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object NeuralNet_XLT extends ModelFactory

The NeuralNet_XLT companion object provides factory functions for buidling three-layer (one hidden layer) neural network classifiers. Note, 'rescale' is defined in ModelFactory in Model.scala.

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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
    Definition Classes
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  4. def allForms(x: MatriD): MatriD

    Create all forms/terms for each row/point placing them in a new matrix.

    Create all forms/terms for each row/point placing them in a new matrix.

    x

    the original un-expanded input/data matrix

    Definition Classes
    ModelFactory
  5. def apply(xy: MatriD, nz: Array[Int], fname: Strings = null, hparam: HyperParameter = hp, af: Array[AFF] = Array (f_tanh, f_tanh, f_id), transfer: NetParam = null): NeuralNet_XL

    Create a NeuralNet_XLT for a combined data matrix.

    Create a NeuralNet_XLT for a combined data matrix.

    xy

    the combined input and output matrix

    nz

    the number of nodes in each hidden layer, e.g., Array (5, 10) means 2 hidden with sizes 5 and 10

    fname

    the feature/variable names

    hparam

    the hyper-parameters

    af

    the array of activation function families over all layers

    transfer

    the saved network parameters from a layer of a related neural network

  6. final def asInstanceOf[T0]: T0
    Definition Classes
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  7. def clone(): AnyRef
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  8. final def eq(arg0: AnyRef): Boolean
    Definition Classes
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  9. def equals(arg0: Any): Boolean
    Definition Classes
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  10. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  11. def forms(xi: VectoD, k: Int, nt: Int): VectoD

    Given a vector/point 'v', compute the values for all of its forms/terms, returning them as a vector (assumes Regression with intercept).

    Given a vector/point 'v', compute the values for all of its forms/terms, returning them as a vector (assumes Regression with intercept). Override for expanded columns, e.g., QuadRegression.

    xi

    the vector/point (i-th row of x) for creating forms/terms

    k

    the number of features/predictor variables (not counting intercept)

    nt

    the number of terms

    Definition Classes
    ModelFactory
  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. def numTerms(k: Int): Int

    The number of terms/parameters in the model (assumes Regression with intercept.

    The number of terms/parameters in the model (assumes Regression with intercept. Override for expanded columns, e.g., QuadRegression.

    k

    the number of features/predictor variables (not counting intercept)

    Definition Classes
    ModelFactory
  19. val rescale: Boolean

    The 'rescale' flag indicated whether the data is to be rescaled/normalized

    The 'rescale' flag indicated whether the data is to be rescaled/normalized

    Attributes
    protected
    Definition Classes
    ModelFactory
  20. def rescaleOff(): Unit

    Turn rescaling off.

    Turn rescaling off.

    Definition Classes
    ModelFactory
  21. def rescaleOn(): Unit

    Turn rescaling on.

    Turn rescaling on.

    Definition Classes
    ModelFactory
  22. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
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  23. def toString(): String
    Definition Classes
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  24. final def wait(arg0: Long, arg1: Int): Unit
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  25. final def wait(arg0: Long): Unit
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  26. final def wait(): Unit
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  1. def finalize(): Unit
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    @throws( classOf[java.lang.Throwable] ) @Deprecated
    Deprecated

Inherited from ModelFactory

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