- calculateAccuracy(Classifier, Instances, Instances) - Static method in class weka.classifiers.collective.util.CollectiveInstances
-
calculates the accuracy for original test and train set
- calculateRMS(Classifier, Instances, Instances, Instances) - Static method in class weka.classifiers.collective.util.CollectiveInstances
-
calculates the RMS for test/original test and train set
- capabilitiesFilterChanged(Explorer.CapabilitiesFilterChangeEvent) - Method in class weka.gui.explorer.CollectiveClassifierPanel
-
method gets called in case of a change event.
- Chopper - Class in weka.classifiers.collective.meta
-
Collective Classifier that uses one classifier for labeling the test data after training on the train set.
- Chopper() - Constructor for class weka.classifiers.collective.meta.Chopper
-
- classifierImproved() - Method in class weka.classifiers.collective.meta.SimpleCollective
-
returns whether the classifier could be improved during the runs/iterations
- classifierImproved() - Method in class weka.classifiers.collective.meta.TwoStageCollective
-
returns whether the classifier could be improved during the runs/iterations
- classifierImproved() - Method in interface weka.classifiers.collective.RestartableCollectiveClassifier
-
returns whether the classifier could be improved during the runs/iterations
- clear() - Method in class weka.classifiers.collective.util.CollectiveLog
-
removes all the values and filenames
- Clock - Class in weka.classifiers.collective.util
-
A little helper class for clocking and outputting times.
- Clock() - Constructor for class weka.classifiers.collective.util.Clock
-
automatically starts the clock with FORMAT_SECONDS format
- Clock(int) - Constructor for class weka.classifiers.collective.util.Clock
-
automatically starts the clock with the given output format
- Clock(boolean) - Constructor for class weka.classifiers.collective.util.Clock
-
starts the clock depending on start
immediately with the
FORMAT_SECONDS output format
- Clock(boolean, int) - Constructor for class weka.classifiers.collective.util.Clock
-
starts the clock depending on start
immediately
- CollectiveClassifier - Interface in weka.classifiers.collective
-
Interface that all collective classifiers implement.
- CollectiveClassifierPanel - Class in weka.gui.explorer
-
This panel allows the user to select and configure a classifier, set the
attribute of the current dataset to be used as the class, and perform an
Experiment (like in the Experimenter) with this Classifier/Dataset
combination.
- CollectiveClassifierPanel() - Constructor for class weka.gui.explorer.CollectiveClassifierPanel
-
Creates the Experiment panel.
- CollectiveClassifierSplitEvaluator - Class in weka.experiment
-
A SplitEvaluator that produces results for a
collective classification scheme on a nominal class attribute.
- CollectiveClassifierSplitEvaluator() - Constructor for class weka.experiment.CollectiveClassifierSplitEvaluator
-
No args constructor.
- CollectiveEM - Class in weka.classifiers.collective.meta
-
The given base classifier trains on the training set and then labels the instances of the test set.
- CollectiveEM() - Constructor for class weka.classifiers.collective.meta.CollectiveEM
-
- CollectiveEvaluation - Class in weka.classifiers
-
- CollectiveEvaluation(Instances) - Constructor for class weka.classifiers.CollectiveEvaluation
-
Initializes all the counters for the evaluation.
- CollectiveEvaluation(Instances, CostMatrix) - Constructor for class weka.classifiers.CollectiveEvaluation
-
Initializes all the counters for the evaluation and also takes a cost
matrix as parameter.
- CollectiveEvaluationUtils - Class in weka.classifiers.evaluation
-
Contains utility functions for generating lists of predictions in
various manners.
- CollectiveEvaluationUtils() - Constructor for class weka.classifiers.evaluation.CollectiveEvaluationUtils
-
- CollectiveForest - Class in weka.classifiers.collective.trees
-
/**
This collective Classifier uses RandomTrees to build predictions on the test set.
- CollectiveForest() - Constructor for class weka.classifiers.collective.trees.CollectiveForest
-
- CollectiveHelper - Class in weka.classifiers.collective.util
-
Contains various useful methods, e.g., for writing to a file.
- CollectiveHelper() - Constructor for class weka.classifiers.collective.util.CollectiveHelper
-
- CollectiveIBk - Class in weka.classifiers.collective.lazy
-
Uses IBk to determine the optimal K for the neighborhood on the training set.
- CollectiveIBk() - Constructor for class weka.classifiers.collective.lazy.CollectiveIBk
-
- CollectiveInstances - Class in weka.classifiers.collective.util
-
A helper class for initializing labels and flipping class labels.
- CollectiveInstances() - Constructor for class weka.classifiers.collective.util.CollectiveInstances
-
Creates a new instance of CollectiveInstances
- CollectiveLog - Class in weka.classifiers.collective.util
-
Used to output some internal statistics for a Collective Classifier.
- CollectiveLog() - Constructor for class weka.classifiers.collective.util.CollectiveLog
-
initializes the log
- CollectiveNeighbor - Class in weka.classifiers.collective.meta
-
Dummy classifier for TwoStageCollective - only used to keep experiments working!
Valid options are:
- CollectiveNeighbor() - Constructor for class weka.classifiers.collective.meta.CollectiveNeighbor
-
- CollectiveRandomizableClassifier - Class in weka.classifiers.collective
-
Abstract classifier for a randomizable collective classifer.
- CollectiveRandomizableClassifier() - Constructor for class weka.classifiers.collective.CollectiveRandomizableClassifier
-
initializes the classifier
- CollectiveRandomizableMultipleClassifiersCombiner - Class in weka.classifiers.collective
-
Abstract classifier for a randomizable collective classifer combining
multiple classifiers.
- CollectiveRandomizableMultipleClassifiersCombiner() - Constructor for class weka.classifiers.collective.CollectiveRandomizableMultipleClassifiersCombiner
-
initializes the classifier
- CollectiveRandomizableSingleClassifierEnhancer - Class in weka.classifiers.collective
-
Abstract classifier for a randomizable collective classifer with a single
base classifier.
- CollectiveRandomizableSingleClassifierEnhancer() - Constructor for class weka.classifiers.collective.CollectiveRandomizableSingleClassifierEnhancer
-
initializes the classifier
- CollectiveTree - Class in weka.classifiers.collective.trees
-
The CollectiveTree classifier works similar to the RandomTree.But it differs in some ways:
- Binary splits:
~ it tries to find attributes that split the data in two,
roughly equal sized, parts.
~ in case of nominal attributes it groups those values
together that split the training and test instances
roughly in two
- Stops growing if
~ only training instances would be covered
~ only test instances in the leaf
~ only training instances of one class in a leaf
Valid options are:
- CollectiveTree() - Constructor for class weka.classifiers.collective.trees.CollectiveTree
-
- CollectiveTreeModel - Class in weka.classifiers.collective.trees.model
-
A generic class for storing a collective tree model.
- CollectiveTreeModel(CollectiveTreeNode) - Constructor for class weka.classifiers.collective.trees.model.CollectiveTreeModel
-
Creates a tree in which any node can have children.
- CollectiveTreeNode - Class in weka.classifiers.collective.trees.model
-
A generic class for storing a node in a CollectiveTree.
- CollectiveTreeNode(DecisionTreeNode) - Constructor for class weka.classifiers.collective.trees.model.CollectiveTreeNode
-
initializes the node, adds itself to the parents children if parent
is not null.
- CollectiveWoods - Class in weka.classifiers.collective.trees
-
This collective Classifier uses CollectoveTrees to build predictions on the test set.
- CollectiveWoods() - Constructor for class weka.classifiers.collective.trees.CollectiveWoods
-
- CollectiveWrapper - Class in weka.classifiers.collective.meta
-
Represents a wrapper around any normal WEKA classifier.
- CollectiveWrapper() - Constructor for class weka.classifiers.collective.meta.CollectiveWrapper
-
- compare(Object, Object) - Method in class weka.classifiers.collective.util.RankedInstanceComparator
-
compares the two instances, returns -1 if o1 is smaller than o2, 0
if equal and +1 if greater.
- COMPARE_ACC_TRAIN - Static variable in class weka.classifiers.collective.util.CollectiveInstances
-
the accuracy on the training set is used for comparing models
- COMPARE_RMS - Static variable in class weka.classifiers.collective.util.CollectiveInstances
-
the RMS is used for comparing models
- COMPARE_RMS_TEST - Static variable in class weka.classifiers.collective.util.CollectiveInstances
-
the RMS on the test set is used for comparing models
- COMPARE_RMS_TRAIN - Static variable in class weka.classifiers.collective.util.CollectiveInstances
-
the RMS on the training set is used for comparing models
- COMPARE_TAGS - Static variable in class weka.classifiers.collective.util.CollectiveInstances
-
the tags to select in the GUI
- compareTo(Object) - Method in class weka.classifiers.collective.lazy.ibk.Neighbors
-
Compares this object with the specified object for order.
