SGDText (weka-stable 3.8.3 API)

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

All Implemented Interfaces:: Serializable, Cloneable, Classifier, UpdateableBatchProcessor, UpdateableClassifier, Aggregateable<SGDText>, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, Randomizable, RevisionHandler, WeightedInstancesHandler

public class SGDText
extends RandomizableClassifier
implements UpdateableClassifier, UpdateableBatchProcessor, WeightedInstancesHandler, Aggregateable<SGDText>

Implements stochastic gradient descent for learning a linear binary class SVM or binary class logistic regression on text data. Operates directly (and only) on String attributes. Other types of input attributes are accepted but ignored during training and classification.

Valid options are:

 -F
  Set the loss function to minimize. 0 = hinge loss (SVM), 1 = log loss (logistic regression)
  (default = 0)

 -outputProbs
  Output probabilities for SVMs (fits a logsitic
  model to the output of the SVM)

 -L
  The learning rate (default = 0.01).

 -R <double>
  The lambda regularization constant (default = 0.0001)

 -E <integer>
  The number of epochs to perform (batch learning only, default = 500)

 -W
  Use word frequencies instead of binary bag of words.

 -P <# instances>
  How often to prune the dictionary of low frequency words (default = 0, i.e. don't prune)

 -M <double>
  Minimum word frequency. Words with less than this frequence are ignored.
  If periodic pruning is turned on then this is also used to determine which
  words to remove from the dictionary (default = 3).

 -min-coeff <double>
  Minimum absolute value of coefficients in the model.
  If periodic pruning is turned on then this
  is also used to prune words from the dictionary
  (default = 0.001

 -normalize
  Normalize document length (use in conjunction with -norm and -lnorm)

 -norm <num>
  Specify the norm that each instance must have (default 1.0)

 -lnorm <num>
  Specify L-norm to use (default 2.0)

 -lowercase
  Convert all tokens to lowercase before adding to the dictionary.

 -stopwords-handler
  The stopwords handler to use (default Null).

 -tokenizer <spec>
  The tokenizing algorihtm (classname plus parameters) to use.
  (default: weka.core.tokenizers.WordTokenizer)

 -stemmer <spec>
  The stemmering algorihtm (classname plus parameters) to use.

 -S <num>
  Random number seed.
  (default 1)

 -output-debug-info
  If set, classifier is run in debug mode and
  may output additional info to the console

 -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).

Author:: Mark Hall (mhall{[at]}pentaho{[dot]}com), Eibe Frank (eibe{[at]}cs{[dot]}waikato{[dot]}ac{[dot]}nz)
See Also:: Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type Class and Description

static class SGDText.Count

Nested Classes
Modifier and Type	Class and Description
`static class`	`SGDText.Count`

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`HINGE` the hinge loss function.
`static int`	`LOGLOSS` the log loss function.
`static Tag[]`	`TAGS_SELECTION` Loss functions to choose from

Fields inherited from class weka.classifiers.AbstractClassifier
BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors
Constructor and Description

SGDText()

