weka.filters.unsupervised.attribute
Class StringToWordVector

java.lang.Object
  extended by weka.filters.Filter
      extended by weka.filters.unsupervised.attribute.StringToWordVector
All Implemented Interfaces:
java.io.Serializable, CapabilitiesHandler, OptionHandler, RevisionHandler, UnsupervisedFilter

public class StringToWordVector
extends Filter
implements UnsupervisedFilter, OptionHandler

Converts String attributes into a set of attributes representing word occurrence (depending on the tokenizer) information from the text contained in the strings. The set of words (attributes) is determined by the first batch filtered (typically training data).

Valid options are:

 -C
  Output word counts rather than boolean word presence.
 
 -R <index1,index2-index4,...>
  Specify list of string attributes to convert to words (as weka Range).
  (default: select all string attributes)
 -V
  Invert matching sense of column indexes.
 -P <attribute name prefix>
  Specify a prefix for the created attribute names.
  (default: "")
 -W <number of words to keep>
  Specify approximate number of word fields to create.
  Surplus words will be discarded..
  (default: 1000)
 -prune-rate <rate as a percentage of dataset>
  Specify the rate (e.g., every 10% of the input dataset) at which to periodically prune the dictionary.
  -W prunes after creating a full dictionary. You may not have enough memory for this approach.
  (default: no periodic pruning)
 -T
  Transform the word frequencies into log(1+fij)
  where fij is the frequency of word i in jth document(instance).
 
 -I
  Transform each word frequency into:
  fij*log(num of Documents/num of documents containing word i)
    where fij if frequency of word i in jth document(instance)
 -N
  Whether to 0=not normalize/1=normalize all data/2=normalize test data only
  to average length of training documents (default 0=don't normalize).
 -L
  Convert all tokens to lowercase before adding to the dictionary.
 -S
  Ignore words that are in the stoplist.
 -stemmer <spec>
  The stemmering algorihtm (classname plus parameters) to use.
 -M <int>
  The minimum term frequency (default = 1).
 -O
  If this is set, the maximum number of words and the 
  minimum term frequency is not enforced on a per-class 
  basis but based on the documents in all the classes 
  (even if a class attribute is set).
 -stopwords <file>
  A file containing stopwords to override the default ones.
  Using this option automatically sets the flag ('-S') to use the
  stoplist if the file exists.
  Format: one stopword per line, lines starting with '#'
  are interpreted as comments and ignored.
 -tokenizer <spec>
  The tokenizing algorihtm (classname plus parameters) to use.
  (default: weka.core.tokenizers.WordTokenizer)

Version:
$Revision: 9563 $
Author:
Len Trigg (len@reeltwo.com), Stuart Inglis (stuart@reeltwo.com), Gordon Paynter (gordon.paynter@ucr.edu), Asrhaf M. Kibriya (amk14@cs.waikato.ac.nz)
See Also:
Stopwords, Serialized Form

Field Summary
static int FILTER_NONE
          normalization: No normalization.
static int FILTER_NORMALIZE_ALL
          normalization: Normalize all data.
static int FILTER_NORMALIZE_TEST_ONLY
          normalization: Normalize test data only.
static Tag[] TAGS_FILTER
          Specifies whether document's (instance's) word frequencies are to be normalized.
 
Constructor Summary
StringToWordVector()
          Default constructor.
StringToWordVector(int wordsToKeep)
          Constructor that allows specification of the target number of words in the output.
 
