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A

AbstractCostFunction - Class in weka.classifiers.functions.gmlvq.core.cost
An abstract implementation of the CostFunction interface.
AbstractCostFunction(SigmoidFunction) - Constructor for class weka.classifiers.functions.gmlvq.core.cost.AbstractCostFunction
 
add(Vector, Vector) - Static method in class weka.classifiers.functions.gmlvq.utilities.LinearAlgebraicCalculations
 
addAdditionalCostFunction(CostFunctionValue) - Method in class weka.classifiers.functions.GMLVQ
 
addAdditionalCostFunction(CostFunctionValue) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
addLatestValues(Map<CostFunctionValue, Double>) - Method in class weka.classifiers.functions.gmlvq.visualization.CostFunctionChartPanel
 
addVisualization(Visualizer) - Static method in class weka.classifiers.functions.gmlvq.visualization.VisualizationSingleton
 
AVAILIABLE_ADDITIONAL_COST_FUNCTIONS - Static variable in interface weka.classifiers.functions.GMLVQ.CostFunctionsSettings
 
AVAILIABLE_COST_FUNCTIONS - Static variable in interface weka.classifiers.functions.GMLVQ.CostFunctionsSettings
 

B

brightness(float) - Method in class weka.classifiers.functions.gmlvq.visualization.ColorScale.Builder
 
build(List<DataPoint>) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
builds the classifier without showing live visualization
build() - Method in class weka.classifiers.functions.gmlvq.visualization.ColorScale.Builder
 
buildAndShow(List<DataPoint>, Instances) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
builds the classifier and shows the live visualization
buildClassifier(Instances) - Method in class weka.classifiers.functions.GMLVQ
 
buildClassifier() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
starts everything
Builder() - Constructor for class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
Builder(float, float) - Constructor for class weka.classifiers.functions.gmlvq.visualization.ColorScale.Builder
 

C

calculateCovarianceFromMeanVector(List<DataPoint>) - Static method in class weka.classifiers.functions.gmlvq.utilities.LinearAlgebraicCalculations
calculates the covariance matrix based on the definition of
calculateSquaredEuclideanDistance(Vector, Vector) - Static method in class weka.classifiers.functions.gmlvq.utilities.LinearAlgebraicCalculations
calculates the squared euclidean distance between 2 double vectors/arrays of the same dimension
this is the some what low-level function used to find nearest prototypes to a given data point
ClassificationErrorFunction - Class in weka.classifiers.functions.gmlvq.core.cost
Can be used to compute the classification error.
IMPORTANT: For 2 class problems, use the ConfusionMatrix implementation.
ClassificationErrorFunction(SigmoidFunction) - Constructor for class weka.classifiers.functions.gmlvq.core.cost.ClassificationErrorFunction
 
classifyInstance(Instance) - Method in class weka.classifiers.functions.GMLVQ
 
classifyInstance(DataPoint) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
collectDatapointsWithClassLabel(List<DataPoint>, double) - Static method in class weka.classifiers.functions.gmlvq.utilities.LinearAlgebraicCalculations
return all data points with a requested class label
ColorScale - Class in weka.classifiers.functions.gmlvq.visualization
This object provides a color scale between the given minimal and maximal values.
ColorScale.Builder - Class in weka.classifiers.functions.gmlvq.visualization
 
compare(Map.Entry<K, V>, Map.Entry<K, V>) - Method in class weka.classifiers.functions.gmlvq.visualization.HashMapValueComparator
 
computeFMeasure(double) - Method in class weka.classifiers.functions.gmlvq.core.cost.ConfusionMatrix
 
computeFMeasureUpdate(DataPoint, double) - Method in class weka.classifiers.functions.gmlvq.core.cost.ConfusionMatrix
 
computePrecisionRecall(double, double) - Method in class weka.classifiers.functions.gmlvq.core.cost.ConfusionMatrix
 
computePrecisionRecallUpdate(DataPoint, double, double) - Method in class weka.classifiers.functions.gmlvq.core.cost.ConfusionMatrix
 
computeWeightedAccuracy(double, double) - Method in class weka.classifiers.functions.gmlvq.core.cost.ConfusionMatrix
 
computeWeightedAccuracyUpdate(DataPoint, double, double) - Method in class weka.classifiers.functions.gmlvq.core.cost.ConfusionMatrix
 
ConfusionMatrix - Class in weka.classifiers.functions.gmlvq.core.cost
Provides the implementation of any confusion matrix based cost function.
ConfusionMatrix(SigmoidFunction, List<DataPoint>, List<Prototype>, OmegaMatrix) - Constructor for class weka.classifiers.functions.gmlvq.core.cost.ConfusionMatrix
 
COST_FUNCTION_BETA_OPTION - Static variable in interface weka.classifiers.functions.GMLVQ.CostFunctionsSettings
the beta parameter used within confusion dependent cost functions (currently only F-measure)
COST_FUNCTION_TO_OPTIMIZE_OPTION - Static variable in interface weka.classifiers.functions.GMLVQ.CostFunctionsSettings
 
COST_FUNCTION_WEIGHTS_OPTION - Static variable in interface weka.classifiers.functions.GMLVQ.CostFunctionsSettings
the weights used for confusion matrix based cost functions
CostFunction - Interface in weka.classifiers.functions.gmlvq.core.cost
Defines the contract of each cost function.
costFunctionBeta(double) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
CostFunctionCalculator - Class in weka.classifiers.functions.gmlvq.core.cost
A wrapping class for all CostFunctions to be calculated during training.
CostFunctionCalculator(SigmoidFunction, double, double[], CostFunctionValue, CostFunctionValue...) - Constructor for class weka.classifiers.functions.gmlvq.core.cost.CostFunctionCalculator
 
CostFunctionCalculator(SigmoidFunction, CostFunctionValue, CostFunctionValue...) - Constructor for class weka.classifiers.functions.gmlvq.core.cost.CostFunctionCalculator
 
CostFunctionChartPanel - Class in weka.classifiers.functions.gmlvq.visualization
 
CostFunctionChartPanel(Map<CostFunctionValue, Double>) - Constructor for class weka.classifiers.functions.gmlvq.visualization.CostFunctionChartPanel
 
costFunctionToOptimize(CostFunctionValue) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
CostFunctionValue - Enum in weka.classifiers.functions.gmlvq.core.cost
Gathers all CostFunction implementations.
costFunctionWeights(double[]) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
costFunctionWeights(String) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
createDataPoint(Instance) - Static method in class weka.classifiers.functions.gmlvq.model.WekaModelConverter
 
createDataPoints(Instances) - Static method in class weka.classifiers.functions.gmlvq.model.WekaModelConverter
 
createMeanVectorFromListOfVectors(List<DataPoint>) - Static method in class weka.classifiers.functions.gmlvq.utilities.LinearAlgebraicCalculations
computes the average vector of a set of vectors - each feature is set to the average of these feature for all input data points

D

DATA_POINTS_PER_ROUND_OPTION - Static variable in interface weka.classifiers.functions.GMLVQ.AlgorithmSettings
 
