Package adams.flow.transformer

Class Summary

AbstractGlobalWekaClassifierEvaluator	Ancestor for classifier evaluators that make use of a global classifier.
AbstractInstanceGenerator<T extends DataContainer>	Ancestor for transformers that turn data containers into WEKA Instance objects.
AbstractProcessWekaInstanceWithModel<T>	Ancestor for transformers that user models for processing Instance objects, e.g., classifiers making predictions.
AbstractWekaClassifierEvaluator	Ancestor for transformers that evaluate classifiers.
AbstractWekaInstanceAndWekaInstancesTransformer	Transformer that processes weka.core.Instance, weka.core.Instances or adams.data.instance.Instance objects.
AbstractWekaModelReader	Ancestor for actors that deserialize models.
AbstractWekaPredictionsTransformer	Ancestor for transformers that convert the predictions stored in an Evaluation object into a different format.
WekaAccumulatedError	Generates plot containers from an evaluation object's predictions.
WekaAccumulatedError.SortablePrediction	Container for a classifier prediction, used for sorting.
WekaAttributeIterator	Iterates through all attributes of a dataset and outputs the names. The attributes can be limited with the range parameter and furthermore with the regular expression applied to the names.
WekaClassifier	Trains a classifier based on the incoming dataset and outputs the built classifier alongside the training header (in a model container). Incremental training is performed, if the input are weka.core.Instance objects and the classifier implements weka.classifiers.UpdateableClassifier. If the incoming token does not encapsulate a dataset or instance, then only a new instance of the classifier is sent around.
WekaClassifierOptimizer	Evaluates a classifier optimizer on an incoming dataset.
WekaClassifierRanker	Performs a quick evaluation using cross-validation on a single dataset (or evaluation on a separate test set if the number of folds is less than 2) to rank the classifiers received on the input and forwarding the x best ones.
WekaClassifierRanker.RankingJob	A job class specific to ranking classifiers.
WekaClassifying	Uses a serialized model to perform predictions on the data being passed through.
WekaClassSelector	Sets the class index.
WekaClusterer	Trains a clusterer based on the incoming dataset and output the built clusterer alongside the training header (in a model container). Incremental training is performed, if the input are weka.core.Instance objects and the clusterer implements weka.clusterers.UpdateableClusterer. If the incoming token does not encapsulate a dataset, then only a new instance of the clusterer is sent around.
WekaClustering	Uses a serialized model to cluster data being passed through. The model can also be obtained from a global actor, if the model file is pointing to a directory.
WekaCrossValidationEvaluator	Cross-validates a classifier on an incoming dataset.
WekaCrossValidationSplit	Generates train/test pairs like during a cross-validation run.
WekaEvaluationSummary	Generates a summary string of the weka.classifiers.Evaluation objects that it receives.
WekaEvaluationValuePicker	Picks a specific value from an evaluation object.
WekaEvaluationValues	Generates a spreadsheet from statistics of an Evaluation object.
WekaExperiment	Represents a Weka experiment, stored in a file.
WekaExperimentEvaluation	Generates evaluation output of an experiment that was run previously.
WekaExtractArray	Extracts a column or row of data from a weka.core.Instances or SpreadSheet object. Only numeric columns can be returned.
WekaFileReader	Reads any file format that Weka's converters can handle and returns the full dataset or single weka.core.Instance objects.
WekaFilter	Filters Instances/Instance objects using the specified filter.
WekaGetInstanceValue	Retrieves a value from a WEKA Instance object. Notes: - date and relational values are forwarded as strings - missing values are output as '?' (without the single quotes) - the 'attribute name' option overrides the 'index' option Input/output: - accepts: weka.core.Instance - generates: java.lang.Double java.lang.String Valid options are:
WekaInstanceBuffer	Can act in two different ways: 1.
WekaInstanceDumper	Dumps weka.core.Instance objects into an ARFF file.
WekaInstanceEvaluator	Adds a new attribute to the data being passed through (normally 'evaluation') and sets the value to the evaluation value returned by the chosen evaluator scheme.
WekaInstanceFileReader	Loads a WEKA dataset from disk with a specified reader and passes on the adams.core.instance.Instance objects.
WekaInstancesInfo	Outputs statistics of a weka.core.Instances object.
WekaInstancesMerge	Merges multiple datasets. If no 'ID' attribute is named, then all datasets must contain the same number of rows. Attributes can be excluded from ending up in the final dataset via a regular expression.
WekaInstancesStatistic	Generates statistics from a weka.core.Instances object. The actor just uses the internal format (double array) and does not check whether the attributes are actually numeric.
WekaInstanceStreamPlotGenerator	Generates plot containers from a range of attributes of the weka.core.Instance objects being passed through. The generator merely uses the internal data representation for generating the Y value of the plot container.
WekaModelReader	Actor for loading a model (classifier or clusterer).
WekaMultiLabelSplitter	Splits a dataset containing multiple class attributes ('multi-label') into separate datasets with only a single class attribute.
WekaNewInstance	Creates a new weka.core.Instance-derived object, with all values marked as missing. The class implementing the weka.core.Instance interface needs to have a constructor that takes the number of attributes as sole parameter.
WekaPredictionsToInstances	Generates weka.core.Instances from the predictions of an Evaluation object.
WekaRandomSplit	Splits a dataset into a training and test set according to a specified split percentage.
WekaRegexToRange	Produces a range string from a regular expression describing attributes.
WekaRelationName	Extracts the relation name of a weka.core.Instances or weka.core.Instance object.
WekaRenameRelation	Modifies relation names.
WekaSetInstanceValue	Sets a value in a WEKA Instance. Notes: - relational values cannot be set - '?' (without single quotes) is interpreted as missing value Input/output: - accepts: weka.core.Instance - generates: weka.core.Instance Valid options are:
WekaStoreInstance	Appends the incoming weka.core.Instance to the dataset in storage.
WekaSubsets	Splits the dataset based on the unique values of the specified attribute: all rows with the same unique value form a subset.
WekaTestSetEvaluator	Evaluates a trained classifier (obtained from input) on the dataset obtained from the global actor.
WekaTextDirectoryReader	Loads all text files in a directory and uses the subdirectory names as class labels.
WekaTrainTestSetEvaluator	Trains a classifier on an incoming training dataset (from a container) and then evaluates it on the test set (also from a container). The classifier setup being used in the evaluation is a global 'Classifier' actor.

Enum Summary

WekaClassifierRanker.Measure	The performance measure to use.
WekaExtractArray.ExtractionType	The type of extraction to perform.
WekaFileReader.OutputType	Defines how to output the data.
WekaInstanceBuffer.Operation	Defines how the buffer actor operates.
WekaInstanceDumper.OutputFormat	The format to output the data in.
WekaInstancesInfo.InfoType	The type of information to generate.
WekaInstancesStatistic.DataType	Defines what data to retrieve from an Instances object.