- AbstractIterator<T> - Class in com.clearspring.analytics.util
-
Rough and ready clone of the Guava AbstractIterator.
- AbstractIterator() - Constructor for class com.clearspring.analytics.util.AbstractIterator
-
- acceptFailure(FailureEvent) - Method in interface weka.gui.beans.FailureListener
-
Accept a failure event
- acceptSuccess(SuccessEvent) - Method in interface weka.gui.beans.SuccessListener
-
Accept a success event
- add(TDigest.Group) - Method in class com.clearspring.analytics.stream.quantile.GroupTree
-
- add(double) - Method in class com.clearspring.analytics.stream.quantile.TDigest
-
Adds a sample to a histogram.
- add(double, int) - Method in class com.clearspring.analytics.stream.quantile.TDigest
-
Adds a sample to a histogram.
- add(TDigest) - Method in class com.clearspring.analytics.stream.quantile.TDigest
-
- add(double, int) - Method in class com.clearspring.analytics.stream.quantile.TDigest.Group
-
- add(double) - Method in class weka.core.stats.IncrementalQuantileEstimator
-
Add a new observation
- add(String, double) - Method in class weka.core.stats.NominalStats
-
Adds to the count for a given label.
- add(double) - Method in class weka.core.stats.TDigest
-
Add a point to the estimator
- add(double, int) - Method in class weka.core.stats.TDigest
-
Add a point to the estimator
- add(Instance, double) - Method in class weka.core.WeightedReservoirSample
-
(Potentially) add an instance to the reservoir
- addPreconstructedFilterToUse(PreconstructedFilter) - Method in class weka.distributed.WekaClassifierMapTask
-
Add a Preconstructed filter (such as PreConstructedPCA) to use with the
classifier.
- addReservoirToCurrentSketch() - Method in class weka.clusterers.CentroidSketch
-
Add the reservoir to the current sketch.
- addToTrainingHeader(Instances) - Method in class weka.distributed.WekaClassifierMapTask
-
Add the supplied instances to the training header
- addToTrainingHeader(Instance) - Method in class weka.distributed.WekaClassifierMapTask
-
Add the supplied instance to the training header
- addValue(double, double) - Method in class weka.core.stats.NumericAttributeBinData
-
Add a value to the histogram.
- aggregate(AggregateableFilteredClassifier) - Method in class weka.classifiers.meta.AggregateableFilteredClassifier
-
- aggregate(NumericAttributeBinData) - Method in class weka.core.stats.NumericAttributeBinData
-
- aggregate(WeightedReservoirSample) - Method in class weka.core.WeightedReservoirSample
-
- aggregate(int, List<double[]>, List<int[]>, Instances, boolean, boolean, boolean) - Method in class weka.distributed.CorrelationMatrixRowReduceTask
-
Aggregate a list of partial rows of the matrix.
- aggregate(List<Instances>) - Static method in class weka.distributed.CSVToARFFHeaderReduceTask
-
Aggregates a list of Instances (headers) into a final Instances object.
- aggregate(List<Evaluation>) - Method in class weka.distributed.WekaClassifierEvaluationReduceTask
-
Aggregate a list of Evaluation objects.
- aggregate(List<Classifier>) - Method in class weka.distributed.WekaClassifierReduceTask
-
Aggregate the supplied list of classifiers
- aggregate(List<Classifier>, List<Integer>, boolean) - Method in class weka.distributed.WekaClassifierReduceTask
-
Aggregated the supplied list of classifiers.
- AggregateableEvaluationWithPriors - Class in weka.classifiers.evaluation
-
A version of AggregateableEvaluation that allows priors to be provided
(rather than being computed from the training data).
- AggregateableEvaluationWithPriors(Instances) - Constructor for class weka.classifiers.evaluation.AggregateableEvaluationWithPriors
-
Constructs a new AggregateableEvaluation object
- AggregateableEvaluationWithPriors(Instances, CostMatrix) - Constructor for class weka.classifiers.evaluation.AggregateableEvaluationWithPriors
-
Constructs a new AggregateableEvaluationWithPriors object
- AggregateableEvaluationWithPriors(Evaluation) - Constructor for class weka.classifiers.evaluation.AggregateableEvaluationWithPriors
-
Constructs a new AggregateableEvaluationWithPriors based on another
Evaluation object
- AggregateableFilteredClassifier - Class in weka.classifiers.meta
-
A FilteredClassifier that implements Aggregateable.
- AggregateableFilteredClassifier() - Constructor for class weka.classifiers.meta.AggregateableFilteredClassifier
-
- AggregateableFilteredClassifierUpdateable - Class in weka.classifiers.meta
-
An extension of AggregateableFilteredClassifier that implements
UpdateableClassifier, For use with Aggregateable base classifiers that are
also UpdateableClassifiers
- AggregateableFilteredClassifierUpdateable() - Constructor for class weka.classifiers.meta.AggregateableFilteredClassifierUpdateable
-
- aggregateHeadersAndQuartiles(List<CSVToARFFHeaderMapTask.HeaderAndQuantileDataHolder>) - Static method in class weka.distributed.CSVToARFFHeaderReduceTask
-
Performs aggregation over a list of header and quantile data holder
objects.
- aggregateReservoir(WeightedReservoirSample) - Method in class weka.clusterers.CentroidSketch
-
Aggregate the supplied reservoir into our reservoir.
- applyFilters(Instances) - Method in class weka.distributed.KMeansMapTask
-
Apply the filters (if any) setup for this map task to the supplied
instances
- applyFilters(Instance) - Method in class weka.distributed.KMeansMapTask
-
Apply the filters (if any) for this map task to the supplied instance
- ARFF_SUMMARY_ATTRIBUTE_PREFIX - Static variable in class weka.distributed.CSVToARFFHeaderMapTask
-
Attribute name prefix for a summary statistics attribute
- ArffSummaryNumericMetric - Enum in weka.core.stats
-
An enumerated utility type for the various numeric summary metrics that are
computed.
- asBytes(ByteBuffer) - Method in class com.clearspring.analytics.stream.quantile.TDigest
-
Outputs a histogram as bytes using a particularly cheesy encoding.
- asBytes(ByteBuffer) - Method in class weka.core.stats.TDigest
-
Encode the estimator into a byte buffer
- assignStartPointsFromList(int, int, List<Instance>, Instances) - Static method in class weka.distributed.KMeansMapTask
-
Utility method to choose start points for a number of runs of k-means given
a list of randomly selected instance objects.
