weka.classifiers.functions

## Class SGDText

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

- All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, Classifier, UpdateableBatchProcessor, UpdateableClassifier, Aggregateable<SGDText>, CapabilitiesHandler, CapabilitiesIgnorer, 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`

  - ### 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

  - ### Constructor Summary

    Constructors
    Constructor and Description
    `SGDText()`

  - ### Method Summary

    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.
    `java.lang.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.
    `java.util.LinkedHashMap<java.lang.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
    `java.lang.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
    `java.lang.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.
    `java.lang.String` | `globalInfo()` Returns a string describing classifier
    `java.lang.String` | `lambdaTipText()` Returns the tip text for this property
    `java.lang.String` | `learningRateTipText()` Returns the tip text for this property
    `java.util.Enumeration<Option>` | `listOptions()` Returns an enumeration describing the available options.
    `java.lang.String` | `LNormTipText()` Returns the tip text for this property
    `java.lang.String` | `lossFunctionTipText()` Returns the tip text for this property
    `java.lang.String` | `lowercaseTokensTipText()` Returns the tip text for this property
    `static void` | `main(java.lang.String[] args)` Main method for testing this class.
    `java.lang.String` | `minAbsoluteCoefficientValueTipText()` Returns the tip text for this property
    `java.lang.String` | `minWordFrequencyTipText()` Returns the tip text for this property
    `java.lang.String` | `normalizeDocLengthTipText()` Returns the tip text for this property
    `java.lang.String` | `normTipText()` Returns the tip text for this property
    `java.lang.String` | `outputProbsForSVMTipText()` Returns the tip text for this property
    `java.lang.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(java.lang.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.
    `java.lang.String` | `stemmerTipText()` Returns the tip text for this property.
    `java.lang.String` | `stopwordsHandlerTipText()` Returns the tip text for this property.
    `java.lang.String` | `tokenizerTipText()` Returns the tip text for this property.
    `java.lang.String` | `toString()`
    `void` | `updateClassifier(Instance instance)` Updates the classifier with the given instance.
    `java.lang.String` | `useWordFrequenciesTipText()` Returns the tip text for this property

    - ### Methods inherited from class weka.classifiers.RandomizableClassifier

      `getSeed, seedTipText, setSeed`

    - ### Methods inherited from class weka.classifiers.AbstractClassifier

      `classifyInstance, debugTipText, doNotCheckCapabilitiesTipText, forName, getDebug, getDoNotCheckCapabilities, makeCopies, makeCopy, runClassifier, setDebug, setDoNotCheckCapabilities`

    - ### 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 java.lang.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 java.lang.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 java.lang.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.
      Parameters:
        `u` - true if word frequencies are to be used.

    - #### lowercaseTokensTipText

          public java.lang.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 java.lang.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 java.lang.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 java.lang.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.
      Parameters:
        `return` - the minimum word frequency to use

    - #### minAbsoluteCoefficientValueTipText

          public java.lang.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 java.lang.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 java.lang.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 java.lang.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 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

    - #### 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 java.lang.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 java.lang.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 java.lang.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 java.lang.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 java.util.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(java.lang.String[] options)
                          throws java.lang.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:
        `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 `OptionHandler`
      Overrides:
        `getOptions` in class `RandomizableClassifier`
      Returns:
        an array of strings suitable for passing to setOptions

    - #### globalInfo

          public java.lang.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 java.lang.Exception

      Method for building the classifier.
      Specified by:
        `buildClassifier` in interface `Classifier`
      Parameters:
        `data` - the set of training instances.
      Throws:
        `java.lang.Exception` - if the classifier can't be built successfully.

    - #### updateClassifier

          public void updateClassifier(Instance instance)
                                throws java.lang.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:
        `java.lang.Exception` - if the instance could not be incorporated in the model.

    - #### distributionForInstance

          public double[] distributionForInstance(Instance inst)
                                           throws java.lang.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:
        `java.lang.Exception` - if distribution could not be computed successfully

    - #### toString

          public java.lang.String toString()

      Overrides:
        `toString` in class `java.lang.Object`

    - #### getDictionary

          public java.util.LinkedHashMap<java.lang.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 java.lang.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 java.lang.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:
        `java.lang.Exception` - if the supplied object can't be aggregated for some reason

    - #### finalizeAggregation

          public void finalizeAggregation()
                                   throws java.lang.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:
        `java.lang.Exception` - if the aggregation can't be finalized for some reason

    - #### batchFinished

          public void batchFinished()
                             throws java.lang.Exception

      Description copied from interface: `UpdateableBatchProcessor`
      Signal that the training data is finished (for now).
      Specified by:
        `batchFinished` in interface `UpdateableBatchProcessor`
      Throws:
        `java.lang.Exception` - if a problem occurs

    - #### main

          public static void main(java.lang.String[] args)

      Main method for testing this class.