The SymRidgeRegression object provides several factory methods.
Attributes
- Companion
- class
- Graph
-
- Supertypes
-
class Objecttrait Matchableclass Any
- Self type
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SymRidgeRegression.type
Members list
Value members
Concrete methods
Create a SymRidgeRegression object from a data matrix and a response vector. Partial support for "Symbolic Ridge Regression" as matrix x can be raised to several powers (e.g., x^1 and x^2). Note, x^1 is automatically included. NOTE, Ridge Regression will NOT have an INTERCEPT column.
Create a SymRidgeRegression object from a data matrix and a response vector. Partial support for "Symbolic Ridge Regression" as matrix x can be raised to several powers (e.g., x^1 and x^2). Note, x^1 is automatically included. NOTE, Ridge Regression will NOT have an INTERCEPT column.
Value parameters
- cross
-
whether to include 2-way cross/interaction terms x_i x_j (defaults to true)
- cross3
-
whether to include 3-way cross/interaction terms x_i x_j x_k (defaults to false)
- fname
-
the feature/variable names (defaults to null)
- hparam
-
the hyper-parameters (defaults to RidgeRegression.hp)
- powers
-
the set of powers to raise matrix x to (defaults to null)
- terms
-
custom terms to add into the model, e.g., Array ((0, 1.0), (1, -2.0)) adds x0 x1^(-2)
- x
-
the initial data/input m-by-n matrix (before expansion) must not include an intercept column of all ones
- y
-
the response/output m-vector
Attributes
- See also
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SymbolicRegression.buildMatrix
Create a SymRidgeRegression object that uses multiple regression to fit a cubic surface to the data. For example in 2D, the cubic regression equation is y = b dot x + e = [b_0, ... b_k] dot [x_0, x_0^2, x_0^3, x_1, x_1^2, x_1^3, x_0x_1, x_0^2x_1, x_0*x_1^2] + e NOTE, Ridge Regression will NOT have an INTERCEPT column.
Create a SymRidgeRegression object that uses multiple regression to fit a cubic surface to the data. For example in 2D, the cubic regression equation is y = b dot x + e = [b_0, ... b_k] dot [x_0, x_0^2, x_0^3, x_1, x_1^2, x_1^3, x_0x_1, x_0^2x_1, x_0*x_1^2] + e NOTE, Ridge Regression will NOT have an INTERCEPT column.
Value parameters
- cross
-
whether to include 2-way cross/interaction terms x_i x_j (defaults to false)
- cross3
-
whether to include 3-way cross/interaction terms x_i x_j x_k (defaults to false)
- fname
-
the feature/variable names (defaults to null)
- hparam
-
the hyper-parameters (defaults to RidgeRegression.hp)
- x
-
the initial data/input m-by-n matrix (before quadratic term expansion) must not include an intercept column of all ones
- y
-
the response/output m-vector
Attributes
Create a SymRidgeRegression object that uses multiple regression to fit a quadratic surface to the data. For example in 2D, the quadratic regression equation is y = b dot x + e = [b_0, ... b_k] dot [x_0, x_0^2, x_1, x_1^2] + e NOTE, Ridge Regression will NOT have an INTERCEPT column.
Create a SymRidgeRegression object that uses multiple regression to fit a quadratic surface to the data. For example in 2D, the quadratic regression equation is y = b dot x + e = [b_0, ... b_k] dot [x_0, x_0^2, x_1, x_1^2] + e NOTE, Ridge Regression will NOT have an INTERCEPT column.
Value parameters
- cross
-
whether to include cross terms x_i * x_j (defaults to false)
- fname
-
the feature/variable names (defaults to null)
- hparam
-
the hyper-parameters (defaults to RidgeRegression.hp)
- x
-
the initial data/input m-by-n matrix (before quadratic term expansion) must not include an intercept column of all ones
- y
-
the response/output m-vector
Attributes
Create a SymRidgeRegression object from a data matrix and a response vector. This method provides data rescaling via normalization (z-transform). NOTE, Ridge Regression will NOT have an INTERCEPT column.
Create a SymRidgeRegression object from a data matrix and a response vector. This method provides data rescaling via normalization (z-transform). NOTE, Ridge Regression will NOT have an INTERCEPT column.
Value parameters
- cross
-
whether to include 2-way cross/interaction terms x_i x_j (defaults to true)
- cross3
-
whether to include 3-way cross/interaction terms x_i x_j x_k (defaults to false)
- fname
-
the feature/variable names (defaults to null)
- hparam
-
the hyper-parameters (defaults to Regression.hp)
- powers
-
the set of powers to raise matrix x to (defualts to null)
- terms
-
custom terms to add into the model, e.g., Array ((0, 1.0), (1, -2.0)) adds x0 x1^(-2)
- x
-
the data/input m-by-n matrix (augment with a first column of ones to include intercept in model)
- y
-
the response/output m-vector
Attributes
Create a SymRidgeRegression object from a data matrix and a response vector. This method provides data rescaling via min-max-transform.
Create a SymRidgeRegression object from a data matrix and a response vector. This method provides data rescaling via min-max-transform.
Value parameters
- cross
-
whether to include 2-way cross/interaction terms x_i x_j (defaults to true)
- cross3
-
whether to include 3-way cross/interaction terms x_i x_j x_k (defaults to false)
- fname
-
the feature/variable names (defaults to null)
- hparam
-
the hyper-parameters (use Regression.hp for default)
- powers
-
the set of powers to raise matrix x to
- terms
-
custom terms to add into the model, e.g., Array ((0, 1.0), (1, -2.0)) adds x0 x1^(-2)
- x
-
the data/input m-by-n matrix (augment with a first column of ones to include intercept in model)
- y
-
the response/output m-vector
Attributes
Search for a good symbolic regression model by trying several combinations of powers.
Search for a good symbolic regression model by trying several combinations of powers.
Value parameters
- cross
-
whether to include 2-way cross/interaction terms x_i x_j (defaults to true)
- cross3
-
whether to include 3-way cross/interaction terms x_i x_j x_k (defaults to false)
- fname
-
the feature/variable names (defaults to null)
- hparam
-
the hyper-parameters (use Regression.hp for default)
- terms
-
custom terms to add into the model, e.g., Array ((0, 1.0), (1, -2.0)) adds x0 x1^(-2)
- x
-
the initial data/input m-by-n matrix (before expansion) must not include an intercept column of all ones
- y
-
the response/output m-vector