Prototypes
void	project_onto_basis (gsl_vector weight, gsl_matrix RB, gsl_matrix TS, gsl_matrix projections, INT4 idx)
	Function to project the training set onto a given basis vector. More...

void	complex_project_onto_basis (gsl_vector weight, gsl_matrix_complex RB, gsl_matrix_complex TS, gsl_matrix_complex projections, INT4 idx)
	Function to project the complex training set onto a given basis vector. More...

REAL8	weighted_dot_product (const gsl_vector weight, gsl_vector a, gsl_vector *b)
	The dot product of two real vectors scaled by a given weight factor. More...

gsl_complex	complex_weighted_dot_product (const gsl_vector weight, gsl_vector_complex a, gsl_vector_complex *b)
	The dot product of two complex vectors scaled by a given weight factor. More...

void	normalise (const gsl_vector weight, gsl_vector a)
	Normalise a real vector with a given weighting. More...

void	complex_normalise (const gsl_vector weight, gsl_vector_complex a)
	Normalise a complex vector with a given (real) weighting. More...

void	normalise_training_set (gsl_vector weight, gsl_matrix TS)
	Normalise the set of training waveforms. More...

void	complex_normalise_training_set (gsl_vector weight, gsl_matrix_complex TS)
	Normalise the set of complex training waveforms. More...

double	normalisation (const gsl_vector weight, gsl_vector a)
	Get normalisation constant for a real vector. More...

double	complex_normalisation (const gsl_vector weight, gsl_vector_complex a)
	Get normalisation constant for a complex vector. More...

void	modified_gs_complex (gsl_vector_complex ru, gsl_vector_complex ortho_basis, const gsl_matrix_complex RB_space, const gsl_vector wQuad, const int dim_RB)
	Modified Gram-Schmidt algorithm for complex data. More...

void	iterated_modified_gm_complex (gsl_vector_complex ru, gsl_vector_complex ortho_basis, const gsl_matrix_complex RB_space, const gsl_vector wQuad, const int dim_RB)
	Iterated modified Gram-Schmidt algorithm for complex data. More...

void	modified_gs (gsl_vector ru, gsl_vector ortho_basis, const gsl_matrix RB_space, const gsl_vector wQuad, const int dim_RB)
	Modified Gram-Schmidt algorithm for real data. More...

void	iterated_modified_gm (gsl_vector ru, gsl_vector ortho_basis, const gsl_matrix RB_space, const gsl_vector wQuad, const int dim_RB)
	Iterated modified Gram-Schmidt algorithm for real data. More...

REAL8Array *	B_matrix (gsl_matrix V, gsl_matrix RB)
	Get the interpolant of a reduced basis set. More...

COMPLEX16Array *	complex_B_matrix (gsl_matrix_complex V, gsl_matrix_complex RB)
	Get the interpolant of a complex reduced basis set. More...

int	complex_vector_maxabs_index (gsl_vector_complex *c)
	Get the index of the maximum absolute value for a complex vector. More...

REAL8	LALInferenceGenerateREAL8OrthonormalBasis (REAL8Array *RBin, const REAL8Vector delta, REAL8 tolerance, REAL8Array TS, UINT4Vector greedypoints)
	Create a orthonormal basis set from a training set of real waveforms. More...

REAL8	LALInferenceGenerateCOMPLEX16OrthonormalBasis (COMPLEX16Array *RBin, const REAL8Vector delta, REAL8 tolerance, COMPLEX16Array TS, UINT4Vector greedypoints)
	Create a orthonormal basis set from a training set of complex waveforms. More...

void	LALInferenceValidateREAL8OrthonormalBasis (REAL8Vector *projerr, const REAL8Vector delta, const REAL8Array RB, REAL8Array *testmodels)
	Validate the real reduced basis against another set of waveforms. More...

INT4	LALInferenceTestREAL8OrthonormalBasis (const REAL8Vector delta, REAL8 tolerance, const REAL8Array RB, REAL8Array **testmodels)
	Test the reduced basis against another set of waveforms. More...

REAL8	LALInferenceEnrichREAL8Basis (const REAL8Vector delta, REAL8 tolerance, REAL8Array RB, UINT4Vector greedypoints, const REAL8Array testmodels, REAL8Array **testmodelsnew)
	Expand the real training waveforms with ones from a set of new training waveforms. More...

void	LALInferenceValidateCOMPLEX16OrthonormalBasis (REAL8Vector *projerr, const REAL8Vector delta, const COMPLEX16Array RB, COMPLEX16Array *testmodels)
	Validate the complex reduced basis against another set of waveforms. More...

