MultilayerPerceptron (interruptible-weka 0.1.2 API)

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.functions.MultilayerPerceptron

All Implemented Interfaces:: java.io.Serializable, java.lang.Cloneable, Classifier, IterativeClassifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, Randomizable, RevisionHandler, WeightedInstancesHandler

public class MultilayerPerceptron
extends AbstractClassifier
implements OptionHandler, WeightedInstancesHandler, Randomizable, IterativeClassifier

A Classifier that uses backpropagation to classify instances.
This network can be built by hand, created by an algorithm or both. The network can also be monitored and modified during training time. The nodes in this network are all sigmoid (except for when the class is numeric in which case the the output nodes become unthresholded linear units).

Valid options are:

 -L <learning rate>
  Learning Rate for the backpropagation algorithm.
  (Value should be between 0 - 1, Default = 0.3).

 -M <momentum>
  Momentum Rate for the backpropagation algorithm.
  (Value should be between 0 - 1, Default = 0.2).

 -N <number of epochs>
  Number of epochs to train through.
  (Default = 500).

 -V <percentage size of validation set>
  Percentage size of validation set to use to terminate
  training (if this is non zero it can pre-empt num of epochs.
  (Value should be between 0 - 100, Default = 0).

 -S <seed>
  The value used to seed the random number generator
  (Value should be >= 0 and and a long, Default = 0).

 -E <threshold for number of consequetive errors>
  The consequetive number of errors allowed for validation
  testing before the netwrok terminates.
  (Value should be > 0, Default = 20).

 -G
  GUI will be opened.
  (Use this to bring up a GUI).

 -A
  Autocreation of the network connections will NOT be done.
  (This will be ignored if -G is NOT set)

 -B
  A NominalToBinary filter will NOT automatically be used.
  (Set this to not use a NominalToBinary filter).

 -H <comma seperated numbers for nodes on each layer>
  The hidden layers to be created for the network.
  (Value should be a list of comma separated Natural
  numbers or the letters 'a' = (attribs + classes) / 2,
  'i' = attribs, 'o' = classes, 't' = attribs .+ classes)
  for wildcard values, Default = a).

 -C
  Normalizing a numeric class will NOT be done.
  (Set this to not normalize the class if it's numeric).

 -I
  Normalizing the attributes will NOT be done.
  (Set this to not normalize the attributes).

 -R
  Reseting the network will NOT be allowed.
  (Set this to not allow the network to reset).

 -D
  Learning rate decay will occur.
  (Set this to cause the learning rate to decay).

See Also:: Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type	Class and Description
`protected class`	`MultilayerPerceptron.NeuralEnd` This inner class is used to connect the nodes in the network up to the data that they are classifying, Note that objects of this class are only suitable to go on the attribute side or class side of the network and not both.

Field Summary

Fields
Modifier and Type	Field and Description
`protected double`	`bestError`
`protected double`	`driftOff` Drift off counter
`protected double`	`lastRight` To keep track of error
`protected int`	`numInVal` The number of instances in the validation set (if any)
`protected Instances`	`originalFormatData` Data in original format (in case learning rate gets reset
`protected double`	`totalValWeight` Total weight of the instances in the validation set (if any)
`protected double`	`totalWeight` Total weight of the instances in the training set
`protected Instances`	`valSet` The instances in the validation set (if any)

Fields inherited from class weka.classifiers.AbstractClassifier
BATCH_SIZE_DEFAULT, m_BatchSize, m_Debug, m_DoNotCheckCapabilities, m_numDecimalPlaces, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors
Constructor and Description

MultilayerPerceptron()
The constructor.