- asSmallBytes(ByteBuffer) - Method in class com.clearspring.analytics.stream.quantile.TDigest
-
- asSmallBytes(ByteBuffer) - Method in class weka.core.stats.TDigest
-
More compact encoding
- attributeToStats(Attribute) - Static method in class weka.core.stats.NominalStats
-
Convert a summary meta attribute to a NominalStats
- attributeToStats(Attribute) - Static method in class weka.core.stats.NumericStats
-
Convert a summary meta attribute into a NumericStats object (does not
recover the internal TDigest quantile estimator)
- attributeToStats(Attribute) - Static method in class weka.core.stats.StringStats
-
Convert a meta summary attribute containing string stats into a StringStats
object
- generateNames(int, int) - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Generate attribute names.
- generateNames(int) - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Generate attribute names.
- getAdditionalWekaPackageNames(DistributedJobConfig) - Method in class distributed.core.DistributedJob
-
Get a list of weka packages to use from the supplied config
- getAggregatedCentroidSummaries() - Method in class weka.distributed.KMeansReduceTask
-
Get the aggregated summary data for each individual centroid.
- getAttributeName() - Method in class weka.core.stats.NumericAttributeBinData
-
Get the name of the attribute that this histogram is for
- getAxisColour() - Method in class org.tc33.jheatchart.HeatChart
-
Returns the colour that is set to be used for the axis bars.
- getAxisLabelColour() - Method in class org.tc33.jheatchart.HeatChart
-
Returns the current colour of the axis labels.
- getAxisLabelsFont() - Method in class org.tc33.jheatchart.HeatChart
-
Returns the font that describes the visual style of the labels of the axis.
- getAxisThickness() - Method in class org.tc33.jheatchart.HeatChart
-
Returns the width of the axis bars in pixels.
- getAxisValuesColour() - Method in class org.tc33.jheatchart.HeatChart
-
Returns the colour of the axis values as they will be painted along the
axis bars.
- getAxisValuesFont() - Method in class org.tc33.jheatchart.HeatChart
-
Returns the font which describes the visual style of the axis values.
- getBackgroundColour() - Method in class org.tc33.jheatchart.HeatChart
-
Returns an object that represents the colour to be used as the background
for the whole chart.
- getBaseFilter() - Method in class weka.filters.MakePreconstructedFilter
-
Get the base filter being wrapped
- getBatchSize() - Method in class weka.classifiers.meta.BatchPredictorVote
-
- getBatchTrainedIncremental() - Method in class weka.distributed.WekaClassifierEvaluationMapTask
-
Get whether the classifier is an incremental one that has been batch
trained
- getBinFreqs() - Method in class weka.core.stats.NumericAttributeBinData
-
Get a list of bin frequencies for this histogram
- getBinLabels() - Method in class weka.core.stats.NumericAttributeBinData
-
Get a list of bin labels for this histogram
- getBinWidth() - Method in class weka.core.stats.NumericAttributeBinData
-
Get the bin width for this attribute
- getCanopyAssignments() - Method in class weka.clusterers.InstanceWithCanopyAssignments
-
- getCapabilities() - Method in class weka.filters.MakePreconstructedFilter
-
- getCapabilities(Instances) - Method in class weka.filters.MakePreconstructedFilter
-
- getCapabilities() - Method in class weka.filters.unsupervised.attribute.PreConstructedPCA
-
Returns the capabilities of this evaluator.
- getCellHeight() - Method in class org.tc33.jheatchart.HeatChart
-
- getCellSize() - Method in class org.tc33.jheatchart.HeatChart
-
Returns the size of each individual data cell that constitutes a value in
the x,y,z space.
- getCellWidth() - Method in class org.tc33.jheatchart.HeatChart
-
- getCentroidsForRun() - Method in class weka.distributed.KMeansReduceTask
-
Return the centroids for the run
- getCentroidStats() - Method in class weka.distributed.KMeansMapTask
-
Get the summary stats for each centroid
- getChartHeight() - Method in class org.tc33.jheatchart.HeatChart
-
- getChartImage(boolean) - Method in class org.tc33.jheatchart.HeatChart
-
Generates and returns a new chart Image configured according
to this object's currently held settings.
- getChartImage() - Method in class org.tc33.jheatchart.HeatChart
-
Generates and returns a new chart Image configured according
to this object's currently held settings.
- getChartMargin() - Method in class org.tc33.jheatchart.HeatChart
-
Returns the width of the margin in pixels to be left as empty space around
the heat map element.
- getChartSize() - Method in class org.tc33.jheatchart.HeatChart
-
Returns the size of the chart in pixels as calculated according to the cell
dimensions, chart margin and other size settings.
- getChartWidth() - Method in class org.tc33.jheatchart.HeatChart
-
- getClassifier() - Method in class weka.distributed.WekaClassifierEvaluationMapTask
-
Get the classifier used
- getClassifier() - Method in class weka.distributed.WekaClassifierMapTask
-
Get the classifier to use
- getClassifier() - Method in interface weka.gui.beans.ClassifierProducer
-
Get the trained classifier
- getClusterer() - Method in interface weka.gui.beans.ClustererProducer
-
Get the trained clusterer
- getColourScale() - Method in class org.tc33.jheatchart.HeatChart
-
Returns the scale that is currently in use to map z-value to colour.
- getCompressionLevelForQuartileEstimation() - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Get the compression level to use in the TDigest quantile estimators
- getComputeQuartilesAsPartOfSummaryStats() - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Get whether to include estimated quartiles in the profiling stats
- getContinueTrainingUpdateableClassifier() - Method in class weka.distributed.WekaClassifierMapTask
-
Get whether to continue training an incremental (updateable) classifier.
- getConverged() - Method in class weka.distributed.KMeansMapTask
-
Get whether the run of k-means that this map tasks is associated with has
converged
- getCoOccurrenceCounts() - Method in class weka.distributed.CorrelationMatrixMapTask
-
The co-occurrence counts (will be null if missings are replaced by means)
- getCount(String) - Method in class weka.core.stats.NominalStats
-
Get the count for a given label
- getCovariance() - Method in class weka.distributed.CorrelationMatrixMapTask
-
Get whether to compute a covariance matrix rather than a correlation one
- getCurrentSketch() - Method in class weka.clusterers.CentroidSketch
-
Get the current sketch as a set of instances
- getDateAttributes() - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Returns the current attribute range to be forced to type date.
- getDateFormat() - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Get the format to use for parsing date values.
- getDefaultValue(int) - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Get the default label for a given attribute.
