scalation.metamodel

QuadraticFit

class QuadraticFit extends AnyRef

The QuadraticFit class uses multiple regression to fit a quadratic surface to the function 'f'. This is useful when computing 'f' is costly, for example in simulation optimization. The fit is over a multi-dimensional grid and can be used for interpolation and limited extrapolation.

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Instance Constructors

  1. new QuadraticFit(f: FunctionV2S, n: Int = 2)

    f

    the vector-to-scalar function to fit.

    n

    the dimensionality of the domain of f

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  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  9. def fit(result: (MatrixD, VectorD)): Unit

    Given a design matrix and response vector, use multiple regression to fit the surface, i.

    Given a design matrix and response vector, use multiple regression to fit the surface, i.e., determine the coefficients of the regression equation.

    result

    the design matrix and response vector grouped in a tuple

  10. def formGrid(x: VectorD, i: Int, d: Double = 1.0, m: Int = 2): Unit

    Given a center point x, form a square grid around it.

    Given a center point x, form a square grid around it. This can be used to create a design matrix for use in multiple regression.

    x

    the center point/vector of the grid

    i

    the current dimension (facilitates recursion)

    d

    the distance to move on each step

    m

    move m steps above and below x(i) for each dimension i

  11. final def getClass(): Class[_]

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  13. final def isInstanceOf[T0]: Boolean

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  14. final def ne(arg0: AnyRef): Boolean

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  15. final def notify(): Unit

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  16. final def notifyAll(): Unit

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  17. def qForms(x: VectorD): VectorD

    Given a vector 'x', compute the values for all of its quadratic, linear and constant forms/terms, returning them as a vector.

    Given a vector 'x', compute the values for all of its quadratic, linear and constant forms/terms, returning them as a vector. for 1D: VectorD (1., x(0), x(0)~2.) for 2D: VectorD (1., x(0), x(0)~2., x(1), x(1)*x(0), x(1)~^2.)

    x

    the source vector for creating forms/terms

  18. def qFormsEval(x: VectorD): Double

    Given a point x, use the quadratic regression equation to estimate a value for the function at x.

    Given a point x, use the quadratic regression equation to estimate a value for the function at x. for 1D: b(0) + b(1)*x(0) + b(2)*x(0)~2. for 2D: b(0) + b(1)*x(0) + b(2)*x(0)~2. + b(3)*x(1) + b(4)*x(1)*x(0) + b(5)*x(1)~^2.

    x

    the point whose functional value is to be predicted

  19. def response(): (MatrixD, VectorD)

    Given a grid of design points, create a design matrix 'xx' and response vector 'yy' returning them as a tuple.

  20. final def synchronized[T0](arg0: ⇒ T0): T0

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