- compareTo(Object) - Method in class weka.classifiers.collective.meta.SimpleCollective
-
compares the two classifiers and returns -1 if itself is worse than o, 0 if
they both are equally good and 1 if itself ist better than o; if o is not a
Collective Classifier, null or an Exception happens it returns 0
- compareTo(Object) - Method in class weka.classifiers.collective.meta.TwoStageCollective
-
compares the two classifiers and returns -1 if itself is worse than o, 0 if
they both are equally good and 1 if itself ist better than o; if o is not a
Collective Classifier, null or an Exception happens it returns 0
- compareTo(Object) - Method in class weka.classifiers.collective.util.RankedInstance
-
Compares this object with the specified object for order.
- comparisonTypeTipText() - Method in class weka.classifiers.collective.meta.SimpleCollective
-
Returns the tip text for this property
- comparisonTypeTipText() - Method in class weka.classifiers.collective.meta.TwoStageCollective
-
Returns the tip text for this property
- ConfidentFlipper - Class in weka.classifiers.collective.util
-
Flips a label only if the previous and the current prediction differ more than a given percentage.
- ConfidentFlipper() - Constructor for class weka.classifiers.collective.util.ConfidentFlipper
-
- crossValidateModel(Classifier, Instances, int, Random, Object...) - Method in class weka.classifiers.collective.evaluation.Evaluation
-
Performs a (stratified if class is nominal) cross-validation for a
classifier on a set of instances.
- cutOffTipText() - Method in class weka.classifiers.collective.meta.Chopper
-
Returns the tip text for this property
- cutOffTipText() - Method in class weka.classifiers.collective.meta.Weighting
-
Returns the tip text for this property
- cutOffTipText() - Method in class weka.classifiers.collective.trees.CollectiveForest
-
Returns the tip text for this property
- generateMD5(byte[]) - Static method in class weka.classifiers.collective.util.CollectiveHelper
-
Generates an MD5 hash over the given buffer.
- generateMD5(String) - Static method in class weka.classifiers.collective.util.CollectiveHelper
-
Generates an MD5 hash over the given String.
- getAlpha() - Method in class weka.classifiers.collective.functions.LLGC
-
Gets the alpha parameter for the iterations.
- getAttribute() - Method in class weka.classifiers.collective.trees.model.DecisionTreeNode
-
returns the attribute the split was performed on.
- getAttributeRef() - Method in class weka.classifiers.collective.trees.model.DecisionTreeNode
-
returns the reference of the attribute the split was performed on.
- getAverage(Instance) - Method in class weka.classifiers.collective.util.FlipHistory
-
returns the average distribution for the given instance, only 0s if it
cannot be found
- getBagSize() - Method in class weka.classifiers.collective.trees.CollectiveForest
-
Get the size of the bag.
- getCapabilities() - Method in class weka.classifiers.collective.functions.LLGC
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.collective.lazy.CollectiveIBk
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.collective.meta.Chopper
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.collective.meta.CollectiveEM
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.collective.meta.FilteredCollectiveClassifier
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.collective.meta.SimpleCollective
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.collective.meta.TwoStageCollective
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.collective.meta.YATSI
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.collective.trees.CollectiveForest
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.collective.trees.CollectiveTree
-
Returns default capabilities of the classifier.
- getClassProbabilities() - Method in class weka.classifiers.collective.trees.model.DecisionTreeNode
-
returns the class probabilities.
- getClassValue() - Method in class weka.classifiers.collective.lazy.ibk.Neighbors
-
Returns the current class associated with the neighbors, i.e., majority
vote, in case of a tie, the first class label.
- getComparisonType() - Method in class weka.classifiers.collective.meta.SimpleCollective
-
Gets how the models are compared.
- getComparisonType() - Method in class weka.classifiers.collective.meta.TwoStageCollective
-
Gets how the models are compared.
- getCount(Instance) - Method in class weka.classifiers.collective.util.FlipHistory
-
returns the count for the given instances, -1 if it cannot be found
- getCutOff() - Method in class weka.classifiers.collective.meta.Chopper
-
Gets the fold cut-off
- getCutOff() - Method in class weka.classifiers.collective.meta.Weighting
-
Gets the fold cut-off
- getCutOff() - Method in class weka.classifiers.collective.trees.CollectiveForest
-
Get the fold cutoff value.
- getDebug() - Method in class weka.classifiers.collective.util.CollectiveInstances
-
Gets whether debugging is turned on.
- getDebug() - Method in class weka.classifiers.collective.util.Flipper
-
Gets whether debugging is turned on.
- getDebugLevel() - Method in class weka.classifiers.collective.trees.CollectiveTree
-
Returns the current debug level (0 = off).
- getDebugLevel() - Method in class weka.classifiers.collective.trees.model.DecisionTreeModel
-
Returns the current debug level (0 means off).
- getDebugLevel() - Method in class weka.classifiers.collective.trees.model.DecisionTreeNode
-
Returns the current debug level (0 means off).
- getDelta() - Method in class weka.classifiers.collective.util.ConfidentFlipper
-
Gets the minimum disagreement to enable flipping
- getDistanceFunction() - Method in class weka.classifiers.collective.functions.LLGC
-
returns the distance function currently in use
- getDistanceWeighting() - Method in class weka.classifiers.collective.lazy.CollectiveIBk
-
Gets the distance weighting method used.
- getDistribution() - Method in class weka.classifiers.collective.trees.model.DecisionTreeNode
-
returns the class distribution from the data.
- getDistribution() - Method in class weka.classifiers.collective.util.RankedInstance
-
returns the normalized distribution
- getDistribution(Instance) - Method in class weka.classifiers.collective.util.RankedList
-
returns the distribution for the instance
- getEvaluation() - Method in class weka.gui.explorer.DataGenerator
-
Returns the underlying Evaluation object.
- getEvaluationType() - Method in class weka.classifiers.collective.meta.SimpleCollective
-
Gets how the classifiers are evaluated.
- getEvaluationType() - Method in class weka.classifiers.collective.meta.TwoStageCollective
-
Gets how the classifiers are evaluated.
- getExplorer() - Method in class weka.gui.explorer.CollectiveClassifierPanel
-
returns the parent Explorer frame.
- getFilename(String) - Method in class weka.classifiers.collective.util.CollectiveLog
-
returns the filename for the given identifier
- getFilter() - Method in class weka.classifiers.collective.meta.FilteredCollectiveClassifier
-
Gets the filter used.
- getFilterType() - Method in class weka.classifiers.collective.functions.LLGC
-
Gets how the training data will be transformed.
- getFlippedLabels() - Method in class weka.classifiers.collective.util.CollectiveInstances
-
returns the percentage of labels that were flipped in the flipLabels
method
- getFlipper() - Method in class weka.classifiers.collective.meta.SimpleCollective
-
Gets the flipping algrithm.
- getFlipper() - Method in class weka.classifiers.collective.meta.TwoStageCollective
-
Gets the flipping algrithm.
- getFlipper() - Method in class weka.classifiers.collective.util.CollectiveInstances
-
returns the current flipping algorithm
- getFolds() - Method in class weka.classifiers.collective.meta.Chopper
-
Gets the number of folds the ranked test instances are divided into
- getFolds() - Method in class weka.classifiers.collective.trees.CollectiveForest
-
Get the value of folds.
