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Java Source Code / Java Documentation » Science » weka » weka.classifiers.bayes

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Class Diagram

Java Document (Java Doc)

java.lang .Object

weka.classifiers .Classifier

weka.classifiers.bayes .BayesNet

All known Subclasses:   weka.classifiers.bayes.net .BIFReader,  weka.classifiers.bayes.net .BayesNetGenerator,
BayesNet
public class BayesNet extends Classifier implements OptionHandler,WeightedInstancesHandler,Drawable,AdditionalMeasureProducer(Code)
Bayes Network learning using various search algorithms and quality measures.
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.

For more information see:

http://www.cs.waikato.ac.nz/~remco/weka.pdf
Valid options are:

-D Do not use ADTree data structure

-B <BIF file> BIF file to compare with

-Q weka.classifiers.bayes.net.search.SearchAlgorithm Search algorithm

-E weka.classifiers.bayes.net.estimate.SimpleEstimator Estimator algorithm

author:
   Remco Bouckaert (rrb@xm.co.nz)
version:
   $Revision: 1.30 $

Field Summary
ADNode m_ADTree
     Datastructure containing ADTree representation of the database.
BayesNetEstimator m_BayesNetEstimator
     Search algorithm used for learning the structure of a network.
Discretize m_DiscretizeFilter

public Estimator[][] m_Distributions
     The attribute estimators containing CPTs.
public Instances m_Instances

ReplaceMissingValues m_MissingValuesFilter

protected int m_NumClasses

protected ParentSet[] m_ParentSets
     The parent sets.
SearchAlgorithm m_SearchAlgorithm
     Search algorithm used for learning the structure of a network.
boolean m_bUseADTree

int m_nNonDiscreteAttribute

protected BIFReader m_otherBayesNet
     Bayes network to compare the structure with.
final static long serialVersionUID


Method Summary
public String BIFFileTipText()

String XMLNormalize(String sStr)
     XMLNormalize converts the five standard XML entities in a string g.e.
public void buildClassifier(Instances instances)
     Generates the classifier.
public void buildStructure()
     buildStructure determines the network structure/graph of the network.
public double[] countsForInstance(Instance instance)
     Calculates the counts for Dirichlet distribution for the class membership probabilities for the given test instance.
public double[] distributionForInstance(Instance instance)
     Calculates the class membership probabilities for the given test instance.
public Enumeration enumerateMeasures()
     Returns an enumeration of the measure names.
public void estimateCPTs()
     estimateCPTs estimates the conditional probability tables for the Bayes Net using the network structure.
public String estimatorTipText()
     This will return a string describing the BayesNetEstimator.
public ADNode getADTree()
     get ADTree strucrture containing efficient representation of counts.
public String getBIFFile()

public Capabilities getCapabilities()
     Returns default capabilities of the classifier.
public int getCardinality(int iNode)

public Estimator[][] getDistributions()
     Get full set of estimators.
public BayesNetEstimator getEstimator()

public double getMeasure(String measureName)

public String getName()

public String getNodeName(int iNode)

public String getNodeValue(int iNode, int iValue)

public int getNrOfNodes()

public int getNrOfParents(int iNode)

public String[] getOptions()
     Gets the current settings of the classifier.
public int getParent(int iNode, int iParent)
     get node index of a parent of a node in the network structure
Parameters:
  iNode - index of the node
Parameters:
  iParent - index of the parents, e.g., 0 is the first parent, 1 the second parent, etc.
public int getParentCardinality(int iNode)

public ParentSet getParentSet(int iNode)

public ParentSet[] getParentSets()
     Get full set of parent sets.
public double getProbability(int iNode, int iParent, int iValue)
     get particular probability of the conditional probability distribtion of a node given its parents.
public SearchAlgorithm getSearchAlgorithm()

public boolean getUseADTree()

public String globalInfo()
     This will return a string describing the classifier.
public String graph()
     Returns a BayesNet graph in XMLBIF ver 0.3 format.
public int graphType()
     Returns the type of graph this classifier represents.
public void initCPTs()

public void initStructure()
     Init structure initializes the structure to an empty graph or a Naive Bayes graph (depending on the -N flag).
public Enumeration listOptions()

public static void main(String[] argv)
     Main method for testing this class.
public double measureAICScore()

public double measureBDeuScore()

public double measureBayesScore()

public double measureDivergence()

public double measureEntropyScore()

public double measureExtraArcs()

public double measureMDLScore()

public double measureMissingArcs()

public double measureReversedArcs()

Instances normalizeDataSet(Instances instances)

