstances;
options[current++] = "-C";
options[current++] = "" + m_nCardinality;
options[current++] = "-S";
options[current++] = "" + m_nSeed;
if (m_sBIFFile.length() != 0) {
options[current++] = "-F";
options[current++] = "" + m_sBIFFile;
}
// Fill up rest with empty strings, not nulls!
while (current < options.length) {
options[current++] = "";
}
return options;
} // getOptions
/**
* prints all the options to stdout
*/
protected static void printOptions(OptionHandler o) {
Enumeration enm = o.listOptions();
System.out.println("Options for " + o.getClass().getName() + ":\n");
while (enm.hasMoreElements()) {
Option option = (Option) enm.nextElement();
System.out.println(option.synopsis());
System.out.println(option.description());
}
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 8034 $");
}
/**
* Main method
*
* @param args the commandline parameters
*/
static public void main(String [] args) {
BayesNetGenerator b = new BayesNetGenerator();
try {
if ( (args.length == 0) || (Utils.getFlag('h', args)) ) {
printOptions(b);
return;
}
b.setOptions(args);
b.generateRandomNetwork();
if (!b.m_bGenerateNet) { // skip if not required
b.generateInstances();
}
System.out.println(b.toString());
} catch (Exception e) {
e.printStackTrace();
printOptions(b);
}
} // main
} // class BayesNetGenerator