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 Summary

    All Methods Instance Methods Abstract Methods
    Modifier and Type | Method and Description
    --- | ---
    `double[][]` | `distributionsForInstances(Instances insts)` Batch scoring method
    `java.lang.String` | `getBatchSize()` Get the batch size to use.
    `boolean` | `implementsMoreEfficientBatchPrediction()` Returns true if this BatchPredictor can generate batch predictions in an efficient manner.
    `void` | `setBatchSize(java.lang.String size)` Set the batch size to use.

- - ### Method Detail

    - #### setBatchSize

          void setBatchSize(java.lang.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

          java.lang.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 java.lang.Exception

      Batch scoring method
      Parameters:
        `insts` - the instances to get predictions for
      Returns:
        an array of probability distributions, one for each instance
      Throws:
        `java.lang.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