object NeuralNet_3L1 extends ModelFactory
The NeuralNet_3L1 companion object provides factory functions for buidling three-layer
(one hidden layer) neural nets. 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: VectoD, nz: Int, fname: Strings, hparam: HyperParameter, f0: AFF, f1: AFF): NeuralNet_3L1
Create a
NeuralNet_3L1for a data matrix and response vector.Create a
NeuralNet_3L1for a data matrix and response vector.- x
the m-by-nx input/data matrix
- y
the output/response m-vector
- nz
the number of nodes in hidden layer
- fname
the feature/variable names
- hparam
the hyper-parameters
- f0
the activation function family for layers 1->2 (input to output)
- f1
the activation function family for layers 2->3 (hidden to output)
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def
apply(xy: MatriD, nz: Int = -1, fname: Strings = null, hparam: HyperParameter = hp, f0: AFF = f_tanh, f1: AFF = f_id): NeuralNet_3L1
Create a
NeuralNet_3L1for a combined data matrix.Create a
NeuralNet_3L1for a combined data matrix.- xy
the combined input/data and output/response matrix
- nz
the number of nodes in hidden layer
- fname
the feature/variable names
- hparam
the hyper-parameters
- f0
the activation function family for layers 1->2 (input to output)
- f1
the activation function family for layers 2->3 (hidden to output)
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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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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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