MixtureFactory

class otmixmod.MixtureFactory(*args)

Mixture inference.

Parameters:

atomsNumberint

The number of atoms

covarianceModelstr, optional

The covariance model. Default is ‘Gaussian_pk_Lk_C’

Other possible values include:

• Gaussian_p_L_I

• Gaussian_p_Lk_I

• Gaussian_p_L_B

• Gaussian_p_Lk_B

• Gaussian_p_L_Bk

• Gaussian_p_Lk_Bk

• Gaussian_p_L_C

• Gaussian_p_Lk_C

• Gaussian_p_L_D_Ak_D

• Gaussian_p_Lk D_Ak_D

• Gaussian_p_L_Dk_A_Dk

• Gaussian_p_Lk_Dk_A_Dk

• Gaussian_p_L_Ck

• Gaussian_p_Lk_Ck

• Gaussian_pk_L_I

• Gaussian_pk_Lk_I

• Gaussian_pk_L_B

• Gaussian_pk_Lk_B

• Gaussian_pk_L_Bk

• Gaussian_pk_Lk_Bk

• Gaussian_pk_L_C

• Gaussian_pk_Lk_C

• Gaussian_pk_L_D_Ak_D

• Gaussian_pk_Lk D_Ak_D

• Gaussian_pk_L_Dk_A_Dk

• Gaussian_pk_Lk_Dk_A_Dk

• Gaussian_pk_L_Ck

• Gaussian_pk_Lk_Ck

Methods

BuildClusters(Sample data, Indices labels, OT)
build(...)	Build the distribution.
buildAsMixture(MixtureFactory self, ...)	Mixture inference.
buildEstimator(*args)	Build the distribution and the parameter distribution.
getAtomsNumber(MixtureFactory self)	Atoms number accessor.
getBootstrapSize()	Accessor to the bootstrap size.
getClassName(MixtureFactory self)	Accessor to the object's name.
getCovarianceModel(MixtureFactory self)
getId()	Accessor to the object's id.
getName()	Accessor to the object's name.
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.
setAtomsNumber(MixtureFactory self, OT)	Atoms number accessor.
setBootstrapSize(bootstrapSize)	Accessor to the bootstrap size.
setCovarianceModel(MixtureFactory self, OT)
setName(name)	Accessor to the object's name.
setSeed(MixtureFactory self, OT)	Mixmod RNG seed accessor.
setShadowedId(id)	Accessor to the object's shadowed id.
setVisibility(visible)	Accessor to the object's visibility state.

__init__(MixtureFactory self) → MixtureFactory

__init__(MixtureFactory self, OT::UnsignedInteger const atomsNumber, OT::String const covarianceModel="Gaussian_pk_Lk_C") → MixtureFactory

__init__(MixtureFactory self, MixtureFactory other) → MixtureFactory

static BuildClusters(Sample data, Indices labels, OT::UnsignedInteger const nbClusters) → SampleCollection

build(MixtureFactory self, Sample sample) → Distribution

build(MixtureFactory self, Point parameters) → Distribution

build(MixtureFactory self, Sample sample) → Distribution

build(MixtureFactory self) → Distribution

Build the distribution.

Available usages:

build()

build(sample)

build(param)

Parameters:

sample2-d sequence of float

Data.

paramsequence of float

The parameters of the distribution.

Returns:

distDistribution

The estimated distribution.

In the first usage, the default native distribution is built.

buildAsMixture(MixtureFactory self, Sample sample) → Mixture

Mixture inference.

Parameters:

sampleopenturns.Sample

Sample

Returns:

mixtureopenturns.Mixture

Inferred distribution

buildEstimator(*args)

Build the distribution and the parameter distribution.

Parameters:

sample2-d sequence of float

Data.

parametersDistributionParameters

Optional, the parametrization.

Returns:

resDistDistributionFactoryResult

The results.

Notes

According to the way the native parameters of the distribution are estimated, the parameters distribution differs:

• Moments method: the asymptotic parameters distribution is normal and estimated by Bootstrap on the initial data;

• Maximum likelihood method with a regular model: the asymptotic parameters distribution is normal and its covariance matrix is the inverse Fisher information matrix;

• Other methods: the asymptotic parameters distribution is estimated by Bootstrap on the initial data and kernel fitting (see KernelSmoothing).

If another set of parameters is specified, the native parameters distribution is first estimated and the new distribution is determined from it:

• if the native parameters distribution is normal and the transformation regular at the estimated parameters values: the asymptotic parameters distribution is normal and its covariance matrix determined from the inverse Fisher information matrix of the native parameters and the transformation;

• in the other cases, the asymptotic parameters distribution is estimated by Bootstrap on the initial data and kernel fitting.

getAtomsNumber(MixtureFactory self) → OT::UnsignedInteger

Atoms number accessor.

Returns:

atomsNumberint

The number of atoms

getBootstrapSize()

Accessor to the bootstrap size.

Returns:

sizeinteger

Size of the bootstrap.

getClassName(MixtureFactory self) → OT::String

Accessor to the object’s name.

Returns:

class_namestr

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

getCovarianceModel(MixtureFactory self) → OT::String

getId()

Accessor to the object’s id.

Returns:

idint

Internal unique identifier.

getName()

Accessor to the object’s name.

Returns:

namestr

The name of the object.

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.

setAtomsNumber(MixtureFactory self, OT::UnsignedInteger const & number)

Atoms number accessor.

Parameters:

atomsNumberint

The number of atoms

setBootstrapSize(bootstrapSize)

Accessor to the bootstrap size.

Parameters:

sizeinteger

The size of the bootstrap.

setCovarianceModel(MixtureFactory self, OT::String const covarianceModel)

setName(name)

Accessor to the object’s name.

Parameters:

namestr

The name of the object.

setSeed(MixtureFactory self, OT::SignedInteger const seed)

Mixmod RNG seed accessor.

Parameters:

seedint

Seed used to initialize the Mixmod RNG seed before the learning step. A negative seed will randomly initialize the RNG. The default value is 0.

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.