- getGraph() - Method in class weka.classifiers.collective.trees.model.DecisionTreeModel
-
the decision tree as a graph
- getIncludeClass() - Method in class weka.classifiers.collective.util.RankedInstanceComparator
-
returns TRUE if the class is included in the comparison
- getIncludeNumAttributes() - Method in class weka.classifiers.collective.functions.LLGC
-
returns whether the number of attributes is used in the calculation of the
affinity matrix
- getInformation() - Method in class weka.classifiers.collective.trees.model.DecisionTreeModel
-
returns the information about the dataset, not the data itself
- getInformation() - Method in class weka.classifiers.collective.trees.model.DecisionTreeNode
-
returns the information about the dataset
- getInstance() - Method in class weka.classifiers.collective.lazy.ibk.Neighbors
-
returns the instance the neighbors are determined for (returns the
original instance, not having the missing values replaced)
- getInstance(boolean) - Method in class weka.classifiers.collective.lazy.ibk.Neighbors
-
returns the instance the neighbors are determined for, either the original
one or the the one after replacing missing values
- getInstance() - Method in class weka.classifiers.collective.util.RankedInstance
-
returns the instance we collect the distributions for
- getInvertSplitFolds() - Method in interface weka.classifiers.collective.CollectiveClassifier
-
Gets whether to use the first fold as training set (= FALSE) or as
test set (= TRUE)
- getInvertSplitFolds() - Method in class weka.classifiers.collective.CollectiveRandomizableClassifier
-
Gets whether to use the first fold as training set (= FALSE) or as
test set (= TRUE)
- getInvertSplitFolds() - Method in class weka.classifiers.collective.CollectiveRandomizableMultipleClassifiersCombiner
-
Gets whether to use the first fold as training set (= FALSE) or as
test set (= TRUE)
- getInvertSplitFolds() - Method in class weka.classifiers.collective.CollectiveRandomizableSingleClassifierEnhancer
-
Gets whether to use the first fold as training set (= FALSE) or as
test set (= TRUE)
- getInvertSplitFolds() - Method in class weka.classifiers.collective.util.Splitter
-
Gets whether to use the first fold as training set (= FALSE) or as
test set (= TRUE)
- getKey() - Method in class weka.experiment.CollectiveClassifierSplitEvaluator
-
Gets the key describing the current SplitEvaluator.
- getKeyNames() - Method in class weka.experiment.CollectiveClassifierSplitEvaluator
-
Gets the names of each of the key columns produced for a single run.
- getKeyTypes() - Method in class weka.experiment.CollectiveClassifierSplitEvaluator
-
Gets the data types of each of the key columns produced for a single run.
- getKNN() - Method in class weka.classifiers.collective.lazy.CollectiveIBk
-
Gets the number of neighbours the learner will use.
- getKNN() - Method in class weka.classifiers.collective.lazy.ibk.Neighbors
-
returns the number of neighbors
- getKNN() - Method in class weka.classifiers.collective.meta.YATSI
-
Gets the number of neighbours the learner will use.
- getLast(Instance) - Method in class weka.classifiers.collective.util.FlipHistory
-
returns the last distribution for the given instance, only 0s if it cannot
be found
- getLastAccTrain() - Method in class weka.classifiers.collective.meta.SimpleCollective
-
returns the Accuracy on the train set in which the last improvement
happened
- getLastAccTrain() - Method in class weka.classifiers.collective.meta.TwoStageCollective
-
returns the Accuracy on the train set in which the last improvement
happened
- getLastIteration() - Method in class weka.classifiers.collective.meta.SimpleCollective
-
returns the iteration in which the last improvement happened
- getLastIteration() - Method in class weka.classifiers.collective.meta.TwoStageCollective
-
returns the iteration in which the last improvement happened
- getLastIteration() - Method in interface weka.classifiers.collective.RestartableCollectiveClassifier
-
returns the iteration in which the last improvement happened
- getLastRestart() - Method in class weka.classifiers.collective.meta.SimpleCollective
-
returns the run in which the last improvement happened
- getLastRestart() - Method in class weka.classifiers.collective.meta.TwoStageCollective
-
returns the run in which the last improvement happened
- getLastRestart() - Method in interface weka.classifiers.collective.RestartableCollectiveClassifier
-
returns the run in which the last improvement happened
- getLastRMS() - Method in class weka.classifiers.collective.meta.SimpleCollective
-
returns the RMS on both sets in which the last improvement happened
- getLastRMS() - Method in class weka.classifiers.collective.meta.TwoStageCollective
-
returns the RMS on both sets in which the last improvement happened
- getLastRMSTest() - Method in class weka.classifiers.collective.meta.SimpleCollective
-
returns the RMS on the test set in which the last improvement happened
- getLastRMSTest() - Method in class weka.classifiers.collective.meta.TwoStageCollective
-
returns the RMS on the test set in which the last improvement happened
- getLastRMSTestOriginal() - Method in class weka.classifiers.collective.meta.SimpleCollective
-
returns the RMS on the (original) test set in which the last improvement
happened
- getLastRMSTestOriginal() - Method in class weka.classifiers.collective.meta.TwoStageCollective
-
returns the RMS on the (original) test set in which the last improvement
happened
- getLastRMSTrain() - Method in class weka.classifiers.collective.meta.SimpleCollective
-
returns the RMS on the train set in which the last improvement happened
- getLastRMSTrain() - Method in class weka.classifiers.collective.meta.TwoStageCollective
-
returns the RMS on the train set in which the last improvement happened
- getLog() - Method in class weka.classifiers.collective.meta.SimpleCollective
-
Gets whether internal data is logged to tmp directory.
- getLog() - Method in class weka.classifiers.collective.meta.TwoStageCollective
-
Gets whether internal data is logged to tmp directory.
- getMeasure(String) - Method in class weka.classifiers.collective.CollectiveRandomizableClassifier
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.classifiers.collective.CollectiveRandomizableMultipleClassifiersCombiner
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.classifiers.collective.CollectiveRandomizableSingleClassifierEnhancer
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.classifiers.collective.lazy.CollectiveIBk
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.classifiers.collective.meta.CollectiveEM
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.classifiers.collective.meta.SimpleCollective
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.classifiers.collective.meta.TwoStageCollective
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.classifiers.collective.meta.YATSI
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.classifiers.collective.trees.CollectiveForest
-
Returns the value of the named measure
- getMeasure(String) - Method in class weka.classifiers.collective.trees.CollectiveTree
-
Returns the value of the named measure
- getMinNum() - Method in class weka.classifiers.collective.trees.CollectiveForest
-
Get the value of MinNum.
- getMinNum() - Method in class weka.classifiers.collective.trees.CollectiveTree
-
Get the value of MinNum.
- getNearestNeighbourSearchAlgorithm() - Method in class weka.classifiers.collective.lazy.CollectiveIBk
-
Returns the current nearestNeighbourSearch algorithm in use.
- getNearestNeighbourSearchAlgorithm() - Method in class weka.classifiers.collective.meta.YATSI
-
Returns the current nearestNeighbourSearch algorithm in use.
- getNodeAt(int) - Method in class weka.classifiers.collective.trees.model.DecisionTreeNode
-
same as getChildAt(int), but automatically casts to DecisionTreeNode
- getNominalSplit() - Method in class weka.classifiers.collective.trees.model.DecisionTreeNode
-
returns the nominal values that go into each branch.
- getNoWeights() - Method in class weka.classifiers.collective.meta.YATSI
-
Gets whether no weighting is done at all.
- getNumFeatures() - Method in class weka.classifiers.collective.trees.CollectiveForest
-
Get the number of features used in random selection.
- getNumFeatures() - Method in class weka.classifiers.collective.trees.CollectiveTree
-
Get the number of features used in random selection.
- getNumIterations() - Method in class weka.classifiers.collective.meta.CollectiveEM
-
Gets the number of iterations
- getNumIterations() - Method in class weka.classifiers.collective.meta.SimpleCollective
-
Gets the number of iterations
- getNumIterations() - Method in class weka.classifiers.collective.meta.TwoStageCollective
-
Gets the number of iterations
- getNumIterations() - Method in interface weka.classifiers.collective.RestartableCollectiveClassifier
-
Gets the number of iterations
- getNumRestarts() - Method in class weka.classifiers.collective.meta.SimpleCollective
-
Gets the number of restarts
- getNumRestarts() - Method in class weka.classifiers.collective.meta.TwoStageCollective
-
Gets the number of restarts
- getNumRestarts() - Method in interface weka.classifiers.collective.RestartableCollectiveClassifier
-
Gets the number of restarts
- getNumTrees() - Method in class weka.classifiers.collective.trees.CollectiveForest
-
Get the value of numTrees.
- getOptions() - Method in class weka.classifiers.collective.CollectiveRandomizableClassifier
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.collective.CollectiveRandomizableMultipleClassifiersCombiner
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.collective.CollectiveRandomizableSingleClassifierEnhancer
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.collective.functions.LLGC
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.collective.lazy.CollectiveIBk
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.collective.meta.Chopper
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.collective.meta.CollectiveEM
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.collective.meta.FilteredCollectiveClassifier
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.collective.meta.SimpleCollective
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.collective.meta.TwoStageCollective
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.collective.meta.Weighting
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.collective.meta.YATSI
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.collective.trees.CollectiveForest
-
Gets options from this classifier.
- getOptions() - Method in class weka.classifiers.collective.trees.CollectiveTree
-
Gets options from this classifier.
