Fit a univariate mixture of normals to simulated data using the EM algorithm, "from scratch".
Output of python xfit_mix.py:
#obs: 10000
#iter_em: 30
p1 p2 m1 m2 sd1 sd2
Guessed: 0.5000 0.5000 0.0000 0.0000 1.0000 3.0000
True: 0.3000 0.7000 0.0000 5.0000 1.0000 1.5000
Fitted: 0.3015 0.6985 0.0038 4.9650 0.9941 1.5060
Diff: 0.0015 -0.0015 0.0038 -0.0350 -0.0059 0.0060
p1 p2 m1 m2 sd1 sd2
Guessed: 0.5000 0.5000 0.0000 0.0000 1.0000 3.0000
True: 0.3000 0.7000 0.0000 5.0000 1.0000 1.5000
Fitted: 0.3049 0.6951 0.0309 4.9970 0.9739 1.5032
Diff: 0.0049 -0.0049 0.0309 -0.0030 -0.0261 0.0032
p1 p2 m1 m2 sd1 sd2
Guessed: 0.5000 0.5000 0.0000 0.0000 1.0000 3.0000
True: 0.3000 0.7000 0.0000 5.0000 1.0000 1.5000
Fitted: 0.2991 0.7009 -0.0105 4.9854 0.9925 1.5317
Diff: -0.0009 0.0009 -0.0105 -0.0146 -0.0075 0.0317
time elapsed (s): 0.062