B C E G L M N R S T W

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
 

B C E G L M N R S T W