- getOptions() - Method in class weka.classifiers.collective.util.ConfidentFlipper
-
Gets the current option settings for the OptionHandler.
- getOptions() - Method in class weka.classifiers.collective.util.Flipper
-
Gets the current option settings for the OptionHandler.
- getOutputEvaluations() - Method in class weka.classifiers.collective.meta.CollectiveEM
-
Gets whether the evaluations are stored in a file or not
- getOutputFormat() - Method in class weka.classifiers.collective.util.Clock
-
returns the output format
- getPlotData() - Method in class weka.gui.explorer.DataGenerator
-
Assembles and returns the plot.
- getPlotInstances() - Method in class weka.gui.explorer.DataGenerator
-
Returns the generated dataset that is plotted.
- getProportions() - Method in class weka.classifiers.collective.trees.model.DecisionTreeNode
-
returns the proportions of instances going down each branch.
- getQValue() - Method in class weka.classifiers.collective.meta.CollectiveEM
-
Gets the value for averaging the distributions
- getRandom() - Method in class weka.classifiers.collective.util.Flipper
-
returns the Random number generator that is used
- getRandomize() - Method in class weka.classifiers.collective.meta.CollectiveEM
-
Gets whether the data is randomize before it is presented to the
classifier
- getRank() - Method in class weka.classifiers.collective.lazy.ibk.Neighbors
-
Returns the rank of this neighbor, i.e., the difference between the
number of instances with known class labels for the two class labels.
- getRepeats() - Method in class weka.classifiers.collective.functions.LLGC
-
Gets the number of repeats of iterations after convergence (= p-1).
- getResult(Instances, Instances) - Method in class weka.experiment.CollectiveClassifierSplitEvaluator
-
Gets the results for the supplied train and test datasets.
- getResultNames() - Method in class weka.experiment.CollectiveClassifierSplitEvaluator
-
Gets the names of each of the result columns produced for a single run.
- getResultTypes() - Method in class weka.experiment.CollectiveClassifierSplitEvaluator
-
Gets the data types of each of the result columns produced for a single
run.
- getRevision() - Method in class weka.classifiers.collective.functions.LLGC
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.collective.lazy.CollectiveIBk
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.collective.meta.Chopper
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.collective.meta.CollectiveEM
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.collective.meta.CollectiveWrapper
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.collective.meta.FilteredCollectiveClassifier
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.collective.meta.SimpleCollective
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.collective.meta.TwoStageCollective
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.collective.meta.Weighting
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.collective.meta.YATSI
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.collective.trees.CollectiveForest
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.collective.trees.CollectiveTree
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.evaluation.CollectiveEvaluationUtils
-
Returns the revision string.
- getRevision() - Method in class weka.experiment.CollectiveClassifierSplitEvaluator
-
Returns the revision string.
- getRootNode() - Method in class weka.classifiers.collective.trees.model.DecisionTreeModel
-
returns the root node, like getRoot(), but casts it to DecisionTreeNode
- getSeed() - Method in class weka.classifiers.collective.util.CollectiveInstances
-
returns the seed value for random number generation
- getSequenceLimit() - Method in class weka.classifiers.collective.functions.LLGC
-
Gets the sequence limit function.
- getSigma() - Method in class weka.classifiers.collective.functions.LLGC
-
Gets the sigma parameter for the calculation of the affinity matrix.
- getSource(String) - Method in class weka.classifiers.collective.trees.model.DecisionTreeModel
-
Returns the tree as if-then statements.
- getSpecification(OptionHandler) - Static method in class weka.classifiers.collective.util.Flipper
-
returns the specification of the given optionhandler (class + options)
- getSplitFolds() - Method in interface weka.classifiers.collective.CollectiveClassifier
-
Gets the split percentage for splitting train set into train and test set
- getSplitFolds() - Method in class weka.classifiers.collective.CollectiveRandomizableClassifier
-
Gets the split percentage for splitting train set into train and test set
- getSplitFolds() - Method in class weka.classifiers.collective.CollectiveRandomizableMultipleClassifiersCombiner
-
Gets the split percentage for splitting train set into train and test set
- getSplitFolds() - Method in class weka.classifiers.collective.CollectiveRandomizableSingleClassifierEnhancer
-
Gets the split percentage for splitting train set into train and test set
- getSplitFolds() - Method in class weka.classifiers.collective.util.Splitter
-
Gets the split percentage for splitting train set into train and test set
- getSplitPoint() - Method in class weka.classifiers.collective.trees.model.DecisionTreeNode
-
returns the split point.
- getStart() - Method in class weka.classifiers.collective.util.Clock
-
returns the start time
- getSteps() - Method in class weka.classifiers.collective.meta.Weighting
-
Gets the number of folds the ranked test instances are divided into
- getStop() - Method in class weka.classifiers.collective.util.Clock
-
returns the stop time or, if still running, the current time
- getString() - Method in class weka.classifiers.collective.trees.model.DecisionTreeModel
-
the decision tree in string representation
- getSwapFolds() - Method in class weka.classifiers.collective.evaluation.Evaluation
-
Returns whether the train/test folds are swapped in case of
cross-validation.
- getSwapFolds() - Method in class weka.classifiers.CollectiveEvaluation
-
Returns whether the train/test folds are swapped in case of
cross-validation.
- getTabTitle() - Method in class weka.gui.explorer.CollectiveClassifierPanel
-
Returns the title for the tab in the Explorer.
- getTabTitleToolTip() - Method in class weka.gui.explorer.CollectiveClassifierPanel
-
Returns the tooltip for the tab in the Explorer.
- getTechnicalInformation() - Method in class weka.classifiers.collective.functions.LLGC
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTechnicalInformation() - Method in class weka.classifiers.collective.meta.YATSI
-
Returns an instance of a TechnicalInformation object, containing
detailed information about the technical background of this class,
e.g., paper reference or book this class is based on.
- getTempPath() - Static method in class weka.classifiers.collective.util.CollectiveHelper
-
returns the temporary directory
- getTestDistribution() - Method in class weka.classifiers.collective.trees.model.CollectiveTreeNode
-
returns the class distribution from the data.
- getTestProportions() - Method in class weka.classifiers.collective.trees.model.CollectiveTreeNode
-
returns the proportions of instances going down each branch.
- getTestSet() - Method in interface weka.classifiers.collective.CollectiveClassifier
-
returns the Test Set
- getTestSet() - Method in class weka.classifiers.collective.CollectiveRandomizableClassifier
-
returns the Test Set
- getTestSet() - Method in class weka.classifiers.collective.CollectiveRandomizableMultipleClassifiersCombiner
-
returns the Test Set
- getTestSet() - Method in class weka.classifiers.collective.CollectiveRandomizableSingleClassifierEnhancer
-
returns the Test Set
- getTestset() - Method in class weka.classifiers.collective.util.Splitter
-
returns the test set
- getTrainingSet() - Method in interface weka.classifiers.collective.CollectiveClassifier
-
returns the Training Set
- getTrainingSet() - Method in class weka.classifiers.collective.CollectiveRandomizableClassifier
-
returns the Training Set
- getTrainingSet() - Method in class weka.classifiers.collective.CollectiveRandomizableMultipleClassifiersCombiner
-
returns the Training Set
- getTrainingSet() - Method in class weka.classifiers.collective.CollectiveRandomizableSingleClassifierEnhancer
-
returns the Training Set
- getTrainset() - Method in class weka.classifiers.collective.util.Splitter
-
returns the training set
- getTrainTestPredictions(Classifier, Instances, Instances) - Method in class weka.classifiers.evaluation.CollectiveEvaluationUtils
-
Generate a bunch of predictions ready for processing, by performing a
evaluation on a test set after training on the given training set.
- getTreeName() - Method in class weka.classifiers.collective.trees.model.DecisionTreeModel
-
returns the currently set tree name.
- getUpdateTraining() - Method in class weka.classifiers.collective.meta.SimpleCollective
-
Gets whether the labels of the training set are updated, too.
- getUpdateTrainingSet() - Method in class weka.classifiers.collective.meta.CollectiveEM
-
Gets whether the training set is updated or not
- getUseBagging() - Method in class weka.classifiers.collective.trees.CollectiveForest
-
Gets whether bagging is used.
- getUseCV() - Method in class weka.classifiers.collective.meta.YATSI
-
Gets whether IBk's internal crossvalidation is used to determine an
optimal K value on the training data, which is then used as K.
- getUseInsight() - Method in interface weka.classifiers.collective.CollectiveClassifier
-
Returns whether we use the labels of the test set to output some more
statistics.