INT4	LALInferenceTestCOMPLEX16OrthonormalBasis (const REAL8Vector delta, REAL8 tolerance, const COMPLEX16Array RB, COMPLEX16Array **testmodels)
	Test the reduced basis against another set of waveforms. More...

REAL8	LALInferenceEnrichCOMPLEX16Basis (const REAL8Vector delta, REAL8 tolerance, COMPLEX16Array RB, UINT4Vector greedypoints, const COMPLEX16Array testmodels, COMPLEX16Array **testmodelsnew)
	Expand the complex training waveforms with ones from a set of new training waveforms. More...

LALInferenceREALROQInterpolant *	LALInferenceGenerateREALROQInterpolant (REAL8Array *RB)
	Create a real empirical interpolant from a set of orthonormal basis functions. More...

LALInferenceCOMPLEXROQInterpolant *	LALInferenceGenerateCOMPLEXROQInterpolant (COMPLEX16Array *RB)
	Create a complex empirical interpolant from a set of orthonormal basis functions. More...

REAL8Vector *	LALInferenceGenerateREAL8LinearWeights (REAL8Array B, REAL8Vector data, REAL8Vector *vars)
	Create the weights for the ROQ interpolant for the linear data and model dot product <d\|h> More...

REAL8Vector *	LALInferenceGenerateQuadraticWeights (REAL8Array B, REAL8Vector vars)
	Create the weights for the ROQ interpolant for the model quadratic model term real(<h\|h>). More...

COMPLEX16Vector *	LALInferenceGenerateCOMPLEX16LinearWeights (COMPLEX16Array B, COMPLEX16Vector data, REAL8Vector *vars)
	Create the weights for the ROQ interpolant for the complex data and model dot product <d\|h> More...

REAL8	LALInferenceROQREAL8DotProduct (REAL8Vector weights, REAL8Vector model)
	Calculate the dot product of two vectors using the ROQ iterpolant. More...

COMPLEX16	LALInferenceROQCOMPLEX16DotProduct (COMPLEX16Vector weights, COMPLEX16Vector model)
	Calculate the dot product of two complex vectors using the ROQ iterpolant. More...

void	LALInferenceRemoveREALROQInterpolant (LALInferenceREALROQInterpolant *a)
	Free memory for a `LALInferenceREALROQInterpolant`. More...

void	LALInferenceRemoveCOMPLEXROQInterpolant (LALInferenceCOMPLEXROQInterpolant *a)
	Free memory for a `LALInferenceCOMPLEXROQInterpolant`. More...

Macros
#define	omp ignore

Macro Definition Documentation

◆ omp

#define omp ignore

Definition at line 26 of file LALInferenceGenerateROQ.c.

Function Documentation

◆ project_onto_basis()

void project_onto_basis	(	gsl_vector *	weight,
		gsl_matrix *	RB,
		gsl_matrix *	TS,
		gsl_matrix *	projections,
		INT4	idx
	)

Function to project the training set onto a given basis vector.

This is an internal function to be used by LALInferenceCreateREAL8OrthonormalBasis

Parameters

[in]	weight	The normalisation weight(s) for the training set waveforms (e.g. time or frequency step(s) size)
[in]	RB	The reduced basis set
[in]	TS	The training set of waveforms
[in]	projections	The set of projections (this is updated in this function)
[in]	idx	The index of the reduced basis vector that the training set will project onto

Definition at line 102 of file LALInferenceGenerateROQ.c.

◆ complex_project_onto_basis()

void complex_project_onto_basis	(	gsl_vector *	weight,
		gsl_matrix_complex *	RB,
		gsl_matrix_complex *	TS,
		gsl_matrix_complex *	projections,
		INT4	idx
	)

Function to project the complex training set onto a given basis vector.

This is an internal function to be used by LALInferenceCreateCOMPLEX16OrthonormalBasis

Parameters

[in]	weight	The normalisation weight(s) for the training set waveforms (e.g. time or frequency step(s) size)
[in]	RB	The reduced basis set
[in]	TS	The training set of waveforms
[in]	projections	The set of projections (this is updated in this function)
[in]	idx	The index of the reduced basis vector that the training set will project onto

Definition at line 142 of file LALInferenceGenerateROQ.c.