Instance normalizeInstance(Instance instance)

public static String[] partitionOptions(String[] options)
     Returns the secondary set of options (if any) contained in the supplied options array.
public String searchAlgorithmTipText()

public void setBIFFile(String sBIFFile)

public void setEstimator(BayesNetEstimator newBayesNetEstimator)

public void setOptions(String[] options)
     Parses a given list of options.
public void setSearchAlgorithm(SearchAlgorithm newSearchAlgorithm)
     Set the SearchAlgorithm used in searching for network structures.
public void setUseADTree(boolean bUseADTree)

public String toString()
     Returns a description of the classifier.
public String toXMLBIF03()
     Returns a description of the classifier in XML BIF 0.3 format.
public void updateClassifier(Instance instance)
     Updates the classifier with the given instance.
public String useADTreeTipText()


Field Detail
m_ADTree
ADNode m_ADTree(Code)
Datastructure containing ADTree representation of the database. This may result in more efficient access to the data.

m_BayesNetEstimator
BayesNetEstimator m_BayesNetEstimator(Code)
Search algorithm used for learning the structure of a network.

m_DiscretizeFilter
Discretize m_DiscretizeFilter(Code)
filter used to quantize continuous variables, if any *

m_Distributions
public Estimator[][] m_Distributions(Code)
The attribute estimators containing CPTs.

m_Instances
public Instances m_Instances(Code)
The dataset header for the purposes of printing out a semi-intelligible model

m_MissingValuesFilter
ReplaceMissingValues m_MissingValuesFilter(Code)
filter used to fill in missing values, if any *

m_NumClasses
protected int m_NumClasses(Code)
The number of classes

m_ParentSets
protected ParentSet[] m_ParentSets(Code)
The parent sets.

m_SearchAlgorithm
SearchAlgorithm m_SearchAlgorithm(Code)
Search algorithm used for learning the structure of a network.

m_bUseADTree
boolean m_bUseADTree(Code)
Use the experimental ADTree datastructure for calculating contingency tables

m_nNonDiscreteAttribute
int m_nNonDiscreteAttribute(Code)
attribute index of a non-nominal attribute

m_otherBayesNet
protected BIFReader m_otherBayesNet(Code)
Bayes network to compare the structure with.

serialVersionUID
final static long serialVersionUID(Code)
for serialization

Method Detail
BIFFileTipText
public String BIFFileTipText()(Code)
a string to describe the BIFFile.

XMLNormalize
String XMLNormalize(String sStr)(Code)
XMLNormalize converts the five standard XML entities in a string g.e. the string V&D's is returned as V&D's
Parameters:
  sStr - string to normalize normalized string

buildClassifier
public void buildClassifier(Instances instances) throws Exception(Code)
Generates the classifier.
Parameters:
  instances - set of instances serving as training data
throws:
  Exception - if the classifier has not been generatedsuccessfully

buildStructure
public void buildStructure() throws Exception(Code)
buildStructure determines the network structure/graph of the network. The default behavior is creating a network where all nodes have the first node as its parent (i.e., a BayesNet that behaves like a naive Bayes classifier). This method can be overridden by derived classes to restrict the class of network structures that are acceptable.
throws:
  Exception - in case of an error

countsForInstance
public double[] countsForInstance(Instance instance) throws Exception(Code)
Calculates the counts for Dirichlet distribution for the class membership probabilities for the given test instance.
Parameters:
  instance - the instance to be classified counts for Dirichlet distribution for class probability
throws:
  Exception - if there is a problem generating the prediction

distributionForInstance
public double[] distributionForInstance(Instance instance) throws Exception(Code)
Calculates the class membership probabilities for the given test instance.
Parameters:
  instance - the instance to be classified predicted class probability distribution
throws:
  Exception - if there is a problem generating the prediction

enumerateMeasures
public Enumeration enumerateMeasures()(Code)
Returns an enumeration of the measure names. Additional measures must follow the naming convention of starting with "measure", eg. double measureBlah() an enumeration of the measure names

estimateCPTs
public void estimateCPTs() throws Exception(Code)
estimateCPTs estimates the conditional probability tables for the Bayes Net using the network structure.
throws:
  Exception - in case of an error

estimatorTipText
public String estimatorTipText()(Code)
This will return a string describing the BayesNetEstimator. The string.

getADTree
public ADNode getADTree()(Code)
get ADTree strucrture containing efficient representation of counts. ADTree strucrture

getBIFFile
public String getBIFFile()(Code)
Get name of network in BIF file to compare with BIF file name

getCapabilities
public Capabilities getCapabilities()(Code)
Returns default capabilities of the classifier. the capabilities of this classifier

getCardinality
public int getCardinality(int iNode)(Code)
get number of values a node can take
Parameters:
  iNode - index of the node cardinality of the specified node

getDistributions
public Estimator[][] getDistributions()(Code)
Get full set of estimators. estimators;

getEstimator
public BayesNetEstimator getEstimator()(Code)
Get the BayesNetEstimator used for calculating the CPTs the BayesNetEstimator used.

