ベイズK 2アルゴリズム
package weka.classifiers.bayes.net.search.local;
import weka.classifiers.bayes.BayesNet;
import weka.core.Instances;
import weka.core.Option;
import weka.core.RevisionUtils;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;
public class K2
extends LocalScoreSearchAlgorithm
implements TechnicalInformationHandler {
/**for serializationシーケンス化*/
static final long serialVersionUID = 6176545934752116631L;
/**Holds flag to indicate ordering should be randomは、ソートがランダムであるべきであることを示すフラグを持っています**/
boolean m_bRandomOrder = false;
public TechnicalInformation getTechnicalInformation() {
TechnicalInformation result;
TechnicalInformation additional;
result = new TechnicalInformation(Type.PROCEEDINGS);
result.setValue(Field.AUTHOR, "G.F. Cooper and E. Herskovits");
result.setValue(Field.YEAR, "1990");
result.setValue(Field.TITLE, "A Bayesian method for constructing Bayesian belief networks from databases");
result.setValue(Field.BOOKTITLE, "Proceedings of the Conference on Uncertainty in AI");
result.setValue(Field.PAGES, "86-94");
additional = result.add(Type.ARTICLE);
additional.setValue(Field.AUTHOR, "G. Cooper and E. Herskovits");
additional.setValue(Field.YEAR, "1992");
additional.setValue(Field.TITLE, "A Bayesian method for the induction of probabilistic networks from data");
additional.setValue(Field.JOURNAL, "Machine Learning");
additional.setValue(Field.VOLUME, "9");
additional.setValue(Field.NUMBER, "4");
additional.setValue(Field.PAGES, "309-347");
return result;
}
/**
* search determines the network structure/graph of the network
* with the K2 algorithm, restricted by its initial structure (which can
* be an empty graph, or a Naive Bayes graph.
*検索は、ネットワークのネットワーク構造/図K 2アルゴリズムを決定し、その初期構造(空の図、または素朴なベイズ図に制限することができる.)
* @param bayesNet the network
* @param instances the data to work with
* @throws Exception if something goes wrong
*/
public void search (BayesNet bayesNet, Instances instances) throws Exception {
int nOrder[] = new int [instances.numAttributes()];
nOrder[0] = instances.classIndex();
int nAttribute = 0;
for (int iOrder = 1; iOrder < instances.numAttributes(); iOrder++) {
if (nAttribute == instances.classIndex()) {
nAttribute++;
}
nOrder[iOrder] = nAttribute++;
}
if (m_bRandomOrder) {
//generate random ordering (if required)
Random random = new Random();
int iClass;
if (getInitAsNaiveBayes()) {
iClass = 0;
} else {
iClass = -1;
}
for (int iOrder = 0; iOrder < instances.numAttributes(); iOrder++) {
int iOrder2 = Math.abs(random.nextInt()) % instances.numAttributes();
if (iOrder != iClass && iOrder2 != iClass) {
int nTmp = nOrder[iOrder];
nOrder[iOrder] = nOrder[iOrder2];
nOrder[iOrder2] = nTmp;
}
}
}
//determine base scores
double [] fBaseScores = new double [instances.numAttributes()];
for (int iOrder = 0; iOrder < instances.numAttributes(); iOrder++) {
int iAttribute = nOrder[iOrder];
fBaseScores[iAttribute] = calcNodeScore(iAttribute);
}
//K2 algorithm: greedy search restricted by ordering
for (int iOrder = 1; iOrder < instances.numAttributes(); iOrder++) {
int iAttribute = nOrder[iOrder];
double fBestScore = fBaseScores[iAttribute];
boolean bProgress = (bayesNet.getParentSet(iAttribute).getNrOfParents() < getMaxNrOfParents());
while (bProgress) {
int nBestAttribute = -1;
for (int iOrder2 = 0; iOrder2 < iOrder; iOrder2++) {
int iAttribute2 = nOrder[iOrder2];
double fScore = calcScoreWithExtraParent(iAttribute, iAttribute2);
if (fScore > fBestScore) {
fBestScore = fScore;
nBestAttribute = iAttribute2;
}
}
if (nBestAttribute != -1) {
bayesNet.getParentSet(iAttribute).addParent(nBestAttribute, instances);
fBaseScores[iAttribute] = fBestScore;
bProgress = (bayesNet.getParentSet(iAttribute).getNrOfParents() < getMaxNrOfParents());
} else {
bProgress = false;
}
}
}
}//buildStructure
/**
* Sets the max number of parents
*
* @param nMaxNrOfParents the max number of parents
*/
public void setMaxNrOfParents(int nMaxNrOfParents) {
m_nMaxNrOfParents = nMaxNrOfParents;
}
/**
* Gets the max number of parents.