◆ weighted_dot_product()

REAL8 weighted_dot_product	(	const gsl_vector *	weight,
		gsl_vector *	a,
		gsl_vector *	b
	)

The dot product of two real vectors scaled by a given weight factor.

Parameters

[in]	weight	A (set of) scaling factor(s) for the dot product
[in]	a	The first vector
[in]	b	The second vector

Returns: The real dot product of the two vectors

Definition at line 180 of file LALInferenceGenerateROQ.c.

◆ complex_weighted_dot_product()

gsl_complex complex_weighted_dot_product	(	const gsl_vector *	weight,
		gsl_vector_complex *	a,
		gsl_vector_complex *	b
	)

The dot product of two complex vectors scaled by a given weight factor.

The dot product is produced using the complex conjugate of the first vector.

Parameters

[in]	weight	A real scaling factor for the dot product
[in]	a	The first complex vector
[in]	b	The second complex vector

Returns: The absolute value of the complex dot product of the two vectors

Definition at line 219 of file LALInferenceGenerateROQ.c.

◆ normalise()

void normalise	(	const gsl_vector *	weight,
		gsl_vector *	a
	)

Normalise a real vector with a given weighting.

Parameters

[in]	weight	The weighting(s) in the normalisation (e.g. time of frequency step(s) between points)
[in]	a	The vector to be normalise (this will be change by the function to return the normalised vector.

Definition at line 259 of file LALInferenceGenerateROQ.c.

◆ complex_normalise()

void complex_normalise	(	const gsl_vector *	weight,
		gsl_vector_complex *	a
	)

Normalise a complex vector with a given (real) weighting.

Parameters

[in]	weight	The weighting(s) in the normalisation (e.g. time of frequency step(s) between points)
[in]	a	The vector to be normalised (this will be change by the function to return the normalised vector).

Definition at line 282 of file LALInferenceGenerateROQ.c.

◆ normalise_training_set()

void normalise_training_set	(	gsl_vector *	weight,
		gsl_matrix *	TS
	)

Normalise the set of training waveforms.

This function will normalise a set of training waveforms. This will be used within the LALInferenceCreateREAL8OrthonormalBasis function.

Parameters

[in]	weight	The e.g. time/frequency step in the waveforms used to normalise the waveforms
[in]	TS	The training set to be normalised.

Definition at line 357 of file LALInferenceGenerateROQ.c.

◆ complex_normalise_training_set()

void complex_normalise_training_set	(	gsl_vector *	weight,
		gsl_matrix_complex *	TS
	)

Normalise the set of complex training waveforms.

This function will normalise a set of complex training waveforms. This will be used within the LALInferenceCreateCOMPLEX16OrthonormalBasis function.

Parameters

[in]	weight	The e.g. time/frequency step in the waveforms used to normalise the waveforms
[in]	TS	The training set to be normalised.

Definition at line 375 of file LALInferenceGenerateROQ.c.

◆ normalisation()

double normalisation	(	const gsl_vector *	weight,
		gsl_vector *	a
	)

Get normalisation constant for a real vector.

Parameters

[in]	weight	The weighting(s) in the normalisation (e.g. time of frequency step(s) between points)
[in]	a	The vector to calculate the normalisation constant for

Returns: The normalisation

Definition at line 306 of file LALInferenceGenerateROQ.c.

◆ complex_normalisation()

double complex_normalisation	(	const gsl_vector *	weight,
		gsl_vector_complex *	a
	)

Get normalisation constant for a complex vector.

Parameters

[in]	weight	The weighting(s) in the normalisation (e.g. time of frequency step(s) between points)
[in]	a	The vector to calculate the normalisation constant for

Returns: The normalisation

Definition at line 331 of file LALInferenceGenerateROQ.c.

◆ modified_gs_complex()

void modified_gs_complex	(	gsl_vector_complex *	ru,
		gsl_vector_complex *	ortho_basis,
		const gsl_matrix_complex *	RB_space,
		const gsl_vector *	wQuad,
		const int	dim_RB
	)

Modified Gram-Schmidt algorithm for complex data.

A modified Gram-Schmidt algorithm taken from the greedycpp code.

Parameters

[out]	ru	A 1-by-(`dim_RB` + 1) complex vector returning a slice of the R matrix
[out]	ortho_basis	A complex vector returning the orthogonal basis vector
[in]	RB_space	A complex matrix containing the existing set of orthonormal basis vectors
[in]	wQuad	A vector of weights for the inner products
[in]	dim_RB	The number of elements in the current `RB_space`

Definition at line 518 of file LALInferenceGenerateROQ.c.