- getUseInsight() - Method in class weka.classifiers.collective.CollectiveRandomizableClassifier
-
Returns whether we use the labels of the test set to output some more
statistics.
- getUseInsight() - Method in class weka.classifiers.collective.CollectiveRandomizableMultipleClassifiersCombiner
-
Returns whether we use the labels of the test set to output some more
statistics.
- getUseInsight() - Method in class weka.classifiers.collective.CollectiveRandomizableSingleClassifierEnhancer
-
Returns whether we use the labels of the test set to output some more
statistics.
- getUseNaiveSearch() - Method in class weka.classifiers.collective.lazy.CollectiveIBk
-
Returns whether the naive search or the KDTree is used to search for the
neighbors
- getUseNaiveSearch() - Method in class weka.classifiers.collective.lazy.ibk.Neighbors
-
Returns whether the naive search or the KDTree is used to search for the
neighbors
- getValues(String) - Method in class weka.classifiers.collective.util.CollectiveLog
-
returns the values for the given identifier
- getVerbose() - Method in interface weka.classifiers.collective.CollectiveClassifier
-
Gets the verbose state
- getVerbose() - Method in class weka.classifiers.collective.CollectiveRandomizableClassifier
-
Gets the verbose state
- getVerbose() - Method in class weka.classifiers.collective.CollectiveRandomizableMultipleClassifiersCombiner
-
Gets the verbose state
- getVerbose() - Method in class weka.classifiers.collective.CollectiveRandomizableSingleClassifierEnhancer
-
Gets the verbose state
- getVerbose() - Method in class weka.classifiers.collective.lazy.ibk.Neighbors
-
Returns whether additional information is printed
- getVerbose() - Method in class weka.classifiers.collective.util.Splitter
-
Gets the verbose state
- getWeightingFactor() - Method in class weka.classifiers.collective.meta.YATSI
-
Gets the weighting factor for the unlabeled instances.
- globalInfo() - Method in class weka.classifiers.collective.functions.LLGC
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.collective.lazy.CollectiveIBk
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.collective.meta.AdvancedCollective
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.collective.meta.Chopper
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.collective.meta.CollectiveEM
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.collective.meta.CollectiveNeighbor
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.collective.meta.CollectiveWrapper
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.collective.meta.FilteredCollectiveClassifier
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.collective.meta.SimpleCollective
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.collective.meta.TwoStageCollective
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.collective.meta.Weighting
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.collective.meta.YATSI
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.collective.trees.CollectiveForest
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.collective.trees.CollectiveTree
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.collective.trees.CollectiveWoods
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.collective.util.ConfidentFlipper
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.collective.util.Flipper
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.collective.util.SimpleFlipper
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.collective.util.TriangleFlipper
-
Returns a string describing classifier
- globalInfo() - Method in class weka.experiment.CollectiveClassifierSplitEvaluator
-
Returns a string describing this split evaluator
- scale(ArrayList) - Method in class weka.gui.explorer.DataGenerator
-
Scales the errors.
- SEQ_LIMIT_GRAPHKERNEL - Static variable in class weka.classifiers.collective.functions.LLGC
-
instance weighting method: graph kernel
- SEQ_LIMIT_STOCHASTICMATRIX - Static variable in class weka.classifiers.collective.functions.LLGC
-
instance weighting method: stoachastic matrix
- SEQ_LIMIT_STOCHASTICMATRIX_T - Static variable in class weka.classifiers.collective.functions.LLGC
-
instance weighting method: transposed stoachastic matrix
- sequenceLimitTipText() - Method in class weka.classifiers.collective.functions.LLGC
-
Returns the tip text for this property
- setAlpha(double) - Method in class weka.classifiers.collective.functions.LLGC
-
Sets the alpha parameter for the iterations.
- setAttribute(int) - Method in class weka.classifiers.collective.trees.model.DecisionTreeNode
-
sets the attribute the split was performed on.
- setBagSize(double) - Method in class weka.classifiers.collective.trees.CollectiveForest
-
Set the size of the bag.
- setClassifier(Classifier) - Method in class weka.classifiers.collective.meta.AdvancedCollective
-
Set the base learner.
- setClassifier(Classifier) - Method in class weka.classifiers.collective.meta.FilteredCollectiveClassifier
-
Set the base learner.
- setClassifierName(String) - Method in class weka.experiment.CollectiveClassifierSplitEvaluator
-
Set the Classifier to use, given it's class name.
- setClassProbabilities(double[]) - Method in class weka.classifiers.collective.trees.model.DecisionTreeNode
-
sets the class probabilities.
- setClassProbabilities(Instances) - Method in class weka.classifiers.collective.trees.model.DecisionTreeNode
-
sets the class probabilities based on the given data
- setComparisonType(SelectedTag) - Method in class weka.classifiers.collective.meta.SimpleCollective
-
Sets how the models are compared.
- setComparisonType(SelectedTag) - Method in class weka.classifiers.collective.meta.TwoStageCollective
-
Sets how the models are compared.
- setCutOff(int) - Method in class weka.classifiers.collective.meta.Chopper
-
Sets the cut-off number for the folds
- setCutOff(int) - Method in class weka.classifiers.collective.meta.Weighting
-
Sets the cut-off number for the folds
- setCutOff(int) - Method in class weka.classifiers.collective.trees.CollectiveForest
-
Set the value for fold cutoff.
- setDebug(boolean) - Method in class weka.classifiers.collective.trees.CollectiveTree
-
Set debugging mode.
- setDebug(boolean) - Method in class weka.classifiers.collective.util.CollectiveInstances
-
Sets debugging mode.
- setDebug(boolean) - Method in class weka.classifiers.collective.util.Flipper
-
Sets debugging mode.
- setDebugLevel(int) - Method in class weka.classifiers.collective.trees.CollectiveTree
-
Sets the new debug level (0 = off), i.e., the higher the level the more
printout on stdout.
- setDebugLevel(int) - Method in class weka.classifiers.collective.trees.model.DecisionTreeModel
-
Sets the new debug level (0 means off, the higher, the more output).
- setDebugLevel(int) - Method in class weka.classifiers.collective.trees.model.DecisionTreeNode
-
Sets the new debug level (0 means off, the higher, the more output).
- setDelta(double) - Method in class weka.classifiers.collective.util.ConfidentFlipper
-
Sets the minimum disagreement to enable flipping
- setDistanceFunction(DistanceFunction) - Method in class weka.classifiers.collective.functions.LLGC
-
sets the distance function to use
- setDistanceWeighting(SelectedTag) - Method in class weka.classifiers.collective.lazy.CollectiveIBk
-
Sets the distance weighting method used.
- setDistribution(double[][]) - Method in class weka.classifiers.collective.trees.model.DecisionTreeNode
-
sets the class distribution from the data.
- setEvaluationType(SelectedTag) - Method in class weka.classifiers.collective.meta.SimpleCollective
-
Sets how the classifiers are evaluated.
- setEvaluationType(SelectedTag) - Method in class weka.classifiers.collective.meta.TwoStageCollective
-
Sets how the classifiers are evaluated.
- setExplorer(Explorer) - Method in class weka.gui.explorer.CollectiveClassifierPanel
-
Sets the Explorer to use as parent frame (used for sending notifications
about changes in the data).
- setFilter(Filter) - Method in class weka.classifiers.collective.meta.FilteredCollectiveClassifier
-
Sets the filter.
- setFilterType(SelectedTag) - Method in class weka.classifiers.collective.functions.LLGC
-
Sets how the training data will be transformed.
- setFlipper(Flipper) - Method in class weka.classifiers.collective.meta.SimpleCollective
-
Sets the flipping algorithm.
- setFlipper(Flipper) - Method in class weka.classifiers.collective.meta.TwoStageCollective
-
Sets the flipping algorithm.
- setFlipper(Flipper) - Method in class weka.classifiers.collective.util.CollectiveInstances
-
sets the flipping algorithm
- setFolds(int) - Method in class weka.classifiers.collective.meta.Chopper
-
Sets the number of folds to divide the ranked test instances into
- setFolds(int) - Method in class weka.classifiers.collective.trees.CollectiveForest
-
Set the value of folds.
- setIncludeClass(boolean) - Method in class weka.classifiers.collective.util.RankedInstanceComparator
-
sets whether the class should be included (= TRUE) in the comparison
- setIncludeNumAttributes(boolean) - Method in class weka.classifiers.collective.functions.LLGC
-
sets whether to include the number of attributes in the calculation of the
affinity matrix
- setInformation(Instances) - Method in class weka.classifiers.collective.trees.model.DecisionTreeNode
-
sets the information about the dataset, i.e., it creates a copy of the
header information.
