src/test/OLD_TEST_MATLAB/generateIO.m

   1 %X is generated following a gaussian mixture \sum pi_r N(meanX_k, covX_r)
   2 %Y is generated then, with Y_i ~ \sum pi_r N(Beta_r.X_i, covY_r)
   3 function[X,Y,Z] = generateIO(meanX, covX, covY, pi, beta, n)
   4
   5     [p, ~, k] = size(covX);
   6     [m, ~, ~] = size(covY);
   7     if exist('octave_config_info')
   8         %Octave statistics package  doesn't have gmdistribution()
   9         X = zeros(n, p);
  10         Z = zeros(n);
  11         cs = cumsum(pi);
  12         for i=1:n
  13             %TODO: vectorize ? http://stackoverflow.com/questions/2977497/weighted-random-numbers-in-matlab
  14             tmpRand01 = rand();
  15             [~,Z(i)] = min(cs - tmpRand01 >= 0);
  16             X(i,:) = mvnrnd(meanX(Z(i),:), covX(:,:,Z(i)), 1);
  17         end
  18     else
  19         gmDistX = gmdistribution(meanX, covX, pi);
  20         [X, Z] = random(gmDistX, n);
  21     end
  22
  23     Y = zeros(n, m);
  24     BX = zeros(n,m,k);
  25     for i=1:n
  26         for r=1:k
  27             %compute beta_r . X_i
  28             BXir = zeros(1, m);
  29             for mm=1:m
  30                 BXir(mm) = dot(X(i,:), beta(:,mm,r));
  31             end
  32             %add pi(r) * N(beta_r . X_i, covY) to Y_i
  33             Y(i,:) = Y(i,:) + pi(r) * mvnrnd(BXir, covY(:,:,r), 1);
  34         end
  35     end
  36
  37 end