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from numpy.random import seed
from scipy.sandbox.pyem import GM, GMM, EM
import copy
# To reproduce results, fix the random seed
seed(1)
#+++++++++++++++++++++++++++++
# Meta parameters of the model
# - k: Number of components
# - d: dimension of each Gaussian
# - mode: Mode of covariance matrix: full or diag (string)
# - nframes: number of frames (frame = one data point = one
# row of d elements)
k = 2
d = 2
mode = 'diag'
nframes = 1e3
#+++++++++++++++++++++++++++++++++++++++++++
# Create an artificial GM model, samples it
#+++++++++++++++++++++++++++++++++++++++++++
w, mu, va = GM.gen_param(d, k, mode, spread = 1.5)
gm = GM.fromvalues(w, mu, va)
# Sample nframes frames from the model
data = gm.sample(nframes)
#++++++++++++++++++++++++
# Learn the model with EM
#++++++++++++++++++++++++
# Create a Model from a Gaussian mixture with kmean initialization
lgm = GM(d, k, mode)
gmm = GMM(lgm, 'kmean')
# The actual EM, with likelihood computation. The threshold
# is compared to the (linearly appromixated) derivative of the likelihood
em = EM()
like = em.train(data, gmm, maxiter = 30, thresh = 1e-8)
# The computed parameters are in gmm.gm, which is the same than lgm
# (remember, python does not copy most objects by default). You can for example
# plot lgm against gm to compare
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