GeneralRegression (interruptible-weka 0.1.2 API)

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.pmml.consumer.PMMLClassifier
  - - weka.classifiers.pmml.consumer.GeneralRegression

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, PMMLModel, RevisionHandler
```
public class GeneralRegression
extends PMMLClassifier
implements java.io.Serializable
```
Class implementing import of PMML General Regression model. Can be used as a Weka classifier for prediction (buildClassifier() raises an Exception).

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`protected java.lang.String`	`m_algorithmName`
`protected java.util.ArrayList<weka.classifiers.pmml.consumer.GeneralRegression.Predictor>`	`m_covariateList`
`protected weka.classifiers.pmml.consumer.GeneralRegression.CumulativeLinkFunction`	`m_cumulativeLinkFunction`
`protected double`	`m_distParameter`
`protected weka.classifiers.pmml.consumer.GeneralRegression.Distribution`	`m_distribution`
`protected java.util.ArrayList<weka.classifiers.pmml.consumer.GeneralRegression.Predictor>`	`m_factorList`
`protected int`	`m_functionType`
`protected weka.classifiers.pmml.consumer.GeneralRegression.LinkFunction`	`m_linkFunction`
`protected double`	`m_linkParameter`
`protected java.lang.String`	`m_modelName`
`protected weka.classifiers.pmml.consumer.GeneralRegression.ModelType`	`m_modelType`
`protected double`	`m_offsetValue`
`protected java.lang.String`	`m_offsetVariable`
`protected java.util.ArrayList<weka.classifiers.pmml.consumer.GeneralRegression.Parameter>`	`m_parameterList`
`protected weka.classifiers.pmml.consumer.GeneralRegression.PCell[][]`	`m_paramMatrix`
`protected weka.classifiers.pmml.consumer.GeneralRegression.PPCell[][]`	`m_ppMatrix`
`protected double`	`m_trialsValue`
`protected java.lang.String`	`m_trialsVariable`

Fields inherited from class weka.classifiers.pmml.consumer.PMMLClassifier
m_creatorApplication, m_dataDictionary, m_fieldsMap, m_initialized, m_log, m_miningSchema, m_pmmlVersion

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

Constructor Summary

Constructors
Constructor and Description
`GeneralRegression(org.w3c.dom.Element model, Instances dataDictionary, MiningSchema miningSchema)` Constructs a GeneralRegression classifier.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`double[]`	`distributionForInstance(Instance inst)` Classifies the given test instance.
`java.lang.String`	`getRevision()` Returns the revision string.
`protected void`	`printParameterMatrix(java.lang.StringBuffer buff)` Format and print the parameter matrix to the supplied StringBuffer.
`protected void`	`printPPMatrix(java.lang.StringBuffer buff)` Format and print the PPMatrix to the supplied StringBuffer.
`protected void`	`readFactorsAndCovariates(org.w3c.dom.Element model, java.lang.String factorOrCovariate)` Read the lists of factors and covariates.
`protected void`	`readParameterList(org.w3c.dom.Element model)` Read the list of parameters.
`protected void`	`readPPMatrix(org.w3c.dom.Element model)` Read the PPMatrix from the xml.
`java.lang.String`	`toString()` Return a textual description of this general regression.

Methods inherited from class weka.classifiers.pmml.consumer.PMMLClassifier
buildClassifier, done, getCreatorApplication, getDataDictionary, getFieldsMappingString, getLog, getMiningSchema, getPMMLVersion, mapToMiningSchema, setCreatorApplication, setLog, setPMMLVersion

