All Packages Class Hierarchy
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D
- DatabaseResultListener class weka.experiment.DatabaseResultListener.
- DatabaseResultListener takes the results from a ResultProducer
and submits them to a central database.
- DatabaseResultListener().
Constructor for class weka.experiment.DatabaseResultListener
- Sets up the database drivers
- DatabaseResultProducer class weka.experiment.DatabaseResultProducer.
- DatabaseResultProducer examines a database and extracts out
the results produced by the specified ResultProducer
and submits them to the specified ResultListener.
- DatabaseResultProducer().
Constructor for class weka.experiment.DatabaseResultProducer
- Creates the DatabaseResultProducer, letting the parent constructor do
it's thing.
- databaseURLTipText().
Method in class weka.experiment.DatabaseUtils
- Returns the tip text for this property
- DatabaseUtils class weka.experiment.DatabaseUtils.
- DatabaseUtils provides utility functions for accessing the experiment
database.
- DatabaseUtils().
Constructor for class weka.experiment.DatabaseUtils
- Sets up the database drivers
- DATASET_FIELD_NAME.
Static variable in class weka.experiment.CrossValidationResultProducer
-
- DATASET_FIELD_NAME.
Static variable in class weka.experiment.RandomSplitResultProducer
-
- dataset().
Method in class weka.core.Instance
- Returns the dataset this instance has access to.
- DatasetListPanel class weka.gui.experiment.DatasetListPanel.
- This panel controls setting a list of datasets for an experiment to
iterate over.
- DatasetListPanel().
Constructor for class weka.gui.experiment.DatasetListPanel
- Create the dataset list panel initially disabled.
- DatasetListPanel(Experiment).
Constructor for class weka.gui.experiment.DatasetListPanel
- Creates the dataset list panel with the given experiment.
- DDConditionalEstimator class weka.estimators.DDConditionalEstimator.
- Conditional probability estimator for a discrete domain conditional upon
a discrete domain.
- DDConditionalEstimator(int, int, boolean).
Constructor for class weka.estimators.DDConditionalEstimator
- Constructor
- debugTipText().
Method in class weka.attributeSelection.RaceSearch
- Returns the tip text for this property
- debugTipText().
Method in class weka.classifiers.AdditiveRegression
- Returns the tip text for this property
- debugTipText().
Method in class weka.filters.AttributeExpressionFilter
- Returns the tip text for this property
- decayTipText().
Method in class weka.classifiers.neural.NeuralNetwork
-
- DecisionStump class weka.classifiers.DecisionStump.
- Class for building and using a decision stump.
- DecisionStump().
Constructor for class weka.classifiers.DecisionStump
-
- DecisionTable class weka.classifiers.DecisionTable.
- Class for building and using a simple decision table majority classifier.
- DecisionTable.hashKey class weka.classifiers.DecisionTable.hashKey.
- Class providing keys to the hash table
- DecisionTable.hashKey(DecisionTable, double[]).
Constructor for class weka.classifiers.DecisionTable.hashKey
- Constructor for a hashKey
- DecisionTable.hashKey(DecisionTable, Instance, int).
Constructor for class weka.classifiers.DecisionTable.hashKey
- Constructor for a hashKey
- DecisionTable.Link class weka.classifiers.DecisionTable.Link.
- Class for a node in a linked list.
- DecisionTable.Link(DecisionTable, BitSet, double).
Constructor for class weka.classifiers.DecisionTable.Link
- The constructor.
- DecisionTable.LinkedList class weka.classifiers.DecisionTable.LinkedList.
- Class for handling a linked list.
- DecisionTable.LinkedList(DecisionTable).
Constructor for class weka.classifiers.DecisionTable.LinkedList
-
- DecisionTable().
Constructor for class weka.classifiers.DecisionTable
- Constructor for a DecisionTable
- decompose().
Method in class weka.classifiers.BVDecompose
- Carry out the bias-variance decomposition
- DEFAULT_SHAPE_SIZE.
Static variable in class weka.gui.visualize.Plot2D
-
- del(int, Instance).
Method in class weka.classifiers.j48.Distribution
- Deletes given instance from given bag.
- delete().
Method in class weka.core.Instances
- Removes all instances from the set.