- getDiscarded() - Method in class weka.distributed.WekaClassifierReduceTask
-
Get list of indices of the classifiers that were discarded (if any)
- getDistanceFunction() - Method in class weka.clusterers.CentroidSketch
-
Get the distance function being used
- getDistanceFunction() - Method in class weka.distributed.CanopyMapTask.ECanopy
-
- getDistanceFunction() - Method in class weka.distributed.KMeansMapTask
-
Get the distance function in use
- getDontReplaceMissingValues() - Method in class weka.distributed.CanopyMapTask
-
Gets whether missing values are to be replaced.
- getDontReplaceMissingValues() - Method in class weka.distributed.KMeansMapTask
-
Gets whether missing values are to be replaced.
- getEnclosureCharacters() - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Get the character(s) to use/recognize as string enclosures
- getErrorsForClusters() - Method in class weka.clusterers.PreconstructedKMeans
-
- getEvaluation() - Method in class weka.distributed.WekaClassifierEvaluationMapTask
-
Get the evaluation object
- getFailureInfo() - Method in class weka.gui.beans.FailureEvent
-
Get the failure reason
- getFieldSeparator() - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Returns the character used as column separator.
- getFilterSpec(Filter) - Static method in class weka.core.StreamableFilterHelper
-
Create an option specification string for a filter
- getFiltersToUse() - Method in class weka.distributed.CanopyMapTask
-
Get the filters to wrap up with the base classifier
- getFiltersToUse() - Method in class weka.distributed.KMeansMapTask
-
Get the user-specified filters to use with the k-means clusterer.
- getFiltersToUse() - Method in class weka.distributed.WekaClassifierMapTask
-
Get the filters to wrap up with the base classifier
- getFinalizedClusterer() - Method in class weka.distributed.CanopyMapTask
-
- getFinalizedClusterer() - Method in class weka.distributed.clusterers.CanopyBuilder
-
Return the finalized clusterer.
- getFoldNumber() - Method in class weka.distributed.WekaClassifierEvaluationMapTask
-
Get the fold number to evaluate on
- getFoldNumber() - Method in class weka.distributed.WekaClassifierMapTask
-
Get the fold number to train the classifier with.
- getForceBatchLearningForUpdateableClassifiers() - Method in class weka.distributed.WekaClassifierMapTask
-
Get whether to force batch training for incremental (Updateable)
classifiers
- getForceVotedEnsembleCreation() - Method in class weka.distributed.WekaClassifierMapTask
-
Get whether to force the creation of a Vote ensemble for Aggregateable
classifiers
- getGlobalDistanceFunctionPrimingData() - Method in class weka.distributed.KMeansReduceTask
-
Get the global distance function priming data.
- getHeader() - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
get the header information (as an Instances object) from what has been seen
so far by this map task
- getHeader(int, List<String>) - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Get a header constructed using the supplied attribute names.
- getHeader() - Method in class weka.distributed.CSVToARFFHeaderMapTask.HeaderAndQuantileDataHolder
-
Get the header
- getHeaderAndQuantileEstimators() - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Get the header information and the encoded quantile estimators
- getHeatMapForMatrix(Matrix, List<String>) - Static method in class weka.core.ChartUtils
-
Generates a heat map from a matrix of correlations
- getHeatMapForMatrix(Matrix, List<String>) - Static method in class weka.distributed.CorrelationMatrixRowReduceTask
-
- getHeuristicT2(Instances) - Static method in class weka.distributed.CanopyMapTask
-
- getHighValue() - Method in class org.tc33.jheatchart.HeatChart
-
Returns the high value.
- getHighValueColour() - Method in class org.tc33.jheatchart.HeatChart
-
Returns the colour that is currently to be displayed for the heat map cells
with the highest z-value in the dataset.
- getHistogramBinLabels() - Method in class weka.core.stats.NumericStats
-
Get the histogram labels
- getHistogramFrequencies() - Method in class weka.core.stats.NumericStats
-
Get the histogram bin frequencies
- getIgnoreMissingValues() - Method in class weka.distributed.CorrelationMatrixMapTask
-
Get whether to ignore missing values
- getImage() - Method in interface weka.gui.beans.ImageProducer
-
Get the image
- getInputFormat() - Method in class weka.filters.MakePreconstructedFilter
-
- getInstance() - Method in class weka.clusterers.InstanceWithCanopyAssignments
-
- getInstances() - Method in interface weka.gui.beans.InstancesProducer
-
Get the set of instances
- getIterationNumber() - Method in class weka.distributed.KMeansReduceTask
-
Get the current iteration number
- getJobName() - Method in class distributed.core.DistributedJob
-
Get the job name
- getJobStatus() - Method in class distributed.core.DistributedJob
-
Get the status of the current job
- getKeepClassAttributeIfSet() - Method in class weka.distributed.CorrelationMatrixMapTask
-
Get whether to keep the class attribute as part of the correlation analysis
- getKeepClassIfSet() - Method in class weka.filters.unsupervised.attribute.PreConstructedPCA
-
Get whether the class should be kept
- getLabels() - Method in class weka.core.stats.NominalStats
-
Get the set of labels seen by this NominalStats
- getLog() - Method in class distributed.core.DistributedJob
-
Get the log in use
- getLowValue() - Method in class org.tc33.jheatchart.HeatChart
-
Returns the low value.
- getLowValueColour() - Method in class org.tc33.jheatchart.HeatChart
-
Returns the colour that is currently to be displayed for the heat map cells
with the lowest z-value in the dataset.
- getMatrix() - Method in class weka.distributed.CorrelationMatrixMapTask
-
Returns the matrix
- getMatrixIsCovariance() - Method in class weka.filters.unsupervised.attribute.PreConstructedPCA
-
Get whether the matrix is a covariance rather than correlation one
- getMaximumAttributeNames() - Method in class weka.filters.unsupervised.attribute.PreConstructedPCA
-
Gets maximum number of attributes to include in transformed attribute
names.
- getMaximumAttributes() - Method in class weka.filters.unsupervised.attribute.PreConstructedPCA
-
Gets maximum number of PC attributes to retain.
- getMaxNumCandidateCanopiesToHoldInMemory() - Method in class weka.distributed.CanopyMapTask
-
Get the maximum number of candidate canopies to retain in memory during
training.
- getMaxNumCanopies() - Method in class weka.distributed.CanopyMapTask
-
Get the number of clusters to find by this map task.
- getMinimumCanopyDensity() - Method in class weka.distributed.CanopyMapTask
-
Get the minimum T2-based density below which a canopy will be pruned during
periodic pruning.
- getMinTrainingFraction() - Method in class weka.distributed.WekaClassifierReduceTask
-
Get the minimum training fraction by which a classifier is discarded.