Constructors
Constructor and Description
`MultilayerPerceptron()` The constructor.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`java.lang.String`	`autoBuildTipText()`
`void`	`blocker(boolean tf)` A function used to stop the code that called buildclassifier from continuing on before the user has finished the decision tree.
`void`	`buildClassifier(Instances i)` Call this function to build and train a neural network for the training data provided.
`java.lang.String`	`decayTipText()`
`double[]`	`distributionForInstance(Instance i)` Call this function to predict the class of an instance once a classification model has been built with the buildClassifier call.
`void`	`done()` Signal end of iterating, useful for any house-keeping/cleanup
`boolean`	`getAutoBuild()`
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`boolean`	`getDecay()`
`boolean`	`getGUI()`
`java.lang.String`	`getHiddenLayers()`
`double`	`getLearningRate()`
`double`	`getMomentum()`
`boolean`	`getNominalToBinaryFilter()`
`boolean`	`getNormalizeAttributes()`
`boolean`	`getNormalizeNumericClass()`
`java.lang.String[]`	`getOptions()` Gets the current settings of NeuralNet.
`boolean`	`getReset()`
`java.lang.String`	`getRevision()` Returns the revision string.
`int`	`getSeed()` Gets the seed for the random number generations
`int`	`getTrainingTime()`
`int`	`getValidationSetSize()`
`int`	`getValidationThreshold()`
`java.lang.String`	`globalInfo()` This will return a string describing the classifier.
`java.lang.String`	`GUITipText()`
`java.lang.String`	`hiddenLayersTipText()`
`void`	`initializeClassifier(Instances data)` Initializes an iterative classifier.
`java.lang.String`	`learningRateTipText()`
`java.util.Enumeration<Option>`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(java.lang.String[] argv)` Main method for testing this class.
`java.lang.String`	`momentumTipText()`
`boolean`	`next()` Performs one iteration.
`java.lang.String`	`nominalToBinaryFilterTipText()`
`java.lang.String`	`normalizeAttributesTipText()`
`java.lang.String`	`normalizeNumericClassTipText()`
`java.lang.String`	`resetTipText()`
`java.lang.String`	`seedTipText()`
`void`	`setAutoBuild(boolean a)` This will set whether the network is automatically built or if it is left up to the user.
`void`	`setDecay(boolean d)`
`void`	`setGUI(boolean a)` This will set whether A GUI is brought up to allow interaction by the user with the neural network during training.
`void`	`setHiddenLayers(java.lang.String h)` This will set what the hidden layers are made up of when auto build is enabled.
`void`	`setLearningRate(double l)` The learning rate can be set using this command.
`void`	`setMomentum(double m)` The momentum can be set using this command.
`void`	`setNominalToBinaryFilter(boolean f)`
`void`	`setNormalizeAttributes(boolean a)`
`void`	`setNormalizeNumericClass(boolean c)`
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setReset(boolean r)` This sets the network up to be able to reset itself with the current settings and the learning rate at half of what it is currently.
`void`	`setSeed(int l)` This seeds the random number generator, that is used when a random number is needed for the network.
`void`	`setTrainingTime(int n)` Set the number of training epochs to perform.
`void`	`setValidationSetSize(int a)` This will set the size of the validation set.
`void`	`setValidationThreshold(int t)` This sets the threshold to use for when validation testing is being done.
`java.lang.String`	`toString()`
`java.lang.String`	`trainingTimeTipText()`
`java.lang.String`	`validationSetSizeTipText()`
`java.lang.String`	`validationThresholdTipText()`

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

Methods inherited from interface weka.classifiers.Classifier
classifyInstance

- Field Detail
  - valSet
```
protected transient Instances valSet
```
    The instances in the validation set (if any)
  - numInVal
```
protected transient int numInVal
```
    The number of instances in the validation set (if any)
  - totalWeight
```
protected transient double totalWeight
```
    Total weight of the instances in the training set
  - totalValWeight
```
protected transient double totalValWeight
```
    Total weight of the instances in the validation set (if any)
  - driftOff
```
protected transient double driftOff
```
    Drift off counter
  - lastRight
```
protected transient double lastRight
```
    To keep track of error
  - bestError
```
protected transient double bestError
```
  - originalFormatData
```
protected transient Instances originalFormatData
```
    Data in original format (in case learning rate gets reset
- Constructor Detail
  - MultilayerPerceptron
```
public MultilayerPerceptron()
```
    The constructor.
- Method Detail
  - main
```
public static void main(java.lang.String[] argv)
```
    Main method for testing this class.
    
    Parameters:
    
    argv - should contain command line options (see setOptions)
  - setDecay
```
public void setDecay(boolean d)
```
    Parameters:
    
    d - True if the learning rate should decay.
  - getDecay
```
public boolean getDecay()
```
    Returns:
    
    the flag for having the learning rate decay.
  - setReset
```
public void setReset(boolean r)
```
    This sets the network up to be able to reset itself with the current settings and the learning rate at half of what it is currently. This will only happen if the network creates NaN or infinite errors. Also this will continue to happen until the network is trained properly. The learning rate will also get set back to it's original value at the end of this. This can only be set to true if the GUI is not brought up.
    