- delete(int).
Method in class weka.core.Instances
- Removes an instance at the given position from the set.
- deleteAttributeAt(int).
Method in class weka.core.Instances
- Deletes an attribute at the given position
(0 to numAttributes() - 1).
- deleteAttributeAt(int).
Method in class weka.core.Instance
- Deletes an attribute at the given position (0 to
numAttributes() - 1).
- deleteItemSets(FastVector, int, int).
Static method in class weka.associations.ItemSet
- Deletes all item sets that don't have minimum support.
- deleteStringAttributes().
Method in class weka.core.Instances
- Deletes all string attributes in the dataset.
- deleteTrailingZerosAndDot(StringBuffer).
Static method in class weka.classifiers.m5.M5Utils
- Deletes the trailing zeros and decimal point in a stringBuffer
- deleteWithMissing(Attribute).
Method in class weka.core.Instances
- Removes all instances with missing values for a particular
attribute from the dataset.
- deleteWithMissing(int).
Method in class weka.core.Instances
- Removes all instances with missing values for a particular
attribute from the dataset.
- deleteWithMissingClass().
Method in class weka.core.Instances
- Removes all instances with a missing class value
from the dataset.
- delRange(int, Instances, int, int).
Method in class weka.classifiers.j48.Distribution
- Deletes all instances in given range from given bag.
- deltaTipText().
Method in class weka.associations.Apriori
- Returns the tip text for this property
- densityForInstance(Instance).
Method in class weka.clusterers.DistributionClusterer
- Computes the density for a given instance.
- densityForInstance(Instance).
Method in class weka.clusterers.EM
- Computes the density for a given instance.
- densityForInstance(Instance).
Method in class weka.clusterers.DistributionMetaClusterer
- Returns the density for an instance.
- description().
Method in class weka.core.Option
- Returns the option's description.
- designatedClassTipText().
Method in class weka.classifiers.ThresholdSelector
-
- determineBounds().
Method in class weka.gui.visualize.Plot2D
- Determine the min and max values for axis and colouring attributes
- determineColumnConstraints(ResultProducer).
Method in class weka.experiment.LearningRateResultProducer
- Determines if there are any constraints (imposed by the
destination) on the result columns to be produced by
resultProducers.
- determineColumnConstraints(ResultProducer).
Method in class weka.experiment.DatabaseResultListener
- Determines if there are any constraints (imposed by the
destination) on any additional measures produced by
resultProducers.
- determineColumnConstraints(ResultProducer).
Method in class weka.experiment.CSVResultListener
- Determines if there are any constraints (imposed by the
destination) on the result columns to be produced by
resultProducers.
- determineColumnConstraints(ResultProducer).
Method in interface weka.experiment.ResultListener
- Determines if there are any constraints (imposed by the
destination) on additional result columns to be produced by
resultProducers.
- determineColumnConstraints(ResultProducer).
Method in class weka.experiment.AveragingResultProducer
- Determines if there are any constraints (imposed by the
destination) on the result columns to be produced by
resultProducers.
- DIAMOND_SHAPE.
Static variable in class weka.gui.visualize.Plot2D
-
- differencesProbability.
Variable in class weka.experiment.PairedStats
- The probability of obtaining the observed differences
- differencesSignificance.
Variable in class weka.experiment.PairedStats
- A significance indicator:
0 if the differences are not significant
> 0 if x significantly greater than y
< 0 if x significantly less than y
- differencesStats.
Variable in class weka.experiment.PairedStats
- The stats associated with the paired differences
- directionTipText().
Method in class weka.attributeSelection.BestFirst
- Returns the tip text for this property
- disconnect(NeuralConnection, NeuralConnection).
Static method in class weka.classifiers.neural.NeuralConnection
- Disconnects two units.
- disconnectFromDatabase().
Method in class weka.experiment.DatabaseUtils
- Closes the connection to the database.
- DiscreteEstimator class weka.estimators.DiscreteEstimator.
- Simple symbolic probability estimator based on symbol counts.
- DiscreteEstimator(int, boolean).
Constructor for class weka.estimators.DiscreteEstimator
- Constructor
- DiscretizeFilter class weka.filters.DiscretizeFilter.
