public class Cross_Validation
extends java.lang.Object
Constructor and Description |
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Cross_Validation() |
Modifier and Type | Method and Description |
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static double |
cIndexWithAbstention(weka.core.Instances data)
Computes the C-Index for the given, ranked data-set (considering abstention/ties)
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static int[][] |
classValsDist(weka.core.Instances data)
Returns the distinct values of the class attribute in the set and their respective occurrence
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static weka.core.Instance |
copyInst(weka.core.Instance inst)
Returns a copy of the given instance
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static weka.core.Instances |
duplicates(weka.core.Instances data)
Identifies duplicates within the data and returns a revised dataset
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static void |
main(java.lang.String[] args) |
static void |
nestedVali(double proportion,
weka.core.Instances orgData,
java.lang.String dataset,
int folds,
int repeat,
int maxGroup,
int maxBins)
Nested cross-validation to 1) determine hyper parameters 2) validate with the parameters determined
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static weka.core.Instances |
rankingLPList(weka.core.Instances data)
Ranks the objects based on the LP-List using insertion sort
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static weka.core.Instances |
resample(double proportion,
weka.core.Instances original_train)
Resample and resize the given data set
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static weka.core.Instances |
transformData(weka.core.Instances input)
Transform a classification data set into a set of pairwise preferences
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static double[] |
validateLpList(weka.core.Instances train,
weka.core.Instances test,
int group,
int bins)
Learn and evaluate the lp-list given a parametrization w.r.t. g and r
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public static void main(java.lang.String[] args) throws java.lang.Exception
java.lang.Exception
public static void nestedVali(double proportion, weka.core.Instances orgData, java.lang.String dataset, int folds, int repeat, int maxGroup, int maxBins) throws java.lang.Exception
proportion
- The proportion to be considered to shrink the dataset if necessaryorgData
- The data setpath
- The path for saving the resultsdataset
- Additional information for the pathfolds
- The amount of foldersrepeat
- The number of repetitionsmaxGroup
- The maximal grouping size for CLeRamaxBins
- The maximum number of bins for discretization of attribute valuesjava.lang.Exception
public static double[] validateLpList(weka.core.Instances train, weka.core.Instances test, int group, int bins) throws java.lang.Exception
train
- The training data settest
- The training data setgroup
- The grouping size for learning the lp-listbins
- The number of bins for discretization of attribute valuesjava.lang.Exception
public static weka.core.Instances rankingLPList(weka.core.Instances data) throws java.lang.Exception
data
- The data to rankjava.lang.Exception
public static double cIndexWithAbstention(weka.core.Instances data) throws java.lang.Exception
data
- The data set for evaluationjava.lang.Exception
public static weka.core.Instances transformData(weka.core.Instances input) throws java.lang.Exception
input
- The data to be transformedjava.lang.Exception
public static weka.core.Instances resample(double proportion, weka.core.Instances original_train) throws java.lang.Exception
proportion
- The proportion of the orginial data-setdataset
- The dataset to be resampledjava.lang.Exception
public static weka.core.Instance copyInst(weka.core.Instance inst)
inst
- The instance to be copiedpublic static int[][] classValsDist(weka.core.Instances data)
data
- The data for explorationpublic static weka.core.Instances duplicates(weka.core.Instances data)
data
- The data for exploration