*
* @return the max number of parents
*/
public int getMaxNrOfParents() {
return m_nMaxNrOfParents;
}
/**
* Sets whether to init as naive bayes
*
* @param bInitAsNaiveBayes whether to init as naive bayes
*/
public void setInitAsNaiveBayes(boolean bInitAsNaiveBayes) {
m_bInitAsNaiveBayes = bInitAsNaiveBayes;
}
/**
* Gets whether to init as naive bayes
*
* @return whether to init as naive bayes
*/
public boolean getInitAsNaiveBayes() {
return m_bInitAsNaiveBayes;
}
/**
* Set random order flag
*
* @param bRandomOrder the random order flag
*/
public void setRandomOrder(boolean bRandomOrder) {
m_bRandomOrder = bRandomOrder;
}//SetRandomOrder
/**
* Get random order flag
*
* @return the random order flag
*/
public boolean getRandomOrder() {
return m_bRandomOrder;
}//getRandomOrder
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(0);
newVector.addElement(new Option("\tInitial structure is empty (instead of Naive Bayes)",
"N", 0, "-N"));
newVector.addElement(new Option("\tMaximum number of parents", "P", 1,
"-P "));
newVector.addElement(new Option(
"\tRandom order."
+ "\t(default false)",
"R", 0, "-R"));
Enumeration enu = super.listOptions();
while (enu.hasMoreElements()) {
newVector.addElement(enu.nextElement());
}
return newVector.elements();
}
/**
* Parses a given list of options.
*
* Valid options are:
*
*
*
*
*
*
*
*
*
*
*
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
setRandomOrder(Utils.getFlag('R', options));
m_bInitAsNaiveBayes = !(Utils.getFlag('N', options));
String sMaxNrOfParents = Utils.getOption('P', options);
if (sMaxNrOfParents.length() != 0) {
setMaxNrOfParents(Integer.parseInt(sMaxNrOfParents));
} else {
setMaxNrOfParents(100000);
}
super.setOptions(options);
}
/**
* Gets the current settings of the search algorithm.
*
* @return an array of strings suitable for passing to setOptions
*/
public String [] getOptions() {
String[] superOptions = super.getOptions();
String [] options = new String [4 + superOptions.length];
int current = 0;
options[current++] = "-P";
options[current++] = ""+ m_nMaxNrOfParents;
if (!m_bInitAsNaiveBayes) {
options[current++] = "-N";
} if (getRandomOrder()) {
options[current++] = "-R";
}
//insert options from parent class
for (int iOption = 0; iOption < superOptions.length; iOption++) {
options[current++] = superOptions[iOption];
}
while (current < options.length) {
options[current++] = "";
}
//Fill up rest with empty strings, not nulls!
return options;
}
/**
* This will return a string describing the search algorithm.
* @return The string.
*/
public String globalInfo() {
return
"This Bayes Network learning algorithm uses a hill climbing algorithm "
+ "restricted by an order on the variables."
+ "For more information see:"
+ getTechnicalInformation().toString() + ""
+ "Works with nominal variables and no missing values only.";
}
/**
* @return a string to describe the RandomOrder option.
*/
public String randomOrderTipText() {
return "When set to true, the order of the nodes in the network is random."+
"Default random order is false and the order"+
"of the nodes in the dataset is used."+
"In any case, when the network was initialized as Naive Bayes Network, the"+
"class variable is first in the ordering though.";
}//randomOrderTipText
/**
* Returns the revision string.
