KernelLogisticRegression

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.RandomizableClassifier
  - - weka.classifiers.functions.KernelLogisticRegression

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, weka.classifiers.Classifier, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.Randomizable, weka.core.RevisionHandler
```
public class KernelLogisticRegression
extends weka.classifiers.RandomizableClassifier
```
This classifier generates a two-class kernel logistic regression model. The model is fit by minimizing the negative log-likelihood with a quadratic penalty using BFGS optimization, as implemented in the Optimization class. Alternatively, conjugate gradient optimization can be applied. The user can specify the kernel function and the value of lambda, the multiplier for the quadractic penalty. Using a linear kernel (the default) this method should give the same result as ridge logistic regression implemented in Logistic, assuming the ridge parameter is set to the same value as lambda, and not too small. By replacing the kernel function, we can learn non-linear decision boundaries.

Note that the data is filtered using ReplaceMissingValues, RemoveUseless, NominalToBinary, and Standardize (in that order).

If a CachedKernel is used, this class will overwrite the manually specified cache size and use a full cache instead.

To apply this classifier to multi-class problems, use the MultiClassClassifier.

This implementation stores the full kernel matrix at training time for speed reasons.
Valid options are:
```
 -S <num>
  Random number seed.
  (default 1)
 
```
```
 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
 
```
```
 -K <classname and parameters>
  The Kernel to use.
  (default: weka.classifiers.functions.supportVector.PolyKernel)
 
```
```
 -L <double>
  The lambda penalty parameter. (default 0.01)
 
```
```
 -G
  Use conjugate gradient descent instead of BFGS.
 
```
```
 -P <int>
  The size of the thread pool, for example, the number of cores in the CPU. (default 1)
 
```
```
 -E <int>
  The number of threads to use, which should be >= size of thread pool. (default 1)
 
```
```
 Options specific to kernel 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: ???? $

Author:

Eibe Frank

See Also:

Serialized Form

Field Summary
- Fields inherited from class weka.classifiers.AbstractClassifier
  BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors
Constructor and Description

KernelLogisticRegression()

Constructors
Constructor and Description
`KernelLogisticRegression()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(weka.core.Instances data)` Method for building the classifier from training data.
`double[]`	`distributionForInstance(weka.core.Instance inst)` Method for applying the classifier to a test instance.
`weka.core.Capabilities`	`getCapabilities()` Returns capabilities of the classifier (i.e.
`weka.classifiers.functions.supportVector.Kernel`	`getKernel()` Returns the kernel to use.
`double`	`getLambda()` Get the value of lambda.
`int`	`getNumThreads()` Gets the number of threads.
`java.lang.String[]`	`getOptions()` Gets the current settings of the classifier.
`int`	`getPoolSize()` Gets the number of threads.
`java.lang.String`	`getRevision()` Need to implement this as well....
`boolean`	`getUseCGD()` Gets whether to use CGD.
`java.lang.String`	`globalInfo()` Returns a string describing this classifier
`java.lang.String`	`kernelTipText()` Returns the tip text for this property
`java.lang.String`	`lambdaTipText()` Returns the tip text for this property
`java.util.Enumeration<weka.core.Option>`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(java.lang.String[] args)` The main method for running this class from the command-line.
`java.lang.String`	`numThreadsTipText()`
`java.lang.String`	`poolSizeTipText()`
`void`	`setKernel(weka.classifiers.functions.supportVector.Kernel value)` Sets the kernel to use.
`void`	`setLambda(double v)` Set the value of lambda.
`void`	`setNumThreads(int nT)` Sets the number of threads
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setPoolSize(int nT)` Sets the number of threads
`void`	`setUseCGD(boolean newUseCGD)` Sets whether to use CGD.
`java.lang.String`	`toString()` Outputs description of classifier as a string.
`java.lang.String`	`useCGDTipText()`

Methods inherited from class weka.classifiers.RandomizableClassifier
getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.AbstractClassifier
batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

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

Methods inherited from interface weka.core.OptionHandler
makeCopy

- Constructor Detail
  - KernelLogisticRegression
```
public KernelLogisticRegression()
```
- Method Detail
  - globalInfo
```
public java.lang.String globalInfo()
```
    Returns a string describing this classifier
    
    Returns:
    
    a description of the classifier suitable for displaying in the explorer/experimenter gui
  - getCapabilities
```
public weka.core.Capabilities getCapabilities()
```
    Returns capabilities of the classifier (i.e. what type of data the classifier can handle).
    
    Specified by:
    
    getCapabilities in interface weka.classifiers.Classifier
    
    Specified by:
    
    getCapabilities in interface weka.core.CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class weka.classifiers.AbstractClassifier
  - listOptions
```
public java.util.Enumeration<weka.core.Option> listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    listOptions in class weka.classifiers.RandomizableClassifier
    