- An instance filter that discretizes a range of numeric attributes in
the dataset into nominal attributes.
- DiscretizeFilter().
Constructor for class weka.filters.DiscretizeFilter
- Constructor - initialises the filter
- distinctCount.
Variable in class weka.core.AttributeStats
- The number of distinct values
- distributedExperimentSelected().
Method in class weka.gui.experiment.DistributeExperimentPanel
- Returns true if the distribute experiment checkbox is selected
- DistributeExperimentPanel class weka.gui.experiment.DistributeExperimentPanel.
- This panel enables an experiment to be distributed to multiple hosts;
it also allows remote host names to be specified.
- DistributeExperimentPanel().
Constructor for class weka.gui.experiment.DistributeExperimentPanel
- Constructor
- DistributeExperimentPanel(Experiment).
Constructor for class weka.gui.experiment.DistributeExperimentPanel
- Creates the panel with the supplied initial experiment.
- Distribution class weka.classifiers.j48.Distribution.
- Class for handling a distribution of class values.
- distribution().
Method in class weka.classifiers.evaluation.NominalPrediction
- Gets the predicted probabilities
- distribution().
Method in class weka.classifiers.j48.ClassifierSplitModel
- Returns the distribution of class values induced by the model.
- Distribution(Distribution).
Constructor for class weka.classifiers.j48.Distribution
- Creates distribution with only one bag by merging all
bags of given distribution.
- Distribution(Distribution, int).
Constructor for class weka.classifiers.j48.Distribution
- Creates distribution with two bags by merging all bags apart of
the indicated one.
- Distribution(double[][]).
Constructor for class weka.classifiers.j48.Distribution
- Creates and initializes a new distribution using the given
array.
- Distribution(Instances).
Constructor for class weka.classifiers.j48.Distribution
- Creates a distribution with only one bag according
to instances in source.
- Distribution(Instances, ClassifierSplitModel).
Constructor for class weka.classifiers.j48.Distribution
- Creates a distribution according to given instances and
split model.
- Distribution(int, int).
Constructor for class weka.classifiers.j48.Distribution
- Creates and initializes a new distribution.
- DistributionClassifier class weka.classifiers.DistributionClassifier.
- Abstract classification model that produces (for each test instance)
an estimate of the membership in each class
(ie.
- DistributionClassifier().
Constructor for class weka.classifiers.DistributionClassifier
-
- distributionClassifierTipText().
Method in class weka.classifiers.ThresholdSelector
-
- distributionClassifierTipText().
Method in class weka.classifiers.MultiClassClassifier
-
- DistributionClusterer class weka.clusterers.DistributionClusterer.
- Abstract clustering model that produces (for each test instance)
an estimate of the membership in each cluster
(ie.
- DistributionClusterer().
Constructor for class weka.clusterers.DistributionClusterer
-
- distributionForInstance(Instance).
Method in class weka.classifiers.DistributionClassifier
- Predicts the class memberships for a given instance.
- distributionForInstance(Instance).
Method in class weka.classifiers.DecisionTable
- Calculates the class membership probabilities for the given
test instance.
- distributionForInstance(Instance).
Method in class weka.classifiers.DecisionStump
- Calculates the class membership probabilities for the given test instance.
- distributionForInstance(Instance).
Method in class weka.classifiers.AdaBoostM1
- Calculates the class membership probabilities for the given test instance.
- distributionForInstance(Instance).
Method in class weka.classifiers.ClassificationViaRegression
- Returns the distribution for an instance.
- distributionForInstance(Instance).
Method in class weka.classifiers.AttributeSelectedClassifier
- Classifies a given instance after attribute selection
- distributionForInstance(Instance).
Method in class weka.classifiers.Bagging
- Calculates the class membership probabilities for the given test instance.
- distributionForInstance(Instance).
Method in class weka.classifiers.ThresholdSelector
- Calculates the class membership probabilities for the given test instance.
- distributionForInstance(Instance).
Method in class weka.classifiers.KernelDensity
- Calculates the class membership probabilities for the given test instance.
- distributionForInstance(Instance).
Method in class weka.classifiers.IBk
- Calculates the class membership probabilities for the given test instance.
- distributionForInstance(Instance).