◆ iterated_modified_gm_complex()

void iterated_modified_gm_complex	(	gsl_vector_complex *	ru,
		gsl_vector_complex *	ortho_basis,
		const gsl_matrix_complex *	RB_space,
		const gsl_vector *	wQuad,
		const int	dim_RB
	)

Iterated modified Gram-Schmidt algorithm for complex data.

An iterated modified Gram-Schmidt algorithm taken from the greedycpp code. This uses the method given in [2]

Parameters

[out]	ru	A complex 1-by-(`dim_RB` + 1) vector returning a slice of the R matrix
[out]	ortho_basis	A complex vector returning the orthogonal basis vector
[in]	RB_space	A complex matrix containing the existing set of orthonormal basis vectors
[in]	wQuad	A vector of weights for the inner products
[in]	dim_RB	The number of elements in the current `RB_space`

Definition at line 609 of file LALInferenceGenerateROQ.c.

◆ modified_gs()

void modified_gs	(	gsl_vector *	ru,
		gsl_vector *	ortho_basis,
		const gsl_matrix *	RB_space,
		const gsl_vector *	wQuad,
		const int	dim_RB
	)

Modified Gram-Schmidt algorithm for real data.

A modified Gram-Schmidt algorithm taken from the greedycpp code.

Parameters

[out]	ru	A 1-by-(`dim_RB` + 1) vector returning a slice of the R matrix
[out]	ortho_basis	A vector returning the orthogonal basis vector
[in]	RB_space	A matrix containing the existing set of orthonormal basis vectors
[in]	wQuad	A vector of weights for the inner products
[in]	dim_RB	The number of elements in the current `RB_space`

Definition at line 564 of file LALInferenceGenerateROQ.c.

◆ iterated_modified_gm()

void iterated_modified_gm	(	gsl_vector *	ru,
		gsl_vector *	ortho_basis,
		const gsl_matrix *	RB_space,
		const gsl_vector *	wQuad,
		const int	dim_RB
	)

Iterated modified Gram-Schmidt algorithm for real data.

An iterated modified Gram-Schmidt algorithm taken from the greedycpp code. This uses the method given in [2]

Parameters

[out]	ru	A 1-by-(`dim_RB` + 1) vector returning a slice of the R matrix
[out]	ortho_basis	A vector returning the orthogonal basis vector
[in]	RB_space	A matrix containing the existing set of orthonormal basis vectors
[in]	wQuad	A vector of weights for the inner products
[in]	dim_RB	The number of elements in the current `RB_space`

Definition at line 677 of file LALInferenceGenerateROQ.c.

◆ B_matrix()

REAL8Array * B_matrix	(	gsl_matrix *	V,
		gsl_matrix *	RB
	)

Get the interpolant of a reduced basis set.

This is used internally by LALInferenceCreateREALROQInterpolant when iteratively calculating the interpolation matrix.

Parameters

[in]	V	The matrix containing the basis vector points at the current interpolation nodes
[in]	RB	The set of basis vectors

Returns: The interpolant matrix

Definition at line 396 of file LALInferenceGenerateROQ.c.

◆ complex_B_matrix()

COMPLEX16Array * complex_B_matrix	(	gsl_matrix_complex *	V,
		gsl_matrix_complex *	RB
	)

Get the interpolant of a complex reduced basis set.

This is used internally by LALInferenceCreateCOMPLEXROQInterpolant when iteratively calculating the interpolation matrix.

Parameters

[in]	V	The matrix containing the basis vector points at the current interpolation nodes
[in]	RB	The set of basis vectors

Returns: The interpolant matrix

Definition at line 445 of file LALInferenceGenerateROQ.c.

◆ complex_vector_maxabs_index()

int complex_vector_maxabs_index ( gsl_vector_complex * c )

Get the index of the maximum absolute value for a complex vector.

Parameters

[in] c A complex vector

Returns: The index of the maximum absolute value of that vector

Definition at line 489 of file LALInferenceGenerateROQ.c.

◆ LALInferenceGenerateREAL8OrthonormalBasis()

REAL8 LALInferenceGenerateREAL8OrthonormalBasis	(	REAL8Array **	RBin,
		const REAL8Vector *	delta,
		REAL8	tolerance,
		REAL8Array **	TS,
		UINT4Vector **	greedypoints
	)

Create a orthonormal basis set from a training set of real waveforms.

