X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=src%2Ftest%2FOLD_TEST_MATLAB%2FgenerateIO.m;fp=src%2Ftest%2FOLD_TEST_MATLAB%2FgenerateIO.m;h=c677fd2cf407a14163f8adff459d4700d574f7ab;hb=7b2720733e9aebe177c211119a9ec160c7e7117b;hp=0000000000000000000000000000000000000000;hpb=4725af564bfdbeb40d0caf7f124ab94ac05a97eb;p=valse.git

diff --git a/src/test/OLD_TEST_MATLAB/generateIO.m b/src/test/OLD_TEST_MATLAB/generateIO.m
new file mode 100644
index 0000000..c677fd2
--- /dev/null
+++ b/src/test/OLD_TEST_MATLAB/generateIO.m
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+%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