DataPoint - Class in weka.classifiers.functions.gmlvq.model
The equivalent to Weks's Instance: the representation of raw input data.
DataPoint(double[], double) - Constructor for class weka.classifiers.functions.gmlvq.model.DataPoint
 
dataPointRatioPerRound(double) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
DataRandomizer - Class in weka.classifiers.functions.gmlvq.utilities
A collection of convenience methods to create subsets of data points or to split them into partition of equal size in order to provide an arbitrary number of threads with data to process.
DataRandomizer(int, double) - Constructor for class weka.classifiers.functions.gmlvq.utilities.DataRandomizer
 
DataRandomizer(int, double, long) - Constructor for class weka.classifiers.functions.gmlvq.utilities.DataRandomizer
 
DataSpaceVector - Class in weka.classifiers.functions.gmlvq.model
A Vector living in the data space (as opposed to the embedded space).
DataSpaceVector(double[], double) - Constructor for class weka.classifiers.functions.gmlvq.model.DataSpaceVector
 
DEFAULT_BETA - Static variable in class weka.classifiers.functions.gmlvq.core.cost.CostFunctionCalculator
 
DEFAULT_COST_FUNCTION - Static variable in interface weka.classifiers.functions.gmlvq.core.GMLVQCore.DefaultSettings
 
DEFAULT_COST_FUNCTION_TO_OPTIMIZE - Static variable in interface weka.classifiers.functions.GMLVQ.CostFunctionsSettings
the cost function to optimize
DEFAULT_DATA_POINT_RATIO_PER_ROUND - Static variable in interface weka.classifiers.functions.GMLVQ.AlgorithmSettings
the default percentage of trainingData points used per round
DEFAULT_DATA_POINT_RATIO_PER_ROUND - Static variable in interface weka.classifiers.functions.gmlvq.core.GMLVQCore.DefaultSettings
the default percentage of trainingData points used per round
DEFAULT_LEARN_RATE_CHANGE - Static variable in interface weka.classifiers.functions.gmlvq.core.GMLVQCore.DefaultSettings
 
DEFAULT_LEARN_RATE_CHANGE - Static variable in interface weka.classifiers.functions.GMLVQ.MethodSettings
 
DEFAULT_MATRIX_LEARNING - Static variable in interface weka.classifiers.functions.gmlvq.core.GMLVQCore.DefaultSettings
the default boolean if mode is GMLVQ (true) or GLVQ (false)
DEFAULT_MATRIX_LEARNING - Static variable in interface weka.classifiers.functions.GMLVQ.MethodSettings
the default boolean if mode is GMLVQ (true) or GLVQ (false)
DEFAULT_NUMBER_OF_EPOCHS - Static variable in interface weka.classifiers.functions.GMLVQ.AlgorithmSettings
the default number of epochs used for training
DEFAULT_NUMBER_OF_EPOCHS - Static variable in interface weka.classifiers.functions.gmlvq.core.GMLVQCore.DefaultSettings
the default number of epochs used for training
DEFAULT_NUMBER_OF_PROTOTYPES_PER_CLASS - Static variable in interface weka.classifiers.functions.GMLVQ.AlgorithmSettings
the default number of prototypes used to represent each class
DEFAULT_NUMBER_OF_PROTOTYPES_PER_CLASS - Static variable in interface weka.classifiers.functions.gmlvq.core.GMLVQCore.DefaultSettings
the default number of prototypes used to represent each class
DEFAULT_OMEGA_DIMENSION - Static variable in interface weka.classifiers.functions.gmlvq.core.GMLVQCore.DefaultSettings
the default dimension of matrix omega
DEFAULT_OMEGA_DIMENSION - Static variable in interface weka.classifiers.functions.GMLVQ.MethodSettings
the default dimension of matrix omega
DEFAULT_OMEGA_LEARNING_RATE - Static variable in interface weka.classifiers.functions.gmlvq.core.GMLVQCore.DefaultSettings
the default learning rate of the omega matrix
DEFAULT_OMEGA_LEARNING_RATE - Static variable in interface weka.classifiers.functions.GMLVQ.MethodSettings
the default learning rate of the omega matrix
DEFAULT_PARALLEL_EXECUTION - Static variable in interface weka.classifiers.functions.gmlvq.core.GMLVQCore.DefaultSettings
true iff GMLVQ should be executed in parallel.
DEFAULT_PARALLEL_EXECUTION - Static variable in interface weka.classifiers.functions.GMLVQ.MethodSettings
true iff GMLVQ shoud be executed in parallel.
DEFAULT_PROTOYPE_LEARNING_RATE - Static variable in interface weka.classifiers.functions.gmlvq.core.GMLVQCore.DefaultSettings
the default prototype learning rate
DEFAULT_PROTOYPE_LEARNING_RATE - Static variable in interface weka.classifiers.functions.GMLVQ.MethodSettings
the default prototype learning rate
DEFAULT_SIGMOID_SIGMA_INTERVAL - Static variable in interface weka.classifiers.functions.GMLVQ.AlgorithmSettings
 
DEFAULT_SIGMOID_SIGMA_INTERVAL_END - Static variable in interface weka.classifiers.functions.GMLVQ.AlgorithmSettings
 
DEFAULT_SIGMOID_SIGMA_INTERVAL_END - Static variable in interface weka.classifiers.functions.gmlvq.core.GMLVQCore.DefaultSettings
 
DEFAULT_SIGMOID_SIGMA_INTERVAL_START - Static variable in interface weka.classifiers.functions.GMLVQ.AlgorithmSettings
 
DEFAULT_SIGMOID_SIGMA_INTERVAL_START - Static variable in interface weka.classifiers.functions.gmlvq.core.GMLVQCore.DefaultSettings
 
DEFAULT_STOP_CRITERION - Static variable in interface weka.classifiers.functions.GMLVQ.AlgorithmSettings
the default value of the stop criterion
DEFAULT_STOP_CRITERION - Static variable in interface weka.classifiers.functions.gmlvq.core.GMLVQCore.DefaultSettings
the default value of the stop criterion
DEFAULT_VISUALIZATION - Static variable in interface weka.classifiers.functions.GMLVQ.AlgorithmSettings
the default setting of matrix omega should be visualized
DEFAULT_VISUALIZATION - Static variable in interface weka.classifiers.functions.gmlvq.core.GMLVQCore.DefaultSettings
the default setting of matrix omega should be visualized
DEFAULT_WEIGHTS - Static variable in class weka.classifiers.functions.gmlvq.core.cost.CostFunctionCalculator
 
DefaultCostFunction - Class in weka.classifiers.functions.gmlvq.core.cost
The default cost function which can be employed in any case.
DefaultCostFunction(SigmoidFunction) - Constructor for class weka.classifiers.functions.gmlvq.core.cost.DefaultCostFunction
 
defaultCostFunctionString() - Method in class weka.classifiers.functions.gmlvq.core.cost.CostFunctionCalculator
 
deregisterAllMappingBut(OmegaMatrix) - Method in class weka.classifiers.functions.gmlvq.model.DataSpaceVector
 
deregisterAllMappingsBut(OmegaMatrix, List<Prototype>) - Method in class weka.classifiers.functions.gmlvq.model.DataPoint
 
deregisterAllWinnersBut(List<Prototype>) - Method in class weka.classifiers.functions.gmlvq.model.EmbeddedSpaceVector
 
