object RidgeRegression extends ModelFactory
The RidgeRegression companion object defines hyper-paramters and provides
factory functions for the RidgeRegression class.
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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, fname: Strings, hparam: HyperParameter, technique: RegTechnique.RegTechnique): RidgeRegression
Create a Ridge Regression from a data matrix and response vector.
Create a Ridge Regression from a data matrix and response vector.
- x
the centered data/input m-by-n matrix, NOT augmented with a first column of ones
- y
the centered repsonse/output vector
- fname
the feature/variable names
- hparam
the shrinkage hyper-parameter (0 => OLS) in the penalty term 'lambda * b dot b'
- technique
the technique used to solve for b in (x.t*x + lambda*I)*b = x.t*y
- def apply(xy: MatriD, fname: Strings = null, hparam: HyperParameter = hp, technique: RegTechnique.RegTechnique = Cholesky): RidgeRegression
Create a Ridge Regression from a combined data matrix.
Create a Ridge Regression from a combined data matrix.
- xy
the centered data/input m-by-n matrix, NOT augmented with a first column of ones and the centered response m-vector (combined)
- fname
the feature/variable names
- hparam
the shrinkage hyper-parameter (0 => OLS) in the penalty term 'lambda * b dot b'
- technique
the technique used to solve for b in (x.t*x + lambda*I)*b = x.t*y
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- val drp: (Null, HyperParameter, RegTechnique.Value)
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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
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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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