Commit | Line | Data |
---|---|---|
7b272073 BA |
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 |