% Make a point move in the 2D plane% State = (x y xdot ydot). We only observe (x y).% Generate data from this process, and try to learn the dynamics back.% X(t+1) = F X(t) + noise(Q)% Y(t) = H X(t) + noise(R)ss = 4; % state sizeos = 2; % observation sizeF = [1 0 1 0; 0 1 0 1; 0 0 1 0; 0 0 0 1]; H = [1 0 0 0; 0 1 0 0];Q = 0.1*eye(ss);R = 1*eye(os);initx = [10 10 1 0]';initV = 10*eye(ss);seed = 1;rand('state', seed);randn('state', seed);T = 100;[x,y] = sample_lds(F, H, Q, R, initx, T);% Initializing the params to sensible values is crucial.% Here, we use the true values for everything except F and H,% which we initialize randomly (bad idea!)% Lack of identifiability means the learned params. are often far from the true ones.% All that EM guarantees is that the likelihood will increase.F1 = randn(ss,ss);H1 = randn(os,ss);Q1 = Q;R1 = R;initx1 = initx;initV1 = initV;max_iter = 10;[F2, H2, Q2, R2, initx2, initV2, LL] =  learn_kalman(y, F1, H1, Q1, R1, initx1, initV1, max_iter);