Disposable - Interface in weka.classifiers.functions.gmlvq.core
Provides a signature which enables wrapping classes to safely dispose resources internally used by the implementing class - somewhat like Closeable does for opened files.
In GMLVQ implementation, it is assigned to classes which utilize ExecutorService so the internal thread pools can be shutdown safely.
dispose() - Method in class weka.classifiers.functions.gmlvq.core.cost.AbstractCostFunction
 
dispose() - Method in interface weka.classifiers.functions.gmlvq.core.Disposable
frees resources associated to this object
dispose() - Method in class weka.classifiers.functions.gmlvq.core.GradientDescent
 
distributionForInstance(DataPoint) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
distributionForInstance(Instance) - Method in class weka.classifiers.functions.GMLVQ
 
draw(Graphics2D, Matrix) - Method in class weka.classifiers.functions.gmlvq.visualization.MatrixRenderer
 
dyadicProduct(Vector) - Static method in class weka.classifiers.functions.gmlvq.utilities.LinearAlgebraicCalculations
calculates the outer product respectively dyadic product of a Vector with itself

E

EmbeddedSpaceVector - Class in weka.classifiers.functions.gmlvq.model
The representation of a Vector in the embedded space.
EmbeddedSpaceVector(double[], double, OmegaMatrix) - Constructor for class weka.classifiers.functions.gmlvq.model.EmbeddedSpaceVector
 
EmbeddedSpaceVector(Vector, OmegaMatrix) - Constructor for class weka.classifiers.functions.gmlvq.model.EmbeddedSpaceVector
 
equals(Object) - Method in class weka.classifiers.functions.gmlvq.model.OmegaMatrix
 
equals(Object) - Method in class weka.classifiers.functions.gmlvq.model.Vector
 
evaluate(List<DataPoint>, List<Prototype>, OmegaMatrix) - Method in class weka.classifiers.functions.gmlvq.core.cost.AbstractCostFunction
 
evaluate(List<DataPoint>, List<Prototype>, OmegaMatrix) - Method in interface weka.classifiers.functions.gmlvq.core.cost.CostFunction
computes the costs for the given configuration of data and prototypes
evaluate(List<DataPoint>, List<Prototype>, OmegaMatrix) - Method in class weka.classifiers.functions.gmlvq.core.cost.CostFunctionCalculator
 
evaluate(double) - Method in class weka.classifiers.functions.gmlvq.core.SigmoidFunction
computes the sigmoid function for the input value
evaluatePrime(double) - Method in class weka.classifiers.functions.gmlvq.core.SigmoidFunction
computes the derivative of the sigmoid function for the input value
exportLambdaMatrixToSVG(File) - Method in class weka.classifiers.functions.gmlvq.visualization.LambdaMatrixPanel
 
extractAttributeNames(Instances) - Static method in class weka.classifiers.functions.gmlvq.model.WekaModelConverter
extracts all attribute names of WEKA instances and converts them to a string array
extractClassLables(Instances) - Static method in class weka.classifiers.functions.gmlvq.model.WekaModelConverter
 

F

FeatureAnalysisPanel - Class in weka.classifiers.functions.gmlvq.visualization
 
FeatureAnalysisPanel(VisualizerMouseAdapter, List<DataPoint>, Map<Double, String>, String[], int) - Constructor for class weka.classifiers.functions.gmlvq.visualization.FeatureAnalysisPanel
 
FeatureImpactPanel - Class in weka.classifiers.functions.gmlvq.visualization
 
FeatureImpactPanel(String[], ColorScale) - Constructor for class weka.classifiers.functions.gmlvq.visualization.FeatureImpactPanel
 

G

generateRandomizedSubListOf(List<T>, int) - Method in class weka.classifiers.functions.gmlvq.utilities.DataRandomizer
returns the specified number of elements at random from the given list
generateRandomizedSubListOf(List<T>) - Method in class weka.classifiers.functions.gmlvq.utilities.DataRandomizer
 
get_1_costFunctionToOptimize() - Method in class weka.classifiers.functions.GMLVQ
 
get_1_numberOfEpochs() - Method in class weka.classifiers.functions.GMLVQ
 
get_1_numberOfPrototypesPerClass() - Method in class weka.classifiers.functions.GMLVQ
 
get_2_costFunctionBeta() - Method in class weka.classifiers.functions.GMLVQ
 
get_2_costFunctionWeights() - Method in class weka.classifiers.functions.GMLVQ
 
get_2_dataPointRatioPerRound() - Method in class weka.classifiers.functions.GMLVQ
 
get_2_omegaDimension() - Method in class weka.classifiers.functions.GMLVQ
 
get_2_omegaLearningRate() - Method in class weka.classifiers.functions.GMLVQ
 
get_2_prototypeLearningRate() - Method in class weka.classifiers.functions.GMLVQ
 
get_2_sigmoidSigmaInterval() - Method in class weka.classifiers.functions.GMLVQ
 
getAdditionalCostFunctions() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
getCapabilities() - Method in class weka.classifiers.functions.GMLVQ
 
getClassificationErrorFunction() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
getClassLabel() - Method in class weka.classifiers.functions.gmlvq.model.Vector
 
getColor(float) - Method in class weka.classifiers.functions.gmlvq.visualization.ColorScale
Gets the color as specified by this gradient for this value between the minimal and maximal value.
getConfusionMatrix() - Method in class weka.classifiers.functions.gmlvq.core.cost.CostFunctionCalculator
 
getCostFunction() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
getCostFunctionBeta() - Method in class weka.classifiers.functions.gmlvq.core.cost.CostFunctionCalculator
 
getCostFunctionBeta() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
getCostFunctionToOptimize() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
getCostFunctionWeights() - Method in class weka.classifiers.functions.gmlvq.core.cost.CostFunctionCalculator
 
getCostFunctionWeights() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
getCurrentSigmoidSigma() - Method in class weka.classifiers.functions.gmlvq.core.SigmoidFunction
 
getDataDimension() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
getDataDimension() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
getDataDimension() - Method in class weka.classifiers.functions.GMLVQ
 
getDataPointRatioPerRound() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
getDataPointRatioPerRound() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
getDataPoints() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
getDataRandomizer() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
getDetailString() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
getDimension() - Method in class weka.classifiers.functions.gmlvq.model.Vector
 
getDistanceOtherClass() - Method in class weka.classifiers.functions.gmlvq.model.WinningInformation
 
getDistanceSameClass() - Method in class weka.classifiers.functions.gmlvq.model.WinningInformation
 
getElementSize() - Method in class weka.classifiers.functions.gmlvq.visualization.LambdaMatrixPanel
 
getEmbeddedSpaceVector(OmegaMatrix) - Method in class weka.classifiers.functions.gmlvq.model.DataSpaceVector
 
