scalation.modeling.RidgeBridgeRegression
See theRidgeBridgeRegression companion class
The RidgeBridgeRegression companion object provides default hyper-parameters and convenience factory methods.
Attributes
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Companion
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class
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Graph
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Supertypes
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class Object
trait Matchable
class Any
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Self type
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Members list
Create a Ridge-Bridge Regression from a combined xy matrix.
Create a Ridge-Bridge Regression from a combined xy matrix.
Value parameters
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col
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the column used for response variable
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fname_
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the feature/variable names (defaults to null)
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hparam
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the regularization hyper-parameters (lambda for ridge, beta for bridge, q)
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xy
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the centered combines x and y matrix
Attributes
Create a Ridge-Bridge Regression from an x matrix and y vector.
Create a Ridge-Bridge Regression from an x matrix and y vector.
Value parameters
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fname_
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the feature/variable names (defaults to null)
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hparam
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the regularization hyper-parameters (lambda for ridge, beta for bridge, q)
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x
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the centered data/input matrix
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y
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the centered response/output vector
Attributes
Create a Ridge-Bridge Regression object from a data matrix and a response vector. This method provides data rescaling of x and centering of y.
Create a Ridge-Bridge Regression object from a data matrix and a response vector. This method provides data rescaling of x and centering of y.
Value parameters
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fname
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the feature/variable names (defaults to null)
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hparam
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the shrinkage hyper-parameter (0 => OLS) in the penalty term 'lambda * b dot b'
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x
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the un-centered data/input m-by-n matrix, NOT augmented with a first column of ones
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y
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the un-centered response/output vector
Attributes