comparison toolboxes/alps/generate_matrix.m @ 154:0de08f68256b ivand_dev

ALPS toolbox - Algebraic Pursuit added to smallbox

+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. [e-mail], 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;