getEmbeddedSpaceVector(OmegaMatrix) - Method in class weka.classifiers.functions.gmlvq.model.Prototype
 
getFalseNegativeApprox() - Method in class weka.classifiers.functions.gmlvq.core.cost.ConfusionMatrix
 
getFalsePositiveApprox() - Method in class weka.classifiers.functions.gmlvq.core.cost.ConfusionMatrix
 
getFeatureAnalysisPanel() - Method in class weka.classifiers.functions.gmlvq.visualization.Visualizer
 
getGradientDescent() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
getHorizontalMargin() - Method in class weka.classifiers.functions.gmlvq.visualization.LambdaMatrixPanel
 
getIndexWinnerOtherClass() - Method in class weka.classifiers.functions.gmlvq.model.WinningInformation
 
getIndexWinnerSameClass() - Method in class weka.classifiers.functions.gmlvq.model.WinningInformation
 
getLambdaMatrix() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
getLambdaMatrix() - Method in class weka.classifiers.functions.gmlvq.visualization.FeatureImpactPanel
 
getLambdaMatrix() - Method in class weka.classifiers.functions.gmlvq.visualization.LambdaMatrixPanel
 
getLambdaMatrixScalingFactor() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
getLastVisualizalizer() - Static method in class weka.classifiers.functions.gmlvq.visualization.VisualizationSingleton
 
getLearnRateChange() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
getLearnRateChange() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
getListHideByAttribute() - Method in class weka.classifiers.functions.gmlvq.visualization.FeatureAnalysisPanel
 
getListHideByClass() - Method in class weka.classifiers.functions.gmlvq.visualization.FeatureAnalysisPanel
 
getListModelHideByAttribute() - Method in class weka.classifiers.functions.gmlvq.visualization.FeatureAnalysisPanel
 
getListModelHideByClass() - Method in class weka.classifiers.functions.gmlvq.visualization.FeatureAnalysisPanel
 
getListModelShowByAttribute() - Method in class weka.classifiers.functions.gmlvq.visualization.FeatureAnalysisPanel
 
getListModelShowByClass() - Method in class weka.classifiers.functions.gmlvq.visualization.FeatureAnalysisPanel
 
getListModelShowingPrototypes() - Method in class weka.classifiers.functions.gmlvq.visualization.FeatureAnalysisPanel
 
getListShowByAttribute() - Method in class weka.classifiers.functions.gmlvq.visualization.FeatureAnalysisPanel
 
getListShowByClass() - Method in class weka.classifiers.functions.gmlvq.visualization.FeatureAnalysisPanel
 
getListShowingPrototypes() - Method in class weka.classifiers.functions.gmlvq.visualization.FeatureAnalysisPanel
 
getMatrixDrawHeight() - Method in class weka.classifiers.functions.gmlvq.visualization.MatrixRenderer
 
getMatrixDrawWidth() - Method in class weka.classifiers.functions.gmlvq.visualization.MatrixRenderer
 
getMaximalValue() - Method in class weka.classifiers.functions.gmlvq.visualization.ColorScale
 
getMinAndMaxValuesFromMatrix(Matrix) - Static method in class weka.classifiers.functions.gmlvq.utilities.LinearAlgebraicCalculations
Retrieves the minimal and maximal values in the given matrix at the same time, traversing every value only once.
getMinimalValue() - Method in class weka.classifiers.functions.gmlvq.visualization.ColorScale
 
getNumberOfClasses() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
getNumberOfClasses() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
getNumberOfClasses() - Method in class weka.classifiers.functions.GMLVQ
 
getNumberOfEpochs() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
getNumberOfMappings() - Method in class weka.classifiers.functions.gmlvq.model.DataSpaceVector
 
getNumberOfPrototypesPerClass() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
getNumberOfPrototypesPerClass() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
getNumberOfTotalEpochs() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
getNumberOfTrainingData() - Method in class weka.classifiers.functions.gmlvq.utilities.DataRandomizer
 
getOmegaDimension() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
getOmegaDimension() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
getOmegaLearningRate() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
getOmegaLearningRate() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
getOmegaLearningRate() - Method in class weka.classifiers.functions.gmlvq.core.UpdateManager
 
getOmegaMatrix() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
getOptions() - Method in class weka.classifiers.functions.GMLVQ
 
getPrototypeLearningRate() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
getPrototypeLearningRate() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
getPrototypeLearningRate() - Method in class weka.classifiers.functions.gmlvq.core.UpdateManager
 
getPrototypes() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
getPrototypesPerClass() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
getPrototypesPerClass() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
getPrototypesPerClass() - Method in class weka.classifiers.functions.GMLVQ
 
getRandom() - Method in class weka.classifiers.functions.gmlvq.utilities.DataRandomizer
 
getRatio() - Method in class weka.classifiers.functions.gmlvq.utilities.DataRandomizer
 
getRenderer() - Method in class weka.classifiers.functions.gmlvq.visualization.LambdaMatrixPanel
 
getSeed() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
getSeed() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
getSeed() - Method in class weka.classifiers.functions.gmlvq.utilities.DataRandomizer
 
getSigmoidFunction() - Method in class weka.classifiers.functions.gmlvq.core.cost.ConfusionMatrix
 
getSigmoidFunction() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
getSigmoidSigmaInterval() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
getSigmoidSigmaIntervalEnd() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
getSigmoidSigmaIntervalEnd() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
getSigmoidSigmaIntervalEnd() - Method in class weka.classifiers.functions.gmlvq.core.SigmoidFunction
 
getSigmoidSigmaIntervalStart() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
getSigmoidSigmaIntervalStart() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
getSigmoidSigmaIntervalStart() - Method in class weka.classifiers.functions.gmlvq.core.SigmoidFunction
 
getStopCriterion() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
getStopCriterion() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
getTechnicalInformation() - Method in class weka.classifiers.functions.GMLVQ
 
getToolTipText(MouseEvent) - Method in class weka.classifiers.functions.gmlvq.visualization.LambdaMatrixPanel
 
getTotalNumberOfEpochs() - Method in class weka.classifiers.functions.gmlvq.core.SigmoidFunction
 
getTreePrototypes() - Method in class weka.classifiers.functions.gmlvq.visualization.FeatureAnalysisPanel
 
getTrueNegativeApprox() - Method in class weka.classifiers.functions.gmlvq.core.cost.ConfusionMatrix
 
getTruePositiveApprox() - Method in class weka.classifiers.functions.gmlvq.core.cost.ConfusionMatrix
 
getUpdatedOmegaMatrix() - Method in class weka.classifiers.functions.gmlvq.core.ProposedUpdate
 
getUpdatedPrototypes() - Method in class weka.classifiers.functions.gmlvq.core.ProposedUpdate
 
getUpdateManager() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
getValue(int) - Method in class weka.classifiers.functions.gmlvq.model.Vector
 
getValues() - Method in class weka.classifiers.functions.gmlvq.model.Vector
 
getVerticalMargin() - Method in class weka.classifiers.functions.gmlvq.visualization.LambdaMatrixPanel
 
getWinnerOtherClass() - Method in class weka.classifiers.functions.gmlvq.model.WinningInformation
 
getWinnerSameClass() - Method in class weka.classifiers.functions.gmlvq.model.WinningInformation
 
getWinningInformation(List<Prototype>) - Method in class weka.classifiers.functions.gmlvq.model.EmbeddedSpaceVector
 
globalInfo() - Method in class weka.classifiers.functions.GMLVQ
Returns a string describing classifier
GMLVQ - Class in weka.classifiers.functions
the adapter of GMLVQCore to weka's data structure, input options as well as its GUI integration
see GMLVQCore for details on GMLVQ's implementation
GMLVQ() - Constructor for class weka.classifiers.functions.GMLVQ
 
