StandardDistributionPolynomialFactory¶

class StandardDistributionPolynomialFactory(*args)¶

Build orthonormal or orthogonal univariate polynomial families.

Parameters:

argDistribution or OrthonormalizationAlgorithm

Either a Distribution implementing the probability measure according to which the polynomial family is orthonormal or an OrthonormalizationAlgorithm.

In the first case, the implementation will switch to the suitable specific orthonormal univariate polynomial family if any (see the notes below), or it will default to the AdaptiveStieltjesAlgorithm to build an orthonormal univariate polynomial family.

Methods

`build`(degree)	Build the $k$ -th order orthogonal univariate polynomial.
`buildCoefficients`(degree)	Build the $k$ -th order orthogonal univariate polynomial coefficients.
`buildRecurrenceCoefficientsCollection`(degree)	Build the recurrence coefficients.
`getClassName`()	Accessor to the object's name.
`getHasSpecificFamily`()	Accessor to the specific family boolean.
`getMeasure`()	Accessor to the associated probability measure.
`getName`()	Accessor to the object's name.
`getNodesAndWeights`(n)	Build the $k$ -th order quadrature scheme.
`getOrthonormalizationAlgorithm`()	Accessor to the orthonormalization algorithm.
`getRecurrenceCoefficients`(n)	Accessor to the recurrence coefficients of the $k$ -th order.
`getRoots`(n)	Accessor to the recurrence coefficients of the $k$ -th order.
`getSpecificFamily`()	Accessor to the specific orthonormal polynomial family.
`hasName`()	Test if the object is named.
`setName`(name)	Accessor to the object's name.

See also

Orthogonal polynomials, AdaptiveStieltjesAlgorithm

Notes

Use this functionality with caution:

The polynomials exist if and only if the distribution admits finite moments of all orders. Even if some algorithms manage to compute something, it will be plain numerical noise.

Even if the polynomials exist, they form an Hilbertian basis with respect to the dot product induced by the distribution if and only if the distribution is determinate, ie is characterized by its moments. For example, the LogNormal distribution has orthonormal polynomials of arbitrary degree but the projection onto the functional space generated by these polynomials (see FunctionalChaosAlgorithm) may converge to a function that differs significantly from the function being projected (see [ernst2012]). See Functional Chaos Expansion for more details on this topic.

OpenTURNS implements the following specific orthonormal [1] univariate polynomial families together with their associated standard distributions.

Standard distribution	Polynomial
`Normal` $\cN(\mu = 0, \sigma = 1)$	`HermiteFactory`
`Uniform` $\cU(a = -1, b = 1)$	`LegendreFactory`
`Gamma` $\Gamma(k = k_a + 1, \lambda = 1, \gamma = 0)$	`LaguerreFactory`
`Beta` ${\rm B}(r = \beta + 1, t = \alpha + \beta + 2, a = -1, b = 1)$	`JacobiFactory`
`Poisson` $\cP(\lambda)$	`CharlierFactory`
`Binomial` $\cB(n, p)$	`KrawtchoukFactory`
`Polya` $\cB^-(r, p)$	`MeixnerFactory`

Aside, OpenTURNS also implements generic algorithms for building orthonormal univariate polynomial families with respect to any arbitrary probability measure (implemented as a Distribution). OpenTURNS implements the following OrthonormalizationAlgorithm’s, AdaptiveStieltjesAlgorithm (default).

Examples

>>> import openturns as ot

Build the specific orthonormal polynomial factory associated to the normal distribution (Hermite):

>>> polynomial_factory = ot.StandardDistributionPolynomialFactory(ot.Normal())
>>> for i in range(3):
...     print(polynomial_factory.build(i))
1
X
-0.707107 + 0.707107 * X^2

Build an orthonormal polynomial factory for the WeibullMin distribution with the default orthonormalization algorithm:

>>> polynomial_factory = ot.StandardDistributionPolynomialFactory(ot.WeibullMin())
>>> for i in range(3):
...     print(polynomial_factory.build(i))
1
-1 + X
1 - 2 * X + 0.5 * X^2

Build an orthonormal polynomial factory for the log-normal distribution with Chebychev’s othonormalization algorithm:

>>> algorithm = ot.AdaptiveStieltjesAlgorithm(ot.WeibullMin())
>>> polynomial_factory = ot.StandardDistributionPolynomialFactory(algorithm)
>>> for i in range(3):
...     print(polynomial_factory.build(i))
1
-1 + X
1 - 2 * X + 0.5 * X^2

__init__(*args)¶

build(degree)¶

Build the $k$ -th order orthogonal univariate polynomial.

Parameters:

kint, $0 \leq k$: Polynomial order.

