Uses of Interface adams.ml.data.Dataset (adams-pom 25.11.0-SNAPSHOT API)

Packages that use Dataset
Package	Description
adams.data.binning
adams.flow.container
adams.ml.capabilities
adams.ml.data
adams.ml.evaluation
adams.ml.model
adams.ml.model.classification
adams.ml.model.clustering
adams.ml.model.regression
adams.ml.preprocessing
adams.ml.preprocessing.supervised
adams.ml.preprocessing.unsupervised

Uses of Dataset in adams.data.binning

Methods in adams.data.binning that return Dataset
Modifier and Type	Method	Description
`static Dataset`	BinnableDataset.`toDataset(List<Binnable<DataRow>> list)`	Turns a binnable list back into Rows.

Uses of Dataset in adams.flow.container

Constructors in adams.flow.container with parameters of type Dataset
Constructor	Description
`TrainTestSetContainer(Dataset train, Dataset test)`	Initializes the container.
`TrainTestSetContainer(Dataset train, Dataset test, Long seed)`	Initializes the container.
`TrainTestSetContainer(Dataset train, Dataset test, Long seed, Integer foldNumber, Integer foldCount)`	Initializes the container.
`TrainTestSetContainer(Dataset train, Dataset test, Long seed, Integer foldNumber, Integer foldCount, int[] trainOriginal, int[] testOriginal)`	Initializes the container.

Uses of Dataset in adams.ml.capabilities

Methods in adams.ml.capabilities that return Dataset
Modifier and Type	Method	Description
`static Dataset`	CapabilitiesHelper.`adjust(Dataset data, Capabilities caps)`	Tries to adjust the dataset to the capabilities.
`static Dataset`	CapabilitiesHelper.`adjust(Dataset data, CapabilitiesHandler handler)`	Tries to adjust the dataset to the capabilities of the handler.

Methods in adams.ml.capabilities with parameters of type Dataset
Modifier and Type	Method	Description
`static Dataset`	CapabilitiesHelper.`adjust(Dataset data, Capabilities caps)`	Tries to adjust the dataset to the capabilities.
`static Dataset`	CapabilitiesHelper.`adjust(Dataset data, CapabilitiesHandler handler)`	Tries to adjust the dataset to the capabilities of the handler.
`static Capabilities`	CapabilitiesHelper.`forDataset(Dataset data)`	Returns capabilities that are required for the specified dataset.
`static Capabilities`	CapabilitiesHelper.`forDataset(Dataset data, int col)`	Returns capabilities that are required for the specified dataset column.
`static String`	CapabilitiesHelper.`handles(CapabilitiesHandler handler, Dataset data)`	Returns whether the capabilities handler handles the dataset.
`static String`	CapabilitiesHelper.`handles(Capabilities caps, Dataset data)`	Returns whether the capabilities can handle the dataset.
`static String`	CapabilitiesHelper.`handles(Capabilities caps, Dataset data, int col)`	Returns whether the capabilities can handle the dataset column.

Uses of Dataset in adams.ml.data

Classes in adams.ml.data that implement Dataset
Modifier and Type	Class	Description
`class`	`DatasetView`	Provides a view of another dataset.
`class`	`DefaultDataset`	Extended `DefaultSpreadSheet` class, providing additional machine learning functionality.
`class`	`InstancesView`	Provides a view of an `Instances` object.

Fields in adams.ml.data declared as Dataset
Modifier and Type	Field	Description
`protected Dataset`	DatasetView.`m_Dataset`	the underlying spreadsheet.
`protected Dataset`	DatasetInfo.`m_Header`	the header.