*
* @returnthe revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.8 $");
}
}
import weka.classifiers.bayes.BayesNet;
import weka.core.Instances;
import weka.core.Option;
import weka.core.RevisionUtils;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;
public class K2
extends LocalScoreSearchAlgorithm
implements TechnicalInformationHandler {
/**for serializationシーケンス化*/
static final long serialVersionUID = 6176545934752116631L;
/**Holds flag to indicate ordering should be randomは、ソートがランダムであるべきであることを示すフラグを持っています**/
boolean m_bRandomOrder = false;
public TechnicalInformation getTechnicalInformation() {
TechnicalInformation result;
TechnicalInformation additional;
result = new TechnicalInformation(Type.PROCEEDINGS);
result.setValue(Field.AUTHOR, "G.F. Cooper and E. Herskovits");
result.setValue(Field.YEAR, "1990");
result.setValue(Field.TITLE, "A Bayesian method for constructing Bayesian belief networks from databases");
result.setValue(Field.BOOKTITLE, "Proceedings of the Conference on Uncertainty in AI");
result.setValue(Field.PAGES, "86-94");
additional = result.add(Type.ARTICLE);
additional.setValue(Field.AUTHOR, "G. Cooper and E. Herskovits");
additional.setValue(Field.YEAR, "1992");
additional.setValue(Field.TITLE, "A Bayesian method for the induction of probabilistic networks from data");
additional.setValue(Field.JOURNAL, "Machine Learning");
additional.setValue(Field.VOLUME, "9");
additional.setValue(Field.NUMBER, "4");
additional.setValue(Field.PAGES, "309-347");
return result;
}
/**
* search determines the network structure/graph of the network
* with the K2 algorithm, restricted by its initial structure (which can
* be an empty graph, or a Naive Bayes graph.
*検索は、ネットワークのネットワーク構造/図K 2アルゴリズムを決定し、その初期構造(空の図、または素朴なベイズ図に制限することができる.)
* @param bayesNet the network
* @param instances the data to work with
* @throws Exception if something goes wrong
*/
public void search (BayesNet bayesNet, Instances instances) throws Exception {
int nOrder[] = new int [instances.numAttributes()];
nOrder[0] = instances.classIndex();
int nAttribute = 0;
for (int iOrder = 1; iOrder < instances.numAttributes(); iOrder++) {
if (nAttribute == instances.classIndex()) {
nAttribute++;
}
nOrder[iOrder] = nAttribute++;
}
if (m_bRandomOrder) {
//generate random ordering (if required)
Random random = new Random();
int iClass;
if (getInitAsNaiveBayes()) {
iClass = 0;
} else {
iClass = -1;
}
for (int iOrder = 0; iOrder < instances.numAttributes(); iOrder++) {
int iOrder2 = Math.abs(random.nextInt()) % instances.numAttributes();
if (iOrder != iClass && iOrder2 != iClass) {
int nTmp = nOrder[iOrder];
nOrder[iOrder] = nOrder[iOrder2];
nOrder[iOrder2] = nTmp;
}
}
}
//determine base scores
double [] fBaseScores = new double [instances.numAttributes()];
for (int iOrder = 0; iOrder < instances.numAttributes(); iOrder++) {
int iAttribute = nOrder[iOrder];
fBaseScores[iAttribute] = calcNodeScore(iAttribute);
}
//K2 algorithm: greedy search restricted by ordering
for (int iOrder = 1; iOrder < instances.numAttributes(); iOrder++) {
int iAttribute = nOrder[iOrder];
double fBestScore = fBaseScores[iAttribute];
boolean bProgress = (bayesNet.getParentSet(iAttribute).getNrOfParents() < getMaxNrOfParents());
while (bProgress) {
int nBestAttribute = -1;
for (int iOrder2 = 0; iOrder2 < iOrder; iOrder2++) {
int iAttribute2 = nOrder[iOrder2];
double fScore = calcScoreWithExtraParent(iAttribute, iAttribute2);
if (fScore > fBestScore) {
fBestScore = fScore;
nBestAttribute = iAttribute2;
}
}
if (nBestAttribute != -1) {
bayesNet.getParentSet(iAttribute).addParent(nBestAttribute, instances);
fBaseScores[iAttribute] = fBestScore;
bProgress = (bayesNet.getParentSet(iAttribute).getNrOfParents() < getMaxNrOfParents());
} else {
bProgress = false;
}
}
}
}//buildStructure
/**
* Sets the max number of parents
*
* @param nMaxNrOfParents the max number of parents
*/
public void setMaxNrOfParents(int nMaxNrOfParents) {
m_nMaxNrOfParents = nMaxNrOfParents;
}
/**
* Gets the max number of parents.
