RaRParamsFixed

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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 clone(): AnyRef

   

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

   

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7. def finalize(): Unit

   

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

   

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9. final def isInstanceOf[T0]: Boolean

   

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10. val k: Int

    

    Maximum size of the subset to select in each random trial.

    Maximum size of the subset to select in each random trial. This inherently defines the maximal size of the cluster that can be detected. Because the algorithm always only evaluates a subset of the features, it will never find correlated clusters of a size bigger than k. Nevertheless, setting k to high leads to a low specificity of the contrast measure.

    Definition Classes

    RaRParamsFixed → RaRParams

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

    

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12. final def notify(): Unit

    

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13. final def notifyAll(): Unit

    

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14. def numberOfMonteCarlos(numFeatures: Int): Int

    

    Defines how many random trials are used to fill the tables.

    Defines how many random trials are used to fill the tables. The more tries we allow the more accurate the results will be and the smaller correlations we will find. Can be static or dependent on the number of features.

    numFeatures

    number of features in the dataset

    Definition Classes

    RaRParamsFixed → RaRParams

15. val numberOfMonteCarlosFixed: Int

    

16. val parallelismFactor: Int

    

    Defines how many operations are allowed to be performed in parallel

    Defines how many operations are allowed to be performed in parallel

    Definition Classes

    RaRParamsFixed → RaRParams

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

    

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18. final def wait(): Unit

    

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

    

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

    

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