MLPModel

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

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, weka.core.WeightedInstancesHandler

Direct Known Subclasses:

MLPClassifier, MLPRegressor
```
public abstract class MLPModel
extends weka.classifiers.RandomizableClassifier
implements weka.core.WeightedInstancesHandler
```
Abstract super class for MLPClassifier and MLPRegressor.

Version:

$Revision: 10949 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz)

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

MLPModel()

Constructors
Constructor and Description
`MLPModel()`

Method Summary

All Methods Instance Methods Abstract Methods Concrete Methods
Modifier and Type	Method and Description
`java.lang.String`	`activationFunctionTipText()`
`void`	`buildClassifier(weka.core.Instances data)` Builds the MLP network classifier based on the given dataset.
`double[]`	`distributionForInstance(weka.core.Instance inst)` Calculates the output of the network after the instance has been piped through the fliters to replace missing values, etc.
`ActivationFunction`	`getActivationFunction()` Returns the ActivationFunction object.
`weka.core.Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`LossFunction`	`getLossFunction()` Returns the LossFunction object.
`int`	`getNumFunctions()` Gets the number of functions.
`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.
`double`	`getRidge()` Gets the value of the ridge parameter.
`double`	`getTolerance()` Gets the tolerance parameter for the delta values.
`boolean`	`getUseCGD()` Gets whether to use CGD.
`java.lang.String`	`globalInfo()` This will return a string describing the classifier.
`java.util.Enumeration<weka.core.Option>`	`listOptions()` Returns an enumeration describing the available options.
`java.lang.String`	`lossFunctionTipText()`
`abstract java.lang.String`	`modelType()` Returns the model type as a string.
`java.lang.String`	`numFunctionsTipText()`
`java.lang.String`	`numThreadsTipText()`
`java.lang.String`	`poolSizeTipText()`
`java.lang.String`	`ridgeTipText()`
`void`	`setActivationFunction(ActivationFunction func)` Sets the loss function.
`void`	`setLossFunction(LossFunction loss)` Sets the loss function.
`void`	`setNumFunctions(int newNumFunctions)` Sets the number of functions.
`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`	`setRidge(double newRidge)` Sets the value of the ridge parameter.
`void`	`setTolerance(double newTolerance)` Sets the tolerance parameter for the delta values.
`void`	`setUseCGD(boolean newUseCGD)` Sets whether to use CGD.
`java.lang.String`	`toleranceTipText()`
`java.lang.String`	`toString()` Outputs the network 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, getRevision, 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
  - MLPModel
```
public MLPModel()
```
- Method Detail
  - getCapabilities
```
public weka.core.Capabilities getCapabilities()
```
    Returns default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface weka.classifiers.Classifier
    
    Specified by:
    
    getCapabilities in interface weka.core.CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    the capabilities of this classifier
  - buildClassifier
```
public void buildClassifier(weka.core.Instances data)
                     throws java.lang.Exception
```
    Builds the MLP network classifier based on the given dataset.
    
    Specified by:
    
    buildClassifier in interface weka.classifiers.Classifier
    
    Throws:
    
    java.lang.Exception
  - distributionForInstance
```
public double[] distributionForInstance(weka.core.Instance inst)
                                 throws java.lang.Exception
```
    Calculates the output of the network after the instance has been piped through the fliters to replace missing values, etc.
    
    Specified by:
    
    distributionForInstance in interface weka.classifiers.Classifier
    
    Overrides:
    
    distributionForInstance in class weka.classifiers.AbstractClassifier
    
    Throws:
    
    java.lang.Exception
  - globalInfo
```
public java.lang.String globalInfo()
```
    This will return a string describing the classifier.
    
    Returns:
    
    The string.
  - toleranceTipText
```
public java.lang.String toleranceTipText()
```
    Returns:
    
    a string to describe the option
  - getTolerance
```
public double getTolerance()
```
    Gets the tolerance parameter for the delta values.
  - setTolerance
```
public void setTolerance(double newTolerance)
```
    Sets the tolerance parameter for the delta values.
  - numFunctionsTipText
```
public java.lang.String numFunctionsTipText()
```
    Returns:
    
    a string to describe the option
  - getNumFunctions
```
public int getNumFunctions()
```
    Gets the number of functions.
  - setNumFunctions
```
public void setNumFunctions(int newNumFunctions)
```
    Sets the number of functions.
  - ridgeTipText
```
public java.lang.String ridgeTipText()
```
    Returns:
    
    a string to describe the option
  - getRidge
```
public double getRidge()
```
    Gets the value of the ridge parameter.
  - setRidge
```
public void setRidge(double newRidge)
```
    Sets the value of the ridge parameter.
  - 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.
  - 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
  - lossFunctionTipText
```
public java.lang.String lossFunctionTipText()
```
    Returns:
    
    a string to describe the option
  - getLossFunction
```
public LossFunction getLossFunction()
```
    Returns the LossFunction object.
    
    Returns:
    
    the LossFunction object
  - setLossFunction
```
public void setLossFunction(LossFunction loss)
```
    Sets the loss function.
    
    Parameters:
    
    loss - the loss function to use.
  - activationFunctionTipText
```
public java.lang.String activationFunctionTipText()
```
    Returns:
    
    a string to describe the option
  - getActivationFunction
```
public ActivationFunction getActivationFunction()
```
    Returns the ActivationFunction object.
    
    Returns:
    
    the ActivationFunction object
  - setActivationFunction
```
public void setActivationFunction(ActivationFunction func)
```
    Sets the loss function.
    
    Parameters:
    
    func - the loss function to use.
  - 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:
```
 -N <int>
  Number of hidden units (default is 2).
 
```
```
 -R <double>
  Ridge factor for quadratic penalty on weights (default is 0.01).
 
```
```
 -O <double>
  Tolerance parameter for delta values (default is 1.0e-6).
 
```
```
 -G
  Use conjugate gradient descent (recommended for many attributes).
 
```
```
 -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)
 
```
```
 -L <classname and parameters>
  The loss function to use.
  (default: weka.classifiers.functions.loss.SquaredError)
```
```
 -S <num>
  Random number seed.
  (default 1)
 
```
    Options after -- are passed to the designated classifier.
    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
  - modelType
```
public abstract java.lang.String modelType()
```
    Returns the model type as a string.
  - toString
```
public java.lang.String toString()
```
    Outputs the network as a string.
    
    Overrides:
    
    toString in class java.lang.Object

Class MLPModel

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

MLPModel

Method Detail

getCapabilities

buildClassifier

distributionForInstance

globalInfo

toleranceTipText

getTolerance

setTolerance

numFunctionsTipText

getNumFunctions

setNumFunctions

ridgeTipText

getRidge

setRidge

useCGDTipText

getUseCGD

setUseCGD

numThreadsTipText

getNumThreads

setNumThreads

poolSizeTipText

getPoolSize

setPoolSize

lossFunctionTipText

getLossFunction

setLossFunction

activationFunctionTipText

getActivationFunction

setActivationFunction

listOptions

setOptions

getOptions

modelType

toString