SOM

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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 equals(arg0: Any): Boolean

   

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8. def finalize(): Unit

   

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

   

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10. def hashCode(): Int

    

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11. final def isInstanceOf[T0]: Boolean

    

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12. def load(path: String): Try[SOM]

    

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. def random(xdim: Int, ydim: Int, fdim: Int, seed: Option[Int] = None): SOM

    

    Initialize a self-organizing map with random weights, xdim columns, ydim rows, and fdim features per cell.

    Initialize a self-organizing map with random weights, xdim columns, ydim rows, and fdim features per cell.

17. def save(som: SOM, path: String): Try[Unit]

    

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

    

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19. def toString(): String

    

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20. def train(xdim: Int, ydim: Int, fdim: Int, iterations: Int, examples: RDD[Vector], seed: Option[Int] = None, sigmaScale: Double = 0.95, minSigma: Double = 1, hook: (Int, SOM) ⇒ Unit = { case (_,_) => }): SOM

    

    Train a self-organizing map from an RDD of examples for iterations iterations, starting with a randomly-weighted map with xdim columns, ydim rows, and fdim features per cell.

    Train a self-organizing map from an RDD of examples for iterations iterations, starting with a randomly-weighted map with xdim columns, ydim rows, and fdim features per cell. Optional parameters include sigmaScale, to specify the width of the neighborhood function as a factor of each map dimension, minSigma, which sets the neighborhood width at the last iteration, and hook, which is a callback function to run after each iteration.

21. def trainDF(xdim: Int, ydim: Int, fdim: Int, iterations: Int, examples: DataFrame, seed: Option[Int] = None, sigmaScale: Double = 0.95, minSigma: Double = 1, exampleCol: String = "features", hook: (Int, SOM) ⇒ Unit = { case (_,_) => }): SOM

    

    Train a self-organizing map from a data frame of examples for iterations iterations, starting with a randomly-weighted map with xdim columns, ydim rows, and fdim features per cell.

    Train a self-organizing map from a data frame of examples for iterations iterations, starting with a randomly-weighted map with xdim columns, ydim rows, and fdim features per cell. Optional parameters include sigmaScale, to specify the width of the neighborhood function as a factor of each map dimension, minSigma, which sets the neighborhood width at the last iteration, and hook, which is a callback function to run after each iteration.

22. final def wait(): Unit

    

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

    

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

    

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