GMLVQ.AlgorithmSettings - Interface in weka.classifiers.functions
The interface provides all default values and options essential for the algorithm.
GMLVQ.CostFunctionsSettings - Interface in weka.classifiers.functions
The interface provides all the cost function related settings.
GMLVQ.MethodSettings - Interface in weka.classifiers.functions
The interface provides all default values and options essential for the method in general.
GMLVQCore - Class in weka.classifiers.functions.gmlvq.core
The implementation of the generalized matrix learning vector quantization.
GMLVQCore.Builder - Class in weka.classifiers.functions.gmlvq.core
 
GMLVQCore.DefaultSettings - Interface in weka.classifiers.functions.gmlvq.core
 
GMLVQDefaultObserver - Class in weka.classifiers.functions.gmlvq.core
Provides a light-weight, neutral, non-Weka implementation of the Observer interface, so a Visualizer can be available even when GMLVQCore was invoked from Tests or operated upon by directly using its API.
GMLVQDefaultObserver(GMLVQCore, Instances, int, Map<CostFunctionValue, Double>) - Constructor for class weka.classifiers.functions.gmlvq.core.GMLVQDefaultObserver
 
GradientDescent - Class in weka.classifiers.functions.gmlvq.core
This class wraps the stochastic gradient descent of GMLVQ.
GradientDescent(DataRandomizer, SigmoidFunction, CostFunctionCalculator) - Constructor for class weka.classifiers.functions.gmlvq.core.GradientDescent
 

H

hashCode() - Method in class weka.classifiers.functions.gmlvq.model.OmegaMatrix
 
hashCode() - Method in class weka.classifiers.functions.gmlvq.model.Vector
 
HashMapValueComparator<K,V extends java.lang.Comparable<? super V>> - Class in weka.classifiers.functions.gmlvq.visualization
 
HashMapValueComparator() - Constructor for class weka.classifiers.functions.gmlvq.visualization.HashMapValueComparator
 
hideAttribute(String) - Method in class weka.classifiers.functions.gmlvq.visualization.FeatureAnalysisPanel
 
hideClass(String) - Method in class weka.classifiers.functions.gmlvq.visualization.FeatureAnalysisPanel
 
hideProtoype(String) - Method in class weka.classifiers.functions.gmlvq.visualization.FeatureAnalysisPanel
 

I

incorporate(DataPoint) - Method in class weka.classifiers.functions.gmlvq.core.ProposedUpdate
analyzes the given data point and its WinningInformation (such as the closest prototypes and their distances), this information is added to the prototype and omega deltas
increaseSigmoidSigma(int) - Method in class weka.classifiers.functions.gmlvq.core.SigmoidFunction
increase the SigmoidFunction.currentSigmoidSigma depending on the current epoch number
initializeTrainingData() - Method in class weka.classifiers.functions.gmlvq.visualization.FeatureAnalysisPanel
 
is_1_visualization() - Method in class weka.classifiers.functions.GMLVQ
 
is_2_matrixLearning() - Method in class weka.classifiers.functions.GMLVQ
 
is_2_parallelExecution() - Method in class weka.classifiers.functions.GMLVQ
 
is_3_visualizeClassificationAccuracy() - Method in class weka.classifiers.functions.GMLVQ
 
is_3_visualizeDefaultCost() - Method in class weka.classifiers.functions.GMLVQ
 
is_3_visualizeFMeasure() - Method in class weka.classifiers.functions.GMLVQ
 
is_3_visualizePrecisionRecall() - Method in class weka.classifiers.functions.GMLVQ
 
is_3_visualizeWeightedAccuracy() - Method in class weka.classifiers.functions.GMLVQ
 
isMatrixLearning() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
isMatrixLearning() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
isParallelExecution() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
isParallelExecution() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
isRelevanceLearning(Matrix) - Static method in class weka.classifiers.functions.GMLVQ
 
isShowScale() - Method in class weka.classifiers.functions.gmlvq.visualization.MatrixRenderer
 
isVisualization() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
isVisualization() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
isVisualizingClassificationAccuracy() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
isVisualizingDefaultCost() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
isVisualizingFMeasure() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
isVisualizingPrecisionRecall() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
isVisualizingWeightedAccuracy() - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
itemStateChanged(ItemEvent) - Method in class weka.classifiers.functions.gmlvq.visualization.CostFunctionChartPanel
 

L

LambdaMatrixPanel - Class in weka.classifiers.functions.gmlvq.visualization
 
LambdaMatrixPanel(String[], ColorScale) - Constructor for class weka.classifiers.functions.gmlvq.visualization.LambdaMatrixPanel
 
LEARN_RATE_CHANGE_OPTION - Static variable in interface weka.classifiers.functions.GMLVQ.MethodSettings
 
learnRateChange(double) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
LinearAlgebraicCalculations - Class in weka.classifiers.functions.gmlvq.utilities
Collection of common calculation based on vectors and double arrays.
listOptions() - Method in class weka.classifiers.functions.GMLVQ
 
LOG_DATE_FORMAT - Static variable in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 
LOGGER - Static variable in class weka.classifiers.functions.gmlvq.core.GMLVQCore
 

M

MATRIX_LEARNING_OPTION - Static variable in interface weka.classifiers.functions.GMLVQ.MethodSettings
 
matrixLearning(boolean) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
MatrixRenderer - Class in weka.classifiers.functions.gmlvq.visualization
 
MatrixRenderer(LambdaMatrixPanel, ColorScale) - Constructor for class weka.classifiers.functions.gmlvq.visualization.MatrixRenderer
 
MAXIMAL_INDEX - Static variable in class weka.classifiers.functions.gmlvq.utilities.LinearAlgebraicCalculations
Index of the maximal value in the double array returned by LinearAlgebraicCalculations.getMinAndMaxValuesFromMatrix(Matrix)
maximalHue(float) - Method in class weka.classifiers.functions.gmlvq.visualization.ColorScale.Builder
 
maximalHue(Color) - Method in class weka.classifiers.functions.gmlvq.visualization.ColorScale.Builder
 
