add test folder

[valse.git] / src / test / OLD_TEST_MATLAB / generateIO.m

diff --git a/src/test/OLD_TEST_MATLAB/generateIO.m b/src/test/OLD_TEST_MATLAB/generateIO.m
new file mode 100644 (file)
index 0000000..c677fd2
--- /dev/null
+++ b/src/test/OLD_TEST_MATLAB/generateIO.m
@@ -0,0 +1,37 @@
+%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