OrthogonalUniVariatePolynomialFunctionFactory

class OrthogonalUniVariatePolynomialFunctionFactory(*args)

Polynomial specific orthogonal univariate function factory.

Available constructor:

OrthogonalUniVariatePolynomialFunctionFactory()

OrthogonalUniVariatePolynomialFunctionFactory(polynomialFactory)

Parameters:
polynomialFactoryOrthogonalUniVariatePolynomialFamily

The polynomial factory

Methods

build(order)

Build the n-th order orthogonal univariate function.

getClassName()

Accessor to the object's name.

getMeasure()

Accessor to the associated probability measure.

getName()

Accessor to the object's name.

hasName()

Test if the object is named.

setName(name)

Accessor to the object's name.

Examples

>>> import openturns as ot
>>> polynomialFactory = ot.HermiteFactory()
>>> functionFactory = ot.OrthogonalUniVariatePolynomialFunctionFactory(polynomialFactory)
__init__(*args)
build(order)

Build the n-th order orthogonal univariate function.

Parameters:
nint, 0 \leq n

Function order.

Returns:
functionUniVariateFunction

Requested orthogonal univariate function.

getClassName()

Accessor to the object’s name.

Returns:
class_namestr

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

getMeasure()

Accessor to the associated probability measure.

Returns:
measureDistribution

The associated probability measure (according to which the functions are orthogonal).

Notes

Two functions P and Q are orthogonal with respect to the probability measure w(x) \di{x} if and only if their scalar product:

\langle P, Q \rangle = \int_{\alpha}^{\beta} P(x) Q(x) w(x) \di{x}
                     = 0

where \alpha \in \Rset \cup \{-\infty\} and \beta \in \Rset \cup \{+\infty\}.

getName()

Accessor to the object’s name.

Returns:
namestr

The name of the object.

hasName()

Test if the object is named.

Returns:
hasNamebool

True if the name is not empty.

setName(name)

Accessor to the object’s name.

Parameters:
namestr

The name of the object.

Examples using the class

Create multivariate functions

Create multivariate functions