- getMissingFreq() - Method in class weka.core.stats.NumericAttributeBinData
-
Get the number of missing values
- getMissingMismatchAttributeInfo() - Method in class weka.distributed.WekaScoringMapTask
-
Get a string summarizing missing and type mismatches between the incoming
data and what the model expects
- getMissingValue() - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Returns the current placeholder for missing values.
- getMode() - Method in class weka.core.stats.NominalStats
-
Get the index of the mode
- getModeLabel() - Method in class weka.core.stats.NominalStats
-
Get the most frequent label (not including missing values)
- getName() - Method in class weka.core.stats.Stats
-
Get the name of the attribute that this Stats pertains to
- getNominalAttributes() - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Returns the current attribute range to be forced to type nominal.
- getNominalDefaultLabelSpecs() - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Get the default label specifications for nominal attributes
- getNominalLabelSpecs() - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Get label specifications for nominal attributes.
- getNumBins() - Method in class weka.core.stats.NumericAttributeBinData
-
Get the number of bins for this attribute
- getNumDecimalPlaces() - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Get the number of decimal places for outputting summary stats
- getNumericAttributeStatsSparse(Instances, int) - Static method in class distributed.core.DistributedUtils
-
- getNumMissing() - Method in class weka.core.stats.NominalStats
-
Get the number of missing values for this attribute
- getNumTrainingInstances() - Method in class weka.distributed.WekaClassifierMapTask
-
Get the number of training instances actually used to train the classifier.
- getOptions() - Method in class distributed.core.DistributedJobConfig
-
- getOptions() - Method in class weka.classifiers.meta.AggregateableFilteredClassifier
-
- getOptions(PreconstructedFilter) - Static method in class weka.core.StreamableFilterHelper
-
Utility method to return an array of options representing the configuration
of one or more filters wrapped in a PreconstructedFilter
- getOptions() - Method in class weka.distributed.CanopyMapTask
-
- getOptions() - Method in class weka.distributed.CorrelationMatrixMapTask
-
- getOptions() - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
- getOptions() - Method in class weka.distributed.KMeansMapTask
-
- getOptions() - Method in class weka.distributed.WekaClassifierMapTask
-
- getOptions() - Method in class weka.filters.unsupervised.attribute.PreConstructedPCA
-
Gets the current settings of the filter.
- getOutputFormat() - Method in class weka.filters.MakePreconstructedFilter
-
- getPathToHeaderWihtSummaryAtts() - Method in class weka.filters.unsupervised.attribute.PreConstructedPCA
-
Get the path to the ARFF header (including summary attributes) used when
the matrix was constructed.
- getPathToMatrix() - Method in class weka.filters.unsupervised.attribute.PreConstructedPCA
-
Get the path to the correlation/covariance matrix
- getPathToPreConstructedFilter() - Method in class weka.classifiers.meta.AggregateableFilteredClassifier
-
- getPayloadElement(String) - Method in class weka.gui.beans.FailureEvent
-
Get a payload element
- getPayloadElement(String) - Method in class weka.gui.beans.SuccessEvent
-
Get a payload element
- getPeriodicPruningRate() - Method in class weka.distributed.CanopyMapTask
-
Get the how often to prune low density canopies during training
- getPreConstructedFilter() - Method in class weka.classifiers.meta.AggregateableFilteredClassifier
-
Get the PreconstructedFilter to use
- getPredictionLabels() - Method in class weka.distributed.WekaScoringMapTask
-
Get a list of labels that the model can predict
- getPreprocessingFilters() - Method in class weka.distributed.KMeansMapTask
-
Gets the full set of preprocessing filters
- getPrimingDataForDistanceFunction(Instances) - Static method in class weka.clusterers.ClusterUtils
-
Compute priming data for a distance function (i.e.
- getPrimingDataForDistanceFunction(Instances) - Static method in class weka.distributed.CanopyReduceTask
-
- getProperty(String) - Method in class distributed.core.DistributedJobConfig
-
Get a configuration property.
- getPropertyDescriptors() - Method in class weka.distributed.CSVToARFFHeaderMapTaskBeanInfo
-
Get an array of PropertyDescriptors for the CSVToArffHeaderMapTask's public
properties.
- getPropertyDescriptors() - Method in class weka.distributed.KMeansMapTaskBeanInfo
-
Get an array of PropertyDescriptors for the CSVToArffHeaderMapTask's public
properties.
- getPropertyDescriptors() - Method in class weka.distributed.WekaClassifierMapTaskBeanInfo
-
Get an array of PropertyDescriptors for the CSVToArffHeaderMapTask's public
properties.
- getPropertyNames() - Method in class distributed.core.DistributedJobConfig
-
Get a list of all the properties that have values.
- getQuantile() - Method in class weka.core.stats.IncrementalQuantileEstimator
-
Return the current estimate of the quantile
- getQuantile(String, double) - Method in class weka.core.stats.QuantileCalculator
-
Get a specific quantile for the named attribute
- getQuantileEstimator() - Method in class weka.core.stats.NumericStats
-
Get the quantile estimator in use (if any)
- getQuantileEstimator(String) - Method in class weka.distributed.CSVToARFFHeaderMapTask.HeaderAndQuantileDataHolder
-
Return a decoded TDigest quantile estimator
- getQuantiles(String) - Method in class weka.core.stats.QuantileCalculator
-
Get the computed quantiles for the named attribute
- getReservoirSample() - Method in class weka.clusterers.CentroidSketch
-
Get the reservoir sample
- getReservoirSampleSize() - Method in class weka.distributed.WekaClassifierMapTask
-
Get the sample size for reservoir sampling
- getRevision() - Method in class weka.classifiers.meta.FilteredClassifierUpdateable
-
Returns the revision string.
- getRunNumber() - Method in class weka.distributed.KMeansReduceTask
-
Get the run number
- getSample() - Method in class weka.core.WeightedReservoirSample
-
Get the sample
- getSampleAsInstances() - Method in class weka.core.WeightedReservoirSample
-
Get the current sample as a set of unweighted instances
- getSampleAsWeightedInstances() - Method in class weka.core.WeightedReservoirSample
-
Get the current sample as a set of weighted instances
- getSeed() - Method in class weka.distributed.WekaClassifierMapTask
-
Get the seed for randomizing the data when batch learning and for reservoir
sampling.
- getStats() - Method in class weka.core.stats.NumericStats
-
Return the array of statistics
- getStringAttributes() - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Returns the current attribute range to be forced to type string.
- getStringLengthStats() - Method in class weka.core.stats.StringStats
-
Get the underlying NumericStats object that is tracking string length stats
- getT1MapPhase() - Method in class weka.distributed.CanopyMapTask
-
Get the T1 distance.
