Interpolation method for interpolating between experimental and model data. I need to work on inputs/outputs to this method. For now, this assumes interpolation of functional data. More...

Public Member Functions
	ExperimentData ()
	default constructor

	ExperimentData (const ProblemDescDB &prob_desc_db, const SharedResponseData &srd, short output_level)
	typical DB-based constructor

	ExperimentData (size_t num_experiments, size_t num_config_vars, const boost::filesystem::path &data_prefix, const SharedResponseData &srd, const StringArray &variance_types, short output_level, std::string scalarDataFilename="")
	temporary? constructor for testing

	ExperimentData (size_t num_experiments, const SharedVariablesData &svd, const SharedResponseData &srd, const VariablesArray &configVars, const IntResponseMap &all_responses, short output_level)
	Bayesian experimental design constructor. Passed SVD has calibration parameters as active and config vars as inactive variables. Passed configVars have config vars as active. More...

void	load_data (const std::string &context_message, const Variables &vars_with_state_as_config)
	Load experiments from data files (simple scalar or field)

void	add_data (const SharedVariablesData &svd, const Variables &one_configvars, const Response &one_response)
	Add one data point to the experimental data set. Used for Bayesian experimental design. Passed SVD has calibration parameters as active and config vars as inactive variables. Passed configVars have config vars as active.

size_t	num_experiments () const
	retrieve the number of experiments

size_t	num_total_exppoints () const
	retrieve the total number of experimental data points over all experiments

size_t	num_scalar_primary () const
	retrieve the number of scalars (applies to all experiments)

size_t	num_fields () const
	retrieve the number of fields (applies to all experiments)

size_t	num_config_vars () const
	number of configuration variables

std::vector< RealVector >	config_vars_as_real () const
	values of the configuration variables, 1 RealVector per experiment omits string variables as historically used in NonDBayes More...

const std::vector< Variables > &	configuration_variables () const

const RealVector &	all_data (size_t experiment)
	return contiguous vector of all data (scalar, followed by field) for the specified experiment

const Response &	response (size_t experiment)
	return response for the specified experiment

void	per_exp_length (IntVector &per_length) const
	return the individual sizes of the experimental data lengths (all function values, scalar and field)

const IntVector &	field_lengths (size_t experiment) const
	return the field lengths for specified experiment index

Real	scalar_data (size_t response, size_t experiment)
	retrieve the data value for the given response, for the given experiment

RealVector	field_data_view (size_t response, size_t experiment) const
	retrieve a view of the field data for the given response, for the given experiment

RealMatrix	field_coords_view (size_t response, size_t experiment) const
	retrieve a view of the field data coordinates for the given response, for the given experiment

void	fill_primary_function_labels (StringArray &expanded_labels) const
	populate the passed array with num_total_exppoints labels

bool	variance_type_active (short variance_type) const
	whether the specified variance type (enum value) is present and active

bool	variance_active () const
	whether any variance type is active

Real	apply_covariance (const RealVector &residuals, size_t experiment) const
	apply the covariance responses to compute the triple product v'inv(C)v for the given experiment

void	apply_covariance_inv_sqrt (const RealVector &residuals, size_t experiment, RealVector &weighted_residuals) const
	apply inverse sqrt of the covariance to compute weighted residuals

void	apply_covariance_inv_sqrt (const RealMatrix &gradients, size_t experiment, RealMatrix &weighted_gradients) const
	apply inverse sqrt of the covariance to compute weighted gradients

void	apply_covariance_inv_sqrt (const RealSymMatrixArray &hessians, size_t experiment, RealSymMatrixArray &weighted_hessians) const
	apply inverse sqrt of the covariance to compute weighted Hessians

void	apply_simulation_error (const RealVector &simulation_error, size_t experiment)
	apply simulation error to experiment data

void	get_main_diagonal (RealVector &diagonal, size_t experiment) const
	return a (copy) vector containing the main diagonal entries of a specified experimental covariance matrix

void	cov_std_deviation (RealVectorArray &std_deviation) const
	get the standard deviation of the observation error process, one vector per experiment

void	cov_as_correlation (RealSymMatrixArray &corr_matrix) const
	get the observation error covariance as a correlation matrix, one vector per experiment

void	covariance (int exp_ind, RealSymMatrix &cov_mat) const
	retrieve an individual covariance entry as a dense matrix

void	form_residuals (const Response &sim_resp, Response &residual_resp) const
	form residuals for all experiments, interpolating if necessary; one simulation response maps to all experiments More...