Methods in adams.ml.data that return Dataset
Modifier and Type	Method	Description
`Dataset`	Dataset.`getClone()`	Returns a clone of itself.
`Dataset`	DatasetView.`getClone()`	Returns a clone of itself.
`Dataset`	DefaultDataset.`getClone()`	Returns a clone of itself.
`Dataset`	InstancesView.`getClone()`	Returns a clone of itself.
`Dataset`	DatasetView.`getDataset()`	Returns the underlying dataset.
`Dataset`	Dataset.`getHeader()`	Returns the a spreadsheet with the same header and comments.
`Dataset`	DatasetInfo.`getHeader()`	Returns the header.
`Dataset`	DatasetView.`getHeader()`	Returns the view with the same header and comments.
`Dataset`	DefaultDataset.`getHeader()`	Returns the a spreadsheet with the same header and comments.
`Dataset`	InstancesView.`getHeader()`	Returns the a spreadsheet with the same header and comments.
`static Dataset`	WekaConverter.`toDataset(weka.core.Instances data)`	Converts a Weka Instances object into an ADAMS Dataset.

Methods in adams.ml.data with parameters of type Dataset
Modifier and Type	Method	Description
`static int[]`	DatasetUtils.`getNumericColumns(Dataset data)`	Determines the numeric columns in a dataset.
`static List<double[]>`	DatasetUtils.`numericToArrays(Dataset data)`	Turns all the numeric columns into a list of double arrays.
`static List<double[]>`	DatasetUtils.`numericToArrays(Dataset data, int[] cols)`	Turns the specified numeric columns into a list of double arrays.
`static Jama.Matrix`	DatasetUtils.`numericToJama(Dataset data)`	Turns all the numeric columns into a Jama Matrix.
`static Jama.Matrix`	DatasetUtils.`numericToJama(Dataset data, int[] cols)`	Turns the specified numeric columns into a Jama Matrix.
`static com.github.waikatodatamining.matrix.core.Matrix`	DatasetUtils.`numericToMatrixAlgo(Dataset data)`	Turns all the numeric columns into a Jama Matrix.
`static com.github.waikatodatamining.matrix.core.Matrix`	DatasetUtils.`numericToMatrixAlgo(Dataset data, int[] cols)`	Turns the specified numeric columns into a Jama Matrix.
`static weka.core.Instances`	WekaConverter.`toInstances(Dataset data)`	Converts an ADAMS Dataset to Weka Instances.

Constructors in adams.ml.data with parameters of type Dataset
Constructor	Description
`DatasetInfo(Dataset data)`	Initializes the dataset structure.
`DatasetView(Dataset dataset, int[] rows, int[] columns)`	Initializes the view.

Uses of Dataset in adams.ml.evaluation

Fields in adams.ml.evaluation declared as Dataset
Modifier and Type	Field	Description
`protected Dataset`	AbstractSplitGenerator.`m_Data`	the original dataset.

Methods in adams.ml.evaluation that return Dataset
Modifier and Type	Method	Description
`Dataset`	AbstractSplitGenerator.`getData()`	Returns the original data.

Methods in adams.ml.evaluation with parameters of type Dataset
Modifier and Type	Method	Description
`void`	AbstractSplitGenerator.`setData(Dataset value)`	Sets the original data.
`void`	DefaultCrossValidationFoldGenerator.`setData(Dataset value)`	Sets the original data.

Constructors in adams.ml.evaluation with parameters of type Dataset
Constructor	Description
`DefaultCrossValidationFoldGenerator(Dataset data, int numFolds, long seed, boolean stratify)`	Initializes the generator.
`DefaultCrossValidationFoldGenerator(Dataset data, int numFolds, long seed, boolean randomize, boolean stratify)`	Initializes the generator.
`DefaultRandomSplitGenerator(Dataset data, double percentage)`	Initializes the generator.
`DefaultRandomSplitGenerator(Dataset data, long seed, double percentage)`	Initializes the generator.
`DefaultRandomSplitGenerator(Dataset data, long seed, double percentage, boolean preserveOrder)`	Initializes the generator.

