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java.lang.Objectrelational.helper.DataSetHelper
Contains helper methods (e.g. preprocessing)
| Constructor Summary | |
DataSetHelper()
|
|
| Method Summary | |
static weka.core.FastVector |
add(weka.core.FastVector v1,
weka.core.FastVector v2)
Merge vectors |
static int[] |
computeDistribution(weka.core.Instances instances)
Computes distribution of instances (regarding the categories) |
static double[] |
computePriors(weka.core.FastVector items,
java.util.HashMap labels,
java.lang.String type)
Computes the prior from the training set |
static double[] |
computePriors(weka.core.FastVector items,
int iLabel,
java.lang.String type)
Computes the prior from the training set (for binary classification) |
static java.util.HashMap |
countNeighborsClassesMultiLabelTest(weka.core.FastVector items,
int klasse,
java.util.HashMap initial,
java.util.HashMap weightsMap,
java.util.HashMap nodes)
Counts the frequency (weighted by their probabilities) of the classes of neighbors in the test set (for binary classification) |
static java.util.HashMap |
countNeighborsClassesMultiLabelTrain(weka.core.FastVector items,
java.util.HashMap initial,
java.util.HashMap weightsMap,
java.util.HashMap nodes,
int labelSize)
Counts the frequency of the classes of neighbors in the train set (for binary classification) |
static java.util.HashMap |
countNeighborsClassesTest(weka.core.FastVector items,
java.util.HashMap initial,
java.util.HashMap weightsMap,
java.util.HashMap nodes,
int labelSize)
Counts the frequency (weighted by their probabilities) of the classes of neighbors in the test set |
static java.util.HashMap |
countNeighborsClassesTrain(weka.core.FastVector items,
java.util.HashMap initial,
java.util.HashMap weightsMap,
java.util.HashMap nodes,
int labelsSize)
Counts the frequency of the classes of neighbors in the train set |
static weka.classifiers.Classifier[] |
loadClassifier(java.lang.String filename,
int numClassifier)
Loads classifier |
static weka.core.FastVector |
loadFastVector(java.lang.String filename)
Loads a fastvector |
static java.util.HashMap |
loadHashmap(java.lang.String filename)
Loads hashmap |
static java.util.Hashtable |
loadHashtable(java.lang.String filename)
Loads hashtable |
static double[][] |
loadMatrix(java.lang.String filename)
Loads matrix |
static java.util.Vector |
loadVector(java.lang.String filename)
Loads a vector |
static void |
main(java.lang.String[] args)
|
static java.util.HashMap |
normalizeWeights(BipartGraph graph,
weka.core.FastVector test,
java.util.HashMap weights)
Normalizes the weights of the graph, so that the values are between 0 and 1 |
static java.util.HashMap |
normalizeWeights(edu.uci.ics.jung.graph.impl.SparseGraph graph,
weka.core.FastVector test,
java.util.HashMap weights)
Normalizes the weights of the graph, so that the values are between 0 and 1 |
static void |
saveClassifier(java.lang.String filename,
weka.classifiers.Classifier[] cl)
serialize a classifier |
static void |
saveHashMap(java.lang.String filename,
java.util.HashMap h)
serialize hashmap |
static void |
saveMatrix(java.lang.String filename,
double[][] m)
serialize matrix |
static void |
saveTrainTest(weka.core.FastVector traintest,
java.lang.String outputPath,
boolean multilabel,
int numFolds,
java.lang.String type)
Write training and test sets into a xml-file |
static void |
saveVector(java.lang.String filename,
weka.core.FastVector items)
serialize vector |
static void |
saveVector(java.lang.String filename,
java.util.Vector items)
serialize vector |
| Methods inherited from class java.lang.Object |
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
public DataSetHelper()
| Method Detail |
public static java.util.HashMap countNeighborsClassesTrain(weka.core.FastVector items,
java.util.HashMap initial,
java.util.HashMap weightsMap,
java.util.HashMap nodes,
int labelsSize)
items - - data setinitial - - initialization of test setweightsMap - - weights of edgesnodes - - nodes of the graphlabelsSize - - number of categories
public static java.util.HashMap countNeighborsClassesTest(weka.core.FastVector items,
java.util.HashMap initial,
java.util.HashMap weightsMap,
java.util.HashMap nodes,
int labelSize)
items - - data setinitial - - initialization of test setweightsMap - - weights of edgesnodes - - nodes of the graph
public static java.util.HashMap countNeighborsClassesMultiLabelTrain(weka.core.FastVector items,
java.util.HashMap initial,
java.util.HashMap weightsMap,
java.util.HashMap nodes,
int labelSize)
items - - data setinitial - - initialization of test setweightsMap - - weights of edgesnodes - - nodes of the graph
public static java.util.HashMap countNeighborsClassesMultiLabelTest(weka.core.FastVector items,
int klasse,
java.util.HashMap initial,
java.util.HashMap weightsMap,
java.util.HashMap nodes)
items - - data setinitial - - initialization of test setweightsMap - - weights of edgesnodes - - nodes of the graph
public static weka.core.FastVector loadFastVector(java.lang.String filename)
filename -
public static java.util.Vector loadVector(java.lang.String filename)
filename -
public static weka.classifiers.Classifier[] loadClassifier(java.lang.String filename,
int numClassifier)
filename - numClassifier - - number of classifiers to be loaded
public static java.util.HashMap loadHashmap(java.lang.String filename)
filename -
public static double[][] loadMatrix(java.lang.String filename)
filename -
public static java.util.Hashtable loadHashtable(java.lang.String filename)
filename -
public static int[] computeDistribution(weka.core.Instances instances)
instances - - data instances
public static double[] computePriors(weka.core.FastVector items,
int iLabel,
java.lang.String type)
items - - train settype - - indicates the type of problem (heterogenous or homogenous)
public static double[] computePriors(weka.core.FastVector items,
java.util.HashMap labels,
java.lang.String type)
items - - train setlabels - - categoriestype - - indicates the type of problem (heterogenous or homogenous)
public static void saveTrainTest(weka.core.FastVector traintest,
java.lang.String outputPath,
boolean multilabel,
int numFolds,
java.lang.String type)
throws java.io.IOException
traintest - - train and test setoutputPath - - location where to savemultilabel - - indicates if have to cope with multilabel problemnumFolds - - number of folds (cross-validation)type - - indicates the type of problem (heterogenous or homogenous)
java.io.IOException
public static void saveVector(java.lang.String filename,
weka.core.FastVector items)
filename - items - vector
public static void saveVector(java.lang.String filename,
java.util.Vector items)
filename - items - vector
public static void saveClassifier(java.lang.String filename,
weka.classifiers.Classifier[] cl)
filename - cl - classifier
public static void saveHashMap(java.lang.String filename,
java.util.HashMap h)
filename - h - hashmap
public static void saveMatrix(java.lang.String filename,
double[][] m)
filename - m - matrix
public static weka.core.FastVector add(weka.core.FastVector v1,
weka.core.FastVector v2)
v1 - vectorv2 - vector
public static java.util.HashMap normalizeWeights(BipartGraph graph,
weka.core.FastVector test,
java.util.HashMap weights)
graph - - graph (jung)test - - test setweights - - weights of the edges in the graph
public static java.util.HashMap normalizeWeights(edu.uci.ics.jung.graph.impl.SparseGraph graph,
weka.core.FastVector test,
java.util.HashMap weights)
graph - - graph (jung)test - - test setweights - - weights of the edges in the graph
public static void main(java.lang.String[] args)
throws java.lang.Exception
java.lang.Exception
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