void	form_residuals (const Response &sim_resp, const size_t curr_exp, Response &residual_resp) const
	Populate the portion of residual_resp corresponding to experiment curr_exp; the passed simulation response maps only to the specified experiment.

void	form_residuals (const Response &sim_resp, size_t exp_num, const ShortArray &total_asv, size_t residual_resp_offset, Response &residual_resp) const
	form residuals for an individual experiment, interpolating if necessary More...

void	recover_model (size_t num_pri_fns, RealVector &model_fns) const
	recover original model from the first experiment block in a full set of residuals; works in no interpolation case only (sizes same) More...

bool	interpolate_flag () const
	flag for interpolation. If 0, no interpolation. If 1, interpolate.

void	interpolate_simulation_data (const Response &sim_resp, size_t exp_num, const ShortArray &total_asv, size_t exp_offset, Response &interp_resp) const
	Interpolate simulation data (values, gradients and hessians) onto the coordinates of the experimental data.

void	scale_residuals (const Response &residual_response, RealVector &scaled_residuals) const
	Apply the experiment data covariance to the residual data (scale functions by Gamma_d^{-1/2}), returning in scaled_residuals.

void	scale_residuals (Response &residual_response) const
	Apply the experiment data covariance to the residual data in-place (scale functions, gradients, and Hessians by Gamma_d^{-1/2})

void	build_gradient_of_sum_square_residuals (const Response &resp, RealVector &ssr_gradient)
	Build the gradient of the ssr from residuals and function gradients based on the response's active set request vector.

void	build_gradient_of_sum_square_residuals (const Response &resp, const ShortArray &asrv, RealVector &ssr_gradient)
	Build the gradient of the ssr from residuals and function gradients using the passed active set request vector (overrides the response's request vector)

void	build_gradient_of_sum_square_residuals_from_response (const Response &resp, const ShortArray &asrv, int exp_ind, RealVector &ssr_gradient)
	Update the gradient of ssr with the values from the gradient associated with a single experiment.

void	build_gradient_of_sum_square_residuals_from_function_data (const RealMatrix &func_gradients, const RealVector &residuals, RealVector &ssr_gradient, const ShortArray &asrv)
	Construct the gradient of the sum of squares of residuals. More...

void	build_hessian_of_sum_square_residuals (const Response &resp, RealSymMatrix &ssr_hessian)
	Build the hessian of the ssr from residuals, function gradients and function hessians based on the response's active set request vector.

void	build_hessian_of_sum_square_residuals (const Response &resp, const ShortArray &asrv, RealSymMatrix &ssr_hessian)
	Build the hessian of the ssr from residuals, function gradients and function hessians using the passed active set request vector (overrides the response's request vector)

void	build_hessian_of_sum_square_residuals_from_response (const Response &resp, const ShortArray &asrv, int exp_ind, RealSymMatrix &ssr_hessian)
	Update the hessian of ssr with the values from the hessian associated with a single experiment.

void	build_hessian_of_sum_square_residuals_from_function_data (const RealSymMatrixArray &func_hessians, const RealMatrix &func_gradients, const RealVector &residuals, RealSymMatrix &ssr_hessian, const ShortArray &asrv)
	Construct the hessian of the sum of squares of residuals. More...

void	scale_residuals (const RealVector &multipliers, unsigned short multiplier_mode, size_t num_calib_params, Response &residual_response) const
	in-place scale the residual response (functions, gradients, Hessians) by sqrt(multipliers), according to blocks indicated by multiplier mode More...

Real	cov_determinant (const RealVector &multipliers, unsigned short multiplier_mode) const
	returns the determinant of (covariance block-scaled by the passed multipliers) More...

Real	half_log_cov_determinant (const RealVector &multipliers, unsigned short multiplier_mode) const
	returns the log of the determinant of (covariance block-scaled by the passed multipliers) More...

void	half_log_cov_det_gradient (const RealVector &multipliers, unsigned short multiplier_mode, size_t hyper_offset, RealVector &gradient) const
	populated the passed gradient with derivatives w.r.t. the hyper-parameter multipliers, starting at hyper_offset (must be sized) More...

void	half_log_cov_det_hessian (const RealVector &multipliers, unsigned short multiplier_mode, size_t hyper_offset, RealSymMatrix &hessian) const
	populated the passed Hessian with derivatives w.r.t. the hyper-parameter multipliers, starting at hyper_offset (must be sized) More...