Uses of Dataset in adams.ml.model

Methods in adams.ml.model with parameters of type Dataset
Modifier and Type	Method	Description
`T`	Algorithm.`buildModel(Dataset data)`	Builds a model from the data.
`String`	Algorithm.`handles(Dataset data, boolean strict)`	Checks whether the data can be handled.
`String`	Model.`isCompatible(Dataset data)`	Checks whether the dataset is compatible with the model.
`static String`	ModelHelper.`isCompatible(Model model, Dataset data)`	Checks whether the model is compatible with the dataset.
`void`	IncrementalAlgorithm.`updateModel(T model, Dataset data)`	Updates the model with the given data.

Uses of Dataset in adams.ml.model.classification

Methods in adams.ml.model.classification that return Dataset
Modifier and Type	Method	Description
`protected Dataset`	AbstractClassifier.`check(Dataset data)`	Performs checks on the data.
`protected Dataset`	AbstractMultiTargetClassifier.`check(Dataset data)`	Performs checks on the data.

Methods in adams.ml.model.classification with parameters of type Dataset
Modifier and Type	Method	Description
`ClassificationModel`	AbstractClassifier.`buildModel(Dataset data)`	Builds a model from the data.
`MultiTargetClassificationModel`	AbstractMultiTargetClassifier.`buildModel(Dataset data)`	Builds a model from the data.
`protected Dataset`	AbstractClassifier.`check(Dataset data)`	Performs checks on the data.
`protected Dataset`	AbstractMultiTargetClassifier.`check(Dataset data)`	Performs checks on the data.
`protected abstract ClassificationModel`	AbstractClassifier.`doBuildModel(Dataset data)`	Builds a model from the data.
`protected abstract MultiTargetClassificationModel`	AbstractMultiTargetClassifier.`doBuildModel(Dataset data)`	Builds a model from the data.
`protected ClassificationModel`	WekaClassifier.`doBuildModel(Dataset data)`	Builds a model from the data.
`String`	AbstractClassifier.`handles(Dataset data, boolean strict)`	Checks whether the data can be handled.
`String`	AbstractMultiTargetClassifier.`handles(Dataset data, boolean strict)`	Checks whether the data can be handled.
`String`	WekaClassificationModel.`isCompatible(Dataset data)`	Checks whether the dataset is compatible with the model.

Constructors in adams.ml.model.classification with parameters of type Dataset
Constructor	Description
`WekaClassificationModel(weka.classifiers.Classifier model, Dataset data, weka.core.Instances inst)`	Initializes the model.

Uses of Dataset in adams.ml.model.clustering

Methods in adams.ml.model.clustering that return Dataset
Modifier and Type	Method	Description
`protected Dataset`	AbstractClusterer.`check(Dataset data)`	Performs checks on the data.

Methods in adams.ml.model.clustering with parameters of type Dataset
Modifier and Type	Method	Description
`ClusteringModel`	AbstractClusterer.`buildModel(Dataset data)`	Builds a model from the data.
`protected Dataset`	AbstractClusterer.`check(Dataset data)`	Performs checks on the data.
`protected abstract ClusteringModel`	AbstractClusterer.`doBuildModel(Dataset data)`	Builds a model from the data.
`protected ClusteringModel`	WekaClusterer.`doBuildModel(Dataset data)`	Builds a model from the data.
`String`	AbstractClusterer.`handles(Dataset data, boolean strict)`	Checks whether the data can be handled.
`String`	WekaClusteringModel.`isCompatible(Dataset data)`	Checks whether the dataset is compatible with the model.

Constructors in adams.ml.model.clustering with parameters of type Dataset
Constructor	Description
`WekaClusteringModel(weka.clusterers.Clusterer model, Dataset data, weka.core.Instances inst)`	Initializes the model.

Uses of Dataset in adams.ml.model.regression

Methods in adams.ml.model.regression that return Dataset
Modifier and Type	Method	Description
`protected Dataset`	AbstractMultiTargetRegressor.`check(Dataset data)`	Performs checks on the data.
`protected Dataset`	AbstractRegressor.`check(Dataset data)`	Performs checks on the data.