- getT2MapPhase() - Method in class weka.distributed.CanopyMapTask
-
Get the T2 distance to use.
- getTechnicalInformation() - Method in class weka.clusterers.CentroidSketch
-
- getTechnicalInformation() - Method in class weka.core.stats.IncrementalQuantileEstimator
-
- getText() - Method in interface weka.gui.beans.TextProducer
-
Get the text
- getTitle() - Method in class org.tc33.jheatchart.HeatChart
-
Returns the String that will be used as the title of any successive calls
to generate a chart.
- getTitleColour() - Method in class org.tc33.jheatchart.HeatChart
-
Returns the Color that represents the colour the title text
should be painted in.
- getTitleFont() - Method in class org.tc33.jheatchart.HeatChart
-
Returns the Font that describes the visual style of the title.
- getTotalNumFolds() - Method in class weka.distributed.WekaClassifierEvaluationMapTask
-
Get the total number of folds
- getTotalNumFolds() - Method in class weka.distributed.WekaClassifierMapTask
-
Get the total number of folds to use.
- getTotalWithinClustersError() - Method in class weka.distributed.KMeansReduceTask
-
Get the total within cluster error for this run
- getTrainingHeader() - Method in interface weka.gui.beans.ClassifierProducer
-
Get the header of the data used to train the classifier
- getTrainingHeader() - Method in interface weka.gui.beans.ClustererProducer
-
Get the header of the data used to train the clusterer
- getTransformedHeader() - Method in class weka.distributed.KMeansMapTask
-
Get the header of the data after it has been through any pre-processing
filters specified by the user
- getTreatUnparsableNumericValuesAsMissing() - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Get whether, for hitherto thought to be numeric columns, to treat any
unparsable values as missing value.
- getTreatZerosAsMissing() - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Get whether to treat zeros as missing values for numeric attributes when
computing summary statistics.
- getUseAbsValZ() - Method in class org.tc33.jheatchart.HeatChart
-
- getUseReservoirSamplingWhenBatchLearning() - Method in class weka.distributed.WekaClassifierMapTask
-
Get whether to use reservoir sampling when batch learning
- getUserSuppliedProperties() - Method in class distributed.core.DistributedJobConfig
-
Get the full map of user-supplied properties
- getUserSuppliedProperty(String) - Method in class distributed.core.DistributedJobConfig
-
Get a user-supplied property
- getUserSuppliedPropertyNames() - Method in class distributed.core.DistributedJobConfig
-
Get a list of the user-supplied property names
- getVarianceCovered() - Method in class weka.filters.unsupervised.attribute.PreConstructedPCA
-
Gets the proportion of total variance to account for when retaining
principal components.
- getWordCountStats() - Method in class weka.core.stats.StringStats
-
Get the underlying NumericStats object that is tracking word count stats
- getXAxisLabel() - Method in class org.tc33.jheatchart.HeatChart
-
Returns the String that will be displayed as a description of the x-axis in
any generated charts.
- getXAxisValuesFrequency() - Method in class org.tc33.jheatchart.HeatChart
-
Returns the frequency of the values displayed along the x-axis.
- getXValues() - Method in class org.tc33.jheatchart.HeatChart
-
Returns the x-values which are currently set to display along the x-axis.
- getYAxisLabel() - Method in class org.tc33.jheatchart.HeatChart
-
Returns the String that will be displayed as a description of the y-axis in
any generated charts.
- getYAxisValuesFrequency() - Method in class org.tc33.jheatchart.HeatChart
-
Returns the frequency of the values displayed along the y-axis.
- getYValues() - Method in class org.tc33.jheatchart.HeatChart
-
Returns the y-values which are currently set to display along the y-axis.
- getZValues() - Method in class org.tc33.jheatchart.HeatChart
-
Returns the 2-dimensional array of z-values currently in use.
- globalInfo() - Method in class weka.classifiers.meta.FilteredClassifierUpdateable
-
Returns a string describing this classifier.
- globalInfo() - Method in class weka.clusterers.CentroidSketch
-
Overview information for this class
- globalInfo() - Method in class weka.core.stats.IncrementalQuantileEstimator
-
Global information about this estimator
- globalInfo() - Method in class weka.filters.unsupervised.attribute.PreconstructedMissingValuesReplacer
-
- globalInfo() - Method in class weka.filters.unsupervised.attribute.PreConstructedPCA
-
Returns a string describing this filter.
- Group(double) - Constructor for class com.clearspring.analytics.stream.quantile.TDigest.Group
-
- Group(double, int) - Constructor for class com.clearspring.analytics.stream.quantile.TDigest.Group
-
- Group(double, int, boolean) - Constructor for class com.clearspring.analytics.stream.quantile.TDigest.Group
-
- GroupTree - Class in com.clearspring.analytics.stream.quantile
-
A tree containing TDigest.Group.
- GroupTree() - Constructor for class com.clearspring.analytics.stream.quantile.GroupTree
-
- GroupTree(TDigest.Group) - Constructor for class com.clearspring.analytics.stream.quantile.GroupTree
-
- GroupTree(GroupTree, GroupTree) - Constructor for class com.clearspring.analytics.stream.quantile.GroupTree
-
- saveToFile(File) - Method in class org.tc33.jheatchart.HeatChart
-
Generates a new chart Image based upon the currently held
settings and then attempts to save that image to disk, to the location
provided as a File parameter.
- SCALE_EXPONENTIAL - Static variable in class org.tc33.jheatchart.HeatChart
-
A basic exponential scale value of 3.0.
- SCALE_LINEAR - Static variable in class org.tc33.jheatchart.HeatChart
-
The linear scale value of 1.0.
- SCALE_LOGARITHMIC - Static variable in class org.tc33.jheatchart.HeatChart
-
A basic logarithmic scale value of 0.3.
- seedTipText() - Method in class weka.distributed.WekaClassifierMapTask
-
The tool tip text for this property.
- SEPARATOR - Static variable in class weka.clusterers.InstanceWithCanopyAssignments
-
- serializeAllQuantileEstimators() - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Serialize all TDigest quantile estimators in use
- serializeCurrentQuantileEstimator() - Method in class weka.core.stats.NumericStats
-
Serialize the current TDigest quantile estimator
- setAggregationT1(double) - Method in class weka.distributed.CanopyReduceTask
-
- setAggregationT2(double) - Method in class weka.distributed.CanopyReduceTask
-
- setAxisColour(Color) - Method in class org.tc33.jheatchart.HeatChart
-
Sets the colour to be used on the axis bars.