Constructors
Constructor and Description
`SGDText()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`SGDText`	`aggregate(SGDText toAggregate)` Aggregate an object with this one
`void`	`batchFinished()` Signal that the training data is finished (for now).
`double`	`bias()`
`void`	`buildClassifier(Instances data)` Method for building the classifier.
`double[]`	`distributionForInstance(Instance inst)` Predicts the class memberships for a given instance.
`String`	`epochsTipText()` Returns the tip text for this property
`void`	`finalizeAggregation()` Call to complete the aggregation process.
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`LinkedHashMap<String,SGDText.Count>`	`getDictionary()` Get this model's dictionary (including term weights).
`int`	`getDictionarySize()` Return the size of the dictionary (minus any low frequency terms that are below the threshold but haven't been pruned yet).
`int`	`getEpochs()` Get current number of epochs
`double`	`getLambda()` Get the current value of lambda
`double`	`getLearningRate()` Get the learning rate.
`double`	`getLNorm()` Get the L Norm used.
`SelectedTag`	`getLossFunction()` Get the current loss function.
`boolean`	`getLowercaseTokens()` Get whether to convert all tokens to lowercase
`double`	`getMinAbsoluteCoefficientValue()` Get the minimum absolute magnitude for model coefficients.
`double`	`getMinWordFrequency()` Get the minimum word frequency.
`double`	`getNorm()` Get the instance's Norm.
`boolean`	`getNormalizeDocLength()` Get whether to normalize the length of each document
`String[]`	`getOptions()` Gets the current settings of the classifier.
`boolean`	`getOutputProbsForSVM()` Get whether to fit a logistic regression (itself trained using SGD) to the outputs of the SVM (if an SVM is being learned).
`int`	`getPeriodicPruning()` Get how often to prune the dictionary
`String`	`getRevision()` Returns the revision string.
`Stemmer`	`getStemmer()` Returns the current stemming algorithm, null if none is used.
`StopwordsHandler`	`getStopwordsHandler()` Gets the stopwords handler.
`Tokenizer`	`getTokenizer()` Returns the current tokenizer algorithm.
`boolean`	`getUseWordFrequencies()` Get whether to use word frequencies rather than binary bag of words representation.
`String`	`globalInfo()` Returns a string describing classifier
`String`	`lambdaTipText()` Returns the tip text for this property
`String`	`learningRateTipText()` Returns the tip text for this property
`Enumeration<Option>`	`listOptions()` Returns an enumeration describing the available options.
`String`	`LNormTipText()` Returns the tip text for this property
`String`	`lossFunctionTipText()` Returns the tip text for this property
`String`	`lowercaseTokensTipText()` Returns the tip text for this property
`static void`	`main(String[] args)` Main method for testing this class.
`String`	`minAbsoluteCoefficientValueTipText()` Returns the tip text for this property
`String`	`minWordFrequencyTipText()` Returns the tip text for this property
`String`	`normalizeDocLengthTipText()` Returns the tip text for this property
`String`	`normTipText()` Returns the tip text for this property
`String`	`outputProbsForSVMTipText()` Returns the tip text for this property
`String`	`periodicPruningTipText()` Returns the tip text for this property
`void`	`reset()` Reset the classifier.
`void`	`setBias(double bias)`
`void`	`setEpochs(int e)` Set the number of epochs to use
`void`	`setLambda(double lambda)` Set the value of lambda to use
`void`	`setLearningRate(double lr)` Set the learning rate.
`void`	`setLNorm(double newLNorm)` Set the L-norm to used
`void`	`setLossFunction(SelectedTag function)` Set the loss function to use.
`void`	`setLowercaseTokens(boolean l)` Set whether to convert all tokens to lowercase
`void`	`setMinAbsoluteCoefficientValue(double minCoeff)` Set the minimum absolute magnitude for model coefficients.
`void`	`setMinWordFrequency(double minFreq)` Set the minimum word frequency.
`void`	`setNorm(double newNorm)` Set the norm of the instances
`void`	`setNormalizeDocLength(boolean norm)` Set whether to normalize the length of each document
`void`	`setOptions(String[] options)` Parses a given list of options.
`void`	`setOutputProbsForSVM(boolean o)` Set whether to fit a logistic regression (itself trained using SGD) to the outputs of the SVM (if an SVM is being learned).
`void`	`setPeriodicPruning(int p)` Set how often to prune the dictionary
`void`	`setStemmer(Stemmer value)` the stemming algorithm to use, null means no stemming at all (i.e., the NullStemmer is used).
`void`	`setStopwordsHandler(StopwordsHandler value)` Sets the stopwords handler to use.
`void`	`setTokenizer(Tokenizer value)` the tokenizer algorithm to use.
`void`	`setUseWordFrequencies(boolean u)` Set whether to use word frequencies rather than binary bag of words representation.
`String`	`stemmerTipText()` Returns the tip text for this property.
`String`	`stopwordsHandlerTipText()` Returns the tip text for this property.
`String`	`tokenizerTipText()` Returns the tip text for this property.
`String`	`toString()`
`void`	`updateClassifier(Instance instance)` Updates the classifier with the given instance.
`String`	`useWordFrequenciesTipText()` Returns the tip text for this property

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

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

- Field Detail
  - HINGE
```
public static final int HINGE
```
    the hinge loss function.
    
