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java.lang.Objectweka.classifiers.Classifier
weka.classifiers.SingleClassifierEnhancer
weka.classifiers.RandomizableSingleClassifierEnhancer
weka.classifiers.meta.MultiClassClassifier
public class MultiClassClassifier
A metaclassifier for handling multi-class datasets with 2-class classifiers. This classifier is also capable of applying error correcting output codes for increased accuracy.
Valid options are:-M <num> Sets the method to use. Valid values are 0 (1-against-all), 1 (random codes), 2 (exhaustive code), and 3 (1-against-1). (default 0)
-R <num> Sets the multiplier when using random codes. (default 2.0)
-P Use pairwise coupling (only has an effect for 1-against1)
-S <num> Random number seed. (default 1)
-D If set, classifier is run in debug mode and may output additional info to the console
-W Full name of base classifier. (default: weka.classifiers.functions.Logistic)
Options specific to classifier weka.classifiers.functions.Logistic:
-D Turn on debugging output.
-R <ridge> Set the ridge in the log-likelihood.
-M <number> Set the maximum number of iterations (default -1, until convergence).
Field Summary | |
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static int |
METHOD_1_AGAINST_1
1-against-1 |
static int |
METHOD_1_AGAINST_ALL
1-against-all |
static int |
METHOD_ERROR_EXHAUSTIVE
exhaustive correction code |
static int |
METHOD_ERROR_RANDOM
random correction code |
static Tag[] |
TAGS_METHOD
The error correction modes |
Constructor Summary | |
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MultiClassClassifier()
Constructor. |
Method Summary | |
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void |
buildClassifier(Instances insts)
Builds the classifiers. |
double[] |
distributionForInstance(Instance inst)
Returns the distribution for an instance. |
Capabilities |
getCapabilities()
Returns default capabilities of the classifier. |
SelectedTag |
getMethod()
Gets the method used. |
java.lang.String[] |
getOptions()
Gets the current settings of the Classifier. |
double |
getRandomWidthFactor()
Gets the multiplier when generating random codes. |
java.lang.String |
getRevision()
Returns the revision string. |
boolean |
getUsePairwiseCoupling()
Gets whether to use pairwise coupling with 1-vs-1 classification to improve probability estimates. |
java.lang.String |
globalInfo()
|
double[] |
individualPredictions(Instance inst)
Returns the individual predictions of the base classifiers for an instance. |
java.util.Enumeration |
listOptions()
Returns an enumeration describing the available options |
static void |
main(java.lang.String[] argv)
Main method for testing this class. |
java.lang.String |
methodTipText()
|
static double[] |
pairwiseCoupling(double[][] n,
double[][] r)
Implements pairwise coupling. |
java.lang.String |
randomWidthFactorTipText()
|
void |
setMethod(SelectedTag newMethod)
Sets the method used. |
void |
setOptions(java.lang.String[] options)
Parses a given list of options. |
void |
setRandomWidthFactor(double newRandomWidthFactor)
Sets the multiplier when generating random codes. |
void |
setUsePairwiseCoupling(boolean p)
Set whether to use pairwise coupling with 1-vs-1 classification to improve probability estimates. |
java.lang.String |
toString()
Prints the classifiers. |
java.lang.String |
usePairwiseCouplingTipText()
|
Methods inherited from class weka.classifiers.RandomizableSingleClassifierEnhancer |
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getSeed, seedTipText, setSeed |
Methods inherited from class weka.classifiers.SingleClassifierEnhancer |
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classifierTipText, getClassifier, setClassifier |
Methods inherited from class weka.classifiers.Classifier |
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classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
Field Detail |
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public static final int METHOD_1_AGAINST_ALL
public static final int METHOD_ERROR_RANDOM
public static final int METHOD_ERROR_EXHAUSTIVE
public static final int METHOD_1_AGAINST_1
public static final Tag[] TAGS_METHOD
Constructor Detail |
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public MultiClassClassifier()
Method Detail |
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public Capabilities getCapabilities()
getCapabilities
in interface CapabilitiesHandler
getCapabilities
in class SingleClassifierEnhancer
Capabilities
public void buildClassifier(Instances insts) throws java.lang.Exception
buildClassifier
in class Classifier
insts
- the training data.
java.lang.Exception
- if a classifier can't be builtpublic double[] individualPredictions(Instance inst) throws java.lang.Exception
inst
- the instance to get the prediction for
java.lang.Exception
- if the predictions can't be computed successfullypublic double[] distributionForInstance(Instance inst) throws java.lang.Exception
distributionForInstance
in class Classifier
inst
- the instance to get the distribution for
java.lang.Exception
- if the distribution can't be computed successfullypublic java.lang.String toString()
toString
in class java.lang.Object
public java.util.Enumeration listOptions()
listOptions
in interface OptionHandler
listOptions
in class RandomizableSingleClassifierEnhancer
public void setOptions(java.lang.String[] options) throws java.lang.Exception
-M <num> Sets the method to use. Valid values are 0 (1-against-all), 1 (random codes), 2 (exhaustive code), and 3 (1-against-1). (default 0)
-R <num> Sets the multiplier when using random codes. (default 2.0)
-P Use pairwise coupling (only has an effect for 1-against1)
-S <num> Random number seed. (default 1)
-D If set, classifier is run in debug mode and may output additional info to the console
-W Full name of base classifier. (default: weka.classifiers.functions.Logistic)
Options specific to classifier weka.classifiers.functions.Logistic:
-D Turn on debugging output.
-R <ridge> Set the ridge in the log-likelihood.
-M <number> Set the maximum number of iterations (default -1, until convergence).
setOptions
in interface OptionHandler
setOptions
in class RandomizableSingleClassifierEnhancer
options
- the list of options as an array of strings
java.lang.Exception
- if an option is not supportedpublic java.lang.String[] getOptions()
getOptions
in interface OptionHandler
getOptions
in class RandomizableSingleClassifierEnhancer
public java.lang.String globalInfo()
public java.lang.String randomWidthFactorTipText()
public double getRandomWidthFactor()
public void setRandomWidthFactor(double newRandomWidthFactor)
newRandomWidthFactor
- the new width multiplierpublic java.lang.String methodTipText()
public SelectedTag getMethod()
public void setMethod(SelectedTag newMethod)
newMethod
- the new method.public void setUsePairwiseCoupling(boolean p)
p
- true if pairwise coupling is to be usedpublic boolean getUsePairwiseCoupling()
public java.lang.String usePairwiseCouplingTipText()
public static double[] pairwiseCoupling(double[][] n, double[][] r)
n
- the sum of weights used to train each modelr
- the probability estimate from each model
public java.lang.String getRevision()
getRevision
in interface RevisionHandler
getRevision
in class Classifier
public static void main(java.lang.String[] argv)
argv
- the options
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