Method Summary
 java.lang.String attributeIndicesTipText()
          Returns the tip text for this property.
 java.lang.String attributeNamePrefixTipText()
          Returns the tip text for this property.
 boolean batchFinished()
          Signify that this batch of input to the filter is finished.
 java.lang.String doNotOperateOnPerClassBasisTipText()
          Returns the tip text for this property.
 java.lang.String getAttributeIndices()
          Gets the current range selection.
 java.lang.String getAttributeNamePrefix()
          Get the attribute name prefix.
 Capabilities getCapabilities()
          Returns the Capabilities of this filter.
 boolean getDoNotOperateOnPerClassBasis()
          Get the DoNotOperateOnPerClassBasis value.
 boolean getIDFTransform()
          Sets whether if the word frequencies in a document should be transformed into:
fij*log(num of Docs/num of Docs with word i)
where fij is the frequency of word i in document(instance) j.
 boolean getInvertSelection()
          Gets whether the supplied columns are to be processed or skipped.
 boolean getLowerCaseTokens()
          Gets whether if the tokens are to be downcased or not.
 int getMinTermFreq()
          Get the MinTermFreq value.
 SelectedTag getNormalizeDocLength()
          Gets whether if the word frequencies for a document (instance) should be normalized or not.
 java.lang.String[] getOptions()
          Gets the current settings of the filter.
 boolean getOutputWordCounts()
          Gets whether output instances contain 0 or 1 indicating word presence, or word counts.
 double getPeriodicPruning()
          Gets the rate at which the dictionary is periodically pruned, as a percentage of the dataset size.
 java.lang.String getRevision()
          Returns the revision string.
 Range getSelectedRange()
          Get the value of m_SelectedRange.
 Stemmer getStemmer()
          Returns the current stemming algorithm, null if none is used.
 java.io.File getStopwords()
          returns the file used for obtaining the stopwords, if the file represents a directory then the default ones are used.
 boolean getTFTransform()
          Gets whether if the word frequencies should be transformed into log(1+fij) where fij is the frequency of word i in document(instance) j.
 Tokenizer getTokenizer()
          Returns the current tokenizer algorithm.
 boolean getUseStoplist()
          Gets whether if the words on the stoplist are to be ignored (The stoplist is in weka.core.StopWords).
 int getWordsToKeep()
          Gets the number of words (per class if there is a class attribute assigned) to attempt to keep.
 java.lang.String globalInfo()
          Returns a string describing this filter.
 java.lang.String IDFTransformTipText()
          Returns the tip text for this property.
 boolean input(Instance instance)
          Input an instance for filtering.
 java.lang.String invertSelectionTipText()
          Returns the tip text for this property.
 java.util.Enumeration listOptions()
          Returns an enumeration describing the available options.
 java.lang.String lowerCaseTokensTipText()
          Returns the tip text for this property.
static void main(java.lang.String[] argv)
          Main method for testing this class.
 java.lang.String minTermFreqTipText()
          Returns the tip text for this property.
 java.lang.String normalizeDocLengthTipText()
          Returns the tip text for this property.
 java.lang.String outputWordCountsTipText()
          Returns the tip text for this property.
 java.lang.String periodicPruningTipText()
          Returns the tip text for this property.
 void setAttributeIndices(java.lang.String rangeList)
          Sets which attributes are to be worked on.
 void setAttributeIndicesArray(int[] attributes)
          Sets which attributes are to be processed.
 void setAttributeNamePrefix(java.lang.String newPrefix)
          Set the attribute name prefix.
 void setDoNotOperateOnPerClassBasis(boolean newDoNotOperateOnPerClassBasis)
          Set the DoNotOperateOnPerClassBasis value.
 void setIDFTransform(boolean IDFTransform)
          Sets whether if the word frequencies in a document should be transformed into:
fij*log(num of Docs/num of Docs with word i)
where fij is the frequency of word i in document(instance) j.
 boolean setInputFormat(Instances instanceInfo)
          Sets the format of the input instances.
 void setInvertSelection(boolean invert)
          Sets whether selected columns should be processed or skipped.
 void setLowerCaseTokens(boolean downCaseTokens)
          Sets whether if the tokens are to be downcased or not.
 void setMinTermFreq(int newMinTermFreq)
          Set the MinTermFreq value.
 void setNormalizeDocLength(SelectedTag newType)
          Sets whether if the word frequencies for a document (instance) should be normalized or not.
 void setOptions(java.lang.String[] options)
          Parses a given list of options.
 void setOutputWordCounts(boolean outputWordCounts)
          Sets whether output instances contain 0 or 1 indicating word presence, or word counts.
 void setPeriodicPruning(double newPeriodicPruning)
          Sets the rate at which the dictionary is periodically pruned, as a percentage of the dataset size.
 void setSelectedRange(java.lang.String newSelectedRange)
          Set the value of m_SelectedRange.
 void setStemmer(Stemmer value)
          the stemming algorithm to use, null means no stemming at all (i.e., the NullStemmer is used).
 void setStopwords(java.io.File value)
          sets the file containing the stopwords, null or a directory unset the stopwords.
 void setTFTransform(boolean TFTransform)
          Sets whether if the word frequencies should be transformed into log(1+fij) where fij is the frequency of word i in document(instance) j.
 void setTokenizer(Tokenizer value)
          the tokenizer algorithm to use.
 void setUseStoplist(boolean useStoplist)
          Sets whether if the words that are on a stoplist are to be ignored (The stop list is in weka.core.StopWords).
 void setWordsToKeep(int newWordsToKeep)
          Sets the number of words (per class if there is a class attribute assigned) to attempt to keep.
 java.lang.String stemmerTipText()
          Returns the tip text for this property.
 java.lang.String stopwordsTipText()
          Returns the tip text for this property.
 java.lang.String TFTransformTipText()
          Returns the tip text for this property.
 java.lang.String tokenizerTipText()
          Returns the tip text for this property.
 java.lang.String useStoplistTipText()
          Returns the tip text for this property.
 java.lang.String wordsToKeepTipText()
          Returns the tip text for this property.
 
