Package weka.core

Interface BatchPredictor

All Known Implementing Classes:
AbstractClassifier, AdaBoostM1, AdditiveRegression, AttributeSelectedClassifier, Bagging, BayesNet, BayesNetGenerator, BIFReader, ClassificationViaRegression, CostSensitiveClassifier, CVParameterSelection, DecisionStump, DecisionTable, EditableBayesNet, FilteredClassifier, GaussianProcesses, GeneralRegression, HoeffdingTree, IBk, InputMappedClassifier, IteratedSingleClassifierEnhancer, IterativeClassifierOptimizer, J48, JRip, KStar, LinearRegression, LMT, LMTNode, Logistic, LogisticBase, LogitBoost, LWL, M5Base, M5P, M5Rules, MultiClassClassifier, MultiClassClassifierUpdateable, MultilayerPerceptron, MultipleClassifiersCombiner, MultiScheme, NaiveBayes, NaiveBayesMultinomial, NaiveBayesMultinomialText, NaiveBayesMultinomialUpdateable, NaiveBayesUpdateable, NeuralNetwork, OneR, ParallelIteratedSingleClassifierEnhancer, ParallelMultipleClassifiersCombiner, PART, PMMLClassifier, PreConstructedLinearModel, RandomCommittee, RandomForest, RandomizableClassifier, RandomizableFilteredClassifier, RandomizableIteratedSingleClassifierEnhancer, RandomizableMultipleClassifiersCombiner, RandomizableParallelIteratedSingleClassifierEnhancer, RandomizableParallelMultipleClassifiersCombiner, RandomizableSingleClassifierEnhancer, RandomSubSpace, RandomTree, Regression, RegressionByDiscretization, REPTree, RuleNode, RuleSetModel, SerializedClassifier, SGD, SGDText, SimpleLinearRegression, SimpleLogistic, SingleClassifierEnhancer, SMO, SMOreg, Stacking, SupportVectorMachineModel, TreeModel, Vote, VotedPerceptron, WeightedInstancesHandlerWrapper, ZeroR
public interface BatchPredictor
Interface to something that can produce predictions in a batch manner when presented with a set of Instances.
Version:
$Revision: 11958 $
Author:
Mark Hall (mhall{[at]}pentaho{[dot]}com)

  • Method Details

    • setBatchSize

      void setBatchSize(String size)
      Set the batch size to use. The implementer will prefer (but not necessarily expect) this many instances to be passed in to distributionsForInstances().
      Parameters:
      size - the batch size to use

    • getBatchSize

      String getBatchSize()
      Get the batch size to use. The implementer will prefer (but not necessarily expect) this many instances to be passed in to distributionsForInstances(). Allows the preferred batch size to be encapsulated with the client.
      Returns:
      the batch size to use

    • distributionsForInstances

      double[][] distributionsForInstances(Instances insts) throws Exception
      Batch scoring method
      Parameters:
      insts - the instances to get predictions for
      Returns:
      an array of probability distributions, one for each instance
      Throws:
      Exception - if a problem occurs

    • implementsMoreEfficientBatchPrediction

      boolean implementsMoreEfficientBatchPrediction()
      Returns true if this BatchPredictor can generate batch predictions in an efficient manner.
      Returns:
      true if batch predictions can be generated efficiently