    Returns:
    
    an enumeration of all the available options.
  - setOptions
```
public void setOptions(java.lang.String[] options)
                throws java.lang.Exception
```
    Parses a given list of options.
    Valid options are:
```
 -S <num>
  Random number seed.
  (default 1)
 
```
```
 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
 
```
```
 -K <classname and parameters>
  The Kernel to use.
  (default: weka.classifiers.functions.supportVector.PolyKernel)
 
```
```
 -L <double>
  The lambda penalty parameter. (default 0.01)
 
```
```
 -G
  Use conjugate gradient descent instead of BFGS.
 
```
```
 -P <int>
  The size of the thread pool, for example, the number of cores in the CPU. (default 1)
 
```
```
 -E <int>
  The number of threads to use, which should be >= size of thread pool. (default 1)
 
```
```
 Options specific to kernel 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 interface weka.core.OptionHandler
    
    Overrides:
    
    setOptions in class weka.classifiers.RandomizableClassifier
    
    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 the classifier.
    
    Specified by:
    
    getOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    getOptions in class weka.classifiers.RandomizableClassifier
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - numThreadsTipText
```
public java.lang.String numThreadsTipText()
```
    Returns:
    
    a string to describe the option
  - getNumThreads
```
public int getNumThreads()
```
    Gets the number of threads.
  - setNumThreads
```
public void setNumThreads(int nT)
```
    Sets the number of threads
  - poolSizeTipText
```
public java.lang.String poolSizeTipText()
```
    Returns:
    
    a string to describe the option
  - getPoolSize
```
public int getPoolSize()
```
    Gets the number of threads.
  - setPoolSize
```
public void setPoolSize(int nT)
```
    Sets the number of threads
  - lambdaTipText
```
public java.lang.String lambdaTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getLambda
```
public double getLambda()
```
    Get the value of lambda.
    
    Returns:
    
    Value of lambda.
  - setLambda
```
public void setLambda(double v)
```
    Set the value of lambda.
    
    Parameters:
    
    v - Value to assign to lambda.
  - kernelTipText
```
public java.lang.String kernelTipText()
```
    Returns the tip text for this property
  - setKernel
```
public void setKernel(weka.classifiers.functions.supportVector.Kernel value)
```
    Sets the kernel to use.
  - getKernel
```
public weka.classifiers.functions.supportVector.Kernel getKernel()
```
    Returns the kernel to use.
  - useCGDTipText
```
public java.lang.String useCGDTipText()
```
    Returns:
    
    a string to describe the option
  - getUseCGD
```
public boolean getUseCGD()
```
    Gets whether to use CGD.
  - setUseCGD
```
public void setUseCGD(boolean newUseCGD)
```
    Sets whether to use CGD.
  - buildClassifier
```
public void buildClassifier(weka.core.Instances data)
                     throws java.lang.Exception
```
    Method for building the classifier from training data.
    
    Throws:
    
    java.lang.Exception
  - distributionForInstance
```
public double[] distributionForInstance(weka.core.Instance inst)
                                 throws java.lang.Exception
```
    Method for applying the classifier to a test instance.
    
    Specified by:
    
    distributionForInstance in interface weka.classifiers.Classifier
    
    Overrides:
    
    distributionForInstance in class weka.classifiers.AbstractClassifier
    
    Throws:
    
    java.lang.Exception
  - toString
```
public java.lang.String toString()
```
    Outputs description of classifier as a string.
    
    Overrides:
    
    toString in class java.lang.Object
  - getRevision
```
public java.lang.String getRevision()
```
    Need to implement this as well....
    
    Specified by:
    
    getRevision in interface weka.core.RevisionHandler
    
    Overrides:
    
    getRevision in class weka.classifiers.AbstractClassifier
  - main
```
public static void main(java.lang.String[] args)
```
    The main method for running this class from the command-line.

Class KernelLogisticRegression

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.RandomizableClassifier

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.core.OptionHandler

Constructor Detail

KernelLogisticRegression

Method Detail

globalInfo

getCapabilities

listOptions

setOptions

getOptions

numThreadsTipText

getNumThreads

setNumThreads

poolSizeTipText

getPoolSize

setPoolSize

lambdaTipText

getLambda

setLambda

kernelTipText

setKernel

getKernel

useCGDTipText

getUseCGD

setUseCGD

buildClassifier

distributionForInstance

toString

getRevision

main