The PolyORegression companion object provides factory methods for creating orthogonal polynomial regression models and methods for creating functional forms.
Attributes
- Companion
- class
- Graph
-
- Supertypes
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class Objecttrait Matchableclass Any
- Self type
-
PolyORegression.type
Members list
Value members
Concrete methods
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.
Value parameters
- ord
-
the order (max degree) of the polynomial
- x
-
the original un-expanded input/data matrix
Attributes
Create a PolyORegression object from a combined data-response matrix.
Create a PolyORegression object from a combined data-response matrix.
Value parameters
- fname_
-
the feature/variable names (defaults to null)
- hparam
-
the hyper-parameters (defaults to PolyRegression.hp)
- ord
-
the order (k) of the polynomial (max degree)
- xy
-
the initial combined data-response matrix (before polynomial term expansion)
Attributes
Create a PolyORegression object from a combined data-response matrix.
Create a PolyORegression object from a combined data-response matrix.
Value parameters
- fname_
-
the feature/variable names
- hparam
-
the hyper-parameters
- ord
-
the order (k) of the polynomial (max degree)
- t
-
the initial data/input vector: t_i expands to x_i = [1, t_i, t_i^2, ... t_i^k]
- y
-
the response/ouput vector
Attributes
Given a 1-vector/point 'v', compute the values for all of its polynomial forms/terms, returning them as a vector.
Given a 1-vector/point 'v', compute the values for all of its polynomial forms/terms, returning them as a vector.
Value parameters
- k
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number of features/predictor variables (not counting intercept) = 1
- nt
-
the number of terms
- v
-
the vector/point (i-th row of t) for creating forms/terms
Attributes
Get the multipliers for orthogonal polynomials, matrix 'a'. FIX - collecting the 'a' matrix this way may fail for parallel processing
Get the multipliers for orthogonal polynomials, matrix 'a'. FIX - collecting the 'a' matrix this way may fail for parallel processing
Attributes
The number of terms/parameters in the model (assumes Regression with intercept).
The number of terms/parameters in the model (assumes Regression with intercept).
Value parameters
- k
-
the number of features/predictor variables (not counting intercept)
Attributes
Orthogonalize the data/input matrix x using Gram-Schmidt Orthogonalization, returning the a new orthogonal matrix z and the orthogonalization multipliers a. This will eliminate the multi-collinearity problem.
Orthogonalize the data/input matrix x using Gram-Schmidt Orthogonalization, returning the a new orthogonal matrix z and the orthogonalization multipliers a. This will eliminate the multi-collinearity problem.
Value parameters
- x
-
the matrix to orthogonalize
Attributes
Create a PolyORegression object from a data matrix and a response vector. This method provides data rescaling.
Create a PolyORegression object from a data matrix and a response vector. This method provides data rescaling.
Value parameters
- fname
-
the feature/variable names (defaults to null)
- hparam
-
the hyper-parameters (defaults to PolyRegression.hp)
- ord
-
the order (k) of the polynomial (max degree)
- x
-
the initial data/input matrix (before polynomial term expansion)
- y
-
the response/output m-vector