    Parameters:
    
    r - True if the network should restart with it's current options and set the learning rate to half what it currently is.
  - getReset
```
public boolean getReset()
```
    Returns:
    
    The flag for reseting the network.
  - setNormalizeNumericClass
```
public void setNormalizeNumericClass(boolean c)
```
    Parameters:
    
    c - True if the class should be normalized (the class will only ever be normalized if it is numeric). (Normalization puts the range between -1 - 1).
  - getNormalizeNumericClass
```
public boolean getNormalizeNumericClass()
```
    Returns:
    
    The flag for normalizing a numeric class.
  - setNormalizeAttributes
```
public void setNormalizeAttributes(boolean a)
```
    Parameters:
    
    a - True if the attributes should be normalized (even nominal attributes will get normalized here) (range goes between -1 - 1).
  - getNormalizeAttributes
```
public boolean getNormalizeAttributes()
```
    Returns:
    
    The flag for normalizing attributes.
  - setNominalToBinaryFilter
```
public void setNominalToBinaryFilter(boolean f)
```
    Parameters:
    
    f - True if a nominalToBinary filter should be used on the data.
  - getNominalToBinaryFilter
```
public boolean getNominalToBinaryFilter()
```
    Returns:
    
    The flag for nominal to binary filter use.
  - setSeed
```
public void setSeed(int l)
```
    This seeds the random number generator, that is used when a random number is needed for the network.
    
    Specified by:
    
    setSeed in interface Randomizable
    
    Parameters:
    
    l - The seed.
  - getSeed
```
public int getSeed()
```
    Description copied from interface: Randomizable
    
    Gets the seed for the random number generations
    
    Specified by:
    
    getSeed in interface Randomizable
    
    Returns:
    
    The seed for the random number generator.
  - setValidationThreshold
```
public void setValidationThreshold(int t)
```
    This sets the threshold to use for when validation testing is being done. It works by ending testing once the error on the validation set has consecutively increased a certain number of times.
    
    Parameters:
    
    t - The threshold to use for this.
  - getValidationThreshold
```
public int getValidationThreshold()
```
    Returns:
    
    The threshold used for validation testing.
  - setLearningRate
```
public void setLearningRate(double l)
```
    The learning rate can be set using this command. NOTE That this is a static variable so it affect all networks that are running. Must be greater than 0 and no more than 1.
    
    Parameters:
    
    l - The New learning rate.
  - getLearningRate
```
public double getLearningRate()
```
    Returns:
    
    The learning rate for the nodes.
  - setMomentum
```
public void setMomentum(double m)
```
    The momentum can be set using this command. THE same conditions apply to this as to the learning rate.
    
    Parameters:
    
    m - The new Momentum.
  - getMomentum
```
public double getMomentum()
```
    Returns:
    
    The momentum for the nodes.
  - setAutoBuild
```
public void setAutoBuild(boolean a)
```
    This will set whether the network is automatically built or if it is left up to the user. (there is nothing to stop a user from altering an autobuilt network however).
    
    Parameters:
    
    a - True if the network should be auto built.
  - getAutoBuild
```
public boolean getAutoBuild()
```
    Returns:
    
    The auto build state.
  - setHiddenLayers
```
public void setHiddenLayers(java.lang.String h)
```
    This will set what the hidden layers are made up of when auto build is enabled. Note to have no hidden units, just put a single 0, Any more 0's will indicate that the string is badly formed and make it unaccepted. Negative numbers, and floats will do the same. There are also some wildcards. These are 'a' = (number of attributes + number of classes) / 2, 'i' = number of attributes, 'o' = number of classes, and 't' = number of attributes + number of classes.
    
    Parameters:
    
    h - A string with a comma seperated list of numbers. Each number is the number of nodes to be on a hidden layer.
  - getHiddenLayers
```
public java.lang.String getHiddenLayers()
```
    Returns:
    
    A string representing the hidden layers, each number is the number of nodes on a hidden layer.
  - setGUI
```
public void setGUI(boolean a)
```
    This will set whether A GUI is brought up to allow interaction by the user with the neural network during training.
    
    Parameters:
    
    a - True if gui should be created.
  - getGUI
```
public boolean getGUI()
```
    Returns:
    
    The true if should show gui.
  - setValidationSetSize
```
public void setValidationSetSize(int a)
```
    This will set the size of the validation set.
    