Methods in adams.ml.model.regression with parameters of type Dataset
Modifier and Type	Method	Description
`MultiTargetRegressionModel`	AbstractMultiTargetRegressor.`buildModel(Dataset data)`	Builds a model from the data.
`RegressionModel`	AbstractRegressor.`buildModel(Dataset data)`	Builds a model from the data.
`protected Dataset`	AbstractMultiTargetRegressor.`check(Dataset data)`	Performs checks on the data.
`protected Dataset`	AbstractRegressor.`check(Dataset data)`	Performs checks on the data.
`protected abstract MultiTargetRegressionModel`	AbstractMultiTargetRegressor.`doBuildModel(Dataset data)`	Builds a model from the data.
`protected abstract RegressionModel`	AbstractRegressor.`doBuildModel(Dataset data)`	Builds a model from the data.
`protected RegressionModel`	WekaRegressor.`doBuildModel(Dataset data)`	Builds a model from the data.
`String`	AbstractMultiTargetRegressor.`handles(Dataset data, boolean strict)`	Checks whether the data can be handled.
`String`	AbstractRegressor.`handles(Dataset data, boolean strict)`	Checks whether the data can be handled.
`String`	WekaRegressionModel.`isCompatible(Dataset data)`	Checks whether the dataset is compatible with the model.

Constructors in adams.ml.model.regression with parameters of type Dataset
Constructor	Description
`WekaRegressionModel(weka.classifiers.Classifier model, Dataset data, weka.core.Instances inst)`	Initializes the model.

Uses of Dataset in adams.ml.preprocessing

Fields in adams.ml.preprocessing declared as Dataset
Modifier and Type	Field	Description
`protected Dataset`	AbstractFilter.`m_OutputFormat`	the output format.

Methods in adams.ml.preprocessing that return Dataset
Modifier and Type	Method	Description
`protected abstract Dataset`	AbstractBatchFilter.`doFilter(Dataset data)`	Filters the dataset coming through.
`protected abstract Dataset`	AbstractColumnSubsetBatchFilter.`doFilter(Dataset data)`	Filters the dataset coming through.
`Dataset`	AbstractBatchFilter.`filter(Dataset data)`	Filters the dataset coming through.
`Dataset`	AbstractColumnSubsetBatchFilter.`filter(Dataset data)`	Filters the dataset coming through.
`Dataset`	AbstractColumnSubsetStreamFilter.`filter(Dataset data)`	Filters the dataset coming through.
`Dataset`	AbstractStreamFilter.`filter(Dataset data)`	Filters the dataset coming through.
`Dataset`	BatchFilter.`filter(Dataset data)`	Filters the dataset coming through.
`Dataset`	AbstractFilter.`getOutputFormat()`	Returns the output format.
`Dataset`	ColumnSubsetFilter.`getOutputFormat()`	Returns the output format.
`Dataset`	Filter.`getOutputFormat()`	Returns the output format.
`protected abstract Dataset`	AbstractBatchFilter.`initOutputFormat(Dataset data)`	Initializes the output format.
`protected abstract Dataset`	AbstractColumnSubsetBatchFilter.`initOutputFormat(Dataset data)`	Initializes the output format.
`protected abstract Dataset`	AbstractColumnSubsetStreamFilter.`initOutputFormat(Row data)`	Initializes the output format.
`protected abstract Dataset`	AbstractStreamFilter.`initOutputFormat(Row data)`	Initializes the output format.

