LinearTaylor

class LinearTaylor(*args)

First order polynomial response surface by Taylor expansion.

Available constructors:

LinearTaylor(center, function)

Parameters:
centersequence of float

Point \vect{x}_0 where the Taylor expansion of the function h is performed.

functionFunction

Function h to be approximated.

See also

QuadraticTaylor, LinearLeastSquares, QuadraticLeastSquares

Notes

The approximation of the model response \vect{y} = h(\vect{x}) around a specific set \vect{x}_0 = (x_{0,1},\dots,x_{0,n_{X}}) of input parameters may be of interest. One may then substitute h for its Taylor expansion at point \vect{x}_0. Hence h is replaced with a first or second-order polynomial \widehat{h} whose evaluation is inexpensive, allowing the analyst to apply the uncertainty anaysis methods.

We consider here the first order Taylor expansion around \ux=\vect{x}_0.

\vect{y} \, \approx \, \widehat{h}(\vect{x}) \, = \, h(\vect{x}_0) \, + \, \sum_{i=1}^{n_{X}} \; \frac{\partial h}{\partial x_i}(\vect{x}_0).\left(x_i - x_{0,i} \right)

Introducing a vector notation, the previous equation rewrites:

\vect{y} \, \approx \, \vect{y}_0 \, + \, \vect{\vect{L}} \: \left(\vect{x}-\vect{x}_0\right)

where:

  • \vect{y_0} = (y_{0,1} , \dots, y_{0,n_Y})^{\textsf{T}} = h(\vect{x}_0) is the vector model response evaluated at \vect{x}_0;

  • \vect{x} is the current set of input parameters;

  • \vect{\vect{L}} = \left( \frac{\partial y_{0,j}}{\partial x_i} \,,\, i=1,\ldots, n_X \,,\, j=1,\ldots, n_Y \right) is the transposed Jacobian matrix evaluated at \vect{x}_0.

Examples

>>> import openturns as ot
>>> formulas = ['x1 * sin(x2)', 'cos(x1 + x2)', '(x2 + 1) * exp(x1 - 2 * x2)']
>>> myFunc = ot.SymbolicFunction(['x1', 'x2'], formulas)
>>> myTaylor = ot.LinearTaylor([1, 2], myFunc)
>>> myTaylor.run()
>>> responseSurface = myTaylor.getMetaModel()
>>> print(responseSurface([1.2,1.9]))
[1.13277,-1.0041,0.204127]

Methods

getCenter()

Get the center.

getClassName()

Accessor to the object's name.

getConstant()

Get the constant vector of the approximation.

getId()

Accessor to the object's id.

getInputFunction()

Get the function.

getLinear()

Get the gradient of the function at \vect{x}_0.

getMetaModel()

Get an approximation of the function.

getName()

Accessor to the object's name.

getShadowedId()

Accessor to the object's shadowed id.

getVisibility()

Accessor to the object's visibility state.

hasName()

Test if the object is named.

hasVisibleName()

Test if the object has a distinguishable name.

run()

Perform the Linear Taylor expansion around \vect{x}_0.

setName(name)

Accessor to the object's name.

setShadowedId(id)

Accessor to the object's shadowed id.

setVisibility(visible)

Accessor to the object's visibility state.
__init__(*args)
getCenter()

Get the center.

Returns:
centerPoint

Point \vect{x}_0 where the Taylor expansion of the function is performed.

getClassName()

Accessor to the object’s name.

Returns:
class_namestr

The object class name (object.__class__.__name__).

getConstant()

Get the constant vector of the approximation.

Returns:
constantVectorPoint

Constant vector of the approximation, equal to h(x_0).

getId()

Accessor to the object’s id.

Returns:
idint

Internal unique identifier.

getInputFunction()

Get the function.

Returns:
functionFunction

Function h to be approximated.

getLinear()

Get the gradient of the function at \vect{x}_0.

Returns:
gradientMatrix

Gradient of the function h at the point \vect{x}_0 (the transposition of the jacobian matrix).

getMetaModel()

Get an approximation of the function.

Returns:
approximationFunction

An approximation of the function h by a Linear Taylor expansion at the point \vect{x}_0.

getName()

Accessor to the object’s name.

Returns:
namestr

The name of the object.

getShadowedId()

Accessor to the object’s shadowed id.

Returns:
idint

Internal unique identifier.

getVisibility()

Accessor to the object’s visibility state.

Returns:
visiblebool

Visibility flag.

hasName()

Test if the object is named.

Returns:
hasNamebool

True if the name is not empty.

hasVisibleName()

Test if the object has a distinguishable name.

Returns:
hasVisibleNamebool

True if the name is not empty and not the default one.

run()

Perform the Linear Taylor expansion around \vect{x}_0.

setName(name)

Accessor to the object’s name.

Parameters:
namestr

The name of the object.

setShadowedId(id)

Accessor to the object’s shadowed id.

Parameters:
idint

Internal unique identifier.

setVisibility(visible)

Accessor to the object’s visibility state.

Parameters:
visiblebool

Visibility flag.

Examples using the class

Taylor approximations

Taylor approximations