MINIMAL_INDEX - Static variable in class weka.classifiers.functions.gmlvq.utilities.LinearAlgebraicCalculations
Index of the minimal value in the double array returned by LinearAlgebraicCalculations.getMinAndMaxValuesFromMatrix(Matrix)
minimalHue(float) - Method in class weka.classifiers.functions.gmlvq.visualization.ColorScale.Builder
 
minimalHue(Color) - Method in class weka.classifiers.functions.gmlvq.visualization.ColorScale.Builder
 
mouseClicked(MouseEvent) - Method in class weka.classifiers.functions.gmlvq.visualization.VisualizerMouseAdapter
 
moveAll(DefaultListModel, DefaultListModel, String) - Method in class weka.classifiers.functions.gmlvq.visualization.FeatureAnalysisPanel
 
moveOne(DefaultListModel, DefaultListModel, String, String) - Method in class weka.classifiers.functions.gmlvq.visualization.FeatureAnalysisPanel
 
multiply(Vector, double) - Static method in class weka.classifiers.functions.gmlvq.utilities.LinearAlgebraicCalculations
multiplies a vector by a scalar
multiply(Vector, Matrix) - Static method in class weka.classifiers.functions.gmlvq.utilities.LinearAlgebraicCalculations
multiplies a vector with a matrix - is used to map a Vector to an EmbeddedSpaceVector

N

NEGATIVE_CLASS_LABEL - Static variable in class weka.classifiers.functions.gmlvq.core.cost.ConfusionMatrix
 
normalizeWithMaximalValue(List<Prototype>) - Static method in class weka.classifiers.functions.gmlvq.utilities.LinearAlgebraicCalculations
used to normalize the prototype update
normalizeWithMaximalValue(Matrix) - Static method in class weka.classifiers.functions.gmlvq.utilities.LinearAlgebraicCalculations
used to normalized the omega update
NUMBER_OF_EPOCHS_OPTION - Static variable in interface weka.classifiers.functions.GMLVQ.AlgorithmSettings
 
NUMBER_OF_PROTOTYPES_OPTION - Static variable in interface weka.classifiers.functions.GMLVQ.AlgorithmSettings
 
numberOfEpochs(int) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
numberOfPrototypesPerClass(int) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
NUMERIC_CUTOFF - Static variable in class weka.classifiers.functions.gmlvq.utilities.LinearAlgebraicCalculations
the numeric cutoff used to limit calculations

O

observe(Observer) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
Observer - Interface in weka.classifiers.functions.gmlvq.model
Enables implementing classes to be informed about data to visualized propagated by the UpdateManager.
OMEGA_DIMENSION_OPTION - Static variable in interface weka.classifiers.functions.GMLVQ.MethodSettings
 
OMEGA_LEARNING_RATE_OPTION - Static variable in interface weka.classifiers.functions.GMLVQ.MethodSettings
 
OMEGA_MATRIX_INITIALIZATION_AND_REGULARIZATION_NUMBER_OF_DATA_POINTS - Static variable in interface weka.classifiers.functions.gmlvq.core.GMLVQCore.DefaultSettings
number of data points used for initialization and regularization of the omega matrix
OMEGA_MATRIX_INITIALIZATION_MINIMAL_EXPECTED_VALUE - Static variable in interface weka.classifiers.functions.gmlvq.core.GMLVQCore.DefaultSettings
 
omegaDimension(int) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
omegaLearningRate(double) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
OmegaMatrix - Class in weka.classifiers.functions.gmlvq.model
The matrix describing the mapping rule which is used to project DataPoints and Prototypes to their respective EmbeddedSpaceVector.
This matrix is of dimension dataDimension x omegaDimension.
OmegaMatrix(double[][]) - Constructor for class weka.classifiers.functions.gmlvq.model.OmegaMatrix
 
OmegaMatrix(Matrix) - Constructor for class weka.classifiers.functions.gmlvq.model.OmegaMatrix
 

P

paintComponent(Graphics) - Method in class weka.classifiers.functions.gmlvq.visualization.LambdaMatrixPanel
 
PARALLEL_EXECUTION_OPTION - Static variable in interface weka.classifiers.functions.GMLVQ.MethodSettings
 
parallelExecution(boolean) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
partition(List<E>, int) - Static method in class weka.classifiers.functions.gmlvq.utilities.DataRandomizer
distributes a set of object evenly to subset of equal (+-1) size
performStochasticGradientDescent(List<DataPoint>, List<Prototype>, OmegaMatrix, double, double) - Method in class weka.classifiers.functions.gmlvq.core.GradientDescent
performs the stochastic gradient descent on the given data points and will result in a proposed update which will either be rejected or accepted and subsequently used in the next epoch
POSITIVE_CLASS_LABEL - Static variable in class weka.classifiers.functions.gmlvq.core.cost.ConfusionMatrix
 
ProposedUpdate - Class in weka.classifiers.functions.gmlvq.core
Each stochastic gradient descent composes a update which consists of updated prototypes and an updated omega matrix (which defines how data points and prototypes are mapped to the embedded space).
Most essential, this class provides the ProposedUpdate.incorporate(DataPoint) method which processes individual data points selected by the GradientDescent and utilizes their information to build the potential update.
ProposedUpdate(List<Prototype>, SigmoidFunction, OmegaMatrix, double, double, CostFunctionCalculator) - Constructor for class weka.classifiers.functions.gmlvq.core.ProposedUpdate
 
ProposedUpdate(List<Prototype>, SigmoidFunction, OmegaMatrix, double, double, List<ProposedUpdate>, CostFunctionCalculator) - Constructor for class weka.classifiers.functions.gmlvq.core.ProposedUpdate
 
Prototype - Class in weka.classifiers.functions.gmlvq.model
More or less only existing to increase readability.
Prototype(double[], double) - Constructor for class weka.classifiers.functions.gmlvq.model.Prototype
 
Prototype(Vector) - Constructor for class weka.classifiers.functions.gmlvq.model.Prototype
 
prototypeLearningRate(double) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
PROTOYPE_LEARNING_RATE_OPTION - Static variable in interface weka.classifiers.functions.GMLVQ.MethodSettings
 

R

redraw(Graphics2D) - Method in class weka.classifiers.functions.gmlvq.visualization.MatrixRenderer
 
requiresBeta() - Method in enum weka.classifiers.functions.gmlvq.core.cost.CostFunctionValue
 
requiresConfusionMatrix() - Method in enum weka.classifiers.functions.gmlvq.core.cost.CostFunctionValue
 
requiresWeightVector() - Method in enum weka.classifiers.functions.gmlvq.core.cost.CostFunctionValue
 
RunDetailsPanel - Class in weka.classifiers.functions.gmlvq.visualization
 
RunDetailsPanel(GMLVQCore) - Constructor for class weka.classifiers.functions.gmlvq.visualization.RunDetailsPanel
 

