LeastSquaresStrategy¶

class LeastSquaresStrategy(*args)¶

Least squares strategy for the approximation coefficients.

Available constructors:

LeastSquaresStrategy(weightedExp)

LeastSquaresStrategy(weightedExp, approxAlgoImpFact)

LeastSquaresStrategy(approxAlgoImpFact)

LeastSquaresStrategy(measure, approxAlgoImpFact)

LeastSquaresStrategy(measure, weightedExp, approxAlgoImpFact)

LeastSquaresStrategy(inputSample, outputSample, approxAlgoImpFact)

LeastSquaresStrategy(inputSample, weights, outputSample, approxAlgoImpFact)

Parameters:

weightedExpWeightedExperiment: Experimental design used for the transformed input data. By default the class MonteCarloExperiment is used.
approxAlgoImpFactApproximationAlgorithmImplementationFactory: The factory that builds the desired ApproximationAlgorithm. By default the class PenalizedLeastSquaresAlgorithmFactory is used.
measureDistribution: Distribution $\mu$ with respect to which the basis is orthonormal. By default, the limit measure defined within the class WeightedExperiment is used.
inputSample2-d sequence of float: The input random observations $\left\{\vect{X}^{(1)}, ..., \vect{X}^{(n)}\right\}$ where $\vect{X}=(X_1, \dots, X_{n_X})^T$ is the input of the physical model, $n_X$ is the input dimension and $n$ is the sample size.
outputSample2-d sequence of float: The output random observations $\left\{\vect{Y}^{(1)}, ..., \vect{Y}^{(n)}\right\}$ where $\vect{Y}=(Y_1, \dots, Y_{n_Y})^T$ is the output of the physical model, $n_Y$ is the output dimension and $n$ is the sample size.
weightssequence of float: Numerical point that are the weights associated to the input sample points such that the corresponding weighted experiment is a good approximation of $\mu$ . If not precised, all weights are equals to $\omega_i = \frac{1}{n}$ , where $n$ is the size of the sample.

Methods

`getClassName`()	Accessor to the object's name.
`getCoefficients`()	Accessor to the coefficients.
`getDesignProxy`()	Accessor to the design proxy.
`getExperiment`()	Accessor to the experiments.
`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.
`hasName`()	Test if the object is named.
`involvesModelSelection`()	Get the model selection flag.
`isLeastSquares`()	Get the least squares flag.
`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.