getMeasure
public double getMeasure(String measureName)(Code)
Returns the value of the named measure
Parameters:
  measureName - the name of the measure to query for its value the value of the named measure
throws:
  IllegalArgumentException - if the named measure is not supported

getName
public String getName()(Code)
get name of the Bayes network name of the Bayes net

getNodeName
public String getNodeName(int iNode)(Code)
get name of a node in the Bayes network
Parameters:
  iNode - index of the node name of the specified node

getNodeValue
public String getNodeValue(int iNode, int iValue)(Code)
get name of a particular value of a node
Parameters:
  iNode - index of the node
Parameters:
  iValue - index of the value cardinality of the specified node

getNrOfNodes
public int getNrOfNodes()(Code)
get number of nodes in the Bayes network number of nodes

getNrOfParents
public int getNrOfParents(int iNode)(Code)
get number of parents of a node in the network structure
Parameters:
  iNode - index of the node number of parents of the specified node

getOptions
public String[] getOptions()(Code)
Gets the current settings of the classifier. an array of strings suitable for passing to setOptions

getParent
public int getParent(int iNode, int iParent)(Code)
get node index of a parent of a node in the network structure
Parameters:
  iNode - index of the node
Parameters:
  iParent - index of the parents, e.g., 0 is the first parent, 1 the second parent, etc. node index of the iParent's parent of the specified node

getParentCardinality
public int getParentCardinality(int iNode)(Code)
get number of values the collection of parents of a node can take
Parameters:
  iNode - index of the node cardinality of the parent set of the specified node

getParentSet
public ParentSet getParentSet(int iNode)(Code)
get the parent set of a node
Parameters:
  iNode - index of the node Parent set of the specified node.

getParentSets
public ParentSet[] getParentSets()(Code)
Get full set of parent sets. parent sets;

getProbability
public double getProbability(int iNode, int iParent, int iValue)(Code)
get particular probability of the conditional probability distribtion of a node given its parents.
Parameters:
  iNode - index of the node
Parameters:
  iParent - index of the parent set, 0 <= iParent <= getParentCardinality(iNode)
Parameters:
  iValue - index of the value, 0 <= iValue <= getCardinality(iNode) probability

getSearchAlgorithm
public SearchAlgorithm getSearchAlgorithm()(Code)
Get the SearchAlgorithm used as the search algorithm the SearchAlgorithm used as the search algorithm

getUseADTree
public boolean getUseADTree()(Code)
Method declaration whether ADTree structure is used or not

globalInfo
public String globalInfo()(Code)
This will return a string describing the classifier. The string.

graph
public String graph() throws Exception(Code)
Returns a BayesNet graph in XMLBIF ver 0.3 format. String representing this BayesNet in XMLBIF ver 0.3
throws:
  Exception - in case BIF generation fails

graphType
public int graphType()(Code)
Returns the type of graph this classifier represents. Drawable.TREE

initCPTs
public void initCPTs() throws Exception(Code)
initializes the conditional probabilities
throws:
  Exception - in case of an error

initStructure
public void initStructure() throws Exception(Code)
Init structure initializes the structure to an empty graph or a Naive Bayes graph (depending on the -N flag).
throws:
  Exception - in case of an error

listOptions
public Enumeration listOptions()(Code)
Returns an enumeration describing the available options an enumeration of all the available options

main
public static void main(String[] argv)(Code)
Main method for testing this class.
Parameters:
  argv - the options

measureAICScore
public double measureAICScore()(Code)

measureBDeuScore
public double measureBDeuScore()(Code)

measureBayesScore
public double measureBayesScore()(Code)

measureDivergence
public double measureDivergence()(Code)

measureEntropyScore
public double measureEntropyScore()(Code)

measureExtraArcs
public double measureExtraArcs()(Code)

measureMDLScore
public double measureMDLScore()(Code)

measureMissingArcs
public double measureMissingArcs()(Code)

measureReversedArcs
public double measureReversedArcs()(Code)

normalizeDataSet
Instances normalizeDataSet(Instances instances) throws Exception(Code)
ensure that all variables are nominal and that there are no missing values
Parameters:
  instances - data set to check and quantize and/or fill in missing values filtered instances
throws:
  Exception - if a filter (Discretize, ReplaceMissingValues) fails

normalizeInstance
Instance normalizeInstance(Instance instance) throws Exception(Code)
ensure that all variables are nominal and that there are no missing values
Parameters:
  instance - instance to check and quantize and/or fill in missing values filtered instance
throws:
  Exception - if a filter (Discretize, ReplaceMissingValues) fails

partitionOptions
public static String[] partitionOptions(String[] options)(Code)
Returns the secondary set of options (if any) contained in the supplied options array. The secondary set is defined to be any options after the first "--" but before the "-E". These options are removed from the original options array.
Parameters:
  options - the input array of options the array of secondary options

searchAlgorithmTipText
public String searchAlgorithmTipText()(Code)
a string to describe the SearchAlgorithm.