S

saturation(float) - Method in class weka.classifiers.functions.gmlvq.visualization.ColorScale.Builder
 
saveLambdaMatrixToSVG() - Method in class weka.classifiers.functions.gmlvq.visualization.Visualizer
 
scaledTranslate(Vector, double, Vector) - Static method in class weka.classifiers.functions.gmlvq.utilities.LinearAlgebraicCalculations
condensed addition of one vector with another vector multiplied by a scalar - used e.g.
seed(long) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
seedTipText() - Method in class weka.classifiers.functions.GMLVQ
 
set_1_costFunctionToOptimize(SelectedTag) - Method in class weka.classifiers.functions.GMLVQ
 
set_1_numberOfEpochs(int) - Method in class weka.classifiers.functions.GMLVQ
 
set_1_numberOfPrototypesPerClass(int) - Method in class weka.classifiers.functions.GMLVQ
 
set_1_visualization(boolean) - Method in class weka.classifiers.functions.GMLVQ
 
set_2_costFunctionBeta(double) - Method in class weka.classifiers.functions.GMLVQ
 
set_2_costFunctionWeights(String) - Method in class weka.classifiers.functions.GMLVQ
 
set_2_dataPointRatioPerRound(double) - Method in class weka.classifiers.functions.GMLVQ
 
set_2_matrixLearning(boolean) - Method in class weka.classifiers.functions.GMLVQ
 
set_2_omegaDimension(int) - Method in class weka.classifiers.functions.GMLVQ
 
set_2_omegaLearningRate(double) - Method in class weka.classifiers.functions.GMLVQ
 
set_2_ParallelExecution(boolean) - Method in class weka.classifiers.functions.GMLVQ
 
set_2_prototypeLearningRate(double) - Method in class weka.classifiers.functions.GMLVQ
 
set_2_sigmoidSigmaInterval(String) - Method in class weka.classifiers.functions.GMLVQ
 
set_3_visualizeClassificationAccuracy(boolean) - Method in class weka.classifiers.functions.GMLVQ
 
set_3_visualizeDefaultCost(boolean) - Method in class weka.classifiers.functions.GMLVQ
 
set_3_visualizeFMeasure(boolean) - Method in class weka.classifiers.functions.GMLVQ
 
set_3_visualizePrecisionRecall(boolean) - Method in class weka.classifiers.functions.GMLVQ
 
set_3_visualizeWeightedAccuracy(boolean) - Method in class weka.classifiers.functions.GMLVQ
 
setClassLabel(double) - Method in class weka.classifiers.functions.gmlvq.model.Vector
 
setCostFunctionToOptimize(CostFunctionValue) - Method in class weka.classifiers.functions.GMLVQ
 
setDistanceOtherClass(double) - Method in class weka.classifiers.functions.gmlvq.model.WinningInformation
 
setDistanceSameClass(double) - Method in class weka.classifiers.functions.gmlvq.model.WinningInformation
 
setElementSize(int) - Method in class weka.classifiers.functions.gmlvq.visualization.LambdaMatrixPanel
 
setHorizontalMargin(int) - Method in class weka.classifiers.functions.gmlvq.visualization.LambdaMatrixPanel
 
setIndexWinnerOtherClass(int) - Method in class weka.classifiers.functions.gmlvq.model.WinningInformation
 
setIndexWinnerSameClass(int) - Method in class weka.classifiers.functions.gmlvq.model.WinningInformation
 
setLambdaMatrix(Matrix) - Method in class weka.classifiers.functions.gmlvq.visualization.FeatureImpactPanel
 
setLambdaMatrix(Matrix) - Method in class weka.classifiers.functions.gmlvq.visualization.LambdaMatrixPanel
 
setLambdaMatrix(Matrix) - Method in class weka.classifiers.functions.gmlvq.visualization.MatrixRenderer
 
setListModelShowingPrototypes(DefaultListModel) - Method in class weka.classifiers.functions.gmlvq.visualization.FeatureAnalysisPanel
 
setListShowingPrototypes(JList) - Method in class weka.classifiers.functions.gmlvq.visualization.FeatureAnalysisPanel
 
setOptions(String[]) - Method in class weka.classifiers.functions.GMLVQ
 
setPrototypes(List<Prototype>) - Method in class weka.classifiers.functions.gmlvq.visualization.FeatureAnalysisPanel
 
setRenderer(MatrixRenderer) - Method in class weka.classifiers.functions.gmlvq.visualization.LambdaMatrixPanel
 
setShowScale(boolean) - Method in class weka.classifiers.functions.gmlvq.visualization.MatrixRenderer
 
setTreePrototypes(JTree) - Method in class weka.classifiers.functions.gmlvq.visualization.FeatureAnalysisPanel
 
setValues(double[]) - Method in class weka.classifiers.functions.gmlvq.model.DataPoint
 
setValues(double[]) - Method in class weka.classifiers.functions.gmlvq.model.Prototype
 
setValues(Vector) - Method in class weka.classifiers.functions.gmlvq.model.Vector
 
setValues(double[]) - Method in class weka.classifiers.functions.gmlvq.model.Vector
 
setVerticalMargin(int) - Method in class weka.classifiers.functions.gmlvq.visualization.LambdaMatrixPanel
 
setWinnerOtherClass(Prototype) - Method in class weka.classifiers.functions.gmlvq.model.WinningInformation
 
setWinnerSameClass(Prototype) - Method in class weka.classifiers.functions.gmlvq.model.WinningInformation
 
showAttribute(String) - Method in class weka.classifiers.functions.gmlvq.visualization.FeatureAnalysisPanel
 
showClass(String) - Method in class weka.classifiers.functions.gmlvq.visualization.FeatureAnalysisPanel
 
showProtoype(DefaultMutableTreeNode) - Method in class weka.classifiers.functions.gmlvq.visualization.FeatureAnalysisPanel
 
showVisualizations() - Static method in class weka.classifiers.functions.gmlvq.visualization.VisualizationSingleton
 
SIGMOID_SIGMA_INTERVAL_OPTION - Static variable in interface weka.classifiers.functions.GMLVQ.AlgorithmSettings
 
SigmoidFunction - Class in weka.classifiers.functions.gmlvq.core
GMLVQ utilizes a sigmoid/Heaviside function to scale e.g.
SigmoidFunction(double, double, int) - Constructor for class weka.classifiers.functions.gmlvq.core.SigmoidFunction
 
sigmoidSigmaInterval(String) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
sigmoidSigmaIntervalEnd(double) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
sigmoidSigmaIntervalStart(double) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
STOP_CRITERION_OPTION - Static variable in interface weka.classifiers.functions.GMLVQ.AlgorithmSettings
 
stopCriterion(double) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
substract(Vector, Vector) - Static method in class weka.classifiers.functions.gmlvq.utilities.LinearAlgebraicCalculations
 
switchScale() - Method in class weka.classifiers.functions.gmlvq.visualization.Visualizer
 

T

toString() - Method in class weka.classifiers.functions.gmlvq.core.cost.ConfusionMatrix
 
toString() - Method in class weka.classifiers.functions.gmlvq.model.DataPoint
 
toString() - Method in class weka.classifiers.functions.gmlvq.model.EmbeddedSpaceVector
 
toString() - Method in class weka.classifiers.functions.gmlvq.model.OmegaMatrix
 
toString() - Method in class weka.classifiers.functions.gmlvq.model.Prototype
 
toString() - Method in class weka.classifiers.functions.gmlvq.model.Vector
 
toString() - Method in class weka.classifiers.functions.gmlvq.model.WinningInformation
 