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

- Field Detail
  - m_modelType
```
protected weka.classifiers.pmml.consumer.GeneralRegression.ModelType m_modelType
```
  - m_modelName
```
protected java.lang.String m_modelName
```
  - m_algorithmName
```
protected java.lang.String m_algorithmName
```
  - m_functionType
```
protected int m_functionType
```
  - m_cumulativeLinkFunction
```
protected weka.classifiers.pmml.consumer.GeneralRegression.CumulativeLinkFunction m_cumulativeLinkFunction
```
  - m_linkFunction
```
protected weka.classifiers.pmml.consumer.GeneralRegression.LinkFunction m_linkFunction
```
  - m_linkParameter
```
protected double m_linkParameter
```
  - m_trialsVariable
```
protected java.lang.String m_trialsVariable
```
  - m_trialsValue
```
protected double m_trialsValue
```
  - m_distribution
```
protected weka.classifiers.pmml.consumer.GeneralRegression.Distribution m_distribution
```
  - m_distParameter
```
protected double m_distParameter
```
  - m_offsetVariable
```
protected java.lang.String m_offsetVariable
```
  - m_offsetValue
```
protected double m_offsetValue
```
  - m_parameterList
```
protected java.util.ArrayList<weka.classifiers.pmml.consumer.GeneralRegression.Parameter> m_parameterList
```
  - m_factorList
```
protected java.util.ArrayList<weka.classifiers.pmml.consumer.GeneralRegression.Predictor> m_factorList
```
  - m_covariateList
```
protected java.util.ArrayList<weka.classifiers.pmml.consumer.GeneralRegression.Predictor> m_covariateList
```
  - m_ppMatrix
```
protected weka.classifiers.pmml.consumer.GeneralRegression.PPCell[][] m_ppMatrix
```
  - m_paramMatrix
```
protected weka.classifiers.pmml.consumer.GeneralRegression.PCell[][] m_paramMatrix
```
- Constructor Detail
  - GeneralRegression
```
public GeneralRegression(org.w3c.dom.Element model,
                         Instances dataDictionary,
                         MiningSchema miningSchema)
                  throws java.lang.Exception
```
    Constructs a GeneralRegression classifier.
    
    Parameters:
    
    model - the Element that holds the model definition
    
    dataDictionary - the data dictionary as a set of Instances
    
    miningSchema - the mining schema
    
    Throws:
    
    java.lang.Exception - if there is a problem constructing the general regression object from the PMML.
- Method Detail
  - readParameterList
```
protected void readParameterList(org.w3c.dom.Element model)
                          throws java.lang.Exception
```
    Read the list of parameters.
    
    Parameters:
    
    model - the Element that contains the model
    
    Throws:
    
    java.lang.Exception - if there is some problem with extracting the parameters.
  - readFactorsAndCovariates
```
protected void readFactorsAndCovariates(org.w3c.dom.Element model,
                                        java.lang.String factorOrCovariate)
                                 throws java.lang.Exception
```
    Read the lists of factors and covariates.
    
    Parameters:
    
    model - the Element that contains the model
    
    factorOrCovariate - holds the String "FactorList" or "CovariateList"
    
    Throws:
    
    java.lang.Exception - if there is a factor or covariate listed that isn't in the mining schema
  - readPPMatrix
```
protected void readPPMatrix(org.w3c.dom.Element model)
                     throws java.lang.Exception
```
    Read the PPMatrix from the xml. Does not handle multiple PPMatrixes yet.
    
    Parameters:
    
    model - the Element that contains the model
    
    Throws:
    
    java.lang.Exception - if there is a problem parsing cell values.
  - toString
```
public java.lang.String toString()
```
    Return a textual description of this general regression.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a description of this general regression
  - printPPMatrix
```
protected void printPPMatrix(java.lang.StringBuffer buff)
```
    Format and print the PPMatrix to the supplied StringBuffer.
    
    Parameters:
    
    buff - the StringBuffer to append to
  - printParameterMatrix
```
protected void printParameterMatrix(java.lang.StringBuffer buff)
```
    Format and print the parameter matrix to the supplied StringBuffer.
    
    Parameters:
    
    buff - the StringBuffer to append to
  - distributionForInstance
```
public double[] distributionForInstance(Instance inst)
                                 throws java.lang.Exception
```
    Classifies the given test instance. The instance has to belong to a dataset when it's being classified.
    
    Specified by:
    
    distributionForInstance in interface Classifier
    
    Overrides:
    
    distributionForInstance in class AbstractClassifier
    
    Parameters:
    
    inst - the instance to be classified
    
    Returns:
    
    the predicted most likely class for the instance or Utils.missingValue() if no prediction is made
    
    Throws:
    
    java.lang.Exception - if an error occurred during the prediction
  - getRevision
```
public java.lang.String getRevision()
```
    Description copied from class: AbstractClassifier
    
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class AbstractClassifier
    
    Returns:
    
    the revision

Class GeneralRegression

Field Summary

Fields inherited from class weka.classifiers.pmml.consumer.PMMLClassifier

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.pmml.consumer.PMMLClassifier

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Field Detail

m_modelType

m_modelName

m_algorithmName

m_functionType

m_cumulativeLinkFunction

m_linkFunction

m_linkParameter

m_trialsVariable

m_trialsValue

m_distribution

m_distParameter

m_offsetVariable

m_offsetValue

m_parameterList

m_factorList

m_covariateList

m_ppMatrix

m_paramMatrix

Constructor Detail

GeneralRegression

Method Detail

readParameterList

readFactorsAndCovariates

readPPMatrix

toString

printPPMatrix

printParameterMatrix

distributionForInstance

getRevision