setBIFFile
public void setBIFFile(String sBIFFile)(Code)
Set name of network in BIF file to compare with
Parameters:
  sBIFFile - the name of the BIF file

setEstimator
public void setEstimator(BayesNetEstimator newBayesNetEstimator)(Code)
Set the Estimator Algorithm used in calculating the CPTs
Parameters:
  newBayesNetEstimator - the Estimator to use.

setOptions
public void setOptions(String[] options) throws Exception(Code)
Parses a given list of options.
Valid options are:

-D Do not use ADTree data structure

-B <BIF file> BIF file to compare with

-Q weka.classifiers.bayes.net.search.SearchAlgorithm Search algorithm

-E weka.classifiers.bayes.net.estimate.SimpleEstimator Estimator algorithm

Parameters:
  options - the list of options as an array of strings
throws:
  Exception - if an option is not supported

setSearchAlgorithm
public void setSearchAlgorithm(SearchAlgorithm newSearchAlgorithm)(Code)
Set the SearchAlgorithm used in searching for network structures.
Parameters:
  newSearchAlgorithm - the SearchAlgorithm to use.

setUseADTree
public void setUseADTree(boolean bUseADTree)(Code)
Set whether ADTree structure is used or not
Parameters:
  bUseADTree - true if an ADTree structure is used

toString
public String toString()(Code)
Returns a description of the classifier. a description of the classifier as a string.

toXMLBIF03
public String toXMLBIF03()(Code)
Returns a description of the classifier in XML BIF 0.3 format. See http://www-2.cs.cmu.edu/~fgcozman/Research/InterchangeFormat/ for details on XML BIF. an XML BIF 0.3 description of the classifier as a string.

updateClassifier
public void updateClassifier(Instance instance) throws Exception(Code)
Updates the classifier with the given instance.
Parameters:
  instance - the new training instance to include in the model
throws:
  Exception - if the instance could not be incorporated inthe model.

useADTreeTipText
public String useADTreeTipText()(Code)
a string to describe the UseADTreeoption.

Fields inherited from weka.classifiers.Classifier
protected boolean m_Debug(Code)(Java Doc)

Methods inherited from weka.classifiers.Classifier
abstract public void buildClassifier(Instances data) throws Exception(Code)(Java Doc)
public double classifyInstance(Instance instance) throws Exception(Code)(Java Doc)
public String debugTipText()(Code)(Java Doc)
public double[] distributionForInstance(Instance instance) throws Exception(Code)(Java Doc)
public static Classifier forName(String classifierName, String[] options) throws Exception(Code)(Java Doc)
public Capabilities getCapabilities()(Code)(Java Doc)
public boolean getDebug()(Code)(Java Doc)
public String[] getOptions()(Code)(Java Doc)
public Enumeration listOptions()(Code)(Java Doc)
public static Classifier[] makeCopies(Classifier model, int num) throws Exception(Code)(Java Doc)
public static Classifier makeCopy(Classifier model) throws Exception(Code)(Java Doc)
protected static void runClassifier(Classifier classifier, String[] options)(Code)(Java Doc)
public void setDebug(boolean debug)(Code)(Java Doc)
public void setOptions(String[] options) throws Exception(Code)(Java Doc)

Methods inherited from java.lang.Object
native protected Object clone() throws CloneNotSupportedException(Code)(Java Doc)
public boolean equals(Object obj)(Code)(Java Doc)
protected void finalize() throws Throwable(Code)(Java Doc)
final native public Class getClass()(Code)(Java Doc)
native public int hashCode()(Code)(Java Doc)
final native public void notify()(Code)(Java Doc)
final native public void notifyAll()(Code)(Java Doc)
public String toString()(Code)(Java Doc)
final native public void wait(long timeout) throws InterruptedException(Code)(Java Doc)
final public void wait(long timeout, int nanos) throws InterruptedException(Code)(Java Doc)
final public void wait() throws InterruptedException(Code)(Java Doc)

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