queens.parameters package

Parameters.

Modules grouping deterministic and continuous random variables or random fields.

Subpackages

• queens.parameters.random_fields package
  • Submodules
  • queens.parameters.random_fields.fourier module
    • DimensionMethods1D
      • DimensionMethods1D.calculate_basis()
      • DimensionMethods1D.calculate_covariance()
      • DimensionMethods1D.get_dim()
    • DimensionMethods2D
      • DimensionMethods2D.calculate_basis()
      • DimensionMethods2D.calculate_covariance()
      • DimensionMethods2D.get_dim()
    • DimensionMethods3D
      • DimensionMethods3D.calculate_basis()
      • DimensionMethods3D.calculate_covariance()
      • DimensionMethods3D.get_dim()
    • Fourier
      • Fourier.mean
      • Fourier.std
      • Fourier.corr_length
      • Fourier.variability
      • Fourier.trunc_threshold
      • Fourier.basis_dimension
      • Fourier.latent_index
      • Fourier.covariance_index
      • Fourier.covariance
      • Fourier.basis
      • Fourier.coordinates
      • Fourier.field_dimension
      • Fourier.number_expansion_terms
      • Fourier.dimension
      • Fourier.convex_hull_size
      • Fourier.__init__()
      • Fourier.check_convergence()
      • Fourier.draw()
      • Fourier.expanded_representation()
      • Fourier.grad_logpdf()
      • Fourier.latent_gradient()
      • Fourier.logpdf()
  • queens.parameters.random_fields.karhunen_loeve module
    • KarhunenLoeve
      • KarhunenLoeve.nugget_variance
      • KarhunenLoeve.explained_variance
      • KarhunenLoeve.std
      • KarhunenLoeve.corr_length
      • KarhunenLoeve.cut_off
      • KarhunenLoeve.mean
      • KarhunenLoeve.cov_matrix
      • KarhunenLoeve.eigenbasis
      • KarhunenLoeve.eigenvalues
      • KarhunenLoeve.eigenvectors
      • KarhunenLoeve.dimension
      • KarhunenLoeve.__init__()
      • KarhunenLoeve.calculate_covariance_matrix()
      • KarhunenLoeve.draw()
      • KarhunenLoeve.eigendecomp_cov_matrix()
      • KarhunenLoeve.expanded_representation()
      • KarhunenLoeve.grad_logpdf()
      • KarhunenLoeve.latent_gradient()
      • KarhunenLoeve.logpdf()
  • queens.parameters.random_fields.piece_wise module
    • PieceWise
      • PieceWise.distribution
      • PieceWise.latent_1d_distribution
      • PieceWise.__init__()
      • PieceWise.draw()
      • PieceWise.expanded_representation()
      • PieceWise.grad_logpdf()
      • PieceWise.latent_gradient()
      • PieceWise.logpdf()

Submodules

queens.parameters.parameters module

Parameters.

class HasGradLogPDF

Bases: Protocol

Dummy class to check if an object has a gradient log-PDF function.

__init__(*args, **kwargs)

grad_logpdf(samples)

Gradient of the log-PDF.

Parameters:

samples (ndarray)

Return type:

ndarray

class HasPPF

Bases: Protocol

Dummy class to check if an object has a percent point function.

__init__(*args, **kwargs)

ppf(quantiles)

Percent point function (inverse of CDF).

Parameters:

quantiles (ndarray)

Return type:

Any

class Parameters

Bases: object

Parameters class.

dict

Random variables and random fields stored in a dict.

parameters_keys

List of keys for all parameter members.

num_parameters

Number of (truncated) parameters.

random_field_flag

Specifies if random fields are used.

names

Parameter names.

__init__(**parameters)

Initialize Parameters object.

Parameters:

**parameters (Continuous | Discrete | RandomField) – Parameters as keyword arguments

Return type:

None

dict_as_sample(sample_dict)

Opposite of sample_as_dict: Convert sample_dict to sample array.

Parameters:

sample_dict (dict) – dict containing the sample. The keys must correspond to parameter keys. The order of the keys does not matter.

Returns:

sample – sample array in correct order

Return type:

ndarray

draw_samples(num_samples)

Draw samples from all parameters.

Parameters:

num_samples (int) – The number of samples to draw for each parameter.

Returns:

Drawn samples

Return type:

ndarray

expand_random_field_realization(truncated_sample)

Expand truncated representation of random fields.

Parameters:

truncated_sample (ndarray) – Truncated representation of sample

Returns:

sample_expanded – Expanded representation of sample

Return type:

ndarray

grad_joint_logpdf(samples)

Evaluate the gradient of the joint log-PDF w.r.t. the samples.

Parameters:

samples (ndarray) – Samples for which to evaluate the gradient of the joint log-PDF. Each row represents a sample and each column corresponds to a parameter dimension.

Returns:

Gradient of the joint log-PDF w.r.t. the samples

Return type:

ndarray

inverse_cdf_transform(samples)

Transform samples to unit interval.

Parameters:

samples (ndarray) – Samples that should be transformed.

Returns:

Transformed samples

Return type:

ndarray

joint_logpdf(samples)

Evaluate the log-PDF summed over all parameters.

Parameters:

samples (ndarray) – Samples for which to evaluate the joint log-PDF. Each row represents a sample and each column corresponds to a parameter dimension.

Returns:

Log-PDF summed over all parameters

Return type:

ndarray

latent_grad(upstream_gradient)

Gradient of the rvs and rfs w.r.t. latent variables.

Parameters:

upstream_gradient (ndarray) – Upstream gradient

Returns:

Gradient of the joint rvs/rfs w.r.t. the samples

Return type:

ndarray

sample_as_dict(sample)

Return sample as a dict.

Parameters:

sample (ndarray) – A single sample

Returns:

sample_dict – Dictionary containing sample members and the corresponding parameter keys

Return type:

dict

to_distribution_list()

Return the distributions of the parameters as list.

Returns:

List of distributions of parameters

Return type:

list[Continuous | Discrete]

to_list()

Return parameters as list.

Returns:

List of parameters

Return type:

list[Continuous | Discrete | RandomField]