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UnsupportedOperationException.GeneticAlgorithm, the
GeneratingGeneticAlgorithm generates new attributes and thus can
change the length of an individual.Condition serves to accept all examples.DataTableRow to the table.
Attributes in newAttributes to the end
of the list of attributes, creating new data columns if necessary.
Attributes in newAttributes to the end
of the list of attributes, creating new data columns if necessary.
Averagable to the list of criteria.
GeneratingGeneticAlgorithm operator.Model repeatedly running a weak learner,
reweighting the training example set accordingly, and combining the
hypothesis using the available weighted performance values.
ExampleSet.
switchOffFeature().
ValueReplenishment.getReplenishmentValue(int, Attribute, double) if
ValueReplenishment.replenishValue(double) returns true.
lib/plugins subdirectory of Yale.
Attribute.
ExampleSet.AttributeDataSources.
epsilon * frequency.
ExampleSet from a
parameter string
ExampleSet as input and maps the values
of certain attributes to other values.AttributeWeights IOObject.AttributeWeightsDialog.Bins providing some additional information and methods
for histogram plotting.BreakpointListener.breakpointReached(Operator, IOContainer, int) is invoked
every time a breakpoint is reached during the experiment.Operator.getDeprecationInfo() method does not return null.
Operator.getDeprecationInfo() method does not return null.
other to this.
ColorHistogramPlotter for each of the plots.ColorQuartilePlotter for each of the plots.commandList.
PCA, GHA
and FastICA you can create the AttributeWeights
for a component.ConditionExampleReaders, a special sub class of
ExampleReader that skips all examples that do not fulfill this
condition.Examples that do not fulfill a given Condition.Condition.ConfigurationListener.Ontology).
Condition serves to accept all examples which are
correctly predicted.countExample(double, double) and counts the
deviation.
AttributeDataSources read from the file.
DataTable from this example set.
Model.createPredictedLabel(ExampleSet) in order to lower
memory consumption.
createTempFile, additionally the given name is part
of the file name.
createTempFile, additionally the given name is part
of the file name.
createTempFile(File), additionally the given name is
part of the file name.
SparseFormatDataRowReader.next()
FileDataRowReader that reads examples from a file, objects
of this class read examples from a ResultSet, a data structure that
is returned from a database query.ExampleSet from an SQL
database.DataRows.
DataRows, e.g. from memory, a file or a
database.KernelModel.DataTable).
Component.repaint().
DataTableViewerTable.MetaDataViewerTable.DatabaseExampleSource operators.DatabaseExampleSource operator.PropertyTable either bold or plain depending on
whether or not the parameter is optional.ExampleSet as input and maps all
nominal values to randomly created strings.get(i) nor put(i,v)
throw a runtime exception for all 0 <= i="i" <="numberOfColumns</i">.
peaksPerPeak peaks in the range of
epsilon * frequency.
a1.equals(a2)
returns true.
EstimatedPerformance.PerformanceEvaluator.evaluate(ExampleSet,PerformanceVector).
Example2DataTableRowWrapper objects.Examples as basis for
DataTableRow.ExampleSet as input and returns a new
ExampleSet including only the Examples that fulfill a
condition.ExampleSet to a file.ExampleSource operators.ExampleSource
operator.FastICA.skip_features_of_type.skip_features_with_name.skip_features_of_type.Averagable.toString() method.
Averagable.toString() method.
Averagable.toString() method.
GeneticAlgorithm, the
GeneratingGeneticAlgorithm generates new attributes and thus can
change the length of an individual.GenericOperatorFactory.registerOperators().Attribute's index.
attribute.
attribute.
maxPeaks highest peaks.
Attributes.
Attributes.
Example.getAttributesAsSparseString(String,String) using
default separator characters.
UnbalancedCrossover.
DataRow
objects.
DataRow
objects.
IOObject in this container.
Iterable.iterator() instead
getAllRefinedRules.
AttributeGenerator.
AttributeGenerator
Attribute.mapIndex(int).
BinaryAttribute.mapIndex(int).
NominalAttribute.mapIndex(int).
BreakpointListener.BREAKPOINT_WITHIN.
PerformanceCriterion.
Averagable.buildAverage(Averagable) method.
Averagable.buildAverage(Averagable) method.
Averagable.buildAverage(Averagable) method.
DirectedGeneratingMutation.
PopulationOperator.
PopulationOperator.
Parameters of this
Operator.
BasicPlotterCondition allowing for all DataTables.
value.
value.
ResultSet.
ExampleSet.size() instead
Example.getValue(Attribute) for the i-th regular attribute.
Plotter.NO_SELECTION.
Plotter.SINGLE_SELECTION.
ResultObjectAdapter.toResultString() result
encoded as html.
ResultObjectAdapter.toResultString() result
encoded as html.
ResultObjectAdapter.toResultString() result
encoded as html.
GenericWekaMetaLearner to specify the
learners name.
GenericWekaMetaLearner to specify the
learners name.
GenericWekaMetaLearner to specify the
learners name.
GenericWekaMetaLearner to specify the
learners parameters.
GenericWekaMetaLearner to specify the
learners parameters.
