SymLassoRegression
The SymLassoRegression object supports symbolic ridge regression that allows variables/columns to be raised to various powers, e.g., x^2, x^3, x^.5. Note, x~^p is a column-wise power function (each column raised to p-th power). IMPORTANT: must not include INTERCEPT (column of ones) in initial data matrix), i.e., DO NOT include a column of ones in x (will cause singularity in expanded matrix). Method signatures are the as same as for SymbolicRegression, except there is NO intercept ARGUMENT.
Attributes
- Graph
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- Supertypes
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class Objecttrait Matchableclass Any
- Self type
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SymLassoRegression.type
Members list
Value members
Concrete methods
Create a LassoRegression object from a data matrix and a response vector. Partial support for "Symbolic Lasso Regression" as matrix x can be raised to several powers (e.g., x^1 and x^2). Note, x^1 is automatically included.
Create a LassoRegression object from a data matrix and a response vector. Partial support for "Symbolic Lasso Regression" as matrix x can be raised to several powers (e.g., x^1 and x^2). Note, x^1 is automatically included.
Value parameters
- cross
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whether to include 2-way cross/interaction terms x_i x_j (defaults to true)
- cross3
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whether to include 3-way cross/interaction terms x_i x_j x_k (defaults to false)
- fname
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the feature/variable names (defaults to null)
- hparam
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the hyper-parameters (defaults to LassoRegression.hp)
- intercept
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whether to include the intercept term (column of ones) _1 (defaults to true)
- powers
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the set of powers to raise matrix x to (defaults to null)
- terms
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custom terms to add into the model, e.g., Array ((0, 1.0), (1, -2.0)) adds x0 x1^(-2)
- x
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the initial data/input m-by-n matrix (before expansion) must not include an intercept column of all ones
- y
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the response/output m-vector
Attributes
- See also
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SymbolicRegression.buildMatrix
Create a LassoRegression 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
Create a LassoRegression 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
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
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the feature/variable names (defaults to null)
- hparam
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the hyper-parameters (defaults to LassoRegression.hp)
- intercept
-
whether to include the intercept term (column of ones) _1 (defaults to true)
- x
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the initial data/input m-by-n matrix (before quadratic term expansion) must not include an intercept column of all ones
- y
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the response/output m-vector
Attributes
Create a LassoRegression 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
Create a LassoRegression 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
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 LassoRegression.hp)
- intercept
-
whether to include the intercept term (column of ones) _1 (defaults to true)
- 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 SymLassoRegression object from a data matrix and a response vector. This method provides data rescaling.
Create a SymLassoRegression object from a data matrix and a response vector. This method provides data rescaling.
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)
- intercept
-
whether to include the intercept term (column of ones) _1 (defaults to true)
- powers
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the set of powers to raise matrix x to (defualts to null)
- terms
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custom terms to add into the model, e.g., Array ((0, 1.0), (1, -2.0)) adds x0 x1^(-2)
- x
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the data/input m-by-n matrix (augment with a first column of ones to include intercept in model)
- y
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the response/output m-vector