- setAxisLabelColour(Color) - Method in class org.tc33.jheatchart.HeatChart
-
Sets the colour of the text displayed as axis labels.
- setAxisLabelsFont(Font) - Method in class org.tc33.jheatchart.HeatChart
-
Sets the font that describes the visual style of the axis labels.
- setAxisThickness(int) - Method in class org.tc33.jheatchart.HeatChart
-
Sets the width of the axis bars in pixels.
- setAxisValuesColour(Color) - Method in class org.tc33.jheatchart.HeatChart
-
Sets the colour to be used for the axis values as they will be painted
along the axis bars.
- setAxisValuesFont(Font) - Method in class org.tc33.jheatchart.HeatChart
-
Sets the font which describes the visual style of the axis values.
- setBackgroundColour(Color) - Method in class org.tc33.jheatchart.HeatChart
-
Sets the colour to be used on the background of the chart.
- setBaseFilter(Filter) - Method in class weka.filters.MakePreconstructedFilter
-
Set the base filter to wrap
- setBatchSize(String) - Method in class weka.classifiers.meta.BatchPredictorVote
-
- setBatchTrainedIncremental(boolean) - Method in class weka.distributed.WekaClassifierEvaluationMapTask
-
Set whether the classifier is an incremental one that has been batch
trained
- setCellHeight(int) - Method in class org.tc33.jheatchart.HeatChart
-
- setCellSize(Dimension) - Method in class org.tc33.jheatchart.HeatChart
-
Sets the size of each individual cell that constitutes a value in x,y,z
data space.
- setCellWidth(int) - Method in class org.tc33.jheatchart.HeatChart
-
- setCentroids(Instances) - Method in class weka.distributed.KMeansMapTask
-
Set the cluster centroids to use for this iteration.
- setChartMargin(int) - Method in class org.tc33.jheatchart.HeatChart
-
Sets the width of the margin in pixels to be left as empty space around the
heat map element.
- setClassifier(Classifier) - Method in class weka.classifiers.meta.AggregateableFilteredClassifierUpdateable
-
- setClassifier(Classifier) - Method in class weka.classifiers.meta.FilteredClassifierUpdateable
-
Set the base learner.
- setClassifier(Classifier) - Method in class weka.distributed.WekaClassifierEvaluationMapTask
-
Set the classifier to use
- setClassifier(Classifier) - Method in class weka.distributed.WekaClassifierMapTask
-
Set the classifier to use
- setClusterCentroids(Instances) - Method in class weka.clusterers.PreconstructedKMeans
-
- setClusterStats(List<Instances>) - Method in class weka.clusterers.PreconstructedKMeans
-
- setColourScale(double) - Method in class org.tc33.jheatchart.HeatChart
-
Sets the scale that is currently in use to map z-value to colour.
- setCompression(double) - Method in class weka.core.stats.NumericStats
-
Set the compression level
- setCompressionLevelForQuartileEstimation(double) - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Set the compression level to use in the TDigest quantile estimators
- setComputeQuartilesAsPartOfSummaryStats(boolean) - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Set whether to include estimated quartiles in the profiling stats
- setConstructed() - Method in class weka.filters.MakePreconstructedFilter
-
Mark this pre-constructed filter as "constructed" - i.e.
- setContinueTrainingUpdateableClassifier(boolean) - Method in class weka.distributed.WekaClassifierMapTask
-
Set whether to continue training an incremental (updateable) classifier.
- setConverged(boolean) - Method in class weka.distributed.KMeansMapTask
-
Set whether the run of k-means that this map is associated with has
converged or not
- setCovariance(boolean) - Method in class weka.distributed.CorrelationMatrixMapTask
-
Set whether to compute a covariance matrix rather than a correlation one
- setDateAttributes(String) - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Set the attribute range to be forced to type date.
- setDateFormat(String) - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Set the format to use for parsing date values.
- setDistanceFunction(NormalizableDistance) - Method in class weka.clusterers.CentroidSketch
-
Set the distance function to use
- setDontReplaceMissingValues(boolean) - Method in class weka.distributed.CanopyMapTask
-
Sets whether missing values are to be replaced.
- setDontReplaceMissingValues(boolean) - Method in class weka.distributed.KMeansMapTask
-
Sets whether missing values are to be replaced.
- setDummyDistancePrimingData(Instances) - Method in class weka.distributed.KMeansMapTask
-
Set the dummy priming data (two-instance dataset that contains global
min/max for numeric attributes) for the distance function to use when
normalizing numeric attributes.
- setEnclosureCharacters(String) - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Set the character(s) to use/recognize as string enclosures
- setEnvironment(Environment) - Method in class distributed.core.DistributedJob
-
- setEnvironment(Environment) - Method in class weka.distributed.CanopyMapTask
-
- setEnvironment(Environment) - Method in class weka.distributed.WekaClassifierMapTask
-
- setFieldSeparator(String) - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Sets the character used as column separator.
- setFilter(Filter) - Method in class weka.clusterers.PreconstructedFilteredClusterer
-
- setFilteredHeader(Instances) - Method in class weka.clusterers.PreconstructedFilteredClusterer
-
Set the header for the filtered data.
- setFiltersToUse(Filter[]) - Method in class weka.distributed.CanopyMapTask
-
Set the filters to wrap up with the base classifier
- setFiltersToUse(Filter[]) - Method in class weka.distributed.KMeansMapTask
-
Set the user-specified filters to use with the k-means clusterer.
- setFiltersToUse(Filter[]) - Method in class weka.distributed.WekaClassifierMapTask
-
Set the filters to wrap up with the base classifier
- setFinalNumberOfIterations(int) - Method in class weka.clusterers.PreconstructedKMeans
-
- setFoldNumber(int) - Method in class weka.distributed.WekaClassifierEvaluationMapTask
-
Set the fold number to evaluate on.
- setFoldNumber(int) - Method in class weka.distributed.WekaClassifierMapTask
-
Set the fold number to train the classifier with.
- setForceBatchLearningForUpdateableClassifiers(boolean) - Method in class weka.distributed.WekaClassifierMapTask
-
Set whether to force batch training for incremental (Updateable)
classifiers
- setForceVotedEnsembleCreation(boolean) - Method in class weka.distributed.WekaClassifierMapTask
-
Set whether to force the creation of a Vote ensemble for Aggregateable
classifiers
- setHighValueColour(Color) - Method in class org.tc33.jheatchart.HeatChart
-
Sets the colour to be used to fill cells of the heat map with the highest
z-values in the dataset.