- setInstances(Instances) - Method in class weka.gui.explorer.CollectiveClassifierPanel
-
Tells the panel to use a new set of instances.
- setInvertSplitFolds(boolean) - Method in interface weka.classifiers.collective.CollectiveClassifier
-
Sets whether to use the first fold as training set (= FALSE) or as
test set (= TRUE).
- setInvertSplitFolds(boolean) - Method in class weka.classifiers.collective.CollectiveRandomizableClassifier
-
Sets whether to use the first fold as training set (= FALSE) or as
test set (= TRUE).
- setInvertSplitFolds(boolean) - Method in class weka.classifiers.collective.CollectiveRandomizableMultipleClassifiersCombiner
-
Sets whether to use the first fold as training set (= FALSE) or as
test set (= TRUE).
- setInvertSplitFolds(boolean) - Method in class weka.classifiers.collective.CollectiveRandomizableSingleClassifierEnhancer
-
Sets whether to use the first fold as training set (= FALSE) or as
test set (= TRUE).
- setInvertSplitFolds(boolean) - Method in class weka.classifiers.collective.util.Splitter
-
Sets whether to use the first fold as training set (= FALSE) or as
test set (= TRUE).
- setKNN(int) - Method in class weka.classifiers.collective.lazy.CollectiveIBk
-
Set the number of neighbours the learner is to use.
- setKNN(int) - Method in class weka.classifiers.collective.meta.YATSI
-
Set the number of neighbours the learner is to use.
- setLog(boolean) - Method in class weka.classifiers.collective.meta.SimpleCollective
-
Sets whether to use log internal data to tmp directory.
- setLog(boolean) - Method in class weka.classifiers.collective.meta.TwoStageCollective
-
Sets whether to log internal data to tmp directory.
- setLog(Logger) - Method in class weka.gui.explorer.CollectiveClassifierPanel
-
Sets the Logger to receive informational messages.
- setMinNum(double) - Method in class weka.classifiers.collective.trees.CollectiveForest
-
Set the value of MinNum.
- setMinNum(double) - Method in class weka.classifiers.collective.trees.CollectiveTree
-
Set the value of MinNum.
- setNearestNeighbourSearchAlgorithm(NearestNeighbourSearch) - Method in class weka.classifiers.collective.lazy.CollectiveIBk
-
Sets the nearestNeighbourSearch algorithm to be used for finding nearest
neighbour(s).
- setNearestNeighbourSearchAlgorithm(NearestNeighbourSearch) - Method in class weka.classifiers.collective.meta.YATSI
-
Sets the nearestNeighbourSearch algorithm to be used for finding nearest
neighbour(s).
- setNominalSplit(double[][]) - Method in class weka.classifiers.collective.trees.model.DecisionTreeNode
-
sets the nominal values that go into each branch.
- setNoWeights(boolean) - Method in class weka.classifiers.collective.meta.YATSI
-
Disables any weighting if TRUE.
- setNumFeatures(int) - Method in class weka.classifiers.collective.trees.CollectiveForest
-
Set the number of features to use in random selection.
- setNumFeatures(int) - Method in class weka.classifiers.collective.trees.CollectiveTree
-
Set the number of features to use in random selection.
- setNumIterations(int) - Method in class weka.classifiers.collective.meta.CollectiveEM
-
Sets the number of iterations
- setNumIterations(int) - Method in class weka.classifiers.collective.meta.SimpleCollective
-
Sets the number of iterations
- setNumIterations(int) - Method in class weka.classifiers.collective.meta.TwoStageCollective
-
Sets the number of iterations
- setNumIterations(int) - Method in interface weka.classifiers.collective.RestartableCollectiveClassifier
-
Sets the number of iterations
- setNumRestarts(int) - Method in class weka.classifiers.collective.meta.SimpleCollective
-
Sets the number of restarts
- setNumRestarts(int) - Method in class weka.classifiers.collective.meta.TwoStageCollective
-
Sets the number of restarts
- setNumRestarts(int) - Method in interface weka.classifiers.collective.RestartableCollectiveClassifier
-
Sets the number of restarts
- setNumTrees(int) - Method in class weka.classifiers.collective.trees.CollectiveForest
-
Set the value of numTrees.
- setOptions(String[]) - Method in class weka.classifiers.collective.CollectiveRandomizableClassifier
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.collective.CollectiveRandomizableMultipleClassifiersCombiner
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.collective.CollectiveRandomizableSingleClassifierEnhancer
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.collective.functions.LLGC
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.collective.lazy.CollectiveIBk
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.collective.meta.AdvancedCollective
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.collective.meta.Chopper
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.collective.meta.CollectiveEM
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.collective.meta.FilteredCollectiveClassifier
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.collective.meta.SimpleCollective
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.collective.meta.TwoStageCollective
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.collective.meta.Weighting
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.collective.meta.YATSI
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.collective.trees.CollectiveForest
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.collective.trees.CollectiveTree
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.collective.util.ConfidentFlipper
-
Sets the OptionHandler's options using the given list.
- setOptions(String[]) - Method in class weka.classifiers.collective.util.Flipper
-
Sets the OptionHandler's options using the given list.
- setOutputEvaluations(boolean) - Method in class weka.classifiers.collective.meta.CollectiveEM
-
Sets whether the evaluations are stored in a file
- setOutputFormat(int) - Method in class weka.classifiers.collective.util.Clock
-
sets the format of the output
- setProportions(double[]) - Method in class weka.classifiers.collective.trees.model.DecisionTreeNode
-
sets the proportions of instances going down each branch.
- setQValue(double) - Method in class weka.classifiers.collective.meta.CollectiveEM
-
Sets the value for averaging the distributions
- setRandom(Random) - Method in class weka.classifiers.collective.util.Flipper
-
sets the random number generator to use
- setRandomize(boolean) - Method in class weka.classifiers.collective.meta.CollectiveEM
-
Sets whether the data is randomized before it is presented to the
classifier
- setRepeats(int) - Method in class weka.classifiers.collective.functions.LLGC
-
Sets the number of repeats of iteration after convergence (= p-1).
- setSeed(int) - Method in class weka.classifiers.collective.util.CollectiveInstances
-
sets the seed value for random number generation
- setSequenceLimit(SelectedTag) - Method in class weka.classifiers.collective.functions.LLGC
-
Sets the sequence limit function.
- setSigma(double) - Method in class weka.classifiers.collective.functions.LLGC
-
Sets the sigma parameter for the calculation of the affinity matrix.
- setSplitFolds(int) - Method in interface weka.classifiers.collective.CollectiveClassifier
-
Set the percentage for splitting the train set into train and test set.
- setSplitFolds(int) - Method in class weka.classifiers.collective.CollectiveRandomizableClassifier
-
Set the percentage for splitting the train set into train and test set.
- setSplitFolds(int) - Method in class weka.classifiers.collective.CollectiveRandomizableMultipleClassifiersCombiner
-
Set the percentage for splitting the train set into train and test set.
- setSplitFolds(int) - Method in class weka.classifiers.collective.CollectiveRandomizableSingleClassifierEnhancer
-
Set the percentage for splitting the train set into train and test set.
- setSplitFolds(int) - Method in class weka.classifiers.collective.util.Splitter
-
Set the percentage for splitting the train set into train and test set.
- setSplitPoint(double) - Method in class weka.classifiers.collective.trees.model.DecisionTreeNode
-
sets the split point.
- setSteps(int) - Method in class weka.classifiers.collective.meta.Weighting
-
Sets the number of folds to divide the ranked test instances into
- setSwapFolds(boolean) - Method in class weka.classifiers.collective.evaluation.Evaluation
-
Sets whether to swap the train/test folds in case of cross-validation.
- setSwapFolds(boolean) - Method in class weka.classifiers.CollectiveEvaluation
-
Sets whether to swap the train/test folds in case of cross-validation.
- setTestDistribution(double[][]) - Method in class weka.classifiers.collective.trees.model.CollectiveTreeNode
-
sets the class distribution from the data.
- setTestProportions(double[]) - Method in class weka.classifiers.collective.trees.model.CollectiveTreeNode
-
sets the proportions of instances going down each branch.
