Mercurial > hg > smallbox
view toolboxes/alps/generate_vector.m @ 199:751fa3bddd30 luisf_dev
Added config file for SMALL_solve (removed the if/else code from SMALL_solve); added headers to both config files;
author | luisf <luis.figueira@eecs.qmul.ac.uk> |
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date | Tue, 20 Mar 2012 14:28:51 +0000 |
parents | 0de08f68256b |
children |
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function [x] = generate_vector(N, K, ensemble, sigma, p) % ========================================================================= % Sparse vector generator % ========================================================================= % INPUT ARGUMENTS: % N Size of sparse vector. % K Sparsity of vector. % ensemble Ensemble type of measurement matrix. Possible % values are: % -'Gaussian': K non-zero elements of the sparse vector % are drawn from normal distribution N(0,1). % -'sGaussian': K non-zero elements of the sparse vector % are drawn from normal distribution N(0,sigma^2). % -'Bernoulli': K non-zero elements of the sparse vector % are drawn from Bernoulli distribution (1/2,1/2). % -'pBernoulli': K non-zero elements of the sparse vector % are drawn from Bernoulli distribution (p,1-p). % sigma Standard deviation of Gaussian distribution. % p Parameter of Bernoulli distribution. % ========================================================================= % OUTPUT ARGUMENTS: % x K-sparse vector. % ========================================================================= % 01/04/2011, by Anastasios Kyrillidis. anastasios.kyrillidis@epfl.ch, EPFL. % ========================================================================= if nargin < 3 ensemble = 'Gaussian'; end; if nargin < 4 sigma = 1; p = 0.5; end; x = zeros(N,1); rand_indices = randperm(N); switch ensemble case 'Gaussian' x(rand_indices(1:K)) = randn(K,1); % Standard normal distribution ~ N(0,1) case 'sGaussian' x(rand_indices(1:K)) = sigma*randn(K,1); % Normal distribution ~ N(0,sigma^2) case 'Uniform' x(rand_indices(1:K)) = rand(K,1); % Uniform [0,1] distribution case 'Bernoulli' x(rand_indices(1:K)) = (-1).^round(rand(K,1)); % Bernoulli ~ (1/2, 1/2) distribution case 'pBernoulli' x(rand_indices(1:K)) = (-1).^(rand(K,1) > p); % Bernoulli ~ (p, 1-p) distribution end; x = x/norm(x);