KStar (interruptible-weka 0.1.4 API)

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.lazy.KStar

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, KStarConstants, UpdateableClassifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, TechnicalInformationHandler
```
public class KStar
extends AbstractClassifier
implements KStarConstants, UpdateableClassifier, TechnicalInformationHandler
```
K* is an instance-based classifier, that is the class of a test instance is based upon the class of those training instances similar to it, as determined by some similarity function. It differs from other instance-based learners in that it uses an entropy-based distance function.

For more information on K*, see

John G. Cleary, Leonard E. Trigg: K*: An Instance-based Learner Using an Entropic Distance Measure. In: 12th International Conference on Machine Learning, 108-114, 1995.
BibTeX:
```
 @inproceedings{Cleary1995,
    author = {John G. Cleary and Leonard E. Trigg},
    booktitle = {12th International Conference on Machine Learning},
    pages = {108-114},
    title = {K*: An Instance-based Learner Using an Entropic Distance Measure},
    year = {1995}
 }
 
```
Valid options are:
```
  -B <num>
  Manual blend setting (default 20%)
 
```
```
  -E
  Enable entropic auto-blend setting (symbolic class only)
 
```
```
  -M <char>
  Specify the missing value treatment mode (default a)
  Valid options are: a(verage), d(elete), m(axdiff), n(ormal)
 
```
See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`protected int`	`m_BlendMethod` 0 = use specified blend, 1 = entropic blend setting
`protected KStarCache[]`	`m_Cache` A custom data structure for caching distinct attribute values and their scale factor or stop parameter.
`protected int`	`m_ClassType` The class attribute type
`protected int`	`m_ComputeRandomCols` Flag turning on and off the computation of random class colomns
`protected int`	`m_GlobalBlend` default sphere of influence blend setting
`protected int`	`m_InitFlag` Flag turning on and off the initialisation of config variables
`protected int`	`m_MissingMode` missing value treatment
`protected int`	`m_NumAttributes` The number of attributes
`protected int`	`m_NumClasses` The number of class values
`protected int`	`m_NumInstances` The number of instances in the dataset
`protected int[][]`	`m_RandClassCols` Table of random class value colomns
`protected Instances`	`m_Train` The training instances used for classification.
`static Tag[]`	`TAGS_MISSING` Define possible missing value handling methods

Fields inherited from class weka.classifiers.AbstractClassifier
BATCH_SIZE_DEFAULT, m_BatchSize, m_Debug, m_DoNotCheckCapabilities, m_numDecimalPlaces, NUM_DECIMAL_PLACES_DEFAULT

Fields inherited from interface weka.classifiers.lazy.kstar.KStarConstants
B_ENTROPY, B_SPHERE, EPSILON, FLOOR, FLOOR1, INITIAL_STEP, LOG2, M_AVERAGE, M_DELETE, M_MAXDIFF, M_NORMAL, NUM_RAND_COLS, OFF, ON, ROOT_FINDER_ACCURACY, ROOT_FINDER_MAX_ITER

Constructor Summary

Constructors
Constructor and Description

KStar()

Constructors
Constructor and Description
`KStar()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(Instances instances)` Generates the classifier.
`double[]`	`distributionForInstance(Instance instance)` Calculates the class membership probabilities for the given test instance.
`java.lang.String`	`entropicAutoBlendTipText()` Returns the tip text for this property
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`boolean`	`getEntropicAutoBlend()` Get whether entropic blending being used
`int`	`getGlobalBlend()` Get the value of the global blend parameter
`SelectedTag`	`getMissingMode()` Gets the method to use for handling missing values.
`java.lang.String[]`	`getOptions()` Gets the current settings of K*.
`java.lang.String`	`getRevision()` Returns the revision string.
`TechnicalInformation`	`getTechnicalInformation()` Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
`java.lang.String`	`globalBlendTipText()` Returns the tip text for this property
`java.lang.String`	`globalInfo()` Returns a string describing classifier
`java.util.Enumeration<Option>`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(java.lang.String[] argv)` Main method for testing this class.
`java.lang.String`	`missingModeTipText()` Returns the tip text for this property
`void`	`setEntropicAutoBlend(boolean e)` Set whether entropic blending is to be used.
`void`	`setGlobalBlend(int b)` Set the global blend parameter
`void`	`setMissingMode(SelectedTag newMode)` Sets the method to use for handling missing values.
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`java.lang.String`	`toString()` Returns a description of this classifier.
`void`	`updateClassifier(Instance instance)` Adds the supplied instance to the training set

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Field Detail
  - m_Train
```
protected Instances m_Train
```
    The training instances used for classification.
  - m_NumInstances
```
protected int m_NumInstances
```
    The number of instances in the dataset
  - m_NumClasses
```
protected int m_NumClasses
```
    The number of class values
  - m_NumAttributes
```
protected int m_NumAttributes
```
    The number of attributes
  - m_ClassType
```
protected int m_ClassType
```
    The class attribute type
  - m_RandClassCols
```
protected int[][] m_RandClassCols
```
    Table of random class value colomns
  - m_ComputeRandomCols
```
protected int m_ComputeRandomCols
```
    Flag turning on and off the computation of random class colomns
  - m_InitFlag
```
protected int m_InitFlag
```
    Flag turning on and off the initialisation of config variables
  - m_Cache
```
protected KStarCache[] m_Cache
```
    A custom data structure for caching distinct attribute values and their scale factor or stop parameter.
  - m_MissingMode
```
protected int m_MissingMode
```
    missing value treatment
  - m_BlendMethod
```
protected int m_BlendMethod
```
    0 = use specified blend, 1 = entropic blend setting
  - m_GlobalBlend
```
protected int m_GlobalBlend
```
    default sphere of influence blend setting
  - TAGS_MISSING
```
public static final Tag[] TAGS_MISSING
```
    Define possible missing value handling methods
- Constructor Detail
  - KStar
```
public KStar()
```
- Method Detail
  - globalInfo
```
public java.lang.String globalInfo()
```
    Returns a string describing classifier
    
