weka.classifiers.functions
Class MultilayerPerceptron

java.lang.Object
  |
  +--weka.classifiers.Classifier
        |
        +--weka.classifiers.functions.MultilayerPerceptron

All Implemented Interfaces:

java.lang.Cloneable, OptionHandler, java.io.Serializable, WeightedInstancesHandler

public class MultilayerPerceptron

extends Classifier

implements OptionHandler, WeightedInstancesHandler

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).

Version:

$Revision: 1.1 $

Author:

Malcolm Ware (mfw4@cs.waikato.ac.nz)

See Also:

Serialized Form

Constructor Summary

MultilayerPerceptron()
The constructor.

Method Summary

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.
boolean	getAutoBuild()
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.
long	getRandomSeed()
boolean	getReset()
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()
java.lang.String	learningRateTipText()
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	momentumTipText()
java.lang.String	nominalToBinaryFilterTipText()
java.lang.String	normalizeAttributesTipText()
java.lang.String	normalizeNumericClassTipText()
java.lang.String	randomSeedTipText()
java.lang.String	resetTipText()
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	setRandomSeed(long l) This seeds the random number generator, that is used when a random number is needed for the network.
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	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 weka.classifiers.Classifier

classifyInstance, debugTipText, forName, getDebug, makeCopies, setDebug

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

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.

setRandomSeed

public void setRandomSeed(long l)

This seeds the random number generator, that is used when a random number is needed for the network.

Parameters:

l - The seed.

getRandomSeed

public long getRandomSeed()

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).

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 class Classifier

Parameters:

i - The training data.

Throws:

Throws - exception if can't build classification properly.

java.lang.Exception - if the classifier has not been generated successfully

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.

Overrides:

distributionForInstance in class Classifier

Parameters:

i - The instance to classify.

Returns:

A double array filled with the probabilities of each class type.

Throws:

if - can't classify instance.

java.lang.Exception - if distribution could not be computed successfully

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class Classifier

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 num
Set the learning rate. (default 0.3)

-M num
Set the momentum (default 0.2)

-N num
Set the number of epochs to train through. (default 500)

-V num
Set the percentage size of the validation set from the training to use. (default 0 (no validation set is used, instead num of epochs is used)

-S num
Set the seed for the random number generator. (default 0)

-E num
Set the threshold for the number of consequetive errors allowed during validation testing. (default 20)

-G
Bring up a GUI for the neural net.

-A
Do not automatically create the connections in the net. (can only be used if -G is specified)

-B
Do Not automatically Preprocess the instances with a nominal to binary filter.

-H str
Set the number of nodes to be used on each layer. Each number represents its own layer and the num of nodes on that layer. Each number should be comma seperated. There are also the wildcards 'a', 'i', 'o', 't' (default 4)

-C
Do not automatically Normalize the class if it's numeric.

-I
Do not automatically Normalize the attributes.

-R
Do not allow the network to be automatically reset.

-D
Cause the learning rate to decay as training is done.

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class Classifier

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 Classifier

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.

randomSeedTipText

public java.lang.String randomSeedTipText()

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.