view tests/GAPgentest.m @ 51:eb4c66488ddf

Split algos.py and stdparams.py, added nesta to std1, 2, 3, 4

% Run GAP and save parameters and solutions as reference test data
% to check if other algorithms are correct

% numA = 10;
% numY = 100;
% 
% sizesA{1} = [50 100];
% sizesA{2} = [20 25];
% sizesA{3} = [10 120];
% sizesA{4} = [15 100];
% sizesA{5} = [70 100];
% sizesA{6} = [80 100];
% sizesA{7} = [90 100];
% sizesA{8} = [99 100];
% sizesA{9} = [100 100];
% sizesA{10} = [250 400];
% for i = 1:numA sizesA{i} = fliplr(sizesA{i}); end
% 
% for i = 1:numA
%     sz = sizesA{i};
%     cellA{i} = randn(sz);
%     m = round((0.2 + 0.6*rand)*sz(2));
%     cellM{i} = randn(m,sz(2));
%     cellY{i} = randn(m, numY);
%     cellXinit{i} = zeros(sz(2), numY);
%     for j = 1:numY
%         cellEps{i}(j) = rand / 100; % restrict from 0 to 1% of measurements
%     end
% end
% opt_num_iteration = 1000;
% opt_greedy_level = 0.9;
% opt_stopping_coefficient_size = 1e-4;
% opt_l2solver = 'pseudoinverse';

load GAPtestdata
tic
for i = 1:numA
    for j = 1:numY
        gapparams.num_iteration = opt_num_iteration;
        gapparams.greedy_level = opt_greedy_level;
        gapparams.stopping_coefficient_size = opt_stopping_coefficient_size;
        gapparams.l2solver = opt_l2solver;
        gapparams.noise_level = cellEps{i}(j);
        
        cellXr{i}(:,j) = GAP(cellY{i}(:,j), cellM{i}, cellM{i}', cellA{i}, cellA{i}', gapparams, cellXinit{i}(:,j));
    end
end
toc

% save GAPtestdata