Given a gsl_matrix containing a training set of real waveforms (where the waveforms are created at time or frequency steps seperated by delta) an orthonormal basis will be generated using the greedy binning Algorithm 1 of [1] . The stopping criteria for the algorithm is controlled by the tolerance value, which defined the maximum residual between the current basis set (at a given iteration) and the training set (and example tolerance is \(10^{-12}\)). In this function the training set will be normalised, so the input TS will be modified.

If the RBin value is NULL then a new reduced basis will be formed from the given training set. However, if RBin already contains a previously produced basis, then this basis will be enriched with bases if possible using the new training set. NOTE: when using small tolerances enriching the basis in this way can lead to numerical precision issues, so in general you should use LALInferenceEnrichREAL8Basis for enrichment.

Parameters

[out]	RBin	A `REAL8Array` to return the reduced basis.
[in]	delta	The time/frequency step(s) in the training set used to normalise the models. This can be a vector containing just one value.
[in]	tolerance	The tolerance used as a stopping criteria for the basis generation.
[in]	TS	A `REAL8Array` matrix containing the complex training set, where the number of waveforms in the training set is given by the rows and the waveform points by the columns. This will be modified by this function, as the training set will be normalised.
[out]	greedypoints	A `UINT4Vector` to return the indices of the training set rows that have been used to form the reduced basis.

Returns: A REAL8 with the maximum projection error for the final reduced basis.

See also: LALInferenceEnrichREAL8Basis

Definition at line 762 of file LALInferenceGenerateROQ.c.

◆ LALInferenceGenerateCOMPLEX16OrthonormalBasis()

REAL8 LALInferenceGenerateCOMPLEX16OrthonormalBasis	(	COMPLEX16Array **	RBin,
		const REAL8Vector *	delta,
		REAL8	tolerance,
		COMPLEX16Array **	TS,
		UINT4Vector **	greedypoints
	)

Create a orthonormal basis set from a training set of complex waveforms.

Given a gsl_matrix containing a training set TS of complex waveforms (where the waveforms are created at time or frequency steps seperated by delta) an orthonormal basis will be generated using the greedy binning Algorithm 1 of [1] . The stopping criteria for the algorithm is controlled by the tolerance value, which is defined the maximum residual between the current basis set (at a given iteration) and the training set (and example tolerance is \(10^{-12}\)). In this function the training set will be normalised, so the input TS will be modified.

If the RBin value is NULL then a new reduced basis will be formed from the given training set. However, if RBin already contains a previously produced basis, then this basis will be enriched with bases if possible using the new training set. NOTE: when using small tolerances enriching the basis in this way can lead to numerical precision issues, so in general you should use LALInferenceEnrichCOMPLEX16Basis for enrichment.

Note that in this function we have to cast the COMPLEX16 array as a double to use gsl_matrix_view_array, which assume that the data is passed as a double array with memory laid out so that adjacent double memory blocks hold the corresponding real and imaginary parts.

Parameters

[out]	RBin	A `COMPLEX16Array` to return the reduced basis.
[in]	delta	The time/frequency step(s) in the training set used to normalise the models. This can be a vector containing just one value.
[in]	tolerance	The tolerance used as a stopping criteria for the basis generation.
[in]	TS	A `COMPLEX16Array` matrix containing the complex training set, where the number of waveforms in the training set is given by the rows and the waveform points by the columns. This will be modified by this function, as the training set will be normalised.
[out]	greedypoints	A `UINT4Vector` to return the indices of the training set rows that have been used to form the reduced basis.

Returns: A REAL8 with the maximum projection error for the final reduced basis.

See also: LALInferenceEnrichCOMPLEX16Basis

Definition at line 968 of file LALInferenceGenerateROQ.c.

◆ LALInferenceValidateREAL8OrthonormalBasis()

void LALInferenceValidateREAL8OrthonormalBasis	(	REAL8Vector **	projerr,
		const REAL8Vector *	delta,
		const REAL8Array *	RB,
		REAL8Array **	testmodels
	)

Validate the real reduced basis against another set of waveforms.

This function projects a set of waveforms onto the reduced basis and checks that the residuals are within a given tolerance. It returns the projection errors.

Note that the projection error returned are the square of the residual errors, as is used as the criterion for adding new bases in the reduced basis generation function LALInferenceGenerateREAL8OrthonormalBasis. This is different to the validation.cpp code in greedycpp, which returns the square root of the value we return.