Method in class weka.classifiers.ZeroR
- Calculates the class membership probabilities for the given test instance.
- distributionForInstance(Instance).
Method in class weka.classifiers.LogitBoost
- Calculates the class membership probabilities for the given test instance.
- distributionForInstance(Instance).
Method in class weka.classifiers.HyperPipes
- Classifies the given test instance.
- distributionForInstance(Instance).
Method in class weka.classifiers.Id3
- Computes class distribution for instance using decision tree.
- distributionForInstance(Instance).
Method in class weka.classifiers.MultiClassClassifier
- Returns the distribution for an instance.
- distributionForInstance(Instance).
Method in class weka.classifiers.UserClassifier
- Call this function to get a double array filled with the probability
of how likely each class type is the class of the instance.
- distributionForInstance(Instance).
Method in class weka.classifiers.NaiveBayes
- Calculates the class membership probabilities for the given test
instance.
- distributionForInstance(Instance).
Method in class weka.classifiers.SMO
- Outputs the distribution for the given output.
- distributionForInstance(Instance).
Method in class weka.classifiers.Logistic
- Computes the distribution for a given instance
- distributionForInstance(Instance).
Method in class weka.classifiers.VotedPerceptron
- Outputs the distribution for the given output.
- distributionForInstance(Instance).
Method in class weka.classifiers.NaiveBayesSimple
- Calculates the class membership probabilities for the given test instance.
- distributionForInstance(Instance).
Method in class weka.classifiers.DistributionMetaClassifier
- Returns the distribution for an instance.
- distributionForInstance(Instance).
Method in class weka.classifiers.VFI
- Classifies the given test instance.
- distributionForInstance(Instance).
Method in class weka.classifiers.FilteredClassifier
- Classifies a given instance after filtering.
- distributionForInstance(Instance).
Method in class weka.classifiers.adtree.ADTree
- Returns the class probability distribution for an instance.
- distributionForInstance(Instance).
Method in class weka.classifiers.j48.J48
- Returns class probabilities for an instance.
- distributionForInstance(Instance).
Method in class weka.classifiers.j48.MakeDecList
- Returns the class distribution for an instance.
- distributionForInstance(Instance).
Method in class weka.classifiers.j48.ClassifierDecList
- Returns class probabilities for a weighted instance.
- distributionForInstance(Instance).
Method in class weka.classifiers.j48.PART
- Returns class probabilities for an instance.
- distributionForInstance(Instance).
Method in class weka.classifiers.kstar.KStar
- Calculates the class membership probabilities for the given test instance.
- distributionForInstance(Instance).
Method in class weka.classifiers.neural.NeuralNetwork
- Call this function to predict the class of an instance once a
classification model has been built with the buildClassifier call.
- distributionForInstance(Instance).
Method in class weka.clusterers.DistributionClusterer
- Predicts the cluster memberships for a given instance.
- distributionForInstance(Instance).
Method in class weka.clusterers.EM
- Predicts the cluster memberships for a given instance.
- distributionForInstance(Instance).
Method in class weka.clusterers.DistributionMetaClusterer
- Returns the distribution for an instance.
- distributionForInstance(Instance, boolean).
Method in class weka.classifiers.j48.ClassifierTree
- Returns class probabilities for a weighted instance.
- DistributionMetaClassifier class weka.classifiers.DistributionMetaClassifier.
- Class that wraps up a Classifier and presents it as a DistributionClassifier
for ease of programmatically handling Classifiers in general -- only the
one predict method (distributionForInstance) need be worried about.
- DistributionMetaClassifier().
Constructor for class weka.classifiers.DistributionMetaClassifier
- Default constructor
- DistributionMetaClassifier(Classifier).
Constructor for class weka.classifiers.DistributionMetaClassifier
- Creates a new
DistributionMetaClassifier
instance,
specifying the Classifier to wrap around.
- DistributionMetaClusterer class weka.clusterers.DistributionMetaClusterer.
- Class that wraps up a Clusterer and presents it as a DistributionClusterer
for ease of programmatically handling Clusterers in general -- only the
one predict method (distributionForInstance) need be worried about.
- DistributionMetaClusterer().
Constructor for class weka.clusterers.DistributionMetaClusterer
-
- DKConditionalEstimator class weka.estimators.DKConditionalEstimator.