Methods inherited from class weka.filters.Filter
batchFilterFile, filterFile, getCapabilities, getOutputFormat, isFirstBatchDone, isNewBatch, isOutputFormatDefined, makeCopies, makeCopy, mayRemoveInstanceAfterFirstBatchDone, numPendingOutput, output, outputPeek, runFilter, toString, useFilter, wekaStaticWrapper
 
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait
 

Field Detail

FILTER_NONE

public static final int FILTER_NONE
normalization: No normalization.

See Also:
Constant Field Values

FILTER_NORMALIZE_ALL

public static final int FILTER_NORMALIZE_ALL
normalization: Normalize all data.

See Also:
Constant Field Values

FILTER_NORMALIZE_TEST_ONLY

public static final int FILTER_NORMALIZE_TEST_ONLY
normalization: Normalize test data only.

See Also:
Constant Field Values

TAGS_FILTER

public static final Tag[] TAGS_FILTER
Specifies whether document's (instance's) word frequencies are to be normalized. The are normalized to average length of documents specified as input format.

Constructor Detail

StringToWordVector

public StringToWordVector()
Default constructor. Targets 1000 words in the output.


StringToWordVector

public StringToWordVector(int wordsToKeep)
Constructor that allows specification of the target number of words in the output.

Parameters:
wordsToKeep - the number of words in the output vector (per class if assigned).
Method Detail

listOptions

public java.util.Enumeration listOptions()
Returns an enumeration describing the available options.

Specified by:
listOptions in interface OptionHandler
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:

 -C
  Output word counts rather than boolean word presence.
 
 -R <index1,index2-index4,...>
  Specify list of string attributes to convert to words (as weka Range).
  (default: select all string attributes)
 -V
  Invert matching sense of column indexes.
 -P <attribute name prefix>
  Specify a prefix for the created attribute names.
  (default: "")
 -W <number of words to keep>
  Specify approximate number of word fields to create.
  Surplus words will be discarded..
  (default: 1000)
 -prune-rate <rate as a percentage of dataset>
  Specify the rate (e.g., every 10% of the input dataset) at which to periodically prune the dictionary.
  -W prunes after creating a full dictionary. You may not have enough memory for this approach.
  (default: no periodic pruning)
 -T
  Transform the word frequencies into log(1+fij)
  where fij is the frequency of word i in jth document(instance).
 
