 
       trueWeights(:,c) = x0;

       % resulting y for linear model
       %y = A*x0 + noise*(2*rand(m,1)-1);  %  Uniform noise
       y = A*x0 + noise*randn(m,1);       %  Gaussian noise

       % add some non-linearity?
       %y = 7*tanh(3*y/max(abs(y)));

       % find all posterior modes (MAP sols under a flat prior)
       for i = 1:Ns
           x(:) = 0;
           idx = sets{i};
           x(idx) = A(:,idx) \ y;
           Xmode(i,:) = x';
           Emode(i) = norm(A*x-y)^2;  % 