LinearLeastSquaresCalibration

class LinearLeastSquaresCalibration(*args)

Linear least squares calibration algorithm.

Available constructors:

LinearLeastSquaresCalibration(model, inputObservations, outputObservations, candidate, methodName)

LinearLeastSquaresCalibration(modelObservations, gradientObservations, outputObservations, candidate, methodName)

Parameters
modelFunction

The parametric function to be calibrated.

inputObservations2-d sequence of float

The sample of input observations. Can have dimension 0 to specify no observations.

outputObservations2-d sequence of float

The sample of output observations.

candidatesequence of float

The reference value of the parameter.

methodNamestr

The name of the least-squares method to use for the calibration. By default, equal to QR. Possible values are SVD, QR, Cholesky.

modelObservations2-d sequence of float

The sample of output values of the model.

gradientObservations2-d sequence of float

The Jacobian matrix of the model with respect to the parameter.

See also

GaussianLinearCalibration, NonLinearLeastSquaresCalibration, GaussianNonLinearCalibration

Notes

LinearLeastSquaresCalibration is the minimum variance estimator of the parameter of a given model under the assumption that this parameter acts linearly in the model.

The prior distribution of the parameter is a noninformative prior emulated using a flat Normal centered on the candidate and with a variance equal to SpecFunc.MaxScalar.

The posterior distribution of the parameter is Normal and reflects the variability of the optimum parameter depending on the observation sample. The associated covariance matrix may be regularized depending on the value of the key LinearLeastSquaresCalibration-Regularization in the ResourceMap. Let us denote by \sigma_1 the largest singular value of the covariance matrix. The default value of the LinearLeastSquaresCalibration-Regularization, zero, ensures that the singular values of the covariance matrix are left unmodified. If this parameter is set to a nonzero, relatively small, value denoted by \epsilon, then all singular values of the covariance matrix are increased by \epsilon \sigma_1.

The resulting distribution of the output error is Normal with a zero mean and a diagonal covariance matrix computed from the residuals. The residuals are computed based on the linearization of the model, where the Jacobian matrix is evaluated at the candidate point. The diagonal of the covariance matrix of the output error is constant and is estimated with the unbiased variance estimator.

Examples

Calibrate a nonlinear model using linear least-squares:

>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> m = 10
>>> x = [[0.5 + i] for i in range(m)]
>>> inVars = ['a', 'b', 'c', 'x']
>>> formulas = ['a + b * exp(c * x)']
>>> model = ot.SymbolicFunction(inVars, formulas)
>>> p_ref = [2.8, 1.2, 0.5]
>>> params = [0, 1, 2]
>>> modelX = ot.ParametricFunction(model, params, p_ref)
>>> y = modelX(x)
>>> y += ot.Normal(0.0, 0.05).getSample(m)
>>> candidate = [1.0]*3
>>> method = 'SVD'
>>> algo = ot.LinearLeastSquaresCalibration(modelX, x, y, candidate, method)
>>> algo.run()
>>> print(algo.getResult().getParameterMAP())
[8.24019,0.0768046,0.992957]

Methods

getCandidate()

Accessor to the parameter candidate.

getClassName()

Accessor to the object's name.

getGradientObservations()

Accessor to the model gradient at the candidate.

getId()

Accessor to the object's id.

getInputObservations()

Accessor to the input data to be fitted.

getMethodName()

Accessor to the name of least-squares method used for the resolution.

getModel()

Accessor to the model to be fitted.

getModelObservations()

Accessor to the model evaluation at the candidate.

getName()

Accessor to the object's name.

getOutputObservations()

Accessor to the output data to be fitted.

getParameterPrior()

Accessor to the parameter prior distribution.

getResult()

Get the result structure.

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()

Launch the algorithm.

setName(name)

Accessor to the object's name.

setResult(result)

Accessor to optimization result.

setShadowedId(id)

Accessor to the object's shadowed id.

setVisibility(visible)

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

Accessor to the parameter candidate.

Returns
candidatePoint

Parameter candidate.

getClassName()

Accessor to the object’s name.

Returns
class_namestr

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

getGradientObservations()

Accessor to the model gradient at the candidate.

Returns
gradientObservationMatrix

Gradient of the model at the candidate point.

getId()

Accessor to the object’s id.

Returns
idint

Internal unique identifier.

getInputObservations()

Accessor to the input data to be fitted.

Returns
dataSample

The input data to be fitted.

getMethodName()

Accessor to the name of least-squares method used for the resolution.

Returns
nameString

Name of least-squares method used for the resolution.

getModel()

Accessor to the model to be fitted.

Returns
dataFunction

The model to be fitted.

getModelObservations()

Accessor to the model evaluation at the candidate.

Returns
modelObservationSample

Evaluation of the model at the candidate point.

getName()

Accessor to the object’s name.

Returns
namestr

The name of the object.

getOutputObservations()

Accessor to the output data to be fitted.

Returns
dataSample

The output data to be fitted.

getParameterPrior()

Accessor to the parameter prior distribution.

Returns
priorDistribution

The parameter prior distribution.

getResult()

Get the result structure.

Returns
resCalibration: CalibrationResult

The structure containing all the results of the calibration problem.

Notes

The structure contains all the results of the calibration problem.

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()

Launch the algorithm.

Notes

It launches the algorithm and creates a CalibrationResult, structure containing all the results.

setName(name)

Accessor to the object’s name.

Parameters
namestr

The name of the object.

setResult(result)

Accessor to optimization result.

Parameters
resultCalibrationResult

Result class.

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.