object NeuralNet_3L1_4TS extends ModelFactory
The NeuralNet_3L1_4TS companion object provides factory functions and functions
for creating functional forms.
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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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- ModelFactory
- def apply(x: MatriD, y: VectoD, nz: Int, fname: Strings, hparam: HyperParameter, f0: AFF, f1: AFF): NeuralNet_3L1_4TS
Create a
NeuralNet_3L1_4TSobject from a data matrix and a response vector.Create a
NeuralNet_3L1_4TSobject from a data matrix and a response vector. This factory function provides data rescaling.- x
the initial input/data matrix (before expansion)
- y
the output/response m-vector
- nz
the number of nodes in hidden layer (-1 => use default formula)
- fname
the feature/variable names (if null, use x_j's)
- hparam
the hyper-parameters for the model/network
- f0
the activation function family for layers 1->2 (input to hidden)
- f1
the activation function family for layers 2->3 (hidden to output)
- See also
ModelFactory
- def apply(xy: MatriD, nz: Int = -1, fname: Strings = null, hparam: HyperParameter = hp, f0: AFF = f_tanh, f1: AFF = f_id): NeuralNet_3L1_4TS
Create a
NeuralNet_3L1_4TSobject from a combined data-response matrix.Create a
NeuralNet_3L1_4TSobject from a combined data-response matrix.- xy
the m-by-(nx+1) combined input/data matrix and output/response vector
- nz
the number of nodes in hidden layer (-1 => use default formula)
- fname
the feature/variable names (if null, use x_j's)
- hparam
the hyper-parameters for the model/network
- f0
the activation function family for layers 1->2 (input to hidden)
- 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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- val hp: HyperParameter
Base hyper-parameter specification for
NeuralNet_3L1_4TS - final def isInstanceOf[T0]: Boolean
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- def numTerms(k: Int): Int
The number of terms include current value and lag one value.
The number of terms include current value and lag one value. when there are no cross-terms.
- k
number of features/predictor variables (not counting intercept)
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- NeuralNet_3L1_4TS → ModelFactory
- 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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