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## weka.attributeSelection
Class LatentSemanticAnalysis

    java.lang.Object
      weka.attributeSelection.ASEvaluation
          weka.attributeSelection.UnsupervisedAttributeEvaluator
              weka.attributeSelection.LatentSemanticAnalysis

All Implemented Interfaces:
  java.io.Serializable, AttributeEvaluator, AttributeTransformer, CapabilitiesHandler, OptionHandler, RevisionHandler

    public class LatentSemanticAnalysis

extends UnsupervisedAttributeEvaluator
implements AttributeTransformer, OptionHandler

Performs latent semantic analysis and transformation of the data. Use in conjunction with a Ranker search. A low-rank approximation of the full data is found by specifying the number of singular values to use. The dataset may be transformed to give the relation of either the attributes or the instances (default) to the concept space created by the transformation.

Valid options are:

     -N
      Normalize input data.

     -R
      Rank approximation used in LSA. May be actual number of
      LSA attributes to include (if greater than 1) or a proportion
      of total singular values to account for (if between 0 and 1).
      A value less than or equal to zero means use all latent variables.
      (default = 0.95)

     -A
      Maximum number of attributes to include in
      transformed attribute names. (-1 = include all)

Version:
  $Revision: 5728 $
Author:
  Amri Napolitano
See Also:
  Serialized Form

Constructor Summary
`LatentSemanticAnalysis()`

Method Summary
`void` | `buildEvaluator(Instances data)` Initializes the singular values/vectors and performs the analysis
`Instance` | `convertInstance(Instance instance)` Transform an instance in original (unnormalized) format
`double` | `evaluateAttribute(int att)` Evaluates the merit of a transformed attribute.
`Capabilities` | `getCapabilities()` Returns the capabilities of this evaluator.
`int` | `getMaximumAttributeNames()` Gets maximum number of attributes to include in transformed attribute names.
`boolean` | `getNormalize()` Gets whether or not input data is to be normalized
`java.lang.String[]` | `getOptions()` Gets the current settings of LatentSemanticAnalysis
`double` | `getRank()` Gets the desired matrix rank (or coverage proportion) for feature-space reduction
`java.lang.String` | `getRevision()` Returns the revision string.
`java.lang.String` | `globalInfo()` Returns a string describing this attribute transformer
`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` | `maximumAttributeNamesTipText()` Returns the tip text for this property
`java.lang.String` | `normalizeTipText()` Returns the tip text for this property
`java.lang.String` | `rankTipText()` Returns the tip text for this property
`void` | `setMaximumAttributeNames(int newMaxAttributes)` Sets maximum number of attributes to include in transformed attribute names.
`void` | `setNormalize(boolean newNormalize)` Set whether input data will be normalized.
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`void` | `setRank(double newRank)` Sets the desired matrix rank (or coverage proportion) for feature-space reduction
`java.lang.String` | `toString()` Returns a description of this attribute transformer
`Instances` | `transformedData(Instances data)` Transform the supplied data set (assumed to be the same format as the training data)
`Instances` | `transformedHeader()` Returns just the header for the transformed data (ie.

Methods inherited from class weka.attributeSelection.ASEvaluation
`forName, makeCopies, postProcess`

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

Constructor Detail

### LatentSemanticAnalysis

    public LatentSemanticAnalysis()

Method Detail

### globalInfo

    public java.lang.String globalInfo()

  Returns a string describing this attribute transformer

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

### 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:

       -N
        Normalize input data.

       -R
        Rank approximation used in LSA. May be actual number of
        LSA attributes to include (if greater than 1) or a proportion
        of total singular values to account for (if between 0 and 1).
        A value less than or equal to zero means use all latent variables.
        (default = 0.95)

       -A
        Maximum number of attributes to include in
        transformed attribute names. (-1 = include all)

  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

### normalizeTipText

    public java.lang.String normalizeTipText()

  Returns the tip text for this property

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

### setNormalize

    public void setNormalize(boolean newNormalize)

  Set whether input data will be normalized.

