X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=src%2Ftest%2Fgenerate_test_data%2Fhelpers%2FgenerateIO.m;fp=src%2Ftest%2Fgenerate_test_data%2Fhelpers%2FgenerateIO.m;h=0000000000000000000000000000000000000000;hb=ef67d338c7f28ba041abe40ca9a8ab69f8365a90;hp=c677fd2cf407a14163f8adff459d4700d574f7ab;hpb=c3bc47052f3ccb659659c59a82e9a99ea842398d;p=valse.git

diff --git a/src/test/generate_test_data/helpers/generateIO.m b/src/test/generate_test_data/helpers/generateIO.m
deleted file mode 100644
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