    Parameters:
    
    a - The size of the validation set, as a percentage of the whole.
  - getValidationSetSize
```
public int getValidationSetSize()
```
    Returns:
    
    The percentage size of the validation set.
  - setTrainingTime
```
public void setTrainingTime(int n)
```
    Set the number of training epochs to perform. Must be greater than 0.
    
    Parameters:
    
    n - The number of epochs to train through.
  - getTrainingTime
```
public int getTrainingTime()
```
    Returns:
    
    The number of epochs to train through.
  - blocker
```
public void blocker(boolean tf)
```
    A function used to stop the code that called buildclassifier from continuing on before the user has finished the decision tree.
    
    Parameters:
    
    tf - True to stop the thread, False to release the thread that is waiting there (if one).
  - getCapabilities
```
public Capabilities getCapabilities()
```
    Returns default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface Classifier
    
    Specified by:
    
    getCapabilities in interface CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class AbstractClassifier
    
    Returns:
    
    the capabilities of this classifier
    
    See Also:
    
    Capabilities
  - initializeClassifier
```
public void initializeClassifier(Instances data)
                          throws java.lang.Exception
```
    Initializes an iterative classifier.
    
    Specified by:
    
    initializeClassifier in interface IterativeClassifier
    
    Parameters:
    
    data - the instances to be used in induction
    
    Throws:
    
    java.lang.Exception - if the model cannot be initialized
  - next
```
public boolean next()
             throws java.lang.Exception
```
    Performs one iteration.
    
    Specified by:
    
    next in interface IterativeClassifier
    
    Returns:
    
    false if no further iterations could be performed, true otherwise
    
    Throws:
    
    java.lang.Exception - if this iteration fails for unexpected reasons
  - done
```
public void done()
          throws java.lang.Exception
```
    Signal end of iterating, useful for any house-keeping/cleanup
    
    Specified by:
    
    done in interface IterativeClassifier
    
    Throws:
    
    java.lang.Exception - if cleanup fails
  - buildClassifier
```
public void buildClassifier(Instances i)
                     throws java.lang.Exception
```
    Call this function to build and train a neural network for the training data provided.
    
    Specified by:
    
    buildClassifier in interface Classifier
    
    Parameters:
    
    i - The training data.
    
    Throws:
    
    java.lang.Exception - if can't build classification properly.
  - distributionForInstance
```
public double[] distributionForInstance(Instance i)
                                 throws java.lang.Exception
```
    Call this function to predict the class of an instance once a classification model has been built with the buildClassifier call.
    
    Specified by:
    
    distributionForInstance in interface Classifier
    
    Overrides:
    
    distributionForInstance in class AbstractClassifier
    
    Parameters:
    
    i - The instance to classify.
    
    Returns:
    
    A double array filled with the probabilities of each class type.
    
    Throws:
    