StringArray	hyperparam_labels (unsigned short multiplier_mode) const
	generate variable labels for the covariance (error) multiplier hyperparams

Protected Member Functions
ShortArray	determine_active_request (const Response &resid_resp) const
	Perform check on the active request vector to make sure it is amenable to interpolation of simulation data and application of apply covariance.

SizetArray	residuals_per_multiplier (unsigned short multiplier_mode) const
	count the number of residuals influenced by each multiplier More...

void	generate_multipliers (const RealVector &multipliers, unsigned short multiplier_mode, RealVector &expanded_multipliers) const
	Generate a set of multipliers commensurate with the residual size for the total experiment data set. Instead of repeating the loops all over the place, generate an expanded set of multipliers; the conditionals get too complicated otherwise.

void	resid2mult_map (unsigned short multiplier_mode, IntVector &resid2mult_indices) const
	return the index of the multiplier that affects each residual

Private Member Functions
void	initialize (const StringArray &variance_types, const SharedResponseData &srd)
	shared body of constructor initialization

void	parse_sigma_types (const StringArray &sigma_types)
	parse user-provided sigma type strings and populate enums More...

void	update_data_properties ()
	After constructing or adding data, update properties like experiment lengths, determinant, etc.

void	load_experiment (size_t exp_index, std::ifstream &scalar_data_stream, size_t num_field_sigma_matrices, size_t num_field_sigma_diagonals, size_t num_field_sigma_scalars, size_t num_field_sigma_none, Response &exp_resp)
	Load a single experiment exp_index into exp_resp. More...

void	read_scalar_sigma (std::ifstream &scalar_data_stream, RealVector &sigma_scalars, IntVector &scalar_map_indices)
	read or default populate the scalar sigma

RealVector	residuals_view (const RealVector &residuals, size_t experiment) const
	Return a view (to allowing updaing in place) of the residuals associated with a given experiment, from a vector contaning residuals from all experiments.

RealMatrix	gradients_view (const RealMatrix &gradients, size_t experiment) const
	Return a view (to allowing updaing in place) of the gradients associated with a given experiment, from a matrix contaning gradients from all experiments.

RealSymMatrixArray	hessians_view (const RealSymMatrixArray &hessians, size_t experiment) const
	Return a view (to allowing updaing in place) of the hessians associated with a given experiment, from an array contaning the hessians from all experiments.

Private Attributes
bool	calibrationDataFlag
	whether the user specified a calibration data block

size_t	numExperiments
	the total number of experiments

size_t	numConfigVars
	number of configuration (state) variables to read for each experiment

UShortArray	varianceTypes
	type of variance specified for each variable, one per response group; empty varianceType indicates none specified by user

Real	covarianceDeterminant
	cached product of each experiment covariance's determinant

Real	logCovarianceDeterminant
	cached sum of each experiment covariance's log determinant

boost::filesystem::path	dataPathPrefix
	path to prepend to any data file names

String	scalarDataFilename
	the user-specied scalar data filename

unsigned short	scalarDataFormat
	tabular format of the simple scalar data file; supports TABULAR_NONE, TABULAR_HEADER, TABULAR_EVAL_ID, TABULAR_EXPER_ANNOT

size_t	scalarSigmaPerRow
	number of sigma values to read from each row in simple data file format (calculated from variance types strings

bool	readSimFieldCoords
	whether to read coordinate data files for simulation fields

SharedResponseData	simulationSRD
	archived shared data for use in sizing fields, total functions (historically we read all functions, including constraints, which might not be correct)

bool	interpolateFlag
	flag for interpolation.

short	outputLevel
	output verbosity level

std::vector< Response >	allExperiments
	Vector of numExperiments ExperimentResponses, holding the observed data and error (sigma/covariance) for each experiment.

std::vector< Variables >	allConfigVars
	Vector of numExperiments configurations at which data were gathered; empty if no configurations specified. The inactive state variables are used to store the configuration settings.

IntVector	experimentLengths
	Length of each experiment.

IntVector	expOffsets
	function index offsets for individual experiment data sets

Detailed Description

Interpolation method for interpolating between experimental and model data. I need to work on inputs/outputs to this method. For now, this assumes interpolation of functional data.

As Brian suggested, thsi class has the experimental data (coordinates and RealVectorArray interpolatedResults; The ExperimentData class is used to read and populate data (currently from user-specified files and/or the input spec) relating to experimental (physical observations) data for the purposes of calibration. Such data may include (for example): number of experiments, configuration variables, type of data (scalar vs. functional), treatment of sigma (experimental uncertainties). This class also provides an interpolation capability to interpolate between simulation or experimental data so that the differencing between simulation and experimental data may be performed properly.