*
* @return the max number of parents
*/
public int getMaxNrOfParents() {
return m_nMaxNrOfParents;
}
/**
* Sets whether to init as naive bayes
*
* @param bInitAsNaiveBayes whether to init as naive bayes
*/
public void setInitAsNaiveBayes(boolean bInitAsNaiveBayes) {
m_bInitAsNaiveBayes = bInitAsNaiveBayes;
}
/**
* Gets whether to init as naive bayes
*
* @return whether to init as naive bayes
*/
public boolean getInitAsNaiveBayes() {
return m_bInitAsNaiveBayes;
}
/**
* Set random order flag
*
* @param bRandomOrder the random order flag
*/
public void setRandomOrder(boolean bRandomOrder) {
m_bRandomOrder = bRandomOrder;
}//SetRandomOrder
/**
* Get random order flag
*
* @return the random order flag
*/
public boolean getRandomOrder() {
return m_bRandomOrder;
}//getRandomOrder
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(0);
newVector.addElement(new Option("\tInitial structure is empty (instead of Naive Bayes)",
"N", 0, "-N"));
newVector.addElement(new Option("\tMaximum number of parents", "P", 1,
"-P "));
newVector.addElement(new Option(
"\tRandom order."
+ "\t(default false)",
"R", 0, "-R"));
Enumeration enu = super.listOptions();
while (enu.hasMoreElements()) {
newVector.addElement(enu.nextElement());
}
return newVector.elements();
}
/**
* Parses a given list of options.
*
* Valid options are:
*
*
-N
* Initial structure is empty (instead of Naive Bayes)
*
*
-P <nr of parents>
* Maximum number of parents
*
*
-R
* Random order.
* (default false)
*
*
-mbc
* Applies a Markov Blanket correction to the network structure,
* after a network structure is learned. This ensures that all
* nodes in the network are part of the Markov blanket of the
* classifier node.
*
*
-S [BAYES|MDL|ENTROPY|AIC|CROSS_CLASSIC|CROSS_BAYES]
* Score type (BAYES, BDeu, MDL, ENTROPY and AIC)
*
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
setRandomOrder(Utils.getFlag('R', options));
m_bInitAsNaiveBayes = !(Utils.getFlag('N', options));
String sMaxNrOfParents = Utils.getOption('P', options);
if (sMaxNrOfParents.length() != 0) {
setMaxNrOfParents(Integer.parseInt(sMaxNrOfParents));
} else {
setMaxNrOfParents(100000);
}
super.setOptions(options);
}
/**
* Gets the current settings of the search algorithm.
*
* @return an array of strings suitable for passing to setOptions
*/
public String [] getOptions() {
String[] superOptions = super.getOptions();
String [] options = new String [4 + superOptions.length];
int current = 0;
options[current++] = "-P";
options[current++] = ""+ m_nMaxNrOfParents;
if (!m_bInitAsNaiveBayes) {
options[current++] = "-N";
} if (getRandomOrder()) {
options[current++] = "-R";
}
//insert options from parent class
for (int iOption = 0; iOption < superOptions.length; iOption++) {
options[current++] = superOptions[iOption];
}
while (current < options.length) {
options[current++] = "";
}
//Fill up rest with empty strings, not nulls!
return options;
}
/**
* This will return a string describing the search algorithm.
* @return The string.
*/
public String globalInfo() {
return
"This Bayes Network learning algorithm uses a hill climbing algorithm "
+ "restricted by an order on the variables."
+ "For more information see:"
+ getTechnicalInformation().toString() + ""
+ "Works with nominal variables and no missing values only.";
}
/**
* @return a string to describe the RandomOrder option.
*/
public String randomOrderTipText() {
return "When set to true, the order of the nodes in the network is random."+
"Default random order is false and the order"+
"of the nodes in the dataset is used."+
"In any case, when the network was initialized as Naive Bayes Network, the"+
"class variable is first in the ordering though.";
}//randomOrderTipText
/**
* Returns the revision string.
*
* @returnthe revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.8 $");
}
}