- setTestSet(Instances) - Method in interface weka.classifiers.collective.CollectiveClassifier
-
sets the instances used for testing
- setTestSet(Instances) - Method in class weka.classifiers.collective.CollectiveRandomizableClassifier
-
sets the instances used for testing
- setTestSet(Instances) - Method in class weka.classifiers.collective.CollectiveRandomizableMultipleClassifiersCombiner
-
sets the instances used for testing
- setTestSet(Instances) - Method in class weka.classifiers.collective.CollectiveRandomizableSingleClassifierEnhancer
-
sets the instances used for testing
- setTreeName(String) - Method in class weka.classifiers.collective.trees.model.DecisionTreeModel
-
sets the name of the tree.
- setUpdateTraining(boolean) - Method in class weka.classifiers.collective.meta.SimpleCollective
-
Sets the updating of the training labels.
- setUpdateTrainingSet(boolean) - Method in class weka.classifiers.collective.meta.CollectiveEM
-
Sets whether the instances of the training set are updated
- setUseBagging(boolean) - Method in class weka.classifiers.collective.trees.CollectiveForest
-
Sets whether bagging is used.
- setUseCV(boolean) - Method in class weka.classifiers.collective.meta.YATSI
-
Sets whether to use IBk's internal crossvalidation to determine an
optimal K value on the training data, which is then used as K.
- setUseInsight(boolean) - Method in interface weka.classifiers.collective.CollectiveClassifier
-
Whether to use the labels of the test set to output more statistics
(not used for learning and only for debugging purposes)
- setUseInsight(boolean) - Method in class weka.classifiers.collective.CollectiveRandomizableClassifier
-
Whether to use the labels of the test set to output more statistics
(not used for learning and only for debugging purposes)
- setUseInsight(boolean) - Method in class weka.classifiers.collective.CollectiveRandomizableMultipleClassifiersCombiner
-
Whether to use the labels of the test set to output more statistics
(not used for learning and only for debugging purposes)
- setUseInsight(boolean) - Method in class weka.classifiers.collective.CollectiveRandomizableSingleClassifierEnhancer
-
Whether to use the labels of the test set to output more statistics
(not used for learning and only for debugging purposes)
- setUseNaiveSearch(boolean) - Method in class weka.classifiers.collective.lazy.CollectiveIBk
-
Sets whether to use the naive list search (= TRUE) or the KDTree (= FALSE)
for finding the neighbors
- setUseNaiveSearch(boolean) - Method in class weka.classifiers.collective.lazy.ibk.Neighbors
-
Sets whether to use the naive list search (= TRUE) or the KDTree (= FALSE)
is used for finding the neighbors
- setValues(String, String) - Method in class weka.classifiers.collective.util.CollectiveLog
-
stores the given values under the specified identifier
- setVerbose(boolean) - Method in interface weka.classifiers.collective.CollectiveClassifier
-
Set the verbose state.
- setVerbose(boolean) - Method in class weka.classifiers.collective.CollectiveRandomizableClassifier
-
Set the verbose state.
- setVerbose(boolean) - Method in class weka.classifiers.collective.CollectiveRandomizableMultipleClassifiersCombiner
-
Set the verbose state.
- setVerbose(boolean) - Method in class weka.classifiers.collective.CollectiveRandomizableSingleClassifierEnhancer
-
Set the verbose state.
- setVerbose(boolean) - Method in class weka.classifiers.collective.lazy.ibk.Neighbors
-
Sets whether to output some additional information
- setVerbose(boolean) - Method in class weka.classifiers.collective.util.Splitter
-
Set the verbose state.
- setWeightingFactor(double) - Method in class weka.classifiers.collective.meta.YATSI
-
Set the weighting factor for the unlabeled instances
- sigmaTipText() - Method in class weka.classifiers.collective.functions.LLGC
-
Returns the tip text for this property
- SimpleCollective - Class in weka.classifiers.collective.meta
-
Uses collective classification (cf.
- SimpleCollective() - Constructor for class weka.classifiers.collective.meta.SimpleCollective
-
- SimpleFlipper - Class in weka.classifiers.collective.util
-
label = (rnd < p0) ? 0 : 1
Valid options are:
- SimpleFlipper() - Constructor for class weka.classifiers.collective.util.SimpleFlipper
-
- size() - Method in class weka.classifiers.collective.trees.model.DecisionTreeModel
-
returns the number of nodes in the tree
- size() - Method in class weka.classifiers.collective.trees.model.DecisionTreeNode
-
Returns the size of the subtree, including the node itself.
- splitFoldsTipText() - Method in interface weka.classifiers.collective.CollectiveClassifier
-
Returns the tip text for this property
- splitFoldsTipText() - Method in class weka.classifiers.collective.CollectiveRandomizableClassifier
-
Returns the tip text for this property
- splitFoldsTipText() - Method in class weka.classifiers.collective.CollectiveRandomizableMultipleClassifiersCombiner
-
Returns the tip text for this property
- splitFoldsTipText() - Method in class weka.classifiers.collective.CollectiveRandomizableSingleClassifierEnhancer
-
Returns the tip text for this property
- Splitter - Class in weka.classifiers.collective.util
-
A helper class for splitting a single training set into train and test
set.
- Splitter(Instances) - Constructor for class weka.classifiers.collective.util.Splitter
-
initializes the splitting
- Splitter(Instances, int) - Constructor for class weka.classifiers.collective.util.Splitter
-
initializes the splitting
- Splitter(Instances, int, boolean) - Constructor for class weka.classifiers.collective.util.Splitter
-
initializes the splitting
- start() - Method in class weka.classifiers.collective.util.Clock
-
saves the current system time in msec as start time
- stepsTipText() - Method in class weka.classifiers.collective.meta.Weighting
-
Returns the tip text for this property
- stop() - Method in class weka.classifiers.collective.util.Clock
-
saves the current system in msec as stop time


GetAsSVGReturns text converted to the SVG (Scalable Vector Graphics) format.SVG is an internet text format similar to HTML or CSS. SVG supports more text formats than HTML, so SVG will represent what you have typed more accurately.Note The GetAsSVG function does not return formats that are set in the Conditional Formatting dialog box.GetAsSVG(text) returns the example result (below) when the field text contains the word “Frank” and the word Frank has the following text attributes: Font = Helvetica, Font Size = 12 points, Font Color = red, Font Style = bold.


a80
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22 1000
Interpreting SQL TraceThis section looks at some of the statistics available from SQL Trace and how to interpret them. For each SQL statement executed, SQL Trace provides the following information:
By looking at each of these parameters, you can get an idea of how your SQL statements are being processed and which statements are taking the most time. By analyzing which statements are taking the longest, you may be able to find some inefficiencies you can correct. The SQL Trace facility was enabled in a session by using this Oracle command: EXECUTE RDBMS_system.set_sql_trace_in_session(7,3,TRUE); In another session (with SID = 7 and Serial# = 3), the following SQL statement was executed: SELECT SUBSTR(dogname,1,20) "Dog Name", SUBSTR(description,1,20) "Breed", SUBSTR(owner,1,20) "Owner" FROM dogs, breeds WHERE dogs.breed = breeds.breed ORDER BY dogs.breed; The SQL Trace facility was later disabled using this Oracle command from the fist session: EXECUTE RDBMS_system.set_sql_trace_in_session(7,3,FALSE); Following the execution of the SQL statements, the trace file was translated by running TKPROF as follows: tkprof orclshad.trc trace.out sys=no explain=ed/ed In this syntax, the following are true:
TKPROF gene f09 rated the output file shown in Listing 25.1. Listing 25.1 The TKPROF Output File for a Sample Trace TKPROF: Release <IP_ADDRESS>.0 - Beta on Sun Nov 26 13:03:20 1995 Copyright Oracle Corporation 1979, 1994. All rights reserved. Trace file: orclshad.trc Sort options: default ****************************************************************************** count = number of times OCI procedure was executed cpu = cpu time in seconds executing elapsed = elapsed time in seconds executing disk = number of physical reads of buffers from disk query = number of buffers gotten for consistent read current = number of buffers gotten in current mode (usually for update) rows = number of rows processed by the fetch or execute call ****************************************************************************** SELECT SUBSTR(dogname,1,20) "Dog Name", SUBSTR(breed_name,1,20) "Breed", SUBSTR(owner,1,20) "Owner" FROM dogs, breeds WHERE dogs.breed = breeds.breed ORDER BY dogs.breed call count cpu elapsed disk query current rows ------- ------ -------- ---------- ---------- ---------- ---------- ---------- Parse 1 0.00 0.57 2 0 4 0 Execute 1 0.00 0.00 0 0 0 0 Fetch 2 0.00 0.08 2 2 6 25 ------- ------ -------- ---------- ---------- ---------- ---------- ---------- total 4 0.00 0.65 4 2 10 25 Misses in library cache during parse: 1 Optimizer hint: CHOOSE Parsing user id: 10 (ED) Rows Execution Plan ------ -------------------------------------------------- 0 SELECT STATEMENT HINT: CHOOSE 60 MERGE JOIN 0 SORT (JOIN) 0 TABLE ACCESS (FULL) OF BREEDS 0 SORT (JOIN) 0 TABLE ACCESS (FULL) OF DOGS ******************************************************************************** OVERALL TOTALS FOR ALL NON-RECURSIVE STATEMENTS call count cpu elapsed disk query current rows ------- ------ -------- ---------- ---------- ---------- ---------- ---------- Parse 1 0.00 0.57 2 0 4 0 Execute 1 0.00 0.00 0 0 0 0 Fetch 2 0.00 0.08 2 2 6 25 ------- ------ -------- ---------- ---------- ---------- ---------- ---------- total 4 0.00 0.65 4 2 10 25 Misses in library cache during parse: 1 1 user SQL statements in session. 8 internal SQL statements in session. 9 SQL statements in session. 1 statement EXPLAINed in this session. ******************************************************************************** Trace file: orclshad.trc Trace file compatibilty: 7.02.01 Sort options: default 1 session in tracefile. 1 user SQL statements in trace file. 8 internal SQL statements in trace file. 9 SQL statements in trace file. 6 unique SQL statements in trace file. 1 SQL statements EXPLAINed using schema: ED.prof$plan_table Default table was used. Table was created. Table was dropped. 109 lines in trace file.
