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: 11821 $

Author:

Amri Napolitano

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description

LatentSemanticAnalysis()

Constructors
Constructor and Description
`LatentSemanticAnalysis()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`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
clean, 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)

Class LatentSemanticAnalysis

Constructor Summary

Method Summary

Methods inherited from class weka.attributeSelection.ASEvaluation

Methods inherited from class java.lang.Object

Constructor Detail

LatentSemanticAnalysis

Method Detail

globalInfo

listOptions

setOptions

normalizeTipText

setNormalize

getNormalize

rankTipText

setRank

getRank

maximumAttributeNamesTipText

setMaximumAttributeNames

getMaximumAttributeNames

getOptions

getCapabilities

buildEvaluator

transformedHeader

transformedData

evaluateAttribute

convertInstance

toString

getRevision

main