    Returns:
    
    a description suitable for displaying in the explorer/experimenter gui
  - getTechnicalInformation
```
public TechnicalInformation getTechnicalInformation()
```
    Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
    
    Specified by:
    
    getTechnicalInformation in interface TechnicalInformationHandler
    
    Returns:
    
    the technical information about this class
  - getCapabilities
```
public Capabilities getCapabilities()
```
    Returns default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface Classifier
    
    Specified by:
    
    getCapabilities in interface CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class AbstractClassifier
    
    Returns:
    
    the capabilities of this classifier
    
    See Also:
    
    Capabilities
  - buildClassifier
```
public void buildClassifier(Instances instances)
                     throws java.lang.Exception
```
    Generates the classifier.
    
    Specified by:
    
    buildClassifier in interface Classifier
    
    Parameters:
    
    instances - set of instances serving as training data
    
    Throws:
    
    java.lang.Exception - if the classifier has not been generated successfully
  - updateClassifier
```
public void updateClassifier(Instance instance)
                      throws java.lang.Exception
```
    Adds the supplied instance to the training set
    
    Specified by:
    
    updateClassifier in interface UpdateableClassifier
    
    Parameters:
    
    instance - the instance to add
    
    Throws:
    
    java.lang.Exception - if instance could not be incorporated successfully
  - distributionForInstance
```
public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception
```
    Calculates the class membership probabilities for the given test instance.
    
    Specified by:
    
    distributionForInstance in interface Classifier
    
    Overrides:
    
    distributionForInstance in class AbstractClassifier
    
    Parameters:
    
    instance - the instance to be classified
    
    Returns:
    
    predicted class probability distribution
    
    Throws:
    
    java.lang.Exception - if an error occurred during the prediction
  - missingModeTipText
```
public java.lang.String missingModeTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getMissingMode
```
public SelectedTag getMissingMode()
```
    Gets the method to use for handling missing values. Will be one of M_NORMAL, M_AVERAGE, M_MAXDIFF or M_DELETE.
    
    Returns:
    
    the method used for handling missing values.
  - setMissingMode
```
public void setMissingMode(SelectedTag newMode)
```
    Sets the method to use for handling missing values. Values other than M_NORMAL, M_AVERAGE, M_MAXDIFF and M_DELETE will be ignored.
    
    Parameters:
    
    newMode - the method to use for handling missing values.
  - listOptions
```
public java.util.Enumeration<Option> listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class AbstractClassifier
    
    Returns:
    
    an enumeration of all the available options.
  - globalBlendTipText
```
public java.lang.String globalBlendTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setGlobalBlend
```
public void setGlobalBlend(int b)
```
    Set the global blend parameter
    
    Parameters:
    
    b - the value for global blending
  - getGlobalBlend
```
public int getGlobalBlend()
```
    Get the value of the global blend parameter
    
    Returns:
    
    the value of the global blend parameter
  - entropicAutoBlendTipText
```
public java.lang.String entropicAutoBlendTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setEntropicAutoBlend
```
public void setEntropicAutoBlend(boolean e)
```
    Set whether entropic blending is to be used.
    
    Parameters:
    
    e - true if entropic blending is to be used
  - getEntropicAutoBlend
```
public boolean getEntropicAutoBlend()
```
    Get whether entropic blending being used
    
    Returns:
    
    true if entropic blending is used
  - setOptions
```
public void setOptions(java.lang.String[] options)
                throws java.lang.Exception
```
    Parses a given list of options.
    Valid options are:
```
  -B <num>
  Manual blend setting (default 20%)
 
```
```
  -E
  Enable entropic auto-blend setting (symbolic class only)
 
```
```
  -M <char>
  Specify the missing value treatment mode (default a)
  Valid options are: a(verage), d(elete), m(axdiff), n(ormal)
 
```
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class AbstractClassifier
    
    Parameters:
    
    options - the list of options as an array of strings
    
    Throws:
    
    java.lang.Exception - if an option is not supported
  - getOptions
```
public java.lang.String[] getOptions()
```
    Gets the current settings of K*.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class AbstractClassifier
    
    Returns:
    
    an array of strings suitable for passing to setOptions()
  - toString
```
public java.lang.String toString()
```
    Returns a description of this classifier.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a description of this classifier as a string.
  - main
```
public static void main(java.lang.String[] argv)
```
    Main method for testing this class.
    
    Parameters:
    
    argv - should contain command line options (see setOptions)
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class AbstractClassifier
    
    Returns:
    
    the revision

Class KStar

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Fields inherited from interface weka.classifiers.lazy.kstar.KStarConstants

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Field Detail

m_Train

m_NumInstances

m_NumClasses

m_NumAttributes

m_ClassType

m_RandClassCols

m_ComputeRandomCols

m_InitFlag

m_Cache

m_MissingMode

m_BlendMethod

m_GlobalBlend

TAGS_MISSING

Constructor Detail

KStar

Method Detail

globalInfo

getTechnicalInformation

getCapabilities

buildClassifier

updateClassifier

distributionForInstance

missingModeTipText

getMissingMode

setMissingMode

listOptions

globalBlendTipText

setGlobalBlend

getGlobalBlend

entropicAutoBlendTipText

setEntropicAutoBlend

getEntropicAutoBlend

setOptions

getOptions

toString

main

getRevision