Constructor & Destructor Documentation

◆ ExperimentData()

ExperimentData	(	size_t	num_experiments,
		const SharedVariablesData &	svd,
		const SharedResponseData &	srd,
		const VariablesArray &	config_vars,
		const IntResponseMap &	all_responses,
		short	output_level
	)

Bayesian experimental design constructor. Passed SVD has calibration parameters as active and config vars as inactive variables. Passed configVars have config vars as active.

Used in Hi2Lo Bayesian experimental design; passed config vars are active, but stored here as inactive.

References ExperimentData::allConfigVars, ExperimentData::allExperiments, SharedResponseData::copy(), Response::copy(), ExperimentData::numConfigVars, ExperimentData::numExperiments, ExperimentData::outputLevel, SharedResponseData::response_type(), ExperimentData::simulationSRD, Dakota::size_and_fill(), Dakota::svd(), Response::update(), and ExperimentData::update_data_properties().

Member Function Documentation

◆ config_vars_as_real()

std::vector< RealVector > config_vars_as_real ( ) const

values of the configuration variables, 1 RealVector per experiment omits string variables as historically used in NonDBayes

Skips string vars rather than converting to indices

References ExperimentData::allConfigVars, Dakota::copy_data_partial(), and Dakota::merge_data_partial().

Referenced by NonDGPMSABayesCalibration::fill_experiment_data().

◆ form_residuals() [1/2]

void form_residuals	(	const Response &	sim_resp,
		Response &	residual_resp
	)		const

form residuals for all experiments, interpolating if necessary; one simulation response maps to all experiments

This assumes the souce gradient/Hessian are size less or equal to the destination response, and that the leading part is to be populated.

References ExperimentData::determine_active_request(), ExperimentData::numExperiments, and ExperimentData::per_exp_length().

Referenced by DataTransformModel::archive_submodel_responses(), DataTransformModel::derived_evaluate(), ExperimentData::form_residuals(), DataTransformModel::primary_resp_differencer(), and DataTransformModel::transform_response_map().

◆ form_residuals() [2/2]

void form_residuals	(	const Response &	sim_resp,
		size_t	exp_ind,
		const ShortArray &	total_asv,
		size_t	exp_offset,
		Response &	residual_resp
	)		const

form residuals for an individual experiment, interpolating if necessary

This assumes the souce gradient/Hessian are size less or equal to the destination response, and that the leading part is to be populated.

◆ recover_model()

void recover_model	(	size_t	num_pri_fns,
		RealVector &	best_fns
	)		const

recover original model from the first experiment block in a full set of residuals; works in no interpolation case only (sizes same)

Add the data back to the residual to recover the model, for use in surrogated-based LSQ where DB lookup will fail (need approx eval DB). best_fns contains primary and secondary responses

References Dakota::abort_handler(), ExperimentData::allExperiments, Response::function_value(), ExperimentData::interpolateFlag, SharedResponseData::num_primary_functions(), and Response::shared_data().

◆ build_gradient_of_sum_square_residuals_from_function_data()

void build_gradient_of_sum_square_residuals_from_function_data	(	const RealMatrix &	func_gradients,
		const RealVector &	residuals,
		RealVector &	ssr_gradient,
		const ShortArray &	asrv
	)

Construct the gradient of the sum of squares of residuals.

Parameters

func_gradients	A matrix containing the gradients of the residual vector
residuals	A vector of residuals (mismatch between experimental data and the corresponding function values
asrv	The active set request vector

Referenced by ExperimentData::build_gradient_of_sum_square_residuals_from_response().

◆ build_hessian_of_sum_square_residuals_from_function_data()

void build_hessian_of_sum_square_residuals_from_function_data	(	const RealSymMatrixArray &	func_hessians,
		const RealMatrix &	func_gradients,
		const RealVector &	residuals,
		RealSymMatrix &	ssr_hessian,
		const ShortArray &	asrv
	)

Construct the hessian of the sum of squares of residuals.

Parameters

func_hessians	A list of matrices containing the Hessians of the function elements in the residual vector
func_gradients	A matrix containing the gradients of the residual vector
residuals	A vector of residuals (mismatch between experimental data and the corresponding function values
asrv	The active set request vector

Referenced by ExperimentData::build_hessian_of_sum_square_residuals_from_response().