Methods in adams.ml.preprocessing with parameters of type Dataset
Modifier and Type	Method	Description
`protected abstract Dataset`	AbstractBatchFilter.`doFilter(Dataset data)`	Filters the dataset coming through.
`protected abstract Dataset`	AbstractColumnSubsetBatchFilter.`doFilter(Dataset data)`	Filters the dataset coming through.
`protected abstract void`	AbstractBatchFilter.`doInitFilter(Dataset data)`	Filter-specific initialization.
`protected abstract void`	AbstractColumnSubsetBatchFilter.`doInitFilter(Dataset data)`	Filter-specific initialization.
`Dataset`	AbstractBatchFilter.`filter(Dataset data)`	Filters the dataset coming through.
`Dataset`	AbstractColumnSubsetBatchFilter.`filter(Dataset data)`	Filters the dataset coming through.
`Dataset`	AbstractColumnSubsetStreamFilter.`filter(Dataset data)`	Filters the dataset coming through.
`Dataset`	AbstractStreamFilter.`filter(Dataset data)`	Filters the dataset coming through.
`Dataset`	BatchFilter.`filter(Dataset data)`	Filters the dataset coming through.
`protected void`	AbstractColumnSubsetFilter.`initColumns(Dataset data)`	Initializes the columns to use for the filter.
`protected void`	AbstractBatchFilter.`initFilter(Dataset data)`	Initializes the filter.
`protected void`	AbstractColumnSubsetBatchFilter.`initFilter(Dataset data)`	Initializes the filter.
`protected abstract Dataset`	AbstractBatchFilter.`initOutputFormat(Dataset data)`	Initializes the output format.
`protected abstract Dataset`	AbstractColumnSubsetBatchFilter.`initOutputFormat(Dataset data)`	Initializes the output format.
`protected void`	AbstractBatchFilter.`postInitFilter(Dataset data)`	After the filter has been initialized.
`protected void`	AbstractColumnSubsetBatchFilter.`postInitFilter(Dataset data)`	After the filter has been initialized.
`protected void`	AbstractBatchFilter.`preInitFilter(Dataset data)`	Before the actual filter initialization.
`protected void`	AbstractColumnSubsetBatchFilter.`preInitFilter(Dataset data)`	Before the actual filter initialization.

Uses of Dataset in adams.ml.preprocessing.supervised

Methods in adams.ml.preprocessing.supervised that return Dataset
Modifier and Type	Method	Description
`protected Dataset`	PLS.`doFilter(Dataset data)`	Filters the dataset coming through.
`protected Dataset`	PLS.`initOutputFormat(Dataset data)`	Initializes the output format.

Methods in adams.ml.preprocessing.supervised with parameters of type Dataset
Modifier and Type	Method	Description
`protected Dataset`	PLS.`doFilter(Dataset data)`	Filters the dataset coming through.
`protected void`	PLS.`doInitFilter(Dataset data)`	Filter-specific initialization.
`protected Dataset`	PLS.`initOutputFormat(Dataset data)`	Initializes the output format.

Uses of Dataset in adams.ml.preprocessing.unsupervised

Methods in adams.ml.preprocessing.unsupervised that return Dataset
Modifier and Type	Method	Description
`protected Dataset`	MultiBatch.`doFilter(Dataset data)`	Filters the dataset coming through.
`protected Dataset`	PCA.`doFilter(Dataset data)`	Filters the dataset coming through.
`protected Dataset`	MultiBatch.`initOutputFormat(Dataset data)`	Initializes the output format.
`protected Dataset`	MultiStream.`initOutputFormat(Row data)`	Initializes the output format.
`protected Dataset`	PassThrough.`initOutputFormat(Row data)`	Initializes the output format.
`protected Dataset`	PCA.`initOutputFormat(Dataset data)`	Initializes the output format.
`protected Dataset`	SavitzkyGolay.`initOutputFormat(Row data)`	Initializes the output format.

Methods in adams.ml.preprocessing.unsupervised with parameters of type Dataset
Modifier and Type	Method	Description
`protected Dataset`	MultiBatch.`doFilter(Dataset data)`	Filters the dataset coming through.
`protected Dataset`	PCA.`doFilter(Dataset data)`	Filters the dataset coming through.
`protected void`	MultiBatch.`doInitFilter(Dataset data)`	Filter-specific initialization.
`protected void`	PCA.`doInitFilter(Dataset data)`	Filter-specific initialization.
`protected Dataset`	MultiBatch.`initOutputFormat(Dataset data)`	Initializes the output format.
`protected Dataset`	PCA.`initOutputFormat(Dataset data)`	Initializes the output format.