    See Also:
    
    Constant Field Values
  - LOGLOSS
```
public static final int LOGLOSS
```
    the log loss function.
    
    See Also:
    
    Constant Field Values
  - TAGS_SELECTION
```
public static final Tag[] TAGS_SELECTION
```
    Loss functions to choose from
- Constructor Detail
  - SGDText
```
public SGDText()
```
- Method Detail
  - 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
  - setStemmer
```
public void setStemmer(Stemmer value)
```
    the stemming algorithm to use, null means no stemming at all (i.e., the NullStemmer is used).
    
    Parameters:
    
    value - the configured stemming algorithm, or null
    
    See Also:
    
    NullStemmer
  - getStemmer
```
public Stemmer getStemmer()
```
    Returns the current stemming algorithm, null if none is used.
    
    Returns:
    
    the current stemming algorithm, null if none set
  - stemmerTipText
```
public String stemmerTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setTokenizer
```
public void setTokenizer(Tokenizer value)
```
    the tokenizer algorithm to use.
    
    Parameters:
    
    value - the configured tokenizing algorithm
  - getTokenizer
```
public Tokenizer getTokenizer()
```
    Returns the current tokenizer algorithm.
    
    Returns:
    
    the current tokenizer algorithm
  - tokenizerTipText
```
public String tokenizerTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - useWordFrequenciesTipText
```
public String useWordFrequenciesTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setUseWordFrequencies
```
public void setUseWordFrequencies(boolean u)
```
    Set whether to use word frequencies rather than binary bag of words representation.
    
    Parameters:
    
    u - true if word frequencies are to be used.
  - getUseWordFrequencies
```
public boolean getUseWordFrequencies()
```
    Get whether to use word frequencies rather than binary bag of words representation.
    
    Returns:
    
    true if word frequencies are to be used.
  - lowercaseTokensTipText
```
public String lowercaseTokensTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setLowercaseTokens
```
public void setLowercaseTokens(boolean l)
```
    Set whether to convert all tokens to lowercase
    
    Parameters:
    
    l - true if all tokens are to be converted to lowercase
  - getLowercaseTokens
```
public boolean getLowercaseTokens()
```
    Get whether to convert all tokens to lowercase
    
    Returns:
    
    true true if all tokens are to be converted to lowercase
  - setStopwordsHandler
```
public void setStopwordsHandler(StopwordsHandler value)
```
    Sets the stopwords handler to use.
    
    Parameters:
    
    value - the stopwords handler, if null, Null is used
  - getStopwordsHandler
```
public StopwordsHandler getStopwordsHandler()
```
    Gets the stopwords handler.
    
    Returns:
    
    the stopwords handler
  - stopwordsHandlerTipText
```
public String stopwordsHandlerTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - periodicPruningTipText
```
public String periodicPruningTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setPeriodicPruning
```
public void setPeriodicPruning(int p)
```
    Set how often to prune the dictionary
    
    Parameters:
    
    p - how often to prune
  - getPeriodicPruning
```
public int getPeriodicPruning()
```
    Get how often to prune the dictionary
    
