Mercurial > hg > smallbox
view toolboxes/alps/generate_matrix.m @ 218:c38d965b5a1d luisf_dev
Moved CVX_add_const_Audio_declipping.m from solvers to the examples/AudioInpainting folder.
author | Aris Gretsistas <aris.gretsistas@eecs.qmul.ac.uk> |
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date | Thu, 22 Mar 2012 15:37:45 +0000 |
parents | 0de08f68256b |
children |
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function [A] = generate_matrix(M, N, ensemble, p) % ========================================================================= % Measerement matrix generator % ========================================================================= % INPUT ARGUMENTS: % M Number of measurements (number of rows). % N Size of sparse vector (number of columns). % ensemble Ensemble type of measurement matrix. Possible % values are: % -'Gaussian': creates a MxN measurement matrix with % elements drawn from normal distribution N(0,1).% % -'Bernoulli': creates a MxN measurement matrix with % elements drawn from Bernoulli distribution (1/2,1/2). % -'pBernoulli': creates a MxN measurement matrix with % elements drawn from Bernoulli distribution (p,1-p). % Parameter of Bernoulli distribution. % -'sparseGaussian': creates a MxN sparse measurement % matrix with elements drawn from normal distribution N(0,1). % ========================================================================= % OUTPUT ARGUMENTS: % A MxN measurement matrix with normalized columns. % ========================================================================= % 01/04/2011, by Anastasios Kyrillidis. anastasios.kyrillidis@epfl.ch, EPFL. % ========================================================================= if nargin < 3 ensemble = 'Gaussian'; end; if nargin < 4 p = 0.5; end; switch ensemble case 'Gaussian' A = randn(M,N); % Standard normal distribution for i = 1:N % Normalize columns A(:,i) = A(:,i)/norm(A(:,i)); end; case 'Bernoulli' A = (-1).^round(rand(M,N)); % Bernoulli ~ (1/2, 1/2) distribution for i = 1:N % Normalize columns A(:,i) = A(:,i)/norm(A(:,i)); end; case 'pBernoulli' A = (-1).^(rand(M,N) > p); % Bernoulli ~ (p, 1-p) distribution for i = 1:N % Normalize columns A(:,i) = A(:,i)/norm(A(:,i)); end; case 'sparseGaussian' leftd = 8; A = zeros(M,N); for i = 1:N ind = randperm(M); A(ind(1:leftd),i)=1/leftd; end for i = 1:N % Normalize columns A(:,i) = A(:,i)/norm(A(:,i)); end; end;