 -I
  Transform each word frequency into:
  fij*log(num of Documents/num of documents containing word i)
    where fij if frequency of word i in jth document(instance)
 -N
  Whether to 0=not normalize/1=normalize all data/2=normalize test data only
  to average length of training documents (default 0=don't normalize).
 -L
  Convert all tokens to lowercase before adding to the dictionary.
 -S
  Ignore words that are in the stoplist.
 -stemmer <spec>
  The stemmering algorihtm (classname plus parameters) to use.
 -M <int>
  The minimum term frequency (default = 1).
 -O
  If this is set, the maximum number of words and the 
  minimum term frequency is not enforced on a per-class 
  basis but based on the documents in all the classes 
  (even if a class attribute is set).
 -stopwords <file>
  A file containing stopwords to override the default ones.
  Using this option automatically sets the flag ('-S') to use the
  stoplist if the file exists.
  Format: one stopword per line, lines starting with '#'
  are interpreted as comments and ignored.
 -tokenizer <spec>
  The tokenizing algorihtm (classname plus parameters) to use.
  (default: weka.core.tokenizers.WordTokenizer)

Specified by:
setOptions in interface OptionHandler
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 filter.

Specified by:
getOptions in interface OptionHandler
Returns:
an array of strings suitable for passing to setOptions

getCapabilities

public Capabilities getCapabilities()
Returns the Capabilities of this filter.

Specified by:
getCapabilities in interface CapabilitiesHandler
Overrides:
getCapabilities in class Filter
Returns:
the capabilities of this object
See Also:
Capabilities

setInputFormat

public boolean setInputFormat(Instances instanceInfo)
                       throws java.lang.Exception
Sets the format of the input instances.

Overrides:
setInputFormat in class Filter
Parameters:
instanceInfo - an Instances object containing the input instance structure (any instances contained in the object are ignored - only the structure is required).
Returns:
true if the outputFormat may be collected immediately
Throws:
java.lang.Exception - if the input format can't be set successfully

input

public boolean input(Instance instance)
              throws java.lang.Exception
Input an instance for filtering. Filter requires all training instances be read before producing output.

Overrides:
input in class Filter
Parameters:
instance - the input instance.
Returns:
true if the filtered instance may now be collected with output().
Throws:
java.lang.IllegalStateException - if no input structure has been defined.
java.lang.NullPointerException - if the input format has not been defined.
java.lang.Exception - if the input instance was not of the correct format or if there was a problem with the filtering.

batchFinished

public boolean batchFinished()
                      throws java.lang.Exception
Signify that this batch of input to the filter is finished. If the filter requires all instances prior to filtering, output() may now be called to retrieve the filtered instances.

Overrides:
batchFinished in class Filter
Returns:
true if there are instances pending output.
Throws:
java.lang.IllegalStateException - if no input structure has been defined.
java.lang.NullPointerException - if no input structure has been defined,
java.lang.Exception - if there was a problem finishing the batch.

globalInfo

public java.lang.String globalInfo()
Returns a string describing this filter.

Returns:
a description of the filter suitable for displaying in the explorer/experimenter gui

getOutputWordCounts

public boolean getOutputWordCounts()
Gets whether output instances contain 0 or 1 indicating word presence, or word counts.

Returns:
true if word counts should be output.

setOutputWordCounts

public void setOutputWordCounts(boolean outputWordCounts)
Sets whether output instances contain 0 or 1 indicating word presence, or word counts.

Parameters:
outputWordCounts - true if word counts should be output.

outputWordCountsTipText

public java.lang.String outputWordCountsTipText()
Returns the tip text for this property.

Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui

getSelectedRange

public Range getSelectedRange()
Get the value of m_SelectedRange.

Returns:
Value of m_SelectedRange.

setSelectedRange

public void setSelectedRange(java.lang.String newSelectedRange)
Set the value of m_SelectedRange.

Parameters:
newSelectedRange - Value to assign to m_SelectedRange.

attributeIndicesTipText

public java.lang.String attributeIndicesTipText()
Returns the tip text for this property.

Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui

getAttributeIndices

public java.lang.String getAttributeIndices()
Gets the current range selection.

Returns:
a string containing a comma separated list of ranges

setAttributeIndices

public void setAttributeIndices(java.lang.String rangeList)
Sets which attributes are to be worked on.

Parameters:
rangeList - a string representing the list of attributes. Since the string will typically come from a user, attributes are indexed from 1.
eg: first-3,5,6-last
Throws:
java.lang.IllegalArgumentException - if an invalid range list is supplied

setAttributeIndicesArray

public void setAttributeIndicesArray(int[] attributes)
Sets which attributes are to be processed.

Parameters:
attributes - an array containing indexes of attributes to process. Since the array will typically come from a program, attributes are indexed from 0.
Throws:
java.lang.IllegalArgumentException - if an invalid set of ranges is supplied

invertSelectionTipText

public java.lang.String invertSelectionTipText()
Returns the tip text for this property.

Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui

getInvertSelection

public boolean getInvertSelection()
Gets whether the supplied columns are to be processed or skipped.

Returns:
true if the supplied columns will be kept

setInvertSelection

public void setInvertSelection(boolean invert)
Sets whether selected columns should be processed or skipped.

Parameters:
invert - the new invert setting

getAttributeNamePrefix

public java.lang.String getAttributeNamePrefix()
Get the attribute name prefix.

Returns:
The current attribute name prefix.

setAttributeNamePrefix

public void setAttributeNamePrefix(java.lang.String newPrefix)
Set the attribute name prefix.

Parameters:
newPrefix - String to use as the attribute name prefix.

attributeNamePrefixTipText

public java.lang.String attributeNamePrefixTipText()
Returns the tip text for this property.

Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui

getWordsToKeep

public int getWordsToKeep()
Gets the number of words (per class if there is a class attribute assigned) to attempt to keep.

Returns:
the target number of words in the output vector (per class if assigned).

setWordsToKeep

public void setWordsToKeep(int newWordsToKeep)
Sets the number of words (per class if there is a class attribute assigned) to attempt to keep.

Parameters:
newWordsToKeep - the target number of words in the output vector (per class if assigned).

wordsToKeepTipText

public java.lang.String wordsToKeepTipText()
Returns the tip text for this property.

Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui

getPeriodicPruning

public double getPeriodicPruning()
Gets the rate at which the dictionary is periodically pruned, as a percentage of the dataset size.

Returns:
the rate at which the dictionary is periodically pruned

setPeriodicPruning

public void setPeriodicPruning(double newPeriodicPruning)
Sets the rate at which the dictionary is periodically pruned, as a percentage of the dataset size.

Parameters:
newPeriodicPruning - the rate at which the dictionary is periodically pruned

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

getTFTransform

public boolean getTFTransform()
Gets whether if the word frequencies should be transformed into log(1+fij) where fij is the frequency of word i in document(instance) j.

Returns:
true if word frequencies are to be transformed.

setTFTransform

public void setTFTransform(boolean TFTransform)
Sets whether if the word frequencies should be transformed into log(1+fij) where fij is the frequency of word i in document(instance) j.

Parameters:
TFTransform - true if word frequencies are to be transformed.

TFTransformTipText

public java.lang.String TFTransformTipText()
Returns the tip text for this property.

Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui

getIDFTransform

public boolean getIDFTransform()
Sets whether if the word frequencies in a document should be transformed into:
fij*log(num of Docs/num of Docs with word i)
where fij is the frequency of word i in document(instance) j.

