ProjectionStrategy

class ProjectionStrategy(*args)

Base class for the evaluation strategies of the approximation coefficients.

Available constructors:

ProjectionStrategy(projectionStrategy)

Parameters
projectionStrategyProjectionStrategy

A projection strategy which is a LeastSquaresStrategy or an IntegrationStrategy.

See also

FunctionalChaosAlgorithm, LeastSquaresStrategy, IntegrationStrategy

Notes

Consider \vect{Y} = g(\vect{X}) with g: \Rset^d \rightarrow \Rset^p, \vect{X} \sim \cL_{\vect{X}} and \vect{Y} with finite variance: g\in L_{\cL_{\vect{X}}}^2(\Rset^d, \Rset^p).

The functional chaos expansion approximates \vect{Y} using an isoprobabilistic transformation T and an orthonormal multivariate basis (\Psi_k)_{k \in \Nset} of L^2_{\mu}(\Rset^d,\Rset). See FunctionalChaosAlgorithm to get more details.

The meta model of g, based on the functional chaos decomposition of f = g \circ T^{-1} writes:

\tilde{g} = \sum_{k \in K} \vect{\alpha}_k \Psi_k \circ T

where K is a non empty finite set of indices, whose cardinality is denoted by P.

We detail the case where p=1.

The vector \vect{\alpha} = (\alpha_k)_{k \in K} is equivalently defined by:

(1)\vect{\alpha} = \argmin_{\vect{\alpha} \in \Rset^K} \Expect{ \left( g \circ T^{-1}(\vect{Z}) - \sum_{k \in K} \alpha_k \Psi_k (\vect{Z})\right)^2 }

and:

(2)\alpha_k = <g \circ T^{-1}(\vect{Z}), \Psi_k (\vect{Z})>_{\mu} = \Expect{ g \circ T^{-1}(\vect{Z}) \Psi_k (\vect{Z}) }

where \vect{Z} = T(\vect{X}) and the mean \Expect{.} is evaluated with respect to the measure \mu.

It corresponds to two points of view:

  • relation (1) means that the coefficients (\alpha_k)_{k \in K} minimize the quadratic error between the model and the polynomial approximation. Use LeastSquaresStrategy.

  • relation (2) means that \alpha_k is the scalar product of the model with the k-th element of the orthonormal basis (\Psi_k)_{k \in \Nset}. Use IntegrationStrategy.

In both cases, the mean \Expect{.} is approximated by a linear quadrature formula:

(3)\Expect{ f(\vect{Z})} \simeq \sum_{i \in I} \omega_i f(\Xi_i)

where f is a function in L^1(\mu).

In the approximation (3), the set I, the points (\Xi_i)_{i \in I} and the weights (\omega_i)_{i \in I} are evaluated from different methods implemented in the WeightedExperiment.

The convergence criterion used to evaluate the coefficients is based on the residual value defined in the FunctionalChaosAlgorithm.

Methods

getClassName()

Accessor to the object's name.

getCoefficients()

Accessor to the coefficients.

getExperiment()

Accessor to the experiments.

getId()

Accessor to the object's id.

getImplementation()

Accessor to the underlying implementation.

getInputSample()

Accessor to the input sample.

getMeasure()

Accessor to the measure.

getName()

Accessor to the object's name.

getOutputSample()

Accessor to the output sample.

getRelativeError()

Accessor to the relative error.

getResidual()

Accessor to the residual.

getWeights()

Accessor to the weights.

setExperiment(weightedExperiment)

Accessor to the design of experiment.

setInputSample(inputSample)

Accessor to the input sample.

setMeasure(measure)

Accessor to the measure.

setName(name)

Accessor to the object's name.

setOutputSample(outputSample)

Accessor to the output sample.

setWeights(weights)

Accessor to the weights.

computeCoefficients
__init__(*args)
getClassName()

Accessor to the object’s name.

Returns
class_namestr

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

getCoefficients()

Accessor to the coefficients.

Returns
coefPoint

Coefficients (\alpha_k)_{k \in K}.

getExperiment()

Accessor to the experiments.

Returns
expWeightedExperiment

Weighted experiment used to evaluate the coefficients.

getId()

Accessor to the object’s id.

Returns
idint

Internal unique identifier.

getImplementation()

Accessor to the underlying implementation.

Returns
implImplementation

The implementation class.

getInputSample()

Accessor to the input sample.

Returns
XSample

Input Sample.

getMeasure()

Accessor to the measure.

Returns
muDistribution

Measure \mu defining the scalar product.

getName()

Accessor to the object’s name.

Returns
namestr

The name of the object.

getOutputSample()

Accessor to the output sample.

Returns
YSample

Output Sample.

getRelativeError()

Accessor to the relative error.

Returns
efloat

Relative error.

getResidual()

Accessor to the residual.

Returns
erfloat

Residual error.

getWeights()

Accessor to the weights.

Returns
wPoint

Weights of the design of experiments.

setExperiment(weightedExperiment)

Accessor to the design of experiment.

Parameters
expWeightedExperiment

Weighted design of experiment.

setInputSample(inputSample)

Accessor to the input sample.

Parameters
XSample

Input Sample.

setMeasure(measure)

Accessor to the measure.

Parameters
mDistribution

Measure \mu defining the scalar product.

setName(name)

Accessor to the object’s name.

Parameters
namestr

The name of the object.

setOutputSample(outputSample)

Accessor to the output sample.

Parameters
YSample

Output Sample.

setWeights(weights)

Accessor to the weights.

Parameters
wPoint

Weights of the design of experiments.