UQTk: Uncertainty Quantification Toolkit 3.1.5
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Header for the implementations of Bayesian compressive sensing algorithm. More...
Go to the source code of this file.
Macros | |
#define | MAX_IT 1000 |
Functions | |
void | WBCS (Array2D< double > &PHI, Array1D< double > &y, Array1D< double > &sigma2, double eta, Array1D< double > &lambda_init, int adaptive, int optimal, double scale, int verbose, Array1D< double > &weights, Array1D< int > &used, Array1D< double > &errbars, Array1D< double > &basis, Array1D< double > &alpha, Array2D< double > &Sig) |
Implements weighted version of the original Bayesian Compressive Sensing algorithm. | |
void | BCS (Array2D< double > &PHI, Array1D< double > &y, double &sigma2, double eta, Array1D< double > &lambda_init, int adaptive, int optimal, double scale, int verbose, Array1D< double > &weights, Array1D< int > &used, Array1D< double > &errbars, Array1D< double > &basis, Array1D< double > &alpha, double &lambda) |
Essentially same functionality as WBCS, but slightly altered I/O. | |
Header for the implementations of Bayesian compressive sensing algorithm.
#define MAX_IT 1000 |
void BCS | ( | Array2D< double > & | PHI, |
Array1D< double > & | y, | ||
double & | sigma2, | ||
double | eta, | ||
Array1D< double > & | lambda_init, | ||
int | adaptive, | ||
int | optimal, | ||
double | scale, | ||
int | verbose, | ||
Array1D< double > & | weights, | ||
Array1D< int > & | used, | ||
Array1D< double > & | errbars, | ||
Array1D< double > & | basis, | ||
Array1D< double > & | alpha, | ||
double & | lambda ) |
Essentially same functionality as WBCS, but slightly altered I/O.
void WBCS | ( | Array2D< double > & | PHI, |
Array1D< double > & | y, | ||
Array1D< double > & | sigma2, | ||
double | eta, | ||
Array1D< double > & | lambda_init, | ||
int | adaptive, | ||
int | optimal, | ||
double | scale, | ||
int | verbose, | ||
Array1D< double > & | weights, | ||
Array1D< int > & | used, | ||
Array1D< double > & | errbars, | ||
Array1D< double > & | basis, | ||
Array1D< double > & | alpha, | ||
Array2D< double > & | Sig ) |
Implements weighted version of the original Bayesian Compressive Sensing algorithm.
[in] | PHI | : design matrix |
[in] | y | : data vector |
[in,out] | sigma2 | : initial noise variance (usually var(y)/1e2) : re-estimated on output |
[in] | eta | : stopping criterion (usually 1e-5) |
[in] | lambda_init | : regularization weight vector, if empty array, it automatically computes the optimal, uniform weights |
[in] | adaptive | : generate basis for adaptive CS (usually 0) |
[in] | optimal | : use the rigorous implementation of adaptive CS (usually 1) |
[in] | scale | : diagonal loading parameter (usually 0.1) |
[in] | verbose | : verbosity flag |
[out] | weights | : sparse weights |
[out] | used | : the positions of sparse weights |
[out] | errbars | : one standard deviation around the sparse weights |
[out] | basis | : if adaptive==1, then this is the next projection vector, see [Ji:2008] |
[out] | alpha | : estimated sparse hyperparameters (1/gamma), see [Babacan:2010] |
[out] | Sig | : covariance matrix of the weights |
Implements weighted version of the original Bayesian Compressive Sensing algorithm.