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B
buildEvaluator(Instances)
- Method in class weka.attributeSelection.
LatentSemanticAnalysis
Initializes the singular values/vectors and performs the analysis
C
convertInstance(Instance)
- Method in class weka.attributeSelection.
LatentSemanticAnalysis
Transform an instance in original (unnormalized) format
E
evaluateAttribute(int)
- Method in class weka.attributeSelection.
LatentSemanticAnalysis
Evaluates the merit of a transformed attribute.
G
getCapabilities()
- Method in class weka.attributeSelection.
LatentSemanticAnalysis
Returns the capabilities of this evaluator.
getMaximumAttributeNames()
- Method in class weka.attributeSelection.
LatentSemanticAnalysis
Gets maximum number of attributes to include in transformed attribute names.
getNormalize()
- Method in class weka.attributeSelection.
LatentSemanticAnalysis
Gets whether or not input data is to be normalized
getOptions()
- Method in class weka.attributeSelection.
LatentSemanticAnalysis
Gets the current settings of LatentSemanticAnalysis
getRank()
- Method in class weka.attributeSelection.
LatentSemanticAnalysis
Gets the desired matrix rank (or coverage proportion) for feature-space reduction
getRevision()
- Method in class weka.attributeSelection.
LatentSemanticAnalysis
Returns the revision string.
getSigma()
- Method in class weka.attributeSelection.
LatentSemanticAnalysis
Gets the singular values
getU()
- Method in class weka.attributeSelection.
LatentSemanticAnalysis
Gets the left singular vectors.
getV()
- Method in class weka.attributeSelection.
LatentSemanticAnalysis
Gets the right singular vectors.
globalInfo()
- Method in class weka.attributeSelection.
LatentSemanticAnalysis
Returns a string describing this attribute transformer
L
LatentSemanticAnalysis
- Class in
weka.attributeSelection
Performs latent semantic analysis and transformation of the data.
LatentSemanticAnalysis()
- Constructor for class weka.attributeSelection.
LatentSemanticAnalysis
listOptions()
- Method in class weka.attributeSelection.
LatentSemanticAnalysis
Returns an enumeration describing the available options.
M
main(String[])
- Static method in class weka.attributeSelection.
LatentSemanticAnalysis
Main method for testing this class
maximumAttributeNamesTipText()
- Method in class weka.attributeSelection.
LatentSemanticAnalysis
Returns the tip text for this property
N
normalizeTipText()
- Method in class weka.attributeSelection.
LatentSemanticAnalysis
Returns the tip text for this property
R
rankTipText()
- Method in class weka.attributeSelection.
LatentSemanticAnalysis
Returns the tip text for this property
S
setMaximumAttributeNames(int)
- Method in class weka.attributeSelection.
LatentSemanticAnalysis
Sets maximum number of attributes to include in transformed attribute names.
setNormalize(boolean)
- Method in class weka.attributeSelection.
LatentSemanticAnalysis
Set whether input data will be normalized.
setOptions(String[])
- Method in class weka.attributeSelection.
LatentSemanticAnalysis
Parses a given list of options.
setRank(double)
- Method in class weka.attributeSelection.
LatentSemanticAnalysis
Sets the desired matrix rank (or coverage proportion) for feature-space reduction
T
toString()
- Method in class weka.attributeSelection.
LatentSemanticAnalysis
Returns a description of this attribute transformer
transformedData(Instances)
- Method in class weka.attributeSelection.
LatentSemanticAnalysis
Transform the supplied data set (assumed to be the same format as the training data)
transformedHeader()
- Method in class weka.attributeSelection.
LatentSemanticAnalysis
Returns just the header for the transformed data (ie.
W
weka.attributeSelection
- package weka.attributeSelection
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