IterativeMoments

class IterativeMoments(*args)

Iterative moments.

Parameters

orderint

Maximum order of the moments wanted

dimensionint

Dimension of the input data

See also

IterativeAlgorithm

Notes

This class iteratively computes the centered moments of an iteratively increasing dataset without storing any data in memory.

Examples

In the following example, we iteratively compute the sample statistics of a sample. This is based on a sample with size equal to 2000. In the for loop, each realization of the distribution is used to update the statistics up to the 4th order moments.

>>> import openturns as ot
>>> distNormal = ot.Normal()
>>> order = 4
>>> dim = 1
>>> iterMoments = ot.IterativeMoments(order, dim)
>>> size = 2000
>>> meanEvolution = ot.Sample()
>>> for i in range(size):
...     point = distNormal.getRealization()
...     iterMoments.increment(point)
>>> print('Mean:     ', iterMoments.getMean())
Mean:      [-0.00726852]
>>> print('Variance: ', iterMoments.getVariance())
Variance:  [0.99156]
>>> print('Skewness: ', iterMoments.getSkewness())
Skewness:  [0.0577277]
>>> print('Kurtosis: ', iterMoments.getKurtosis())
Kurtosis:  [3.11128]

The statistics can also be updated with a single sample.

>>> distNormal = ot.Normal()
>>> dim = 1
>>> order = 1
>>> iterMoments = ot.IterativeMoments(order, dim)
>>> size = 2000
>>> sample = distNormal.getSample(size)
>>> iterMoments.increment(sample)
>>> print('Mean: ', iterMoments.getMean())
Mean:  [-0.017342]

Methods

getCenteredMoments()	Returns the centered moments
getClassName()	Accessor to the object's name.
getCoefficientOfVariation()	Returns the coefficient of variation
getDimension()	Get the dimension of the algorithm
getId()	Accessor to the object's id.
getIterationNumber()	Get the current iteration of the algorithm
getKurtosis()	Returns the current value of the unbiased estimator of the kurtosis
getMean()	Returns the values of the mean
getName()	Accessor to the object's name.
getOrder()	Returns the maximum order prescribed
getShadowedId()	Accessor to the object's shadowed id.
getSkewness()	Returns the current value of the unbiased estimator of the skewness
getStandardDeviation()	Returns the standard deviation
getStandardErrorOfTheMean()	Returns the standard error of the mean
getVariance()	Returns the current value of the unbiased estimator of the variance
getVisibility()	Accessor to the object's visibility state.
hasName()	Test if the object is named.
hasVisibleName()	Test if the object has a distinguishable name.
increment(*args)	Increment the internal data.
setName(name)	Accessor to the object's name.
setShadowedId(id)	Accessor to the object's shadowed id.
setVisibility(visible)	Accessor to the object's visibility state.

__init__(*args)

getCenteredMoments()

Returns the centered moments

Returns

centeredMomentsSample

Current value of the centered moments.

getClassName()

Accessor to the object’s name.

Returns

class_namestr

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

getCoefficientOfVariation()

Returns the coefficient of variation

Returns

coefficientOfvariationPoint

Current value of the coefficients of variation.

getDimension()

Get the dimension of the algorithm

Returns

dimensionint

Dimension of the algorithm

getId()

Accessor to the object’s id.

Returns

idint

Internal unique identifier.

getIterationNumber()

Get the current iteration of the algorithm

Returns

iterationint

Current iteration of the algorithm

getKurtosis()

Returns the current value of the unbiased estimator of the kurtosis

Returns

kurtosisPoint

Current values of the iterative kurtosis.

getMean()

Returns the values of the mean

Returns

meanPoint

Current value of the mean.

getName()

Accessor to the object’s name.

Returns

namestr

The name of the object.

getOrder()

Returns the maximum order prescribed

Returns

orderint

Prescribed order of the iterative object.

getShadowedId()

Accessor to the object’s shadowed id.

Returns

idint

Internal unique identifier.

getSkewness()

Returns the current value of the unbiased estimator of the skewness

Returns

skewnessPoint

Current values of the skewness.

getStandardDeviation()

Returns the standard deviation

Returns

standardDeviationPoint

Current value of the standard deviation.

getStandardErrorOfTheMean()

Returns the standard error of the mean

Returns

errorPoint

Current value of the standard error of the mean.

getVariance()

Returns the current value of the unbiased estimator of the variance

Returns

variancePoint

Current values of the variance.

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.

increment(*args)

Increment the internal data.

Parameters

datasequence of float or 2-d sequence of float

New input point or sample.

setName(name)

Accessor to the object’s name.

Parameters

namestr

The name of the object.

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.