- setHistogramData(List<String>, List<Double>) - Method in class weka.core.stats.NumericStats
-
Set histogram data for this numeric stats
- setHistogramMap(Map<Integer, NumericAttributeBinData>) - Method in class weka.core.stats.QuantileCalculator
-
Set a map of initialized numeric histogram data to be updated for each
incoming row/instance
- setIgnoreMissingValues(boolean) - Method in class weka.distributed.CorrelationMatrixMapTask
-
Set whether to ignore missing values
- setInitialStartingPoints(Instances) - Method in class weka.clusterers.PreconstructedKMeans
-
- setInputFormat(Instances) - Method in class weka.filters.MakePreconstructedFilter
-
- setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.PreConstructedPCA
-
Sets the format of the input instances.
- setJobDescription(String) - Method in class distributed.core.DistributedJob
-
Set the job description
- setJobName(String) - Method in class distributed.core.DistributedJob
-
Set the job name
- setJobStatus(DistributedJob.JobStatus) - Method in class distributed.core.DistributedJob
-
Set the status of the current job
- setKeepClassAttributeIfSet(boolean) - Method in class weka.distributed.CorrelationMatrixMapTask
-
Set whether to keep the class attribute as part of the correlation analysis
- setKeepClassIfSet(boolean) - Method in class weka.filters.unsupervised.attribute.PreConstructedPCA
-
Set whether the class should be kept
- setLog(Logger) - Method in class distributed.core.DistributedJob
-
Set the log to use
- setLowValueColour(Color) - Method in class org.tc33.jheatchart.HeatChart
-
Sets the colour to be used to fill cells of the heat map with the lowest
z-values in the dataset.
- setMatrixIsCovariance(boolean) - Method in class weka.filters.unsupervised.attribute.PreConstructedPCA
-
Set whether the matrix is a covariance rather than correlation one
- setMaxFinalNumCanopies(int) - Method in class weka.distributed.CanopyReduceTask
-
- setMaximumAttributeNames(int) - Method in class weka.filters.unsupervised.attribute.PreConstructedPCA
-
Sets maximum number of attributes to include in transformed attribute
names.
- setMaximumAttributes(int) - Method in class weka.filters.unsupervised.attribute.PreConstructedPCA
-
Sets maximum number of PC attributes to retain.
- setMaxNumCandidateCanopiesToHoldInMemory(String) - Method in class weka.distributed.CanopyMapTask
-
Set the maximum number of candidate canopies to retain in memory during
training.
- setMaxNumCanopies(String) - Method in class weka.distributed.CanopyMapTask
-
Set the maximum number of clusters to find in this map task.
- setMinimumCanopyDensity(String) - Method in class weka.distributed.CanopyMapTask
-
Set the minimum T2-based density below which a canopy will be pruned during
periodic pruning.
- setMinTrainingFraction(double) - Method in class weka.distributed.WekaClassifierReduceTask
-
Set the minimum training fraction by which a classifier is discarded.
- setMissingValue(String) - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Sets the placeholder for missing values.
- setModel(Object, Instances, Instances) - Method in class weka.distributed.WekaScoringMapTask
-
Set the model to use
- setNominalAttributes(String) - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Sets the attribute range to be forced to type nominal.
- setNominalDefaultLabelSpecs(Object[]) - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Set the default label specifications for nominal attributes
- setNominalLabelSpecs(Object[]) - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Set label specifications for nominal attributes.
- setNumDecimalPlaces(int) - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Set the number of decimal places for outputting summary stats
- setNumMissing(double) - Method in class weka.core.stats.NominalStats
-
Set the number of missing values for this attribute
- setOptions(String[]) - Method in class distributed.core.DistributedJobConfig
-
- setOptions(String[]) - Method in class weka.classifiers.meta.AggregateableFilteredClassifier
-
- setOptions(String[]) - Method in class weka.distributed.CanopyMapTask
-
- setOptions(String[]) - Method in class weka.distributed.CorrelationMatrixMapTask
-
- setOptions(String[]) - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
- setOptions(String[]) - Method in class weka.distributed.KMeansMapTask
-
- setOptions(String[]) - Method in class weka.distributed.WekaClassifierMapTask
-
- setOptions(String[]) - Method in class weka.filters.MakePreconstructedFilter
-
Set the options for this filter
- setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.PreConstructedPCA
-
Parses a list of options for this object.
- setPathToHeaderWithSummaryAtts(String) - Method in class weka.filters.unsupervised.attribute.PreConstructedPCA
-
Set the path to the ARFF header (including summary attributes) used when
the matrix was constructed.
- setPathToMatrix(String) - Method in class weka.filters.unsupervised.attribute.PreConstructedPCA
-
Set the path to the correlation/covariance matrix
- setPathToPreConstructedFilter(String) - Method in class weka.classifiers.meta.AggregateableFilteredClassifier
-
- setPayloadElement(String, T) - Method in class weka.gui.beans.FailureEvent
-
Set a payload element
- setPayloadElement(String, T) - Method in class weka.gui.beans.SuccessEvent
-
Set a payload element
- setPeriodicPruningRate(String) - Method in class weka.distributed.CanopyMapTask
-
Set the how often to prune low density canopies during training
- setPreConstructedFilter(Filter) - Method in class weka.classifiers.meta.AggregateableFilteredClassifier
-
Set the PreconstructedFilter to use
- setPriors(double[], double) - Method in class weka.classifiers.evaluation.AggregateableEvaluationWithPriors
-
Set the priors to use.
- setProperty(String, String) - Method in class distributed.core.DistributedJobConfig
-
Set a configuration property
- setQuantileEstimator(TDigest) - Method in class weka.core.stats.NumericStats
-
Set the quantile estimator to use
- setReservoirSampleSize(int) - Method in class weka.distributed.WekaClassifierMapTask
-
Set the sample size for reservoir sampling
- setSeed(String) - Method in class weka.distributed.WekaClassifierMapTask
-
Set the seed for randomizing the data when batch learning and for reservoir
sampling.
- setShowXAxisValues(boolean) - Method in class org.tc33.jheatchart.HeatChart
-
Sets whether axis values are to be shown at all for the x-axis.
- setShowYAxisValues(boolean) - Method in class org.tc33.jheatchart.HeatChart
-
Sets whether axis values are to be shown at all for the y-axis.
- setStats(double[]) - Method in class weka.core.stats.NumericStats
-
Sets the array of statistics.
- setStatusMessagePrefix(String) - Method in class distributed.core.DistributedJob
-
Set the prefix to use for log status messages (primarily for use in the
Knowledge Flow's status area)
- setStringAttributes(String) - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Sets the attribute range to be forced to type string.
- setT1MapPhase(String) - Method in class weka.distributed.CanopyMapTask
-
Set the T1 distance.