    Returns:
    
    how often to prune the dictionary
  - minWordFrequencyTipText
```
public String minWordFrequencyTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setMinWordFrequency
```
public void setMinWordFrequency(double minFreq)
```
    Set the minimum word frequency. Words that don't occur at least min freq times are ignored when updating weights. If periodic pruning is turned on, then min frequency is used when removing words from the dictionary.
    
    Parameters:
    
    minFreq - the minimum word frequency to use
  - getMinWordFrequency
```
public double getMinWordFrequency()
```
    Get the minimum word frequency. Words that don't occur at least min freq times are ignored when updating weights. If periodic pruning is turned on, then min frequency is used when removing words from the dictionary.
    
    Returns:
    
    the minimum word frequency to use
  - minAbsoluteCoefficientValueTipText
```
public String minAbsoluteCoefficientValueTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setMinAbsoluteCoefficientValue
```
public void setMinAbsoluteCoefficientValue(double minCoeff)
```
    Set the minimum absolute magnitude for model coefficients. Terms with weights smaller than this value are ignored. If periodic pruning is turned on then this is also used to determine if a word should be removed from the dictionary
    
    Parameters:
    
    minCoeff - the minimum absolute value of a model coefficient
  - getMinAbsoluteCoefficientValue
```
public double getMinAbsoluteCoefficientValue()
```
    Get the minimum absolute magnitude for model coefficients. Terms with weights smaller than this value are ignored. If periodic pruning is turned on this then is also used to determine if a word should be removed from the dictionary
    
    Returns:
    
    the minimum absolute value of a model coefficient
  - normalizeDocLengthTipText
```
public String normalizeDocLengthTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setNormalizeDocLength
```
public void setNormalizeDocLength(boolean norm)
```
    Set whether to normalize the length of each document
    
    Parameters:
    
    norm - true if document lengths is to be normalized
  - getNormalizeDocLength
```
public boolean getNormalizeDocLength()
```
    Get whether to normalize the length of each document
    
    Returns:
    
    true if document lengths is to be normalized
  - normTipText
```
public String normTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getNorm
```
public double getNorm()
```
    Get the instance's Norm.
    
    Returns:
    
    the Norm
  - setNorm
```
public void setNorm(double newNorm)
```
    Set the norm of the instances
    
    Parameters:
    
    newNorm - the norm to wich the instances must be set
  - LNormTipText
```
public String LNormTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getLNorm
```
public double getLNorm()
```
    Get the L Norm used.
    
    Returns:
    
    the L-norm used
  - setLNorm
```
public void setLNorm(double newLNorm)
```
    Set the L-norm to used
    
    Parameters:
    
    newLNorm - the L-norm
  - lambdaTipText
```
public String lambdaTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setLambda
```
public void setLambda(double lambda)
```
    Set the value of lambda to use
    
    Parameters:
    
    lambda - the value of lambda to use
  - getLambda
```
public double getLambda()
```
    Get the current value of lambda
    
    Returns:
    
    the current value of lambda
  - setLearningRate
```
public void setLearningRate(double lr)
```
    Set the learning rate.
    
    Parameters:
    
    lr - the learning rate to use.
  - getLearningRate
```
public double getLearningRate()
```
    Get the learning rate.
    
    Returns:
    
    the learning rate
  - learningRateTipText
```
public String learningRateTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - epochsTipText
```
public String epochsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setEpochs
```
public void setEpochs(int e)
```
    Set the number of epochs to use
    
    Parameters:
    
    e - the number of epochs to use
  - getEpochs
```
public int getEpochs()
```
    Get current number of epochs
    
    Returns:
    
    the current number of epochs
  - setLossFunction
```
public void setLossFunction(SelectedTag function)
```
    Set the loss function to use.
    
    Parameters:
    
    function - the loss function to use.
  - getLossFunction
```
public SelectedTag getLossFunction()
```
    Get the current loss function.
    
