Package weka.classifiers.trees.m5
Class PreConstructedLinearModel
- java.lang.Object
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- weka.classifiers.AbstractClassifier
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- weka.classifiers.trees.m5.PreConstructedLinearModel
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- All Implemented Interfaces:
java.io.Serializable,java.lang.Cloneable,Classifier,BatchPredictor,CapabilitiesHandler,CapabilitiesIgnorer,CommandlineRunnable,OptionHandler,RevisionHandler
public class PreConstructedLinearModel extends AbstractClassifier implements java.io.Serializable
This class encapsulates a linear regression function. It is a classifier but does not learn the function itself, instead it is constructed with coefficients and intercept obtained elsewhere. The buildClassifier method must still be called however as this stores a copy of the training data's header for use in printing the model to the console.- Version:
- $Revision: 15358 $
- Author:
- Mark Hall (mhall@cs.waikato.ac.nz)
- See Also:
- Serialized Form
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Field Summary
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Fields inherited from class weka.classifiers.AbstractClassifier
BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT
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Constructor Summary
Constructors Constructor Description PreConstructedLinearModel(double[] coeffs, double intercept)Constructor
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description voidbuildClassifier(Instances instances)Builds the classifier.doubleclassifyInstance(Instance inst)Predicts the class of the supplied instance using the linear model.double[]coefficients()Return the array of coefficientsjava.lang.StringgetRevision()Returns the revision string.doubleintercept()Return the interceptintnumParameters()Return the number of parameters (coefficients) in the linear modeljava.lang.StringtoString()Returns a textual description of this linear model-
Methods inherited from class weka.classifiers.AbstractClassifier
batchSizeTipText, debugTipText, distributionForInstance, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getCapabilities, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, getOptions, implementsMoreEfficientBatchPrediction, listOptions, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces, setOptions
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Method Detail
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buildClassifier
public void buildClassifier(Instances instances) throws java.lang.Exception
Builds the classifier. In this case all that is done is that a copy of the training instances header is saved.- Specified by:
buildClassifierin interfaceClassifier- Parameters:
instances- anInstancesvalue- Throws:
java.lang.Exception- if an error occurs
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classifyInstance
public double classifyInstance(Instance inst) throws java.lang.Exception
Predicts the class of the supplied instance using the linear model.- Specified by:
classifyInstancein interfaceClassifier- Overrides:
classifyInstancein classAbstractClassifier- Parameters:
inst- the instance to make a prediction for- Returns:
- the prediction
- Throws:
java.lang.Exception- if an error occurs
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numParameters
public int numParameters()
Return the number of parameters (coefficients) in the linear model- Returns:
- the number of parameters
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coefficients
public double[] coefficients()
Return the array of coefficients- Returns:
- the coefficients
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intercept
public double intercept()
Return the intercept- Returns:
- the intercept
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toString
public java.lang.String toString()
Returns a textual description of this linear model- Overrides:
toStringin classjava.lang.Object- Returns:
- String containing a description of this linear model
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getRevision
public java.lang.String getRevision()
Returns the revision string.- Specified by:
getRevisionin interfaceRevisionHandler- Overrides:
getRevisionin classAbstractClassifier- Returns:
- the revision
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