object NeuralNet_Classif_XL extends ModelFactory
The NeuralNet_Classif_XL 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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- 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
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- def apply(x: MatriD, y: VectoI, nz: Array[Int] = null, fname: Strings = null, hparam: HyperParameter = hp, f: Array[AFF] = Array (f_tanh, f_tanh, f_sigmoid)): NeuralNet_Classif_XL
Create a
NeuralNet_XLfor a data matrix and response vector.Create a
NeuralNet_XLfor a data matrix and response vector.- x
the input/data matrix
- y
the output/response vector
- nz
the number of nodes in each hidden layer, e.g., Array (9, 8) => 2 hidden of sizes 9 and 8
- hparam
the hyper-parameters for the model/network
- f
the array of activation function families between every pair of layers
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- 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
Regressionwith intercept).Given a vector/point 'v', compute the values for all of its forms/terms, returning them as a vector (assumes
Regressionwith 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
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- val hp: HyperParameter
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- def numTerms(k: Int): Int
The number of terms/parameters in the model (assumes
Regressionwith intercept.The number of terms/parameters in the model (assumes
Regressionwith intercept. Override for expanded columns, e.g.,QuadRegression.- k
the number of features/predictor variables (not counting intercept)
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- 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
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- def rescaleOff(): Unit
Turn rescaling off.
Turn rescaling off.
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- def rescaleOn(): Unit
Turn rescaling on.
Turn rescaling on.
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