Returns:
true if the word frequencies are to be transformed.

setIDFTransform

public void setIDFTransform(boolean IDFTransform)
Sets whether if the word frequencies in a document should be transformed into:
fij*log(num of Docs/num of Docs with word i)
where fij is the frequency of word i in document(instance) j.

Parameters:
IDFTransform - true if the word frequecies are to be transformed

IDFTransformTipText

public java.lang.String IDFTransformTipText()
Returns the tip text for this property.

Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui

getNormalizeDocLength

public SelectedTag getNormalizeDocLength()
Gets whether if the word frequencies for a document (instance) should be normalized or not.

Returns:
true if word frequencies are to be normalized.

setNormalizeDocLength

public void setNormalizeDocLength(SelectedTag newType)
Sets whether if the word frequencies for a document (instance) should be normalized or not.

Parameters:
newType - the new type.

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

getLowerCaseTokens

public boolean getLowerCaseTokens()
Gets whether if the tokens are to be downcased or not.

Returns:
true if the tokens are to be downcased.

setLowerCaseTokens

public void setLowerCaseTokens(boolean downCaseTokens)
Sets whether if the tokens are to be downcased or not. (Doesn't affect non-alphabetic characters in tokens).

Parameters:
downCaseTokens - should be true if only lower case tokens are to be formed.

doNotOperateOnPerClassBasisTipText

public java.lang.String doNotOperateOnPerClassBasisTipText()
Returns the tip text for this property.

Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui

getDoNotOperateOnPerClassBasis

public boolean getDoNotOperateOnPerClassBasis()
Get the DoNotOperateOnPerClassBasis value.

Returns:
the DoNotOperateOnPerClassBasis value.

setDoNotOperateOnPerClassBasis

public void setDoNotOperateOnPerClassBasis(boolean newDoNotOperateOnPerClassBasis)
Set the DoNotOperateOnPerClassBasis value.

Parameters:
newDoNotOperateOnPerClassBasis - The new DoNotOperateOnPerClassBasis value.

minTermFreqTipText

public java.lang.String minTermFreqTipText()
Returns the tip text for this property.

Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui

getMinTermFreq

public int getMinTermFreq()
Get the MinTermFreq value.

Returns:
the MinTermFreq value.

setMinTermFreq

public void setMinTermFreq(int newMinTermFreq)
Set the MinTermFreq value.

Parameters:
newMinTermFreq - The new MinTermFreq value.

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

getUseStoplist

public boolean getUseStoplist()
Gets whether if the words on the stoplist are to be ignored (The stoplist is in weka.core.StopWords).

Returns:
true if the words on the stoplist are to be ignored.

setUseStoplist

public void setUseStoplist(boolean useStoplist)
Sets whether if the words that are on a stoplist are to be ignored (The stop list is in weka.core.StopWords).

Parameters:
useStoplist - true if the tokens that are on a stoplist are to be ignored.

useStoplistTipText

public java.lang.String useStoplistTipText()
Returns the tip text for this property.

Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui

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

setStopwords

public void setStopwords(java.io.File value)
sets the file containing the stopwords, null or a directory unset the stopwords. If the file exists, it automatically turns on the flag to use the stoplist.

Parameters:
value - the file containing the stopwords

getStopwords

public java.io.File getStopwords()
returns the file used for obtaining the stopwords, if the file represents a directory then the default ones are used.

Returns:
the file containing the stopwords

stopwordsTipText

public java.lang.String stopwordsTipText()
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

getRevision

public java.lang.String getRevision()
Returns the revision string.

Specified by:
getRevision in interface RevisionHandler
Overrides:
getRevision in class Filter
Returns:
the revision

main

public static void main(java.lang.String[] argv)
Main method for testing this class.

Parameters:
argv - should contain arguments to the filter: use -h for help