U

update(DataPoint) - Method in class weka.classifiers.functions.gmlvq.core.cost.CostFunctionCalculator
 
update(ProposedUpdate) - Method in class weka.classifiers.functions.gmlvq.core.UpdateManager
 
updateCostFunctions(Map<CostFunctionValue, Double>) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQDefaultObserver
 
updateCostFunctions(Map<CostFunctionValue, Double>) - Method in interface weka.classifiers.functions.gmlvq.model.Observer
hand over the current cost values to visualize
updateCostFunctions(Map<CostFunctionValue, Double>) - Method in class weka.classifiers.functions.GMLVQ
 
updateCostFunctions(Map<CostFunctionValue, Double>) - Method in class weka.classifiers.functions.gmlvq.visualization.Visualizer
 
updateLambdaMatrix(Matrix) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQDefaultObserver
 
updateLambdaMatrix(Matrix) - Method in interface weka.classifiers.functions.gmlvq.model.Observer
hand over the new lambda matrix to visualize
updateLambdaMatrix(Matrix) - Method in class weka.classifiers.functions.GMLVQ
 
updateLambdaMatrix(Matrix) - Method in class weka.classifiers.functions.gmlvq.visualization.Visualizer
 
UpdateManager - Class in weka.classifiers.functions.gmlvq.core
The instance directing the learning process.
UpdateManager(GMLVQCore, CostFunctionCalculator, Observer) - Constructor for class weka.classifiers.functions.gmlvq.core.UpdateManager
 
updatePrototypes(List<Prototype>) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQDefaultObserver
 
updatePrototypes(List<Prototype>) - Method in interface weka.classifiers.functions.gmlvq.model.Observer
hand over the new prototypes to visualize
updatePrototypes(List<Prototype>) - Method in class weka.classifiers.functions.GMLVQ
 
updatePrototypes(List<Prototype>) - Method in class weka.classifiers.functions.gmlvq.visualization.Visualizer
 

V

valueOf(String) - Static method in enum weka.classifiers.functions.gmlvq.core.cost.CostFunctionValue
Returns the enum constant of this type with the specified name.
values() - Static method in enum weka.classifiers.functions.gmlvq.core.cost.CostFunctionValue
Returns an array containing the constants of this enum type, in the order they are declared.
Vector - Class in weka.classifiers.functions.gmlvq.model
GMLVQ's internal data structure.
Vector(double[], double) - Constructor for class weka.classifiers.functions.gmlvq.model.Vector
 
visualization - Variable in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
visualization(boolean) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
VISUALIZATION_OPTION - Static variable in interface weka.classifiers.functions.GMLVQ.AlgorithmSettings
 
VisualizationSingleton - Class in weka.classifiers.functions.gmlvq.visualization
 
VISUALIZE_CLASSIFICATION_ACCURACY - Static variable in interface weka.classifiers.functions.GMLVQ.CostFunctionsSettings
additional classification functions
VISUALIZE_DEFAULT_COST - Static variable in interface weka.classifiers.functions.GMLVQ.CostFunctionsSettings
 
VISUALIZE_FMEASURE - Static variable in interface weka.classifiers.functions.GMLVQ.CostFunctionsSettings
 
VISUALIZE_PRECISION_RECALL - Static variable in interface weka.classifiers.functions.GMLVQ.CostFunctionsSettings
 
VISUALIZE_WEIGHTED_ACCURACY - Static variable in interface weka.classifiers.functions.GMLVQ.CostFunctionsSettings
 
visualizeClassificationAccuracy(boolean) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
visualizeDefaultCost(boolean) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
visualizeFMeasure(boolean) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
visualizePrecisionRecall(boolean) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 
Visualizer - Class in weka.classifiers.functions.gmlvq.visualization
 
Visualizer(GMLVQCore, List<DataPoint>, Map<Double, String>, String[], int, Map<CostFunctionValue, Double>) - Constructor for class weka.classifiers.functions.gmlvq.visualization.Visualizer
 
VisualizerMouseAdapter - Class in weka.classifiers.functions.gmlvq.visualization
 
VisualizerMouseAdapter(Visualizer) - Constructor for class weka.classifiers.functions.gmlvq.visualization.VisualizerMouseAdapter
 
visualizeWeightedAccuracy(boolean) - Method in class weka.classifiers.functions.gmlvq.core.GMLVQCore.Builder
 

W

weka.classifiers.functions - package weka.classifiers.functions
 
weka.classifiers.functions.gmlvq.core - package weka.classifiers.functions.gmlvq.core
 
weka.classifiers.functions.gmlvq.core.cost - package weka.classifiers.functions.gmlvq.core.cost
 
weka.classifiers.functions.gmlvq.model - package weka.classifiers.functions.gmlvq.model
 
weka.classifiers.functions.gmlvq.utilities - package weka.classifiers.functions.gmlvq.utilities
 
weka.classifiers.functions.gmlvq.visualization - package weka.classifiers.functions.gmlvq.visualization
 
WekaModelConverter - Class in weka.classifiers.functions.gmlvq.model
Converts the internal data structure to WEKA format and vice versa.
WinningInformation - Class in weka.classifiers.functions.gmlvq.model
Contains information about the closest prototypes regarding to the parent EmbeddedSpaceVector.
WinningInformation() - Constructor for class weka.classifiers.functions.gmlvq.model.WinningInformation
 

_

_1_costFunctionToOptimizeTipText() - Method in class weka.classifiers.functions.GMLVQ
 
_1_numberOfEpochsTipText() - Method in class weka.classifiers.functions.GMLVQ
 
_1_numberOfPrototypesPerClassTipText() - Method in class weka.classifiers.functions.GMLVQ
 
_1_visualizationTipText() - Method in class weka.classifiers.functions.GMLVQ
 
_2_costFunctionBetaTipText() - Method in class weka.classifiers.functions.GMLVQ
 
_2_costFunctionWeightsTipText() - Method in class weka.classifiers.functions.GMLVQ
 
_2_dataPointRatioPerRoundTipText() - Method in class weka.classifiers.functions.GMLVQ
 
_2_matrixLearningTipText() - Method in class weka.classifiers.functions.GMLVQ
 
_2_omegaDimensionTipText() - Method in class weka.classifiers.functions.GMLVQ
 
_2_omegaLearningRateTipText() - Method in class weka.classifiers.functions.GMLVQ
 
_2_parallelExecutionTipText() - Method in class weka.classifiers.functions.GMLVQ
 
_2_prototypeLearningRateTipText() - Method in class weka.classifiers.functions.GMLVQ
 
_2_sigmoidSigmaIntervalTipText() - Method in class weka.classifiers.functions.GMLVQ
 
_3_visualizeClassificationAccuracyTipText() - Method in class weka.classifiers.functions.GMLVQ
 
_3_visualizeDefaultCostTipText() - Method in class weka.classifiers.functions.GMLVQ
 
_3_visualizeFMeasureTipText() - Method in class weka.classifiers.functions.GMLVQ
 
_3_visualizePrecisionRecallTipText() - Method in class weka.classifiers.functions.GMLVQ
 
_3_visualizeWeightedAccuracyTipText() - Method in class weka.classifiers.functions.GMLVQ
 
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