Parameters

[out]	projerr	The projection errors for each test waveform.
[in]	delta	The time/frequency step(s) in the training set used to normalise the models. This can be a vector containing just one value.
[in]	RB	The reduced basis set
[in]	testmodels	The set of waveform models to project onto the basis (these will be changed by this function, as the waveforms will get normalised).

See also: LALInferenceTestREAL8OrthonormalBasis

Definition at line 1159 of file LALInferenceGenerateROQ.c.

◆ LALInferenceTestREAL8OrthonormalBasis()

INT4 LALInferenceTestREAL8OrthonormalBasis	(	const REAL8Vector *	delta,
		REAL8	tolerance,
		const REAL8Array *	RB,
		REAL8Array **	testmodels
	)

Test the reduced basis against another set of waveforms.

This function projects a set of waveforms onto the reduced basis and checks that the residuals are within a given tolerance

Parameters

[in]	delta	The time/frequency step(s) in the training set used to normalise the models. This can be a vector containing just one value.
[in]	tolerance	The allowed (squared) residual tolerence for the test waveforms
[in]	RB	The reduced basis set
[in]	testmodels	The set of waveform models to project onto the basis

Returns: Returns XLAL_SUCCESS if all test waveforms meet the tolerance

See also: LALInferenceValidateREAL8OrthonormalBasis

Definition at line 1240 of file LALInferenceGenerateROQ.c.

◆ LALInferenceEnrichREAL8Basis()

REAL8 LALInferenceEnrichREAL8Basis	(	const REAL8Vector *	delta,
		REAL8	tolerance,
		REAL8Array **	RB,
		UINT4Vector **	greedypoints,
		const REAL8Array *	testmodels,
		REAL8Array **	testmodelsnew
	)

Expand the real training waveforms with ones from a set of new training waveforms.

Expands an original set of training waveforms with waveforms from a new set a training waveforms if, when projected onto the reduced basis, those new waveforms are greater than the given tolerance. The reduced basis will then be recomputed using the expanded training set.

Parameters

[in]	delta	The time/frequency step(s) in the training set used to normalise the models. This can be a vector containing just one value.
[in]	tolerance	The allowed residual tolerence for the test waveforms
[in]	RB	The reduced basis set
[in]	greedypoints	The rows in `testmodels` that formed the reduced basis
[in]	testmodels	The set of waveform models used to produce the reduced basis (already normalised)
[in]	testmodelsnew	A new set of waveforms to project onto the current reduced basis (these will be changed by this function, as the waveforms will get normalised when pass to `LALInferenceValidateREAL8OrthonormalBasis`, and editted to contain the new set of training waveforms from which the enriched basis was formed).

Returns: Returns the maximum projection error of the new basis set

Definition at line 1291 of file LALInferenceGenerateROQ.c.

◆ LALInferenceValidateCOMPLEX16OrthonormalBasis()

void LALInferenceValidateCOMPLEX16OrthonormalBasis	(	REAL8Vector **	projerr,
		const REAL8Vector *	delta,
		const COMPLEX16Array *	RB,
		COMPLEX16Array **	testmodels
	)

Validate the complex reduced basis against another set of waveforms.

This function projects a set of waveforms onto the reduced basis and checks that the residuals are within a given tolerance. It returns the projection errors.

Note that the projection error returned are the square of the residual errors, as is used as the criterion for adding new bases in the reduced basis generation function LALInferenceGenerateCOMPLEX16OrthonormalBasis. This is different to the validation.cpp code in greedycpp, which returns the square root of the value we return.

Parameters

[out]	projerr	The projection errors (square of the residual) for each test waveform.
[in]	delta	The time/frequency step(s) in the training set used to normalise the models. This can be a vector containing just one value.
[in]	RB	The reduced basis set
[in]	testmodels	The set of waveform models to project onto the basis (these will be changed by this function, as the waveforms will get normalised).

See also: LALInferenceTestCOMPLEX16OrthonormalBasis

Definition at line 1416 of file LALInferenceGenerateROQ.c.

◆ LALInferenceTestCOMPLEX16OrthonormalBasis()

INT4 LALInferenceTestCOMPLEX16OrthonormalBasis	(	const REAL8Vector *	delta,
		REAL8	tolerance,
		const COMPLEX16Array *	RB,
		COMPLEX16Array **	testmodels
	)

Test the reduced basis against another set of waveforms.

