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java.lang.Objectweka.attributeSelection.ASEvaluation
weka.attributeSelection.UnsupervisedAttributeEvaluator
weka.attributeSelection.LatentSemanticAnalysis
public class LatentSemanticAnalysis
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)
Constructor Summary | |
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LatentSemanticAnalysis()
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Method Summary | |
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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 |
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forName, makeCopies, postProcess |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
Constructor Detail |
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public LatentSemanticAnalysis()
Method Detail |
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public java.lang.String globalInfo()
public java.util.Enumeration listOptions()
listOptions
in interface OptionHandler
public void setOptions(java.lang.String[] options) throws java.lang.Exception
-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)
setOptions
in interface OptionHandler
options
- the list of options as an array of strings
java.lang.Exception
- if an option is not supportedpublic java.lang.String normalizeTipText()
public void setNormalize(boolean newNormalize)
newNormalize
- true if input data is to be normalizedpublic boolean getNormalize()
public java.lang.String rankTipText()
public void setRank(double newRank)
newRank
- the desired rank (or coverage) for feature-space reductionpublic double getRank()
public java.lang.String maximumAttributeNamesTipText()
public void setMaximumAttributeNames(int newMaxAttributes)
newMaxAttributes
- the maximum number of attributespublic int getMaximumAttributeNames()
public java.lang.String[] getOptions()
getOptions
in interface OptionHandler
public Capabilities getCapabilities()
getCapabilities
in interface CapabilitiesHandler
getCapabilities
in class ASEvaluation
Capabilities
public void buildEvaluator(Instances data) throws java.lang.Exception
buildEvaluator
in class ASEvaluation
data
- the instances to analyse/transform
java.lang.Exception
- if analysis failspublic Instances transformedHeader() throws java.lang.Exception
transformedHeader
in interface AttributeTransformer
java.lang.Exception
- if the header of the transformed data can't
be determined.public Instances transformedData(Instances data) throws java.lang.Exception
transformedData
in interface AttributeTransformer
java.lang.Exception
- if transformed data can't be returnedpublic double evaluateAttribute(int att) throws java.lang.Exception
evaluateAttribute
in interface AttributeEvaluator
att
- the attribute to be evaluated
java.lang.Exception
- if attribute can't be evaluatedpublic Instance convertInstance(Instance instance) throws java.lang.Exception
convertInstance
in interface AttributeTransformer
instance
- an instance in the original (unnormalized) format
java.lang.Exception
- if instance can't be transformedpublic java.lang.String toString()
toString
in class java.lang.Object
public java.lang.String getRevision()
getRevision
in interface RevisionHandler
getRevision
in class ASEvaluation
public static void main(java.lang.String[] argv)
argv
- should contain the command line arguments to the
evaluator/transformer (see AttributeSelection)
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