www.gusucode.com > matlab写的贝叶斯的压缩感知的代码 > BCS_CODE\bcs_ver0.1\BCS_demo\Fig4_ab.m
%------------------------------------------------------ % This code generates Figures 4(a,b) of the following paper: % "Bayesian Compressive Sensing" (Preprint, 2007) % This example is modified from l1qc_example.m, an example % from l1magic. % Coded by: Shihao Ji, ECE, Duke University % last change: Jan. 2, 2007 %------------------------------------------------------ clear all % load random_results.mat rand_mean = mean(err); rand_std = std(err); load optimized_results.mat opt_mean = mean(err); opt_std = std(err); load approx_results.mat app_mean = mean(err); app_std = std(err); % base = 40; ns = 80; dN = 1; % plot the mean figure plot(base+(1:ns)*dN,rand_mean,'b-o'); hold on; plot(base+(1:ns)*dN,opt_mean,'r-*'); hold on; %plot(base+(1:ns)*dN,app_mean,'k-s'); xlabel('Number of Measurements'); ylabel('Reconstruction Error'); box on; legend('Random','Optimized','Approx.',1); % plot the variance figure errorbar(base+(1:ns)*dN,rand_mean,rand_std,'b-o'); hold on; errorbar(base+(1:ns)*dN,opt_mean,opt_std,'r-*'); % hold on; % errorbar(base+(1:ns)*dN,app_mean,app_std,'k-s'); xlabel('Number of Measurements'); ylabel('Reconstruction Error'); box on; axis([39,121,-0.3,1.6]); legend('Random','Optimized',1);