    java.lang.Exception - if can't classify instance.
  - listOptions
```
public java.util.Enumeration<Option> listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class AbstractClassifier
    
    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:
```
 -L <learning rate>
  Learning Rate for the backpropagation algorithm.
  (Value should be between 0 - 1, Default = 0.3).
 
```
```
 -M <momentum>
  Momentum Rate for the backpropagation algorithm.
  (Value should be between 0 - 1, Default = 0.2).
 
```
```
 -N <number of epochs>
  Number of epochs to train through.
  (Default = 500).
 
```
```
 -V <percentage size of validation set>
  Percentage size of validation set to use to terminate
  training (if this is non zero it can pre-empt num of epochs.
  (Value should be between 0 - 100, Default = 0).
 
```
```
 -S <seed>
  The value used to seed the random number generator
  (Value should be >= 0 and and a long, Default = 0).
 
```
```
 -E <threshold for number of consequetive errors>
  The consequetive number of errors allowed for validation
  testing before the netwrok terminates.
  (Value should be > 0, Default = 20).
 
```
```
 -G
  GUI will be opened.
  (Use this to bring up a GUI).
 
```
```
 -A
  Autocreation of the network connections will NOT be done.
  (This will be ignored if -G is NOT set)
 
```
```
 -B
  A NominalToBinary filter will NOT automatically be used.
  (Set this to not use a NominalToBinary filter).
 
```
```
 -H <comma seperated numbers for nodes on each layer>
  The hidden layers to be created for the network.
  (Value should be a list of comma separated Natural
  numbers or the letters 'a' = (attribs + classes) / 2,
  'i' = attribs, 'o' = classes, 't' = attribs .+ classes)
  for wildcard values, Default = a).
 
```
```
 -C
  Normalizing a numeric class will NOT be done.
  (Set this to not normalize the class if it's numeric).
 
```
```
 -I
  Normalizing the attributes will NOT be done.
  (Set this to not normalize the attributes).
 
```
```
 -R
  Reseting the network will NOT be allowed.
  (Set this to not allow the network to reset).
 
```
```
 -D
  Learning rate decay will occur.
  (Set this to cause the learning rate to decay).
 
```
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class AbstractClassifier
    
    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 NeuralNet.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class AbstractClassifier
    
    Returns:
    
    an array of strings suitable for passing to setOptions()
  - toString
```
public java.lang.String toString()
```
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    string describing the model.
  - globalInfo
```
public java.lang.String globalInfo()
```
    This will return a string describing the classifier.
    
    Returns:
    
    The string.
  - learningRateTipText
```
public java.lang.String learningRateTipText()
```
    Returns:
    
    a string to describe the learning rate option.
  - momentumTipText
```
public java.lang.String momentumTipText()
```
    Returns:
    
    a string to describe the momentum option.
  - autoBuildTipText
```
public java.lang.String autoBuildTipText()
```
    Returns:
    
    a string to describe the AutoBuild option.
  - seedTipText
```
public java.lang.String seedTipText()
```
    Returns:
    
    a string to describe the random seed option.
  - validationThresholdTipText
```
public java.lang.String validationThresholdTipText()
```
    Returns:
    
    a string to describe the validation threshold option.
  - GUITipText
```
public java.lang.String GUITipText()
```
    Returns:
    
    a string to describe the GUI option.
  - validationSetSizeTipText
```
public java.lang.String validationSetSizeTipText()
```
    Returns:
    
    a string to describe the validation size option.
  - trainingTimeTipText
```
public java.lang.String trainingTimeTipText()
```
    Returns:
    
    a string to describe the learning rate option.
  - nominalToBinaryFilterTipText
```
public java.lang.String nominalToBinaryFilterTipText()
```
    Returns:
    
    a string to describe the nominal to binary option.
  - hiddenLayersTipText
```
public java.lang.String hiddenLayersTipText()
```
    Returns:
    
    a string to describe the hidden layers in the network.
  - normalizeNumericClassTipText
```
public java.lang.String normalizeNumericClassTipText()
```
    Returns:
    
    a string to describe the nominal to binary option.
  - normalizeAttributesTipText
```
public java.lang.String normalizeAttributesTipText()
```
    Returns:
    
    a string to describe the nominal to binary option.
  - resetTipText
```
public java.lang.String resetTipText()
```
    Returns:
    
    a string to describe the Reset option.
  - decayTipText
```
public java.lang.String decayTipText()
```
    Returns:
    
    a string to describe the Decay option.
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class AbstractClassifier
    
    Returns:
    
    the revision

Class MultilayerPerceptron

Nested Class Summary

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.classifiers.Classifier

Field Detail

valSet

numInVal

totalWeight

totalValWeight

driftOff

lastRight

bestError

originalFormatData

Constructor Detail

MultilayerPerceptron

Method Detail

main

setDecay

getDecay

setReset

getReset

setNormalizeNumericClass

getNormalizeNumericClass

setNormalizeAttributes

getNormalizeAttributes

setNominalToBinaryFilter

getNominalToBinaryFilter

setSeed

getSeed

setValidationThreshold

getValidationThreshold

setLearningRate

getLearningRate

setMomentum

getMomentum

setAutoBuild

getAutoBuild

setHiddenLayers

getHiddenLayers

setGUI

getGUI

setValidationSetSize

getValidationSetSize

setTrainingTime

getTrainingTime

blocker

getCapabilities

initializeClassifier

next

done

buildClassifier

distributionForInstance

listOptions

setOptions

getOptions

toString

globalInfo

learningRateTipText

momentumTipText

autoBuildTipText

seedTipText

validationThresholdTipText

GUITipText

validationSetSizeTipText

trainingTimeTipText

nominalToBinaryFilterTipText

hiddenLayersTipText

normalizeNumericClassTipText

normalizeAttributesTipText

resetTipText

decayTipText

getRevision