    Returns:
    
    the current loss function.
  - lossFunctionTipText
```
public String lossFunctionTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setOutputProbsForSVM
```
public void setOutputProbsForSVM(boolean o)
```
    Set whether to fit a logistic regression (itself trained using SGD) to the outputs of the SVM (if an SVM is being learned).
    
    Parameters:
    
    o - true if a logistic regression is to be fit to the output of the SVM to produce probability estimates.
  - getOutputProbsForSVM
```
public boolean getOutputProbsForSVM()
```
    Get whether to fit a logistic regression (itself trained using SGD) to the outputs of the SVM (if an SVM is being learned).
    
    Returns:
    
    true if a logistic regression is to be fit to the output of the SVM to produce probability estimates.
  - outputProbsForSVMTipText
```
public String outputProbsForSVMTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - listOptions
```
public Enumeration<Option> listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class RandomizableClassifier
    
    Returns:
    
    an enumeration of all the available options.
  - setOptions
```
public void setOptions(String[] options)
                throws Exception
```
    Parses a given list of options.
    Valid options are:
```
 -F
  Set the loss function to minimize. 0 = hinge loss (SVM), 1 = log loss (logistic regression)
  (default = 0)
```
```
 -outputProbs
  Output probabilities for SVMs (fits a logsitic
  model to the output of the SVM)
```
```
 -L
  The learning rate (default = 0.01).
```
```
 -R <double>
  The lambda regularization constant (default = 0.0001)
```
```
 -E <integer>
  The number of epochs to perform (batch learning only, default = 500)
```
```
 -W
  Use word frequencies instead of binary bag of words.
```
```
 -P <# instances>
  How often to prune the dictionary of low frequency words (default = 0, i.e. don't prune)
```
```
 -M <double>
  Minimum word frequency. Words with less than this frequence are ignored.
  If periodic pruning is turned on then this is also used to determine which
  words to remove from the dictionary (default = 3).
```
```
 -min-coeff <double>
  Minimum absolute value of coefficients in the model.
  If periodic pruning is turned on then this
  is also used to prune words from the dictionary
  (default = 0.001
```
```
 -normalize
  Normalize document length (use in conjunction with -norm and -lnorm)
```
```
 -norm <num>
  Specify the norm that each instance must have (default 1.0)
```
```
 -lnorm <num>
  Specify L-norm to use (default 2.0)
```
```
 -lowercase
  Convert all tokens to lowercase before adding to the dictionary.
```
```
 -stopwords-handler
  The stopwords handler to use (default Null).
```
```
 -tokenizer <spec>
  The tokenizing algorihtm (classname plus parameters) to use.
  (default: weka.core.tokenizers.WordTokenizer)
```
```
 -stemmer <spec>
  The stemmering algorihtm (classname plus parameters) to use.
```
```
 -S <num>
  Random number seed.
  (default 1)
```
```
 -output-debug-info
  If set, classifier is run in debug mode and
  may output additional info to the console
```
```
 -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).
```
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class RandomizableClassifier
    
    Parameters:
    
    options - the list of options as an array of strings
    
    Throws:
    
    Exception - if an option is not supported
  - getOptions
```
public String[] getOptions()
```
    Gets the current settings of the classifier.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class RandomizableClassifier
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - globalInfo
```
public String globalInfo()
```
    Returns a string describing classifier
    
    Returns:
    
    a description suitable for displaying in the explorer/experimenter gui
  - reset
```
public void reset()
```
    Reset the classifier.
  - buildClassifier
```
public void buildClassifier(Instances data)
                     throws Exception
```
    Method for building the classifier.
    
    Specified by:
    
    buildClassifier in interface Classifier
    
    Parameters:
    
    data - the set of training instances.
    
    Throws:
    
    Exception - if the classifier can't be built successfully.
  - updateClassifier
```
public void updateClassifier(Instance instance)
                      throws Exception
```
    Updates the classifier with the given instance.
    
    Specified by:
    
    updateClassifier in interface UpdateableClassifier
    
    Parameters:
    
    instance - the new training instance to include in the model
    
    Throws:
    
    Exception - if the instance could not be incorporated in the model.
  - distributionForInstance
```
public double[] distributionForInstance(Instance inst)
                                 throws Exception
```
    Description copied from class: AbstractClassifier
    
    Predicts the class memberships for a given instance. If an instance is unclassified, the returned array elements must be all zero. If the class is numeric, the array must consist of only one element, which contains the predicted value. Note that a classifier MUST implement either this or classifyInstance().
    
    Specified by:
    
    distributionForInstance in interface Classifier
    
    Overrides:
    
    distributionForInstance in class AbstractClassifier
    
    Parameters:
    
    inst - the instance to be classified
    
    Returns:
    
    an array containing the estimated membership probabilities of the test instance in each class or the numeric prediction
    
    Throws:
    
    Exception - if distribution could not be computed successfully
  - toString
```
public String toString()
```
    Overrides:
    
    toString in class Object
  - getDictionary
```
public LinkedHashMap<String,SGDText.Count> getDictionary()
```
    Get this model's dictionary (including term weights).
    
    Returns:
    
    this model's dictionary.
  - getDictionarySize
```
public int getDictionarySize()
```
    Return the size of the dictionary (minus any low frequency terms that are below the threshold but haven't been pruned yet).
    
    Returns:
    
    the size of the dictionary.
  - bias
```
public double bias()
```
  - setBias
```
public void setBias(double bias)
```
  - getRevision
```
public String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class AbstractClassifier
    