Returns:

polynomialOrthogonalUniVariatePolynomial: Requested orthogonal univariate polynomial.

Examples

>>> import openturns as ot
>>> polynomial_factory = ot.HermiteFactory()
>>> print(polynomial_factory.build(2))
-0.707107 + 0.707107 * X^2

buildCoefficients(degree)¶

Build the $k$ -th order orthogonal univariate polynomial coefficients.

Parameters:

kint, $0 \leq k$: Polynomial order.

Returns:

coefficientsPoint: Coefficients of the requested orthogonal univariate polynomial.

Examples

>>> import openturns as ot
>>> polynomial_factory = ot.HermiteFactory()
>>> print(polynomial_factory.buildCoefficients(2))
[-0.707107,0,0.707107]

buildRecurrenceCoefficientsCollection(degree)¶

Build the recurrence coefficients.

Build the recurrence coefficients of the orthogonal univariate polynomial family up to the $k$ -th order.

Parameters:

kint, $0 \leq k$: Polynomial order.

Returns:

recurrence_coefficientslist of Point: All the tecurrence coefficients up to the requested order.

Examples

>>> import openturns as ot
>>> polynomial_factory = ot.HermiteFactory()
>>> print(polynomial_factory.buildRecurrenceCoefficientsCollection(2))
0 : [  1         0         0        ]
1 : [  0.707107  0        -0.707107 ]

getClassName()¶

Accessor to the object’s name.

Returns:

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

getHasSpecificFamily()¶

Accessor to the specific family boolean.

Returns:

hasSpecificFamilybool: True if the factory has a specific orthogonal polynomial family.

getMeasure()¶

Accessor to the associated probability measure.

Returns:

measureDistribution: The associated probability measure (according to which the polynomials are orthogonal).

Notes

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

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

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

Examples

>>> import openturns as ot
>>> polynomial_factory = ot.HermiteFactory()
>>> print(polynomial_factory.getMeasure())
Normal(mu = 0, sigma = 1)

getName()¶

Accessor to the object’s name.

Returns:

namestr: The name of the object.

getNodesAndWeights(n)¶

Build the $k$ -th order quadrature scheme.

Associated with the orthogonal univariate polynomials family.

Parameters:

kint, $0 < k$: Polynomial order.

Returns:

nodesPoint: The nodes of the $k$ -th order quadrature scheme.
weightsPoint: The weights of the $k$ -th order quadrature scheme.

Examples

>>> import openturns as ot
>>> polynomial_factory = ot.HermiteFactory()
>>> nodes, weights = polynomial_factory.getNodesAndWeights(3)
>>> print(nodes)
[-1.73205,...,1.73205]
>>> print(weights)
[0.166667,0.666667,0.166667]

getOrthonormalizationAlgorithm()¶

Accessor to the orthonormalization algorithm.

Returns:

orthonormalizationAlgorithmOrthonormalizationAlgorithm: The univariate polynomial orthonormalization algorithm.

getRecurrenceCoefficients(n)¶

Accessor to the recurrence coefficients of the $k$ -th order.

Of the orthogonal univariate polynomial.

Parameters:

kint, $0 \leq k$: Polynomial order.

Returns:

recurrence_coefficientsPoint: The recurrence coefficients of the $k$ -th order orthogonal univariate polynomial.

Notes

Any sequence of orthogonal polynomials has a recurrence formula relating any three consecutive polynomials as follows:

$P_{-1}=0, P_0=1, P_{n + 1} = (a_n x + b_n) P_n + c_n P_{n - 1}, \quad n > 1$

Examples

>>> import openturns as ot
>>> polynomial_factory = ot.HermiteFactory()
>>> print(polynomial_factory.getRecurrenceCoefficients(3))
[0.5,0,-0.866025]

getRoots(n)¶

Accessor to the recurrence coefficients of the $k$ -th order.

Of the orthogonal univariate polynomial.

Parameters:

kint, $k > 0$: Polynomial order.

Returns:

rootsPoint: The roots of the $k$ -th order orthogonal univariate polynomial.

Examples

>>> import openturns as ot
>>> polynomial_factory = ot.HermiteFactory()
>>> print(polynomial_factory.getRoots(3))
[-1.73205,...,1.73205]

getSpecificFamily()¶

Accessor to the specific orthonormal polynomial family.

Returns:

specificFamilyOrthogonalUniVariatePolynomialFamily: The specific orthonormal univariate polynomial family.

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 a full or sparse polynomial chaos expansion

Advanced polynomial chaos construction

OpenTURNS

An Open source initiative for the Treatment of Uncertainties, Risks'N Statistics

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Examples using the class¶