This function projects a set of waveforms onto the reduced basis and checks that the residuals are within a given tolerance

Parameters

[in]	delta	The time/frequency step(s) in the training set used to normalise the models. This can be a vector containing just one value.
[in]	tolerance	The allowed residual (squared) tolerence for the test waveforms
[in]	RB	The reduced basis set
[in]	testmodels	The set of waveform models to project onto the basis

Returns: Returns XLAL_SUCCESS if all test waveforms meet the tolerance

See also: LALInferenceValidateCOMPLEX16OrthonormalBasis

Definition at line 1508 of file LALInferenceGenerateROQ.c.

◆ LALInferenceEnrichCOMPLEX16Basis()

REAL8 LALInferenceEnrichCOMPLEX16Basis	(	const REAL8Vector *	delta,
		REAL8	tolerance,
		COMPLEX16Array **	RB,
		UINT4Vector **	greedypoints,
		const COMPLEX16Array *	testmodels,
		COMPLEX16Array **	testmodelsnew
	)

Expand the complex training waveforms with ones from a set of new training waveforms.

Expands an original set of training waveforms with waveforms from a new set a training waveforms if, when projected onto the reduced basis, those new waveforms are greater than the given tolerance. The reduced basis will then be recomputed using the expanded training set.

Parameters

[in]	delta	The time/frequency step(s) in the training set used to normalise the models. This can be a vector containing just one value.
[in]	tolerance	The allowed residual tolerence for the test waveforms
[in]	RB	The reduced basis set
[in]	greedypoints	The rows in `testmodels` that formed the reduced basis
[in]	testmodels	The set of waveform models used to produce the reduced basis
[in]	testmodelsnew	A new set of waveforms to project onto the current reduced basis (these will be changed by this function, as the waveforms will get normalised when pass to `LALInferenceValidateCOMPLEX16OrthonormalBasis`, and editted to contain the new set of training waveforms from which the enriched basis was formed)

Returns: Returns the maximum projection error of the new basis set

Definition at line 1560 of file LALInferenceGenerateROQ.c.

◆ LALInferenceGenerateREALROQInterpolant()

LALInferenceREALROQInterpolant * LALInferenceGenerateREALROQInterpolant ( REAL8Array * RB )

Create a real empirical interpolant from a set of orthonormal basis functions.

Given a real REAL8Array containing a set of orthonormal basis functions generate an empirical intopolant, and set of interpolation points, using Algorithm 2 of [1] .

Parameters

[in] RB The set of basis functions

Returns: A LALInferenceREALROQInterpolant structure containing the interpolant and its nodes

Definition at line 1679 of file LALInferenceGenerateROQ.c.

◆ LALInferenceGenerateCOMPLEXROQInterpolant()

LALInferenceCOMPLEXROQInterpolant * LALInferenceGenerateCOMPLEXROQInterpolant ( COMPLEX16Array * RB )

Create a complex empirical interpolant from a set of orthonormal basis functions.

Given a complex gsl_matrix_complex containing a set of orthonormal basis functions generate an empirical intopolant, and set of interpolation points, using Algorithm 2 of [1] .

Parameters

[in] RB The set of basis functions

Returns: A LALInferenceCOMPLEXROQInterpolant structure containing the interpolant and its nodes

Definition at line 1771 of file LALInferenceGenerateROQ.c.

◆ LALInferenceGenerateREAL8LinearWeights()

REAL8Vector * LALInferenceGenerateREAL8LinearWeights	(	REAL8Array *	B,
		REAL8Vector *	data,
		REAL8Vector *	vars
	)

Create the weights for the ROQ interpolant for the linear data and model dot product <d|h>

Parameters

[in]	B	The interpolant matrix
[in]	data	The real data vector
[in]	vars	A vector of data noise variance values (or a single value) to weight the "weights"

Returns: The vector of weights

Definition at line 1860 of file LALInferenceGenerateROQ.c.

◆ LALInferenceGenerateQuadraticWeights()

REAL8Vector * LALInferenceGenerateQuadraticWeights	(	REAL8Array *	B,
		REAL8Vector *	vars
	)

Create the weights for the ROQ interpolant for the model quadratic model term real(<h|h>).

Parameters

[in]	B	The interpolant matrix
[in]	vars	A vector of data noise variance values (or a single value) to weight the "weights"

Returns: The vector of weights

Definition at line 1899 of file LALInferenceGenerateROQ.c.