  Parameters:
    `newNormalize` - true if input data is to be normalized

### getNormalize

    public boolean getNormalize()

  Gets whether or not input data is to be normalized

  Returns:
    true if input data is to be normalized

### rankTipText

    public java.lang.String rankTipText()

  Returns the tip text for this property

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

### setRank

    public void setRank(double newRank)

  Sets the desired matrix rank (or coverage proportion) for feature-space reduction

  Parameters:
    `newRank` - the desired rank (or coverage) for feature-space reduction

### getRank

    public double getRank()

  Gets the desired matrix rank (or coverage proportion) for feature-space reduction

  Returns:
    the rank (or coverage) for feature-space reduction

### maximumAttributeNamesTipText

    public java.lang.String maximumAttributeNamesTipText()

  Returns the tip text for this property

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

### setMaximumAttributeNames

    public void setMaximumAttributeNames(int newMaxAttributes)

  Sets maximum number of attributes to include in transformed attribute names.

  Parameters:
    `newMaxAttributes` - the maximum number of attributes

### getMaximumAttributeNames

    public int getMaximumAttributeNames()

  Gets maximum number of attributes to include in transformed attribute names.

  Returns:
    the maximum number of attributes

### getOptions

    public java.lang.String[] getOptions()

  Gets the current settings of LatentSemanticAnalysis

  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 evaluator.

  Specified by:
    `getCapabilities` in interface `CapabilitiesHandler`
  Overrides:
    `getCapabilities` in class `ASEvaluation`
  Returns:
    the capabilities of this evaluator
  See Also:
    `Capabilities`

### buildEvaluator

    public void buildEvaluator(Instances data)
                        throws java.lang.Exception

  Initializes the singular values/vectors and performs the analysis

  Specified by:
    `buildEvaluator` in class `ASEvaluation`
  Parameters:
    `data` - the instances to analyse/transform
  Throws:
    `java.lang.Exception` - if analysis fails

### transformedHeader

    public Instances transformedHeader()
                                throws java.lang.Exception

  Returns just the header for the transformed data (ie. an empty set of instances. This is so that AttributeSelection can determine the structure of the transformed data without actually having to get all the transformed data through getTransformedData().

  Specified by:
    `transformedHeader` in interface `AttributeTransformer`
  Returns:
    the header of the transformed data.
  Throws:
    `java.lang.Exception` - if the header of the transformed data can't be determined.

### transformedData

    public Instances transformedData(Instances data)
                              throws java.lang.Exception

  Transform the supplied data set (assumed to be the same format as the training data)

  Specified by:
    `transformedData` in interface `AttributeTransformer`
  Returns:
    the transformed training data
  Throws:
    `java.lang.Exception` - if transformed data can't be returned

### evaluateAttribute

    public double evaluateAttribute(int att)
                             throws java.lang.Exception

  Evaluates the merit of a transformed attribute. This is defined to be the square of the singular value for the latent variable corresponding to the transformed attribute.

  Specified by:
    `evaluateAttribute` in interface `AttributeEvaluator`
  Parameters:
    `att` - the attribute to be evaluated
  Returns:
    the merit of a transformed attribute
  Throws:
    `java.lang.Exception` - if attribute can't be evaluated

### convertInstance

    public Instance convertInstance(Instance instance)
                             throws java.lang.Exception

  Transform an instance in original (unnormalized) format

  Specified by:
    `convertInstance` in interface `AttributeTransformer`
  Parameters:
    `instance` - an instance in the original (unnormalized) format
  Returns:
    a transformed instance
  Throws:
    `java.lang.Exception` - if instance can't be transformed

### toString

    public java.lang.String toString()

  Returns a description of this attribute transformer

  Overrides:
    `toString` in class `java.lang.Object`
  Returns:
    a String describing this attribute transformer

### getRevision

    public java.lang.String getRevision()

  Returns the revision string.

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

### main

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

  Main method for testing this class

  Parameters:
    `argv` - should contain the command line arguments to the evaluator/transformer (see AttributeSelection)

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