fix generateRunTest for constructionModelesEMGrank; remove man pages (will be roxygen...

[valse.git] / src / test / generate_test_data / helpers / generateIO.m

diff --git a/src/test/generate_test_data/helpers/generateIO.m b/src/test/generate_test_data/helpers/generateIO.m
deleted file mode 100644 (file)
index c677fd2..0000000
--- a/src/test/generate_test_data/helpers/generateIO.m
+++ /dev/null
@@ -1,37 +0,0 @@
-%X is generated following a gaussian mixture \sum pi_r N(meanX_k, covX_r)
-%Y is generated then, with Y_i ~ \sum pi_r N(Beta_r.X_i, covY_r)
-function[X,Y,Z] = generateIO(meanX, covX, covY, pi, beta, n)
-
-       [p, ~, k] = size(covX);
-       [m, ~, ~] = size(covY);
-       if exist('octave_config_info')
-               %Octave statistics package      doesn't have gmdistribution()
-               X = zeros(n, p);
-               Z = zeros(n);
-               cs = cumsum(pi);
-               for i=1:n
-                       %TODO: vectorize ? http://stackoverflow.com/questions/2977497/weighted-random-numbers-in-matlab
-                       tmpRand01 = rand();
-                       [~,Z(i)] = min(cs - tmpRand01 >= 0);
-                       X(i,:) = mvnrnd(meanX(Z(i),:), covX(:,:,Z(i)), 1);
-               end
-       else
-               gmDistX = gmdistribution(meanX, covX, pi);
-               [X, Z] = random(gmDistX, n);
-       end
-       
-       Y = zeros(n, m);
-       BX = zeros(n,m,k);
-       for i=1:n
-               for r=1:k
-                       %compute beta_r . X_i
-                       BXir = zeros(1, m);
-                       for mm=1:m
-                               BXir(mm) = dot(X(i,:), beta(:,mm,r));
-                       end
-                       %add pi(r) * N(beta_r . X_i, covY) to Y_i
-                       Y(i,:) = Y(i,:) + pi(r) * mvnrnd(BXir, covY(:,:,r), 1);
-               end
-       end
-
-end