◆ LALInferenceGenerateCOMPLEX16LinearWeights()

COMPLEX16Vector * LALInferenceGenerateCOMPLEX16LinearWeights	(	COMPLEX16Array *	B,
		COMPLEX16Vector *	data,
		REAL8Vector *	vars
	)

Create the weights for the ROQ interpolant for the complex data and model dot product <d|h>

Parameters

[in]	B	The interpolant matrix
[in]	data	The complex data vector
[in]	vars	A vector of data noise variance values (or a single value) to weight the "weights"

Returns: The vector of weights

Definition at line 1936 of file LALInferenceGenerateROQ.c.

◆ LALInferenceROQREAL8DotProduct()

REAL8 LALInferenceROQREAL8DotProduct	(	REAL8Vector *	weights,
		REAL8Vector *	model
	)

Calculate the dot product of two vectors using the ROQ iterpolant.

This function calculates the dot product of two real vectors using the ROQ interpolant. This required the interpolant weights computed with LALInferenceCreateREAL8DataModelWeights and the waveform model defined at the interpolation node.

Parameters

[in]	weights	The ROQ interpolation weights
[in]	model	The waveform model (or quadratic model) defined at the interpolation points

Returns: The dot product weights and model

Definition at line 1982 of file LALInferenceGenerateROQ.c.

◆ LALInferenceROQCOMPLEX16DotProduct()

COMPLEX16 LALInferenceROQCOMPLEX16DotProduct	(	COMPLEX16Vector *	weights,
		COMPLEX16Vector *	model
	)

Calculate the dot product of two complex vectors using the ROQ iterpolant.

This function calculates the dot product of two complex vectors using the ROQ interpolant. This required the interpolant weights computed with LALInferenceCreateCOMPLEX16DataModelWeights and the waveform model defined at the interolation node.

Parameters

[in]	weights	The ROQ interpolation weights
[in]	model	The waveform model defined at the interpolation points

Returns: The dot product of the data and model

Definition at line 2005 of file LALInferenceGenerateROQ.c.

◆ LALInferenceRemoveREALROQInterpolant()

void LALInferenceRemoveREALROQInterpolant ( LALInferenceREALROQInterpolant * a )

Free memory for a LALInferenceREALROQInterpolant.

Parameters

[in] a A pointer to a LALInferenceREALROQInterpolant

Definition at line 2022 of file LALInferenceGenerateROQ.c.

◆ LALInferenceRemoveCOMPLEXROQInterpolant()

void LALInferenceRemoveCOMPLEXROQInterpolant ( LALInferenceCOMPLEXROQInterpolant * a )

Free memory for a LALInferenceCOMPLEXROQInterpolant.

Parameters

[in] a A pointer to a LALInferenceCOMPLEXROQInterpolant

Definition at line 2036 of file LALInferenceGenerateROQ.c.

Prototypes

Macros

Macro Definition Documentation

◆ omp

Function Documentation

◆ project_onto_basis()

◆ complex_project_onto_basis()

◆ weighted_dot_product()

◆ complex_weighted_dot_product()

◆ normalise()

◆ complex_normalise()

◆ normalise_training_set()

◆ complex_normalise_training_set()

◆ normalisation()

◆ complex_normalisation()

◆ modified_gs_complex()

◆ iterated_modified_gm_complex()

◆ modified_gs()

◆ iterated_modified_gm()

◆ B_matrix()

◆ complex_B_matrix()

◆ complex_vector_maxabs_index()

◆ LALInferenceGenerateREAL8OrthonormalBasis()

◆ LALInferenceGenerateCOMPLEX16OrthonormalBasis()

◆ LALInferenceValidateREAL8OrthonormalBasis()

◆ LALInferenceTestREAL8OrthonormalBasis()

◆ LALInferenceEnrichREAL8Basis()

◆ LALInferenceValidateCOMPLEX16OrthonormalBasis()

◆ LALInferenceTestCOMPLEX16OrthonormalBasis()

◆ LALInferenceEnrichCOMPLEX16Basis()

◆ LALInferenceGenerateREALROQInterpolant()

◆ LALInferenceGenerateCOMPLEXROQInterpolant()

◆ LALInferenceGenerateREAL8LinearWeights()

◆ LALInferenceGenerateQuadraticWeights()

◆ LALInferenceGenerateCOMPLEX16LinearWeights()

◆ LALInferenceROQREAL8DotProduct()

◆ LALInferenceROQCOMPLEX16DotProduct()

◆ LALInferenceRemoveREALROQInterpolant()

◆ LALInferenceRemoveCOMPLEXROQInterpolant()