queens.parameters package

Parameters.

Modules grouping deterministic and continuous random variables or random fields.

Subpackages

• queens.parameters.fields package
  • Submodules
  • queens.parameters.fields.fourier_field 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()
    • FourierRandomField
      • FourierRandomField.mean
      • FourierRandomField.std
      • FourierRandomField.corr_length
      • FourierRandomField.variability
      • FourierRandomField.trunc_threshold
      • FourierRandomField.basis_dimension
      • FourierRandomField.latent_index
      • FourierRandomField.covariance_index
      • FourierRandomField.covariance
      • FourierRandomField.basis
      • FourierRandomField.coordinates
      • FourierRandomField.field_dimension
      • FourierRandomField.number_expansion_terms
      • FourierRandomField.dimension
      • FourierRandomField.convex_hull_size
      • FourierRandomField.check_convergence()
      • FourierRandomField.draw()
      • FourierRandomField.expanded_representation()
      • FourierRandomField.grad_logpdf()
      • FourierRandomField.latent_gradient()
      • FourierRandomField.logpdf()
  • queens.parameters.fields.kl_field module
    • KarhunenLoeveRandomField
      • KarhunenLoeveRandomField.nugget_variance
      • KarhunenLoeveRandomField.explained_variance
      • KarhunenLoeveRandomField.std
      • KarhunenLoeveRandomField.corr_length
      • KarhunenLoeveRandomField.cut_off
      • KarhunenLoeveRandomField.mean
      • KarhunenLoeveRandomField.cov_matrix
      • KarhunenLoeveRandomField.eigenbasis
      • KarhunenLoeveRandomField.eigenvalues
      • KarhunenLoeveRandomField.eigenvectors
      • KarhunenLoeveRandomField.dimension
      • KarhunenLoeveRandomField.calculate_covariance_matrix()
      • KarhunenLoeveRandomField.draw()
      • KarhunenLoeveRandomField.eigendecomp_cov_matrix()
      • KarhunenLoeveRandomField.expanded_representation()
      • KarhunenLoeveRandomField.grad_logpdf()
      • KarhunenLoeveRandomField.latent_gradient()
      • KarhunenLoeveRandomField.logpdf()
  • queens.parameters.fields.piece_wise_field module
    • PieceWiseRandomField
      • PieceWiseRandomField.distribution
      • PieceWiseRandomField.latent_1d_distribution
      • PieceWiseRandomField.create_latent_distribution()
      • PieceWiseRandomField.draw()
      • PieceWiseRandomField.expanded_representation()
      • PieceWiseRandomField.grad_logpdf()
      • PieceWiseRandomField.latent_gradient()
      • PieceWiseRandomField.logpdf()
  • queens.parameters.fields.random_fields module
    • RandomField
      • RandomField.dimension
      • RandomField.coords
      • RandomField.dim_coords
      • RandomField.distribution
      • RandomField.draw()
      • RandomField.expanded_representation()
      • RandomField.grad_logpdf()
      • RandomField.latent_gradient()
      • RandomField.logpdf()

Submodules

queens.parameters.parameters module

Parameters.

class Parameters(**parameters)

Bases: object

Parameters class.

dict

Random variables and random fields stored in a dict.

Type:

dict

parameters_keys

List of keys for all parameter members.

Type:

list

num_parameters

Number of (truncated) parameters.

Type:

int

random_field_flag

Specifies if random fields are used.

Type:

bool

names

Parameter names.

Type:

list

draw_samples(num_samples)

Draw samples from all parameters.

Parameters:

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

Returns:

samples (np.ndarray) – Drawn samples

expand_random_field_realization(truncated_sample)

Expand truncated representation of random fields.

Parameters:

truncated_sample (np.ndarray) – Truncated representation of sample

Returns:

sample_expanded (np.ndarray) – Expanded representation of sample

grad_joint_logpdf(samples)

Evaluate the gradient of the joint logpdf w.r.t. the samples.

Parameters:

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

Returns:

grad_logpdf (np.ndarray) – Gradient of the joint logpdf w.r.t. the samples

inverse_cdf_transform(samples)

Transform samples to unit interval.

Parameters:

samples (np.ndarray) – Samples that should be transformed.

Returns:

transformed_samples (np.ndarray) – Transformed samples

joint_logpdf(samples)

Evaluate the logpdf summed over all parameters.

Parameters:

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

Returns:

logpdf (np.ndarray) – logpdf summed over all parameters

latent_grad(upstream_gradient)

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

Parameters:

upstream_gradient (np.array) – Upstream gradient

Returns:

gradient (np.ndarray) – Gradient of the joint rvs/rfs w.r.t. the samples

sample_as_dict(sample)

Return sample as a dict.

Parameters:

sample (np.ndarray) – A single sample

Returns:

• sample_dict (dict) – Dictionary containing sample members and the corresponding parameter

• keys

to_distribution_list()

Return the distributions of the parameters as list.

Returns:

distribution_list (list) – List of distributions of parameters

to_list()

Return parameters as list.

Returns:

parameter_list (list) – List of parameters

from_config_create_parameters(parameters_options, pre_processor=None)

Create a QUEENS parameter object from config.

Parameters:

• parameters_options (dict) – Parameters description

• pre_processor (obj, optional) – Pre-processor object to read coordinates of random field discretization