1 function sheet05
   2 
   3 % generate some data
   4 T = linspace(0, 10, 1000);
   5 X1 = sin(pi*T);
   6 X2 = 2*(T - floor(T)) - 1;
   7 X3 = 0.1*randn(1, 1000);
   8 
   9 X = [X1; X2; X3];
  10 
  11 % plot the sources
  12 figure(1)
  13 plotsources(T, X)
  14 
  15 % generate a random mixing matrix
  16 A = randn(3, 3);
  17 Y = A * X;
  18 
  19 % plot the mixed sources
  20 figure(2)
  21 plotsources(T, Y);
  22 
  23 % compute time-lagged 
  24 B = tdsep(Y, 5);
  25 
  26 figure(3)
  27 plotsources(T, B'*Y)
  28 
  29 function plotsources(T, X)
  30 N = size(X, 1);
  31 for I = 1:N
  32   subplot(N, 1, I)
  33   plot(T, X(I, :))
  34 end
  35 
  36 % Compute the TDSEP estimate by diagonalizing both matrices
  37 % simultaneously. Use matlabs EIG function for two matrices.
  38 function B = tdsep(Y, T)
  39 C0 = tdcov(Y, 0);
  40 CT = tdcov(Y, T);
  41 % ...
  42 
  43 % Compute the time-lagged covariance matrices. Extract a "normal" and a
  44 % lagged version of the signal and estimate the covariance matrix for
  45 % both parts.
  46 function C = tdcov(X, T)
  47 % ...