- 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