    Returns:
    
    the revision
  - aggregate
```
public SGDText aggregate(SGDText toAggregate)
                  throws Exception
```
    Aggregate an object with this one
    
    Specified by:
    
    aggregate in interface Aggregateable<SGDText>
    
    Parameters:
    
    toAggregate - the object to aggregate
    
    Returns:
    
    the result of aggregation
    
    Throws:
    
    Exception - if the supplied object can't be aggregated for some reason
  - finalizeAggregation
```
public void finalizeAggregation()
                         throws Exception
```
    Call to complete the aggregation process. Allows implementers to do any final processing based on how many objects were aggregated.
    
    Specified by:
    
    finalizeAggregation in interface Aggregateable<SGDText>
    
    Throws:
    
    Exception - if the aggregation can't be finalized for some reason
  - batchFinished
```
public void batchFinished()
                   throws Exception
```
    Description copied from interface: UpdateableBatchProcessor
    
    Signal that the training data is finished (for now).
    
    Specified by:
    
    batchFinished in interface UpdateableBatchProcessor
    
    Throws:
    
    Exception - if a problem occurs
  - main
```
public static void main(String[] args)
```
    Main method for testing this class.

Class SGDText

Nested Class Summary

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

Field Detail

HINGE

LOGLOSS

TAGS_SELECTION

Constructor Detail

SGDText

Method Detail

getCapabilities

setStemmer

getStemmer

stemmerTipText

setTokenizer

getTokenizer

tokenizerTipText

useWordFrequenciesTipText

setUseWordFrequencies

getUseWordFrequencies

lowercaseTokensTipText

setLowercaseTokens

getLowercaseTokens

setStopwordsHandler

getStopwordsHandler

stopwordsHandlerTipText

periodicPruningTipText

setPeriodicPruning

getPeriodicPruning

minWordFrequencyTipText

setMinWordFrequency

getMinWordFrequency

minAbsoluteCoefficientValueTipText

setMinAbsoluteCoefficientValue

getMinAbsoluteCoefficientValue

normalizeDocLengthTipText

setNormalizeDocLength

getNormalizeDocLength

normTipText

getNorm

setNorm

LNormTipText

getLNorm

setLNorm

lambdaTipText

setLambda

getLambda

setLearningRate

getLearningRate

learningRateTipText

epochsTipText

setEpochs

getEpochs

setLossFunction

getLossFunction

lossFunctionTipText

setOutputProbsForSVM

getOutputProbsForSVM

outputProbsForSVMTipText

listOptions

setOptions

getOptions

globalInfo

reset

buildClassifier

updateClassifier

distributionForInstance

toString

getDictionary

getDictionarySize

bias

setBias

getRevision

aggregate