Numerical experiments are often used to validate ideas, evaluate algorithms, and compare different approaches on a set of problems. They take time to complete and can be very tiring and even boring sometimes. The post collects tricks that may help make your experience of numerical experiments faster, more productive, and hopefully more fun.

1. Generate unique file names in mass numerical simulations [download m-file]
   

   % Usage:
   %   filename = gen_filename(base_name, varargin)
   %   where varargin can be ‘hostname’, ‘date’, ‘datetime’,suffix
   %
   % Example:
   %   call
   %     filename = gen_filename(mfilename,’hostname’,’datetime’,’mat’);
   %   in foo.m on PC “yind20” at 23:42:25 on Jan 24, 2011 gives
   %     filename = ‘foo_yind20_20110124T234225.mat’
   %
   % Note: mfilename returns the name of the current m-file.

2. A template for repeated numerical experiments. [download m-file]

   function simulation_xxx()
   
   %% generate data file's name
   datafile = gen_filename(mfilename,'hostname','datetime','mat');
   [~,host] = system('hostname'); host = host(1:end-1); % get hostname
   
   %% testset's parameters
   par.set1 = 1:2:10;
   par.set2 = 0:0.1:0.5;
   par.nrep = 100;
   
   set1_len = length(par.set1);
   set2_len = length(par.set2);
   
   %% initialize rand gen's seed
   par.seed = 2010; % use fixed seed
   % par.seed = sum(100*clock); % use clock seed
   if isfield(par,'seed')
      fprintf('Seed = %d\n',par.seed);
      RandStream.setDefaultStream(RandStream('mt19937ar','seed',par.seed));
   end
   
   %% generate stationary part of the data
   data.st = generate_st_data( parameters );
   
   %% initialize arrays to save test statistics
   data.resu = cell (set1_len,set2_len,par.nrep);  % store test output, if not too large
   data.psnr = zeros(set1_len,set2_len,par.nrep);  % record PSNRs
   data.mser = zeros(set1_len,set2_len,par.nrep);  % record mean square error
   data.time = zeros(set1_len,set2_len,par.nrep);  % record time
   data.rand = cell (set1_len,set2_len);           % record rand state
   
   %% main iterations
   for ii = 1:set1_len
       par1 = par.set1(ii);
   
       for jj = 1:set2_len
           par2 = par.set2(jj);
   
           data.rand{ii,jj} = get(RandStream.getDefaultStream,'state');
   
           for rep = 1:par.nrep
   
               %% generate complete data
               thisdata = generate_data(data.st, par1, par2);
   
               %% call solver
               tic;
               resu = solver(thisdata,par1,par2);
               data.time(ii,jj,rep) = toc;
   
               %% save and analyze result
               data.resu{ii,jj,rep} = resu; % store test output, if not too large
               [psnr, mser] = analysis(thisdata, resu);
               data.psnr(ii,jj,rep) = psnr;
               data.mser(ii,jj,rep) = mser;
           end
   
           fprintf('%s par1:%4.2f par2:%4.2f avg.psnr:%4.2f avg.mser:%4.2e\n',...
                    host, par1, par2, mean(data.psnr(ii,jj,:)),...
                    mean(data.mser(ii,jj,:)));
           save(datafile,'par','data','ii','jj');
       end
   end
   
   end % function

3. Obtain the folder of the current m-file in use:

   fileparts(mfilename('fullpath'));

   Example: add subfolder “utilities” to search path

   addpath([fileparts(mfilename('fullpath')) '/utilities']);

Recent updates on Matlab’s random generator

This entry was posted on Tuesday, January 25th, 2011 at 12:43 am and is filed under Talks. You can follow any responses to this entry through the RSS 2.0 feed. Both comments and pings are currently closed.