NaiveBayes

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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. def apply(xy: MatrixI, fn: Array[String], k: Int, cn: Array[String], vc: VectorI = null, me: Int = 3): NaiveBayes

   

   Create a 'NaiveBayes object, passing 'x' and 'y' together in one table.

   Create a 'NaiveBayes object, passing 'x' and 'y' together in one table.

   xy

   the data vectors along with their classifications stored as rows of a matrix

   fn

   the names of the features

   k

   the number of classes

   vc

   the value count (number of distinct values) for each feature

   me

   use m-estimates (me == 0 => regular MLE estimates)

5. final def asInstanceOf[T0]: T0

   

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6. def clone(): AnyRef

   

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

   

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8. def equals(arg0: Any): Boolean

   

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9. def finalize(): Unit

   

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

    

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11. def hashCode(): Int

    

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12. final def isInstanceOf[T0]: Boolean

    

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

    

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14. final def notify(): Unit

    

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15. final def notifyAll(): Unit

    

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

    

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17. def toString(): String

    

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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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