Package weka.classifiers.functions
Class SMOreg
- java.lang.Object
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- weka.classifiers.Classifier
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- weka.classifiers.functions.SMOreg
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- All Implemented Interfaces:
java.io.Serializable
,java.lang.Cloneable
,AdditionalMeasureProducer
,CapabilitiesHandler
,OptionHandler
,RevisionHandler
,TechnicalInformationHandler
,WeightedInstancesHandler
public class SMOreg extends Classifier implements WeightedInstancesHandler, AdditionalMeasureProducer, TechnicalInformationHandler
SMOreg implements the support vector machine for regression. The parameters can be learned using various algorithms. The algorithm is selected by setting the RegOptimizer. The most popular algorithm (RegSMOImproved) is due to Shevade, Keerthi et al and this is the default RegOptimizer.
For more information see:
S.K. Shevade, S.S. Keerthi, C. Bhattacharyya, K.R.K. Murthy: Improvements to the SMO Algorithm for SVM Regression. In: IEEE Transactions on Neural Networks, 1999.
A.J. Smola, B. Schoelkopf (1998). A tutorial on support vector regression. BibTeX:@inproceedings{Shevade1999, author = {S.K. Shevade and S.S. Keerthi and C. Bhattacharyya and K.R.K. Murthy}, booktitle = {IEEE Transactions on Neural Networks}, title = {Improvements to the SMO Algorithm for SVM Regression}, year = {1999}, PS = {http://guppy.mpe.nus.edu.sg/\~mpessk/svm/ieee_smo_reg.ps.gz} } @techreport{Smola1998, author = {A.J. Smola and B. Schoelkopf}, note = {NeuroCOLT2 Technical Report NC2-TR-1998-030}, title = {A tutorial on support vector regression}, year = {1998} }
Valid options are:-C <double> The complexity constant C. (default 1)
-N Whether to 0=normalize/1=standardize/2=neither. (default 0=normalize)
-I <classname and parameters> Optimizer class used for solving quadratic optimization problem (default weka.classifiers.functions.supportVector.RegSMOImproved)
-K <classname and parameters> The Kernel to use. (default: weka.classifiers.functions.supportVector.PolyKernel)
Options specific to optimizer ('-I') weka.classifiers.functions.supportVector.RegSMOImproved:
-T <double> The tolerance parameter for checking the stopping criterion. (default 0.001)
-V Use variant 1 of the algorithm when true, otherwise use variant 2. (default true)
-P <double> The epsilon for round-off error. (default 1.0e-12)
-L <double> The epsilon parameter in epsilon-insensitive loss function. (default 1.0e-3)
-W <double> The random number seed. (default 1)
Options specific to kernel ('-K') weka.classifiers.functions.supportVector.PolyKernel:
-D Enables debugging output (if available) to be printed. (default: off)
-no-checks Turns off all checks - use with caution! (default: checks on)
-C <num> The size of the cache (a prime number), 0 for full cache and -1 to turn it off. (default: 250007)
-E <num> The Exponent to use. (default: 1.0)
-L Use lower-order terms. (default: no)
- Version:
- $Revision: 8126 $
- Author:
- Remco Bouckaert (remco@cs.waikato.ac.nz,rrb@xm.co.nz)
- See Also:
- Serialized Form
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Field Summary
Fields Modifier and Type Field Description static int
FILTER_NONE
The filter to apply to the training data: Nonestatic int
FILTER_NORMALIZE
The filter to apply to the training data: Normalziestatic int
FILTER_STANDARDIZE
The filter to apply to the training data: Standardizestatic Tag[]
TAGS_FILTER
The filter to apply to the training data
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Constructor Summary
Constructors Constructor Description SMOreg()
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description void
buildClassifier(Instances instances)
Method for building the classifier.double
classifyInstance(Instance instance)
Classifies the given instance using the linear regression function.java.lang.String
cTipText()
Returns the tip text for this propertyjava.util.Enumeration
enumerateMeasures()
Returns an enumeration of the measure names.java.lang.String
filterTypeTipText()
Returns the tip text for this propertydouble
getC()
Get the value of C.Capabilities
getCapabilities()
Returns default capabilities of the classifier.SelectedTag
getFilterType()
Gets how the training data will be transformed.Kernel
getKernel()
Returns the kernel to usedouble
getMeasure(java.lang.String measureName)
Returns the value of the named measurejava.lang.String[]
getOptions()
Gets the current settings of the classifier.RegOptimizer
getRegOptimizer()
returns the learning algorithmjava.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
globalInfo()
Returns a string describing classifierjava.lang.String
kernelTipText()
Returns the tip text for this propertyjava.util.Enumeration
listOptions()
Returns an enumeration describing the available options.static void
main(java.lang.String[] args)
Main method for running this classifier.java.lang.String
regOptimizerTipText()
Returns the tip text for this propertyvoid
setC(double v)
Set the value of C.void
setFilterType(SelectedTag newType)
Sets how the training data will be transformed.void
setKernel(Kernel value)
sets the kernel to usevoid
setOptions(java.lang.String[] options)
Parses a given list of options.void
setRegOptimizer(RegOptimizer regOptimizer)
sets the learning algorithmjava.lang.String
toString()
Prints out the classifier.-
Methods inherited from class weka.classifiers.Classifier
debugTipText, distributionForInstance, forName, getDebug, makeCopies, makeCopy, setDebug
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Field Detail
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FILTER_NORMALIZE
public static final int FILTER_NORMALIZE
The filter to apply to the training data: Normalzie- See Also:
- Constant Field Values
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FILTER_STANDARDIZE
public static final int FILTER_STANDARDIZE
The filter to apply to the training data: Standardize- See Also:
- Constant Field Values
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FILTER_NONE
public static final int FILTER_NONE
The filter to apply to the training data: None- See Also:
- Constant Field Values
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TAGS_FILTER
public static final Tag[] TAGS_FILTER
The filter to apply to the training data
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Method Detail
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globalInfo
public java.lang.String globalInfo()
Returns a string describing classifier- Returns:
- a description suitable for displaying in the explorer/experimenter gui
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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 interfaceTechnicalInformationHandler
- Returns:
- the technical information about this class
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listOptions
public java.util.Enumeration listOptions()
Returns an enumeration describing the available options.- Specified by:
listOptions
in interfaceOptionHandler
- Overrides:
listOptions
in classClassifier
- Returns:
- an enumeration of all the available options.