GenericWekaMetaLearner to specify the
learners parameters.
ParameterTypes for the Yale system properties.
GHA The number of
components is initially specified by the GHA.ExperimentLogOperator to a file in gnuplot
format to a file.GridViz is a simple extension of RadViz that
places the dimensional anchors on a rectangular grid instead of using the
perimeter of a circle.HistogramPlotter for each of the plots.global
section of the configuration file.
ParameterTypeInnerOperator.IOContainer from a file.IOContainer, i.e. all objects
passed to this operator, to a file.readData(ObjectInputStream) method after having been
constructed.Double.parseDouble(String).
DataTableRows.
ConditionExampleReader.
Condition that are useful independently of special applications.
Model repeatedly running a weak learner,
reweighting the training example set accordingly, and combining the
hypothesis using the available weighted performance values.
Model repeatedly running a base learner on subsamples.
Model repeatedly running a weak learner,
reweighting the training example set accordingly, and combining the
hypothesis using the available weighted performance values.
Model repeatedly running a weak learner,
reweighting the training example set accordingly, and combining the
hypothesis using the available weighted performance values.
ListPropertyTable.ParameterTypeList the
parameter values are parameter lists themselves.ListPropertyDialog.MacroDefinitionOperator.str.
str.
str.
MetaDataViewerTable.MinMaxWrapper).MinMaxCriterion around each performance criterion of type
MeasuredPerformance.Condition serves to excludes all examples containing
no missing values from an example set.Condition serves to exclude examples with known
labels from an example set.Model to an ExampleSet.Model from a file that was generated
by an operator like Learner in a
previous experiment.double
value between lowerBound and upperBound
from this random number generator's sequence (exclusive of the interval
endpoint values).
int
value between lowerBound and upperBound
from this random number generator's sequence (exclusive of the interval
endpoint values).
long
value between lowerBound and upperBound
from this random number generator's sequence (exclusive of the interval
endpoint values).
Condition serves to excludes all examples containing
missing values from an example set.Condition serves to exclude examples with unknown
labels from an example set.Tools.formatNumber(double) method.
ExampleSet as input and maps all
nominal values to randomly created strings.ObjectVisualizers.Operator or of one of its subclasses.ParameterTypeValue, i.e. the parameter type for
values which are provided by operators.ParameterOptimizationOperator operator this
operators simply uses the defined parameters and perform the inner operators
for all possible combinations.Parameters for the given list of
ParameterTypes.
ParameterOptimizationOperator.ParameterOptimizationOperator.ParameterTypeValue.Tools.formatPercent(double) method.
PerformanceVectors.ExampleSet
as input, whose elements have both true and predicted labels, and delivers as
output a list of performance values according to a list of performance
criteria that it calculates.ColorPlotterPoints for a plot.DataTable object.
seed
RandomSplitValidationChain splits up the example set into a
training and test set and evaluates the model.UnsupportedOperationException since DataRowReader does not have
to implement remove.
UnsupportedOperationException since DataRowReader does not have
to implement remove.
UnsupportedOperationException since DataRowReader does not have
to implement remove.
removeAttribute(attribute.getTableIndex()).
removeAttribute(attribute.getTableIndex()).
Averagable from the list of criteria.
IOObject in this container.
Tools class in order to allow resource loading
for both the YALE core and the plugin.ResultObject.FileDataRowReader that reads examples from a file, objects
of this class read examples from a ResultSet, a data structure that
is returned from a database query.ExampleSet via a ResultSet.WeightedPerformanceMeasures object.
BayesianBoosting operator
This method reweights the example set with respect to the
WeightedPerformanceMeasures object.
ScatterPlotter for each of the plots.Attribute to value.
AverageVector.addAveragable(Averagable))
ResultSet does not provide information about possible
values of nominal attributes, subclasses must set these by implementing
this method.
MAX_MODEL_NUMBER allows to discard
all but the first n models for specified n.
MAX_MODEL_NUMBER allows to discard
all but the first n models for specified n.
MAX_MODEL_NUMBER allows to discard
all but the first n models for specified n.
Attribute to the example set.
DataTable.maxPeaks new
peaks.
IOObjects in this container.
SparseFormatDataRowReaderAttributes
ExampleSet into two disjunct parts
and applies the first child operator on the first part and applies the second
child on the second part and the result of the first child.JMySVMModel into an ExampleSet of the
support vectors and AttributeWeights representing the normal
of the hyperplane.Partition.Partition.ExampleSetGenerator operator.Object.toString().
Attribute to a Weka attribute.
OperatorTree and a PropertyTable in a SplitPane.ExampleSet into
a training and test set and return a performance vector.WeightingMutation.GaussianMutation.WeightApplier.
BayesianBoosting algorithm and the similarly working
ModelBasedSampling operator.Classifier which can be used to classify
Examples.ExampleSet as source for Weka instead of
copying the complete data.XMLSerialization.
XMLSerialization.
ExampleSourceConfigurationWizard.writeData(File) must have been called.
Condition serves to accept all examples which are
wrongly predicted.XValidation encapsulates a cross-validation experiment.ExampleSet into a training and test
sets similar to XValidation, but returns the test set predictions instead of
a performance vector.
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