◆ scale_residuals()

void scale_residuals	(	const RealVector &	multipliers,
		unsigned short	multiplier_mode,
		size_t	num_calib_params,
		Response &	residual_response
	)		const

in-place scale the residual response (functions, gradients, Hessians) by sqrt(multipliers), according to blocks indicated by multiplier mode

In-place scaling of residual response by hyper-parameter multipliers

References Dakota::abort_handler(), Response::active_set_request_vector(), Response::function_gradient_view(), Response::function_hessian_view(), Response::function_value(), Response::function_value_view(), ExperimentData::num_total_exppoints(), and ExperimentData::resid2mult_map().

◆ cov_determinant()

Real cov_determinant	(	const RealVector &	multipliers,
		unsigned short	multiplier_mode
	)		const

returns the determinant of (covariance block-scaled by the passed multipliers)

Determinant of the total covariance used in inference, which has blocks mult_i * I * Cov_i.

References Dakota::abort_handler(), ExperimentData::covarianceDeterminant, ExperimentData::generate_multipliers(), and ExperimentData::num_total_exppoints().

◆ half_log_cov_determinant()

Real half_log_cov_determinant	(	const RealVector &	multipliers,
		unsigned short	multiplier_mode
	)		const

returns the log of the determinant of (covariance block-scaled by the passed multipliers)

Determinant of half the log of total covariance used in inference, which has blocks mult_i * I * Cov_i.

References Dakota::abort_handler(), ExperimentData::generate_multipliers(), ExperimentData::logCovarianceDeterminant, and ExperimentData::num_total_exppoints().

Referenced by NonDBayesCalibration::log_likelihood(), NonDMUQBayesCalibration::print_results(), and NonDQUESOBayesCalibration::print_results().

◆ half_log_cov_det_gradient()

void half_log_cov_det_gradient	(	const RealVector &	multipliers,
		unsigned short	multiplier_mode,
		size_t	hyper_offset,
		RealVector &	gradient
	)		const

populated the passed gradient with derivatives w.r.t. the hyper-parameter multipliers, starting at hyper_offset (must be sized)

Compute the gradient of scalar f(m) 0.5*log(det(mult*Cov)) w.r.t. mults. Since this is the only use case, we include the 0.5 factor and perform an update in-place.

References ExperimentData::num_total_exppoints(), and ExperimentData::residuals_per_multiplier().

Referenced by NonDBayesCalibration::neg_log_post_resp_mapping().

◆ half_log_cov_det_hessian()

void half_log_cov_det_hessian	(	const RealVector &	multipliers,
		unsigned short	multiplier_mode,
		size_t	hyper_offset,
		RealSymMatrix &	hessian
	)		const

populated the passed Hessian with derivatives w.r.t. the hyper-parameter multipliers, starting at hyper_offset (must be sized)

Compute the gradient of scalar f(m) log(det(mult*Cov)) w.r.t. mults

References ExperimentData::num_total_exppoints(), and ExperimentData::residuals_per_multiplier().

Referenced by NonDBayesCalibration::calculate_evidence(), and NonDBayesCalibration::neg_log_post_resp_mapping().

◆ residuals_per_multiplier()

SizetArray residuals_per_multiplier ( unsigned short multiplier_mode ) const

protected

count the number of residuals influenced by each multiplier

Calculate how many residuals each multiplier affects

References ExperimentData::allExperiments, SharedResponseData::num_field_response_groups(), ExperimentData::num_fields(), SharedResponseData::num_response_groups(), SharedResponseData::num_scalar_primary(), ExperimentData::numExperiments, and ExperimentData::simulationSRD.

Referenced by ExperimentData::half_log_cov_det_gradient(), and ExperimentData::half_log_cov_det_hessian().

◆ parse_sigma_types()

void parse_sigma_types ( const StringArray & sigma_types )

private

parse user-provided sigma type strings and populate enums

Validate user-provided sigma specifcation. User can specify 0, 1, or num_response_groups sigmas. If specified, sigma types must be the same for all scalar responses.

References Dakota::abort_handler(), SharedResponseData::num_field_response_groups(), SharedResponseData::num_scalar_primary(), ExperimentData::scalarDataFilename, ExperimentData::scalarSigmaPerRow, ExperimentData::simulationSRD, and ExperimentData::varianceTypes.

Referenced by ExperimentData::initialize().

◆ load_experiment()

void load_experiment	(	size_t	exp_index,
		std::ifstream &	scalar_data_stream,
		size_t	num_field_sigma_matrices,
		size_t	num_field_sigma_diagonals,
		size_t	num_field_sigma_scalars,
		size_t	num_field_sigma_none,
		Response &	exp_resp
	)