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Make your selection
Your amplifier and speaker combination are always unique.
Sound will be perceived louder and clearer with some combinations.
While others will sound quiet and less clean.
Select one amplifier and one speaker to see how these will match.
You will find that not all combinations work.
A speaker may require an amplifier to handle the ohm of the speaker.
Or the speaker may take more energy to play at minimal volumes than your amplifier can handle.
While most combinations 'work.' You will find some are clearly better than others.


1 write to HadMatches
Microsoft.PowerShell.Commands.Management (1)
cimSupport\cmdletization\cim\clientSideQuery.cs (1)
108
this.
HadMatches
= this.HadMatches || isMatch;
2 references to HadMatches
Microsoft.PowerShell.Commands.Management (2)
cimSupport\cmdletization\cim\clientSideQuery.cs (2)
108
this.HadMatches = this.
HadMatches
|| isMatch;
134
if (this.
HadMatches
)


An exception is an error that happens during execution of a program. When that error occurs, Python generate an exception that can be handled, which avoids your
program to crash.
Try: it prevents termination of program.
Except: To display message to the user .
Finally: To close all the Database and files.
Syntax:
Try:
Statement 1
Except:
Statement 2
Except:
Statement 3
Finally:
Statement 4
Else:
Statement 5
assert stmt:
to display a msg when a condition is not fulfilled
syntax :
assert
# to raise assertion error if the user i/p is not b/w 5 and 10
x=int(input("Enter number"))
assert x>5 and x<10,'please enter value b/w 5 and 10'
print(x)
o/p:
Enter number6
6
# to raise assertion error if the user i/p is not b/w 5 and 10
try:
x=int(input("Enter number"))
assert x>5 and x<10,'please enter value b/w 5 and 10'
print(x)
except AssertionError as e:
print(e)
o/p:
Enter number6
6
Enter number4
please enter value b/w 5 and 10
=> Compile-time error
=> Runtime error
=> Logical error
Compile-time error:
Compile-time checking occurs during the compile time. Compile time errors are error occurred due to typing mistake, if we do not follow the proper
syntax and semantics of any programming language then compile time errors are thrown by the compiler.
Example
x=1
if x==1
print("where is mistake?")
o/p:
File "
Two type 1. Built-in Exception
2. User-define exception
Built-in Exception:
exception Exception
All built-in, non-system-exiting exceptions are derived from this class. All user-defined exceptions should also be derived from this class.
exception StandardError
The base class for all built-in exceptions except StopIteration, GeneratorExit, KeyboardInterrupt and SystemExit. StandardError itself is derived from
Exception.
exception ArithmeticError
The base class for those built-in exceptions that are raised for various arithmetic errors: OverflowError, ZeroDivisionError, FloatingPointError.
exception BufferError
Raised when a buffer related operation cannot be performed.
exception LookupError
The base class for the exceptions that are raised when a key or index used on a mapping or sequence is invalid: IndexError, KeyError. This can be raised
directly by codecs.lookup().
exception AssertionError
Raised when an assert statement fails.
exception AttributeError
Raised when an attribute reference (see Attribute references) or assignment fails. (When an object does not support attribute references or attribute
assignments at all, TypeError is raised.)
exception EOFError
Raised when one of the built-in functions (input() or raw_input()) hits an end-of-file condition (EOF) without reading any data. (N.B.: the file.read()
and file.
readline() methods return an empty string when they hit EOF.)
exception FloatingPointError
Raised when a floating point operation fails. This exception is always defined, but can only be raised when Python is configured with the --with-fpectl
option, or the WANT_SIGFPE_HANDLER symbol is defined in the pyconfig.h file.
exception GeneratorExit
Raised when a generator’s close() method is called. It directly inherits from BaseException instead of StandardError since it is technically not an
error.
exception IOError
Raised when an I/O operation (such as a print statement, the built-in open() function or a method of a file object) fails for an I/O-related reason,
e.g., "file not found" or "disk full".
exception ImportError
Raised when an import statement fails to find the module definition or when a from ... import fails to find a name that is to be imported.
exception IndexError
Raised when a sequence subscript is out of range. (Slice indices are silently truncated to fall in the allowed range; if an index is not a plain
integer, TypeError is raised.)
exception KeyError
Raised when a mapping (dictionary) key is not found in the set of existing keys.
exception KeyboardInterrupt
Raised when the user hits the interrupt key (normally Control-C or Delete). During execution, a check for interrupts is made regularly. Interrupts
typed when a built-infunction input() or raw_input() is waiting for input also raise this exception. The exception inherits from BaseException so
as to not be accidentally caught by code that catches Exception and thus prevent the interpreter from exiting.
exception NameError
Raised when a local or global name is not found. This applies only to unqualified names. The associated value is an error message that includes the
name that could not be found.
exception RuntimeError
Raised when an error is detected that doesn’t fall in any of the other categories. The associated value is a string indicating what precisely
went wrong.
exception SyntaxError
Raised when the parser encounters a syntax error. This may occur in an import statement, in an exec statement, in a call to the built-in function
eval() or input(), or when reading the initial script or standard input (also interactively).
exception SystemExit
This exception is raised by the sys.exit() function. When it is not handled, the Python interpreter exits; no stack traceback is printed. If the
associated value is a plain integer, it specifies the system exit status (passed to C’s exit() function); if it is None, the exit status is zero; if
it has another type (such as a string), the object’s value is printed and the exit status is one.
exception TypeError
Raised when an operation or function is applied to an object of inappropriate type. The associated value is a string giving details about the type
mismatch.
exception UnicodeError
Raised when a Unicode-related encoding or decoding error occurs. It is a subclass of ValueError.
Example
#syntax error
try:
x=eval(input("Enter Date"))
except SyntaxError:
print("Invalid Date...")
else:
print("Date:",x)
o/p:
Enter Date2016,10,3
Date: (2016, 10, 3)
Enter Date2016,06b,6
Invalid Date...
User-Defined Exceptions
Programmers may name their own exceptions by creating a new exception class.
Example
Eeample for User-Defined Exceptions
class MyError(Exception):
# Constructor or Initializer
def __init__(self, value):
self.value = value
def pro(self):
try:
if(self.value>5 and self.value<15 ):
raise(MyError(self.value))
except MyError as error:
print('Exception occured: ',error.value)
else:
print("No Exception",self.value)
x=int(input("Enter value"))
ob=MyError(x)
ob.pro()
o/p:
Enter value18
No Exception 18
Enter value11
Exception occured: 11
Creating log file
Example
import logging # logging is module name
logging.basicConfig(filename='mylog.txt',level=logging.ERROR)
try:
a=int(input("Enter 1st no."))
b=int(input("Enter 2nd no."))
c=a/b
except Exception as e:
logging.exception(e) #there store in the file
print("Sucessfully save")
else:
print("Result:\t",c)
#log file create by tester
o/p:
Enter 1st no.10
Enter 2nd no.ab
Sucessfully save
The message of this exception also added to our mylog.txt file.Now open the file and see the following exceptions:
ERROR:root:invalid literal for int() with base 10: 'ab'
Traceback (most recent call last):
File "