- Conditional probability estimator for a discrete domain conditional upon
a numeric domain.
- DKConditionalEstimator(int, double).
Constructor for class weka.estimators.DKConditionalEstimator
- Constructor
- DNConditionalEstimator class weka.estimators.DNConditionalEstimator.
- Conditional probability estimator for a discrete domain conditional upon
a numeric domain.
- DNConditionalEstimator(int, double).
Constructor for class weka.estimators.DNConditionalEstimator
- Constructor
- doHistory(KeyEvent).
Method in class weka.gui.SimpleCLI
- Changes the currently displayed command line when certain keys
are pressed.
- done().
Method in interface weka.classifiers.IterativeClassifier
- Signal end of iterating, useful for any house-keeping/cleanup
- done().
Method in class weka.classifiers.adtree.ADTree
- Frees memory that is no longer needed for a final model - will no longer be able
to increment the classifier after calling this.
- doRun(int).
Method in interface weka.experiment.ResultProducer
- Gets the results for a specified run number.
- doRun(int).
Method in class weka.experiment.LearningRateResultProducer
- Gets the results for a specified run number.
- doRun(int).
Method in class weka.experiment.CrossValidationResultProducer
- Gets the results for a specified run number.
- doRun(int).
Method in class weka.experiment.RandomSplitResultProducer
- Gets the results for a specified run number.
- doRun(int).
Method in class weka.experiment.AveragingResultProducer
- Gets the results for a specified run number.
- doRun(int).
Method in class weka.experiment.DatabaseResultProducer
- Gets the results for a specified run number.
- doRunKeys(int).
Method in interface weka.experiment.ResultProducer
- Gets the keys for a specified run number.
- doRunKeys(int).
Method in class weka.experiment.LearningRateResultProducer
- Gets the keys for a specified run number.
- doRunKeys(int).
Method in class weka.experiment.CrossValidationResultProducer
- Gets the keys for a specified run number.
- doRunKeys(int).
Method in class weka.experiment.RandomSplitResultProducer
- Gets the keys for a specified run number.
- doRunKeys(int).
Method in class weka.experiment.AveragingResultProducer
- Gets the keys for a specified run number.
- doRunKeys(int).
Method in class weka.experiment.DatabaseResultProducer
- Gets the keys for a specified run number.
- doTests().
Method in class weka.classifiers.CheckClassifier
- Begin the tests, reporting results to System.out
- doubleToString(double, int).
Static method in class weka.core.Utils
- Rounds a double and converts it into String.
- doubleToString(double, int, int).
Static method in class weka.core.Utils
- Rounds a double and converts it into a formatted decimal-justified String.
- doubleToStringF(double, int, int).
Static method in class weka.classifiers.m5.M5Utils
- Rounds a double and converts it into a formatted right-justified String.
- doubleToStringG(double, int, int).
Static method in class weka.classifiers.m5.M5Utils
- Rounds a double and converts it into a formatted right-justified String.
- Drawable interface weka.core.Drawable.
- Interface to something that can be drawn as a graph.
- drawHighlight(Graphics, int, int).
Method in class weka.classifiers.neural.NeuralConnection
- Call this function to draw the node highlighted.
- drawInputLines(Graphics, int, int).
Method in class weka.classifiers.neural.NeuralConnection
- Call this function to draw the nodes input connections.
- drawNode(Graphics, int, int).
Method in class weka.classifiers.neural.NeuralConnection
- Call this function to draw the node.
- drawOutputLines(Graphics, int, int).
Method in class weka.classifiers.neural.NeuralConnection
- Call this function to draw the nodes output connections.
- dumpDistribution().
Method in class weka.classifiers.j48.Distribution
- Prints distribution.
- dumpLabel(int, Instances).
Method in class weka.classifiers.j48.ClassifierSplitModel
- Prints label for subset index of instances (eg class).
- dumpModel(Instances).
Method in class weka.classifiers.j48.ClassifierSplitModel
- Prints the split model.
- Dvector class weka.classifiers.m5.Dvector.
- Class for handling a double vector.
- Dvector().
Constructor for class weka.classifiers.m5.Dvector
-
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
All Packages Class Hierarchy