- setT2MapPhase(String) - Method in class weka.distributed.CanopyMapTask
-
Set the T2 distance to use.
- setTitle(String) - Method in class org.tc33.jheatchart.HeatChart
-
Sets the String that will be used as the title of any successive calls to
generate a chart.
- setTitleColour(Color) - Method in class org.tc33.jheatchart.HeatChart
-
Sets the Color that describes the colour to be used for the
chart title String.
- setTitleFont(Font) - Method in class org.tc33.jheatchart.HeatChart
-
Sets a new Font to be used in rendering the chart's title
String.
- setTotalNumFolds(int) - Method in class weka.distributed.WekaClassifierEvaluationMapTask
-
Set the total number of folds (1 for evaluating on all the data)
- setTotalNumFolds(int) - Method in class weka.distributed.WekaClassifierMapTask
-
Set the total number of folds to use.
- setTreatUnparsableNumericValuesAsMissing(boolean) - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Set whether, for hitherto thought to be numeric columns, to treat any
unparsable values as missing value.
- setTreatZerosAsMissing(boolean) - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Set whether to treat zeros as missing values for numeric attributes when
computing summary statistics.
- setup(Instances) - Method in class weka.distributed.CorrelationMatrixMapTask
-
Initialize the map task
- setup(Instances, double[], double, long, double) - Method in class weka.distributed.WekaClassifierEvaluationMapTask
-
Setup the task.
- setup(Instances) - Method in class weka.distributed.WekaClassifierMapTask
-
Initialize the map task
- setUseAbsValZ(boolean) - Method in class org.tc33.jheatchart.HeatChart
-
- setUseReservoirSamplingWhenBatchLearning(boolean) - Method in class weka.distributed.WekaClassifierMapTask
-
Set whether to use reservoir sampling when batch learning
- setUserSuppliedProperty(String, String) - Method in class distributed.core.DistributedJobConfig
-
Set a user-supplied property
- setVarianceCovered(double) - Method in class weka.filters.unsupervised.attribute.PreConstructedPCA
-
Sets the amount of variance to account for when retaining principal
components.
- setWithinClustersSumOfErrors(double[]) - Method in class weka.clusterers.PreconstructedKMeans
-
- setXAxisLabel(String) - Method in class org.tc33.jheatchart.HeatChart
-
Sets the String that will be displayed as a description of the x-axis in
any generated charts.
- setXAxisValuesFrequency(int) - Method in class org.tc33.jheatchart.HeatChart
-
Sets the frequency of the values displayed along the x-axis.
- setXValues(double, double) - Method in class org.tc33.jheatchart.HeatChart
-
Sets the x-values which are plotted along the x-axis.
- setXValues(Object[]) - Method in class org.tc33.jheatchart.HeatChart
-
Sets the x-values which are plotted along the x-axis.
- setXValuesHorizontal(boolean) - Method in class org.tc33.jheatchart.HeatChart
-
Sets whether the text of the values along the x-axis should be drawn
horizontally left-to-right, or vertically top-to-bottom.
- setYAxisLabel(String) - Method in class org.tc33.jheatchart.HeatChart
-
Sets the String that will be displayed as a description of the y-axis in
any generated charts.
- setYAxisValuesFrequency(int) - Method in class org.tc33.jheatchart.HeatChart
-
Sets the frequency of the values displayed along the y-axis.
- setYValues(double, double) - Method in class org.tc33.jheatchart.HeatChart
-
Sets the y-values which are plotted along the y-axis.
- setYValues(Object[]) - Method in class org.tc33.jheatchart.HeatChart
-
Sets the y-values which are plotted along the y-axis.
- setYValuesHorizontal(boolean) - Method in class org.tc33.jheatchart.HeatChart
-
Sets whether the text of the values along the y-axis should be drawn
horizontally left-to-right, or vertically top-to-bottom.
- setZValues(double[][]) - Method in class org.tc33.jheatchart.HeatChart
-
Replaces the z-values array.
- setZValues(double[][], double, double) - Method in class org.tc33.jheatchart.HeatChart
-
Replaces the z-values array.
- size() - Method in class com.clearspring.analytics.stream.quantile.GroupTree
-
- size() - Method in class com.clearspring.analytics.stream.quantile.TDigest
-
Returns the number of samples represented in this histogram.
- SMALL_ENCODING - Static variable in class com.clearspring.analytics.stream.quantile.TDigest
-
- smallByteSize() - Method in class com.clearspring.analytics.stream.quantile.TDigest
-
Returns an upper bound on the number of bytes that will be required to
represent this histogram in the tighter representation.
- smallByteSize() - Method in class weka.core.stats.TDigest
-
Number of bytes required for the compact encoding
- splitInstanceAndEncodedCanopies(String) - Static method in class weka.clusterers.InstanceWithCanopyAssignments
-
- stackTraceToString(Throwable) - Static method in class distributed.core.DistributedJob
-
Convert a stack trace from a Throwable to a string
- Stats - Class in weka.core.stats
-
Stats base class for the numeric and nominal summary meta data
- Stats(String) - Constructor for class weka.core.stats.Stats
-
Construct a new Stats
- StatsFormatter - Class in weka.core.stats
-
Class for formatting summary stats into nice readable output
- StatsFormatter() - Constructor for class weka.core.stats.StatsFormatter
-
- statusMessage(String) - Method in class distributed.core.DistributedJob
-
Send a message to the status
- stopJob() - Method in class distributed.core.DistributedJob
-
Signal that the job should abort (if it is currently running)
- StreamableFilterHelper - Class in weka.core
-
Utility class for wrapping one or more StreamableFilters in a
MakePreconstructedFilter
- StreamableFilterHelper() - Constructor for class weka.core.StreamableFilterHelper
-
- stringAttributesTipText() - Method in class weka.distributed.CSVToARFFHeaderMapTask
-
Returns the tip text for this property.
- StringStats - Class in weka.core.stats
-
Class for computing string-related stats.
- StringStats(String) - Constructor for class weka.core.stats.StringStats
-
Constructs a new StringStats
- stripSummaryAtts(Instances) - Static method in class weka.distributed.CSVToARFFHeaderReduceTask
-
Utility method that returns a header Instances object without any summary
attributes.
- SuccessEvent - Class in weka.gui.beans
-
Success event for Hadoop KF steps
- SuccessEvent(Object) - Constructor for class weka.gui.beans.SuccessEvent
-
Constructor
- SuccessListener - Interface in weka.gui.beans
-
Interface to something that is interested in receiving SuccessEvents
- sum() - Method in class com.clearspring.analytics.stream.quantile.GroupTree
-