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U
- UnassignedClassException exception weka.core.UnassignedClassException.
UnassignedClassException
is used when
a method requires access to the Attribute designated as
the class attribute in a set of Instances, but the Instances does not
have any class attribute assigned (such as by setClassIndex()).- UnassignedClassException().
Constructor for class weka.core.UnassignedClassException
- Creates a new
UnassignedClassException
instance
with no detail message.
- UnassignedClassException(String).
Constructor for class weka.core.UnassignedClassException
- Creates a new
UnassignedClassException
instance
with a specified message.
- UnassignedDatasetException exception weka.core.UnassignedDatasetException.
UnassignedDatasetException
is used when
a method of an Instance is called that requires access to
the Instance structure, but that the Instance does not contain
a reference to any Instances (as set by Instance.setDataset(), or when
an Instance is added to a set of Instances)).- UnassignedDatasetException().
Constructor for class weka.core.UnassignedDatasetException
- Creates a new
UnassignedDatasetException
instance
with no detail message.
- UnassignedDatasetException(String).
Constructor for class weka.core.UnassignedDatasetException
- Creates a new
UnassignedDatasetException
instance
with a specified message.
- unclassified().
Method in class weka.classifiers.Evaluation
- Gets the number of instances not classified (that is, for
which no prediction was made by the classifier).
- UNCONNECTED.
Static variable in class weka.classifiers.neural.NeuralConnection
- This unit is not connected to any others.
- uniqueCount.
Variable in class weka.core.AttributeStats
- The number of values that only appear once
- UnsupervisedAttributeEvaluator class weka.attributeSelection.UnsupervisedAttributeEvaluator.
- Abstract unsupervised attribute evaluator.
- UnsupervisedAttributeEvaluator().
Constructor for class weka.attributeSelection.UnsupervisedAttributeEvaluator
-
- UnsupervisedSubsetEvaluator class weka.attributeSelection.UnsupervisedSubsetEvaluator.
- Abstract unsupervised attribute subset evaluator.
- UnsupervisedSubsetEvaluator().
Constructor for class weka.attributeSelection.UnsupervisedSubsetEvaluator
-
- UnsupportedAttributeTypeException exception weka.core.UnsupportedAttributeTypeException.
UnsupportedAttributeTypeException
is used in situations
where the throwing object is not able to accept Instances with the
supplied structure, because one or more of the Attributes in the
Instances are of the wrong type.- UnsupportedAttributeTypeException().
Constructor for class weka.core.UnsupportedAttributeTypeException
- Creates a new
UnsupportedAttributeTypeException
instance
with no detail message.
- UnsupportedAttributeTypeException(String).
Constructor for class weka.core.UnsupportedAttributeTypeException
- Creates a new
UnsupportedAttributeTypeException
instance
with a specified message.
- UnsupportedClassTypeException exception weka.core.UnsupportedClassTypeException.
UnsupportedClassTypeException
is used in situations
where the throwing object is not able to accept Instances with the
supplied structure, because the class Attribute is of the wrong type.- UnsupportedClassTypeException().
Constructor for class weka.core.UnsupportedClassTypeException
- Creates a new
UnsupportedClassTypeException
instance
with no detail message.
- UnsupportedClassTypeException(String).
Constructor for class weka.core.UnsupportedClassTypeException
- Creates a new
UnsupportedClassTypeException
instance
with a specified message.
- UpdateableClassifier interface weka.classifiers.UpdateableClassifier.
- Interface to incremental classification models that can learn using
one instance at a time.
- updateChildPropertySheet().
Method in class weka.gui.GenericObjectEditor.GOEPanel
- Updates the child property sheet, and creates if needed
- updateClassifier(Instance).
Method in interface weka.classifiers.UpdateableClassifier
- Updates a classifier using the given instance.
- updateClassifier(Instance).
Method in class weka.classifiers.IBk
- Adds the supplied instance to the training set
- updateClassifier(Instance).
Method in class weka.classifiers.IB1
- Updates the classifier.
- updateClassifier(Instance).
Method in class weka.classifiers.HyperPipes
- Updates the classifier.
- updateClassifier(Instance).
Method in class weka.classifiers.NaiveBayes
- Updates the classifier with the given instance.
- updateClassifier(Instance).
Method in class weka.classifiers.LWR
- Adds the supplied instance to the training set
- updateClassifier(Instance).
Method in class weka.classifiers.kstar.KStar
- Adds the supplied instance to the training set
- upDateCounter(Instance).
Method in class weka.associations.ItemSet
- Updates counter of item set with respect to given transaction.
- upDateCounters(FastVector, Instances).
Static method in class weka.associations.ItemSet
- Updates counters for a set of item sets and a set of instances.
- updatePriors(Instance).
Method in class weka.classifiers.Evaluation
- Updates the class prior probabilities (when incrementally
training)
- updateResult(String).
Method in class weka.gui.ResultHistoryPanel
- Tells any component currently displaying the named result that the
contents of the result text in the StringBuffer have been updated.
- updateWeights(double, double).
Method in class weka.classifiers.neural.NeuralConnection
- Call this function to update the weight values at this unit.
- updateWeights(double, double).
Method in class weka.classifiers.neural.NeuralNode
- Call this function to update the weight values at this unit.
- updateWeights(NeuralNode, double, double).
Method in class weka.classifiers.neural.LinearUnit
- This function will calculate what the change in weights should be
and also update them.
- updateWeights(NeuralNode, double, double).
Method in class weka.classifiers.neural.SigmoidUnit
- This function will calculate what the change in weights should be
and also update them.
- updateWeights(NeuralNode, double, double).
Method in interface weka.classifiers.neural.NeuralMethod
- This function will calculate what the change in weights should be
and also update them.
- upperBoundMinSupportTipText().
Method in class weka.associations.Apriori
- Returns the tip text for this property
- upperSizeTipText().
Method in class weka.experiment.LearningRateResultProducer
- Returns the tip text for this property
- useBetterEncodingTipText().
Method in class weka.filters.DiscretizeFilter
- Returns the tip text for this property
- useFilter(Instances, Filter).
Static method in class weka.filters.Filter
- Filters an entire set of instances through a filter and returns
the new set.
- useKononenkoTipText().
Method in class weka.filters.DiscretizeFilter
- Returns the tip text for this property
- useMDLTipText().
Method in class weka.filters.DiscretizeFilter
- Returns the tip text for this property
- UserClassifier class weka.classifiers.UserClassifier.
- Class for generating an user defined decision tree.
- UserClassifier().
Constructor for class weka.classifiers.UserClassifier
- Constructor
- userCommand(TreeDisplayEvent).
Method in class weka.classifiers.UserClassifier
- Receives user choices from the tree view, and then deals with these
choices.
- userCommand(TreeDisplayEvent).
Method in interface weka.gui.treevisualizer.TreeDisplayListener
- Gets called when the user selects something, in the tree display.
- userDataEvent(VisualizePanelEvent).
Method in class weka.classifiers.UserClassifier
- This receives shapes from the data view.
- userDataEvent(VisualizePanelEvent).
Method in interface weka.gui.visualize.VisualizePanelListener
- This method receives an object containing the shapes, instances
inside and outside these shapes and the attributes these shapes were
created in.
- useTrainingTipText().
Method in class weka.attributeSelection.ClassifierSubsetEval
- Returns the tip text for this property
- Utils class weka.core.Utils.
- Class implementing some simple utility methods.
- Utils().
Constructor for class weka.core.Utils
-
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All Packages Class Hierarchy