StochasticOutlierSelection

Value Members

1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. final def asInstanceOf[T0]: T0

   

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5. def chainPredictor[P <: Predictor[P]](predictor: P): ChainedPredictor[StochasticOutlierSelection, P]

   

   Chains a Transformer with a Predictor to form a ChainedPredictor.

   Chains a Transformer with a Predictor to form a ChainedPredictor.

   P

   Type of the Predictor

   predictor

   Trailing Predictor of the resulting pipeline

   Definition Classes

   Transformer

6. def chainTransformer[T <: Transformer[T]](transformer: T): ChainedTransformer[StochasticOutlierSelection, T]

   

   Chains two Transformer to form a ChainedTransformer.

   Chains two Transformer to form a ChainedTransformer.

   T

   Type of the Transformer

   transformer

   Right side transformer of the resulting pipeline

   Definition Classes

   Transformer

7. def clone(): AnyRef

   

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   protected[java.lang]

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8. final def eq(arg0: AnyRef): Boolean

   

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9. def equals(arg0: Any): Boolean

   

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10. def finalize(): Unit

    

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    protected[java.lang]

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    @throws( classOf[java.lang.Throwable] )

11. def fit[Training](training: DataSet[Training], fitParameters: ParameterMap = ParameterMap.Empty)(implicit fitOperation: FitOperation[StochasticOutlierSelection, Training]): Unit

    

    Fits the estimator to the given input data.

    Fits the estimator to the given input data. The fitting logic is contained in the FitOperation. The computed state will be stored in the implementing class.

    Training

    Type of the training data

    training

    Training data

    fitParameters

    Additional parameters for the FitOperation

    fitOperation

    FitOperation which encapsulates the algorithm logic

    Definition Classes

    Estimator

12. final def getClass(): Class[_]

    

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13. def hashCode(): Int

    

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14. final def isInstanceOf[T0]: Boolean

    

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15. final def ne(arg0: AnyRef): Boolean

    

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16. final def notify(): Unit

    

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17. final def notifyAll(): Unit

    

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18. val parameters: ParameterMap

    

    Definition Classes

    WithParameters

19. def setErrorTolerance(errorTolerance: Double): StochasticOutlierSelection

    

    The accepted error tolerance to reduce computational time when approximating the affinity.

    The accepted error tolerance to reduce computational time when approximating the affinity.

    errorTolerance

    the accepted error tolerance with respect to the affinity

20. def setMaxIterations(maxIterations: Int): StochasticOutlierSelection

    

    The maximum number of iterations to approximate the affinity of the algorithm.

    The maximum number of iterations to approximate the affinity of the algorithm.

    maxIterations

    the maximum number of iterations.

21. def setPerplexity(perplexity: Double): StochasticOutlierSelection

    

    Sets the perplexity of the outlier selection algorithm, can be seen as the k of kNN For more information, please read the Stochastic Outlier Selection algorithm technical paper.

    Sets the perplexity of the outlier selection algorithm, can be seen as the k of kNN For more information, please read the Stochastic Outlier Selection algorithm technical paper.

    perplexity

    the perplexity of the affinity fit

22. final def synchronized[T0](arg0: ⇒ T0): T0

    

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23. def toString(): String

    

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24. def transform[Input, Output](input: DataSet[Input], transformParameters: ParameterMap = ParameterMap.Empty)(implicit transformOperation: TransformDataSetOperation[StochasticOutlierSelection, Input, Output]): DataSet[Output]

    

    Transform operation which transforms an input DataSet of type I into an output DataSet of type O.

    Transform operation which transforms an input DataSet of type I into an output DataSet of type O. The actual transform operation is implemented within the TransformDataSetOperation.

    Input

    Input data type

    Output

    Output data type

    input

    Input DataSet of type I

    transformParameters

    Additional parameters for the TransformDataSetOperation

    transformOperation

    TransformDataSetOperation which encapsulates the algorithm's logic

    Definition Classes

    Transformer

25. final def wait(): Unit

    

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26. final def wait(arg0: Long, arg1: Int): Unit

    

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27. final def wait(arg0: Long): Unit

    

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