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setOptions
public void setOptions(java.lang.String[] options) throws java.lang.Exception
Parses a given list of options. Valid options are:-C <double> The complexity constant C. (default 1)
-N Whether to 0=normalize/1=standardize/2=neither. (default 0=normalize)
-I <classname and parameters> Optimizer class used for solving quadratic optimization problem (default weka.classifiers.functions.supportVector.RegSMOImproved)
-K <classname and parameters> The Kernel to use. (default: weka.classifiers.functions.supportVector.PolyKernel)
Options specific to optimizer ('-I') weka.classifiers.functions.supportVector.RegSMOImproved:
-T <double> The tolerance parameter for checking the stopping criterion. (default 0.001)
-V Use variant 1 of the algorithm when true, otherwise use variant 2. (default true)
-P <double> The epsilon for round-off error. (default 1.0e-12)
-L <double> The epsilon parameter in epsilon-insensitive loss function. (default 1.0e-3)
-W <double> The random number seed. (default 1)
Options specific to kernel ('-K') weka.classifiers.functions.supportVector.PolyKernel:
-D Enables debugging output (if available) to be printed. (default: off)
-no-checks Turns off all checks - use with caution! (default: checks on)
-C <num> The size of the cache (a prime number), 0 for full cache and -1 to turn it off. (default: 250007)
-E <num> The Exponent to use. (default: 1.0)
-L Use lower-order terms. (default: no)
- Specified by:
setOptions
in interfaceOptionHandler
- Overrides:
setOptions
in classClassifier
- Parameters:
options
- the list of options as an array of strings- Throws:
java.lang.Exception
- if an option is not supported
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getOptions
public java.lang.String[] getOptions()
Gets the current settings of the classifier.- Specified by:
getOptions
in interfaceOptionHandler
- Overrides:
getOptions
in classClassifier
- Returns:
- an array of strings suitable for passing to setOptions
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getCapabilities
public Capabilities getCapabilities()
Returns default capabilities of the classifier.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classClassifier
- Returns:
- the capabilities of this classifier
- See Also:
Capabilities
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buildClassifier
public void buildClassifier(Instances instances) throws java.lang.Exception
Method for building the classifier.- Specified by:
buildClassifier
in classClassifier
- Parameters:
instances
- the set of training instances- Throws:
java.lang.Exception
- if the classifier can't be built successfully
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classifyInstance
public double classifyInstance(Instance instance) throws java.lang.Exception
Classifies the given instance using the linear regression function.- Overrides:
classifyInstance
in classClassifier
- Parameters:
instance
- the test instance- Returns:
- the classification
- Throws:
java.lang.Exception
- if classification can't be done successfully
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regOptimizerTipText
public java.lang.String regOptimizerTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setRegOptimizer
public void setRegOptimizer(RegOptimizer regOptimizer)
sets the learning algorithm- Parameters:
regOptimizer
- the learning algorithm
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getRegOptimizer
public RegOptimizer getRegOptimizer()
returns the learning algorithm- Returns:
- the learning algorithm
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kernelTipText
public java.lang.String kernelTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setKernel
public void setKernel(Kernel value)
sets the kernel to use- Parameters:
value
- the kernel to use
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getKernel
public Kernel getKernel()
Returns the kernel to use- Returns:
- the current kernel
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cTipText
public java.lang.String cTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getC
public double getC()
Get the value of C.- Returns:
- Value of C.
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setC
public void setC(double v)
Set the value of C.- Parameters:
v
- Value to assign to C.
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filterTypeTipText
public java.lang.String filterTypeTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getFilterType
public SelectedTag getFilterType()
Gets how the training data will be transformed. Will be one of FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.- Returns:
- the filtering mode
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setFilterType
public void setFilterType(SelectedTag newType)
Sets how the training data will be transformed. Should be one of FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.- Parameters:
newType
- the new filtering mode
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toString
public java.lang.String toString()
Prints out the classifier.- Overrides:
toString
in classjava.lang.Object
- Returns:
- a description of the classifier as a string
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enumerateMeasures
public java.util.Enumeration enumerateMeasures()
Returns an enumeration of the measure names. Additional measures must follow the naming convention of starting with "measure", eg. double measureBlah()- Specified by:
enumerateMeasures
in interfaceAdditionalMeasureProducer
- Returns:
- an enumeration of the measure names
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getMeasure
public double getMeasure(java.lang.String measureName)
Returns the value of the named measure- Specified by:
getMeasure
in interfaceAdditionalMeasureProducer
- Parameters:
measureName
- the name of the measure to query for its value- Returns:
- the value of the named measure
- Throws:
java.lang.IllegalArgumentException
- if the named measure is not supported
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getRevision
public java.lang.String getRevision()
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classClassifier
- Returns:
- the revision
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main
public static void main(java.lang.String[] args)
Main method for running this classifier.- Parameters:
args
- the commandline options
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